designing social protection programs: using theory and experimentation ... · protection from...
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DesigningSocialProtectionPrograms:
UsingTheoryandExperimentationtoUnderstandhowtoHelpCombatPoverty
RemaHannaHarvardUniversity
DeanKarlanYaleUniversity
March2016
Abstract
“Anti-poverty”programscomeinmanyvarieties,rangingfrommulti-faceted,complexprogramstomore simple cash transfers. Articulating and understanding the root problem motivatinggovernmentandnongovernmentalorganizationinterventioniscriticalforchoosingamongstmanyanti-poverty policies, or combinations thereof. Policies should differ depending on whether theunderlyingproblemisaboutuninsuredshocks,liquidityconstraints,informationfailures,orsomecombination of all of the above. Experimental designs and thoughtful data collection can helpdiagnose the root problems better, thus providing better predictions for what anti-povertyprogramstoemploy inspecificconditionsandcontexts.However, themorecomplex theoriesarelikewisemorechallengingtotest,requiringlargersamples,andoftenmorenuancedexperimentaldesigns, as well as detailed data on many aspects of household and community behavior andoutcomes.Weprovideguidanceonthesedesignandtestingissuesforsocialprotectionprograms,from how to target programs, to who should implement the program, to whether and whatconditionstorequireforprogramparticipation.Inshort,carefulexperimentationdesignedtestingcanhelpprovidea stronger conceptualunderstandingofwhyprogramsdoornotwork, therebyallowing one to ultimately make stronger policy prescriptions that further the goal of povertyreduction.
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I. IntroductionIn low-income countries, more than one billion individuals are enrolled in at least one
safetynetprogram(Gentilinietal.2014).1Theseprogramscomeinavarietyofformsand
sizes. Some aim to simply supplement consumption in hard times. Other newer, more
nuanced, social protection programs aim to address the underlyingmarket failures that
mayhavecontributedtoahousehold’spersistentstateofpovertyinthefirstplace,driven
byabeliefthatdirectlyaddressingthesefailuresmayhelpfamiliesbreakoutofapoverty
trap.Theultimate choiceofprogram—or combination therein—that countries choose to
implementwilldependgreatlyontheirsocialgoals,institutionalcapabilitiesandresources.
However, evenwithin eachbroad categoryof program, the specific design choicesmade
andmethodsofimplementationmayaffectwhethertheseprogramsactuallyachievetheir
statedgoals.
Inordertostartthinkingabouthowtodesign—andthentesttheimpactsof—safety
net programs, we begin by classifying them into four main categories based on the
underlying motivation for intervening. The first and simplest category is comprised of
programsdesignedforredistributivepurposes,e.g.,recognizingthatthemarginalutilityof
consumptionishigherforthepoorthanfortherich,andthustransfersaresociallyoptimal
fromautilitarianperspective(assumingnaturallythatthetaxationprocessdoesnotcreate
considerable deadweight losses).While these programs can differ in actual design, they
sharethecommonfeatureoffirstidentifyingtheneediestfamiliesalongaparticularmetric
and then providing themwith cash transfers. Of course, these programs could still have
long-rungrowthimpacts,e.g.iftheyarelargeenoughtoprovidesufficientcapitaltostart
new agricultural, migratory, or business activities (e.g., A. Banerjee and Newman 1993;
McKenzie and Woodruff 2006) or if they persuade risk-averse households to invest in
riskier, butmoreprofitable endeavors (Chetty andLooney2006) and so forth.2 But, the
1Throughoutthischapterwereferto“socialprotection”and“safetynet”programsasoneandthesame.2Notable examples that have found changes increases in investment as a result of cash transfer programsincludeCovarrubias,etal(2012)whofindthatMalawi’sSocialCashTransferledtoanincreaseinagriculturalinvestments; and Gertler, et al (2012) who find that Mexico’s CCT (Progresa) led to higher levels ofagriculturalincome,ashouseholdspartiallyinvestedaportionofthecashinproductiveassets.
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primarygoalofthesetypesofprogramsissimplytolimitpovertyandhungerbyensuring
thathouseholdsattainaminimumlivingstandard.
Second,amissinginsurancemarketmaymotivateasocialprotectionprogram.The
poor facemanyrisks, suchasunexpectedhealthcosts,agriculturaldamageor job losses.
Without fully functioning financial markets, households may be unable to borrow to
smoothconsumption.Informalcreditandinsurancemarketsprovideanotheravenuetodo
so,buttheyoftenunderperformandhavebecomeevenlesseffectiveascountriesgrowand
urbanizationbreaksdowntraditionalsocialnetworks(Coady2004).Furthermore,evenif
householdssmoothconsumptioninthefaceofincomeshocks,theymaybedoingsointhe
short run by making long-term sacrifices, such as pulling children out of school. The
insurancemarketmayexistbutneedsasubsidytoincreasequantitydemanded,oritmay
notexistatallandthesafetynetprogramdirectlyprovidesprotectioninbadtimes.Social
protection from natural disasters is an extreme example of the insurance motivation;
however,economic theoryandempiricalworkhashad less tosayabout thestructureof
suchpolicies.Lastly,unemploymentandhealthinsuranceprogramsalsofitintothisoften-
(atleastpartially)missinginsurancemarketcategory.
Third, theremaybebehavioralorhouseholdbargainingconstraints that influence
the choices of low income households, and perpetuate poverty. For example, difficulty
resisting immediate temptations can lead to undersaving and thus underinvestment in
lumpygoods. Intra-householdbargaining issuescan lead tosuboptimaloutcomes for the
underpowered,typicallywomenandthuschildren.Manytransferprogramsaimtoaccount
forthesebehavioralfactors,byprovidingin-kindtransfers(suchasfood)ratherthancash
to prevent temptation or by providing transfers towomen rather thanmen to increase
female bargainingpowering in thehousehold. Importantly,manyprograms—conditional
cashorin-kindprograms—evendirectlyconditionassistancetopoorfamiliesonbehaviors
thatsocietywouldliketoaddress,e.g.childrenattendingschoolandreceivingvaccinations
orotherpreventivehealthmeasures.Finally,“workfare”programsalsosharethisaim,by
providingtransfersconditionalonlaborforcetrainingand/orparticipation.
Fourth,andfinally,theremaybemarketfailurespreventingassetaccumulation.We
focusontwodomainswhereassetaccumulationcouldbeimportantforsocialprotection.
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First, short andmediumrunproductiveassetbuilding could increasehousehold income,
thus allowing individuals to no longer need consumption transfers from government.
Rather than cash transfers, productive asset transfers as well as training, coaching and
informational programs may be critical to motivate and improve investment choices to
makesuchagoalattainable.Second, long termfinancialasset-buildingmaybedifficult if
savingsmarketsaremissing,or ifbehavioral andhousehold constraintsdiscussedabove
bind. The current savings market infrastructure does not facilitate pension savings for
individuals,andbuildingsuchmarketscouldbecriticalforimprovingconsumptionforthe
elderlyindevelopingcountries,justasindevelopedcountries.
Naturallymanyprogramscanandoftendo targetmultiple issuesatonce, for two
reasons. First, individuals may face multiple market failures, thus motivating a more
complexprogram.Forexample,recentlymulti-facetedprogramshavearisenthatexplicitly
aim to “graduate” house holds out of extreme poverty by providing households with a
varietyofinputs,includingworkingcapital,assets,andjobstraining.Theseprogramsaim
to increase household earnings capacity by concurrently relieving a number of different
barriers to economic growth. Second, low income households even within a particular
settingmayfacedifferentmarketfailuresandthustheoptimalpolicymaynotbethesame
for all. This difference across households may motivate targeting different aspects of a
programtodifferentpeople,ordesigningprogramsthatmanagetoaddressdifferentissues
fordifferentpeople.
Inshort,awiderangeoftoolsareavailabletopolicy-makersinthegoalofpoverty
alleviation.Thisvarietynaturallycausesustoquestion:whataretherightprograms,who
should they be targeted to, and how do we know that they are working? Randomized
evaluations can answer this question by providing clear answers to whether or not a
program “works.” However, importantly, a randomized evaluation can go even further,
providing insights into why it works, i.e. the underlying mechanisms that drive the
observed treatment effects. By doing so, the evaluation can offer greater insight into
whetherasimilarprogramwouldworkelsewhereorhowtheprogramshouldchangeas
circumstanceschangeevenwithinthesamecontext.Inordertogeneratetheseinsights,a
well-designed evaluation must pay careful attention to the theory behind the different
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forms of social protection programs, consider multiple treatment variations to isolate
theoreticalchannels,andbecreativeindatacollection.
Wewill firstdiscuss issues toconsider in testingprograms that fallundereachof
the four categories we lay about above, weaving in examples and knowledge from the
currentstateof the literature.3We thenaddress issuespertaining to implementation,as
success not only relies on whether the proposed program theoretically can achieve the
socialgoalitwasdesignedtoaddress,butalsoreliesonhowitisimplementedinpractice.
Finally, we offer advice on further research needed in this space, including a better
understandingofboththegeneralequilibriumandlong-runimpactsoftheseprograms.
II. RedistributiveProgramsMotivationsforredistribution,absentspecificmarketfailures,inevitablycomefromsocial
preferences for lower levels of inequality or from philosophical tenets such as
utilitarianism. In the simplest utilitarian form, elegantly put forward by Peter Singer
(1997),redistributionismotivatedbyanawarenessofthestarktradeoffsbetweenmoreof
one’s own consumption (for the wealthy) versus more consumption for the poor.
Philosophicalmotivations abound, naturally: for example, JohnRawls’ATheoryofJustice
(1971)argues for a veil of ignorance inwhich onemakesmoral and policy judgements
withoutknowledgeof one’sownposition in society.This hypothetical construct leads to
argumentsforredistributiontotheworst-offmembersofsociety.
With evenminimalweighting on other people’s utility in one’s own utility, some
redistribution typically becomes the individually optimal policy for all. This naturally
motivateswhyanindividualmayredistributetheirwealthtoothers(i.e.throughcharitable
actions),butnotnecessarilywhyagovernment,throughtaxation,mayeffectivelymandate
such redistribution. However, there aremany reasonswhy a governmentmaymandate
such redistribution, rather than leave it to the voluntary actions of individuals. First,
3Summarizing the broad and sizable literature on poverty alleviation programs poses a unique set ofchallenges. We have tried to focus on key ideas and use the literature to help provide examples whenpossible,aswellastogiveinsightintowhereopenquestionspersist. Wehavetriedtocovertheimportantpapers in the literature, but cannot cover all of themwhile keeping this chapter a reasonable length.Weapologizeinadvancetothosewhosepapersthatwedonotcoverindetail.
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transaction costsmaybe high for individuals to target the poorest effectively. Second, it
maybecostlyforwealthyindividualstotransferwealthtothepoorest.Third,highlevelsof
inequalitycancausesocialstrife,orevenviolentuprising,andcollectiveactionproblems
make it difficult for a purely voluntary system to generate sufficient redistribution to
achieve the social optimal. Fourth, behavioral theories could explain why individuals
under-redistribute if left to their own device, but do have a stated preference formore
redistribution. This is akin to models of quasi-hyperbolic preferences or the dual-self
(FudenbergandLevine2006)appliedtoredistribution:ifoneismaximizingthewelfareof
themoredeliberativeandsharingself(asopposedtotheimpulsiveandselfishself),then
one may want a commitment device to help stay in line with their more deliberative
preferences.Agovernmentprogram for redistribution thusbecomessuchadevice.Fifth,
individualsmaybemisinformedaboutthecurrentlevelofinequalityandpoverty,andtheir
relative wealth, whereas policymakers may be more informed. In the United States, for
example,recentworkshowsthatmostpeoplevastlyunderestimatethelevelofinequality
in the United States, and state a preference for more equality, even though they are
simultaneouslyopposedtomoretaxes.Thisfindingdemonstratesaclearinformationgap
(NortonandAriely2011;Kuziemkoetal.2015).
In short, there are a number of rationales for governments to engage in
redistributiveactivities.Akeyempiricalquestionishowtobestdesigntheseprogramsand
test if they accomplish their goals. In doing so, the first aspect to consider is how to
identify the poor to direct resources towards them (“targeting”). In Section A, we first
discuss the potential strengths and weaknesses of common targeting methodologies, as
wellasdescribethekeyfactorsthatonemustconsiderwhenplanningrandomizedcontrol
trials(RCTs)ontargetingmethodologies.InSectionB,wethendiscusshowtoredistribute:
forexample,howbigshouldthetransfersbe?Howlongshouldthetransfers last? Inthe
processof thisdiscussion,werecountwhatweknowanddonotknow from thecurrent
experimental literature.We also provide a guide for the design of RCTs to evaluate the
typesofsocialprotectionprogramsoutlinedabove.
A.HowtoTargetthePoor?
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Inhigh-incomecountries,targetingisoftenachievedbymeanstesting:householdsbringa
proofofincomeorunemploymenttoabenefitsoffice,ortheyreceivetransfersthroughtax
systems, such as through theUnited States Earned IncomeTax Credit. However, in low-
income countries, a lack of formal labor markets with a paper trail of income and
employment status, coupledwith underdeveloped tax systems, results in limited data to
verifyincome.
Inordertofillthisdatagap,low-incomecountry’sgovernmentscanconductincome
orconsumptioncensuses.However,suchcensusesalsopresenttheirownsetofchallenges,
asanyonewhohasevertriedtoconductasurveymoduletoelicitthesekindsofdatacan
attest:themodulestakeaninordinateamountoftimeandrequirecertainskills,sinceone
needstomapoutallthedifferentcomponentsofincome(e.g.farmingyourownlandone
day, casual labor the next) or consumption (e.g. items purchased, the crops that one
grows).Plus,withouta formalmechanismwithwhich to cross-checkdata, there isoften
nothingstoppingsurveyedpopulationsfromlyingiftheyknowthatacashprizeisattached
totheiranswers.
As such, low-incomecountries tend todevelopalternativemethodswithwhich to
identifythepoor.Themethodchosenwilldependontheprioritiesofthegovernment:for
example,istheaimtotargetbasedonaparticularpovertyline?4Isitpreferabletotarget
thepoorbasedon income,consumption,orsomeothermetricofpoverty?Howspatially
dispersedare thepoor? Itwill alsodependon the institutions inplaceandcontext:how
good is the implementing agency’s ability to conduct surveys?How responsive are local
leaders to citizens?We first outline key categories of targetingmethodologies and then
discusshowexperimentalmethodscanhelpdistinguishbetweenvaryingfeaturesofthese
methods.
Finally,note thatwe focusontargetingpoorhouseholds in thissection,giventhat
the goal of redistributive programs is to provide impoverished householdswith a basic
standardof living. Transferprograms thathope toenact longer runchangesmay target
4Note that in addition to targeting poverty status, some programs also target particular demographiccharacteristics, suchaswhetherawoman ispregnantorhaschildren.As targetingon thesecharacteristicstendstobeeasier—duetotheirverifiability—wewillnotdiscussthemindetailhereforconciseness.
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differently,dependingagainontheirgoals.Forexample,Barrera-OsorioandFilmer(2013)
comparetheeffectivenessofscholarshipswhentheyaretargetedtothepoorversuswhen
they are merit-based: while both increase school enrollment, only merit scholarships
increasetestscores.Thus,themethodthatyouwouldchoosewoulddependuponwhether
youwould liketoredistributescholarshipstothepoor,ortoredistributescholarshipsto
thosewhowillhavethehighestmarginalreturn fromthemgivenametricof testscores.
We will revisit the idea of differences in targeting methods and goals below, when we
discussprogramsthataimtochangelong-runoutcomes.
1.TargetingMethodsWhile there are many variations in practice, there are four primary categories that
encompassmosttargetingmethodologies:
• Geographic:Ifthepoorareconcentratedinparticularvillages,districts,orregions,
givingeveryonewithin thoseareasaccess tosocialprotectionmaybeaneffective
methodtotransferresourcestothepoor(see,forexample,BakerandGrosh1994;
Elbersetal.2007).Moreover, this formof targetingmaybeparticularlyattractive
when the institutional capacityneeded to collect individual information is low, as
onlyaggregateinformationisneeded,e.g.povertymappings,rainfalldata,etc.Note,
however,thatthismethodmayalsobepoliticallysensitiveasitdisbursesbenefitsto
someareas,butnotothers.
• Proxy-means testing (PMT): In thismethod, thegovernment collectsdemographic
andassetdatafromhouseholdsandusesthesedatatopredictor“proxy”incomeor
consumption.5 Sometimes this method involves a quick-and-dirty poverty score
card with just a few questions. Other times, a longer, more detailed asset and
demographic survey is conducted. However, in either case, the key is to choose
5Thisistypicallydonewithanationallyrepresentativedatasetthatincludesthevariableonwhichtotarget(e.g. income),aswellasnumerousdemographicandassetvariables.Next, income isregressedondifferenthousehold characteristics, looping through different combinations and permutations of the variables, untilthe set of characteristics that best predicts income is identified you find (often regional fixed effects orregional variables are also included for better precision). After conducting a census to obtain the chosenhousehold characteristics, it is then possible to compute predicted income for each household using theformula.Householdsbelowachosencutoffofpredictedincomewouldthusbeeligiblefortheprogram.
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variables that are simple to collect, relatively easy to verify (e.g. whether the
householdhasadirtorconcretefloor,oriftheyhavetelephoneline),andthatare
less likely tobedistortionary (e.g. school enrollmentmaypredictpoverty,butwe
may not want to incentivize households to keep their kids out of school).
Householdsthatpasstheproxymeanstest—i.e.arebelowacertainpovertyline—
are then automatically enrolled. The effectiveness of the PMT will depend upon
differentfactors:theformula’spredictivepower,thequalityofsurveyteam,etc.
• Self-targeting: Self-targeted programs are those in which everyone is allowed to
apply,but inwhich somesortof “barrier” isput intoplace inorder to reduce the
probabilitythatrichhouseholdstrytoaccesstheprogram.Theoretically,thereare
differentbarriersthatcangeneratethis formofselection(NicholsandZeckhauser
1982), ranging from time costs to apply, means testing on arrival (Alatas et al.
2015), and work requirements (Besley and Coate 1992; Ravallion 1991).6 When
done right, this can effectively screen out the rich (see, for example Alatas et al.
2015;Christian2014).Giventhathouseholdsmayfallintoandoutofpoverty,these
programs also have the potential advantage of flexibility, allowing households to
accesstheprogramswhentheyhaveabadshock.However,theseprogramsalsorun
a substantial risk: itmaybehard for governments to initiallypredict, in advance,
howmanypeoplewillactuallyenrollorparticipate,providingadditionalchallenges
tobudgetingandimplementation.
• Community basedmethods: In this method, community members choose who in
their locality are needy. Theoretically, thismethod could not only bring in better
local information on who is poor, but it could also incorporate the community’s
perceptions as to what determines poverty in their location (Seabright 1996). A
6Forcertaintypesofin-kindgoods—subsidizedhealthproducts,insuranceproducts—thepricechargedmayalsobeusedtoselectaparticularly typeofpersonwhomayvaluetheparticularproduct(see forexample,CohenandDupas,2010;Beaman,etal,2014).WediscussthisinmoredetailinSectionIV,whenwediscussprogramsthataregearedatchanginglonger-runincomeorbehavior.
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potential benefit is that this may make the programmore politically popular, as
peoplemay feel that the list ismore in-linewith their vision ofwho is needy or
deserving.Akeyworryisthatbyallowingforlocaldiscretioninchoosingprogram
recipients,elitesmaypossiblycapturetheprocess(BardhanandMookherjee2000).
Moreover,anotherpotentialdownside is thatwhile thismethodelicitsbetterdata
onrelativepovertywithinanarea, itdoesnotprovideinformationacrossvillages.
Forexample,Alatasetal. (2012)showthat thedifferencebetween thePMT’sand
community’sabilitytotargetbasedonconsumptionnearlydoubleswhenthePMT
isallowedtouseitscross-villageinformation.
Ultimately, themethod anddesignwill depend on a number of factors: the goals,
context,institutionalcapacity,targetingbudget,etc.Forexample,Alatasetal.(2012)shows
that thechoiceofcommunitymethodsversusPMTcandependonwhetheronewants to
specificallytargetahardmeasureofpoverty(e.gincome)orasoftone(e.g.perceptions).
Similarly,choosingtherightdesignofthePMT—e.g.thenumberofquestionsthatgoesinto
the formula—will also depend on the institutional capacity to administer the PMT.
Moreover,Beathetal.(2013)showsthatwhetheraidallocationsreachtheneediestornot
candependonthetypeofcommunityinstitutionsthatparticipateintargeting.
Inpractice,while therearedistinctcategoriesofmethods,governmentsoftenmix
andmatch themethods depending on circumstance. For example, they often try to save
money by conducting a PMT on a selected sample of peoplewho are likely poor, rather
than conducting a full PMT census. For example, in its earlier form, Mexico’s Progresa
programconductedaPMT todetermineeligibilityonly in the areas thatwere chosenas
likely poor based on geographic targeting (Schultz 2004). Similarly, Indonesia’s Data
CollectiononSocialProtectionProgramme(PPLS)usescommunity-basedmethodstohelp
determine the list of households that will receive the PMT survey (Alatas et al. 2012).
Kenya’sCashTransfer forOrphansandVulnerableChildrenactuallyuses threemethods:
first,geographictargetingisusedtodeterminelocations,thencommunitytargetingisused
in the selectedareas todeterminea list ofhouseholds, and finally, thosehouseholds are
givenaPMTtodetermineactualeligibility(TheKenyaCT-OVCEvaluationTeam2012).
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2.ExperimentallyTestingBetweenTargetingMethods
Are experimental methods important for testing across targeting approaches? Not
necessarily.Forexample,onesimpleresearchdesignwouldjustbetotryouttwodifferent
methodologies in the same areas and then compare the income levels of those selected
underbothmethods.
Whilethisresearchstrategymayproveattractiveinsomeways,itmaymissouton
the nuances of targeting that may ultimately be quite important in understanding the
relative effectiveness of the differing methods. First, many of these targeting methods
require considerable effort on the part of citizens and the program staff.7Simulating
conditionstobeasrealaspossible,withatleastasmallamountofcashonthelineforthe
households that are to be selected, is important to understand how themethodswould
actuallyworkinpractice.8Intermsofcitizens,peoplemightbehavedifferentlyduringthe
process if they do not believe that the targeting list will actually be used to distribute
resources.Forexample,theymaynotexertanyeffortindiscussingandrankinghouseholds
incommunitytargeting,orindividualsunderself-targetingmayjustnotbothertoshowup
eventhoughtheywouldiftherewererealcashinvolved.Moreover,peoplemaygivemore
truthfulanswers in thePMT,ornot claimagreaterpoverty status than their realityata
communitymeeting, if they believe that their answers do not have any consequence. In
termsofstaff,theresultsmaynoticeablydifferifyoutestoutmethodswithoutstakeswith
ahighlytrainedsetofstaffthaniftryingoutthemethodswiththetypicaltypeofstaffthat
wouldbehiredandtrained(Alatasetal.2012).
Second,thechoiceoftargetingmethodmayaffectboththeprogramandhousehold
outcomes. For example, the targeting method chosen may help determine the ultimate
7One obvious exception to this is geographic targeting, which does not rely on field operations, such ashouseholdandcommunity leader interactions. Ifadifferentmethodwasconductedfor theactualprogram,then simulating geographic targeting over the same areas based on administrative data can provide anaccurate comparison of the type of person selected under both methods. However, by not conductinggeographic targeting in practice, it will not be feasible to test program outcomes other than just who isselected(e.g.politicalacceptability,leakages)thatresultfromusingdifferenttargetingmethods.8You could, for example, try out two methods in the same area and offer a transfer to everyone who isselectedbyeitherofthedifferentmethods,butthenstafforvillagesmaycoordinatesothatdifferentpeopleareoneachlist.And,itrunstheriskofconfusingpeople,sothattheydonottaketheexerciseveryseriously.
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satisfaction of the program, which in turn may affect program acceptance and the
government’sability to implementtheprogram.Targetedprogramscanbecontroversial,
withsomehouseholdsreceivingatransfer,whiletheirneighborsdonot.Ifcitizensbelieve
that a certainmethod isunfair and that itwouldproducea flawed list, theymaybe less
likely to support the overall social protection program and potentially block the
distribution of benefits.9 Moreover, if people believe that the wrong individuals were
chosenduetothefactthatacertainmethodwasused,itmaypossiblyleadtodistortionsin
howinformalinsuranceorlendingoperateswithinacommunity.
Finally, who is chosen may be different than who actually receives the transfer
(Alatasetal.2013)andthismayalsovarybythetargetingmethodthatwasemployed.For
example, suppose that thePMTbetter selected thepoor than a communitymethod.But,
that thePMThad less legitimacy than thecommunitymethod, so thatvillage leadersdid
notadheretothePMTinpracticewhendistributingthetransfers,buttheydidadhereto
thecommunitylist.Inthiscase,simplysimulatingbothmethodsinthesameareatoelicita
beneficiary listwouldpossiblywronglysuggestthatoneshouldselect thePMTsinceyou
wouldonlybeable tostudywhowaschosen,butnotbeable tomeasurewhoultimately
wouldgetthebenefits.
Inshort,forallofthesereasons,wewouldwanttorandomizethetargetingmethods
todifferentareastoseehowthemethodaffectswhoischosenandwhoultimatelyreceives
the transfers. The optimal design for a field experiment in the domain of targeting will
dependonanumberoffactors,includingwhichmethodisbeingstudied,butalsowhatthe
particularcontextlookslike.However,thereareanumberofkeyquestionstokeepinmind
regardlessofthegivendesign.
The firstquestion iswhetherornot it isnecessary tohaveacontrolgroup, in the
traditional sense. RCTs generally compare the outcomes from a treatment group that
receivesaninterventionwiththoseofacontrolgroupthatdoesnot.Inthiscase,sincethe
9Forexample,Alatasetal. (2012)showthat community targeting led tomuchhigher levelsof satisfactionthan the PMT,with village leaders feeling less comfortablemaking the transfers under the guise of publicscrutinywhen thePMThadbeenused.Theyprovide suggestiveevidence that thisdifference isdue to theperceptionsofthemethods,andnottheultimateliststhatthedifferentmethodsproduced.
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outcomeof interest iswhoisselectedunderdifferenttargetingmethodswithinthesame
program, it may be viable to simply have multiple treatment groups where each is
randomlyassignedadifferenttargetingmethod,buteveryonereceivestheprogram.
Second, at what level should the randomization take place? Randomizing at the
individualleveloffersthemoststatisticalpower,butinthiscase,thetargetingtreatments
often involve some sort of group participation (e.g. community, self-targeting) or group
data (e.g. geographic).Moreover, evenwith a PMT,where it is possible to vary how the
survey is conducted across individuals, a question of interestmight also bewhether the
programadministratorschangehowtheyrespondtothetargetinglistintheirareabased
onwhichmethod generated the list. Thus, inmost cases, it is appropriate to randomize
across a sensible geographic unit; as such, power calculations to determine sample size
shouldaccountforthegroupstructureofthedata.
Third, is a baseline survey needed? With most experiments, the answer is not
necessarily:theanalysiswillconsistofcomparingoutcomesofthoseinthetreatmentand
the control group. A baseline might help for power if the outcome measures within a
personarehighlycorrelatedacrosstimeanditmayallowyoutotestfortheheterogeneous
treatment effects by various baseline characteristics (Duflo et al. 2006). But, it is not
necessaryperseinatypicalrandomizedexperiment.Inthiscase,abaselineisessential:a
key outcome of any targeting experiment will be the baseline income or consumption
levels—priortothetargeting—ofthosewhoareactuallychosen.
Fourth,whatkindsofdatashouldbecollected?Theexactvariableswould,ofcourse,
dependonwhatmethodsarebeingtestedandwhatoutcomesareexpected.But,typically,
inthebaseline,itisessentialthatdatabecollectedonthevariablesbeingtargetedon(e.g.
income,consumption,etc.), so that inclusionandexclusionerrorscanbecomputed.10To
measuredistortions inwho is chosenalong certaindimensions, itmaybeworthwhile to
collectbaselinedataonpoliticalaffiliationsandrelationstopoliticalleaders.Intheendline
surveys, valuable data to collect could include who actually received the program,
satisfactionlevels,andmetricsongeneralprogramfunctioning.
10Alatas et al. (2012) also ask villagers to rank one another to gain the “average” person’s belief aboutanotherhousehold’spovertystatusinavillage,aswellasaskhouseholdtoassesstheirownpovertystatus.
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Finally,will theexperiment just aim tomeasure the reduced formeffectorwill it
also attempt to understand why it is working (or not)? If the only relevant question is
comparingmethod one versusmethod two (i.e. the reduced form difference of the two
programs),onlytwotreatmentarmsareneeded.But,weknowthattheeffectivenessofa
methodmayvarybasedon itsowndesign, and therefore, itmaybeworthwhile to learn
more about amethod’s effectiveness if certain details of the implementation are varied.
Here,theorycanhelpguidetheappropriatesub-randomizations:Forexample,Alatasetal.
(2015) experimentally compare the outcomes of a PMTwith a self-targetingmechanism
within Indonesia’s conditional cash transfer program. Importantly, they experimentally
varythedistanceoftheapplicationsiteunderself-targetinginordertogenerateexogenous
variationinthecostoftheapplication“barrier.”Theythenusethisvariationtoestimatea
modelofthedecisiontoapplyfortheprogramandsimulateself-targetingoutcomesunder
differentlevelsandtypesofapplicationcosts.
B.EvaluatingtheImpactsofRedistributivePrograms
Once the poor have been identified, the next task at hand is to think aboutwhether the
redistributiveprogramsareachievingtheirgoals inpractice. Thesimplestredistributive
programsarethosethatentitletheidentifiedpoortosomeformofcashstipendinorder
provide a certain standard of living and are unconditional on any behaviors (an
unconditionalcashtransfer,orUCT).TheChineseDi-BaoProgramisthelargestUCTinthe
developingworld,reaching78millionhouseholds(seeChenetal.(2006)foradescription
of the program). Other prominent examples include South Africa’s Child Support Grant,
India’sNationalOldAgePensionScheme,andKenya’sHungerSafetyNetProgram.
Evaluating theseprogramsusually followsa typicaldesign.First,poorhouseholds
aretargetedinthesamplearea.Then,potentialbeneficiariesarerandomlyassignedtothe
treatmentgroup(“receivestransfers”)andthecontrolgroup(“doesnotreceivetransfers”)
toassess theprogram impacts.Examplesofunconditionalcashprograms thathavebeen
evaluatedinthisfashionincludetheZambiaChildgrantprogram(Jesseeetal.2013),the
Kenya Hunger Safety Net program (Merttens et al. 2013), Kenya's Cash Transfer for
- 15 -
Orphans andVulnerable Children (see for example, TheKenya CT-OVCEvaluationTeam
(2012)andCovarrubiasetal.(2012),ontheMalawiSocialCashTransferScheme).11
Eventhoughtheoverallevaluationstrategyisrelativelystraightforward,thereare
stillanumberofdecisionstobemade—concerningthelevelofrandomization,thetypeof
datacollected,thetimingofdatacollection,etc.—thatareimportantinassessingimpacts.
Thelevelandformofrandomization: transfers are generallyprovided to an individual or
household,andsoitistemptingtorandomizeattheindividual-leveltomaximizestatistical
power.Forexample,Schadyetal.(2008)doexactlythis.However,AngeluccianddeGiorgi
(2009), among others, show that there may be spillovers of transfers from eligible to
ineligiblehouseholdswithinavillage.12Thisimpliesthattherandomizationlikelyneedsto
be done at a higher level, at the village-level or sub-district level depending uponwhat
typesofspilloversonemightexpect.13
Ifrandomizationisatagrouplevel, it isvitaltohaveenough“units”torandomize
overoritwillbechallengingtomeasureimpacts.Forexample,theKenya'sCashTransfer
forOrphansandVulnerableChildren(TheKenyaCT-OVCEvaluationTeam2012)hadonly
28 units of randomization, which might account for their difficulty in detecting any
programimpacts;theMalawiSocialCashTransferSchemehadonly8clustersanddidnot
fullyaccountforthegroupednatureofthedataintheanalysis(Covarrubiasetal.2012).14
Thus, one shoulddetermine in advancewhat thedesired sizeof treatment effects is (i.e.
11ThereweretwoRCTsconductedonBonodeDesarrolloHumano–oneonchildhealth(PaxsonandSchady2010;FernaldandHidrobo2011)andoneoneducation(Schadyetal.2008;EdmondsandSchady2012).Theprogram was initially supposed to be conducted as a conditional cash transfer program and someannouncementsweremade to this effect (Paxson and Schady (2010) hadmultiple treatment arms to testbetweenpure cash and cashwith conditions), but the conditionswere never enforced. Given that just theframingoftheprogramasaCCTcouldstillhaveimpacts,wedonotincludethisinourdiscussionofUCTs,butinsteaddiscussitbelow.12Aswediscussbelow,AngeluccianddeGiorgi(2009)evaluateaconditionalcashtransferprogram,butthebasicideasonspilloversholdforunconditionalcashtransfersaswell.13Aswediscussbelow,onemightevenwanttodesignthestudytocapturedifferenttypesofspilloversandgeneralequilibriumeffects.14In the analysis of these cases, it would be necessary to adjust standard errors to account for both thegroupednatureoftherandomizationandthesmallnumberofgroupsinkeepingwiththeprocedureoutlinedbyCameronandMiller(2010).
- 16 -
largeenoughfortheprogramtobecost-effective,etc.)andusethistoassessthestatistical
poweroftheproposeddesign.
Importantly,itisalsokeytoidentifythebeneficiariesinthecontrolgroup,notjust
thetreatmentgroup.SupposearandomizationdeterminedwhichvillagesobtainedaUCT
andwhichwereinthecontrolgroup.IntheUCTvillages,targetingwouldhavefirstbeen
conducted to choose the beneficiaries within the village. However, unless a similar
targetingstrategywasalsoconductedinadvanceforthecontrolgroup,itwouldbedifficult
know who were the hypothetical beneficiaries in the control group (see, for example,
Covarrubiasetal.(2012)).Inthiscase,itwouldbepossibletoestimatetheimpactonthe
entire village as awhole, but not easy to estimate the impact on just the beneficiaries.15
Thus,itisessential,whenpossible,tousethesametargetingmethodsinthetreatmentand
control group, even if the control group is not receiving the program at the time of the
study.
Data Collection: What data should be collected and when? If the goal is simply
redistributive,wemaysimplycarewhetherpoorhouseholdswereactuallyidentifiedand
whetherornottheywerereceivingtheirentitlements.Inmanydevelopingcountries,due
to weaker institutional structures, corruption, or imperfect information on their
entitlements, households do not receive the full transfer. So, an important set of survey
questions should be focused on whether households actually received the transfer,
whether they received the full transfer, whether the village elites held them up for a
portion of their transfer, and so forth. Next, one may want to administer a household
incomeorconsumptionmodeltomeasurehousehold’sincomestatus.
Ifwe simply care about redistribution,wewould not necessarily care about how
households spend the transfers—we would just care about whether they receive the
money. Thus, if motivated by such a philosophy, one may argue to only measure the
15Ofcourse,thisisnotaproblemwithinthevillageiftheprogramisgeographicallytargetedandeveryoneinallsamplevillagesiseligible.However,thismayalsogeneratespilloversoverlargergeographicregionsthatonemaywanttobeawareof,e.g.ifeveryoneinadistrictofprovincereceivesadditionalincome,wouldthisincreasedemandforfood,raisingfoodprocess?
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transfers, and not bother examiningwhat happenswith themoney transferred. But, for
many reasons researchers do collect more data. For example, one political rationale of
manyredistributiveprogramsistoprovideabasicstandardof livingforchildren,soone
maywanttocollectmeasuresonchildhealth,nutrition,andeducation.Thereareanumber
ofissuestoconsiderindoingthistypeoflargerdatacollection,butwewillrevisititbelow
whenwediscussbroaderissuessurroundingbehavioralchangeforprogrambeneficiaries.
Next,akeyquestioniswhetherornotabaselinesurveyisneeded.Inassessingthe
impactofatransferprograms,abaselinesurveyisnotnecessarilyneeded,sincetreatment
wasassignedrandomly,meaningthatapost-interventionofthetwogroupsisanunbiased
estimateofthetrueimpact.However,therearetwokeyexceptionstothis.First,ifwewant
toassesswhetherthepoorwereindeedtargetedandwhetherthepoorestofthepoorwere
able to receive their entitlements, thenwewould need baseline consumption or income
data. Second, if particularly vulnerable sub-groups areof importance (e.g. the verypoor,
familieswith childrenwhoare less likely toattendschool, etc.), onemaywant to collect
data on these groups—if administrative data of this sort is unavailable—to be able to
stratifytherandomization.
Finally,whendoyouconductthefollow-upsurveys?Again,itdependsabitonthe
researchaims.Afollow-upsurveyshouldgenerallybeconductedwithinthedurationofthe
program to understand whether households are receiving the transfer. However, as we
discussbelow,ifthelonger-runimpactsoftransferprogramsareofinterest,onemayalso
want to do additional surveys sometime after a household is no longer enrolled in the
program.
III. MissingInsuranceMarketsThe poor face many risks, such as unexpected health costs, agricultural damage or job
losses.Withoutfullyfunctioningfinancialmarkets,householdsmaybeunabletoborrowto
smoothconsumption.Furthermore,evenifhouseholdsdosmoothconsumptionintheface
ofshocks,theymaybedoingsointheshortrunbymakinglonger-termsacrifices,suchas
pullingchildrenoutofschoolornotinvestinginhealth.Attheextreme,naturaldisasters—
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e.g. earthquakes, flooding, famine—may cause group shocks that further limit the
functioningabilityofinformalinsurancemechanismswithinaregion.
Thus,an importantroleofsocialprotectionmaybetohelpalleviate the impactof
such shocks by providing social insurance of various forms. Two prominent forms of
insuranceareagriculturalinsuranceandhealthinsurance,butwewillnotcoverthemhere
sincetheyarediscussedelsewhereinthisHandbook. Ratherwewillbrieflydiscusstwo
otherformsofinsurance:disasterreliefandunemploymentinsurance.
Disaster relief is an important form of social insurance. While some quasi-
experimentalworkhasbeendone to studyboth the consequences of a disaster, and the
distribution of aid, to our knowledge, there has been less experimental evidence in
evaluating social protection inhumanitarian settings.Notable exceptions includedeMel,
McKenzie, and Woodruff (2008), who studied the effect of cash transfers to
microenterprises in post-tsunami Sri Lanka, as well as Aker (2014) and Hidrobo et al.
(2014) that examined cash versus other types of transfer programs in informal refugee
campsintheDemocraticRepublicofCongoandNorthernEcuador,respectively.Partofthe
reasonforthelackofexperimentalevidencecomesfromethicalconcerns,withaneedto
provide immediate assistance trumping research and evaluation. Related is a logistical
challenge:thechaosandhighly-transientnatureofrefugeecampscanmakerandomization
particularly challenging to implement.However, given theabout60million refugeesand
internally displaced people worldwide (UNHCR 2014), understanding how safety net
programscanbebettertailoredtoprovideassistanceinhumanitariansettingshasbeenan
increasingly urgent need, particularly as many refugee camps persist for long after the
immediacyofthedisaster.
A second important form of insurance is unemployment insurance (UI), the
provisionoftemporaryassistancetothoseoutofwork.Thisformofsocialprotectionhas
been traditionallymissing from poor countries, as the data-poor environments preclude
easy identification of those who are working in full-time positions in order to then
determinewhoistemporarilyoutofwork.But,itisbecomingmorecommoninrelatively
more developed, low-income countries with formal labor markets (e.g. Brazil, Egypt).
WhiletherehavebeennumerousexperimentsthathavetriedtounderstandaspectsofUI
- 19 -
in developed countries, to our knowledge, there is little experimental evidence on UI in
lowertomiddleincomesettings.16OnenotableexceptionisMickelwrightandNagy(2010),
whichrandomizedunemploymentbeneficiaries,inHungarytovaryinglevelsofmonitoring
(i.e.visitingtheemploymentofficeeverythreemonthswithnojobsearchquestionsversus
visitingevery threeweeks toanswerquestionson jobsearchbehavior). However,asUI
spreads and becomes a more important component of social protection in developing
countries, empirical evidencewillbeneeded tounderstand its impactson labormarkets
and household outcomes. Empirical evidencewill also be needed to understand how to
betterdesigntheseprograms—addressinghowtobestverifyemploymentstatus,howto
best distribute benefits, andhow to ensure that theprogramsprovide incentives to find
work.
IV. BehavioralConstraintsItisoftenarguedthatbehavioralorhouseholdbargainingconstraintsinfluencethechoices
of low-income households, thereby perpetuating poverty. Regardless ofwhether or not
thesebehavioralconstraintsarepresent,safetynetprogramsareoftendesignedtocorrect
thesetypesofconstraintsandencouragesociallyoptimalbehavior.
There are two key forms that behaviorally-focused social protection programs
usuallytake. Thefirstisprovidingin-kindtransfersratherthancash,underassumptions
suchaspeoplewillbetootemptedtospendmoneyon“bad”goods(suchastobaccoand
alcohol)or thathouseholdbargainingconstraintswould imply that cash transferswould
cause socially undesirable outcomes, such as an underinvestment in children or an exit
fromthelabormarket.
The second is to directly condition the receipt of transfers to households on a
household’scompliancewithcertainlong-terminvestments,suchasthechildrenattending
schoolorvisitinghealthclinics. Theseconditionalcashtransferprograms,orCCTs,have
16Forexample,Meyer(1995)summarizesanumberofearlyexperimentsonUIintheUnitedStates,focusingonfourcashbonusexperimentsandsixjobsearchexperiments.MorerecentexamplesincludevandenBergandvanderKlaauw(2006),whoexploredtheeffectofadditionalmonitoringandcounselingonUIrecipientsintheNetherlands;GrenierandPattanayak(2011),whomeasuredtheimpactoflegalassistanceonUIintheUnitedStates.
- 20 -
becomeincreasinglycommoninLatinAmericaandhavebeguntospreadtootherpartsof
theworld,existinginmorethan52developingcountriesasof2013(FiszbeinandSchady
2009; Saavedra and Garcia 2012; Gentilini et al. 2014). And, finally, note that some
programsdoacombinationofbothin-kindandconditions,with130countriesdoingsome
sortofconditional,in-kindprogram.
Carefulexperimentationthathelpstesttheunderlyingrationalefortheseprograms
canhelpdetermine if behavioral constraints exist, and if so,what form they take. Thus,
they can provide insights into whether these kind of constraints on behavior actually
improvewelfareorsimplymakeredistributiveprogramsmoreexpensive.
A.ProvidingIn-kindTransferstoConstrainSpendingChoices
1.TheRationaleforIn-KindPrograms
Unconditional, in-kindredistributiveprogramstypicallyprovidefreeorhighlysubsidized
goods,toprogramrecipients.Theycanentailadirectprovisionofthegoods,suchasafood
orfueltransferprogram;thesubsidyofaproductdistributedthroughlocalgovernments,
NGOs,ordesignatedshops;orasystemofvouchersthatareconstrainedtoparticulartypes
ofgoods,suchasafoodstampprogram.17Gentilinietal.(2014)notesthat89low-income
countries have unconditional in-kind transfer programs, with examples including
Indonesia’sRiceSubsidyProgram(“Raskin”)and thePublicDistributionSystems inboth
BangladeshandIndia.
There’smuchdebateaboutwhethertransfersshouldbein-kind.Ifyouaskatypical
economist,mostwill favor cashprograms,under the idea thathouseholdswillmaximize
utility if theyhavechoiceoverwhattheypurchase,ratherthanreceivingagoodofequal
monetary worth that they may not value as much. However, there are a number of
argumentsproposed in favorof in-kindsubsidies (see CurrieandGahvari (2008) foran
excellentreview),whichmayexplaintheirgeneralpersistenceworldwide.
17Itisimportanttonotethatthereareothertypesofin-kindtransfers,suchasonesthatprovidefreehealthproducts(CohenandDupas2010;Dupasetal.2013;Maetal.2013;Glewwe,Park,andZhao2014),prizesorscholarshipsforschool(Berry2014;Kremer,Miguel,andThornton2009),schoolmealsprograms(Kazianga,DeWalque, andAlderman2012;Vermeersch andKremer2004), public housing (Kling et al. 2004). Theseprogramsmay alsohavedifferent aims, such as solving the externality issue inhealthproduct take-up.AstheyarediscussedinotherchaptersinthisHandbook,werefrainfromdiscussingthemhere.
- 21 -
Themostcitedexplanationisthatofpaternalism:peopleoftenhaveanimageofthe
lazy,out-of-workhusbandco-optingthefamilycashandwastingitonalcohol,tobaccoand
otherformsofentertainment,ratherthanmakingspendingdecisionsthatcanimprovethe
family’slivingsituationorinvestinginchildren.Thus,theargumentfollowsthatanin-kind
subsidy could reduce the husband’s ability to do so, forcing redistribution within the
householdtothewomenandchildrenwhotypicallyhavelesshouseholdbargainingpower.
However,othersarguethatifthein-kindsubsidyisinfra-marginal—orifitiseasytoresell
goods—thenin-kindsubsidieswillnotalterthehousehold’sconsumptionbundle,anditis
simplyamorecostlymechanismto redistribute to thepoor thancash.Theexperimental
evidence thus far suggests that cash programs do not generate more spending on
temptationgoods, suchasalcohol and tobacco. Nonetheless, in-kindprogramsareoften
“sold” as targeted towomenand children and thus tend tobemorepopular among tax-
payerswhowanttoensurethattheirtaxdollarsarenotwasted,butratherusedto“feed
children” and reduce social ills such as school dropouts and crime (for example, see de
Janvryetal.1991;EppleandRomano2008).
Asecondpotentialreasontofavorinkindtransfersovercashtransfersisthatthey
might have lower de-incentive effects on work, and may in fact spur work. A common
worrywithcashtransfersisthattheycouldprovideadisincentivetowork,particularlyif
householdsworryaboutlosingtheirbenefitsastheirincomerisesabovetheeligibilityline.
Ithasbeenarguedthatin-kindtransfergeneratefewerlabormarketdistortions,andmay
in fact be labor market enhancing if the provided good is a complement to work. For
example,inareaswhereproductivityislowduetonutritionalconstraints,afoodtransfer
programcouldeasethisconstraint.However,theexistingevidencethusfardoesnotimply
thatcashtransfersgreatlyreducelabormarketparticipation(forexample,seeAlzúaetal.
(2013);Banerjeeetal.(2015)),perhapsduetothelongdurationofbenefitsanduncertain
processes for re-certification observed in many developing countries. Furthermore, the
cash transfersmayalsohelpeasecreditconstraints for thoseengaged in theagricultural
sector,increasingtheproductivityofagriculturallabor(Gertleretal.2012).
A third reason in support of in-kind programs is their self-targeting properties
(BesleyandCoate1991;Christian2014):byprovidingagoodthatthepoordifferentially
- 22 -
valuerelativetotherich, thepoorwillapplyandtherichwilloptout.Again, ifmoney is
fullyfungible,richerhouseholdscouldsimplyopt-inandsellthesegoodsforthecash,etc.
However,wemightexpectthattransitioncostsandotherconstraintswouldimplythatin-
kind goods are viewed differently than just pure cash. Note that despite this rationale,
manyin-kindtransferprogramsareindependentlytargetedpriortodistribution,shutting
offachannelthroughwhichanin-kindprogrammaygeneratethesetypesofeffects.And,
whilethenon-experimentalevidencesuggeststhatin-kindprogramsarebetteratselecting
thepoor(seeforexampleJacoby(1997)),thereislittleexperimentalworkthatcompares
the magnitude of its targeting properties against different forms of means-tested cash
programs.
A final argument in favor of in-kind transfers comes from the idea of missing
markets, i.e. if for some reason the market does not provide the good on its own, just
providingcashmaynotbeenough.Thismaybeaparticular issueinremoteareaswhere
hightransportcostsdiscouragethespreadofproductsorindisasterorwarzones,where
foodandotherproductsmaybeinshortsupply.
2.EvaluatingIn-KindPrograms
One can evaluate an in-kind transfer program in a manner similar to evaluating a cash
program, i.e. randomize some areas to receive in-kind transfers (treatment group) and
otherstonot(controlgroup).Thenonecancollectdatatounderstandifpoorhouseholds
were indeed properly targeted and whether or not they were able to access their
entitlementorsubsidizedproducts.
However, unlike pure redistributive programs, onemay also want to understand
whether the theoretical behavioral constraint actually exists, as well as whether the
behavioral change thatoneaimed to inducehasoccurred. As such, thereare twoother
designfeaturestoconsider.First,whileanevaluationofaredistributiveprogramwouldbe
focused on collecting variables to determine whether or not the redistribution has
successfully occurred, an evaluation of an in-kind program would additionally require
collectingvariablestotestforthehypothesizedbehavioralchanges.
- 23 -
Forexample, in evaluatinga food transferprogram, if thegoal is to increase food
consumption forchildren,onewouldcareaboutcollectingdetailedmoduleson foodand
caloriesconsumed,aswellashealthindicatorsforchildren.However,notetwoimportant
caveatsofthisdatacollectionprocess.First,asSchadyetal.(2008)pointout,peoplemay
hesitate to provide accurate information on surveys if they believe that the surveys are
connected to re-verification for the program. In which case, one may want to collect
variables that are easier to verify: assets that one can observe, vaccination records on a
card,bodymassindex(BMI)andotheranthropometricvariablestoassessthehealthstatus
ofchildren,etc.Second,asthetransferprogrammaybedesignedtoaddressanumberof
behavioralchanges,oneworryisthatincollectingmanydifferentvariables,wewouldfind
somesignificant impacts justbychance.Thus,onemightwanttopre-specifysomeofthe
keyhypothesesandoutcomevariablesinadvance(Migueletal.2014;Olken2015).
Second, and perhaps more important, one may also want to consider evaluation
strategiesthatisolatethebehavioralconstraint.ThebasicRCTdescribedabovecomparing
theeffectofaspecificprogramagainstcontrolareasthatdonotreceiveanyassistanceis
importantforunderstandingtotalprogrameffects,butitdoesnottellushowtheparticular
constraint(e.g.providingriceversuscash)affectstheobservedoutcomes.Understanding
the relevance of specific behavioral constraints can be important, if they imply specific
changestoprogramdesign.
Therefore, comparing in-kind transfer programs to cash transfer programs can
provide useful insights into whether or not constraining behavior is welfare improving.
For example,Aker (2014) explores the effect of cash versus food vouchers for displaced
households living in an informal camp in the Congo, and shows that the level of food
consumptionwasthesameregardlessofthemechanismsincevoucherhouseholdssimply
boughtfoodthatisrelativelyeasytosell(e.g.salt).Similarly,theMexicangovernmenttook
advantageofmultiple treatment arms tomeasureboth the effect of in-kindprograms to
cash,andtodoingnothingatall. Specifically,theycomparedthreetreatments:(1)anin-
kindtransferprogramthatgavehouseholds10differentitemsoffood(2)acashtransfer
- 24 -
intended to be of equivalent value (3) a control group that did not receive transfers.18
Whilethein-kindprogramdistortedconsumptionofsomeindividualtypesoffood(Cunha,
2012),overall foodconsumptionwassimilaracrossbothprograms(Skoufiasetal.2013)
andtherewasnodifferenceinobservedweightamongwomen(Leroyetal.2013).
Otherstudieshavealsocollectedvaluabledatathathighlightthetradeoffsbetween
programsthatmaysatisfyasociety’sspecificgoalthatanin-kindprogrammaybedesigned
to address (e.g. greater food consumption for kids) versus other important social goals.
Hidroboet al. (2014) compare cash, food, foodvouchersanda control group inEcuador
andfindthatallthreetypesofprogramsimprovepercapitafoodconsumptionandcaloric
intake.However,while food and food vouchers increase calories and diet diversity a bit
morerelativetocash, thecashprogramismucheasierandcheaperto implement,which
maybeofrealconcernforcountrieswithweakerinstitutionalquality.19
Similarly,Hoddinottetal. (2014)comparecashversusfoodtransfers inNigerand
find that the food transfer program had a larger effect on food consumption and diet
variety thancash.However,householdswerenotwastingthe fundsontemptationgoods
such as alcohol; theyused cash to invest in greater agricultural inputs.Thus, using their
estimates, one can think about how tomodel the tradeoff between the additional utility
householdsreceivefromspendingastheychoose,relativetosociety’sutilityfromtheshift
infoodconsumption.
In short, the theory suggested that food, cash and vouchers could have different
effects onhousehold outcomes, but the existing evidencemostly shows that this didnot
materialize in practice—likely because the particular items of food distributed in these
contextswereinfra-marginal.
B.AddingConditionstoIncentivizeBehavior
18Notethataparticularchallengeisensuringthatthein-kindtransferisequivalenttothecashtransfer.Forexample, in thePALprogram, thevalueof the food transferwas30percentmore than thecash treatment,sincethefoodbasketwasbasedonwholesalepricestothegovernmentratherthanthepricesthatconsumerpay.SeeCunha,2012foradiscussionofhowtomakethemex-postequivalent.19 In an interesting follow-up paper to this experiment, Hidrobo, Peterman and Heise (2013) shows that transfers (irrespective of whether food, cash or vouchers) reduce intimate partner violence.
- 25 -
Since the 1990s, it has been increasingly common formanydeveloping country transfer
programs to layer a level of “conditionality” onto redistributive transfer programs, e.g.
requiringchildrentogotoschoolandgethealthcheckupsforthefamilytoreceiveitsfull
transfer. The conditions usually aim to correct an underinvestment in the family’s
wellbeingthatstemsfromsomesortofmarketfailurewithinthehousehold—parentsnot
internalizingtheirchildren’sfullreturnstoschool,thefamilynotinternalizingthebenefits
ofvaccinationforsociety,etc.
Conditional cash transfer programs (CCT) worldwide have been studied by
randomized control trials—from Mexico’s Progressa/Oportunidades to the Philippines’
Pantawid Pamilyang Pilipino Program (PPPP)—showing important effects. Aswith the
otherprogramswehavediscussed,onecanevaluateCCTsbysimplyrandomizingareasto
receiveornot receive theCCTand thenstudying theoutcomes; this tellsus the reduced
formeffectofhavingtheprogramrelativetonoprogram.Indoingso,manyoftheissues
thatwehaveraisedabove—whatdatatocollectandwhen,howtotarget,etc.—wouldthus
beimportanttothinkaboutinthiscontextaswell.
However,giventheuniquenatureoftheCCTinattemptingtocorrectaninvestment
failure within the family—in addition to redistributing to poor households—more
sophisticated analysis can be done to better understand the role of the conditions on
householdbehavior.Below,wediscussthreekeyquestionsthatexperimentationcanhelp
shed light on: (1) What is the impact of the conditions relative to basic redistributive
programs?;(2)Whatshouldtheconditionsbe?;andfinally,(3)Howdoweenforcethem?
1.EvaluatingConditionsrelativetoBasicRedistributivePrograms
Evaluationsthattestaconditionaltransferversusnoprogramhavedifficultyteasingapart
the conditionality mechanism from the liquidity or income effect of the transfer itself.
Whether to includeconditions inacash transferprogram isanessentialdesigndecision.
Conditions require a budget to fund them and staff to enforce them. They may also
generateselectioneffectsonwhochoosestoparticipate.
Toisolatetheeffectsoftheconditionality,asimpleexperimentaldesignwouldhave
onetreatmentwithaconditionalcashtransfer,asecondtreatmentwithunconditionalcash
- 26 -
transfers,andacontrol.Thusthesecondtreatmentgroupgeneratesapureincomeeffect,
whichallowsonetolearnwhethertheconditionalitychangesbehavior,orwhethertheCCT
changedbehaviorsimplythroughitsincomeeffect.
ThisistheapproachtakenbyBaird,McIntoshandÖzler(2011)intheZombaCash
TransferPrograminMalawi.TheCCTsperformedbetteratinducingthedesiredbehavior:
schooldropoutrateswerelowerintheCCTarmthantheUCTarmandpersistedbeyond
the program’s end. Moreover, attendance rates improved and cognitive ability,
mathematics, andEnglish reading comprehension test scores increased for theCCTarm,
butnottheUCTarm.Thus,theconditionshadeffectsonhouseholdsaboveandbeyondjust
providing cash. However, the UCT arm had significantly lowermarriage and pregnancy
rates. This initially may feel like a counter-intuitive result since schooling is thought to
postpone marriage and lower pregnancy rates. However, the UCT is provided to
householdseveniftheteenagegirlsdonotattendschool,whereasonlythosewhoattend
schoolreceivetheCCT.Thus,theeffectonmarriagewilldependlargelyontherelativesize
oftwogroups,thegroupofhouseholdsthatdoesnotattendschoolundereithertheUCTor
CCT,comparedtothegroupofhouseholdsthatattendsschoolundertheCCTbutnotthe
UCT. If the income effect onmarriage and pregnancy in the first group is large enough
(Duflo,Dupas,andKremer2010;Ferre2009;Osili andLong2008;Ozier2011), theUCT
willleadto,onnet,lowermarriageandpregnancyrates.TheauthorsoftheMalawistudy
found this tobe true.Thisdynamic illustrates that theconditions themselvescanreduce
redistributive aid to thepoor—andanyassociatedbenefits of the aid—bychangingwho
participates in the program. And, it reinforces the need for careful data collection on
ancillaryoutcomes—e.g.inthiscase,marriageandpregnancy—inordertounderstandthe
full setofmechanisms throughwhichaprogramworks so thatpolicy-makers canbetter
understand the tradeoffs that they would make by implementing one program over
another.
Further insights on the tradeoff between a UCT and a CCT can come through
examiningwhat beneficiarieswould choose, given a choice.Most programs naturally do
notprovidesuchachoice,butprovidingachoiceinanexperimentalcontextcanhelpoffer
insightsintowhichprogramswouldyieldhigherutilitytocitizens.Forexample,individuals
- 27 -
couldusetheconditionsintheCCTtogenerateapersonalorfamilycommitmentdeviceto
engageinfuturebehavior.Ifrespondentsweretoopt-intosuchaCCToveraUCT,thisis
strongevidenceofeitherindividualdemandforacommitmentdevice(Ashraf,Karlan,and
Yin 2006; Bryan, Karlan, and Nelson 2010) or of demand due to family conflict over
educationor healthdecisions. Similarly, inBrazil, researchers examinedhouseholds that
weregivenachoicebetweenaUCTandaCCTonschoolattendance,andalsotestedasub-
treatment within the UCT in which households were informed of whether the children
wereattendingschoolornot(BursztynandCoffman2012).Theparentsexhibitedastrong
preference for the CCT, unless the UCT included monitoring of their children’s school
attendance, in which case they were content with the UCT. These preferences lend
important insight into the underlying mechanisms of the CCT, and suggest that the
conditionalityinthiscontextwassimplyatoolforparentstobettermonitorchildren.Thus,
interventionsthatimprovemonitoringandcommunicationsbetweenschoolsandparents
maybeabettersolutionthanthecomplicatednatureofCCTsoverUCTs.
2.WhichConditionsshouldbeImposed?
Policy-makershavetomakechoicesaboutwhatconditionstoimpose: addingconditions
adds additional costs of monitoring and as we discuss above can have important
implications onwho participates in programs, potentially screening out the very people
whom you want to reach. Thus, experimentation can also help along this dimension,
helpingtodeterminewhichconditionsaremoreimpactfulandworthfocusingon.
Indoingso,itismorecomplicatedthanjustchoosingtoconditiononasinglefactor,
say“schooling.” Thestructureisalsoimportant:conditionscanbeimposedoninputsor
activities(e.g.attendanceofchildren)oroutcomes(e.g.testscores)orboth.Andthen,the
payment structuremust alsobedefined.Forexample,Barrera-Osorioet al. (2011) show
thatwhetheryousimplyconditionamonthlypaymentbyattendance,orholdbackpartof
the payment and provide it only if the child re-enrolls in school, can affect schooling
outcomes.
Another type of structure provides conditions for participation, but does not
financiallypenalizehouseholds if theydonotmeetthem. Thus, thisstructureessentially
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provideshouseholdswitha“nudge.” Forexample,comparingaCCTwithacashtransfer
program thatwas “labeled” for schooling (LCT),Benhassineetal. (2015) show thatboth
types of programs improved school participation relative to the control group, but they
were not statistically different from each other. The conditionality costs more to
implement,andsowasmoreexpensiverelativetotheLCT.
Aswediscussedabove,applyingconditionsmayleadtoatradeoffbetweenthegoals
oftheconditionsandtheinitialgoalofredistribution.Onelegitimateworryisthatcertain
typesofconditionsmaydiscouragepoorhouseholdsfromapplyingsincetheyfindsomeof
conditionstoooneroustocomplywith,thusdiminishingtheultimateredistributivegoalof
theseprograms.Forexample,inareaswithstrongculturalbeliefsagainstvaccines,would
requiring vaccines for children reduce the probability that the poorest households
participate in the program? One can imagine extensions of the CCT literature with
randomizationnotonlyforwhethertheprogramhasconditions,butalsoforthetypesof
conditions in different areas—measuring not only the effect on recipients, but also the
effect onwho applies for the programs and howwell the implementers can realistically
enforcethem.
3.EnforcingtheConditions
Theenforcementoftheconditionalityiscriticaltoexamine.Withouttheenforcement,the
program isperhaps theoretically equivalent to anunconditional cash transfer. Politics at
the policy level, and corruption at the implementation level, both can lead to de facto
removal of the conditionality. This occurred, for example, in Ecuador with the Bonode
DesarrolloHumano(BDH)program.Anevaluationoftheprogramthusanalyzestheresults
asiftheprogramisanunconditionalcashtransfer(FernaldandHidrobo2011;Paxsonand
Schady 2010). However, a CCT program that fails to enforcemay ultimately generate a
differentbehavioralresponsethanjustapureUCT.Forexample,intheEcuadorcase,many
households believed that the fundswere indeed conditional, even though in reality they
werenot.
Enforcement of conditions is an area worthy of further research, but it is not
obvious this is appropriate randomized trial territory. While one could randomize the
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enforcementof theprogram, in an approach similar towhatOlken (2007)uses for road
building,thepoliticalenvironmentthatledagovernmenttofailtoenforcetheconditions
may matter, and it may be that merely randomizing enforcement in one setting does
provide insights to settings in which enforcement is not viable for political or social
reasons.Thereasonforlackofenforcementisimportant,andcannotbesimulatedmerely
through randomization. For example, lack of viability of enforcement couldbedrivenby
constituentexpectations,localsocialnorms(whichbothdrivethelevelofenforcementand
the treatment effect of the program), or simultaneous policies that interact with the
treatmenteffectsofthetransferprogram.
However,anunenforcedconditionmaybesimilartoasuggestionora“nudge.”For
example, an unenforced condition of school attendance may serve a similar role as the
governmentmerelylabellingatransferasan“educationsupportprogram.”Benhassineet
al.(2015)examinesthisquestionbydesigningatwo-prongedexperiment:conditionalcash
transfer versus labelled cash transfer (those two prongswere crossedwith a household
structure test,providing theprogramtomothersversus fathers). Inorder tounderstand
theunderlyingmechanisms, thestudycollecteddataonprocesschangesandalsouseda
multi-armed experimental design. For example, to understand if the conditionality (or
labelling) leads to changes by signaling information about returns to education,
researcherscollecteddataonparentalbeliefsaboutreturnstoeducation(nochangewas
found).Attendance(conditionalonenrollment)increasedafterCCTsandLCTs, leadingto
an increase in time spent studying, at school, and traveling to school and a decrease in
leisure and productive labor (but not a decrease in chores); this suggests the barrier to
schoolingwasduemoretoalackofstudentinterest(thusdrawingasimilarconclusionon
mechanisms as the Brazilian study above (Bursztyn and Coffman 2012). The multi-arm
experimentaldesignthentestedexplicitly themarginalbenefitof thecondition,overand
beyondamerelylabeledtransfer,andthestudyfoundnoadditiveeffectbeyondthelabel.
V. MarketFailuresPreventingAssetAccumulationMost of the issues we discussed thus far focus on providing short-term relief through
redistributive aid or insurance to households, with some programs paying attention to
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ensuring that themoney is spentongoodsand services thatbenefithouseholds inways
beyondanincreaseincashorliquidity.However,certainformsofanti-povertyprograms
alsoaimtoaddressunderlyingmechanismsthatmaybecreatingpovertytrapsinorderto
improvelong-termincomeforthepoor,ortoprovidemechanismsforindividualstobuild
long-termfinancialassetsforwhentheyareelderly.Theseare, indeed,morecomplicated
challenges: if the underlying market frictions are beyond credit and savings market
constraints,thenthesolutionwillrequiremorethanredistribution.Ifredistributionalone
isemployed,itmayprovideimportantshort-runbenefits,butmayultimatelyactasmoreof
a band aid on immediate symptoms without helping individuals achieve a sustained
increaseinincome.
A.BuildingProductiveAssets
Forthepastthirtyyears,microcredithasbeenaleadingdevelopmentpolicyinthefightto
reduce poverty. Unfortunately, seven recent randomized trials have shown that
microcredit,20while it does provide important benefits, does not improve long term
income,onaverage,forparticipantsinitscurrentform(forareview,seeBanerjee,Karlan,
and Zinman2015; the seven randomized trials areAngelucci, Karlan, and Zinman2015;
Attanasioetal.2015;Augsburgetal.2015;Banerjeeetal.2015;Créponetal.2015;Karlan
and Zinman 2011; Tarozzi, Desai, and Johnson 2015). In addition, Meager (2015)
aggregates themicrodata across these studies, andusesBayesianhierarchicalmodels to
show that theeffectofmicrocreditonhouseholdprofits is likelyverysmall and that the
effects from each individual study site are reasonably informative for each other. This
suggests thateithercredit constraintsarenotdrivingstagnatedgrowth for thepoor, the
current designs of microcredit programs do not fully address credit constraints, or
microcreditmaynotworkwithoutchangesinotherconditionsthatalsogeneratemarket
failuresforthepoor.Multi-sitestudiesprovidetremendousopportunitiesforsuchanalysis,
e.g. through building more robust theories and then examining, using appropriate
statisticaltools,howwellresultsfrommultiplesitesfitbroadtheoreticalframeworks.
20 Note that this lesson was only learned by running similar experiments across different project sites and countries, showing how valuable replication studies can be in changing perspective.
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Indeed,thepoordofacemultiplefailuresatthesametimethatmayhinderlong-run
investmentandincomegrowth.But,wetypicallyobserveprogramstacklingoneproblem
at a time, rather than a coordinated system. Poverty alleviation policy, as with many
government programs, often operates in silos. One silo, described above and typically
managed under the umbrella of “social protection,” focuses on redistribution policies
through either conditional or unconditional cash transfers.A second silo, oftenmanaged
under the ministries of trade or agriculture, focuses on livelihood support, such as the
transferoraproductiveassetoragricultural input, alongside some training.A third silo,
financial inclusion, is often administered through for-profit or nonprofit (and sometimes
subsidized)financialinstitutions.But,naturally,thecausesofpovertymaybemultifaceted.
Thus, uncoordinated programs across different ministries may fail to provide the right
bundleofinterventionsthatahouseholdwouldneedtoimprovetheirlivingstandard.This
lack of coordination in itself poses an interesting research agenda to understand the
impactsofthesemulti-approachprograms.
Onerecentexampleofsuchaprogramisthe“Graduation”approach—anintegrated,
multi-faceted program with livelihood promotion at its core that aims to “graduate”
individualsoutofextremepovertyandontoalong-term,sustainablehigherconsumption
path.BRAC,theworld’slargestnongovernmentalorganization,hasscaled-upthisprogram
inBangladesh(Bandieraetal.2016),whileNGOsaroundtheworldhaveengagedinsimilar
livelihood-based efforts. Six randomized trials across the world (Ethiopia, Ghana,
Honduras,India,Pakistan,andPeru)foundthattheintegratedmulti-facetedprogramwas
“sufficient”to increase long-termincome,where long-termisdefinedasthreeyearsafter
theproductiveasset transferBanerjeeetal. (2015).Theresults fromthepooledanalysis
acrossallsixcountriesfoundthattheprogramledtosustainableandsignificantimpactsin
10 out of 10 categories of impact. Using an index approach to account for multiple
hypotheses testing, positive impacts were found for consumption, income and revenue,
assetwealth,foodsecurity,financialinclusion,physicalhealth,mentalhealth,laborsupply,
politicalinvolvementandwomen’sdecision-makingaftertwoyears.Afterathirdyear,the
resultsremainedthesamein8outof10outcomecategories(withpointestimatesfalling
to below statistical significance for physical health and women’s empowerment).
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Furthermore,theWestBengalsitefoundevenlargertreatmenteffectsaftersevenyears(A.
Banerjeeetal.2016).ThepatternofresultsarestrikinglysimilartotheBangladeshstudy
(Bandieraetal.2016).
Theseresultsarepromisinginthattheyshowthatasufficientsetofinterventionsis
capableofalleviatingpovertysustainablyandare thus important forpolicy.Theyshould
whettheappetite,bothforamoretheoreticallygroundedunderstandingofexactlywhich
marketfailuresledtoapovertytrap,aswellasamorepracticallygroundedunderstanding
ofwhetheralloftheinterventionsweretrulynecessaryorifcertaincomponentscouldbe
removed. In the event that some components are unnecessary, costs could be lowered
considerably,allowingtheprogramtoreachmorepeopleusingthesamebudget.Returning
to the theme of this paper, there are two complementarymethods to tackle testing the
importantmechanismsbehindthetheory,andsuccessor failure,of theseprograms:data
andexperimentaldesign.
The idealmethod, if unconstrained by budget and organizational constraints, is a
complex experimental design that randomizes all permutations of each component. The
productive asset transfer, if the only issuewere a creditmarket failure,may have been
sufficient to generate these results, and if no other component enabled an individual to
accumulate sufficient capital to acquire the asset, the transfer alone may have been a
necessary component. The savings component on the other hand may have been a
substitute for the productive asset transfer, by lowering transaction costs to save and
serving as a behavioral intervention which facilitated staying on task to accumulate
savings.Clearly it isnotrealistic inonesettingtotestthenecessityorsufficiencyofeach
component, and interaction across components: Even if treated simplistically with each
componenteitherpresentornot,thiswouldimply2x2x2x2=16experimentalgroups.
Data can also provide important insights, even absent experimental design
variation. Take the savings component, for example. For the savings component to be
eitheranecessaryorsufficientcomponent,presumablyanincreaseintheflowofsavings
must be observed (but not necessarily the stock, since withdrawals for investment
purposesmay bring the stock back down). The evidence from the Graduation programs
showswidelyvaryingimpactsonsavings, farmorethantheresultsoftheprogramitself.
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Forexample,inthemostextremecase,savingsincreasedinEthiopiabyPurchasingPower
Parity(PPP)US$70721, comparedtoonlyPPPUS$17 inGhana.Thissuggests thatsavings
maybean importantcomponent,but isneitheranecessarynorsufficientcomponent for
somelevelofsuccess.
Several studies have tackled pieces of the puzzle, and theway forward is clearly
going to be the development of amosaic, rather than any onedefinitive study that both
testseachcomponentandalsoincludessufficientcontextualandmarketvariationsthatit
can help set policy for a myriad of countries and populations. For example, in a post-
conflict setting in Uganda, a NGO-led program provided youth groupswith training and
cash (US$150) towards non-agricultural self-employment activity and found a 57%
increaseinbusinessassets,17%increaseinworkhours,and38%increaseinearningsfour
yearsafterthecashgrants(Blattman,Fiala,andMartinez2014).Thisprogramdiffersfrom
theabovementionedGraduationprogramsinthreepotentiallyimportantandilluminating
dimensions:post-conflictversusnonpost-conflict,youthversusgeneralpopulationofthe
extreme poor, group-level intervention versus household-level, and no inclusion of
ancillary components such as life coaching, savings, and health care. The first two
differences speak to the applicability of the program to alternative sample frames and
settings,whereasthethirdandfourthprogramvariations,suggestthateitherthedriverof
theimpactoftheprogramlieswiththecashgrantsandtrainingnottheothercomponents,
orthatthegroup-levelaspectimprovestheimpactandeffectivelysubstitutesfortheother
components.
A second study in Uganda sheds insight into the value of the group-level
intervention, as it randomly varies the group aspect of the intervention, as well as the
intensityofsupervision(Blattmanetal.2014).Theseprograms,aswiththeaboveUganda
program,differfromtheGraduationstudiesinthattheydonotincludesavings,healthand
lifecoachingcomponents,andarefocusedonenterprisedevelopment(ratherthananimal
husbandry,thedominantlivelihoodintheGraduationstudies).
21AlthoughnotethattheEthiopiaprogramdesignincludedamuchstrongerpushforsavingsthantheotherprograms, with savings put forward as almost a “mandatory” component, even though there was noconsequenceifhouseholdsdidnotsave.
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Thus, the initial studies above have established a base case, that there exists a
sufficientinterventionpackagethatincreaseslong-termincome.Wehighlightfourlinesof
inquirytounderstandmoreabouttheunderlyingmechanisms.First,long-termimpactsare
critical for assessing whether the short-run interventions actually addressed the
underlyingproblems,orrather just lastedabit longer thanacash transfer.Forexample,
Graduation programs typically last two years while the Graduation studies cited above
measured impacts three years after the assets were transferred. If the household visits
were a critical component in driving the observed impacts, longer-term measurement
wouldbeimportant,toassesswhetherthebehavioralchangesmotivatedbythehousehold
visitspersistedformorethanjustoneyearafterthehouseholdvisitsceased.
Second, as some of the studies above have begun to do,morework is needed to
tease apart the different components: asset transfer (addresses capitalmarket failures),
savings account (lowers savings transaction fee), information (addresses information
failures), life-coaching (addresses behavioral constraints, and perhaps changes
expectations and beliefs about possible return on investment), health services and
information(addresseshealthmarketfailures),consumptionsupport(addressesnutrition-
basedpovertytraps),etc.Therewillbenosimpleanswertotheabovequeries,butfurther
work can help isolate the conditions under which each of these components should be
deemednecessary to address.And furthermore, for several of thesequestions, there are
keyopenissuesforhowtoaddressthem;forexample,life-coachingcantakeonaninfinite
number of manifestations. Some organizations conduct life-coaching through religion,
othersthroughinteractiveproblem-solving,andothersthroughpsychotherapyapproaches
(Boltonetal.2003;Boltonetal.2007;Pateletal.2010).Muchremainstobelearnednot
justaboutthepromiseofsuch life-coachingcomponents,buthowtomakethemwork(if
theyworkatall).
Third, general equilibrium effects should be considered, particularly as the
programsaretakentoscale.Here,thefirsttaskistobemorespecificindatacollection,as
general equilibriumeffects encompass awide varietyof indirect effects, such aspriceof
transferred assets; spillovers from explicit sharing of granted resources; and increased
economicactivityfromincreasingthepoor’swealth.Atypicalexperimentaldesignwould
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eitherrandomizeacrossandwithinvillages(assumingthatthevillageistheboundaryfor
generating general equilibrium effects), or for some issues, examining spillovers to non-
participantsintreatmentversuscontrol(asinAngelucciandDeGiorgi2009).
Fourth,importantlessonscanbelearnedfromunderstandingtheconsumptionpath
taken by households after participating in these programs. The Graduation program for
example found important and cost-effective, but still modest, increases in long-term
consumption.Thisfindingsuggeststhathouseholdsarenotcaughtinanextremepoverty
trap, where one simply needs to get households over a particular hump and they will
immediately converge to the equivalent of the middle class. Further work is needed to
understand the long-term dynamics of such programs andwhat can be done to further
increaseincomemobility.
B.BuildingLong-TermFinancialAssets-Pensions
Non-contributory pension programs are an important form of social protection inmany
developing countries—such as Brazil, South Africa, India, etc.—, and given the shift in
demographics and the risk of poverty for the elderly (UNDESA 2013) they are likely to
growinimportance.Theprogramsvaryinshapeandform:Rofmanetal.(2015)compare
pension programs across 14 different Latin American countries, showing differences in
paymentsizes, in timingofpayment, inwhether thepensionsare targeted,etc. While to
the best of our knowledge there are few experimental studies of pension programs in
developing countries,22RCTs can help us understand how differences in these design
choices affect the labor market choices of working-age adults, retirement age, saving
patterns,andhowfundsareusedwithinthehousehold.
VI. IdeasOnlyGoSoFar:ImplementationMattersTooAtransferprogrammaylooklikeawinneronpaper,butmaybeatotalflopinpracticeif
theimplementationishaphazard.Someofthismaybepurelyadministrative,e.g.,ensuring
that therightnumberof staff ishired,and that theyareproperly trainedandmotivated.
22Althoughasofthetimeofthischapter,thereareseveralexcitingongoingstudiesinChileandIndia.
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This may require incentives to not shirk on the job, as well as to not engage in bad
behaviors, e.g. siphon off funds or food, or reallocate the funds to friends or political
supportersratherthanthosewhoaremostinneed.
Therefore,indesigningrandomizedevaluationsofanti-povertyprograms,itisalso
importanttothinkaboutwhether,theoretically,aparticularaspectoftheimplementation
is likely tobeparticularlyvulnerable toproblems.Thereare two typesofvariationsone
can think about: (1) evaluations that vary the underlying structure of the program (2)
evaluations that layer on complementary actions that can be undertaken to improve
programimplementationgivenafixedprogramdesign.
Experimentallyvaryingthecoreelementsoftheunderlyingstructureofaprogram
is challenging, especially if aspectsof theprogramhavebeenwritten into law.However,
thedetailsoftheunderlyingstructure—fromwhoshouldimplementtheprogramtohow
oneshouldmakethetransfers—maymattertremendously,affectingthe levelof leakages
and corruption, the targeting, the costs for beneficiaries to access the program, and
potentially how beneficiaries spend their entitlements. For example, there is extensive
work, in general, exploring how officials’ incentives affect theirwork output, but to our
knowledgelessworkonhowtheincentivesprovidedtoofficialsaffectstransferprogram
delivery.Similarly,thereisastrainofresearchthatshowsthatthetypeofpersonrecruited
mayaffectgovernmentefficiency(see,forexample,recentempiricalevidencefromAshraf
etal.2014;HannaandWang2014),butlessspecificallyonhowchangingwhoisselectedto
implement transfer programs affects the ultimate outcomes of households. For future
research, one could vary the salary and incentive structure (amount and conditions) for
currentworkers—andduring the recruitmentofnewworkers—toexplorehow itaffects
targetinganddelivery.
One important question is whether governments should even directly implement
theseprograms,orwhethertheyshouldcontractoutdeliverymechanisms.Banerjeeetal.
(2014)experimentallyvarywhetherlocalofficialsdistributeagovernment-runsubsidized
riceprogramorwhetherprivate citizensalsobid for the right to run theprogram.They
findthatthebiddingreducestheprice-markupthatcitizenspay,withoutreducingquality.
Follow-upwork can include testing out differentways of contracting out, from changing
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whoiseligibletobidtohowthebiddingprocessoccurstohownewimplementersarere-
evaluated. Moreover, the bidding process in that paper focuses on local government
provision(i.e.atthevillagelevel). Futureresearchcouldalsohelpshedlightonwhether
theprocurementprocessshouldbedoneatthatlocallevelwhereindividualspossesslocal
informationabouthowtogetthingsdoneinthatvillage,orshould itbedoneatahigher
level of government (e.g. district or province level) where one may also benefit from
economiesofscale.
Aniceseriesofrecentpaperstestswhetherthenatureofthedeliverymechanismin
itselfaffectsoutcomes.Forexample,Akeretal.(2011)experimentallytestfortheimpactof
providing cash versus mobile money in a short-run transfer program in Niger. An
innovative feature of their experimental designwas to also have a treatment group that
simplygotcashandacellphone,tonetoutpotentialeffectsofahavingacellphonemore
generallyfrommobilemoney.Mobilemoneynotonlyreducedthenon-profit’sdistribution
costs, but it also reduced thehouseholds’ costs topickup their entitlement.This second
feature of mobile money many be particularly important if we believe high transaction
costsinducebeneficiaryhouseholds“leavemoney”onthetable(CurrieandGahvari2008).
Importantly, they also showed that spending patterns changed due to mobile money,
hypothesizing that it also conferred greater privacy over one’s finances. Further testing
thesemechanismsinthecontextoflargergovernmentprogramstounderstandlonger-run
effectswould be an important extension of thiswork: For example,would the ease and
potential secrecy of payments attract richer people to apply for these types of transfer
programs?Inthelong-run,wouldlocalofficialswhomayhavepreviouslysiphonedoffcash
duringdisbursements findotherwaysto“tax”citizenswhonowreceivecashdirectlyvia
mobilemoney?
AnambitiousprojectbyMuralidharanetal. (2014) alsoaims toaddress someof
these types of questions. They evaluated the impact of biometrically-authenticated
payments infrastructure ("Smartcards") on beneficiaries of employment (NREGS) and
pension (SSP) programs in Andhra Pradesh, India. The smart cards changed both how
householdscollectedtheirpayments,aswellaswhowasinchargeofthecashdistribution
(asbanksandtechnologyserviceprovidersmanagedthenewcashdisbursalsystem).The
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statewasrollingouttheprogramacross its158sub-districts,sotheauthorsrandomized
which sub-districtswere converted first. Following the introduction of the program, not
onlydidthetimeit tookbeneficiariestocollectapayment fall,butthedelay inreceiving
thepaymentwasreducedbyalmost30percent.Theeaseofpaymentinducedhouseholds
to actually work more. Households, thus, earned more, while payments to officials
remained the same—hence, leakages fell quitedramatically. Similarly, anRCT conducted
by Banerjee et al. (2014) shows that by simply asking local officials to input all of the
namesofthepeoplewhoparticipatedinNREGAintoadatabasesysteminordertoreceive
the funds transfer—i.e. increasing informational requirements for releasing funds and
reducing the administrative tiers in the flow of funds process—led to a stark decline in
leakagesofpublictransfers,withnocorrespondingdecreaseinactualNREGAwork.
Experimentallytestingcomplementaryprogramsthatarelayeredontopofexisting
programscanalsobe important in improving thedeliveryof socialprotectionprograms.
These programs do not necessarily require changing the existing program rules or
functioning,but insteadprovideadditional informationorservices tohelpcitizensbetter
access their entitlements. For example, one could test how increased information on
eligibility andprogram rules affects overall program leakages. Ravallion et al. (2013) do
this: they experimentally vary whether beneficiaries see a half hour video on their
entitlementsunderNREGA.Theyshowthatthisformofinformationhasverylittleimpact
onemployment.Giventhattheformandlevelofinformationmaymatter,onemayalsotest
betweenvaryingtypesofinformation:forexample,Banerjeeetal.(2014)showthatacard
that informs households of their eligible status and entitlements reduces leakages in a
subsidizedriceprogram,andthatmakingthecardinformationpublicwithinthevillagehas
even larger impacts. Moreover, one can imagine experiments designed to test how
providing householdswith direct helpwith their paperworkwhen applying affectswho
enrolls.
Questionsabout implementationatscalealsorelatetocriticalquestionsaboutthe
roleofrandomizedtrialsgivenhowtheyareoftenconductedindevelopingcountries.For
example, there is a broader debate about whether we would observe similar program
results in NGO and government settings, given differences in implementation capacity
- 39 -
between two (see, forexampleBoldetal.2013;DhaliwalandHanna2014).Of course, if
oneisevaluatingaprogramwithanNGOthatwillbescaledupbythatNGOorsimilarones,
wemaynotparticularlycareiftheprogramwouldlookdifferentifrunbythegovernment.
However,oftentimeswemayalsowanttounderstandhowevaluationswithNGOswould
differ in government and vice versa. Naturally, the fundamental problem here is of
generalizabilityandsamplesize:acomparisonofanyoneNGOtoanyonegovernmentonly
comparesthatNGOtothatgovernment.NGOsarenotamonolithicsetofinstitutions,and
neitheraregovernments.Thusitmaybewrongtoaskwhetheragovernmentisbetter,or
worse, at implementing than an NGO, and be more appropriate to ask whether “an”
institution with certain specific characteristics or in certain specific cultural or political
environments will be better at implementing than an institution with a different set of
specificcharacteristicsorenvironmentalfactors.
Treatmenteffectsmaydependon institutional type (governmentorNGO) for two
broadreasons:behavioralresponsesandimplementationefficiency.Intermsofbehavioral
responses,treatmenteffectsmaydependuponthelegitimacyofwhodeliverstheprogram.
Inthespecificcaseofsocialprotectionprograms,howweexpecthouseholdstorespondto
aparticular transferof foodorcash isunlikely tochangebasedonwho isdistributing it.
But,howpeoplerespondtothespecificsof theprogrammaymatter.Forexample, inthe
studyinMoroccowithlabeledcashtransfers(Benhassineetal.2015),itcouldbethatsuch
labelsonlywork from trustedandwell-known institutions.Again, this is lessof an issue
overwhethertheNGOorgovernmentisdeliveringtheservice,butabouttheoveralllevel
oflegitimacyoftheinstitution.Thus,oneinterestingdesignwouldbetoseeiftheresponse
to the nudges changes when households are randomized to receive more or less
informationonhowlegitimatetheorganizationhasbeeninimplementingtheseprograms
inthepast.
In termsof implementationefficiency,onecanalso imaginethatdifferent typesof
organizations may have different strengths and weaknesses in terms of the types of
programs that they can deliver. Suppose, for example, that citizenswould be indifferent
between cash andan in-kind transfer if bothwere implementedperfectly.However, one
typeoforganizationhasstrengthinreducingtheleakagesinthedeliveryofcashrelativeto
- 40 -
asecondorganization,andthesecondorganizationisbetteratreducingleakagesinthein-
kind transfer. Thus, citizens would ultimately prefer different types of transfers from
different types of organizations due to the relative differences in leakages. Again, this
preference may be symptomatic of a difference between government and an NGO, but
speaks to larger differences in the relative implementation abilities of different
organizationsandhowcanoneimproveupontheirweaknesses.
VII. Conclusion:KeyAreasforFurtherWorkSincetheinnovativeandinstrumentalrandomizedevaluationofProgresainMexico,there
has been a burst of important and exciting experiments in this area. This has greatly
informedour understanding ofwhat can “work” in trying to redistribute to the poor, as
wellaswhatcanreducebothbehavioralconstraintsandmarketfailures.
So,thenthequestionbecomes,whereshouldwefocusourresearcheffortsnext?We
highlight three areas for furtherwork, aside from thosediscussed above: interactions of
demandandsupply,long-termeffects,andgeneralequilibriumeffects.
A.KeyAreasforFurtherWork1.InteractionsofDemandandSupply
A vital question is how transfer programs work across different contexts. For example,
GalianiandMcEwan(2013)documentthattheeffectoftheHonduranPRAFCCTprogram
wasmuchlargerinthetwopooreststrata,withtheeffectnotbeingstatisticallysignificant
inthethreericherareas.
Thisquestionissimilartotheoneatthecenterofthedebateoverwhoimplements
(e.g.anon-profitorgovernment):ifonewantstounderstandhowaprogramwillworkina
specificcontext,wemaynotcarewhethertheevaluationfindingsareportabletoanother
context. But, ifwewant tounderstandwhetheraprogramwouldhavesimilarresults in
another area, or if the results will change with policy changes in the current area, it is
important to understand how the theoretically important underlying features of an area
impactoutcomes.
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More broadly, there are lots of unanswered questions about the interaction of
transfer programs with existing conditions, particularly supply-side conditions: for
example, howdoes school quality or health care availability affect the adherence to CCT
conditions?Do foodorother in-kind transfersworkbetter thancash inareaswithmore
limitedfoodsupplies?Dotransfersfacilitateaccesstofinancebyreducingrisktolenders?
Andsoforth.
To answer these kinds of questions, one would ideally not only vary the
introductionofatransferprogram,butwouldalsocrossthiswithanexperimentalchange
in a supply side feature. For example, to isolate how increased health care availability
affects the adherence to CCT health conditions, one would randomize areas to four
treatments:apurecontrol,CCTonly,anincreaseinnursesonly,andCCTandanincreasein
nurses.
Anextensionofthiswouldbetotesttheeffectivenessofdifferenttypesoftransfer
programsunderdifferentconditions:forexample,wemightthinkthataUCTmaybemore
effective at redistributing to the poor than a CCT in areaswhere there is limited health
availability, since the inability to adhere to the conditions may scare off or reduce
payments to the poor. Thus, rather than having just a pure control, one may want to
compare CCTs to UCTs across areas with and without the induced increases in nurse
availability.
2.Long-TermEffects
Thereisatensionbetweentryingtomeasureaprogram’slong-runimpactsversusscaling
upa“working”programtothecontrolgroup.However,long-runimpactsareimportantto
measure, especially if there are reasons to believe that a program’s impactsmay evolve
differentlyastimegoeson(andpotentiallyhavegeneralequilibriumeffectsaswediscuss
below).
For example, whilewe know quite a bit about the short-run impacts of different
targeting methods, we know less about their relative long-run effects. Using quasi-
experimental variation, Camacho and Conover (2011) show that Colombia’s targeting
systemwasmanipulatedovertime,as localofficialsbetter learnedtherulesof thegame.
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One can imagine experimentally varying different targeting methods across different
locations,and thenrepeating thesamemethod ineachrespective locationduring there-
certification process, to determine whether the relative efficacy of different methods
changeasbothhouseholdsandofficialslearnthesystemsovertime.
Similarly, there are many questions about the long-run impacts of the transfers
themselves:What happens to households after the transfers are complete? For example,
did CCTs achieve the goal of changing the outcomes the next generation, i.e. did the
childrenwhoattendedschoolforlonger,orhadimprovedtestscores,ultimatelydobetter
inthelabormarket?InthecaseoftheGraduationstudiesdiscussedabove,theBangladesh
andWestBengal,Indiasiteshavefollowedhouseholdsforsevenyears,andfoundthatthe
positivetreatmenteffectsincreasedfromthreetosevenyears(A.Banerjeeetal.2016).
3.GeneralEquilibriumEffects
With relatively large sums being distributed, anti-poverty programs may have broader
effects than one initially expects, with these effects being potentially quite large. These
effects can take various forms. Anti-poverty programs can affect insurance and lending
marketswithinvillages(AngeluccianddeGiorgi2009),naturalresourcedemand(Gertler
etal.2013;HannaandOliva2015)andlabormarkets(Muralidharanetal.2015).Someof
theeffectscouldbepositive,whileotherscanbenegative.Whilewetouchedontheideaof
general equilibrium effects above, it is important enough of a topic to warrant its own
sectionhere.
Importantly, the general equilibrium effects may differ by type of transfers. For
example,Cunhaetal.(2013)documentdifferenteffectsonpricesofconsumergoodswhen
villages have been randomized to cash versus in-kind transfer programs, particularly in
remoteareas.Thegeneralequilibriumeffectsmayalsovaryacrosscontexts:forexample,
CCTs induce positive peer effects on the schooling outcomes of ineligible children in
Mexico’sProgressa(BobonisandFinan2009;LaliveandCattaneo2009),butnoeffectson
ineligible children in the Honduran PRAF (Galiani and McEwan 2013). At the more
extreme,Barrera-Osorioetal.(2011)findnegativespillovers:siblings(particularlysisters)
ofCCTrecipientsarelesslikelytoattendschoolandmorelikelytodropout.
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While there have been a few studies, including those discussed above, that have
triedtocapturebroadereffects,thisisstillanareawhereourunderstandingisrelatively
sparse andwhere there is a need for further research. However, given themultitude of
types of general equilibrium effects that may be possible, this is a case where we are
particularly worried about multiple hypothesis testing. Careful theory detailing
predictions,coupledwithpre-specifiedhypotheses,maybeimportantinbuildingarobust
model that successfully predicts outcomes in new settings, thus properly guides
policymaking.
Identifying spillover effects should be built into the research planwhenplausible
andviable.Threebasicapproacheshavebeenemployed:(a)throughexperimentaldesign:
randomizingthedensityoftreatmentwithinageographicarea(orwithinanyunitwithin
whichoneexpectstheretobespilloversorgeneralequilibriumeffects),(b)throughdata
collectiononineligibles:thisisstrengthenedwhencombinedwiththefirst,butevenonits
own can shed important insights (Angelucci and de Giorgi 2009), and (c) through data
collectiononprocesschanges: forexample, collectingspecificdataon informal transfers,
credit and savings could identify behaviors that indicate the presence of general
equilibriumeffects.
B.FinalThoughts
Putting these elements together poses its own challenges. Naturally, there is no simple
diagnostic thatassesseswhichmarketsaremissingforasociety, tothenprovideaneasy
prescriptionforwhichoftheaboveprogramsto implement.And,ontheotherendofthe
spectrum,itissimplynotpracticaltoimplementatscaleaprogramthatassessesforeach
individualwhat constraints they face, and then provides the exact program that targets
theirparticularsituation.Thepolicychallengeliesinhowtofindthepolicythatbalances
theoperational constraintsof scalewith the targeting constraintsofboth identifying the
poorest and minimizing the false positives, i.e., policies targeting an issue that are not
relevantforahouseholdorcommunity.Nationalsocialprotectionstrategiesshouldthink
holistically: how do specific policies interact with each other as either complements or
substitutes,whatpopulationsarecriticallymissingfromexistingpolicies,andhowwelldo
- 44 -
existingpolicies improve long-termoutcomessoastoreducetheeventual taxburdenon
society.
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