Understanding vulnerability. Three papers on Chile

Understanding vulnerability.

Three papers on Chile

A thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy (PhD) in the Faculty of Humanities

2018

Amanda Telias Simunovic

Global Development Institute

School of Environment, Education and Development (SEED)

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List of Contents

List of Contents ........................................................................................................................................................3

List of Tables .............................................................................................................................................................7

List of Figures ...........................................................................................................................................................9

Abstract ................................................................................................................................................................... 11

Declaration .............................................................................................................................................................. 13

Copyright statement .............................................................................................................................................. 13

Acknowledgments ................................................................................................................................................. 15

1 Introduction .................................................................................................................................................. 17

1.1 The relevance of vulnerability and its role in poverty reduction ................................................. 20

1.2 Alternative approaches to understanding vulnerability and their low predictive effectiveness

23

1.3 Why is Chile an interesting case study? ............................................................................................ 24

1.4 Data for the empirical research: Chile’s Casen and Panel Casen ................................................. 25

1.5 The three papers and methodology .................................................................................................. 27

1.6 Contributions ....................................................................................................................................... 32

1.7 References ............................................................................................................................................ 35

2 Paper I: Has vulnerability to poverty been increasing in Chile? A distributional analysis of

household income 1990-2013 .............................................................................................................................. 41

2.1 Introduction ......................................................................................................................................... 42

2.2 Why vulnerability matters and how can it be measured? .............................................................. 49

2.2.1 The importance of risk understanding poverty ..................................................................... 49

2.2.2 How to measure vulnerability to poverty ............................................................................... 52

2.3 Vulnerability in practice: The Chilean Social Protection System ................................................. 55

2.4 An empirical approach to vulnerability to poverty ........................................................................ 60

2.5 Data ....................................................................................................................................................... 64

2.6 Methodology ........................................................................................................................................ 67

2.6.1 The Relative Distribution method: a non-parametric framework ...................................... 67

2.6.2 Decomposition by covariates ................................................................................................... 70

2.6.3 Income distribution polarization ............................................................................................. 74

2.7 Empirical results .................................................................................................................................. 76

2.7.1 Relative Density .......................................................................................................................... 78

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2.7.2 Location and Shape Effects ...................................................................................................... 90

2.7.3 The Polarization Index............................................................................................................... 95

2.7.4 Decomposition by socio-demographic characteristics ......................................................... 98

2.8 Conclusions ......................................................................................................................................... 106

2.9 References ........................................................................................................................................... 109

2.10 Annexes ............................................................................................................................................... 114

2.10.1 La Capacidad Generadora de Ingresos (The income generating capacity) ...................... 114

2.10.2 The covariate-adjusted relative density of total income ..................................................... 115

3 Paper II: Vulnerability to poverty in Chile: the differences between current poverty and the risk of

being in poverty in the future ............................................................................................................................. 118

3.1 Introduction ........................................................................................................................................ 119

3.2 Literature review: Poverty and vulnerability definitions, measurements and differences ...... 124

3.2.1 What does poverty mean? ....................................................................................................... 124

3.2.2 What does vulnerability to poverty mean? ............................................................................ 125

3.3 Theoretical Framework: An empirical approach of vulnerability to poverty ........................... 127

3.3.1 How can we operationalize the vulnerability to poverty concept? ................................... 127

3.3.2 The rise of vulnerability to poverty in Latin American countries ..................................... 129

3.4 Data ...................................................................................................................................................... 131

3.5 Econometric Methodology............................................................................................................... 132

3.5.1 The three-stage methodology for approaching vulnerability to poverty ......................... 133

3.5.2 How can the socio-demographic characteristics of people in poverty, vulnerability, the

middle class and upper middle class be compared? ............................................................................... 139

3.6 Results and discussion ....................................................................................................................... 140

3.6.1 Defining the relation between the probability of falling into poverty and households'

observed income ......................................................................................................................................... 141

3.6.2 Are the socio-demographic characteristics of people living in vulnerability to poverty

different from those of people living with less or with more income? .............................................. 154

3.7 Conclusions ......................................................................................................................................... 163

3.8 References ........................................................................................................................................... 168

3.9 Annexes ............................................................................................................................................... 174

3.9.1 Approaches to and Methodologies for measuring vulnerability to poverty .................... 174

3.9.2 Panel CASEN 1996-2001-2006. Kinds of people interviewed by year ............................ 176

3.9.3 Descriptive statistics of CASEN Panel Database ................................................................ 177

3.9.4 Robustness Checks of logistic and lineal estimations ......................................................... 179

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4 Paper III: Protecting vulnerable groups in Chile: the contribution of social Assistance to the

poverty exit rate of children and older people ................................................................................................ 185

4.1 Introduction ....................................................................................................................................... 186

4.2 The vulnerable groups approach .................................................................................................... 191

4.3 Are children and older persons overrepresented in poverty? .................................................... 194

4.3.1 Poverty and deprivation among children ............................................................................. 194

4.3.2 Poverty and deprivation among old people ......................................................................... 196

4.4 Poverty rates by age in Chile ........................................................................................................... 199

4.4.1 Income Poverty ........................................................................................................................ 200

4.4.2 Multi-dimensional Poverty...................................................................................................... 203

4.5 The theoretical framework ............................................................................................................... 205

4.5.1 Theories of change behind cash transfers to children and older people ......................... 205

4.5.2 Fiscal mobility ........................................................................................................................... 211

4.6 The data base ..................................................................................................................................... 212

4.7 Methodology ...................................................................................................................................... 213

4.7.1 The three income measures in partial fiscal incidence analysis......................................... 214

4.7.2 Poverty Exit Rate ..................................................................................................................... 219

4.7.3 Poverty and vulnerability lines ............................................................................................... 222

4.8 Results ................................................................................................................................................. 225

4.8.1 Population ................................................................................................................................. 225

4.8.2 Children and Older persons ................................................................................................... 229

4.8.3 Households with children and/or/without older persons ................................................ 231

4.9 Conclusions ........................................................................................................................................ 260

4.10 References .......................................................................................................................................... 263

4.11 Annexes ............................................................................................................................................... 267

4.11.1 The new methodology for measuring poverty in Chile and recent figures of poverty

reduction under both methodologies ...................................................................................................... 267

4.11.2 Dimensions, indicators and cut-offs of the multi-dimensional poverty index used in

Chile 269

4.11.3 Description of the Direct Cash Transfers considered in the analysis: ............................. 270

4.11.4 Rates of taxes ............................................................................................................................ 280

4.11.5 Poverty by age ........................................................................................................................... 283

4.11.6 Tables and Figures ................................................................................................................... 283

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4.11.7 Multidimensional Poverty Index-Chile. Dimensions, indicators and cutoff points ¡Error!

Marcador no definido.

4.11.8 Fiscal mobility matrices ............................................................................................................ 284

5 Conclusions .................................................................................................................................................. 286

5.1 Findings and contributions from each of the three papers ......................................................... 286

5.2 Policy implications ............................................................................................................................. 294

5.3 Further research ................................................................................................................................. 297

5.4 References ........................................................................................................................................... 299

Word Count: 68,816

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List of Tables

Table 2- 1 Summary of CASEN statistics by years of application ................................................................ 65

Table 2- 2 Covariates and Equivalent autonomous income in 1990 and 2013 ......................................... 100

Table 3-1 Original members of the survey interviewed both years and three (domestic servants

excluded) ............................................................................................................................................................... 132

Table 3-2 National and International poverty lines expressed in 2005 Chilean Pesos ............................. 134

Table 3-3 Poverty transition matrices 1996-2001 and 2001-2006 ............................................................... 141

Table 3-4 Poverty transition: 2 and 4 categories 1996-2001 and 2001-2006 ............................................. 142

Table 3-5 Logit and Linear estimation 1996-2001 ......................................................................................... 143

Table 3-6 Logit and Linear estimation 2001-2006 ......................................................................................... 144

Table 3-7 Monthly equivalised income thresholds 2005 Chilean Pesos ..................................................... 151

Table 3-8 Distribution of individuals among income groups in 2001 under two probability thresholds

................................................................................................................................................................................ 152

Table 3-9 Comparison between households’ characteristics in poverty, in vulnerability and middle class,

year 2001 ............................................................................................................................................................... 160

Table 3-10 Approaches to and Methodologies for vulnerability ................................................................. 174

Table 3-11 Kinds of people interviewed by year ............................................................................................ 176

Table 3-12 Descriptive statistics: covariates, income and poverty. CASEN Panel Database ................. 177

Table 4-1 Cash transfers considered in the analysis, CASEN 2015 ............................................................ 219

Table 4-2 Groups of benefits ............................................................................................................................ 221

Table 4-3 Poverty and vulnerability thresholds .............................................................................................. 223

Table 4-4 Fiscal mobility matrix, from market income to disposable income, Total percentage

distribution of population .................................................................................................................................. 228

Table 4-5 Fiscal mobility matrix, from market income to disposable income, Row percentage

distribution of population .................................................................................................................................. 228


distribution of people living in households with children and no older persons ...................................... 239


distribution of people living in households with children and no older persons ...................................... 239


distribution of people living in households with older persons and no children ...................................... 240


distribution of people living in households with older persons and no children ...................................... 240

Table 4-10 Poverty Rate for four poverty lines, composition of individuals in poverty and Exit Rate by

Household Groups. ............................................................................................................................................. 243

Table 4-11 Poverty Exit Rate, Probability of being covered and probability of leaving poverty given

coverage, by households groups ........................................................................................................................ 245

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Table 4-12 Household type by population groups, all households, Row percentage ............................... 249

Table 4-13 Household type by population groups, exit poverty households ............................................. 250

Table 4-14 Benefits groups, all individuals and individuals in moderate poverty under market income

................................................................................................................................................................................. 254

Table 4-15 Decomposition of the exit rate from poverty, national extreme and moderate poverty lines,

5 and 10 groups of programmes ........................................................................................................................ 256

Table 4-16 Decomposition of the exit rate from poverty, national moderate poverty lines, 5 groups of

programmes, groups of households .................................................................................................................. 259

Table 4-17 Dimensions, indicators and cut-offs of the multi-dimensional poverty index used in Chile

................................................................................................................................................................................. 269

Table 4-18 Household type by population groups, all population, Row percentage ................................ 283

Table 4-19 Household type by population groups, exit poverty individuals .............................................. 284


distribution of people living in households with children and older persons ............................................ 284


distribution of people living in households with children and older persons ............................................ 284


distribution of people living in households with no children and no older persons ................................. 285


distribution of people living in households with no children and no older persons ................................. 285

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List of Figures

Figure 2- 1 Kernel Densities 1990 and 2013. Autonomous income distributions ...................................... 77

Figure 2- 2 Kernel Densities 1990, 2000 and 2013. Autonomous income distributions ........................... 78

Figure 2- 3 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Autonomous Income ... 79

Figure 2- 4 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Monetary Income ......... 80

Figure 2- 5 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Total Income ................. 80


Figure 2- 7 Relative PDF and Relative CDF years 1990 and 2000, Equivalent Monetary Income ......... 85

Figure 2- 8 Relative PDF and Relative CDF years 1990 and 2000, Equivalent Total Income ................. 85


Figure 2- 10 Relative PDF and Relative CDF years 2000 and 2013, Equivalent Monetary Income ....... 86

Figure 2- 11 Relative PDF and Relative CDF years 2000 and 2013, Equivalent Total Income ............... 86

Figure 2- 12 Relative PDF every year compared with the base year 1990, Equivalent Autonomous

Income ..................................................................................................................................................................... 88

Figure 2- 13 Relative PDF every year compared with the base year 1990, Equivalent Monetary Income

.................................................................................................................................................................................. 89

Figure 2- 14 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Autonomous

Income ..................................................................................................................................................................... 90

Figure 2- 15 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Monetary Income

.................................................................................................................................................................................. 91

Figure 2- 16 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Total Income ... 91


Income ..................................................................................................................................................................... 93


.................................................................................................................................................................................. 93

Figure 2- 19 Relative Density 1990 and 2000, Location and Shape Effect, Equivalent Total Income .. 93


Income ..................................................................................................................................................................... 94


.................................................................................................................................................................................. 94

Figure 2- 22 Relative Density 2000 and 2013, Location and Shape Effect, Equivalent Total Income .. 94

Figure 2- 23 Relative Polarization Indices, Equivalent Autonomous Income ........................................... 97

Figure 2- 24 Relative Polarization Indices, Equivalent Monetary Income .................................................. 97

Figure 2- 25 Covariates composition effect, Equivalent Autonomous Income ...................................... 103

Figure 2- 26 Covariate-adjusted relative density of income, Equivalent Autonomous Income ............ 104

Figure 2- 27 Covariates composition effect, Equivalent Total Income ..................................................... 115

Figure 2- 28 Covariate-adjusted relative density of income, Equivalent Total Income .......................... 116

Figure 3- 1 Correlation between the estimated probability of falling into poverty and the predicted

equalized income.................................................................................................................................................. 148

Figure 3- 2 1996 Monthly equivalised incomes by probability of falling into poverty ............................. 149

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Figure 3- 3 2001 Monthly equivalised incomes by probability of falling into poverty.............................. 149

Figure 3- 4 2001 Kernel distribution of monthly equalized incomes 2001................................................. 153

Figure 4-1 Percentage of people in income poverty Chile 2006-2015, by age group ................................ 201

Figure 4-2 14 Latin American Countries. Relation between the poverty rate for population between 0

and 14 years and the poverty rate for the population over 55 years. Relation between the poverty rate

for population between 15 and 24 years and the poverty rate for the population over 55 years. Surveys

around the year 2013 ........................................................................................................................................... 202

Figure 4-3 Percentage of people in income and multi-dimensional poverty in Chile 2015, by age group

................................................................................................................................................................................. 204

Figure 4-4 Partial fiscal incidence analysis incomes ........................................................................................ 214

Figure 4-5 Poverty Headcount by income concepts ...................................................................................... 226

Figure 4-6 Poverty Headcount (Incidence), Poverty Gap (Intensity), Poverty Gap Squared (Severity), by

income concepts ................................................................................................................................................... 227

Figure 4-7 Distribution of age groups among all population, population in poverty under market

income and population in poverty under disposable income, 3 age groups ............................................... 229

Figure 4-8 Moderate Poverty headcount, by income concept and age group ............................................ 231

Figure 4-9 Distribution of individuals by household type, all population, population in poverty under

market income, population in poverty under disposable income ................................................................ 232

Figure 4-10 Distribution of households by household type, all population, population in poverty under

market income, population in poverty under disposable income ................................................................ 233

Figure 4-11 Kernel density of market income, by type of household ......................................................... 234

Figure 4-12 Kernel density of disposable income, by type of household ................................................... 235

Figure 4-13 TIP curve of market income by household type, official moderate poverty line ................. 236

Figure 4-14 TIP curve of net market income by household type, official moderate poverty line .......... 236

Figure 4-15 TIP curve of disposable income by household type, official moderate poverty line........... 236

Figure 4-16 Percentage of population in poverty, by income concept and household composition,

National extreme poverty line ............................................................................................................................ 237

Figure 4-17 Percentage of population in poverty, by income concept and household composition,

National moderate poverty line.......................................................................................................................... 238

Figure 4-18 Household size, all households, by household type .................................................................. 248

Figure 4-19 Household size, by household types, all households, households in poverty and exit poverty

households ............................................................................................................................................................. 250

Figure 4-20 Poverty gap national moderate poverty line, by type of household and income ................. 252

Figure 4-21 Poverty gap national extreme poverty line, by type of household and income .................... 252

Figure 4-22 Percentage of people in income poverty by New Methodology (2006-2013) and Traditional

Methodology (1990-2013) ................................................................................................................................... 268

Figure 4-23 Distribution of age groups among all population, population in poverty under market

income and population in poverty under disposable income, 6 age groups ............................................... 283

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Abstract

Poverty eradication has been one of the most important, if not the most important,

development goals of recent decades. It still represents one of the major challenges of our

time. The first objective of the U.N.’s Sustainable Development Goals agreed in 2015 states:

"End poverty in all its forms everywhere" (United Nations 2015). To meet the main objective

of eliminating poverty by 2030, it has been recognized that protection must go not only to

those in poverty but also to those who are in danger of falling into poverty in the future.

Although vulnerability to poverty can be broadly defined as the likelihood of someone falling

into poverty in the future, there is no agreement on how best to measure it or determine its

impact on well-being. The main research question addressed in the thesis is: How can

vulnerability to poverty be operationalized and measured? It explores this question empirically

in three papers covering: (i) what are the shifts in vulnerability to poverty along the distribution

of income over time; (ii) what do the measurements of vulnerability to poverty tell us about

the sociodemographic characteristics of people in situations of vulnerability to poverty

compared with those living in poverty and the middle class; (iii) what is the relationship

between poverty, vulnerability and age and what is the role of social assistance in addressing

these. The three papers take Chile as a case study to understand and measure vulnerability

from three different approaches. Chile is a high-income country with a successful poverty

reduction strategy but still facing the challenge of eradicating it. Most of its social programs are

designed to reach the 60% most vulnerable sector of the population. The first paper employs a

relative understanding of vulnerability. It examines population shifts along the distribution of

income from deciles in poverty in an earlier period to deciles of vulnerability in a later period.

Methods to analyse relative distribution proposed by Handcock & Morris (1999) are used to

perform this analysis. The findings emphasize that poverty reduction can be accompanied by

vulnerability reduction. The second paper measures vulnerability to poverty using the approach

proposed by López-Calva & Ortiz-Juarez (2014). This paper estimates the probability of falling

into poverty and uses this to establish a vulnerability income threshold. The findings underline

the differences between the group of people living in vulnerability, those living in poverty and

people who belong to middle class. This paper contributes to the recognition of the group of

people in vulnerability as a different group to those in poverty and the middle class providing

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the recommendation of different social programmes for these groups. Poverty reduction

strategies should consider these differences. The third paper moves the analysis onto the

vulnerable groups. It focuses on children and older people as vulnerable groups in need of

state protection. A partial fiscal analysis is carried out following the guidelines of the

Commitment to Equity Institute to compare the situation of these groups before and after

direct taxes and cash transfers. It shows that current cash transfers have an age bias, being

more effective in reducing poverty among the elderly than among children. The findings

confirm the view that age bias in welfare institutions creates generational inequity in the

allocation of public benefits. In the context of the general lack of agreement regarding what

vulnerability to poverty is and how it can be measured, this thesis thus tries out three different

ways to conceptualize and measure it.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

Copyright statement

i. The author of this thesis (including any appendices and/or schedules to this thesis)

owns certain copyright or related rights in it (the “Copyright”) and he has given

The University of Manchester certain rights to use such Copyright, including for

administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or electronic

copy, may be made only in accordance with the Copyright, Designs and Patents

Act 1988 (as amended) and regulations issued under it or, where appropriate, in

accordance with licensing agreements which the University has from time to time.

This page must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “Intellectual Property”) and any reproductions of

copyright works in the thesis, for example graphs and tables (“Reproductions”),

which may be described in this thesis, may not be owned by the author and may

be owned by third parties. Such Intellectual Property and Reproductions cannot

and must not be made available for use without the prior written permission of the

owner(s) of the relevant Intellectual Property and/or Reproductions.

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property

and/or Reproductions described in it may take place is available in the University

IP Policy (http://documents.manchester.ac.uk/DocuInfo. aspx?DocID=24420),

in any relevant Thesis restriction declarations deposited in the University Library,

The University Library’s regulations

(http://www.manchester.ac.uk/library/aboutus/regulations) and in the

University’s policy on Presentation of Theses.

http://documents.manchester.ac.uk/DocuInfo.%20aspx?DocID=24420

http://www.manchester.ac.uk/library/aboutus/regulations

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To Luciana and José Manuel

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Acknowledgments

This thesis is more than a piece of work. It is the conclusion of a journey that I started

seven years ago through different countries. Throughout this journey, I have met many

important people for this thesis and for me. I would like to thank all of them for being part, in

different ways, of this adventure. It was not difficult to remember them all. They are part of

this.

The Washington DC friends: Elena Costas-Pérez, Fernando Vargas, Sebastián

Kraljevich, Javiera Díaz-Valdés, Gonzalo Araya, Nelson, Nicolás Lillo, Paulina Sepúlveda,

Natalia Ortiz, Felipe Avilés. Catalina, Silvia, Fernando, Manolo. José Ignacio Sembler, Andrea

and Santana.

The Lancastrian family: Carolina Pérez, Macarena Rioseco, Daniela Silva, Derly

Sánchez, Oscar Maldonado, Felipe, Gabriel, Rodrigo. Soledad Dávalos, Julio Prado, Antonella-

Samantha-Joaquín Prado (Los pradalos). Carla, Victor, Julieta, Gastón, Leah, Phil, John,

Virginie, James, Sten, Kristof, Ruth, Johnny.

The Londoners: Paula Graetzer, Marité Chavira, Robinson Rojas, Cristian Olmos,

Macarena León, Max Valdés, Ian Mackinnon, Rocío, Antonia Asenjo, Tito Coddou.

The Mancunians: The completo italiano (Daniele Malerba, Felipe González, Gabriela

Zapata), Nayaret Inaipil. Lucía Mantelli, Maria Montt, Joaquín Mantelli. Fabiola, Flo, Liu,

Victoria, Víctor, Javier Castillo, Valentina. Laura Rodríguez, Vidhya Unnikrishnan, Aarti

Krishnan, Eyob Gebremariam, Elena, Denisse.

The supervisors, advisors, and experts: Armando Barrientos, Katsushi Imai, Eduardo

Ortiz-Juarez, Sandra Martinez-Aguilar, Marisa Bucheli, Mark Handcock, Francesco Schettino.

Osvaldo Larrañaga, Rodrigo Herrera, Alina Oyarzún, César Cancho. Abhishek Chakravarty,

Jennifer Golan, Solava Ibrahim, Kunal Sen, David Hulme.

The closest friends: Claudia Petric, Daniela Lagos, Daniela Aceituno, Genoveva Tapia,

Gabriela Bertrand, Alejandra Arellano, Rodrigo Martel, Francisca Palomas, Javiera Palomas,

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Francisca Keller. Mariana Huepe, Alejandra Núñez, Belén Valdés, Catalina Pantoja, Catalina

Valdés, Carla Parra, Pía Mora. Edmundo Hermosilla, Pedro Asenjo, Sebastián Guinguis, Pablo

Egaña, Alvaro Ruíz, Lucas Orellana, Daniela Guasch. Paula Castro, Alejandra Sanhueza,

Francisco Pinto.

The unconditional family: Benjamín, Dezanka and Mauricio. Ana María, Mario, Pablo,

Cristina and Ángeles. Irene, Ximena, Rosa, Salvador, Moisés, Olivia, Gaspar and Isabel. Vesna,

Milenko, Ivo, Marisol, Lauri, Virna, Nono.

The most important: Luciana and José Manuel. The life with you has purpose, inspiration and

love. Let start together a new journey! This time, back home.

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Amartya Sen (Asia Week, October 1999), “….the challenge of development includes not only the

elimination of persistent and endemic deprivation, but also the removal of vulnerability to sudden and severe

destitution”

1 Introduction

Poverty eradication is one of the biggest challenges of our time. Although poverty has been

decreasing during the last two decades, even so, 767 million people were living on less than

$1.90 dollars a day in 2013 (2011 PPP) (World Bank, 2016). Extreme1 and moderate2 poverty

have fallen dramatically since 19903 but the challenge of eradicating it again still appears as the

first objective in the global development agenda for the forthcoming years. The first goal of

the Sustainable Development Goals (SDG4) established in 2015 is to end poverty in all its forms

everywhere, which is composed of the following three more detailed goals:

1.1 By 2030, eradicate extreme poverty for all people everywhere, currently measured as

people living on less than $1.25 a day.

1.2 By 2030, reduce at least by half the proportion of men, women and children of all ages

living in poverty in all its dimensions according to national definitions.

1.3 Implement nationally appropriate social protection systems and measures for all,

including floors, and by 2030 achieve substantial coverage of the poor and the vulnerable.

1 Extreme poverty is defined by the World Bank as living on less than US $1.90 a day in 2011 PPP. 2 Moderate poverty is defined by the World Bank as living on less than US $3.10 a day in 2011 PPP. 3 The proportion of the world population living in extreme poverty fell from 34.82% in 1990 to 10.67% in 2013. People living in moderate poverty also decreased over this period. The proportion of people living on less than $3.10 dollars a day dropped from 66% in 1990 to 35% in 2012 (World Bank indicators) (World Bank, 2016). http://povertydata.worldbank.org/poverty/home/ 4 http://www.un.org/sustainabledevelopment/sustainable-development-goals/

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After acknowledging that poverty must be eradicated from a global perspective in point 1.1

and reduced according to national agreements in point 1.2, the importance of providing social

protection systems to the poor and the vulnerable is recognized. The question that emerges is

how to define the poor and the vulnerable that social protection systems must protect

nationally? The answer is easier regarding the poor than the vulnerable. There is a long

tradition in economics of literature regarding the definition and measurement of poverty.

However, research in vulnerability is more recent in the field increasing since the beginning of

this century. In general terms, vulnerability means the probability of being exposed to a variety

of risks, such as, natural disasters, crime, violence, poverty, illness, among others (World Bank,

2001). Hence, the study of vulnerability has been concentrated on the risk of negative

outcomes in the future (Hoddinott & Quisumbing, 2003b). Although different approaches

have been developed in pursuing a definition and measurement of vulnerability not much

consensus has been reached, in particular over its measurement.

The understanding of vulnerability is the main focus of this thesis. The main research

question of the thesis is: How can vulnerability to poverty be operationalized and measured?

This broad main research question is broken down into three subsequent questions that

can be directly addressed through empirical research. Each of the following questions is the

object of one of the three papers that comprise this thesis. :

Is the reduction of poverty accompanied by an increase in vulnerability to poverty?

What are the determinants of vulnerability to poverty? And what distinguishes people

living in vulnerability from people in poverty and the middle-class?

Is vulnerability related with age? And if so, what is the role played by cash transfers in this

relation?

The aim of this thesis is to trial three ways of understanding vulnerability. Therefore each

of the three research papers of this thesis provides a different way of understanding

vulnerability. The first and the second paper put attention on vulnerability in one dimension:

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income. The first paper employs a relative understanding of vulnerability. In this, vulnerability

is understood as belonging inherently to certain deciles of income distribution. This

understanding of vulnerability is in line with empirical definitions of vulnerability. The second

paper characterizes vulnerability to poverty. Usually vulnerability in the income dimension is

measured through the reduction in income below the poverty line (Calvo & Dercon, 2013).

This paper estimates vulnerability to poverty defined as the current probability of being in

poverty in the future. This paper characterizes the group of people in vulnerability to poverty

and compares them with the socio-demographic characteristics of people in poverty and

middle class people. Although the third paper still focuses on poverty, it broadens the analysis

to vulnerable groups. It focuses on children and older people as particularly vulnerable groups

in need of state protection. It analyses the effectiveness of monetary transfers in moving these

two vulnerable groups out of poverty. The three papers aim to contribute to the understanding

of vulnerability to poverty, with a specific focus on how a Social Protection System can tackle

a household’s vulnerability to poverty. As vulnerability and its measurement are the main

concerns of the three papers, some subsections of the three papers are very close. In particular,

the literature review of the first two papers describes the same approaches and measurements

available in the understanding of vulnerability. More detailed summaries of each of the three

papers are presented in Section 1.5.

In the following sections are presented the background and contextual factors of this

research. The next sections are organized as follows. In Section 1.1 the importance of

understanding vulnerability is briefly presented. The purpose of this section is to provide the

motivation for the increasing literature on vulnerability during the last two decades and the

justification for the research questions of this thesis. This section contextualizes the

importance of vulnerability reduction for poverty eradication goals; increasing the well-being

of a population, among other benefits. In Section 1.2 alternative approaches and

methodologies for defining and measuring vulnerability are presented. The focus is put on

vulnerability to poverty which is the main concern of this research. In addition, a discussion of

the low effectiveness that the available vulnerability to poverty measures have is presented in

this section, showing the importance of new approaches to understanding vulnerability. In

Section 1.3 the context of Chile is presented. The reasons that make Chile an interesting case

study are presented in this section. The data from Chile used in this research is presented in

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Section 1.4. The cross-section survey used in paper I and III and the panel data used in paper

II are described. The three papers and the methods used to conduct the empirical analysis in

each paper are presented in Section 1.5. Finally, in Section 1.6 the contributions of this thesis

are presented.

1.1 The relevance of vulnerability and its role in poverty

reduction

A Social Protection System is defined as a policy response5 to conditions of poverty and

vulnerability considered unacceptable within a society (Barrientos et al., 2005; Norton et al.,

2000). Under this definition, any Social Protection System should consider not only

individual’s or household’s current level of deprivation but also their vulnerability to being

deprived. This has been one of the main drivers of the growing literature on vulnerability

during the last two decades. The identification of the vulnerable groups that need protection

from the State is at the core of research on vulnerability. The possibility of identifying the

determinants of vulnerability has great importance in the design and implementation of social

policies.

Putting the focus on vulnerability to poverty, a Social Protection System should be able to

reduce the probability of households being in poverty. The importance of vulnerability to

poverty to the design and implementation of social policies has motivated the growing recent

literature in the topic. The possibility to design appropriate forward-looking anti-poverty

interventions is a motivation to find better definitions and measurements for vulnerability to

poverty (Chaudhuri, 2003). In this context, the reduction of vulnerability is an ex-ante

mechanism for reducing poverty.

5 This policy response can be through social insurance, social assistance and labour market policies (Barrientos, 2013a; Barrientos & Hulme, 2008). Social insurance is a protection against uncertain risk (unemployment or sickness) or life-course contingencies (maternity or old-age) that individuals or households can face (Barrientos & Hulme, 2008). Social assistance represents all public actions, from the government or otherwise, that transfer resources to deprived groups or other groups with entitlements with a moral justification. A Social Protection System helps to meet the aims of labour market regulation to consolidate decent working conditions (Cecchini & Martínez, 2011).

21

Beyond the importance of vulnerability to poverty as a forward-looking strategy for

reducing poverty, vulnerability has negative consequences today. It has been shown that the

uncertainty associated with it has direct adverse effects on current levels of wellbeing (Calvo &

Dercon, 2007; Chaudhuri, 2003). Vulnerability is about the uncertainty over what the future

will bring which generates suffering and anxiety among people. This fact has also motivated

the literature on vulnerability putting the focus on the reduction of vulnerability to improve the

well-being of the population.

The growing evidences of into and out of poverty transitions stresses the importance of

vulnerability to poverty. This research understands poverty as a state in which individuals can

find themselves at a particular moment in time. Poverty is not a permanent characteristic of

individuals or households (Barrientos 2013). This research refers to people or households in

poverty. Here, households or individuals are not intrinsically poor. In fact, many people are

vulnerable to being in poverty because poverty is transient. They can be in poverty for a short

period of time because of changes or shocks that they confront. Other groups of people stay

in poverty for longer periods. In that case, poverty is chronic or persistent. The duration of

poverty is one of the dimensions important in the context of vulnerability to poverty. Poverty

transitions configure the vulnerability to poverty notion. Moreover, it has been argued that

vulnerability to poverty estimations can contribute to targeted social programmes

distinguishing between transient and chronic poverty (Bérgolo et al., 2010).

In addition, poverty measures are not able to capture these transitions. Poverty measures

based on static data drawn from a single cross-section survey do not capture the movements in

and out of poverty that people experience, and fail to identify temporarily poor or non-poor

individuals (Baulch & Hoddinott, 2000). Poverty measures are a static measure of welfare that

can lead to errors of inclusion -people who are in poverty temporarily because of a short-term

misfortune- and exclusion -who are out of poverty because of favourable short-term

circumstances. In addition, the poverty headcount 6 is discontinuous at the poverty line

meaning that incomes below the poverty line classify people as poor and incomes above the

poverty threshold identify people as not-poor. From a social welfare perspective, this measure

6 This is part of the family of poverty measures developed by Foster-Greer-Thorbecke (FGT) (1984). The poverty headcount rate shows the share of the population in poverty.

22

captures the welfare loss of being poor when a person is too poor to acquire the poverty-level

basket of goods (Bourguignon & Fields, 1997). For the purposes of the poverty headcount

rate, incomes matter if people are below the poverty line, but they do no matter above it. The

concept of vulnerability cares about the well-being of people above the poverty line but facing

the risk of being in poverty in the future. This is another fact that has motivated its study.

The fact that all of us are exposed to suffering shocks has also increased the interest in

vulnerability. People are exposed to suffer aggregate7 and idiosyncratic8 shocks (Chaudhuri et

al., 2002; Dercon, 2006; Hoddinott & Quisumbing, 2008) that make them vulnerable to being

in poverty in the future. There are households out of poverty but that do not have enough

resources to confront these shocks. Furthermore, this is a global concern. This is a shared

concern for rich and poor countries because as countries improve their living standards,

attention tends to shift away from the poorest population exclusively.

The impossibility to smooth out consumption that many households confront makes the

consequences of shocks even greater. A shock that reduces a household’s income can generate

long lasting consequences if the household does not have access to insurance or insufficient

assets to smooth out consumption. Some children in the house can drop out of school to start

working or they can reduce their nutrient intake affecting their current well-being and also

their future productive capacity. The household can sell productive assets that will also reduce

their income in the future. All these mechanisms for coping with shocks can lead to

irreversible consequences in the future.

All the arguments presented motivate the study of vulnerability. Different approaches and

measures have been developed to improve the understanding and measurement of

vulnerability. A brief summary of the most important of them follows.

7 Among the aggregate shocks are natural disasters (earthquakes, floods, etc.), bad economic conditions (price increases, unemployment increases, recessions, etc.), or socio-political instability (violence, war, etc.) (Hoddinott & Quisumbing, 2008). 8 Idiosyncratic shocks are the unemployment, illness, or death of one member of the family, among others (Hoddinott & Quisumbing, 2008).

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1.2 Alternative approaches to understanding vulnerability and

their low predictive effectiveness

Although attention to vulnerability to poverty has increased during the last decade there is

not an agreed definition and measurement of it. The stochastic nature of the future adds a

layer of complexity to the ex-ante estimation of vulnerability (Dutta et al., 2011).

Competing conceptualisations of poverty and vulnerability emphasise different

explanations for vulnerability to poverty and different methods of measuring it. Authors such

as Hoddinott and Quisumbing (2003a, 2003b, 2008), Ligon and Schechter (2003), Gaiha and

Imai (2008), Calvo and Dercon (2013) and Klasen and Povel (2013) have analyzed and

compare the most influential approaches on vulnerability to poverty. As many of these authors

establish, the conceptualization of vulnerability at individual or household level can be grouped

in the following categories: vulnerability as uninsured exposure to risk (VER)9; vulnerability as

low expected utility (VEU)10; vulnerability as expected poverty (VEP)11; and vulnerable groups.

Vulnerability to poverty measures can be assessed through their capacity to predict future

episodes of poverty. This has been done to different vulnerability to poverty methods (Jha et

al. (2010); Bérgolo et al. (2010; 2012); Celidoni (2011); Feeny & McDonald (2016); Ligon &

Schechter (2004); Zhang & Wan (2009)). The majority of them have been concentrated in the

most popular measures of vulnerability such as Suryahadi et al. (2000); Chaudhuri (2003);

Foster, Greer and Thorbecke (1984; 2010); Calvo and Dercon (2005); Dutta et al. (2011).

However, these evaluations have been more concentrated on ranking these measurements

Celidoni (2011) than proving support for the use of one particular measure. One of the more

9 Among the approaches that see vulnerability as exposure to risks (VER) are ‘Vulnerability as exposure to

risks of low income households’ (Glewwe and Hall (1998); Dercon and Krishnan (2000); Amin et al. (2003); Cochrane (1991); Ravallion and Chaudhuri (1997); Townsend (1994); Jalan and Ravallion (1999)); ‘Vulnerability as extended poverty’ (Cafiero & Vakis, 2006); ‘Vulnerability as subjective perception of downward risk’ (Povel, 2010); ‘Dutta-Foster-Mishra Proposal’ (Dutta et al., 2011).

10 This approach was rigorously formulated by Ligon and Schechter (2003) 11 Ravallion (1988); Jorgensen and Holzmann (1999); Christiaensen and Boisvert (2000); Suryahadi et al. (2000); Chaudhuri (2003); Chaudhuri and Christiaensen (2002); Kamanou and Morduch (2002); Christiaensen and Subbarao (2005); Ligon and Schechter (2003); Calvo and Dercon (2007). There are several empirical applications, among them, Hoddinott and Quisumbing (2003a), Suryahadi and Sumarto (2003), Kamanou and Morduch (2002), Christiaensen and Subbarao (2005), Gaiha and Imai (2008), and Gunther and Harttgen (2009).

24

conclusive pieces of research, made by Bergolo et al. (2010) using Chaudhuri et al. (2002)

methodology12, indicates that vulnerability measures predict better aggregate poverty trends

than at the micro level. The authors conclude that vulnerability estimates through this

methodology should be complemented with additional information on aggregate trends and

shocks in order to target policy intervention Bergolo et al. (2010). The low predictive capacity

of currently available vulnerability measures and the complex application of axiomatic

approaches proposed justify the use of alternative approaches.

1.3 Why is Chile an interesting case study?

The three papers of this thesis use Chile as a case study. There are several reasons to select

this South American country to conduct empirical research. First, the concept of the

vulnerable has been incorporated by its Social Protection System since the year 2000. The main

distinction of the adoption of a vulnerability approach is the coverage of the Social Protection

System. Since the year 2000, the government of Chile began to include in their Social

Protection System people living in poverty, and also middle and low-income households in

unstable economic situations at risk of falling into poverty.

Second, Chile is one of the countries that have followed the global tendency of poverty

reduction during the last decades. Considering a per capita poverty line of around 4.5 dollars a

day, the poverty rate decreased from 38.6% in 1990 to 7.8% in 2013, and extreme poverty

from 13% to 2.5% during the same period. 13 In addition, poverty reduction has been

accompanied by an improvement in other social indicators. Chile has the highest UNDP

Human Development Index (HDI) in Latin America14, and it has one of the lowest poverty

rates in the continent measured by ECLAC15. Today, Chile is a high-income country with one

12 The methodology is applied to 18 Latin American countries and a validation exercise was carried out to assess their capacity to predict poverty in two countries with Panel data available: Chile and Argentina. 13 Under a new methodology (higher poverty lines) that started to be applied in the year 2013, poverty levels decreased from 29.1% in 2006 to 14.4% in 2013, and the reduction in extreme poverty was from 12.6% to 4.5% between these years. 14 In 2013 the Human Development Index for Chile was 0.822 points. 15 Chile is behind Argentina and Uruguay. Source: ECLAC

25

of the highest incomes per capita 16 in Latin America 17 . In this context of significant

improvements in well-being, economic and social vulnerabilities gain relevance.

Third, there is strong evidence of poverty transitions confirming that vulnerability to

poverty is significant even among groups that have escaped poverty. Neilson et al. (2008)

show, using 1996 and 2001 panel data, that while poverty levels have fallen significantly in

Chile, a large percentage of the population is threatened by poverty at some time. The national

moderate poverty rate fell from 22% to 18% between 1996 and 2001. However, more than

34% of the population was in poverty at least once during this time, and 46% of people in

poverty in 2001 were not in poverty in 1996 (Neilson et al., 2005). The group of people that

fell into poverty came, in the majority, from the third up to the sixth decile of the initial

income distribution. They found large income volatility among the first seven deciles of the

population, with the implication that an important share of this population is vulnerable to

falling into poverty. This supports selecting the 60th percentile of the income distribution as the

threshold of vulnerability to poverty.

Fourth, children and older persons are not equally represented in poverty. This evidence

configures Chile as a country where children are over-represented among people in poverty

while older persons are under-represented among this group, and where a higher number of

children in the household is related with higher levels of vulnerability to poverty and a higher

number of older persons means lower levels of vulnerability to poverty. These disparities

between these two age groups raise questions regarding the different levels of protection that

they are receiving, and in particular, the role of Social Assistance programmes over these

poverty rate disparities by age.

1.4 Data for the empirical research: Chile’s Casen and Panel

Casen

16 The GDP per capita PPP for the year 2012 corresponds to US$ 22,363. Source: World Bank, World Development Indicators. 17 Chile in conjunction with Argentina and Mexico.

26

Two main data bases from Chile are used in this thesis. One is the National

Socioeconomic Characterization Survey (CASEN) from 1990 up to 2015 and the other one is

the Panel version of CASEN. A brief description of each data base follows.

The CASEN is applied between November and December every two or three years by the

Ministry of Social Development and it is representative at the level of country, regions, and

rural and urban areas. The samples have on average 59 thousand households in each survey.

The publicly available CASEN have been applied in the years, 1990, 1992, 1994, 1996, 1998,

2000, 2003, 2006, 2009, 2011, 2013 and 2015. Paper I of this thesis makes use of all the

CASEN between 1990 and 2013. The last available CASEN 2015 was not included in the

analysis because it was not adjusted by national accounts as had been done in all previous

versions. This innovation had been in place since CASEN 2013. However, CASEN 2013 was

still comparable with previous CASEN because the data was released with and without

adjustment that year. Since CASEN 2015, all the innovations made in CASEN 2013 have been

in place, among them the new methodology for poverty measurement. Paper III of this thesis

makes use of CASEN 2015 in order to estimate a vulnerability income threshold taking into

account all the changes in the methodology.

Paper II, instead, makes use of the first18 Panel version of the CASEN. In order to support

analysis of the same households throughout the years, the Ministry of Social Development has

conducted a Panel CASEN since 1996. The first Panel CASEN was carried out by the Ministry

of Social Development in conjunction with the Social Observatory at Universidad Alberto

Hurtado (OSUAH) and the Foundation of Poverty Reduction (FSP). A subsample of 5,209

households -comprising 20,942 individuals- was selected from four regions of the country (III,

VI, VIII, and the Metropolitan Region) in CASEN 1996 who were re-interviewed in 2001 and

2006. These regions represent 60 per cent of the national population and of the GDP. The

main advantage of the Panel CASEN is that it provides information from the same households

over a long span of time -10 years.

18 A second Panel from CASEN was collected in the years 2006, 2007, 2008 and 2009. The base line was taken from the cross section CASEN 2006. As the attrition rate of this Panel was very high, being not very used to research.

27

1.5 The three papers and methodology

As stated above, the first research paper explores vulnerability from a relative perspective.

It examines population shifts along the distribution of income from poverty deciles in an

earlier period to deciles of vulnerability in a later period. This is a starting point to understand

vulnerability as inherent in certain deciles of the distribution of income, in particular, the first

six deciles of it. The literature shows that a group of people in developing countries who

moved out of poverty are “bunching up” just above the poverty line and remain vulnerable to

falling into poverty after any shock (Chen & Ravallion, 2004). This research aims to analyse the

movements between poverty and vulnerability in conjunction. The relative distribution method

is applied to analyze the changes along the distribution of income in Chile between 1990 and

2013. As is detailed later, Chile represents the case study and the National Socioeconomic

Characterization Survey (CASEN) between 1990 and 2013 is the survey used to conduct the

empirical analysis. The findings emphasize that poverty reduction can be accompanied by

vulnerability reduction. Rising incomes in real terms between 1990 and 2013 have led to a

decrease in both poverty and vulnerability. This study finds that the main driver of the

reduction of poverty and vulnerability was the increase in the income that households generate

by themselves –their autonomous income19. However, monetary transfers also contribute to

the reduction of poverty and vulnerability. People in vulnerability dropped after monetary

transfers from the government were in place showing the effectiveness of Social Assistance

over a larger population than people in poverty. These results also confirm the empirical 6th

decile as the threshold to define vulnerability. The proportion of people in 2013 who remain in

the first 6 deciles of the income distribution of 1990 fell. In opposition, the proportion of

those in 2013 that were in the top four deciles of the income distribution in 1990 increased.

However, the results indicate that the distribution of incomes shows an increase in polarization

in the first decile. These results suggest that while the great majority of individuals experienced

growth in their autonomous income during this period, the poorest fraction of them has been

left behind. The contribution of monetary transfers to increasing income in the lower deciles is

19 Autonomous income is the name given to the income generated by household members. This income does not consider monetary transfers or imputed rent. Autonomous income is the best income measure to reflect the standard of living of households because it represents their ability to generate income for themselves.

28

not enough to equalize the increase of autonomous income in all the rest of the deciles.

Regarding the variables that explain this reduction in poverty and vulnerability, it is found that

the educational level of heads of household is the most important. Except for education, the

impact of the other variables is small. Higher educational levels reduce the proportion of

people in deciles of vulnerability, but they do not explain all the changes in the distribution of

income nor its polarization. Overall, this study provides evidence that the reduction of poverty

can be accompanied by a reduction in vulnerability where, as in the case of Chile, it has been

led by the increase in the autonomous income that households generate.

The focus of the second paper is the estimation and characterization of vulnerability to

poverty. Although, overall, vulnerability to poverty can be defined as the probability that

individuals or households will find themselves in poverty in the future (Barrientos, 2013), there

is no agreement on how to measure it or determine its impact on well-being. This study

addresses the gaps in understanding and measuring vulnerability to poverty. Specifically, this

study estimates the probability of falling into poverty identifying the vulnerability income

threshold associated with these probabilities. This empirical threshold of 10% of probability is

used to distinguish those in vulnerability and those in the middle class. After the establishment

of the groups in poverty, vulnerability, middle class and upper middle class, they are compared

in their socio-demographic characteristics. The findings show that people in vulnerability to

poverty are distinct in many respects to people in poverty and those who belong to the middle

class. It can be said that people living in vulnerability are in between those living in poverty and

the middle-class group. The socio-demographic characteristics and the propensity to suffer

shocks of people in vulnerability are getting closer to middle-class characteristics and getting

further away from poverty determinants. People in vulnerability have more resources for

avoiding deprivation than those in poverty but not enough to be protected against the risk of

being in poverty in the future. The substantial and statistically significant differences among

the group in vulnerability and the other two income groups stress the importance of making a

distinction among these groups. The results also show that this inverse relationship between

higher income and lower probability of falling into poverty remains pronounced with incomes

decreasing sharply up to a 20% probability of falling into poverty. Furthermore, the rate of

decline of incomes is sharper up to around the 10% degree of probability of falling into

poverty. From that point onwards, although incomes still decrease while the probability of

29

falling into poverty increases, they do so at a slower pace. This makes 10% and 20% of

probability of falling into poverty natural thresholds for identifying the group of people in

vulnerability to poverty. In methodological terms, this study finds that the estimations are very

sensitive to the poverty line considered and the probability threshold used. In this context, this

study uses the new poverty line implemented in Chile since 2013 and the 10% probability

threshold which is in connection with the 60% of the population that the State of Chile uses to

identify the vulnerable.

The third paper moves the analysis to two vulnerable groups: children and older people.

The over-representation of these two age groups in income poverty and vulnerability make

them the focus of many Social Assistance programmes (Barrientos, 2013). The consequences

of living in poverty over their well-being are observed in the short and long term. As a

consequence, children and older persons are commonly recognized as vulnerable groups that

need protection from the State. In addition, one of the main findings of Paper II of this thesis

is that the age composition of the household is an important determinant of vulnerability to

poverty. Households with more children face a higher probability of being in poverty in the

future while having more elderly persons in the households is related with a lower probability

of facing episodes of poverty in the forthcoming years. However, these two age groups do not

necessarily receive the same level of protection from the State. Lynch (2006) distinguishes

between welfare institutions biased towards older groups and pensions –called occupational-

based- and those giving more attention to families, children and to groups with weak ties to

the labour market –named citizenship-based. The age-related bias of welfare institutions

concerning protection from poverty and destitution creates a generational inequity in the

allocation of public benefits. The main objective of this paper is to examine the generational

equity of cash transfers in Chile. In particular, to compare the effectiveness on poverty

reduction of cash transfers targeted to households with children and households with older

persons. A partial fiscal analysis is carried out following the guidelines of the Commitment to

Equity Institute (CEQ20) to compare the situation of these groups before and after direct taxes

and cash transfers. This study finds that the effectiveness of cash transfers to reduce poverty is

higher among people living in households with older persons than in households with children.

20 The Commitment to Equity (CEQ) Institute at Tulane University. http://www.commitmentoequity.org/

30

The main reason behind the higher effectiveness of cash transfers to exiting poverty in

households with older persons is the higher amount of cash transfers that they receive. They

are highly covered by cash transfers that are more effective in exiting poverty. In addition, this

study finds a complementarity of benefits increasing the exit poverty rate. This evidence

supports the argument that the per capita amount of benefit is an important element in

increasing the exit poverty rate and not only the coverage of cash transfers among people in

poverty. Overall, this study provides further evidence that the effectiveness of cash transfers in

reducing poverty depends on the kind and amount of the cash transfer and those in the case of

Chile are strictly in connection with the age composition of the household. The findings

confirm the view that age bias in welfare institutions creates generational inequity in the

allocation of public benefits.

The three papers use different approaches and methodologies to define and measure

vulnerability. All of them are presented in detail in each paper. This section briefly summarizes

each of them.

The Relative Distribution method introduced by Handcock and Morris (1998; 1999) is

applied in Paper I. The Relative Distribution method is a non-parametric framework for

analyzing data taking into account all distributional changes. This framework uses a

combination of graphical tools for exploratory data analysis with statistical summaries,

decomposition, and inference. The Relative Distribution Method helps us observe the way in

which the deciles of income distribution have moved relative to the past from a non-

parametric perspective. It explores whether the movements across income distribution have

benefited the vulnerable deciles in the same way as the rest of the deciles. Moreover, incomes

pre- and post-social assistance are considered in order to evaluate the contribution of monetary

transfers from the government to reducing vulnerability to poverty. In addition, the relative

distribution is decomposed into its covariates in order to observe the contribution of different

socio-economic characteristics, such as gender, age and education of the head of the

household or geographical location, to the income distribution changes. Finally, the Relative

Distribution Method also allows the polarization of income distribution to be measured

through the Median Relative Polarization index (MRP).

31

The estimation strategy and the econometric model used in Paper II follow the

vulnerability to poverty approach proposed by López-Calva and Ortiz-Juarez’s (2014) which

can be carried out in three steps. First, the probability of falling into poverty in the next period

is estimated from socio-demographic characteristics in the current period and some changes

along the period, where the individual is the unit of analysis. Taking advantage of the Panel

data, these estimations are made. Second, the estimation of income is done using the same

covariates used in the first step. Third, the probability and income estimation are combined,

obtaining a predicted income associated with a particular probability of falling into poverty.

From this three-stage method, the definition of the probability of falling into poverty as a

vulnerability threshold has an income threshold associated. This is a clear advantage of this

method because the income vulnerability threshold can be compared with the poverty line;

they are in the same language. These thresholds are used to identify four groups: those in

poverty, vulnerability, the middle and upper middle classes. The first three groups are

compared in terms of their socio-demographic characteristics and propensity of suffer shocks

between the initial and final years.

Paper III carries out a partial fiscal incidence analysis through the comparison of incomes

before and after direct taxes and direct cash transfers. It is partial because it does not consider

indirect taxes and transfers that are usually considered in a full fiscal incidence analysis. The

partial fiscal incidence analysis was carried out following the framework proposed by the

Commitment to Equity (CEQ) (Lustig & Higgins, 2013, 2016). Therefore, the focus of this

paper is on fiscal interventions that affect the total income of the household: direct taxes and

cash transfers. In order to identify the effects of fiscal intervention, the following three

different income measures are compared: market income (pre-fiscal income before direct taxes

and direct cash transfers), net market income (after direct taxes and before direct cash

transfers) and disposable income (after direct taxes and direct cash transfers). The market and

disposable incomes per equivalent person are used to estimate the poverty exit rate. The

poverty exit rate is defined as the proportion of people 0in poverty under market income that

is not in poverty under disposable income. Four poverty rates are considered: the extreme

international, extreme national, international moderate and national moderate. In addition, the

poverty exit rate is decomposed between the probability of being covered -receiving the cash

transfer- and the probability of leaving poverty given that the individual is covered. This

32

decomposition allows us to separate the effect of the programme’s coverage and the

importance of the amount of the cash transfer for being taken out of poverty, given that the

individual is covered. The poverty exit rate is compared between children, adults and older

persons. In addition, in order to identify if households with children and households with

older people are equally protected, households are classified in four groups: households with

children, households with older persons, households with children and older persons, and

households without children and older persons.

1.6 Contributions

The research in this thesis makes several contributions to the existing literature related to

vulnerability to poverty. These contributions are presented in the following paragraphs and

presented in more detail in each paper of the thesis and in the conclusions.

The overall contribution of this thesis is to uncover the meaning of vulnerability to

poverty. In the context of the general lack of agreement regarding what vulnerability is and

how it can be measured, this thesis tries out three different ways to conceptualize and measure

vulnerability focusing on vulnerability to poverty. In doing so, this thesis adds to the literature

in several ways.

The first contribution is the analysis of the relation between poverty and vulnerability to

poverty. Poverty reduction means that people move above the poverty line. Their new position

in the income distribution is unknown. This research empirically analyzes the transition from

poverty to out of poverty identifying if people move to vulnerability to poverty or out of it.

This establishes that poverty can be reduced at the same time as vulnerability to poverty. An

additional contribution is the use of the relative distribution method developed by Handcock

and Morris (1998; 1999) for the study of vulnerability.

Second, this research contributes to the incorporation of the polarization concept in

vulnerability to poverty analysis. Understanding income polarization as the occurrence of a few

large groups with different income levels that feel identified with their group and alienated

from each other (Esteban & Ray, 1994), vulnerability can be explored from this approach.

33

People in vulnerability to poverty can be understood as a group alienated from other income

groups and with a high degree of identification among themselves.

Third, the relation between the probability of falling into poverty and income is

established. Using the vulnerability to poverty approach proposed by López-Calva & Ortiz

Juarez (2014), this research contributes to the estimation of a vulnerability threshold for a

high-income country like Chile. The income threshold definition allows the operationalization

of the concept of vulnerability to poverty. This is crucial for vulnerability to poverty reduction

and for the goal of poverty eradication. In methodological terms, this study finds that the

estimations are very sensitive to the poverty line considered and the probability threshold used.

In this context, this study uses the new poverty line implemented in Chile since 2013 and the

10% probability threshold which is in connection with the 60% of the population that the

State of Chile uses to identify the vulnerable. The estimation taking into consideration the

contextual factors of a particular country is a contribution to the literature.

Fourth, this research contributes to the characterization of the group of people in

vulnerability to poverty. Through the use of panel data for Chile, the socio-demographic

characteristics between people in vulnerability to poverty and people in poverty and the

middle-class are compared. The comparison identifies a group defined as being in vulnerability

to poverty with socio-demographic characteristics that differ from people in poverty and the

middle-class. This is decisive for the design of social protection programmes to reduce

vulnerability to poverty and eradicate poverty.

Fifth, a direct connection between vulnerability to poverty and household composition is

found. A higher proportion of children appears to be related with a higher level of

vulnerability to poverty and a higher proportion of older persons with lower levels of

vulnerability. The literature on vulnerability describes the shocks that people confront as a

central explanation of vulnerability to poverty. Shocks are unpredictable making the estimation

of vulnerability to poverty inaccurate. The identification of household characteristics as

determinants of the probability of being in poverty in the future is a contribution of this

research.

Sixth, this is the first study to compare the effectiveness of cash transfers depending on the

age composition of the household in Chile. The importance of anti-poverty programmes’

34

contribution to exit poverty depending on the proportion of children and/or older person is

presented. This thesis contributes by bringing two topics into the vulnerability literature. First,

age comparison brings the issue of the protection-promotion trade-off that anti-poverty

programmes confront. Second, it raises the issue of welfare institutions biased towards older

groups while children are less protected against poverty and destitution.

Finally, this research also contributes to the literature on the effectiveness of cash transfers

by using a partial fiscal incidence analysis. This kind of analysis, although leaving behavioural

responses apart, allows vulnerable groups to be compared before and after direct taxes and

cash transfers. The priority groups can be easily identified through fiscal incidence analysis.

35

1.7 References

Amin, S., Rai, A. S., & Topa, G. (2003). Does microcredit reach the poor and vulnerable? Evidence from northern Bangladesh. Journal of Development Economics, 70(1), 59–82. https://doi.org/10.1016/S0304-3878(02)00087-1

Barrientos, A. (2013). Social assistance in developing countries. Cambridge ; New York: Cambridge University Press.

Barrientos, A., & Hulme, D. (2008). Social protection for the poor and poorest: an introduction. In Social protection for the poor and poorest (pp. 3–24). Palgrave Macmillan.

Barrientos, A., Hulme, D., & Shepherd, A. (2005). Can Social Protection Tackle Chronic Poverty? The European Journal of Development Research, 17(1), 8–23. https://doi.org/10.1080/09578810500066456

Baulch, B., & Hoddinott, J. (2000). Economic mobility and poverty dynamics in developing countries. Journal of Development Studies, 36(6), 1–24. https://doi.org/10.1080/00220380008422652

Bérgolo, M., Cruces, G., Gasparini, L., & Ham, A. (2010). Vulnerability to poverty in Latin America. Empirical evidence from cross-sectional data and robustness analysis with panel data. Chronic Poverty Research Centre. Working Paper. No 170.

Bérgolo, M., Cruces, G., & Ham, A. (2012). Assessing the predictive power of vulnerability

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41

2 Paper I: Has vulnerability to poverty been increasing

in Chile? A distributional analysis of household income

1990-2013

Abstract

While it is well known that a prospective approach to poverty is necessary to prevent

and eradicate poverty, there is no agreement on how to do so. There is no single definition of

vulnerability to poverty or a consensual mechanism for measuring and quantifying its impact

on well-being. This paper attempts to contribute to narrowing the knowledge gap in the

understanding of vulnerability. This study employs a relative understanding of vulnerability. It

examines population shifts along the distribution of income from poverty deciles in an earlier

period to deciles of vulnerability in a later period. Methods to analyze relative distribution

proposed by Handcock & Morris (1999) are used to perform this analysis. The case study is of

Chile and its’ National Socio-economic Characterization Survey (CASEN) between 1990 and

2013 provides the data used to conduct this analysis. It is found that the vulnerable population

decreased between 1990 and 2013, mainly because of the increase in household income. The

findings emphasize that poverty reduction can be accompanied by vulnerability reduction.

People in vulnerability decreased after monetary transfers from the government showing the

effectiveness of the Social Assistance provided over a larger population than people in poverty.

However, the income distribution shows an increase in polarization, revealing that the poorest

fraction of the population was left behind.

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2.1 Introduction

Poverty and vulnerability have been recognized as close but different concepts. Although

both of them are well-being measures, poverty is an ex-post and vulnerability is an ex-ante

measure of well-being. In general terms, poverty can be understood as significant deficit in

well-being experienced by individuals, households or communities considered unacceptable in

a given society (Barrientos, 2013). Vulnerability to poverty can be broadly defined as the

probability that individuals or households will find themselves in poverty in the future

(Barrientos, 2013). The main distinction between poverty and vulnerability to poverty is the

uncertainty about the future as a consequence of the risks that households and individuals face

(Dercon, 2006).

The motivation to study vulnerability to poverty as something different from being in

poverty came from several theoretical developments and new empirical evidence. First,

poverty has the characteristic of being dynamic. This dynamism is not captured by poverty

measures. The use of static welfare measures can lead to errors of inclusion -people who are in

poverty temporarily because of short-term misfortune- and exclusion -people out of poverty

because of favourable short-term circumstances. Poverty measures based on static data drawn

from a single cross-section survey do not capture the movements in and out of poverty that

people experience, and therefore fail to identify temporarily poor or non-poor individuals

(Baulch & Hoddinott, 2000). Second, people are exposed to aggregate and idiosyncratic shocks

(Dercon, 2006) that make them vulnerable to being in poverty in the future. There are

households out of poverty that do not have enough resources to confront these shocks. Third,

definitions and measures of vulnerability to poverty allow the design of appropriate forward-

looking anti-poverty interventions Chaudhuri (2003). The reduction of vulnerability is an ex-

ante mechanism to reduce poverty. Fourth, interest in vulnerability to poverty is global. This is

a shared concern for both rich and poor countries because as countries improve their living

standards, attention tends to shift away from the poorest population exclusively. Fifth, it has

been shown that uncertainty has direct adverse effects on current levels of well-being (Calvo &

Dercon, 2007; Chaudhuri 2003). Uncertainty about what the future will bring generates

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suffering and anxiety among people. The poverty headcount21 is discontinuous at the poverty

line meaning that incomes below the poverty line classify people as poor and incomes above

the poverty threshold identify people as not-poor. From a social welfare perspective, this

measure captures the welfare loss of being poor when a person is too poor to acquire the

poverty-level basket of goods (Bourguignon & Fields, 1997). For the purposes of the poverty

headcount rate, incomes matter if people are below the poverty line but they do not matter

above it. The concept of vulnerability takes into consideration the well-being of people above

the poverty line but facing the risk of being in poverty in the future. Sixth, the impossibility to

smooth out consumption that many households confront makes the consequences of shocks

even greater. A shock that reduces a household’s income can generate long lasting

consequences if the households have no access to insurance or insufficient assets to smooth

out consumption. As a consequence of this, it has been said that policies directed at reducing

vulnerability are instrumental in reducing poverty (Chaudhuri et al., 2002).

Overall, its importance for the well-being of populations and its strategic role in Social

Protection programmes has motivated the literature on vulnerability to poverty. The study of

vulnerability to poverty increased over the last two decades. The World Development Report

2000/2001 (World Bank, 2001) spotlighted the concept of vulnerability and since then many

studies have been trying to contribute to its understanding and measurement. Among the

available approaches to understanding vulnerability today are vulnerability as expected poverty

(VEP), vulnerability as expected utility (VEU) and vulnerability as exposure to risks (VER)

(Ligon & Schechter, 2003). Although attention to vulnerability to poverty has increased since

2000 there is still no agreed definition or measurement of it. The stochastic nature of the

future adds a layer of complexity to the ex-ante estimation of vulnerability (Dutta et al., 2011).

The main objective of this paper is to provide further insights into vulnerability. It argues

that the welfare loss associated with poverty can be significantly larger than that measured by

cross-section data, and that a better measure of welfare loss can be obtained by measuring

both poverty and vulnerability to poverty. A reduction of poverty achieved by shifting large

numbers of people to just above the poverty line might not reduce the welfare loss from

21 This is part of the family of poverty measures developed by Foster-Greer-Thorbecke (FGT) (1984). The poverty headcount rate shows the proportion of the population in poverty.

44

poverty proportionally. It has been shown that poverty reduction has been accompanied by an

increase in vulnerability. Chen and Ravallion (2004) show evidence of poverty reduction in

developing countries from 1981 to 2001. The authors indicate that the percentage of the

population living below $1 per day was almost halved over the 20 year period falling from 40%

to 21%. The poverty measures for $2 per day also decreased over this period but at a lower

rate. Poverty rated by this higher standard dropped from 67% in 1981 to 53% in 2001.

However, the group of people living between $1 and $2 rose abruptly over these two decades,

from about 1 billion to 1.6 billion. This evidence of clear “bunching up” of people in poverty

just above the $1 line suggests that a considerable number of people in developing countries

remain vulnerable to falling into poverty after any shock.

This study uses an empirical approach as a first approximation to identify vulnerability to

poverty. Instead of focusing on a particular method to measure vulnerability to poverty –that is

done in the second paper of this thesis- this paper focuses on an empirical conceptualization

of being vulnerable to poverty. In general terms, we could identify people who are vulnerable

to poverty as those who populate the bottom six deciles of the per capita income distribution.

This is a definition used by the State of Chile, the case study in this analysis, to target its Social

Protection System. Social Protection is therefore targeted to the 40 or to the 60 percent most

vulnerable people of the population depending on the specific social programme. These

empirical thresholds are also used by academics, governments and international organizations.

For instance, the World Bank and the Palma Ratio focus on the bottom 40 percent of income

distribution to provide an empirical counterpart to the concepts of shared prosperity and

inequality measures respectively. In both cases, the bottom 40 percent represents a group of

people who are not benefiting from income growth as the other deciles of the income

distribution are. Some other governments have also started to target Social Protection

programmes to people in vulnerability to poverty. For instance, Brazil expanded the coverage

of its Bolsa Família Programme (Programa Bolsa Família, PBF) in 2009 because of the

programme’s inclusion and exclusion errors due to the income volatility of the poorest

families.

45

The main objective of this research is to explore the changes in the vulnerability deciles

through time. The analysis in this study examines whether people who have moved out of

poverty remain vulnerable to poverty or have managed to move out of vulnerability altogether.

The main research questions of this paper are the following:

Does poverty reduction mean an increase in vulnerability to poverty?

What is the effect of public monetary transfers on vulnerability to poverty?

What factors underpin a reduction of vulnerability?

In order to explore the relation between poverty and vulnerability reduction the Relative

Distribution Method introduced by Handcock and Morris (1998; 1999) is applied. The

objective is to analyze how the population has moved across the income distribution over the

1990-2013 period, putting attention on the poverty and vulnerability deciles. This method

allows the identification of the proportion of people in the vulnerability deciles of 1990 who

remained in vulnerability in 2013. Additionally, incomes pre- and post-social assistance are

considered in order to evaluate the contribution of monetary transfers from the government to

reducing vulnerability to poverty. The Relative Distribution Method helps us observe the way

in which the deciles of income distribution have moved relative to the past from a non-

parametric perspective. It explores if the movements across income distribution have benefited

the vulnerable deciles in the same way as the rest of the deciles. In addition, the relative

distribution is decomposed into its covariates in order to observe the contribution of different

socio-economic characteristics, such as gender, age and education of the head of the

household or geographical location, over the income distribution changes.

For this purpose, the Chilean National Socio-economic Characterization Survey (CASEN)

available for the years between 1990 and 2013 is used. This survey is collected by the Ministry

of Social Development of Chile and representative of the country, its regions, and rural and

urban strata.

There are several reasons making Chile an interesting case for studying vulnerability to

poverty. First, as it was mentioned above, the incorporation of the vulnerable group in its

Social Protection System. The main distinction of its adoption of a vulnerability approach is

46

the coverage of the Social Protection System. Since the year 2000, the government of Chile

began to include in their Social Protection System not only people living in poverty, but also

middle and low-income households with an unstable economic situation at risk of falling into

poverty22. Second, Chile is one of the countries that have followed the global tendency of

poverty reduction during the last decades. Considering a per capita poverty line of around 4.5

dollars a day, the poverty rate decreased from 38.6 per cent in 1990 to 7.8 per cent in 2013,

and extreme poverty from 13 to 2.5 per cent during the same period.23 In addition, poverty

reduction has been accompanied by an improvement in other social indicators. Chile has the

highest UNDP Human Development Index (HDI) in Latin America24, and it has one of the

lowest poverty rates in the continent measured by ECLAC25. Today, Chile is a high-income

country with one of the highest incomes per capita26 in Latin America27. In this context of

significant improvements in well-being, it is important to analyze the changes in economic and

social vulnerabilities.

Third, there is strong evidence showing that people move in and out of poverty

throughout time. Studies confirm that vulnerability to poverty is significant even among

groups that have escaped poverty. Using 1996 and 2001 panel data, Neilson et al. (2008) show,

that while poverty levels have fallen significantly in Chile, a large percentage of the population

is threatened by poverty at some time mainly those who belong to the bottom six deciles of

the initial income distribution. This supports the selecting of the 60th percentile of the income

distribution as the threshold of vulnerability to poverty.

The main findings of this study show that rising incomes in real terms between 1990 and

2013 led to a decrease in both poverty and vulnerability. Poverty reduction was accompanied

by a reduction in vulnerability in Chile between 1990 and 2013. The proportion of people in

22 Social Protection is targeted to the 40 or to the 60 percent most vulnerable people of the population depending on the specific social programme through a verification of means. 23 Under a new methodology (Higher Poverty Lines) that started to be applied in the year 2013, poverty levels decreased from 29.1% in 2006 to 14.4% in 2013, and the reduction in extreme poverty was from 12.6% to 4.5% between these years. 24 In 2013 the Human Development Index for Chile was 0.822 points. 25 Chile is behind Argentina and Uruguay. Source: ECLAC 26 The GDP per capita PPP for the year 2012 corresponds to US$ 22,363. Source: World Bank, World Development Indicators. 27 Chile in conjunction with Argentina and Mexico.

47

the year 2013 with a per capita income in the lowest 6th deciles of the income distribution in

1990 decreased considerably. A smaller percentage of people in 2013 remain in the

vulnerability deciles of 1990. Only 30% of the population in 2013 remained in the lowest 6th

decile of 1990 and 15% of them were in first 4th decile of 1990. The number of people in 2013

that remained in the same degree of vulnerability as in 1990 more than halved.

The results also show that the 6th decile is a natural inflection point between the two years

under analysis. The proportion of people in 2013 who remained in the first 6 deciles of the

income distribution of 1900 fell. In opposition, the proportion of those in 2013 that were in

the top four deciles of the income distribution in 1990 increased. More than twice as many

recent incomes fell into the higher deciles than the original cohort. This provides evidence to

support the empirical approach that considers the 60th percentile as the threshold of

vulnerability to poverty.

This study finds that the main driver of the reduction of poverty and vulnerability was the

increase in the income that households generate by themselves –their autonomous income28.

However, monetary transfers also contribute to the reduction of poverty and vulnerability.

Their effect was even more important between the years 2000 and 2013 than it was throughout

the first decade under analysis. That means that the reduction of the population living in 2013

with incomes in 2000’s vulnerability deciles was more important than the decrease of the

population in vulnerability in 2000 compared with the 1990 vulnerability deciles. This reflects

the expansion of the Social Protection System since 2000 onwards.

The polarization of incomes29 between 1990 and 2013 is an additional finding of this

research. The lower tail of the distribution –mainly the first decile- contributes more

importantly to the polarization of incomes than the upper tail. This suggests that while the

great majority of individuals experienced growth in their autonomous income during this

period, the poorest fraction of them was already falling behind. Although monetary transfers

28 Autonomous income is the name given to the income generated by household members. This income does not consider monetary transfers or imputed rent. Autonomous income is the best income to reflect the standard of living of households because it represents their ability to generate income for themselves. 29 From the polarization approach of Esteban and Ray (1994), the polarization of a population occurs when people belonging to a group that share an attribute - such as income, religion, race - feel identification with the members of their group and alienation from other groups with different attributes. In the context of income distribution, polarization occurs when there are few large groups with different incomes that feel identified with their group and alienated from each other.

48

reduce the proportion of people in vulnerability, and especially in the first 2 deciles of the 1990

income distribution, they are not enough to reduce considerably the polarization of income

distribution. Their main contribution is in reducing polarization in the lower tail, and this effect

increased throughout the years. However, as it was observed before, the contribution of state

financial support to increasing income in the lower deciles is not enough to equalize the

increase of autonomous income in all the rest of the deciles.

This study finds that an increase in educational level of heads of household is behind the

average higher income in 2013 than in 1990. In addition, a small but positive effect on income

is generated by a reduction in the proportion of children in the household. In the opposite

direction, there are the following variables: greater proportion of women heads of household

and young people. These variables increased the proportion of people in the lowest decile and

reduced the proportion in the top decile. Except for education, the impact of the other

variables is small. The higher educational levels reduce the proportion of people in deciles of

vulnerability, but they do not explain all the changes in the distribution of income nor its

polarization.

Overall, this study provides evidence that the reduction of poverty can be accompanied by

a reduction in vulnerability where, as in the case of Chile, it has been led by the increase in the

autonomous income that households generate.

The contributions of this research are centered in a better understanding of the

vulnerability to poverty concept. From an empirical point of view, this research presents

evidence that poverty reduction can be accompanied by vulnerability to poverty reduction in a

middle-income country like Chile. An additional contribution is the application of the relative

distribution method to the study of vulnerability. This non-parametric framework is used to

analyze the changes in vulnerability to poverty from a relative and empirical point of view.

Finally, this research contributes to the incorporation of the concept of polarization in

vulnerability to poverty analysis. People in vulnerability to poverty can be explored as a group

alienated from other income groups and with a high degree of identification among

themselves.

The paper is organized as follows. Section 2.2 presents the literature review regarding the

importance of understanding vulnerability to poverty and the different approaches to

49

measuring it, providing a context to this understanding of vulnerability. In section 2.3

vulnerability to poverty in the context of the Chilean Social Protection System is presented. In

Section 2.4, the theoretical framework of the analysis is presented. The following sections

explain the data base used (Section 2.5) and the methodology conducted (Section 2.6). Section

2.7 presents the main results of the paper. The final section draws out the main conclusions of

the research.

2.2 Why vulnerability matters and how can it be measured?

This section focuses on two areas, foundational to the research which follows. First, it

examines the literature that makes the case for using vulnerability to poverty as a welfare

measure. Second, it examines the range of methods available for measuring vulnerability to

poverty. As interest in vulnerability to poverty has increased, many different approaches and

measures have been developed and proposed. However, there is no consensus on either its

definition or its measure.

2.2.1 The importance of risk understanding poverty

Poverty and vulnerability have been recognized as close but different concepts.

Traditionally, poverty has been seen as multiple deficits in the basic goods and services

required for human life available to individuals or households. There has been a tendency to

measure poverty in terms of individuals’ economic resources – measured with an income or

expenditure metric – to cover these needs and thereby avoid poverty. In general terms,

vulnerability to poverty refers to the threat of experiencing poverty at some point in the future.

Viewed from this perspective, the main distinction between poverty and vulnerability to

poverty lies in the uncertainty about the future that households and individual face. Chambers

(1989) distinguished between external factors –shocks, risks and stress- and internal factors –

defencelessness -in defining the degree of uncertainty faced by households. His study placed

social risks and resilience, as the capacity to cope with risks without damaging loss, as the key

50

dimensions of vulnerability. More recently, these shocks have been categorized as aggregate

shocks or idiosyncratic shocks (Chaudhuri et al., 2002; Dercon, 2006; Hoddinott &

Quisumbing, 2008). The former are those that affect several people at the same time while the

latter only affect one individual or household. Among aggregate shocks are natural disasters

(earthquakes, floods, etc.), bad economic conditions (price increases, unemployment increases,

recessions, etc.), or socio-political instability (violence, war, etc.) (Hoddinott & Quisumbing,

2008), whereas idiosyncratic shocks refer to unemployment, illness, or the death of one

member of the family, among others.

The motivation to study vulnerability to poverty as something different from poverty came

from several theoretical and empirical contributions.

A key factor encouraging the study of vulnerability to poverty has been the growing

evidence supporting a dynamic understanding of poverty. There is strong evidence showing

that many individuals and households move in and out of poverty at different times meaning

that poverty is a not a static state that remains stable throughout time. Aggregate or

idiosyncratic shocks as described above can reduce the income or expenditure of households

moving them into poverty. For instance, a household can face employment shocks, health

shocks, or agro-climatic shocks that can reduce their income generation and consequently

affect their poverty status. The evidence of poverty dynamics indicates there is a group of

people who are not in poverty but lack enough resources to avoid the possibility of being in

poverty in the future.

The population groups vulnerable to poverty are an important target for any anti-poverty

programme. This is another important reason for the growing interest in the study of

vulnerability to poverty. The identification of which sectors of the population face a high risk

of being in poverty in the future is a priority in the design of Social Assistance programmes. In

order to identify these groups of people, a clear definition of vulnerability to poverty is needed

and a way to measure it. As Chaudhuri (2003) clearly states, in order to define appropriate

forward-looking anti-poverty interventions, “it is necessary to go beyond a cataloging of who is

currently poor, how poor they are, and why they are poor to an assessment of households’

vulnerability to poverty–who is likely to be poor, how likely are they to be poor, how poor are

they likely to be, and why are they likely to be poor” (Chaudhuri, 2003, p. 3). The determinants

51

of being in poverty at a particular time can differ from the determinants of being in poverty in

the future. A forward-looking policy to reduce poverty must consider these possible

differences.

In addition, the distinction between the ex-post measures of poverty and the ex-ante

measures of vulnerability lead to a distinction between ex-post measures to reduce poverty and

ex-ante measures to prevent poverty. The reduction of vulnerability is an ex-ante mechanism

to reduce poverty.

The interest in vulnerability to poverty is now global. At the same time that poverty rates

have been decreasing around the world during the last few years (World Bank, 2016b)30 ,

interest in the risk of falling into poverty again that these people face has grown. This is a

shared concern for both rich and poor countries because as countries improve their living

standards, attention tends to shift away from the poorest population exclusively. This provides

an additional motivation for the understanding of vulnerability to poverty and the need for

social protection of the poorest people in richer economies.

Another motivation for the understanding of vulnerability to poverty came from the

possibility of an intrinsic negative relationship between uncertainty and well-being. Risk and

uncertainty might lead to adverse behavioural responses leading to lower levels of well-being.

They might also have direct intrinsic effects on well-being. It has been shown that uncertainty

has direct adverse effects on current levels of well-being (Chaudhuri, 2003; Calvo & Dercon,

2007). Uncertainty about what the future will bring generates suffering and anxiety among

people. As it was established in the World Development Report 2000/2001, risk and

uncertainty are a crucial source of worry to people living in poverty (World Bank, 2001).

In addition, the impossibility to smooth out consumption that many households confront

makes the consequences of shocks even greater. A shock that reduces a household’s income

can generate long lasting consequences if the household does not have access to insurance or

insufficient assets to smooth out consumption. Some children in the house can drop out from

school to start working or they can reduce their nutrient intake affecting their current well-

being and also their future productive capacity. The household can sell productive assets that

30 The World Development Indicators, http://povertydata.worldbank.org/poverty/home/

52

will also reduce their income in the future. All these mechanisms for coping with shocks can

lead to irreversible consequences in the future. As a consequence of this, it has been said that

policies directed at reducing vulnerability are instrumental in reducing poverty. This is the case

in the majority of households in low and middle-income countries. The market failure of

uninsured economic risks taken is another reason that motivates the study of populations that

are vulnerable to poverty.

2.2.2 How to measure vulnerability to poverty

After acknowledging that vulnerability to poverty matters and understanding it is important

to poverty reduction strategies, the key question arises as to how to properly define and

measure it. Several proposals are available. Competing conceptualisations of poverty and

vulnerability emphasise different explanations for vulnerability to poverty and different

methods of measuring it.

Some authors argue that vulnerability to poverty is explained by, and can be measured

through, exposure to risk. Risk-related vulnerability puts the downside risks that people face at

the centre of the analysis. The literature regarding this understanding of vulnerability explores

the “exposure to risk and uncertainty, the response to these, the welfare consequences, and the

implications for policy” (Dercon, 2006).

Among the approaches that see vulnerability as exposure to risks (VER) are ‘Vulnerability

as exposure to risks of low income households’ (Glewwe and Hall (1998); Dercon and

Krishnan (2000); Amin et al. (2003); Cochrane (1991); Ravallion and Chaudhuri (1997);

Townsend (1994); Jalan and Ravallion (1999)); ‘Vulnerability as extended poverty’ (Cafiero &

Vakis, 2006); ‘Vulnerability as subjective perception of downward risk’ (Povel, 2010); ‘Dutta-

Foster-Mishra Proposal’ (Dutta et al., 2011).

Usually, vulnerability as exposure to risks uses an ex-post, backward-looking perspective,

which concentrates on observed past outcomes rather than on an aggregate measure of

vulnerability (Tesliuc & Lindert, 2002; Cruces, 2005; Cruces & Wodon, 2007). For instance,

one method of doing it is to focus on counting the poverty spells observed for a household

53

(Baulch & Hoddinott, 2000). Through the use of panel data, it counts the number of times that

a particular household has been in poverty. Households that have always been in poverty are

persistently poor, but households that have only experienced poverty some of the time can be

defined as vulnerable to being in poverty.

Other authors propose an understanding of vulnerability as expected poverty (Ravallion

(1988); Jorgensen and Holzmann (1999); Christiaensen and Boisvert (2000); Suryahadi et al.

(2000); Chaudhuri (2003); Chaudhuri and Christiaensen (2002); Kamanou and Morduch

(2002); Christiaensen and Subbarao (2005); Ligon and Schechter (2003); Calvo and Dercon

(2007). Among these approaches, the most popular focuses on measuring vulnerability to

poverty understood as the probability that a household will find itself in poverty in the future

(Barrientos, 2013). The ‘vulnerability as expected poverty’ (VEP) approach tries to predict ex-

ante the likelihood that a household will experience poverty in the future. It tries to estimate

the probability that welfare may drop below the minimum standard of living in the future

(Chaudhuri et al., 2002). The methodology for this approach was initially provided by

Chaudhuri et al. (2002), who applied it to Indonesian household data. In this approach, the

measure of vulnerability to poverty is defined by the probability that a household or an

individual, whether currently in poverty or not, will be in poverty in the future. In this

approach, the usual metric to measure vulnerability to poverty is the income or consumption

space.

This methodology is based on the assumption that cross-section variation in household

consumption could be a proxy for cross-time variation. As a consequence of this, it does not

need panel data to predict the likelihood of future poverty. It only requires transversal data to

calculate vulnerability measures. This is the main reason for its popularity. However, this

assumption is not accepted in the literature and therefore, this methodology is not widely

adopted.

An intrinsic difficulty with the approach of vulnerability as expected poverty, as a forward-

looking approach to poverty, is the fact that it tries to predict ex-ante the probability of being

in poverty in the future, which can only be done stochastically. Another source of criticism of

this methodology comes from the fact that it makes several assumptions to predict future

probabilities: households' probability distributions of consumption are log-normal, these

54

distributions are time invariant, and these distributions are equal for all households. All these

assumptions are questionable. In addition, it establishes a counter-intuitive relationship

between a lower probability of being in poverty in the future and more variance (risk) in

consumption. Under this perspective, a social policy that increases variance in consumption

could reduce the likelihood of being in poverty in the future. The transition of this

understanding of vulnerability to social policies focused on vulnerability reduction is not clear.

A third approach proposed to understand vulnerability to poverty has been called

‘vulnerability as expected utility’ (VEU). This approach was rigorously formulated by Ligon

and Schechter (2003) who propose a utilitarian-based methodology to estimate household

vulnerability. They propose that vulnerability can be measured through the difference between

two utilities: the utility resulting from a threshold income and an individual’s expected utility

resulting from incomes in a vulnerable situation. The higher a person’s vulnerability the higher

the difference between these two utility values. An individual is not in vulnerability when his

income is above the threshold. The advantage of this approach is that it includes the

individual’s attitude towards risks through the utility function. However, it assumes that all

individuals have the same attitudes towards risks. While appealing in terms of incorporating

risk in the analysis of vulnerability, it has some drawbacks. The approach needs a specification

of a utility functional form and a value for the risk aversion parameter. The VEU has been less

used as an approach than the VEP because vulnerability is presented in terms of utility making

the interpretation of results less straightforward and less applicable to real contexts.

A fourth perspective for understanding vulnerability to poverty is through identifying

groups of vulnerable people. They can be the elderly, orphans, widows, the landless or low-

paid workers. They are described as vulnerable to poverty because they are in a fragile

condition, in more general ‘weakness’ or ‘defencelessness’ (Dercon, 2006). They can face more

risks as well, such as those described in the vulnerability as exposure to risks approach, but

they are in a disadvantaged position to take profitable opportunities. If they do not receive

support or protection, they can end up in severe and persistent poverty. As a consequence, this

approach is generally used by policy makers that aim to protect these vulnerable groups

through social policies. However, this approach risks including people in better conditions

who do not need any protection (Barrientos, 2013).

55

2.3 Vulnerability in practice: The Chilean Social Protection

System

Following the same path as other developing countries, Chile has expanded its social

protection programmes since the 1990s but mainly since the 2000s. The decade of the 1990s

was characterized by the implementation of programmes and policies that were focused on

poverty reduction according to the basic needs framework. However, it was at the end of the

1990s when a new model of social policy started, based on guaranteeing social rights (Hardy,

2006).

This was accompanied by an increase in social public spending as a percentage of GDP,

from 12.5% in 1990 to 16.5% in 2009. This translated into an average of 13.2% during the

period 1990-2000 and an average of 14% between 2001-2009 (Rodríguez and Flores, 2010).

However, social protection spending, which is the main component of social public

expenditure, decreased as a percentage of GDP, from 8% in 1990 to 7.4% in 2009. In contrast,

education and health expenditure rose during the same period, from 2.3% to 4.4%,

representing from 1.8% to 4.0% of GDP respectively (Rodríguez and Flores, 2010). This

shows that social protection spending had been decreasing relatively from the 1990s and that

the public social expenditure was still low compared with the other countries within the region

(an average31 of 18.4% of GDP in 2007-2008) (ECLAC, 2010) and also with countries. Today

Chile has the third lowest levels of poverty and extreme poverty in the region32 and this is

mainly attributed to its social policies applied since the 1990s (Robles, 2011).

The objective of the Chilean Social Protection System is to provide inclusive support to

the population through their life-cycle. It provides access to education and work opportunities

covering the risks of illness and disability and also guaranteeing an adequate pension in old age.

The foundations of this System were the health reform AUGE (which provides explicit

guarantees for a set of health conditions that have been progressively increasing over time), the

31 Figures for individual countries range from less than 8% of GDP in Ecuador, Guatemala and Peru (central government) to more than a fifth of GDP in Argentina, Brazil, Cuba and Uruguay. 32The level of poverty has been decreasing during the last two decades in Chile, from 38.6% in 1990 to 14.4% in 2012. Only the estimation of 2009 reported an increase (15.1%). These official figures of absolute poverty in Chile originated from the National Survey of Socio-economic Characterization (CASEN).

56

Chile Solidario (a poverty reduction programme, which provides a guaranteed minimum income

for the poorest in the country), and unemployment insurance for formally employed (Hardy,

2006). All these programmes were introduced by the government of President Ricardo Lagos

(2000 - 2006).

The following government of President Michelle Bachelet (2006-2010) formally

institutionalized the Social Protection System, under the name Red Protege (Protection

Network). This system included the previously created programmes and also some

programmes created during this government. It includes the Integral Protection System for

Children Chile Crece Contigo for children between 0 and 4 years and their mothers during and

after pregnancy, the Social Protection System of Labour, designed for men and women

workers to promote decent working conditions for working lives, and the Pension Reform that

also created a solidarity-based pillar for the old age pensions.

The implementation of the new Social Protection System widened the coverage of

targeted programmes, from the previous focus on households in poverty to the inclusion of

households vulnerable to poverty. This new target population includes not only households

living in poverty but also middle and low-income households in unstable economic situations

who face the risk of falling into poverty.

This change in the orientation of social policy required a new mechanism to select its

beneficiaries. A new targeting tool was designed named Ficha de Protección Social (FPS) which

started to be implemented in the year 2006. The FPS was a proxy means test. Considering the

importance of informal sources of income in the poor population, the use of either a simple

means test or an unverified means test is difficult. The FPS was the tool determining access to the

Social Protection System, based on applications made by local councils. Over 10 million

Chileans have had their data recorded by the FPS in all municipalities (more than half of the

national population considering that by 2010 the estimated population was approximately 17

million (Larrañaga et al., 2010).

The FPS introduced a conceptual and methodological change compared with the previous

mechanisms for selecting beneficiaries, named Ficha CAS. Instead of focusing on durable

goods and housing characteristics, the FPS focuses on the individual’s capacity to generate

income and the household’s needs.

57

The income generation capacity (CGI), the permanent incomes of the household,

including pensions or monetary subsidies, and the reported current income, are taken into

consideration. This emphasizes permanent income 33 in the ranking households. The idea

behind the estimation of the CGI for all the individuals of the household able to work is to

consider not only the current and declared income but their potential to generate income.

Behind this method is a view of vulnerability as insufficient permanent income, in the sense

that the income that a family has today matters less compared with the permanent income that

they can generate. For example, a family could be in poverty today but be out of poverty in the

future and vice versa. In addition, this estimation attempts to minimize the under reporting of

income that is usual in household surveys, especially when the goal of the survey is to qualify

to be the beneficiary of a social programme. Despite these advances, the CGI still does not

measure vulnerability directly.

After the calculation of all the CGI inside a household, and in order to obtain the

permanent income that a household has, some household needs are also taken into

consideration, such as the number of household inhabitants, their gender and age, and adjusted

by equivalence scales. In addition, special needs are also taken into consideration if there are

any persons with physical or mental disabilities belonging to the household. Behind this

consideration of special needs depending on the composition, size and demographic

characteristics of the household is also the idea of vulnerability, but this time, the

vulnerabilities associated with certain groups, such as small children or people with disabilities

who require care and someone to look after them. The assumption is that either someone in

the household must look after them, who consequently cannot work or they need to pay

someone to care of them. The presence of a carer in the household reduces the CGI of the

household.

33 Here, permanent income means income that is not affected by transitory income changes. It represents the income that households on average should have regardless of their current income. This is not strictly the same as the definition of permanent income in economic theory. The hypothesis of permanent income (PIH), developed for the first time by Milton Friedman (Friedman, 1957), tries to incorporate the idea that people smooth out consumption throughout their lives. The differences between current consumption and income can be explained by expectations of future income. Under this hypothesis, people smooth out consumption to avoid transitory changes in income affecting their current consumption.

58

After estimating the capacity of the household to generate income, the FPS creates,

through econometric techniques, a ranking of the families in Chilean society in order to

prioritize social expenditure. At the end of the process, each family is assigned a score to locate

them in one percentile of the distribution of the household survey representative of the whole

population (CASEN).

With this score and location in the ‘permanent income’ distribution, families can apply to

social programmes. There are two important FPS score thresholds that allow families to apply

to some social programmes, the first being in the 40th percentile of the distribution and the

second in the 60thpercentile. This means that if a household belongs to the 40th percentile of

the population classed as most vulnerable they can access some programmes or be eligible for

other programmes if they are under the threshold that identifies the 60th percent most

vulnerable of the population. Social Programmes use these thresholds, or set their own

thresholds in conjunction with other requirements in order to select beneficiaries.

The 40th percentile most vulnerable sector of the population qualify to apply to specific

social programmes. In this respect, it can be said that the FPS considers vulnerability as well as

poverty. Under this approach, not only are people in poverty qualified to receive Social

Protection but also people out of poverty. The wider coverage of the Social Protection System

shows a social policy concerned with vulnerability to poverty.

Summarizing, it would still be an exaggeration to say that the FPS tool is an instrument

designed to measure the vulnerability of households. The variables used by the FPS could be

included to evaluate a household’s resources in a targeting tool based on poverty (Larrañaga et

al., 2014). However, there are some aspects that can be considered as vulnerability sensitive,

for example the consideration of groups with special needs and the wider coverage of social

programmes beyond poverty thresholds.

Since its introduction in 2006, the FPS has been criticized as an imperfect instrument for

assessing household poverty (Larrañaga et al., 2010). One of the critiques is in relation to the

transparency of the calculation of the FPS score because the algorithm of calculation is kept

secret to avoid manipulation. This is not in line with the right of individuals to know how their

score has been calculated. Another critique refers to the updating that the data collected by the

FPS have (Larrañaga et al., 2010). The FPS provided a score that remained valid for several

59

years without any need for updating in between. This meant that households could apply to

social programmes without being evaluated again. The recertification of a score did not happen

unless the family asked for it. Recertification only happened when households believed their

conditions had deteriorated since the previous certification. In addition, it was shown that

there were considerable distortions in the data collected by the FPS compared with the

National Survey of Socio-economic Characterization (CASEN) (Larrañaga et al., 2010). This

meant that some households had altered some characteristics or some interviewers had

collected the information erroneously. In both cases, the ranking of a household would

become distorted. This undermined the principle of fairness that should be present in any

Social Protection System. For example, in the FPS data, a higher percentage of households

with people with disabilities was observed than in household surveys (2.5 times more than in

CASEN); the same applied to the percentage of households with women as head of the

household (1.6 times higher than CASEN). A similar effect occurred with household size,

reported to be 15% smaller than by CASEN. This potential manipulation of data was more

prominent in the lower deciles, perhaps because they were closer to getting a low score in the

FPS. This potentially false data generated a generalized fall in the FPS scores. In January 2010,

there were 3.77 million, 22.5% of the population, with scores below the threshold (6,035

points) that delimited the 10th percentile. This means that in the lowest decile there were more

than double the numbers of people than expected (Larrañaga et al., 2010).

As a consequence of these issues with the FPS, it was replaced with the Registro Social de

Hogares (RSH) in January 2016. The RSH incorporated the three main suggestions for

improvement made by the technical commission. First, it relies on information from

administrative sources instead of information self-reported by the households. Second, instead

of relying on a ranking of the population in terms on their level of poverty or vulnerability, the

focus is on excluding the richest groups. Third, each social programme will rely on information

specific to its objectives, instead of a unique score used for all equally (Larrañaga et al., 2014).

The RSH is a registry of information drawn from different sources: FPS or Ficha Social and

the State’s data bases. Among the latter are the Servicio de Impuestos Internos (SII), Registro Civil,

Administradora del Fondo de Cesantía (AFC), Instituto de Previsión Social (IPS), Superintendencia de

Salud y Ministerio de Educación among others.

60

There are two main changes that the RSH proposes in order to improve on the FPS

(Larrañaga et al., 2014). The first one is the use of State administrative data instead of just self-

reported data from households. The FPS only used data provided by the individuals creating

the potential for misreporting explained above. The RSH still uses data provided by individuals

but reduces the potential for error by using administrative data too. The second change

introduced by the RSH is using criteria for exclusion as well as inclusion in defining the

vulnerable group. Individuals who have vehicles, properties or other assets above some value

are excluded from receiving social assistance. The RHS tries to exclude the population on

higher incomes through the use of available administrative data. The objective is to reduce the

error of including people who should not be receiving social benefits, called Error Type II. At

the same time, the RSH still allows some exceptions in the exclusion component depending on

the characteristics of the household. For instance, the value of their property is not considered

for individuals older than 60 years; income from work is not considered for younger people

between 18 and 24 years who are studying and earning wages lower than double the minimum

wage; the value of a car is not considered for households with a disabled person/s.

Independently of the mechanism for collecting the data and selecting the beneficiary, the

Chilean Social Protection System is still targeted to the 40 or the 60 per cent most vulnerable

of the population, depending on the programme. This means that the idea of vulnerability to

poverty is still embraced. The only thing that has changed between the mechanisms for

selecting the beneficiaries is the better identification of this group within the whole population.

In this context, this research pays attention to the lowest 60 percent of the income distribution.

This research uses income distribution to identify the 60 percent most vulnerable.

2.4 An empirical approach to vulnerability to poverty

In general terms, vulnerability to poverty can be defined as the probability that individuals

or households will find themselves in poverty in the future (Calvo & Dercon, 2007; Chaudhuri,

2003). The main objective of a vulnerability to poverty measure would be to identify this

probability of falling into poverty that every household or individual faces. After the

61

identification of this probability, a threshold to distinguish between the vulnerable and non-

vulnerable is needed.

There is no consensus on a unique definition, measurement and threshold of vulnerability

to poverty. Considering this fact, this research uses an empirical approach to identify the

vulnerable to being in poverty in the future. This study combines the theory and the evidence

to evaluate the changes of vulnerability to poverty focusing on a fixed group in the population.

People in vulnerability are those who are in the six lowest deciles of income distribution. Let

us see in detail the argument behind this decision.

From a theoretical point of view, we can start from the notion of an ‘augmented’ poverty

line proposed by Cafiero and Vakis’s (2006). The authors argue that this line should consider

not only the package of consumption goods and services necessary to satisfy the minimum

standard of living but also a basic ‘basket of insurance’ against ‘unacceptable risks’. Their

objective is to incorporate risks into the measurement of poverty. Under this

conceptualization, the ‘augmented’ poverty line incorporates vulnerability in a broader

perspective. It is not only the fact of being subject to shocks but also the welfare consequences

of being exposed to risks.

The notion of an ‘augmented’ poverty line has been incorporated into the vulnerability

approach to defining the middle class proposed by López-Calva & Ortiz-Juárez (2014). They

define as middle class a household that faces a probability of falling into poverty lower than

10%. The authors estimate the income per capita per day associated with this probability,

finding a threshold that works as an ‘augmented’ poverty line. The group of people whose per

capita incomes are below this threshold have incomes at or below US$10 dollars PPP a day

and above the poverty line of US$4 dollars PPP a day. They are considered vulnerable. The

middle class includes households with per capita income between US$10 and $US50 dollars a

day.

The concept of the most vulnerable of the population takes into consideration not only

people in vulnerability to poverty but also those in poverty. The most vulnerable are people

currently in poverty and people not in poverty but facing a high risk of being in poverty

because of fragile economic conditions. Considering both these two groups as the vulnerable,

people under the proposed threshold of US$10 dollars are vulnerable. The application of this

62

threshold to Chile shows that the most vulnerable comprises around 50 percent of the

population. The threshold of US$10 dollars PPP a day is around the 50th percentile of income

distribution, meaning that 50 per cent of the population is vulnerable (Hardy, 2014). This

approach is used in Chapter 3 to estimate the vulnerability income threshold for Chile

considering the new poverty line implemented since 2013.

The evidence of poverty dynamics also shows that the first 6 deciles of income

distribution are the most vulnerable. The Chilean population has shown mobility between

states of poverty and non-poverty. As noted in the Introduction, Neilson et al. (2008) show

from 1996 and 2001 panel data that although poverty levels have been reduced in Chile, a high

percentage of the population is at risk of being in poverty at some time. The poverty rate fell

from 22% to 18% between 1996 and 2001. However, more than 34% of the population was in

poverty as least once during this time, and 46% of people in poverty in 2001 were not in

poverty in 1996 (Neilson et al., 2008). The group of people that fell into poverty came, in the

majority, from the third up to the sixth decile of income distribution. They found high relative

mobility among the first seven deciles of the population, meaning that an important percentage

of the population is vulnerable to falling into poverty.

Another relevant approach is the shared prosperity concept embraced by the World Bank.

It focuses on the income of the bottom 40 percent of income distribution over time (Basu,

2013; World Bank, 2016a; World Bank Group, 2015) which ideally should grow at a higher rate

than, or at least in proportion to, the rate of income growth for the general population. This

measure has been discussed for some time but criticism pointing to the arbitrariness of the

threshold of the 40th percentile still remains. The shared prosperity concept suggests that in the

same way that countries may vary in their definition of poverty lines they could vary in their

definition of shared prosperity. They can decide how many deciles of their income distribution

are used to define their own shared prosperity. As a starting point, the 40th percentile is

proposed as the proportion of the population that must benefit substantially from growth.

The so-called Palma Ratio proposed by Palma (2011; 2014) also considers the bottom 40

per cent of income distribution as the proportion of the population capturing the

disadvantaged segment of the income distribution. Palma (2011; 2014) found that the relation

between the highest decile and the bottom four deciles of the income distribution can best

63

summarise a country’s income inequality. The remaining half of the population, represented by

the middle and upper-middle deciles of the distribution from decile 5 to 9, are homogenous

with respect to the proportion of national income that they get. This implies that income

inequality is explained by the relative share of national income captured by the richest and the

poorest segments of the population. They acquire over half of the national income. In Palma's

view, high income inequality in Latin American countries is mainly explained by the low

proportion of national income that is captured by the bottom 40 per cent relative to that

captured by the top decile.

This ‘augmented’ poverty line has also been used in applied social policies. In the case of

Chile, the government defines that the Social Protection System goes to the 40 or to the 60

percent most vulnerable people of the population depending on each Social Programme's

particular target. This is made through the use of a targeting tool named the Registro Social de

Hogares, that was described in section 2.3 of this paper.

Brazil is another Latin American country where the definition of the target population for

social assistance is through the notion of vulnerability as extended poverty. The initial pre-

fixed target of covering 11 million families through Brazil’s Bolsa Família programme (Programa

Bolsa Família, PBF) was expanded to 12.5 million in the year 2009. This increase in the

coverage of the programme was decided because of the programme’s errors of inclusion due

to the income volatility of the poorest families. Soares et al. (2010) showed that 45 per cent of

those receiving the benefit were not actually eligible, mainly because their income level was

slightly above the income threshold. They provide evidence showing that poor individuals face

income insecurity, meaning that their eligibility may vary from month to month. This makes

the real target population much larger than those identified as poor at a given moment in time.

However, from their estimations, they proposed that the PBF would effectively need to cover

15 million families in order to include the families most vulnerable to poverty.

Based on this empirical evidence and on the theory of an augmented poverty line, this

study will rely on the bottom six deciles of the income distribution as the population

considered vulnerable. People who belong to the lowest six deciles of the income distribution

can be considered as more vulnerable to poverty than the rest of the population. The empirical

research below focuses on this group in the population and its changes over time.

64

2.5 Data

The data is drawn from the National Socio-economic Characterization Survey (CASEN)

from 1990 up to 2013. The CASEN is applied between November and December every two

or three years by the Ministry of Social Development and it is representative at the level of

country, regions, and rural and urban areas. The samples have on average 59 thousand

households in each survey. The publicly available CASEN have been applied in the years,

1990, 1992, 1994, 1996, 1998, 2000, 2003, 2006, 2009, 2011 and 2013. This research will

consider all these years to make a comparison between income distributions and to explore

how the vulnerable groups have been changing during this period.

This research compares households’ income distributions during the mentioned period,

understanding household earnings as a measure of living standards. Household earnings are

best captured by one of the three different definitions of household income that the Chilean

government considers in defining their social programmes: autonomous household income.

From the point of view of this research, this income is the most appropriate way to understand

a household's permanent living standards because it represents the income that the household

generates. Formally defined, autonomous income represents the income from salaries and wages,

earnings from self-employment, self-provision of goods produced by the household,

allowances, bonuses, rents, interest, pensions, life insurance payouts and transfers between

individuals. It does not consider the monetary transfers that the state provides to families, such

as contributions in cash distributed through social programmes34. Autonomous income plus

monetary transfers is named monetary income. Finally, there is another category of income,

named total income, which considers the monetary income plus the imputed rent of the

household. Imputed rent applies to households who do not pay rent for a dwelling. The

imputed value is equivalent to the rent to be paid in the market for a similar house.

34 CASEN records the subsidies received by households for Basic Solidarity Pension Old Age and Disability, Solidarity Pension Contribution Elderly and Disability, Family Subsidy, the Mental Disability Allowance, Family Protection Bond and Bond Exit Chile Solidario, Extraordinary Bonds Family Support (March) / (August), Consumer Subsidy Payment Water, Sewerage and Sewage Treatment (SAP), Electrical Grant, Unemployment Allowance and Family Allowance.

Table 2- 1 Summary of CASEN statistics by years of application

Source: Author’s elaboration from CASEN 1990, 1992, 1994, 1996, 1998, 2000, 2003, 2006, 2009, 2011, 2013. All equivalent incomes are expressed in 2013

Chilean Pesos. Weighted?, To correct last two rows.

CASEN

years

Survey

Observations

Extreme

Poverty

Non-Extreme

Poverty

Out of

povertyMean

Standard

DeviationMedian Mean

Standard

DeviationMedian Mean

Standard

DeviationMedian

1990 104,391 12.7% 25.7% 61.6% 257,754 456,385 141,934 259,472 456,111 143,617 273,592 466,589 154,575

1992 142,703 8.8% 23.9% 67.3% 298,195 522,099 165,006 299,775 521,762 166,973 312,218 532,948 176,254

1994 177,340 7.2% 20.1% 72.6% 320,926 1,164,004 178,462 322,129 1,163,859 179,866 332,770 1,167,795 188,875

1996 133,886 5.6% 17.5% 76.9% 359,431 599,757 197,431 362,945 598,755 201,510 384,826 615,745 217,980

1998 187,809 5.4% 16.1% 78.5% 379,825 707,783 207,977 383,590 706,667 211,198 405,970 719,552 229,968

2000 252,217 5.3% 14.7% 80.0% 388,992 731,987 211,956 392,999 730,887 215,430 417,105 744,383 235,717

2003 256,447 4.5% 14.0% 81.5% 381,616 788,468 214,653 385,948 787,386 218,801 408,101 803,166 236,288

2006 268,508 3.1% 10.5% 86.4% 410,244 643,376 246,661 415,193 641,739 251,303 435,543 653,289 268,224

2009 246,782 3.5% 11.4% 85.1% 442,276 711,499 262,054 453,726 708,117 273,104 474,136 720,600 289,683

2011 200,160 2.6% 11.7% 85.7% 456726.8 673717.9 277843 466740.9 670562.4 286025.1 489746.8 684732.7 303496.5

2013 217,967 2.3% 5.3% 92.4% 504756.9 771161.8 310252.4 517384.6 767605.1 321599.1 542108.6 781582.9 341809.8

Equivalent Autonomous

Income

Equivalent Monetary Income Equivalent Total IncomePoverty Levels

Autonomous income is the best option to consider because it is the income that the

household itself generates. Monetary income and total income are also considered when making the

estimations in this research. The comparison between the relative densities of autonomous income

and monetary income is crucial to analyze the effect of monetary transfers from government to

households over the most vulnerable deciles. The estimation of relative density to the total

income is a useful benchmark because it is the income used in Chile to calculate poverty rates.

Taking into account that the objective is to understand household income changes, all the

income definitions in the survey are considered in order to get a more complete picture.

In order to maintain the comparability of incomes over the years, all incomes were

deflated using information from the National Statistics Institute (INE) to express them in 2013

values. In addition, incomes have been adjusted considering equivalences of scale. A square

root scale has been used, which divides household income by the square root of household

size35. This scale has been used in recent OECD publications (e.g. OECD 2011, OECD 2008)

to compare income inequality and poverty across countries. Considering Chile is an OECD

country, this scale is a useful reference. After these adjustments, we can find the equivalent

household income distribution for each year available. This means that although when

household income is the variable considered initially, the final information is expressed by

individuals in order to consider the size of the household. Instead of labelling this "equivalent

household income distribution" every time, it is referred to simply as income distribution from

now on. The only distinction made will be between autonomous, monetary, or total income

distributions.

Table 2-1 shows a description of CASEN for each year of application. It contains the

number of observations and the poverty rates. The mean, median and standard deviation of

the three incomes is provided. Incomes are expressed in 2013 Chilean Pesos and they

correspond to equivalent income.

35 This implies that, for instance, a household of four persons has needs twice as large as one composed of a single person.

67

2.6 Methodology

This section presents the method used to analyze the changes in vulnerability deciles

between 1990 and 2013 in detail. The first part presents the Relative Distribution method which

allows two income distributions to be compared by deciles. The second section describes the

polarization indexes that this method proposed.

This study uses the R statistical package reldist developed by Handcock and Morris (1998;

1999) to implement the method. The authors explain in detail the steps to use this method

here http://www.stat.ucla.edu/~handcock/RelDist from where the package reldist can be

downloaded to use in the R statistical package. Handcock and Aldrich (2002) also provide the

syntaxes of one of the examples provided in Handcock and Morris (1999).

2.6.1 The Relative Distribution method: a non-parametric framework

In order to closely examine patterns of changes in income distribution during the last two

decades, this research uses the Relative Distribution method introduced by Handcock and Morris

(1998; 1999). The Relative Distribution method is a non-parametric framework for analyzing data

taking into account all distributional changes. This framework uses a combination of graphical

tools for exploratory data analysis with statistical summaries, decomposition, and inference.

In this study, a comparison is made between income distributions at different time points.

It is not exactly the same group of people, because the same people are not interviewed each

time, but they always represent the national population. A comparison is made between the

incomes of the reference population, 𝑌0, and the incomes of the comparison population, 𝑌. In

this case, the reference population is the income distribution in the base year, 1990, and the

comparison population is the income distribution for the comparison year, 2013. This

represents the longest span of years and their comparison shows the changes during the period

under consideration. However, other comparisons are also made. The distribution in the base

year 1990 is compared with every year of the survey application, to explore the change in every

short period with available information. In addition, the 23-year series is split in two, from

http://www.stat.ucla.edu/~handcock/RelDist

68

1990 to 2000 and from 2000 to 2013. In this case, the beginning year of the decade is the

reference distribution and the end year of the decade is the comparison. This display allows us

to observe the changes that took place within each decade.

The relative distribution method allows us to observe the differences between the

reference distribution and the comparison distribution. The comparison between them takes

advantage of the information implicit in the distributions and goes beyond comparison of

means and variances. It helps explore whether the low, middle, and upper deciles of the

income distributions have changed and if the distribution is more or less polarized.

In order to interpret the results from the comparison between income distributions for

different years, it is important to understand what is called the relative distribution. The relative

distribution is a random variable obtained by transforming a variable from a comparison

group, Y, by the cumulative distribution function (CDF) of that variable for a reference group,

𝑌0. The relative data, r, obtained from this transformation represents the rank of the original

comparison value in terms of the reference group's CDF. It is interpreted as the percentile

rank that the original comparison value would have in the reference group. Then, the CDF and

the density of the relative data can be used to completely represent and analyze distributional

differences.

To exemplify relative density in this research, the 1990 and 2013 income distribution are

considered. In this case, the income in the year of comparison (2013) is assigned the rank it

would have had in the income distribution for the reference year (1990). These ranks are

represented in a histogram in which bin cut-off points are defined by the deciles of the 1990

distribution. The frequency in each bin represents the fraction of 2013 individuals falling into

each decile of the 1990 income distribution. If both income distributions were the same, the

relative deciles would take a uniform value of 10% over the income scale, because 10% of

2013 individuals would fall into each 1990 income decile.

The relative probability density function (PDF), 𝑔(𝑟), can be interpreted as a density ratio.

It is the ratio of the fraction of respondents in the comparison group to the fraction in the

reference group at a given level of the outcome attribute. For this research, it corresponds to

the ratio of the income density in the comparison year (2013),𝑓(𝑦𝑟), to the income density in

the reference year (1990) , 𝑓0(𝑦𝑟), evaluated at each decile of the income distribution in the

69

reference year (1990). In other words, the density ratio is the fraction of individuals in the

comparison population that are in each reference income decile.

In addition, the relative data can be interpreted as the percentile rank that the original

comparison value would have in the reference group. And the relative CDF, 𝐺(𝑟), can be

interpreted as the proportion of the comparison group whose attribute (income in this case)

falls below the rth quantile of the reference group.

The relative PDF, is then,

𝑔(𝑟) =f(y

r)

f0(yr) y

r≥ 0

𝑔(𝑟) =f(y

r)→ density comparison distribution

f0(yr)→ density reference distribution

The relative PDF, 𝑔(𝑟) , is greater than 1 when there is a greater frequency of

observations in the comparison distribution, 𝑌 , than in the reference distribution, 𝑌0 . The

opposite happens, the relative density is lower than 1, when there is a lower frequency of

observations in the comparison distribution, 𝑌 . In the case of no difference between

distributions, the relative distribution is 1. It always happens when the distributions intersect.

An advantage of the relative method is the option to decompose the relative distribution

into changes in location and in shape. The changes in location are usually attributed to changes

in the median or mean of the income distribution and the changes in shape can be related with

changes in variance, asymmetry, or other distributional characteristics. Let r the percentile rank

that an income value y from the comparison year has in the reference year.

The relative distribution for the comparison year can be decomposed in the following

way,

𝑔𝑡(𝑟) =𝑓(y

r)

𝑓0(𝑦𝑟)=

𝑓𝐴(𝑦𝑟)

𝑓0(𝑦𝑟)×

𝑓(yr)

𝑓𝐴(𝑦𝑟)

70

where 𝑓𝐴(𝑦) = 𝑓0(𝑦 + 𝜌) is a density function adjusted by a shift with the same shape as the

reference distribution, 𝑓0(𝑦𝑟) , but with the median of the comparison one, 𝑓(𝑦𝑟) . This

research uses a median adjustment instead a mean adjustment because it is more appropriate in

the case of the skewed income distribution considered. The value of 𝜌 is the difference

between the medians of the comparison and reference distributions. When the two

distributions have different medians, the ‘location effect’ increases if the comparison median is

higher than the reference median, and it decreases if the comparison median is lower. In the

case where the two distributions have the same median, the density ratio for the location

difference is uniform [0,1].

The second component of the decomposition is the density ratio for the shape difference

which represents the relative density net of the location effect. This shape effect isolates any

re-distribution that happened between the reference and comparison populations. The density

ratio for the shape allows us to observe if there is polarization of the income distribution.

2.6.2 Decomposition by covariates

The relative distribution method also allows the impact of covariates on the variable of

interest to be identified. In the same way as the regression setting explores the impact of

covariates over the outcome, the relative distribution method explores the distributional

changes related to covariates. The impact of covariates over the income distribution can

happen through two channels. The first is a compositional shift in the covariates from one

period to the other. For instance, the proportion of heads of household with higher education,

or the proportion of women heads of household could have changed between one period and

the other. The method quantifies the impact of this change on the distribution of income. The

second effect of covariates over income can be through a change in the relationship between

the covariates and the response variable –income in this case. Taking the same example, even

Overall relative

density

Density ratio

for the location

effect

Density ratio

for the shape

effect

= x

71

if the proportion of women heads of household has not changed, the conditional distribution

of income by sex of head of household could have changed, changing the overall income

distribution.

Usually, the covariate composition and the covariate response relation change

simultaneously and this method proposes a mechanism to separate out the effects. The

approach is similar to location and shape decomposition, in that it creates a counter-factual

distribution to isolate every shift. It adjusts the reference population to have the same covariate

composition as the comparison population. As it is clearly detailed in Handcock and Morris

(1999), this adjustment allows us to answer the question: “How would the income distribution

have looked if there had been no changes in a covariate?” The residual differences in the

relative distribution are interpreted as the covariate-response relationship.

Let’s see in detail how a counter-factual distribution is constructed for the response

variable in the reference population adjusted by the composition of covariates in the

comparison population. The response variables, as before, are Y and 𝑌0, which correspond to

the variables we want to compare across the two periods. The covariates are represented by Z

and 𝑍0 which, in this case, are categorical 36 ranging from {1,2, . . . 𝐾} . Let {𝜋𝐾0 }𝑘=1

𝐾 be the

probability mass function of 𝑍0 and {𝜋𝑘}𝑘=1𝐾 be the probability mass function of Z which

together denote the population composition with respect to the covariate. For conditional

comparison of the response variable Y between the two periods, the densities are considered

of

𝑌0 given that 𝑍0 = 𝑘:

𝑓𝑌0|𝑧0(𝑦|𝑘) 𝑘 = 1, … , 𝐾

and the density of Y given that Z=k:

36 The extension to continuous covariate or to multivariate covariate can be found in Handcock and Morris (1999)

72

𝑓𝑌|𝑍(𝑦|𝑘) 𝑘 = 1, … , 𝐾

The two densities represent the covariate-response relationship. The marginal densities of

Y and 𝑌0 can be represented by, respectively:

𝑓0(𝑦) ≡ ∑ 𝜋𝑘0

𝐾

𝑘=1

𝑓𝑌0|𝑧0(𝑦|𝑘) = 𝐸𝜋0[𝑓𝑌0|𝑧0

(𝑦|𝑍0)] (𝑎)

𝑓(𝑦) ≡ ∑ 𝜋𝑘𝐾𝑘=1 𝑓𝑌|𝑧(𝑦|𝑘) = 𝐸𝜋[𝑓𝑌|𝑧(𝑦|𝑍)] (𝑏)

Equations (a) and (b) represent “the overall distribution of the response as a weighted

average of the distributions given the covariate where the weights are the proportions of the

population with that value of the covariate” (Handcock & Morris, 1999, p. 91). From these

equations it is clear how the covariate affects the overall distribution.

Consider the case where the conditional distributions of the response are the same for

each value of the covariate, that is, fY0|z0(y|k) = fY|z(y|k), k = 1, … , K . Therefore “the

subgroups defined by the covariate have identical distribution of the response and the relative

distributions for each group across the two populations will each be uniform” (Mark Stephen

Handcock & Morris, 1999, p. 91). The two populations are equivalent given the covariate. The

marginal density of Y0 can be written by:

𝑓0(𝑦) ≡ ∑ 𝜋𝑘0

𝐾

𝑘=1

𝑓𝑌|𝑧(𝑦|𝑘)

The comparison between this equation to (b), identifies the differences between 𝑓(𝑦) and

𝑓0(𝑦), which are because of the differences in 𝜋𝐾0 and 𝜋𝐾 , the compositions of the covariate in

each population. The alternative situation is obtained “where the probability mass function of

73

the covariate is the same in each population, that is 𝜋𝐾0 = 𝜋𝐾 , 𝑘 = 1, … , 𝐾 . Then the

marginal density of 𝑌0 can be written as”: (Handcock & Morris, 1999, p. 91).

𝑓0(𝑦) ≡ ∑ 𝜋𝑘

𝐾

𝑘=1

𝑓𝑌0|𝑧0(𝑦|𝑘)

Here, the differences between𝑓0(𝑦) and 𝑓(𝑦) in (b) are because of the differences in the

conditional densities 𝑓𝑌0|𝑧0(𝑦|𝑘) and 𝑓𝑌|𝑧(𝑦|𝑘), 𝑘 = 1, … , 𝐾 . They are the differences in the

covariate-response relationship between the two populations.

Using the ideas presented, a counter-factual distribution can be constructed for the

compositional difference. In it, the distribution of 𝑌0 composition-adjusted to Y is defined as:

𝑓0𝐶(𝑦) ≡ ∑ 𝜋𝑘𝐾𝑘=1 𝑓𝑌0|𝑧0

(𝑦|𝑘) (c)

The comparison between (a) and (b) and (c) allows us to observe that the density 𝑓0𝐶(𝑦)

“corresponds to a counter-factual population with the covariate composition of the

comparison population and the covariate-response relationship of the reference population”

(Handcock & Morris, 1999, p. 91). Population composition remains constant when 𝑓0𝐶(𝑦) to

𝑓(𝑦) are compared, and then differences are isolated in the covariate-response relationship. In

contrast the comparison between 𝑓0(𝑦) and 𝑓0𝐶(𝑦) isolates the impact of the compositional

shifts because they have the same covariate-response relationship.

Through the use of the composition-adjusted response distribution, it is possible to

decompose the overall relative distribution in two: a “component that represents the effect of

changes in the marginal distribution of the covariate (the composition effect), and a

component that represents the residual changes” (Handcock & Morris, 1999, p. 91).

Two relative distributions are constructed from the three distributions -𝑌0, 𝑌0𝐶 , and 𝑌.

They represent the effect of the covariate composition and effect of residual changes. In order

74

to isolate the composition effect, the relative distribution is taken of 𝑌0𝐶 to 𝑌0, denoted R00𝐶 =

𝐹(𝑌0𝐶). When the comparison and reference population have the same marginal covariate

distribution, R00𝐶 will have a uniform distribution. In order to isolate the residual effect the

relative distribution is taken of 𝑌 to 𝑌0𝐶 , denoted R0𝐶 = 𝐹(𝑌0𝐶) . When the conditional

response distributions are the same in both populations, R0𝐶 will have a uniform distribution.

The decomposition is represented in terms of the density ratios in the following

expression:

𝑓(yr)

𝑓0(𝑦𝑟)=

𝑓0𝐶(𝑦𝑟)

𝑓0(𝑦𝑟)×

𝑓(yr)

𝑓0𝐶(𝑦𝑟)

2.6.3 Income distribution polarization

In addition, the Relative Method approach also allows the polarization of income

distribution to be measured through the Median Relative Polarization index (MRP). The

polarization index and its decomposition are a method for measuring the relative density in the

centre or tails of the distribution. The MRP is defined in the following way:

𝑀𝑅𝑃(𝑓, 𝑓0) = 4 ∫ |𝑟 −1

2| 𝑔0

𝐴(𝑥)𝑑𝑟 − 11

0

Or,

Overall relative

density

Density ratio for

the compositional

effect

Density ratio

for the residual

effect

= x

75

𝑀𝑅𝑃(𝑓, 𝑓0) =4

𝑛𝑡(∑ |𝑟𝑖 −

1

2|

𝑛𝑡

𝑖=1

) − 1

The MRP “is the mean absolute deviation around the median of the location-matched

relative distribution 𝑔(𝑎)” (Handcock & Morris, 1999, p. 71). It is weighted by -2 in order to

emphasize deviations in the tails, and scaled to produce an index that varies between - 1 and 1

(Handcock & Morris 1998). When there is a convergence toward the centre of the distribution,

the index is negative, which means less polarization. In the opposite direction, a positive value

represents more polarization and an increase in the tails of the distribution. A zero value of the

index means no differences in distributional shape. When the only difference between F and

𝐹0 is location, (that is, 𝐹0(𝑦) = 𝐹(𝑦 + 𝜌) for some 𝜌, then 𝑔0𝐿 is the uniform distribution,

and MRP (F, F0) is zero. In this case, “none of the differences between F and 𝐹0 are because

of differences in distributional shape” (Handcock & Morris, 1999, p. 71).

Among several useful characteristics of the MRP index37, there is that it can be interpreted

in terms of a proportional shift of observations in the distribution from more central values to

less central values. For example, an MRP of 0.1 means a 10% population shift from the centre

of the distribution to the upper and lower quartiles. Another characteristic of this index is its

decomposition along the scale of y. Through this decomposition, it is possible to compare the

contribution of each section of the distribution to the overall polarization. This allows, for

example, the decomposition of the overall polarization in the contributions made by the

components above and below the median of g(r), which are defined by the following

equations:

𝐿𝑅𝑃(𝑓, 𝑓0) = 8 ∫ |𝑟 −1

2| 𝑔𝑚(𝑥)𝑑𝑟 − 1 (𝑎)

1/2

0

37 To see this in detail, see Handcock & Morris 1998 p.71.

76

𝑈𝑅𝑃(𝑓, 𝑓0) = 8 ∫ |𝑟 −1

2| 𝑔𝑚(𝑟)𝑑𝑟 − 1 (𝑏)

1

1/2

𝑀𝑅𝑃(𝑓, 𝑓0) =1

2𝐿𝑅𝑃(𝑓, 𝑓0) +

1

2𝑈𝑅𝑃(𝑓, 𝑓0) (𝑐)

These are the Lower Relative Polarization Index (LRP) and the Upper Relative

Polarization Index (URP). The LRP reflects the contribution to the median index of the

relative distribution below its median and the URP, of that above its median. The properties of

these indices are very similar to the MRP. They vary between -1 and 1 values; more

polarization is represented by positive values (increases in the tail of the distribution) while less

polarization is reflected by negative values (convergences towards the centre of the

distribution). When the shape component of the relative distribution is unchanging below the

median, then the lower index is zero. However, when it is above the median, then the upper

index is zero.

2.7 Empirical results

As a first approximation to exploring the differences between income distributions we can

compare their kernel densities. Figure 2-1 shows the kernel density estimates of the

autonomous income distributions at the two end points of the period 1990-2013, and Figure 2-

2 includes the kernel density estimates for the year 2000, a middle point between the two

extremes. It can be observed that the period 1990-2013 was characterized by an increase in the

median of the equivalent autonomous household income. The figures show a right shift

movement of the distributions each year, meaning an increase in the median of income. In

addition, the shape of the income distributions has changed during the period, with a higher

concentration of low incomes in 1990 compared with the distributions in the other two years.

77

With later observations, the income distribution is flatter than the previous one showing a

higher dispersion of values along the income distribution.

However, observing the kernel densities do not say very much about what is behind this

means increase and shape change of the income distribution. In order to understand how these

changes have affected different percentiles of the distribution, it is necessary to implement a

different method. The relative distribution method appears as a very useful framework to

answer these questions and to provide insights regarding how different groups of the

population have confronted these changes, especially the most vulnerable.

We already know that incomes are higher in 2013 than in 1990, but does this

improvement reach all the population? Are the higher deciles getting more benefits than the

lower deciles? Is the population in deciles of vulnerability increasing or decreasing with the

increase in incomes? Are the incomes more or less polarized now? All these questions can be

answered with this relative density method.

Figure 2- 1 Kernel Densities 1990 and 2013. Autonomous income distributions

Source: Author’s elaboration from CASEN surveys: 1990, 2000, 2013. Ministerio de Desarrollo Social.

78

Figure 2- 2 Kernel Densities 1990, 2000 and 2013. Autonomous income distributions


2.7.1 Relative Density

The following plots describe the relative probability density function (PDF) and the

relative cumulative distribution function (CDF) for all the periods under analysis –from 1990

to 2013- and also divided by decades: the decade from 1990 to 2000 and the little bit more

than a decade from 2000 to 2013. In the cases of both decades, the relative PDF and CDF are

calculated with the beginning year of the decade as the reference distribution and the end year

of the decade as the comparison.

The distinction between decades is interesting because of the difference between the

Social Protection System in each period. As it was explained in section 2.3, Chile initially

expanded its social protection programmes in the 1990s, but increased them considerably in

the early 2000s. This should be reflected in the most vulnerable deciles.

In addition, a distinction is made between the three measures of incomes mentioned in

section 2.5: autonomous income being that generated by household members; monetary income being

autonomous income plus monetary transfers from the government; and total income being

monetary income plus imputed rent. Relative densities are calculated for each kind of income

79

in order to compare differences among them. The comparison between the relative density of

autonomous income and the relative density of monetary income allows the effects of

monetary transfers on the deciles in vulnerability to be analyzed.

As it was explained, the relative PDF can be interpreted as a density ratio. In Figure 2-3, it

represents the ratio of the fraction of respondents in the comparison year, 2013, to the fraction

in the reference year, 1990, at the given level of income of each decile of the reference year,

1990. In other words, it is a comparison between the densities of observations in both years at

the income level associated with each decile in the reference year 1990. Values above 1

represent more density in the recent distribution -2013, while values below 1 represent less

density in the recent distribution. The value of the relative density represents the multiplicative

factor more or less. The second plot of Figure 2-3 shows the relative CDF in which each point

represents a specific earning level, and as you travel along the relative CDF curve, you can read

off the x and y axes the proportion of the 1990 individuals and the relative proportion of the

2013 individuals who live with that level of income.

Therefore, in Figure 2-3, the movements of the 2013 incomes relative to 1990 incomes

can be observed from two different angles.

Figure 2- 3 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Autonomous Income

80

Figure 2- 4 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Monetary Income

Figure 2- 5 Relative PDF and Relative CDF years 1990 and 2013, Equivalent Total Income

Source: Author’s elaboration from CASEN surveys: 1990, 2013. Ministerio de Desarrollo Social

Figures 2-3, 2-4, and 2-5 display the PDF and CDF for all the periods under analysis,

where 1990 is the reference population and 2013 the comparison. Each figure represents the

different incomes considered. It is important to remember that the 1990 incomes were deflated

using information from the National Statistics Institute (INE) to express them in 2013 values

considering the inflation over the period. This means that these incomes are comparable and

the increase observed in 2013 income compared to 1990 income is independent of the changes

in the value of the money.

81

These figures show that the 6th decile is the inflection point between the two periods for

any of the three incomes under analysis. Under this decile, a relative distribution of less than

one means that if we choose any decile between the 1st and the 6th in the 1990 income

distribution, the fraction of individuals in 2013 that earn an amount of income corresponding

to these deciles is less than the analogous fraction of individuals in 1990. For instance, the

relative density at the sixth decile of original income is 0.76, so 24 percent fewer recent income

individuals attained this level of income. The relative density in the second decile, the lowest, is

0.17, meaning that only 17 percent of the 2013 individuals reach this level of 1990 income.

Considering the empirical approach, with individuals under the 60th percentile considered

vulnerable, these results show that the population in vulnerability, measured in 1990 terms, is

lower in 2013 than in 1990. The percentage of individuals in 2013 whose equivalent income is

under the 6th decile of the year 1990 has decreased considerably. Considering autonomous

income in Figure 2-3, the percentage of the population in the first decile is 37%, 17% in the

second and in the third one 27%. This value is less than one but increases little by little in each

consequent decile up to the 7th decile, where the relative density is more than one. This means

that the proportion of individuals who remain in the 1990 deciles of vulnerability in the year

2013 is substantially less. It can be said that the population living with household income levels

associated with vulnerability in the year 1990 is much smaller in the year 2013.

A higher concentration of individuals is observed in the first decile of 1990 autonomous

income in the year 2013 compared with the 2nd and the 3rd decile. However, this percentage is

reduced by half when the monetary transfers from the government are considered. After the

subsidies are considered in household income, the proportion of individuals in 2013 receiving

the income of the lowest 1990 decile is 18%. In Figure 2-4, it is observed that the

concentration of individuals in the 1st decile is reduced when monetary income is considered,

meaning that the monetary transfers are effective at increasing households' incomes and

getting them out of the lowest deciles, which represent poverty. However, this proportion is

not zero, which means that in 2013 there are still people living with the lowest decile income

of 1990.

The reduction of the proportion of individuals after the monetary transfers can be

observed in all the deciles that represent the vulnerability area of income distribution.

82

However, the reduction is much more important in the 1st decile than in the rest of the deciles.

Furthermore, in the 6th decile, the proportion of the population after monetary transfer is

higher and not lower than when autonomous income is considered. This could be reflecting

that monetary transfers are more focalized in the lowest decile of the population, or that

monetary transfers have a bigger impact on households with lower incomes. However, because

this is aggregate data, it reflects that people from lower deciles move to higher deciles,

increasing the proportion of people from the 6th decile on. The availability of panel data

explored in the second paper of this thesis will permit analyzing these movements in more

detail, allowing us to identify from which to which decile individuals are moving.

Independently of the limitation of the aggregate data, these results show that the

focalization of monetary transfers from the government has generated results that not only

reduce poverty, something that is already known, but also in terms of vulnerability. After the

monetary subsidies are received, the proportion of individuals living in the deciles of

vulnerability is reduced even more between the years 1990 and 2013. This means that the

action of the government’s social protection system has contributed to reducing the population

living in vulnerability further than the contribution of the increase in autonomous income of

the households.

By definition, the reduction of people living in the area of vulnerability means that

individuals are moving to the zones of more secure income represented by the 7th decile

onwards. From this decile, there is a greater frequency of individuals earning the incomes of

these 1990 deciles in 2013 than in 1990. This is represented by a relative density higher than 1.

The 7th decile shows relative densities a little bit higher than one, meaning that the proportion

of individuals in 2013 is slightly higher than in 1990. Moreover, the 8th decile contains 50%

more individuals in 2013 than in 1990. In the case of the 9th and the 10th decile, in the year

2013, it is more than two times more probable to reach those levels of income than in 1990.

This means that more than two times as many recent incomes moved into the higher deciles

than in the original cohort.

The relative CDF allows us to observe these numbers from another perspective. The

relative CDF can be interpreted as the proportion of the comparison year 2013 whose income

falls below the rth quantile of the reference year 1990. For example, at the median of

83

autonomous income in the reference year 1990 – meaning that half of the reference population

earns this income – the relative CDF at this point is 0.2. This means that 20% of the

comparison group experienced lower gains than this.

Considering again the deciles in vulnerability, the proportion of the comparison year 2013

whose autonomous income falls below the 4th decile of the reference year 1990 is almost 15%

and in the 6th decile of the reference year 1990, it is almost 30%. This means that the

proportion of the population living in vulnerability deciles in 1990 has decreased considerably

in 2013.

When monetary transfers are included in the equivalent household income, these

proportions are even lower, representing around 10% under the 4th decile and a little more

than 25% under the 6th decile. This shows again that Social Assistance has been effective in

reducing the population in the 1990 deciles of vulnerability. However, the reduction in

vulnerability mainly came from the increase in the income that households generate, their

autonomous income.

Considering that between 1990 and 2013 is a long span of time, this study calculates the

Relative PDF and Relative CDF over a shorter period. The decades from 1990 to 2000 and

2000 to 2013 – the last year with information – appear as a natural division because of the

increased emphasis on Social Protection from the year 2000 onwards. Figures 2-6, 2-7 and 2-8

show the relative PDF and CDF between 1990 and 2000 for the three kinds of income

respectively and Figures 2-9, 2-10 and 2-11 show the same estimations but for the years

between 2000 and 2013.

The reduction of the number of individuals in the vulnerability area of income distribution

happens in both decades. In 2000, less of the population were in deciles of vulnerability than in

1990, and the same happens in 2013 compared with 2000. The increase in household

autonomous income generated similar reductions in each decile in both periods but only

slightly higher in the second decade. For example, 58% of individuals are in 2000 in the 1st

decile of the 1990 incomes, and 52% are in 2013 in the 1st decile of 2000’s income. These

values in the 2nd decile are 47% for the first decade and 45% for the second decade. The 3rd

decile has a value of 64% in both periods. This means that the increase in household

autonomous income has generated similar reductions in vulnerability in both periods.

84

However, the reduction of individuals in vulnerability deciles changes in both decades

when the monetary transfers from the government are considered. The percentage of

individuals in vulnerability is lower after a household receives subsidies from the government.

The decomposition of the relative distribution by decade shows that this reduction is bigger in

the second decade than in the first one, as was expected because of the expansion of the Social

Protection System.

The monetary transfers from the government are responsible for the decrease in the

population in vulnerability deciles more importantly between the years 2000 and 2013 than

1990 and 2000. The effect of the action of government subsidies is concentrated mainly in the

first two deciles of the distribution in the first decade and the first three in the second period.

From those deciles onwards, the percentage of individuals in vulnerability deciles is very

similar considering autonomous or monetary income. However, when monetary income is

considered, the percentage from the 4th decile in the period 2000-2013 is a little bit higher than

when autonomous income is considered. These percentage points –around 5 and 10–

represent the fact that after monetary transfers, some individuals reach higher deciles. This is

present in the second decade but not so clearly in the first one. This could have happened

because of the expansion in coverage of the Social Protection System.

85





86




Source: Author’s elaboration from CASEN surveys: 2000, 2013. Ministerio de Desarrollo Social.

87

The proportion of people facing vulnerability in 1990 has decreased in 2013 and that this

reduction was more pronounced between the years 2000 and 2013 because of a wider action of

the government through monetary transfers. In order to observe how these changes happened

throughout the years, the information can be even more disaggregated by every year with

information.

The baseline year 1990 is compared with every year in which the survey was applied

identifying where the major changes were concentrated. The following two figures show

graphically how the income distributions – autonomous income in Figure 2-12 and monetary

income in Figure 2-13 – have changed compared with 1990. For the baseline year, 1990, the

relative density has been set at 1 by design.

In Figure 2-12, in general terms, it can be seen that the changes in the income

distributions are marked by a general upwards shift in income in every year compared with

1990. The density of individuals in the top deciles in 1990 increased during the period, while

the density of individuals in the bottom deciles in 1990 fell.

The increase in the upper deciles was considerably bigger than the decrease in the lower

deciles, showing a polarization of incomes. The density of individuals in the top decile in 2013

was more than 2.5 times than in 1990, while the density of individuals in the bottom decile was

almost 40% of the original in 1990. However, the behaviour of the first decile is not the same

as the following deciles up to the 6th decile. The reduction of the population in the first decile

in 1990 over the years is lower than the reduction in the deciles that represent vulnerability.

There is a group of the population that is stuck in the lowest decile and their autonomous

income is not growing enough for them to leave their 1990 level of income behind.

There was a reduction of the number of individuals across all the deciles that represent

vulnerability. Although the relative density in the 6th decile in 1990 was a little bit higher than

the one in 2000, since this year, it started to decrease. From the 7th decile on, the percentage of

individuals is always higher than in 1990, increasing sharply in the highest decile of the

distribution.

88

Figure 2- 12 Relative PDF every year compared with the base year 1990, Equivalent Autonomous Income


The higher concentration in the 1st decile of equivalent autonomous income in 1990 is

eliminated when monetary income is considered. In Figure 2-13, it can be observed that the

percentage of the population that remains in the 1st decile was decreasing throughout the years

up to a level of 18% in 2013 compared with 1990. This means that monetary transfers from

government to households in the 1st decile are successfully increasing their income and helping

them to leave the first poverty decile behind. However, 18% of population in 2013 still earns

incomes equivalent to the first decile in 1990.

Monetary transfers have thus contributed to the reduction in the number of individuals in

the vulnerability area. However, this reduction is more pronounced in the first two deciles over

the years. Their contribution in the 3rd, 4th, and 5th decile started to be noticeable from the year

1996 onwards. But it is from the year 2009 on that the monetary transfers have made a big

difference in the 3rd and 4th deciles, reducing the population in vulnerability. However, the

effect of the action of the government on the 5th decile is less than in lower deciles. Actually,

1

3

5

79

0

0,5

1

1,5

2

2,5

3

1 2 3 4 5 6 7 8 9 10

1990

1992

1994

1996

1998

2000

2003

2006

2009

2011

2013

89

the 6th decile contained more people after the monetary transfers. This can reflect the

movement of people from lower deciles up to the 6th decile and onwards.

Figure 2- 13 Relative PDF every year compared with the base year 1990, Equivalent Monetary Income

Source: Own elaboration from CASEN surveys: 1990, 2013. Ministerio de Desarrollo Social.


The evidence presented shows that equivalised incomes have been moving up in the

income distribution. People have moved away from the levels of poverty and vulnerability in

the 1990 deciles. The monetary transfers from the government have played a role in this

change but the main engine was the increase in income that households generate by

themselves. The following decomposition between location and shape effects disentangle if

this positive effect has been experienced equally by deciles.

1

4

7

10

0

0,5

1

1,5

2

2,5

3

1 2 3 4 5 6 7 8 9 10

1990

1992

1994

1996

1998

2000

2003

2006

2009

2011

2013

90

2.7.2 Location and Shape Effects

In order to have a more detailed picture regarding the income distribution movements,

the relative distribution can be decomposed into location and shape effects. As it was

presented in Section 2.6, location effects represent changes in position of the distributions

attributed to changes in the median of the income distribution. The median shift is understood

as the pattern that the relative distribution would have displayed if there had been no change in

distributional shape, but only a location shift of the distribution. For the other side, shape

effects reflect changes in the form of the income distribution that can be related with changes

in variance, asymmetry, or other distributional characteristics. The shape effect represents the

relative distribution net of the median influence.

The relative impact of location and shape shifts to the overall relative density of each

period can be seen in the following figures. Again, they are displayed for each income type

considered – autonomous, monetary, and total – and for three time spans, 1990-2013, 1990-

2000, and 2000-2013. Each figure has two graphs in a row. The first shows the effect due to

the median shift and the second displays the shape effect.

Figure 2- 14 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Autonomous Income

(a) Location Effect (b) Shape Effect

Source: Authors’ elaboration from CASEN surveys: 1990, 2013. Ministerio de Desarrollo Social.

91

Figure 2- 15 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Monetary Income (a) Location Effect (b) Shape Effect


Figure 2- 16 Relative Density 1990 and 2013, Location and Shape Effect, Equivalent Total Income

(a) Location Effect (b) Shape Effect


In Figure 2-14, for all the periods under analysis, it can be observed that the location

effect reduces the share of individuals in the bottom deciles and increases that in the higher

ones. This makes sense considering that the median shift was positive in this period. The

median upshift in autonomous income between 1990 and 2013 was a dominant factor in the

total relative density observed. However, there was also an important polarization trend that

was not evident in the overall relative density figure. The shape effect shows a prominent

increase in the fraction of individuals in the poorest decile of the distribution, an important

92

decline of the mass between the 2nd and the 8th decile and an increase in the higher two deciles

of the distribution. This suggests that while the great majority of individuals experienced

growth in their income during this period, the poorest fraction of them was already falling

behind. This happens mainly in the 1st decile in which the relative growth is considerable. On

the contrary, the higher two deciles of the distribution increased, contributing to the

polarization.

When autonomous income is considered, as in the case of relative densities, the location

effect and shape effect are very similar between decades. The increase in the median of the

autonomous income was present in both decades. However, the change in the shape of the

distribution forces a higher proportion of individuals into the lower deciles, mainly into the 1st

one. Here, there is a small difference between decades; between the years 1990-2000, the

increase in the lowest decile was a little bit bigger than in the period 2000-2013, indicating that

the first decade was even less generous with the most vulnerable of the population. At the

same time, the relative increase of the two higher deciles was even greater during the first

decade, 1990-2000.

It can be observed that monetary transfers from the government do not have a big impact

on reducing the polarization observed. They have a small impact on the median effect and

slightly reduce the proportion of individuals in the lowest decile in the shape effect. This

means that even when monetary transfers have the effect of reducing the population in

vulnerability areas, they are not enough to equalize the increase in autonomous income of the

rest of the population.

93

Figure 2- 17 Relative Density 1990 and 2000, Location and Shape Effect, Equivalent Autonomous Income (a) Location Effect (b) Shape Effect


Figure 2- 19 Relative Density 1990 and 2000, Location and Shape Effect, Equivalent Total Income (a) Location Effect (b) Shape Effect

94

Figure 2- 20 Relative Density 2000 and 2013, Location and Shape Effect, Equivalent Autonomous Income (a) Location Effect (b) Shape Effect


Figure 2- 22 Relative Density 2000 and 2013, Location and Shape Effect, Equivalent Total Income (a) Location Effect (b) Shape Effect

95

2.7.3 The Polarization Index

The polarization index helps quantify, and to corroborate, the polarization that was

observed in the previous graphs. This index can be used to keep track of changes in the shape

of the income distribution across the whole 1990-2013 period. It measures the magnitude and

direction of differences between distributions in each successive year with 1990 as the

reference year. It is very useful in a context where polarization could be happening in the

vulnerability area of the distribution.

As was detailed in Section 2.6, the polarization index and its decomposition are method

for measuring the relative density in the centre or tails of a distribution. Figure 2-23 displays

the set of three indices calculating the graphs (a), (b), and (c) using the equivalent household

autonomous income, and Figure 2-24 the equivalent household monetary income. The median

relative polarization index (MRP) varies between - 1 and 1. When this index is zero, it means

there is no difference in the distributional shape. When there is a convergence toward the

centre of the distribution, the index is negative, which means less polarization. In the opposite

direction, a positive value represents more polarization and an increase in the tails of the

distribution.

The MRP is decomposed to compare the contribution of each section of the distribution

to the overall polarization. In this case, the overall polarization in the contributions made by

components above and below the median of g(r) is decomposed. The Lower Relative

Polarization index (LRP) reflects the contribution to the median index of the relative

distribution below its median, and the Upper Relative Polarization index (URP), that from

above its median.

The properties of these indices are very similar to the MRP. They vary between -1 and 1

values, more polarization being represented by positive values (increases in the tail of the

distribution), while less polarization is reflected by negative values (convergences towards the

centre of the distribution). When the shape component of the relative distribution is

unchanging below the median, then the lower index is zero. However, when it is above the

median, then the upper index is zero.

96

Figures 2-23 and 2-24 show the evolution of the relative polarization indexes from 1990,

the first with autonomous income and the second with monetary income. It can be observed

that the polarization has been increasing every year compared with the reference year, 1990.

The indexes are positive, indicating more polarization in both tails of the distribution.

It is clear from the figures that the income distribution has polarized during this period

and this polarization came from both tails of the income distribution. However, the lower tail

of the distribution contributes more importantly to the polarization of incomes than the upper

tail of it. It is observed from the fact that the Lower Index (LRP) is always more positive than

the Upper Index (URP), indicating more polarization in the lower than in the upper tail. This

was observed through the shape effect identified in section 2.7.2, when it was appreciated that

the lowest deciles of the distribution – mainly the lowest one – were left behind by the median

increase of the autonomous income per capita.

The monetary transfers contribute to reduce the polarization index MRP, and the LRP

and URP. Their main contribution is reducing polarization in the lower tail, and its effects have

increased throughout the years. Since the year 1996, the polarization index MRP has been

lower after monetary transfers have been received by households. However, it is from 2009

that this reduction is more than 1%. This can be interpreted in terms of a proportional shift of

observations in the distribution from less central values to more central values. For example, in

2009 the MRP is 0.01, meaning a 1% population shift from the upper and lower quartiles to

the centre of the distribution.

The contribution of monetary transfers to less polarization mainly came from the

population shift from the lower quartiles to the centre of the distribution. The LRP in 2013 is

0.17 and the URP is 0.007. This means that within the total 1.2% population movement to the

centre of the distribution between 1990 and 2013, 1.7% is from the lower quartiles and 0.7% is

from the upper quartiles of the income distribution. However, as was observed before, the

contribution of monetary transfers to increasing income in the lower deciles is not enough to

equalize the increase in autonomous income in all the rest of deciles. In this case, they are not

enough to eliminate the polarization of income distribution in both tails.

97

Figure 2- 23 Relative Polarization Indices, Equivalent Autonomous Income

Source: Author’s elaboration from CASEN 1990, 1992, 1994, 1996, 1998, 2000, 2003, 2006. 2009, and 2013, Ministry of Social Development.

Figure 2- 24 Relative Polarization Indices, Equivalent Monetary Income

Source: Author’s elaboration from CASEN 1990, 1992, 1994, 1996, 1998, 2000, 2003, 2006. 2009, and 2013, Ministry of Social Development.

The concern about income polarization came from the theory that polarization is linked to

the source of social tension (Esteban & Ray, 1994; J. E. Foster & Wolfson, 2010). Esteban &

Ray (1994) developed an alienation-identification framework that characterizes polarization

0

0,1

0,2

0,3

0,4

0,5

0,6

1990 1992 1994 1996 1998 2000 2003 2006 2009 2013

Median Index Lower Index Upper Index

0

0,1

0,2

0,3

0,4

0,5

0,6

1990 1992 1994 1996 1998 2000 2003 2006 2009 2011 2013

Median Index Lower Index Upper Index

98

from three main elements: group size, identification and alienation. The polarization of a

population happens when people who belong to a group that share an attribute–i.e income,

religion, race- feel identification with members of their group and alienation from other groups

with different attributes. Groups of significant size with different attributes may feel

identification with their group and alienation from each other. The authors say that these three

elements produce antagonism among people and can lead to social tension. In this study, a

greater distance between incomes in the upper and lower tail of the distribution can be

interpreted as an increase in alienation. At the same time, the fact that the lower decile has

been left behind can increase the self-identification of the group of people in poverty. The fact

that the income growth has benefited a proportion of the population more than the rest is a

potential source of social tension.

2.7.4 Decomposition by socio-demographic characteristics

This section presents the results of applying the covariate adjustment technique of the

relative distribution method explained in Section 2.6. It allows the impacts of changes in the

population composition of covariates to be distinguished from changes in the covariate-

response relationship. In other words, it tries to identify how some covariates have affected the

income distribution through changes in the covariates’ composition or through their impact

over income over time.

The covariates considered describe the household. Some covariates analyzed correspond

to characteristics of the heads of household such as, current work activity; age; and length of

school education. Other groups of covariates reflect characteristics of the household such as,

household with children younger than 6 years; household with children younger than 18 years;

household in the central region of the country; urban or rural household. Table 2-2 shows the

covariates considered in the analysis, following the work of Clementi and Schettino (2015) who

also used the Relative Distribution Method to analyze income distribution changes in Brazil.

Table 2-2 summarizes the population share in each covariate category and the median,

standard deviation and median of autonomous, monetary and total income by categories of the

covariates. The variables are presented for the years 1990 and 2013.

99

An increase in mean and median equivalent incomes was experienced by all the subgroups

during this period. Household heads are more educated and older in 2013 than 1990. The

proportion of households with a female head increased during this period. The proportion of

households with children fell and with older persons increased between 1990 and 2013. This

shows that the age composition of households is changing. Households have older heads,

fewer children and more elderly members. Occupation status, occupation category and the

contract status do not show a considerable change between the two years. People are working

more as employed workers and less as self-employed and a higher proportion has a job

contract in 2013 than 1990.

The manner in which these changes in covariates have affected income distribution

movement is explored through the covariate adjustment presented in the flowing figures.

Table 2- 2 Covariates and Equivalent autonomous income in 1990 and 2013

1990 2013

Population Share

Equivalent Autonomous Income

Population Share

Equivalent Autonomous Income

Mean Standard Deviation

Median

Mean Standard Deviation

Median

Household Head's education

Uneducated and less than 1 year 6.1% 138,133 172,767 105,029 2.6% 239,230 214,571 190,919

1-7 years 37.5% 165,102 253,267 117,943 21.6% 284,186 287,465 222,110

8-11 years 24.6% 190,683 326,584 118,954 24.3% 329,472 338,800 253,884

12 years 15.1% 321,917 595,361 188,919 27.8% 424,824 586,504 306,462

>12 years 16.6% 547,454 704,122 328,622 23.8% 1,001,725 1,249,179 631,583

Household location

Rural 17.0% 193,578 484,962 102,968 12.7% 322,408 601,062 209,863

Urban 83.0% 269,959 448,610 152,185 87.3% 530,319 789,019 328,455

Household region

North 21.5% 243,585 427,030 142,876 21.4% 488,987 663,170 328,076

Metropolitan Region 39.7% 305,060 456,902 171,335 40.6% 635,531 965,898 373,774

South 38.8% 215,116 465,643 117,501 38.0% 371,491 531,589 247,337

Household with young children

At least one children younger than 6 years 46.0% 215,204 361,508 118,903 32.5% 429,575 565,359 286,497

Without children younger than 6 years 54.0% 292,498 520,362 163,891 67.6% 539,485 849,676 322,594

Household with children At least one children younger than 18 years 79.9% 228,174 369,719 129,771 64.6% 448,895 629,168 291,160

Without children younger than 18 years 20.1% 371,529 688,956 200,725 35.4% 604,034 969,600 352,526

Household with older persons

At least one person 65 years or more 19.7% 272,276 541,304 159,202 26.7% 437,225 756,141 285,530

Without individuals 65 years or more 80.3% 253,203 432,310 137,879 73.3% 528,140 774,598 318,928

101

Household Head 's gender

Female 16.5% 195,247 255,985 124,719 34.3% 392,646 562,491 253,884

Male 83.5% 269,152 484,838 144,548 65.7% 561,835 853,891 342,500

Household Head 's age

10-24 years 2.7% 145,381 272,451 93,778 2.0% 368,937 491,086 232,678

25-44 years 44.5% 223,676 367,527 117,065 33.1% 520,860 761,350 302,243

45-64 years 39.1% 296,958 501,503 172,456 45.0% 548,346 862,447 338,384

65 years or more 13.7% 273,133 579,006 153,848 19.9% 387,856 541,633 268,941 Household Head 's occupational status

employed 72.9% 284,951 512,104 150,434 71.7% 588,440 862,667 357,573

unemployed 3.9% 93,974 148,433 59,451 2.6% 229,796 413,226 140,000

Inactive 23.3% 196,452 240,559 134,200 25.7% 295,717 384,241 218,517 Household Head 's occupational category

Unemployed 3.9% 93,974 148,433 59,451 2.6% 229,796 413,226 140,000

Inactive 23.3% 196,452 240,559 134,200 25.7% 295,717 384,241 218,517

Employer 3.0% 1,203,615 1,507,450 750,431 2.1% 1,803,978 2,333,660 1,101,503

Self-employed 20.6% 294,414 483,903 179,088 15.7% 629,171 882,843 400,505

Employee/worker 46.9% 230,272 322,495 133,824 50.8% 536,533 708,331 337,359

Domestic service 1.3% 106,059 101,434 85,752 2.2% 298,020 316,294 223,660

Unpaid family member 0.0% 268,818 199,902 285,746 0.1% 384,490 436,792 250,000

Armed Forces 1.1% 189,579 157,410 152,072 0.7% 673,909 589,008 504,069

Household Head 's contract

Without contract 13.2% 150,006 272,613 91,965 10.6% 555,767 721,075 354,210

With contract 86.8% 237,127 320,025 141,830 89.4% 299,991 353,510 210,815

Source: Author’s elaboration from CASEN 1990 and 2013. Incomes are expressed in 2013’s values.

Figure 2-25 shows the composition effect for autonomous income between 1990 and

2013 (total income is in Appendix 2.10.2). It represents the changes that would have occurred

in the 1990 to 2013 relative distribution of Chilean households if only the covariate

composition of the median-adjusted 1990 reference population had changed. The fact that the

majority of the figures are very close to a uniform distribution suggests that the changes in the

composition of covariates are not so influential on the overall changes in income distribution

over time. However, some covariates have an impact.

The education variable appears as an exception suggesting that the improvement in

education has had an impact in the movements of equivalent income to the upper deciles of

the income distribution. Household heads with more education in 2013 than 1990 contributes

to an increase in the population from the 6th decile on. The relative density is higher than 1

from this decile on, which increases to around 1.3 in the top 10th decile. This means that the

higher education levels that household heads reached in 2013 had increased by 30 per cent the

population in the top decile compared to 1990. Higher educational levels decreased the

population living in vulnerability deciles.

The decrease in the proportion of children in households shows a slight increase in

population in the upper deciles and a decrease in the lower deciles. Its effect is not as

important as the education variables but still shows that part of the movement to the upper

deciles of income distribution is explained by the smaller proportion of children at home. The

equivalent incomes reach higher deciles in 2013 because there are fewer children –hence fewer

inhabitants- in the households. This suggests that a tendency towards smaller households has

contributed to the movement out of the deciles of poverty and vulnerability of 1990.

Exactly the opposite happens with the increase in proportion of female household heads

and older household heads. These changes have slightly increased the proportion of people in

the first decile and reduced the proportion of people in the top decile. Both covariates are then

associated with higher vulnerability.

The slight increases and decreases of these covariates, although they are associated with

some changes in the income distribution, are far from explaining all the changes and

polarization of it.

103

Figure 2- 25 Covariates composition effect, Equivalent Autonomous Income

HH Education H Location H Region

H with young children H with children H with older

HH gender HH age HH occup


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Figure 2- 26 Covariate-adjusted relative density of income, Equivalent Autonomous Income



person


Source: Author’s elaboration from CASEN surveys: 1990, 2000, 2013. Ministerio de Desarrollo Social

Figures 2-25 and 2-26 depict the covariate-adjusted relative density of autonomous

income. What is called the residual effect holds the population covariates composition constant

and hence isolates changes of income distribution generated by changes in the return to a

105

particular covariate over time. The fact that the compositional effect is small for the majority

of the covariates means that the covariate-adjusted distributions are not much different from

the original relative distribution in Figure 2-3. This happens with the majority of the covariates

with the exception of the covariates that showed a small but positive composition effect.

In the case of level of education of the head of household, it can be observed that the

increase in the proportion of population in the top deciles in 2013 would have been lower if

the covariates had not changed between the two periods. In addition, the residual effect shows

that household head education would have moved more people to the 7th decile in 2013 than

actually moved to this decile –Figure 2-3. This indicates that education has contributed to the

reduction of vulnerability through changes in its composition –higher levels of education- and

also through its positive impacts on income.

The covariate ‘households with children’ also increased slightly the proportion of the

population in the 7th decile not as a consequence of the change in the covariate composition

but through their impact on income. This suggests that the higher proportion of people in the

upper deciles in 2013, compared to 1990, is not only affected by the reduction of the

proportion of children but also by its relation to income. It is a residual effect that indicates

that the drop in the number of children is a variable that has produced more income in 2013

than in 1990.

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2.8 Conclusions

Chile does not have a measure of vulnerability to poverty itself, but it has in a sense

embraced the concept of vulnerability. The targeting tool used to select the beneficiaries of

social programmes of the government of Chile tried to incorporate vulnerability through

different mechanisms. However, this tool remains in between upholding a vulnerability

approach and a poverty one. What extends this approach to include vulnerability is the fact of

the widening coverage of the Social Protection System. Some social programmes are targeted

to the 40% most vulnerable of the population, and others to the 60% most vulnerable.

This increase in coverage was adopted in order to address the economic insecurities faced

by the population. At the same time as poverty levels have reduced considerably during the last

two decades or so, the population’s perception of vulnerability has been rising. Studies have

shown that the growing population out of poverty faces significant risks and uncertainties

making them vulnerable to poverty.

The non-parametric Relative Distribution Method helped generate insights into the deciles

of income distribution constituting the most vulnerable groups in the population. The years

between 1990 and 2013 were characterized by an average upgrading of autonomous incomes.

Equivalent autonomous income increased throughout the period, generating a higher

concentration of individuals in the higher 1990 income deciles in the year 2013 and a lower

proportion in the lowest deciles. Under the 6th decile, which can be interpreted as the 60%

most vulnerable of the population in the year 1990, the proportion of individuals had

decreased by the year 2013. This means that, on average, individuals have incomes ensuring

more economic security. The proportion of people in the 8th, 9th, and 10th deciles of 1990

increased in 2013. More than twice as many recent incomes fell into the higher decile than in

the original cohort. Only the 7th decile shows the same proportion of individuals both years.

The monetary transfers from the government to people make the proportion of

individuals in vulnerability lower in 2013 than in 1990. The targeting of monetary transfers

from the government has not only reduced poverty, something which was already known, but

it has also reduced the population in vulnerability. This means that the action of the

government’s social protection system has contributed more to a reduction in the population

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living in vulnerability than the contribution of the increase in autonomous income of the

households.

The expansion of the Social Protection System since the year 2000 onwards reduced the

population in vulnerability more than in the first decade. The reduction of the population

living in 2013 with incomes classifying them as vulnerable in 2000 was more important than

the decrease of population in vulnerability in 2000 compared with the 1990 vulnerability

deciles. The effect of the action of the government’s subsidies was concentrated mainly in the

first two deciles of the distribution in the first decade and the first three in the second period.

The shift to the right of the income distribution – the location effect – reduced the share

of individuals in the bottom deciles and increased it in the higher ones. The median upward

shift in autonomous income between 1990 and 2013 was the dominant factor in the total

relative density observed. However, there was also an important polarization trend that was

not evident in the overall relative density figure. The shape effect shows a prominent increase

in the fraction of individuals in the poorest decile of the distribution, an important decline in

the mass between the 2nd and the 8th decile and an increase in the higher two deciles of the

distribution. This suggests that, while the great majority of individuals experienced growth in

their income during this period, the poorest fraction of them was already falling behind. This

happens mainly in the 1st decile in which the relative growth is considerable. On the contrary,

the higher two deciles of the distribution increased, contributing to the polarization.

Although the income polarization came from both tails of the income distribution, the

lower tail of the distribution contributes more importantly to the polarization of incomes than

the upper tail. This is observed from the fact that the Lower Index (LRP) is always more

positive than the Upper Index (URP), indicating more polarization in the lower than in the

upper tail.

It is observed that the monetary transfers contribute to a reduction in the polarization

index MRP, and the LRP and URP. Its main contribution is reducing polarization in the lower

tail, and this effect increased throughout the years. This contribution to less polarization by

monetary transfers mainly came from the population shift from the lower quartiles to the

centre of the distribution. However, as was observed before, the contribution of monetary

transfers to increasing income in the lower deciles is not enough to equalize the increase of

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autonomous income in all the rest of the deciles. In this case, monetary transfers are not

enough to eliminate the polarization of income distribution in both tails.

One of the limitations of this approach is the impossibility of observing in detail the

movements between deciles. On average, people are rising in the distribution of income.

However, it is not possible to identify where the deciles move from. The availability of the

panel data will allow these movements to be analyzed in more detail, allowing the identification

of what decile individuals move from. Paper II of this research explores panel data to estimate

individual vulnerability and to characterize further the group of people who are vulnerable to

poverty.

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2.9 References



Basu, K. (2013). Shared Prosperity and the Mitigation of Poverty: In Practice and in Precept. World Bank Policy Research Working Paper No. 6700. Available at SSRN: https://ssrn.com/abstract=2354167.


Bourguignon, F., & Fields, G. (1997). Discontinuous losses from poverty, generalized Pα measures, and optimal transfers to the poor. Journal of Public Economics, 63(2), 155–175. https://doi.org/10.1016/S0047-2727(96)01589-7



Chambers, R. (1989). Editorial Introduction: Vulnerability, Coping and Policy. IDS Bulletin, 20(2), 1–7. https://doi.org/10.1111/j.1759-5436.1989.mp20002001.x

Chaudhuri, S. (2003). Assessing vulnerability to poverty: concepts, empirical methods and illustrative examples. Department of Economics Columbia University.



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Clementi, F., & Schettino, F. (2015). Declining Inequality in Brazil in the 2000s: What is Hidden Behind?: Declining Inequality in Brazil in the 2000s. Journal of International Development, 27(7), 929–952. https://doi.org/10.1002/jid.3076







ECLAC. (2010). Public social spending in Latin America: general trends and investment in developing the skills of the new generations. In Social Panorama of Latin America. Economic Commission for Latin America and the Caribbean (ECLAC).

Esteban, J.-M., & Ray, D. (1994). On the Measurement of Polarization. Econometrica, 62(4), 819. https://doi.org/10.2307/2951734

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Foster, J. E., & Wolfson, M. C. (2010). Polarization and the decline of the middle class: Canada and the U.S. The Journal of Economic Inequality, 8(2), 247–273. https://doi.org/10.1007/s10888-009-9122-7


Friedman, M. (1957). The Permanent Income Hypothesis (Reprint, 1. pbk. print). Princeton, NJ: Princeton Univ. Press.




Hardy, C. (2006). Hacia un sistema de protección social fundado en derechos. Ministerio de Planificación, Chile.

Hardy, C. (2014). Estratificación social en América Latina: retos de cohesión social. LOM.






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Larrañaga, O., Herrera, R., & Telias, A. (2010). La Ficha de Protección Social. In Las Nuevas Políticas de Protección Social en Chile (pp. 265–296). Uqbar.

Larrañaga, O., Herrera, R., Telias, A., & Falk, D. (2014). La Ficha de Protección Social. In Las Nuevas Políticas de Protección Social en Chile (2nd ed., pp. 265–296). Uqbar.


López-Calva, L. F., & Ortiz-Juárez, E. (2011). A Vulnerability Approach to the Definition of the Middle Class. Policy Research Working Paper 5902. The World Bank.


Palma, G. (2011). Homogeneous Middles vs. Heterogeneous Tails, and the End of the “Inverted-U”: It’s All About the Share of the Rich. Development and Change. International Institute of Social Studies, 42(1), 87–153.

Palma, J. G. (2014). Has the Income Share of the Middle and Upper-middle Been Stable around the “50/50 Rule”, or Has it Converged towards that Level? The “Palma Ratio” Revisited: The “Palma Ratio” Revisited. Development and Change, 45(6), 1416–1448. https://doi.org/10.1111/dech.12133




Robles, C. (2011). El sistema de protección social de Chile: Una mirada desde la igualdad. Comisión Económica para América Latina y el Caribe (CEPAL).

Rodríguez Cabello, J., & Flores Serrano, L. (2010). Protección del gasto público social a través de la

política fiscal : el caso de Chile. Santiago, Chile: United Nations. Economic Commission for Latin America and the Caribbean. CEPAL. Retrieved from http://www.eclac.org/publicaciones/xml/9/40379/lcl3235e.pdf

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Soares, S., Perez Ribas, R., & Veras Soares, F. (2010). Targeting and coverage of the Bolsa Familia Programme: Why knowing what you measure is important in choosing the numbers. International Policy Centre for Inclusive Growth (IPC - IG). Working Paper number 71.






World Bank. (2016a). Poverty and Shared Prosperity 2016: Taking on Inequality. The World Bank. Retrieved from http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0958-3

World Bank. (2016b). World Development Indicators. Washington, DC: World Bank.

World Bank Group. (2015). A Measured Approach to Ending Poverty and Boosting Shared Prosperity: Concepts, Data, and the Twin Goals. The World Bank. Retrieved from http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0361-1

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2.10 Annexes

2.10.1 La Capacidad Generadora de Ingresos (The income generating

capacity)

The capacity to generate income (CGI) measures the labour capacities of individuals that

can work38. The CGI is calculated based on the observable characteristics of each individual,

such as, years of schooling, work experience, kind of work contract, and others. It is estimated

separately by gender and work status 39 . There also included in the CGI’s estimation are

environmental characteristics in order to reflect the territorial risks40 (Larrañaga et al., 2010,

2014).

38 It excludes students, mothers with small children, people with disabilities, and other groups with difficulties to participate in the labour market. 39 Dependent workers, independent workers, and for inactive people. 40 Such as, level of unemployment of the county and other local features.

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2.10.2 The covariate-adjusted relative density of total income

Figure 2- 27 Covariates composition effect, Equivalent Total Income





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Figure 2- 28 Covariate-adjusted relative density of income, Equivalent Total Income





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3 Paper II: Vulnerability to poverty in Chile: the

differences between current poverty and the risk of

being in poverty in the future

Abstract

Latin American countries have considerably reduced their levels of poverty during the last

two decades. Simultaneously, the concern about the risk of these groups that are currently in

better conditions returning to poverty has increased. The eradication of poverty in the

continent needs not only to lift people currently in deprivation out of poverty, but also to

prevent new people from falling into poverty. Protection should also go to people vulnerable

to being in poverty in the future. However, the information regarding people living in

vulnerability to poverty is scarce. This study addresses the gaps in understanding and

measuring vulnerability to poverty. Specifically, this study characterizes the group of people

living in vulnerability to poverty and compares them to people living in poverty and those who

belong to the middle class. The context of empirical research is Chile, a high-income country

that has significantly reduced its poverty levels during the last decades. The findings show that

people living in vulnerability to poverty are a different group than those living in poverty and

those who belong to the middle class. Substantial and statistically significant differences in

several socio-demographic characteristics between the vulnerable group and the other two

income groups emphasize the importance of distinguishing between these groups. Therefore,

this research contributes both to the understanding of vulnerability and to the design of

strategies against poverty.

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3.1 Introduction

The risk of being in poverty in the future, which is called vulnerability to poverty, has been

a matter of concern for academics and policy makers for more than a decade. While countries

have been improving their living standards and reducing their poverty levels, vulnerability to

poverty has emerged as a priority for policy makers. Rich and poor countries alike want to

protect their citizens in order to avoid episodes of poverty and increase their levels of well-

being.

In general terms, vulnerability to poverty can be defined as the probability that individuals

or households will find themselves in poverty in the future (Barrientos, 2013). The importance

of measuring vulnerability to poverty is the identification of the group of people who are not

currently in poverty but have a high probability of falling into poverty in the future (Wan &

Zhang, 2008). Appropriate policies to prevent them from falling into poverty can be design

from their identification and characterization. As Chaudhuri et al. (2002) state, what really

matters for forward-looking anti-poverty interventions is vulnerability to poverty.

While it is well known that a prospective approach to poverty is necessary to prevent and

eradicate poverty, there is no agreement on how to identify people in vulnerability neither on

what features characterize them. This paper attempts to contribute to narrowing the

knowledge gap in the understanding of vulnerability to poverty and its differences with respect

to poverty and middle class. This study aims to identify and characterize the group of people in

vulnerability to poverty based on their probability of being in poverty. The specific objectives

are to identify the group of people living in vulnerability to poverty; characterize them by their

socio-demographic characteristics and compare them with people in poverty and the middle

class.

In addition, this research contributes to answering whether people in situations of

vulnerability to poverty need different social protection programs than people living in

poverty. The results can contribute to the discussion on public policies to protect vulnerable

people from falling into poverty.

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The analysis in this chapter attempts to provide a detailed description on the characteristics

of households living in vulnerability to poverty. It examines whether people living in poverty,

vulnerability and the middle class are different with respect to their socio-demographic

characteristics and if they are exposed differently to shocks. This document aims to find

answers to the following research questions:

• What characterizes people in vulnerability to poverty?

• Are the socio-economic characteristics of people living in poverty and people

living in vulnerability different?

• Are the socio-economic characteristics of people living in poverty and people

living in the middle class different?

• Do these differences justify differentiated protection for these groups?

In order to identify the group of people living in vulnerability to poverty and compare

them with the other two income groups, the approach proposed by López-Calva & Ortiz-

Juárez (2014) is used. They propose a three-stage methodology that allows obtaining an

individual predicted income associated with a probability of falling into poverty. This is a clear

advantage of this method because the income vulnerability threshold can be compared with

the poverty line; they are in the same language.

Chile is the case study of this research because it has an interesting set-up for studying

vulnerability. Chile is considered a successful case in Latin America of targeted public policies

designed to reduce poverty. For over 25 years, it has achieved successful economic growth

rates and witnessed an improvement in social indicators. Considering a per capita poverty line

of around 4.5 dollars a day, the poverty rate has decreased from 38.6% in 1990 to 7.8% in

2013, and extreme poverty from 13.0% to 2.5% during the same period of time41. Chile is the

country with the highest UNDP Human Development Index (HDI) in Latin America42, and it

41 Under a new methodology (higher poverty lines) that started to be applied in the year 2013, poverty levels

decreased from 29.1% in 2006 to 14.4% in 2013, and the reduction in extreme poverty was from 12.6% to 4.5%

between these years. 42 In 2013 the Human Development Index for Chile was 0.822 points.

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has one of the lowest poverty rates in the continent measured by ECLAC43. Today, Chile is a

high income country44 with one of the highest incomes per capita45 in Latin America46.

However, poverty remains dynamic even in the context of poverty reduction of Chile.

Neilson et al. (2008) using the same CASEN Panel Database that this study uses, show that

although poverty levels have been reduced in Chile, a high percentage of the population is

under the threat of being in poverty at some time. The poverty rate fell from 22% to 18%

between 1996 and 2001. However, poverty remains dynamic even in the context of poverty

reduction of Chile. Neilson et al. (2008) using the same CASEN Panel Database that this study

uses, show that although poverty levels have been reduced in Chile, a high percentage of the

population is under the threat of being in poverty at some time.

This study makes use of a panel version of the National Socio-economic Characterisation

Survey (Encuesta de Caracterización Económica Nacional, CASEN). This Panel follows a subsample

of 5,209 households -20,942 individuals- from four regions of the country (III, VI, VIII, and

the Metropolitan Region) in CASEN 1996 that were re-interviewed in 2001 and 2006. These

regions represent 60 per cent of the national population and of the GDP. This research

exploits the advantage of Panel Data to identify trajectories in and out of poverty in order to

identify the probability of falling into poverty. The moderate poverty income line established

by Chile since 2013 is used. The probability of falling into poverty in the next period is

estimated from characteristics in the initial period and changes between the two periods.

The findings of this study show a negative correlation between income and the probability

of falling into poverty. People with lower incomes in the initial year face, on average, a higher

probability of falling into poverty in the latest year than those with higher incomes. Those with

lower incomes have less availability of monetary and asset resources to overcome the adverse

shocks that may occur over the period. Reduced availability of resources makes people more

vulnerable to poverty in the future.

43 Chile is behind Argentina and Uruguay. Source: ECLAC 44 Chile is classified as High income country by the World Bank. High-income economies are those in which 2015 GNI per capita was $12,476 or more. Chile has a GNI per capita equal to $13,530 in 2016. Data source: World Bank national accounts data, and OECD National Accounts data files. https://data.worldbank.org. World Bank Open Data. 45 The GDP per capita PPP (current international $) for the year 2016 correspond to US$ 23,960 and US$13,793 in GDP per capita (current US$). Source: World Bank, World Development Indicators. 46 Chile in conjunction with Argentina and Mexico.

https://data.worldbank.org/

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The results also show that this inverse relationship between higher income and lower

probability of falling into poverty remains pronounced with incomes decreasing sharply up to a

20% probability of falling into poverty. Furthermore, the rate of decline of incomes is sharper

up to around the 10% degree of probability of falling into poverty. From that point onwards,

although incomes still decrease while the probability of falling into poverty increases, they do

so at a slower pace. This makes 10% and 20% of probability of falling into poverty natural

thresholds for identifying the group of people in vulnerability to poverty.

This study estimates a vulnerability income threshold for the probability of falling into

poverty equal to 10%. This threshold is almost 17% higher than the threshold obtained by

López-Calva & Ortiz-Juárez (2014). Although both of them are income thresholds associated

with a probability of falling into poverty equal to 10%, the threshold estimated by this research

uses the ‘new poverty line’ implemented in Chile since 2013. This new poverty line is 30%

higher than the US $4 PPP per day poverty line used by López-Calva & Ortiz-Juárez (2014)47.

As expected, the vulnerability threshold estimated by this research is higher than the estimated

by López-Calva & Ortiz-Juárez (2014) by 20%. These results corroborate the importance of

measuring vulnerability to poverty, as with poverty, considering the welfare standards of the

specific context. The vulnerability threshold obtained by this research is in harmony with the

current welfare measures used in Chile.

The proportion of people living in vulnerability to poverty is very sensitive to the

vulnerability threshold selected. The density of people around the income thresholds

associated with a 10% and 20% of probability of falling into poverty is very high making the

selection of the threshold a sensitive issue. The recommendation here is that the selection of

the threshold must be relevant to the specific context to operationalize the vulnerability to

poverty measurement. From an empirical point of view, the threshold of 10% of probability of

falling into poverty is more appropriate in the context of Chile. The use of the vulnerability

income threshold associated with this 10% probability of falling into poverty indicates that

19.2% of people live in vulnerability. This percentage plus the 38.8% of people in poverty is

very close to the 60% of the population that the State of Chile uses to identify the vulnerable.

47 To make the comparison poverty lines were expressed at 2005 values. That year was established the moderate poverty line in US $4 PPP per day by the World Bank.

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As a consequence, this percentage is considered as relevant in the Chilean case in order to

compare the group of people living in vulnerability with other income groups.

The comparison among groups shows that those in vulnerability to poverty are distinct in

many respects from both people in poverty and those who belong to middle class. It can be

said that people living in vulnerability are in between those living in poverty and the group in the

middle class. The socio-demographic characteristics and the propensity to suffer shocks of

people in vulnerability are getting closer to middle-class characteristics and getting far away

from poverty determinants. People in vulnerability have more resources to avoid deprivation

than those in poverty but not enough to be protected against the risk of being in poverty in the

future. They have more education, better household conditions and more qualified

occupations than households in poverty. They have similar household sizes to those in poverty

and bigger than middle class households. They are characterized by having more extended

families than households in poverty and the middle class and by having more single

households than those in poverty. They are in between households in poverty and those in the

middle class regarding the proportion of children and older people at home. They are in a

younger stage in the household life-cycle than middle class households but older than

households in poverty. They have the same proportion of female heads of households than

middle class households which is higher than those in households in poverty. The substantial

and statistically significant differences among the group in vulnerability and the other two

income groups stress the importance of making a distinction among these groups.

This research contributes to the literature in two important ways. First, it contributes to the

discussion on the measurement of vulnerability to poverty. It operationalizes the approach

proposed by López-Calva & Ortiz-Juárez (2014) to measure the middle class and applies it to

the measurement of vulnerable groups in a high income country such as Chile. It provides a

conceptualization and measurement of vulnerability to poverty. Second, from this

measurement, the group of people vulnerable to poverty is characterized and compared to the

group of people living in poverty and those belonging to the middle class. The comparison of

their socio-demographic characteristics brings the issue of different social programmes that are

required for people in vulnerability and people in poverty for poverty reduction strategies.

The paper is divided into 7 sections. Section 3.2 examines the importance of understanding

vulnerability to poverty and the different approaches to measuring it, providing a context to

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this understanding of vulnerability. In Section 3.3, the theoretical framework of the analysis is

presented. The following sections explain the data base used (Section 3.4) and the econometric

methodology used (Section 3.5). Section 3.6 presents the main results of the paper. The final

section draws out the main conclusions and a discussion of the policy implications of the

research.

3.2 Literature review: Poverty and vulnerability definitions,

measurements and differences

This section provides a brief review of the concepts of poverty and vulnerability to poverty

and their differences, and then outlines the motivations under their conceptualizations and

measurements.

3.2.1 What does poverty mean?

In general terms, poverty can be conceptualized as significant deficits in well-being which

are unacceptable in a given society (Barrientos, 2013). People in poverty are those at the

bottom of the distribution of well-being in a particular society. Usually, the poverty line is the

threshold in the distribution of well-being that divides those who are in poverty from those

who are not. This threshold can be defined in absolute or relative terms. In the case of

absolute poverty, shortfalls in well-being are measured with respect to a particular level of well-

being. The poverty line is a fixed value that represents a particular level of well-being. In

contrast, relative poverty defines people in poverty as those in the lower segments of the

distribution of well-being.

There are different viewpoints regarding the metric or informational basis of well-being.

From one angle, it has been defined as disposability of resources or their monetary equivalents.

In this case, a shortfall in well-being is understood as a scarcity control over resources or

income. Households or individuals are in poverty because they have low income or available

resources. An alternative option has been the understanding of well-being through a welfarist

approach. The metric of well-being under this approach is utility or, put simply, people’s

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happiness (Sen, 1987, 1995). Poverty is understood as low individual utility under this

perspective. Another alternative to conceptualizing well-being was proposed by Rawls (1971),

through which well-being should be measured through the ‘primary goods’ that contribute to

the realization of people's life plans. A shortfall in the ownership of, or access to, these primary

goods defines poverty. In the capability approach proposed by Sen (1987, 1995), poverty is the

lack of capabilities. These approaches use different informational bases of well-being. Despite

these differences, almost all of them define poverty by an income or consumption threshold.

Even multidimensional poverty, which is based on the capability approach, is highly correlated

with monetary measures of poverty (Sumner, 2016).

3.2.2 What does vulnerability to poverty mean?

There is no unique definition of vulnerability to poverty neither a unique way of measuring

it. Questions remain open regarding how to define it, measure it and how to calculate its

impacts on welfare. Different approaches have been proposed for the understanding and

measurement of vulnerability to poverty. All of them try to incorporate the idea that people

face diverse risks. All of us face risks that create uncertainty about our future. In the

vulnerability to poverty context, all of us have a probability of being in poverty in the future.

When this vulnerability to poverty is high it can also affect our well-being seriously. This

means that vulnerability to poverty matters when it affects people well-being. Vulnerability to

poverty is not important when the threat of poverty is low affecting people’s well-being little

or not at all (Dercon, 2005).

The growing interest in the analysis of vulnerability is related to its negative effect over

people’s welfare. Its incorporation into the design and implementation of Social Protection

Systems appears as a natural path. Not only because vulnerability to poverty affects people’s

welfare negatively but also because poverty levels can fall in the future through tackling

vulnerability to poverty in the present. It can be said that Social Protection targeted to people

in vulnerability to poverty has twofold benefits. On one side, by reducing levels of vulnerability

to poverty, people’s well-being can positively benefit. On the other side, reduction of the

probability of falling into poverty means a probable reduction in poverty levels in the future.

Social protection programmes seeking to tackle the determinants of vulnerability to poverty

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can contribute to the reduction in poverty. Also, the protection of vulnerable groups during

episodes of macro-economic contraction has been recognized as vital to poverty reduction in

developing countries (World Bank, 2001). The threat of being in poverty in the future can be

experienced by people who are currently in poverty and people who are out of poverty but

facing a high probability of being in poverty in the future. People in poverty today are the most

vulnerable group which is therefore the main focus of any Social Protection System.

As Chaudhuri et al. (2002) say vulnerability is a combination of two elements: poverty and

risk. Vulnerability to poverty today represents the risk of being in poverty in the future. It is

the probability of failing to reach the minimum level of income in the future. In other words,

vulnerability to poverty reflects the chances of being below the poverty line in the future.

People in vulnerability to poverty are those who face a high probability of being in deprivation

in forthcoming months or years.

Vulnerability to poverty can be affected by many factors and their interrelation. Among

them there is the environment that households are surrounded by, such as the macro-

economic, socio-political and physical contexts in which a household operates (Chaudhuri,

2003). Additionally, the human, physical and financial resources that a household possesses,

determine their behavioural responses (Chaudhuri, 2003).

As it has been presented throughout this work, interest in vulnerability to poverty has

increased during the last decade and with this, different approaches to understanding and

measuring it have been developed. The most influential approaches and measurements were

presented in Section 2.2.2 of this thesis. To see their authors, and the advantages and

disadvantages of each approach listed in detail, go to Table 3-14 in the Annex 3.9.1.

Poverty and vulnerability have been recognized as close but different concepts. Although

both of them are well-being measures, poverty is an ex-post measure and vulnerability is an ex-

ante measure of well-being. In general terms too, vulnerability to poverty refers to the threat of

experiencing poverty – being below the poverty threshold – at some point in the future.

The main distinction between poverty and vulnerability to poverty is the uncertainty about

the future as a consequence of risks that households and individuals confront. These risks were

initially identified by Chambers (1989), who distinguished between those external (shocks, risks

and stress) to the household and those internal – chiefly the condition of defencelessness to

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the external defined as the lack of means to cope with them without damaging loss. More

recently, these shocks have been categorized as aggregate shocks or idiosyncratic shocks

(Chaudhuri et al., 2002; Dercon, 2006; Hoddinott & Quisumbing, 2008). The first are those

that affect several people at the same time while the latter only affect one individual or

household. Among aggregate shocks are natural disasters (earthquakes, floods, etc.), bad

economic conditions (price increases, unemployment increases, recessions, etc.), or socio-

political instability (violence, war, etc.) (Hoddinott & Quisumbing, 2008). On the other hand,

idiosyncratic shocks are the unemployment, illness, or death of one member of the family,

among others.

3.3 Theoretical Framework: An empirical approach of

vulnerability to poverty

This section provides a theoretical framework for understanding and measuring

vulnerability to poverty and it shows how this conceptualization has been applied in the Latin

American context.

3.3.1 How can we operationalize the vulnerability to poverty concept?

Lopez-Calva and Ortiz-Juarez (2014) establish that the vulnerability to poverty is linked to

social class. In particular, the authors explored the link between vulnerability to poverty and

the middle class. Based on the work of Goldthorpe and McKnight (2004), they relate the

concepts of class and economic vulnerability. Goldthorpe and McKnight (2004) established

that class position affects the risk and opportunities that individuals face. Analyzing workers

and their contracts48, they found that economic insecurity49 was higher for contracts held by

non-skilled workers. These workers confronted higher risks of job loss and unemployment

48 Their analysis focuses in three categories of workers and their contracts: non-skilled workers with simple contracts, professional workers and managers with comprehensive and stable contracts, and intermediate workers with “mixed” forms of contracts. 49 as well as on three dimensions: economic security, economic stability and economic prospects

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than other classes. These results suggest the importance of class in defining economic

vulnerability.

Lopez-Calva and Ortiz-Juarez (2014) follow a vulnerability to poverty approach to identify

the middle class. They establish that the middle class “is defined by the level of income that

allows individuals to protect themselves from falling into poverty over time” (López-Calva &

Ortiz-Juarez, 2014, p. 26). In other words, the middle class is formed by people who face a low

probability of falling into poverty in the future. In contrast, those who face a high probability

of falling into poverty in the future but are not in poverty today, are defined as vulnerable.

Defining what is enough protection against poverty or, in other words, what is a low

probability of falling into poverty in the future is problematic. The selection of this threshold

that distinguishes between vulnerability to poverty and non-vulnerability to poverty is a

normative decision that must be taken by any vulnerability to poverty approach. Common

thresholds used are the vulnerability to poverty average a 50% probability of falling into

poverty, 75% probability of falling into poverty, the poverty rate as a threshold of vulnerability,

among others (Chaudhuri (2003); Gaiha & Imai (2008); Imai et al. (2010); Imai et al. (2011);

Bronfman (2014)).

Lopez-Calva and Ortiz-Juarez (2014) define the vulnerability to poverty threshold as 10%.

People who face a probability of falling into poverty higher than 10% are considered

vulnerable and those with a probability below this threshold belong to the middle class. The

authors justify the selection of this threshold from a theoretical and an empirical point of view.

They argue that the threshold is derived from a well-defined concept of economic security and

that this can be made operational for specific contexts. This opens the possibility of fixing a

different threshold of economic security depending on the context making the approach

flexible. In addition, they justified the selection of the 10% probability threshold as a lower

boundary for the middle class, based on empirical regularities found using synthetic panels,

indicating that 10% was the share of population falling into poverty in a fifteen-year period.

This research uses the same probability threshold of 10% and compares these results with

a higher probability threshold of 20%. The objective is to compare the sensitivity of the

estimations to the threshold. Later in the analysis will be presented the fact that the 10%

threshold fits better to a high income country as Chile.

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One of the advantages of the approach proposed by López-Calva & Ortiz-Juárez (2014) is

the translation from a probability threshold to an income threshold. Based on the ‘augmented’

poverty line concept proposed by Cafiero and Vakis (2006), the authors propose to find a

vulnerability income threshold that incorporates the risk that people face. The ‘augmented’

poverty line proposed by Cafiero and Vakis (2006) includes not only the minimum basket of

consumption and services but also basket of insurance against risks. The main objective of the

‘augmented’ poverty line is to incorporate the risk that people face in the poverty line. The aim

is to incorporate welfare loss as a consequence of vulnerability to poverty into the poverty

measurement and not only the income losses as a consequence of having been subject to

shocks.

This research using the approach of López-Calva & Ortiz-Juárez (2014) and the

‘augmented’ poverty line concept from Cafiero and Vakis (2006) estimates an income

vulnerability threshold for a high income country as Chile. The poverty line used by the

government of Chile was updated in 2013 in order to reflect the higher living standards and

current population needs. This ‘new poverty line’ is higher than the previous poverty line used.

This research uses the new poverty line to estimate vulnerability to poverty. The more

demanding standards for defining the minimum acceptable level of life entail a higher standard

of vulnerability to poverty. This research makes a contribution by estimating the vulnerability

to poverty threshold in connection with the new poverty line.

3.3.2 The rise of vulnerability to poverty in Latin American countries

Latin American countries have decreased their poverty levels considerably during the last

two decades. The percentage of the population living on less than US$2.5 per capita decreased

from 28.8% in 2000 to 15.9% in 2013 and that of those living on less than US$4 per capita

decreased from 46.3% to 29.7% between the same years (BID). This decline in poverty means

that 50 million people escaped poverty between 2000 and 2010 (Ferreira et al.).

One of the main questions that emerged after this reduction in poverty was about the

vulnerability of people who had left poverty behind. Were they vulnerable to being in poverty

again? Had any of them moved to the middle class? Were they secure or insecure

economically?

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In order to answer these questions, a great deal of effort has been put into the

identification of the threshold that distinguishes between those in vulnerability, the middle

class and upper middle class. The establishment of an income threshold that can be compared

with poverty thresholds has been pursued. In this context, the thresholds proposed by Lopez-

Calva and Ortiz-Juarez (2014) have gained relevance and popularity. They have been used by

international organizations such as the World Bank (Ferreira et al.) and the Inter-American

Development Bank (IDB) to identify the groups of people in poverty, vulnerability, the middle

class and upper middle class in Latin American countries and to analyze the mobility between

groups over the last few decades in the continent.

These works show that the reduction in poverty and inequality since 1990 in Latin

American countries has generated an increase of people in both the vulnerable and middle

class groups (Ferreira et al. and BID). BID show that the vulnerable group increased from

32% in 2000 to 38% in 2013, the middle class from 20% to 30%, while the upper middle class

remained stable at 2% throughout the period. They describe the heterogeneity among Latin

American countries regarding the proportion of their populations living in these income

groups. While some of them, such as Argentina, Chile and Uruguay, have poverty levels of

around 10%, other countries have more than 65% of their population living in poverty and

more than 50% living in extreme poverty. However, a common characteristic for all the

countries is the large proportion of people living in vulnerability, around 30%-40%. This

indicates that a large proportion of the population still faces a high probability of falling into

poverty.

Both Ferreira et al. and BID describe Chile as a country where poverty rates are lower and

the middle class is bigger than in other countries on the continent. Poverty rates in Chile were

reduced by more than half between 2000 and 2013 and the majority of its population belonged

to the middle and upper middle class in 2013. However, these numbers are obtained with

thresholds described which are low for the Chilean context. The new poverty line

implemented since 2013 changed the vulnerability threshold which is estimated by this

research.

Most effort hitherto has gone into the characterization of the middle class. This research

wants to contribute by providing evidence regarding the vulnerable groups. This group needs

policy attention because they need security in order to avoid future episodes of poverty.

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The only known study that estimated vulnerability to poverty in the region is the work of

Bergolo et al. (2010). The authors, using Chaudhuri’s methodology, estimated vulnerability to

poverty for 18 Latin American countries and carried out a validation exercise to assess their

capacity to predict poverty in two countries with panel data available: Chile and Argentina.

Their results indicate that vulnerability measures predict better aggregate poverty trends than at

the micro level. The authors conclude that vulnerability estimates through this methodology

should be complemented with additional information on aggregate trends and shocks in order

to target policy intervention (Bérgolo et al., 2010).

3.4 Data

This study makes use of the first 50 Panel version of the National Socio-economic

Characterisation Survey (Encuesta de Caracterización Económica Nacional, CASEN). The CASEN is

a cross-section survey which has been collected every two or three years since 1985 by the

Ministry of Social Development. It is nationally and regionally representative and also

representative of some counties. The CASEN is the main survey that analyzes the socio-

economic characteristics of the Chilean population. In order to support the analysis of the

same households throughout the years, the Ministry of Social Development has also conducted

a CASEN Panel Database since 1996. The CASEN Panel Database is carried out by the

Ministry of Social Development in conjunction with the Social Observatory at Universidad

Alberto Hurtado (OSUAH) and the Foundation of Poverty Reduction (FSP).

A subsample of 5,209 households -20,942 individuals- was selected from four regions of

the country (III, VI, VIII, and the Metropolitan Region) in CASEN 1996 who were re-

interviewed in 2001 and 2006. These regions represent 60 per cent of the national population

and of the GDP. The main advantage of the CASEN Panel Database is that provides

information from the same households over a long span of time -10 years. It follows the same

households including the new people that have been incorporated into the household. The

Annex N°3.9.2 describes the mechanisms to incorporate new members in the survey.

50 A second Panel from CASEN was collected in the years 2006, 2007, 2008 and 2009. The base line was taken from the cross section 2006 CASEN. As the attrition rate of this panel was very high, it is not much used for research.

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Longitudinal weights are used to avoid the possible bias in the estimations if the attrition

between waves is not random51. This research takes the individuals as the unit of analysis and

excludes from poverty estimates people who work in domestic service and who live in the

household in which they work52. In addition, in order to maintain the consistency of the Panel,

this research takes into consideration in the analysis individuals who were interviewed in all

three waves, 10,286 as shows Table 3-1.

Table 3-1 Original members of the survey interviewed both years and three (domestic servants excluded)

Source: Author’s elaboration from CASEN Panel Database 1996-2001-2006

The main advantage of longitudinal data is the possibility to follow the same household

throughout the time. This research exploits the advantage to identify trajectories in and out of

poverty in order to identify the probability of falling into poverty. Here, the probability of

falling into poverty in the next period is estimated from the current period and changes

between the two of them. The following section explains the method to estimate the

probability of falling into poverty used by this research.

3.5 Econometric Methodology

The estimation strategy and the econometric model used in this study follow the

vulnerability to poverty approach proposed by Lopez-Calva and Ortiz-Juarez (2014). They

51 To see the detail about the construction of the longitudinal weights in the Panel CASEN 1996-2001-2006 go to (Bendezú et al., 2007; PNUD, 2009). 52 They are not considered as a member of the household that contributes to the total income nor in the calculus of equivalised income. This group represent a small proportion of people. This practice is followed by the Ministry of Social Development in Chile in the estimation of poverty rates as well.

1996-2001 15,030

2001-2006 12,091

1996-2001-2006 10,286

CASEN 1996-2001-2006 Interviewed people both

years and three years without domestic service

133

propose a three-stage methodology that would allow researchers to obtain an individual’s

predicted income associated with a probability of falling into poverty. First, poverty transition

matrices from panel data are constructed. Second, the probability of falling into poverty in the

next period is estimated from socio-demographic characteristics in the current period and

some changes along the period. Third, income estimation is done using the same covariates

used in the second step. After that, the probability and income estimations are combined,

obtaining the predicted income associated with a particular probability of falling into poverty.

From this three-stage method, the definition of the probability of falling into poverty as a

vulnerability threshold has an income threshold associated. This is a clear advantage of this

method because the income vulnerability threshold can be compared with the poverty line;

they are in the same language.

Section 3.5.1 explains in detail the three-stage approach used by this research. Then, in

section 3.5.2, an estimation strategy to compare the characteristics of people living in

vulnerability with people living in poverty and middle class is presented.

3.5.1 The three-stage methodology for approaching vulnerability to

poverty

This study follows the vulnerability to poverty approach proposed by López-Calva &

Ortiz-Juárez (2014) which define middle class in terms of economic security. The authors

define middle class as people who face a low probability, less than 10%, of being in poverty in

the future. This paper focuses on economic insecurity instead of economic security that

defines the middle class. It focuses on the rest of the population who face a probability of

falling into poverty in excess of 10% who are defined as vulnerable to poverty. This research

uses this vulnerability to poverty approach to characterize the group of people living in

vulnerability to poverty and to identify the difference between them and other income groups.

This approach follows a methodology that comprises three stages explained below.

3.5.1.1 First Stage

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In the first stage, poverty transition matrices are constructed using the Chilean poverty line

for each year. Transitions matrices are calculated for the two periods of comparison available:

between 1996 and 2001 and between 2001 and 2006. The comparison between 1996 and 2006

is not possible because the shocks variables required for the next stages are only available for

consecutive samples. From these poverty transitions, households are classified as ‘never poor’,

‘always in poverty’ ‘entering poverty’ or ‘out of poverty’. ‘Never poor’ means that households

were above the poverty line in both periods and ‘always poor’ when they were below the

poverty line in both periods. Households are classified as ‘out of poverty’ when they were in

poverty in the initial period but had got out of poverty in the final period and ‘entering

poverty’ in the situation of being out of poverty in the first period but having fallen into

poverty in the second period. Households are classified into one of these four categories and

this information is used in the second stage of the methodology.

The new poverty line implemented since 2013 is used to construct the poverty transition

matrices. The value of the poverty line in 2013 is expressed in 1996, 2001 and 2006 values to

carry out the analysis In Section 3.3.1 were presented the methodological changes that the new

poverty line incorporated. Table 3-2 shows the international poverty line and the old and new

national poverty lines expressed in 2005 Chilean Pesos, allowing them to be compared.

Table 3-2 National and International poverty lines expressed in 2005 Chilean Pesos

2001 2005 2006 2013 2001 2005 2006 2013

Extreme 23,155 - 23,102 40,998 761 - 760 1,348

Moderate 46,312 - 46,204 61,497 1,523 - 1,519 2,022

Extreme US $2.5 29,456 968

Moderate US $4 47,129 1,549

Vulnerability US $10 117,822 3,874

Middle Class US $50 589,110 19,368

Source: Author’s calculation based on CASEN Panel Database 1996-2001-2006 and World Bank PPP Private Consumption.

Poverty line values on 2001 and 2006 are obtained through the old methodology.

The 2013 poverty line came f rom the new methodology for poverty measurement.

The 2013 poverty line represents the poverty line in the average household size of 4.43 inhabitants.

Poverty lines depend on household size ranging f rom 96,112 for a single household to 48,170 per capita in a household with 10 members.

Per month Per day

Poverty line values expressed in 2005 Chilean Pesos

National

International

Poverty line

135

It can be seen that the new poverty line implemented in 2013 is much higher than the

poverty lines in 2001 and 2006. It is also much higher than the international moderate poverty

line. This is the first research which uses the new poverty line in order to estimate the

vulnerability line. López-Calva & Ortiz-Juárez (2014) used the international moderate poverty

line commonly used for Latin American countries of US$4 dollars PPP since the year 2005.

That poverty line was very close to the national poverty line in the years 2001 and 2006.

Considering that the changes in the methodology to measure poverty line were looking to

bring the measurement into line with the higher living standards and population needs of Chile

today, any measure of vulnerability to poverty must do the same. The more demanding

standards for defining the minimum acceptable level of life are in connection with a higher

standard of vulnerability to poverty. This research makes a contribution by estimating a

vulnerability to poverty threshold in connection with a higher poverty line.

3.5.1.2 Second Stage

In the second stage of the methodology, a logistic model53 (Aldrich & Nelson, 1984) is

estimated to analyze the correlates of the probability of falling into poverty between years. For

a household “i”, the estimated probability (pit) of falling into poverty between the initial (t0) and

final year (t1) is given by:

pit = E(poori,t+1|Xit) = F(Xitβit) (1)

Where poori,t+1 is the dependent variable taking the value of 1 if households are identified

as falling into poverty (‘always poor’ or ‘entering poverty’) and 0 otherwise; βit is a vector of

model parameters, and Xit is a vector of observable characteristics including labour market

resources, demographic indicators and shocks affecting the household. These variables

constitute the set of characteristics known to be related to poverty and the income generating

process.

53 Logit and Probit models usually return quite similar results and therefore the choice between them is not crucial. Thus, in this research a Logit model is used, as do López-Calva & Ortiz-Juárez (2014).

136

Among the labour market resources in t0 the head’s education level and occupational status

are considered. The head’s education level is a proxy for the household’s human capital which

is grouped into seven categories: no formal education; incomplete and complete primary;

incomplete and complete secondary; and incomplete and complete tertiary education. ‘No

formal education’ means 0 years of education; ‘complete primary education’ means 8 years of

education and ‘incomplete’ less than that; ‘complete secondary education’ means 12 years of

education and ‘incomplete’ less than that; ‘completed’ or ‘incompleted tertiary education’ is

reported by the survey’s respondent. To record the occupational status of the head of the

household, a collapsed 6-class version of the Erikson, Goldthorpe, & Portocarero (1979)

(EGP) class classification is used. This collapsed 6-class version is commonly used to simplify

the Erikson et al. (1979) occupational classification. The household head’s occupation is

classified in one of the following classes: professional and managers, clerical workers, self-

employed, skilled manual workers, non-skilled manual workers, and agricultural workers54.

Regarding demographic indicators in t0, urban or rural residence, region of residence,

access to water and sanitation, floor material; and age, sex, and marital status of the household

head are considered. Marital status is gathered into three categories: married, cohabiting, or

single. The last category is made up of widowed, separated or never married people. The

household’s point in the life cycle can be measured by the age of the household head and by

the proportion of children and the proportion of older people living in the household. In this

context, the proportion of children under 15 years and the percentage of people over 64 years

living in the household are also considered.

The household access to basic services is also considered through two variables: unfinished

floors and absence of adequate sanitation system. The thresholds are taken from the

thresholds used by the Ministry of Social Development of Chile to define deprivation in these

dimensions. Unfinished floor is characterized by dirt floor, uncovered or covered by plastic or

wood and unfinished concrete floor55. A household does not have adequate sanitation when it

has a water closet –w.c- that is not connected to a sewer or septic tank. Any other solution

54 To see in detail the occupational classification: Erikson, Goldthorpe, & Portocarero (1979). 55 This is a more demanding threshold to describe the absence of a good quality floor. López-Calva & Ortiz-Juárez (2014) consider unfinished floor as a dirt floor, uncovered or covered by plastic or wood which is a less demanding threshold.

137

such as a sanitary latrine, drawer over a well, drawer over a ditch or drawer connected to any

other system, absence of connection to any system, represents deprivation in sanitation56.

Changes and shocks that the household confronted between t0 and t1 are also used. They

include any change in the number of household members engaged in paid employment, and

any change in household size. The model also takes into account self-reported health shocks,

which required hospitalization, affecting the household between t0 and t1.

From this vector of observable characteristics the probability for each household of falling

into poverty in the final year (t1) is estimated.

3.5.1.3 Third Stage

The average characteristics associated with an estimated probability of being poor in the

next period are obtained. In particular, the average of the independent variables, Xjt , for j

ranges of estimated probabilities of falling into poverty are calculated. For instance, the average

characteristics of the group of people who have an estimated probability of falling into poverty

of between 4 and 6 percent are obtained, X4−6,0. In order to have enough information for the

group, ranges of 2% probabilities are considered (0%-2%, 2%-4%, 4%-6%,….., 98%-100%).

This provides information on the average characteristics of the group of individuals that face a

similar probability of falling into poverty.

In addition, the same independent variables Xit used in equation (1) are included to

estimate the following income equation:

lnYit = α: + Xitβit + εi (2)

56 A less demanding threshold is defined by no access to any system of sanitation. Again, this threshold is very low for Chilean standards.

138

In equation (2) lnYit is the equivalised income in the logarithmic scale at the initial time

point. The resulting coefficients from equation (2) βt and the average of the independent

variables Xjt are used to solve the income equation, and therefore to obtain the predicted

income associated with each range of probability Yjt. This is the mechanism for combining the

estimated probability of falling into poverty with an estimated income57. Following the same

example, an estimated income is obtained associated with a probability of falling into poverty

between 4% and 6%. This is done for each range of estimated probability.

To estimate equation (2) Ordinary Least Squares (OLS) regression can be used. However,

these estimations can be affected by measurement errors, omitted variables and reverse

causation (Bourguignon, 2003). The use of robust standard errors accounts for

heteroscedasticity. The possibility to use random or fixed effects or first differences can

control for the presence of unobserved household-specific effects. The problem of using fixed

effects estimations here is that it eliminates time invariant characteristics. In this research,

many characteristics of the heads of households, such as education or sex, and household

characteristics, such as regional or urban/rural location, do not change between years. Fixed

effect estimation attenuates bias because of omitted variables but sacrifices the elimination of

invariant characteristics important for this research. In the case of this study, the use of fixed

effects estimation is problematic because of the presence of a lot of invariant characteristics

through time. The fact that the use of fixed effects estimations are associated with coefficients

biased to zero due to a low indication-to-noise ratio has already been documented in the

literature (Hauk & Wacziarg, 2009; Ravallion, 2015). Fixed effect estimations were conducted

by this study as robustness checks. However, they were discarded because the problem was

exposed. In the literature OLS estimations with robust standard errors are usually employed as

this study does.

The three-stage methodology allows us, at this point, to obtain the predicted income

associated with the probability of falling into poverty. The income threshold related to the

vulnerability threshold can be obtained. Considering the two thresholds of vulnerability

57 The justification for using predicted income comes from the fact that it has less volatility than observed income and it represents the income generation capacity of the household based on their assets.

139

adopted by this research -10 and 20 per cent- the equivalised income can be obtained that, on

average, people facing those probabilities earn.

As explained above, comparison can only be made between two consecutive measures.

The three- stage methodology is applied for the period 1996-2001 and for the period 2001-

2006 separately. This allows the results from both periods to be compared in order to validate

them.

3.5.2 How can the socio-demographic characteristics of people in

poverty, vulnerability, the middle class and upper middle class be

compared?

After the three-stage methodology is applied, a vulnerability income threshold is obtained.

This vulnerability line represents the predicted equivalised income associated with a probability

of falling into poverty equal to 10% or 20% depending on the probability threshold. This

threshold allows the identification of the group of people living in vulnerability to poverty.

Individuals with an equivalised income below the poverty line in the initial year are in

poverty irrespective of their probability of falling into poverty in the next period. A second

group is formed by individuals with equivalised income above the poverty line but below the

vulnerability threshold. People having equalized incomes below that threshold and above the

poverty line are considered vulnerable to being in poverty. A third group is formed by people

whose equalized income is between the vulnerability threshold and the middle-class threshold.

This last threshold is taken from Lopez-Calva and Ortiz-Juarez (2014) in which it is equivalised

to 50 dollars per day in 2005 PPP. This threshold can be used in this research because it is not

the objective here to estimate the upper threshold of the middle class. In addition, Lopez-

Calva and Ortiz-Juarez (2014) describe that the selection of this upper threshold has a small

impact on the size of the middle class. They describe that moving the upper threshold of the

middle class up or down in the income distribution has only a small impact on the percentage

of people in the middle class. In contrast, they explain that a variation in the lower-threshold

of the middle class, which is the vulnerability threshold here, has a larger impact on the size of

the middle class. The higher sensitivity of the vulnerability threshold requires it to be very

carefully estimated. As the authors state, making the vulnerability to poverty concept

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operational requires the definition of thresholds that are relevant to the specific context

(Lopez-Calva and Ortiz-Juarez, 2014). This is what this research is doing by using the new

poverty line implemented in Chile since 2013.

The fourth and last group is formed by people having equalized incomes higher than 50

dollars per day in 2005 PPP. They are called the upper middle class.

After these groups have been identified, their socio-economic characteristics are compared.

The main objective is to characterize the group of people living in vulnerability to poverty and

to identify their similarities and differences with the rest of the groups. The representation of

the group of people living in vulnerability to poverty will emerge from this comparison. The t-

test is used in order to identify if the differences in covariates between the different groups are

statistically significant. Table 3-3 shows descriptive statistics for the covariates used in the

analysis. These covariates have been selected from similar studies in the literature (Bérgolo et

al., 2010; López-Calva & Ortiz-Juarez, 2014; Neilson et al., 2008). The variables for each year

under analysis and their mean and standard deviation are reported in Table 3-12 in the Annex

N°3.9.3

3.6 Results and discussion

The results obtained from the econometric strategy are presented in two sub-sections. The

first sub-section presents the results of the three-stage methodology carried out to estimate the

vulnerability threshold. Here, the results of each stage of the method are presented in detail:

the transition between poverty status throughout the years; the estimation of the probability of

falling into poverty and the predicted income associated with the probability of falling into

poverty. The vulnerability income thresholds associated with both a 10% and a 20%

probability of falling into poverty are presented. The distribution of people in poverty,

vulnerability, the middle class and upper middle class is described. It is shown that a 10%

probability of falling into poverty is more appropriate for the Chilean context. The second

sub-section shows the comparison between the socio-economic characteristics of people living

in poverty, vulnerability and the middle class.

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3.6.1 Defining the relation between the probability of falling into poverty

and households' observed income

Transition matrices in Table 3-3 show a cross-classification of poverty status at the initial

time point in the rows, and the poverty status at the final time point in the columns. They are

presented for the periods 1996-2001 and 2001-2006 using the new poverty line implemented in

Chile since 2013.

Table 3-3 Poverty transition matrices 1996-2001 and 2001-2006

The percentage of people who were out of poverty in 1996 but had fallen into poverty in

2001 was 19.4% while this percentage was 14.3% between 2001 and 2006. Not only was falling

into poverty less probable during the last period than the first period but also moving out of

poverty was more likely between 2001 and 2006 than between 1996 and 2001. 34.5% of people

in poverty in 1996 had left poverty in 2001 and 51.7% did it between 2001 and 2006. These

numbers show that the higher reduction in poverty in the period 2001-2006 was accompanied

by a lower proportion of people falling into poverty and a higher percentage moving out of

poverty.

Table 3-4 presents the distribution of individuals between the two and among the four

more differentiated categories of poverty transitions explained in the methodology section.

Out of poverty In poverty Total

Out of poverty 80.6 19.4 100

In poverty 34.5 65.5 100

Out of poverty In poverty Total

Out of poverty 86 14.3 100

In poverty 51.7 48.4 100

Source: Author's calculations f rom CASEN Panel Database 1996-2001-2006

Row percentage distribution of individuals

Initial

Period

2001

1996-2001

2001-2006

Final Period 2001

Initial

Period

1996

Final Period 2006

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Table 3-4 Poverty transition: 2 and 4 categories 1996-2001 and 2001-2006

The proportion of people ‘always poor’ was 27.5% and 18.7% in the period 1996-2001 and

2001-2006 respectively. The group ‘entering poverty’ decreased from 11.3% between 1996 and

2001 to 8.7% for the period 2001-2006. These two groups were in poverty in the final year,

representing 38% of the population in 2001 and 27.5% in 2006.

The variable that represents poverty status in the final year is used as a dependent variable

in the logit estimation to estimate the ex-ante probability of being in poverty in the final year.

The dependent variable takes the value of 1 if the individual is in poverty in the final year and 0

if it is not. In other words, the dependent variable is 1 when people are ‘in poverty’ and 0 when

they are ‘out of poverty’ in the ‘poverty transition 2 categories’. The main objective is to

predict the probability of falling into poverty in the final year from the initial year’s variables

and some changes along the period. This logit estimation comprises the second stage of the

methodology and the lineal estimations the third stage. The results of both estimations are

presented in Table 3-5 for the period 1996-2001 and Table 3-6 for the period 2001-2006.

1996-2001 2001-2006 2001 2006

Always poor 27.5 18.7

Entering poverty 11.3 8.7

Out of poverty 14.5 20.0

Never poor 46.8 52.5

Total 100 100 Total 100 100

Source: Author's calculations f rom CASEN Panel Database 1996-2001-2006

Column percent distribution of individuals

Poverty Transition 4 categories Poverty Transition 2 categories

Period Years

In poverty 38.8 27.5

Out of

poverty61.2 72.5

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Table 3-5 Logit and Linear estimation 1996-2001

1996-2001

Model:

Dependent Variable:

Coeff. S.E. Coeff. S.E.

Education of head of household -0.185*** 0.039 0.100*** 0.014

Age of head of household -0.040 0.028 0.0226*** 0.007

Age squared of head of household 0.000 0.000 -0.000152* 0.000

Sex of head of household (male=1) -0.227 0.275 -0.165 0.109

Head of household without social insurance 0.429*** 0.109 -0.255*** 0.033

Household with unfinished floor 0.579*** 0.166 -0.083 0.047

Household without water sanitation 0.965*** 0.148 -0.106 0.059

Head of household married (omitted)

Head of household cohabiting 0.330* 0.142 -0.0396 0.030

Head of household without partner -0.787** 0.282 -0.131 0.121

Head of household in agriculture (omitted)

Head of household as unskilled manual worker -0.0587 0.162 0.0758 0.045

Head of household as skilled manual worker -0.848*** 0.196 0.0688 0.048

Head of household as independent worker -0.959*** 0.175 0.446*** 0.046

Head of household in clerical activities -1.520*** 0.193 0.200** 0.061

Head of household as professional manager -1.675*** 0.252 0.787*** 0.076

Region VII 0.703*** 0.190 -0.322*** 0.050

Region III 0.686*** 0.137 -0.283*** 0.036

Region VIII 1.143*** 0.114 -0.313*** 0.040

Metropolitan region (omitted)

Rurality -0.261 0.170 -0.026 0.049

Occurrence of health shocks 2001-2006 0.398* 0.190 -0.0622 0.043

Change in number of members working 2001-2006 -0.546*** 0.058 -0.140*** 0.019

Change in household size 2001-2006 0.231*** 0.037 0.0434*** 0.010

Percentage of children 3.372*** 0.301 -1.047*** 0.096

Percentage of elderly 0.0605 0.573 0.219 0.123

Constant 0.659 0.759 10.86*** 0.231

Observations 7,272 7,272

Pseudo R2/ R2 0.29 0.47

Source: Author's calculations from CASEN Panel Database 1996-2001-2006

Dependent variables: poverty status of households in logistic model,

household per capita income (ln-scale) in lineal model

Robust standard errors in parentheses, * p<0.05, **p<0.01, *** p<0.001

Poverty Income (ln scale)

Logistic Linear

144

Table 3-6 Logit and Linear estimation 2001-2006

2001-2006

Model:

Dependent Variable:

Coeff. S.E. Coeff. S.E.

Education of head of household -0.310*** 0.046 0.153*** 0.014

Age of head of household 0.042 0.048 0.001 0.013

Age squared of head of household -0.001 0.001 0.000 0.000

Sex of head of household (male=1) 0.485 0.317 0.000101 0.113

Head of household without social insurance 0.186 0.136 -0.063 0.033

Household with unfinished floor 0.193 0.116 -0.112** 0.038

Household without water sanitation 0.654*** 0.169 -0.268*** 0.032

Head of household married (omitted)

Head of household cohabiting -0.0605 0.175 0.0451 0.050

Head of household without partner 0.11 0.313 0.208 0.110

Head of household in agriculture (omitted)

Head of household as unskilled manual worker -0.16 0.194 0.0413 0.040

Head of household as skilled manual worker -0.550** 0.183 0.0998* 0.044

Head of household as independent worker 0.145 0.175 0.117** 0.044

Head of household in clerical activities -0.408 0.225 0.126* 0.052

Head of household as professional manager -1.486*** 0.318 0.365*** 0.076

Region VII -0.139 0.180 -0.0465 0.050

Region III 0.491*** 0.131 -0.167*** 0.035

Region VIII 1.064*** 0.138 -0.200*** 0.041


Rurality 0.283 0.147 0.029 0.036

Occurrence of health shocks 2001-2006 0.0327 0.125 0.0151 0.041

Change in number of members working 2001-2006 -0.567*** 0.069 -0.111*** 0.013

Change in household size 2001-2006 0.133** 0.047 0.0397** 0.013

Percentage of children 3.760*** 0.399 -1.092*** 0.097

Percentage of elderly 0.263 0.611 -0.206 0.135

Constant -2.45 1.253 10.95*** 0.356

Observations 6,650 6,650

Pseudo R2/ R2 0.25 0.44

Source: Author's calculations from CASEN Panel Database 1996-2001-2006




Logistic Linear

Poverty Income (ln scale)

145

The first two rows of Table 3-5 and Table 3-6 present the coefficients and the robust

standard errors of the logistic estimation and the third and the fourth column the same two

variables but for the lineal estimation. The statistical significance of the coefficients is

represented through the asterisks explained in the same table. The estimations show similar

results to the estimations of previous studies using the same approach, such as Hertova et al.

(2010); López-Calva & Ortiz-Juarez (2014); De la Fuente et al. (2017).

The number of observations is 7,272 for the estimation between 1996 and 2001 and 6,650

for the estimation between 2001 and 2006. These numbers are lower than the 10,286

individuals who were interviewed in all three waves presented in Table 3-1. The reason for this

reduction in the number of observations used in the estimations is because many household

heads do not have or do not provide all the information regarding their occupation. Around

70% of household heads are employed and almost all of them provided information regarding

their occupation which is used to build the EGP occupational classification. However, a small

proportion of people who are not working report their previous occupation’s characteristics.

Although some of them are unemployed the rest are out of the labour market. They are not

looking for a job and many of them have no work experience. This fact means that only some

of the household heads’ occupations can be classified into one of the following classes:

professional and managers, clerical workers, self-employed, skilled manual workers, non-skilled

manual workers, and agricultural workers. This is the reason why the number of observations

used in the regressions is reduced.

In order to analyze if this reduction in the number of observations has biased the

estimations, the logit and lineal regressions are conducted taking out the EGP classification.

The main objective of this second model is to compare how the estimations change in the

absence of occupational class. In addition, other robustness checks are carried out. A third

scenario is estimated replacing the EGP classification by a dummy of occupation (1 employed,

0 not employed) in order to incorporate people who are not working in the estimation. It is

important to remember that the variable ‘change in number of members working’ between one

year and another incorporates in a way the occupation status of all the members of the

household. A fourth model is estimated using the household as the unit of analysis. This

research uses the individual as the unit of analysis following the advice of the data collectors

146

(Annex N°3.9.2). This means using the household information for each household member.

The results of these robustness checks of the estimations are included in the Annex 3.10.4.

We already know that the potential bias that attrition between waves can generate is solved

through the use of longitudinal weights. It is also confirmed that the potential bias in the

estimation as a consequence of incorporating fewer observations of occupational class is not

present. The robustness checks done confirm that the four scenarios compared show similar

results. Coefficient signs are the same for the four models and their significance is very similar

in all four models. It is confirmed that Model 1 is the best among the four models compared.

The R-square is higher and it has more significant coefficients. Additionally, the incorporation

of the EGP classification variables is important for the predictability of the model. Four of the

five variables included are statistically significant in predicting the probability of falling into

poverty and low income. To conclude, Model 1 which includes the occupational classification

of the household heads predicts better than the other three models under comparison. The

incorporation of these variables does not bias the results. Moreover, they improve the

predictability of the model.

Regarding the variables included in the models, the expected signs and significance are

found. As expected, education is a relevant characteristic associated negatively with the

probability of being in poverty in the future and positively with household income. More

educated heads of households do have a smaller chance of falling into poverty and do earn

higher incomes. Again, these results are in line with previous evidence for Chile provided by

other authors such as Neilson et al. (2008).

Household conditions are also a significant determinant of the probability of falling into

poverty and family income. People who live in households without sanitation or with an

unfinished floor have a higher probability of being in poverty in the future and lower incomes

than households with better conditions. The degree of significance varies among the

estimations for 1996-2001 and 2001-2006 but still they are significant for some of the two

estimations.

The occupation of the household head shown explains an individual’s probability of falling

into poverty and their equivalised income. Skilled manual workers and professionals or

managers display a lower probability of falling into poverty than workers in the agricultural

sector in both periods. Additionally, independent workers, workers in clerical activities and

147

professionals or managers have higher incomes than workers in occupations with fewer

qualifications such as agriculture or unskilled manual labour.

The evidence shows that a household’s point in the life cycle matters in predicting the

probability of falling into poverty and household income. Younger households, measured

through having a younger head of household and households with children under the age of

15, have increased chances of being in poverty in the future and having lower incomes.

After both estimations are done, they are combined in order to obtain the relation between

the probability of being in poverty and equivalent income per person. The predicted

probability of falling into poverty is obtained for each individual from the logit estimation.

Then, the averages of the independent variables for an array of these estimated probabilities of

falling into poverty are estimated. These covariates that describe the interval of probability of

falling into poverty are combined with the second estimation, the lineal regression. The

average covariates from the logit estimation are multiplied with the resulting coefficient from

the lineal regression. The predicted income is obtained from this multiplication. This is the way

in which the probabilities of falling into poverty and predicted income are combined. The

advantage of using predicted income –a mean, conditional to characteristics- instead of the

observed income of the households is that predicted income has lower volatility and it reflects

the income generation capacity of the household. It is a measurement which is more related to

the assets that the household has than to the contingency of the moment (Lopez-Calva and

Ortiz-Juarez, 2014).

The estimation shows that higher probabilities of falling into poverty are correlated with

lower equalized income. Figure 3-1 shows the correlation between the estimation of equalized

income in the initial period –in log scale- and the probability in the initial year of falling into

poverty in the next year. Individuals with lower equalized incomes in the initial year face on

average a higher probability of falling into poverty in the future. They have less income and

probably less assets available to overcome any adverse shocks that may occur along the period,

making them more vulnerable to being in poverty in the future.

148

Figure 3- 1 Correlation between the estimated probability of falling into poverty and the predicted equalized income

Source: Author’s elaboration based on CASEN Panel Database 1996, 2001, 2006. Ministry of Social Development,

Chile.

Both periods show the same negative correlation between the estimation of the probability

of falling into poverty and the estimated equalized income. People with lower incomes in the

initial year confront a higher probability of falling into poverty in the final year than people

with higher incomes.

Moving to the real equalized incomes that people have, it can be observed that the inverse

relationship between incomes and probability of falling into poverty remains. The following

two figures show the equalized income per month in the initial year related to intervals of

probabilities of falling into poverty in the final year. Figure 3-2 shows the 1996 predicted

equalized income associated with the interval of probability of falling into poverty in 2001.

Figure 3-3 shows the same for the period 2001-2006. In order to facilitate the comparison,

equalized incomes in both figures are expressed in 2013 values. The advantage of using the

year 2013 is because the new poverty line has been used since then. The vulnerability threshold

will also be presented in 2013 values.

1996-2001

0.2

.4.6

.81

pro

ba

bili

ty to

fall

into

po

vert

y

10 11 12 13 14predicted household per capita income at log-scale

2001-2006

0

.2.4

.6.8

1

pro

ba

bili

ty to

fall

into

po

vert

y

10 11 12 13predicted household equivalised income at log-scale

149

Figure 3- 2 1996 Monthly equivalised incomes by probability of falling into poverty

Monthly incomes expressed in Chilean pesos year 2013. Source: Author’s calculation based on CASEN Panel Database 1996-2001-2006

Figure 3- 3 2001 Monthly equivalised incomes by probability of falling into poverty

Monthly incomes expressed in Chilean pesos year 2013. Source: Author’s calculation based on CASEN Panel Database 1996-2001-2006

0

50000

100000

150000

200000

250000

300000

350000

Ho

use

ho

ld a

du

lt e

qu

ival

en

t in

com

e p

er

mo

nth

Probability of falling into poverty

0

50000

100000

150000

200000

250000

300000

350000

400000

Ho

use

ho

ld a

du

lt e

qu

ival

en

t in

com

e p

er

mo

nth

Probability of falling into poverty

150

The inverse relationship between higher incomes and lower probabilities of falling into

poverty declines fast up to 20% of probability of falling into poverty. It is more pronounced

up to the 10-12 per cent interval of probability in 1996 and up to the 8-10 per cent interval in

2001. From that point onwards, although income still decreases with higher probabilities, it

does so at a slower pace. The reduction of income continues at this pace up to the 24-26 per

cent interval of probability in 1996 and to the 22-24 interval in 2001. Income decreases at a

lower rate after those levels of probabilities of falling into poverty. This makes 10% and 20%

of probability of falling into poverty natural thresholds for identifying the group of people in


The income poverty and vulnerability thresholds are presented in Table 3-7. This is the

same as Table 3-2 presented above but with the vulnerability thresholds obtained from this

research’s estimations. They are presented in the 2013 column because they were obtained

using the new poverty line used since that year. They are expressed in 2005 values in order to

make all the thresholds comparable. They represent the vulnerability income threshold

associated with the 10% and 20% probabilities of falling into poverty. The vulnerability

threshold estimated in this research is almost 17% higher than the vulnerability threshold

estimated by Lopez-Calva and Ortiz-Juarez (2014). Both of them represent the predicted

income associated with a 10% of probability of falling into poverty but they use different

poverty lines to find it. While the authors use the international poverty line of US $4 2005 PPP

this research uses the new poverty line established by the Chilean State in 2013. The

vulnerability threshold obtained in this research is in line with the current welfare measures

used in Chile.

151

Table 3-7 Monthly equivalised income thresholds 2005 Chilean Pesos

The distribution of individuals in each income category in the initial year 2001 is presented

in Table 3-8. The proportion of people in each category is presented using both vulnerability

income thresholds, one associated with a 10% and the other to a 20% probability of falling

into poverty. The percentage of people in poverty in 2001 is 38.8%. Their income is under the

poverty line independently of the probability of being in poverty that they face. The

proportion of people whose income is between the poverty line and the vulnerability line

represents 19.2% of the population if the vulnerability threshold is 10% and 8.3% if it is 20%.

Poverty line 2001 2005 2006 2013 2001 2005 2006 2013

Extreme 23,155 - 23,102 40,998 761 - 760 1,348

Moderate 46,312 - 46,204 61,497 1,523 - 1,519 2,022

Vulnerability 10% 141,238 4,643

Vulnerability 20% 114,646 3,769

Extreme US $2.5 29,456 968

Moderate US $4 47,129 1,549

Vulnerability US $10 117,822 3,874

Middle Class US $50 589,110 19,368

Source: Author’s calculation based on CASEN Panel Database 1996-2001-2006 and World Bank PPP Private Consumption.

Poverty line values on 2001 and 2006 are obtained through the old methodology.

The 2013 poverty line came f rom the new methodology for poverty measurement.

The 2013 poverty line represents the poverty line in the average household size of 4.43 inhabitants.

Poverty lines depend on household size ranging f rom 96,112 for a single household to 48,170 per capita in a household with 10 members.

Per day

International

National

Per month

Poverty line values expressed in 2005 Chilean Pesos

152

Table 3-8 Distribution of individuals among income groups in 2001 under two probability thresholds

Source: Author’s calculation based on CASEN Panel Database 1996-2001-2006 Values reported as percentages.

People who belong to the middle class are defined as those whose income is above the

vulnerability threshold and below the middle class threshold. The proportion of people in the

middle class is higher under a higher vulnerability threshold. Less people face a probability of

falling into poverty higher than 20% than at a probability higher than 10%. As a consequence

of this, 38.3% of individuals are in the middle class when the 10% threshold is considered and

49.2% when the 20% is taken. The middle class is smaller the lower the threshold for

economic security is.

The proportion of people in vulnerability halves if the vulnerability income threshold

associated with a 10% of probability of falling into poverty is used instead of the 20%

probability threshold. This happens because of the high density of people around these two

vulnerability thresholds. Figure 3-4 shows the kernel distribution of equalized incomes in 2001.

The red vertical line represents the poverty line for the average household size of 4.43

inhabitants. As was discussed previously, the new poverty line depends on household sizes

ranging from $95,342 Chilean Pesos in 2001 for a single household to $47,784 Chilean Pesos

in 2001 per capita in a household with 10 members. The red vertical line is a reference to the

poverty line for the average household size, $61,497 Chilean Pesos in 2001. The vulnerability

threshold of 20% expressed in equalized income is shown through the black line in the figure

and the higher vulnerability threshold of 10% through the blue line. Finally, the green line

shows the middle-income threshold.

Income groups 10% prob 20% prob

In poverty 38.8 38.8

In vulnerability 19.2 8.3

Middle Class 38.3 49.2

Upper middle class 3.7 3.7

Total percentage 100 100

Total observations 10,286 10,286

Vulnerability thresholds

153

Figure 3- 4 2001 Kernel distribution of monthly equalized incomes 2001

Author’s calculation based on CASEN Panel Database 1996-2001-2006 Monthly equalized income expressed in 2001 values Vertical lines: moderate poverty line (red line), vulnerability threshold 10% (black line), vulnerability threshold 20% (blue line), middle class threshold (green line).

A movement from the black line to the blue line covers 10.9% of the population. The high

density of population in this part of the income distribution makes the vulnerability threshold

selection very sensitive. This was also documented by López-Calva and Ortiz Juárez (2014)

who showed that varying the lower threshold of the middle class had a larger impact on the

size of the middle class. Considering that the lower threshold of the middle class is the upper

vulnerability threshold, its variation will also affect the proportion of people in vulnerability.

They note that where the poverty line falls with respect to the initial distribution is important

to determine poverty elasticity to growth. In this context, they argue that the operationalization

of vulnerability to poverty requires the defining of thresholds that are relevant to the specific

context and being consistent with the framework that supports the analysis. The conceptual

framework is even more relevant in a context of absolute welfare measures estimations which

are commonly made operational with arbitrariness in one specific dimension.

0

2.0

00e

-06

4.0

00e

-06

6.0

00e

-06

De

nsity

0 200000 400000 600000monthly equivalent income

154

The Chilean context has particularities that must be included in the operationalization in

the definition of our vulnerability threshold. First, the use of the new poverty line is crucial to

the identification of the vulnerability to poverty threshold. As was discussed before, the new

poverty line is used by this research to establish the vulnerability threshold. The considerable

difference between the income vulnerability threshold from the international poverty line or

the new national poverty line was already discussed and presented in Table 3-12. Second, the

concept of vulnerability that the country currently uses is important. Chile uses 60% of the

population to describe the vulnerable. This comprises people in poverty and people out of

poverty but facing a high risk of being in poverty. The majority of social assistance and social

protection programmes are targeted to the 60% most vulnerable of the population (Ministerio

de Desarrollo Social, Chile, 2015). This empirical threshold was used in the first paper of this

research to identify vulnerability to poverty in income distributions from 1990 onwards.

From this research’s estimations and using the 10% of probability of falling into poverty as

the vulnerability threshold, the 38.8% of the population in poverty plus the 19.2% of people in

vulnerability to poverty is equal to 58%. This percentage is almost the same that the 60% used

by the State of Chile to target their Social Protection System.

From an empirical point of view, it can be argued that the threshold of 10% probability of

falling into poverty is more appropriate in the context of Chile today. The use of the new

poverty line identifies this threshold as relevant to defining the income groups in the Chilean

context. In the following section the income groups are characterized and compared.

3.6.2 Are the socio-demographic characteristics of people living in

vulnerability to poverty different from those of people living with

less or with more income?

Comparisons of the socio-demographic characteristics of people living in poverty,

vulnerability and the middle class are presented here. The variables used are those used in the

logit and lineal estimation plus other covariates. Among the additional covariates compared is

the family composition of the household. Households are classified into the following

categories: single; nuclear two-parent; extended two-parent; nuclear single-parent; extended

single-parent. Nuclear families consist of parent/s and children. Extended families are those

155

formed by nuclear families with additional relatives or non-relatives. ‘Single-parent’ is when

only one parent is present and ‘two-parent’ when both of them live in the household.

The variables used to characterize the income groups represent all the socio-demographic

characteristics available in the CASEN Panel Database. Table 3-13 presents the mean of these

variables for each group; the mean differences among the groups with their statistical

significance; the p-values of the difference mean test; and the number of observations in each

group of income. To analyze the statistical significance of mean differences among income

groups, mean t-tests are conducted. The main objective of these comparisons is to analyze

whether people in vulnerability to poverty differ from the other two groups of incomes or not.

In other words, the aim is to know if people whose incomes are above the poverty line but

below the vulnerability income threshold associated with a 10% probability of falling into

poverty have socio-demographic characteristics that differ from people in poverty and the

middle class. The analysis is presented for the year 2001 which is the most recent data available

for an initial year.

The figures in Table 3-9 show the noteworthy differences between the socio-demographic

characteristics of people living in vulnerability and the other two income groups. In general

terms, it can be said that the group of people living in vulnerability to poverty differ from both

those people living in poverty and people who belong to the middle class. The differences

between people in vulnerability and those in the other two groups are large and statistically

significant. The main differences are presented in detail in the next paragraphs.

Human capital variables show large and significant differences among groups. Heads of

households in vulnerability have less years of education than heads of households in the

middle class but more years than those of households in poverty. The proportion of heads of

households with incomplete or complete basic education is higher in households in poverty

followed by households in vulnerability and the middle class. Heads of households in

vulnerability have higher levels of complete secondary education than heads of households in

poverty and equal levels to those in the middle class. A higher proportion of heads of

households in vulnerability has university education than those in poverty but lower than heads

of households in the middle class. In general terms, heads of households in vulnerability have

higher levels of education than heads of household in poverty but lower levels than heads of

156

middle class households. Education appears as a variable protecting against poverty and the

risk of being in poverty in the future.

The proportion of male heads of household is higher in households in poverty than the

other two groups. 79% of the heads of household in vulnerability and middle-class households

are male while this proportion is 82% of households in poverty. Heads of household in

vulnerability are more similar to heads of households in the middle class regarding their marital

status too. They have a similar proportion of heads of household without a partner which is

higher than those in households in poverty. Additionally, the proportion of heads of

household cohabiting is higher in households in poverty than in the other two groups which

show no difference in this variable. The proportion of married heads of household is not

statistically different among the three groups.

Regarding household composition, households in vulnerability and those in the middle

class have a higher proportion of single households than those in poverty. Middle class

households have the highest proportion of single households and nuclear single-parent

households. Households in vulnerability also have a higher proportion of single households

than those in poverty but they show an equal percentage of nuclear single-parent households

as those in poverty. Households in vulnerability present the highest proportion of extended

families in their households. Extended families, with one or two parents, are more common in

households in vulnerability than households in poverty. This suggests that extended families

are a characteristic of households in vulnerability. Households in poverty and those in

vulnerability are similar regarding household size with or without scales of equivalence being

taken into account. They do not differ importantly in the number of members of household

but in family composition. Households in vulnerability have a lower proportion of nuclear

families with two parents, a higher proportion of extended families and a higher percentage of

single households than households in poverty.

The occupations of heads of households also present clear difference among these income

groups. Heads of households in vulnerability work less in agriculture than those in poverty and

more than those in middle class. They are employed more in manual work than heads of

households in poverty and in similar rates to those in the middle class. Again, they are in

between heads of households in poverty and the middle class regarding the proportion

working as an independent worker. This proportion increases while incomes are higher

157

representing 9% for heads of households in middle class. The same happens with clerical

activities and professional managers. In both cases, the rate increases while the income

increases. Middle class heads of households work more in these kinds of occupations than

heads of households in vulnerability, who at the same time work more in these occupations

than those in poverty. Occupation categories such as self-employed, in administrative activities

and professional managers are related to higher incomes. Middle class heads of households are

more likely to work in these sectors followed by those in vulnerability. Heads of households

who work in these occupations face a lower probability of being in poverty in the future.

These results agree with the idea, proposed by Goldthorpe and McKnight (2004), that

workers’ occupations are related to economic vulnerability. Here, occupations such as self-

employed, administrative activities and professional managers are related to less vulnerability.

Heads of households who work in these occupations have higher incomes that allow them to

protect themselves from risks. The majority of them live in middle class households. In

opposition there are occupations like agriculture and unskilled manual activities which have a

higher prevalence among heads of households in poverty and vulnerability.

Household basic services are less available in households in poverty than households in

vulnerability. At the same time, they are less available in households in vulnerability than

middle class households. Households without sanitation or with unfinished floors are a

variable with higher prevalence among households in poverty followed by households in

vulnerability. The proportion of households without sanitation or with unfinished floors is

almost zero in middle class households when less demanding thresholds are used.

Another significant difference among people living in poverty, vulnerability or the middle

class is the rural or urban location of their households. A higher proportion of people in

poverty live in rural areas than people in vulnerability and the middle class. Here, a

demographic variable is related with lower incomes. People in poverty are more concentrated

in rural areas than people in vulnerability and people in vulnerability live more in rural areas

than those who belong to the middle class. Rural location appears as a variable related with

poverty and the risk of being in poverty in the future. Urban households are more likely to be

protected against the risk of being in poverty.

The point of a household in the life cycle is different among the three groups. One of the

variables that can be used as a proxy of the household’s point in the life cycle is the age of the

158

household head. Heads of households in poverty are younger than those in vulnerability, who,

at the same time, are younger than heads of middle-class households. In other words,

households with younger heads of households are more prone to being in poverty now and in

the future. Additionally, the percentage of children under 15 years or under 18 years is higher

in households with less income. Households in the middle class are those with the lowest

proportion of children at home. Households in vulnerability are in between households in

poverty and those in the middle class with a percentage of 25% of children under 15 years and

30% under 18 years. The incidence of older people in the household behaves exactly the

opposite. Middle class households have a higher proportion of elders at home than the other

two income groups. Again, households in vulnerability are in between the other two groups

where 11% of their members are individuals older than 64 years. This percentage is 7% in

households in poverty and 17% in middle class households. This suggests that younger

households are more prone to being in poverty and vulnerability. Households in poverty are

those with the youngest heads of households, they have the highest proportion of children and

the lowest proportion of older persons at home. They are followed by households in

vulnerability which have a higher proportion of children and lower proportion of elders than

middle-class households. This relation between the demographic composition of the

household and the household’s vulnerability to poverty will be explored in detail in the third

paper of this research. Children and older persons as vulnerable groups are analyzed. In

particular, the role of social assistance programmes over the poverty reduction of households

with children and/or older persons is explored.

Regarding variables that can change along the period or health shocks that can happen

between the initial and final year, significant differences among income groups are observed.

The number of heads of households who have reported an important health problem between

the initial and final year is not statistically different between households in poverty and

vulnerability. Middle class heads of households reported a higher incidence of health shocks

than households in poverty and vulnerability. This suggests that health shocks, at least as they

are collected by this survey, are not a significant variable to explain the higher probability of

being in poverty in the future. A higher incidence of shocks is actually related with higher

incomes.

159

The change in number of members of the household working between the initial and final

year is different among the income groups. While households in poverty increased their

number of members working, households in vulnerability almost did not change it and middle-

class households in decreased this number. This suggests that the increase of members

working in households is not a protection against the risk of being in poverty in the future.

The fact that this increase is higher in households in poverty may reflect the fact that they need

to increase the number of members working to increase their income. In that case, a higher

number of household members working represents the necessity to find more sources of

income for the household. As usually workers living in households in poverty earn lower

incomes, they need more people working to increase their income.

A variable that also changes differently between the initial and final year among groups is

household size. Households in vulnerability decreased their household size along the period at

the same level that those in the middle class but this drop was higher than in households in

poverty. Households in poverty are those who experienced the lowest reduction in household

size along the period. The drop in household size of those in vulnerability and the middle class

could have affected their lower probability of being in poverty as a consequence of producing

a higher income per person.

Table 3-9 Comparison between households’ characteristics in poverty, in vulnerability and middle class, year 2001

In poverty In

vulnerability

Middle

Class

In poverty-

In

vulnerability

In

vulnerability-

Middle

Class

In poverty-

In

vulnerability

In

vulnerability-

Middle

Class

In poverty In

vulnerability

Middle

Class

Years of education 6.88 7.70 8.69 -0.82*** -0.99*** 0.000 0.000 5,259 2,146 2,738

Non-formal education 0.07 0.05 0.05 0.017*** -0.003* 0.006 0.630 5,259 2,146 2,738

Uncomplete basic education 0.39 0.29 0.23 0.1*** 0.06*** 0.000 0.003 5,259 2,146 2,738

Complete basic education 0.20 0.18 0.14 0.01 0.04*** 0.251 0.000 5,259 2,146 2,738

Uncomplete secondary education 0.22 0.26 0.24 -0.04*** 0.02* 0.000 0.082 5,259 2,146 2,738

Complete secondary education 0.10 0.16 0.17 -0.05*** -0.02 0.000 0.103 5,259 2,146 2,738

Uncomplete university education 0.01 0.01 0.03 0.00* -0.02*** 0.874 0.000 5,259 2,146 2,738

Complete university education 0.01 0.03 0.12 -0.02*** -0.08*** 0.000 0.000 5,259 2,146 2,738

Age of head 47.6 52.6 56.0 -5.09*** -3.35*** 0.000 0.000 5,259 2,146 2,738

Sex of head (male) 0.82 0.79 0.79 0.04*** -0.01*** 0.000 0.536 5,259 2,146 2,738

Head without social insurance (the head is not affiliated)0.32 0.29 0.31 0.03** -0.01

0.012 0.2985,259 2,146 2,738

Unfinished floor (less demanding) 0.07 0.04 0.02 0.04*** 0.02*** 0.000 0.003 5,259 2,146 2,738

Unfinished floor (more demanding) 0.28 0.17 0.11 0.12*** 0.06*** 0.000 0.000 5,259 2,146 2,738

Household without water sanitation (less demanding) 0.06 0.01 0.00 0.05*** 0.01** 0.000 0.016 5,259 2,146 2,738

Household without water sanitation (more demanding) 0.31 0.12 0.07 0.19*** 0.05*** 0.000 0.000 5,259 2,146 2,738

Household without water 0.19 0.06 0.03 0.13*** 0.03*** 0.000 0.000 5,259 2,146 2,738

Head cohabiting 0.11 0.09 0.07 0.03*** 0.02* 0.000 0.067 5,259 2,146 2,738

Head married 0.71 0.69 0.69 0.02 0.00* 0.163 0.800 5,259 2,146 2,738

Head without partner 0.18 0.22 0.24 -0.05*** -0.02 0.000 0.235 5,259 2,146 2,738

Head in agriculture 0.09 0.06 0.04 0.03*** 0.02*** 0.001 0.004 5,259 2,146 2,738

Head as unskilled manual worker 0.08 0.10 0.10 -0.02*** 0.01 0.003 0.397 5,259 2,146 2,738

Head as skilled manual worker 0.03 0.06 0.05 -0.03*** 0.01* 0.000 0.068 5,259 2,146 2,738

Head as independent worker 0.04 0.06 0.09 -0.02*** -0.03*** 0.000 0.000 5,259 2,146 2,738

Head in clerical activities 0.03 0.06 0.10 -0.03*** -0.04*** 0.000 0.000 5,259 2,146 2,738

Head as professional manager 0.01 0.02 0.07 -0.01*** -0.05*** 0.000 0.000 5,259 2,146 2,738

Number of observationsMean Mean Difference Pr(|T| > |t|)

Household's Characteristics

161

Rurality 0.28 0.15 0.11 0.12*** 0.04*** 0.000 0.000 5,259 2,146 2,738

Occurrence of health shocks to the head of the household

during the last 5 years 0.25 0.26 0.30 -0.01 -0.04*** 0.251 0.009 5,259 2,146 2,738

Change in number of members working 0.37 0.01 -0.14 0.36*** 0.15*** 0.000 0.000 5,259 2,146 2,738

Change in household size -0.17 -0.28 -0.23 0.11** -0.05 0.011 0.253 5,259 2,146 2,738

Percentage of child in the household (15 years and below) 0.33 0.25 0.16 0.08*** 0.09*** 0.000 0.000 5,259 2,146 2,738

Percentage of children in the household (18 years and below) 0.40 0.30 0.21 0.01*** 0.09*** 0.000 0.000 5,259 2,146 2,738

Percentage of elderly in the household (over 64 years) 0.07 0.11 0.17 -0.04*** -0.06*** 0.000 0.000 5,259 2,146 2,738

Household size 4.93 4.84 4.23 0.09* 0.61*** 0.060 0.000 5,259 2,146 2,738

Equivalent household size 3.01 2.96 2.69 0.05 0.27*** 0.000 0.000 5,259 2,146 2,738

Equivalent income 58,066 115,187 216,611 -57,121*** -101,424*** 0.000 0.000 5,259 2,146 2,738

Per capita income 37,154 74,328 147,400 -37,174*** -73,071*** 0.000 0.000 5,259 2,146 2,738

Single 0.01 0.02 0.04 -0.01*** -0.02*** 0.000 0.000 5,259 2,146 2,738

Nuclear two-parent 0.61 0.55 0.55 0.06*** 0.00* 0.000 0.888 5,259 2,146 2,738

Extended two-parent 0.10 0.14 0.08 -0.04*** 0.06*** 0.000 0.000 5,259 2,146 2,738

Nuclear single-parent 0.17 0.16 0.20 0.01 -0.04*** 0.413 0.001 5,259 2,146 2,738

Extended single-parent 0.08 0.11 0.10 -0.03*** 0.01 0.000 0.211 5,259 2,146 2,738

Source: Author's elaboration f rom CASEN Panel Database 1996-2001-2006.

Percentages and amounts by individuals. Income variables are in 2001 Chilean Pesos.

Statistically signif icant mean comparisons *** p<0.01, ** p<0.05, * p<0.1. P-values are in the appendix.

The results show that there are significant differences among the three income groups in

almost all the covariates analyzed. It can be said that households in vulnerability are in between

households in poverty and middle-class households. They are getting closer to middle-class

socio-economic characteristics and getting far away from households in poverty. They have

more education, better household conditions and more qualified occupations than households

in poverty. They are similar in size to those in poverty and bigger than middle class

households. They are characterized by having more extended families than households in

poverty and middle class and by having more single households than those in poverty. They

are in between households in poverty and those in the middle class regarding the proportion of

children and older people at home. They are in a younger stage of the household’s point in the

life cycle than middle class households but older than households in poverty. They have the

same proportion of female heads of households than middle class households, which is higher

than those in households in poverty. The substantial and statistically significant differences

among the group in vulnerability and the other two income groups stress the importance of

making a distinction among these groups.

163

3.7 Conclusions

This research investigates the characteristics of the population vulnerable to poverty, using

a theoretical framework for identifying the populations either side of them, those in poverty

and the middle class. This approach supports an empirical approach to measuring vulnerability.

Estimating a vulnerability income threshold allows the group of people living in vulnerability

to be identified. The socio-demographic characteristics and the prevalence of shocks in this

group are compared with the group of people living in poverty and those belonging to the

middle class. A discussion of the results of the study, as well as their empirical and theoretical

implications, follows.

A first finding of the study is to confirm the inverse relationship between incomes and

probability of falling into poverty. People with lower incomes in the initial year face, on

average, a higher probability of falling into poverty in the last year than those with higher

incomes. This result was also presented by López-Calva & Ortiz-Juarez (2014). Those with

lower incomes have less availability of monetary and asset resources to overcome any adverse

shocks that may occur over the period. Reduced availability of resources makes people more

vulnerable to poverty in the future.

The results show that income reduces fast as the probability of falling into poverty

increases fast up to the 20% level of probability of falling into poverty. From that point

onwards, although income still decreases while probabilities are higher, it does so at a lower

pace. This makes the 10% and 20% levels of probability of falling into poverty natural

thresholds for identifying the group of people in vulnerability to poverty. Empirically, the

threshold of 10% of probability of falling into poverty is closer to the definition used by the

State of Chile to target its Social Protection System. The majority of their social programmes

go to the 60% most vulnerable of the population. This definition includes people in poverty

and vulnerability. The threshold of 10% of probability indicates that 19. 2% of the population

is in vulnerability and 38.8% are in poverty. In conjunction, these almost equal the 60%

considered by the State of Chile. This makes the threshold of a 10% probability of falling into

poverty more relevant for the Chilean context.

A second finding of this study shows the sensitivity of the vulnerability income threshold

to the definition of the poverty threshold. The threshold estimated by this study is almost 17%

164

higher than the threshold obtained by López-Calva & Ortiz-Juárez (2014). In their study, the

income threshold associated with a probability of falling into poverty is equal to 10%, but the

threshold estimated by this research uses the ‘new poverty line’ implemented in Chile since

2013. This new poverty line is 30% higher than the US $4 PPP per day poverty line used by

López-Calva & Ortiz-Juárez (2014)58. These results corroborate the importance of measuring

vulnerability to poverty, considering, as with poverty, the welfare standards of specific context.

The vulnerability threshold obtained by this research is in line with the current welfare

measures used in Chile.

The key findings from this research refer to the characterization of vulnerability. This study

concludes that the population in vulnerability to poverty differs significantly from those in

poverty and from the middle class. The analysis from Chile suggests that the socio-

demographic characteristics and the prevalence of shocks are different across these three

income groups. It can be said that the group in vulnerability is in between people in poverty and

those in the middle class. They are not as deprived as those in poverty but they are not as

affluent as those in the middle class. A discussion of the main differences across these groups

follows.

Human capital variables show large and significant differences across the groups.

Households in vulnerability have heads of households with more years of education than those

in poverty but fewer years of education than those of middle class households. More of them

have completed the education cycle, similar to those in the middle class. However, much fewer

of them have completed university studies compared to the middle class. Education appears to

have a protective function against poverty and the risk of being in poverty in the future.

Households in vulnerability have a higher proportion of female heads of households than

households in poverty but are more or less equal to households in the middle class.

Households in vulnerability are in between those in poverty and middle class regarding single

heads of households. The highest proportion of single households is in the middle class. The

same applies to single person households. Middle class households have the highest

proportion of single person households followed by those in vulnerability and then by those in

poverty. Households in vulnerability are similar in size to those in poverty and smaller than

58 To make the comparison poverty lines were expressed at 2005 values. That year the moderate poverty line was established by the World Bank at US $4 PPP per day.

165

those in the middle class. Although households in vulnerability and in poverty are almost equal

in size, they differ in their composition. Households in vulnerability have more extended

families than nuclear families. The highest proportion of extended families, with one or two

parents, is in households in vulnerability.

Heads of households in vulnerability are again in between heads of households in poverty

and middle class regarding the occupations in which they work. Occupations such as self-

employed, administrative activities and professional managers show less vulnerability. Heads of

households who work in these occupations have higher incomes that allow them to protect

themselves from risks. The majority of them live in middle-class households. In contrast there

are occupations like agriculture and unskilled manual activities which have a higher prevalence

among heads of households in poverty and vulnerability. These results confirm the view that

occupations can be stratified by economic vulnerability as suggested by Goldthorpe and

McKnight (2004).

Households in vulnerability have better access to basic services than households in poverty

but worse access than those in the middle class. The incidence of unfinished floors and

absence of water and sanitation services is highest in households in poverty followed by those

in vulnerability. This is in line with the higher propensity of households in poverty to be

located in rural areas than the other two groups. Rural areas have more limited access to basic

services. Households in vulnerability are again in between the other two groups regarding their

rural location.

The household’s point in the life cycle is different among the three income groups.

Households in poverty are those with the youngest heads of households, the highest

proportion of children and the lowest proportion of older persons at home. They are followed

by households in vulnerability that have a higher proportion of children and lower of elderly

than middle-class households. Younger households are more likely to be in poverty and

vulnerability.

Regarding the changes that households experience between the initial and final year,

important difference across the groups emerge. The incidence of health shocks in the period is

not statistically different when comparing households in poverty and vulnerability. Middle class

heads of households reported a higher incidence of health shocks than households in

vulnerability suggesting that this shock is not a determinant of higher levels of vulnerability.

166

Households in vulnerability show almost no change in the number of members of the

household working between the initial and final year. In contrast, households in poverty

increased their number of members working and middle-class households decreased this

number. This suggests that the increase of members working in households is not a sufficient

protection against being in poverty in the future. Households in vulnerability do not change

their number of members working but they decreased their household size along the period at

the same level that those in middle class but higher than those in poverty. Smaller household

sizes could have affected their lower probability of being in poverty.

These findings have important policy implications. First, there is a need to expand the

literature on vulnerability to poverty to actively consider contextual factors. Measurements of

the risk of being in poverty in the future, their characteristics, and consequences for well-being

must be analyzed in any particular context. The sensitivity of the vulnerability income

threshold to the definition of the poverty threshold and to the density of the population along

the income distribution highlights the importance of measuring vulnerability to poverty in a

specific context.

Second, this research confirms the importance of securing high incomes as a protection

against risks, in this case the specific risk of falling into poverty. This confirms that the

"augmented" poverty line concept proposed by Cafiero and Vakis (2006) can be estimated and

operationalized as a ‘vulnerability income threshold’. This threshold incorporates not only a

minimum basket of consumption and services, but also a basket of insurance against risks. It

represents income related to socio-economic characteristics and the assets of households as

ensuring less vulnerability to poverty as a result of idiosyncratic or asymmetric shocks (López-

Calva & Ortiz-Juárez 2014). Its estimation is feasible and it must be used in addition to

measuring poverty lines to identify the most vulnerable of the population.

Finally, the differences between people in vulnerability and the other two groups throw

light on the importance of designing social programmes specifically for people in vulnerability.

People in vulnerability differ from people in poverty and those in the middle class in almost all

the variables analyzed by this study. In order to reduce their vulnerability they need to increase

their opportunity to complete tertiary education and to have access to more qualified jobs.

Considering the fact that they have more extended families in their households they can be

helped to increase the chances of finding a job for all these members. Ensuring the wide

167

provision of child-care is a policy that can allow these households to increase their levels of

employment. Their high incidence of children at home reduces the chances of all the

household members to go out to find a job. Considering the fact that the households in

poverty and those in vulnerability are at a younger point of the household life-cycle, social

policies can be designed to protect households with younger heads of households or with

children.

As mentioned, the protection of the group of people in vulnerability to poverty has two-

fold benefits. On the one hand, reducing levels of vulnerability to poverty has a positive

impact on the well-being of people. On the other hand, reducing the likelihood of falling into

poverty contributes to the reduction of poverty levels in the future. Reducing vulnerability to

poverty needs a better understanding of its meaning in a particular context. This study has

contributed to this line of research.

168

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3.9 Annexes

3.9.1 Approaches to and Methodologies for measuring vulnerability to

poverty

Table 3-10 Approaches to and Methodologies for vulnerability

Approaches and

Methodologies Brief description Authors Adventages Disadventages

First attempts to understand

vulnerability as exposure to

risks.

It is not only vulnerability to

poverty. Vulnerability to

changes on wellbeing.

It measures vulnerability as

insecurity that generate losses

of wellbeing.

The changes on income (risk)

can be upward or downward.

It is difficult the estimation of

the cost of the insurance for

each individual or household.

It creates N poverty lines for

each individual or household.

As a consequence, poverty

gaps are not comparable

between individuals or

groups of people.

It considers only downward

variations of wellbeing. It

excludes upward changes of

wellbeing.

It's not a vulnerability to

poverty approach only.

It’s the first proposal regarding

subjective vulnerability.

It's a relative perception of

vulnerability instead of

absolute thresholds.

It reinforces the idea that only

downward variations of

wellbeing should be included.

It excludes upward changes of

wellbeing in the analysis of


Some individuals in chronic

poverty are not vulnerable

under this approach. This

happens because they will be

in poverty in the future with

certainty (not vulnerability).

Identification of people in

vulnerability is confused

because it defines two

thresholds: poverty line and

poverty line plus inicial

wellbeing.

It is a theorical approach and

it does not explain how to

calculate the vulnerability

proposed.

The subjective perception of

wellbeing can be misaligned

with the objective state of

wellbeing of an individual.

Axiomatic argumentation

Dutta-Foster-Mishra

Proposal

It is an hibrid approach of vulnerability

because it combines an absolute

threshold (poverty line) with a relative

threshold (inicial level of income). The

inicial level of wellbeing is a proxi of the

capacity to manage risks. It is not only

important the fall into poverty but also

from which inicial level of income this

happened.

Dutta, Foster,

Mishra (2011)

It represents a step forward to

consider uncertainty in

poverty measures.

Vulnerability as

subjective perception

of downward risk

It defines vulnerability as the risk of

getting future losses of wellbeing. It

uses subjective information from

individuals regarding their own

perception of future shocks that they can

live and their probabilities.

Povel (2010)

It defines a vulnerability threshold

including the consumption needed to

satisfy basic needs plus the cost of an

insurance for a social acceptable risk.

Thus, the extended poverty considers

losses over wellbeing but risk exposition

too.

Vulnerability as

extended poverty

Cafiero and Vakis

(2006)

Vulnerability as exposure to risks

Vulnerability as

exposure to risks of

low income

households

It shows the relation between poverty

and exposure to risk of low income

households. Vulnerability as uninsured

exposure to risks. A household is

vulnerable if and only if it doesn't has

the capacity to smooth the consumption

in response to idiosyncratic income

fluctuations (Amin et al, 2003)

Glewwe and Hall

(1998); Dercon and

Krishnan (2000);

Amin et al. (2003);

Cocgrane (1991);

Ravallion and

Chauduri (1997);

Towsend (1994);

Jalan and Ravallion

(1999)

175

Source: Author’s elaboration following Hoddinott, J., & Quisumbing, A. (Hoddinott & Quisumbing, 2003a,

2003b) and Gallardo (2017).

It requires only transversal

data to calculate vulnerability

measures. It is a reason of its

popularity.

It makes several assumptions

to predict future

probabilities: household's

probability distributions of

consumption are log-normal,

these distributions are time

invariant, these distributions

are equal for all household.

All these assumptions are

questionable.

It is needed the

determination of a

vulnerability threshold

It establishes a confused

relation between a lower

probability of being in

poverty in the future and

more variance (risk) in

consumption.

Vulnerability can be

decomposed between a

poverty component and a risk

component (idiosyncratic risk

and aggregate risk).

The risk included in the

concavity of the utility

function is sensitive to

upward or downward

changes of wellbeing. Only

dwonward movements of

wellbeing are important to

determine vulnerability.

It uses expected poverty

approach expressed in utility

terms.

The risk component is very

sensitive to the choice of a

utility function and its

specifications and is not to

individual preferences and

particular household

situation.

It proposes a vulnerability

index based on axiomatic

propierties.

It proposes an asymetric

conception of vulnerability in

which only the downward

changes of wellbeing are

important to evaluate

vulnerability.

It includes expected poverty in

the analysis of vulnerability

and not only risk.

Vulnerability as expected poverty

Vulnerability as the

probability of future

poverty

It defines vulnerability to poverty as the

probability, in the current state, of being

in poverty in the future. It uses expected

poverty to measure the risk of being

under the poverty threshold in the

future. A household is vulnerable to

poverty if their probability to be in

poverty in the future is equal to or

greater than zero. This vulnerability is

relevant if the probability is equal to or

graeter than a defined threshold

(usually 0.5 or the proportion of people

living in poverty).

Christiaensen and

Boisvert (2000);

Prichett et al.

(2000); Chaudhuri

(2002); Chaudhuri

and Christiaensen

(2002); Kamanou

and Morduch (2002);

Chaudhuri (2003);

Christiaensen and

Subbarao (2005)

However, the election of the

convexity of the indicator

allow symetry of the risks.

Vulnerability as low

expected utility

It defines vulnerability like the

difference between the utility from

certain consumption level (usually the

poverty threshold) and the expected

utility under uncertainty. In the other

way around, a household is not

vulnerable when the expected utility of

their consumption es equal to or greater

than the utility of the basket that define

the poverty line. It uses the expected

utility of Von Neumann and

Morgenstern (1944) and the risk of

Rothschild and Stiglitz (1970).

Ligon and Schechter

(2002, 2003)

Calvo and Dercon

It defines vulnerability like the measure

of the magnitude in which a household

suffers the threat to confront poverty

states in the future.

Calvo and Dercon

(2005, 2007, 2008)

176

3.9.2 Panel CASEN 1996-2001-2006. Kinds of people interviewed by year

New babies and new members who have arrived at the household are also considered as

temporary members of the survey (miembros temporales de la muestra, MTM). The original

members of the survey (miembros originales de la muestra, MOM) who were interviewed from the

first wave of the survey are followed throughout time even when they have moved to live with

other households in the same region of the country. The objective of the survey is to follow

the MOM even when they live with new people.

Table 9 shows the number of MOM and MTM in every year of the Panel. The reduction

of MOM in the second and the third wave indicates the extent of attrition in the survey. Every

year there are people who do not respond to the questionnaire for different reasons. Some of

them may have moved to another house with an address unknown to the data collectors.

Other households may have moved to another region of the country which makes it not

possible to follow them. A group of households refuse to participate again in the survey. The

attrition rate is 28.2% for the 1996-2001 period and 50.9% between 1996 and 2006. “Although

this attrition rate may seem high, for a 10-year three-wave panel data set it is a reasonable rate,

when compared to international standards 59 (Bendezú et al. (2007); Czajka et al. (2008);

Fitzgerald et al. (1998)).

Table 3-11 Kinds of people interviewed by year

Source: Author’s elaboration from CASEN Panel Database 1996-2001-2006

59 eg. Panel Study of Income Dynamics, European Community Household Panel.

1996 2001 2006

MOM 20,942 15,038 10,287

MTM 0 3,549 4,281

Total 20,942 18,587 14,568

CASEN 1996-2001-2006

Interviewed people by years

177

3.9.3 Descriptive statistics of CASEN Panel Database

Table 3-12 Descriptive statistics: covariates, income and poverty. CASEN Panel Database

Household characteristics

(Percentage and averages of individuals) Mean S.d Mean S.d Mean S.d

Education of household head in the initial year

Years of education 8.6 4.3 9.1 4.4 9.6 4.4

Non-formal education 4.2% 20.2% 3.9% 19.5% 3.1% 17.4%

Incomplete primary education 22.0% 41.4% 22.9% 42.0% 22.0% 41.4%

Complete primary education 16.7% 37.3% 16.9% 37.5% 18.2% 38.6%

Incomplete secundary education 23.3% 42.2% 22.8% 41.9% 20.1% 40.1%

Complete secundary education 20.5% 40.4% 17.7% 38.2% 18.6% 38.9%

Incomplete universitary education 3.9% 19.4% 3.9% 19.3% 4.6% 20.9%

Complete universitary education 8.1% 27.3% 11.4% 31.7% 13.1% 33.8%

Marital status of the household head in the initial year

Cohabiting 8.3% 27.6% 9.3% 29.0% 10.4% 30.5%

Married 73.5% 44.2% 68.5% 46.5% 63.9% 48.0%

Without partner 18.2% 38.6% 22.1% 41.5% 25.7% 43.7%

Sector of activity of the household head in the initial year

Agriculture 4.5% 20.8% 4.8% 21.5% 5.0% 21.9%

Unskilled manual worker 10.3% 30.3% 9.7% 29.5% 10.7% 31.0%

Skilled manual worker 5.1% 22.0% 3.8% 19.1% 5.5% 22.8%

Independent worker 6.7% 25.0% 7.1% 25.6% 7.5% 26.4%

Clerical activities 6.7% 25.1% 8.9% 28.5% 8.5% 28.0%

Professional manager 4.7% 21.1% 4.5% 20.7% 8.3% 27.6%

Other characteristics of the household head in the initial year

Age (years) 47.05 13.73 50.85 13.95 54.34 13.98

Age squared 2,402 1,405 2,781 1,522 3,148 1,621

Male 81.8% 38.6% 80.9% 39.3% 77.0% 42.1%

Without social insurance (the head is not afiliated) - - 29.0% 45.4% 73.4% 44.2%

Other characteristics of the household in the initial year

Unfinished floor 16.8% 37.4% 16.0% 36.7% 20.2% 40.1%

Without water sanitation 18.7% 39.0% 12.5% 33.0% - -

Region III 2.9% 16.7% 2.9% 16.7% 2.9% 16.7%

Region VII 10.9% 31.2% 10.9% 31.2% 10.9% 31.2%

Region VIII 21.3% 41.0% 21.3% 41.0% 21.3% 41.0%

Metropolitan region 64.9% 47.7% 64.9% 47.7% 64.9% 47.7%

Rural 11.9% 32.4% 11.9% 32.4% 11.9% 32.4%

Percentage of child in the household (15 and below) 30.9% 21.8% 25.5% 20.9% 15.1% 17.6%

Percentage of child in the household (18 and below) 36.3% 21.9% 31.0% 22.5% 20.9% 20.1%

Percentage of elder in the household (over 64) 8.7% 19.5% 10.7% 22.7% 24.5% 29.1%

Household size 4.66 1.72 4.51 1.87 4.38 1.88

Equivalent household size 2.89 0.76 2.82 0.83 2.76 0.83

1996 2001 2006

Year

178

Changes and shocks in the household

Occurrence of health shocks to any member of the

household during the last 5 years

- - 32.3% 46.8% 43.2% 49.5%

Occurrence of health shocks to the head of the

household during the last 5 years

- - 14.6% 35.3% 26.4% 44.1%

Change in number of members working - - -0.10 1.08 0.19 1.11

Change in household size - -

Income and poverty

Moderate Poverty rate 23.5% 42.4% 20.2% 40.2% 10.5% 30.7%

Household equivalent income (Chilean Pesos 2006) 182,441 211,863 227,480 265,716 223,125 220,668

Household per capita income (Chilean Pesos 2006) 121,512 147,464 156,346 207,534 150,795 156,528

Total observation

Source: Author’s elaboration based on CASEN Panel Database 1996-2001-2006. Ministerio de Desarrollo Social, Chile.

10,286

3.9.4 Robustness Checks of logistic and lineal estimations

Model 1: All the variables available

Model 2: Without EGP classification variables

Model 3: Replace EGP classification variables by occupation status variable

Model 4: All the variables available by household instead by individuals

1996-2001

1996-2001 Logistic poverty

Model: Model 1 Model 2 Model 3 Model 4

Dependent Variable:

Coeff. S.E. Coeff. S.E. Coeff. S.E. Coeff. S.E.

Education of the head of the household -0.185*** 0.039 -0.342*** 0.031 -0.363*** 0.034 -0.149 0.078

Age of the head of the household -0.040 0.028 -0.0545** 0.021 -0.020 0.024 -0.048 0.059

Age squared of head of the household 0.000 0.000 0.000413* 0.000 0.000 0.000 0.000 0.001

Sex of thehead of the household (male=1) -0.227 0.275 -0.301 0.211 -0.013 0.239 -0.381 0.461

Head of the household without social insurance 0.429*** 0.109 0.166 0.101 0.257* 0.116 0.598** 0.229

Household with unfinished floor 0.579*** 0.166 0.623*** 0.142 0.715*** 0.151 0.59 0.326

Household without water sanitation 0.965*** 0.148 1.066*** 0.132 0.977*** 0.139 0.794** 0.281

Head of the household married (omitted)

Head of the household cohabiting 0.330* 0.142 0.237 0.129 0.172 0.140 0.238 0.309

Head of the household without partner -0.787** 0.282 -0.548* 0.225 -0.724** 0.246 -1.087** 0.408

Head of household working -0.812*** 0.160

Head of the household in agriculture (omitted)

180

Head of the household as unskilled manual worker -0.0587 0.162 -0.258 0.298

Head of the household as skilled manual worker -0.848*** 0.196 -0.854* 0.364

Head of the household as independent worker -0.959*** 0.175 -1.294*** 0.339

Head of the household in clerical activities -1.520*** 0.193 -1.546*** 0.373

Head of the household as professional manager -1.675*** 0.252 -2.011*** 0.506

Household Region VII 0.703*** 0.190 0.713*** 0.169 0.639*** 0.176 0.954* 0.388

Household Region III 0.686*** 0.137 0.698*** 0.113 0.563*** 0.122 0.770** 0.260

Region VIII 1.143*** 0.114 0.936*** 0.096 0.798*** 0.103 1.215*** 0.222


Rurality -0.261 0.170 -0.208 0.132 -0.2 0.139 -0.302 0.297

Occurrence of health shocks 2001-2006 0.398* 0.190 0.319* 0.147 0.452** 0.157 0.323 0.350

Change in number of members working 2001-2006 -0.546*** 0.058 -0.379*** 0.044 -0.447*** 0.049 -0.440*** 0.130

Change in household size 2001-2006 0.231*** 0.037 0.189*** 0.028 0.227*** 0.032 0.325*** 0.077

Percentage of child in the household 3.372*** 0.301 3.295*** 0.284 3.172*** 0.287 2.789*** 0.527

Percentage of elder in the household 0.0605 0.573 -0.0521 0.352 0.007 0.386 0.357 0.906

Constant 0.659 0.759 0.896 0.603 0.922 0.696 1.155 1.550

Observations 7,272 10,026 8,923 1,876

Pseudo R2/ R2 0.29 0.24 0.24 0.28

Source: Own calculations from Panel CASEN 1996-2001-2006




1996-2001 Linear Income (ln scale)


Dependent Variable:


181

Education of the head of the household 0.100*** 0.014 0.166*** 0.011 0.167*** 0.012 0.102*** 0.027

Age of the head of the household 0.0226*** 0.007 0.0213*** 0.006 0.0175** 0.007 0.0414** 0.014

Age squared of head of the household -0.000152* 0.000 -0.000137* 0.000 0.000 0.000 -0.000366* 0.000

Sex of thehead of the household (male=1) -0.165 0.109 -0.00316 0.081 -0.0702 0.084 0.042 0.149

Head of the household without social insurance -0.255*** 0.033 -0.113*** 0.030 -0.154*** 0.036 -0.302*** 0.068

Household with unfinished floor -0.083 0.047 -0.139** 0.044 -0.168*** 0.047 -0.0123 0.084

Household without water sanitation -0.106 0.059 -0.223*** 0.054 -0.214*** 0.057 -0.00567 0.106


Head of the household cohabiting -0.0396 0.030 -0.0683* 0.032 -0.0752* 0.034 -0.0454 0.065

Head of the household without partner -0.131 0.121 -0.033 0.089 -0.027 0.094 0.1 0.144

Head of household working 0.219*** 0.042


Head of the household as unskilled manual worker 0.0758 0.045 0.106 0.084

Head of the household as skilled manual worker 0.0688 0.048 0.0583 0.090

Head of the household as independent worker 0.446*** 0.046 0.491*** 0.089

Head of the household in clerical activities 0.200** 0.061 0.287** 0.110

Head of the household as professional manager 0.787*** 0.076 0.784*** 0.136

Household Region VII -0.322*** 0.050 -0.327*** 0.046 -0.331*** 0.047 -0.392*** 0.091

Household Region III -0.283*** 0.036 -0.277*** 0.035 -0.279*** 0.037 -0.342*** 0.063

Region VIII -0.313*** 0.040 -0.343*** 0.037 -0.354*** 0.038 -0.345*** 0.075


Rurality -0.026 0.049 -0.080 0.051 -0.0828 0.054 -0.060 0.094

Occurrence of health shocks 2001-2006 -0.0622 0.043 -0.0528 0.037 -0.102* 0.041 -0.0973 0.080


Change in household size 2001-2006 0.0434*** 0.010 0.0444*** 0.009 0.0341*** 0.010 0.037 0.020

Percentage of child in the household -1.047*** 0.096 -1.081*** 0.084 -1.075*** 0.087 -1.203*** 0.159

Percentage of elder in the household 0.219 0.123 0.088 0.081 0.156 0.088 0.163 0.184

Constant 10.86*** 0.231 10.69*** 0.194 10.63*** 0.220 10.35*** 0.418

182

Observations 7,272 9,989 8,888 1,872

Pseudo R2/ R2 0.47 0.42 0.44 0.48



household per capita income (ln-scale) in lineal model Robust standard errors in parentheses, * p<0.05, **p<0.01, *** p<0.001

2001-2006 Logistic poverty


Dependent Variable:


Education of the head of the household -0.310*** 0.046 -0.316*** 0.039 -0.304*** 0.042 -0.232** 0.087

Age of the head of the household 0.042 0.048 -0.0951** 0.037 -0.0893* 0.043 0.096 0.076

Age squared of head of the household -0.001 0.001 0.000959* 0.000 0.001 0.000 -0.001 0.001

Sex of thehead of the household (male=1) 0.485 0.317 -0.0354 0.192 0.189 0.209 0.144 0.458

Head of the household without social insurance 0.186 0.136 0.0732 0.099 0.0801 0.106 0.300 0.266

Household with unfinished floor 0.193 0.116 0.370*** 0.093 0.305** 0.098 0.012 0.223

Household without water sanitation 0.654*** 0.169 0.677*** 0.129 0.653*** 0.132 0.439 0.307


Head of the household cohabiting -0.0605 0.175 0.0977 0.146 0.0831 0.160 -0.065 0.324

Head of the household without partner 0.11 0.313 -0.31 0.226 -0.297 0.242 0.049 0.445

Head of household working -0.615*** 0.120


Head of the household as unskilled manual worker -0.16 0.194 -0.334 0.393

Head of the household as skilled manual worker -0.550** 0.183 -0.981** 0.362

Head of the household as independent worker 0.145 0.175 -0.121 0.342

Head of the household in clerical activities -0.408 0.225 -1.006* 0.465

Head of the household as professional manager -1.486*** 0.318 -2.104** 0.677

Household Region VII -0.139 0.180 0.19 0.143 0.0435 0.146 -0.081 0.346

183

Household Region III 0.491*** 0.131 0.639*** 0.109 0.563*** 0.113 0.550* 0.238

Region VIII 1.064*** 0.138 1.042*** 0.111 0.938*** 0.116 0.830** 0.257


Rurality 0.283 0.147 0.318** 0.115 0.436*** 0.123 0.191 0.269

Occurrence of health shocks 2001-2006 0.0327 0.125 0.0529 0.099 0.0203 0.106 -0.109 0.255


Change in household size 2001-2006 0.133** 0.047 0.107*** 0.031 0.136*** 0.036 0.135 0.091

Percentage of child in the household 3.760*** 0.399 2.889*** 0.323 2.972*** 0.343 3.792*** 0.736

Percentage of elder in the household 0.263 0.611 -0.535 0.482 -0.718 0.520 0.731 0.868

Constant -2.45 1.253 1.111 0.940 1.253 1.058 -3.416 1.973

Observations 6,650 10,070 9,240 1,873

Pseudo R2/ R2 0.25 0.19 0.19 0.23





2001-2006 Linear Income (ln scale)


Dependent Variable:


Education of the head of the household 0.153*** 0.014 0.187*** 0.010 0.182*** 0.010 0.138*** 0.026

Age of the head of the household 0.001 0.013 0.0264*** 0.008 0.0179* 0.009 0.008 0.022

Age squared of head of the household 0.000 0.000 -0.000156* 0.000 0.000 0.000 0.000 0.000

Sex of thehead of the household (male=1) 0.000101 0.113 0.175** 0.067 0.0635 0.069 0.188 0.133

Head of the household without social insurance -0.063 0.033 -0.0484 0.029 -0.0654* 0.031 -0.067 0.066

Household with unfinished floor -0.112** 0.038 -0.128*** 0.032 -0.105*** 0.032 0.003 0.074

184

Household without water sanitation -0.268*** 0.032 -0.280*** 0.032 -0.303*** 0.032 -0.337*** 0.069


Head of the household cohabiting 0.0451 0.050 0.00117 0.046 0.015 0.050 0.070 0.091

Head of the household without partner 0.208 0.110 0.258*** 0.067 0.228** 0.070 0.177 0.118

Head of household working

0.271*** 0.036


Head of the household as unskilled manual worker 0.0413 0.040 0.046 0.083

Head of the household as skilled manual worker 0.0998* 0.044 0.051 0.089

Head of the household as independent worker 0.117** 0.044 0.059 0.091

Head of the household in clerical activities 0.126* 0.052 0.251* 0.110

Head of the household as professional manager 0.365*** 0.076 0.452** 0.148

Household Region VII -0.0465 0.050 -0.170*** 0.047 -0.133** 0.048 -0.137 0.120

Household Region III -0.167*** 0.035 -0.170*** 0.031 -0.156*** 0.032 -0.278*** 0.059

Region VIII -0.200*** 0.041 -0.200*** 0.032 -0.163*** 0.033 -0.207** 0.067


Rurality 0.029 0.036 0.025 0.030 0.014 0.031 0.035 0.072

Occurrence of health shocks 2001-2006 0.0151 0.041 0.0179 0.031 0.0328 0.033 0.068 0.074


Change in household size 2001-2006 0.0397** 0.013 0.0515*** 0.009 0.0481*** 0.010 0.050 0.026

Percentage of child in the household -1.092*** 0.097 -0.932*** 0.084 -0.927*** 0.089 -1.230*** 0.168

Percentage of elder in the household -0.206 0.135 0.0581 0.079 0.0891 0.083 -0.225 0.223

Constant 10.95*** 0.356 10.25*** 0.233 10.28*** 0.262 11.01*** 0.555

Observations 6,650 10,056 9,226 1,873

Pseudo R2/ R2 0.44 0.42 0.43 0.45





4 Paper III: Protecting vulnerable groups in Chile: the

contribution of social Assistance to the poverty exit

rate of children and older people

Abstract

Children and older persons are commonly over-represented in income poverty and

vulnerability making them the focus of many Social Assistance programmes. However, these

two vulnerable groups do not necessarily receive the same level of protection from the State.

The age-related bias of welfare institutions in relation to protection from poverty and

destitution creates a generational inequity in the allocation of public benefits. This paper

examines the generational equity of cash transfers targeted to children and older persons. The

empirical research is in Chile, a country where children are over-represented in poverty and

vulnerability while older persons are under-represented. The question that this research lays

out is the role of Social Assistance programmes over these disparities in poverty rates by age. A

partial fiscal analysis is carried out following the guidelines of the Commitment to Equity

Institute to compare the situation of these groups before and after direct taxes and cash

transfers. Overall, this study provides further evidence that the effectiveness of cash transfers

to reduce poverty depends on the kind and amount of cash transfer, and these, in the case of

Chile, are strictly in connection with the age composition of the household. The findings

confirm age bias towards households with older persons rather than households with children

that creates generational inequity.

186

4.1 Introduction

Children and older people are two age groups that are commonly targeted by Social

Assistance (Barrientos, 2013). The over-representation of these two age groups in income

poverty and vulnerability make them the focus of many anti-poverty programmes. The

consequences of living in poverty over their well-being are observed in the short and long

term. In the case of children, there is a great deal of evidence supporting the view that poverty

during childhood affects not only developmental outcomes but also has damaging effects

extending over the whole life of an individual (Magnuson & Votruba-Drzal, 2009; Melchior et

al. 2007). In the case of older people, poverty can affect not only their quality of life but also

their quantity of life. For all these reasons, children and older persons are recognized as

vulnerable groups that need protection from the State.

Appropriate policy responses to poverty and vulnerability for each age group have been

developed in different countries. Social insurance and social assistance programmes targeted to

children and older persons in poverty are common in developing countries (Barrientos, 2013a).

However, these two age groups do not necessarily receive the same level of protection from

the State. Lynch (2006) distinguishes between welfare institutions biased towards older groups

and pensions –called occupational-based- and those putting more attention on families,

children and groups with weak ties to the labour market –named citizenship-based. The age-

related bias of welfare institutions in relation to protection from poverty and destitution

creates a generational inequity in the allocation of public benefits.

Social Assistance programmes in Chile, and cash transfers in particular, have contributed to

poverty reduction (Martinez-Aguilar et al., 2017). Since 1990 many different anti-poverty

programmes have been implemented which were expanded in 2002 to create a more integral

Social Protection System (Larrañaga, 2010). Cash transfers go not only to people in poverty

but also those in vulnerability to poverty during the last decade and a half. However, the effect

of cash transfers on poverty reduction by age has not been explored. The main objective of

this paper is to examine the generational equity of cash transfers in Chile, and in particular, to

compare the effectiveness of poverty reduction of cash transfers targeted to households with

children and households with older persons.

187

Chile is an interesting case to study because it has one of the highest disparities between

child poverty rates and older persons’ poverty rates among Latin American countries (ECLAC,

2014). The poverty rate among children under 15 is 2.9 times the poverty rate among people

older than 55 (ECLAC, 2014). National figures show that 6.6% of people older than 60 were

in poverty in Chile in 2015 which was 70% less than the incidence of poverty among children

younger than 18 –which was 19.5%. In addition, older people have experienced a higher

decrease in poverty than the average population since 2006. The child poverty rate decreased

50% between 2006 and 2015 –from 39.5% to 19.5%- and the elderly poverty rate fell 70% in

the same period –from 22.8% to 6.6%. This shows that people aged 65 and over are not only

the group with the lowest incidence of poverty in the population but also the group that has

experienced the highest reduction in poverty between 2006 and 2015.

In addition, children are more vulnerable to being in poverty than older persons. One of

the main findings of Paper II of this thesis is that the age composition of the household is an

important determinant of vulnerability to poverty. Households with more children face a

higher probability of being in poverty in the future while having more elderly persons in the

household is related with a lower probability of confronting episodes of poverty in the

forthcoming years.

This evidence configures Chile as a country where children are over-represented among

people in poverty while older persons are under-represented among this group, and where

having a higher number of children in the household is related to higher levels of vulnerability

to poverty and a higher number of older persons means lower levels of vulnerability to

poverty. The question that this research lays out is the role of Social Assistance programmes

over these disparities in poverty rate by age.

The analysis in this chapter examines whether the effectiveness of cash transfers in Chile

on poverty reduction are age dependent. This paper aims to find answers to the following

research questions:

Do cash transfers reduce poverty for children and older persons equally?

Are children and older people equally covered by cash transfers?

Is the effectiveness of monetary transfers on poverty reduction dependent on the

recipient's age?

188

What factors might underpin the effectiveness of differences in cash transfers on

poverty reduction between age groups?

In order to identify the effectiveness of cash transfers over poverty rates for both age

groups, a partial fiscal incidence analysis is carried out following the framework proposed by

Commitment to Equity (CEQ60) (Lustig & Higgins, 2013). The objective is to identify any

changes in poverty rate after the direct fiscal intervention: direct taxes and cash transfers. The

analysis measures the poverty exit rate as a consequence of fiscal intervention. This paper restricts

the analysis to direct taxes and cash transfers because the objective is to identify their effects

on income poverty. Among the cash transfers considered are both conditional and

unconditional types, and the pensions that households receive. Although direct taxes are

considered in the analysis, they are not very important in changes in poverty rates. The fact

that households in poverty do not pay direct taxes and just a small proportion of them pay

social contributions makes the effect of taxes over poverty rates very small.

The data source of this research is the 2015 National Socioeconomic Characterization

Survey (CASEN) –its most recent application- which is collected by The Ministry of Social

Development of Chile, and is representative at the level of country, regions, and rural and

urban areas. CASEN collects data from 83,887 households including the details of all the

incomes and cash transfers received from Social Assistance programmes.

The findings of this study show that cash transfers have an age bias in Chile reducing in a

higher proportion the poverty rates among older persons than among children. The incidence

of moderate poverty in the group of people over 64 years decreases by 57% after monetary

transfers, from 17% to 7.2%. Although children also experience a decrease in moderate

poverty -from 20.4% for children under 3 years and 19.3% for children between 4 and 18 years

to 17.6% and 16% for each group respectively- this represents a reduction of around 14% of

initial poverty rates. This means that the reduction of poverty after monetary transfers among

older persons is around 4 and 3 times greater than the reduction of poverty among younger

and older children respectively. This age bias is also observed in the proportion of both age


189

groups among people in poverty. Children remain over-represented among the population in

poverty after cash transfers while older people move from being over-represented in poverty

to being under-represented in poverty after monetary transfers.

The results also show that the effectiveness of cash transfers in reducing poverty is higher

among people living in households with older persons than in households with children. The

highest poverty exit rate after monetary transfers belongs to people living in households with

older persons and no children and the lowest to people living in households with children and

no elderly. People living in households with both children and older persons are in between.

This indicates that the effectiveness of cash transfers on exit poverty depends on the age

composition of the households. In particular, the effectiveness of cash transfers on exit

poverty is biased towards households where older persons live.

This study finds that the main reason behind the higher effectiveness of cash transfers on

exit poverty in households with older persons is the higher amount of cash transfers that they

receive. They are highly covered by cash transfers that are more effective in enabling exiting

poverty. The main cash transfer behind this is the non-contributory pension called Pensión

Basica Solidaria (PBS). The fact that older persons live in smaller households than children is

also part of the explanation behind their higher poverty exit rates. However, the much higher

exit poverty rates of people living in households with children and older persons –the biggest

in size- than for individuals who live in households with children and no older persons

indicates the fact that having an elder at home is more important than having a small

household size. Furthermore, the cash transfers received by people who live in households

with older person/s are more effective in exiting poverty even when poverty gaps are much

higher in those households than in household with children.

The complementarity of benefits in increasing the exit poverty rate is an additional finding

of this research. For instance, when benefits for families are combined with benefits for

workers or other benefits, the exit poverty rate of households with children and no older

persons increases. The same happens when benefits for families are combined with pensions.

This evidence supports the argument that the per capita amount of benefit is an important

element in increasing the exit poverty rate and not only the coverage of cash transfers among

people in poverty.

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Overall, this study provides further evidence that the effectiveness of cash transfers in

reducing poverty depends on the kind and amount of the cash transfer and those in the case of

Chile are strictly in connection with the age composition of the household.

This research contributes to the literature in several important ways. First, this paper

presents further evidence for the effectiveness of cash transfers on poverty reduction in a high-

income country like Chile. The importance of the contribution of anti-poverty programmes to

exit poverty in the short-run is presented. Second, this is the first study comparing the

effectiveness of cash transfers depending on the age composition of the household in Chile.

The age comparison raises the issue of the protection-promotion trade-off that anti-poverty

programmes confront. Third, this research also contributes to the literature on the

effectiveness of cash transfers by using a partial fiscal incidence analysis.

The paper is organized as follows. Section 4.2 presents the definition of vulnerable groups

and the justification for their protection. In Section 4.3 the context of poverty among children

and older persons is presented. Section 4.4 presents the context of poverty among these two

age groups in the context of Chile. In Section 4.5, the theoretical framework of the analysis is

presented. The following sections explain the data base used (Section 4.6) and the

methodology conducted (Section 4.7). Section 4.8 presents the main results of the paper. The

final section draws out the main conclusions and a discussion of the conclusions of the

research and its implications for policy.

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4.2 The vulnerable groups approach

A Social Protection System groups all the policy responses to conditions of poverty and

vulnerability considered unacceptable within a society (Barrientos, Hulme, & Shepherd, 2005;

Norton, Conway, & Foster, 2000). This policy response can be through social insurance, social

assistance and labour market policies (Barrientos, 2013; Barrientos & Hulme, 2008). Social

insurance is a protection against uncertain risk (unemployment or sickness) or life-course

contingencies (maternity or old-age) that individuals or households can face (Barrientos &

Hulme, 2008). Social assistance represents all public actions, from the government or

otherwise, that transfer resources to deprived groups or other groups with entitlements with a

moral justification. Deprivation is usually defined in terms of income poverty, alongside other

dimensions such as nutritional or social status. While social assistance is financed by

government taxation, social insurance is usually financed by employees’ and workers’

contributions.

Under this conceptualization of a Social Protection System, social assistance is focused on

morally or institutionally relevant social groups who are in a disadvantaged position in the

society. Among them are the groups called vulnerable groups. Commonly defined vulnerable

groups are children, teenagers living in the streets, orphans, single parents, migrants, disabled

people, old people, widows and indigenous groups, among others. They are characterized by

an accumulation of disadvantaging events that make it more difficult to achieve a high level of

welfare and these disadvantages are a combination of social and idiosyncratic characteristics.

Vulnerable groups confront a high propensity of finding themselves in fragile contexts ending

up in poverty or vulnerability many times.

Vulnerable groups are characterized by having a low productive capacity by themselves or

living in households with low productive capacity. This means that they usually live in a low-

income context and often in poverty. As a consequence of this, vulnerable groups have been

broadly used to select the beneficiaries of social assistance programmes targeted to poverty

alleviation. This method of beneficiary selection has been called categorical selection because it

is based on categorizing people from their identifiable demographic characteristics (Barrientos,

2013). However, not all the individuals who belong to a vulnerable group are in poverty. It

may be true that many orphans or children live in poverty but that does not mean that all of

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them are in that situation. The correlation between the characteristics that define belonging to

a vulnerable group and the incidence of poverty or extreme poverty is not always very strong

(Barrientos, 2013). This has made categorical selection a weaker instrument for selecting

beneficiaries on its own. As a consequence of this, categorical selection is usually combined

with a means or proxy means test that allows the identification of vulnerable groups living in

poverty or vulnerability.

As was mentioned, there is a broad list of vulnerable groups. National governments and

international organizations claim for different groups that they consider vulnerable. For

instance, the Food and Agriculture Organization of the United Nations (FAO) (2005) defines

as vulnerable groups who suffer from food insecurity or confront a risk of suffering from it.

From their perspective, the individual or household level of vulnerability is determined by their

exposure to risk factors and their capacity to confront or resist shocks.

From a Human Rights perspective, there are some groups of people vulnerable to

violations of their fundamental human rights that must be protected. This approach identifies

some particular groups “who, for various reasons, are weak and vulnerable or have

traditionally been victims of violations and consequently require special protection for the

equal and effective enjoyment of their human rights” 61 . The Human Rights approach

establishes that additional guarantees must be provided for persons belonging to these groups

and Human Rights instruments must be used as a “vehicle for the protection of vulnerable

groups within society, requiring states to extend special protective measures to them and

ensure some degree of priority consideration, even in the face of severe resource constraints”.

This approach defines vulnerable groups as people especially vulnerable to abuse of human

rights. Among these groups are: women and girls, children, refugees, migrant workers,

internally displaced person, stateless persons, national minorities, indigenous people, disabled

persons, physically and mentally disabled persons, the elderly, HIV-positive persons and AIDS

victims, Roma/Gypsies/Sinti, lesbian, gay and transgendered people, among others. Many of

them suffer from discrimination and oppression and need special protection. For instance,

UNICEF -mandated by the United Nations General Assembly- advocates for the protection

61 http://www.humanrights.is/en/human-rights-education-project/human-rights-concepts-ideas-and-fora/the-human-rights-protection-of-vulnerable-groups

193

of children’s rights. They promote the rights of every child, emphasizing the most

disadvantaged, excluded and vulnerable children (UNICEF, 2013).

In the context of Chile, the Social Protection System recognizes some vulnerable groups in

connection with their propensity for being in poverty (Ministerio de Desarrollo Social, 2013).

They recognize the existence of individuals living above the poverty line but facing a high

probability of being in poverty in the future. Among this vulnerable population they identify

specific groups that face a higher probability of being in poverty. These groups are defined as

vulnerable groups because their condition of defencelessness increases their probability of

being in poverty. Among these groups are disabled people, who confront higher rates of

poverty and lower rates of inclusion than the rest of the country’s population. In addition, are

considered people that belong to an indigenous group. They confront higher rates of poverty

and lower rates of inclusion than the rest of the country’s population. The homeless, who

were identified with a specific census in 2011, are also identified as a vulnerable group. Elderly

individuals -over 65 years- are recognized as a vulnerable group too. Families with a member in

prison are also in this category. Finally, children and young people under 18 years are

recognized as a vulnerable group.

Children and older persons, the two vulnerable groups that this research examines, are

characterized by the need for protection. They should not work, hence, their productive

capacity should be null. In the case of children, responsibility for them falls on their parents

who should but may not have the productive capacity to satisfy their needs. In the case of

older persons, they do not necessarily have relatives to care for them and their wellbeing

satisfaction falls mainly on the pensions that they have. Children and older persons living in

poverty define a specific vulnerable group. They live in households with a per capita income

below the poverty line, meaning that they do not have enough resources to satisfy alimentary

needs and many times non-alimentary necessities. As a consequence, many of them cannot

fulfil their economic and social activities properly. Poverty and extreme poverty configure a

vulnerable environment in themselves. As a consequence of this, these vulnerable groups living

in poverty are the most fragile groups. Children and older persons living in poverty or

vulnerability have been the priority for the majority of welfare states and social assistance

programmes. The importance of these groups is a widely shared societal value. They are the

focus of this research.

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4.3 Are children and older persons overrepresented in poverty?

This section presents evidence of poverty in children and the older population. The

evidence is presented using the dimensions of income and non-income, and using one-

dimensional or multi-dimensional poverty measures. Figures from developed and developing

countries are showed. The difference between age groups with respect to the propensity to be

in poverty, the consequences of being in poverty and the role of social policies in these

differences are presented in the following two subsections.

4.3.1 Poverty and deprivation among children

Children are the most affected by poverty around the world, representing half of the

extreme poor population in developing countries (UNICEF, 2016). Child poverty is pervasive

and persistent throughout the world and children are more vulnerable and at greater risk of

poverty than adults (Barrientos & DeJong, 2004; Save the Children, 2012; UNICEF, 2016)). A

recent study made by UNICEF (2016) shows that out of 767 million people living in extreme

poverty in 2013 almost 385 million were children62. The same report indicates that 19.2% of

children live in households in extreme poverty –less than US$1.90 a day- compared with 9.2%

of adults. This means that children are more than twice as likely to be in extreme poverty than

adults in developing countries. Moreover, children are the worst affected also when higher

thresholds for measuring poverty are considered. Data drawn from the World Bank shows that

45% of children are living in households that have less than US$3.10 per day per person

compared with 27% of adults. This evidence shows that children are the worst affected by

poverty transversely to different income poverty lines63.

Non-income indicators also show the high levels of poverty and deprivation that children

are experiencing today. Evidence for developing countries is provided by Gordon et al. (2003)

62 These numbers were based on data from 89 countries representing 85% of the developing world’s population. 63 UNICEF and The World Bank tested their findings against different economies of scales (families with more members have less marginal costs and share public goods) and equivalence scales (different ratios of children and adults’ consumption). They find that children were the worst off across developing countries in all the cases considered (UNICEF, 2016).

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who used survey data sampling 46 households from developing countries to examine the

incidence of deprivation among children. They considered the following eight dimensions of

wellbeing: food, water, sanitation, health, shelter, education, information and access to services.

They estimated that one in two children in this sample suffered from at least one kind of

severe deprivation and one in three suffered from two or more dimensions of severe

deprivation.

A multi-dimensional worldwide perspective on poverty among children under 18 years is

provided by the disaggregation of the global Multidimensional Poverty Measure (global MPI)

by age group made by the Oxford Poverty & Human Development Initiative (OPHI) in 2017

considering 103 countries 64 . The global MPI considers the overlapping deprivations in

education65, health66 and living standards67 (Alkire et al. 2017). An individual is considered

multi-dimensionally poor if she or he lives in a household deprived of at least one third of the

weighted MPI indicators. The report shows that 48% of the multi-dimensionally poor people

are children68 compared with the 21% of adults in the same situation. This means that 37% of

children are multi-dimensionally poor –nearly two out of every five (Alkire et al. 2017). It

shows that children are over-represented among the poor because they represent 34% of the

total population but 48% of the poor. The report also highlights two important facts; the first

is that children have a higher incidence and intensity of poverty than adults and the second is

that younger children, aged between 0 and 9 years, are the poorest.

Although impoverished children suffer more hardship in developing than in developed

countries (Gordon et al., 2003), developed countries still have children living in poverty. The

percentage of children living in poverty according to the child poverty rate proposed by

UNICEF69 varies from about 5 to 7% in Northern European countries to 12.1% in United

Kingdom, and 23.1% in USA (Fernandez & Ramia, 2015; UNICEF Innocenti Research

64 They use a sample of 103 countries providing data between 2006 and 2016. The sample covers 5.4 billion persons or 76% of the world’s population and 92% of people living in low and middle-income countries. To see more detail about the sample, methodology and results go to OPHI (2017). 65 It considers two indicators: years of schooling and school attendance. 66 It considers two indicators: nutrition and child mortality. 67 It includes six indicators: cooking fuel, improved sanitation, safe drinking water, electricity, flooring, asset ownership. 68 Poor children are on average deprived in 52% of the weighted indicators. 69 UNICEF Innocenti Research Centre (2012) defines the child poverty rate as the proportion of children living in households with income below 50% of the national medium income.

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Centre, 2012). The UNICEF (2016) report shows that deprivation 70 among children in

developed countries varies between 3% in Netherlands and 72% in Romania. This indicates

that children suffer deprivation beyond income poverty in developed countries.

All these studies show that children experience poverty, deprivation and social exclusion at

a considerably higher rate than adults irrespective of the approach used to measure poverty

and the level of development of the country. It is a fact that a large number of children have

their basic rights compromised around the world affecting their future trajectories and

capabilities.

There are different channels through which episodes of poverty during childhood can

affect future outcomes. Among them are inadequate nutrition (Barrientos & DeJong, 2006),

poorer health outcomes (Fernandez & Ramia, 2015), lower levels of stimulation and learning

(Bradbury, 2007; Duncan & Brooks-Gunn, 1999; Votruba-Drzal, 2006) and changes and

stress, such as changes in family, shortages of social care and early entrance to the labour

market (Fernandez, 2015; Chamberlain & Johnson, 2013; Stein, 2012), that children in poverty

are usually exposed to.

All the evidence presented shows that children are the most affected by poverty, being

over- represented among the poor. In addition, it has been shown that the poverty experienced

by them has consequences that last a lifetime. This fact highlights the importance of

eradicating child poverty.

4.3.2 Poverty and deprivation among old people

The relation between poverty and older persons is different. Poverty rates among old

people relative to the rest of the population are not unequivocally higher around the world as

70 UNICEF identifies deprivation as when children lack at least 2 out of 14 items: three meals a day, at least one meal a day with meat, chicken or fish (or a vegetarian equivalent), fresh fruit and vegetable every day, books suitable for the child’s age and knowledge level, outdoor leisure equipment (bicycle, roller-skates, etc.), regular leisure activities (swimming, playing an instrument, etc.), indoor games (computer games, etc.), money to participate in school trips and events, a quiet place with enough room and light to do homework, an internet connection, some new clothes (i.e., not all second-hand), two pairs of properly fitting shoes, the opportunity, from time to time, to invite friends home to play and eat, the opportunity to celebrate special occasions, birthday, etc.

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are poverty rates among children. As was discussed above, child poverty is higher than national

poverty rates in any country but that is not the case for the group of people older than 60. In

some countries, older persons are in poverty equally as the rest of the population while in

other countries this group is under or over-represented among the poor.

The evidence of old-age poverty in Latin America shows significant differences across

countries (Gasparini et al. 2009; Popolo, 2001). Old-age poverty incidence, that is, the

percentage of people over 60 who live in households in poverty, ranges from 7.9% in Chile to

38.4% in Ecuador. The headcount ratio for the US$2 a day poverty line among the older

population goes from 0.8% in Uruguay to 66.4% in Haiti. The poverty rate among older

people –considering the US$2 a day poverty line- is considerably lower than for the rest of the

population in Argentina, Chile, Brazil and Uruguay and just slightly lower in Bolivia, Ecuador,

El Salvador, Guatemala, Haiti, Panama, Paraguay, Peru and Venezuela. In countries like

Honduras, Jamaica and Mexico, older people are over-represented among the poor. In

Argentina, Chile, Brazil and Uruguay the poverty rate for those over 60 is between 10%

(Uruguay) and 40% (Argentina) of the poverty rate for the total population. In contrast, in

countries like Mexico or Jamaica poverty rates for older persons are around 20% higher than

for the rest of the population.

Different arguments have been provided trying to explain the differences in the incidence

of poverty among older people across countries. One argument establishes that older people

might be poorer than younger generations just because they have received less education.

Under this perspective the higher gap in years of education between older persons and adults

must correlate positively with relative old age poverty. A second theory argues that older

people tend to live in households of smaller size than adults and this can explain the lower

degree of poverty. In this scenario, relative old age poverty is positively correlated with a

higher gap in household size between the older population and adults. However, it can also be

considered a negative consequence of this different demographic structure of older persons

because it can reduce the advantages of household consumption economies of scale (Gasparini

et al., 2009). A fourth idea establishes that the majority of people have less income after a

certain age. Different reasons can lie behind this reduction in income that increases the

probability of being in poverty. Under this conceptualization, the higher risk of old age must

be reduced through pension systems for older persons. Poverty in old age must be negatively

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correlated with deeper pension systems. The last argument implies that the differences among

old age poverty rates just come from the fact that some countries are richer than others.

Countries with a higher economic development as indicated by Gross National Income per

capita have the ability to establish better pensions systems.

The evidence for Latin American countries shows that the third argument is the most

important in explaining the differences in old age poverty among countries (Barrientos, 2006;

Gasparini et al., 2009). The strongest explanation can be found in the extent of support that an

old age population receives. Gasparini et al. (2009) estimate a significant negative relationship

between relative old age poverty and the development of the pension system –measured

though the share of old people in the population receiving pension payments. The rest of the

arguments are not as important as this one. Gasparini et al. (2009) finds that the correlation

between relative old age poverty with education and household size seems weaker. The effect

of the educational upgrading of the younger generation shows a positive but low correlation

with the higher level of poverty among older people. The lower size of the household is

positively correlated with lower relative old age poverty but driven by Argentina and Uruguay

where a large fraction of older people lives alone.

The negative relation between relative old age poverty and the share of old people in the

population receiving a pension is driven by the Southern Cone countries: Argentina, Chile,

Brazil and Uruguay. They have pension schemes with a higher coverage than the rest of the

countries. On average 66% of the population is covered Gasparini et al. (2009)71. These four

countries have a lower incidence of poverty of older persons meaning a significant under-

representation among the poor. These four countries and Costa Rica and Bolivia have cash

transfer programmes focused on older people in poverty (Barrientos, 2006). This indicates that

old-age poverty incidence in Latin America is strongly influenced by social policy (Barrientos,

2006).

The same relationship between lower old age poverty and comprehensive pension schemes

can be observed in developed countries. In these countries, the relative living standard of older

persons is higher because of the combination of small households, robust social security

systems, and well-developed capital markets (Gasparini et al. 2010). The majority of developing

71 Compared with the rest of countries where on average 14% of older people are covered.

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countries are characterized by exactly the opposite scenario. Older persons live in extended

households sharing the household budget among a large number of inhabitants and children;

pension systems are not available for all the population and formal credit is not accessible for

everyone (Gasparini et al. 2010).

The extent to which older persons are in poverty compared with the rest of the population

must be considered in any social policy discussion. This is even more important in the context

of the population ageing process leading to an increase in the share of older people in

populations throughout the world. The reduction in fertility rates and the increase in life

expectancy lead to an increase in the proportion of the population above a certain age. It is

expected that the numbers of persons aged 60 or above will double by 2050 and triple by 2100

(United Nations, Department of Economic and Social Affairs, Population Division, 2015).

The ageing of the population is projected for most regions of the world. The 11% of people

over 60 years in Latin America in 2015 is expected to increase to 25% by 2050. These numbers

are even higher for Chile. The projection is that the15.7% of people over 60 years in 2015 will

be 32.9% in 2050. Under this scenario, fiscal and political pressure for better health care,

pension and social protection systems are going to be in place sooner than later.

The evidence presented shows that, while children and the elderly are vulnerable groups

that are often over-represented in poverty, the pension system in some countries makes

poverty among the elderly lower than the average population. Countries with high coverage of

pension systems show lower poverty rates in the older population. This suggests that Social

Policy provides different levels of protection between age groups

4.4 Poverty rates by age in Chile

This research focuses on a comparison between the poverty rates of children and older

people. The relative poverty rates among older persons and children are presented here, both

of income poverty, which has traditionally been the way of measuring poverty in Chile, and

multi-dimensional poverty, figures for which have been provided since 2013. Although the

analysis of this study focuses on income poverty, this section presents both approaches to

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measuring poverty to provide a broader perspective of poverty among groups in Chile. The

reason to focus the analysis just on income poverty is because only direct taxes and monetary

transfers are considered by this research. These variables affect directly household income and

then their income poverty status.

4.4.1 Income Poverty

Income poverty –which this research is focused on- is measured through the absolute

poverty approach72. In the Chilean context, the poverty threshold to identify people living in

poverty is defined in terms of the satisfaction of basic needs: food and non-food needs.

Extreme poverty, instead, is evaluated in terms of the satisfaction of food needs only. Annex

N°4.11.1 describes the changes of the new methodology in measuring poverty and producing

the figures of poverty reduction throughout the years using both methodologies.

Under this conceptualization of poverty, the indicator of welfare used is income by

equivalent person. This measure considers, as income per capita does, the effect of the number

of household members on welfare, but in addition, it considers the economies of scale in

consumption inside households73. As a consequence of this, there is not a unique poverty line

per capita because of the equivalent scale. This means that the poverty line depends on the

number of members that a household has. In this way, there is a poverty line for each

household size. The following poverty rates presented –income and multi-dimensional- assume

that household resources are allocated equally among the members of the household. This

assumes complete within-household equality in living standards (Deaton, 1997).

Historically, the incidence of poverty among children has been higher than for the rest of

the age groups and also among older persons. Figure 4-1 shows poverty rates, measured

according to the new methodology, across age groups based on household equivalent income

for the five years with available data from 2006 to 2015. It shows large differences between age

72 This method establishes an absolute threshold of basic needs and the income poverty line needed to satisfy these basic needs. Under this approach, absolute poverty is identified where income is insufficient to provide basic needs such as food or shelter (Townsend, 1979). 73 The scale of equivalence was defined as 0.7 for each member of the household.

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groups. Poverty headcount rates are considerably higher for children below 18 years of age

than older persons above 60 years for all the years considered. The first year with information

available, 2006, the incidence of poverty among the older group was 22.8 % whereas among

children it was 39.6%. The difference between their incidences of poverty was 16.8 % in 2006.

The sharp reduction of poverty in the period between 2006 and 2015 was experienced by all

age groups. Poverty rates decreased more among children, in absolute terms, than among the

rest of the groups. The incidence of poverty among children decreased by 50% between 2006

and 2015, from 39.5% to 19.5%. However, the reduction of the incidence of poverty among

the older group was greater than among children from a relative perspective. The 6.6% of

older people in poverty in the year 2015 represents a reduction of 70% in the incidence of

poverty among this age group in 2006 which was 22.8%. This shows that people aged 60 and

over are not only the group with the lowest incidence of poverty in the population but also the

group who experienced the highest reduction in poverty between 2006 and 2015.

Figure 4-1 Percentage of people in income poverty Chile 2006-2015, by age group

Source: CASEN. Ministerio de Desarrollo Social. Chile. The differences between age groups are statistically significant at 95% between the years 2006-2009, 2009-2011, 2011-2013 and 2013-2015, except for the group 0-3 years and 18-29 years 2006-2009.

Chile is among the Latin American countries with the highest disparities between child

poverty rates and older persons’ poverty rates. The data provided by the ECLAC in the

39,6 38,5

24,5

29,2

22,5 22,819,5

17,8

11 11,39

6,6

0

5

10

15

20

25

30

35

40

45

0 to 3 4 to 17 18 to 29 30 to 44 45 to 59 60 plus

2006

2009

2011

2013

2015

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Panorama Social de América Latina (2014) compared the incidence of poverty of people younger

than 15 years and older than 55 years. It shows that the poverty rate among children is 2.8

times the poverty rate among older people. These figures just represent the average for the

region. The four countries of the southern cone of the continent have a greater disparity

between these two age groups. The poverty rate for children younger than 15 years is 8 times

higher than the poverty rate for older persons in Uruguay, 5.1 times in Argentina, 5.5 times in

Brazil and 2.9 times in Chile (ECLAC, 2014)(Figure 4-2). Countries like Colombia, Panamá,

México and Venezuela have lower disparities between these two groups but still the poverty

rate among children is more than double the poverty rate among older people. A similar

regularity but with lower disparities between the groups can be observed from the comparison

between younger population between 15 and 24 years and the older population over 55 years.

Figure 4-2 14 Latin American Countries. Relation between the poverty rate for population between 0 and 14 years and the poverty rate for the population over 55 years. Relation between the poverty rate for population between 15 and 24 years and the poverty rate for the population over 55 years. Surveys around the year 2013

Source: Comisión Económica para America Latina y el Caribe, ECLAC 2014. Figures came from data from household surveys in each country. Light blue bars represent the poverty rate of the group between 0 and 14 years/the poverty rate of the group over 55 years old. Blue bars represent the poverty rate of the group between 15 and 24 years/the poverty rate of the group over 55 years old.

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4.4.2 Multi-dimensional Poverty

The Chilean multi-dimensional poverty measure incorporates the following five

dimensions: Education, Health, Social Security and Work, Dwelling and Environment, and

Social Cohesion and Networks74. The Table N°4-17 in the Annex N° 4.11.2 describes the

indicators and cut-offs by dimension.

The difference between children’s and older people’s multi-dimensional poverty rate is not

so pronounced as it is with the income poverty rate. 26.8% of children between 0 and 3 years

and 22.4% of children between 4 and 17 years are in multi-dimensional poverty in 2015

compared with 21.6% of older people in multi-dimensional deprivation (see Figure 4-3). In this

case, both age groups are more in multi-dimensional poverty than the average of the

population – the 20.9% represented by the horizontal green line-. This is absolutely different

than income poverty where children are fairly above the average poverty rate of 11.6% - the

horizontal blue line - and people over 60 years are well below the average. This indicates that

the main disparity of poverty rates between these two age groups emerges when the focus is

on income. The reason for this finding is explored in the analysis of this research, as is the role

of the monetary transfers in these disparities.

74 These five dimensions have been used since 2015 onwards.

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Figure 4-3 Percentage of people in income and multi-dimensional poverty in Chile 2015, by age group

Source: Author’s elaboration based on CASEN, Ministry of Social Development. Horizontal lines represent average

income and multidimensional poverty rates.

The evidence presented shows that the incidence of poverty in children and older persons

is not the same. While children are the group with the highest incidence of poverty, older

people are the group with the lowest incidence of poverty among the population. This is very

clear when talking about income poverty. However, multi-dimensional poverty shows a

different picture. Even when children are still the group most affected by multi-dimensional

poverty, the older population is not the least affected. Both age groups are above the average

multi-dimensional poverty rate showing their higher vulnerability than the rest of the

population. The hypothesis behind the disparity between income and multi-dimensional

poverty is that non-contributory pensions play an important role in reducing income poverty

among the elderly. This is analyzed in the empirical section 4.8.

19,517,8

11,0 11,3

9,0

6,6

26,8

22,4 22,7

17,7 18,3

21,6

0,0

5,0

10,0

15,0

20,0

25,0

30,0

0 to 3 4 to 17 18 to 29 30 to 44 45 to 59 60 plus

Income poverty Multidimensional Poverty

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4.5 The theoretical framework

The theoretical framework is structured in two parts. The first part presents the theory of

change of cash transfers for the two vulnerable groups under analysis. There are presented the

arguments for poverty alleviation behind the cash transfers focused on children and older

persons. The second part presents the theory of fiscal mobility which is used in this research to

analyze the effectiveness of cash transfers on poverty.

4.5.1 Theories of change behind cash transfers to children and older

people

Although there is a diverse range of social assistance programmes with different specific

objectives, they share two main core objectives: improvements in household conditions and

productive capacity (Barrientos, 2012). They also share some intermediary objectives to

achieve the main objectives such as asset protection, asset accumulation, nutrition and service

utilization (Barrientos, 2012). It can be said that they share a common theory of change

composed of a sequence of intended positive impacts. It is expected that predictable modest

cash transfers to households in a scenario of good provision of basic services and favorable

economic conditions will generate immediate effects on household expenditure, saving and

investment decisions. In the short term, the cash transfer increases the household’s purchasing

power which is expected to affect household conditions and the productive capacity of the

household leading to poverty reduction. In the mid and long term, the cash transfer has

potential positive effects on household asset accumulation –physical and human- and

livelihood strategies leading to poverty and vulnerability reduction (Bastagli et al., 2016). Let us

see in detail how these positive effects are expected to happen.

Imagine a household beginning to receive a regular monetary transfer from a new cash

transfer programme recently implemented by the government. The amount is modest and is

provided every month meaning an increase in the household regular income. The household

confronts the decision regarding how best to use these new resources to improve their welfare.

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Considering the fact that these transfers usually go to households in poverty or vulnerability,

the new resources will contribute to addressing the household’s deficits. The additional

resources can be spent, invested or saved to cope with these deficits (Bastagli et al., 2016).

They can be spent on individual or household goods such as food, clothes, or furniture or on

access to services such as education, health or transport. The additional cash can also increase

the expenditure on less desirable goods such as tobacco or alcohol. The extra cash can also

contribute to increasing savings or to making small investments. All these effects of cash

transfers over expenditure, saving and investments have been called first-order outcomes which are

triggered as a consequence of the new cash transfers (Bastagli et al., 2016). In addition to the

outcomes generated by the use of these additional resources, the cash transfer can generate

some behavioural changes inside the beneficiaries’ households. The first-order outcomes can

trigger what are called second-order or intermediate outcomes. Among these outcomes are an increase

in school enrolment, attendance and retention; an improvement in food intake, food security

and dietary diversity; a higher utilization of health services; a higher diversification of economic

activities; a higher labour participation and an improvement in the sector of work; greater self-

acceptance, dignity, pride and hopefulness (Bastagli et al., 2016). Finally, the cash transfer can

have an impact over the medium or long term. These impacts are called third-order or final

outcomes. Among those impacts are an increase in school performance and progression; higher

nutritional outcomes and a higher health status; better livelihood strategies diversification and a

safer transition to adulthood; better income potential and productivity; and a higher resilience

(Bastagli et al., 2016).

The ways in which these outcomes are reached are affected by the design and

implementation of the programme, household behaviour and decision-making processes and

also by the environmental conditions of the households (Barrientos, 2012).

In terms of the design and implementation of the programme it is clear how they can

influence the outcomes of the programme. For instance, in terms of the design of the

programme, a higher level of transfer may be expected to lead to higher impacts on poverty

measures or a regular and predictable transfer should facilitate investment hence poverty

reduction. A longer duration of the transfer can also encourage more investment and a

sustained impact on poverty. In addition, the conditionality of the cash transfer and the

modality of the payments can also affect their potential impacts on poverty indicators. The

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selection of beneficiaries of the programme is also a crucial issue to see the impact of cash

transfer on poverty. Targeting cash transfers to populations in poverty can increase their

impact on poverty but it also means costs and challenges to political economy.

Household preferences and decision-making process can also affect the impact of the cash

transfer programme. They are usually less tangible with usually unpredictable impacts. The new

cash transfer can affect decisions like the way in which household members distribute their

time between labour and free time. For example, cash transfers can generate a reduction in

hours in work because the additional income may replace the salary earned in work. In

addition, cash transfers can generate the incentive to maintain income below the eligibility

threshold, thus reducing hours worked (Bastagli et al., 2016). Many cash transfers put school

attendance as a condition seeking to increase school hours and to reduce child labour. They are

seeking to generate behavioural changes regarding child education. In addition, the risk

preferences of the household can affect the way in which the cash transfer is spent. Moreover,

cash transfers can also affect the intra-household bargaining power and decision-making. Cash

transfers have the potential to empower the recipients of the cash, such as women, children or

older persons. In general terms, the additional cash can affect the set of choices of the

household leading to different behavioural responses. They can be positive, negative,

predictable and unpredictable.

Although the main objective of cash transfers to children or older persons is still poverty

alleviation, the channels in which the monetary transfer helps to reach the objectives can differ.

The following two subsections explain in more detail the mechanisms through which cash

transfers are expected to help children and older population to exit poverty.

4.5.1.1 Children

An anti-poverty transfer targeted to children, as any other anti-poverty transfer, is

expected to generate an improvement in household conditions and productive capacity. Here

are presented in detail the channels through which children can be benefited by the cash

transfers contributing to exit poverty in the short and long term.

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One of the main arguments of the provision of cash transfers targeted to children –

whether universally or at children in poverty- is the reduction of intergenerational poverty

persistence. As was explained in section 4.5, children living in poverty confront a vicious cycle

of negative long-term consequences of living in poverty during childhood. The possibility of

breaking this cycle is the main objective of cash transfers targeted to children. The cash

transfer programmes try to promote the increase in educational and health services use among

children through monetary support and usually setting conditions on education and health care

attendance to qualify. The main objective is to increase the likelihood of better education,

health and nutrition for children in poverty in the short run. This increases their human capital,

and that can allow them to exit poverty in adulthood. This is the main theory of change of

cash transfers targeted to children. What are called human development transfers pursue the

reduction of intergenerational poverty persistence through adding conditions to transfers

focused on children’s schooling and health care.

A first-order or immediate outcome of the cash transfer is the possibility of increasing

household expenditure on food leading to improved dietary diversity. These extra resources

can translate into a greater quantity and variety of food. The wider diversity of food increases

children’s nutritional status having immediate impacts on health status and potential

consequences on schooling such as higher concentration and lower absenteeism. This can be

reinforced through improvements in overall food security reducing the probability of skipping

meals. It is important to highlight that intra-household decision-making dynamics can affect

whether children are benefiting from such improved nutrition. In addition, the possibility to

invest the cash transfer in farm assets such as seed to grow more food or a cow to have more

milk has the potential to improve the nutritional status of children. This is a second-order or

medium-term effect if the cash transfer is invested.

Another channel in which the cash transfer can reduce child poverty is through making

access to services cheaper. The additional cash provided can boost household income reducing

direct, indirect and opportunity costs of education and health. The extra cash can help to cover

direct costs for education such as fees, school materials, uniforms, and books and/or direct

costs for health care such as fees and medicines. Indirect costs of education and health include

travel costs especially when children need to travel long distances to go to secondary school or

to obtain specialized health care. In addition, the cash transfer can reduce the opportunity cost

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of children being in school or of patients or carers while seeking care. The additional resources

can cover the foregone earnings because children are not working or they are not doing

household chores anymore. The reduction in child labour also has a positive outcome on

school attendance and retention than can lead in to a long-term increase in productivity.

Furthermore, cash transfers can be spent on other individual goods such as soap or shoes.

They have the potential to increase children’s participation in school as a consequence of an

increase in self-acceptance, dignity and pride (Bastagli et al., 2016). These goods can increase

children’s willingness to participate in school or to attend health services as a consequence of a

reduction in stigma (Bastagli et al., 2016).

The way in which an increase in school enrolment, attendance at school and retention

affects learning is not univocal because it is affected by many other factors, such as the

different quality of schools and of the household environment as well. However, it is expected

that improved educational outcomes in conjunction with better nutritional status and

improved self-acceptance means a gain in terms of cognitive and non-cognitive development

for children benefited by a cash transfer programme. These gains from schooling can influence

their productive capacity and as a consequence their longer-term employability and earning

potential (Bastagli et al., 2016).

An additional effect that cash transfers targeted to children can generate is a better and safe

transition to adulthood (Bastagli et al., 2016). The additional cash, the higher access to

education and health services all have the potential to reduce the exposure to many risks that

adolescents in poverty are usually exposed to, such as, transactional sex, violence, HIV

infection, early pregnancy or marriage, among others. The possibility of having role models at

school giving them aspirations for the future is among the benefits of higher levels of

education (Bastagli et al., 2016).

4.5.1.2 Older Persons

Cash transfers targeted to the oldest population have a different rationale than transfers

focused on children. Instead of putting attention on the future it concentrates on the present

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poverty of the older population. Older people who are in poverty need protection of their

assets, well-being and access to services.

Older persons should not have to work and in many cases they really cannot work. Cash

transfers given to them are not seeking to build their productive capacity because they should

not have to work anymore. They should be able to live on their pensions. Cash transfers

targeted to older persons are often non-contributory pensions for those who were not able to

save for pensions during their working life. Cash transfers targeted to older persons in poverty

usually replace an insufficient or non-existent pension.

Unlike the case of children, the elderly do not necessarily live with working adults. No

adults have legal responsibility over them, as is the case with children. This means that many

times they live without any relatives earning salaries. In this context, the cash transfer plays a

crucial role in the satisfaction of basic needs.

Older persons can have assets such as a dwelling or farm and non-farm assets and with a

high probability live from them. As a consequence of that, they need protection for their assets

being sometimes their only source of income. In this context, cash transfers can be an asset

protection.

As in the case of children, cash transfers can improve the nutrition of older persons. The

additional cash can be spent on more and/or better food, and as with children again, the

additional resources can increase service utilization. Health services are even more important

with age, becoming crucial for older persons.

Some cash transfers are income maintenance schemes. Their aim is to cover the poverty

gaps of households in poverty through income transfers. When these cash transfers are

targeted to households with older persons they reduce the problems of incentives and

information. They cannot work and their age is an objective parameter.

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4.5.2 Fiscal mobility

In order to analyse the effect of public transfers on poverty and vulnerability, the concept

of Fiscal Mobility is used, as proposed by Lustig (2011) and Lustig and Higgins (2012, 2013).

Fiscal mobility refers to the movements between the before and after situations among pre-

defined income categories (Lustig & Higgins, 2012). These pre-defined categories can be any

one chooses; for instance, extreme poverty, poverty, vulnerable, no-poverty. The Fiscal Mobility

concept identifies the movements across income distributions as a consequence of fiscal policy

in a particular period. A Fiscal Mobility Matrix “measures the proportion of individuals that

move from a before taxes and transfers income group (e.g. non-poor) to another income

group (e.g. poor) after their income is changed by taxes and transfers” (Lustig & Higgins, 2012,

p. 2).

There are many definitions and measures of income mobility that can be seen in detail in

Fields (2008). The concept of mobility used by Lustig and Higgins (2012) is called directional

mobility which is a subcategory of the ‘mobility of movement’ described by (Fields, 2008).

Under this conceptualization of mobility, Fiscal Mobility is formally described as “the directional

movement between the before and after net taxes situations among k pre-defined income

categories” (Lustig & Higgins, 2012, p. 3).

The fiscal mobility matrix depicts how fiscal policy contributes to the movement of

individuals from one income group to other income groups. The main interest of this research

is the transition between poverty and out of poverty states of two vulnerable groups: children

and older persons. The transition from poverty to out of poverty is called exit poverty. Again,

the fiscal mobility matrix allows us to identify how social assistance programmes contribute to

improving the poverty exit rate of households with children and households with older

persons.

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4.6 The data base

The data source of this research is the 2015 National Socioeconomic Characterization

Survey (CASEN). The Chilean Ministry of Social Development is the organization

responsible for its gathering who bid for its application every two or three years. The

National Institute of Statistics (INE) is responsible for the design of the sample and the

elaboration of weights and the Centro de Microdatos of The University of Chile performs the

data collection and processing. The sampling frame of CASEN is probabilistic and

stratified by clusters and in multiple stages. The final unit of selection is the dwelling. The

sample of CASEN 2015 has 83,887 households and 266,968 individuals being

representative at the level of country, regions, and rural and urban areas 75 (Ministerio de

Desarrollo Social, Chile, 2015).

The objective of this survey is to measure the socio-economic circumstances of the

population, mainly households living in poverty and priority groups for social policy,

considering aspects like education, health, housing, work and income. In particular, the

main objectives are to estimate the magnitude of poverty and income distribution; to

identify the shortcomings and demands of the population in the areas defined above; and

to evaluate the gaps between social segments and between territories. In addition, it has the

aim to evaluate the impact of social policy: to estimate the coverage, target and distribution

of public expenditure on the main national social programmes and to finally evaluate the

impact of this expenditure on household income and its distribution.

CASEN collects data about household’ incomes. Every household reports all the

sources of their monthly income. The detail of the cash transfers received by every

household is provided, which is particularly useful for this research.

75 The representativeness of the sample is also for some municipalities (139 of 350).

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4.7 Methodology

This research carries out a partial fiscal incidence analysis (Lustig & Higgins, 2013) through

the comparison of incomes before and after direct taxes and direct cash transfers. It is partial

because it does not consider the indirect taxes and transfers that are usually considered in a full

fiscal incidence analysis. This partial fiscal incidence analysis follows the framework proposed

by the Commitment to Equity (CEQ) (Lustig & Higgins, 2013; 2016).

The interest of this research is to see the effect of social assistance programmes on the

poverty and vulnerability of children and older persons. Social Assistance in Chile can be

grouped into three main areas: unconditional and conditional cash transfers, non-contributory

pensions, and near-cash transfers. The analysis carried out in this research considers all the

cash transfers –conditional, unconditional and pensions- but excludes the near-cash transfers.

This decision came from the fact that income poverty and vulnerability are evaluated from a

monetary perspective. Poverty and vulnerability statuses are assessed using the total income of

the household divided among its members. Therefore, the focus of this research is on fiscal

interventions that affect the total income of the household: direct taxes and cash transfers. It is

true that provision in kind, such as free meals at school, should reduce the proportion of

household income spent on food consequently increasing the household disposable income to

buy other things. However, the total household income remains the same after receiving the

in-kind transfer. Many fiscal incidence analyses try to monetarize in-kind transfers to create the

artificial income after a benefit. However, official poverty measures do not consider this

monetarization of in-kind transfers. Therefore, this research only considers fiscal interventions

that strictly change the total household income.

This kind of analysis has been described as an ‘accounting approach’ because it focuses on

what is paid and what is received in a fiscal intervention without assessing how this can affect

household decisions (Lustig & Higgins, 2016). It is plausible to think that direct taxes and cash

transfers may trigger behavioural responses among individuals or households, as was discussed

in Section 4.5.1. Individuals can take different decisions depending on the fiscal intervention

that are not captured in this analysis. However, these kinds of changes are difficult to isolate

and measure. The fiscal incidence analysis is point-in-time rather than life-cycle and does not

include behavioural responses or general equilibrium modelling. It represents a first-order

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approximation of the fiscal intervention. In the context of this research, the partial fiscal

incidence analysis is carried out to identify one first-order outcome of the cash transfers:

poverty reduction.

4.7.1 The three income measures in partial fiscal incidence analysis

In order to identify the effects of fiscal intervention, the following three different income

measures are compared: market income (pre-fiscal income before direct taxes and direct cash

transfers), net market income (after direct taxes and before direct cash transfers) and

disposable income (after direct taxes and direct cash transfers) – Figure 4-4.

Figure 4-4 Partial fiscal incidence analysis incomes

Source: Author’s elaboration from Lustig (2011); Martinez-Aguilar et al. (2017) and Bucheli (2016).

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4.7.1.1 Market income (MI)

This is a pre-fiscal income which is composed of wages and salaries from the informal and

formal sectors, income from capital, pensions (contributive and non-contributive), private

transfers (remittances or alimonies for example), and an imputed rent from owner-occupied

housing. The imputed rent applies to households who do not pay rent due to owning a

dwelling. The imputed value is equivalent to the rent to be paid in the market for a similar

house. The market income is the sum of the income called in the CASEN survey autonomous

household income 76 plus the imputed rent. This income is the most appropriate way to

understand a household’s permanent living standard because its represents the income that the

household members generate by themselves.

Fiscal incidence analysis can consider the contributory pensions of the Pay-as-you-go

system in different ways. These pensions can be treated either as government transfers or as

deferred income. The way in which pensions are considered will affect the fiscal incidence

analysis because on it depends in which income –market or disposable - the pensions are

included. It is usual to compare the fiscal incidence analysis in both scenarios in order to

analyze the importance of the different treatments for contributory pensions. Chile has a small

sized Pay-as-you-go system meaning that the fiscal incidence analysis conclusions are the same

whether pensions are considered as government transfers or as deferred income (Martinez-

Aguilar et al., 2017). As a consequence of this, this research presents the analysis considering

contributory pensions of the Pay-as-you-go system as deferred income. This means that they

are included in the market income because they are understood as an individual’s savings and

not as government monetary transfers. The contributory pensions that came from the

individual’s compulsory saving system –AFP- are included in market income too.

Every household’s members report their disposable income in the survey. This means that

the incomes reported by individuals in the CASEN survey are after direct taxes and social

contributions are in place. As a consequence of this, in order to identify the market income, it

is necessary to simulate the direct taxes and social contributions that individuals should have

76 Autonomous income represents the income from salaries and wages, earnings from self-employment, self-provision of goods produced by the household, allowances, bonuses, rents, interest and retirements, pensions, widow’s pensions and transfers between individuals (Ministerio de Desarrollo Social. Chile, 2014).

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paid. This has been called a ‘reverse engineering process’ because it starts from income after

direct taxes (net market income) to the final simulated income before taxes (market income)

(Martinez-Aguilar et al., 2017).

The household income is obtained through the method of direct identification from the

CASEN survey. Every individual reports all the incomes they are receiving and the amount

received. The taxes that individuals pay are obtained through simulation. This means that the

taxpayers and the amount paid are simulated based on the tax system rules. The direct taxes

considered in the analysis are explained in the next subsection.

4.7.1.2 Net Market Income (NMI)

NMI corresponds to the market income minus direct taxes and social contributions. Direct

taxes are formed by taxes on salaries and remunerations called Secondary Category Tax (SCT) and

taxes on other personal income named Complementary Global Tax (CGT) of which rates range

from 0 to 40 percent. The method of simulation applies the statutory rate and discounts of each

taxable bracket stipulated by the Internal Revenue Service (IRS) to the taxable income reported

by each individual in the CASEN.

The Secondary Category Tax (SCT) is levied on income from dependent work, such as

salaries, contributory pensions and any other complementary remuneration. It is applied with

progressive tax rates over any dependent labour activity. Monthly incomes must be higher than

13.5 Monthly Tax Unit (UTM) which is equal to 607,000 Chilean Pesos and US$1,467 in 2015

to be taxable. The detail of the SCT rates is in the Annex 4.11.4.1

The Complementary Global Tax (CGT) is a personal, global, progressive and complementary tax.

It is levied on the annual total income obtained by an individual. The SCT or First Category Tax

(FCT) paid monthly is creditable against the CGT. The FCT is levied on income from capital

of commercial, industrial, mining and services enterprises, among others. The FCT paid by an

enterprise is a credit granted to the CGT when owners, shareholders, managers or partners

receive dividends from the enterprise. Tables in Annex 4.11.4 describe in detail the tax rates

for the three taxes described.

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Households in poverty do not pay taxes but what they do pay are social security

contributions. Formal employees pay health care 77 , unemployment insurance 78 , and

contributory pensions 79 . Contributions to health include the National Health Fund

(FONASA), and the health system of the armed forces (CAPREDENA) and the police

(DIPRECA). However, a small proportion of people in poverty pay social contributions as

well because they do not work as dependents. As a consequence of this, net market income

and market income are very similar for most of the people in poverty. Net market income is

not so relevant for the analysis of people in poverty.

4.7.1.3 Disposable Income (DI)

Disposable income represents post-fiscal income. This is after direct taxes and direct cash

transfers are in place. Disposable income is different from market income for people in

poverty because it takes social assistance into account. Social assistance here is composed by all

the cash transfers described in Table 4-1 and the discount on health contributions for older

people. People above 65 years who belong to the fourth most vulnerable quintile of the

population pay less in health contribution. This means that people receiving Aporte Previsional

Solidario or Pension Básica Solidaria have a reduction in their health contributions meaning an

increase in their disposable income. This reduction was from 7% to 3% in 2015 –the year of

the survey- and it was eliminated completely on 1st November 2016. Pensioners from

CAPEDRENA and DIPRECA do not have access to this discount. Again, as a small

proportion of pensioners in poverty pay for health contribution this discount is not very

important in this analysis.

The allocation method of cash transfers is direct identification. The CASEN survey provides

information about all the cash transfers that people receive and their exact amounts. The

health contribution discount is simulated in the survey.

77 It represents 7% of the salary. 78 It represents 0.3% of the salary. 79 It represents 20% of salary for workers in the Pay-as-you-go system.

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The cash transfers are grouped into benefits for families, benefits for pensioners, benefits

for workers and other benefits. These beneficiary groups are created and defined by the

Chilean Ministry of Social Development showing which groups are of interest for protection.

Among benefits for families are included the benefits for families in extreme poverty from the

main two Anti-Poverty programmes: Chile Solidario and Sistema de Igualdad y Oportunidades.

Among these benefits are: Bono de protección familiar; Bono de egreso; Bono base familiar; Bono Control

Niño Sano; Bono por Asistencia Escolar; Bono Logro Escolar. The periodicities with which these

benefits are delivered are also provided in Table 4-1. The details of the cash transfers in terms

of beneficiaries, beneficiary selection, periodicity, and objectives are presented in Annex 4.11.3.

As was explained in Section 2.3, during the government of President Michelle Bachelet

(2006-2010) the Social Protection System, under the name Red Protege (Protection Network),

was formally institutionalized. The main monetary transfer included in Red Protege was the

solidarity-based pillar for the old age pensions. The Pension Reform was implemented in 2008

and created the Aporte Previsional Solidario (APS, basic solidarity supplement) or Pension Básica

Solidaria (PBS, Basic Solidarity Pension) for those who were not able to save enough in their

compulsory individual saving system. The PBS represents the minimum pension that all

Chilean citizens are guaranteed. This monetary transfer is targeted to people 65 years old and

older that do not have a contributory pension, belong to the 60% most vulnerable of the

population and who have lived in Chile for 20 years or more (Larrañaga et al. 2014). The APS

is for people who have a contributory pension lower than the PBS. The amount of the APS is

the difference between these two pensions in order to guarantee the minimum PBS for all

people over 64 years that satisfies the characteristics already mentioned. The number of older

people who received PBS in 2014 was 400,436 and 627,020 people received APS.

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Table 4-1 Cash transfers considered in the analysis, CASEN 2015

Source: Author’s elaboration from Guia de Beneficios Sociales 2017, Ministerio de Desarrollo Social

4.7.2 Poverty Exit Rate

After market, net and disposable incomes are obtained for each household of the survey,

they are divided among household members to obtain per capita incomes. The allocation of

total incomes, taxes and benefits to each individual in the household assumes that the

resources of the household are equally distributed. This means that when an individual is

receiving a cash transfer this goes to the pooled income of the household. This analysis

Groups of beneficiaries Name of the benefit Amount, Chilean Pesos, year

2017

Periodicity

Aporte familiar permanente (ex bono marzo) $43,042 Once a yer (March)

Subsidio familiar al menor o recién nacido $10,844 Monthly

Subsidio Familiar (SUF) Mujer embarazada $10844 Monthly

Subsidio familiar a la madre $10,844 Monthly

Subsidio familiar para personas con Certificacion de Invalidez y

con Discapacidad mental

$21,688 Monthly

Subsidio de Discapacidad Mental para personas menores de 18

anos.

$66,105 Monthly

Asignación familiar depending on salary: up to $

10,844

Monthly

Asignacion maternal $10,844 Monthly

Pension Basica Solidaria de Invalidez $102,897 Monthly

Aporte Previsional Solidario de invalidez $102,897 Monthly

Subsidio al Consumo de Agua Potable % of the bill (maximum 15

monthly cubic metres)

Monthly

bono de protección familiar - meses 1 a 6 $15,516 Monthly




bono de egreso - meses 25 a 60 $9,899 Monthly

Bono base familiar depending on the poverty gap Monthly

Bono Control Niño Sano $6,000 Monthly

Bono por Asistencia Escolar $6,000 Monthly

Bono Logro Escolar between $56,253 - $33,752 Once a year

Pension Basica Solidaria de Vejez $102,897 Monthly

Aporte Previsional Solidario de vejez difference between PBS or

PMAS

Monthly

Bono de invierno $57,353 Once a year

Bono bodas de oro $307,516 Once in a life time

Bono al Trabajo de la Mujer % of salarie lower than

$453,282

Monthly

Subsidio Empleo Joven % of salarie lower than

$453,282

Monthly

Pensión por leyes especiales de reparación $102,897 Monthly

Otro subsidio del estado - -

Source: Guía de benef icios Sociales 2017. Ministerio de Desarrollo Social. Chile

Other benefits

Benefits for pensioners

Benefits for workers

Benefits for families

Cash Transfers Programmes

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considers that all the household members receive a benefit if one of them is covered by a

benefit. Here the analysis unit is the individual who has a market, net and disposable, per capita

income.

The per capita market and disposable incomes are used to estimate the poverty exit rate.

The poverty exit rate is defined as the proportion of people in poverty under market income

that is not in poverty under disposable income. It represents the probability of leaving poverty

due to cash transfers and it is represented as P(Em,d)80.

The poverty exit rate can be decomposed between the probability of being covered P(C) –

receiving the cash transfer- and the probability of leaving poverty given that the individual is

covered, P(Em,d/C). This decomposition allows us to separate the effect of the programme’s

coverage and the importance of the amount of the cash transfer for being taken out of

poverty, given that the individual is covered (Bucheli, 2016). The decomposition is expressed

in the following equation:

P(Em,d) = P(C)P(Em,d/C) (1)

The poverty exit rate can also be obtained for particular groups of cash transfers. This

research follows the strategy used by (Bucheli, 2016) and used in poverty dynamic studies to

decompose the poverty exit rate by groups of benefits. The author decomposed the transition

over time between “the frequency with which the population at risk experiences a relevant

event and the probability of transition, given the occurrence of the event” (Bucheli, 2016, p. 4)

following a strategy used in poverty dynamics studies (Beccaria et al., 2013; Jenkins & Schluter,

2001). Bucheli (2016) interprets the occurrence of an event as being covered by a social

assistance programme. Under this conceptualization, the population in poverty is divided in

terms of market income according to their coverage status. This research uses the classification

developed by the Social Development Ministry of Chile (Guía de beneficios Sociales 2017) which

assembles benefits for pensioners, benefits for families and benefits for workers. In addition,

80 The notation of this research follows the notation in (Bucheli, 2016).

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the category of other benefits is identified here because in the CASEN survey some people

report cash transfers from other programmes. Following Bucheli (2016), individuals are

classified according to these four categories of benefits.

Table 4-2 Groups of benefits

Groups of cash transfers

Household covered by (at least one member)

1 2 Benefits for pensioners

Benefits for families

Benefits for workers

Other benefits

I 1 Yes No No No

2 Yes No Yes (at least one of the benefits)

II 3 No Yes No No

4 No Yes Yes (at least one of the benefits)

III

5 No No Yes No

6 No No No Yes

7 No No Yes Yes

IV 8 Yes Yes No No

9 Yes Yes Yes (at least one of the benefits)

V 10 No No No No Source: Author’s elaboration.

Individuals belong to a particular group depending on the combination of benefits that

they receive. Two main categories of groups are created. The first category has five groups

represented in the first column of Table 4-2 from I to V. The second category is more

disaggregated into 10 groups. The first category puts attention on receiving or not benefits for

pensioners and/or benefits for families. The second category distinguishes more between the

other two kinds of benefits: for workers and other benefits. The results are presented using

these two categories.

All individuals can be classified into only one group meaning the groups are mutually

exclusive and cover all the possible combinations of benefits. This means that the probability

of transition from poverty under market income to non-poverty under disposable income is

equal to the sum of the transition probabilities of each coverage group (Bucheli, 2016). In

other words, the total poverty exit is equal to the sum of poverty exit of each category covered

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by the group of programmes i. The equation (2) describes the total poverty exit rate -the

transition from poverty under market income to non-poverty under disposable income- as the

sum of the probability of leaving poverty conditional on being covered by the group of

programmes i, from i=1 to n=10 or n=5 depending on the amount of groups considered.

P(Em,d) = ∑ P(Em,d/𝐶𝑖)𝑛𝑖=1 (2)

The equation (2) can be decomposed in the following two terms:

P(Em,d) = ∑ P(𝐶𝑖)P(Em,d/𝐶𝑖)𝑛𝑖=1 (3)

Where “P(Ci) is the probability that a poor person according to market income is covered by

the groups of programme i” (Bucheli, 2016, p. 5). The term P(Em,d/Ci) “is the probability that

a poor person leaves poverty conditional to being covered by i” (Bucheli, 2016, p. 5). The

decomposition allows us to disentangle the effect of the coverage of a group of programmes

from the amount of the transfers to that group for alleviate poverty (Bucheli, 2016).

4.7.3 Poverty and vulnerability lines

The objective of this research is to identify the effectiveness of cash transfers in taking out

of poverty and vulnerability to poverty two vulnerable groups: children and older persons. For

doing that, different poverty and vulnerability lines are considered. The idea is to compare

different welfare measures through the use of different international and national thresholds.

In addition, these different thresholds allow us to observe the transition between income

groups such as those in poverty, vulnerability and the middle class. The first two poverty lines

correspond to the extreme and moderate poverty lines used by international agencies: US$2.5

dollars and US$4 per capita per day at 2005 purchasing power parity (PPP). These poverty

thresholds are expressed in 2015 using the National Consumer Price Index (CPI) calculated by

the Institute of National Statistics (INE) and the 2015 Power Parity Purchase (PPP) provided

223

by the World Bank. The US$2.5 poverty line in 2005 PPP terms corresponds to US$3.3 in

2015 PPP and the US$4 is equivalent to US$5.3 in 2015 PPP terms. National poverty lines are

also considered in the analysis. These lines are higher than the international poverty lines

described and they embed the equivalence scale in the household.

The vulnerability threshold used in this research came from López-Calva & Ortiz-Juarez

(2014) and The World Bank. International vulnerability thresholds in López-Calva & Ortiz-

Juarez (2014) are US$4 and US$10 2005 PPP per day. Expressed in Chilean Pesos and dollars

PPP for the year 2015 this threshold represents 1,380.9 and 2,209.5 (PPP) respectively. Table

4-3 presents the values of the poverty and vulnerability lines expressed in Chilean Pesos and

US Dollars.

Table 4-3 Poverty and vulnerability thresholds

Values per day

2005 2015

Poverty and vulnerability thresholds

US Dollar Chilean Pesos PPP 2005

Chilean Pesos CPI 2005-2015

US Dollar PPP 2015

International

Extreme 2.5 968.4 1,380.9 3.3

Moderate 4.0 1,549.4 2,209.5 5.3

Vulnerability 10.0 3,873.6 5,523.8 13.3

Middle Class 50.0 19,368.0 27,618.8 66.7

National

Extreme - - 2,159.3 5.2

Moderate - - 3,238.9 7.8

Source: Author’s elaboration from World Bank PPP Private Consumption; Social Development Ministry, Chile

Although poverty is evaluated at the individual level the information used to define it

comes from the household in which persons live. That means that poverty status evaluation

considers household characteristics such as its total income and composition. In this context, it

is important if the household is composed of two members or more and their age. As a

consequence of that, poverty lines consider the equivalence of scales described in Section 4.4.

Having in mind the importance of household composition in determining individual poverty

this research also considers the age composition of the household to evaluate exit poverty.

224

This allows us to evaluate the effectiveness of cash transfers in moving out of poverty the

households where children and older persons live. This research uses a methodological

strategy, which was followed by Bucheli (2015) that builds population groups according to the

age composition of the household. The households are classified into four groups: households

with children; households with older persons; households with children and older persons; and

households without children and older persons.

The three indicators developed by Foster-Greer-Thorbecke (FGT) (1984) are used to

measure income poverty of the household. The FGT aggregate poverty measures can be

derived from the following equation:

𝑃𝛼(𝑦, 𝑧, 𝛼) =1

𝑁∑ (

𝑧 − 𝑦𝑖

𝑧)

𝛼𝐻

𝑖=1

where 𝑧 is the poverty line, 𝑦𝑖 is the income of individual 𝑖, 𝐻 represents the people in

poverty. 𝛼 can be interpreted to reflect society’s ‘aversion to poverty’. The Poverty Headcount

(P0), also called poverty incidence, is obtained when 𝛼 = 0 . It measures the share of the

population that is in poverty81. The Poverty gap (P1) is obtained when 𝛼 = 1. It measures the

depth or intensity of poverty, the extent to which individuals fall below the poverty line (the

poverty gaps) as a proportion of the population82. The Poverty gap squared (P2) is obtained

when 𝛼 = 2. This is called poverty severity. It represents the average poverty gaps squared as a

proportion of the poverty line. It takes into account inequality between the poor.

81 (By definition 𝑥0 = 1 , so that for 𝛼 = 0, 𝑃0 = 𝐻/𝑁)

82 It reads as: the poverty gap for each household if the total poverty gap is divided equally among households

225

4.8 Results

Are monetary transfers reducing poverty in Chile? We already know that the answer is yes

(Martinez-Aguilar et al., 2017). The poverty headcount is lower after fiscal interventions are

implemented and the extent of this reduction depends on the definition of incomes and

poverty lines considered. The question that remains is whether the monetary transfers’

contribution to poverty reduction is dependent on age. The following results show that it is.

4.8.1 Population

The changes in poverty rates as a consequence of fiscal interventions –direct taxes and

transfers- are presented in Figure 4-5. It can be observed that poverty headcounts increase

after direct taxes are applied and decrease after monetary transfers are provided

independently of the poverty line considered. The increase in poverty headcounts after

direct transfers and social contributions are in place is not very large because people in

poverty do not pay taxes but they pay health contributions. They must have formal

contracts to pay these kinds of social contributions. As a consequence of this, the poverty

rates of net market income are very similar to those obtained from market income. The

range of variation between poverty headcounts of market income and net market income is

between 0.3 and 0.8 percentage points depending on the poverty line considered.

226

Figure 4-5 Poverty Headcount by income concepts

Source: Author’s elaboration based on CASEN, Ministry of Social Development.

In contrast, monetary transfers reduce poverty levels in a sizable proportion. Poverty

headcounts under disposable income are lower than those obtained under market income

considering any poverty line. As expected, the reduction of the poverty headcount is higher

as a consequence of monetary transfers when lower poverty lines are considered. The

reduction of the poverty headcount by using the international extreme poverty line of

US$2.5 dollars is 55%, from 2.3% to just 1%. At the same time, poverty headcounts

decrease by 23% (3.2 percentage points) by using the national moderate poverty line and

by 42% (2.5 percentage points) by using the national extreme poverty line. The moderate

poverty headcount goes from 14.1% to 10.8% and the extreme poverty headcount moves

from 6.1% to 3.7% after fiscal intervention.

Furthermore, after direct taxes and transfers not only is the incidence of poverty lower,

but also its intensity and severity. The incidence corresponds to the results already

presented which measured the proportion of the population under the poverty line. The

intensity -measured through the poverty gap- and the severity of poverty –measured using

the poverty gap squared- decreased after fiscal intervention. As expected, the higher the

reduction, the lower the poverty line, – Figure 4-6.

2,3% 2,5% 1,0%

6,1% 6,4%

3,7%6,0% 6,3%

3,4%

14,1% 14,9%

10,8%

0,0%

5,0%

10,0%

15,0%

20,0%

Market Income Net Market Income Disposable Income

Percentage of population in poverty, by income concepts and poverty lines

US $ 2.5 PPP US $ 4 PPP

Extreme National Poverty Line Moderate National Poverty Line

227

Figure 4-6 Poverty Headcount (Incidence), Poverty Gap (Intensity), Poverty Gap Squared (Severity), by income concepts


The fiscal mobility matrices –Table 4-4 and Table 4-5 - show the movements across

income groups after fiscal intervention. In general terms, upward economic mobility

happens after direct taxes and transfers are applied. 31.2% of people in extreme poverty

under market income move to moderate poverty. While 9.4% move to vulnerability, 2.3%

move up to the middle class. In addition, while 30% of people in moderate poverty move

to vulnerability, 3.8% move to the middle class. This indicates that 7.3% of the population

–all the cells right and above the diagonal in Table 4-4- experience upward economic

mobility and 5.4% move to groups out of poverty. As should be expected, the fiscal

intervention is not enough to move anyone to the upper middle-class group. A small

proportion of the population experience downward economic mobility after taxes and

transfers are in place. 0.3% of the population move from upper middle class to middle

class -which represents 4.8% of the upper middle class under market income. 8% of the

population move from middle class to vulnerability –1.4% of the middle class under

market income-, and a small 0.1% of the population move from vulnerability to moderate

poverty –0.7 of the group in vulnerability before fiscal intervention.

In general terms, the percentage of people living in extreme and moderate poverty

declines - from 6% to 4.3% and from 8.1% to 7.4% respectively- and the proportion in

-54,7%

-62,0%

-62,9%

-39,3%

-52,4%

-57,7%

-42,4%

-56,1%

-61,1%

-23,3%

-40,4%

-49,6%

-70,0% -60,0% -50,0% -40,0% -30,0% -20,0% -10,0% 0,0%

Incidence

Intensity

Severity

Moderate National Poverty Line Extreme National Poverty Lines

US $ 4 PPP US $ 2.5 PPP

228

vulnerability and middle- class income groups increases after fiscal intervention –from

21.3% to 22.9% and from 57.8% to 59.8% respectively. Only the population in the upper

middle class decreases a little bit after taxes and transfers.

Table 4-4 Fiscal mobility matrix, from market income to disposable income, Total percentage distribution of population


Table 4-5 Fiscal mobility matrix, from market income to disposable income, Row percentage distribution of population


Market Income Extreme

poverty

Moderate

Poverty

Vulnerability Middle

Class

Upper

Middle Class

Total

Extreme poverty 3.4% 1.9% 0.6% 0.1% 0.0% 6.0%

Moderate Poverty 0.0% 5.4% 2.4% 0.3% 0.0% 8.1%

Vulnerability 0.0% 0.1% 19.1% 2.0% 0.0% 21.3%

Middle Class 0.0% 0.0% 0.8% 56.9% 0.0% 57.8%

Upper Middle Class 0.0% 0.0% 0.0% 0.3% 6.5% 6.8%

Total 3.4% 7.4% 22.9% 59.8% 6.5% 100%

Disposable Income


poverty

Moderate

Poverty


Class

Upper

Middle Class

Total

Extreme poverty 57.1% 31.2% 9.4% 2.3% 0.0% 100.0%

Moderate Poverty 0.3% 66.0% 30.0% 3.8% 0.0% 100.0%

Vulnerability 0.0% 0.7% 89.8% 9.6% 0.0% 100.0%

Middle Class 0.0% 0.0% 1.4% 98.6% 0.0% 100.0%

Upper Middle Class 0.0% 0.0% 0.0% 4.8% 95.2% 100.0%

Total 3.4% 7.4% 22.9% 59.8% 6.5% 100%

Disposable Income

229

4.8.2 Children and Older persons

In Section 4.4 it was discussed that children are over-represented among people in

poverty and older persons are under-represented in poverty when equivalent total income

was considered. The official methodology for measuring poverty after taxes and transfers

clearly indicates the disparity between these two age groups. The question that remains is

whether this disparity is the same before monetary transfers and direct taxes. The answer is

no. Monetary transfers contribute to poverty reduction for all the age groups but in a

higher proportion for people over 60 years. Figure 4-7 shows the age composition of three

different groups: all population, people in poverty under market income and people in

poverty under disposable income –presenting moderate poverty levels here83.

Figure 4-7 Distribution of age groups among all population, population in poverty under market income and population in poverty under disposable income, 3 age groups


The two vulnerable groups under analysis are over-represented among people in

poverty before fiscal intervention. The proportion of children and older persons among

83 Figure 4-23 in the Annex N 4.11.5 disaggregates age into more groups than Figure 4-7.

26,6%36,3% 40,1%

60,8% 48,5%51,5%

12,6% 15,2% 8,5%

all population poor under market income poor under disposableincome

Proportion of individuals, by age groups and income

0 to 18 19 to 59 64 plus

230

people in poverty under market income is higher than their proportion among all the

population. 15.2% of people in moderate poverty under market income are persons over

64 years whereas they represent 12.6% of the population as a whole. This difference is

even greater for children younger than 19 years who represent 26.6% of the population but

36.3% of people in poverty under market income. In contrast, the group of people that are

neither children nor older persons –between 19 and 64 years- are under-represented

among the poor before any fiscal intervention. They represent 60.8% of the population

and 48.5% of people in poverty under market income. This evidence shows that children

and older persons represent more than half of the population in poverty under market

income even though they just represent almost 40% of the total population. These two age

groups are effectively more in poverty than the average population, showing their

vulnerability.

The picture changes when taxes and monetary transfers are considered. The age

composition of people in poverty is completely different between market and disposable

income. The group of people over 64 years decrease their participation among people in

poverty from 15.2% under market income to 8.5% under disposable income. The opposite

happens with the rest of the age groups who increase their proportion among people in

poverty. The most affected group is that composed of children who represent 36.3% of

population in poverty under market income but 40.1% of people in poverty after monetary

transfers. This evidence indicates an age bias in cash transfers which benefit elderly

persons more than children. People over 64 years exit poverty more than children as a

consequence of the benefits from the government.

The same age bias can be observed through the moderate poverty headcount reduction

by age groups after monetary transfers–Figure 4-8. The reduction of poverty incidence is

around 2 and 3 percentage points depending on the age group representing a decrease

between 13% and 22% of the poverty headcount before monetary transfers. Children are

the group with the highest poverty incidence under market income (20.4% for 0-3 years,

19.3% for 4-18 years) and remain first in the poverty rate ranking under disposable income

(17.6% for 0-3 years, 16% for 4-18 years). However, they reduce their poverty indicator

after monetary transfers, a reduction between 13% and 17% of their initial poverty rate.

231

A completely different story is experienced by people over 64 years. They experience a

diminution of 57% in their poverty incidence after monetary transfers, almost 10

percentage points less moving from 17% to 7.2%. This reduction of 9.7 percentage points

of the poverty rate after monetary transfers is almost three times the 3.3 percentage point

reduction for all the population. This reduction in the poverty rate is more than 4 times the

reduction of poverty rate of children under 3 years and 3 times of children between 4 and

18 years. Older persons are the group most benefited from monetary transfers which help

them to escape poverty in a higher proportion than the rest of the population.

Figure 4-8 Moderate Poverty headcount, by income concept and age group


4.8.3 Households with children and/or/without older persons

The figures presented already were all in individual terms. All the population was grouped

by their age to analyze their poverty incidence. However, their individual income per month

was obtained from the division of the total resources of the household among all its members.

People are in poverty when the household does not have enough resources to satisfy a

minimum for each inhabitant. It is not obvious the way in which they share the benefits they

20,4%

17,6%17,0%

7,2%

14,1%

10,8%

0,0%

5,0%

10,0%

15,0%

20,0%

25,0%

Market Income Disposable Income

Moderate poverty headcount, by income concept and age group

0 to 3 4 to 18 19 to 29 30 to 44

45 to 64 65 plus Population

232

receive but anyway they experience an increase in the resources available for all the inhabitants

of the household. As a consequence, it is interesting to identify the kind of household whose

exit from poverty is facilitated by monetary transfers. What is the composition by age of the

households that monetary transfers are taking out of poverty? Are households with children

and households with older people equally protected?

The households are classified in four groups: households with children; households with

older persons; households with children and older persons; and households without children

and older persons.

Figure 4-9 Distribution of individuals by household type, all population, population in poverty under market income, population in poverty under disposable income


54,0%

62,9%

71,3%

16,1%

16,8%

9,6%

11,5%

12,3%

10,0%

18,5%

8,0%

9,1%

0% 20% 40% 60% 80% 100%

population

poor under market income

poor under disposable income

with children and without old persons

with old persons and without children

with children and old persons

without children and without old persons

233

Figure 4-10 Distribution of households by household type, all population, population in poverty under market income, population in poverty under disposable income


The majority of the population (54%) lives in households with children and without old

persons–Figure 4-9. This kind of household represents 42.3% of total households–Figure 4-10.

Households composed of older persons but without children represent almost 23.4% of total

households but 16.1% of the population. This reflects the fact that on average households with

older persons are smaller than households with children. Many older persons live alone or with

a partner. Households with children are on average formed by one or two parents and siblings.

This is important in analyzing exit poverty. It is possible that monetary transfers are more

effective in reducing poverty among older persons because they share the new resources with

fewer people. This will be explored later.

At the same time, a minority of households are those composed by children and older

persons at the same time (7.3%). This shows that children and grandparents usually do not live

together. This characteristic restricts the possibility of reducing poverty levels of children and

elders at the same time by just targeting only one group of them. At the same time, there is an

important proportion of households where there are neither children nor older persons (27%).

As expected, the distribution of these four kinds of households is different among all the

households in the population, households in poverty under market income and those in

42,3%

49,7%

59,9%

23,4%

28,0%

16,6%

7,3%

8,8%

7,5%

27,0%

13,5%

16,0%

0% 20% 40% 60% 80% 100%

population

poor under market income

poor under disposable income

with children and without old persons

with old persons and without children

with children and old persons

without children and without old persons

234

poverty under disposable income. Households with children and no older persons are over-

represented among households in poverty under market income and even more among

households in poverty under disposable income. Furthermore, individuals living in those

households represent the majority of people in poverty under market income -62.9%- and

under disposable income -71.3%. This indicates that the major incidence of poverty is in

households where children do live and older persons do not, and this is more pronounced

after monetary transfers.

A first approximation to compare the importance of monetary transfers over the income

of the four groups of households in poverty is to observe their market and disposable income

distributions. The way in which these distributions change reflects the relative importance of

monetary transfers for households in poverty. It can be observed from Figures 4-11 and 4-12

that income distributions change their location and their shape after monetary transfers are

received. The income distributions for the four groups of households show a rightward shift,

meaning an increase in the median of income. The changes in the shape of the income

distributions show a more uni-modal income distribution under disposable income than

market income. However, the movements are more intense for some groups than others. The

rightward shift of the disposable income distribution is more pronounced for households with

older persons. The density of individuals living in this kind of household moves from being

the most concentrated on the left of the income distributions to being the most concentrated

on the right.

Figure 4-11 Kernel density of market income, by type of household

Source: Author’s elaboration based on CASEN 2015, Ministry of Social Development.

0

5.0

00e

-06

.000

01

.000

01

5.0

00

02

De

nsity

0 50000 100000 150000market income

with children with elders

with children and elders without children and elders

235

Figure 4-12 Kernel density of disposable income, by type of household


The TIP curves are a useful graphical instrument that allows the poverty levels of these

four groups of households to be ranked without the specification of a proper poverty line. The

TIP curve is a plot that shows the cumulative population proportion in the x axis and the

cumulative per capita poverty gap in the y axis. The gap is normalized and is defined only for

people below the poverty line. It represents the difference between income and the maximum

poverty line. The TIP curve shows the gap ordered from highest to lowest. The intensity of

poverty is represented by the height of the TIP curve. The height is the average poverty gap

for the maximum poverty line. The inequality among people in poverty is represented through

the curvature of the TIP curve. TIP curves have the advantage of representing the incidence,

intensity and inequality for all lines below the maximum line.

Figure 4-13 shows the TIP curve of market income by household groups with the

maximum poverty line set at the official moderate poverty line. The highest levels of incidence,

intensity and severity of poverty are experienced by households with elders. The group

corresponds to households with children and elders followed closely by households with

children but no elders. The lower levels of incidence, intensity and severity of poverty are

experienced by households without elders and children. This reflects the fact that households

with children -and/or elders- live more in poverty than households just composed of adults

older than 64 years.

0

5.0

00e

-06 .0

00

01

.000

01

5.0

00

02

.000

02

5D

ensity

0 50000 100000 150000disposable income



236

Figure 4-13 TIP curve of market income by household type, official moderate poverty line


Figure 4-14 TIP curve of net market income by household type, official moderate poverty line


Figure 4-15 TIP curve of disposable income by household type, official moderate poverty line


0

.02

.04

.06

.08

0 .1 .2 .3 .4Cumulative proportion of population

children elder childen & elder no childen no elder

TIP curves

0

.02

.04

.06

.08



TIP curves

0

.01

.02

.03

.04

.05



TIP curves

237

The TIP curves for market (Fig. 4-13) and net market income (Fig. 4-14) are very

similar because the majority of people in poverty do not pay taxes and social security

contributions. However, the TIP curve for disposable income (Fig 4-15) is completely

different to the previous two curves changing the order of dominance of the different

household groups. The provision of cash transfers moves households with elders from

being the most in poverty to being the least in poverty. Households with elders share, after

monetary transfers, the same incidence of poverty as households without elders and

children. On the contrary, households with children become the most in poverty compared

with the other groups of household composition.

The different trajectories of poverty reduction after cash transfers by household

composition are presented in Figure 4-16 –for the national extreme poverty line- and

Figure 4-17 –for the national moderate poverty line. It is clear from Figure 4-16 that the

reduction of the poverty headcount for households with older persons and no children is

more pronounced than the average. The 8.1% of people living in this kind of household

were in extreme poverty before monetary transfers. This percentage decreases to just 1.7%

after monetary transfers representing half of the average extreme poverty rate of 3.4%.

Households with elders move from being the most in extreme poverty under market

income to the least in extreme poverty under disposable income after fiscal intervention.

Figure 4-16 Percentage of population in poverty, by income concept and household composition, National extreme poverty line


6,4%

4,7%

8,1%

1,7%

2,7%

2,0%

6,0%

3,4%

0,0%

1,0%

2,0%

3,0%

4,0%

5,0%

6,0%

7,0%

8,0%

9,0%




All the population

238

Figure 4-17 Percentage of population in poverty, by income concept and household composition, National moderate poverty line


In the case of moderate poverty the picture is similar. The highest decrease of poverty

after monetary transfers is experienced by individuals of households with elders and no

children. They move from 14.8% of poverty rate under market income –just above the

average poverty rate of 14%- to far below the average poverty rate under disposable

income -6.4%. This reduction of 8.3 percentage points after cash transfers is much higher

than the 2.1 percentage point reduction for individuals living in households with children

and without elders. However, it has already been shown that the proportion of individuals

living in households with elders and without children was much lower (16%) than

households with children and without elders (52%).

The following fiscal mobility matrices show that the movements across income groups

as a consequence of fiscal intervention also depend on the kind of household individuals

live in.

16,5%

14,3%14,8%

6,4%6,1%

5,3%

0,0%

2,0%

4,0%

6,0%

8,0%

10,0%

12,0%

14,0%

16,0%

18,0%




All the population

239

Table 4-6 Fiscal mobility matrix, from market income to disposable income, Row percentage distribution of people living in households with children and no older persons

Source: Author’s elaboration based on CASEN, Ministry of Social Development

Table 4-7 Fiscal mobility matrix, from market income to disposable income, Total percentage distribution of people living in households with children and no older persons


It can be observed that households with children and without older persons remain in

a higher proportion in the same income group after fiscal intervention than households

with older persons but without children –Table 4-6 and Table 4-7. Households with older

people move to higher income groups in a higher proportion that the rest of the

household groups after fiscal intervention. Table 4-8 and Table 4-9 show the lower

proportion of people in households with children moving to higher income groups than

households with older people. The Annex 4.11.7 has the fiscal mobility matrices for the

other two kinds of households.


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 71.9% 26.7% 1.3% 0.0% 0.0% 100%

Moderate Poverty 0.3% 77.1% 22.5% 0.0% 0.0% 100%

Vulnerability 0.0% 0.8% 96.0% 3.2% 0.0% 100%

Middle Class 0.0% 0.0% 2.1% 97.9% 0.0% 100%

Upper Middle Class 0.0% 0.0% 0.0% 5.3% 94.7% 100%

Total 4.7% 9.7% 29.0% 52.1% 4.6% 100%

Row percentage distribution of population

Disposable Income


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 4.6% 1.7% 0.1% 0.0% 0.0% 6%


Vulnerability 0.0% 0.2% 25.5% 0.8% 0.0% 27%

Middle Class 0.0% 0.0% 1.1% 51.0% 0.0% 52%


Total 4.7% 9.7% 29.0% 52.1% 4.6% 100%

Total percentage distribution of population

Disposable Income

240

Table 4-8 Fiscal mobility matrix, from market income to disposable income, Row percentage distribution of people living in households with older persons and no children


Table 4-9 Fiscal mobility matrix, from market income to disposable income, Total percentage distribution of people living in households with older persons and no children


Table 4-10 summarizes poverty and exit poverty rates considering the four poverty

lines. As expected, the higher the poverty exit the lower the poverty line. As expected from

the evidence so far, it is observed that the poverty exit is higher if households have an

older person among their members.

Let us consider the international poverty line first. The average poverty headcount is

2.3% under market income, declining to just 1% under disposable income when the

extreme international poverty line is used. Public benefits are effective in reducing the

population living in extreme poverty: the exit poverty rate is equal to 54.6%. This means


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 21.6% 37.1% 32.1% 9.3% 0.0% 100%


Vulnerability 0.0% 0.2% 52.1% 47.7% 0.0% 100%

Middle Class 0.0% 0.0% 0.3% 99.6% 0.1% 100%


Total 1.7% 4.6% 12.6% 74.3% 6.8% 100%


Disposable Income


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 1.7% 3.0% 2.6% 0.7% 0.0% 8%


Vulnerability 0.0% 0.0% 6.4% 5.8% 0.0% 12%

Middle Class 0.0% 0.0% 0.2% 65.8% 0.0% 66%


Total 1.7% 4.6% 12.6% 74.3% 6.7% 100%


Disposable Income

241

that cash transfers contribute to reducing extreme poverty –by international standards- by

more than half. Again, it is observed that public benefits help households with elders more

than households without any them. These differences in exit poverty rates among

household groups remain for all the poverty lines considered.

The poverty headcount, considering the moderate international poverty line, is 6.1%

under market income and 3.7% under disposable income. That means that monetary

transfers contribute to a poverty exit rate of 39.7%. Similar average numbers are obtained

when the national extreme poverty line is considered. This happens because both lines

expressed in Chilean pesos are very close. The average exit poverty rate considering the

national extreme poverty line is 42.9%. Again, the exit poverty rates differ among

household groups. The 28% of people living in households with children leave extreme

poverty as a consequence of monetary transfers. In contrast, the 78.4% of people living in

extreme poor households with elders get out of poverty as a consequence of monetary

transfers. The older persons contribute to a higher exit poverty rate even when there are

children in the household. Around 61% of individuals living in extreme poor households

with elders and children get out of poverty after monetary transfers.

The exit poverty differences among household types remains under the national

moderate poverty line. 14.1% of the population is in moderate poverty under market

income and this percentage decreased to 10.8% under disposable income. The capacity of

monetary transfers to reduce the poverty headcount is lower –the exit rate equal to 24.3%-

because the poverty line is higher. However, the exit poverty rate is still higher for

households with elders (56.8%) and elders and children (37.4%) than for households with

children and no elders (14.3%) and no children and no elders (14.8%).

The evidence shows that monetary transfers contribute to poverty exit in a higher

proportion in the group of people over 64 years and the households where they live. This

means that the effectiveness of the cash transfers on poverty exit depends on the age

composition of the household. The question that emerges is whether households with

elders are receiving proportionally more monetary transfers than the rest of the household

or the amount that they are receiving is bigger. The decomposition of the poverty exit rate

presented in equation (1) allows us to separate the effect of the programme’s coverage and

242

the importance of the amount of the cash transfer for being taken out of poverty, given

that the individual is covered. This decomposition is presented in the next subsection to try

to answer whether it is the coverage or the amount of the benefit that plays the more

important role in the exit poverty rates.

Table 4-10 Poverty Rate for four poverty lines, composition of individuals in poverty and Exit Rate by Household Groups.


Households with children 61.0% 63.8%

0.025 0.026 0.015 0.015 0.413 0.417 0.072 0.072 0.053 0.053 0.264 0.266

Households with elders 18.2% 16.6%

0.025 0.026 0.002 0.002 0.925 0.929 0.062 0.063 0.011 0.011 0.821 0.825

Households with children and elders 13.7% 13.7%

0.027 0.027 0.006 0.007 0.754 0.761 0.072 0.073 0.034 0.034 0.531 0.536

Households without children and elders 7.2% 6.0%

0.009 0.009 0.006 0.006 0.293 0.304 0.020 0.020 0.013 0.014 0.318 0.325

All the population 100.0% 100.0%

0.022 0.023 0.010 0.010 0.545 0.548 0.061 0.061 0.037 0.037 0.396 0.398

Households with children 58.1% 62.9%

0.064 0.064 0.046 0.047 0.279 0.282 0.164 0.165 0.143 0.143 0.142 0.143

Households with elders 21.7% 16.8%

0.080 0.081 0.017 0.018 0.783 0.786 0.147 0.148 0.064 0.065 0.566 0.569

Households with children and elders 11.8% 12.3%

0.061 0.062 0.025 0.025 0.609 0.614 0.150 0.151 0.094 0.095 0.373 0.376

Households without children and elders 8.4% 8.0%

0.027 0.020 0.020 0.275 0.281 0.061 0.061 0.053 0.053 0.146 0.149

All the population 100.0% 100.0%

0.060 0.060 0.034 0.035 0.428 0.430 0.141 0.141 0.108 0.108 0.243 0.244

10.8%

Population Groups

6.1% 2.5% 61.2% 15.1% 9.5% 37.4%

8.1% 1.7% 78.4% 14.8% 6.4% 56.8%

6.4% 4.7% 28.1% 16.5% 14.3% 14.3%

Poverty Rate Comp. poor

under market

income

Exit Rate Poverty Rate Comp. poor

under market

income

Exit Rate

Market

Income

Disposable

Income

Market

Income

Disposable

Income

US$ 2.5

National Extreme National Moderate

2.3% 1.0% 54.6% 6.1% 3.7% 39.7%

0.9% 29.9% 2.0% 1.4% 32.2%

2.7% 75.7% 7.3% 3.4% 53.4%

2.5% 92.7% 6.3% 1.1% 82.3%

2.5% 41.5% 7.2% 5.3% 26.5%

Market

Income

Disposable

Income

Market

Income

Disposable

Income

Exit RatePoverty Rate Comp. poor

under market

income

Exit Rate Poverty Rate Comp. poor

under market

income

0.6%

0.7%

0.2%

1.5%

US$ 4

6.0% 3.4% 42.9% 14.1% 24.3%

5.3%2.7% 2.0% 27.8% 6.1% 14.8%

4.8.3.1 The role of coverage and amount of cash transfers in the exit from poverty

We have already seen that poverty exit rates decrease when poverty lines increase. The

poverty exit rate is 54.6% when the international extreme poverty line is applied, and

decreases to 39.7% for the international moderate poverty line, and moves from 42.9% for

the national extreme poverty line to 24.3% for the national moderate poverty line. Higher

poverty lines make the capacity of monetary transfers to reduce poverty rates more

difficult. This may be happening because the coverage of people in poverty is lower –there

are more people in poverty to target- or because the amount of benefit is not enough to

move household income above the higher poverty line –it is more difficult to close the

poverty gap-.

The probability of coverage –the probability that an individual in poverty under market

income will receive monetary transfers- is very similar for all the poverty lines: between

82% and 85% depending on the line taken, column P(Ci), Table 4-11. People in poverty

under market income have a high probability of receiving public benefit for all the poverty

lines but the probability of leaving poverty as a consequence of the transfer is lower while

poverty lines are higher. The probability of living in poverty conditional on being covered

ranges from 65% for the international extreme poverty line to 29.7% for the national

moderate poverty line –the highest considered. This evidence suggests that the average

amount of transfers received by people in poverty lift half of them out of extreme poverty

and almost 30% out of moderate poverty. From another angle, this also means that 70% of

the individuals in poverty receiving benefits are not able to exit moderate poverty.

However, the picture again depends on the age composition of the household.

Table 4-11 Poverty Exit Rate, Probability of being covered and probability of leaving poverty given coverage, by households groups


Households with children

0.413 0.417 0.827 0.830 0.499 0.503 0.264 0.266 0.835 0.837 0.315 0.318 0.279 0.282 0.829 0.831 0.337 0.339 0.142 0.143 0.815 0.816 0.175 0.176

Households with elders

0.925 0.929 0.962 0.964 0.962 0.964 0.821 0.825 0.963 0.965 0.852 0.855 0.783 0.786 0.962 0.963 0.813 0.816 0.566 0.569 0.933 0.935 0.606 0.609

Households with children and elders

0.754 0.761 0.975 0.977 0.773 0.780 0.531 0.536 0.951 0.953 0.558 0.563 0.609 0.614 0.956 0.958 0.636 0.642 0.373 0.376 0.932 0.934 0.399 0.403

Households without children and elders

0.293 0.304 0.372 0.383 0.784 0.799 0.318 0.325 0.458 0.466 0.691 0.701 0.275 0.281 0.467 0.474 0.587 0.597 0.146 0.149 0.426 0.430 0.341 0.348

All the population

0.545 0.548 0.839 0.841 0.649 0.652 0.396 0.398 0.850 0.851 0.466 0.468 0.428 0.430 0.843 0.844 0.507 0.509 0.243 0.244 0.819 0.819 0.296 0.297

93.4% 60.8%

93.3% 40.1%

14.8% 42.8% 34.5%27.8% 47.0% 59.2%

14.3%

96.4% 85.3%

28.1%

78.4% 96.2% 81.5%

61.2% 95.7% 63.9% 37.4%

56.8%

53.4% 95.2% 56.1%

32.2% 46.2% 69.6%37.7% 79.1%

41.5% 82.8% 50.1%

92.7% 96.3% 96.3%

Poverty Lines

US$ 2.5

P(Em,d/

Ci)

P(Em,d)

National Extreme

P(Em,d) P(Ci) P(Em,d/

Ci)

US$ 4


Ci)

81.9% 29.7%24.3%42.9% 84.3% 50.8%

National Moderate


Ci)

83.0% 33.8% 81.6% 17.5%

46.7%

Population Groups

85.1%

P(Ci)

54.6% 84.0% 65.0% 39.7%

26.5% 83.6% 31.6%

82.3%

75.7% 97.6% 77.6%

29.9%

Although the average coverage of people in poverty under market income is high,

there are differences among the different groups of households. The least covered are the

individuals living in households without children and elders who are from 38% to 47%

covered depending on the poverty line. They represent a low proportion of individuals in

poverty –between 7% and 8% depending on the poverty line- who are far from being fully

covered by monetary transfers.

A higher coverage by monetary transfers is experienced by individuals who live in

households with children: around 83% of them are covered. Considering that they

represent more than 60% of people in poverty, the evidence shows that the biggest group

in poverty is highly covered by cash transfers. However, children are even more likely to be

covered when they are living in households with elders as well. This happens because

elders benefit from more extensive coverage than children. Households with elders and no

children and households with both of them have a higher coverage by monetary transfers

than households without older persons. Around 96% of individuals in extreme poverty

living in households with elders are covered by public benefits and 93.4% of them if

moderate poverty is considered. This shows that the majority of monetary transfers are

focused on households with children or elders being recognized as vulnerable groups with

a higher probability of being in poverty.

In addition, the probability of exiting poverty conditional on being covered is also

higher for households with older persons. That means that elders not only have a higher

probability of receiving cash transfers but also those transfers are more effective in exiting

poverty. People living in households with elders have a probability of exiting poverty, given

being covered, equal to 96.3% for the international extreme poverty line, declining to

around 81% for the extreme poverty line and 61% for the national moderate poverty line.

The probability of leaving poverty conditional on being covered decreases considerably

with higher poverty lines but coverage, as we saw above, does not change too much

depending on poverty lines. This suggests that the decrease in the exit poverty rate is not

explained by a lower coverage of elders in poverty but monetary transfers lose the capacity

to lift them out of poverty when poverty rates are higher.

247

In the opposite scenario are individuals living in households with children in poverty

who have a probability of leaving poverty conditional on being covered that ranges from

50% for the international extreme poverty line to 17.5% for the national moderate poverty

line. These probabilities are much lower than those confronted by the rest of the

households and also lower than the average that range between 65% and 29.7% depending

on the poverty line considered.

Even though people living in households without children and elders are the lowest

covered by benefits they confront a higher probability of leaving poverty when they are

covered than people living in households with children and no elders. Let us see the

differences considering the national moderate poverty line. 34.5% of the 42.8% of

households without children and elders covered by cash transfers leave poverty as a

consequence of receiving benefits. This is completely different from the 17.5% of the

81.6% of households with children and no elders covered that leave poverty. This makes

them to have very similar poverty exit rates –between 14.3 and 14.8 percent- but with a

different story behind. This suggests the lower effectiveness of cash transfers in

households with children and no older persons than the rest of households.

This evidence shows that some households are more covered by monetary transfers

than others. In particular, households with elders are more covered by monetary transfers

than the rest. But there are also some households with a higher probability of leaving

poverty as a consequence of the monetary transfers that they receive. Again, households

with elders are in this group but also households without children and elders. This higher

probability can be arising for different reasons. First, it is possible that the number of

household members varies among household groups. Households with a higher probability

of leaving poverty can be smaller in size than the rest of the households covered by cash

transfers. In that case, monetary transfers do not need to be shared among many

inhabitants, increasing the income per person in a higher proportion and hence the

effectiveness of the cash transfers to move them out of poverty. A second reason can be

that poverty gaps are different among the household groups. Households with a higher

probability of leaving poverty can be closer to the poverty line making the monetary

transfers more effective in helping to lift them out of poverty. A third argument is that the

amount of monetary transfers received by the different groups of households can be

248

different. Again, these kinds of households can be receiving higher monetary transfers than

the rest of the individuals covered. In the following section, each argument is explored in

detail.

4.8.3.1.1 Household members

The smaller size of some households can explain the fact that they are exiting poverty

in a higher proportion as a consequence of monetary transfers. Under this hypothesis,

monetary transfers are more effective in exiting poverty because they are shared among

fewer household members increasing the per capita income proportionally more than the

average. This is true but it is not the most important thing to explain the higher

effectiveness of monetary transfers in contributing to exit poverty.

Households with older people have a different household size depending on whether

there are children or not. Households with elders and children are the biggest in size

having on average 5 members -Figure 4-18- Households with elders but no children are the

smallest in size being formed by 2.2 inhabitants on average, well below the average

household size of 3.2 inhabitants in the population.

Figure 4-18 Household size, all households, by household type


0,0

2,0

4,0

6,0

8,0

10,0

12,0

14,0

16,0

18,0

withchildren no

elders

with eldersno children

withchildren and

elders

withoutchildren and

elders

All thepopulation

Mean

sd

min

max

249

The composition of these two kinds of households is also different. Households with

children and older persons are the biggest in size because they are households

comprised of extended families. The majority of the households where children and older

persons live together are formed by the nuclear family –mother and/or father and

children- and some others who do not belong to the core family, such as grandparents,

other relatives or non-relatives. In more than 96% of the households with elders and

children live extended families –Table 4-12. This percentage is much lower in households

with elders and no children where extended families just represent around 25%. This kind

of household are highly single -25.8%- or comprised of nuclear families with one or two

parents.

Table 4-12 Household type by population groups, all households, Row percentage


Single households are even more important among households with elders who exit

poverty –Table 4-13. Households with elders and no children who exit poverty were more

single and less extended than the same kind of households among all the population. The

same happens with households without children and elders that exit poverty as a

consequence of monetary transfers. A higher proportion of households that exit poverty

are single person households and a lower proportion are those formed by extended

families. This suggests that household size matters because there is an over-representation

of single households among those who left poverty as a consequence of monetary

transfers.

single nuclear two-

parent

extended

two-parent

nuclear single-

parent

extended

single-parent

Total

Households with children 0.0% 48.9% 21.7% 18.5% 10.9% 100%

Households with elders 25.8% 38.2% 9.0% 12.2% 14.8% 100%

Households with children and elders 0.0% 3.1% 54.0% 0.4% 42.6% 100%

Households without children and elders 27.9% 44.2% 4.6% 15.0% 8.3% 100%

All the population 13.6% 41.8% 16.5% 14.8% 13.4% 100%

Population Groups

Household type

250

Table 4-13 Household type by population groups, exit poverty households


This fact is corroborated when the household size of households that exit poverty is

compared with household sizes in the population Figure 4-19. The majority of households

who have exited poverty –with the exception of households with children and no elders-

are smaller in size than the average. This suggests that having fewer household members

affects the effectiveness of monetary transfers in reducing poverty.

Figure 4-19 Household size, by household types, all households, households in poverty and exit poverty households


However, the smaller household size is not all that lies behind a higher probability of

leaving poverty. The household size of households with children and no elders and

households with both of them are very similar especially when households in poverty

single nuclear two-

parent

extended

two-parent

nuclear single-

parent

extended

single-parent

Total






Population Groups

Household type

0,0

1,0

2,0

3,0

4,0

5,0

6,0

All households Households inpoverty undermarket income

Exit povertyhousehold

with children no elders

with elders no children

with children and elders

without children andelders

All the population

251

under market income are considered. However, their exit poverty rates are very different -

17.5% and 40.1% under the moderate poverty line. The difference in household size

between these two kinds of households shrinks -4.2 and 4.6- considering just households

in poverty. The difference is even smaller among households who exit poverty as a

consequence of monetary transfers -4.3 and 4.5. This indicates that the higher probability

of leaving poverty of households with children and elders than households with children

and no elders is not because of differences in their household size. On average they need

to share the cash transfers among the same numbers of members. However, households

with elders and children are receiving more effective monetary transfers to exit poverty.

This can mean that they receive a higher amount of cash transfers and/or they are closer to

the poverty line. This will be explored in the following subsections.

4.8.3.1.2 Poverty Gap

Another possible explanation of a higher probability of leaving poverty as a

consequence of monetary transfers is a lower poverty gap. Households with a higher

probability of leaving poverty can be closer to the poverty line making the monetary

transfer more effective in helping to lift them out of poverty. The evidence presented here

does not support this argument.

It is observed that the intensity of poverty is different for the four groups of people.

The poverty gap before and after monetary transfers considering the national moderate

poverty line and the extreme poverty line are presented in Figure 4-20 and Figure 4-21

respectively. The highest intensity of poverty under market income -and both poverty

lines- belongs to individuals living in households with older persons and without children.

This is the same group of individuals with the lowest intensity of poverty under disposable

income –after fiscal intervention- and with the highest probability of leaving poverty as a

consequence of monetary transfers. This shows that a lower poverty gap is not behind a

higher probability of leaving poverty conditional on being covered by monetary transfers.

In addition, individuals living in these households -with elders and no children-

experience the highest reduction in their poverty intensity after cash transfers. They are

252

followed by individuals in households with elders and children. This indicates that

individuals in households with elders –with or without children- benefit most from

monetary transfers reducing the incidence and intensity of their poverty in a higher

proportion than the rest of the population.

Figure 4-20 Poverty gap national moderate poverty line, by type of household and income


Figure 4-21 Poverty gap national extreme poverty line, by type of household and income


0,051

0,038

0,057

0,016

0,046

0,029

0,000

0,010

0,020

0,030

0,040

0,050

0,060

0,070




All the population

0,021

0,013

0,030

0,004

0,020

0,010

0,000

0,005

0,010

0,015

0,020

0,025

0,030

0,035




All the population

253

This evidence shows that the poverty gap is not the explanation for the differences in

poverty exit rates among groups. The group of people who live in households with elders

has the highest poverty gap under market income but also the highest poverty exit rate

after monetary transfers. In the opposite, is the group who live with children. They are the

most important group of people in poverty under market income and they are less

intensely poor than the rest of the groups. However, they are the group with the lowest

poverty exit rate after monetary transfers.

The evidence so far suggests that the amount of monetary transfers plays a role in the

differences in exit poverty rate among groups.

4.8.3.2 The role of the amount and kind of monetary transfers

Taking into account that a lower household size and a lower intensity of poverty are

not the main drivers of the higher probability of leaving poverty that some households

show, the kind and amount of monetary transfers appear the most feasible explanation. In

order to explore this, the cash transfers were grouped in benefits for pensioners, for

families, for workers and others benefits. The detail of every monetary transfer in each

group was presented in Table 4-1 in Section 4.7. There are elaborated two groups of

benefits: one with 5 and the other with 10 possible combinations of benefits. Table 4-14

presents the distribution of individuals in each group. In addition, it presents the

distribution of individuals in each group distinguishing between all the individuals in the

population and those in poverty under market income.

254

Table 4-14 Benefits groups, all individuals and individuals in moderate poverty under market income


The majority of individuals that receive cash transfers from the government live in

households that do receive benefits for families and for families in extreme poverty and do

not receive pensions –Group II in Table 4-14. 36.4% of all individuals are in this group

and 3.2% receive in addition some benefits for workers or others. They represent in

conjunction almost 39.6% of the population and 56.8% of the population in poverty

before monetary transfers. This indicates that the majority of people in poverty receive just

cash transfers targeted to children and working age people instead of older persons.

In the opposite scenario, there is a group that receives just pensions and no other

benefits from the government –Group I in Table 4-14. They represent 7.1% of the

population and 9.8% of people in poverty before fiscal intervention. A small proportion

inside this group receives also benefits for workers and others –Group 2 in Table 4-14.

7.8% of the population and 14.4% of people in poverty receive a combination of

benefits from the government –Group IV. They receive benefits for families and benefits

for pensioners and in some cases, benefits for workers and/or other benefits. At the same

Benefits

for

pensioner

s

Benefits

for

families

and

Benefits

for

workers

Other

benefits

5 groups 10 groupsDistribution

5 groups

Distribution

10 groups

Distribution

5 groups

Distribution

10 groups

I 1 Yes No No No 6.8% 7.1% 9.4% 9.8%

2 Yes No 0.3% 0.4%

II 3 No Yes No No 36.4% 39.6% 53.5% 56.8%

4 No Yes 3.2% 3.3%

III 5 No No Yes No 0.7% 1.3% 0.4% 0.9%

6 No No No Yes 0.6% 0.5%

7 No No Yes Yes 0.0% 0.0%

IV 8 Yes Yes No No 7.1% 7.8% 13.4% 14.4%

9 Yes Yes 0.6% 1.0%

V 10 No No No No 44.3% 44.3% 18.1% 18.1%

Total 100% 100% 100% 100%

Population Groups

Yes in one of these

Yes in one of these

Yes in one of these

Social Assistance Programmes

All population People in poverty

255

time, a small proportion of people in poverty under market income receive benefits for

workers and/or other benefits; and no benefits for families and benefits for pensioners –

Group IV. This shows that the majority of people in poverty -81%- receive benefits for

families and/or benefits for pensioners.

A not small 18.1% of people in poverty do not receive any of these government

benefits that represent 44.3% of the population.

In order to see the importance of these groups of benefits in reducing poverty, their

poverty exit rate, P(Em,d), is presented in Table 4-15. In addition, the poverty exit rate is

decomposed as was presented in equation (3) between the probability that a person in

poverty is covered by a program of the group i -P(Ci)- and the probability that those

covered by this group of benefits leave poverty –under both extreme and moderate

poverty lines.

It is observed that the highest contribution to the total exit poverty rate is provided by

the Group II of benefits composed of benefits for families, eventually benefits for workers

and others but no pensions. This is mainly because these benefits reach the majority of

people in poverty -53.6% in extreme poverty and 56.8% in moderate poverty. Among the

Group II is the subgroup 3 composed just by benefits for families that contribute to this

high coverage. Individuals who are receiving benefits for families and benefits for workers

or other benefits are a smaller proportion -2.6%. However, they have a higher probability

of leaving poverty than individuals just receiving benefits for families. In this case, the

combination of benefits increases the probability of exiting poverty.

A higher effectiveness of benefits for reducing poverty is observed also in the

combination of benefits in Group IV. The probability of leaving poverty conditional on

receiving benefits for families and pensioners is higher than when just benefits for families

are received. The coverage of this combination of benefits is lower than the coverage of

Group II but is much more effective in reducing poverty. This indicates that the benefits

for pensioners make the difference between these two groups increasing considerably the

probability of leaving poverty as a consequence of benefits.

Table 4-15 Decomposition of the exit rate from poverty, national extreme and moderate poverty lines, 5 and 10 groups of programmes

All the population

0.428 0.430 0.843 0.844 0.507 0.509 0.243 0.244 0.819 0.819 0.296 0.297

I

0.091 0.092 0.125 0.126 0.729 0.734 0.048 0.049 0.098 0.099 0.494 0.498

1

0.086 0.087 0.119 0.120 0.720 0.725 0.046 0.046 0.094 0.095 0.484 0.488

2

0.005 0.005 0.005 0.006 0.913 0.927 0.003 0.003 0.004 0.004 0.712 0.729

II

0.189 0.190 0.535 0.537 0.352 0.355 0.101 0.102 0.567 0.569 0.178 0.179

3

0.174 0.175 0.509 0.511 0.341 0.343 0.088 0.089 0.534 0.535 0.164 0.166

4

0.015 0.015 0.025 0.026 0.576 0.588 0.013 0.013 0.033 0.034 0.392 0.399

III

0.005 0.005 0.008 0.008 0.616 0.637 0.003 0.003 0.009 0.009 0.362 0.375

5

0.000 0.000 0.002 0.002 0.065 0.087 0.000 0.000 0.004 0.004 0.066 0.076

6

0.005 0.005 0.005 0.006 0.839 0.858 0.003 0.003 0.005 0.005 0.619 0.636

7

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

IV

0.134 0.135 0.174 0.175 0.769 0.772 0.077 0.078 0.144 0.144 0.538 0.541

8

0.125 0.126 0.163 0.165 0.761 0.765 0.070 0.071 0.134 0.134 0.525 0.528

9

0.009 0.009 0.010 0.010 0.877 0.889 0.007 0.007 0.010 0.010 0.712 0.723

V

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.9% 1.0% 88.3% 0.7% 1.0% 71.7%

0.0% 15.7% 0.0% 0.0% 18.1% 0.0%

13.4% 17.4% 77.0% 7.8% 14.4% 54.0%

12.5% 16.4% 76.3% 7.1% 13.4% 52.6%

0.5% 0.6% 84.9% 0.3% 0.5% 62.8%

0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

0.5% 0.8% 62.7% 0.3% 0.9% 36.9%

0.0% 0.2% 7.6% 0.0% 0.4% 7.1%

17.4% 51.0% 34.2% 8.8% 53.5% 16.5%

1.5% 2.6% 58.2% 1.3% 3.3% 39.6%

0.5% 0.6% 92.0% 0.3% 0.4% 72.0%

18.9% 53.6% 35.3% 10.1% 56.8% 17.9%

4.9% 9.8% 49.6%

8.7% 12.0% 72.3% 4.6% 9.4% 48.6%

Poverty Lines

Population Groups

National Extreme National Moderate

P(Em,d) P(Ci) P(Em,d/Ci) P(Em,d) P(Ci) P(Em,d/Ci)

0.429 0.843 0.508 0.243 0.819 0.297

9.2% 12.5% 73.2%

The importance for pensions to exit poverty is also observed in Group I and in group

1 in particular. The 48.6% of people receiving just pensions –who represent 9.4% of

people in moderate poverty- exit poverty as a consequence of this benefit. This percentage

is greater for extreme poverty: the 72.3% of people in extreme poverty exit poverty as a

consequence of benefits for pensioners –group 1. This shows that the higher effectiveness

of monetary transfers in exiting poverty came mainly from benefits for pensioners.

Table 4-16 shows the same decomposition of the poverty exit rate by groups of

benefits but also by groups of households. This allows us to observe which groups of

benefits the different households receive and their relative importance in helping them to

exit moderate poverty.

People living in households with children and no elders mostly receive benefits for

families and benefits for workers and/or other benefits. Although 80% of them receive

these benefits only 16.9% leave poverty because of them. The cash transfers that they

receive are not enough to lift the average size household of 4.2 members out of poverty.

They are not very far from the poverty line but the amount that they receive appears to be

insufficient to help the majority of them to exit poverty.

The opposite happens in households with older persons but no children. They are little

covered just by benefits for families. They are more covered by benefits for pensioners -

44.8%- or by benefits for families and for pensioners -40.7%. In their case, the probability

of exiting poverty as a consequence of these benefits is much higher than the rest of the

groups. 54.4% of people receiving benefits for pensioners –Group I- leave poverty and

63% of those receiving benefits for pensioners and for families exit poverty as a

consequence of monetary transfers. This shows again the complementarity of these

benefits in increasing the probability of exiting poverty.

The majority of households with children and older persons receive benefits for

pensioners and benefits for families. 56.8% of them receive these benefits and/or benefits

for workers and other benefits. The probability of living poverty as a consequence of these

benefits is equal to 45.6%. As was described previously, the household sizes of households

with children and households with children and older persons in poverty were very similar

-4.2 and 4.6 respectively. However, people living in households with children and older

258

persons have a higher probability of leaving poverty than people living in households with

children and no elders. This evidence shows that this difference is mainly explained by the

benefits for pensioners that households with elders receive. The benefits for pensioners

captured in Groups II and IV increase the exit poverty rate of households with children

and older persons. So, it is the benefits for pensioners which are contributing the most to

increasing the probability of exiting poverty for those living in households with children

and older persons. In addition, when these benefits are combined with benefits for families

the exit poverty rate increases and it is almost equal to the exit poverty rate of households

with elders but no children.

Households in poverty with older persons have the most extensive coverage. Only

6.6% are non-covered when they live without children and 6.7% when they live with

children. However, the 18.4% of people living in households with children but no older

persons are non-covered by benefits. This shows an age bias also in the coverage of

benefits. The lowest coverage of benefits is presented by households without children and

elders. This indicates that children and older persons represent the focus of monetary

transfers. However, households with older persons are more covered by benefits and have

a higher probability of leaving poverty as a consequence of the benefits. The main

explanation for this is the amount of the pensions that they receive.

Table 4-16 Decomposition of the exit rate from poverty, national moderate poverty lines, 5 groups of programmes, groups of households


All the population group

0.142 0.143 0.815 0.816 0.175 0.176 0.571 0.574 0.933 0.935 0.611 0.615 0.375 0.379 0.932 0.934 0.402 0.406 0.146 0.149 0.426 0.430 0.341 0.348

I

0.001 0.001 0.002 0.002 0.485 0.521 0.243 0.245 0.447 0.450 0.542 0.546 0.048 0.050 0.163 0.165 0.296 0.304 0.013 0.014 0.019 0.021 0.673 0.702

II

0.134 0.135 0.798 0.799 0.168 0.169 0.021 0.022 0.067 0.069 0.314 0.325 0.037 0.038 0.196 0.199 0.188 0.194 0.103 0.106 0.373 0.377 0.275 0.281

III

0.001 0.001 0.007 0.008 0.138 0.151 0.008 0.008 0.010 0.011 0.731 0.757 0.002 0.003 0.003 0.003 0.775 0.826 0.011 0.012 0.022 0.023 0.487 0.517

IV

0.004 0.004 0.008 0.008 0.448 0.465 0.255 0.258 0.406 0.409 0.628 0.632 0.257 0.261 0.566 0.570 0.454 0.459 0.006 0.007 0.010 0.011 0.603 0.644

V

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

0.2% 0.3% 80.1% 1.1% 2.2% 50.2%

25.9% 56.8% 45.6% 0.7% 1.1% 62.3%

Households with children and eldersHouseholds without children and

elders


Population groups

0.0% 18.4% 0.0% 0.0% 6.6% 0.0% 0.0% 6.7% 0.0% 0.0% 57.2% 0.0%

0.4% 0.8% 45.6% 25.7% 40.7% 63.0%

0.1% 0.8% 14.4% 0.8% 1.1% 74.4%

13.5% 79.8% 16.9% 2.2% 6.8% 32.0% 3.8% 19.8% 19.1% 10.4% 37.5% 27.8%

14.3% 81.6% 17.5% 57.3% 93.4% 61.3% 37.7% 93.3% 40.4% 14.8% 42.8% 34.5%

4.9% 16.4% 30.0% 1.4% 2.0% 68.8%

Group of ProgrammesHouseholds with children Households with elders


0.1% 0.2% 50.3% 24.4% 44.8% 54.4%

4.9 Conclusions

The protection that different age groups should receive is under debate. The evidence

shows that different age groups are protected from poverty and destitution dissimilarly. The

discussion in the literature has acknowledged that the age-related bias of welfare institutions

creates a generational inequity in the allocation of public resources. Considering the fact that

experiencing poverty during childhood has not only negative consequences in developmental

outcomes in the present but also generates damaging effects continuing over the whole life of

an individual, the discussion becomes even more relevant. Where public resources are allocated

is in response to normative decisions taken by governments. This research provides evidence

for this discussion through the analysis of cash transfer allocations among age groups. This

research has investigated the age-bias of cash transfers in a high-income country like Chile, in

particular, in relation to two vulnerable groups: children and older persons.

The relevance of Chile as a case study emerges as a consequence of the higher poverty

incidence and vulnerability to poverty in children than among older populations that it

displays. The role of Social Assistance programmes in these disparities was explored by this

study. A partial fiscal analysis using the framework proposed by Commitment to Equity (CEQ)

(Lustig & Higgins, 2016) was applied to the 2015 National Socioeconomic Characterization

Survey (CASEN). The analysis measured the poverty exit rate as a consequence of fiscal

intervention concentrating the analysis on direct taxes and cash transfers.

A first finding of the study is confirmation of the age bias of cash transfers in Chile. Both

conditional and unconditional cash transfers reduce in a higher proportion the poverty rates

among older persons than among children. The results indicate that the reduction of poverty

after monetary transfers among older persons is 4 times the reduction of poverty among

children. Even when both age groups are over-represented among the population in poverty

before fiscal intervention, older groups move to being under-represented in poverty after

monetary transfers while children remain over-represented among the population in poverty.

This indicates that although both vulnerable groups are highly represented in poverty when

only incomes generated by their families are considered, the fiscal intervention alters this

situation. The vulnerability of older groups is highly reduced through monetary transfers

moving a high proportion of them out of poverty. In contrast, monetary transfers are not as

261

effective in moving children out of poverty as they are with the older group. Fiscal

intervention allocates monetary transfers in a way that generates intergenerational inequality.

The results indicate that the effectiveness of cash transfers in increasing exit poverty is

biased towards households where older persons live, and not only biased to older individuals

alone. People living in households with older persons and no children are those with the

highest poverty exit rate after monetary transfers. In contrast, those who live in households

with children and no elderly have the lowest poverty exit rate after monetary transfers. People

living in households with both children and older persons are in between them. This shows

that the effectiveness of monetary transfers in moving people out of poverty is in connection

with the age composition of the households.

The different reasons that might explain the higher effectiveness of cash transfers in

increasing exit poverty in households with older persons were explored: amount of monetary

transfers; size of the households; and poverty gap of households. The results show that the

most important reason is the higher amount of cash transfers that households with older

persons receive. The main cash transfer behind this is the non-contributory pension called

Pensión Basica Solidaria (PBS). The smaller size of households with older persons than

households with children is also part of the explanation behind higher poverty exit rates

among the former. However, households with children and older persons together, which are

those with the largest size of household, have a much higher exit poverty rate than households

with children and no older persons. Having older people at home is more important than

having fewer members at home. Moreover, older persons receiving pensions allows members

of their households to exit poverty even when poverty gaps are much higher in those

households than in households with children.

The fact that the amount of the monetary transfers matters in their effectiveness in

moving people out of poverty is also observed through the complementarity of benefits. The

results show that the exit poverty rate is higher when more than one monetary transfer is

received. Benefits are complementary in increasing the exit poverty rate. The results show that

the combination of benefits for families with benefits for workers or other benefits increases

the exit poverty rate of households with children and no older persons. The same happens

when benefits for families are combined with pensions. Considering the fact that the coverage

262

of monetary transfers is high among households with children and those with older persons,

the amount of monetary transfer received by equivalent persons appears crucial to

understanding their effectiveness in reducing poverty. The evidence of this paper supports the

argument that the amount of benefit that each individual receives is an important element in

increasing the exit poverty rate and not only the coverage of cash transfers among people in

poverty. The findings show that the coverage of cash transfers and their effectiveness in

reducing poverty have an age bias towards households with older persons rather than

households with children. Overall, this study provides further evidence that the effectiveness

of cash transfers in reducing poverty depends on the kind and amount of the cash transfer,

and those, in the case of Chile, are strictly in connection with the age composition of the

household. These findings have clear policy implications. First, the discussion of the age bias in

the provision of public resources must be incorporated in the design of any social protection

programme. The findings confirm an age bias in the allocation of monetary transfers creating a

generational inequity which was not present before the fiscal intervention. Normative

decisions regarding which vulnerable groups should be more or less protected should consider

the intergenerational inequities that fiscal intervention can generate.

Second, policy makers should consider the effectiveness of the complementarity of

monetary transfers in reducing poverty. Fiscal interventions must be considered in their

conjunctions and not just separately in order to identify their effectiveness. Adding further

protection for children living in households without older persons is an option in the context

in which children living with grandparents are more protected against poverty.

The fact that experiencing poverty during childhood has long lasting consequences on

adult life increases the urgency of child poverty reduction. Children and older people are

vulnerable groups over-represented in poverty before fiscal intervention in Chile. The fact that

monetary transfers create an intergenerational inequity of allocation of resources is a topic that

must be discussed. Any anti-poverty programme must incorporate a discussion of age-related

effectiveness.

263

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4.11 Annexes

4.11.1 The new methodology for measuring poverty in Chile and recent

figures of poverty reduction under both methodologies

The new methodology for measuring poverty implemented in Chile since 2013 considers

household consumption equivalence scales; this means that the expenditure to satisfy the food

and non-food needs increases less than proportionally than the increase of number of

members of households. A household is in poverty if its monthly income by equivalent person

(instead of per capita) is lower than ‘the poverty line by equivalent person’ (instead of the

poverty line per capita). The poverty line represents the amount of income necessary to satisfy

the food and non-food basic needs of an equivalent person. In the same way, an individual is

in extreme poverty when his monthly income by equivalent person is under the ‘extreme

poverty line by equivalent person’. Under the new methodology, the extreme poverty line has

been established as 2/3 of the poverty line value.

The poverty line and the extreme poverty line are estimated from the cost of a basic food

bundle that must satisfy the minimum requirement of calories and reflect the consumption

habits of the Chilean population. The new methodology estimated a new basic food bundle

from the VIIth Family Budget Survey made by the National Institute of Statistics between

2011 and 2012. In this bundle are included the minimum of calories84 and proteins85 needed

nutritionally per day, advised by the World Health Organisation and the Food and Agriculture

Organisation, considering the consumption habits of the Chilean population86. The prices are

updated by the Consumer Price Index in order to update the poverty lines as well.

Figure 4-1 shows the moderate and extreme income poverty rates between 1990 and 2015

using both methodologies. The new methodology of 2013 was applied retrospectively to 2006

to see the evolution from that point on. Chile has shown a considerable decrease in poverty

during the last two decades. Using the ‘traditional methodology’, the poverty headcount rate

84 The minimum is equal to 2,176 kcal 85 The minimum is equal to 54,612 gr 86 A 2,187 kcal per person per day is the average used by Economic Commission for Latin America and the Caribbean (ECLAC).

268

among individuals declined from 38.6% in 1990 to 7.8% in 2013, and extreme poverty declined

from 13.0% to 2.5% during the same period of time87. The ‘new methodology’ indicates that

3.5% of the population is living in extreme poverty and 11.6% in moderate poverty in 2015.

Although poverty levels had constantly decreased, still a considerable proportion of the

population do not have an enough income to satisfy basic needs.

Figure 4-22 Percentage of people in income poverty by New Methodology (2006-2013) and Traditional Methodology (1990-2013)


87 Source: Ministry of Social Development.

38,6

32,9

27,6

23,221,7

20,218,7

13,711,4 10,9

7,8

29,1

25,322,2

14,411,613

9 7,65,7 5,6 5,6 4,7

3,2 3,6 3,1 2,5

12,69,9

8,1

4,5 3,50

5

10

15

20

25

30

35

40

45

1990 1992 1994 1996 1998 2000 2003 2006 2009 2011 2013 2015

old poverty line new poverty line

old extreme poverty line new extreme poverty line

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4.11.2 Dimensions, indicators and cut-offs of the multi-dimensional

poverty index used in Chile

People are in multi-dimensional poverty if they are deprived in at least three indicators of

the following four dimensions; education, health, work and social security, or dwelling; or if

they are deprived in the three indicators of Networks and Social Cohesion and two indicators

of one or two of the rest of the dimensions. This distinction is made because the dimension

Network and Social Cohesion was only included in the measures in 2015 while the other four

dimensions had been included since 2013. Altogether, this means being deprived in more than

22.5% of the total indicators (Ministerio de Desarrollo Social, Chile, 2015a).

Table 4-17 Dimensions, indicators and cut-offs of the multi-dimensional poverty index used in Chile

Dimensions Indicators Deprived if…

Education

School attendance Any school-aged child (between 4-18 years) is not attending school

Years of schooling Any household member has less years of schooling according to their age by law

School lag Any 21 year old or less household member attend school with a lag of two years or more

Health

Nutrition Any child between 0 and 6 years is malnourished (malnutrition or obesity)

Access to health services

Any member is not affiliated to any health system and do not have health insurance

Health attendance Any member did not receive health services during the last 3 months because of reasons beyond their control

Employment and Social Security

Occupation Any member over 18 years is unemployed

Social Security Any member over 15 years employed is not paying social security

Pensions Any member in retirement-ages do not receive any contributory or non-contributory pension

Housing and environment

Housing conditions

Overcrowded household (more than 2.5 people per bedroom) or household in bad conditions (floor, ceiling, walls in bad conditions)

Basic Services Household without basic sanitary services (WC, water according to urban and rural standards)

Environment a.) 2 or more pollution problems in area of residence or b) no household member is employed and no basic services (health, education and transportation) are offered in their area, or c) no basic services (health, education and transportation) are offered in their area and the commute in public transportation of their employed members takes more than an hour

Networks and Social Cohesion

Social Participation

Household has not counted with any member outside the household in 8 situations of care and support; and no household member over 14 years has participated in any social groups during the last 12 months; and no households members over 18 years and employed participate in any organization related with their job

Equal treatment Any member declares being discriminated or unfairly treated during the last 12 months for any reasons described in the question.

Security Any member have experienced or witnessed drug trafficking or shootings in their areas during the last 12 months

Source: Author’s elaboration from (Ministerio de Desarrollo Social, Chile, 2015)

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4.11.3 Description of the Direct Cash Transfers considered in the analysis:

4.11.3.1 Benefits for Families

Aporte Familiar Permanente (AFP). This is a cash transfer which is part of the

Social Security System paid in March of every year to help lower income families

to cope with the higher expenditure at the beginning of the year. There are two

groups of people receiving this transfer. The first group is made up of households

which already receive ‘Asignacion Familiar o Maternal’ or ‘Subsidio Familiar (SUF)’.

The second group is formed by households who belong to the ‘Chile Solidario’ or

‘Seguridades y Oportunidades’ sub-systems. The households who belong to the first

group receive 43,042 Chilean Pesos (at 2016 values)88 for each family dependent

(who is entitled to the benefit). However, the households that belong to the

second group receive the same amount of transfer but only one of them. In this

case the entitlement to the benefit belongs to the family which participates in the

above-mentioned programmes. In cases in which a household belongs to both

groups belonging to group A takes priority.

Subsidio Unico Familiar (SUF). This is a monthly cash transfer to low income

individuals without access to the Asignacion Familiar transfer because they are not

dependent workers affiliated to a benefit system (beneficiaries). The cash transfer

is provided for each family dependent younger than 18 years old (who is the

causative of the benefit89) that also has the right to medical and dental attention.

The SUF is not compatible with the following cash transfers: ‘Asignacion Familiar’,

‘Pension Basica Solidaria’ and ‘Subsidio de Discapacidad Mental’ for children younger

than 18 years old. The cash transfer is equal to 10,844 Chilean Pesos (2017

values). The SUF is provided for 3 years after which it can be renewed if the

conditions still remain90. The allowance is provided until the 31st December of the

88 The amount of the transfer is adjusted by 100% of the variation of the Consumer Price Index (CPI) on 1st March every year. 89 They provide one allowance only even when more than one beneficiary is entitled to it. . 90 However, the municipality can check at any time if the conditions still remain.

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year in which the boy, or the girl teenager turns 18 years old. Children who are

entitled to the allowance must satisfy the following requirements:

- Belong to the Registro Social de Hogares and to the 60% most vulnerable of the

population with respect to the Calificacion Socioeconomica.

- The boys, girls and teenagers entitled to the allowance cannot receive a salary from

any source equal or higher than the SUF 91.

- The girls and boys must verify every year that they have participated in all the

health programmes for children up to 8 years old established by the Ministry of

Health.

- Children between 6 and 18 years old must verify that they are regularly attending

students of public schools or schools recognized by the State.

Subsidio Familiar (SUF) Mujer embarazada. This monthly cash transfer is

paid throughout pregnancy for women earning low incomes (the causatives of the

benefit) who are not receiving ‘Asignacion Maternal’. They have the right to free

medical and dental attention. This allowance is for over 20 weeks pregnant

women who belong to the Registro Social de Hogares (RSH) and the 60% of

lower income or higher socioeconomic vulnerability with respect to the

Clasificacion Socioeconomica (CSE) who are not receiving the SUF a la Madre.

They cannot receive a salary from any source equal or higher than the SUF92.

The cash transfer is equal to 10,844 Chilean Pesos (2017 values) and the payment

is retrospective meaning that the first months of pregnancy are included in the

first payment. The following payments of the allowance are monthly. The women

who receive the ‘SUF Mujer Embarazada’ cannot receive benefits such as the

‘Asignacion Familiar o Maternal’, ‘Pension Basica Solidaria’ and/or the ‘Subsidio de

Discapacidad Mental’ for individuals younger than 18 years old. If they are entitled

to the ‘Asignacion Familiar and SUF’ they will need to choose between them. Once

the baby is born and before the baby will be 3 months old, he or she will receive

91 Except for the orphan’s allowance which is not taken into consideration in this respect. . 92 Except for the orphan’s allowance which is not taken into consideration in this respect.

272

the SUF al Recien Nacido. After 3 months old, the baby becomes the one entitled

to the benefit instead of the mother.

Subsidio Familiar para personas con Certificacion de Invalidez y con

Discapacidad mental. This cash transfer, also known as “SUF duplo”, is

provided to low income households that have a member with physical or mental

disability (who the entitlementto the benefit is attached to) and that are not

receiving ‘Asignacion Familiar’. Entitlement to the SUF comes with the right to

receive free medical and dental care. The SUF is not compatible with the

following benefits: ‘Asignacion Familiar’, ‘Pension Basica Solidaria de Invalidez (PBSI)’

and ‘Subsidio de Discapacidad Mental para personas menores de 18 anos’. If they are

entitled to both ‘Asignacion Familiar’ and this ‘SUF’ they must opt for only one of

them. The amount of the cash transfer is 21,688 Chilean Pesos per month (2017

values). This SUF is provided for 3 years and it can be renewed after that. The

person entitled to the benefit must have medical certification of their mental or

physical disability. They must belong to the 60% of the population on lower

incomes and in higher socio-economic vulnerability with respect to the

Socioeconomic Characterization (CSE). Those entitled to the allowance cannot

receive a salary from any source equal or higher than the SUF93. Girls and boys

must participate in all the health programmes for children established by the

Ministry of Health until they are 8 years old.

Subsidio de Discapacidad Mental para personas menores de 18 anos. This

is a monetary transfer that every month is provided to boys, girls and teenagers

(under 18 years old) with mental disabilities who belong to the most vulnerable

and lower income families of society. This allowance provides also the right to

free medical care for the entitled children. The children must have been registered

in the ‘Registro Social de Hogares’ and be living in a household that is among the

20% most vulnerable households in the Country. The amount of the cash

transfer is equal to 66,105 Chilean Pesos (2017 values).

93 Except for the orphan’s allowance which is not taken into consideration in this respect.

273

Asignacion Familiar. This is a variable cash transfer depending on the salary of

the workers or pensioners provided for every legal dependent of them. This

benefit is incompatible with the SUF. The beneficiaries of ‘Asignacion Familiar’ are

the following:

Dependent workers in the public or private sector; Independent workers;

Recipient of any social security benefit; Widow’s Pensioners and the mother of

the children of a worker or pensioner receiving benefits because of an industrial

injury or a work-related illness; State Institutions or those recognized by the State

where children live; Individuals who look after children under 18 years old

because of a court decision.

The dependent individuals who are entitled to the benefits are the following:

- Wives

- Children under 18 years old who are regular students in school and young students

between 18 and 24 years old who must verify their regular attendance of studies.

- Grandchildren living with their grandparents and without their parents.

- Widowed mothers

- Parents, grandparents or other ancestors older than 65 years old.

- Children living in State Institutions or those recognized by the State.

- Children living with individuals because of a court decision.

Individuals entitled to the benefit must not earn a salary from any source equal or

higher than 50% of the Monthly Minimum Income which is 132,000 Chilean Pesos94

(2017 values). The amount of the cash transfer depends on the salary of the

beneficiary as is shown in the following table:

94 Except for the orphan’s allowance which is not taken into consideration in this respect.

274

CT Amount

Salary

Minimum Maximum

10,844 0 277,016

6,655 277,017 404,613

2,104 404,614 631,058

0 631,058

Values for the period between 01 January to 30 June 2017

If one of the recipients of the benefit also has a disability certification, the

amount of the cash transfer is double the amount reported in the table.

Asignación Maternal. This is a monthly cash transfer for pregnant women

working as a dependent or independent who are affiliated to a provisional

regimen and also for men who have a pregnant wife. Pensioned women or

pregnant women who are the wife of a pensioner are not entitled to receive this.

The amount of the cash transfer is provided every month throughout the

pregnancy and it is the same as provided by the ‘Asignacion Familiar’. The

beneficiaries can demand the benefit once they are 20 weeks pregnant. The

benefits of the first five months are paid retrospectively.

Pensión Básica Solidaria de Invalidez. This is a cash transfer focused on

individuals between 18 and 65 years with disabilities that do not allow them to

work. They receive this cash transfer if they do not receive pension from any

provisional regime. After 65 years old they have access to the ‘Pensión Básica

Solidaria de Vejez’. The beneficiaries must be in the ‘Registro Social de Hogares’ and

living in a household that belongs to the 60% of the lowest provisional target

score (‘Puntaje de Focalizacion Previsional’). The amount of the pension is

102,897 (until 30 June 2017) being adjusted by the PCI on the 1st July of every

year.

Aporte Previsional Solidario de Invalidez. This is a monthly cash transfer that

supports individuals with a disability who are receiving a low amount of disability

pension. The amount of this supplementary contribution is the amount extra

needed to reach the level of the ‘Pension Basica Solidaria de Invalidez’ equivalent to

102,897 (until 30 June 2017). The beneficiaries must satisfy the same conditions

275

required by the ‘Pension Basica Solidaria de Invalidez’. The main difference in this

case is that the individuals have a disability pension from their contributory

pensions. However, this pension is lower than the PBSI and the State contributes

the difference needed to get an amount equivalent to the PBSI.

Asignacion por Muerte. This is a unique payment to help families in the case of

the death of one of their members. The deceased giving rise to the benefit must

have been receiving PBS or APS and have not been entitled to death benefit in

any other social security system. In addition, he or she must have been the

beneficiary of any allowance or have been pensioned or have made a contribution

to social security at some point in the previous six months. Any of these

requirements is needed to receive 510,888 Chilean pesos (2017 values) for the

family.

Subsidio al Consumo de Agua Potable. This is a discount on the monthly

water bill for families on the ‘Registro Social de Hogares’ who spend more than 3%

of their incomes on this service. This benefit is provided without the over 3%

requirement if the household belongs to the ‘Chile Solidario’ or ‘Oportunidades y

Seguridades’ programmes. The benefit is a variable percentage of the bill with a

maximum of 15 cubic metres monthly. This percentage depends on the region

and county in which the household is located and also the household’s

vulnerability depending on its ‘Calificacion Socioeconomica’ (below or above the

40% vulnerability threshold). The ‘Chile Solidario’ or ‘Oportunidades y Seguridades’

households receive 100% of their bill with a maximum of 15 cubic metres.

Subsidio Calefaccion para Aysen. This is a monetary contribution to the

expenditure on heating for households located in Aysen Region. This subsidy is

provided only once a year per household in the ‘Registro Social de Hogares’ which

belong to the 80% of lowest incomes and highest vulnerability with respect to the

‘Calificacion Socioeconomica’. This subsidy corresponds to 100,000 Chilean

Pesos (2017 values).

276

4.11.3.2 Benefits for Households in Extreme Poverty (Chile Solidario and Sistema de

Igualdad y Oportunidades)

Households living in extreme poverty are targeted to offer to them Conditional

Cash Transfers (CCT). They have access to other benefits and subsidies from the

State.

Bono Control del Niño Sano. This cash transfer is directed to families who

belong to the Ingreso Etico Familiar Programme and have started the Social Support

and/or the Job Support Programme or are receiving the Bono Base Familiar because

they belong to the Chile Solidario Programme. The main objective of this cash

transfer is to promote the fulfilment of regular medical check-ups (control del niño

sano) of the children in the participating households. This is a monthly cash

transfer of 6,000 Chilean pesos per child younger than 6 years. They must have

up to date medical check-ups and verify them at the local municipality. They will

receive this bonus for a maximum of 24 months.

Bono por Asistencia Escolar. This cash transfer is directed to families who

belong to the Ingreso Etico Familiar Programme and have started the Social Support

and/or the Job Support Programme or families who are receiving the Bono Base

Familiar from the Chile Solidario Programme. They must have children between 6

and 18 years going to school regularly (no less than 85% of assistance). This is a

monthly cash transfer of 6,000 Chilean pesos per child which is received after the

Ministry of Education has confirmed the fulfilment of the minimum of

attendance required. This bonus is received for a maximum of 24 months.

Bono Logro escolar. This is focused on families who belong to the Ingreso Etico

Familiar Programme and to the 30% most vulnerable sector of the population

who have children with a good performance in school. In order to know if they

belong to this particular vulnerability group information is used from the Proxy

Means Test Ficha de Proteccion Social and the income of the family during the last 12

months from the register of the Administradora de Fondos de Cesantia. Good

performance in schools is defined as belonging to the higher 30% of the class

grades. The children must be between the 5th and 12th grades. In order to assess

277

good performance the ranking of the students in the class the year before is

considered. This information is collected by the Ministry of Education who

provide this information to the Ministry of Social Development who finally

determine the beneficiaries of the bonus for school achievement. The amount of

the cash transfer depends on the level of achievement. The 30% of students with

the highest scores are divided into two halves. The first half is composed of the

students with the highest scores who receive 56,253 Chilean pesos (value as set in

the 2015 round). The second-best group of students receive 33,752 Chilean

pesos. This cash transfer is received once per year.

4.11.3.3 Benefits for Pensioners

Pension Basica Solidaria (PBS). This is a cash transfer for individuals from 65

years old onwards who do not receive any other pension from a social security

system. They must be part of the ‘Registro Social de Hogares’ and belong to the 60%

of households with the lowest score of the provisional focalization instrument.

They must have been living in Chile for at least 20 years, continuously or

discontinuously. The amount of the cash transfer is equal to 102,897 Chilean

pesos per month (values until 30 June 2017).

Aporte Previsional Solidario de Vejez (APS). This is a cash transfer to

improve the pensions of pensioners in the private system who do not have a

good pension. It is for individuals from 65 years onwards that receive a pension

from AFP, an insurance company, or the former Social Security System (IPS)

lower than 304,062 (values until 30 June 2017). This value represents the

Maximum pension with a supplementary previsional contribution (PMAS). They

must be part of the ‘Registro Social de Hogares’ and to belong to the 60% of

households with the lowest score of the provisional focalization instrument. They

must have been living in Chile for at least 20 years, continuously or

discontinuously. The amount of the cash transfer depends on the base pension of

the beneficiary. Pensions lower than 102,897 (the PBS value) are going to receive

the difference up to the PBS level. Pensions between the PBS and PMAS levels

278

receive the difference between the PBS and a percentage of the base pension of

the beneficiary.

Bono Invierno. This is a cash transfer for low income pensioners facing higher

expenditure during winter. It is for pensioners in the Social Security System (IPS),

Dirección de Previsión de Carabineros de Chile (Dipreca), Caja de Previsión de la

Defensa Nacional (Capredena), AFP and insurance companies receiving APS,

and PBS beneficiaries. The amount is equal to 57,353 pesos (2016 values).

Bono por Hijo. This is a monetary transfer to a woman’s individual national

insurance account for each child born or adopted with the aim of increasing her

pension amount. It is paid once per child.

Bono Bodas de Oro. This is a one-off payment for couples or widows that have

reached their50th wedding anniversary.They must belong to the ‘Registro Social de

Hogares’ and to the 80% of lowest incomes and highest vulnerability with respect

to the ‘Calificacion Socioeconomica’. The couple must still be living together. If

one of them has died, the widow will receive half of the benefit. The amount is

equal to 307,516 pesos (2017 values) which is split in two equal parts of 153,758

for each of them.

4.11.3.4 Benefits for Workers

Bono al Trabajo de la Mujer. This cash transfer is part of the Ingreso Etico

Familiar Programme and it is focussed on working women in dependent or

independent jobs with their contributions up to date. They must belong to the

40% most vulnerable of the population from the Registro Social de Hogares95 and

95 “El Registro Social de Hogares es un sistema de información construido con antecedentes aportados por el hogar y bases de datos que posee el Estado, como:

Registro Social de Hogares,

Servicio de Impuestos Internos (SII),

Registro Civil,

Administradora del Fondo de Cesantía (AFC),

Instituto de Previsión Social (IPS),

Superintendencia de Salud y

Ministerio de Educación, entre otros. El Registro Social de Hogares, en base a la información aportada por una persona del hogar mayor de 18 años y los datos administrativos que posee el Estado, ubica al hogar en un tramo de Calificación Socioeconómica.

279

their age must be between 25 and 59 years old. The amount of the cash transfer

is calculated from their level of income and will be paid 4 months after

application for the subsidy. Their gross monthly income must be lower than

453,282 Chilean pesos and their gross annual income lower than 5,439,369

Chilean Pesos (at 2017 values). They must work primarily in the private sector

(they cannot work in public enterprises including municipalities) and if the

enterprise receives some contribution from the State this cannot be higher than

50%. This bonus provides an amount to the employer to increase the incentive

for hiring women who belong to the most vulnerable group. The employee can

receive their part during four continuous years but the employer can receive the

subsidy for 24 months.

Subsidio al Empleo Joven (SEJ). This is a cash transfer for young -between 18

and 24 years old- workers to improve their salaries and a contribution for their

employers The SEJ extinguishes if the worker has not completed his/her

secondary school by the age of 21 years. They can be dependent or independent

workers and must belong to the 40% most vulnerable of the population from the

Registro Social de Hogares. The amount of the cash transfer is calculated from their

level of income. Their gross monthly income must be lower than 453,281 Chilean

pesos and their gross annual income lower than 5,439,369 Chilean Pesos (at 2017

values).

La Calificación Socioeconómica se construye a partir de la suma de ingresos efectivos de las personas que componen un hogar, y son ajustados por el nivel de dependencia de personas con discapacidad, menores de edad y adultos mayores que integran el hogar. En caso de que los integrantes del hogar no registren información de ingresos en las bases administrativas que posee el Estado, se toma en consideración los valores de ingresos reportados por el integrante del hogar que realiza la solicitud de ingreso al Registro Social de Hogares. De esta manera, para resguardar que la Calificación Socioeconómica represente las verdaderas características de los hogares, el Registro Social de Hogares aplica a toda la base, una validación de las condiciones de vida de la familia, considerando según corresponda:

Tasación Fiscal de Vehículos

Avalúo Fiscal de Bienes Raíces

Valor de Cotización de Salud

Valor de mensualidad de Establecimiento Educacional” http://www.registrosocial.gob.cl/que-es-el-registro-social/

280

4.11.3.5 Other Benefits

Pensión por leyes especiales de reparación. This is a pension for the families

of victims of human rights violations or political violence which where included

in the Informe de la Comisión Nacional de Verdad y Reconciliación and the

Corporación Nacional de Reparación y Reconciliación.

4.11.4 Rates of taxes

4.11.4.1 Impuesto a la Renta de Primera Categoría (Artículo 20 Ley de Impuesto a la

Renta)

Año Tributario Año Comercial Tasa Circular SII

2002 2001 15% N° 44, 24.09.1993

2003 2002 16% N° 95, 20.12.2001

2004 2003 16,5% N° 95, 20.12.2001

2005 al 2011 2004 al 2010 17% N° 95, 20.12.2001

2012 al 2014 2011 al 2013 20% N° 63 30.09.2010

N° 48 19.10.2012

2015 2014 21% N° 52, 10.10.2014

2016 2015 22,5% N° 52, 10.10.2014

2017 2016 24% N° 52, 10.10.2014

2018 y sgtes. 2017 y sgtes. 25% N° 52, 10.10.2014

2018 2017 25,5% N° 52, 10.10.2014

2019 y sgtes. 2018 y sgtes. 27% N° 52, 10.10.2014

281

4.11.4.2 Escala de tasas del Impuesto Único de Segunda Categoría según N° 1 artículo

43 LIR para trabajadores dependientes

VIGENCIA

(1)

N° DE

TRAMOS (2)

RENTA IMPONIBLE MENSUAL

DESDE HASTA

(3)

FACTOR

(4)

CANTIDAD A REBAJAR

(5)

RIGE A CONTAR

DEL 01.01.2013

Y HASTA EL

31.12.2016(CIR.

N° 6, DE 2013)

1 0,0 UTM a 13,5 UTM Exento -.-

2 13,5 " a 30 " 4% 0,54 UTM

3 30 " a 50 " 8% 1,74 "

4 50 " a 70 " 13,5% 4,49 "

5 70 " a 90 " 23% 11,14 "

6 90 " a 120 " 30,4% 17,80 "

7 120 " a 150 " 35,5% 23,92 "

8 150 " y MAS 40% 30,67 "

VIGENCIA

(1)

N° DE

TRAMOS (2)

RENTA IMPONIBLE

MENSUAL DESDE HASTA

(3)

FACTOR

(4)

CANTIDAD A

REBAJAR

(5)

RIGE A CONTAR DEL

01.01.2017, SEGÚN N°

30 DEL ARTÍCULO 1°

LEY N° 20.780/2014 E

INCISO 1° ARTÍCULO 1°

TRANSITORIO DE

DICHA LEY.

1 0 UTM 13,5 UTM 0% 0 UTM

2 13,5 “ 30 “ 4% 0,54 “

3 30 “ 50 “ 8% 1,74 “

4 50 “ 70 “ 13,5% 4,49 “

5 70 “ 90 “ 23% 11,14 “

6 90 “ 120 “ 30,4% 17,80 “

7 120 “ Y más “ 35% 23,32 “

282

4.11.4.3 Escala de tasas del Impuesto Global Complementario según artículo 52 de la

LIR para personas naturales con domicilio y residencia en Chile

VIGENCIA

(1)

N° DE

TRAMOS (2)

RENTA IMPONIBLE

ANUAL DESDE HASTA

(3)

FACTOR

(4)

CANTIDAD A REBAJAR

(5)

RIGE A

CONTAR DEL

AÑO

TRIBUTARIO

2014 Y HASTA

EL AÑO

TRIBUTARIO

2017 (CIR. N°

6, DE 2013).

1 0,0 UTA a 13,5 UTA Exento -.-

2 13,5 " a 30 " 4% 0,54 UTA

3 30 " a 50 " 8% 1,74 "

4 50 " a 70 " 13,5% 4,49 "

5 70 " a 90 " 23% 11,14 "

6 90 " a 120 " 30,4% 17,80 "

7 120 " a 150 " 35,5% 23,92 "

8 150 " y más 40% 30,67 "

VIGENCIA

(1)

N° DE

TRAMOS (2)

RENTA IMPONIBLE

MENSUAL DESDE HASTA

(3)

FACTOR

(4)

CANTIDAD A

REBAJAR

(5)

RIGE A CONTAR DEL

AÑO TRIBUTARIO 2018,

SEGÚN N° 32 DEL

ARTÍCULO 1° LEY N°

20.780/2014 E INCISO 1°

ARTÍCULO 1°

TRANSITORIO DE DICHA

LEY.

1 0 UTM 13,5 UTA 0% 0 UTA

2 13,5 “ 30 “ 4% 0,54 “

3 30 “ 50 “ 8% 1,74 “

4 50 “ 70 “ 13,5% 4,49 “

5 70 “ 90 “ 23% 11,14 “

6 90 “ 120 “ 30,4% 17,80 “

7 120 “ Y más “ 35% 23,32 “

283

4.11.5 Poverty by age

Figure 4-23 Distribution of age groups among all population, population in poverty under market income and population in poverty under disposable income, 6 age groups


4.11.6 Tables and Figures

Table 4-18 Household type by population groups, all population, Row percentage


5,3% 7,6% 8,6%

21,3%28,7% 31,5%

18,1%14,2%

15,8%18,5%

16,3%17,4%

24,2% 18,0%18,3%

12,6% 15,2% 8,5%

all population poor under market income poor under disposableincome

Proportion of individuals, by age groups and income

0 to 3 4 to 18 19 to 29 30 to 44 45 to 64 64 plus

single nuclear two-

parent

extended

two-parent

nuclear single-

parent

extended

single-parent

Total






Population Groups

Household type

284

Table 4-19 Household type by population groups, exit poverty individuals


4.11.7 Fiscal mobility matrices

Table 4-20 Fiscal mobility matrix, from market income to disposable income, Row percentage distribution of people living in households with children and older persons


Table 4-21 Fiscal mobility matrix, from market income to disposable income, Total percentage distribution of people living in households with children and older persons


single nuclear two-

parent

extended

two-parent

nuclear single-

parent

extended

single-parent

Total






Population Groups

Household type


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 38.6% 51.8% 9.6% 0.0% 0.0% 100%


Vulnerability 0.0% 0.1% 85.9% 14.0% 0.0% 100%

Middle Class 0.0% 0.0% 1.0% 99.0% 0.0% 100%


Total 2.4% 7.1% 32.7% 56.1% 1.7% 100%


Disposable Income


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 2.4% 3.2% 0.6% 0.0% 0.0% 6%


Vulnerability 0.0% 0.0% 26.6% 4.3% 0.0% 31%

Middle Class 0.0% 0.0% 0.5% 51.7% 0.0% 52%


Total 2.4% 7.1% 32.7% 56.1% 1.7% 100%


Disposable Income

285

Table 4-22 Fiscal mobility matrix, from market income to disposable income, Row percentage distribution of people living in households with no children and no older persons


Table 4-23 Fiscal mobility matrix, from market income to disposable income, Total percentage distribution of people living in households with no children and no older persons



poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 72.2% 18.6% 6.1% 3.1% 0.0% 100%


Vulnerability 0.0% 1.3% 88.8% 9.9% 0.0% 100%

Middle Class 0.0% 0.0% 0.8% 99.2% 0.0% 100%


Total 2.0% 3.3% 8.2% 71.6% 14.9% 100%


Disposable Income


poverty

Moderate

Poverty


Class

Upper

Middle

Class

Total

Extreme poverty 2.0% 0.5% 0.2% 0.1% 0.0% 3%


Vulnerability 0.0% 0.1% 6.9% 0.8% 0.0% 8%

Middle Class 0.0% 0.0% 0.6% 69.9% 0.0% 70%


Total 2.0% 3.3% 8.2% 71.6% 14.9% 100%


Disposable Income

286

5 Conclusions

The main theme of this thesis is vulnerability, with a particular emphasis on vulnerability to

poverty. This topic is important for several reasons that were explained in the Introduction,

Section I. Alternative approaches to understanding and measuring vulnerability were also

described. Consensus regarding its importance has not been reached with respect to its

definition and measurement. Although in general terms vulnerability to poverty can be defined

as the probability that a household will find itself in poverty in the future (Barrientos 2013),

there is no a unique way to measure and characterize it. The aim of this thesis is to contribute

to the understanding of vulnerability by trialling three novel approaches.

Each of three papers of this thesis addresses a specific theme related to the main research

question of how vulnerability can be conceptualized and characterized. The three papers

explore the following questions: (i) Are people who have left poverty vulnerable to being in

poverty again? (ii) What are the determinants of vulnerability to poverty? And what

distinguishes people living in vulnerability from people in poverty and the middle class? (iii) Is

vulnerability related with age? And what is the role played by cash transfers in this relation?

In this final section, the main findings and contributions of the research are presented.

Additionally, the policy implications and areas that will need to be addressed in future research

are discussed. The manner in which each paper provides an answer to each question follows.

5.1 Findings and contributions from each of the three papers

The question that the first paper tries to answer is whether people who have left poverty

are still vulnerable to being in poverty again. Evidence has shown that a group of people in

developing countries who have moved out of poverty are “bunching up” just above the

poverty line and remain vulnerable to falling into poverty after any subsequent shock (Chen &

Ravallion, 2004). From this evidence emerges the question regarding what is happening with

vulnerability to poverty while poverty is falling. This needs to go beyond poverty analysis to

incorporate vulnerability and analyse them in conjunction. This paper proposes a relative

287

approximation to poverty and vulnerability. Here, the lowest 2 deciles of the income

distribution represent poverty and the lowest 6 deciles are considered the deciles in

vulnerability. The aim is to explore their shifts along the distribution of income over time. The

empirical context of the paper is Chile between 1990 and 2013 and the data recorded by the

National Socioeconomic Characterization Survey (CASEN) between those years, the survey

used to conduct the empirical analysis. In this context, the paper examines population shifts

along the distribution of income from poverty and vulnerability deciles in 1990 to income

deciles in 2013. The Relative Distribution method introduced by Handcock and Morris (1998;

1999) was applied to analyze these shifts along the deciles of the income distribution. The main

advantage of this method is the possibility it offers to analyze all distributional changes from a

non-parametric framework. It helps us to observe the way in which the deciles of income

distribution have moved relative to the past from a non-parametric perspective and whether

some of them have become polarized over the period. Additionally, the role of monetary

transfers and the importance of several co-variates over these changes are provided.

The results show that reduction in poverty can happen simultaneously with reduction in

vulnerability. The evidence from Chile indicates that as real incomes increased between 1990

and 2013 the proportion of people in both 1990’s poverty and vulnerability decreased. The

following two things happened. First, the proportion of people in 2013 who remained in the

bottom deciles of 1990 decreased. This indicates that the chances of remaining in the poverty

deciles of 1990 decreased throughout the period. The people in poverty deciles moved up the

income distribution. Second, the people moving up the income distribution were not

concentrated in vulnerability deciles in 1990. The percentages of people that in 2013 remained

in deciles of vulnerability in 1990 also fell. This shows that people who left poverty do not

necessarily move into the deciles just above, remaining vulnerable. Many of them are moving

further up the income distribution. Answering the question, people moving out of poverty are

not increasing the group of people in vulnerability. Some of them remain in the vulnerability

deciles but others move to the upper part of the income distribution. The main driver of this

reduction in poverty and vulnerability levels is the increase in the autonomous income96 of the

households which is generated by themselves. Although monetary transfers also contribute to

96 Autonomous income is the name given to the income generated by household members. This income does not consider monetary transfers or imputed rent. Autonomous income is the best income to reflect the standard of living of households because it represents their ability to generate income for themselves.

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both of these reductions, the main explanation is the increase in the income generated by

household members. The fact that monetary transfers reduce not only people in poverty but

also in vulnerability reflects the coverage of Social Assistance over a larger population than

people in poverty. The expansion of Social Assistance to vulnerable segments of the

population since 2000 onwards is reflected in the results. The reduction in vulnerability after

monetary transfers is more noticeable between 2000 and 2013 than between 1990 and 2000.

The results also confirm the 6th decile as the best threshold that empirically define vulnerability.

It appears as a natural threshold between the vulnerability and non-vulnerability deciles. While

the proportion of people in the lower 6 deciles of 1990 decreased in 2013, the percentage of

people in the upper 4 deciles of 1990 increased in 2013. However, the increase in real incomes

was not in the same proportion for everyone. The distribution of incomes shows an increase in

polarization in the first decile. The proportion of people in the first decile of 1990 that

remained there in 2013 did not increase their incomes in the same proportion as the people in

the rest of the deciles. This means that the poorest fraction of the population has been left

behind. Although monetary transfers increase income in the lowest decile of income

distribution, this increase is not enough to equal the increase of autonomous income in all the

rest of the deciles. The educational level of heads of household appears as the most important

variable that explains the reduction in poverty and vulnerability. Except for education, the

impact of the other variables is small. However, higher educational levels are far from

explaining all the changes in the distribution of income and its polarization.

This first paper contributes to the literature of vulnerability in several ways. First, it

provides evidence from an empirical point of view that poverty and vulnerability can be

reduced simultaneously. This means that poverty reduction does not necessarily mean an

increase in the deciles just above poverty deciles. Second, the application of the relative

distribution method to vulnerability is a contribution of this paper. The non-parametric

framework allows the analysis of the changes in vulnerability to poverty from a relative and

empirical point of view. Finally, this paper contributes to the incorporation of the concept of

polarization in vulnerability to poverty analysis. The polarization of some income groups when

poverty and vulnerability are falling can create social tension. The appearance of income

groups alienated from other groups and with a high degree of identification among themselves

must be incorporated in the research on poverty and vulnerability reduction.

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The second paper of this research moved the focus towards the characterization of

vulnerability and its differences with poverty and the middle class. The approach proposed by

López-Calva & Ortiz-Juárez (2014) was used by this paper to identify the group of people

living in vulnerability to poverty. The authors established that people in the middle class are

those who face a low degree of economic insecurity which empirically means facing a

probability of falling into poverty lower than 10%. The vulnerable group is defined as a

default: those people who are not currently in poverty but who confront a probability higher

than 10% of falling into poverty in the future. Although the second paper uses the same

probability threshold to distinguish between vulnerability and the middle class, it is also

compared with a 20% threshold of falling into poverty. The final comparison between people

in poverty, vulnerability and the middle class was made using the threshold of 10% because it

fit better with the context of Chile where the core of the Social Protection System is targeted

to the 60% most vulnerable of the population. The main advantage of this approach is that it

estimates an income threshold associated with the probability threshold of 10%. This

‘vulnerability income threshold’ represents the level of income that distinguishes between

people living in vulnerability or in the middle class. It represents income related to socio-

economic characteristics and the assets of households that demonstrate less vulnerability to

poverty as a result of idiosyncratic or asymmetric shocks (López-Calva & Ortiz-Juárez 2014).

After the estimation of this threshold, the groups of people living in poverty, vulnerability, the

middle and upper middle class were identified and compared in their socio demographic

characteristics.

The main finding of this paper is that people in vulnerability to poverty differ in their

socio- demographic characteristics and their propensity to suffer shocks with people in poverty

and those who belong to the middle class. People in vulnerability are in between those living in

poverty and the middle-class group. The results show that the group in vulnerability have more

resources to avoid deprivation than the group in poverty but not enough to be protected

against the risk of being in poverty in the future. The group of people in vulnerability have

more education, better household conditions and better qualified occupations than households

in poverty. Their household sizes are similar to those in poverty but bigger than middle class

households. They are in a younger stage in the household life-cycle than middle class

households but older than households in poverty. This is reflected through older (young)

heads of household, a lower (higher) proportion of children and a higher (lower) proportion of

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older people than households in poverty (middle class). There are more single households than

those in poverty and the same proportion of female heads of households as among middle-

class households which is higher than those in households in poverty. Finally, extended

families are more highly represented in households in vulnerability than the other two groups.

These differences stress the importance of distinguishing between these three groups for the

design and implementation of social programmes.

As expected, the paper finds an inverse relationship between higher incomes and lower

probabilities of falling into poverty. A higher probability of falling into poverty in the future is

experienced in average by people with lower incomes in the initial year. A low income means

less availability of monetary and asset resources to overcome adverse shocks that people can

experience throughout the years. A reduced availability of physical and human resources

increases the vulnerability of being in poverty in the future. In addition, the results also indicate

that the inverse relationship between higher incomes and lower probabilities of falling into

poverty is clearer up to the 20% of probability of falling into poverty. From that point

onwards, although incomes still decrease while probabilities are higher, they do so at a slower

pace. These results show the 10% and 20% degrees of probability of falling into poverty are

natural thresholds for identifying the group of people in vulnerability to poverty.

From a methodological point of view, the estimation for Chile indicates the sensitivity of

the vulnerability income threshold and the proportion of people in vulnerability, to the poverty

line under consideration. This paper uses the same 10% probability of falling into poverty as

the threshold as López-Calva & Ortiz-Juárez (2014) but instead of using the international

poverty line of US$4 PPP per day, it uses the ‘new poverty line’ used in Chile since 2013,

which is 30% higher than the international one. The results show that the vulnerability income

threshold estimated by this paper is higher than the threshold estimated by López-Calva &

Ortiz-Juárez (2014) by 20%. Furthermore, the proportion of people living in vulnerability to

poverty is also very sensitive to the vulnerability threshold selected. This happens because the

high density of people around the income thresholds associated with a 10% and 20% of

probability of falling into poverty. The recommendation is that the selection of the threshold

must be relevant to the specific context to operationalize the vulnerability to poverty

measurement. In this context, this paper uses the new poverty line implemented in Chile since

2013 and the 10% probability threshold which is connected to the 60% of the population level

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that the State of Chile uses to identify the vulnerable. These results corroborate the importance

of measuring vulnerability to poverty, as with poverty, considering the welfare standards of the

specific context.

The contributions to the literature of this paper can be summarized in two main areas.

First, it contributes to the literature on vulnerability to poverty through its conceptualization

and measurement of vulnerability to poverty. The operationalization of the approach

developed by López-Calva & Ortiz-Juárez (2014) to measure the middle class for measuring

vulnerability to poverty in a high-income country as Chile is another contribution of this paper.

In addition, it offers an estimation of a vulnerability income threshold in the same metric as

that of the poverty line. This provides an advantage in the operationalization of vulnerability to

poverty measures. Second, this paper contributes to the characterization of vulnerability. This

paper provides answers to the main questions it raised regarding the determinants of

vulnerability to poverty and the distinguishing factors between vulnerability, poverty and the

middle class. These three income groups are compared through their socio-demographic

characteristics and their exposure to suffering shocks or changes over time. This paper

contributes to the recognition of the group of people in vulnerability as a different group to

those in poverty and the middle class providing the recommendation of different social

programmes to these groups. Poverty reduction strategies should consider these differences.

The third paper of this research approaches vulnerability as the understanding of

vulnerable groups. These groups need protection from the State, in particular, protection

against poverty and destitution. This paper puts attention on two commonly recognized

vulnerable groups: children and the elderly. They are differently represented among groups in

poverty, vulnerability and the middle class. One of the findings of Paper II of this research was

that the the age composition of the household is an important determinant of poverty and

vulnerability to poverty, in part because of the distribution of public subsidies. While having

elders at home means a lower probability to being in poverty, the presence of children at home

is related to a higher probability to being in poverty. In addition, while children are the group

with the highest incidence of poverty, older people are the group with the lowest incidence of

poverty among the population in Chile. This evidence configures Chile as a country where

children face a higher probability of being in poverty today and tomorrow than older persons.

This paper explores the role of Social Assistance programmes in contributing to these

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disparities between these two vulnerable groups. For doing so, a partial fiscal incidence analysis

was carried out following the framework proposed by Commitment to Equity (CEQ97) Lustig

and Higgins (2012, 2013). The data source of this research was the 2015 National

Socioeconomic Characterization Survey (CASEN) of Chile. This paper measures the poverty exit

rate as a consequence of fiscal intervention. This means the percentage of people in poverty

who leave poverty after direct taxes and cash transfers are in place. Among the cash transfers

considered are both conditional and unconditional types, and the pensions that households

receive. Although direct taxes are considered in the analysis, they are not very important in

changes in poverty rates. The fact that households in poverty do not pay direct taxes and just a

small proportion of them pay social security contributions makes the effect of taxes on poverty

rates very small.

The findings show that both children and older people are vulnerable groups having more

prevalence of being in poverty than the average population. This answers the question

regarding the relation between vulnerability and age. Both age groups are over-represented in

poverty when only the income that their households generate is considered. This means that

before any fiscal intervention, these two age groups are those that are most prevalent in

income poverty. However, this scenario changes after direct taxes and transfers are in place.

The reduction of poverty after fiscal intervention is around 4 times greater among older

persons than among children. While children remain over-represented among the population

in poverty after cash transfers, older people are under-represented. The results show that

although both age groups are highly covered by monetary transfers, those received by the

elderly are more effective for taking them out of poverty. Not only do older persons benefit

the most by monetary transfers but also the people who live with them. The results show that

the highest poverty exit rate after monetary transfers belongs to people living in households

with older persons and no children and the lowest to people living in households with children

and no elderly. This indicates the importance of the age composition of the household to

explain the effectiveness of cash transfers in reducing poverty. The most important reason that

explains the higher effectiveness of cash transfers on exit poverty in households with older

persons is the higher amount of cash transfers that they receive. The non-contributory pension


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called Pensión Basica Solidaria plays the most important role in enabling the exit of poverty.

Although it is true that the fact that households with older people are smaller than households

with children is also part of the explanation behind the higher poverty exit rates in households

with elders, this is not the most important reason. The much higher exit poverty rates of

people living in households with children and older persons –the biggest in size- than for

individuals who live in households with children and no older persons indicates the fact that

having an elder at home is more important than having a small household size. Furthermore,

the cash transfers received by people who live in households with older person/s are more

effective in exiting poverty even when poverty gaps are much higher in those households than

in household with children. In addition, the results show that different monetary transfers

complement each other increasing the exit poverty rate. The exit poverty rate of households

with children but without older people increases when benefits for families are combined with

benefits for workers or with other benefits. The same is observed when benefits for families

are combined with pensions in households with older people. These results show that the

amount of per capita income that households receive as monetary transfers determines their

chances of getting out of poverty. Overall, the results of this paper show that cash transfers

have an age bias towards older persons in Chile. Cash transfers reduce in a higher proportion

the poverty rates among older persons than among children. The cash transfers targeted to

older people increases the probability of exiting poverty of all people living with them at home.

The group of people benefiting most by monetary transfers is those living with older people

and no children at home. Answering the question of this paper, cash transfers play a crucial

role shaping the relation between vulnerability and age. While children are a vulnerable group

that remains highly unprotected against poverty after fiscal intervention, the vulnerability of

older persons is reduced after fiscal intervention.

This third paper of the research contributes to the literature of vulnerability in several

important ways. First, the results of this paper provide further evidence of the effectiveness of

cash transfers in reducing poverty in a high-income country like Chile. The short-run impact of

monetary transfers on reducing poverty among beneficiaries is a contribution of this paper. A

second contribution of this paper is the comparison between the ages of the recipients of cash

transfers. This paper compares the effectiveness of cash transfers depending on the presence

of children and/or older persons in the households. The comparison between these two

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vulnerable groups raises the issue for discussion about whether Social Assistance programmes

should protect or promote. While, anti-poverty programmes targeted to children contribute to

promoting a better present and future to them, those targeted to older persons protect them

for poverty and destitution in the present. Finally, the use of a partial fiscal incidence analysis

to examine the effectiveness of cash transfers is a contribution to the literature of cash transfer

effectiveness. Fiscal incidence analysis provides a detailed framework to analyze the effects of

fiscal intervention on poverty in the short run.

5.2 Policy implications

The policy implications arising from the empirical analysis of the three research papers are

discussed in this section. Particular attention is given to social protection policies considering

their importance in poverty eradication and vulnerability reduction. Four main policy

implications are discussed in the following pages: the importance of the reduction of poverty

and vulnerability to poverty in conjunction; the recognition of vulnerability as a different state

than poverty and the middle class; the reduction of the age bias in poverty and vulnerability to

poverty and the role of monetary transfers in this; and the need of complementary benefits to

increase the effectiveness of monetary transfers in reducing poverty.

The first paper of this thesis provides new evidence to suggest that poverty reduction can

be accompanied by vulnerability reduction. This fact reduces the chances of falling into

poverty again after being out of poverty. A first message for policy makers is that, the

reduction of poverty needs reduction in vulnerability to poverty as well. The reduction of the

chances of being in poverty in the future is a necessary condition to reduce poverty. The

evidence for Chile shows that although monetary transfers have played a role in both falls, the

main driver of these reductions has been the increase in the autonomous income that the

households generate by themselves. However, these increases in real incomes have not

benefited all the population in the same proportion. The polarization of incomes in the first

decile shows that there is a group of people who did not increase their incomes in the same

proportion as people in the rest of the income deciles. Their autonomous income did not grow

at the same rate as that of the rest of the population, leaving them behind. Although monetary

transfers contributed to the decrease of the group of people concentrated in the first decile of

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income distribution, they were not enough to equalize the increase of autonomous income in

all the rest of the deciles. A second message for policy makers is that, despite the fact that

efforts must be focused on all people in poverty and vulnerability, people concentrated in the

lowest decile of the income distribution need further protection. The reduction of polarization

has been suggested because of its negative consequences increasing social tensions.

The evidence of the second paper shows that people in vulnerability have different socio-

demographic characteristics than people living in poverty or those to belong to the middle

class. Any Social Protection System that wants to reduce vulnerability to poverty must consider

these differences in the design and implementation of social programmes. The importance of

reducing vulnerability to poverty reduction makes this point even more important. Although

people in vulnerability have more resources to avoid deprivation than the group in poverty,

these are not enough to be protected against the risk of being in poverty in the future. Social

protection policies may help in improving the factors that increase the economic security of

people in vulnerability. One of the variables commonly suggested to alleviate poverty also

applies in tackling vulnerability: education. Developing the human capital that increases the

proportion of head of households in more qualified occupations is one of the suggestions.

Another option is focusing on the group of variables that define the household life-cycle. The

evidence shows that households in younger stages -measured through the age of the head of

the household, the proportion of children and the proportion of older population- face higher

economic insecurity than households in an older phase of their life-cycle. Social protection

should consider these differences in the household life-cycle of those in poverty and

vulnerability. Social programmes targeted to households with children and young parents can

contribute to reducing their levels of vulnerability. Policies oriented to increasing the offer for

childhood care, to foster hiring young workers and the incorporation of mothers in the labour

market are in this line.

The second and third papers of this thesis raise the issue of the age bias observed in

poverty, vulnerability to poverty and monetary transfers. Households with children are more

prevalent to being in poverty and vulnerability to poverty and to receiving lower cash transfers

than households with elders. Although Social Assistance programmes in Chile, and cash

transfers in particular, have contributed to poverty reduction (Martinez-Aguilar et al., 2017),

they have been more effective among older groups. Monetary transfers are biased towards

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older people reducing their incidence in poverty and vulnerability to poverty faster and deeper

than the reduction in other age groups. When fiscal interventions are biased towards older

groups and pensions, they are called occupational-based (Lynch, 2006). This kind of welfare

institutions are in opposition to those putting more attention on families, children and groups

with weak ties to the labour market, named citizenship-based. In the context of the evidence

showing the long-lasting consequences of experiencing poverty in childhood (Magnuson &

Votruba-Drzal, 2009; Melchior et al., 2007), the biased monetary transfers towards older

groups must be revisited by policy makers. Although the debate regarding which age group

should be more or less protected is open, it should be present in any conceptualization of

social programmes. A useful starting point would be that the State's protection against poverty

and destitution should not create a generational inequity that was not present before the

allocation of public resources.

The third paper of the thesis highlights the importance of the amount and the

complementarity of cash transfers in contributing to poverty reduction. A suggestion to policy

makers is to not only focus on the coverage of vulnerable groups but also, and most

importantly, put attention on the effectiveness of the benefits in protecting these groups

against poverty and destitution. The two vulnerable groups in the analysis, children and older

persons, are highly covered by monetary transfers. Those of them in poverty do not pay direct

taxes and around 90% of them receive monetary transfers from Social Assistance programmes.

However, the majority that exit poverty as a consequence of the benefits are older people.

People who live in households with older persons but without children benefit the most from

social assistance. The explanation of this is the age bias of poverty and vulnerability to poverty

explained before. Further protection for children living in households without older persons

can be a proposal in the context in which children living with grandparents are more protected

against poverty.

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5.3 Further research

The main policy implication of this thesis is that vulnerability must be recognized as a

different state of poverty and needs to be incorporated in anti-poverty strategies to eradicate

poverty. Further research to deepen knowledge of vulnerability and its operationalization

would be crucial to identify the most appropriate policies to reduce vulnerability in different

contexts. As this thesis has highlighted, the results are very dependent on normative decisions

taken locally. Poverty and vulnerability thresholds are determinants in the definition of

vulnerable groups. In this context of increasing knowledge of vulnerability, further research is

recommended in the following areas:

- The elaboration of a common theoretical framework for vulnerability to poverty from

available research. Comparison among the different approaches to understanding and

measuring vulnerability to poverty regarding the main determinants found. To move

forward in an elaboration of a common theoretical framework of vulnerability.

- Sensitivity analysis to different poverty and vulnerability thresholds can be explored

further. The evidence indicates the importance of the normative decisions that each

society takes in the identification of their populations in poverty and vulnerability. In

the same line of poverty thresholds defined from a global perspective, vulnerability

thresholds could be defined from an international point of view. Research in this area

can foster a global use of vulnerability conceptualization and operationalization.

- Vulnerability to poverty could be explored further from a multi-dimensional

perspective. In the same way that poverty measures have been extended to go beyond

the dimension of income alone, vulnerability to poverty could be studied further in this

aspect. The main challenge here is the common invariability of many of the dimensions

usually used in multidimensional measures. In other words, fluctuations in household

conditions, years of education, access to health, among others are lower than income

changes over time. Income appears as the more sensitive variable to measure the risks

and changes that people usually face.

- More and richer panel data will allow us to explore vulnerability to poverty better.

Fixed-effect or random-effect estimations can be done when panel data captures more

variables that change throughout the time.

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- Vulnerability to poverty in childhood appears as a main topic for future research. The

evidence confirms the higher incidence of children in poverty and vulnerability to

poverty than the rest of the age groups. The importance of tackling vulnerability to

poverty is even more important in the case of children. Developing, introducing,

evaluating and assessing mechanisms to tackle vulnerability among the youngest

population are all areas that need more research.

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5.4 References





Lustig, N., & Higgins, S. (2012). Commitment to Equity Assessment (CEQ): Estimating the Incidence of Taxes and Benefits Handbook. Tulane Economics Department Working Paper and CIPR (Center for Inter-American Policy & Research) Working Paper, New Orleans, Louisiana, July.




Magnuson, K., & Votruba-Drzal, E. (2009). Enduring influences on childhood poverty. In Changing Poverty, Changing Policies. Focus. New York: Russell Sage Foundation.

Martinez-Aguilar, S., Fuchs, A., Ortiz-Juarez, E., & Del Carmen, G. (2017). The Impact of Fiscal Policy on Inequality and Poverty in Chile. Policy Research working paper,no. WPS 7939;; Policy Research Working Paper;No. 7939. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/25948 License: CC BY 3.0 IGO.”.

Melchior, M., Moffitt, T. E., Milne, B. J., Poulton, R., & Caspi, A. (2007). Why Do Children from Socioeconomically Disadvantaged Families Suffer from Poor Health When They Reach Adulthood? A Life-Course Study. American Journal of Epidemiology, 166(8), 966–974. https://doi.org/10.1093/aje/kwm155

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