STOCKHOLM UNIVERSITY

DEPARTMENT OF ECONOMICS

Schooling in poor

families The effect of a Conditional Cash Transfer on

school attendance in Brazil

A Minor Field Study

Pierre Liljefeldt

9/29/2009

Presented autumn 2009

Abstract: Brazil is a country of striking inequality. Higher education levels have been

identified as a crucial measure to break the poverty trap. In this paper the Brazilian

population sample PNAD2006 is used to carry out the method Propensity Score

Matching to estimate the effect of the Brazilian cash transfer Bolsa Família on school

attendance. The results show that the program has a positive, although limited, effect on

school attendance and that little would be achieved in terms of school attendance by

raising the handout for the whole eligible population. The quality of the results can

however be questioned, since all the properties of the method have not been fulfilled.

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Table of content

1 Introduction 3

2. Theory and Rationale 5 3. The literature 9 4. Method 10 4.1 Propensity Score Matching 12 4.2 The covariates 13 4.3 The matching 13 5. Conditional Cash Transfers 15 5.1 Bolsa Família 15 5.2 Design of Bolsa Família 16 6. Data 20 6.1 The core variables 21 6.2 Descriptive statistics 22 7. Results 22 7.1 Results of Bolsa Família on Schooling 25 7.2 Results of extra cash on Schooling 26 7.3 Analysis of Results 27

8. Summary 28

9. Reference list 31

Graphs and Tables

Graph 1 – Income/Schooling p. 8 Graph 2 – Histogram income p. 19 Graph 3 – Common support p. 23

Table 1 – Nearest neighbor matching p. 14 Table 2 – Cases of matching p. 15 Table 3 – Levels of benefits p. 17 Table 4 – Conditionalities p. 18 Table 5 – Descriptive statistics p. 22 Table 6 – Means of covariates (Bolsa Família) p. 25 Table 7 – Estimation of ATT (Bolsa Família) p. 26 Table 8 – Means of covariates (extra cash) p. 26 Table 9 - Estimation of ATT (extra cash) on school attendance p. 27 Table 1.1 A – Means of each variable in each block (Bolsa Familia) Ap. 1 Table 1.2 A – Observations, common support, st. dev. in each block Ap. 1 Table 2.1 A – Means of each variable in each block (Extra cash) Ap. 2 Table 2.2 A – Observations, propensity scores, st. dev. in each block Ap. 2 Table 3.1 A – Probabilities of the covariates to predict inclusion (Bolsa Família) Ap. 3 Table 3.2 A – Probabilities of the covariates to predict inclusion (Extra cash) Ap. 3

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1. Introduction

Poverty and the ways out of it is a broadly debated issue in the field of economics.

Social programs and different types of cash transfers of innumerous designs have been

tried out since the creation of the welfare state in the first half of the twentieth century.

The latest trend in this field is to attach strings to the cash transfer, demanding a contra-

action of some kind. These have come to be called Conditional Cash Transfers (CCT).

The first programs of large scale were adopted by Brazil and Mexico in the mid 1990s.

Since then it has become a model for many developing countries. The basic thought is

to tackle poverty on more than one frontier. The acute poverty is handled through a

small cash transfer that can be spent as the beneficiary considers best. The long run

poverty trap is aimed to be broken through conditioning the money to activities that are

good for the individual in the long run, but relatively costly in the short run, such as

schooling for children, vaccinations, eating nutritionally right, etc.

The programs have received much attention in the academic world, especially the

Mexican program Oportunidades (earlier Progresa)1. It has been showed by many2 that

the programs has a positive impact on both poverty and school attendance. Even though

the positive effects have been proved, there are still many question marks. Handa et. al

(2008) points out that the CCTs have been treated as a “black box” in the evaluations,

without examining each component of the programs. One can identify several parts that

on their own are crucial to the impact of a CCT; 1) the targeting 2) the levels of

benefits, and 3) the conditionality3. Each and every one of these parts needs to be

examined in detail to understand the true potential of the cash transfers and

consequently, how to use tax payers’ money most efficiently. The purpose of this paper

is to study the Brazilian CCT Bolsa Família (BF) and how being a part of the program

affects school attendance. Further, it is intended to examine how a substantial increase

in the handout, all other things equal, affects school attendance. More specifically, the

1 Papers evaluating Progresa include Coady (2000), de Brauw and Hoddinott (2008), Handa and Davies (2008). 2 See for example Bourguignon et. al. (2003), Coady (2000) Janvry (2006), Rawlings (2003) among others. 3 De Brauw and Hoddinott (2008) Schady and Arauju (2006) and Monnerat et. al (2007) all question the need of conditionality. They argue that it costs more than it gives in return. De Brauw points out that the monitoring of fulfillment of conditionality in Mexico take up 19 % of the administrative costs of the program. Schady mean that the money in itself is the only thing needed to incentivize the families to schooling and Monnerat et. al argues, on basis of the UN resolution of human rights, for the unconditional right to social care. The discussion on conditionality is of course closely connected to the one on targeting, in the sense that it is basically about being pro or against universal social programs.

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question is: How the educational choice of poor families in Brazil is affected by extra

income, and if a “lump sum of cash on the door step”4 changes this choice?

To reach a conclusion, this paper make use of survey data from a national sample of

Brazilian households from 2006 called National Household Survey (Nacional por

Amostra de Domícilios - PNAD) concluded by the Brazilian Institute of Geography and

Statistics (Instituto Brasileiro de Geográfia e Estatística - IBGE) year 2006. Since, for

obvious reasons, it is impossible to compare the same individual inside and outside the

program at the same time one has to find an adequate method to generalize causal

interference. Conducting an ex-post5 evaluation on BF using Propensity Score Matching

(PSM) is the most appropriate way of creating a (as close to) contra factual case. As a

second best option, PSM allows me to analyze the impact of the program on

beneficiaries and comparing them to “the nearest neighbor”, which represents

individuals who are as similar as possible (most probable of becoming participants of

the program) to the beneficiaries.

This paper contributes to an expanding literature on CCTs by shedding light on how

higher handouts affect family behavior in the choice of schooling for their children. It

exist several similar studies with slightly different approaches. Duryea and Morrison

(2004) use PSM to evaluate a Costa Rican CCT with survey data from 2001, in which

they find that the program has significant effects, rising school attendance with 5 %.

Eliana Cardoso and André Portela Souza (2004) evaluate how Bolsa Escola

(predecessor to Bolsa Família) has affected school attendance and child labor using the

PNAD 2000. They adapt PSM and find non-significant effects on child labor but a

strong significant effect by an average of 8 % on school attendance. See the literature

section below for a more systematic review of the literature.

The thesis is constructed as follows: first a theoretical background to CCTs; second a

review of previous research and its findings; third you will find the method; fourth the

history and design of Bolsa Família; fifth the data and descriptive statistics; sixth are

the results, seventh and last, the summary followed by acknowledgments and

references.

4 As questioned by Shea in his paper Does parent’s money matter? (Shea, 1997; p. 2). 5 Based on facts from an event that already occurred to analyze what effects that event has had.

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2. Rationales and theory

There are three clear rationales for implementing CCTs:

1) Short term poverty alleviation - Reduce acute poverty through a direct cash transfer

2) Long term poverty alleviation by Human capital accumulation - Break the poverty

trap by attaching requirements of education and health check-ups to the cash transfer.

3) Economic stability – Secure consumption and living standard for the poorest in

society under a safety net in case of economic crisis (Reimers et al. 2006).

Rationale number two is my focus and also what the rest of this section is about.

According to Jenkins and Schulter (2002) there are two main competing hypotheses

about the effect of income on human capital accumulation: the investment theory and

the good parenting theory. The former holds that income has a direct effect on

education for children. Time and money allocation on children is based on family

income, monetary transfers ease the income constraint of financing children’s

education. Becker (1964), Becker and Tomes (1986) and more recently Shea (1997) all

argues for this theory. The good parenting theory, on the other hand, means that there is

an indirect relationship between income and human capital investment. It says that low

income induces stress for the parents and hence poor parenting. The driving factor is a

dysfunctional family, which is affected by income. Mayer (2002) is the main advocate

of this theory.

Nobel Prize laureate Gary Becker is the father of modern theory on human capital

investment and family economics. In his theory, the choice of the family of whether or

not to enroll their kids in school is based on the direct and indirect costs versus the

future returns to education, limited by the budget constraint. The model assumes that

children have negligible bargaining power in the household, and that they are

instruments of parents’ maximization effort (Cardoso, 2004). The direct cost may be

admission fees, school lunch, transportation, etc. whereas the indirect costs are the

income that is lost when the child no longer spend his/her time working. That makes

child labor the flipside to school attendance in the context of poverty, or economically

speaking, the tradeoff between labor income today versus in the future. The costs are

weighted against the future returns to education, the extra income and life quality the

family will gain from the child attending school. Becker puts this as follows in his

article from 1964:

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� = �� − �

� = ��� − ��� − �� + ��

� = ��� − �

Where W is the net earnings, MP is the actual marginal product (equal earnings), k is the

direct cost, ��� is the marginal product that could have been received, C is hence the

sum of direct cost and forgone income. It is broadly accepted/observed that wage

increases with age and education. This means that ��� constantly increases, making

education more expensive as the child grows older (in terms of indirect cost) (Becker,

1964).

Jenkins and Schulter (2002) list a number of studies and which implications their results

have in their paper, including that 1) the income-effect on education is bigger for poor

than for rich families, which implies non-linearity. 2) The effect of income on education

is larger in early childhood stages. 3) Income has a relatively small effect on schooling

choice relatively to other factors such as race and parental education.

In a perfect market, where parents can borrow against children’s future earnings, the

family will invest in the children until the marginal product of further investment equals

the interest rate (Shea, 1997 and Becker and Tomes, 1986). In the real world, credit

constraints are more common than uncommon, even more so in the poorest income

segments of the population (which to some extent explains point 1 in the previous

paragraph). Public policy can shift the choice either by reducing the direct costs, for

example by making public schools free for the student (as in Brazil) or by changing the

budget constraint with a cash transfer (as with BF).

Laibson (1997) apud6 Varian (2006; p. 557) points out that the household often acts

under hyperbolic discounting, which means that it acts time inconsistent in its allocation

of resources, preferring to consume today than more tomorrow. In a plain economic

world, it can be due to that parents are not sure if their investments on their children’s

education will return to them, which in turn can explain the lower attendance levels

observed on the countryside. The educated youth will more probably seek themselves to

a city to continue their studies or look for more qualified jobs, therefore the returns of

education for the parent are smaller on the countryside (Kochar apud Das et al. 2005).

6 Definition: in the work of; according to

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Another argument is that the time-inconsistency is just risk management from the

families; poor families cannot afford to plan ahead. The low quality school systems that

are often found on the countryside do most often not guarantee any returns to the

investment on the unstable and informal labor market (Patrinos and Safiq, 2008).

Janvry and Sadoulet (2004) claim that social returns to education exceed private returns.

The market fail to fully reward the educated for the benefits they generate for society in

terms of higher employment rates, higher wages (cities with a high supply of college

graduates has observably higher wages for high school graduates), healthier children,

less crime, etc. The Conditional Cash Transfers work to internalize these positive

externalities by incentivizing private behavior that is close(er) to the social optimum. In

other words, it is worthwhile investing in education not only because of the private

returns that the citizens experience, but also the greater social returns to society. In this

sense, BF is not just income distribution to the poor for their gain; it benefits society as

a whole.

The internalization of the externalities is done by adding the aspect of conditionality to

the cash transfer. Like this, the government hopes to shift the families’ behavior to

choosing education over work for the children by adding income with “strings attached”

(as written in the newsmagazine The Economist, Happy Families, 7th of February

2008). If the goal of the cash transfer was only to accumulate human capital,

distributing the cash to families that already would send their kids to school would be a

failure. For them, this would mean just additional income, without any change in

behavior, hence a pure income effect. This, however, is in accordance with rationale

number one of CCTs and is not considered a failure. Recent research (Handa et al 2008

and Janvry and Saudolet, 2006, among others) have been trying to unveil the

substitution effect concerning school consumption, under what circumstances the added

income makes the family reallocate their resources to cover the costs of education

(when education gets cheaper thanks to the benefit). Handa et al. (2008) illustrate the

tradeoff between income and schooling and the possible reactions to a CCT with the

following graph:

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Graph 1

A-E is the budget line before CCT, C-D is the budget line when the program has been

implemented, which is only available if the student attends school at least 85 % of the

classes. Handa et al. (2008) identifies three possible responses from the household to the

program:

1) Households near point A will not react to the program at all, since their indifference

curve (U2) lies above the C-D line; these households have very low preferences for

education and will not comply with the conditionalities – non-participants.

2) Households close to point B will be induced to change their behavior and move from

indifference curve U3 to U1’. For them the program means both a substitution and an

income effect.

3) Households in the segment B-E already consume enough of schooling to receive the

cash transfer, which means that for them the program only exerts an income effect

(move from U1 to U1’).

The effect of the program depends of how the population is distributed over these three

groups. Additionally, the intensity of the handout (part of distance B-C) clearly has the

potential to influence both group one and two. Last, if the minimum school attendance

requirement is lowered, it has the potential to shift the behavior of group one at the

same time as making the cash transfer exclusively an income effect for group two. What

my thesis looks at is the distance B-C, first examining if the handout in BF is large

enough to affect the distance and subsequently the preferences for education; and

second if the extra cash might further change these preference (Handa et. al. 2008).

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3. The literature

There is a vast literature on Conditional Cash Transfers and their effects. Do we know

what works? By César Patricio Bouillon, 2006, lists 51 articles that evaluate 48 CCTs

on their effects. The majority finds that they have positive impact on a range of societal

illnesses, such as school attendance, family’s food consumption, infant mortality and

child labor. Most papers that look at how school attendance rates are affected by CCTs

also look at child labor7, since it intuitionally is the alternative activity of the child in the

context of poverty (also according to Becker’s theory).

One of the papers mentioned above is written by Cardoso and Souza (2004). They take

use of data gathered in Brazil during the years 1992 to 2001 (PNAD) to evaluate the

impact of different cash transfers, including Bolsa Escola, on child labor and school

attendance. The percentage of boys going to school increased from 76.1 % in 1992 to

90.6 % in 2001. The percentage of girls going to school increased from 79.8 % in 1992

to 90.5 % in 2001. The results of the program on school attendance are robust. No

significant net-reduction in child labor can be found, with the conclusion that the

handouts are too low to make a poor family withdraw their children from the labor

market. Their results implicate that school and part time work does not exclude one

another. In some cases, working makes it possible for children to go to school according

to Cardoso and Souza.

Bourguignon, Ferreira and Leite (2003) uses PNAD 1999 with which they conduct an

ex-ante analysis to simulate the impact of the program. The authors examine how time-

allocation for children (work/school) has shifted due to Bolsa Escola and also how the

program has affected the Gini-coefficient. They find that the Gini has decreased by half

a point due to the program. They also find that about 40 % of the 10-15 year old that

were not previously enrolled in school enrolled as a response to the program and among

very poor households the number corresponded to 60 %.

Oliveira (2008) uses a household sample that was gathered in 2005 to evaluate BF,

called Impact Evaluation of the Bolsa Família program (Avaliação do Impacto do

Programa Bolsa Família - AIBF). She uses the PSM method. The results point at robust

negative results of being a beneficiary of BF on school attendance. It is explained by the

existence of other CCTs that run parallel with BF, which the non-treated are a part of, 7 I have decided not to do this to restrict the subject to a reasonable level for a bachelor thesis.

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and that has been run for more time. When studying the time allocation between school

and work Oliveira finds that BF beneficiaries spends significantly more time in school

than the others. Child labor drops as a consequence of BF according to Oliveira (2008),

contrary to results from for example Cardoso and Souza. Caccimali and Tatei (2007) do

however find the same results concerning child labor for children aged 5-15, using

PNAD 2004. They find that the cash transfers have had positive effects in reducing

child labor. The effect is larger in families where the head has a higher average of

schooling and lower if the family makes its living in agriculture.

Carvalho Filho (2008) uses survey data collected in Brazil over four years to examine

the effect of household income on school attendance and child labor. He utilizes an

elder benefit program to reach the effect of income. The results show that higher income

directly increases school attendance. However, he does not find any effect of income on

school attendance for young children (10-11 years old), but large effect on older

children (14-15 years old). Further of interest is that the elder benefit program does not

require the children to go to school, contrary to BF. The possibility to generalize the

results can although be questioned, since children living in households where elders also

reside may possess certain unobservable characteristics.

Shea (1997) studies how family income affects labor outcomes later in life. He uses the

Panel Study of Income Dynamics annual survey, which tracks families and its offspring

over time. Shea finds that it is only in low income groups that income per se matters,

over the national sample there is no statistical significance. He draws the conclusion

that due to imperfection in the capital markets, poor families are constrained to invest in

their children and they are, therefore, more sensitive to income as a determinant of

human capital accumulation. This makes it interesting to look at how almost equal

households react when one of them receives some extra cash.

4. Conditional Cash Transfers – Background

The CCTs first emerged in the middle income Latin American countries of Brazil

(1995) and Mexico (1997). Soon thereafter, it spread all over the continent and today

more than 30 countries over the world have some form of CCT program. The World

Bank is today funding 13 of these programs with a budget of 2,4 billion USD in 2009.

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The World Bank, or regional counterparts, has also been involved in the start-up phase

in basically all the programs. In a recent World Bank report the CCTs were given a

crucial role in dampening the damage of the current financial crisis, keeping recent

poverty alleviation from falling back to earlier levels (Fiszbein 2009).

The first Brazilian CCT, Bolsa Escola, started out on small scale in 1995 as a response

to an academic and public debate8 going on since the 80’s on that policy must not only

address the symptoms of poverty, but also the underlying structural sources. Education

(or more accurately, the lack of it) was identified as one of the most central sources to

poverty. Although schools were available, the poorest children were not inclined to

attend them, due to both direct costs of school uniforms, meals and material and the

high indirect cost of not working9. In the beginning it was not a national program, each

municipality decided on implementation. By 2001 more than one hundred

municipalities and 200 000 families were covered. The programs did although differ in

its design from municipality to municipality because of the lack of formalization of the

program from a central level. As a result, Bolsa Escola (BE), together with Bolsa

Alimentação (BA) and Auxilio Gás (BG) (also CCTs), became national (BE 2001, BA

2001 and BG 2002). Brazil now had three national cash transfer programs, all of them

targeting more or less the same group of people, but with different goals (Lindert et. al.

2007). Due to high administrative costs and the difficulty to get an overview of the

programs, President Luiz Inácio Lula da Silva merged the programs into BF in 2003, a

decision that was the fruit of a long academic debate10 and a discussion between Lula,

James Wolfensohn (World Bank president at the time) and Santiago Levy (designer of

the Mexican CCT Progresa).

Bolsa Família has come to be an important tool in lifting people out of poverty in Brazil

(a country famous as one of the most unequal countries in the world) and a central part

in the current government’s political profile. The government has been successful with

reducing the high inequality numbers lately. During the period 1995 to 2004 the gini-

8 Reflected in 1) The Brazilian constitution from 1988 which formalized social assistance and minimum wages. 2) Articles in the newspaper Folha de São Paulo by economist José Márcio Camargo (December 26, 1991) arguing for social assistance to families with requirements of education 3) In policy documents (A Revolução nas Prioridades) from a group of social scientists from the Universidade de Brasília (led by the today senator Cristovam Buarque) in the beginning of the 90´s. (Lindert et. al. 2007, p. 12). 9 The low quality of schools makes it hard for the labor market to reward schooling. There was a general disbelief that further education was actually going to improve the students’ future wages. 10 Including for example: Camargo and Ferreira 2001; Lavinas et. al., 2001; Costa Cotinho et. al. 2002; Paes de Barros 2003; Ferreira; Ferreira, and Lindert 2003 (Lindert et. al. 2007, P. 14).

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coefficient was reduced from 59,9 to 56 and is today at its lowest in more than 30 years.

The inequality is still striking though, according to the governmental research institute

IPEA it would take Brazil 20 years (with a reduction rate observed the latest years) to

achieve inequality levels of countries at a similar development level (Ipea, 2006).

5.1 Design of Bolsa Família

It does not exist any formal poverty line in Brazil, it does however exist poverty line

specifically for the administration of BF. As of June 2009 this is at 137 BRL monthly

per capita income / family for poverty and 69 BRL monthly per capita income /family

for extreme poverty. Per capita family income is calculated as all income by all family

members, divided by the number of family members. Transfers from social programs

must not be included in the calculation of family income (Lindert er al. p 16 2007).

The benefits of the program stretch from a minimum of 20 BRL to a maximum of 182

BRL per family and month. It varies depending on income and number of children up to

15 years old and between 16 – 17 years old. If the family is extremely poor (makes less

than 69 BRL/month) they receive a basic grant of 62 BRL. If the family makes 69,01 –

137 BRL/month they only get a variable grant of 20 BRL per child up to 15 years old

that attends school, to a maximum of 60 BRL. For children at age 16-17 the family gets

30 BRL/child in school, to a maximum of 60 BRL. There is thus a range of possible

levels of the grant. Once the data used in the calculations is from 2006 and the above is

from 2009, the levels of grants used in this study are as follows:

Table 3 – Levels of benefits

Level of poverty Montly per capita

income in family

Number of children 0-15,

or pregnant or

brestfeeding mothers

Quantity and

type of

benefit

Amount

received

from BF

Poor 51-100 BRL 1 1 variable 15 BRL

2 2 variable 30 BRL

3 or more 3 variable 45 BRL

Extremely poor 0-50 BRL 0 Base 50 BRL

1 Base+1 var 65 BRL

2 Base+2 var 80 BRL

3 or more Base+3 var 95 BRL

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Except that the values of the handouts has changed lately, the Ministry for Social

Development and Fight Against Hunger (Ministério de Desenvolvimento Social e

Combate a Fome - MDS) has also included the ages 16-17. These ages has been

observed to be when most youngsters drop out of school (by for example Janvry and

Saudolet, 2006), which is in accordance with Becker’s theory of higher indirect costs of

education as age increases.

The conditions of BF say that the children need to attend school at least 85 % of the

classes. The benefit is capped at 45/95 BRL to not create incentives for getting pregnant

to receive the grant11. In addition, mothers have to go to pre and post natal care and

children 0-6 are obliged to take vaccinations. Despite the fact that the conditions are

formally requirements that need to be fulfilled to receive the benefit, it is unclear how

harsh the control and implementation of these are. By taking the means of, for example,

school attendance of the beneficiaries and a control group consisting on non-

beneficiaries in the same income segment, one soon realizes that the difference is very

small between the two groups (see results), which could be an indication of weak

control that the requirements are fulfilled. It may however also be an indication that the

control is not needed; if the un-treated population meets the conditionalities even

without control.

Table 4 - Conditionalities

Conditionalities Health Education

Children -All children 0-7 have to go

through vaccine schedules and

regular health checkups

-Children 6-15 enrolled in school

with at least 85 % school

attendance

Women (pregnant or

breastfeeding)

-Pre-natal checkups

-Post-natal checkups

-Participate in nutritional

seminars

(Both parents)

-Inform the school when the

child misses class

-Inform BF administrator if the

child moves school

While the unit of assistance of the program is defined as the family, the benefits are

made preferentially to the woman in the family. Currently, 93% of the responsible

beneficiaries are women (Lindert, 2007). This preference for payments to women

reflects international experience that suggests that women are more likely than men to

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This is common by folk wisdom in Brazil, although not according to any scientific article I have seen.

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invest marginal income in improving the well being (education, health) of their

family12. This point is questioned by Handa et. al. (2008) that empirically shows that

consumer behavior has not changed although the money is in the hands of women and

that female decision making-power has not increased in Mexico due to the program

(Handa et. al, 2008).

The targeting of BF resources is done through a combination of methods: geographic

allocations and family assessments based on per capita incomes. The geographical

targeting is done at two levels, first at federal level through allocating quotas based on

estimates of poverty of the municipalities. Second, it is done on municipal level, where

spatial maps of poverty (created by the MDS) are used to allocate where the families

most in need of the benefit are located (Lindert, 2007). Family eligibility is determined

centrally based on household data which are collected locally (self-reported) and passed

on to a central database known as Cadastro Único. This means that the families that are

going to get BF is chosen by MDS on the basis on this data. This is called means

targeting13. The implication is that, since the families are not directly screened14, there

is a potential problem evaluating the program. This is because of the imprecision in the

means targeting. One might suspect a big spread of income among the families,

stretching quite high above the eligibility criteria, see graph 2 below:

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See for example Hoddinott & Haddad (1995) in Ivory Coast, Thomas (1997) in Brazil and Quisumbing & Maluccio (2000) in Bangladesh, Indonesia, Ethiopia, and South Africa, as mentioned in Handa et al. (2008; p. 4). 13 As with any data, it is not 100 % correct. According to Das et. al. (2005) BF has although one of the better targeting procedures among the Latin American CCTs. (Das et. al 2005; p. 64). 14 The self-reported incomes are however cross-checked with proxies of poverty and by other databases (Lindert, 2007; p. 36).

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Today about 11,1 million families (about 46 million individuals) is covered by the

program. That number, according to the MDS, represents 100 % of the poor families in

Brazil and 25 % of the total population. This is however an estimate that assumes no

leakage (payment to non-eligible’s). Official numbers show that about 2,2 million

families that are above the eligibility threshold receives BF, which means that there are

another ca. 2,2 million families that are eligible that are not beneficiaries15. Targeting

errors are although inevitable in a program of the scale of BF. Soares et. al (2009)

estimates that the program needs to cover 15 million families to cover all individuals

that are eligible, taking into account the volatility of income. See Medeiros et. al (2008)

for a discussion on why it might be good not to withdraw the handouts just because the

income temporarily exceeds the eligible criteria.

5. Method

This study aims to evaluate the impact of the social program BF on school attendance.

Further it examines how an extra 50 BRL of handout to the family affects school

attendance when all other things are held equal. This means observing the outcomes of

school attendance in the so called treated group (receives BF/receiving the extra

handout) and comparing the results to an untreated control group (do not receive

BF/extra handout). This implicates an assumption that everyone in the chosen

population is potentially exposed to the treatment. At this point the problem of all social

sciences arises, Holland (1986), as cited in Essama-Nssah (2006), calls it the

Fundamental Problem of Causal Inference, which consists of that we cannot observe

the same individual both with and without treatment at the same time. Consequently, we

have to ask ourselves how to generalize individual’s behavior (treatment effect) without

being able to observe the counterfactual? Herein lays the challenge of who to include in

the control group to minimize selection bias. Holland (1986) makes an assumption

called unit homogeneity which is crucial to generalization. Suppose that we can find

individuals that do not receive the treatment ( �) but possess the same pre-treatment16

characteristics as the ones who receive treatment ( �). Like this, the � become proxies

for what would have happened to � if it did not get exposed to the treatment. The

15 However, the program is not designed for full coverage. There is a budget constraint so from a design point of view it is acceptable to have eligible’s not receiving transfers. 16 Characteristics that are not affected by receiving the treatment.

16

difference in means between � and � is generally called The Average Treatment effect

on the Treated (ATT) and is denoted:

��� − ��| � = 1� = ���|� = 1� − ���|� = 1� (1)

Some further denotation to make the paragraph above clearer:

��� = the outcome (in school attendance) for i when receiving the treatment (BF/extra cash). ��� = the outcome for i when not receiving the treatment. �� ∈ �0,1� = the possible treatments for i. �� = ��� − ��� is the causal effect of the treatment on i.

In equation (1) ���|� = 1� represents the contra factual case that we cannot observe

but want to estimate.

The best perquisite when evaluating any treatment/policy by observational data is to

have the treatment applied randomly at the startup stage of the program. In that case we

would possibly have a random control group and would be able to conduct quasi

natural-experiments17 with data retrieved from treated and un-treated, without having to

worry about the selection bias of the sample. This can although be expensive, or just not

taken into consideration when implementing the program. Whatever was the reason in

Brazil, BF was not designed to facilitate evaluation research.

Even though it is tempting to just take the means of the treated (receiving BF) and

untreated (all families that do not receive BF with a per capita income under 200

BRL/month), by doing this, there is a risk of biased selection because of the non-

randomization in the sample. Families that already are participants of the BF program

might be more prone to send their kids to school even if they would not receive the

benefit (self-selection bias) or might for other reasons be systematically different. Thus

what has to be done to artificially create the contra factual scenario, (���|� = 1�, is to

construct a control group as similar as possible to the treated group.

The challenge of choosing the control group still persists: i can still not take on 1 and 0

so we have to find i’s with a set of similar characteristics taking on either 1 or 0 of the

treatment dummy.

17

Observable events that approximate a controlled experiment - these events aren't created by

scientists, but yield data which nonetheless can be used to make causal inferences.

17

4.1 Propensity Score Matching

One way to circumvent the selection bias is by using a method developed by

Rosenbaum and Rubin (1983) and enhanced by Heckman, Ichimura and Todd (1997)

called Propensity Score Matching (PSM). PSM strives to identify a control group that is

as similar as possible to the treated group. This is based on various pre-program

covariates (x) that resemble the characteristics of the families before the treatment was

implemented. The estimation of propensity scores is done through a logit regression

with a dummy outcome looking like:

��� = Pr [� = 1|� = �] (2)

The propensity score (p) stands for the probability of an individual to be treated (D),

given a vector of observable variables (x). From the observables captured in x an index

is created that measures the propensity of being treated. The thought is thus that when

we control for the differences in X, balancing concept should occur between the treated

and un-treated. The two different groups do however differ, but only in the error term. D

is thus independent of Y, given the propensity scores created by X. This is commonly

denoted as equation (3) and is called the balancing property.

Y ╨ D | X (3)

It needs to be fulfilled to be able to generalize the results of ATT (Chen, 2008). In

practice, the balancing means that blocks containing treated and untreated observations

are created to yield identical propensity scores between the groups in each block.

When the propensity scores has been estimated, every treated i is matched with an i

taking on the (close to) equal propensity score in the un-treated group. When the

matching is done the ATT can be calculated as the difference between the matches of

treated and un-treated outcomes. Beyond the assumptions of causal effect brought

forward by Holland (1986) and the balancing property, PSM further demands some

central assumptions that are worth mentioning. One is called the Conditional

Independence Assumption (CIA). It states that if we can control for all the observable

differences between the treated and un-treated, the outcome would be identical without

the treatment. CIA is quite a strong assumption but it needs to be made to calculate

ATT, since it ensures that only the difference of the outcome variable is captured in the

18

error term. Another requirement when applying PSM is the Common support (also

called overlap condition) which rules out perfect predictability of D by X. Additionally,

the common support ensures that i’s have a positive probability of being both

participants and non-participants. In practice this means that we may have i’s both from

the treated and un-treated with the same propensity score (Caliendo 2005). If the

overlap condition is not met, it would be impossible to find matches and therefore also

an estimate of ATT18.

4.2 The choice of covariates

The choice of the covariates is crucial to the outcome in the propensity score and is

explained in the data section and under results. It is important to choose variables that

are not influenced by the program but influence the selection into the program and

simultaneously affect the outcome variable. Therefore my choice is basically a set of

household characteristics that most probably were the same even before the treatment

and affects school attendance either negatively, or, positively. The choice is basically

based on theory developed in previous studies and intuition. A central tradeoff in the

choice of X’s is demonstrated in Heckman, Ichimura, Smith and Todd (1998), and

shows that the matching estimators perform best when many variables are chosen to

predict the PS. The bias of the results increased substantially when only a core of the

variables was kept. At the same time, it gets more and more difficult to achieve a

balanced PS as more variables are included (Caliendo 2005).

4.3 The matching

When the propensity score has been estimated, there is one more choice to be made to

conclude and proceed to the results. That is how to match the propensity scores. The

most straightforward is the Nearest-Neighbor (NN) approach (or one-to-one), where the

control i’s are matched to the treatment i’s based on their distance from each other in

relation to the propensity scores. Every treated observation is thus being compared with

one “nearest neighbor” among all potential controls, that is, the control with the least

deviating propensity score. The problem of this approach is that the distance of the

propensity scores could be much greater for some matches than others; even so, these

matches contribute to the result with the same weight as the perfect matches, resulting

18 I use the common-support option in the statistical software STATA to omit un-treated i’s that do not overlap the treated propensity scores.

19

in potentially bad estimates (Caliendo 2005). To avoid these less fortunate estimates NN

was used with replacement, which means that every un-treated is allowed to be matched

to more than one treated. In the case of matching without replacement, once an un-

treated has found a match he drops out of consideration. Choosing replacement as an

option means an increase in the variance of the impact estimator, but at the same time it

improves the quality of the matches.

Table 1 - Nearest-neighbor matching

Propensity score

Treatment Control

0.9 i i

0.8 i

0.7

0.6 i

0.5 i

Because of the large dataset, with plentiful of treated and un-treated, Smith’s statement

can be relied upon “…all PSM estimators should yield the same results, because with

growing sample size they all become closer to comparing only exact matches” (Smith

2000 apud Caliendo 2005) 19. On this assumption the model is restricted to using the

intuitionally easiest form of matching, that is NN.

First the propensity scores of i’s receiving BF and not receiving BF are estimated, then

their different outcomes in school attendance may be observed. Second the propensity

scores of beneficiaries receiving the extra income of 50 BRL in BF, is matched, against

the ones who does not receive it, and outcomes in schooling are compared.

Table 2

Case 1) BF-beneficiaries → ← Non BF-beneficiaries

Case 2) Beneficiaries with “extra cash” → ← Beneficiaries without “extra” benefit

19 To read about the different matching algorithms, see Caliendo (2005, p. 8-12).

20

6. Sample and Data

The National Brazilian Institute of Geography and Statistics (IBGE) annually gathers

information on the Brazilian population for a national sample. This dataset is called

Pesquisa Nacional por Amostra de Domicílios (PNAD) and is based on interviews in a

total of 145 547 households, making up 410 241 observations. This is about 1/500 of the

whole Brazilian population. The sampling of PNAD was conducted through three stages

1) primary sampling units – municipalities 2) secondary sampling units – census areas

3) tertiary sampling units – residential units (private residences and rooms in collective

households) (IBGE).

The month of reference is September 2006, when about 2000 interviewers collected the

information. The response rate was 97,3 % with a refusal rate of 1,4 %. All the data is

based on the answers from the interviews, which in some cases lead to difficulties in

interpreting contradictive answerers (explained below). There is only one respondent in

each household, which contributes with info on the other members of the home. The

definition of a household in PNAD is quite broad, including not only members on basis

of kinship, but also on domestic dependence and norms of common living. The

household head is the one identified as such by the others in the house.

Unfortunately the PNAD 2006 has no variable that explicitly states the amount of cash

received from social programs. This money is included in the variable income from

interest and other sources. One has to assume that poor families do not have any

income from interest. Consequently, if the variable income from interest and other

sources equals the handouts in BF, it can be assumed that it is the source.

This thesis follows existing research in this field (Teixeira and Oliveira, 2008) and

defines poor families as those who have a per capita income below 200 BRL/month.

Because of the lack of control and the design of the targeting there are many families

that receive BF that has a higher income than the program permits. To not lose out on

too many observations it has been decided to include all observations that have a family

per capita income up until 200 BRL/month (see graph 2 for a histogram of income over

the sample).

21

A final specification is done to include only the observations in the ages of 6-15, which

is the obligatory school age and also the years when the family receives BF for sending

the kids to school. After the data reduction we end up with 43 833 observations.

6.1 The core variables

The PNAD 2006 is a very rich dataset with several hundreds of variables. Below the

outcome variable (Y) is stated and then the main explanatory variables (X) for case 1

and 2 respectively:

Y = School attendance) Binary variable that takes on the value of 1 if the individual

attends school in the week of reference and 0 if not. The variable is derived from

question “do you attend school?” in the questionnaire.

X = Household receives Bolsa Família) The main explanatory variable in case 1. A

binary variable that takes on 1 if the family is a part of BF and 0 if not. The variable is

derived from two sources, the first from the question “does anyone in this household

receive money from the social program Bolsa Família?”. The second is based on a

suspicion that arises when one looks at descriptive statistics, comparing how many

answers yes to the question above and how many poor families that receive an amount

that exactly corresponds to the benefit in income from interest and other sources20. On

the assumption that poor families have no income from interest, the interpretation is that

there has to be a misunderstanding when answering the questionnaires. This might be

due to confusion of which social program the family receives (Teixeira and Oliveira,

2008). To come around this, the variable Receives BF consist of 1) the yes respondents

to the question above plus; 2) the families that receive 15, 30, 45, 50, 65, 80, or 95 BRL

from interest and other sources (if the family at the same time has the amount of

children required and/or are pregnant/breastfeeding and do not exceed 200 BRL

month/capita).

X = Extra cash) The main explanatory variable in case 2. The variable is a binary,

taking on 0 if the family receives 15, 30 or 45 BRL and 1 if 50, 65, 80 and 95 BRL.

Hence, if extra cash takes on 1 it means that the family receives an extra lump of cash

of 50 BRL.

20 What justifies this assumption is that the percentage of ones that declare themselves participant is lower than the one reported by de government.

22

6.2 Descriptive statistics

Table 5 – descriptive statistics Range: Description Mean St.dev. Obs.

(1)Personal Characteristics

Non-white 0-1 =1 if black, yellow, indigenous or mix .692 .461 43833

Go to school (Y) 0-1 =1 if going to school .949 .219 43833

(2)Household Characteristics

Age of household head 0-101 Age in years of head of family 38.698 13.595 43833

Literate head 0-1 = 1 if the head of family knows how to read .791 .406 43833

Years of schooling of head of

family

1-13 Time in years that head has spent in school 1-12

and 13 or more

3.682 2.333 43833

Number of adults 1-5 Amount of 16-64 year olds. 1-4 and 5 or more. 2.376 1.023 43833

Number of children 0-3 Amount of 0-15 year olds. 0-2 and 3 or more 2.264 .855 43833

Partner present 0-1 =1 if the head of the family has a partner present in

the household

.741 .437 43833

Income per capita in family

/month

0-199.75 Amount in liquid income (not from social

programs)

90.567 55.994 43833

Family recieves Bolsa família

(X)

0-1 =1 if receives BF .451 . .497 43833

Family receives the extra

benefit (X)

0-1 = if receives ‘extra benefit’ for being extremely

poor

.755 .429 19795

(3)Regional Characteristics

North or northeast 0-1 =1 if residing in north or northeast parts of Brazil .612 .487 43833

Urban 0-1 = 1 if residing in urban environment .734 . .441 43833

7. Results

The objective of BF are (as already mentioned) not just one; it is first of all to reduce

acute poverty and second to reduce future poverty. Human capital accumulation,

achieved by education, is one of the most important ways to reach the second objective.

Here the results of 1) to what extent BF affect school attendance and; 2) how the extra

handout adds to this effect; are presented.

23

Before proceeding to the results of the estimations it is useful to pinpoint some

problems that have occurred in the statistical progress and which limitations these mean

for the final results. These problems consist mainly in that the balancing property has

not been fulfilled in neither of the cases of PS matching. See appendix 1.1 for the

balancing in each block of beneficiaries’ contra non-beneficiaries’ and appendix 2.1 for

extra-cash beneficiaries’ contra non-extra-cash beneficiaries. The variables that are

unbalanced are written in red in respective block. The unbalance is due to that the

treatment and control group are basically too different from each other. Various models

have been tried, but after all it was decided in favor of a quite basic model with all the

variables that have been identified as important. As a consequence, no potentially

important variables has been omitted to increase balance, since that would have created

weak results of another kind. Even though it exists some unbalance, most variables are

balanced in most blocks, and when table 1.2 A and 2.2 A is studied, we can conclude

that the propensity scores and standard errors are close to identical between treated and

untreated in both cases. The unbalance should nonetheless be ignored and we bear it in

mind for the final results and conclusion.

The area of common support is .118 to .850 for the BF inclusion and .181 to .981 for

receiving the extra cash. This is the area where both treated and un-treated encounter

nearest-neighbors. Even though it is difficult to see from graph 3 below, 11

observations were found to be outside the overlapping area in the case of BF and thus

dropped from further calculations, leaving me with 43821 observations. In the case of

Extra cash only one observation was omitted, resulting in 19794 observations.

24

In appendix 3 the variables’ performance in predicting inclusion to be treated, is listed,

which is received from running a logistic function such as equation 2. The results are

statistically significant at 0.01 based on the low p-values, we can say that all the

variables are statistically significant in predicting inclusion into Bolsa Família. The

same can be done for Extra cash. In which direction (-/+) they influence inclusion is not

important, only that they do it.

The basis of inclusion of the covariates is although worth mentioning. Non-white is

included since being white is highly positively correlated with wealth in Brazil. Age of

the head of the family is included on the intuition that, in general, an older head would

prioritize education to a greater extent than a younger. Years of studies of household

head is intuitionally related to schooling choice of his/her children. The more children

in the family, less prone the family is to send the marginal child to school. The older

siblings might for example be expected to take care of the younger to a higher extent in

a large family. The number of adults is related to the schooling choice based on the

same thought as for the number of children On the contrary, it is positively related since

more adults mean more potential income and since the adults can take on the

household/work burdens instead of the children. Income per capita is included for the

methodical reason of being able to hold it equal while looking at how the outcome

variable is affected by the treatment. The dummy north and northeast is included since

these regions differ substantially in levels of development from the more southern

regions. The public school system in the south and southwest of the country are in

general of higher quality than those in the regions of the north and northeast, with the

implication that the returns to education are relatively higher in the south and southwest.

Urban has three basis for inclusion: first is that urban schools have higher quality in

general; second that the transportation to school can be costly or insufficient in rural

areas; and third that the returns for the parents of sending their children to school on the

countryside (working with agriculture) is unclear. The variables chosen do to large

extent correspond to earlier work in the area, see for example Dureya et al. (2004).

7.1 Results of Bolsa Família on School attendance

The following task is to compare the mean values of the treated i’s with the mean values

of their matched controls. If the critical assumptions of the matching method hold, the

matched means function as proxies for the unobservable means the un-treated

25

individuals would have experienced (if they had been treated). Further, Table 6 shows

the reduction in bias that most variables have experienced by using PSM21. This

indicates that the comparability of the control group was enhanced by the matching.

Table 6 – results of covariates, Bolsa Família

Variable Sample Mean %Bias % Bias

reduction

T-stat P-value (Bias

reduction) Treated Control

Non-white Unmatched Matched

.724

.726 .650 .700

16.1 5.5

65.6

31.03 5.57

0.000 0.000

Age of head of family

Unmatched Matched

39.846 39.663

38.848 38.813

6.4 5.5

14.8

11.98 6.36

0.000 0.000

Literate head Unmatched Matched

.724

.723 .843 .820

-29.1 -23.6

18.7

-58.39 -22.79

0.000 0.000

Years of Schooling

Unmatched Matched

4.370 3.673

5.213 3.718

-23.3 -1.2

94.6

-44.33 -2.03

0.000 0.042

# adults Unmatched Matched

2.646 2.465

2.503 2.375

12.7 8.0

36.9

24.66 8.53

0.000 0.000

# kids in family Unmatched Matched

2.088 2.394

1.579 2.276

47.5 11.0

76.8

90.68 14.18

0.000 0.000

Partner Present Unmatched Matched

.810

.789 .696 .749

26.7 9.4

64.8

50.83 9.44

0.000 0.000

Income per capita/month

Unmatched Matched

83.824 80.333

99.984 90.873

-28.1 -18.3

34.8

-53.62 -19.30

0.000 0.000

North /northeast

Unmatched Matched

.731

.715 .555 .615

37.5 21.2

43.5

71.80 21.07

0.000 0.000

Urban Unmatched Matched

.648

.656 .796 .762

-33.4 -24.0

27.9

-66.66 -23.28

0.000 0.000

Thanks to having a very big dataset with thousands of controls and treated, the results

yield very low p-values, meaning that the difference of the means between treated and

un-treated are statistically significant with an alpha of 0.01, except for years of study

where the result is significant at 0.05.

Although it is a sign that the results are unbalanced, it is worth noting that the treated

has “worse” results on all variables in relation to poverty. Higher average of non-white,

more illiteracy, less years of study, more kids in the family, less income per capita,

higher mean of north/northeast and less Urban. But still, they have higher school

attendance.

Table 7 - Estimation of Average Treatment of the Treated (Bolsa Família beneficiaries) on school attendance

Sample Treated Controls Difference St. Err T-stat P-value

Unmatched .9599 .9352 .0246 .0021 11.43 0.000

Matched .9601 .9280 .0320 .0035 9.07 0.000

21 The unmatched is the simple difference in means between the one who receives BF and the ones who does not.

26

One of two questions stated in the introduction to this thesis was how school attendance

is affected by being a part of BF. The answer is, according to the result on Average

Treatment effect of the Treated (ATT) that it increases school attendance by 3,2 % in

the examined sample, statistically significant at a confidence level of 0.01. See the

analysis of the results below for a discussion.

7.2 Results of receiving the extra 50 BRL on School attendance

Table 8

Variable Sample Mean % bias % Bias reduction

T-stat. P-value

Treated Control

Non-white Unmatched Matched

. .74496

. .75135 .6629 .73292

18.0 4.1

77.5

19.18 3.66

0.000 0.000

Age of family head

Unmatched Matched

40.12 39.961

39 40.707

8.8 -5.9

33.4

9.14 -5.22

0.000 0.000

Literate head Unmatched Matched

.68486

.68137 .84782 .69384

-39.2 -3.0

92.4

-38.43 -2.32

0.000 0.020

Years of Schooling

Unmatched Matched

4.1999 3.5754

4.8965 3.4585

-20.8 3.5

83.2

-21.99 4.51

0.000 0.000

# adults Unmatched Matched

2.7019 2.5195

2.4762 2.572

21.0 -4.9

76.7

21.32 -4.23

0.000 0.000

# kids Unmatched Matched

2.1222 2.4544

1.9849 2.3946

14.2 6.2

56.4

14.37 6.84

0.000 0.000

Partner Present

Unmatched Matched

.81357

.7962 .80077 .76859

3.2 7.0

-115.7

3.40 5.81

0.001 0.000

Income Unmatched Matched

76.405 72.061

106.76 72.395

-59.3 -0.7

98.9

-61.55 -0.59

0.000 0.554

North /northeast

Unmatched Matched

.78089

.7676 .57899 .77842

44.3 -2.4

94.6

48.33 -2.22

0.000 0.027

Urban Unmatched Matched

.60611

.61339 .78024 .61407

-38.4 -0.1

99.6

-38.42 -0.12

0.000 0.904

Continuing to question number two “does a substantial increase in the level of handout,

all other things equal, increase school attendance?”, first, the means of the treated and

controls are listed, as matched and unmatched respectively, in table 8. All variables

except Income and Urban are robust at a significance level of 0.05. The high p-values

of these variables indicate that the difference between the two means of treated and

controls are not statically robust. When comparing the matched results in table 8 to the

ones in table 6, one soon realizes that the controls and treated are more alike in table 8.

This is not very surprising, since they have all gone through the selection process by

MDS to be targeted to receive BF.

From table 9 we can read that the effect of the extra 50 BRL on school attendance is

positive with 1,38 %. The result is statistically significant at 0.05. Interestingly, the

27

PSM method corrected the result from being negative in the unmatched sample to being

positive after matching.

Table 9 - Estimation of Average Treatment of the Treated (receives extra cash) on school attendance

Sample Treated Controls Difference St. Err T-stat P-value

Unmatched .95682 .9695 -.0127 .0032 -3.94 0.000

Matched .9568 .9430 .0138 .0055 2.49 0.013

7.3 Analysis of the results

In terms of graph 1, and the indifference curves, it can be concluded that 3,2 % moves

from u3 to u1 in response to BF and 1,38 % from u3 to u1 in response to extra cash.

The rest of the families whose children attend school move from the lower u1 to the

higher u1 when receiving BF, meaning a pure income effect since they would send their

children to school anyway. Either the handout is too small to make any bigger

difference, that is, move more i’s from u3 to u1, (and still too small if it would be

increased by 50 BRL) or, more likely, the main part of the population is already located

along the line E-B. For them the benefit just exerts an income effect and they will not

need to alter their behavior. The rest of the population that still does not send their

children to school, have very low preferences for education and is not sensitive to

income changes (as preference curve U2 in graph 1).

Bolsa Família and its predecessor Bolsa Escola has been in effect for almost a decade

on national scale. Looking back at studies by Bourguignon et al. and Cardoso and Souza

one sees a great increase in school attendance during the first years of the CCTs. Today

one may suspect that the income from the program has become more of ordinary

income and not the type of “lump sum of cash on the doorstep” that it meant in the

beginning. Implying that, the benefits had a large substitution effect in the beginning of

the program and today have more of an income effect, since most of the eligible

population already complies with the condition of school attendance.

The results of other papers (Carvalho Filho, 2008) strengthen Becker’s theory that says

that schooling gets more expensive with age. The approach of this paper does not

answer any such question, but the assumption that schooling is directly affected by

income might be questioned on the grounds of the results. The results indicate that there

are families that are very poor but do not comply with the program, even though it

28

would mean higher income. Further studies are needed to identify what type of families

these are and what can be done to incentivize them to send their children to school.

In the context of already high school attendance it would be interesting to look at how

sub-groups of the population react to the benefits; for example, different age segments

(having Carvalho Filho’s results in mind that the effect of income is stronger on older

children). It would further be of interest to identify extra vulnerable groups, such as the

families on indifference curve u2 in graph 1, to recognize what it is that makes them

have low preferences for education.

Referring back to the conditionalities, it is not until recently that control of these

became effective. When people start being driven out of the program for not complying

with the conditionalities, a further increase in school attendance can be expected as a

reaction to BF. Also on conditionalities, one might suppose that social control keeps it

high even without the cash being conditioned. If the neighboring family sends their kids

to school oneself doesn’t want to be stigmatized as a bad parent by letting the kid’s

work all day (leaning towards Mayer’s (2002) good parenting theory).

As mentioned, the balancing property has not been met and therefore the possible

generalization of the results is limited. Another assumption that is difficult to know if it

has been fulfilled is CIA. If CIA is not met (that is, if all the characteristics that effect

inclusion into the program have not included) the error term does not only consist of the

difference in school attendance, but also of differences not included in the regression.

Hence, a type of omitted variable bias.

8. Summary and future challenges

This thesis set out to estimate the effects of the Conditional Cash Transfer Bolsa

Família on School attendance. To do this, Propensity Score estimates with Nearest

Neighbor matching was used to create a control group almost identical to the treated

sample. The micro-data was attained from the Brazilian Institute of Geographics and

Statistics’ population census PNAD, gathered in 2006. From PNAD, the dataset is

reduced to include 43 388 observations, restricting it to observations with (in the

family) per capita income below 200 BRL and of mandatory school age (6-15).

29

The theoretical assumptions put forth in the paper are: 1) income has a larger effect the

poorer the family is due to imperfect credit markets; 2) the child is a tool in the parents’

maximization efforts; 3) The costs of education (direct + indirect) increases with the

child’s age; 4) Child labor is the flipside to education in the context of poverty; 5) due

to risk management the parent’s need financial incentives to do what is good for them,

and society as a whole, in the long run: send their children to school.

The estimations show that BF has a positive and statistically significant effect on school

attendance by raising the amount of children attending school by 3,2 %. The results

from receiving extra cash points in the same direction: the results are significant, but

with limited effect. An increase of 1,38 % in school attendance due to 50 BRL more in

each family is a modest result. In the context of already high school attendance levels

one can expect that it is more difficult to make the marginal child in the family to attend

school. With that background even a small increase is an important achievement. The

effect of BF was evaluated on the whole eligible population. In a future study it would

be interesting to divide the sample into subgroups to see who the children that still do

not attend school are, in terms of living standards and parenting backgrounds. It might

be that more money has a limited impact on these families and children.

The main concerns of this thesis are that in neither one of the cases the needed

balancing property (that is needed to generalize the results using PSM) has been

achieved. It is also not sure whether the strong CIA has been met.

Future challenges for the Brazilian government is to complement the now achieved

(high) school attendance with (high) quality education, for everyone. The recent change

in BF to give higher benefits for students in the ages of 16-17 is a welcome

modification with a broad academic basis in both Becker’s time allocation theory and

empirics by, for example, Cardoso and Souza (2008). It is important to remember that

the objectives of the program are more than just achieving school attendance for the

children. Brazil is today a less unequal country than before BF and has started to see the

benefits of having a tool to smooth economic chocks (such as the current economic

crisis) for the poorest population.

30

Acknowledgements

In the process of writing this bachelor thesis I have encountered a lot of helpful people

that deserve mentioning. All steps in the writing have been done in Brasília, Brazil

which was made possible through the financial support by the Swedish International

Development Agency (SIDA) and the trust given by Anneli Eriksson at Gothenburg

University. At the location I have been received with great care from the Swedish

embassy and its staff, I would especially like to thank Ambassador Annika Markovic

for letting me use the facilities of the embassy during this time. In the practical work the

people at UNDPs International Poverty Centre for Inclusive Growth have been of great

assistance and without Fabio Veras Soares and Clarissa Teixeira I would still be

struggling with the statistical software. Last but not least, I would like to thank Thaís

Cardoso de Melo for the thorough (linguistic) support.

Obrigado.

31

9. List of references

Becker, Gary (1964) Human Capital - A Theoretical and Empirical Analysis,

with Special Reference to Education, New York: Columbia University Press. Becker, Gary and Nigel Tomes (1986) An equilibrium Theory of the Distribution of

Income and Intergenerational Mobility, Journal of Political Economy, vol 87 no 61, University of Chicago. Bourguigon, François, Francisco Ferreira and Phillippe Leite (2003) Conditional Cash

Transfers, Schooling and Child Labor: Micro-Simulating Bolsa Escola,, Washington DC: The World Bank. Cacciamali, Maria Cristina, Fábio Tatei (2007) Uma Análise Regional do Atendimento

aos Mais Pobres: Os Programas de Transferência de Renda, Universidade de São Paulo. Caliendo, Marco and Sabine Kopeinig (2005) Some practical guidance for the

implementation of Propensity Score Matching, Discussion paper 1588, Bonn, Institute for the Study of Labor (IZA). Carvalho Filho, Irineu Evangelista de (2008) Household Income As A Determinant of

Child Labor and School Enrollment in Brazil: Evidence From A Social Security Reform,

IMF Working Paper. Chen, Vivien and Krissy Zeiser (2008) Implementing Propensity Score Matching

Causak Analysis with Stata, Penn State University. Coady, David (2000) The application of Social Cost-Benefit Analysis to the Evaluation

of Progresa, International Food Policy Research Institute, Washington D.C. Cardoso, Eliana and André Portela Souza, (2004), The impact of cash transfers on child

labor and school attendance in Brasil, Vanderbilt University, Nashville. Das, Jishnu, Quy-Toan Do and Berk Özler, (2005), Reassessing Conditional Cash

Transfer Programs, Oxford University Press, The World Bank. Duryea, Suzanne and Andrew Morrison, (2004), The Effect of Conditional Cash

Transfers on School Performance and Child Labor: Evidence from an Ex-Post Impact

Evaluation in Costa Rica, Inter-American Development Bank. Essama-Nssah, B, (1986) Statistics and Causal Inference, Journal of the American Statistical Association, 81.

32

Fiszbein, Ariel and Norbert Schady (2009) Conditional Cash Transfers – Reducing

present and future poverty, World Bank, Washington. Heckman, J., H. Ichimura and P. Todd (1997) Matching as an Econometric Evaluation

Estimator: Evidence from Evaluating a Job Training Program, Review of Economic Studies, 64. Heckman, J., H. Ichimura, J. Smith, and P. Todd (1998) Characterizing Selection Bias

Using Experimental Data. Econometrica, 66.

Heckman, James, Hidehiko Ichimura and Petra Todd (1997) Matching As An

Econometric Evaluation Estimator: Evidence from Evaluating a

Job Training Programme, Review of Economic Studies, no 64. Handa, Sudhanshu, Benjamin Davies and Marco Stampini, (2008) Opening Up

Pandora’s Box: The Effect of Gender Targeting and Conditionality on Household

Spending Behavior in Mexico’s Progresa Program, World Development Review. IBGE, PDF about the PNAD2006 - http://www.lisproject.org/techdoc/br/br06survey.pdf retrieved 2009-08-31.

IPEA (2005), On the Recent Fall in Income Inequality in Brazil – Technical note,

Ministry of Planning, Brasília

Janvry, Alain de and Elisabeth Sadoulet (2004) Conditional Cash Transfer Programs:

Are they Really Magic Bullets? University of California at Berkley. Janvry, Alain de and Elisabeth Sadoulet (2006) Making Conditional Cash Transfer

Programs More Efficient: Designing for Maximum Effect of the Conditionality, Oxford University Press, The World Bank. Jenkins, Stephen and Chrstian Schulter (2002) The effect of family income during

childhood on later-life attainment: evidence from Germany, Lindert, Kathy, Anja Linder, Jason Hobbs and Bénédicte de la Brière, (2007) The Nuts

and Bolts of Brazil’s Bolsa Família Program: Implementing Conditional Cash

Transfers in a Decentralized Context, The World Bank. Mayer, S, (2002) The Influence of Parental Income on Children’s Outcomes, Ministry of Social Development, Wellington, New Zeeland. Medeiros, Marcelo, Tatiana Britto and Fábio Veras Soares (2008), Targeted Cash

Transfer Programmes in Brazil: BPC and Bolsa Família, International Poverty Centre – UNDP.

33

Oliveira, Ana Maria Hermeto Camilo de (2008) An Evaluation of the Bolsa Família

Program in Brazil: Expenditures, Education and Labor Outcomes Patrinos, Harry Anthony and M. Najeeb Shafiq (2008) A Positive Stigma for Child

Labor? Reimers, Fernando, Carol deShano da Silva and Ernesto Trevino, (2006), Where is the

“Education” in Conditional Cash Transfers in Education, UNESCO Institute for Statistics, Montreal. Rosenbaum, Paul and Donald Rubin (1983) The Central Role of the Propensity Score in

Observational Studies for Causal Effects. Biometrika, Vol.70, No.1 Shea, John (1997) Does Parents’ Money Matter? National Bureau of Economic Research, Cambrige. Skoufias, Emmanuel and Susan Parker (2001) Conditional Cash Transfers and Their

Impact on Child Work and Schooling: Evidence from the Progresa Program in Mexico, Economía. Soares, Sergei, Rafael Perez Ribas and Fábio Veras Soares (2009) Focalização e

Cobertura do programa Bolsa-Família: Qual o Significado dos 11 milhões de

Famílias? Ipea. Teixeira, Clarissa and H. C Oliveira (2008) Impact Analysis of the Bolsa Família

Program Effect on Men and Women’s Work Supply – an Application of the Generalized

Propensity Score Method. The economist, Happy Families, published 7th of February 2008. Varian, Hal R (2006) Intermediate Micro economics – A modern approach, 7th edition, W. W Norton & Company, New York.

Appendix

1

Appendix 1

Means of un-treated and treated in each block of the estimation of the propensity score of inclusion into Bolsa Família22.

22 Red numbers means that the variable is unbalanced in this block

Table 1.1 A Income per

capita

Years of

study of

head of

family

Number of

adults

partner

present in

household

age of head

of family

urban North or

northe

east

region

Non-

white

number of

kids

head

knows

how to

read Un-treated Treated

Block 1 151.39 162.20 3.31 3.67 1.60 1.69 .18 .18 27.76 36.80 .98 .97 0 0 .41 .48 1.01 .88 .98 .97

Block 2 133.40 140.80 3.49 3.93 1.84 1.79 .36 .37 30.86 35.26 .98 .97 .01 .01 .42 .41 1.37 1.37 .97 .97

Block 3 137.46 146.53 3.68 3.60 2.06 2.03 .54 .51 35.59 37.00 .97 .97 .04 .04 .43 .43 1.52 1.61 .96 .93

Block 4 131.83 130.24 3.87 3.81 2.06 2.12 .59 .56 35.40 35.25 .96 .98 .06 .04 .45 .52 1.77 1.80 .97 .95

Block 5 120.92 126.18 3.89 3.62 2.12 2.17 .64 .63 36.15 36.76 .92 .95 .07 .07 .47 .49 1.91 2.02 .95 .97

Block 6 119.62 121.87 3.77 3.59 2.15 2.14 .68 .67 36.82 37.42 .90 .92 .11 .11 .50 .53 2.12 2.19 .95 .95

Block 7 113.57 115.10 3.94 3.50 2.22 2.18 .72 .71 36.93 37.47 .90 .93 .14 .15 .58 .63 2.28 2.30 .95 .94

Block 8 97.71 100.18 3.92 3.78 2.29 2.22 .75 .72 37.79 37.28 .86 .86 .24 .25 .61 .61 2.23 2.31 .92 .93

Block 9 92.87 95.54 3.90 3.98 2.35 2.39 .76 .76 39.54 39.39 .81 .78 .44 .46 .67 .65 2.19 2.13 .88 .88

Block 10 91.61 87.37 3.79 3.83 2.40 2.39 .75 .75 39.17 39.25 .74 .75 .65 .65 .71 .71 2.22 2.16 .85 .82

Block 11 86.80 86.14 3.87 3.75 2.38 2.43 .74 .75 39.81 39.53 .74 .72 .77 .79 .75 .72 2.33 2.25 .82 .81

Block 12 83.03 78.08 3.70 3.68 2.49 2.43 .76 .77 39.38 39.18 .72 .73 .84 .84 .77 .72 2.38 2.37 .79 .80

Block 13 73.75 70.60 3.56 3.66 2.46 2.48 .81 .87 40.01 39.25 0.70 0.70 0.92 0.90 .79 .76 2.50 2.50 .79 .79

Block 14 61.00 58.03 3.40 3.48 2.60 2.58 .86 .86 42.57 41.37 .49 .48 .97 .96 .80 .79 2.67 2.63 .58 .55

Block 15 41.34 39.17 3.19 3.46 2.83 2.85 .96 .96 43.72 43.39 .12 .09 1 1 .82 .81 2.87 2.87 .22 .28

Block 16 16.99 18.93 4.04 4.42 3.25 3.86 1 1 53.72 50.82 0 0 1 1 .88 .94 3 3 0 0

Appendix

2

Table 1.2 A – Number of observations for treated and un-treated, means of propensity scores and standard deviance, in respective block for Bolsa Família.

Mean of

Propensity scores

Observations Mean St. dev.

Un-treated Treated Un-treated Treated Un-treated Treated

Block 1 882 169 .173 .173 .021 .019

Block 2 701 172 .213 .214 .007 .006

Block 3 949 319 .238 .238 .007 .006

Block 4 1,205 393 .262 .262 .007 .007

Block 5 1,272 502 .287 .288 .007 .007

Block 6 1,478 615 .312 .313 .007 .007

Block 7 1,542 739 .337 .337 .007 .007

Block 8 3,059 1,702 .374 .375 .014 .014

Block 9 2,604 1,789 .424 .425 .014 .014

Block 10 2,486 1,938 .474 .474 .014 .014

Block 11 1,197 959 .512 .512 .007 .007

Block 12 1,045 1,075 .537 .537 .007 .007

Block 13 1,936 2,353 .573 .574 .014 .014

Block 14 2,453 4,097 .647 .648 .028 .028

Block 15 1,167 2,726 .742 .743 .027 .028

Block 16 50 247 .810 .811 .006 .009

Sum 24,026 19,795 .433 .528 .149 .152

Appendix

3

Appendix 2

Table 2.1 A - Means of un-treated and treated in each block of the estimation of the propensity score of inclusion into receiving the extra 50 BRL23.

23 Red numbers means that the variable is unbalanced in this block

Table 2.1 A Income per

capita

Years of

study of

head of

family

Number of

adults

partner

present in

household

age of head

of family

urban North or

northe

east

region

Non-

white

number of

kids

head

knows

how to

read Un-treated Treated

Block 1 195 179.01 7 5.53 1 1.24 0 .48 32 42.83 1 1 0 0 0 0 1 .24 1 1

Block 2 167.38 170.15 4.90 4.62 1.98 2.05 .73 .70 37.41 39.02 .97 .96 .02 .04 .33 .36 1.70 1.52 .99 .95

Block 3 150.14 154.63 4.46 4.42 2.03 2.22 .72 .80 36.98 38.25 .95 .95 .07 .14 .40 .43 2.09 2.00 .96 .97

Block 4 144.98 147.94 4.04 4.09 2.08 2.17 .76 .80 36.46 38.79 .92 .91 .17 .19 .48 .52 2.16 2.10 .95 .95

Block 5 131.70 136.23 4.15 4.28 2.14 2.22 .84 .79 38.25 38.09 .89 .90 .24 .28 .56 .59 2.16 2.10 .96 .94

Block 6 131.35 128.94 4.34 4.16 2.21 2.30 .83 .82 39.59 38.68 .87 .88 .34 .31 .64 .64 2.18 2.16 .93 .94

Block 7 120.03 120.32 3.85 3.94 2.15 2.23 .78 .78 38.19 38.38 .83 .84 .31 .27 .53 .57 2.25 2.29 .94 .94

Block 8 107.42 111.32 4.16 3.97 2.23 2.24 .77 .77 38.04 38.87 .83 .83 .36 .41 .59 .59 2.19 2.14 .93 .92

Block 9 99.41 101.14 3.70 3.84 2.27 2.29 .76 .72 38.08 39.22 .79 .81 .42 .48 .64 .65 2.28 2.20 .89 .91

Block 10 91.69 90.19 3.74 3.92 2.46 2.36 .79 .75 39.61 38.81 .78 .77 .60 .57 .69 .71 2.19 2.25 .88 .87

Block 11 84.47 77.61 3.95 3.80 2.45 2.39 .79 .77 39.24 39.10 .70 .74 .67 .69 .75 .70 2.38 2.34 .78 .83

Block 12 70.59 65.20 3.76 3.58 2.43 2.40 .78 .79 40.06 38.82 .67 .66 .88 .79 .76 .76 2.31 2.46 .74 .77

Block 13 61.20 57.21 3.24 3.45 2.43 2.51 .63 .83 40.69 40.64 .57 .63 .84 .87 .75 .79 2.44 2.53 .69 .72

Block 14 56.66 57.14 3.67 3.60 2.30 2.50 .69 .82 38.85 40.63 .51 .57 .91 .90 .83 .80 2.33 2.53 .56 .62

Block 15 51.56 54.82 3.38 3.58 2.49 2.57 .81 .85 41.43 39.99 .44 .51 .92 .90 .82 .80 2.27 2.57 .54 .54

Block 16 51.71 45.39 3.05 3.19 2.83 2.56 91 80 45.62 40.00 .48 .49 .95 .90 .78 .80 2.38 2.62 .50 .56

Block 17 44.82 40.56 3.36 3.20 2.80 2.63 .81 .79 42.79 40.67 .33 .36 .97 .95 .79 .81 2.59 2.66 .46 .47

Block 18 42.97 35.34 2.83 2.89 2.98 2.68 .77 .85 42.22 42.78 .28 .27 1 .97 .94 .87 2.57 2.77 .37 .42

Block 19 45.69 33.53 1.85 3.15 2.75 2.68 .70 .86 38.81 42.67 .34 .19 1 .98 .86 .82 2.81 2.80 .21 .32

Block 20 32.62 32.93 2.94 3.00 3.33 2.90 .59 .86 49.94 43.03 .56 .17 1 .99 .78 .81 2.73 2.85 .21 .19

Block 21 17.99 21.76 1.97 2.72 3.39 3.42 .79 .85 49.17 45.08 .06 .05 1 1 .92 .92 2.90 2.93 .28 .12

Appendix

4

Table 2.2 A - Number of observations for treated and un-treated, means of propensity scores and standard deviance, in respective block for Extra cash.

Mean of

Propensity scores

Observations Mean St. dev.

Un-treated Treated Un-treated Treated Un-treated Treated

Block 1 1 4 .195 .188 0 .005

Block 2 377 278 .339 .339 .043 .046

Block 3 244 196 .427 .429 .014 .014

Block 4 281 306 .475 .476 .014 .014

Block 5 397 433 .525 .526 .014 .014

Block 6 220 221 .561 .562 .007 .007

Block 7 205 322 .587 .587 .006 .007

Block 8 470 699 .623 .624 .014 .014

Block 9 529 915 .674 .676 .014 .014

Block 10 504 1,154 .724 .725 .014 .014

Block 11 483 1,583 .775 .775 . 014 .014

Block 12 456 2,109 .824 .825 . 014 .014

Block 13 115 606 .855 .856 .003 .003

Block 14 85 645 .868 .868 . 003 .003

Block 15 106 648 .880 .881 . 003 .003

Block 16 72 749 .893 .893 . 003 .003

Block 17 157 1,560 .913 .912 .006 .007

Block 18 50 391 .927 .928 .001 .001

Block 19 24 401 .933 .934 .001 .001

Block 20 29 775 .941 .943 .003 .003

Block 21 28 966 .958 .960 .007 .007

Sum 4,833 14,961 .47585 .1580242

Appendix

5

Appendix 3

Table 3.1 A Probabilities of the covariates to predict inclusion in Bolsa Familia Table 3.2 A Probabilities of the covariates to predict inclusion to the group that receives the extra 50 BRL

Variable Coeff. Std. Er. z-stat P-value

Non-white .08052 .0247 3.25 0.001

Age of head .0061 .0009 6.61 0.000

Head literate -.3489 .0288 -12.08 0.000

Years studied .0275 .0048 5.67 0.000

Number adults .0525 .0121 4.32 0.000

Number kids .3030 .0139 21.78 0.000

Partner .2214 .0289 7.65 0.000

Income -.0035 .0002 -16.74 0.000

North/northeast .7274 .0238 30.55 0.000

Urban -.3871 .0259 -14.90 0.000

Constant -.9616 . 0648 -14.82 0.000

Variable Coeff. Std. Err. z-static P-value

Non-white .2483 . 0426 5.83 0.000

Age of head .0065 .0018 3.49 0.000

Head literate -.5377 .0521 -10.32 0.000

Years studied -.0381 .0086 -4.41 0.000

Number adults .1980 .0235 8.40 0.000

Number kids .3294 .0264 12.46 0.000

Partner -.2490 .0532 -4.68 0.000

Income -.0092 .0004 -22.22 0.000

North/northeast .7133 .0415 17.17 0.000

Urban -.4751 .0461 -10.30 0.000

Constant .9301 .1370 6.79 0.000