Development Economics (micro)

Lecture 3: Conditional Cash Transfers

Måns Söderbom, University of Gothenburg

11 November 2009

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1 Introduction

Core reference:

Parker, Susan W., Luis Rubalcava and Graciela Teruel (2008), �Evaluating Con-ditional Schooling and Health Programs,� in T. Paul Schultz and John Strauss(eds.), Handbook of Development Economics, Amsterdam: North-Holland, pp.3963-4035. Note: Section 6 (which describes conditional schooling-health pro-grams around the world) is optional.

� Human capital is thought an important policy instrument for breakingthe transmission of poverty from one generation to another.

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� Supply side interventions: reduce the private cost of schooling e.g.through constructing better infrastructure or by building more schools.

� Demand side interventions: provide incentives for parents to keep theirkids longer in school.

� Today�s topic: a new genre of social programs known as conditional cashtransfer programs, now widespread across Latin America. Such programslink monetary transfers to human capital investment - schooling & health.That is, monetary bene�ts are conditional on, for example, regular schoolattendance and preventive clinic visits.

� Distinguish between two e¤ects of conditional cash transfer programs:They change incentives for parents to keep their kids in school and at-

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tend clinics, which presumably has positive long-term e¤ects on poverty.But they also reduce current poverty - short-term.

� Most give the cash to the mother, thus potentially a¤ecting the powerstructure in the households.

� Most famous program: Progresa (the Education, Health and NutritionProgram) in Mexico. Initiated in 1997. Has served as a model for theimplementation of similar programs elsewhere. Was subject to a largescale evaluation e¤ort in rural areas, including an experimental design.

� The paper by Parker, Rubalcava and Teruel surveys what we know aboutthe actual success and potential success of conditional cash transfer pro-grams as a mechanism for reducing poverty. For obvious reasons we don�tknow much yet about the long-term e¤ects.

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� Conditional cash transfer programs are a subsidy to schooling & health. Isthe conditioning always welfare enhancing - why not simply give the cashto the poorest people with no strings attached? Let�s have a look at sometheoretical results.

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2 Theoretical considerations

� Aim of conditional cash transfer programs: alleviate current poverty throughmonetary transfers; and future poverty by enhancing the human capital ofchildren.

� Let�s have a look at two models in order to clarify some theoretical predic-tions about the e¤ects of such programs.

2.1 Model 1: Income & substitution e¤ects

� Two-period model; in period 1, individuals are children, in period 2 adults.At the beginning of period 1, individuals make decisions so as to maximize

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total discounted utility:

U1 (C1; L1) + � � U2 (C2; L2) ;

where C1 and C2 denote consumption in period 1 and 2, respectively; L1and L2 denote amount of leisure enjoyed in periods 1 and 2; U1 and U2are the utility functions in the two periods; and � is the discount factor.

� There is no uncertainty in the model, so all decisions are made in thebeginning of period 1.

� The decisions for period 1 concern how much time to devote to schooling,market work, home production and leisure. Total time is �xed at T ; hencethe time constraint in period 1 is as follows:

S + t1 + h1 + L1 � T; (1)

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where S denotes schooling, t1 is time spent on market work, h1 is timespent in home production.

� The time constraint for period 2 is the same except individuals don�t attendschool:

t2 + h2 + L2 � T . (2)

� Schooling, market work and home production are di¤erent ways of gener-ating income (thus a¤ecting consumption and utility):

� Schooling raises market wages in period 2:

W2 =W2 (S) :

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Schooling also enters the production function for home production inperiod 2:

M2 =M2 (h2; S)

Schooling is also associated with a subsidy (grant) paid for the amountof schooling attended:

grant = psS:

� Market work generates wages. Total wages earned in period 1:

t1W1;

and total wages in period 2:

t2W2 (S) ;

where the wage function is concave in education.

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� Home production in period 1 is writtenM1 (h1) ;while home productionin period 2 is given by M2 (h2; S). Both production functions areconcave.

� Finally, we allow for a term B measuring initial assets of the individual(e.g. transfers from parents).

� We now have all the ingredients for the life-time budget constraint:

C1 +1

1 + rC2 = B + psS + t1W1 +M1 (h1)

+t2W2 (S)

1 + r+M2 (h2; S)

1 + r(3)

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In general, utility at the optimum is de�ned as

maxS;h1;h2;t1;t2;L1;L2

U1 (C1; L1) + � � U2 (C2; L2) ;

subject to the budget constraint and the time constraints. Note that schoolingdoes not enter the utility function directly. The only role played by schooling inthe model is to increase income so as to allow for higher consumption. Thus,optimal schooling chosen at period 1 maximizes total discounted consumption.

The problem can be expressed as follows:

maxS;h1;h2;t1;t2;L1;L2

fU1 (C1; L1) + � � U2 (C2; L2)

��1 C1 +

1

1 + rC2 �B � psS � t1W1 �M1 (h1)� t2

W2 (S)

1 + r�M2 (h2; S)

1 + r

!��2 (S + t1 + h1 + L1 � T )��3 (t2 + h2 + L2 � T )g;

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where �1, �2, �3 are non-negative Lagrange multipliers. Assuming the solutionswon�t be in the corners, optimal levels of schooling (S) and child labour (t1)must satisfy the following �rst-order conditions, respectively:

�1

0@ps + t2W 02 (S)

1 + r+M 02;2 (h2; S)

1 + r

1A� �2 = 0

�1W1 � �2 = 0;

hence

ps + t2W 02 (S)

1 + r+M 02;2 (h2; S)

1 + r=W1

must hold at the optimum. We can rearrange this in order to facilitate inter-pretation:

W1 � ps =t2W

02 (S) +M

02;2 (h2; S)

1 + r:

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This simply says that, at the optimal level of schooling, the marginal cost ofschooling (the LHS) is equal to the discounted marginal income associated withschooling. Insights:

� An increase in the subsidy ps tends reduces the marginal cost of schoolingand hence leads to a higher level of optimal schooling;

� A reduction in the child wage rate works in exactly the same way as anincrease in the subsidy ps.

We can relate these results to the intertemporal condition for current and futureconsumption. It is straightforward to show that the following intertemporalcondition must hold at the optimum:

MUC1 = � (1 + r)MUC2;

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where MUX denotes the marginal utility with respect to argument X. Itfollows that

MUC1 (W1 � ps) = �MUC2�t2W

02 (S) +M

02;2 (h2; S)

�:

This is the f.o.c. shown on p. 3968 in the paper. It simply says that themarginal utility of schooling in period 1 must equalize the discounted marginalutility of schooling in period 2.

Another interesting e¤ect of the subsidy concerns the time allocation in period2. How does an increase in ps a¤ect the time spent on home production (h2)and market work (t2)? To �nd out, consider the f.o.c. for h2 and t2:

�1M 02;1 (h2; S)

1 + r� �3 = 0;

�1W2 (S)

1 + r� �3 = 0;

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hence

W2 (S) =M02;1 (h2; S) :

Hence, given that an increase in ps leads to an increase in schooling S, whichin turn leads to an increase in the period 2 wage rate, it must be that h2decreases (sinceM2 is assumed concave). In other words, the subsidy reducesthe amount of time spent on home production in the second period.

Computer Exercise: The subsidy has both income e¤ects (raising life timeincome) and substitution e¤ects (changing relative prices). In the frameworkabove, it is not possible to sign (I think) the e¤ect of an increase in ps on leisureand child labour in period 1 (though t1 + h1 must fall, as S unambiguouslyrises). Write down a fully parametric version of the model (e.g. CES util-ity function) and use (for example) Matlab to solve the optimization problemnumerically (one possibility is to use fmincon to impose the budget and time

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constraints). Investigate what happens to the decision variables in the modelas ps increases from 0 to 0:5W1. Investigate how these e¤ects change as youvary the elasticity of substitution.

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2.2 Bargaining e¤ects

In the model above, what are the welfare e¤ects of the schooling subsidy?

If we think of psS as a �gift�given to the child by the government, we mightask whether the child�s welfare, de�ned as

U1 (C1; L1) + � � U2 (C2; L2)

would not be higher if the government simply gave the child an unconditionalgrant equal to the same amount, thus increasing B. Indeed, in the modelabove, this would lead to higher child welfare than the conditional schoolingsubsidy, as the subsidy implies the child will �over-invest�in schooling.

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Alternatively, we might insist the schooling subsidy has to be �nanced withinthe modelling framework; the child could thus borrow against future incomes:

receive psS in period 1

pay back (1 + r) psS in period 2.

It is easy to show that the �rst-order condition for schooling becomes

W1 =t2W

02 (S) +M

02 (h2; S)

1 + r;

which is exactly the same solution as you get with zero subsidy. Thus, aschooling level higher than that optimal under zero subsidy will be - by de�nition- suboptimal.

So what kind of theoretical argument can be advanced in favour of the ideathat a conditional schooling subsidy are welfare improving?

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� One potential motivation is externalities - e.g. there are some social re-turns to schooling that are not reaped by the individual. Consequently, theindividual will under-invest (from the point of view of aggregate welfare)in schooling. This type of market failure might be tackled by means ofconditional schooling subsidies.

� Intrahousehold bargaining leads to too little schooling from the point ofview of the child (since the adults make the decisions). Let�s have a lookat a model.

� Model components: two periods; family = one man, one woman, one child;a single consumption good.

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� Preferences: Each parent cares only about his/her own consumption andthe consumption of the child:

Um = Um (Cm; Ck) (male)

Uf = Uf�Cf ; Ck

�(female)

� Period 1: m (male) and f (female) consume, k (kid) does not; m andf supply T units of time in a labour market (hence parent worker supplynot endogenous); m and f decide (exactly how will be clear later) howto allocate the child�s total time between child labour and schooling S;and how much to leave as a non-negative bequest (B � 0) to the child.Wage rates for the three types of labour are de�ned W1. At the end ofperiod 1, the parents die.

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� The budget constraint for the family in period 1:

Cm + Cf � 2TW1 + (T � S)W1 �BCm + Cf � 3TW1 � SW1 �B;

which simply says that total expenditure on consumption is equal to totallabour income minus what is left as a bequest. (Note: there appears to bea typo in the exposition in Parker et al. - schooling does not get multipliedby W1).

� Period 2: k consumes; the income of k is equal to the sum of the bequestand the labour income, hence the budget constraint:

Ck � B + TW2 (S) ;

where W2 (S) indicates the wage rate in period 2 depends on the level ofschooling.

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� Bargaining: Decisions about Cm; Cf ; S and B are the outcomes of Nashbargaining between the adults in the �rst period:

maxCm;Cf ;Ck;S;B

�Uf

�Cf ; Ck

�� Udf

� ��Um (Cm; Ck)� Udm

��;

subject to

Cm + Cf � 3TW1 � SW1 �B;Ck � B + TW2 (S)

B � 0

where Udf ; Udm represent the �threat point�utilities for the two adults (i.e.

what they would get if negotiations were to break down; e.g. because ofdivorce). (Note that we are giving equal weight to the male and the femalein the Nash bargaining process). The problem can be expressed as

maxCm;Cf ;Ck;S;B

ln�Uf

�Cf ; Ck

�� Udf

�+ ln

��Um (Cm; Ck)� Udm

����1

�Cm + Cf � 3TW1 + SW1 +B

�� �2 (Ck �B � TW2 (S)) + �3Bt;

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(I �nd it somewhat more convenient to express the Nash function in loga-rithmic form) where �1; �2, �3 are non-negative Lagrange multipliers. Ifthe non-negativity constraint on bequests is not binding, so that Bt > 0,then �3 = 0.

� First-order condition with respect to schooling:

��1W1 + �2TW02 (S) = 0:

� First-order condition with respect to bequests:

��1 + �2 + �3 = 0:

� Now consider two cases: B > 0, and B = 0.

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� If B > 0 is the optimal solution, then �3 = 0, and so �1 = �2. The f.o.c.for schooling reduces to

W1 = TW02 (S) :

De�ne S� as the solution to this condition.

� Now consider B = 0, in which case �3 > 0. We refer to a family inthis situation as bequest constrained, i.e. the family would like to leave anegative bequest to the child (i.e. borrow in period 1 for the child to payback in period 2) - but this is not allowed! It follows that, in this case,

�1 � �2 = �3 > 0;

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hence �1 > �2: The relevant f.o.c. for schooling

��1W1 + �2TW02 (S) = 0

��1W1 + (�1 � �3)TW 02 (S) = 0

��1�W1 � TW 0

2 (S)�= �3TW

02 (S)

W1 � TW 02 (S) = �

�3TW02 (S)

�1< 0:

De�ne Sbc as the solution to this condition. We established above that,if the bequest constraint does not bind, W1 � TW 0

2 (S�) = 0. We now

see that, if the bequest constraint binds, W1 � TW 02

�Sbc

�< 0. Hence

it must be that

W 02

�Sbc

�> W 0

2 (S�) ;

and since the wage function is assumed concave, this in turn implies

Sbc � S�.

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� Now suppose a policy maker with a positive budget G wants to maximizethe utility of a family. What would be the best policy: to give G uncon-ditionally; or to provide a schooling subsidy ps, such that psS = G (thepolicy maker�s budget constraint)?

� The answer depends on whether the family is bequest-constrained or not.

� Suppose G is given unconditionally to the adults. This raises the incomeof the family. The distortion caused by the bequest-constraint is now lesssevere - e.g. adults can enjoy a �reasonable level�of consumption withouthaving to subject the child to a lot of child labour; instead, the child getsmore schooling.

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� If a conditional schooling subsidy is given to the adults, the f.o.c. forschooling changes to

��1 (W1 � ps) + �2TW 02 (S) = 0:

The subsidy has two e¤ects: it raises the income of the family by psS =G (i.e. the same amount as under unconditional grants) which tendsto increase schooling as we have seen. But in addition to that thereis a substitution e¤ect: the opportunity cost of schooling has fallen to(W1 � ps) per time unit, hence it is optimal for the family to invest morein schooling than under the unconditional grant.

� What are the welfare e¤ects? First consider the bequest-constrained family.

� Well, for the child, assuming G is not high enough to make the familybequest-unconstrained, her consumption in period 2 is given by

Ck = TW2 (S) :

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Hence, assuming the child�s utility is increasing in consumption, theschooling subsidy unambiguously makes the child better o¤ than theunconditional grant.

� The parents, however, are worse o¤ with the subsidy than with thegrant. Indeed, family utility is bound to be lower with the subsidy thanwith the unconditional grant. To justify the schooling subsidy, it mustthus be that the policy makers cares more about the utility of the childthan that of the parents.

� For a bequest-unconstrained family it will always be better (in this model!)to receive an unconditional grant than a schooling subsidy, as the latterleads to overinvestment in schooling, hence less child labour in period 1,resulting in lower adult consumption in period 1 and a lower bequest passedon the child in period 2.

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3 The Progresa program

� Progresa began operating in 1997. The principal anti-poverty strategyof the Mexican government (~50% of the anti-poverty budget; almost aquarter of the population were receiving Progresa bene�ts).

� Cash transfers are conditional on children�s enrollment and regular schoolattendance; and clinic attendance. Also some in-kind health bene�ts andnutritional supplements for children aged less than 5.

3.1 Education

� Monthly educational grants given for children aged 22 or less and enrolledin school between grades 3 (at which point most kids are aged 8) and 12.

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� Grants increase as children progress to higher grades, and from grade 7onwards they are somewhat higher for girls than for boys. At grade 3, thegrant is 120 pesos per month; for comparison, the monthly minimum wagein Mexico is about 1,000 pesos.

� Failure to comply with the rules of the program (>85% attendance ratemonthly & annually) will lead to the loss of the bene�t. Grants are givento the mother of the family.

[Table 1, Parker et al.]

3.2 Health and nutrition

� Provides basic health care for all members of the family.

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� Nutritional components: �xed grant of 180 pesos per month, speci�edfor "improved food consumption" (no monitoring), plus nutritional sup-plements targeting children aged 4 months - 2 years.

� All members of the family must adhere to a regular schedule of healthclinic visits (see Table 3, p.3976, in Parker et al. for details).

3.3 Size of monetary transfers

� Maximum limit of monthly bene�ts for each family: USD100 if children inprimary school; USD 175 if children (at least one) in senior high school.

� This limit is meant to reduce incentives to have additional children inresponse to the bene�ts.

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3.4 Targeting & continued program eligibility

� Program is means tested. The mechanism varies somewhat betweenrural & urban areas. In both rural & urban areas, the �rst stage of themeans testing is geographic: based on aggregate local indicators, poorrural communities and urban blocks are selected.

� Identi�cation of household level bene�ciaries.

� Rural areas: Households in selected communities are surveyed, andthe data are used to identify eligible households. Key variables: Percapita income; dwelling characteristics; dependency ratios; ownershipof durables, etc. Nearly all families that were identi�ed as eligibleenrolled in the program - self-selection is thus not a major issue inrural areas.

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� Urban areas: Individuals apply for the program. Basic socioeconomicfactors are assessed, followed by a home visit. In urban areas manyeligible households do not apply, making self-selection a potential issue.

� Once families become bene�ciaries, the stay in the program for 3 yearswithout being subjected to further veri�cation. After 3 years, programhouseholds are assessed again.

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4 The Progresa evaluation (rural)

� 1998: Initial evaluation design in rural areas.

� Randomized design: a subset of communities eligible for the program wasrandomly assigned to treatment (320 communities) or control (186 com-munities).

� The control group began to receive treatment 18 months after the exper-iment began.

� A new comparison group of communities never receiving bene�ts wasbrought into the evaluation design in 2003.

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� Thus three groups: T1998; T2000; and C2003.

� For T1998 households, bene�ts were dispersed in May 1998. T2000 house-holds received bene�ts in December 1999. Households were informed inadvanced they would be eligible, however it�s unclear when they were giventhis information.

� Individuals belonging to the C2003 group are, on average, less poor thanindividuals belonging to T1998 or T2000 (check out Table 4 in Parker etal.). To compare C2003 to T1998 or T2000 thus requires non-experimentalmethods (e.g. propensity score matching).

� [Table 5 in Parker et al.: Time line for Progresa rural evaluation and datasources]

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In the lab we will use data from surveys in October 1997 & November 1999.

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4.1 Estimators of the evaluation

� Common estimators:

� Di¤erence-in-means estimator (only appropriate under randomization)

� Cross-sectional estimator with selection on observables (e.g. regressionof outcome on treatment dummy and a vector of control variables):

Yi = �0 + �2Ti +Xj=1

�jXjit + "it

� Di¤erence-in-di¤erences estimator:

Yit = �0 + �1Rt + �2Ti + �3 (Ti �Rt) +Xj=1

�jXjit + "it;

where �3 Ti is a treatment dummy, Rt is a round dummy, and the restof the notation is obvious. �3 measures the impact of treatment while

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�2 measures the pre-program di¤erence in expected outcome acrosstreatment and control groups.

� How should we de�ne treatment here? One option is to set Ti = 1 if thefamily is a bene�ciary of the program and Ti = 0 if not - i.e. to focus onactual treatment. However, given that the program targets poor familiesthis would raise serious concerns about selection-into-treatment. Sure, youcan try to control for this but you will probably never fully convince youraudience you�ve solved the problem entirely.

� Another option is to restrict attention to the sample eligible for the pro-gram. This is especially attractive in the case where initial eligibility wasrandomized: de�ne the treated group as consisting of those communitieseligible for treatment in the initial round, while the control group consists

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of those communities randomized out of eligibility (i.e. the communitiessubject to delay in the implementation of the program). Thus, we ignorewhether households take up treatment or not. This procedure gives rise tothe intent-to-treat (ITT) estimator.

� The initial studies of the Progresa program are based on comparisons be-tween the T1998 group and the T2000 group. Because of the randomiza-tion, the causal e¤ects of interest can be convincingly estimated. However,the nature of the data implies that only short-run e¤ects can be identi�ed.Recall that a major objective underlying the Progresa is to raise humancapital in order to spur growth in the long run.

� In order to shed some light on longer run program impacts, comparisonsof the C2003 and the T1998 groups were undertaken. These surveys were

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made 5.5 years apart, i.e. we are looking at a reasonably long time win-dow. Because of the non-experimental nature of the selection of the newcomparison group, non-experimental estimators - mainly propensity scorematching estimators - were used. In this, the �second generation�of Pro-gresa studies, researchers typically estimate the average treatment e¤ecton the treated, as distinct from the ITT (i.e. they compare bene�ciariesand non-bene�ciaries).

� Two types of propensity score matching estimators:

� Cross-sectional matching estimator

� Di¤erence-in-di¤erences matching estimator

(see my lecture notes on treatment e¤ects, part I).

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� Attrition has caused some headache for the researchers. For example,migrants are not followed. Why did some people migrate? Could themigration decision be related to if/when households were treated?

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5 Progresa rural evaluation results

5.1 Short term results: Rural areas 1998-2000

5.1.1 Poverty

� The main hypothesis: Progresa will reduce the income poverty of its ben-e�ciaries.

� But, there may be a number of incentive e¤ects that tend to reduce income,for example:

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� Subsidizing schooling results in less child labour. Thus children�s in-come may fall.

� The income e¤ect of the program may result in less labour supply bythe adults (they want to consume more leisure). This reduces familyincome.

� The program might a¤ect other income sources, e.g. remittances.

� Comparing poverty and income trends using the T1998 and the T2000groups, the data indicate that Progresa reduced the headcount povertyrate by 11.7 percentage points, a large e¤ect (Skou�as, 2005).

� Investigating the e¤ect of the program on calorie consumption, Hoddinottand Skou�as (2004) �nd a positive and signi�cant e¤ect even conditional

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on total expenditures (which is meant to proxy for income). That is, notall of the increase in the calorie consumption is due to an income e¤ect;the authors argue health talks attended by bene�ciaries may have playeda role.

� Does these result suggest that the concerns about negative incentive e¤ectsof the program may have been exaggerated? Possibly. However, it couldbe that these negative incentive e¤ects only become important after sometime. To �nd out, long-run studies need to be undertaken.

� Most common parameter: average treatment on the treated:

TT = E (Y1jX;D = 1)� E (Y0jX;D = 1) ;

where the notation should be very familiar.(Y1,Y0 denote potential out-comes etc.).

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5.1.2 E¤ects on education

� Shultz (2004): The e¤ects on school enrollment di¤er depending on thelevel of schooling. The e¤ects are strongest at the transition from primaryto junior high level, where many children prior to the program tendedto leave school. The impact on enrollment in primary is small, howeverenrollment rates at this level are high to start with.

� At junior high level, the estimated program e¤ect is to increase enrollmentrates by 7-9 percentage points for girls and by 5-6 percentage points forboys.

� The implied long-run e¤ect (backed out by means of extrapolating theshort-run e¤ects; be careful) of the program is to increase the average

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years of education by 0.64-0.72 years This will raise earnings through thereturn to education; Schultz estimates that the internal rate of return onthe program - i.e. gains divided by costs - is 8%.

5.1.3 Health and nutrition

� The baseline data are problematic for various reasons, so results shouldprobably be interpreted with caution. Still, several studies report resultsindicating a positive e¤ect of the program on child health. Behrman andHoddinott (2005), for example, �nd that receiving and taking the nutri-tional supplements provided by the program results in an increase in childheight by 1 cm (children aged between 12 and 36 months were studied)

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� However, there are a lot of children who don�t take their supplements;those who do tend to have a greater degree of malnutrition than thosewho don�t. So there are some selection issues here that might a¤ect theresults.

5.1.4 Work

� Di¤-in-Di¤ type analysis of the time use of children by Skou�as and Parker(2001) indicates that the program reduces child labour. This e¤ect ismost clear for boys aged 12-14.

� Similar type of analysis suggests the program has no e¤ects on adult laboursupply, hence there is no short-run disincentive e¤ect on work.

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5.1.5 Migration

� Migration rates in rural Mexico are high

� Potential e¤ects of the program:

� Higher income relaxes credit constraints and makes migration morefeasible.

� However, conditionality makes you less mobile (children have to attendschool)

� Angelucci (2004) �nds that international migration (to the US) is sub-stantially increased (by 60%) by the program; however the e¤ect is onlythere in 1998 & disappears in 1999. Domestic migration, however, is notsigni�cantly a¤ected by the program according to this study.

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� The study by Stecklov et al. (2005) reports that the program reducesinternational migration by almost 40%. Contradicts Angelucci�s results.

� Why these di¤erent results are obtained is not well understood. Parker etal. argue that both studies su¤er from weaknesses in the empirical analysis.More research is needed here - clearly an important area for policy.

5.1.6 Fertility

� Until you hit the family limit (cap), having more children will raise the sizeof the grant. Does the program, therefore, increase fertility?

� Schultz (2004), Stecklov et al. (2006): No statistically signi�cant e¤ecton fertility in the �rst 18 months of the program.

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5.2 Medium term results: Rural areas 1998-2003

The previous studies indicate signi�cant impacts on indicators or inputs tohuman capital. By 2003, the T1998 group had received bene�ts for 5.5 years.Researchers brought in the C2003 control group and a follow up survey �eldedin 2003 so as to investigate whether longer term impacts can be observed inthe data. This is the �second generation�of Progresa studies, all using non-experimental estimators, generally matching estimators.

5.2.1 Infant development

� Gertler & Fernald (2005). Sample consists of children aged 3-6 in 2003,some of whom are born to mothers who were taking the program; somewho were infants at the time treatment began; and some who have notreceived treatment (Progresa�s nutritional supplement) at all.

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� A wide range of outcome variables such as cognitive development (e.g. vo-cabulary), physical development, and socio-emotional development. Thesedata were collected for the �rst time in 2003, thus cross-sectional match-ing.

� Results indicate important impacts on physical development (boys as wellas girls) and some improvement in socio-emotional development for girls.

� But no e¤ects on cognitive development, perhaps because of lack of stim-ulation within the household.

5.2.2 Education and work

� Given the positive e¤ects of the program on infant developed, one mightexpect the program to in�uence the educational performance of children

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once they start school.

� Focus is on children aged 0 to 8 prior to the program (=6 to 14 in 2003).Results:

� Receiving 5.5 years of bene�ts (T1998) vs. never having received bene�ts(C2003) leads to a reduction in the age of entry to primary school for girls(i.e. program reduces late entry)

� Program causes an increase in expected completed years of schooling equalto 0.45 years. This e¤ect is strongest for those aged between 6 and 8 whenthe program was started.

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� Recall that the results based on comparing the �rst waves of the dataindicated that the strongest e¤ect of the program on enrollment is for chil-dren at or near the transition to junior high school pre-program. Lookingspeci�cally at this group (those aged 9 to 15 prior to the program), Bher-man, Parker and Todd (2007) report matching estimates indicating that5.5 years of exposure to bene�ts lead to an increase in schooling between0.7 and 1.0 years (no major di¤erence between boys & girls).

� So the evidence seems quite clear that the program increases schooling.Does it also cause better performance in school? On this issue, theevidence is not so clearcut. Analysis of data on achievement tests (reading,writing, maths) for youths aged 15-21 in 2003 indicate no signi�cante¤ects of the program on achievement tests.

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� However, a potentially serious problem here is that no baseline data exists,so di¤erences-in-di¤erences analysis cannot be undertaken. Instead, wehave to compare di¤erences between treated and non-treated children; theproblem is that the control group is somewhat less disadvantaged (e.g. lesspoor) than the treatment group pre-program.

� To the extent that the propensity score fails to take this into account,the counterfactuals for treated observations will likely not be valid; inparticular, it seems likely the counterfactuals will be overestimated, thusleading to a downward bias in the estimated impacts.

� What are the e¤ects of the program on labour supply?

� One anticipated mechanisms is that the program delays entry into thelabour market (because of its positive impact on schooling)

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� Another anticipated mechanism is that the increase in education raises thelikelihood of �nding a good job.

� Results are in line with these expectations:

� For boys aged 15-16 in 2003 there is a negative and signi�cant impacton the probability of employment

� For girls aged 19-21 in 2003 there is a positive and signi�cant (andquantitatively large) e¤ect on the probability of employment.

[Other conditional schooling-health programs around the world - Table10 in Parker et al. - read Section 6 if you are interested]

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6 Summing up

� Conditional cash transfer programs aim to both alleviate current povertyand future poverty.

� The ��rst generation� of studies of the impact of Progresa focused oncurrent poverty (short-term) and indicators likely to be correlated withhuman capital accumulation, notably enrollment.

� The evidence seems clear the program led to higher enrollment rates. Other�ndings: better health, less child work, improved household consumption(calorie intake).

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� So that sounds great - but how do we know the money spent on theprogram would not have done more good if invested in other projects? It�simportant to adopt a cost-bene�t perspective when evaluating programsof this type.

� Drawing on the impact of the program on school enrollment, which willfeed into higher productivity and earnings in the future (because the returnsto education are positive), Schultz compares costs and bene�ts and arrivesat an internal rate of return equal to 8%. Incidentally, this is similar tothe median return on equity in micro�nance, reported in Cull, Robert, AsliDemirguc-Kunt & Jonathan Morduch (2009). �Micro�nance meets themarket,� Journal of Economic Perspectives 23(1), 167-192.

� For obvious reasons the long-term e¤ects of the program remain uncertainat this point. Medium term impacts may take 15-20 years to observe if

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one is interested in evaluating the e¤ects into early adulthood. This is,and will be for some time, an area of active research. Note, however, thatthe randomization design won�t be very useful in this context.

� More generally, randomized experiments would seem unlikely to survivea long period of time for practical reasons. You may be able to justifydelaying treatment by 12-18 months, but it will hardly be possible to delaytreatment by several years. In my view, this is one of the main limitationsof randomization design.

� Thus, recognizing that if you are interested in long-term outcomes you willhave to make do with non-experimental data, it would be extremely usefulto collect baseline data for groups that will constitute a control group forlong-term analysis. The point is that this control group almost inevitably

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will self-select into treatment, and so a di¤erence-in-di¤erences analysiswould seem much more convincing than a simple analysis of cross-sectiondi¤erences.

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