Schooling in poor families: The effect of a Conditional Cash Transfer on school attendance in Brazil
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Schooling in poor families: The effect of a Conditional Cash Transfer on school attendance in Brazil

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

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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  • 1. 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.
  • 2. 2 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
  • 3. 3 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.
  • 4. 4 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.
  • 5. 5 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:
  • 6. 6 ܹ = ‫ܲܯ‬ − ݇ ܹ = ‫ܲܯ‬଴ − ሺ‫ܲܯ‬଴ − ‫ܲܯ‬ + ݇ሻ ܹ = ‫ܲܯ‬଴ − ‫ܥ‬ Where W is the net earnings, MP is the actual marginal product (equal earnings), k is the direct cost, ‫ܲܯ‬଴ 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 ‫ܲܯ‬଴ 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
  • 7. 7 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:
  • 8. 8 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).
  • 9. 9 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.
  • 10. 10 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.
  • 11. 11 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).
  • 12. 12 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
  • 13. 13 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 11 This is common by folk wisdom in Brazil, although not according to any scientific article I have seen.
  • 14. 14 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: 12 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).
  • 15. 15 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 (݅ଵ). Like this, the ݅଴ become proxies for what would have happened to ݅ଵ 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. 16 difference in means between ݅଴ and ݅ଵ is generally called The Average Treatment effect on the Treated (ATT) and is denoted: ‫ܧ‬ሺܻଵ − ܻ଴| ‫ܦ‬ = 1ሻ = ‫ܧ‬ሺܻଵ|‫ܦ‬ = 1ሻ − ‫ܧ‬ሺܻ଴|‫ܦ‬ = 1ሻ (1) Some further denotation to make the paragraph above clearer: ܻଵ௜ = the outcome (in school attendance) for i when receiving the treatment (BF/extra cash). ܻ଴௜ = the outcome for i when not receiving the treatment. ‫ܦ‬௜ ∈ ሼ0,1ሽ = the possible treatments for i. ܻ௜ = ܻଵ௜ − ܻ଴௜ is the causal effect of the treatment on i. In equation (1) ‫ܧ‬ሺܻ଴|‫ܦ‬ = 1ሻ 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, (‫ܧ‬ሺܻ଴|‫ܦ‬ = 1ሻ, 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. 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: ‫݌‬ሺ‫ݔ‬ሻ = Pr [‫ܦ‬ = 1|ܺ = ‫]ݔ‬ (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. 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. 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. 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. 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. 22 6.2 Descriptive statistics Table 5 – descriptive statistics Range: Description Mean 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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 - 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. 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.
  • 34. 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 readUn-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
  • 35. 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
  • 36. 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 readUn-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
  • 37. 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
  • 38. 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