Peda - Food Aid PDF 1

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Agricultural & Applied Economics Association

Child Growth, Shocks, and Food Aid in Rural EthiopiaAuthor(s): Takashi Yamano, Harold Alderman, Luc ChristiaensenSource: American Journal of Agricultural Economics, Vol. 87, No. 2 (May, 2005), pp. 273-288Published by: Oxford University Press on behalf of the Agricultural & Applied Economics Association

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CHILD GROWTH, SHOCKS,AND FOOD AIDIN RURAL ETHIOPIATAKASHIYAMANO,HAROLDALDERMAN,ANDLUC CHRISTIAENSEN

Childstunting n Ethiopiahas persistedat alarming ates,despiteenormousamountsof food aid,oftenprocuredn response o shocks.Usingnationally epresentative ata,thestudy inds hat whileharvest ailure eads to childgrowth altering,oodaid affectedchildgrowthpositivelyandoffsetthenegativeffects f shocksncommunitieshatreceivedoodaid.However,many ommunitieshatexperienced hocksdid notreceive ood aid. Insum,whilefoodaid hashelpedreducechildmalnutri-tion,inflexible ood aidtargeting,ogetherwithendemicpovertyand imitedmaternal ducation,hasleft theprevalenceof childstuntingat alarmingevels.Keywords: hildgrowth, hildmalnutrition, thiopia, oodaid,shocks.

Children that grow slowly experience poorerpsychomotor development and interact lessfrequently in their environment (Grantham-McGregor et al.). They tend to delay school en-rolment, and score less well on cognitive tests(Martorell, 1997). Moreover, the detrimen-tal effects of slow height growth during earlychildhood may be long lasting. For example,Alderman, Hoddinott, and Kinsey find that inZimbabwe, lowered stature as a pre-schoolerfollowing exposure to the 1982-84 drought re-sulted in a permanent loss of stature of 2.3 cm,a delay in startingschool of 3.7 months, and 0.4grades less of completed schooling. The com-bined effect of these factors was estimated toreduce lifetime earnings by 7%.Rural households in developing countriesoften live in riskyenvironments, unable to fullyprotect their consumption against temporaryincome shocks such as droughts (Dercon). Theavailable empirical evidence to date on the ef-fect of such income shocks on child growth sug-gests pervasive growth retardation (Martorell,1999; Hoddinott and Kinsey). As such tem-porary income shocks may cause permanentdamage to children's future welfare and cog-

nitive abilities (World Bank), furtherempiricalinvestigation to quantify the magnitude of theeffect of such shocks on early child growth iscalled for.A common intervention to alleviate theeffects of drought shocks is food aid, oftenmotivated by explicit reference to its bene-ficial effect on child malnutrition. Ironically,however, there is limited research on the ef-fect of food aid on child growth (Barrett).The literature has so far mainly focused onfood aid targeting, i.e., whether the poor arereached or not (von Braun;Sharp;Clay,Molla,and Habtewold; Jayne et al.), without exam-ining the actual welfare effects of food aidfor its beneficiaries. One notable exception isQuisumbing who finds positive effects of foodaid programs on weight-for-height z-scores ofchildren using panel data from Ethiopia.'Examining the effect of shocks and foodaid on child growth is often complicated bythe lack of sufficiently integrated data setsas well as the methodological difficulties inseparating the causal effects of food aid onchildren's nutritional status from the reversecausality. Food aid programs are generally

TakashiYamano is Fellow at the Foundation for Advanced Studieson International Development. Harold Alderman is Lead HumanDevelopment Economist. Luc Christiaensen is Economist in theAfrica Region of the World Bank.The authors would like to thank John Hoddinott, MartinRavallion, Norbert Schady,andJohn Straussaswell as seminar par-ticipants at the Chronic Poverty Conference at Manchester Univer-sity for useful comments. Nonetheless, the findings, nterpretations,and conclusions expressed are entirely those of the authors, andthey do not necessarily represent the views of the World Bank, itsExecutive Directors, or the countries they represent.

'Other related studies include Dercon and Krishnan whoexamine the extent to which food aid helps households smooththeir consumption (as opposed to nutritionaloutcomes) in the faceof negative income shocks while taking into account the existinginformal risk sharing arrangements. Their results, based on paneldata from Ethiopia, indicate positive effects of food aid on con-sumption smoothing, though largely via intra-village risk sharingand not through direct targeting. Brown, Yohannes, and Webb,andWebb and Kumar look at the relation between child malnutritionand participation in food for work programs. They find positiverelationships but were unable to establish causality.

Amer. J. Agr. Econ. 87(2) (May 2005): 273-288Copyright 005AmericanAgricultural conomicsAssociation


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274 May2005 Amer.J.Agr Econ.targeted to poorer areas and neglecting theendogeneity of program placement may leadto substantial underestimates of their effect(Rosenzweig and Wolpin; Pitt, Rosenzweig,and Gibbons).This study addresses these challenges by in-tegratingthree different nationally representa-tive surveys from Ethiopia conducted over theperiod 1995-96. In doing so, it complementsthe findingsof Quisumbing which are based ona sample from 15 purposively selected villages.Moreover, the study differs from others in theliterature in that it focuses on child growthrather than achieved status, thereby matchingthe flow of food aid with the outcome, and byexplicitly disentangling the effect of the shockper se on nutrition from the effect of the assis-tance.To control for program placement effects,food aid allocations have been instrumentedwith past food aid needs assessments and long-term rainfall patterns as captured by aver-age rainfall and the coefficient of variation ofthe rainfall. Our focus on Ethiopia is moti-vated by the alarmingly high pre-school childstunting rates that have persisted at around60% since the early 1980s and are amongthe highest in the world (Christiaensen andAlderman). Yet, Ethiopia has received mas-sive amounts of food aid over the past decadesoften in response to severe droughts,which area frequently recurring phenomenon.2 Thesefacts have led some to question the effective-ness of food aid in reducing child malnutrition.Our results indicate that crop damage has alarge detrimental effect on early child growth(measured in height) with children aged sixto twenty-four months experiencing about a0.9 cm growth loss over a six-month periodcompared to communities whose percentageof damaged crop area was 50% points lower.We also find that food aid affects child growthpositively, especially among the six to twenty-four months old who grew on average 1.8 cmfaster in the food aid receiving communitiesthan if no food aid would have been available.The empirical analysis further suggests thatthe total amount of food aid distributed off-sets the growth damage from the income shockin food aid receiving communities. However,many communities who experienced shocks,

did not receive food aid, due to inflexible tar-geting rules, leaving many children's growthunprotected from drought shocks, consistentwith the high child stunting rates observed. Inthe next section, we lay out the conceptualframework and our estimation strategy. Sub-sequently, the data are described. We then dis-cuss determinants of food aid allocation andthe effect of food aid and income shocks onchild growth, before making concluding re-marks.Conceptual FrameworkAs outlined in Foster, Deolalikar, and Derconand Hoddinott, we first consider a general in-tertemporal household utility model definedover household consumption and child healthincluding preference shifters (A), such asgender-based parental attitudes towards theirchildren. This maximization problem is fur-ther subjected to an intertemporal budget con-straint and a health production function. Achild i's height at t + 1, hit+l, can then be de-rived as a function of its initial height, hit, itshousehold income, yit,observed characteristicsat the individual, household, and communitylevel, Xit, as well as unobserved individual(eit), household (uit), and community (vjt)characteristics:(1) hit+l = f (hit,yit,Xi,, eit,uiv, t, A).

Household income, Yit, is determined byhousehold characteristics including house-hold assets, and community characteristics.Drought or insect related crop failure alsoaffects income, especially among subsistencefarmers who form the large majority in ruralEthiopia. Income can be defined as inclusiveof transfers, including food aid. Household in-come, yit, can thus be written as:(2) Yit= y(Sit, Fit, Xit, uit, Vjt).

By substituting equation (2) into equa-tion (1), a child i's height becomes(3) hit+1 = f (hit, Sit, Fit, Xit, eit, uit, Vjtr,A) .

Plot damage is assumed to affect householdconsumption negatively, and thus child growth,especially when households are unable to in-sure their consumption from income shocks.Food aid is expected to have a positive effecton child growth by supplementing householdincome and increasing food consumption. Its

2 About one-fifth to a quarter of all food aid deliveries to Africaover the past decades has gone to Ethiopia, with food aid attainingup to 20% of domestic production in drought years (Jayne et al.).According to WorldFood Programme estimates, Ethiopia has beenthe second largest recipient of food aid in the world for 1994-98(after Bangladesh).


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Yamano,Alderman, and Christiaensen Child Growth and Food Aid 275effect on child growth will further depend onthe intra-household allocation and thus ageand gender preferences of the parents (A) re-garding their children as well as the modal-ity under which it is distributed-free distribu-tion (FD) or food for work (FFW). When foodaid is freely distributed, as is mostly the casein our sample, it also frees up time for childcare, another important input in child growth(Engle, Menon, and Haddad). Moreover, tothe degree that the labor requirement in FFWis more strenuous than it is in alternative ac-tivities, food aid increases energy outlay. Thissuggests that free food aidmay have a largeref-fect on child growththan anequivalent amountof food for work. In addition, depending onthe degree of market integration, food aid mayhave an indirectpositive effect on child growthby lowering food prices.The effect of food aid on child growth is, ofcourse, contingent on the amount a householdreceives, i.e., the targeting rules of the food aidprogramas well as individual choices regardinglabor on publicworks. Forexample, if allocatedin response to household income shocks (Fit =F(Sit)), food aid may also mitigate the nega-tive effect of income shocks. While the theoret-ical literature on targeting has devised optimalallocation rules given information constraints(Besley and Kanbur;Besley), the actualalloca-tion of food aid is often the combined result ofahost of factorsincludingconsiderations of op-timal targeting, but also spatial inertia in pro-gram operations due to fixed operational costs(Jayne et al.), and the political economy of re-source allocations at the national and regionallevel. Yet, different political economy theo-ries predict quite different allocation rules.Ac-cording to altruismtheories of public transfers,the least endowed ought to receive the highesttransfer (Roberts), while pressure group the-ories predict that groups small in number andwith considerable resources for lobbying takethe highest share of public transfers (Becker).Thus, in practice, whether and how much foodaid a household is likely to receive is mostly acontext-specific matter that needs to be deter-mined empirically.We are not able to match our informa-tion on food aid and plot-related shocks foreach household with their children's growth.Instead, we use information on the averageamount of food aid received (Fi,) and averageplot damage (Sit) at the community level. As aresult, the take up of food aid by a householdis not modeled in this study. Yet, this choicedoes not follow only from practical consid-

erations; it is also consistent with the policyquestion we investigate. The evaluation liter-ature makes the distinction between the im-pact of a program on those who receive thetreatment and the impact of the "intention totreat." While both convey useful information,Heckman, Lalonde, and Smith observe in theirreview of econometric methodologies for eval-uation that often it is the latter that is of policyrelevance.Thus, we evaluate based on the intention totreat, in this case, child growth conditional onthe allocation of food aid to the community,and not the household choice to take up thisopportunity. If we were to model the impacton self-selecting participants or those chosenby a local administration, it would be neces-sary to make a set of additional assumptions.3While we do not address the marginal impactof additional food (or income) on households,we note that the impact that we do measureallows one to assess the degree to which in-creases in the amount of food aid allocated tothe community prevent child malnutrition.From equation (3), we derive an estimablegrowth equation(4) hit+1 - hit =hhit + PFFit +F FSjt+ Ix Xit+ eit+ uit+ vjt.

However, food aid may be directed to thoseareas where child malnutrition is high, poten-tially leading us to underestimate its effect(E(Fjtit)) : 0. To overcome the food aid pro-gram placement problem, we use the averagefood-aid-need assessments in 1984-88, up toeleven years before the period being studied,and its squared term along with rainfall re-lated variables capturing chronic needs, as in-struments to predict the quantity of food aidreceived. Given the inertia involved in the lo-cation of food aid programs as a result of highfixed start up costs, earlier needs assessmentsduring the second half of the 1980s have beenobserved to be good predictors of future foodaid in Ethiopia (Jayne et al.). The selection ofthe instrumental variables is discussed in moredetail below.To examine the potential differential ef-fect of FD and FFW, the predicted amountsreceived of each kind are also separatelyincluded in the child growth regression

3While the element of self-selection in free distribution is lessthan with food for work, the perspective of evaluation as the in-tention to treat is also relevant to avoid assumptions regarding themechanisms of the intra-community allocation process.


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276 May 2005 Amer. J.Agr. Econ.

1FFjt = IFDFFDjt + FFWFFFWjt. The presenceof intra-household gender differentiation isexplored through the inclusion of interactionterms of the sex of the child with the shockvariables.Child Growth, Food Aid, and Crop Failure inRural EthiopiaOver the period 1995-96, a series of threenationally representativehousehold surveyswereconducted n Ethiopiaaspartof the Ru-ral Integrated Household Survey Program.Weintegrateinformation rom these surveysei-ther at the household level or the enumerationarea (EA)/community level. Anthropometricinformation npre-school hildrensprovidedby the 1995/96 Welfare Monitoring Survey(WMS), which covered twelve randomly se-lected households n each EA/community. nthis survey, 2,414 rural children under fiveyears old were measured twice and matchedsuccessfully across two rounds with a sixmonths interval.4After excludingcases withmore than twelve-monthage differencere-ported, negative height growth, more than25 cm growth in six months, and a HAZ-scorebeyondthe [-6, +6] range,2,089childrenre-mained, spread over 469 EAs out of a total of531 EAs.The Food Security Survey (FSS) was con-ductedon a sub-sampleof the WMS (7 outof 12 households) and collected recall infor-mation on the amounts of food aid receivedby each household in the past twelve months

(June1995-May1996),whichcoversthe 1995Meher(mainrain)season.The 1995/96AnnualAgriculturalSampleSurvey(ASS) coveredalarger etof households n eachcommunity 25in total, including hose covered n theWMS)and collectedinformationon cropdamageoneach plot duringthe 1995Meher season.Asthe plot size of each plot wasphysicallymea-sured,we couldcalculate heproportion fplotarea damaged or each household.In the ab-senceof appropriateommonhousehold den-tifiers,we mergedthe ASS and FSS with theWMSat the EA level. Inparticular,we prox-ied per capitahousehold food aidavailabilityandthe proportionof totalplotareadamagedper householdby theirrespectivecommunityaverages.Food aid in Ethiopia is allocated in twostages.In a firststage,food aid is assignedtotheWoredas, asedontheirneeds assessmentsandtheoverall ood aidquantities eceived ol-lowingthe annualappealto the internationalcommunity or food aid.6Needs assessmentsare estimatesof the proportionof people inneedof food aid,carriedoutannually oreachWoredaby the DisasterPreventionandPre-parednessCommission(formerlythe Reliefand RehabilitationCommission) n consulta-tionwithNGOsandthe donorcommunity.Onthebasisof theseassessments, achyearafoodaid appealgoes out to the international om-munity.The Woredassubsequentlydistributethe food aid in a second step to the house-holdsusinga federalguideline hat mostfoodshould be distributed hroughFFWarrange-ments andthat only householdswith no ablebodiedindividuals houldreceivefree food. Inpractice,however,as illustratedn table1,freefood distributions more commonthanFFW,with the level of effort for publicworksoftenbeingnominal.About one in five communities 116out of531 communities)receivedfood aid betweenJune1995andJanuary 996.Of those commu-nitiesreceivingfood aid,53%reportedusingthis aidexclusively orFD,21%onlyforFFW,and 27%hadbothtypesof distribution.Com-munitiesreceivingaidexperiencedowerrain-fall on averageas well as highervariation ntheir rainfall patterns as captured by the coef-ficient of variation. These findings suggest thatfood aid allocations are somewhat targeted

4While 5,012 children aged six to fifty-four months old weremeasured in the first round and 5,121 children aged twelve to sixtymonths old were measured in the second round, only 2,414 weremeasured twice and matched successfully across two rounds. Yet,even though only 48% of original children were measured twice,86% of the households in the original survey were in the secondround. Thus, it is likely that much of the difference in children insurvey rounds reflects coding errors-a common problem of panelsurveys-and not selective attrition. Indeed, no large mortalityrates among children or migration were reported during the fewmonths between surveys, and further investigations indicated mis-coding child IDs across rounds and a lower rate of follow up amongchildren near the age cut-off as important reasons for the attrition.We also tested for attrition biases following Fitzgerald, Gottschalk,and Moffitt, and Alderman et al. Since all children have an initialheight we were able to test for the influence of unobservable dif-ferences in the sample in the base period. This test confirmed thatthe group with subsequent attrition is on the same nutrition pro-duction function as the rest of the sample. Therefore, it seems thatthe reasons for attrition are not self-selection in nature and thatattrition bias is not a serious concern in our data.

5Those excluded are less likely to be in peri-urban areas, lesslikely to have an educated father, less likely to come from a house-hold that owns land, and more likely to be older. Other variables,including the EA-level food aid and crop damage variables, are notsignificantly correlated to the probability of being excluded due tounreasonable measurements.6 A Woreda is the second lowest administrative unit in Ethiopiaand corresponds to what is commonly known as a district in othercountries. There are about 560 Woredas in the country.


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Yamano,Alderman, ndChristiaensen ChildGrowth nd Food Aid 277Table 1. Food Aid Distribution and Plot Damage at EA Level

EAs with Food Aid EAs withoutFoodAid TotalNumberof EAs 116 415 531Numberof EAs with FD only 61Numberof EAs with FFW 24onlyNumberof EAs with FD 31andFFWAnnualexpenditures er 828.6(518.1) 1,111* 1,172) 1,049(1,070)capita(Birr) (Net of foodaid)Percapita ood aid received 22.5(32.7) 0.0** 0.0) 5.06(18.3)(Birr)(June1995-January1996)Inertia/chronicpoverty measuresNeeds assessmentsn 1984-88 0.288(0.248) 0.089**0.160) 0.133(0.200)Long-runaverage LA) 891.8(332.4) 1,135.9**358.9) 1,082.6 367.1)

rainfall mm) (1967-2001)Coefficientof variationof 0.286(0.092) 0.227**0.094) 0.240(0.096)rainfall 1967-2001)ShocksRatioof damagedplotarea 0.323(0.262) 0.176**0.176) 0.208(0.206)withinEABreakdownbycauses of damageToolittlerain 0.164(0.262) 0.061**0.130) 0.084(0.173)Too muchrain 0.072(0.12) 0.036**0.072) 0.044(0.087)Cropdisease/insectproblem 0.087(0.116) 0.079(0.116) 0.081(0.112)Note: Needs assessments, long-run average rainfall and coefficient of variation of rainfall are measured at the Woreda level. Numbers in parentheses arestandard deviations. In 1996, 1US$ equals about 6.5 Ethiopian Birr.* and ** indicate a statistically significant difference at the 5% and 1% level, respectively, on a particularattribute between communities with and without foodaid.

to chronically poor communities although theallocations may also suffer from inertia. Fi-nally,communities that received food aid werealso observed to be poorer in 1995/96 as re-flected by their lower average household ex-penditure per capita.Food aid programs also appear targetedto communities that experienced crop dam-age in the 1995/96 Meher season. The aver-age percentage of damaged crop areas wasabout 32%in communities with food aid, whileit was only 17.6% in communities withoutfood aid. Most of the damage was caused byrainfall shocks (mostly droughts), though anon-negligible proportion (about 40%) of thedamage was related to insect attacks and cropdiseases. Comparing shock incidence in com-munities with and without food aid, it ap-pears that food aid was especially responsiveto droughts (and flooding) though not to (id-iosyncratic) insect attacks or crop diseases. Toexplore the relationship between child growthand food aid, we plot child growth (in cm) overthe six-month interval against child age at thefirst measurement (figure 1), both for all chil-dren (426) in the food aid receiving communi-

ties as well as for those (1,663 in total) in thenon-food aid receiving communities. Figure 1is created using locally weighted smoothedscatter plots, LOWESS (Cleveland). The ob-served pattern reflects normal growth curves,i.e., growth velocity declining by age. Morestrikingly,we also findthat, in general, childrenin food aid receiving communities grow fasterthan children in communities without food aid,especially those younger than two years old.Consistent with figure 1, table 2 shows that sixto twenty-four month old children grew about0.41 cm faster in communities with food aidcompared to those without, although the dif-ference is not statistically significant (t-stat =1.28). We do not find any difference in growthamong children aged twenty-five to sixtymonths old.

Following common child growth specifica-tions (Deolalikar; Hoddinott, and Kinsey),other variables in our regressions include theindividual child, household, and communitycharacteristics.The descriptive statistics arere-ported in table 3. We control for individualchild characteristicsby including initial height,gender of the child, and child age.


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278 May 2005 Amer. J. Agr. Econ.

7With Food Aid

E O0S. 6-rI o

C Without Food Aid

4-12 24 36 48 60Initial Age in Month

Figure 1. Child growth in height (cm) in a six-month period and food aidTable 2. Child Growth in Height by Food Aid in Rural Ethiopia

ChildrenAged6-24 months ChildrenAged 25-60 monthsA BNumber of childrenAll 1,083 1,006No food aid (F = 0)a 862 801Food aid (F = 1)a 221 205Growth in height (cm)All 6.68(4.27) 5.48(4.04)No food aid (F = 0)a 6.78 (4.25) 5.49 (4.15)Foodaid(F = 1)a 7.19(4.33) 5.44(3.57)Difference(Yes-No)[t-statistics] +0.41 [1.28] -0.05 [0.16]Note: Numbers in parentheses are standard deviations, and numbers in brackets are absolute t-values.aNo Food Aid includes children who live in enumeration areas (EAs) where no food aid program was available between June 1995 and January 1996 (theperiod between the first and second measurement of child height in the Welfare Monitoring Surveys). Food Aid includes children who live in EAs where atleast one food aid program (free distribution, food for work, or both) was available between June 1995 and January 1996.

Household characteristicsncludedin themodelaremother'sage,educational nforma-tion on household members,gender of thehouseholdhead,thecompositionof the house-hold,householdassets,andthesourceof drink-ing water.Weproxythe educational tatus ofthe householdby usingthe highestgradeat-tainedby the most educatedmaleandfemaleadult in the household,as opposedto educa-tion of the parents, o capturepotential ntra-householdexternalities romeducation.Theseareespeciallymportantwheneducation evelsare low (BasuandFoster;Gibson).While thehighestgradeattainedby the most educatedmale adult in the household is twice as largeas the highestgradeattainedbythe most edu-cated femaleadult,at anaverageof 2.3grades

for the most educatedmale adultper house-hold,educationalattainmentsn Ethiopiaareextremely low.7As individualexpenditures eflect food aidreceived,whichwe cannotnet out in the datawe have,the estimatingequationsdo not in-clude householdexpenditures.However, wecontrolfor wealth at the household level us-ing assets.Nearlyall householdsreportown-ing land,and about 60%possesseda plough.Yet,lessthan20% of thehouseholdspossessaradio,a sign of widespreadpoverty;andonly

7Because of a very low education level in Ethiopia, the corre-lation between mother's and the most educated female member'seducation level is quite high, 0.924. Thus, we did not find any sig-nificant differences in estimation results using one or the other.


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Yamano,Alderman, ndChristiaensen ChildGrowth ndFoodAid 279Table 3. Means of Socioeconomic Determinants of Child Growth in Rural Ethiopia

ChildrenAged6-24 months ChildrenAged25-60monthsMean S.D. Mean S.D.

Child growthGrowth n height n sixmonths cm) 6.68 4.27 5.48 4.04EA -level food aidPercapita ood aid received(Birr) 3.7 11.5 3.1 10.3PC food aidfromfreedistributionBirr) 2.8 10.4 2.2 9.3PC food aidfromfood forwork(Birr) 0.9 4.0 0.9 3.7EA-level variablesDamagedplotareas(ratio) 0.21 0.20 0.21 0.20Percapitaexpenditure netof food aid) 1,104 1,159 1,081 1,034In(percapitaexpenditure) 6.79 0.57 6.80 0.53Child characteristicsInitialheight(cm) 67.2 7.7 85.2 7.6Gender(boy= 1) 0.47 0.50 0.54 0.5Age (months) 13.5 6.6 38.7 8.9Household characteristicsMothers'age (years) 28.60 6.94 30.68 7.28No mother nfo (No info= 1) 0.03 0.17 0.05 0.22Maximummale education years) 2.16 3.48 2.45 3.75Maximumemaleeducation years) 1.13 2.68 1.24 2.95Femaleheadedhouseholds = 1) 0.11 0.31 0.11 0.32Numberof men 1.24 0.76 1.32 0.82Numberof women 1.30 0.61 1.32 0.62Ownership= 1):land 0.93 0.25 0.91 0.28Ownership = 1):plough 0.60 0.49 0.59 0.49Ownership = 1):animals 0.19 0.40 0.22 0.41Ownership = 1):radio 0.17 0.37 0.20 0.40Ownership= 1):sickle 0.71 0.45 0.71 0.45Ownership= 1):stove 0.04 0.20 0.05 0.22Water ource:protectedwell (= 1) 0.05 0.22 0.06 0.24Water ource: ap (= 1) 0.15 0.36 0.16 0.37Community characteristicsElevation (m) 1,989 467 1,987 449Pop.density(perarable andkm2)/1,000 0.327 0.399 0.336 0.427Peri-urban= 1) 0.17 0.38 0.18 0.39Tarmac oadavailable nzone (= 1) 0.52 0.50 0.54 0.50Numberof children 1,083 1,006one in five households reports ownership ofanimals. About 15% of the households haveaccess to a tap for drinking water.Dummy variables for peri-urban areas,availability of a tarmac road in the zone aswell as elevation, a proxy for malaria infesta-tion, capture some important location charac-teristics for child growth.8,9About 17% of thechildren in our sample live in peri-urban areasand slightly more than half of the children live

in zones with a tarmac road. We also includepopulation density,measured as the number ofpeople per kilometer squared of arable land.This is a possible proxy for both unobservedinfrastructure and land quality. Finally, we in-clude nine Killil dummies to control for otherspatially correlated characteristicssuch as foodprices, the presence of development programs,and quality of service delivery.From the descriptive discussion (figure1 andtable 2) aswell as from other studies in the liter-ature, it appears that our key variables of inter-est, shocks and food aid, may have differentialeffects according to child age. Consequently,we estimate separate child growth regressionsboth for children younger and older than twoyears old. We begin however by examining thedeterminants of food aid reception.

8 There are about fifty-five Zones in Ethiopia which is the ad-ministrative unit between the Woreda (district) and the Killil (thelargest administrative unit).9Malaria is usually absent in most of the Ethiopian highlands,though its overall incidence in the country has been increasingsteadily over the years in Ethiopia. In 1995, the incidence was 1.1million cases, while it increased to 1.5million in 2000 (WorldHealthOrganization).


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280 May2005 Amer. J. Agr. Econ.FoodAid Allocation n RuralEthiopiaOurmain nteresthere s indetermininghere-sponsivenessof foodaidallocations o incomeshocks,whichcombinedwiththe effectoffoodaid on childgrowth(discussed n the sectionbelow), permitsus to analyzehow effectivefood aidis inmitigating heeffect of shocksonchildgrowth.More detailedempiricaldiscus-sions of the food aid allocationrulesin ruralEthiopiahave been providedby Jayneet al.and Dercon and Krishnan.10 he dependentvariable is the community's otal per capitavalue of food aid received(whetherused forFD or FFW) between the firstand the sec-ondsurveyround,whichcomprisesupto eightmonths.Becauseonlyone-fifthof thesampledcommunitiesreceived food aid,we use Tobitmodels.The results in column A, table 4, indicatethatthe EA-level percapitaexpenditure ex-cludingaverage ood aid) is negativelycorre-lated withfood aid.However,the results alsoindicate hat,evenaftercontrollinghe currentexpenditurelevel, proxies of inertia/chronicpoverty (Z) are importantdeterminantsoffood aid distribution. n particular,he foodaid needs assessmentsduring1984-88,whichencompassed he major1984/85 amine,havea significantpositive effect on the amount offoodaidreceivedbycommunities.Thismay n-dicate thatcommunitieshat wereconsideredvulnerablen 1984-88arestillvulnerableandin need of food aid in 1996.Alternatively, hismay also reflectinertiain food aid programsdue to highfixed costsrelated to programes-tablishment eadingto a high degree of spa-tial continuityn food aidallocationswiththecurrent patialpatternof food allocations tillreflecting he geographical llocation et upinresponseto the 1984/85 amine.Theempiricalevidencepresentedby Jayneet al. favors thelatterinterpretation.We returnto this in theanalysisbelow.An importantproxyfor chronicpovertyisthe coefficient of variation for rainfall,es-pecially in ruralEthiopia which largely de-pends on rain-fedagriculture.Not only mayhigh rainfall variability force farmers to adoptlow-risk-low-return production technologies,trapping them into chronic poverty, but rain-fall variability is also negatively correlated to

long run average rainfall."'In other words, thedepressing effects of low average rainfall onliving standards are exacerbated by increaseduncertainty.The larger the coefficient of varia-tion, the largerthe amount of food aid receivedin the eight-month period. Since the coefficientof variation is distinct from the current shock,the significance of this variable should be in-terpreted in terms of long run conditions inthe communities.We also find that food aid programs are re-sponsive to crop damage, represented by theratio of damaged plots in the community.How-ever, the amount of food aid delivered in re-sponse to shocks seems small compared withthe amount of food aid determined by the in-ertia/chronic poverty measurements.When decomposing the average predictedvalue of total food aid per capita (5.95Ethiopian Birr over eight months) into foodaid allocated in response to inertia and chronicpoverty and food aid allocated in responseto shocks, we find that the lion's share of allfood aid (84% = (4.98/5.95) x 100) has beenallocated in response to inertia and chronicpoverty (as well as the other community char-acteristics). We also find that only a small part(16% = (0.97/5.95) x 100) has been allocatedin response to shocks. While these results maypartly follow from the fact that only 20% ofthe crop area was damaged, they are in keep-ing with Dercon and Krishnanwho also reporta limited response of food aid to shocks in theirpurposively selected sample of fifteen villagesin Ethiopia surveyed three times between 1994and 1995.12,13 Nonetheless, to judge how ef-fective food aid is in mitigating the effect ofshocks on child growth, we also need to knowhow food aid reception and plot damage affectchild growth. We revisit this issue later, whenwe discuss the empirical results on the effectof the different child growth determinants onchild growth.When looking at the determinants offree food aid and food for work alloca-tions separately (table 4, columns B and C,

10To examine the food aid allocation rules we augmented thedata set used by Jayne et al. with more disaggregated rainfall datacovering a much longer time period than used in the earlier study.

" Pearson correlation coefficient = -0.4. This is statistically sig-nificant at the 1% level.12The amount of food aid determined by inertia and chronicpoverty has been estimated by setting the shock variable (S) and

the interaction term with the shock variables (S x P) equal tozero. The amount of food aid responding to the shocks has beenpredicted based on the shock variables and its interaction terms.The latter were included to examine if the responsiveness of thefood aid distribution system to shocks depends on the inertia ofthe system, which was not supported by the data.13Doubling the plot damage ratio to 40% increases the percent-age food aid allocated in response to shocks to 27.4%.


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Yamano,Alderman, ndChristiaensen ChildGrowth ndFoodAid 281Table 4. EA-Level Food Aid Received (Per Capita) in Birr (EA-Level Analysis: Tobit)

Food Aid FD FFWA B CInertia/chronicpoverty measures (Z)Needsassessment n 1984-88 (P) 235.6 2.78)** 332.4 3.30)** -29.50 (0.68)Needsassessment quared p2) -191.6 (2.48)* -211.6 (2.37)* -34.97 (0.85)Assessment P) x long-run ainfall -0.019 (0.32) -0.139 (1.87) 0.102 3.21)**Long-runaveragerainfall1967-2001 -0.002 (0.13) 0.003(0.18) -0.010 (1.45)C.V.of rainfall1967-2001 74.83 1.81) 59.26 1.21) 24.80 1.11)Shocks (S)Damagedplotareas(ratio) 40.94 2.36)* 51.13 2.46)* 6.450(0.73)Damagedplotareas x assessment P) -63.40(1.30) -86.11 (1.53) 9.088 0.34)EA-level variables (X)In(EA-levelpercapitaexpenditure) -18.75 (3.22)** -19.39 (2.83)** -0.809 (0.29)Elevation 12.24 1.95) 9.416 1.29) 8.482 2.57)*Pop. density perarable andkm2) -0.021 (1.98)* -0.033 (2.04)* -0.005 (1.22)Peri-urban -72.52 (3.52)** -85.69 (3.00)** -26.35 (2.35)*Good roadavailable =1) -13.18 (2.03)* -11.15 (1.45) -6.928 (2.04)*Constant 47.34 1.03) 50.89 0.95) -30.75 (1.29)Joint significance tests and predictionsOn inertia/chronicovertymeasures Z) 7.49[0.00]** 5.80[0.00]** 4.31[0.00]**On shocks(S) andS x P 2.92[0.06] 3.10[0.05]* 0.99[0.37]Predicted: otalfood aid 5.95 4.89 1.00Predicted:permanentransfer 5.04 4.19 0.74Predicted: esponse o shocks 0.91 0.70 0.26PseudoR-squared 0.123 0.129 0.127Numberof EAs with food aid 116 92 55Numberof EAs 531 531 531Note:Nine Killildummies re also ncluded ut notreported.Numbersnparenthesesreabsolute -values alculated nheteroskedasticity-robusttandarderrorswithcluster EA) effects.*indicates %significanceevel;and** ndicates %significance.

respectively), we find that while free food aidhas been allocated both in response to thecurrent expenditure level, chronic needs, andshocks, food for work allocations seem largelyunaffected by shocks.14 This would suggestthat, in practice, food for work programs havebeen largely set up to address chronic food in-security,while free food aidmay serve a limitedinsurance function. Yet, furtherinvestigation isneeded, as experience in the sample studied byQuisumbing and Dercon and Krishnan seemsto suggest the opposite.Estimated Effects of Shocks and Food Aid onPre-school Child GrowthThe results on child growth in table 5 showthat children aged six to twenty-four monthsold are quite vulnerable to shocks, consistent

with findings by Hoddinott and Kinsey. A 10%point increase in the proportion of damagedplot areas within a community corresponds toa reduction in child growth by 0.12 cm over asix-month period (column A). Due to the factthat the average growth rate among this agegroup is 6.68 cm, a 0.12 cm decline representsa 1.8% reduction in growth.When we add food aid variables to thegrowth models in columns B and C, the co-efficient on plot damage increases in absolutevalue (from -1.174 to -1.886) as does the pre-cision of the estimate." This suggests that foodaidmitigates the negative effect of plot damageon child growth. When the food aid variable isexcluded (column A), the estimated coefficienton plot damage not only picks up the (nega-tive) effect of plot damage but also the (posi-tive) effect of food aid on child growth because

14The sum of the number of EAs that received food aid in bothregressions exceeds the total number of EAs with food aid in oursample because some EAs used food aid both for free food distri-bution and food for work. In these EAs, we calculated the averageper capita value of each type of food aid.

15While we also control for food aid in model D, the latter modelis applied to a restricted sample, i.e., excluding those communitiesthat distribute food aid both through FD and FFW.As a result, thecoefficient on damaged plot areas is not strictly comparable withthose in models B and C, even though the size, sign, and statisticalsignificance are very similar.


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282 May2005 Amer. .Agr.Econ.Table5. ChildGrowth n Height(cm):ChildrenAged 6-24 Months-IV Models

InitialHeightIs Endogenous Plus,FoodAid Is EndogenousA B C D

EA-level food aidPercapita ood aidreceived 0.042(2.62)** 0.090(2.58)**(Birr)aPCfood aid fromFDa 0.068(0.85)PCfood aid fromFFWa 0.332(0.68)EA-level variablesDamagedplotareas(ratio) -1.174 (1.92) -1.505 (2.42)* -1.886 (2.80)** -2.187 (2.02)*Child characteristicsInitialheight(instrumented)b 0.091(1.23) 0.099(1.33) 0.104(1.40) 0.114(1.49)Gender(boy= 1) -0.473 (1.54) -0.483 (1.56) -0.487 (1.56) -0.531 (1.57)Age (month) -0.242 (1.61) -0.250 (1.66) -0.255 (1.68) -0.236 (1.52)Age squared 0.003(0.80) 0.003(0.80) 0.003(0.78) 0.002(0.59)Household characteristicsMax.maleeducation years) -0.003 (0.13) 0.001(0.04) 0.005(0.22) 0.018(0.78)Max.femaleeducation years) 0.500(0.50) 0.555(0.55) 0.620(0.61) 0.197(0.17)Mother'sage (years) -0.031 (0.63) -0.038 (0.77) -0.046 (0.92) -0.047 (0.76)No mother nfo (no info= 1) 0.075(0.91) 0.074(0.89) 0.073(0.87) 0.077(0.85)Femaleheadedhouseholds -0.039 (0.08) 0.024(0.05) 0.097(0.19) 0.035(0.06)Numberof men -0.014 (0.07) 0.001(0.00) 0.018(0.09) -0.067 (0.32)Numberof women -0.199 (0.87) -0.219 (0.97) -0.243 (1.07) -0.317 (1.21)Ownership:and 0.844(1.39) 0.798(1.32) 0.745(1.23) 0.861(1.39)Ownership: lough -0.270 (0.75) -0.272 (0.76) -0.273 (0.77) -0.142 (0.38)Ownership: nimals -0.134 (0.38) -0.144 (0.40) -0.154 (0.42) -0.124 (0.32)Ownership:adio -0.095 (0.22) -0.042 (0.10) 0.018(0.04) -0.039 (0.09)Ownership:ickle 0.130(0.39) 0.135(0.41) 0.140(0.42) 0.164(0.42)Ownership:tove -0.223 (0.31) -0.257 (0.35) -0.296 (0.40) -0.023 (0.03)Water ource:protectedwell -0.794 (1.43) -0.783 (1.39) -0.773 (1.34) -0.841 (1.27)Water ource: ap 0.056(0.11) 0.030(0.06) -0.003 (0.01) -0.095 (0.19)Elevation -0.204 (0.54) -0.264 (0.69) -0.334 (0.86) -0.238 (0.54)Pop.density(perarable and 0.665(1.80) 0.683(1.84) 0.701(1.88) 0.688(1.62)km2)/1000Peri-urban 0.050(0.08) 0.192(0.32) 0.358(0.58) 0.557(0.90)Good roadavailable = 1) 0.222(0.69) 0.259(0.81) 0.303(0.95) 0.255(0.77)Constant 3.745(0.94) 2.661(0.66) 1.579 0.38) 0.795(0.19)Joint significance testsOnFD and FFW 2.86[0.06]On assets 0.56[0.76] 0.53[0.78] 0.50[0.81] 0.46[0.89]Joint significance testson instrumentsF-stat of IVs on initialheight 60.3 58.5 24.6 23.1F-stat of IVson foodaid 11.9 8.1/2.0(FD/FFW)Over-identificationtests: 3.36 [q = 2] 3.36 [q = 2] 8.23 [q = 5] 6.03 [q = 5]Chi-squaredR-squared 0.03 0.03 0.01 0.01Numberof children 1,083 1,083 1,083 1,005Note: Killil dummies (n = 9) are also included but not reported. Numbers in parentheses are absolute t-values calculated on heteroskedasticity-robust standarderrors.* indicates 5% significance level; and ** indicates 1% significance.aEndogenous variables in columns C and D.bEndogenous variables in all models.

areas with plot damage are more likely to re-ceive food aid, as indicated in table 4. Thus,bycontrolling for food aid programs in columnsB and C, we are able to get a more accurateestimate of plot damage per se.

Food aid has a positive effect on thegrowth of children between six and twenty-four months old. Moreover, the positivecoefficient on food aid on child growthincreases from 0.042 to 0.090 when we


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Yamano, Alderman, and Christiaensen Child Growthand Food Aid 283control for programplacement effects throughthe use of instrumental variables (column C).As discussed previously, the instrumental vari-ables are Woreda-level variables on the iner-tia/chronic poverty measurements (Z). Thesevariables are included in the instrumental vari-ables model as instruments, together with theother variables in the child growth model. TheF-tests on the instruments in the firststage re-gression presented at the bottom of table 5clearly show high predictive power. The in-struments also pass an over-identification test(Wooldridge), presented at the bottom of ta-ble 5 for each IV model providing additionalconfidence in the validity of our identifyingvariables.Two key messages emerge from these re-sults. First, the substantial change in the sizeof the coefficient on food aid-the coefficientmore than doubles, when instrumenting thefood aid allocations-underscores the impor-tance of controlling for program placement inexamining the effect of food aid on individualwelfare. This is consistent with our expecta-tions since food aid programs appeared to belocated in communities with poor child nutri-tion and growth (table 1). It is also in line withPitt, Rosenzweig, and Gibbons, who reportedincreases of up to 100% in the estimated effectof public programs on human developmentoutcomes when accounting for programplace-ment. Second, the effect of food aid on childgrowth among the six to twenty-four monthold children in our sample is considerable.Children in communities who received foodaid grew on average 2.0 cm (0.090 times 22.5Ethiopian Birr) faster in a six-month periodthan if no food aid would have been avail-able.16 This would help compensate poor childnutrition and growth in communities that aretargeted for food aid.We further investigate if the effects of foodaid programs differ by the modality of foodaid utilization (table 5, column D). To do so,we predict both types of food aid separatelywith the same set of instrumental variables,proxies for inertia and chronic poverty mea-surements (Z), using the instrumental variableprocedure. We also restrict the sample to com-munities that do not have dual use. Given thatour food aid variables are matched with in-dividual child growth at the community level,inclusion of communities that use food aid forboth purposes may confound the estimation of

the differential effect of FD andFFW.Applica-tion of model C to the restricted sample yieldsvery similar results as those obtained from thefull sample, apart from the coefficient on to-tal food aid, permitting us to use the restrictedsample for examining the differential effect ofboth uses of food aid.The results indicate that both uses have pos-itive coefficients. While they are both impre-cisely estimated, the F-test indicates that theyare jointly significant. It seems that we are un-able to separate the impact of one programfrom the other. The F-test on instrumentalvariables on FD and FFW indicate that instru-mental variables are weakly correlated withFFW (F-stat = 2.0). In order to separate oneprogram's impact from the other, we needto have instruments that are closely corre-lated with one programbut not with the other.As Gebremedhin and Swinton observe, FFWis partially determined by individual choice aswell as community targeting. This, in turn, ispartially influenced by the efficiency of fac-tor markets (Barrett and Clay). Furthermore,FFW is influenced in some areas by the avail-ability of appropriate projects. Such informa-tion is currently not available for this study touse as instruments.

The estimated coefficient of the initialheightis about 0.10, with a t-statistic around 1.4, sug-gesting no catch-up growth in this short period.We have treated initial height as an endoge-nous variable, using weight and its squaredterm as instrumental variables. Although thisdoes not solve a potential omitted variablesproblem between the initial height and un-observed child or household characteristics,ithelps reduce the measurement error problemin the initial height.17 To further examine therobustness of the estimated coefficients of foodaid and crop damage, we also excluded initialheight (results not reported here), as it is verydifficult to find plausible instruments that are(a) of sufficient magnitude and persistence toaffect a child's height in the initial period, (b)sufficiently variable across households, and (c)sufficiently transitory not to affect the child'sstature in the subsequent period. The esti-mated coefficients of interest are similar to theones reported in table 5.

16Recall from table 1 that the average value of food aid receivedamong the food aid receiving communities was 22.5 Ethiopian Birr.

17When we do not instrument initial height, the estimated coef-ficient of initial height is about -0.25, with a very high t-statistic.While nominally this implies that shorter children grow faster, thisresult should not be interpreted as proof of catch-up growth sincemeasurement error in lagged height would be negatively correlatedwith the dependent variable.


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284 May2005 Amer. J. Agr. Econ.We also ascertainwhether the results arerobust to the additionof the currentrainfallshock to the instrumentalvariables.Thispo-tential instrument s not includedin the re-sultsreported n tables4 and5 becausethereis a possibilitythat rainfall affects nutritionthroughts effects on mobility.However, f thecurrentrainfallshock is included,the coeffi-cient of food aidcorrespondingo the one re-portedin table 5, columnC, is 1.823and thejoint significanceof the instruments s 10.7.These are very close to the ones reported ntable 5.While it might be somewhat of a surprisethat the asset variablesare jointly insignifi-cant, this is less of a contrast to the existingliterature on wealth and nutrition, ncludingstudies from Ethiopia, than it first appears.Most studiesregressachieved status (heightor weight),which is a stockvariable,againstthe stockof assets or expenditureswhichmaybe interpretedas a measureof permanentn-come and, hence, also a stock indicator.Incontrast, hismodelhasa flowvariableas thedependentvariable.This,as well as the factthat manyassets including and had compar-atively little variance,reduces the precisionof the estimates.Nevertheless, heirinclusion

provides omeprotectionagainstmissingvari-able bias.In keepingwiththis logic, it is alsoless of a surprisenot to find statistically ig-nificantresultsfor the coefficientson most oftheotherstockvariables,while thecoefficientson the flow variables(food aid and shocks),which are also ourvariablesof primary nter-est,areestimatedwithmuchgreaterprecision.Wedonoteapositiveeffectofpopulationden-sity on child growththat may be related tothe availability f morefertile landsandpub-lic infrastructuren more denselypopulatedareas.Inaddition o theresultsreported ntable5,wehavealso triedaninteraction ermbetweenthe ratio of damagedplot area and the gen-der of the child (boy = 1). The inclusion ofthe interaction ermindicates haton averagethe growthof girlsunder wosuffers ess fromincome shocks than the growthof boys un-der two, either because of greater biologicalresilience or due to intra-household dynam-ics. The coefficients on the interaction termand the damage variable are -2.06 and -0.81,respectively, and they are jointly significant(F-test = 4.38). This result-which is alsoborne out by other evidence from Ethiopia(Christiaensen and Alderman)-is in keepingwith Svedberg who finds that boys are oftenmore malnourished in Sub-Saharan Africa.

Our findingsregardinggrowthof childrenagedtwenty-five ndsixtymonthsold aresimi-lartothe resultson childrenagedsix totwenty-four months, but less precisely estimated(table 6). The coefficienton the plot damagevariable has a negative sign and its size in-creasesas we control and instrument orfoodaid-as in the case of younger children-though n none of the models(A-D) is itstatis-ticallysignificant.Thepointestimateof thees-timatedcoefficients also muchsmaller.This sconsistentwith otherstudies(Martorell,1997;Jensen;HoddinottandKinsey)thatfindchil-drenbetween twelveandtwenty-fourmonthsto be especiallyvulnerable n the face of in-come shocks.Similarly, he estimatedcoeffi-cientof thefoodaidvariablehas apositivesignwhenfood aidis instrumented,hough,again,it is not statistically ignificant.Thepointesti-mate of the coefficient(0.040)is also smallerthan the one found amongyoungerchildren(0.090).We can use the results n table5, columnC,to examinehow effective ood aid s inprotect-ingchildgrowth romplot damageshocks.Toobtaina marginal esponseof food aid to plotdamage,wedecompose hemarginal esponseinto two components: he marginaleffect onthe probabilityof receivingfood aid timesthe average amount of food aid usuallyre-ceivedandthe marginal ffect on the averageamountof food aidreceived imestheaverageprobability freceivingood aid(Wooldridge).Accordingly,we re-estimate he food aidallo-cationmodel in table4 in a two-stepmethod,Probit and TruncatedOLS,18 nd find that a10%pointincrease n plotdamageareais ex-pectedto increasethe amountof food aidby0.96 Birr.19Using the resultsin columnC in

18 Because Tobit is a nonlinear model, we cannot directly inter-pret Tobit coefficients as marginal effects. Although it is possible tocalculate the marginal effects from Tobit coefficients, we prefer touse a two-step method discussed in text because Tobit constrainscoefficients to be the same in affecting the probability of receivingfood aid and the amount of food aid received among recipients.Thesimulation results, however, are similar in both Tobit and two-stepmethods.19Denote by y the dependent variable (food aid in our exam-ple) and xi independent variable i (e.g., plot damage). The Tobitcoefficient on xi can then be decomposed into:aE[y Ixi] aE[ylxi, y > 0]- Pr ob[yi > 0]axi axi

a Prob[y > 0]+ E[y Ixi, y > 0] axiBy using the results from Probit and Truncated OLS and averagevalues, the total marginal response of a 10% increase in plot dam-age on the amount of food aid can then be calculated as the averageprobability of receiving some food aid times the marginal impactof plot damage on the amount of food aid from the truncated OLS(23.44, t = 1.16) plus the average amount of food aid conditionalaid being non-zero times the marginal effect of plot damage on the


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Yamano,Alderman, nd Christiaensen ChildGrowth nd FoodAid 285Table 6. Child Growth in Height (cm): Children Aged 25-60 Months-IV Models

InitialHeightIs Endogenous Plus,FoodAid Is EndogenousA B C D

EA-level food aidPercapita oodaid received Birr)a -0.001 (0.12) 0.040(0.88)PC foodaidfromFDa 0.059(0.76)PC food aidfromFFWa 0.069(0.22)EA-level variablesDamagedplotareas(ratio) -0.082 (0.11) -0.071 (0.09) -0.411 (0.49) -1.414 (0.86)Child characteristicsInitialheight(instrumented)b 0.005(0.14) 0.004(0.13) 0.011(0.33) 0.003(0.09)Gender(boy= 1) 0.179(0.71) 0.179(0.70) 0.187(0.73) 0.069(0.25)Age (months) -0.048 (0.36) -0.047 (0.35) -0.073 (0.52) -0.119 (0.75)Age squared -0.000 (0.09) -0.000 (0.10) 0.000(0.05) 0.001(0.39)Household characteristicsMax.maleeducation years) 0.025(1.21) 0.025(1.20) 0.026(1.28) 0.032(1.40)Max.femaleeducation years) 0.246(0.40) 0.247(0.40) 0.233(0.38) 0.160(0.22)Mother'sage (years) 0.017(0.33) 0.017(0.33) 0.012(0.23) 0.040(0.73)No mother nfo(no info= 1) -0.004 (0.06) -0.004 (0.06) -0.000 (0.00) -0.019 (0.29)Femaleheadedhouseholds -0.132 (0.28) -0.133 (0.28) -0.100 (0.21) -0.256 (0.47)Numberof men -0.130 (0.69) -0.130 (0.69) -0.125 (0.65) -0.160 (0.79)Numberof women 0.308(1.37) 0.308(1.37) 0.309(1.37) 0.365(1.48)Ownership:and -0.498 (1.07) -0.499 (1.07) -0.489 (1.05) -0.484 (0.98)Ownership: lough 0.158(0.51) 0.158(0.51) 0.154(0.50) 0.338(0.96)Ownership:nimals 0.339(1.01) 0.338(1.00) 0.373(1.09) 0.280(0.75)Ownership:adio -0.160 (0.42) -0.160 (0.42) -0.162 (0.42) -0.204 (0.48)Ownership:ickle -0.159 (0.52) -0.159 (0.52) -0.140 (0.45) -0.207 (0.62)Ownership:tove 0.239(0.45) 0.239(0.45) 0.221(0.42) 0.220(0.40)Water ource:protectedwell -0.904 (1.80) -0.906 (1.80) -0.859 (1.70) -0.800 (1.50)Water ource: ap 0.230(0.47) 0.231(0.47) 0.206(0.42) 0.232(0.47)Elevation 0.061(0.17) 0.062(0.18) 0.024(0.07) 0.001(0.00)Pop.density perarable andkm2)/1,000 0.534(1.59) 0.533(1.59) 0.563(1.65) 0.479(1.38)Peri-urban -0.893 (1.52) -0.897 (1.53) -0.779 (1.29) -0.735 (1.18)Goodroadavailable = 1) 0.173(0.55) 0.173(0.55) 0.164(0.51) 0.069(0.20)Constant 5.439(1.64) 5.452(1.65) 5.083(1.53) 7.042(1.83)Joint significance testsOnFD and FFW 0.58[0.56]On assets 0.55[0.77] 0.55[0.77] 0.55[0.77] 0.52[0.79]Joint significance tests on instrumentsF-statof IVson initialheight 190.8 189.6 74.0 68.9F-statof IVson food aid(FD/FFW) 8.7 7.1/1.3Over-identification ests:Chi-squared 0.70 [q = 2] 0.70 [q = 2] 1.81 [q = 5] 1.69 [q = 5]R-squared 0.05 0.05 0.04 0.01Numberof children 1,006 1,006 1,006 943Note: Killil dummies (n = 9) are also included but not reported. None of the asset ownership variables and water source variables has a significant coefficient.Numbers in parentheses are absolute t-values calculated on heteroskedasticity-robust standard errors.* indicates 5% significance level; and ** indicates 1% significance.aEndogenous variables in columns C and D.bEndogenous variables in all models.table 5, this amount of additional food aidwill increasechild growthby 0.086 cm (0.96Birrtimes0.090).Yet,a 10%pointincrease nplot damage s alsoassociatedwith-0.189 cm

less growth. Food aid compensates on aver-age about 46% of the negative impact on childgrowth following from crop damage.This average marginal response representsthree very different sets of communities. First,consider communities that had not previouslyreceived aid (about 78% of our sampled com-munities). Due to an increase of need result-ing from the 10% increase in plot damage theprobability of receiving food aid increases by

probability of receiving food aid from the probit (0.201, t = 2.06 inour re-estimation), oraE[y Ixi] = 0.218 x 23.44 x 0.1 + 22.5axi x 0.201 x 0.1 = 0.96 Birr.


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286 May 2005 Amer. J. Agr. Econ.2%.Yet,while the marginal ncrease n prob-abilityis incremental, he actualresponse islumpy;a community s either completelyin-cludedor it is not. Forthose that are allowedto enter into the food distributionn responseto thisparticularhock,the averageallocationof foodaid(22.5Birr) ncreasesfarmorethannecessary o offset theimpactof plot damage.Conversely,any communitywith a shockyetnot added to the food aidlists bear the conse-quencesof the shocks.The thirdgroup,com-munitiesthatalreadyreceivefood aid (about22% of thesampledcommunities)alsoappearto be protected, houghthe marginalncreaseis impreciselyestimated.That is, a 10%plotdamageincreases the amountof food aid by2.3 Birr,whichis more than enoughto com-pensatethenegative mpacton growth.Whileit is encouragingo observe that food aid canalleviate the negativeeffect of frequentlyre-curringhocks nEthiopiaon childgrowth, heresultsare also consistentwith the continuingpersistenceof high childmalnutritionas wellaswithresults hatshow nertia n thetargetingof food aid.

Summary ndConcludingRemarksUsing threenationallyrepresentativeurveysconductedduring1995-96,we find hat ncomeshocks, measured by crop damage, reducechild growth substantially, speciallyamongchildrenagedsix to twenty-fourmonths.Chil-dren in this age groupmaylose about 0.9 cmgrowth over a six-monthinterval when halfof theircrop area is damaged.As earlychildgrowth falteringmay cause permanentdam-age,appropriatensurancemechanisms ohelphouseholdsprotect heirconsumptionrom n-come shocksarecrucial.Thisholdsespeciallyin Ethiopia,wherestuntingamongpre-schoolchildrenhas persistedat alarming evels overthe pastdecadesandwheredroughtsarea re-currentphenomenon.Food aid has often been procuredin re-sponse to shocksand has been motivatedbyits beneficial effect on child malnutrition. Thisdepends, of course, critically on the allocationrules and the marginal effects of food aid onchild growth. Our empirical results indicatethat the average value of food aid received ina community has indeed a large positive ef-fect on early child growth. The results furtherunderscore the critical importance of control-ling for program placement effects to prop-

erly estimate the effect of food aid on childgrowth.Inaddition,basedon theempiricalargetingrules derivedfromthe data,the total amountof food aidappearson anaveragesufficient ooffset the negative effectsof plot damageonchildgrowthin food aid receivingcommuni-ties. This result is encouragingas it indicatesthat food aid has indeed been effective npro-tectingearlychildgrowthfromdroughtsandother ncomeshocks n food aidreceiving om-munities.Yet, it appearsthat food aid recep-tion has been largely determinedby factorsother thanshocks,and as a result,manycom-munitieswhoexperience hocks endnot togetfood aid. Indeed,childstuntinghas persistedat alarmingevelsdespitemassiveamountsoffood aid.Thispointsnot only to the inflexiblefood aidtargeting,but alsoto the endemicna-ture of povertyand theextremelyow levels ofmaternaleducation n Ethiopia.Food aid tar-getingrulesmoreresponsive o shocksas wellas other insurancemechanisms re called for.[ReceivedJune2003;accepted une2004.]

ReferencesAlderman,H., J. Behrman,H. Kohler, J.Maluccio,and S. Watkins."Attrition nLongitudinalousehold urveyData:SomeTests orThreeDeveloping ountry amples."Demographic Research 5(2001):78-124.Alderman, H., J. Hoddinott, and B. Kinsey."LongTermConsequences f EarlyChildhoodMalnutrition."Unpublished,WashingtonDC:WorldBankand International oodPolicyRe-searchInstitute, 003.Barrett,C."FoodSecurity ndFoodAssistancePro-grams." n B. Gardner,and G. Rausser,eds.Handbook of Agricultural Economics, vol. 2B.Amsterdam:North HollandPress,2003.Barrett,C.,andD. Clay."HowAccurateIs Food-for-WorkSelf-Targetingn the Presence ofImperfect Factor Markets? Evidence fromEthiopia." Journal of Development Studies39(2003):152-80.Basu, K., andJ.Foster. "OnMeasuringLiteracy."Economic Journal 108(1998):1733-49.Becker,G."ATheoryof CompetitionamongPres-sureGroups orPolitical nfluence."QuarterlyJournal of Economics 98(1983):371-400.Besley, T. "Political Economy of AlleviatingPoverty:TheoryandInstitutions."nM.Bruno,and B. Pleskovic, eds. Annual World Bank


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288 May 2005 Amer.J. Agr. Econ.Webb, P., and S. Kumar. "Food and Cash for Workin Ethiopia: Experiences during Famine andMacroeconomic Reform." In J.von Braun, ed.Employment for Poverty Reduction and FoodSecurity. Washington DC: International Food

Policy Research Institute, 1995.World Health Organization. WHO CooperationStrategies in Ethiopia: Investing for Healthy

Generation. Addis Ababa: World Health Or-ganization, 2002.Wooldridge, J. M. Econometric Analysis of CrossSection and Panel Data. Cambridge, MA: TheMIT Press, 2002.World Bank. WorldDevelopment Report. Investingin Health. New York: Oxford University Press,1993.

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