do conditional cash transfers affect poor students’...
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Do Conditional Cash Transfers Affect Poor Students’ Performance?∗∗∗∗
Mabel Garza Gobierno de Coahuila
Hector J. Villarreal
EGAP / ITESM Campus Monterrey
Abstract The lack of human capital is typically associated as cause and consequence of poverty and low levels of development. Governments look for public policy schemes to enhance its accumulation among the poor, one of these strategies is to apply programs based on Conditional Cash Transfers (CCT). This form of giving a targeted subsidy consists in establishing some conditions that the receiver must fulfill in order to get the benefits. We argue that while the quantity effect in education (enrollment) of these programs has been studied with some detail, much fewer has been written about the quality effect. A theoretical model is presented with some pessimistic predictions: it is expected that participants in CCT should have lower grades. The result is caused by two effects: a switching effect where some students may decide to enroll because of the CCT and will do the minimum to remain in track. Secondly, while the transfer component of the program may cause relief, an incomplete homologation would appear because the program is not able to provide conditions experienced by less poor students. A dataset that includes participants and non-participants in a famous Mexican CCT (i.e. Progresa/Oportunidades), students’ test scores, plus a series of sociodemographic variables allows us to test different econometric specifications. Participants in the CCT do have lower test scores. While we found some evidence of a switching effect, most of the difference appears related to the incomplete homologation. Finally, we conclude that while cash transfers may help, there are other variables to consider if schooling quality is to be equated.
(this version 01/2007)
∗ We want to thank Guadalupe Villarreal for facilitating the data set employed for the empirical analysis of this paper and her invitation to work with it. The data was collected with the financial support of CONACYT grant SEPSEByN-2003-C01-25. Pedro Albuquerque made valuable suggestions regarding the econometrics. Rocio Garcia and Bonnie Palifka diligently commented an older version of the paper. Errors remain our own.
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I. Introduction
The lack of human capital is typically associated as a cause and consequence of poverty and
underdevelopment. The fact that low-income households invest very little on education and
health tends to perpetuate poverty via vicious circles, i.e. poverty traps. As a response, the
last two decades have witnessed the emergence of a series of Conditional Cash Transfers
programs (CCT), (Lindert, Skoufias, and Shapiro 2005; Das, Do, and Özler 2004, 2005;
Bourguignon, Ferreira, and Leite 2003; Rawlings and Rubio 2005). These programs possess
two peculiarities described in their name. On the one hand, CCT involve direct cash transfers
as opposed to in kind transfers or to subsidies of particular goods. In theory, the “cash
transfer” element is associated with short-run welfare alleviation. The second characteristic is
that reception of resources implies the commitment of the household to follow certain
behavior (school attendance by children, medical check-ups, etc.). The objective within this
second element is to align incentives in order for poor households to accumulate human
capital. Thus, this second element is expected to enhance welfare in the longer run.
Several evaluations and assessments of CCT have shown positive effects with respect
to human capital acquisition by participants in the programs, (Behrman, Sengupta, and Todd
2000; Gertler and Fernald 2004). In particular, CCT appear quite successful incrementing
schooling among the poor (enrollment), (Schultz 2000; Skoufias and Parker 2001; Behrman,
Parker, and Todd 2004). The fact that this type of programs permits good targeting enables
them as an efficient tool for social policy, (Lindert, Skoufias, and Shapiro 2005; Das, Do, and
Özler 2004). While the “quantity” component of the acquired human capital has been studied
to a considerable extent, much less has been said about the “quality” component. The issue is
far from trivial, if the quality component is not considered the accumulation of human capital
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may be overestimated (underestimated). With respect to schooling, the latter may have a
profound effect for productivity, the acquisition of further human capital, and signaling in the
labor markets.
In this paper, we investigate the effects of CCT on poor students’ performance. We
believe students’ academic performance (SAP) is a good proxy of the quality of the acquired
human capital. A sensible hypothesis is that CCT do not have a direct effect on SAP once the
poverty level of the household is controlled. However, to the extent that these programs
affect variables that influence SAP, important indirect effects may show up. Moreover,
standard revealed preference logic suggests that the indirect effects may be negative. This
would be bad news, as it implies that that the accumulation of human capital by the poor
facilitated with CCT is overrated.
The paper is organized in the following way. Section II introduces a model of grade
formation and educational investment. The role of costs and expectations is studied as well as
the influence that the aforementioned social programs may have on them. Section III presents
the data; we have a sample of 1243 ninth grade students in Mexico and their exam results in
several subjects. A subset of these students participates in a famous Mexican CCT, i.e.
Progresa/Oportunidades. The availability of a rich set of sociodemographic and personal
variables allows us to test the model presented previously. In section IV the estimation
strategy is described, the econometric results presented and some implications suggested.
Finally, section V concludes and succinctly motivates some avenues for future research.
II.Theoretical Model
We begin by describing the formation of grades (the proxy we are using to describe the
quality of human capital):
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( , )i i i iG G q u= (1)
that is Gi is the vector of grades for the individual i=1,…I, that depends upon her
qualifications (qi) and some randomness or unspecified variables (ui). To some extent the
qualifications account for the accumulated human capital, however they are usually not
observed. Two variables emerge visible, years of education that we relate in this paper to the
quantity component of education, and grades on standardized exams that we consider as a
quality proxy. Notice that this kind of specification is flexible enough to allow qualifications,
sociodemographic variables, and the interaction between both, to affect alternative subjects
in a differentiated way. This is to comply with the literature that has documented these
differences (Alderman et al 1997; Borland and Howsen 1999; McNabb, Pal, and Sloane
2002; Black, Devereux and Salvanes 2004; Dooley and Stewart 2004; McEwan 2004;
Chevalier et al 2005; Paxson and Schady 2005).
The next step is to describe how qualifications are generated. Several components are
recognized to come into play:
( , , , )i i i i i iq q a e x s= (2)
where (qi) are the qualifications of individuals i=1,…I. They depend upon the level of ability
of individual i (ai), her effort level (ei), her expectations about future education/school
enrollment (xi), and sociodemographic variables (si). It should be noticed that possible
endogenous relations might exist between variables. For example, a common model in
microeconomics textbooks suggest that the effort an individual realizes at work or school
depends on his ability (Kreps 1990, Varian 1992). We assume that that the utility of the
individual (Vi) increases with respect to grades, that is 0/ >∂∂ ii gV for each i ig G∈ . Also, and
aligned with related models, effort is costly (increasingly) in terms of utility, that
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is 2 2/ 0, / 0i i i iV e V e∂ ∂ < ∂ ∂ < , (De Fraja and Oliveira 2005)1. As these authors mention, the
effort is not necessarily limited to students themselves. To the extent that costs are involved,
any behavior by other members of the family or household can be considered part of the
educational effort, which will be discussed below.
We define a human capital function that considers both the quantity (years of
schooling) and quality (grades) components:2
( , | 1,..., )i i i ij iK K y G j y= = (3)
so the human capital of a person depends on her years of schooling (yi) and the grades
received at each academic year j , (Gij). Notice, this does not mean that the grades of each
school year have an equal weight, i.e. it is very plausible that the grades at the last year are
more important.
Educational Investment
The next step is to link the formation of human capital at the individual level with the
household framework and circumstances. To understand the accumulation of human capital
by the household we propose a model of educational investment inspired in (Brown and Park
2001). Define:
( ) [(1 ) ( ) ( )]
. .
i
i i i i ie
i i
MaxW I C R K A R K M V
s t I b C N
α α
−
= − + + − +
+ ≥ +
(4)
1 It is a common practice in this kind of models to assume that the utility generated and the costs can be separated into two distinct functions. In that case the cost function is usually assumed to be convex. Both the separability and convexity of the cost function do not stem from economic theory. However, it is very convenient to use that framework to guarantee well-behaved solutions. For a good discussion, please refer to Kreps (1990). 2 To the extent that standardized tests are available, grades may be a sensible measurement of the quality component across schools, regions, etc.
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that is the household tries to maximize its utility or well-being (W)3. We define (Ii) as the
potential income of the household, and assume it is a function of a set of variables (Zi). The
function (Ci) takes into account the costs of children attending school; it includes opportunity
costs (forgone wages), school fees, and related costs such as transportation or school
materials. R(Ki) is the present value of the income flows for particular human capital profiles,
part of it will be kept by the individuals, and part of it will be transferred to the parents (α ∈
[0,1]). The function (A) measures the degree of altruism of the parents (explained below),
meanwhile M(Vi) is the present money metric utility value of the school experience. Finally,
a standard budget constraint is augmented with a borrowing constraint b−
(truncated because
imperfect markets and poverty), and a minimum consumption level (N).
The level of altruism can depend upon a series of variables; among them, the gender
of children tends to play a critical role. However, this effect may not necessarily be uniform
across regions, rural/urban areas, economic levels, etc. The existent literature has sought to at
least partially explain the altruism rationale as intra household bargaining, (Brown and Park
2001; Browning, Chiappori, and Lewbel 2005). Therefore, we proceed to model the
parameter ( A∈ [0, 1]) as:
( , , )f m
i i i iA A s l l= (5)
where (si) is a set of sociodemographic characteristics, and ( ,f m
i il l ) are the educational levels
of the father and the mother respectively.
Now, we should consider the investment problem of the household that participates in
the CCT:
3 Notice that while equations 1-3 refer to an individual, the investment decision is at the household level. Of course the presence of more than one child in the household can create several complementarities that are omitted here. The later is equivalent to assume that there is only one child per household or that the W’s as presented can be added through children.
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( ) [(1 ) ( ) ( )]
. . ,
i
i i i i i ie
o
i i i i
MaxW I C T R K A R K M V
s t I b T C N e e
α α
−
= − + + + − +
+ + ≥ + ≥
(6)
the investment analysis of households participating in CCT and households that do not
participate are very similar. The two differences between (4) and (6) are that the latter
includes the transfer (Ti) in the objective function, and in the restrictions, the effort level has
to be at least ( o
ie ). The intuition behind this minimum level is that the students need to attend
school and obtain passing grades.
Behavior
In order to understand how the human capital investment decisions of the households are
altered when participating in CCT, we will proceed heuristically. First, assume that the CCT
includes the component (Ti) but no additional restrictions. A direct implication is that if a
household has a non-binding budget constraint before the transfer (Ti), it would not modify
its behavior (ei). By contrast, (Ti) would alter the investment decision (ei) of a household with
a binding constraint (more human capital will be acquired and children effort allocated to that
purpose). Now the relevant question is if any differences in children grades should occur
between participating and non-participating households. Consider two identical households
whose only difference is 1 2 iI I T= + . To the extent that / 0, / 0i i i iI Z q Z∂ ∂ > ∂ ∂ > , households
participating in CCT should present lower grades than non-participating households with the
same resources. The intuition is that variables positively correlated with household income
are also positively correlated with children grades (e.g. human capital of the parents, living in
a better neighborhood, etc.). This first effect would be called incomplete homologation. The
hypothesis is that even if income is homologated between households, there maybe
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characteristics affecting both income and qualifications that are not homologated by CCT,
thus non-participating children have better grades.
Now we would consider the effort constraint of CCT. Assume that
0( ( ) [(1 ) ( ) ( )] | ) 0i i i i iC R K A R K M V eα α− + + − + < . Many factors can be producing the result:
high opportunity costs, poor expectations, low level of ability by the students, etc. A
truncation result occurs and children would not do that extra school year. We will call
children under that condition type 1, children with the inequality positive will be catalogued
as type 2. Now, children participating the described CCT have to attend school, an
implication of the model is that 0( ( ) [(1 ) ( ) ( )] | ) 0i i i i i iC R K A R K M V e Tα α− + + − + + > . Notice
that by the convexity assumption, if a child was type 1 and now participates in CCT her
effort level must be ( o
ie ). If some of the children participating in CCT were type 1, type 2
children have * 0i ie e> where *
ie solves the investment problems, and / 0i iq T∂ ∂ = ; then grades
from children participating in CCT would be lower other things equal.4 This second effect
would be called switching. The hypothesis is that some children would not study without
CCT because of opportunity costs. The transfer component (Ti) would make them switch, but
they would try to minimize effort. These children should have lower average grades
compared to non-switchers.
Under the model developed in this section, both the incomplete homologation and the
switching effects, suggest that the children participating in CCT would have lower grades on
average than non-participants.
III. The Empirical Environment
4 These are sufficient conditions, necessary conditions are much weaker.
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The Mexican government created in 1997 the Programa de Educación, Salud y Alimentación
(Progresa). The driving idea was that education is the key to break the vicious cycle of
poverty and that unless the minimum conditions on health and nutrition are satisfied, the
benefits of education will be suboptimal, and therefore poverty would continue. According to
an evaluation made by the Mexican Ministry of Social Development, by the end of the year
2000, the program counted with more than 2.4 millions of families as beneficiaries, living in
53,000 rural communities in 31 states. The program was mildly reformulated and renamed in
year 2002 as Programa de Desarrollo Humano Oportunidades (Oportunidades). Among the
changes there was the inclusion of urban and semi-urban communities (previously it was
limited to rural areas), the promotion of jobs for participants, and access to basic financial
services. By the end of 2005, five million families were beneficiaries from the program.5
One of the distinguishing factors of Progresa/Oportunidades is that it is a conditional
cash transfer program (CCT), which means than once a family has been selected to
participate in the program, they have to fulfill certain requirements in order to keep receiving
the transfers. Particularly on the educational component, the scholarships and economic help
to buy school supplies are granted for children in school age, specifically for those from the
third to the last grade of secondary school (ninth grade). The amount of cash increases as the
child moves from one year to the next. When the child reaches the seventh grade, the transfer
is greater for girls than for boys. The motivation for this is that desertion from school is
greater for girls than for boys during secondary school. The transfer for education is
conditioned to the attendance of children to the school and the participation of the parents in
school activities. Unjustified absences lead to the loss of the scholarship.
5 Source: Sedesol, La Política Social del Gobierno de México, resultados 1995-2000 y retos futuros (http://www.sedesol.gob.mx/)
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Participant’s Selection
In order to determine who would participate in the program, three steps were covered. In the
first place, the Ministry of Social Development (Sedesol) determined the geographic areas
with the most critical conditions of poverty and their access to health and educational
services. Once those areas were identified, the second step was to recollect socioeconomic
data about the families in the area, to determine which should have been beneficiaries of the
transfers. The third and last step was to present in the community assembly6 the list of the
selected families in order to receive feedback, further suggestions and reasons to include any
other family in the program.
Another difference between Progresa and Oportunidades lies in the selection method.
With the addition of families from urban and semi-urban areas, the existent method could not
be used due to excessive costs. For these cases, they adopted an alternative method of sign-
up modules. The problem of this strategy is that some families that should have been selected
maybe were not because they did not sing up (Parker, Todd, and Wolpin 2005).
Previous Evaluations
Shultz (2000) showed that Progresa has had a positive effect in the enrollment in primary
and secondary schools for both boys and girls. At the secondary school level, the enrollment
rates were 67% for girls and 73% before Progresa. Just a couple of years after the program
began the enrollment had an increase of above 8% for the boy’s case and of 14% for the
girls. He also found that the Program itself contributes with .66 years of additional years of
schooling for both sexes. If analyzed separately, the increase in the years of education is
6 Community Assembly is a reunion of the population of a rural community in which the members discuss public matters; in this case, the participation of certain families in the Program.
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greater for girls than for boys. The former have a gain of .72 years of additional education
and the latter of .64 years.
Concerning to dropout rates, Behrman, Sengupta, and Todd (2005) found that
students with a Progresa educational grant have lower dropout rates and higher school re-
entry rates among those who had dropped-out. They also found that the program has a special
effectiveness in the dropout reduction during the transition from sixth to seventh grade.
A previous evaluation of the impact of Progresa on school performance was
conducted by the same authors (Behrman, Sengupta, and Todd 2000). They evaluated the
Program after a school year and a half of exposition to the program and they found no
significant impact on improving student scores but they also recognized the limitations due to
the available data they had and the exposure time to the Program.
The Data Employed in This Paper
The dataset includes 1255 students that finished middle school in 2005, and is drawn from a
survey conducted in April of the same year by Dr. Guadalupe Villarreal. The dataset contains
information about students of two states, Chiapas and Nuevo León, studying in two types of
schools, telesecondarys and general secondary schools. The students in the survey took an
EXANI I test and filled a registry form to obtain sociodemographic data.
Telesecondary is a distance education format available in rural communities in which
the student instead of having a teacher receives the classes through television. This system
has come to alleviate the problem of the shortage of middle school institutions and teachers
in isolated communities. The general middle school is the traditional format institution where
the teacher gives the classes directly to the students.
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The performance of the students is measured with the scores of the EXANI I7 test.
This exam is a high school admission test designed and administered by the National Center
for the Evaluation of Higher Education (Ceneval) that has been applied to more that 5 million
students since 1994. This exam provides a reliable diagnose of basic cognitive skills (verbal
and mathematical skills) and knowledge of eight fields: Mathematics, Spanish, Physics,
History, Civics and Ethics, Geography, Chemistry, and Biology. The results for each section
are transformed into a scale that goes from 700 to 1300, where 700 means 0% of right
answers and 1300 that the exam is 100% right.
Table 1. Basic Information about Chiapas and Nuevo León
Chiapas Nuevo León
Total population 3,920,892 3,834,141 Urban population 46% 93%Literacy 77% 95.6%Number of middle schools 1,403 12,991 Number of houses 778,845 878,600 Houses with tubed-water 74.4% 95.5%Houses with elecricity 87.9% 98.5%Houses with sewer services 62.3% 90.8%Families in Oportunidades 554,525 49,564 Oportunidades scholarships* 153,326 6,864 Gini Coefficient** 0.5729 0.5185 Human Development Index¤ 0.6926 0.8425 Margination Index¤¤ 2.2507 -1.3926
*Only for secondary school
**Authors calculation. Source: Enigh 2004, INEGI.
¤ Source : CONAPO, Indice de Desarrollo Humano, 2000;
available from www.conapo.gob.mx
¤¤Source: CONAPO, Indice de Marginación,2000;
available from www.conapo.gob.mx
Source : INEGI, Anuario Estadístico por Entidad Federativa 2005; availablefrom www.inegi.gob.mx
7 For additional information about the EXANI-I, the reader can visit the CENEVAL’s webpage (http://www.ceneval.edu.mx/portalceneval/index.php?q=info.fichas.ficha1)
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Chiapas and Nuevo León present great differences. While the first state has 54% of
rural population, the second only has 7%. The GDP in Nuevo León is 45,759.7848 million
dollar compared to 10,441.6316 million dollar in Chiapas.8 There are also big differences
among the number of families that are part of Oportunidades and the basic services that the
households have available (Table 1).
IV. Estimation Strategy and Econometric Analysis
Table 2 presents the average scores in different subjects for students in the sample: both
participating in the program and non-participants. Clearly, there is a statistical difference
between both types, with participants’ scores lower than non-participants in the program.
Table 2. Average Scores
We study the correlation between test scores of students and the variables presented
in the model of section II. The purpose of the econometric analysis in this section is to
isolate the effect (or correlation) of participating in Progresa/Oportunidades with respect to
other variables that may potentially be affecting the results. The estimation strategy followed
will be of the parsimonious type: a simpler econometric framework is employed at the
beginning. Afterwards, it would be expanded depending upon the problem to be solved.
8 The data is for 2004 and the peso/dollar exchange rate used was 11.3085 pesos/dollar. Source: INEGI, Producto Interno Bruto por Entidad Fedrativa (http://dgcnesyp.inegi.gob.mx)
Participants Non-participants
Mathematics 877 926 Spanish 870 956 Physics 867 917 Chemistry 866 895 Biology 879 920 History 877 919 Geography 868 912
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Attention is concentrated on identification of the effects described in the theoretical model.
Table 3 resumes the variables that will be employed for the econometric analysis.
OLS Estimates
A natural benchmark to begin the analysis regards ordinary least squares estimation. The
seven scores of the students were regressed on the following variables: an interceptor,
participation in Oportunidades, a dummy variable with value of one if they considered
themselves fast-learners, the educational level of the mother,9 how many hours they study per
week, and the educational level they aim to complete. In the case of Spanish score, an
explanatory variable was added if the language spoken at home was indigenous.10
Table 4 summarizes our findings. It is worth to note that participation in
Oportunidades is negatively correlated and significant with the seven scores, and the effect is
very big for the Spanish score. With respect to the ability to learn fast, it has a considerable
effect in all the scores. The same can be said for the mother’s education, given the variable
range, for mothers with high levels of education, this variable ends explaining an important
part of the score. Hours devoted to study and the educational level that students want to reach
also have significant effects.
9 63 students reported not living with their mother, but only eight of them did not report the educational level of the mother. In those cases, the mothers are assumed to have no education. 10 We did not found any statistical evidence that this variable was correlated with the other scores. Also, (and contrary to our suspicion) the dummy variable of being in a “telesecondary” was not statistical or economical significant in explaining the scores.
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Table 3. Description of the Variables
Variable Description Mean Std. Dev.
Aimed educationDummy. 1 for thos who expect to study for4 or more years.
0.2725 0.4455
Eat legumesNumber of portions of legumes they eat perweek.
3.5007 2.6387
Eat meatNumber of portions of meat they eat perweek.
1.4778 1.4189
Education of the father Years of study of the father. 5.3001 3.1583Education of the mother Years of study of the mother. 4.7033 3.3560
Fast-learnersDummy. 1 for those who considerthemselves as fast-learners.
0.6872 0.4639
Gender Dummy. 1 for males. 0.5195 0.4999Hours of study Hours of study per week. 3.4094 2.3811Income Household montly income in pesos. 2409.87 1806.31
IndigenousDummy. 1 for those who speak anindigenous language at home.
0.3248 0.4686
Rural Dummy. 1 for those who live in rural areas. 0.8406 0.3663
Siblings Number of brothers and sisters. 4.2875 2.1322
StateDummy. 1 for those who live in NuevoLeón.
0.2671 0.4427
Variable Description Mean Std. Dev.
Aimed educationDummy. 1 for thos who expect to study for4 or more years.
0.2950 0.4565
Eat legumesNumber of portions of legumes they eat perweek.
3.5125 2.3416
Eat meatNumber of portions of meat they eat perweek.
2.6619 1.8367
Education of the father Years of study of the father. 8.8807 3.8868Education of the mother Years of study of the mother. 8.6590 4.0202
Fast-learnersDummy. 1 for those who considerthemselves as fast-learners.
0.7845 0.4116
Gender Dummy. 1 for males. 0.4895 0.5004Hours of study Hours of study per week. 4.3954 2.9046Income Household montly income in pesos. 3530.25 3563.45
IndigenousDummy. 1 for those who speak anindigenous language at home.
0.0565 0.2311
Rural Dummy. 1 for those who live in rural areas. 0.5690 0.4957
Siblings Number of brothers and sisters. 2.7128 1.6851
StateDummy. 1 for those who live in NuevoLeón.
0.7761 0.4173
With Oportunidades scholarship (N=745)
Without Oportunidades scholarship (N=478)
A series of auxiliary regressions (also included in Table 4) were performed to
investigate possible correlations among the first set of explanatory variables. In particular, we
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were interested to see if participation in the CCT may affect behavior or expectations. The
first auxiliary regression was studied hours as a dependent variable, keeping the other five as
explanatory variables. As it is shown in the tables, all the variables were significant.
Indirectly participation in the CCT is affecting (in terms of correlation) the scores since
students in the program study on average half an hour less. Both expectations and ability
have a positive and significant effect on studied hours, thus directly and indirectly affecting
the test scores. The education of the mother is playing a role in explaining the studied hours.
Given that the mean educational level (measured in years) of participants’ and non-
participants’ mothers differ (they are 4.71 and 8.66 respectively) another indirect effect is
present.
Two more auxiliary regressions were performed. Using expectation of completed
grades and self-defined ability as dependent variables (both dummies), we did ML logit
regressions on an extended set of explanatory variables. The results are summarized in Table
3. For the first logit regressions, other than the intercept the significant independent variables
were participation in Oportunidades, gender, and the ability to learn fast. The program and
ability have the positive expected sign, meaning that they would also have a positive indirect
effect in the scores. Being a male has a negative effect on the aspired educational level. This
last effect will contradict the Mexican experience where men have on average studied more
years than women have.
The only significant variable (other than the intercept) explaining the ability to learn
fast is the aimed level of study. On the one hand, it is suspected that the causality runs the
other way, and that the regression is just capturing a correlation. Second and very interesting,
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is the fact that the self-declared ability is not correlated with variables that are themselves
correlated with poverty or income level.
Table 4. OLS Estimates
Mathematics Spanish Physics Chemistry Biology History Geography
Intercept 828.5773 839.3169 827.5767 826.1563 835.5236 830.3310 833.2350[10.3336]*** [9.9085]*** [10.0531]*** [8.8330]*** [9.7343]*** [9.1559]*** [8.9700]***
Oportunidades -21.1016 -42.8688 -24.2269 -9.8332 -14.3144 -13.4688 -19.3845[6.9669]*** [6.4991]*** [6.7779]*** [5.9552]* [6.5629]** [6.1729]** [6.0476]***
Fast-learners 34.1595 18.6316 25.6176 20.4563 10.4477 19.2636 17.6660[6.7831]*** [6.2829]*** [6.5990]*** [5.7980]*** [6.3897] [6.0100]*** [5.8880]***
Education of the mother 4.5824 6.7127 4.3095 2.9554 5.0280 5.3904 4.7171[.8406]*** [.8166]*** [.8177]*** [.7185]*** [.7918]*** [.7448]*** [.7296]***
Hours of study 5.8379 7.9044 6.7362 5.3073 5.7973 4.6480 4.7961[1.1812]*** [1.0943]*** [1.1492]*** [1.0097]*** [1.1127]*** [1.0466]*** [1.0254]***
Aimed education 17.5751 27.1689 10.5321 12.8115 26.2906 22.9990 11.6957[6.6987]*** [6.2103]*** [6.5169] [5.7259]** [6.3102]*** [5.9353]*** [5.8148]**
Indigenous -16.6893[6.6005]**
Hours of study Aimed education Fast-learners
Intercept 2.7397 -1.7921 0.7693[.2350]*** [.3369]*** [.3204]**
Oportunidades -0.4636 0.2936 -0.2239[.1663]*** [.1623]* [.1637]
Fast-learners 0.7891 0.3962[.1609]*** [.1545]***
Education of the mother 0.1072 0.0328 0.0214[.0199]*** [.0242] [.0252]
Aimed education 0.2961 0.5642[.1602]* [.1351]***
Gender -0.4031 0.0392[.1306]*** [.1314]
Siblings 0.0193 -0.0243[.0374] [.0364]
Education of the father 0.0373 0.0239[.0238] [.0247]
Rural 0.0692 -0.1216[.1549] [.1658]
Eat meat 0.0402 0.0217[.0419] [.0443]
Eat legumes -0.0084 0.0269[.0265] [.0259]
Indigenous -0.3114 -0.0699[.1964] [.1793]
*Significant at 10% **Significant at 5% ***Significant at 1% Standard errors in brackets
Controlling for Selection Bias
Given that participation in the program is not random, it would be suspected that the
variables that affect, who are part of the CCT, may be biasing the test scores. We would
proceed to test how selectivity bias may be influencing the results (Heckman 1979). The
usage of the Heckman procedure will obey two purposes. On the one hand, the incorporation
18
of the Inverse Mills ratio (IMR) in the second step will aid in detecting a selection bias and in
its correction. Second, the variables employed in the first step probit can be directly linked to
the model of section II.
The first step probit had participation in Oportunidades as the dependent variable and
the independent ones were an intercept, the number of siblings, the consumption of meat, the
students’ state, and both the mother’s and the father’s education. The obtained coefficients
allowed the calculation of the IMR. The second step consisted in estimating the same
equations used in the OLS models, augmented with the IMR as an extra independent
variable. The results of the second step are summarized in table 4.11 In all the cases, except in
the Spanish equation, the participation in Oportunidades is non-significant. Instead, the IMR
absorbs the effect. To what extent this result suggests the presence of a bias in selection will
be discussed in the analysis section. The effects of three of the explanatory variables (ability,
expected educational level, and hours) remain very stable that is its statistic and economic
significance are unaltered. This is not the case for mother’s education, which is still
significantly correlated with three of the test outcomes (History, Biology, and Spanish), but
whose effects vanish in the other subjects. However, some caution is needed given that the
variable mother’s education is also employed in the first stage. We know that it plays a role
in the selection effect and ultimately in the test scores. Also, and consequently, some
endogeneity is hinted, which will be discussed below.
11 Further information about the first step is available under the request of the reader.
19
Table 5. Controlling for Selection Bias Estimates
Mathematics Spanish Physics Chemistry Biology History Geography
Intercept 811.3910 817.9968 805.2945 819.3079 823.8200 818.2072 820.3474[11.0261]*** [10.6469]*** [10.5867]*** [9.4767]*** [10.4160]*** [9.7116]*** [9.5616]***
Oportunidades -3.5963 -23.3106 -3.4243 -4.7217 -4.0549 -2.7435 -8.2412[7.8671] [7.2666]*** [7.5536] [6.7616] [7.4318] [6.9292] [6.8222]
Fast-learners 33.0893 16.1209 25.3900 20.9796 11.2145 18.5419 18.3167[6.9173]*** [6.3815]** [6.6416]*** [5.9452]*** [6.5345]* [6.0926]*** [5.9985]***
Education of the mother 0.5366 2.2695 -0.4528 1.4753 2.8055 2.2915 1.5255[1.1998] [1.1166]** [1.1520] [1.0312] [1.1334]** [1.0567]** [1.0404]
Hours of study 5.6723 7.7028 6.5701 5.1638 5.7101 4.4391 4.8329[1.1918]*** [1.0997]*** [1.1443]*** [1.0243]*** [1.1259]*** [1.0497]*** [1.0335]***
Aimed education 15.4566 25.9269 7.1677 12.5129 23.7675 21.3644 12.0285[6.7852]** [6.2645]*** [6.5148] [5.8317]** [6.4097]*** [5.9763]*** [5.8839]**
Indigenous -12.3708[6.8165]*
IMR 49.8385 57.0345 58.8615 18.6208 28.8331 37.6853 36.4711[10.3421]*** [9.6269]*** [9.9300]*** [8.8888]** [9.7698]*** [9.1092]*** [8.9684]***
Hours of study Aimed education Fast-learners
Intercept 2.5785 -1.7114 0.6357[.2559]*** [.3432]*** [.3328]*
Oportunidades -0.3089 0.2217 -0.1341[.1899] [.1720] [.1735]
Fast-learners 0.7574 0.4029[.1658]*** [.1548]***
Education of the mother 0.0768 0.0487 0.0026[.0289]*** [.0274]* [.0280]
Aimed education 0.2818 0.4064[.1638]* [.1545]***
Gender -0.4107 0.0467[.1308]*** [.1316]
Siblings 0.0045 -0.0063[.0393] [.0382]
Education of the father 0.0534 0.0048[.0272]** [.0276]
Rural 0.0613 -0.1043[.1551] [.1665]
Eat meat 0.0728 -0.0178[.0497] [.0515]
Eat legumes -0.0042 0.0220[.0267] [.0261]
Indigenous -0.3192 -0.0586[.1964] [.1792]
IMR 0.4322 -0.3776 0.4820[.2497]* [.3077] [.3179]
Standard errors in brackets*Significant at 10% **Significant at 5% ***Significant at 1%
Endogeneity
As commonly happens in economic/social empirical analysis, we suspect that the model
suffers of endogeneity, which means that one or some of the independent variables is (are)
correlated with unobservable variables captured in the error term. This problem leads to
biased parameters. A solution is the use of instrumental variables (IV). We employ
instrumental variables via the Generalized Method of Moments. The chosen instruments
20
were state (living in Nuevo Leon or Chiapas) and a self-reported categorical income level
(part of the EXANI I questionnaire), plus all the regressors except participation in the
program. Table 6 summarizes the results. Notice that the model passes the orthogonality test
since the χ2 (7) at a 95% confidence level from tables is 2.17. Our calculated value is 1.97, so
we do not have evidence to reject the orthogonality condition.
Table 6. Controlling for Endogeneity Estimates
Mathematics Spanish Physics Chemistry Biology History Geography
Intercept 899.1329 935.6169 900.8634 859.3887 882.6015 877.7782 850.4930[21.5930]*** [31.5106]*** [21.1129]*** [17.9867]*** [19.9711]*** [18.8367]*** [18.1093]***
Oportunidades -89.9884 -163.3733 -95.7802 -42.2797 -60.2789 -59.7939 -36.2344[19.6738]*** [102.0660] [19.2364]*** [16.3881]*** [18.1960]*** [17.1625]*** [16.4998]**
Fast-learners 30.8814 14.3112 22.2127 18.9123 8.2604 17.0592 16.8642[7.0960]*** [6.7115]** [6.9382]*** [5.9109]*** [6.5630] [6.1902]*** [5.9518]***
Education of the mother 0.8788 2.5571 0.4625 1.2110 2.5568 2.8999 3.8112[1.3150] [1.8283] [1.2857] [1.0954] [1.2162]** [1.1471]** [1.1028]***
Hours of study 4.9198 6.1395 5.7825 4.8749 5.1847 4.0306 4.5716[1.2504]*** [2.1494]*** [1.2226]*** [1.0416]*** [1.1565]*** [1.0908]*** [1.0487]***
Aimed education 20.1812 33.8805 13.2390 14.0390 28.0295 24.7516 12.3331[6.9891]*** [12.0069]*** [6.8337] [5.8219]** [6.4641]*** [6.0970]*** [5.8615]**
Indigenous 36.3612[153.9673]
Minimum distance 1.9766Instruments Fast-learners, Education of the mother, Hours of study, Aimed education, State, Income
Standard errors in brackets*Significant at 10% **Significant at 5% ***Significant at 1%
After the correction for endogeneity, the coefficients of many of the explanatory
variables are similar to the ones of the previous section (both in economic and statistic
effect). The IMR was not significant anymore, so it was dropped from the equations. This
will be discussed in the next section. The drastic change with respect to the previous
regressions is that now participating in the program has a huge negative effect. Even in the
case of Spanish the p-value is marginally above 10%, the economic significance is so large
that the effect becomes non-negligible.
Analysis
How can we reconcile the evidence found? Or put it in other terms: are the econometric
results of the three sections inconsistent? We believe not. To begin with, the exams results
21
summarized in Table 2 suggest that participants in the program do have lower scores than
non-participants. These results were aligned with the predictions of the theoretical model of
section II. The negative correlation between the CCT participation and scores were backed
up by each of the econometric specifications, but in different ways. OLS regressions show
that there exists the negative correlation but that other variables are playing a role too.
Moreover, participation in the program may have indirect effects since it is correlated itself
with the other variables.
When selection bias correction is introduced via the IMR, the significance of
participating in the program vanishes. These results are consistent with the theoretical model:
it is not that participation in CCT lower grades, but participants in the program are expected
to have a poorer performance because of the incomplete homologation and the switching
effect. Thus, selectivity bias seems important.
With the suspicion of endogeneity, participation in the program is instrumented via a
self-reported income level and state of residence (with the two states in our sample showing
very different standards of living). Again, participation is the program is highly negative
correlated with test scores. While the effects of the other explanatory variables remains
qualitatively very similar.
To understand why a negative correlation is showing up, we rely again in the
theoretical model. We found some evidence that supports the switching effect: participants in
the program study less hours, have lower future schooling expectations, and define
themselves as slightly less able. Nonetheless, the major part of the difference in scores
appears to be caused by variables (observed and unobserved) associated to living standards.
In terms of our model, while the cash transfers may cause an immediate relief, they may not
22
solve other problems (e.g. attending quality schools, living in good neighborhoods, etc.).
Thus an incomplete homologation between participants and non-participants may be
occurring.
V. Conclusions
Conditional Cash Transfers (CCT) appear nowadays as the silver bullets of social policy:
constantly more governments are interested in implementing them. The rationale behind this
behavior seems understandable: in general, the assessments of existing CCT show that they
have been effective. In the particular case of education, it seems that targeted CCT are very
good to improve enrollment.
In this paper, we argue that while the quantity effect in education (enrollment) has
been studied with some detail, much fewer has been written about the quality effect. A
theoretical model is presented with some pessimistic predictions: it is expected that
participants in CCT should have lower grades. The result is caused by two effects: a
switching effect (similar in logic to revealed preference) where some students may decide to
switch because of the CCT and will do the minimum to remain in track. Secondly, while the
transfer component of the program may cause relief, an incomplete homologation would
appear because the program is not able to provide conditions experienced by less poor
students (better educated parents, access to services, good neighborhoods, higher quality
schools, etc.).
A dataset that includes participants and non-participants in a famous Mexican CCT
(i.e. Progresa/Oportunidades), students’ test scores, plus a series of sociodemographic
variables allows us to test different econometric specifications. Participants in the CCT do
23
have lower test scores. While we found some evidence of a switching effect, most of the
difference appears related to the incomplete homologation.
CCT are expected to promote the accumulation of human capital. Past studies and
assessments suggest that they are effective, but the quality component is often not
considered. This paper points that “leveling the field” between poor students and others
better off may be harder than expected. That is, while cash transfers may help, there are other
variables to consider if schooling quality is to be equated. Future research on the
determinants of school grades and public policies to enhance them should prove
instrumental.
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