Alternative Poverty Measurements A Multidimensional Approach … · 2012-12-21 · hereby especially poverty alleviation programs, the ability to investigate people’s wellbeing

Alternative Poverty Measurements

– A Multidimensional Approach to Wellbeing in Illinois, US

Daniel Auer

Vienna University of Economics and Business Administration

NEURUS exchange program at University of Illinois at Urbana-Champaign

Spring 2012

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Abstract

“Poverty measurement and research has made important progress by moving from

unidimensional to multidimensional approaches. [...] [The latter] assesses the state of

human well-being by focusing on ‘what one has,’ ‘how much prospect one has’, and

‘how much advantaged or disadvantaged one is in society’ toward improving such

prospect with all contributing to ‘what one can have.’ Although ‘how much one has’ is

important, as it is the means by which one can acquire human well-being, poverty is

a more complex social phenomenon and incorporating more information is necessary

to draw its accurate picture.” (Waglé, 2007, p. 16)

Based on the debate on finding more accurate ways of measuring poverty than the

current income proxies, this paper provides an alternative multidimensional approach

by investigating five specific dimensions of wellbeing (capability, income,

employment, housing & facilities, and health) through interpreting the results of their

aggregated overall index of wellbeing as well as its decomposition. While different

approaches during the process of creating such an index are discussed briefly, the

paper stresses the importance of a more comprehensive picture of poverty than the

conventional income-based measures can provide.

By converting indicators of wellbeing into ratios, an alternative method of poverty

thresholds can be established, basing on the percentage difference to an overall

benchmark. This simple yet powerful approach implies a significant value added for

policy planning and the investigation of quality of life in general.

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Content

1. Introduction ........................................................................................................ 4

1.1. Research Goal ............................................................................................. 7

2. Multidimensional Measures of Wellbeing ........................................................ 8

3. An Alternative Approach ................................................................................. 14

4. Results .............................................................................................................. 26

4.1. Deprivation Count ..................................................................................... 26

4.2. The Aggregated Index of Wellbeing ......................................................... 30

4.3. Decomposing the Index of Wellbeing ...................................................... 34

4.4. Comparison with Conventional Poverty Measures ................................ 38

4.5. Introducing ‘Natural Weights’................................................................... 40

5. Suggested Further Research .......................................................................... 43

6. Conclusion ....................................................................................................... 45

Sources ................................................................................................................... 47

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1. Introduction

According to recent estimations conducted by the U.S. Census Bureau, almost

50 million Americans live in poverty today. (International Herald Tribune, 2011)

Numbers of “children growing in families officially designated as the poor” rise up to

20 percent. (Waglé, 2008, p. 131) But what defines a person or a family as poor?

The conventional practice is to incorporate consumption and income in order to

derive easily understood poverty thresholds. The process involves specifying and

valuing basic needs and expressing the value as the poverty threshold in terms of

income such that those without sufficient income are categorized as the poor.

Consequently poverty is immediately understood as income deficiency. (Waglé,

2008)

The U.S. Census Bureau states that “[f]ollowing the Office of Management and

Budget's (OMB) Statistical Policy Directive 14, the Census Bureau uses a set of

money income thresholds that vary by family size and composition to determine

who is in poverty. If a family's total income is less than the family's threshold,

then that family and every individual in it is considered in poverty. The official

poverty thresholds do not vary geographically, but they are updated for inflation

using Consumer Price Index (CPI-U). The official poverty definition uses money

income before taxes and does not include capital gains or noncash benefits

(such as public housing, Medicaid, and food stamps).” (U.S. Census Bureau,

2011b)

This definition’s origins date back to the 1960s where poverty thresholds were

calculated as three times the cost of a minimum diet. Accordingly, a person or a

family that spends over one-third of its household income on food is considered to be

in poverty. (Fisher, 1992) Derived income-based poverty lines are appealing to

governments all over the world, especially because of their “simplicity, accessibility,

and comparability over time and across societies.” (Citro & Michael, 1995; cited

Waglé, 2008, p. 17)

On the other hand, several scientific sources consider this unidimensional approach

as a limitation for stating a person’s quality of life. (e.g. Betti, Cheli, Lemmi, & Verma,

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2006) For example, measuring income alone can be insufficient for explaining an

individual’s living condition as a relatively low level of income could be more than

compensated for the fact that its recipient owns his house. (Miceli, 2006) This leads

back to the question mentioned above: What defines someone being poor?

Nobel laureate Amartya Sen1, who can be considered as one of the most influential

pioneers trying to change the view of poverty in modern society, argues for two major

aspects: That there is an existence of irreducible absolute human needs such as

food and shelter on the one hand (Sen, 1979), and that poverty in general is a

product of inequality on the other hand.

Referring to Sen, Vero stresses the following: “Poverty is a difficult notion and it

may be defined in various ways which correspond to different philosophical

approaches. The general idea is that poverty is a consequence of an inequality,

between individuals, in the control of certain things, i.e., the result of an unequal

distribution between those who have something and those who are more or less

deprived by it. Poverty is then a situation in which certain individuals are

deprived of this something. Thus, according to Sen, the central question to

define and measure inequality, as well as poverty can be resumed as follows:

"equality of what?" (Sen, 1980; Sen, 1987) Thus, in order to define and

measure poverty one has to formulate a value judgment on what must be the

objects of value. Discussions on normative economics have offered us a wide

menu in answer to this question "equality of what?": for example, income,

wealth, rights, freedom, etc.” (Vero, 2006, p. 211)

Hence, a solely income-based approach excludes non-economic aspects and

therefore creates an imperfect picture of people’s quality of life.

According to numerous critics, inequality plays a minor role in the United States to

understand poverty compared to other countries in the developed world. (e.g. Brady,

2003; Glennerster, 2002; Townsend & Gordon, 2000; Waglé, 2008) As a result, rising

inequality and miss-targeted social policies can be observed. (e.g. Glennerster, 2002;

Gilbert, 2008; Smeeding, 2005) The ‘2010 Report on Illinois Poverty’ (Terpstra & et

1 “The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 1998 was awarded

to Amartya Sen ‘for his contributions to welfare economics‘.“ (The Nobel Prize, 2012)

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al., 2010), for example, estimates the percentage of people living in poverty at 12.2

percent whereas the Corporation for Enterprise Development numbers 26.4 percent

as “[…] ’asset poor’, meaning they don’t have enough money tucked away to cover

basic expenses for three months in case of a layoff or emergency that saps income

[…]” (Yerak, 2012, p. 1f) There should be no doubt about the fact that asset poor are

in a state of deprivation as well.

“Because the definition of poverty has been invariably focused on lowness of

income to meet basic consumption, [policy makers] have never looked outside

of the box to understand what actually constitutes poverty and what causes and

perpetuates it.” (Waglé, 2008, p. 131)

Yet, in a wider scientific community, the understanding of poverty as a

multidimensional phenomenon has already been debated and does not present a

fundamentally new approach. (e.g. Brady, 2003; Lister, 2004; Ravallion, 1996; Sen,

2000; Waglé, 2002) By pinpointing focus areas in order to implement efficient poverty

alleviation policies, it is important to deal with deprivations on a multifaceted level.

There is no doubt that available income is of high importance for determining a

person’s social-economic status. However, various forms of deprivation including

social and environmental aspects have to be considered as well. Only a reciprocal

evaluation of monetary and complementing non-monetary indicators can guarantee a

significant picture of poverty in a certain region. (Coromaldi & Zoli, 2011; Nolan &

Whelan, Rseources, Deprivation and Poverty, 1996; Perry, 2002; Townsend P. ,

Poverty in the United Kingdom, 1979) Non-monetary elements are usually

understood as a lack of access to resources, facilities and individual attributes. (Betti,

Cheli, Lemmi, & Verma, 2006)

The World Bank, for example, stresses in its Development Report 2000/2001: “This

report accepts the now traditional view of poverty […] as encompassing not only

material deprivation (measured by an appropriate concept of income or

consumption), but also low achievements in education and health.” (World Bank,

2001, p. 15)

While the generally accepted multidimensional wellbeing approach reaches slowly

outside of academia into policy-oriented debates as the above-mentioned or the

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Laeken European Council (Atkinson, Cantillon, Marlier, & Nolan, 2002), the United

States are lacking behind in dealing with quality of life contemporarily. (Waglé, 2008)

Poverty should be considered as a shortfall in specific dimensions of a person’s

wellbeing. A quality of life index therefore could provide policy planners and

governments with a more accurate picture of an individual’s or household’s situation

reaching beyond income.

1.1. Research Goal

Effective policy making requires not only administrational and financial resources, but

also valuable information. Regarding policies targeting a society’s quality of life and

hereby especially poverty alleviation programs, the ability to investigate people’s

wellbeing in a more detailed way is fundamental for cost minimization and impact

maximization.

“When poverty statuses are erroneously identified, the consequence will not

only be inappropriate policy prescriptions with ineffective measures to deal with

poverty. It will also be inaccurately identified target population and thus

inaccurately targeted policies.” (Waglé, 2008, p. 67)

A solely monetary – and therefore imperfect - definition of poverty cannot provide as

detailed information as multidimensional measures do. “The role of income and

wealth [...] has to be integrated into a broader and fuller picture of success and

deprivation.” (Sen, 1999, p. 20) Consequently, the goal of this research is to create a

tool which reflects poverty in a more comprehensive and accurate way by

pinpointing the various forms of possible deprivation.

Based on scientific literature as well as empirical studies, this paper proofs the value

added for policy making by broadening the focus from solely income-based poverty

measurements as provided by the U.S. Census to a dimension-based investigation of

people’s quality of life.

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2. Multidimensional Measures of Wellbeing

The International Labor Organization (ILO, 1976) as well as the United States’

President’s Commission on Income Maintenance (1969; cited Spicker, Alvarez

Leguizamón, & Gordon, 2006) stressed that poverty is a state of deprivation

regarding basic human needs like food, shelter, but also in general an acceptable

level of maintaining life. So far, studies like these did not intend to implement

multidimensional measures into policy planning.

Eventually, in 2010, the Oxford Poverty and Human Development Initiative (OPHI) of

the Oxford University and the Human Development Report Office of the United

Nations Development Programme (UNDP) implemented a Multidimensional Poverty

Index (MPI) which replaced the previous Human Poverty Index. (United Nations,

2012) This MPI – based on the works of Sabina Alkire and James Foster (Oxford

Poverty & Human Development Initiative, 2012) – is composed of three dimensions

(health, education, and standard of living) which in turn consist of ten specific

indicators to represent each dimension adequately. Using Alkire and Foster’s

methodology (2007; 2009) with equally weighted indicators and further also equally

weighted dimensions, eventually an aggregated measure is created. Its idea is

considerably based on Maslow’s hierarchy of needs (Maslow, 1943) and should

address basic physical needs referring to fundamental human rights. Although this

reflection of rudimentary services and core human functionings is deeply constrained

by data limitations, “the MPI reveals a different pattern of poverty than income

poverty”. (Alkire & Santos, 2010, p. 7) Similar to the MPI, the established United

Nation Health Development Index (HDI) evoked critiques, mainly referring to

assumed redundancy, meaning that there is no value added in adding non-income

dimensions to existing income measurements. However, several authors (e.g. Lustig,

2011) are rejecting this critique by pinpointing significant differences after comparing

ranks of per capita incomes and HDI.

Basically, multidimensional measures (of poverty or wellbeing in general) rely on a

vector I of K variables, with K > 1. The variables represent indicators of wellbeing and

are usually heterogeneous in their nature; thus, both quantitative and qualitative.

(Asselin, 2009)

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While the United Nation’s MPI is probably the most visual product of recent studies

on the field of multidimensional measures of poverty and quality of life, there have

been several earlier efforts towards more axiomatic changes to this relatively new

field of science: “[…] Adelman and Morris (1967; 1973) and Morris (1979) […] sought

to quantify poverty and well-being under such guises as economic development and

physical quality of life index using infant mortality, life expectancy, adult literacy, and

other macro indicators.” (Waglé, 2008, p. 56f) While the more recent works of

Bourguignon and Chakravarty (2003) and Tsui (2002) provide fundamental

theoretical information, Dewilde (2004) was able to show important conclusions

regarding the higher plausibility of multidimensional approaches compared to

income-based ones when he elaborated a cross-country analysis between the United

Kingdom and Belgium, operationalizing three poverty dimensions (housing, financial

stress, and limited financial means). A central argument of multidimensional

measurements of poverty and well-being is stressed by Moisio (2004) using relative

income poverty, subjective poverty, and housing deprivation measures: “Although the

assumed unidimensionality of the indicators may not be highly consistent with the

multidimensional approach, results using some theoretically justified indicators were

highly encouraging thus providing richer understanding of the quality of deprivation.”

(Moisio, 2004; cited Waglé, 2008, p. 56f)

While the multidimensional approach is more or less stringently considered as

enrichment for poverty alleviation programs and wellbeing measures in general,

regarding the appropriate methodology for these measures a broad consensus is not

existent.

Apriori, a central aspect in the scientific debate is the definition of wellbeing itself.

Separating the various approaches into two major mindsets, one can identify those

who focus on material aspects or resources (e.g. Citro & Michael, 1995) and those

who consider the standard of living including for example an individual’s capabilities

or health as the preferred area of investigation (e.g. Nolan & Whelan, Rseources,

Deprivation and Poverty, 1996). Eventually, some try to combine both approaches.

(e.g. Ringen, 1987) In general, a person’s quality of life will always be a reflection of

needs, available resources, and last but not least of lifestyle choices.

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Inevitable, any approach that requires a certain initial definition of wellbeing is

normative to a certain extent and one has to accept that there may always be several

possible definitions. However, considering measurements of quality of life as

necessary, three basic strategies can be chosen:

(i) The above mentioned absolute approach (e.g. Sen, 1980) takes some basic

needs into account and thereof defines certain thresholds which separate a

population according to their status of wellbeing respectively into poor and non-poor.

(ii) An alternative approach is to define quality of life and potential deprivations

relatively, “by comparing the situation of each individual with the standard of living

prevailing at a certain point in time in a given [region].” (Miceli, 2006, p. 195f) This

method is widely applied in today’s poverty research. (Waglé, 2008) Several

European countries implemented a 60 percent threshold of a benchmarking amount

of income (e.g. Glennerster, 2002; Immervoll, Levy, Lietz, Mantovani, & Sutherland,

2006)

(iii) Eventually, one could evaluate a population’s self-perceived status of wellbeing

which evolves as a subjective index. (e.g. Goedhart, Halberstadt, Kapteyn, & Van

Praag, 1977)

Ultimately, the choice of an approach and further the “important conceptual step”

(Asselin, 2009, p. 7) of which dimensions and indicators are chosen, depends on the

purpose the measurement should serve.

“Money-metric (income or consumption based) poverty measures have been

useful […] in estimating approximately the magnitude of global poverty as well

as individual regions’ and countries’ progress towards the reduction of poverty.

The Human Development Index has provided the development community with

a broader measure of well-being — adding health and educational dimensions

to income — and demonstrating that the correlation between the monetary and

non-monetary dimensions of poverty within and across countries was far from

perfect. Finally, multidimensional indicators such as the Alkire-Foster (A-F)

measure respond to an effective demand for scalar estimates of poverty that

could be used by policymakers in the allocation of funds to reduce poverty in an

efficient and equitable way. Hence, notwithstanding their shortcomings, each of

these indicators fulfills somewhat different yet important functions.” (Thorbecke,

2011, p. 485)

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The variety of dealing with certain dimensions and their indicators is as

encompassing as the literature on multidimensional measures itself. Shall

dimensions be aggregated to an overall index of wellbeing? Consequently, the

measurement allows a complete ordering of the obtained results. (e.g. Alkire &

Sarwar, 2009) At the same time, this approach of creating one index and therefore

one general threshold maneuvers the particular study again towards

unidimensionality. Having a unidimensional index consistent of several dimensions

can still explain poverty/wellbeing more plausible than measurements based on a

single indicator. Due to a relatively easy measurement process and wieldy

applicability these kinds of multidimensional indices enjoy certain popularity in the

scientific community.

However, by incorporating information to an overall index of poverty (whether it is

monetary or non-monetary) a loss of important explanatory value has to be accepted.

Allowing an index to be split into its original describing dimensions can increase the

value of the measurement fundamentally. Consequently when poverty is defined as

deprivation in multiple (simultaneous) dimensions the notion of identifying the poor

changes. Considering several dimensions of wellbeing which are independent from

other dimensions to a certain extent, but correlated with each other under to notion of

general wellbeing, creates a new set of possibilities of identifying the poor: Several

authors stress the so called ‘union approach’ where an individual is considered as

poor when she or he is deprived in at least one of the possible dimensions. In

contrast to that the ‘intersection method’ identifies those as poor who are deprived in

all possible dimensions. Claiming a lack of explanatory value for these

measurements (either too many or too few are classified as poor) the above

mentioned Alkire-Foster Method (Alkire & Foster, 2011) - on which for example the

UNDP Multidimensional Poverty Index is based - embodies a widely used

methodological approach as it is regarded as a useful general framework which

allows various modifications regarding the choice of indicators, measurement of

cutoffs, etc.

Considering union and intersection approach as the two extremes the newly

created index “’[uses] a methodology for measuring poverty in the sense of Sen

(1979) that first identifies who is poor, then aggregates to obtain overall

measures of poverty that reflect the multiple deprivations experienced by the

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poor.’ The multi-dimensional poverty measures derived from applying the M0

methodology — as the authors call it — fulfill desirable axioms, are

decomposable, and include discrete and qualitative data as well as continuous

and cardinal data. The construction of multi-dimensional poverty measures

using the M0 methodology involves the selection of dimensions, dimensional

cutoffs (to determine when a person is deprived in a particular dimension),

dimensional weights (to indicate the relative importance of the different

deprivations), and a poverty cutoff (to determine when a person experiences

enough deprivations to be considered poor). The essence of identifying the poor

in that multi-dimensional framework is to use a ‘dual cutoff’ method. The

‘deprivation cutoff’ is used to determine whether a person is poor in a particular

dimension. It is analogous to the poverty line in the unidimensional analysis.

The second cutoff is called the ‘poverty cutoff.’ It is the number of deprivations

(or the weighted sum if unequal weights are used) that defines a person as

poor. If the number is equal to 1, the multi-dimensional index will be the union of

deprivations. If it equals the number of dimensions, the index will be the

intersection of deprivations. There exists an array of possibilities in between

these two extremes.’ (Lustig, 2011, p. 229)

Eventually, whereas several authors seek to provide a political or philosophical

rationale argue for the use of multidimensional measures (e.g. Lister, 2004;

Ravallion, 1996; Sen, 2000; Waglé, 2002) the critical question is not ‘whether’ to use

this new approach, but rather ‘how’ to create a value added for potential policies.

The connotation of wellbeing is subjective and altered by individual and cultural

preferences as well as it is dependent of time and space. (Waglé, 2008)

Consequently, poverty itself is an ever-changing concept. When voices in the United

States stress that people fall below the poverty threshold while they can afford color

television or a mobile (e.g. Paulin, 2011), they often ignore the fact that today’s

common understanding of poverty cannot be compared to early 1960s. Likewise

space plays a similar crucial role. In the United States the current official poverty

threshold for a single household is 11.491 $ per year (U.S. Census Bureau, 2012b)

while the United Nations define extreme poverty in the world’s most struck regions by

less than 1 $ a day. (United Nations, 2012)

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For poverty measures it is necessary to evolve an understanding of wellbeing in

terms of the ability to realize a certain quality of life as it is deemed appropriate and

therefore measure the availability of certain resources and individual achievements

which allow an individual to reach the same. (Waglé, 2008)

“Because one’s needs are conditioned by the level of overall well-being in

society, those focusing on relative poverty see distributional issues to be central

to developing poverty thresholds. While this invokes broader issues of whether

or not inequality in the distribution of economic resources is justified in a well-

ordered society (Friedman, 1982; Friedman & Friedman, 1980; Nozick, 1980;

Rawls, 1971; 2005), this also has implications for establishing needs as a

reasonable basis for demarcation of the quality of life of the poor relative to

those of the non-poor. Since the purpose of determining who is poor and who is

not is to identify the population that needs policy resources to improve the

quality of life to an acceptable minimum level, this process is essentially

political.” (Waglé, 2008, p. 24f)

Thus, creating a tool which reflects the dynamic nature of wellbeing (Waglé, 2008) as

well as the fact that individual needs are conditioned by their – social – environment

is vital for improving the understanding of poverty and its manifold connections

between a person and the surrounding society.

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3. An Alternative Approach

The multidimensional wellbeing measurement tool established hereinafter integrates

the relative understanding of poverty as well as the basic elements of a so called

fuzzy set approach into the Alkire-Foster Method’s theoretical fundament.

Additionally, a new approach of converting deprivations into percentage-based

differences to certain regional benchmarks will be implemented. Thereof results a

tool which will be able to pinpoint more accurately the particular dimensions of

deprivations and thus obtaining a more powerful instrument regarding poverty

alleviation programs.

“The process of identifying the population in poverty can go wrong in a number

of ways. One is the use of information itself. The availability and use of a more

comprehensive set of information likely result in more accurate measurement

outcomes. The purpose is to thoroughly understand people’s living conditions,

how they operate in day-to-day lives, and what are the challenges facing them

in efforts to improve quality of life. More information on people’s behavior and

activities would help better understand their living conditions. At the same time,

there always exist data constraints in that collecting comprehensive sets of

information will be expensive and operationally infeasible in many policy

circumstances. The goal, therefore, is to create an accurate understanding

given data and resource constraints. The resource constraint further indicates

that policy-makers cannot afford to aimlessly wander around with irrelevant

information. The theoretical framework also needs to be accurate and pertinent

to the given policy context.” (Waglé, 2008, p. 67)

According to Coromaldi and Zoli (2011) specific hypothesis relating to four main

aspects have to be established in order to derive this indicator: (i) The choice of

dimensions and indicators, (ii) establishing a weighting structure, (iii) developing an

aggregation strategy, and (iv) the implementation of thresholds.

(i) The choice of dimensions and their explanatory indicators which should guarantee

an accurate reflection of an individual’s quality of life is a crucial and probably the

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most controversial aspect of multidimensional measures. Considering every possible

element that depicts wellbeing is simply impossible and would always be susceptible

to severe critiques regarding individual preferences. Thus, the challenge is to

introduce indicators that are representatives of a certain dimension and not to cover

every single possible aspect of wellbeing. It is undeniable that any approach has to

be based on normative assumptions and judgments on what should be considered

as central for a desirable quality of life.

A conventional strategy for choosing dimensions and indicators is to conduct existing

scientific research and their elaborate set of indicators on the one hand or work

together with experts and focus groups in order to implement indicators especially

relevant for the actual research. (e.g. Guhathakurta, Pijawka, & Sadalla, 2010)

Regarding the present paper in particular, the choice of dimensions and indicators

was solely based on existing scientific research due to time-constraints. Thereby the

selection had to be tailored to requirements for a multidimensional index capable of

measuring wellbeing in an industrialized country. This conjuncture is especially

relevant regarding the fact that the majority of multidimensional (poverty) indices are

established for the context of developing countries. (e.g. Salahuddin & Zaman, 2011)

As mentioned, human needs and achievements are a dynamic concept which is

dependent of time and space. The access to clean water for example is an essential

good for a healthy life and its introduction into the United Nation’s Multidimensional

Poverty Index is the logical consequence. (United Nations, 2012) At the same time,

its consideration as an indicator for wellbeing in an industrialized country like the

United States may not be useful for any estimation, as the sweeping majority actually

has access to clean water. Thus, in order to obtain relevant information, poverty lines

and further the understanding of poverty itself, have to be relatively changing in the

same direction as the overall quality of life in a specific region. (e.g. Miller & Roby,

1970; Townsend, 1970)

Generally the content of possible dimensions is abundant, ranging from poverty as a

reflection of individual characteristics such as low educational achievement to

wellbeing influenced by social and economic structures such as a lack of jobs or

public health care2. (Crump, 1997, p. 99)

2 Martínez and Ruiz-Huerta (2000) for example introduce a distinction between a so-called “life-style

approach” that includes non-necessary goods, and a “necessities approach” that excludes the same.

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Above all, the United Kingdom study ‘Monitoring Poverty and Social Exclusion 2010’

(Parekh, MacInnes, & Kenway, 2010), the European Union Indicators of Social

Exclusion (‘Laeken Indicators’) (Atkinson, Cantillon, Marlier, & Nolan, 2002; Dennis &

Guio, 2003; Guio, 2005), as well as Waglé’s works on capability deprivation in the

United States (Waglé, 2009) met particular repercussion in the present paper as they

have been adopted for a similar socio-economic context.

Eventually, five dimensions of wellbeing have been identified as playing a major role

regarding an individual’s quality of life: Capability, Income, Employment, Housing &

Facilities, and Health3. If nothing else, the decision for these dimensions is

underpinned by a broad consensus regarding their importance as aspects of

wellbeing.

Since dimensions in general cannot be observed and therefore measured directly,

the use of explanatory indicators is necessary. Potential indicators require as a

minimum to be ordinal. Asselin (2009) emphasizes that variables like ‘main

occupation of household head’ are not admissible poverty indicators. However, those

can still play an important role in poverty analysis. Despite the argument’s plausibility,

the present paper takes into account ordinal or metric variables only as the focus lies

on the creation of a tool for poverty analysis. Regarding further steps of interpretation

and eventually policy making, the information value added of cardinal variables

should be taken into consideration.

The alternative approach compared to existing multidimensional measurements is

the conversion of ordinal and metric variables into ratios from 0 to 1 to allow in further

steps a new way of identifying the poor by comparing the ratio-based scores with

regional benchmarks. Thereby, a value of 0 would be considered as total deprivation

of a specific indicator, whereas 1 equals no or 0 percent deprivation. This method

allows the index to benefit from a typical advantage of unidimensional measures:

Ranking the units of observation according to a complete ordering which is

particularly important for targeting policies and more sophisticated poverty analysis.

(Asselin, 2009) Table 3.1 provides an overview of the five dimensions, their

explanatory indicators as well as the indicators’ mathematical composition of ratios.

3 Several authors stress the consideration of a “social quality of life” (e.g. Asselin, 2009); a possible

introduction to the set of dimensions will be discussed in Chapter 5.

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Dimension Indicator Definition*

Capability

Pre-School enrollment pct of Children enrolled in Pre-School

Educational attainment 1 - pct of population ≥ 25 years with less than High

School Degree

Ability to speaking

English

1 - pct of population with not imputed ability to speak

English

Literacy 1 - pct of population lacking basic prose literacy skills

Income

Relative Income (Household income) / (Median IL-State income) ≤ 1**

GINI - Coefficient 1 - GINI coefficient

Jobless Households 1 - pct of population living in a jobless household

Children in Poverty 1 - pct of Children in Poverty

Employment

Unemployment 1 - pct (Unemployed 20-64 years) - (NAIRU 3%) ≤ 1**

Young Unemployment 1 - pct (Unemployment 16 years and over) - (NAIRU

4%) ≤ 1**

Housing

and

Facilities

Over occupancy 1 - pct of Rooms with more than 2 occupants per room

Mortgage Status 1 - pct of Housing units with second mortgage and

home equity loan

Kitchen Facilities 1 - pct of Households lacking Kitchen Facilities

Plumbing Facilities 1 - pct of Households lacking Plumbing Facilities

Phone availability 1 - pct of Households without Phone

Vehicle availability 1 - pct of Household without Vehicle available

Health

Health Insurance

Coverage 1 - pct of population uninsured

Life Expectancy (County Life Expectancy) / (Median IL-Life Expectancy)

≤ 1**

Table 3.1: Dimensions & Indicators, mathematical structure and geographic scale

* In order to retain ratios with 1 as the highest score, the majority of indicators

had to be measured as (1 – percentage of I).

** Four indicators (Relative Income, Unemployment, Young Unemployment, and

Life Expectancy) receive special treatment regarding their conversion into

ratios. In order to keep up equal weights for every indicator the resulting ratios

of these four indicators are capped at 1 or 100 percent. This method is plausible

insofar that with having for example a higher income than the regional

benchmark (≥1), one would assume a non-deprived situation.

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The indicators’ scale is based on the Census Tract Level, data is retrieved from the

American Community Survey – 5 year estimates 2006-20104. (U.S. Census Bureau,

2011a)

“Census tracts are small, relatively permanent statistical subdivisions of a

county delineated by local participants as part of the U.S. Census Bureau's

Participant Statistical Areas Program. The U.S. Census Bureau delineated

census tracts in situations where no local participant existed or where local or

tribal governments declined to participate. The primary purpose of census

tracts is to provide a stable set of geographic units for the presentation of

decennial census data. […] Census tracts generally have between 1,500 and

8,000 people, with an optimum size of 4,000 people. (Counties with fewer

people have a single census tract.) When first delineated, census tracts are

designed to be homogeneous with respect to population characteristics,

economic status, and living conditions. The spatial size of census tracts varies

widely depending on the density of settlement.” (U.S. Census Bureau, 2012a)

Only three exceptions - Literacy, Health Insurance Coverage, and Life Expectancy -

are county-based and had to be broken down to Census Tracts with the necessary

assumption that all Census Tracts in a specific county obtain the same indicator-

values.

The choice for Census Tracts as the preferred scale reflects a compromise between

data availability, maximization of details, and minimization of complexity in order to

keep up comparability as well as easy usage for potential policy planning.

Undoubtedly, the most detailed scale would be the individual level, including the

maximum information regarding quality of life and especially the connections of an

individual’s several dimensions of wellbeing. Having the individual as the unit of

analysis would allow comparisons across certain socio-economic groups of the

society. (Alkire & Santos, 2010) Although several authors stress the unprecedented

amount of available information especially in the United States (e.g. Alkire & Foster,

2011), it is a well-known fact that multidimensional measurements require

4 One has to be aware of binding constraints like estimation errors on a larger scale, caused using

data conducted by a survey. “[…] [A] key priority for future work on multidimensional poverty must be

gathering more and better data around core areas […].” (Alkire & Santos, 2010, p. 13)

19

comprehensive data on a large scale, which are often difficult to come by. (Ravallion,

2011; Waglé, 2008) Eventually, the necessary data for individual scale is not

available considering this research’s constraint of time and resources.

Capability dimensions can be defined as an individual’s ability to achieve certain

‘functionings’ in the sense of valued elements by a society. Capability is the freedom

to choose in order to realize these functionings. (Sen, 1999) “At a general level,

functionings conceived as the outcomes have constitutive values whereas the

capabilities taken as the means have instrumental values to make such functionings

happen.” (Waglé, 2009, p. 510) The indicators established to reflect a society’s

capability (see Table 3.1.) provide the “freedom to do the things that one wants to do

including earning an income needed to maintain a desired lifestyle. Income or the

type of life maintained with it constitutes the outcome or functioning with education

having an instrumental role in achieving it.” (Waglé, 2009, p. 510) In the present case

an additional indicator has been implemented in order to obtain a fitted value for the

context of the United States as the ‘ability to speak English’ is often a requirement for

– especially well-paid – jobs. Consequently the quality of life should be assessed in

terms of an individual’s capabilities because it is the capability that defines the choice

of alternative combinations of functionings a person can achieve. (Vero, 2006)

Further, the present research does not take into account the assumption of external

capabilities (Alkire & Santos, 2010) and effective literacy (Basu & Foster, 1998),

stating that for example other household members benefit from the abilities of one

literate person in the household. Above that, the data selection basing on Census

Tracts would not allow assumptions on the individual level at all.

Although it is widely recognized that deprivations are – as already mentioned – a

multidimensional phenomenon, monetary indicators still play a fundamental role as

one aspect of the multidimensionality. (Betti, D'Agostino, & Neri, 2003) The plausible

approach of measuring the monetary dimension would be the investigation of

consumption. However, this causes massive complexity, as consumption is not only

influenced by available income, but also by tastes and preferences all together

conditioned by time, place, culture and other factors. Thus, “income has been a

widely used proxy measure of consumption assuming that it can capture not only the

ability to consume but the actual consumption as well.” (Waglé, 2008, p. 17) The

20

income proxy implemented in the present paper is not adjusted for regional variations

in the cost of living. Crump stresses this constraint in particular. (Crump, 1997) A

possible correction for that is to introduce further indicators to the income dimension,

concretely the GINI-coefficient as well as the percentage of jobless households and

children in poverty.

The understanding of employment as a dimension of wellbeing is not only referring to

a certain security of income or available insurance, but also regarding the notion of

employment as one form of an individual’s connection to society. Additionally, special

attention is paid to young unemployment as it is considered as the most critical form

of unemployment. (e.g. Freeman & Wise, 1982) In order to obtain the scores for

unemployment and young unemployment, their specific ratios are corrected for full

employment considering the ‘Non Accelerating Inflation Rate of Unemployment’

(NAIRU) (Gordon, 1996) and capped with a value of 1. This expresses the

assumption that unemployment rates below 3 percent for 24-year-olds and over and

4 percent for 16-year-olds and over are considered as no deprivation.

The Housing & Facilities dimension is notably altered by the indicators’ requirements

for an industrialized country. Considering a lack of plumbing facilities as deprivation is

basically a reflection of a wealthy society at a certain point in time. In the same vein

over occupancy defined as housing units with more than 2 occupants per room is

primarily plausible in the present context5. Of course a major distinction has to be

drawn whether the housing unit is owned by the occupant or not. The economic

consequences of being fraught with both a second mortgage and a home equity loan

obviously can be devastating.

Although some authors question the possession of durable goods as a valuable

poverty indicator (Asselin, 2009), not obtaining these objects is evaluated as a form

of deprivation by the majority of scientific papers (e.g. Miceli, 2006). Though, the

interpretation of goods as poverty indicators has to be done with caution. One can

definitely argue that not possessing a vehicle is a reflection of taste and preferences.

However, the present paper considers this as a deprivation due to the extraordinarily

high value in America’s society.

5 A more detailed discussion about the integration of housing as indicators of wellbeing can be found

in Groh-Samberg (2004).

21

“A true indicator of the physical quality of life, for example, is the status of health as it

can accurately gauge the state of one’s physical life.” (Waglé, 2008, p. 16) Yet,

health was the most difficult dimension of wellbeing to measure. Comparable

information regarding a person’s health condition is missing on a large scale, mainly

due to privacy concerns. The general approach is to measure a long and healthy life,

whereby possible diseases are not taken into account as this would require an

unreasonable judgment over the ranking of certain illnesses. A plausible indicator

that deals with diseases would be child development in the sense of overweight,

malnutrition and disabilities, as shortfalls in child development often lay the

foundation of a general shortfall of wellbeing at adulthood. Eventually, shortfalls in life

expectancy as well as health insurance coverage (Alkire & Foster, 2011)

representing the security of acceptable treatment in case of illness are introduced as

indicators of an individual’s health quality.

(ii) Establishing a weighting structure for both dimensions and indicators describes

another key aspect of multidimensional measures. The scientific debate on how to

introduce weights yields various possibilities. By following the conventional approach

(e.g. Alkire & Santos, 2010; Alkire & Foster, 2011) this methodology implements

equally weighted dimensions and among them equally weighted indicators

(Equation 1).

Equation 1:

Hence, every dimension has an imputed equal importance for wellbeing.

(Townsend, 1979) However, the impact of introducing unevenly weighted indicators

based on statistical methods will be discussed in a subsequent chapter.

(iii) Having specific indicators built up, it is another controversial question whether to

aggregate them to an overall indicator or not. (Lustig, 2011; Brandolini, 2008)

Investigating an overall index postulates an understanding of poverty as a combined

22

shortfall based on multiple deprivations and further using a certain threshold to

identify the poor and non-poor (Equation 2). (Waglé, 2008)

Equation 2:

This is not only a simple, but also often useful interpretation of a multidimensional

context. (Brandolini, 2008) Despite the fact that a simple summary of a complex

phenomenon is better provided by an overall index, Ravallion (2011, p. 237) stresses

a vivid example for a situation in which an aggregated index might not be the first

choice: “Imagine you go for your annual medical checkup. Your doctor does all the

usual tests, but tells you that she will base her assessment solely on a single

composite index — rescaling and averaging all the test results. You would be well

advised to get a new doctor!” Considering the single dimensions of potential

deprivations rather than their aggregation reduces the inevitable loss of information

of aggregation-based approaches. (Coromaldi & Zoli, 2011; Brandolini, 2008)

An elegant way to benefit from the advantages of both approaches is provided by a

special version of comprehensive strategies6: What Ravallion calls a “deprivation

aggregation” (Ravallion, 2011) is basically the identification of the poverty status in

each of the dimensions and then the aggregation into a composite index. Methods

like this (e.g. Oxford Poverty & Human Development Initiative, 2012) allow

aggregation of an overall index as well as the latter’s decomposition in its

dimensions. Therefore they provide policy makers with a powerful tool to investigate

dimensional deprivations of specific groups by unpacking the overall index. “[This

method] embodies Sen's (1993) view of poverty as capability deprivation and is

6 “According to Brandolini (2008), two kinds of strategies can be distinguished: ‘supplementation’

strategies, which consider all well-being items one by one and in conjunction with income, and

‘comprehensive’ strategies, which make an attempt to reduce the multidimensionality into one or more

summary measures. Albeit undoubtedly simpler, the first solution prevents the researchers from

having a synthetic picture of people’s standard of living. Within the second category, a further

classification distinguishes between ‘aggregative’ strategies, where a single well-being indicator

summarizes all the information provided by several life domains, and ‘non-aggregative’ strategies,

which analyze deprivation in distinct dimensions separately, by constructing multidimensional indices

specific for each domain.” (Coromaldi & Zoli, 2011, p. 4)

23

motivated by Atkinson's (2003) discussion of counting methods for measuring

deprivations.” (Alkire & Foster, 2011, p. 477)

The present paper addresses this approach twofold: On the one hand its index

remains easy to be decomposed into the explanatory dimensions (see Table 3.2. and

Chapter 4.4.). Further, it considers ‘deprivation aggregation’ by introducing an

alternative ‘deprivation count’ that emerges being similar in its results (see Chapter

4.1.).

(iv) The fourth and last of the major steps is the implementation of a threshold

identifying the deprived and non-deprived. A large number of poverty measurements

including the income-based methods entail a major limitation: “[T]hey need to

dichotomize the population into the poor and the non-poor by means of the so called

poverty line.” (Betti, Cheli, Lemmi, & Verma, 2006, p. 115) Basing distinction between

poor and not poor on for example a certain exact amount of available income

guarantees a simple measurement process, but can be criticized as too rigorous. In

contrast ‘poor’ is rather a vague predicate with allowance for borderline cases, the

absence of sharp borderlines in general and susceptibility to a Sorites paradox.

Considering the paradox of the heap or so called Sorites paradox, giving a poor

person one dollar will not make that person rich. By repeatedly giving that person a

dollar she will become rich indeed. In that case, drawing a sharp line is impossible.

Likewise is wellbeing a continuum of living standards from poor to rich and therefore

making sharp cutoffs arbitrary. (Mack & Lansley, 1985) Consequently, the clearest

statement possible is to identify a person as “borderline poor” (Qizilbash, 2006), but

any dichotomy segmentation reflects reality in a too simplified and therefore defective

way. Hence, the so called ‘fuzzy set’ approach allows a continuous transition (for

example from 0 to 1) and can therefore eliminate abrupt changes like a single

threshold would reflect a leap between 0 and 17. (Vero, 2006) “In other words,

poverty should be considered as a matter of degree rather than as an attribute that is

simply present or absent for individuals in the population.” (Betti, Cheli, Lemmi, &

Verma, 2006, p. 115f)

However, because it is an easy-to-use method, the value of simple thresholds for

policy planning should be acknowledged. In the present methodology, any

implementation of thresholds or multiple cutoffs for every single dimension as well as

7 For a detailed discussion of the fuzzy approach see Qizilbash (2006).

24

for the aggregated index is left open. This methodological construction of not

dictating exact thresholds provides a maximum of flexibility for any policy making or

analytical approaches.

The present research introduces the median score of the state of Illinois (overall and

for every dimension) as the regional benchmark and therefore the deprivation

threshold (see Table 3.2.). In doing so, the boundary value between deprived and not

deprived is exactly defined. Yet, the existence of this line doesn’t make any point

about the categorization of poor and non-poor. Being deprived by a relatively small

percentage in a certain dimension is not necessarily resulting in being poor,

especially when additional dimensions are introduced into the equation. Thus, the

issue of poverty as a vague concept is addressed indirectly by introducing the

possibility of trade-offs among dimensions8. Standard approaches of

multidimensional measurements don’t consider values above a certain deprivation

threshold. Therefore, possible trade-offs among dimensions as an essential aspect of

wellbeing cannot be measured using a unidimensional indicators or the majority of

multidimensional indices of poverty and wellbeing. For instance, a person classified

as deprived because of the absence of health insurance coverage, but with a relative

income above a certain level could theoretically compensate for the lack of insurance

by drawing on his or her income surplus. Consequently, by ignoring information

above the deprivation threshold any substitutability or complementarity between

dimensions is ruled out. (Thorbecke, 2011)

Although it can be stressed that the estimation of trade-offs and complementary

relations is too complex that it would be inopportune from an operational standpoint,

this approximation represents a crucial value added regarding the understanding of

multidimensionality and beyond that it is able to attenuate the negative side-effects of

sharp thresholds.

Summarizing, the relative approach addresses the fact that the concept of poverty or

wellbeing is dynamic regarding time and place. Building upon ratios, a complete

ordering expressed as percentage difference to the regional benchmark is possible.

8 Due to massively differing rates of substitution across societies/economies (Lustig, 2011, p. 228f),

the introduction of possible trade-offs impedes any comparison attempts. Yet, it is already mentioned

above that the present measurements are intentionally tailored to the demands of the United States

and not subject to international comparisons.

25

Additionally, by using those ratios, the resulting indices for dimensions and indicators

guarantee strong flexibility for drawing poverty lines or creating corridors for policy

making as they are partly implemented in monetary indicators. (e.g. Terpstra & et al.,

2010) And finally, the introduction of estimating possible trade-offs weakens the

strong normative influence of the median as the deprivation threshold and considers

an important aspect of multidimensionality.

Table 3.2.: Measuring the Multidimensional Index

26

4. Results

The measurement outcomes can be rehashed in several possible ways, depending

on which information is required. Following this paper’s central argumentation that

the focus should lie specifically on the multidimensional perspective, the so called

deprivation count (Chapter 4.1.) as well as the overall index in dependence to its

decomposition (Chapter 4.2. and Chapter 4.3.) can be considered as the most

valuable ways of presenting the results. Further, Chapter 4.4. discusses the

measurement outcomes in relation to conventional unidimensional income-based

measures.

4.1. Deprivation Count

Simple counting of the number of dimensions in which a given region or individual is

regarded as deprived is not only a very elementary, but also widely used method for

visualizations because it provides an immediate picture of especially critical areas for

potential poverty alleviation programs.

Most importantly, one has to decide where to draw the line(s) to identify the poor.

Again, this step’s basic thinking is similar to any above-mentioned issues regarding

thresholds. However, building on Waglé’s fundamental approach of identifying

different cases of poverty (Waglé, 2008, p. 70), the introduction of poverty lines turns

out to be less arbitrary than given by the information available: Waglé’s scenario

deals with three dimensions (Economic-Wellbeing, Capability and Social Inclusion).

Being deprived in one of these dimensions ranks an individual as “poor”. Deprivation

in two dimensions is similar to “very poor” whereas being deprived in all three

dimensions is defined as “abject poor”.

Entering this down-to-earth approach into the present methodology with five

dimensions yields following plausible configuration (Definition 1).

27

Definition 1:

Number of dimensions in deprivation (n) = 0;1: Nonpoor

n = 2: Poor

n = 3: Very poor

n = 4; 5: Abject poor

This ranking pays especially attention to the rather rigorous definition of deprivation

by considering the state’s median as the benchmark for deprivation (see Chapter 3).

Accordingly, 808 of 3123 Census Tracts in the state of Illinois would be considered

as nonpoor, 636 as poor, 639 as very poor, and eventually 1038 as abject poor (see

Map 4.1.1.9). At first appearance the results seem exceptionally high. Yet, with due

regard to reminding that deprivation is present when falling below the state median,

the results become more plausible. Additionally, a principal accordance of multiply

deprived Census Tracts and areas with high income-based poverty rates (Terpstra &

et al., 2010) can be observed.

It is essential to take into account that a deprivation count cannot provide statements

regarding the severity of deprivation in a given dimension. Counting deprivations by

scaling dimensions in a dichtomous way fulfils the initial purpose of addressing areas

that deserve special attention only. Drawing detailed conclusions necessitates

deeper analysis.

A simple approach addresses the fact that poverty can feature different directions:

On the one hand – as it is measured with help of a deprivation count – poverty can

manifest itself as a more or less severe deprivation across several dimensions.

Additionally, the extent of deprivation occuring in a single dimension can justify the

term ‘poor’ as well. This plays an important role for policy making as poverty always

tends to be a combination of both vectors.

Consequently, this research defines each a vector regarding the ‘horizontal form of

poverty’ and the ‘vertical form of poverty’ which are shown in Graph 4.1.1.: The

intensitiy of deprivation can be measured with one composite index as well as with a

measure for every single dimension always as a negative percentage difference to

9 For a detailed discourse about poverty maps see Benson, Minot, and Epprecht (2007) as well as

Crump (1997).

28

the regional benachmark from 0 to 1. The volume of deprived dimensions is

represented by a simple deprivation count.

Graph 4.1.1.: Horizontal & Vertical form of poverty

29

Map 4.1.1. Deprivation Count, State of Illinois

30

4.2. The Aggregated Index of Wellbeing

The aggregated index of wellbeing in turn presents the vertical form of poverty as it is

defined as the percentage difference of a Census Tract’s ratio to the regional

benchmark. Yet it cannot provide information regarding the horizontal form of

poverty.

Interpreting the aggregated index serves a similar purpose as counting the number of

deprived dimensions: It can provide an informative basis for further analysis. In

contrast to any deprivation count method, the aggregated index of multiple

dimensions - as it is measured in the present paper – contains information about the

extent of overall wellbeing and therefore allows for statements regarding the severity

of deprivations ergo the vertical extent of deprivation rather than the horizontal one.

Table 4.2.1. provides a summary of the overall deprivation scores for Census Tracts

in the state of Illinois. Showing a range of 0.2969 percentage points from 0.9496 to

the minimum of 0.6527, the median overall score of wellbeing is 0.8832. In other

words, the median Census Tract’s population in Illinois is deprived by 11.68 percent.

Table 4.2.1.: Wellbeing in Illinois, Census Tract Scores

0

100

200

300

400

0.65 0.70 0.75 0.80 0.85 0.90 0.95

Series: MPI_UW_OVERALL

Sample 1 3123

Observations 3123

Mean 0.873794

Median 0.883200

Maximum 0.949600

Minimum 0.652700

Std. Dev. 0.040682

Skewness -0.974774

Kurtosis 3.784237

Jarque-Bera 574.6014

Probability 0.000000

31

Map 4.2.1. presents this equally weighted index whereat the categorization is based

on the values’ standard deviation. Aligning Census Tracts (or any given entities)

according to their standard deviation is a useful method in order to gain a general

view of a region’s shape. Expectably, the majority of more severely deprived

individuals can be assumed in mainly urban areas; besides the existence of some

larger rural Census Tracts spread across the state. Map 4.2.2. provides a

visualization of the Chicago area where the most deprived Census Tracts are

located.

The results go along with the previous deprivation count; essentially producing an

overlap of more severely deprived and multiply deprived Census Tracts. Again,

Waglé’s definition of poverty (2008, p. 70) can be implemented - in an adapted form -

for every Census Tract and consequently its inhabitants (Definition 2).

Definition 2:

[percentage difference to state median (r) > -0.5 * Std. Dev.] = Nonpoor

[-0.5 * Std. Dev.; -1.5 * Std. Dev.] = Poor

[-1.5 * Std. Dev.; -2.5 * Std. Dev.] = Very poor

[r < -2.5 * Std. Dev.] = Abject poor

32

Map 4.2.1.: The aggregated index of multiple dimensions in the state Illinois

33

Map 4.2.2.: The aggregated index of multiple dimensions, clip of Chicago area

34

4.3. Decomposing the Index of Wellbeing

As Lustig (2011, p. 232) refers to Ravallion (2011): “What is the usefulness of

aggregating deprivations into a composite index if, for policy purposes, a

disaggregation will be indispensable?”

The value added of analyzing the aggregated index is limited indeed, although one

can already draw inferences about the situation of poor people in certain areas. This

paper stresses the argument that a more revealing procedure is to decompose the

index of wellbeing into its initial dimensions. (e.g. Nolan & Whelan, 2010)

Map 4.3.1. shows the area of Champaign County, IL and the selected Census Tract

representing the University of Illinois at Urbana-Champaign’s campus. According to

the overall index the campus area (overall score: -0.0689) would be considered as

very poor, being in numerous company of other Census Tract’s located in the urban

area of the twin city of Champaign-Urbana as well as the city of Rantoul in the

northern part of Champaign County. A similar conclusion can be found as well in the

official poverty statistics provided by the U.S. Census Bureau where the campus

area’s income-based poverty rate lies at around 44 percent. (U.S. Census Bureau,

2010) However, comparing unidimensional and multidimensional measures requires

special precaution which will be discussed in the upcoming chapter.

Decomposing the index offers important additional insights. In contrast to the two

previous approaches of counting deprivations and aggregating one index, this

method enables the interpretation of the horizontal as well as the vertical form of

poverty.

Interpreting Map 4.3.2. renders some important indications of how the different

dimensions affect the situation of the poor. Accordingly, the campus area shows

rather good or ‘acceptable’ values regarding employment and capability. In contrast

to that, the situation regarding housing and income is critical, even more as the

campus area stands out as the negative peak in both dimensions (together with a

very small number of other Census Tracts).

Table 4.3.1. offers a comparison of the example area’s dimension scores and its

state benchmarks.

35

Map 4.3.1.: The aggregated index of multiple dimensions, Champaign County, IL

36

Map 4.3.2.: Decomposition, Champaign County, IL

37

Table 4.3.1.: UIUC dimension scores compared to state benchmarks

38

4.4. Comparison with Conventional Poverty Measures

A very important aspect for any evaluation attempts of comparing uni- and

multidimensional measures is the fact that these measures differ not only in regard to

their philosophical definition of poverty itself, but also regarding what they actually

measure. (Asselin, 2009)

So far, this paper presented two different approaches of measuring multidimensional

poverty and interrelated their outcomes with unidimensional measures respectively

the income-based approach as a reference:

- The deprivation count consists of simply counting an individual’s number of

deprived dimensions. Its outcome can be considered – as mentioned above –

as the horizontal form of poverty. (Due to the unavailability of individual data,

the information had to be aggregated to larger scale.)

- An index as it is defined in this paper measures the extent of deprivation,

whether in one or in several dimensions, again referring to an individual or the

aggregation to a certain scale.

- The unidimensional income-based poverty rate expresses the ratio of

individuals being considered as poor in a certain area.

Thus, the main question in the case of official poverty rates is ‘How many individuals

are poor in a given region?’ whereas a multidimensional measure according to this

research asks for ‘How poor is every single person in a given region on average?’

Consequently, comparing the two main readings turns out to be inaccurate when

looking at the numbers only.

Nonetheless, it is possible to treat the official income-based poverty rate in the same

way as the multidimensional approach by measuring the percentage difference of a

Census Tract’s poverty rate to the state’s median poverty rate. The resulting values

contain the same information as their multidimensional counterpart.

The outcomes of comparing the values for every Census Tract presented in Table

4.4.1. show that - to a considerable extent - both measures identify the same

Census Tracts for example either as areas of high poverty rates or as severely

39

deprived areas. The mean difference of both measures is 0.018 percentage points

and therefore can be considered as arbitrary.

Table 4.4.1.: Comparison of uni- and multidimensional measure, percentage points

Thus, one could interpret the similar results as the ‘proof’ that the multidimensional

measure is correct to a certain extent. Or in other words: Either both the income-

based poverty rates and the multidimensional approach are correct or both fail to

capture poverty. However, the latter approach is more versatile as it provides

additional information regarding the composition of poverty and therefore enables to

measure poverty more accurately.

0

100

200

300

400

500

600

700

800

900

-0.2 -0.0 0.2 0.4 0.6 0.8

Series: PPDIFF_INDEX_POVSC

Sample 1 3123

Observations 3123

Mean 0.018405

Median -0.014200

Maximum 0.859400

Minimum -0.178100

Std. Dev. 0.113649

Skewness 1.972039

Kurtosis 8.793031



40

4.5. Introducing ‘Natural Weights’

With reference to the above mentioned discussion about weighting strategies it is

legitimate to investigate the methodology after introducing an alternative weighting

system than that of equal weights. (Thorbecke, 2011)

Among the great variety of different methods, the present paper introduces what shall

be called ‘natural weights’ and is a determination of the optimal weight associated

with each attribute using conventional statistical methods. (e.g. Dewilde, 2004; Nolan

& Whelan, 1996) The explanatory values are obtained running a standard regression

analysis with the income-based poverty rate as the dependent variable. The specific

weights can be gathered from the coefficient column in Table 5.1. However, what is

immediately revealed is the fact that this table presents a drastically reduced set of

indicators. Due to insignificant variables for this configuration of regression analysis

as well as suppression of a smaller contingent of variables, the final model contains

of nine indicators representing the five dimensions10. The actual configuration of the

model presents only one of several possibilities. Most importantly the information

criteria show acceptable values.

10

In order to guarantee at least one representational indicator per dimension the estimates for life

expectancy have been retained in the model, despite their assumed 5-percent-acceptance for H0.

41

Table 5.1.: Introducing an alternative weighting system: regression analysis

Now, the aspect in question is whether the alternative weighting system alters the

outcomes for the multidimensional index in a significant way. A comparison of the

equally weighted index and that composed of ‘natural weights’ (Table 5.2.) shows a

normal distribution of percentage points differences with an average difference of

0.003 percentage points. Consequently, regarding their aggregated indices and using

the present set up for both methods, their outcomes are basically identical.

42

Table 5.2.: Comparing indices composed of equal- and natural weights

A significant difference might be concealed behind the shift in the dimension

composition which implicates different interpretations for single dimension scores and

therefore causes altered results regarding the interpretation of the decomposed

index. However, the major impetus for not implementing a weighting structure such

as the model above is serious consideration concerning wrong causality. Although

the literature is in general not dealing with the issue at all, it seems to be a plausible

conclusion that for example not obtaining a vehicle can be explained due to the fact

of poverty and not vice versa as the actual model would suggest.

0

100

200

300

400

500

-0.10 -0.05 -0.00 0.05 0.10 0.15 0.20

Series: DIFF_MEDIAN_UW_W

Sample 1 3123

Observations 3123

Mean -0.003153

Median -0.011100

Maximum 0.191500

Minimum -0.105700

Std. Dev. 0.042436

Skewness 1.092241

Kurtosis 4.731567



43

5. Suggested Further Research

During the process of creating an alternative measure of wellbeing based on

multidimensional implications, several occurrences evoke issues of various kinds.

Some have already been discussed in this paper. The following chapter seeks to

present the most fervent topics, less to criticize multidimensional indices, but rather to

spur further research.

On the very beginning it is the choice of dimensions and indicators which is subjected

to widespread discussion. While the general debate11 has already been dealt with in

this chapter the focus rests on the actual approach.

First, indicators for the possession of durable goods cannot distinct between not

having these goods because one cannot afford them and not possessing as a matter

of taste. (Brandolini, 2008) Thus, it would be informative to be able to control for the

intentions of consumption or non-consumption. However, this task reaches far

beyond this paper’s ambitions.

Further, as the notion of poverty shifts towards overall quality of life or human-

wellbeing, this tendency should be reflected in the choice of indicators too. The

present paper tries to emphasize quality of life mainly by measuring health (besides

alternative definitions of employment etc.). The index could be enriched with more

social aspects of wellbeing (Waglé, 2008) such as gender and racial equality, child

development, or other politically loaded vocabularies such as choice, opportunities,

and freedom. This would also alter the way poverty is understood in general towards

a more comprehensive notion. (Waglé, 2009) In its initial structure, this approach

included a sixth dimension of ‘social quality of life’. Ultimately it had to be excluded

from the set of indicators due to severe data restrictions. One possible way of gaining

such information would be conducting surveys asking about self-perceived quality of

life. (e.g. Goedhart, Halberstadt, Kapteyn, & Van Praag, 1977; Guhathakurta,

Pijawka, & Sadalla, 2010)

Another aspect which could be taken into consideration is more extensive spatial

analyses such as gravity modeling. For example introducing a proxy such as the

access to goods or markets could provide valuable information not only for the choice

11

“The need for selection and discrimination is neither an embarrassment, nor a unique difficulty, for

conceptualizing functionings and capabilities.” (Sen, 2008; cited Alkire & Santos, 2010, p. 11)

44

of indicators in the first place, but also for explaining interrelations between deprived

areas and their environment in terms of discrepancies to neighboring areas,

employment centers and so on.

As mentioned above, information for this research is gathered or aggregated to

Census Tract level because of data restrictions on the one hand and lucidity

regarding policy making. It has also been mentioned that scaling data on the

individual level would add very important information. On the individual level it is

possible to make clear statements regarding a person’s situation of wellbeing across

the whole set of dimensions. Right now, having obtained Census Tract data, the

wellbeing status can only be estimated as a Census Tract’s inhabitants’ average.

Consequently, one can make rough statements, yet it remains unclear whether

deprivations are distributed rather evenly across a population or whether a smaller

number of individuals are severely deprived across several dimensions and others

are not deprived at all.

Finally, considering longitudinal measures can be undoubtedly vital for any kind of

policy making. “Persistence and movement over time is an equally important aspect

of the intensity of deprivation, requiring longitudinal study at the micro level and in the

aggregate.” (Betti, Cheli, Lemmi, & Verma, 2006, p. 115)

45

6. Conclusion

The possibilities for a science-based treatment of poverty as a multidimensional

phenomenon are abundant, likewise is the debate. Undoubtedly, alternative

measures of wellbeing receive growing attention (Brandolini, 2008), yet

comprehensive integration into policy-oriented analysis is still to come. (Brady, 2003;

Glennerster, 2002) The United States in particular “do not even recognize the

relevance of incorporating this approach in their official poverty measurement

attempts.” (Waglé, 2009, p. 528)

The impression is that multidimensional analysis is sometimes reduced to

bunching together a number of indicators of living standard through some

multivariate technique. But neglecting the role of underlying assumptions may

be extremely misleading. It is of the utmost importance to develop a close link

between analytical characterization and practical application of measurement

tools.” (Brandolini, 2008, p. 28)

A more comprehensive application of wellbeing would guarantee a more sustained

impact. For example, alleviating educational or health-related deprivations tends to

improve other dimensions or capabilities as well and therefore renders a larger

impact than targeting exclusively based on low income. (Sen, 1995)

“It is this direct relationship between the empirical findings and policy planning

that gives appropriate meaning to the application of the multidimensional

approach to poverty.” (Waglé, 2008, p. 134)

This research tried to proof the applicability of alternative measures of wellbeing for

more accurate policy making. Based on the understanding of wellbeing as a dynamic

phenomenon, individual poverty is relatively put in the context of a person’s

environment. The derived measurements are corrected for the society of the United

States offering sophisticated lifestyle choices. Hence, the method should be regarded

as a useful instrument rather than a strict paradigm of how to interpret poverty.

(Bourguignon F. , 1999)

46

Interpreting multidimensional measures can add a very important information gain to

any poverty alleviation programs. The findings go along with existing studies related

to the field of multidimensional poverty measures. (Coromaldi & Zoli, 2011) Whereas

the aggregated results are very similar to those provided by income-based poverty

measurements, a decomposition of the overall index can explain wellbeing and

consequently poverty more accurately. It is necessary not to exclude monetary

indicators, but to broaden their explanatory value by shifting the focus to capability

aspects.

“[Multidimensional Poverty Measurements assess] the state of human well-

being by focusing on ‘what one has,’ ‘how much prospect one has’, and ‘how

much advantaged or disadvantaged one is in society’ toward improving such

prospect with all contributing to ‘what one can have.’ Although ‘how much one

has’ is important, as it is the means by which one can acquire human well-

being, poverty is a more complex social phenomenon and incorporating more

information is necessary to draw its accurate picture.” (Waglé, 2007, p. 16)

47

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