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Heterogeneous Impacts of Conditional Cash Transfers:Evidence from Nicaragua

ana c. dammertCarleton University and IZA

I. IntroductionThe most popular policy tool used in the last decade in developing countriesto increase human capital has been the conditional cash transfer program,which provides cash payments to households conditional on regular schoolattendance and visiting health clinics. Many governments implemented ex-perimental frameworks to assess the impacts of conditional cash transfers onemployment, schooling, and health among poor eligible households (PRO-GRESA in Mexico and PRAF in Honduras, among others). Although con-ditional cash transfers have achieved quantified success in reaching the poorand bringing about short-term improvements in consumption, education, andhealth (Rawlings and Rubio 2003; Gertler 2004; Schultz 2004), most of theliterature has focused on mean impacts. As Heckman, Smith, and Clements(1997) point out, however, judgments about the “success” of a social programshould depend on more than the average impact. For example, it may be ofinterest to investigate whether social programs have differential effects for anysubpopulation defined by covariates, for example, gender effects, or whetherthere is heterogeneity in the effect of treatment. Knowledge of whether aprogram’s impacts are concentrated among a few individuals is important forthe effectiveness of the program in reaching its target population.

This study contributes to the small but growing literature on the estimationof heterogeneous effects of conditional cash transfers in developing countries.This type of program has received a great deal of attention among policymakers, influencing adoption of new policies in Latin and Central America.The assessment of heterogeneous impacts is done with a unique data set from

I am grateful for helpful comments and suggestions from Dan Black, Jeff Kubik, Jose Galdo, HugoNopo, the editor, and two anonymous referees. I also thank participants for comments and sug-gestions received at LACEA in Bogota, the Third IZA/World Bank Conference on Employmentand Development in Rabat, and PEGNet in Accra. I am grateful to the International Food PolicyResearch Institute for permission to use the data. Any errors or omissions are my own responsibility.Please contact the author at [email protected].

54 economic development and cultural change

a social experiment in Nicaragua designed to evaluate a conditional cashtransfer program targeted to poor rural households, the Red de ProteccionSocial (hereafter RPS) or Social Safety Net. The analysis takes advantage ofthe random assignment of localities to treatment and control groups so thatprogram participation is not correlated in expectation with either observed orunobserved individual characteristics and outcome differences provide an un-biased estimate of the true mean impact of the program. The purpose of thisstudy is to investigate the degree of heterogeneity in program impacts of theRPS program for education, health, and nutrition in Nicaragua. This paperexplores the heterogeneity of impacts as a function of observable characteristics(age, gender, poverty, and household head characteristics) and the criteria usedby the RPS to select beneficiaries. This study also investigates the overallheterogeneity of program impacts using quantile treatment effects (QTE),which allows us to test whether conditional transfers lead to larger or smallerchanges in some parts of the outcome distribution.

This study adds to the existing literature in several ways. First, the existingliterature on conditional cash transfers focuses on mean impacts, in the fullsample and in demographic subgroups. This paper goes beyond mean impactsand interaction variables and tests whether there are heterogeneous impactsof conditional cash transfers on the distribution of expenditures. Conditionalcash programs, such as the RPS, have differential effects on household behaviorgiven that transfers affect regular school attendance and health visits. Forexample, the school cash transfer is conditional on regular attendance of chil-dren ages 7–13 who have not yet completed the fourth grade. For householdswith children ages 7–13 years who have not completed fourth grade and arenot attending school, the program has income effects of the cash transfer andsubstitution effects of a lower price of schooling driven by the attendancerequirement. Some households may have to bear the cost of children’s forgonelabor earnings due to the implicit reduction in labor time, in which case theimpact on household expenditures may be negative if the RPS transfer doesnot make up for losses in income from market work. The monetary transferreceived to buy food (or food cash transfer), however, has a positive effect onhousehold expenditure. The net effect on expenditures could be positive ornegative. Conditional cash transfers have differential effects based on whetherthe household is meeting the requirements prior to the implementation ofthe program. Knowing more about this heterogeneity is relevant to antipovertypolicies (Ravallion 2007).

Second, the literature on QTE has been limited mostly to the U.S. context.Recent papers, such as Heckman et al. (1997), Abadie, Angrist, and Imbens(2002), Black et al. (2003), and Firpo (2007), have used QTE to assess the

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impacts of training programs on labor outcomes. Bitler, Gelbach, and Hoynes(2005, 2006) examine the impact of welfare reform experiments on earningsand total income. Overall, the main finding is that variation in the impactof treatment across persons is an important aspect of the evaluation problem.To the best of my knowledge, Djebbari and Smith’s (2005) study representsthe first to analyze heterogeneous impacts of social programs in a developingcountry using QTE.

Third, QTE corresponds, for any fixed percentile, to the horizontal distancebetween two cumulative distribution functions. Under the rank preservationassumption, QTE can be interpreted as the treatment effect for individuals atparticular quantiles of the control group outcome distribution or the treatmenteffect for each quantile in the distribution (Bitler et al. 2005). Without therank preservation assumption, QTE represents how various quantiles of theoutcome distribution change in the treatment and control groups, but wecannot make an inference of the impact on any particular person. This paperpresents evidence of rank invariance in the RPS context to help clarify theinterpretation of the QTE impacts for the development literature.

The main results show that impact estimates vary among the eligible pop-ulation. From the analysis on subgroups, the estimates show that boys ex-perienced a larger positive impact of the program on schooling and a negativeimpact on the probability of engaging in labor activities and hours worked.The estimates also show that older children experienced a smaller impact ofthe program on schooling and participation in labor activities. There are alsodifferential impacts by whether the child is living with a male head of house-hold and with education of the head of household. To assess the effectivenessof the targeting criteria, the analysis considers the interaction between thetreatment indicator and marginality index and household per capita expen-ditures, separately. The main results show that children located in more im-poverished areas experienced a larger impact on schooling and a smaller impacton working hours.

From the QTE analysis, the estimates suggest that the positive programimpact in per capita food expenditures and total per capita expenditures issmaller for households who are in the lower tail of the expenditure distribution.The estimates show that program impacts are larger for households who hadlower levels of food shares prior to the program. These findings are consistentwith the theoretical prediction that treatment effects on expenditures are lowerfor households whose costs of complying with the program requirements arehighest. Tests of the null hypothesis of constant treatment effects reveal thatthese findings could not have been revealed using mean impact analysis. Finally,joint tests of rank preservation show that the distributions of observable char-


acteristics in all ranges of the expenditures distribution do not vary significantlybetween the treatment and control groups.

The rest of the paper is organized as follows. Section II presents the RPSprogram and data, and Section III outlines the theoretical framework. SectionIV outlines the empirical strategy, and this is followed by a discussion of theempirical results in Section V. Section VI concludes.

II. The RPS ProgramA. Program Structure and BenefitsNicaragua is a lower-middle-income country. With an estimated per capitaGDP of US$817 in 2004, Nicaragua remains the second poorest country inthe Latin America and Caribbean region after Haiti. In 2000, the Nicaraguangovernment implemented the Red de Proteccion Social, or Social Safety Net,to encourage educational attainment and help impoverished households inrural areas. Phase I of the program started with a budget of US$11 million,representing approximately 0.2% of Nicaragua’s GDP (Maluccio and Flores2005). With financial assistance from the Inter-American Development Bankand the government of Nicaragua, the RPS program was expanded in 2002with a US$20 million budget for coverage for an additional 3 years. The RPSprogram provided benefits conditional on school attendance and health check-ups, where participants were identified using a detailed targeting process aimedat reaching poor people in rural areas.

For Phase I of the RPS, the government of Nicaragua selected the depart-ments of Madriz and Matagalpa from the northern part of the Central Region.This selection was based on the departments’ ability to implement the programin terms of institutional and local government capacity, high poverty levelswithin the communities, and proximity to the capital of Nicaragua. In 1998,approximately 80% of the rural population in Madriz and Matagalpa was poorand half was extremely poor.

Targeting of poor households was implemented at the RPS headquarters intwo stages: (1) officials selected six municipalities within these two departmentsbased on criteria similar to those used at the department level, and (2) officialsselected eligible comarcas within the selected municipalities based on themarginality index constructed from the 1995 National Population and Hous-ing Census. Comarcas (hereafter called localities) are administrative areas withinmunicipalities including between one and five small communities averaging100 households each. This marginality index used locality-level informationon the illiteracy rate of persons over age 5, access to basic infrastructure(running water and sewage), and average family size. The higher the value of

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TABLE 1NICARAGUAN RPS BENEFICIARY REQUIREMENTS

Household Type

With No Tar-geted

Children(A)

With ChildrenAges 0–5

(B)

With ChildrenAges 7–13

Who Have NotCompleted

Fourth Grade(C)

Attend bimonthly health education workshops � � �

Bring children to prescheduled health careappointments �

Monthly ( 0–2 years)Bimonthly (2–5 years)Adequate weight gain for children under 5a �

Enrollment in grades 1–4 of all targeted children inthe household �

Regular attendance (85%) of all targeted children inthe household �

Promotion at end of school yearb �

Bono a la Oferta or teacher transfer �

Up-to-date vaccination for all children under 5 years �

Source. Maluccio and Flores (2005).a This requirement was discontinued in Phase II in 2003.b This condition was not enforced.

the marginality index, the more impoverished the area. Out of 59 localities,42 eligible rural localities were identified as having a high or very highmarginality index and thus preselected for the program.1

Program benefits are conditional income transfers composed of (table 1):1. Each eligible household received money to buy food (called the

food cash transfer) every other month. In order to receive this transfer, ahousehold member (typically the mother) is required to attend educationalworkshops and bring their children under the age of 5 for preventive healthcare appointments (including vaccinations and growth monitoring). Chil-dren younger than age 2 were seen monthly and those between ages 2 and5, every other month. In September 2000, the food transfer was US$224a year, representing 13% of total annual household expenditures in bene-ficiary households before the program.

2. Contingent on enrollment and regular attendance, each householdwith children ages 7–13 who had not completed the fourth grade of primary

1 The RPS program did not identify poor households within targeted localities, as in PROGRESA.See Maluccio and Flores (2005) for an assessment of the targeting procedure.


school received a fixed cash transfer every other month.2 In addition, foreach eligible child in the household enrolled in school, the household re-ceived an annual lump sum transfer for school supplies and uniforms (calledthe school supplies transfer).3 In September 2000, the school attendancetransfer and the school supplies transfer were US$112 and US$21, respec-tively.To enforce compliance with program requirements, beneficiaries did not

receive the transfer if they failed to carry out the conditions previously de-scribed. Less than 1% of households were expelled during the first two yearsof delivering transfers, though 5% voluntarily left the program, for example,by dropping out or migrating out of the program area (Maluccio and Flores2005).

B. The Experimental Design and DataThe evaluation design is based on an experiment with randomization of lo-calities into treatment and control groups. One-half of the 42 localities wererandomly selected into the program. The selection was done at a public eventin which the localities were ordered by their marginality index scores andstratified into seven groups of six localities each. Within each group, random-ization was achieved by blindly drawing one of six colored balls withoutreplacement; the first three were selected in the program and the other threein the control group.4

All households in selected localities are interviewed before and after therandom assignment. The evaluation data set consists of panel data observationsfor 1,359 households over three rounds of survey (baseline: September 2000;follow-ups: October 2001 and October 2002). Surveys at the individual andhousehold level collected information on socioeconomic and demographic char-acteristics such as parental schooling, labor market outcomes, health, nutrition,

2 Children are required to enroll and attend classes at least 85% of the time, i.e., no more thanfive absences every 2 months without valid excuse.

This design seems to embody a perverse incentive for students to keep repeating the fourthgrade so that families can continue to receive the subsidy. In order to eliminate this problem, theprogram design included a number of causes for which the household may be expelled from theprogram, among them, if the beneficiary child failed to be promoted to the next grade. Thiscondition, however, was deemed unfair and never enforced. Thanks to the referee for pointing thisout.3 The lump sum transfer for school supplies and uniforms varies with the number of eligiblechildren, while the school attendance transfer is a lump sum per household, regardless of thenumber of children.4 The evaluation was designed to last for 1 year, but because of delays in funding the implementationof the program was postponed in control localities until 2003.

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TABLE 2DESCRIPTIVE STATISTICS FOR CHILDREN AND THEIR HOUSEHOLDS

2000 2001 2002

Household level:Per capita total consumption 3,885.08 3,852.74 3,880.91Per capita food consumption 2,672.33 2,669.20 2,634.18Food share .704 .688 .683Head of household:

Age 44.27 46.05 47.01Male .858 .858 .858Years of educationa 1.652 … …

Children 7–13 years at baseline:Gender .521 .521 .521Age 9.847 10.938 11.969School attendance .766 .885 .855Participation in labor activities .150 .109 .177Weekly working hours 3.51 2.90 5.31Weekly working hours conditional on

employment 23.47 26.49 30.07

Note. Expenditures levels are in Nicaraguan Cordobas; the equivalent exchange rate is US$1 p C$13.Sample includes 1,359 households with observations in the panel 2000/2001/2002.a Data available only for 2000.

and attributes of the physical infrastructure of the household, among others.5

Table 2 presents descriptive statistics for each year. Prior to the program, themean per capita consumption was 3,885 Nicaraguan Cordobas (hereafter C$)or about US$298.9 a year, with 70% allocated to food consumption. Table 2also shows that 77% of children ages 7–13 years attend school, and 15%participate in labor activities for an average of 23.5 hours per week.

The randomization is at the locality level rather than at the household orindividual level. One reason for doing the random assignment at the localitylevel was to avoid spillover effects between treated and untreated individualsin the same locality. This was part of the motivation for doing the randomassignment at the village level in the PROGRESA evaluation as well.6 As-signment by randomization at the locality level ensures that the treatmentand control groups are similar on average in terms of observable and unob-

5 In panel data, both nonresponse and attrition are potential concerns for the empirical analysis.In 2001 and 2002, about 92% and 88% of the targeted households were reinterviewed, respectively.The principal reasons for failure to interview targeted sample households were that householdmembers were temporarily absent or that the dwelling appeared to be uninhabited. Maluccio andFlores (2005) examine the correlates of the observed attrition and conclude that attrition is not amajor concern for estimating program effects and emphasize that using only the balanced panelis likely to slightly underestimate the effects.6 For PROGRESA, Behrman and Todd (1999) found that treatment and control groups had similarmean outcomes at the locality level before the program; however, they find small differences atthe household and individual level. Thanks to the referee for pointing this out.


TABLE 3RPS SUMMARY STATISTICS: 2000 BASELINE (STANDARD ERRORS IN PARENTHESES)

Treatment Control Difference

Household level:Per capita total consumption 4,020.90 3,738.24 282.66

(203.22) (212.23) (290.34)Per capita food consumption 2,759.87 2,577.68 182.193

(129.02) (128.33) (179.81)Food share .70 .71 �.01

(.01) (.01) (.01)Household head:

Age 44.64 43.86 .77(.85) (.73) (1.11)

Male .87 .85 .02(.01) (.01) (.02)

Years of education 1.70 1.60 .10(.14) (.09) (.16)

N 706 653 1,359Children 7–13 years old:

Gender .53 .51 .02(.02) (.02) (.02)

Age 9.82 9.87 �.05(.07) (.07) (.10)

School attendance .77 .77 .00(.01) (.01) (.02)

Participation in labor activities .14 .16 �.02(.01) (.01) (.02)

Working hours 3.26 3.78 �.52(.33) (.37) (.49)

N 916 829 1,745

Note. Expenditure levels are in Nicaraguan Cordobas; the equivalent exchange rate is US$1 p C$13.Robust standard errors, clustered at the locality level. Sample includes 1,359 households with obser-vations in the panel 2000/2001/2002.

servable characteristics. There is a chance, however, of observing some non-randomness in terms of differences between localities selected for the controland treatment groups at the household level prior to the program, sinceestimates of average quantities are more reliable with large sample sizes andthe sample subject to randomization is small, 42 localities (Behrman and Todd1999). Table 3 shows t-tests of the equality of means at the household andindividual level. Main results show that the majority of variables measuredprior to the random assignment do not differ between the treatment andcontrol groups, which suggests that the sample is well balanced across thesegroups.7

7 Maluccio and Flores (2005) analyze 15 indicators and find small differences only in householdsize and number of children younger than 5 years old.

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III. Theoretical FrameworkThis section outlines a model of household decision making in the presenceof conditional cash transfers in order to get a better understanding of potentialheterogeneous impacts of the RPS program. The model is based on Skoufiasand Parker (2001) and Djebbari and Smith (2005). The analysis begins firstby considering household time allocation in the absence of the conditionalcash transfer. Neoclassical models of household decision making are commonlyemployed in this analysis. In this framework, parents make decisions aboutthe allocation of a child’s schooling time, the time of other household members,and the purchase of goods and services. Parents will invest in each child’sschooling up to the point where the marginal costs of a child’s time in schoolequal the marginal benefits considering the opportunity cost of schooling,which is the forgone earning from work.

The opportunity cost of children’s time is likely to vary with observedcharacteristics. For example, it is expected to see gender and age differencesin child labor if boys and girls have different returns to education or olderchildren have a comparative advantage in the labor market. It is importantto note that the RPS does not provide higher payments for female enrollmentin school as in PROGRESA where the main idea was to equalize the incentivefor girls in the face of higher wages, on average, for boys in the labor market.Girls in secondary school received slightly higher subsidies (by about $2 permonth) than boys in Mexico.

The existing literature on heterogeneity of treatment effects predominantlylooks at the impact of the program as if it varies with observed characteristicsor subgroups of the population. In the case of conditional cash transfers,other papers have found evidence of differential impacts on schooling andchild labor for girls versus boys, and primary-school-age children versussecondary-school–age children, and by socioeconomic status (e.g., Maluccioand Flores [2005] for RPS; Skoufias [2005] for PROGRESA; Schady andAraujo [2006] in Ecuador; Behrman, Sengupta, and Todd [2005] for PRO-GRESA; Filmer and Schady [2008] for Cambodia; Djebbari and Smith[2005] for PROGRESA).

The conditionality of the transfer has important effects on household be-havior as well. The monetary school and food cash transfers are linked to theschool attendance of children ages 7–13, participation in health clinics, andother criteria. If they were not conditioned, transfers would act as a pureincome effect. Conditionality of the transfers results in changes in the marginalcost of investment in schooling. If children participate in the program withfull compliance of the requirements, time devoted to schooling changes: chil-dren now receive transfers for attendance and school supplies but lose wages


for the extra time the child devotes to schooling. What matters is the ratioof the child’s wage and the marginal increase in income due to the transfer.

Participation and compliance with the RPS program might affect householdbehavior as follows:8

Households with no children in the targeted age ranges or with childrenunder age 5 (but without children ages 7–13 who have not completedthe fourth grade) receive the food cash transfer. These transfers willhave a pure income effect, and it is expected that these householdswill have higher expenditures after the program.

Households with children ages 7–13 years old who have completed fourthgrade at primary school and are attending school without the programwill be eligible to receive food transfers but not school transfers. Foodcash transfers will have a pure income effect, and it is expected thatthese households will have higher expenditures after the program.

Households with children ages 7–13 years who have not completed fourthgrade but are attending school even without the program are eligiblefor both the food and school cash transfers. These transfers will havea pure income effect, and it is expected that these households willhave higher expenditures after the program.

For households with children ages 7–13 years who have not completedfourth grade and are not attending school without the program, theRPS program combines the income effect of the school transfer withthe substitution effect of a lower price of schooling driven by theattendance requirement. Some households may have to bear the costof children’s forgone labor earnings due to the implicit reduction inlabor time, in which case the impact on household expenditures maybe negative if the RPS transfer does not make up for losses in incomefrom market work. Thus the program impact on household expen-ditures may be negative. At the same time, the food cash transfer willhave an income effect. The net effect of the school and food transferon expenditures could be positive or negative for these households.

In sum, the predicted effect on expenditures is heterogeneous. At the topof the expenditures distribution, or the richest households among the eligibleones, households are meeting or almost meeting program requirements priorto the program and, thus, the impacts will be larger. For some part of the

8 In the RPS data, approximately 20% of the beneficiary households had no targeted children,25% only children under age 5, 20% only children ages 7–13, and the remaining 35% bothchildren under 5 and 7–13-year-olds.

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bottom of the expenditures distribution there are households who are notmeeting the requirements (e.g., children are not going to school the minimumrequired time) and for which the cost of participation is the highest (children’scontribution is significant); for them the program impacts could be positiveor negative. These households are likely to be the ones who rely greatly onchild labor. As Basu and Van’s (1998) seminal model shows, a household willsend children to work if adult income or family income from nonchild laborsources becomes very low. In between these extremes, the effect of the programdepends on whether the child is attending school the minimum required timeor not. The extent to which the program has a significant impact on differentparts of the expenditure distribution can only be determined through empiricalanalysis.

IV. Empirical Strategy

Let denote the potential outcomes of interest in the presence of treatment,Y1

, and without treatment, . Each individual experiences onlyT p 1 Y T p 0i 0 i

one of these treatment and untreated outcomes; thus, the critical objective isto establish a credible comparison group or a group of individuals who in theabsence of the program would have had outcomes similar to those who wereexposed to the program. In this study, the treatment and control group arerandomly selected, so that program participation is not correlated in expec-tation with either observed or unobserved individual characteristics, and out-come differences provide an unbiased estimate of the true mean impact of theprogram.9

What we are interested is in estimating the expected average effect thatthe RPS program has on different outcomes, or the average treatment effect(ATE), . The literature also focuses on the mean impact ofD p E(Y � Y )ATE 1 0

treatment on the treated (ATET), given by . UnderD p E(Y � Y FT p 1)ATET 1 0

the assumptions of no equilibrium effects and no randomization bias, therandomized experiment identifies the ATET (Djebbari and Smith 2008).

A. Impacts at the Subgroup LevelThe first method generates impact estimates that vary among the eligiblepopulation by considering variation in impacts as a function of observablecharacteristics through the interaction of the treatment indicator in equation

9 See Duflo, Glennerster, and Kremer (2007) for a methodological discussion of randomization ofexperiments in developing countries.


(1) with a variety of individual and locality characteristics as follows:

y p b � b # C � b T � b T # C � X a � q , (1)i 0 1 i 2 i 3 i i i i

where is some outcome measure, is the characteristic of interest, is ay C Ti i i

dummy variable representing whether the locality was randomly assigned tothe treatment or control group, and represent the interactions betweenT # Ci i

the characteristics and the treatment indicator.The interpretation of the coefficients is as follows: for example, in the

specification that tests for heterogeneous impacts by gender, is a dummyCi

variable equal to one if the child is male, the coefficient is an estimate ofb1

the difference in the outcome between boys and girls, the RPS effect for girlsis given by , the corresponding effect for boys is given by the sum of theb2

coefficients . If is statistically significantly different from zero, thereb � b b2 3 3

is evidence of heterogeneity of treatment effects by gender.The vector of covariates also includes characteristics of the head of house-Ci

hold and locality. I also use a criterion used by PRS to select beneficiaries, thelocality marginality index. Program officials using data from the 1995 Ni-caraguan Household Survey, collected prior to the program, constructed thisindex. Following the analysis in Djebbari and Smith (2005) for Mexico, theimpact of conditional cash transfers is expected to be largest for householdsliving in more impoverished localities as defined by the marginality index. Ifthe targeting mechanism is efficient, then households in the most marginallocalities get a greater program impact than less marginal places. Equation(1) also controls for other baseline household and individual characteristics( ) to take into account any differences that were present despite randomi-Xi

zation and to increase the precision of the coefficient estimates. The standarderrors are clustered at the locality level.

B. Quantile Treatment EffectsThe method described in the previous section emphasized differences in means.While the mean is important, comparisons of means only account for shiftsin the central tendency of a distribution. For many questions, knowledge ofdistributional parameters is required; for example, the proportion that benefitfrom treatment, the proportion that gain at least a fixed amount, or thequantiles of treatment effect (Heckman et al. 1997). One particular feature ofinterest in the RPS context is the behavior at the left tail of the consumptiondistribution, as this measures consumption of those households that most likelyare not meeting the requirements (children are not going to school the min-imum required time) and for which the cost of participation is the highest(children’s contribution is significant). In order to capture responses across the

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entire distribution of consumption, the second econometric method uses theQTE approach.

Most of the existing literature on QTE is based on social experiments inemployment, training, and welfare programs in the United States. Heckmanet al. (1997) find strong evidence that heterogeneity is an important featureof impact distributions using experimental data from the National Job TrainingPartnership Act Study. Black et al. (2003), using experimental data from theWorker Profiling and Reemployment Services program, find that the estimatedimpact of treatment varies widely across quantiles of the outcome distributions.The pattern of impacts suggests that the treatment has its largest effect onpersons whose probability of unemployment insurance benefit exhaustion with-out treatment would be of moderate duration. In evaluating the economiceffects of welfare reform, Bitler et al. (2005, 2006) find strong evidence againstthe common effect assumption using experimental data from the Connecticut’sJob First Waiver program and the Canadian Self-Sufficiency Project. Theirestimates suggest substantial heterogeneity in the impact of welfare reformon earnings and total income, which is consistent with the predictions fromthe static labor supply model.

Let and denote the outcome of interest in the treated and controlY Y1 0

states with corresponding cumulative distribution functions F(y) p Pr [Y ≤1 1

and . Let denote the quantile of each distributiony] F (y) p Pr [Y ≤ y] v0 0

y (T) p inf {y : F (y) ≥ v}, T p 0, 1, (2)v T

where “inf” is the smallest attainable value of y that satisfies the conditionstated in the braces. The quantile treatment effect at quantile is defined asv

. For example, suppose that y represents familyQTED p y (T p 1) � y (T p 0)v v v

income in a given year, is that level of income for households in they0.25

treatment (control) group such that 25% of treatment (control) householdshave income below it. The expression is given by the difference betweenQTED0.25

the income of households in the 25th percentile of the treated distributionand the 25th percentile of the control distribution.

The impact estimate for a given quantile is the coefficient on the treatmentindicator from the corresponding quantile regression as follows:

Q (yFT) p a(v) � b(v)T , v � (0, 1), (3)v i i

where denotes the quantile of expenditures conditional onQ (yFT) vv i

treatment.10

As presented above in table 2, the RPS sample is well balanced, and there

10 See Koenker and Basset (1978).


are few statistical differences in the observable characteristics in the two groups.To correct for any differences not accounted for by the randomization of lo-calities into treatment and control groups and to obtain more precise estimates,I have included covariates as in Djebbari and Smith (2005).11 The vector ofcontrol variables includes characteristics of the head of household (age, edu-cation, gender, employment) and household demographic composition.12 Theadvantage of the QTE approach relative to the common effect model is thatthe impact of the program on different quantiles of the outcome distributiondoes not have to be constant.

Note that although average differences equal differences in averages, thetreatment effect at quantile is not the quantile of the difference ( ). TheY � Y1 0

QTE corresponds, for any fixed percentile, to the horizontal distance betweentwo cumulative distribution functions. Under the rank preservation assump-tion, QTE can be interpreted as the treatment effect for individuals at particularquantiles of the control group outcome distribution or the treatment effectfor the person located at quantile in the distribution (Heckman et al. 1997;Bitler et al. 2005). Without the rank preservation assumption, QTE representshow various quantiles of the outcome distribution change in the treatmentand control groups, but we cannot make an inference about the impact onany particular person.

Rank preservation across treatment status is a strong assumption, as itrequires that the rank of the potential outcome for a given individual wouldbe the same under treatment as under nontreatment. There are two ways todeal with cases where the rank invariance assumption is not valid. Heckmanet al. (1997) suggest computing bounds for the QTE, allowing for severalpossibilities of reordering of the ranks. The second approach argues that evenwithout this assumption, QTE estimates are informative about the overallimpacts of the program and, therefore, still meaningful parameters for policypurposes. In the absence of rank invariance, the interpretation of QTE is thedifference in the treated and control distributions, not the treatment effectsfor identifiable people in either distribution (Bitler et al. 2005, 2006). Thelast section of the paper analyzes whether the rank invariance assumption isvalid in the RPS context.

11 Including covariates in the estimation of quantile treatment effects changes somewhat the natureof the treatment effect being estimated and the assumption that underlies it (see Djebbari andSmith 2008). Thanks to the referee for pointing this out.12 Unreported regressions show that the QTE estimates, without controlling for covariates, are inthe 95% range of the QTE estimates controlling for covariates. With nonexperimental data, theestimation can adjust for differences in baseline observable characteristics by using propensity scoreweighting as in Bitler et al. (2006) and Firpo (2007).

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C. Outcomes of InterestThe outcomes of interest in the empirical section include household and in-dividual level variables. The RPS program aims at improving the educationaland health outcomes of children. I focus on children ages 7–13 years old atthe baseline because they are most likely to be affected by the conditionalityof the cash transfers. Outcomes of interest include child labor (participationand working hours) and school attendance. Child labor refers to children whoare engaged in market work, which includes wage employment, self-employ-ment, agriculture, unpaid work in a family business, and helping on the familyfarm.13

Impacts for schooling and child labor outcomes are estimated using OLS.One important feature of the data is the presence of a substantial number ofchildren reporting zero hours of work; thus I also include the estimates fromthe Tobit regression.

To analyze the QTE on household welfare the empirical literature useshousehold consumption rather than income because data on expenditures arelikely to be more accurate and consumption expenditures have a stronger linkwith current levels of welfare (Deaton 1997). At the household level, thisstudy analyzes three outcomes of interest: per capita total expenditure, percapita food expenditure, and food share of total expenditures. The analysis offood expenditures is important because one of the keys of the program issupplementing income to increase expenditures on food so as to improvehousehold nutrition. The expenditure variables include food, nonfood items,and the value of food produced and consumed at home.14

V. ResultsA. Impacts along Observable CharacteristicsTable 4 reports estimated coefficients of the treatment indicator interactedwith covariates of interest. The main results show that the program has differentimpacts on children with different observable characteristics. For example,boys ages 7–13 years experienced a statistically significant larger impact onschool attendance than girls. Estimates suggest that the RPS program increasedschool attendance by 12 percentage points for girls and by 18 percentagepoints for boys in 2001. In addition, the reduction in the probability of

13 Labor laws in Nicaragua establish age 14 as the basic minimum age for work. Children betweenthe ages of 14 and 17 can work a maximum of 6 hours per day but not at night. The employmentof youth is prohibited in places that endanger their health and safety, such as mines, garbagedumps, and night entertainment venues (i.e., nightclubs, bars, etc.). Government enforcement,however, is far from strict.14 See Maluccio and Flores (2005) for more detailed information on the constructed variables.

68

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engaging in market activities is larger for boys. Estimates show that boysexperienced a greater impact of the program on reducing the probability ofengaging in market work and hours worked. The results show that the RPSprogram decreased participation in labor activities for boys by 11 percentagepoints in 2001 and 14 percentage points in 2002, while the negative effectof the RPS program on labor participation for girls is small, just one percentagepoint in both years. These findings are important given that the program didnot provide differential transfers to boys and girls. For instance, PROGRESAprovided slightly more money to girls enrolled in secondary school, and theresults show that the program had a greater impact on secondary age girls(Skoufias and Parker 2001). In addition, it is important to note that thisdefinition of work does not include other activities usually not remuneratedand performed in the same household, such as taking care of younger siblings,cleaning, and cooking, among other household chores. A broader definitionincluding detailed household chores may decrease this gender difference inparticipation rates.

The coefficient on the interaction term between treatment and age showsthat older children experienced a smaller impact of the program on schoolingas well as on the probability of engaging in market work and hours worked.This is related to previous findings that with higher age potential earningsincrease; thus, transfers might not be high enough to compensate for forgoneearnings. The interaction with household head education shows that childrenwith more educated head of households experienced a smaller impact of theprogram on schooling. The empirical literature has shown that more educatedparents use the information provided in health clinics about nutrition moreefficiently and value schooling more and child labor less (Strauss and Thomas1998). Thus, school attendance among children living in more educated house-holds would be higher and the margins for improvement lower than amongchildren in households with lower parental education. In addition, childrenliving with a male head of household experienced a smaller impact of theprogram on school attendance, participation in labor activities, and hoursworked. Similarly, children living in larger households experienced a smallerimpact of the program on school attendance, labor force participation, andhours worked.

The last rows show the effect of the treatment interacted with the margin-ality index and household per capita expenditures, separately, to analyze thetargeting mechanism of the program. Based on the marginality index, I grouphouseholds into quintiles and interact the treatment indicator with the index

Dammert 71

categories.15 If the actual targeting of the program is efficient, then the impactin schooling should decrease from the poorest index (expenditures) quantilesto the richest index (expenditures) quantiles. As the estimates from table 4show, children living in more impoverished areas experienced larger impactsof the program on school attendance in 2001. Similar results are obtainedwith the interaction of the treatment indicator and quintiles of household percapita expenditures. In 2002, however, children living in more impoverishedareas experienced a smaller impact of the program on schooling. For example,children in the first quintile of the marginality index (poorest) experienced anincreased in school attendance of 8 percentage points, whereas children in thehighest quintile experienced an increase in schooling of 19 percentage points.The estimates also show that children in the poorest households experiencedsmaller impacts of the program on the probability of engaging in labor ac-tivities. Most of the interactions between the treatment indicator and thequintiles of the marginality index (expenditures), however, are not statisticallysignificant. Finally, the last row in table 4 rejects the null hypothesis that allof the coefficients on the interaction terms equal zero.

B. Quantile Treatment Effect RegressionThe quantile treatment effects provide information on how the impact at thehousehold level varies at different points of the expenditure distribution. Fig-ures 1–6 plot the quantiles using posttreatment data. The solid line representsthe estimate of the RPS treatment in a given quantile. The associated 95%confidence intervals are obtained from the bootstrap with 1,000 replicationsclustered at the locality level. These bootstrap confidence intervals are plottedon the graph with dashed lines. For comparison purposes, the mean treatmenteffect is plotted as a small dashed line.16

Overall, RPS treatment group expenditures are greater than control groupexpenditures, yielding positive impacts at each quantile of the distribution.For per capita total expenditures and per capita food expenditures, the differenceincreases from the lowest percentile to the highest percentile of the distribution.These findings suggest that households with lower expenditures tend to receive

15 Quintiles of the marginality index are defined so that the lowest quintile includes people withthe highest marginality index (poorest), whereas the highest quintile includes those with the lowestmarginality index (richest).16 The bootstrap samples are drawn in a manner that mimics the stratified cluster sample designof the RPS survey by first drawing localities for each bootstrap sample and then sampling withinthe selected localities for each bootstrap sample. QTEs are calculated for each bootstrap sample,and the process is repeated 1,000 times. The standard deviation of a QTE over the bootstrapreplications is an estimator of the standard error.


Figure 1. Quantile treatment effect on the distribution of annual per capita total expenditures in 2001.(i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval from the bootstrapwith 1,000 replications clustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sampleincludes 1,359 households with observations in the panel 2000/2001/2002. (v) In Nicaraguan Cordobas,the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the 91st to 99th quantiles, butthey are not included in the figures because their variances are large enough to distort the scale of thefigures. (vii) The estimation controls for household head characteristics and demographic composition ofthe household.

lower positive impacts from the program. As the theoretical framework sug-gests, the impacts are greater for households with higher expenditures, whoare more likely meeting or almost meeting program requirements prior to theprogram. For households with lower expenditures, who are more likely notmeeting the requirements and for whom the cost of participation is thereforethe highest, program impacts are still positive but smaller than for householdsat the upper end of the distribution. These results are similar to Djebbari andSmith’s (2005) QTE findings for the Mexican’s PROGRESA. They find thatprogram impacts on wealth and nutrition are greater for households who wereat higher levels of wealth and nutrition prior to the program. Similarly, forthe share of food expenditure, the difference decreased from the lowest per-centile to the highest percentile, suggesting that the program impacts arehigher for households who had lower levels of food shares prior to theprogram.17

Figure 1 shows that in 2001 the program impact on per capita total ex-penditures varies from about C$707 (US$54) to C$3,087 (US$237). In 2002,the program impact on per capita total expenditures varies from about C$264

17 Figures A1 and A2 show the QTE estimates for the year before random assignment. The structureof the figures is identical to that of figures 1–6 before; they show the mean effect, the QTEs, andthe bootstrap 95% confidence interval of the QTEs. The effects are statistically not significantlydifferent from zero for all quantiles.

Dammert 73

Figure 2. Quantile treatment effect on the distribution of annual per capita total expenditures in 2002.(i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval from the bootstrapwith 1,000 replications clustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sampleincludes 1,359 households with observations in the panel 2000/2001/2002. (v) In Nicaraguan Cordobas,the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the 91st to 99th quantiles, butthey are not included in the figures because their variances are large enough to distort the scale of thefigures. (vii) The estimation controls for household head characteristics and demographic composition ofthe household.

(US$20) to C$1,293 (US$99) for the highest percentile (fig. 2). Many of theimpacts are quite large compared to the mean impacts of C$1,184 and C$820in 2001 and 2002, respectively. These results suggest that households at thetop of the outcome distribution receive more than five times the impact thathouseholds with lower expenditures do.

I test whether a constant treatment effect could lead to a range as large asthat observed for the QTE point estimate as in Bitler et al. (2006). The testis as follows: first, keep only observations in the control group and assign auniformly distributed random number to the ith household in the bth bootstrapsample.18 Second, sort the sample of households using this random numberand assign to households with a random number higher than 0.5 andt p 1in the bth sample and to the remaining households in this bootstrapt p 0sample. Third, add the estimated mean treatment effect to households with

to create a synthetic null treatment group distribution. Finally, use thet p 1synthetic null treatment group and the remaining control group to constructthe QTE under the null hypothesis. From the resulting individual distribu-tions, we can generate a confidence interval for testing the maximum minusminimum range, which compares the distribution for the range under thenull with the real-data QTE range. This confidence interval is estimated with

18 Note that each bootstrap sample has the same size as the control group and sampling is madewith replacement from the observations from the control group only.


Figure 3. Quantile treatment effect on the distribution of annual per capita food expenditures in 2001.(i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval from the bootstrapwith 1,000 replications clustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sampleincludes 1,359 households with observations in the panel 2000/2001/2002. (v) In Nicaraguan Cordobas,the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the 91st to 99th quantiles, butthey are not included in the figures because their variances are large enough to distort the scale of thefigures. (vii) The estimation controls for household head characteristics and demographic composition ofthe household.

1,000 bootstrap replications. The test of constant treatment effects suggeststhat the null constant treatment range is [3,187.1, 3,384.1] and [2,987.9,3,198.2] at a confidence level above 95% for 2001 and 2002, respectively.The QTE range estimated using the data is 2,380.3 and 1,029.8 for 2001and 2002. These results show that the mean treatment effect is not sufficientto characterize RPS’s effects on total per capita expenditures.19

Consistent with the RPS program’s goal, additional expenditures as a resultof the transfers were spent predominantly on food. Results for food expendituressuggest a large degree of treatment impact heterogeneity. In 2001, the programimpact on per capita food expenditures varies from about C$367 (US$28) toC$3,780 (US$290) for the highest percentile of the distribution (fig. 3). In2002, the program impact on per capita food expenditures varies from aboutC$174 (US$13) to C$1,846 (US$142) for the highest percentile of the dis-tribution (fig. 4). The mean impacts are C$1,004 and C$733 for 2002 and2001, which are far below the impacts at the top of the distribution. Theconfidence interval for a null of constant treatment effects is [1,963.6, 2,104.9]and [1,993.8, 2,186.4] at a confidence level of above 95%, while the estimated

19 Using the bootstrap, we can compare the mean range from the null of constant treatment effectwith the mean range from the real data. One-sided tests show that we can reject the null of equalityat the 5% level for all variables and years, separately.

Dammert 75

Figure 4. Quantile treatment effect on the distribution of annual per capita food expenditures in 2002.(i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval from the bootstrapwith 1,000 replications clustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sampleincludes 1,359 households with observations in the panel 2000/2001/2002. (v) In Nicaraguan Cordobas,the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the 91st to 99th quantiles, butthey are not included in the figures because their variances are large enough to distort the scale of thefigures. (vii) The estimation controls for household head characteristics and demographic composition ofthe household.

range over all quantiles in the real data is 3,412.2 and 1,671.7 for 2001 and2002, respectively. The positive impact of the program for households withthe highest per capita food expenditures prior to the program is almost seventimes the impact for households with lower food expenditures, which is notcaptured by the mean treatment effect estimate.

To further explore the impacts of RPS on the distribution of expenditures,figures 5 and 6 show QTEs for the share of food expenditures in the householdbudget. In 2001, the program impact on food share ranges from about 7.79percentage points to �0.18 percentage points for the highest percentile. In2002, the program impact on food share varies from about 8.65 percentagepoints to �1.42 percentage points for the highest percentile. The mean impactsare about 4.0 and 3.8 percentage points in 2001 and 2002. The impact ishigher for households who have a lower share of food expenditures prior tothe program. Maluccio and Flores (2005) have shown that not only the numberof food items purchased increased but also their nutritional value.

C. Rank Preservation and Rank ReversalThe main QTE findings show that the impact of the RPS program variedacross the distribution of total and food expenditures. As previously discussed,the impact of the treatment on the distribution is not the distribution oftreatment effects. This interpretation is valid only under the rank preservation

76

Figure 5. Quantile treatment effect on the distribution of food share in 2001. (i) Solid line is the treatmentquantile effect. (ii) Dashed lines provide confidence interval from the bootstrap with 1,000 replicationsclustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sample includes 1,359households with observations in the panel 2000/2001/2002. (v) QTE is computed for the 1st to 3rd quantiles,but they are not included in the figures because their variances are large enough to distort the scale ofthe figures. (vi) The estimation controls for household head characteristics and demographic compositionof the household.

Figure 6. Quantile treatment effect on the distribution of food share in 2002. (i) Solid line is the treatmentquantile effect. (ii) Dashed lines provide confidence interval from the bootstrap with 1,000 replicationsclustered at the locality level. (iii) Small dashed line is the mean impact. (iv) Sample includes 1,359households with observations in the panel 2000/2001/2002. (v) QTE is computed for the 1st to 3rd quantiles,but they are not included in the figures because their variances are large enough to distort the scale ofthe figures. (vi) The estimation controls for household head characteristics and demographic compositionof the household.

Dammert 77

assumption. This section examines whether there is evidence consistent withrank preservation. As in Bitler et al. (2005), I use the treatment and controldistributions of demographic characteristics to see if there is evidence againstrank preservation or rank reversal in each quartile. For example, if the distri-bution of observable characteristics in some range of the expenditures distri-bution varies significantly between the treatment and control group, this wouldbe evidence against rank preservation. Note, however, that rank reversal mayhave occurred among unobservables even if observable characteristics do notchange.

Tables 5 and 6 present the mean difference by quartile and the p-value forstatistical significance. Each row corresponds to a demographic variable sep-arated by household head characteristics (gender, education, age, and employ-ment) and household demographic composition (girls 0–5 years, boys 0–5years, girls 6–15 years, and boys 6–15 years). The test is performed for a totalof eight variables and, thus, tables 5 and 6 present 32 tests for each distributionand year. Panel A classifies people by their position (quartile) in the per capitatotal expenditure distribution, and panel B classifies people by their positionin the per capita food expenditure distribution. Table 5 presents the resultsfrom the exercise using the 2001 data, whereas table 6 presents the resultsusing the 2002 data.

Of the 128 differences, 18 are statistically significant at the 10% level orbelow in 2001 and 2002. The individual test suggests that some rank reversalmay be present based on these demographic characteristics. The joint test forthe significance of the differences within a given quantile range, however, failsto reject the null for all ranges of per capita food expenditure and per capitatotal expenditure.20 While the individual tests suggest that some rank reversalmay be present along observables, the joint test results show that strict rankreversal is not rejected.

VI. Summary and ConclusionsThis study assesses the importance of heterogeneity in impacts of conditionalcash transfers using a social experiment from a poverty alleviation program inNicaragua. Heterogeneity in program impacts is expected to arise because ofthe design and implementation of the program. The theoretical model showsthat program impacts vary with observable characteristics, the targeting di-mension, and the conditionality of the program.

The first part of the paper analyzes impacts at the subgroup level by es-

20 See Bitler et al. (2005) for a detailed explanation of the joint test for the significance of thedemographic variables.

78

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timating the interaction between the treatment indicator and covariates ofinterest. The estimates also show that children living in more impoverishedlocalities experienced larger impacts of the program on schooling in 2001, butthis result is reversed in 2002. The second part of the paper analyzes quantiletreatment effects. The results suggest evidence against the common effectassumption. The estimates show that the impact of the program is lower forhouseholds who were at a lower level of expenditures prior to the program.That is, the RPS program has a greater effect on households who wouldotherwise have had a high per capita total and food expenditures. Quantiletreatment effect estimates show that there was considerable heterogeneity inthe impacts of the RPS on the distributions of expenditures, which is missedby looking only at average treatment effects. As the theoretical frameworksuggests, the impacts are greater for households with higher expenditures whoare more likely to be meeting or almost meeting program requirements priorto the program. For households with lower expenditures who are more likelyto not be meeting the requirements, and for whom the cost of participationis the highest, program impacts are still positive but smaller than for house-holds at the upper end of the distribution. Tests of the null hypothesis ofconstant treatment effects reveal that these findings could not have been ob-tained using mean impact analysis. In addition, joint tests of rank preservationshow that the distributions of observable characteristics in all ranges of theexpenditures distribution do not vary significantly between the treatment andcontrol group. These results have important implications for the implemen-tation and evaluation of conditional cash transfers that are spreading rapidlyin developing countries.

81

Appendix

Figure A1. Quantile treatment effect on the distribution of annual per capita total expenditures in2000. (i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval fromthe bootstrap with 1,000 replications clustered at the locality level. (iii) Small dashed line is the meanimpact. (iv) Sample includes 1,359 households with observations in the panel 2000/2001/2002. (v) InNicaraguan Cordobas, the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the91st to 99th quantiles, but they are not included in the figures because their variances are large enoughto distort the scale of the figures.

Figure A2. Quantile treatment effect on the distribution of annual per capita food expenditures in2000. (i) Solid line is the treatment quantile effect. (ii) Dashed lines provide confidence interval fromthe bootstrap with 1,000 replications clustered at the locality level. (iii) Small dashed line is the meanimpact. (iv) Sample includes 1,359 households with observations in the panel 2000/2001/2002. (v) InNicaraguan Cordobas, the equivalent exchange rate is US$1 p C$13. (vi) QTE is computed for the91st to 99th quantiles, but they are not included in the figures because their variances are large enoughto distort the scale of the figures.


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