Conditional cash transfer programs for child human capital development:
Lessons derived from experience in Mexico and Brazil1
by
Alain de Janvry and Elisabeth Sadoulet
University of California at Berkeley and
World Bank Development Economics Research Group
Abstract
This paper addresses three questions commonly raised about conditional cash transfer (CCT) programs for
child human capital development: (1) When to use the CCT approach? (2) How to increase the efficiency
of the approach? (3) How to learn more from implementation of the approach to improve its use in
alternative contexts? We use lessons derived from the Oportunidades experience in Mexico and the Bolsa
Escola program in Brazil to propose answers to these questions. Answers suggest that the approach is
highly efficient in inducing a change in behavior among parents toward child human capital development
when the objective is not extreme poverty reduction. They also show that considerable efficiency gains can
be achieved through better targeting and calibration of transfers toward children at risk of not going to
school without a CCT, better understanding of heterogeneity of responses to design complementary supplyside interventions in particular according to parents’ educational levels and distance to school, use of the
approach as a safety net to reduce vulnerability of child human capital to shocks, and introduction of more
effective social accountability mechanisms between providers and stakeholders. There exists, however, a
huge deficit in learning from past experiences and in experimenting with alternative ways of implementing
CCT programs while the approach is being extended to new country contexts quite different from the ones
where experience has been derived.
I. Introduction
Conditional cash transfers (CCTs) are now widely used as an approach in social
assistance programs (Rawlings and Rubio, 2005). Their distinguishing feature is that they impose
a behavioral condition on transfer recipients. The condition typically sets minimum requirements
on beneficiaries’ attention to the education, health, and nutrition of their children. For
beneficiaries that would have met the behavioral condition without the transfer, the program is
equivalent to a pure cash transfer that reduces poverty immediately, but does not induce a change
in child welfare else than through the income effect of the transfer. For those that would not have
met the condition without the transfer, receiving the transfer requires a change in behavior. In
this case, the condition acts as a price subsidy on the conditional service. If the price effect is
more powerful than the income effect from the transfer in inducing a change in behavior, the
conditional cash transfer can then have a double benefit: it not only creates an immediate decline
in poverty among recipients if the transfer is larger than the cost of the condition, but it also
induces a gain in the educational, health, and nutritional achievements of beneficiaries’ children,
thus potentially helping reduce future poverty levels.
Not unsurprisingly, these programs implemented at a large scale in several middleincome countries have had reasonable success in meeting their basic objectives, namely reducing
poverty (with annual budgets of $2.6 billion in Mexico and $700 million in Brazil), increasing
educational achievements (Schultz, 2004), improving child and maternal health (Gertler, 2004),
and reducing malnutrition (Hoddinott and Skoufias, 2003). Other verified impacts from the CCT
are linkage effects on the local economy (Coady and Harris, 2001), multiplier effects of transfers
through self-investment (Gertler, Martinez, and Rubio, 2005), spill-over effects on the
educational achievements of the non-poor (Bobonis and Finan, 2005), and a reduction in child
1
Authors’ addresses: alain@are.berkeley.edu, sadoulet@are.berkeley.edu
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labor (Reference #42). Impact on learning, as imperfectly measured by the progress rate from
grade to grade, are not significant in spite of the requirement on school attendance (Reference
#1). This may be due to the fact that the CCT attracts to school children with low taste or ability
for school, and that increased school enrollment crowds-out the supply side of education.
CCT programs that started mainly in middle-income countries such as Mexico, Brazil,
Turkey, Chile, Colombia, Ecuador, Jamaica, Honduras, Panama, and South Africa are now
spreading to low-income countries such as Nicaragua, Burkina Faso, Lesotho, Cambodia,
Pakistan, and Bangladesh. A number of other countries, typically poor ones especially in Africa,
are currently looking at these successes and considering adoption of this approach. There are,
however, several questions that need to be answered in deciding whether to use the CCT
approach or not and how. The questions debated revolve around three issues: (1) When to use
the CCT approach? (2) How to increase the efficiency of the approach? (3) How to learn more
from implementations of the approach to improve its use in alternative contexts? In what follows,
we discuss these three issues based on current experiences that provide useful, though
incomplete, lessons. We do this by using principally results from the research we did on
Progresa/Oportunidades in Mexico and on Bolsa Escola/Bolsa Familia in Brazil for this paper and
in References #1 to #5 in the bibliography.
II. When to use the CCT approach?
2.1. When the program’s objective function includes changing beneficiary behavior
In considering a CCT approach, two contrasted interpretations of objectives are
immediately apparent. In the first, the transfers have the principal objective of reducing current
poverty. The position is that “even a small amount of cash in the hands of a poor mother can do
wonders” (IFAD project officer). In this case, the transfer should be unconditional. In spite of
this, imposing a condition on behavior may be necessary to secure the political acceptability of a
transfer program. This is because taxpayers and donors may agree to fund a transfer program, but
only if the recipients display socially acceptable behavior: they are required to work in workfare
programs such as Trabajar in Argentina or to send their children to school and health visits in
programs such as Oportunidades and Bolsa Escola. Imposing such conditions on behavior may
well be welfare reducing for both recipients and society (compared to an unconditional cash
transfer), but it is second-best welfare enhancing compared to no program or to a program with a
smaller budget due to low political acceptability. Implementation requires targeting on poverty,
irrespective of whether the selected beneficiaries would have met the condition without a transfer.
This position in favor of unconditional CT tends to be preferred in contexts where poverty is
extreme and where the objective of the transfer is securing immediate survival. If a condition is
imposed on the behavior of recipients for program legitimation purposes, it should minimize the
welfare loss on recipients compared to an unconditional cash transfer.
In a second interpretation, the CCT approach is seen as an instrument to increase the
human capital of the children of the poor. To meet this objective, the transfers would need to be
targeted and calibrated for maximum impact of the program on human capital development. For
the educational objective, this requires identifying children who are most at risk of not going to
school without a transfer, and who have the largest response per unit of transfer. We return
below to the optimal design of a CCT program when maximizing the effect of the conditionality
on changing the behavior of recipients is the objective.
2
Papers on Oportunidades and Bolsa Escola authored by us and collaborators are referred to as References
#1 to #5.
2
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Countries that consider introducing a CCT, where the condition is on child human capital
development, need to have clear what objective is being pursued. The optimum targeting and
calibration of transfers offered to poor households will differ according to the objective being
pursued. Eliminating a potential confusion in objectives is thus a pre-condition for implementing
an efficient CCT program.
2.2. When constraining behavior is justified to create a private or social efficiency gain
A CCT transforms the positive income effect of a cash transfer (CT) into a negative user
fee on the service that is imposed as a condition. Conditionalities can thus be used when the
income effect of a transfer is insufficient to induce the action required by the condition. Clearly,
if the action is met without the transfer, or as a consequence of the transfer alone, imposing a
condition is useless and administratively costly for no benefit on behavior.
It is well known from basic principles of welfare economics that, in a first best world,
cash transfers without any conditions attached will maximize the welfare gains achieved by
recipients. Imposing a condition thus needs to be carefully rationalized. There are three sources
of failures in household decision-making that justify imposing a conditionality on transfers. 3
i) Protecting people against themselves: Child win - Household win
There are situations where public intervention is meant to help protect people against
their own choices. This is the case when uneducated parents may not be informed about the value
of education, especially outside the community, or when the future value of education is
underestimated by households based on the current assessment of the value of education. This
may also be the case when there is bounded rationality such as procrastination in decisionmaking. Finally, programs for child human capital development may assemble complementary
interventions in education, health, and nutrition in a complex fashion that is beyond the
understanding and implementation capacity of poor parents. Under these conditions, imposing
conditionalities on transfers may well be doubly welfare enhancing: it will increase the welfare
of the child (who receives the right amount of schooling and other elements of human capital
development) and of parents (who will benefit from the right level of child human capital
development). The CCT approach is in this case an instrument to secure the first best.
Lack of information about returns to education in marginal rural communities is starkly
illustrated in Figure 1.
3
We do not consider here the use of conditionalities to induce self-selection in targeting, for example in
workfare programs. This is discussed in Das, Do, and Özler (2005).
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2500
Life time earnings (pesos/month)
)
Migration
2000
1500
1000
Ag. wage
Self-employed
500
Non-ag. wage
0
Primary
Secondary 1
Secondary 2
Secondary 3
Higher than
secondary 3
Figure 1. Returns to education in four activities for children in marginal rural communities.
Progresa data. (Source: Reference #1)
The present value of lifetime earnings4 in activities within the marginal community
(agricultural wage employment, self-employment in agriculture or micro-enterprises, and nonagricultural wage employment) do not increase with educational levels. In these communities,
returns to education are very low, deterring private investment in education. By contrast, returns
to education are high outside the community. If parents are only informed about local returns,
they will under-invest in education. Informing parents about the gains from migration is thus
necessary to induce investment in education. Alternatively, a CCT is a way of inducing parents’
behavior toward child education that corresponds to the true returns from migration, including
through migration outside the marginal rural community.
ii) Discrepancy between child and parent optima: Child win - Household loss
This is the case when a decision by a parent may hurt the interest of a child who has no
option in responding. Parents may under-invest in education because they have a higher discount
rate than their children, at a private cost to them (Baland and Robinson, 2000). This also happens
when intra-household bargaining with unequal power between genders may lead to resource
allocation that is sub-optimum for children in terms of investment in their human capital. This is
the case if the mother, who represents the child’s interest, does not have bargaining power in the
household to defend the child’s welfare. Cash transfers to such households should be conditional
to induce them to adapt their behavior to the interests of their children. The CCT approach is in
this case to maximize child welfare, at the cost of a loss for the members of the household whose
choices are altered by the conditionality relative to an unconditional CT. No absolute loss is
incurred since participation is voluntary.
iii) Discrepancy between private and individual optima: Social win - Household loss
It is well known that there are large positive externalities from private investments in
education and health, resulting in under-investment relative to social optimum (see in particular
Currie and Moretti, 2003, and Milligan, Moretti, and Oreopoulos, 2004). In this case, a price
4
The present value of lifetime earnings is calculated as the discounted sum of income received by people of
same education and same gender of different ages in the year of the survey. It captures the perceived
present value of lifetime earnings as if currently observed incomes applied to the future.
4
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subsidy is needed to increase private investment to the social optimum, If households have a
positive utility for education, a price subsidy that reduces the cost of education would be
sufficient. If households have a negative utility for education, the price subsidy needs to be larger
that the cost, resulting in a positive net CCT.
Another situation of positive externality is when there is a collective action problem in
seeking information, such as in learning-by-doing and demonstrating to others the value of
lessons learned (Foster and Rosenzweig, 1995). In this case, the positive externality achieved by
demonstration induces individual under-investment in seeking to generate information, about the
value of education for their children in particular.
Finally, the creation of future poor with high social costs (measured as an opportunity
cost for society or as a direct welfare cost) due to parents’ failure to educate justifies a CCT to
reconcile private behavior and social optimum. In this case, the social cost of future poverty is
higher than the private cost. The social objective in using a CCT approach is then education
today at a minimum public cost, versus a high future social cost.
The CCT approach is in these cases to maximize social welfare, at the cost of a loss for
the household relative to a CT. Because program participation is always voluntary, no absolute
loss needs to be incurred by the household.
Imposing a constraint on behavior in using scarce cash in the hands of a poor mother thus
requires careful consideration. The conditionality needs to be justifiable on the basis of one of
these three arguments: imperfect information by parents, discrepancy between parent and child
optima, and market failures due to the positive spillovers created by investments in child human
capital. When these effects are expected to be large, a CCT approach is justified.
2.3. When the cost of altering behavior is much lower through a price than an income effect
We have four sources of evidence to measure the relative magnitudes of the impact of a
CT versus a CCT on educational response.
The first is from the vast literature on empirical analyses of demand for education. Even
though results vary by context, they indicate that income elasticities of education are notably low
among the poor and frequently insignificant. In their review of 42 studies covering 21 countries,
Behrman and Knowles (1999) find that this relation is insignificant in 40% of the cases. Pure
cash transfers such as initiation of the South Africa pension system have been observed to
increase child schooling, but this effect is small (Edmonds, 2005). This is what has motivated the
use of conditional transfers.
The second argument on the relative impact of CCT versus CT in increasing school
enrollment derives from theory. The sketch of a standard school choice model is as follows.
Consider a household at time t with a single child and with period utility u which is an increasing
function of consumption Ct and of the binary enrollment status St of the child, and a decreasing
function of his binary work status Wt. With a rate of time preference ρ , the household’s optimal
choice of schooling, child work, and consumption is the solution to the maximization of the
discounted value U t of expected utility at t over an infinite time horizon,
∞
Ut = ∑
s=0
1
(1+ ρ )
s
Et u (Ct +s , St +s ,Wt +s ) ,
under the contemporary budget constraint:
Ct + pSt = Yt + wWt ,
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where p is the child’s specific cost or opportunity cost of schooling, w the wage he would secure
on the labor market, and Yt the household’s autonomous income. In this model, we assume no
time constraint, allowing the possibility for the child to both enroll in school and work, if he
chooses to. This is based on the observation that the school day is short (usually half-day) and
that some children combine school and work. The opportunity cost of going to school is thus not
necessarily equal to the wage.
nc
Add to this budget constraint a non-conditional cash transfer T and a conditional cash
c
transfer T . The non-conditional cash transfer simply raises the household income, while a
conditional cash transfer only applies if the child is enrolled. The budget constraint becomes:
Ct + pSt = Yt + wWt + T nc + T c St .
Given w and p, the joint choice of schooling and work is as follows:
St = 1 ⎡⎢ p ≤ p∗ (Yt + T nc ,T c , w)⎤⎥
⎣
⎦
∗
nc
c
⎡
Wt = 1 ⎢ w > w (Yt + T ,T , p)⎤⎥
⎣
⎦
meaning that the child enrolls in school if his opportunity cost of school is lower than a threshold
∗
value p∗ , and works if the wage offer is higher than the threshold value w . The relative effects
of the conditional and non-conditional cash transfers derive from their influence on the
thresholds. Solving the model shows that:
uc (Yt + T nc − p∗ ,1,Wt ) −uc (Yt + T nc , 0,Wt )
dp∗
=
<1
dT nc
uc (Yt + T nc − p∗ ,1,Wt )
and
uc (Yt + T nc − p∗ ,1,Wt )
dp∗
=
=1
dT c uc (Yt + T nc − p∗ ,1,Wt )
where uc represents the marginal utility of income.
The numerator in the first expression exhibits the difference in marginal utility of income
when the child is enrolled and not enrolled. As school has a cost or opportunity cost, the
household is poorer when the child is enrolled, and hence its marginal utility of income is higher
dp∗
than when the child is not in school. This difference is therefore positive, and hence
is
dT nc
positive, meaning that the non-conditional cash transfer increases schooling by raising the
threshold value p∗ under which the child enrolls. Note, however, that the difference is likely
small, and hence the effect of the non-conditional cash transfer small. By contrast, in the
expression for the conditional cash transfer, the numerator is simply the marginal utility of
dp∗
income, and the ratio
is equal to 1. The conditional cash transfer is equivalent to a one to
dT c
one decrease in the price of school. The effect of the conditional cash transfer is thus a strong
price effect, while that of the non-conditional cash transfer is a diluted income effect.
The order of magnitude of the impact of a CCT compared to a CT can be approximated
as follows. The difference in marginal utility of income is approximately equal to the difference
in interest rates at which you will be willing to borrow for consumption. If the cost of schooling
impoverishes the household to a point that its marginal utility of income increases from 1.20 to
1.30 (i.e., it is willing to borrow at 30% rather than 20% just for the effect of the school price),
dp∗
1.20
.10
then
= 1−
=
= .08 . An $8 conditional cash transfer has the same schooling
nc
1.30 1.30
dT
6
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effect as a $100 non-conditional cash transfer. The CCT effect would thus be some 13 times
larger than the CT effect.
The third is from ex-ante simulations deriving from observed changes in school
enrollment choices made by children who work in response to wage changes. This allows
Bourguignon, Ferreira, and Leite (2003) to predict that an unconditional cash transfer would have
no effect on school attendance among the poor compared to a 5.6% increase through a price
effect. The effect is large among poor households, as 58% of the 10-15 years old not in school
would enroll in response to the CCT. For Africa, Kakwani, Veras, and Son (2005) show that cash
transfers would buy very little in increased school attendance, recommending against their use
based on cost considerations. They consequently suggest using CCT instead, but do not provide
results of expected impacts due to insufficient information on income from child labor.
Finally, we can use the ex-post Progresa effect to measure the impact of an unconditional
versus a conditional cash transfer effect on schooling decisions (Reference #3). Here, the
schooling decision is entry into secondary school for children who are graduating from primary
school in poor rural communities. The CCT is exogenous through the randomized experiment.
The CT (household total expenditure) is not a controlled experiment. While this estimate thus
suffers from some endogeneity, stability of the estimated coefficients to introduction of a very
large number of child, household, community, and state variables gives confidence that any
endogeneity bias would be very small. Results in Table 1 show that, using the regression will all
controls, a dollar of CCT is about 16 times more effective on education than a dollar of CT.
Linear probability model of enrollment in secondary school
Mean
(1)
(2)
(3)
CCT: Treatment community (dummy, 1=US$200/year)
0.718
0.130**
(0.019)
CT: Household total expenditure (US$100/year)
8.055
0.127**
(0.020)
0.003*
(0.002)
0.130**
(0.020)
0.004*
(0.002)
Control variables
Child, household, and community characteristics (31 variables)
State of residence (6 variables)
CCT/CT effect on enrollment
Yes
Yes
21.2
16.3
* significant at 5%; ** significant at 1%
Table 1. Relative effectiveness of a CCT vs. a CT in inducing a change in behavior toward child
schooling. Progresa data.
We can thus conclude that, once the decision has been made that imposing a condition on
behavior is acceptable, it is quite evident that a CCT is considerably more effective than an
unconditional CT transfer in altering behavior toward schooling. Poor countries, like in SubSaharan Africa, that could not afford to increase educational achievements via CTs (Kakwani,
Veras, and Son, 2005) may well be able to do this via CCTs if they are able to implement the
approach.
2.4. When reducing current extreme poverty is not the objective
An argument that is frequently made by those who advocate using a CCT with
conditionality on child human capital development as an instrument for poverty reduction is that
7
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it is effective in targeting poverty: poor people tend to have more children, and families with
more dependents tend to be poorer. As a consequence, having children of school age may be a
good correlate of the depth of poverty. We can verify if this is true by analyzing the impact of the
transfers on poverty levels in the Progresa evaluation sample. This is done in Figures 2.1 and 2.2.
In these figures, poverty is measured by per capita consumption expenditures in adult equivalent
(in pesos/month). One can see in Figure 2.1 (where each point represents 4% of the households
in the sample) that there is a higher percentage of poor than non-poor among beneficiaries, even
though there are substantial leakages to the non-poor: of the households covered by the program,
68% are poor and 32% non-poor. There is also a higher incidence of beneficiaries among poorer
households (reaching a high of 80% among some of the poorest categories) than among less poor
(reaching 47% by the poverty line) (line (1) in Figure 2.1). The total transfer is consequently
reaching the poor more than the non-poor, and the poorer more than the less poor. On a per
household basis (line (2)), the transfer is slightly larger for poor beneficiaries ($300) than at the
poverty line ($250), reflecting the fact that they have more children. However, on a capita basis
(line (3)), however, transfers are constant or regressive ($40 among the poorest increasing to $50
by the poverty line). This is not surprising since the transfers are formula-based according to the
number of children (with a cap), their grades, and gender.
The absolute income effect of the transfers is seen in Figure 2.2 that reports the average
transfer for the whole population by income level (total expenditure per adult equivalent): the
transfer per adult equivalent does not benefit the very poor more than the less poor in the village
population. As it is, targeting on the poor who qualify for the CCT is equivalent to a uniform
distribution of the transfers among the poor, as opposed to providing larger transfers to the
poorest of the poor.
Transfer (pesos/month)
Percent beneficiaries
400
90%
POOR
NON-POOR
80%
350
70%
300
60%
(2) Total transfer
(among beneficiaries)
250
50%
200
40%
150
30%
(1) Percent beneficiaries
100
(3) Transfer per capita
(among beneficiaries)
50
10%
0
0
50
20%
100
150
200
250
300
350
Total expenditure per adult equivalent (pesos/month)
(Each point represents 4% of the households)
0%
400
Figure 2.1. CCT by income level for Progresa beneficiaries
8
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Total expenditure + transfer (pesos/month)
400
(4) Expenditures + transfer
(for all)
350
300
250
NON-POOR
200
150
100
POOR
50
0
0
50
100
150
200
250
300
350
400
Total expenditure per adult equivalent (pesos/month)
Figures 2.2. Impact on total expenditure of CCT to Progresa beneficiaries.
(Source: Reference #2)
The conclusion is thus that using a CCT approach for current poverty reduction is
effective in reaching differentially more the poorer households, but that it does not result in larger
transfers for the poorest relative to the less poor.
2.5. When the supply-side of the service is sufficiently in place
A demand-side program to enhance educational levels among the poor via CCTs will
only work if the supply-side of education is sufficiently in place. In the case of Oportunidades,
communities without the minimum educational and health facilities were not included in the
program. However, the difficult question of balance between demand-side and supply-side
investments has not been properly resolved. In the case of Oportunidades and Bolsa Escola, there
has been a notable deficit of experimentation on complementarities between supply-side and
demand-side investments. This issue cannot be fully resolved without proper experimentation.
However, an approximate answer can be obtained by analyzing heterogeneity in responses to
CCTs according to contexts with different qualities of supply.
In Figure 3, we use data from Progresa to analyze the decision to enter in secondary
school according to distance to a school, a supply-side determinant, and the differential response
to a CCT according to distance. Households are ranked by distance to a secondary school. The
non-parametric estimation is performed on 1,500 children, using a sliding window from lowest to
highest, dropping 100 kids each time at the lower end and adding 100 kids at the higher end. In
the figure, we represent by a dotted line the proportion of children that quits school at entry into
secondary in control villages without Progresa (right axis). The dashed line is the impact of the
CCT on enrollment in secondary (left axis). The plain line is the net effect of the impact of
distance and the CCT on the proportion that quits.
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By distance to secondary school
Proportion that quits
without Progresa
(right axis)
0.25
0.5
0.45
0.2
0.4
0.35
Proportion that quits
with Progresa
0.15
0.3
0.25
0.1
0.2
0.15
Impact (left axis)
0.05
0.1
0.05
0
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Distance to secondary school
% of children
Impact of Progresa
Distance to
who quit
on enrollment
secondary school (km) without Progresa
(% points)
0 to 1
23.5
5.9
1 to 3.5
37.4
13.9
more than 3.5
46.7
8.7
% of children
who quit
with Progresa
17.6
23.5
38.1
Figure 3 (and data in figure). Heterogeneity of impact of a CCT on secondary school enrollment.
Progresa data.
The data show that dropping out of school increases with distance to school in the control
villages from 20% when there is a school in the village to 48% for children located at 4km of the
nearest secondary school. Impact of the program is greatest for children located at some 3
kilometers away from a school, declining afterwards. For children located at 3 km, the gain in
enrollment due to the CCT is 15% out of 40% that would have quit. The remaining 25% drop out
rate is about the same as that of children who live close to a school without the program. This
indicates that the CCT basically compensated for the higher transportation costs for children
living 3 km away from a school. For them, a supply-side transportation subsidy would
consequently achieve the same gains as the demand-side price incentive. For children living
further away from a school, the demand-side intervention has very little impact. For these
children, a supply-side intervention would be necessary, either through a school transportation
program or through construction of additional schools.
III. How to increase the efficiency of the CCT approach?
3.1. By targeting and calibrating transfers for maximum effect of the program on behavior
For Mexico, the educational problem in poor rural communities is starkly represented by
Figure 4 that shows the continuation rate by grade across primary school and junior high school.
The dotted line is for control communities, and the full line for treated communities.
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Continuation rate (%)
Lower secondary
school
Progresa villages
100
Primary school
90
76%
80
70
Control villages
60
Secondary 1
64%
Upper
secondary
school
43%
50
PROGRESA INTERVENTION
40
P2
P3
P4
P5
P6
S1
S2
S3
S4
Entering grade
Figure 4. Continuation rates in primary and junior high, Mexican rural communities
(Source: Reference #3)
We see that CCTs for primary school essentially do not buy any change in behavior as
most children attend primary school without transfers. In fact, we calculate that the cost of using
CCT to induce more attendance in primary school is as high as $9,700/year/child as 97 children
who already go to school have to be paid for every one additional child induced to go to school
by the CCT (Reference #2). The main problem is with entry into secondary school. We see that:
• 64% of the children who graduate from primary school would enter secondary school
without a transfer (ineffective transfers or leakages).
• 12% enter as a consequence of the CCT (effective transfers).
• 24% do not accept the offer, suggesting that it was insufficient or irrelevant to induce
enrollment (ineffective offer).
Using the experimental data from the Oportunidades program, we can predict the impact
that a given CCT has on the likelihood that a child will continue into secondary school by
estimating a linear probability model of enrollment (Table 2). This is made possible by using (1)
the data from a randomized treatment of 506 communities and (2) existence of a cap on the total
transfer to a family, which implies that 26% of the children receive an effective transfer inferior
to the full amount, serving as a natural experiment on the level of the transfer. Results show that
the CCT increased secondary school enrollment by 13% (treatment community effect). Largest
effects are found to be on children who combine the attributes of male, 14 years old, indigenous,
and with no school in the community for whom the increase in enrollment is 23%.
Mean
Treatment community (dummy)
0.72
Conditional transfer*Treatment (US$100/year)
1.22
Conditional transfer*Treatment * (Age –12)
1.24
Conditional transfer*Treatment * Father indigenous
0.42
Conditional transfer*Treatment*No sec. school in village
0.95
Child, household, and community characteristics
* significant at 10%, ** significant at 5%.
11
Homogeneous
impact
0.130**
(0.019)
-0.172
(0.156)
0.156*
(0.080)
No
Yes
Heterogenous
impact
-0.159
(0.156)
0.095
(0.083)
0.016**
(0.007)
0.028
(0.019)
0.037*
(0.021)
Yes
12/31/05
Table 2. Linear probability model of enrollment in secondary school. Progresa data.
(Source: Reference #3)
We use this equation to determine the targeting and calibration of CCT that maximize
gains in educational achievement among the poor, under the overall constraint of the Progresa
budget. The optimum transfer can then be either completely idiosyncratic, or function of a
reduced number of indicators that are easy to measure and verify by others, and that cannot be
manipulated by potential beneficiaries. The first solution gives an “optimal variable CCT
scheme” while the simplified score system gives us an “implementable CCT scheme” (see Table
2). In the implementable scheme, indicators used to determine the level of CCT offered to a child
consist in gender and birth order in the family, existence of a secondary school in the village and
distance to the school, and State of residency. Results from the current program compared to the
optimal and implementable schemes are shown in Table 3.
No program
Enrollment rate in secondary school, all children (%)
Efficiency gain over universal uniform CCT scheme (%)
63.2
Eligibility among poor (%)
Average transfer value (US$/year)
Leakage to children that would go to school w/o a CCT (% of total budget)
Cost per additional child enrolled (US$/year)
Universal uniform
CCT scheme
Optimal variable
CCT scheme
Implementable
CCT scheme
75.7
–
81.1
44
79.4
29
100
194
83
1151
78
237
65
802
77
236
75
889
Table 3. Enrollment rates under alternative CCT schemes. Progresa data. (Source: Reference #3)
Results show that secondary school enrollment among children graduating from primary
school rises from 63.2% in the control villages to 75.7% with Progresa’s universal (i.e., 100%
eligibility among poor) uniform scheme, the much heralded 12.5 percentage points gain estimated
by others. This gain can be increased by another 5.4 percentage point under the optimal variable
scheme, a 44 % efficiency gain over the uniform scheme. The implementable scheme reduces this
gain over Progresa to 3.7 percentage points, a 29% efficiency gain.
The targeted and calibrated schemes imply less than universal eligibility among the poor
(78 and 77% for the two schemes analyzed, respectively) due to the need to make larger transfers
to induce more up-take ($237/year and $236 as opposed to Progresa’s $194). The relative
efficiency of the schemes can be measured by calculating the percentage of total transfers that is
ineffective in inducing a change in behavior as children would have gone to school without the
transfer (i.e., leakage costs) and the complementary percentage that induces a change in behavior
(effective transfers or direct costs). This is displayed in Figure 5. Targeting and calibrating for
effectiveness of the conditionality reduces leakages from 83% of the total budget to 65% under
the optimal scheme and 75% under the implementable scheme. Figure 5 shows how payments
are shifted toward children with lower enrollment probability without a CCT, and how these
children receive larger transfers within the program’s overall budget constraint. An important
observation, however, is that leakages remain high, even under the optimal scheme. This reflects
the large informational rent due to adverse selection under imperfect information that is captured
by households who would send their children to school without a transfer. This informational
rent is the cost to be paid for imprecision in the program’s ability to predict school attendance
without and with a CCT.
12
12/31/05
In US$ 1000 per year
400
In US$ 1000 per year
350
350
300
300
Efficiency leakage costs
Overall share = 83.2%
Efficiency leakage costs
Overall share = 64.9%
250
250
200
200
150
150
100
100
50
50
Direct costs
Direct costs
0
0
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95
Enrollment probability without CCT program
Enrollment probability without CCT program
Figure 5a. Actual scheme with uniform CCT
Figure 5b. Optimal scheme variable CCT
Figure 5. Direct costs and leakages under the actual and optimal schemes (Source: Reference #3)
We thus conclude this analysis by observing that:
(1) Important efficiency gains in raising school enrollment (a 29% to 44% gain over the current
scheme) can be achieved by targeting and calibrating CCT among the poor for maximum impact
of the conditionality. Targeting is on children at risk of not going to school and with high
response to transfers. The most important selection criteria are: absence of a secondary school in
the village, distance to school, state of residency, gender and rank of the child, and parents
without education. By simulation, we can show that these efficiency gains are all the larger that
the program’s budget constraint is more severe, requiring to be more selective among the poor.
This makes this exercise all the more relevant in extending the CCT approach to low-income
countries.
(2) Selection criteria for an implementable scheme can be simple, public, with self-registration,
and community verification. As opposed to a secret formula used for targeting in Oportunidades,
Bolsa Escola, and other CCT programs (e.g., Chile Solidario), this allows households to have
recourse in claiming their rights if they feel that they have been unjustly mis-targeted. Recourse
is in turn a fundamental attribute to secure greater accountability in service delivery.
3.2. By designing complementary interventions revealed by heterogeneity of impacts
The efficiency of CCT programs can be raised by designing complementary interventions
targeted at specific categories of children identified by analyzing the heterogeneity of responses
to a CCT offer. We can focus on heterogeneity in the magnitude of impacts across the treated
population. We can also focus more specifically on the population of children that do not go to
school in spite of the offer of a CCT. we do both in what follows.
We explore first the role of heterogeneity in parents’ educational levels to determine the
incidence of benefits across households to whom a CCT offer is made. To do this, we return to a
non-parametric analysis of children who have graduated from primary school and are considering
entry into secondary school. Households are ranked the total number of years of education of
both parents.
13
12/31/05
By parents' total education
0.2
0.5
0.18
0.45
0.16
0.4
0.14
0.35
Proportion that quits
without Progresa
(right axis)
0.12
0.3
0.1
0.25
0.08
0.2
Impact (left axis)
0.06
Proportion that quits
with Progresa
(right axis)
0.15
0.04
0.1
0.02
0.05
0
0
0
2
4
Parents' total
education (years)
1 to 4
4 to 6
6 to 9
more than 9
6
8
10
% of children
Impact of Progresa
who quit
on enrollment
without Progresa
(% points)
41.8
2.7
35.9
9.1
31.3
13.8
24.6
16.9
12
14
% of children
who quit
with Progresa
39.1
26.8
17.4
7.7
Figure 6 (and data in Figure). Impact of a CCT on secondary school enrollment by parents’
educational level
Results show that dropping out of school declines with parents’ educational level
(proportion that quits without Progresa). The impact of the program is, however, also greatest for
children with the most educated parents. Education thus begets education. The CCT largely
solves the educational problem for the children of poor parents with 9 years and more of
combined education. However, it does not solve the educational problem of children with loweducation parents, especially parents with less than four years of education: for these children, the
gain from a CCT is basically zero.
We explore next the vexing issue of the remaining 24% children who qualify for a CCT
and yet do not take the offer and do not go to school. Analyzing the heterogeneity of their
conditions can help target specific programs to them to raise program uptake. In Table 4, we
calculate the weighted average of the characteristics of the population of children that qualify for
Progresa, with weights equal to the predicted probability that each child (1) will enroll with
Progresa but not without, and (2) will not enroll even with the Progresa offer. The first category
is what yields the 12% gain in enrollment due to the impact of Progresa on behavior in Figure 4,
while the second category is the remaining 24% children who drop out of school in spite of the
Progresa offer.
14
12/31/05
Child, household, and community characteristics
Age
Father is literate (%)
Father's education (years)
Father is indigenous (%)
Mother is literate (%)
Mother's education (years)
Mother is indigenous (%)
Number of children 11-19 years old
Number of agricultural workers in the household
Number of unpaid family members in the household
Household's maximum education (years)
Potential transfer (US$100)
Persons per room in dwelling
Dwelling has water (%)
Queretaro (% of households in sample)
Veracruz (% of households in sample)
No secondary school in the village (%)
Distance when there is no school (km)
Goes to school
with Progresa:
Average values
(1)
Does not go to school
even with Progresa:
Average values
(2)
13.22
61.0
2.03
29.6
58.4
2.07
31.7
2.79
1.33
0.39
4.67
1.96
5.30
32.9
6.3
21.6
83.9
3.25
13.73
53.1
1.58
23.2
52.0
1.70
25.4
3.03
1.47
0.49
4.24
1.92
5.53
29.4
9.5
18.2
90.3
3.52
% difference Test of difference
(2)/(1)
p-value
3.8
-13.0
-22.2
-21.6
-10.9
-17.9
-19.6
8.3
10.8
26.8
-9.2
-2.3
4.3
-10.9
52.6
-15.7
7.6
8.1
***
***
***
***
**
***
***
***
**
*
***
***
*
*
**
*
***
**
*** significant at 1% level, ** 5% level, * 10% level.
Table 4. Contrasted characteristics of children who go to school due to the CCT and children who do
not go to school in spite of the CCT offer
Results show that the remaining large uptake failure, given the current operational rules
of Progresa, is associated with several well recognizable child, household, and community
characteristics, most notable:
• Low parents’ and household’s education: Children with illiterate and low education
fathers and mothers, and with low maximum education in the household.
• Parents occupation that does not give value to education: Children of agricultural
workers.
• Poverty: Children living in dwellings with no running water and overcrowding.
• Access to school: Children who live in communities with no secondary school and at a
greater distance from a school.
Analysis of heterogeneity is important in helping define complementary interventions to
increase the efficiency of a CCT program where gains can be selectively achieved. Results on
differential impact show the fundamental role of parents’ own education in the educational
outcome of their children, even when a CCT program is available. Hence, there is strong path
dependency in education which is not eliminated by CCT. This suggests the need for special
assistance to children with uneducated parents, beyond mere access to a CCT. Results also show
that there are well identifiable household and community characteristics that suggest
complementary supply-side interventions for differential assistance or greater access to schools.
3.3. By using the approach as a safety net for the human capital of children in vulnerable
households
We start by observing that poor households in marginal communities are exposed to
many shocks, both individual (health, unemployment) and covariate (natural events). Responses
to shocks to shelter consumption include the sale of liquid assets, use of credit and insurance, and
taking children out of school to save on cost or send them to work. The problem with using
children as risk-coping instruments is that, once out of school, they are much less likely to return
(Jacoby and Skoufias, 1997). Short-run responses to shocks thus have high long-term
15
12/31/05
consequences on their educational achievements, creating a source of new poor. Important is thus
to offer risk-coping instruments to parents that will protect child education from shocks.
We use the panel surveys in the Progresa randomized experiment, in which four rounds
have information on exposure to shocks. The econometric specification for the school decision
is:
Sit = γ Sit −1 + α sit + β sit Ti + θt + µi + ε it
where S it is enrollment in school (0/1) by child i in semester t, Sit-1 enrollment the previous
semester, sit is a shock, Ti the treatment (offer of a CCT), θ t a period fixed effect, and µi a
child fixed effect. This fixed effect controls for the child’s idiosyncratic propensity to go to
school.
Dependent variable: Child at school
Children of Children of
Nonagricultural non-ag.
Indigenous indigenous
worker
worker
Primary
school
Secondary
school
Boys
Girls
State dependence
Child at school last semester
0.057**
0.228**
0.099**
0.121**
0.088**
0.123**
0.086**
0.114**
Head of household unemployed
* Progresa
-0.028**
0.023+
0.001
-0.009
-0.034**
0.020
0.002
0.002
-0.038**
0.029+
-0.006
0.002
-0.029*
0.042**
-0.010
0.005
0.010
-0.006
-0.037*
0.047*
-0.007
0.021
-0.008
0.001
0.007
-0.008
-0.015
0.020+
-0.018
0.020
0.004
0.004
-0.028**
0.036**
-0.013
0.021
-0.020
0.024+
-0.050**
0.057**
-0.049**
0.047**
-0.013
0.024
-0.037**
0.041**
-0.024+
0.029+
Head of household ill
* Progresa
Natural disaster severity in locality
* Progresa
+ significant at 10%; * significant at 5%; ** significant at 1%.
All regressions include round and child fixed-effects. Linear probability model estimated with the Arellano-Bond estimator.
Table 5. Path dependency and vulnerability to shocks. Progresa data.
(Source: Reference #4)
Results in Tables 5 show that:
(1) Irreversibility is important: Short term absences from school have long term consequences: a
child who misses one semester of school have 6% less chance of attending school the following
semester in primary school and 23% in secondary school.
(2) Idiosyncratic shocks due to unemployment and illness of the household head and covariate
shocks due to natural disasters in the community induce children to leave school. The categories
of children for whom assistance to school is most exposed to shocks are primary school students,
indigenous children, and sons/daughters of farm workers.
(3) Progresa fully protected child schooling from exposure to shocks.
(4) Progresa did not protect children from working more when their household is hit by a shock.
Since there is no conditionality on behavior toward work, this indicates that the net income effect
of the shocks and the transfer is such that parents choose to increase the work contribution of
their children as a risk-coping instrument. This implies that, for these children, school and work
are compatible, and that parents derive a double benefit from children as risk-coping instruments:
as a source of income by continuing to attend school, and thus receiving a CCT, and as a source
of work when there is a shock.
While CCT programs targeted at the chronically poor are thus shown to be effective to
protect child schooling when parents are hit by a shock, the education of the children of many
non-poor households is also vulnerable to shocks. As such, they may be the source of future new
poor when they are taken out of school in response to a short-run shock. This source of new poor
can then partially erase the educational gains achieved in the population among chronic poor
16
12/31/05
covered by the program. Recent studies on the origins of poverty have indeed emphasized the
role of vulnerability as a source of poverty (UNDP, 2004). The Oportunidades results suggest a
possible extension of coverage to non-poor vulnerable children when hit by a shock to avoid
detrimental long term consequences on their educational achievements. A program of CCTs used
as a safety net for child human capital would then require the following operational procedures:
1) Identify vulnerable children: predict which non-poor children are vulnerable to dropping
out of school as a consequence of shocks. These are children whose parents do not have
access to sufficiently effective other risk-coping instruments (accumulated liquid assets,
access to credit, possibility of calling on mutual insurance, coverage by safety net programs)
that they risk taking their children out of school when hit by a shock.
2) Use a community supervision committee to verify qualification of a child for incorporation
in the CCT program when the household is hit by a shock.
3) Design a pilot experiment to learn how to use 1) and 2) above to manage CCT as a safety
net for child education.
In the African context, an important source of shock is HIV/AIDS. CCT coverage could be used
to provide a safety net for child education among households affected by the disease.
3.4. By increasing transparency and accountability in implementation
While the Oportunidades program is implemented through centralized provision at the
federal level, implementation of the Bolsa Escola program is done through decentralization of the
selection of beneficiaries and the enforcement of conditionalities to municipal governments. As
argued in the World Development Report 2004 (World Bank, 2004), effective decentralized
provision of social services requires accountability of local providers (in this case elected
municipal mayors) to stakeholders (in this case potential beneficiaries of the Bolsa Escola
program). There are two routes for this downward accountability: the “short route to social
accountability” is via direct relations between clients and providers. In the case of Bolsa Escola,
this is to be achieved by appointment of a municipal Bolsa Escola Social Council to which
stakeholders can appeal in claiming their rights. The “long route to social accountability” is via
the local electoral process, whereby stakeholders can reward or punish incumbent mayorial
candidates or incumbent parties in municipal elections. This is represented in Figure 7.
Federal government:
Federal Bolsa Escola Program
Rules and budgets
Decentralized service provider:
Municipal government
Municipal Bolsa Escola social council
Program implementation:
Targeting: Beneficiary identification and selection
Monitoring and enforcement of conditionalities
Accountability mechanisms
Long route to social accountability
Local political retributions
Short route to social accountability
Appeals to social council
Transparency (information)
Demands for downward
accountability
Bolsa Escola beneficiaries:
Potential beneficiaries
Actual beneficiaries
Program outcomes:
Poverty reduction and human capital formation
Figure 7. Social accountability mechanisms in a decentralized CCT program: Bolsa Escola
17
12/31/05
(Source: Reference #5)
Dependent variable: Mayor was reelected in 2004
Bolsa Escola council exists
(1)
0.264
[0.133]+
(2)
0.262
[0.128]*
-0.263
[0.111]*
-0.003
[0.131]
0.011
[0.119]
0.016
[0.006]**
0.013
[0.006]*
(3)
0.206
[0.147]
-0.25
[0.121]*
-0.053
[0.142]
0.031
[0.122]
0.034
[0.131]
0
[0.003]
0.132
[0.120]
0
[0.115]
0.011
[0.006]+
Y
Y
Y
108
0.38
Y
Y
Y
108
0.43
Y
Y
Y
105
0.45
Public denouncement for Type II (inclusion) error
Public denouncement for politics
Public denouncement for Type I (exclusion) error
Registered beneficiaries in mayor's office
Registered beneficiaris using home visits
Registered beneficiaries with geographic priorization
Misunderstood selection process
Quota
Mayor characteristics
Municipal Characteristics
Political Characteristics
Observations
R-squared
Table 6. Long route to downward accountability. Bolsa Escola data. (Source: Reference #5)
Results from a survey of 261 municipalities in four states of Brazil’s Northeast gave the
following results:
• Short route to social accountability: Social Councils, the instrument designed to
insure a short route to downward accountability, performed incompletely and in an
uneven fashion across municipalities. We find that (a) many municipalities did not form
these councils despite federal requirements to do so; (b) even when social councils
existed, they did not necessarily function properly as many did not meet regularly or were
not informed on who were the program beneficiaries; however, (c) in municipalities
where social councils existed, there was a positive impact on the quality of
implementation of the program.
• Long route to social accountability: Results in Table 6 show that electoral rewards
were effective in providing a long route to downward accountability. Incumbent mayors
were more likely to be reelected as able intermediaries if their municipality had received
a large quota of bolsas (in spite of their having no role in this as the municipal allocation
is formula based and implemented by Brasilia), if they had put into place a Bolsa Escola
council, and if there were no public denouncements that they had allocated bolsas to nonqualifying households (inclusion “error”).
These results suggest that direct accountability mechanisms (short route) are potentially
important but need to be reinforced. The institutional mechanisms to perform this function are
often not in place. And, when they are, they are frequently ineffective due to lack of information
and of authority to act by councils. Accountability mechanisms through the political process are
longer (with a four years political cycle in Brazil) and tend to be diluted over many issues
competing for politicians’ attention. Even if the long route to social accountability performs, it is
a poor substitute for effective short route instruments.
18
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IV. How to learn more from implementation of the CCT approach to improve its use in
alternative contexts?
4.1. By linking impact evaluation and experimentation with a results-based approach
International development agencies and governments are placing greater emphasis on the
need to engage in program evaluation. This is done in response to both (1) demands for greater
upward accountability of service providers to sources of funding, and (2) introduction of systems
of results-based management to improve program efficiency. Results of impact analyses for the
first purpose are largely a public good while results for the latter purpose are largely private
goods.
As a public good, impact evaluation will always be severely under-funded, in spite of
exhortations by academics and social planners that such investments can have handsome social
payoffs. The main reason is the inescapable logic of free riding in the provision of public goods.
As a private good used for program improvement, impact evaluations are also severely
under-funded, because they face resistance by project managers in using scarce project funds for
impact evaluation purposes. This is because results generally come too late for use by the project,
and they are often not informative for program improvement. Hence, it is not surprising that
project managers are not interested in paying the cost of evaluation. Even elected officials will
often say that they are not interested in funding project evaluation because their political time is
shorter that the time span needed for evaluators to obtain results. The unfortunate consequence of
delays in yielding results is that millions of dollars have been spent to pay the cost of evaluation
of CCT projects, and yet that very little useful information for program improvement has been
derived from these evaluations. Probably the most important, and non-negligible, benefit has
been to raise the public visibility of successful projects, thus helping to secure their survival
across electoral cycles, as exemplified by sustainability of the Progresa/Oportunidades program.
Three changes need to be made to internalize some of the benefits of evaluation in the
project, and hence create incentives for at least partial funding of the evaluation by the project
itself:
(1) Just-in-time delivery of results
Evaluation has to be designed so that short-term results are available and delivered to
project managers while the project is still active. This requires caution that short run outcomes
not be confused with longer term outcomes. In some cases, achieving favorable early outcomes
may be at the cost of a worst performance in the longer run. However, there are carefully chosen
short-term indicators of impact that can usually be defined as elements of a logframe approach.
And this is easier for some projects such as remedial education with rapid observable benefits on
educational performance than for other projects that aim at raising incomes or reducing
environmental degradation.
(2) Evaluation as part of results-based management
Impact evaluation for accountability purposes needs to be done by impartial external
auditors. By contrast, impact evaluation for results-based management needs to be part of a
participatory process leading to institutional change. For this, evaluation must be built in a
learning process that engages members of the organization who contribute information on
indicators of success and failure, and internalize results from impact analysis in the design and
practices of the organization. These two objectives are not incompatible, but they have rarely
been implemented jointly. Doing so requires engaging both program personnel and external
auditors in the evaluation, and making sure that the accountability purpose is not being perverted
19
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by the management function. Yet, success in managing evaluation in this fashion allows to
deliver benefits to the organization, and hence to internalize part of the costs of the evaluation as
a private good in the project’s operational budget.
(3) Experimenting with alternative options
Evaluation of the current design of a project is not sufficient to identify opportunities for
improvement. For this, experimentation needs to be done on specific aspects of the program that
appear to be crucial on performance and that are derived from recognized knowledge gaps. For
CCT for human capital formation this would include such aspects as different targeting rules
(secret, vs. transparent, vs. participatory), the calibration and graduation of transfers (as opposed
to the current in/out rules with much arbitrariness around the cut-off boundary), the method of
delivery (through the mother or not, using cash or smart cards, etc.), different types of
complementary programs (supply side, returns to education), and alternative accountability
mechanisms (transparency, community participation, appeals mechanisms). Experiments should
be sustained only for as long as it takes to generate statistically significant outcomes. Results are
then internalized into the results-based management process.
4.2. By addressing unresolved questions such as transactions costs in different contexts
There are several other questions on CCTs that need to be answered and for which
additional research on other programs needs to be made, particularly in the context of low-income
countries. One issue that has been raised is how to reduce transactions costs in program
implementation. CCT programs require to implement the following three administrative
functions:
i) Establishing and updating the list of eligible households.
ii) Enforcing conditionality rules.
iii) Delivering payments.
Methodologies for program implementation are highly specific to context. Brazil uses
electronic debit cards to distribute the CCTs, Ecuador asks beneficiaries to withdraw payments
from a bank, while Mexico uses queuing in front of a table covered with banknotes every other
month. High transactions costs are mentioned as an issue of concern is introducing a CCT
approach in Sub-Saharan Africa (Kakwani, Veras, and Son, 2005). Much experimentation is left
to be done to identify the most efficient approach for each particular context, in particular where
administrative capacity is weak and corruption high.
V. Conclusions
We used results derived from experience in Mexico and Brazil to ask the following three
questions: (1) When to use the CCT approach? (2) How to increase the efficiency of the
approach? (3) How to learn more from the approach to improve its use in alternative contexts?
Answers to the first question indicate that the CCT approach has considerable promise
under many circumstances, but also that a shockingly large number of important questions
remains to be answered, in particular as use of the approach is being extended to more countries
and large sums are being committed to these programs. Compared to a CT approach, CCT is an
enormously efficient way of using transfers to induce a change in parents’ behavior toward child
human capital development, if this is an objective of the program and if imposing a constraint on
behavior is justified to achieve child, household, or social gains. We found that this justification
is generally there for programs that use cash transfers beyond the objective of immediate poverty
reduction among very poor households. The efficiency gains in inducing school attendance may
20
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well be some 16 times larger per dollar received with a CCT compared to a CT. If the objective
is income for assets, CCT is indeed an effective approach.
These programs can, however, be quite expensive and difficult to implement. Because
there are large informational rents due to adverse selection in targeting, leakages of payments to
children that would go to school without a transfer are large, equal to 84% of cost under the
current Progresa design, implying a cost of $1,151/year per additional child enrolled in secondary
school. It is for this reason important not to be complacent with current designs, and to seek ways
of raising their efficiency in reducing poverty and enhancing child human capital development. It
is also important not to elevate this approach to the rank of unqualified panacea, and to recognize
its limitations, specificity to context, and needs for complementary interventions. In particular,
supply-side interventions such as school transportation beyond a certain distance and a greater
density of schools are necessary when CCTs are insufficient to induce a behavioral response.
In answer to the second question, we have explored several ways in which a more
efficient use of the approach can be made. This includes: (1) better targeting and calibration of
transfers for maximum effect of the conditionality on behavior by focusing on children at risk of
not going to school and most responsive to a transfer, with potential large efficiency gains when
enrollment rates are low in the target population and there are severe budget constraints in
incorporating all targeted households in the program; (2) better understanding of heterogeneity in
the incidence of benefits, in particular by parent’s educational levels, to design targeted
complementary interventions; (3) use of the CCT approach as a safety net to reduce vulnerability
to shocks of child human capital development, as short run use of children as risk-coping
instruments leads to long term losses of human capital (a child out of school for one semester in
secondary school has 23% less chance of being at school the following semester), creating a
source of new poor, while CCT can serve as effective insurance mechanisms for child human
capital; (4) better downward accountability systems to improve service delivery in CCT
programs, especially through greater transparency in targeting rules and through a more effective
“short route to social accountability” between providers and stakeholders.
To achieve these efficiency gains, and in answer to the third question, more useful impact
evaluations and experimentations need to be designed, not only for the purpose of ex-post
accountability but also to support a results-based management approach to program improvement.
Implementing these sources of efficiency gains requires urgent investments in learning-byexperimenting for use of the CCT approach in different contexts. While progress has been made
with implementing rigorous impact analyses, the culture of experimentation is yet to enter CCT
programs. This is particularly necessary if the approach is to be implemented in poor country
contexts, most particularly Sub-Saharan Africa, which are quite different from the middle-income
country contexts where experience has been gained. It is indeed notable that large sums are being
invested in the approach while so little is being spent in seeking to learn from past experiences
and in designing learning experiments that can be embedded into results-based management
methods.
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