PROGRESA CCT.pdf

7/28/2019 PROGRESA CCT.pdf

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American Economic Association

Do Conditional Cash Transfers Improve Child Health? Evidence from PROGRESA's ControlRandomized ExperimentAuthor(s): Paul GertlerReviewed work(s):Source: The American Economic Review, Vol. 94, No. 2, Papers and Proceedings of the OneHundred Sixteenth Annual Meeting of the American Economic Association San Diego, CA,January 3-5, 2004 (May, 2004), pp. 336-341Published by: American Economic AssociationStable URL: http://www.jstor.org/stable/3592906 .Accessed: 29/06/2012 04:19

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Do ConditionalCashTransfers mproveChildHealth?Evidence rom PROGRESA's ontrolRandomizedExperimentBy PAUL GERTLER*

One of the greatesttragediesof extremepov-erty is its intergenerationalransmission. Chil-dren who grow up in poorfamilies tend to be inpoorerhealth and have lower levels of educa-tion. They thus enter adulthood without "thebasic capabilities"necessaryto take advantageof labor-market pportunitieso pullthemselvesout of povertyand to enjoy an acceptablequal-ity of life (AmartyaSen, 1999). As a result,children from poor families begin life at a dis-tinct disadvantage.In an effort to improvethe circumstances nwhich childrenfrompoorfamilies startout life,the Mexicangovernmenthas spentconsiderableresources developing an anti-poverty programcalled PROGRESA. This programcombines atraditionalcash-transferprogramwith financialincentives for positive behavior in health,edu-cation,and nutrition.Specifically,cash transfersare disbursedconditional on the household en-gagingin a set of behaviorsdesignedto improvehealth and nutrition, including prenatal care,well-baby care and immunization, nutritionmonitoring and supplementation, preventivecheckups,and participationn educationalpro-gramsregardinghealth,hygiene, and nutrition.An additional cash transfer s given to house-holds with school-age children if the childrenare enrolled and attendschool. While financialincentives to encourage good health behav-ior have been used in Finland and France,PROGRESAis, at least to my knowledge, thefirst such program n a developing country.

* Graduate Program in Health Management, HaasSchool of Business andSchool of PublicHealth,Universityof California,Berkeley,CA 94720. This paper s dedicatedto the memoryof Jos6G6mezde Le6n who was the originalDirectorGeneralof PROGRESAandbelieved that no childshould start out life disadvantaged.I am grateful to theMexicanGovernmentand theMexican NationalInstituteofPublic Healthfor fundingthedata collection and initial dataanalysis and to the U.S. National Institute of Child andHumanDevelopmentfor researchsupport.

In this paper, I investigate the impact ofPROGRESAon child health outcomes includ-ing morbidity,height,andanemia. The analysistakes advantage of a controlled randomizeddesign.

I. The InterventionPROGRESA began in 1997 as a national

program designed to address the immediateneeds of extreme poverty and break its inter-generationaltransmission.Over its first threeyears, PROGRESA extended benefits to ap-proximately2.6 million families in 50,000 ruralvillages, which is about 40 percent of ruralfamilies and 10 percentof all families n Mexico.PROGRESAdeterminedhouseholdeligibilityin two stages, first by identifying under-servedcommunities and then by choosing low-income households within those communities(EmmanuelSkoufias et al., 1999). On average,78 percentof the households in selected com-munitieswere classified as eligible for programbenefits.All eligible householdsliving in treat-ment localities were offered PROGRESA,andalmostall (93 percent)enrolledin the program.Every two months PROGRESAfamilies re-ceive a cash transfer ypicallyworth about 20 to30 percentof householdincomeif thefollowingconditions are met.

(i) Children f age0-23 monthsget immunizedand visit nutritionmonitoringclinics everytwo monthswherethey get well-babycare,theirgrowth s measured, hey obtainnutri-tionsupplementsworth 100percentof dailyrecommendedmicronutrientsnd20 percentof protein,and theirparentsreceive educa-tionon nutrition, ealth,andhygiene.(ii) Children of age 24-60 months attend nu-trition monitoring clinics every fourmonths where their growth is measured,they obtain nutritionsupplementsif theirgrowth is assessed as poor, and they re-

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HEALTH,HEALTHCARE,AND ECONOMICDEVELOPMENTceive education on nutrition, health, andhygiene.(iii) Pregnantwomen visit clinics to obtainpre-natal care, nutritional supplements, andhealtheducation.Theyarerequired o havefive prenatal arevisits startingn theirfirsttrimester.

(iv) Lactating women visit clinics to obtainpostpartum are,growthmonitoring,nutri-tion supplements, and education abouthealth, nutrition,and hygiene.(v) Otherfamily members visit clinics once ayear for physical checkups. During thesecheckupsspecialattention s paidto familyplanningand to thedetection andtreatmentof parasites,arterialhypertension,diabetesmellitus, and cervical cancer. The visitsalso include educationabouthealthhabits,hygiene accident prevention,and first-aidtreatment.

(vi) All adult family members participateinregularmeetingsat which health,hygiene,and nutrition ssues and best practicesarediscussed. Female head of households arerequired to attend bi-monthly meetings,while other adults have to attend once ayear. Physicians and nurses speciallytrained in these topics conduct thesesessions.

II. ExperimentalDesignThe analysis takes advantageof a control-randomizeddesign implemented by the Gov-ernment of Mexico. Due to budgetary andlogistical constraints,the governmentwas un-able to enroll all eligible families simulta-neously.Rather, t needed to phasein enrollmentover a periodof time. For logisticalreasons,thegovernmentdecided that it would enroll wholevillages at a time and that it would enroll themas fast as possible so thatno eligible householdwould be keptout of the program f money wasavailable.Because equityrequiresgiving everyeligible village anequalchance of receivingthebenefits first, the governmentdecided to ran-domly choose which villages would receivebenefits first.As a result of this process, the governmentrandomlychose 320 treatmentand 185 control

villages in seven states for a total of 505 exper-

imental villages. Eligible households in treat-ment villages received benefits immediatelystarting n August-September1998, while ben-efits for eligible households in control villageswere postponedfor two years. In localities as-signed to the controlgroup,none of the house-holds received PROGRESAbenefits,nor werethey informedthatPROGRESAwould providebenefits to them at a laterdate.

III. DataI use three indicators of child health out-comes to assess the impactof PROGRESA.Thefirstmeasure s child morbiditymeasuredas themother'sreportas to whetherthe child experi-

enced an illness in the four weeks priorto thesurvey. Child morbidity and socioeconomiccharacteristicswere collected as partof a largersocioeconomic survey of all households in theexperimentalvillages prior to the interventionbaseline, again two months after the interven-tion began, and then threemore times at aboutsix-monthintervals.The surveyincluded nformation hatallowedme to apply the program's eligibility criteriaand to identify those householdsthat were eli-gible in treatmentareas and those householdsthat would be eligible in control areas. Usingthis information, restricted he analysissampleto householdseligible forPROGRESA.A treat-menthouseholdis definedas an eligible house-hold in a treatment village, and a controlhouseholdwas definedas an eligible householdin a controlvillage.The next set of healthoutcomesmeasuresarebased on objective measurements.These in-clude height measuredin centimeters,"stunt-ing" (defined as being two or more standarddeviations below the age-sex standardizedheight of a healthy [U.S.] referencepopulation[WorldHealth Organization,1979]), and ane-mia (defined as hemoglobin less than 11 g/dLadjusted or altitudeusing standardadjustments[Guillermo Jose Ruiz-Argiielles and AntonioLlorente-Peters,1981]).These objective health indicatorsare basedon height and hemoglobin. However, becauseof the cost of collecting these measures,theywere only collected in a subsampleof the 505experimentalommunities.A sampleof treatment

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AEAPAPERSAND PROCEEDINGScommunities was randomlyselected, and con-trol communitieswere matchedto the treatmentcommunitiesbased on populationsize, a socio-economic index (SES), community infrastruc-ture, and geographicallocation. The objectivehealth information used in this analysis wascollected between 1998 and 2000.

IV. StatisticalMethodsThe randomizationand the fact thatthe con-trol and treatment amplesare well balanced inthe observed characteristicsmply that a simplecomparisonof mean outcomespost-interventionwill likely providean unbiasedestimate of pro-gram impacts.However,I also controlfor other

observed socioeconomic characteristics n orderto reduceidiosyncraticvariationandto improvethe power of the estimates.To test whethermorbiditywas higheramongchildren in PROGRESA-eligibletreatmentar-eas, I estimated a logistic regressionof proba-bility of illness with the key independentvariablebeing a dummyindicatingwhethertheindividual was in a treatmentvillage that waseligible for PROGRESA.The model also con-trols for socioeconomic characteristicsmea-suredjust before the intervention.The specificvariables included in the model are the child'sage and sex; the mother's and father's ages,yearsof schooling,andabilityto speak Spanish;and household ownership, whether the househad electricity,householdincome, and averagemale and female wage rates in the village mea-sured at baseline.The economic variablesweremeasuredat baseline because the cash transferlikely affected their values, biasing the esti-matedimpact.The model allows for an individual randomeffect because of the multipleobservations onthe same child across the longitudinalsurvey,and for a village random effect because of thecluster sampling(Allan Donnerand Neil Klar,2000). Few householdshadmorethanone childless thanage 3 atbaseline. Forthosehouseholdsthat did, I randomly sampled one child to in-clude in the analysis.The above specification (model 1) restrictsthe program mpactto be constantwith respectto programexposure.I estimatea second spec-ification(model 2) thatallows the program m-

pact to vary depending on how long theprogramhas been operating n the village. Spe-cifically, I include separatetreatmentdummiesfor six-monthprogramexposure, 12-monthex-posure, 18-month exposure, and 24-monthexposure.I estimatedthe model separatelyfor babiesbornduring he interventionperiodandforchil-drenaged 0-35 months at baseline. While bothof these cohortsexperiencedthe benefitsof thecash transfers, he well-babycare and nutritionmonitoring,the nutritionsupplements,and thegeneralhealth,hygiene, and nutrition nforma-tion providedtheirparents, he newbornsamplealso benefitedfrom the prenatal nterventions.For the newborn sample, I use only theobservations that first appear in the secondfollow-up survey (i.e., those whose familieshave been on the program or 6-12 monthsatthe time of birth).This is to increase the likeli-hood that mothers have received full prenatalcarebenefits.Using data from the firstfollow-upwould meanthatmost newbornsdid notreceiveprenatalbenefitsuntil well into the pregnancy.While this allows me to capture the prenatalcare effect of PROGRESA, t limits the numberof observations that I can use to estimate theeffect of durationon the program.Therefore,given the samplesize, I did not estimatemodel2. Finally, newborns that were less than onemonth old at the time of the survey were ex-cluded from the analysis.I estimate a model similar to model 1 forstuntingandanemia.Whethera child is stuntedand/or anemic is only observed once in thepost-intervention eriod.Therefore, amunableto include individualrandomeffects or estimatethe effect of the durationof exposure to theprogram.Unlike self-reportedmorbidityand anemia,height is a continuousvariable,and I can uselinearregressionmodels with a village randomeffect to estimateprogram mpact.Inaddition othe socioeconomic characteristicsused in theothermodels, I also includea series of dummyvariables indicating the child's age in thefollow-up survey in three-month ntervals,sep-aratelyfor male and females. This is importantto control for highly nonlinearrelationships n-volving height, age, and sex in the first threeyears of life.

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HEALTH,HEALTHCARE,AND ECONOMICDEVELOPMENTTABLE1-PRE-INTERVENTION ESCRIPTIVETATISTICS

FORTHEMORBIDITYAMPLEOFCHILDRENAGE 0-35 MONTHSATBASELINE

p value forVariable Treatment Control differenceChild was ill in last 4 weeks 0.330 0.323 0.771

(=1)Age 1.625 1.612 0.914Male (=1) 0.511 0.491 0.091Father'syearsof education 3.803 3.840 0.980Mother'syears of education 3.495 3.829 0.062Fatherspeaks Spanish(=1) 0.942 0.929 0.276Motherspeaks Spanish(=1) 0.935 0.917 0.443Own house (=1) 0.923 0.917 0.465House has electricity(=1) 0.644 0.711 0.091Hectaresof land owned 0.809 0.791 0.553Male daily wage rate(pesos) 30.483 31.219 0.370Female daily wage rate(pesos) 27.258 27.844 0.493Sample size: 4,519 3,306

Notes: This tablereportsdescriptivestatisticsfor the sampleof children age 0-35 months at baseline before the inter-vention. Thep values in the thirdcolumn are for the test ofthe hypothesis that the means of the treatmentand controlgroupsare equal and are adjustedfor inter-clustercorrela-tion at the village level.

V. Morbidity ResultsThe response rates to the baseline surveywere quite high (93 percent),and sample attri-

tion was low comparedto other large longitu-dinal surveys. Specifically, over the two-yearexperimentalperiod, 5.5 percentof the house-holds and5.1 percentof the individualsdroppedfrom the sample.More importantly, here wereno differences in attritionbetween the controland treatmentareas, suggesting no systematicattritionbias in the analysis.The morbidity analysis sample consists ofchildren younger than age 3 at baseline andchildren born during the experimentalperiod.The analysis sample consists of 7,703 childrenwho were younger than age 3 at baseline and1,501 newborns(i.e., childrenborn duringtheinterventionperiod).Table 1 reports the means of individual,household, and village characteristicsfor thesample of childrenwho were alive at baseline.The last columnreportsthep value for the testof the null hypothesis that the means of thecontrol and treatmentgroup are equal. The pvalues were calculatedadjusting or inter-clustercorrelationwithin villages. At baseline, all ofthe characteristics were statistically indistin-

TABLE2-ESTIMATED LOGODDS ESTIMATESOFTHEIMPACTOF PROGRESA

ON CHILDREN'S ROBABILITYF ILLNESSChild age0-35 monthsat baseline

Model ModelVariable Newborns 1 2PROGRESAeligible = 1 0.747 0.777(0.013) (0.000)PROGRESAeligible 0.940for 2 months = 1 (0.240)PROGRESAeligible 0.749for 8 months = 1 (0.000)PROGRESAeligible 0.836for 14 months = 1 (0.005)PROGRESAeligible 0.605

for 20 months = 1 (0.000)Notes: The first two columns report he estimatedlog oddsfrom coefficients on dummy variables indicatingwhetherthe child was in a treatment village and eligible forPROGRESA.The p value for the hypothesis test that theestimatedlog odds is equal to 1 is reported n parentheses.The third column reports the results for the length oftime that the child could have been on PROGRESA.Thecoefficients for all three models are estimated from arandom-effects ogit model, which allows for inter-clustercorrelationat the village level and controls for the socio-economic variablesreported n Table 1, measured at base-line priorto intervention.

guishable between control and treatmentsam-ples at the 5-percent significance level. Onlythree characteristicswere statisticallydifferentat the 10-percentsignificancelevel.The results of the logistic regressions re-ported n log odds arepresented n Table2. Thefirstcolumnreportsthe results for the newbornsample.The estimatessuggestthatthe treatmentnewbornswere 25.3 percent ess likely thanthecontrolsto be reportedas being ill in the previ-ous month,a differencethat is statistically sig-nificant at the 5-percent significance level.Similarly,the second columnreportsthattreat-ment 0-3-year-olds were 22.3 percent lesslikely to be ill thancontrols,and this differenceis significantat the 1-percentlevel. The thirdcolumn reports the results for the program-exposure model. While there appearsto be noprogramimpact after only six months of pro-grambenefits, the illness rate of the treatmentgroup was 39.5 percentlower than the control

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AEAPAPERSAND PROCEEDINGSTABLE-ESTIMATED IMPACTFPROGRESAONCHILDREN'SBJECTIVEEALTH EASURES

Statistic Height Stunted AnemiaEstimatedprogram mpact 0.959 0.914 0.745

(0.004) (0.495) (0.012)Treatmentgroupmean 80.725 0.396 0.410Controlgroupmean 79.742 0.410 0.483Sample size: 1,552 1,552 2,010

Notes: The first row in this table reports the estimatedcoefficient on a dummy variable indicating whether thechild was in a treatmentvillage for height from a linearregressionwith village randomeffects, and the estimatedlog odds from a coefficienton a dummyvariable ndicatingwhether hechild was in a treatment illage for stuntingandanemia from a random-effectslogistic regression. The pvalue for the test thatthe coefficientsaredifferent rom zeroin the first two columns and different from 1 in the thirdcolumn are reported n parentheses.

groupwith 24 monthsof program xposure,andthis difference is significant at the 1-percentlevel.VI. Anemiaand HeightResults

Theresponserateforanthropometricswas 97percent,and the response rate for hemoglobinwas 92 percent.The samplefor height consistsof children age 12-36 months at the time ofsurvey and children 12-48 monthsfor anemia.The samplesize for the height analysis is 1,049treatmentsand 503 controls,whereasthe sam-ple size for anemiais 1,404 treatmentsand608controls. I matched the 1999 objective healthsurveyto the 1997 baseline socioeconomic sur-vey. Using these data I tested the hypothesisthatthemeansof thevariables n Table 1 arenotdifferent for the control and treatmentgroupsfor this subsample.Of the 11baselinesocioeco-nomic-characteristicsmeans, only two are sig-nificantlydifferentat the 5-percentlevel.Theestimated mpactsarereportedn the firstrow of Table 3. The second and third rowsreportthe means for the treatmentand controlgroups separately.The first column reportstheresults for height using the cross-section dataset. I find that treatment children are 0.96centimeters taller than control children, andthis difference is statistically significantat the1-percent evel. The second column reportsthe

log-odds difference for the probabilityof beingstunted. The results show that treatmentchil-dren are8.6 percent ess likely to be stunted,butthis difference is not statistically significantatany conventional evel. Finally,the last columnreports he log-oddsdifferencefor the probabil-ity of being anemic. The results show that treat-ment children are 25.5 percentless likely to beanemic, and this difference is statisticallysig-nificant at the 1-percent evel.

VII. DiscussionI found a significant improvement in thehealth of children in response to PROGRESA.

Specifically, childrenbornduringthe two-yearintervention o families benefitingfrom thepro-gramexperiencedan illness ratein the firstsixmonthsof life that was 25.3 percentlower thanthat of control children. Treatment childrenaged 0-35 months at baseline experienced areduction of 39.5 percentin their illness ratesafter 24 monthsin the program.Moreover,theeffect of the program seems to increase thelongerthe childrenstayedon the program,sug-gestingthatprogrambenefitswere cumulative.Ialso found that treatment children were 25.3percent ess likely to be anemic andgrew about1 centimeter more duringthe first year of theprogram.While these resultssuggest that PROGRESAhas had a positiveeffect on childhealth,theydonot indicate which aspectsof this complex pro-gram really matter. PROGRESA combineslarge cash transferswith requirements hat in-dividuals engage in a number of preventivehealthand nutritionactivities.One cannot tell ifthe same results could have been achievedwithjust a large cash transfer and no behavioralrequirements. n is also hardto distinguishbe-tween the relative effects of compliance withthe various requirements.Answers to thesequestionswould facilitatea betterpackageandthereforeimprovethe cost-effectiveness of theintervention.

REFERENCESDonner, Allan and Klar, Neil. Design and anal-

ysis of cluster randomization rials in health

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VOL.94 NO. 2 HEALTH,HEALTHCARE,AND iresearch. London, U.K.: Oxford UniversityPress, 2000.Ruiz-Argiielles,Guillermo,Jose and Llorente-Peters, Antonio. "Predicci6nalgebraica deparametrose serierojadeadultos anosresiden-tes en alturas e 0 a 2,670metros." aRevistadeInvestigacion linica,1981,33, pp. 191-93.Sen,Amartya.Developmentasfreedom.Oxford,U.K.: OxfordUniversityPress, 1999.

ECONOMICDEVELOPMENT 341

Skoufias, Emmanuel; Davis, Benjamin and Behr-man,JereR.An evaluationof the selection ofbeneficiaryhouseholdsin PROGRESA: inalreport. Washington,DC: InternationalFoodPolicy Research Institute,Washington,DC,1999.World Health Organization. Measurement of nu-tritionalimpact.Geneva,Switzerland:WorldHealthOrganization,1979.

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PROGRESA CCT.pdf