Living StandardsMeasurement StudyWorking Paper No. 99

Measuring the Effects of GeographicTargeting on Poverty Reduction

Judy L. BakerMargaret E. Grosh

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

Pub

lic D

iscl

osur

e A

utho

rized

LSMS Working Papers

No. 27 Grootaert, The Role of Employment and Earnings in Analyzing Levels of Living: A General Methodology with Applicationsto Malaysia and Thailand

No. 28 Deaton and Case, Analysis of Household Expenditures

No. 29 Glewwe, The Distribution of Welfare in CUte d'lvoire in 1985

No. 30 Deaton, Quality, Quantity, and Spatial Variation of Price: Estimating Price Elasticitiesfrom Cross-Sectional Data

No. 31 Suarez-Berenguela, Financing the Health Sector in Peru

No. 32 Suarez-Berenguela, Informal Sector, Labor Markets, and Returns to Education in Peru

No. 33 van der Gaag and Vijverberg, Wage Determinants in C6te d'Ivoire

No. 34 Ainsworth and van der Gaag, Guidelinesfor Adapting the LSMS Living Standards Questionnaires to Local Conditions

No. 35 Dor and van der Gaag, The Demandfor Medical Care in Developing Countries: Quantity Rationing in Rural COte d'Ivoire

No. 36 Newman, Labor Market Activity in Cate d'Ivoire and Peru

No. 37 Gertler, Locay, Sanderson, Dor, and van der Gaag, Health Care Financing and the Demandfor Medical Care

No. 38 Stelcner, Arriagada, and Moock, Wage Determinants and School Attainment among Men in Peru

No. 39 Deaton, The Allocation of Goods within the Household: Adults, Children, and Gender

No. 40 Strauss, The Effects of Houselhold and Community Characteristics on the Nutrition of Preschool Children: EvidencefromRural C6te d'lvoire

No. 41 Stelcner, van der Gaag, and Vijverberg, Public-Private Sector Wage Differentials in Peru, 1985-86

No. 42 Glewwe, The Distribution of Welfare in Peru in 1985-86

No. 43 Vijverberg, Profitsfrom Self-Employment: A Case Study of C6te d'Ivoire

No. 44 Deaton and Benjamin, The Living Standards Survey and Price Policy Reform: A Study of Cocoa and Coffee Production inCate d'Ivoire

No. 45 Gertler and van der Gaag, Measuring the Willingness to Payfor Social Services in Developing Countries

No. 46 Vijverberg, Nonagricultural Family Enterprises in C8te d'Ivoire: A Descriptive Analysis

No. 47 Glewwe and de Tray, The Poor during Adjustment: A Case Study of C6te d'Ivoire

No. 48 Glewwe and van der Gaag, Confronting Poverty in Developing Countries: Definitions, Information, and Policies

No. 49 Scott and Amenuvegbe, Sample Designsfor the Living Standards Surveys in Ghana and Mauritania/Plans de sondagepour les enquetes sur le niveau de vie au Ghana et en Mauritanie

No. 50 Laraki, Food Subsidies: A Case Study of Price Reform in Morocco (also in French, 50F)

No. 51 Strauss and Mehra, Child Anthropometry in CUte d'lvoire: Estimatesfrom Two Surveys, 1985 and 1986

No. 52 van der Gaag, Stelcner, and Vijverberg, Public-Private Sector Wage Comparisons and Moonlighting in DevelopingCountries: Evidencefrom C6te d'Ivoire and Peru

No. 53 Ainsworth, Socioeconomic Determinants of Fertility in C6te d'Ivoire

No. 54 Gertler and Glewwe, The Willingness to Payfor Education in Developing Countries: Evidencefrom Rural Peru

No. 55 Levy and Newman, Rigidite des salaires: Donnees microeconomiques et macroeconomiques sur l'ajustement du marche dutravail dans le secteur moderne (in French only)

No. 56 Glewwe and de Tray, The Poor in Latin America during Adjustment: A Case Study of Peru

No. 57 Alderman and Gertler, The Substitutability of Public and Private Health Carefor the Treatment of Children in Pakistan

No. 58 Rosenhouse, Identifying the Poor: Is "Headship" a Usefrl Concept?

No. 59 Vijverberg, Labor Market Performance as a Determinant of Migration

No. 60 Jimenez and Cox, The Relative Effectiveness of Private and Public Schools: Evidencefrom Two Developing Countries

No. 61 Kakwani, Large Sample Distribution of Several Inequality Measures: With Application to C6te d'Ivoire

No. 62 Kakwani, Testingfor Significance of Poverty Differences: With Application to Cote d'Ivoire

(List continues on the inside back cover)

Measuring the Effects of GeographicTargeting on Poverty Reduction

The Living Standards Measurement Study

The Living Standards Measurement Study (LSMS) was established by theWorld Bank in 1980 to explore ways of improving the type and quality of house-hold data collected by statistical offices in developing countries. Its goal is to fosterincreased use of household data as a basis for policy decisionmaking. Specifically,the LSMS is working to develop new methods to monitor progress in raising levelsof living, to identify the consequences for households of past and proposed gov-ernment policies, and to improve communications between survey statisticians, an-alysts, and policymakers.

The LSMS Working Paper series was started to disseminate intermediate prod-ucts from the LSMS. Publications in the series include critical surveys covering dif-ferent aspects of the LSMS data collection program and reports on improvedmethodologies for using Living Standards Survey (LSS) data. More recent publica-tions recommend specific survey, questionnaire, and data processing designs anddemonstrate the breadth of policy analysis that can be carried out using LSS data.

LSMS Working PaperNumber 99

Measuring the Effects of GeographicTargeting on Poverty Reduction

Judy L. BakerMargaret E. Grosh

The World BankWashington, D.C.

Copyright © 1994The International Bank for Reconstructionand Development/THE WORLD BANK

1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

All rights reservedManufactured in the United States of AmericaFirst printing July 1994

To present the results of the Living Standards Measurement Study with the least possible delay, thetypescript of this paper has not been prepared in accordance with the procedures appropriate to formalprinted texts, and the World Bank accepts no responsibility for errors. Some sources cited in this paper maybe informal documents that are not readily available.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s)and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to membersof its Board of Executive Directors or the countries they represent. The World Bank does not guarantee theaccuracy of the data included in this publication and accepts no responsibility whatsoever for anyconsequence of their use. The boundaries, colors, denominations, and other information shown on any mapin this volume do not imply on the part of the World Bank Group any judgment on the legal status of anyterritory or the endorsement or acceptance of such boundaries.

The material in this publication is copyrighted. Requests for permission to reproduce portions of it shouldbe sent to the Office of the Publisher at the address shown in the copyright notice above. The World Bankencourages dissemination of its work and will normally give permission promptly and, when thereproduction is for noncommercial purposes, without asking a fee. Permission to copy portions forclassroom use is granted through the Copyright Clearance Center, Inc., Suite 910,222 Rosewood Drive,Danvers, Massachusetts 01923, U.S.A.

The complete backlist of publications from the World Bank is shown in the annual Index of Publications,which contains an alphabetical title list (with full ordering information) and indexes of subjects, authors,and countries and regions. The latest edition is available free of charge from the Distribution Unit, Office ofthe Publisher, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433, U.S.A., or from Publications,The World Bank, 66, avenue d'I6na, 75116 Paris, France.

ISSN: 02534517

Judy L. Baker and Margaret E. Grosh are economists in the Human Resources Operations Division of theLatin America and the Caribbean-Country Department III of the World Bank.

Library of Congress Cataloging-in-Publication Data is available from the Library of Congress.

v

CONTENTS

Section I: Introduction ......................................... 1

Section II: How to Identify Priority Regions ........................... 3What Poverty Measure Should be Used? .9 ..... . . . . . . . . . . . 3Are the Differences in Poverty Measure SignificantBetween Various Regions? .9 ....... . . . . . . . . . . . . . . . . . . . . . 6Which Regions Should Get Benefits, and Which Should be Excluded? . 7

Section III: Simulated Geographic Targeting Schemes: Targeting Accuracyand the Impact on Poverty ........ . .. . . . . . . . . .. . . . . . . . . . . 9

The Simulation Design ........ . .. . . . . . . . . .. . . . . . . . . . . 9The Basic Simulation Results .......................... . 10Varying Benefit Levels by State ........................ . 14Comparisons Between Countries ........................ . 14State, Municipal and Village Level Targeting Compared ..... . . . . . 16

Section IV: Geographic Targeting in Comparison with Other Methods ..... . . . . . . . 18

Section V: Conclusions ............. .. ... .. ... .. .. ... .. .. ... .. . . 22

Bibliography .................... .... .... .... .... .... .... .. . 24

Annex I: Ranking for Jamaican Parishes ........ . . . . . . . . . . . . . . . . . . . . . 25Annex II: Ranking for Mexican States ........ . . .. . . . . . . . . . . . . . .. . . . . 26Annex III: T Statistics for Testing Significance of Poverty Differences

Among Targeting Methods. Venezuela ...... . . . . . . . . . . . . . . . . . 27Annex IV: T Statistics for Testing Significance of Poverty Differences

Among Targeting Methods. Jamaica ....... . . . . . . . . . . . . . . . . . . 28Annex V: T Statistics for Testing Significance of Poverty Differences

Among Targeting Methods. Mexico ....... . . . . . . . . . . . . . . . . . . 29Annex VI: T Statistics for Testing Significance of Poverty Differences

Among Transfer Schemes. Jamaica ......................... . 30

vi

List of Tables

Table 1: Rankings and Priorities by Different Poverty Measures ..... . . . . . . . . . . 4Table 2: Values of R from Spearman Rank Order Correlation ............... . 6Table 3: Significant Differences Among Selected States Using the

Poverty-Gap Measure of Household Income for Venezuela ..... . . . . . . . . 7Table 4: Venezuela - Comparison of Undercoverage and Leakage

for Geographic Targeting Simulations by Poverty Measure Ranking Method . 12Table 5: Poverty Indices Under Various Targeting Simulations Venezuela .13Table 6: Jamaica-Geographic Targeting ............................ . 15Table 7: Leakage and Undercoverage for State Level Targeting in Three Countries . . 16Table 8: Mexico-Geographic Targeting ........ . . . . . . . .. . . . . . . . . . . . . 17Table 9: Comparison of Transfer Schemes: Jamaica (J$4.9 million budget) ..... . . 20Table 10: Comparison of Transfer Schemes: Jamaica (J$141,120 budget) ......... . 21

vii

FOREWORD

In an attempt to reduce the impact of economic adjustment on the poor, manygovernments in Latin America and the Caribbean have introduced targeted social programs.These programs aim to transfer benefits to the poorest groups in the most efficient way possible.Among the targeting mechanisms used, geographic targeting has been very popular due to itssimplicity. This research serves to evaluate the techniques used for identifying poor areas, anddetermine what impact various simulated transfer schemes will have on reducing poverty.

This paper is part of a broader program of research in the Policy Research Departmenton the extent of poverty in developing countries and on policies to reduce poverty. Thisresearch program is located in the Population and Human Resources Division. Aside from thepolicy implications of this paper, the work is intended to demonstrate the need for and usefulnessof household data collection efforts such as the Living Standards Measurement Study indeveloping countries.

Lyn SquireDirector

Policy Research Department

viii

ACKNOWLEDGEMENTS

The authors would like to thank John L. Newman for his helpful suggestions and inputs,and Masako Ii for her close collaboration on the use of the Venezuela data set. We are gratefulto Lant Pritchett, Kimberly Nead, Paul Glewwe, Steven Moss, and participants of the seminarorganized by the PHRPA and LA3HR divisions of the World Bank for their comments on thepaper. We also thank the Planning Institute and Statistical Institute of Jamaica, Oficina Centralde Estadistica e Informatica of Venezuela, and Instituto Nacional de Estadistica, Geografia eInformatica of Mexico for providing access to the data.

ix

ABSTRACT

Targeting benefits to the poor by geographic location is very popular due to its simplicity.Regions can be assigned priority on the basis of existing aggregate data. Programs to improveor extend infrastructure, social services, or transfer benefits can then operate in those identifiedregions. This paper presents some of the technical issues faced by planners in designinggeographically targeted programs, and what approximate impact they will have on reducingpoverty.

Simulated transfer schemes using household data for Venezuela, Mexico and Jamaicaindicate that geographic targeting is a useful mechanism for transferring benefits to the poor.Poverty can be significantly reduced when compared with transfer schemes involving notargeting. Of the various techniques used to identify priority regions for a targeted program,outcomes proved to be fairly similar. The level of geographic unit does, however, seem to havea notable impact on targeting outcomes. Poverty can be significantly reduced when targetingsmaller geographic units such as the village level, in comparison with municipal or state level.The more finely defined the geographic region, the greater is the reduction in poverty. This hasobvious implications for program design.

Outcomes comparing a general food subsidy scheme, a food stamp program which usesa means test and a self-selection process, and geographic targeting show clearly that the targetedschemes perform better than the untargeted price subsidies. Between the two targetingmechanisms, the results are inconclusive. Identifying the poor individuals based on where theylive rather than their income level (which is difficult to verify), or nutritional status isundoubtedly easier to carry out. There are in practice, however, problems of incentive effectsand political economy which may arise with geographic targeting that should be considered.

SECTION I: INTRODUCTION

Low or negative growth, tight fiscal constraints, and a movement toward reducing therole of the state in the economy have led many Latin American governments to introduce newtargeting mechanisms to improve the effect of poverty programs while reducing their costs.Among targeting options, geographic targeting has been very popular region-wide. Examplesinclude the Mexican Tortilla and Milk programs, the Venezuelan Day Care Centers Program,and the Honduran Food Stamp Program. All of these programs use geographic location inconjunction with other mechanisms to target direct transfer programs to the poor.

The main attraction of geographic targeting is its simplicity. Regions can be assignedpriority on the basis of existing aggregate data. Programs to improve or extend infrastructure,social services, or transfer programs can then operate in those identified regions. Many of thesecan use the same modus operandi of providing services that they did prior to the targeting effort.No new or complicated administrative mechanisms for selecting beneficiaries individually needbe set up. No means test is needed, nor the cadre of social workers to carry it out.

A comprehensive comparison of targeted programs in Latin America indicates that inactual program practice, geographic targeting works as well as other targeting mechanisms (seeGrosh, 1994). The median share of benefits going to the poorest 40 percent of households is72 percent for geographic targeting, 71 percent for those using self-targeting mechanisms, and73 percent for programs with individual assessment mechanisms (means test, nutritional statusor risk). Due to it's relative simplicity in administration, geographic targeting is an importantmechanism for delivering benefits to the poor that deserves extensive consideration in designingtargeted programs.

Datt and Ravallion (1991) and Ravallion (1992) have investigated the potential ofgeographic targeting for India and Indonesia through a model designed to minimize poverty.These studies sought to find the maximum impact possible for geographic targeting. Rather thanstart with a fixed budget of plausible size, they allowed richer regions to be taxed to provide thetransfer to the poorer regions. Results indicate that for both countries the qualitative effect ofreducing regional disparities in average living standards generally favors the poor. The overallmaximum impact for India was equivalent to what could be achieved by a uniform, untargetedtransfer of 1.5 percent of mean consumption. For Indonesia, the effect is higher -- equivalentto 4 percent of mean income. Starting from the more limited case of plausible budget size, weshow that the gains to targeting were about 2 percent of mean consumption for Jamaica andbetween 3 - 10 percent for Venezuela. Compared to the budget of many poverty programs,these results imply substantial monetary savings. As we shall see in the case of Jamaica thiscomes out to be a savings of 43 percent of the program budget, and for Venezuela, between 6-12percent.

This paper will present some of the technical issues faced by planners in designinggeographically targeted programs, and approximate what impact these programs will have onreducing poverty. Section II addresses the question of how priorities should be assigned betweenregions based on several ranking methods. Section III performs targeting simulations using data

2

sets from Venezuela, Mexico and Jamaica to illustrate the targeting accuracy and impact onpoverty that can be expected for several different choices in technique. Section IV presentssome comparisons of the outcomes of geographic targeting versus other transfer mechanisms.Section V summarizes, and makes some recommendations about how to use geographictargeting.

SECTION H: How TO IDENTIFY PRIORI REGIONS

In identifying those regions which should be included by targeted programs, severalquestions must be answered; (i) What measure of poverty should be used? (ii) Are thedifferences in the chosen poverty measure significant between various regions? (iii) Whichregions should get benefits, and which be excluded? We address each question in turn.

What Poverty Measure Should be Used?

Geographic targeting ranks regions on the basis of some poverty measure and thenassigns benefits to the poorest groups. Sometimes the poverty measure used is based only onthe income (or consumption) of the region. Other times indicators of social service availabilityor social outcome are used singly or in a compound index.

Some schools of thought prefer composite poverty measures because they conceive theessence of poverty not as purely economic, but also to include social dimensions. Householdincome or expenditure does not capture aspects of welfare such as health, literacy or access topublic services. The availability of clean drinking water or public health clinics, for example,matter to an individual's standard of living but is not reflected in a measure of consumption orincome. Households with access to free public services are better off than those without, eventhough their income or expenditures may be the same.

There are a number of techniques for constructing composite indices ranging from ad hocweightings based on an individual or committee's opinion of what important factors are to themore rigorously statistical techniques of principal components or factor analysis. It is preferablethat these techniques produce estimates of precision, or confidence intervals, around each scorein order to give the planner a firm notion of when the decision that one state is poorer thananother is justified. These indicators are then used in the format of a poverty map to choosepriority regions for transfers.

Economists usually prefer income (or consumption) based measures of poverty. TheFoster-Greer-Thorbecke (FGT) family of poverty measures is highly regarded because it meetsall the axioms desirable in income-based poverty measures and contains a parameter, a, that canbe set according to society's sensitivity to the income distribution among the poor. When a=Othe FGT measure collapses to the head-count index, or the percentage of the population that isbelow the poverty line. This measure, while useful for general poverty comparisons, isinsensitive to differences in the depth of poverty. When o =1, the FGT measure gives thepoverty-gap, a measure of the average depth of poverty. The poverty-gap is based on theaggregate poverty deficit of the poor relative to the poverty line. When ce=2 , the FGT P2measure weights heavily income inequality among the poor. A dollar's benefit that reaches thepoorest will matter more than one reaching the only slightly poor.

4

Table 1: Rankings and Priorities by Different Poverty Measures

Mapa de Pobreza Mean o.c. Income

State Rank Index' Rank Bs' FGT(ai0) FGT(ar=1) FGT(cr=2) Pov. Mi.2

Apure 1* -13.96 1* 663 3* 2* 2* 104*

Barinas 2 -10.79 16 1243 4* 6* 7 95*

Portuguesa 3* -8.64 9 1035 8 13 13 27*

Trujillo 4* -7.26 3* 852 5* 4* 4* 103*

Guarico 5* -5.00 14 1120 16 16 16 42*

Sucre 6* -4.68 2* 825 2* 3* 5* 101*

Yaracuy 7* -4.62 7 994 14 15 15 50*

Cojedos 8* -4.45 8 1035 11 9 11 75*

Merida 9* -2.83 15 1131 10 8 8 81*

Lara 10 -2.43 13 1074 12 10 10 72*

Tachira 11 -2.17 6* 967 7 7 6* 95*

Monagas 12 -1.03 10 1039 9 11 12 65*

Falcon 13 -0.18 5* 930 13 14 14 59*

Zulia 14 2.06 4* 865 6* 5* 3* 105*

Anzoategui 15 3.23 12 1056 15 12 9 0

Bolivar 16 6.72 21 3998 1* 1* 1* 0

Nueva Espar. 17 8.58 11 1050 17 18 20 0

Aragua 18 8.72 17 1362 18 17 17 0

Carabobo 19 9.15 18 1475 20 20 21 0

Miranda 20 13.42 20 1769 19 19 19 0

Dis. Federal 21 14.84 19 1618 21 21 20 0

Source: Venezuela Ministry of the Family/UNDP (1987).This refers to the transfer amounts given to each region using the poverty minimization algorithm explained in Section III, paragraph 42.This refers to those states included in each targeting scheme.

Use of the FGT class of measures requires the definition of a poverty line and iscalculated on the basis of disaggregated data (either household level, or aggregated for a fewgroups such as quintiles). When disaggregated income data are not available, mean GDP percapita is sometimes available from regionalized systems of national accounts and can be used torank geographic regions by income.

5

Using household level data from Venezuela and a national poverty map based on acomposite poverty measure we were able to compare how individual states would be ranked interms of poverty levels for the various measures. For the simulation in this paper, states areranked from poorest to richest and then admitted into a transfer program based on that rankinguntil 30 percent of the population has been included. The transfer budget is set at a fixed level,and transfers within states are uniform.

The composite index (Mapa de Pobreza) is the result of a principal components analysisof 32 indicators such as unemployment rate, illiteracy rate, the percentage of homes withrunning water, etc. Each region is given an index of poverty based on its score from theanalysis. The FGT and mean per capita rankings were calculated for each region using anationwide household survey, Encuesta de Hogares, which provides disaggregated data foranalysis. The survey, which was carried out by the Oficina Central de Estadistica e Informatica(OCEI) in 1989, provides labor income for close to 30,000 households (excluding Guyana State).Numbers are expressed in 1989 Caracas prices. For the analysis in this paper, we excludehouseholds with zero labor income from all the calculations. The individual is used as the unitof analysis.

Column 1 of Table 1 presents the states listed in the order assigned by the compositeindex. Rankings for each method are given in subsequent columns. The "poorest" state isranked I and the "richest" is ranked 21. The states included in the transfer program for eachranking method are marked by an asterisk.

A Spearman rank order correlation was performed to compare the rankings of the variouspoverty measures. Results indicate that there is a significant association between rankings forall methods at the 95 percent confidence level. Of the techniques, the FGT poverty measuresand mean per capita income though still statistically significant, had the lowest degree ofassociation with a value of R below .50 (see Table 2).

These comparisons show that all ranking methods result in fairly similar rankings ofstates. And as we shall see, despite the differences in which regions have been chosen by thegiven ranking method, targeting accuracy and the impact on poverty of geographically targetedtransfers are similar, except for transfers targeted on the basis of the Mapa de Pobreza and thePoverty Minimization technique. It is therefore recommended that more emphasis be put onusing the individual measure well and getting on with targeted programs than with major newefforts to derive or reweight geographic poverty indices.

6

Table 2: Values of R from Spearman Rank Order Correlation

Mean PC Mapa de FGT FGT FGT Pov.Income Pobreza ca=O cr= I a=2 Min.

Mean PC IncomeMapa de Pob. .58 -

FGT a=O .50 .64 -

FGT a= 1 .46 .52 .93 -

FGT a=2 .46 .51 .92 .99 -

Pov. Min. .77 .71 .77 .79 .78 -

Are the Differences in Poverty Measure Signiricant Between Various Regions?

It is important to be realistic about how different poverty really is in regions once theyare ranked by the chosen poverty measure. The differences may affect the decision of whichregions will be included in a targeted program. Consider the rankings in poverty measurementshown in column 2 of Table 1 for Venezuela according to the composite index (Mapa dePobreza). The range of the index is from -14 to + 14. It seems likely that poverty really isdifferent in Apure, with a score of -14 than in the Distrito Federal with its score of + 14. Soif a planner can only afford to run a poverty program in one of the states, it should be in Apure.But what about Sucre and Yaracuy? Sucre's poverty index is -4.68 and Yaracuy's is -4.62. IsSucre's poverty really any more severe than Yaracuy's? It is difficult to justify that Sucreshould be included and Yaracuay omitted from a program based on this classification.

In order to demonstrate how different the levels of poverty in states really is inVenezuela, we turn to the nationwide household survey, Encuesta de Hogares. For ease ofexposition, we chose six states. Apure and Trujillo are among the poorest, Carabobo andDistrito Federal are among the wealthiest, and Cojedos and Anzoatequi are in the middle ofpoverty rankings. This is true when states are ranked according to either the Poverty MapIndex, or mean per capita income. Using the household income data, we calculate the FGTpoverty measures for u = 0, 1, and 2, and test for significant differences between them. Thepoverty line used is Bs 475 per person per year, which includes 30 percent of the population.Table 3 shows results for a= I only to facilitate interpretation.

7

Table 3: Signiricant Differences Among Selected States Using thePoverty-Gap Measure of Household Income for Venezuela

Poorest Medium RichestApure Trujillo Cojedos Anzoatequi Carabobo D.F.

ApureTrujillo NS -

Cojedos * *

Anzoatequi * * *

Carabobo * * * NSDist. Fed. * * * * NS -

*=significantly different at the 5% level

Of the six states, there is no significant difference in poverty level between Apure andTrujillo, Carabobo and Anzoatequi, and Carabobo and the Distrito Federal for e= 1,2. This isquite a different picture from that given by the Poverty Map scores of -13.96 for Apure and -7.26 for Trujillo, 9.15 for Carabobo and 3.23 for Anzoatequi, etc. Taken without an analysisof the significance of the differences in scores, the Poverty Map gave a false impression of howwell it would work as a device to geographically target poverty programs. This finding indicatesa need to use such poverty maps with caution when targeting by geographic region.

Which Regions Should Get Benefits, and Which Should be Excluded?

Once the planner has ranked the regions by the chosen poverty measure, there remainsthe problem of deciding where to draw the line between those that will receive program benefitsand those that will be excluded. Looking again at the Venezuelan ranking, should those stateswith an index of worse than -7.26 get benefits and the others not? Or should the line be drawnat -4.45? or at 0?

One technique is to give benefits to those that fall below the point where there is astatistically significant difference between poorer regions and non-poor regions. Utilizing thismethod bases decisions about the program budget on need rather than on fiscal availability. Thisapproach has the advantage of wider coverage, but in practice is often hindered by budget andpolitical constraints.

A more common approach to choosing which states participate is to start with a budgetceiling and an estimate of how much it would cost to include each state in the program. Statesare given priority based on their poverty index, and the cut-off point is determined by the budget

8

ceiling. In the targeting simulations we have used this method, by taking a fixed budget andthen uniformly distributing it among states until 30 percent of the population has been included.

A third approach is to allow many states to enter the program, but to vary the budgetassigned to them based on the poverty map's indication of relative need. This may be somewhateasier to achieve politically, as more states will have a stake in the program. It can be,however, more difficult administratively depending on the number of states included.

SECTION m1: SIMULATED GEOGRAPHIC TARGETING SCHEMES:TARGETING ACCuRACY AND THE IMACT ON POVERTY

The Simulation Design

In order to see how well geographic targeting works, this section is devoted to severalsimulation experiments. We use geographic targeting to identify states which will participatein a poverty program that will give all the residents in those states a transfer based on a setbudget. Then we use the household data sets to see how accurate the targeting was, and whatthe impact on poverty of the simulated programs would be. Finally, we compare the results oftargeting using several ranking systems, no targeting, and perfect targeting.

The poverty line is set so that just 30 percent of the individuals are considered to bepoor. The states are ranked by the various poverty measures and then admitted into the programuntil about 30 percent of the total population is included. We take a fixed budget equivalent to10 percent of the poverty line times the number of individuals in the sample. In each simulationwe divide that budget equally among all individuals in the states included in the program. Thistransfer thus reaches the 30 percent of the total population that lives in those states which havebeen ranked as poorest. The simulation is designed to be similar to the way manygeographically targeted programs work. They include as many states as possible, and thendistribute benefits uniformly within those states.

With perfect targeting all poor people in the country would be included in the program,and all non-poor in the country would be excluded. We assess accuracy by comparing to thestandard of perfect targeting. With geographic targeting, poor individuals living in poor statesshould and do receive program benefits. Non-poor individuals living in non-poor states shouldnot and do not receive program benefits. Both these groups are targeting successes. Poorindividuals living in non-poor states should (ideally) be included in the program, but will not be.They constitute a targeting error. Likewise, non-poor individuals living in poor states shouldnot (ideally) receive benefits, but will do so with geographic targeting. Such cases alsoconstitute a targeting error. These four permutations of whether a person should and doesreceive benefits completely describe the results of each simulation, but they are not expressedin a form directly comparable with much of the information on social programs. We willtherefore use the four outcomes to construct two other measures that may seem more familiarto the program manager.

"Undercoverage" is the percent of those meant to be reached by the program who are notreached. It is calculated by dividing the number who should but don't get benefits (the error ofexclusion) by the number who should get benefits (the target population).

"Leakage" is the percent of program benefits that are given to those who ought notreceive them. It is calculated by dividing the error of inclusion by the number of persons servedby the program.

10

Lower rates of undercoverage are preferable to higher rates, as is true for leakages. Ingeneral, the higher the priority assigned to raising the welfare of the poor, the more importantit is to eliminate undercoverage. Conversely, the higher the priority assigned to saving limitedbudget funds, the more important it is to eliminate leakage, that is to minimize errors ofinclusion.

In practice poverty and social programs aim to raise the welfare of the poor as much aspossible within their budget constraints. Both kinds of error are therefore important, and a firmpreference for one over the other is rarely stated. It is perhaps interesting to note that strongconcern with minimizing undercoverage has been a traditional argument for universal subsidies,especially of food prices. With the tighter budgetary constraints of the 1980s and 1990s manygovernments are moving toward more targeted programs that will presumably lower leakage,but introduce the risk of undercoverage.

It should be noted that in order to concentrate on the targeting aspect of the problem athand, we abstract from a number of issues that would need to be considered to study the finalimpact of transfer programs -- the second round effects of the responding of the incometransferred , the effects of the taxes which support the program, and possible offsetting declinesin private transfers or work effort. It is not clear whether the impact on poverty predicted wouldbe greater or less if these were taken into account. Thorbecke and Berrian (1992) use a socialaccounting matrix to show that the multiplier effect of the recipients spending the income gainedin experiments similar to those performed here can double the final impact on povertyalleviation. Of course, this effect would presumably be at least partly offset by the burden ofthe taxes raised to finance such a transfer scheme. Binswanger and Quizon (1988) show thatmodeling of the financing mechanism reduces benefits from somewhat similar programsimulations from two-thirds to one tenth of the level predicted when the tax effects are notconsidered. Cox and Jimenez (1992) show that in simulations of the effects on privateinterhousehold transfers in Peru, about a third of the value of the state transfer was offset byreductions in private transfers.

The Basic Simulation Results

We simulate geographic targeting in Venezuela using five different methods to rank thestates -- the Poverty Map Index, mean per capita income, and FGT poverty indices for ce = 0,1, and 2. This allows us to see how sensitive the outcomes are to the measure used. We alsoperform one variant where we allow benefit levels to differ by state in order to minimizepoverty. This allows us to see how sensitive the outcomes are to how priorities are assigned.

In Venezuela, with geographic targeting at the state level, both undercoverage andleakage rates are high (Table 4). For example, with the composite index ranking system (Mapade Pobreza) undercoverage is 66.5 percent of those the program meant to help. In other words,although 30 percent of the population are poor and meant to be helped by the poverty program,

11

only 10.1 percent were correctly assigned to receive benefits. The rest were incorrectlyexcluded from the program ([19.9/29.9=66.5]). Leakage of program benefits is 66.5 percentof the program budget. In other words, almost two thirds of the transfer budget leaks to thenon-poor who are not meant to be reached by the program.

None of the geographic targeting methods differ greatly in targeting accuracy. Therankings based on the FGT a=2 measure produces the lowest level of undercoverage, 58.5percent. The FGT a=0, and 1 measures produce the lowest level of leakage, 57.9 percent.That the targeting accuracy can be so similar when the states admitted to the program aredifferent may at first seem surprising. It is, however, the result of the inherent difficulty ofgeographic targeting - rich states include poor people and vice versa. Switching which statesbenefit, when they are close to each other in the poverty ranking, will have a small effect ontargeting accuracy.

When comparing the geographical targeting scheme to the hypothetical schemes of notargeting and perfect targeting, we find that geographic targeting lies somewhere in between.These comparisons should, however, be interpreted with caution. Uniform targeting schemescould face high administrative costs in distributing a transfer to everyone. Perfect targeting isthe theoretical "ideal" where only the poor would receive benefits. In reality, however, noprograms are perfectly targeted or even come very close.

With a uniform transfer scheme, we assume that every individual gets the same benefit.Since everyone is included, all of the poor benefit and undercoverage is 0 percent. Leakage isthe 70 percent of the population that is non-poor but, nonetheless, benefits from a uniformtransfer scheme.

At the opposite end of the spectrum, with perfect targeting, 30 percent of the populationshould and would benefit. 70 percent shouldn't and wouldn't. Errors of exclusion andinclusion, undercoverage and leakage would all be zero. Table 4 presents a comparison ofleakage and undercoverage for the various targeting schemes.

12

Table 4: Venezuela - Comparison of Undercoverage and Leakagefor Geographic Targeting Simulations by Poverty Measure Ranking Method

Ranking Method Undercoverage Leakage

Uniform Transfer 0 70.0

Perfect Targeting 0 0

Mapa de Pobreza 66.5 62.5

Mean PC Income 59.8 61.3

FGT (a=0,1) 60.9 57.9

FGT (a=2) 58.5 59.0

Poverty Minimization 27.2 57.0

Note: The poverty line is Be 475 per person per year, at the 30thpercentile.

One way to look at the gains from targeting is to see by how much it can reduce povertyfor a given budget. Here, we compare the FGT poverty measures for the country as a wholefollowing the various transfer schemes, in order to see how well geographic targeting works.For geographic targeting, the various ranking methods again produce similar outcomes. Eachrecipient in the chosen region receives approximately Bs 175. The greatest reduction in povertyfor the head-count index can be achieved by the ranking based on the FGT a =0,1 measures foreach region. Poverty is reduced by 25 percent from the original level. The depth of poverty,as measured by the poverty-gap (a =1) and the FGT a =2 would be reduced from the originallevels even further, by 44.5 percent and 57.8 percent respectively (see Table 5).

Geographic targeting reduces poverty more than a uniform transfer, but much less thanthe ideal of "perfect" targeting. With the uniform transfer, the fixed budget allows a transferof only Bs 50 per person. This reduces the head-count index (FGT(a=0)) by 19 percent fromits initial level of 30.0. The same budget would extend to give each poor person Bs 167 underperfect targeting. Under this scheme, the head-count index would fall to 19.26, a decrease of54 percent from its initial level.

Significance tests for the FGT a =0,1,2 measures following each transfer scheme indicatethat all methods are found to be significantly different than no targeting, and perfect targeting.When the Mapa de Pobreza, Mean Per Capita Income and FGT ranking methods are tested,none are significantly different from each other for the head-count index. The differences dobecome significant as the poverty index increasingly measures the depth of poverty (FGT ax = 1)(Annex III). Using those standards the other ranking methods all perform better than the Mapade Pobreza ranking. Using the poverty-gap as the standard, mean per capita income and theFGT measures all serve equally as well as ranking devices.

13

Table 5: Poverty Indices Under Various Targeting Simulations for Venezuela

Outcome Measures

Benefit per Head- Count Poverty- Distrib.Recipient FGT a=0 Gap Sensitive(in Bs) (% change) FGT a= 1 FGT a=2

Ranking Method (% change) (% change)

Uniform Transfer 47.4 24.93 7.57 3.34(-19.22) (-38.01) (-56.52)

Perfect Targeting 167.0 19.26 2.64 .48(-54.31) (-295.8) (-992.1)

Mapa de Pobreza 187.3 23.99 7.69 3.75(-23.90) (-35.97) (-39.34)

Mean PC Income 173.0 23.80 7.36 3.44(-24.89) (-41.96) (-52.11)

FGT (ct=0,1) 179.8 23.77 7.23 3.31(-25.06) (-44.46) (-57.84)

FGT (a=2) 165.3 23.86 7.20 3.25(-24.58) (-45.10) (-60.94)

Poverty Minimization see Table 24.0 6.8 2.811, col. 6 (-23.51) (-52.53) (-86.36)

Note: The first number is the poverty index. The number in parentheses is thepercentage change from the initial, pretransfer poverty index value. The poverty line isBs 475 per person per year, at the 30th percentile. Initial poverty indices before thetransfer were: for a=0, 29.72, for a =1, 10.45, and for a=2, 5.23. N=148,652individuals.

An alternative way to look at the gains from targeting is to see how much it can reducethe cost of reaching a fixed poverty outcome. The monetary savings are the difference betweenthe budget distributed in the targeted simulation and how much it would cost for a uniformtransfer to lower poverty by the same amount. Using mean per capita income as the rankingmethod for geographic targeting, individuals in 6 states receive a transfer of 173 Bs each whichlowers the FGT a =2 measure to 3.44. In order to achieve the same a=2 value withoutgeographic targeting, an additional Bs 1,591,000 would be needed in the program budget. Thisis equivalent to 21.4 percent of the program budget.

14

The three other ranking methods generate monetary savings that are somewhat lower.For the FGT a =2 ranking the equivalent gain from targeting is 10.5 percent of the programbudget, 6 percent for the FGT a =0, 1 ranking methods, and for the composite index ranking,there is a budget loss of 19.5 percent. This loss reflects the fact that the composite index doesnot measure the depth of poverty. As we saw in Table 5, a uniform transfer actually reducespoverty more than geographic targeting based on the composite index ranking when measuredby the FGT a=2 index.

Varying Benefit Levels by State

The results so far have been for schemes where all participating states had a standardprogram -- i.e. residents in all participating states got the same level of benefit. This is notstrictly necessary -- program benefits can vary by state. In order to see how much differencethat makes, the simulation has been performed with the same budget as the other simulations,but allowing for different transfer levels in each state. The algorithm, designed by Ravallion andChao (1989) minimizes poverty as characterized by the FGT a=2 measure, subject to a budgetconstraint, by calculating the optimal transfer to mutually exclusive sub-groups. The budgetconstraint can be set so that all transfers are positive, or so that some are negative implying thatsome groups will be taxed. In this simulation, which we have called the poverty mninimizationtechnique, regions have been used as the sub-groups and the transfer constraint set so that alltransfers are positive. The same total budget is used as in the previous simulations.

Letting transfer levels vary by state also makes little difference to the poverty outcome.If we refer back to Table 4, we can see that though undercoverage is much lower, leakage isapproximately the same as for the other transfer schemes. The impact on poverty for a=O inTable 5, the head-count index, is no better than for the best of the equal benefit results. Fora =1 and a=2, the differentiated transfers show some improvements. The monetary savingsfrom targeting would be 19 percent of the budget, slightly less than the equal benefit schemebased on the mean per capita rank. The gains in reducing undercoverage and small reductionsin overall poverty may not be enough to justify the greater complexity of program administrationthat would be required to run such a program. If the broader reach of the program raises itspolitical sustainability then the complexity might be justified.

Comparisons Between Countries

How well geographic targeting works will, of course, vary depending on the level ofregional disparities in welfare. To see the range of outcomes, we compare the results of statelevel geographic targeting using mean per capita income as the ranking indicator for threecountries -- Jamaica, Venezuela, and Mexico. The transfer budget in each case is set to be tenpercent of the poverty line multiplied by the sample size.

For Jamaica, data from the Survey of Living Conditions (November, 1989) have beenused. The household data set is a nationwide survey including about 4000 households designedin the format based on the World Bank Living Standard Measurement Studies. The survey wascarried out by the Statistical Institute and Planning Institute of Jamaica.

15

A comparison of ranking methods confirms the conclusion that the method used to rankthe states does not have a significant impact on outcomes. We ranked Jamaican parishes(equivalent to states) using the FGT poverty measures, mean per capita income, and the povertyminimization technique. The results are identical. Under all four transfer schemes, the samefive parishes would be included. The head-count index was reduced by about 23 percent withparish level geographic targeting, the poverty-gap by 62 percent, and the distributionallysensitive measure by 113 percent. With the poverty minimization technique, nine parishes wouldreceive some variable amount. The reductions in poverty were not significantly different. Thereduction in budget from that required by an untargeted transfer to achieve the same impact onpoverty is 43.5 percent for the equal benefit scenarios of geographic targeting. This isapproximately 2 percent of mean per capita consumption (see Table 6).

Table 6: Jamaica-Geographic Targeting

Outcome Measures Ranking Method

Mean PerCapita GDP, Poverty

Uniform Perfect FGT a=0,1, 2 Minimnization

Undercoverage 0 0 48.6 21.3

Leakage 70 0 52.3 55.3

Benefit Per Recipient 305 1017 980 See Annex IV

(In Jamaican Dollars)

Head-count (a= 1) 20.09 20.4 24.74 24.70

(% Change) (-16.3) (-48.53) (-22.72) (-22.89)

Poverty-gap (er= 1) 7.64 2.76 6.47 6.02

(% Change) (-37.04) (-279.9) (-61.82) (-73.66)

Distributionally 2.97 .485 2.24 2.16

Sensitive (a =2) (-60.94) (-884.7) (-113.39) (-120.58)(% Change

Note: The first number is the poverty index. The number in parenthesis is the percentage change from the initial, pretransfer poverty index value. The poverty line is J$3052, at the 30th percentile. Initial poverty indices before thetransfer were for a=0, 30.3, and c=l, 10.47, a=2, 4.78. N = 16,000 individuals.

How well geographic targeting works in each of the three countries is shown in Table 7.Mean per capita income was used as the ranking measure in each case. Undercoverage andleakage were almost identical for Venezuela and Mexico. For Jamaica outcomes are slightlybetter for both. In all three countries, the head-count index would be reduced by approximately20-25 percent through state-level geographic targeting. In Jamaica, the mean welfare in the

16

richest parish is 2.6 times higher than the mean of the poorest. In Mexico and Venezuela, thedifferences are much greater.

Table 7: Leakage and Undercoverage for State Level Targetingin Three Countries

Mexico Venezuela Jamaica

Mean PCI M$21,823 Bs2831 J$9805in Wealthiest State

Mean PCI M$ 4,970 Bs 663 J$3721in Poorest State

Ratio of Per CapitaIncome 4.4 4.3 2.6Wealthiest/Poorest

Leakage Rate 59.3% 61.3% 53.3%

Undercoverage Rate 61.0% 59.8% 48.7%

Reduction in the 19.3% 24.9% 22.7%Head-count Index

State, Municipal and Village Level Targeting Compared

A further methodological issue is what sized region should be used? The geographic unitof analysis could, for example, be the state, county, city, or even neighborhood. Seemingly,using smaller units would make it easier to include poor pockets and to exclude non-poor pocketslocated in the opposite environment. Especially in urban areas or heterogenous countries,smaller units of analysis may improve the accuracy of geographic targeting.

The increased accuracy of geographic targeting using more narrowly defined geographicregions is confirmed for simulations in Mexico. We have performed the simulation at the state,municipality (corresponds approximately to county in the US) and locality (corresponds to avillage) level. The data used are the most recently available income-expenditure survey (IES)for Mexico, carried out by the Instituto Nacional de Estadfstica, Geograffa e Informatica (1984).The survey includes 5200 households. Following our conclusions from the simulations inVenezuela and Jamaica that the measure used for raning states has little impact on targetingoutcomes, we have ranked the geographic units for Mexico by mean per capita income only.

17

The results shown in Table 8 indicate that both leakage and undercoverage improve asthe geographic unit is more narrowly defined. When targeting at the state level, leakage is 61percent and undercoverage, 59 percent. These rates drop significantly to 37 percent whentargeting is done at the locality level. The overall reduction in poverty at the FGT a=2 leveldecreased by 59 percent from the original poverty measure with state level targeting, and bythreefold, 180 percent, when targeting was done at the locality level. Outcomes for thegeographic units are on the whole, significantly different from each other. Only the results forthe comparison between State and Municipal level targeting for the head-count index were not.

Table 8: Mexico-Geographic Targeting

Outcome Ranking MethodsMeasures

Uniform Perfect State Municipal Localidad

Undercoverage 0 0 59.3 46 37.3

Leakage 70 0 61.0 42.3 37.3

Benefit per Recipient 373 1243 1220 1243 1242(in pesos)

Head-count (e = 0) 27.0 22.0 26.03 25.58 24.58(% Change) (-14.97) (-41.04) (-19.28) (-21.36) (-26.34)

Poverty-gap (a= 1) 8.37 3.37 8.12 6.76 5.99(% Change) (-34.80) (-234.96) (-38.85) (-66.85) (-88.35)

DistributionallySensitive (a = 2) 3.42 .67 3.36 2.34 1.92(% Change) (-57.46) (-703.76) (-59.94) (-129.09) (-180.48)

Note: The first number is the poverty index. The number in parenthesis is the permentage change from the initial, pre transferpoverty index value. Poverty line is the 30th percentile Initial poverty indices before the transfer were for a = 0, 31.05, a =1, 11.28, and os = 2, 5.38. N=25,400 individuals.

While using smaller geographical units will improve targeting accuracy, there are limitsas to how far the technique can be carried. For example, there may not be data representativeof small units of analysis. Gathering such data from surveys becomes much more expensive asthe size unit of analysis falls. This is a lesser problem for data that comes from serviceproviders. Although for small geographic units of analysis, there may not be service providersin each. It may also be awkward logistically and politically, for example, to admit some schoolsin a district but to exclude other schools in the same district.

SECTION IV: GEOGRAPEHC TARGETING IN COMPARISON WrmIOTHER METHODS

In order to evaluate the use of geographic targeting in comparison with other targetingmechanisms and with general food subsidies, several final simulations are presented below. Wecompare results from the geographic targeting simulations in Jamaica to results from theJamaican Food Stamp Program, to a targeting scheme which combines geographic targeting andthe methods used by the Food Stamp Program, and finally, to general food subsidies onpowdered milk, wheat and corn maize. The simulations are carried out both using the samefixed budget of J$4.9 million as was used in the previous simulations, as well as a reducedbudget of J$141,120, equivalent to the actual per capita amount transferred by the Food StampProgram.

The Jamaica Food Stamp Program distributes benefits to poor households using twodifferent targeting mechanisms. The first mechanism is a means test. Program eligibility isdetermined by household income. Households register for the program or are nominated bycommunity members. Social workers then visit the candidate household to verify that theapparent living conditions coincide with the reported income. The second targeting mechanismis self-selection. In health clinics, the program is designed to give benefits to all pregnant orlactating women and children under the age of five who use the public health system for regularpreventive care. The value of the food stamps at the time of the survey was J$40 per recipientevery two months, or an annual equivalent of US$7.25.

For the simulation, we allocate a transfer to all individuals who currently benefit fromthe program using the J$4.9 million fixed budget. This is equivalent to J$1583 per capita, forthe 20 percent of the population receiving benefits. Using the same 30 percent poverty line asin the previous simulations, these results show that under this transfer scheme undercoverageis 65 percent and leakage 47.5 percent. The level of poverty, as measured by the head-countindex, is reduced by 28.6 percent from its original level of 30. The severity of poverty (FGToi=2) is lowered by 57.1 percent (Table 9).

In order to make the results for the geographic targeting simulation comparable to thosefrom the food stamp transfer, the geographic simulation was adjusted so that the program wouldalso cover only 20 percent of the population. When coverage is reduced from 30 percent to 20percent, fewer individuals benefit from the program (now only 4 parishes are included), but thetransfer amount is higher. These results are very similar to the earlier simulation. The povertymeasures would be reduced by about the same amount though undercoverage would besubstantially higher.

A question arises as to whether we can improve targeting results even further bycombining the self-targeting and means-tested mechanisms used in the Food Stamp Program withgeographic targeting. In this simulation, we allocate transfers to those individuals currentlyreceiving the food stamps, but limit it to only those living in the four poorest parishes whichwere previously identified. The per capita transfer turns out to be very high, J$9840, due to thesmall number of beneficiaries. The target population of the Jamaican Food Stamp Program,

19

which includes 20 percent of the entire population, is further reduced to eight percent by theinclusion of only the poorest parishes. Outcomes from this simulation show a very highundercoverage rate of 81.6 percent, with comparable leakage of 31.8 percent. The head-countindex is reduced by 20.3 percent, the poverty-gap by 27 percent, and the FGT ci= 2 by 33.6percent.

General food subsidies tend to benefit the rich more than the poor because the richconsume more, a finding confirmed in Jamaica. For this simulation, we once again took thefixed budget of J$4.9 million and proportionately distributed it to income groups based on theirshare of the general food subsidies' benefits as they were calculated for 1988 (see STATIN andWorld Bank, 1988). The poorest 10 percent of the population received 6 percent of the totalvalue of general food subsidies and thus, received 6 percent of the fixed budget'. That amountwas then transferred equally within the income group. Results from this simulation show thatthough general food subsides produce a level of undercoverage of 0 percent, the leakage rate is77 percent. The reductions in poverty would only be 14 percent for the head-count index, 26percent for the poverty-gap, and 38 percent for the FGT a =2 poverty measure.

A comparison of the transfer schemes is presented in Table 9. The Food Stamp andgeographic simulations produce outcomes that are clearly better than those for general foodsubsidies. Through targeting with either of the two mechanisms, the head-count index can bereduced by twice as much. Between the targeting mechanisms used for the Food Stamp Programand the geographic methodology, the conclusions are less clear. More of the poor would bemissed by geographic targeting, though the depth of poverty as measured by FGT a= 2 wouldbe reduced by almost twice as much. This is the point at which program goals and thepracticalities of both targeting mechanisms would dominate the decision making process. Forexample, if the goal in the case of the Food Stamp Program is to bring pregnant women andchildren under the age of five to the health clinics for check-ups rather than explicitly todistribute food stamps, geographic targeting would not be appropriate.

The technique of combining geographic targeting with a self-targeted or means-testedscheme with a large budget does appear to improve results. By narrowly defining the targetingcriteria, many of the poor are eliminated from the pool of those designated to receive benefits.Transferring very large benefits to a small, though poor, proportion of the population does notreduce poverty any more than can be achieved by the regressive general food subsidies (seeTable 9).

The distribution of general food subsidies by deciles was as follows;

Decile 1 2 |3 1 4 5 6 |7 |8 |9 |10 |

Distrib. (%) 6 8 9 10 10 10 10 11 13 13

20

Table 9: Comparison of Transfer Schemes: Jamaica (J$4.9 million budget)

Outcome Measures Targeting Method

Geographic/General Geographic Geographic Food Stamp

Food Food Stamp Targeting Targeting ProgramSubsidy Program 20% coverage 30% coverage combined

Undercoverage 0 65.0 76.8 48.6 81.7

Leakage 77.0 47.5 46.1 52.3 31.8

Benefit per Recipient (183-396) 1583 1406 980 9840(in JS)

Head-count (a=0) 26.59 23.61 24.53 24.74 25.23(% change) (-14.18) (-28.60) (-23.76) (-22.62) (-20.33)

Poverty-gap (a=1) 8.32 7.04 6.61 6.47 8.27(% change) (-25.90) (-48.63) (-58.24) (-61.82) (-26.60)

DistributionallySensitive (ea=2) 3.46 3.04 2.37 2.24 3.58(% change) (-37.99) (-57.13) (-101.02) (-113.39) (-33.63)

Note: The first number is the poverty index. The number in parenthesis is the percentage change from the initial, pre-tranafer povertyindex value. Poverty uine is the 30tb percentile, N = 16,000. Initial poverty indices before the transfer were for a = 0, 30.36. a = 1,10.47. and a = 2,4.78. N=16,000 individuals.

The results of this simulation must be treated with caution given Jamaica's small size,and the limited coverage of the Food Stamp Program. In an attempt to account for this, wereduce the size of the fixed total budget to represent a budget equivalent to the actual per capitatransfer used by the Jamaican Food Stamp Program. We reproduce all the previous simulationsusing the smaller budget of J$141,120 (Table 10). Under this scenario, the transfer amount forthe simulation combining targeting mechanisms would be large relative to the other transfersimulations, but not in relation to per capita consumption.

The impact on poverty reduction achieved by using the small transfer amounts is verylimited. Between the three targeting schemes, the "combination" technique does produce resultsthat are slightly better, though the difference does not turn out to be statistically significant. Theresults from this transfer scheme may be more clear using similar data from a larger country.If for example, data were available on participation in popular programs such as the MexicanTortilla or Milk Program, we could have increased the number of states included in thesimulation, without running the risk of including the whole country (as was the case in Jamaica).Such results may have allowed us to better determine what the effects of combining geographiclocation with other targeting mechanisms would have been on targeting accuracy and povertyreduction.

21

Table 10: Comparison of Transfer Schemes: Jamaica (J$141,120 budget)

Outcome Measures Targeting Method

Geographic/General Geographic Food Stamp

Uniform Food Food Stamp Targeting Programtransfer Subsidy Program 20% coverage combined

Undercoverage 0 0 65.0 76.8 85.7

Leakage 70.0 77.0 47.5 46.1 25.5

Benefit per Recipient 8.67 (5.21-11.29) 45 40 151.4(in J$)

Head-count (a=r0) 30.28 30.29 30.38 30.22 30.26(% change) (-.04) (-.32) (-.03) (-.55) (-.41)

Poverty-gap (ca =1) 10.40 10.41 10.40 10.30 10.10(% change) (-.67) (-.52) (-.62) (-1.62) (-3.63)

DistributionallySensitive (a=2) 4.69 4.74 4.69 4.65 4.46(% change) (-.96) (-.70) (-1.77) (-2.73) (-7.05)

Note: The first number is the poverty index. The number in parenthesis is the percentage change from the initial, prc-transfcr povertyindcx value. Povcrty line is the 30th percentile, N = 16,000.1nitial poverty indices before the transfer were for a = 0, 30.39, a = 1,10.47, and cx = 2, 4.77. N=16,000 individuals.

SECTION V: CONCLUSIONS

The results of the simulated transfer schemes indicate that geographic targeting is a usefulmechanism for transferring benefits to the poor. In an operational sense, it has the advantageof being administratively simple to implement. Leakage and undercoverage rates are somewhatlower than those predicted for a hypothetical uniform transfer scheme that would beadministratively difficult to implement. When compared to an actual generalized food subsidyprogram, geographic targeting's accuracy is much better. Its results do not, however, comparewell to a hypothetical, "perfect" targeting. In the cases of Mexico, Venezuela, and Jamaica,over half of benefits went to those in the higher income groups under a state targeting scheme,and about half of the intended recipients were excluded from the program.

The technique used for ranking states does not appear to have a significant impact ontargeting outcomes. In Venezuela, results for the composite index, FGT poverty measures andmean per capita income rankings were all similar. In Jamaica the outcomes for the differentconsumption-based poverty rankings were identical. The technique of transferring varyingamounts to different regions based on their poverty index does improve undercoveragesignificantly, however, leakage rates increase, and there is little difference in poverty outcomes.It is doubtful that the improved undercoverage rate outweighs the additional administrativeburden of such a transfer scheme.

The simulations for Mexico show that geographic targeting accuracy can be improvedas the size of unit used in decision making gets smaller. The outcome for the locality leveltargeting is distinctly better than that for the state level. The overall reduction in the severityof poverty when measured by the FGT ct= 2 index doubles using municipality-level targetingversus state-level, and triples using the locality-level unit. It is expected that if the samesimulation were done for the neighborhood-level, results would improve even further.

Simulations for Jamaica comparing a general food price subsidy scheme, a food stampprogram which uses a means test and a self-selection process, and geographic targeting showclearly that the targeted schemes perform better than the untargeted price subsidies. Betweenthe two targeting mechanisms, the results are less conclusive. A simulation using a combinationof the two targeting mechanisms produces results which are somewhat better than for eithermechanism used above. The ranking among targeting alternatives is sensitive to the size of thebudget. When the budget is high and the target group narrowly defined so that the transferamount is very large relative to the poverty line, the overall poverty reduction is limited.

Identifying poor individuals based on where they live rather than their income level(which is difficult to verify), or nutritional status is undoubtedly easier to carry out. There arein practice, however, problems of incentive effects and political economy which may arise withgeographic targeting. For example, giving benefits to one region rather than another mayprompt migration between regions. If the poor move from the unserved to the served region,then program coverage and costs will increase. This will be justified because targeting accuracy

23

would increase and the poor will be better served. If the non-poor move from the unserved tothe served region, then the accuracy of the targeting will decline. Migration itself may producecosts to the household in terms of moving costs, income lost in searching for a new job, etc. orto the society in terms of congestion in the area of in-migration. These costs reduce the netbenefit of the poverty program. How important these are in practice will depend upon the costsof moving and the size of program benefits. Few single poverty programs in developingcountries will provide sufficient impetus for whole families to migrate from one state to another,especially non-poor families. But at a fine level of geographic targeting, say at theneighborhood or district level, persons may attend health centers or schools that are not theclosest to home if the more distant center provides food supplements, school lunches, free textbooks, etc. This can lead to overcrowding in the participating schools or clinics and a declinein service quality. Concurrently, the non-participating schools and clinics may be underutilized.And, of course, the targeting outcome may be different than foreseen because of the migration.

Giving benefits to one region rather than another may also prompt political protest,especially if the areas by which programs assigned coincide with political units. It is natural thatif some states or cities receive nationally funded programs and others do not, that the governors,legislators, mayors or citizens of the non-recipient states or cities will lobby to be included inthe program, or vote against funding for programs that do not include their constituencies. Thiscan be addressed by targeting witin states. In the simulation shown here, this would allowbenefits to go to some less poor municipalities and would exclude some poor municipalities vis-a-vis the politically unconstrained case. The change in the mean welfare level of targetedmunicipalities was not, however, marked.

24

Bibliography

Binswanger, Hans and Jaime Quizon (1988), "Distributional Consequences of Alternative FoodPolicies in India," in Per Pinstrup-Anderson, ed. Food Subsidies in Developing Countries,Baltimore, Johns Hopkins Press.

Cox, Donald and Emmanuel Jimenez (1992), "Social and Private Transfers in DevelopingCountries: The Case of Peru," The World Bank Economic Review, Vol. 6, pp 155-1970.

Datt, Guarav and Martin Ravallion (1991) "Regional Disparities, Targeting and Poverty inIndia" in Michael Lipton and Jacques van der Gaag (eds), Including the Poor, World Bank.

Foster, James, Joel Greer, and Erik Thorbecke "A Class of Decomposable Poverty Measures"Econometrica Vol. 52, 1984, pp: 761-765.

Grosh, M. (1994) Administering Targeted Social Programs in Latin America: From Platitudesto Practice, The World Bank, Washington, D.C.

Honduran Social Investment Fund (1991) Presupuesto de Inversi6n, 1991, Tegucigalpa,Honduras.

Ravallion, Martin (1991) "Poverty Alleviation through Regional Targeting: A Case Study forIndonesia", in Avishay Braverman, Karla Hoff, and Joseph Stiglitz (eds) Agricultural Policy andthe Theory of Rural Organization.

Ravallion, Martirl and K. Chao (1989) "Targeted Policies for Poverty Alleviation UnderImperfect Information: Algorithms and Applications: Journal of Policy Modeling Vol. 11 pp2. 13-224.

STATIN and World Bank (1988) Preliminary Results of the Survey of Living Conditions, 1988,Kingston, Jamaica.

Thorbecke, E. and 0. Berrian (1992) "Budgetary Rules to Minimize Societal Poverty in aGeneral Equilibriurm Context," Journal of Development Economics, Vol. 39, No. 2, NorthHolland Press.

Venezuelan Ministry of the Family/United Nations Development Program. (1987) MetodologiaMultivariable Para Determinar Zonas Homogeneas de Pobreza en Venezuela, Caracas,Venezuela.

25

Annex I

Ranking for Jamaican Parishes

Poverty Mean per CapitaParish Rank Minimization Consumption

St. Elizabeth 1* 1119* 3721

Hanover 2* 502* 3912

St. Mary 3* 901* 4098

Westmoreland 4** 1006* 4330

Clarendon 5* 1530* 4881

Trelawny 6 0 5791

St. James 7 0 6352

Portland 8 0 6483

Manchester 9 0 6492

St. Catherine 10 0 6770

St. Anne 1 0 7028

St. Thomas 12 0 7589

St. Andrew 13 0 8614

Kingston 14 0 9805

*Refers to those parishes included in the transfer scheme.

26

Annex II

Ranking for Mexican States

Mean Per Capita IncomeState Rank (in 1984 pesos)

Chiapas 1* 4970

Hidalgo 2* 5826

Guerrero 3* 5878

Zacatecas 4* 6440

Oaxaca 5* 6463

Morelos 6* 6580

Tiaxcala 7* 6771

Michoacan 8* 6793

Puebla 9* 7172

Sinaloa 10* 7224

Colima 11* 7627

Guanajuato 12 7714

San Luis Potosi 13 8050

Durango 14 8152

Quintana Roo 1S 8625

Aguascalientes 16 8670

Mexico 17 8875

Tamaulipas 18 9243

Chihuahua 19 9263

Nayarit 20 9338

Veracruz 21 9356

Sonora 22 9471

Queretaro 23 9590

Coahuila 24 9591

Jalisco 25 10640

Campeche 26 11352

Nuevo Leon 27 11421

Yucatan 28 11440

Baia California Sur 29 11740

Distrito Federal 30 14609

Tabasco 31 14633

Baja California 32 21823

*=states including in the transfer scheme (30% of the population).

27

Annex III

r T Statistics for Teaung Significaan of Poverty Differences

Among Targeting Methods.

Venezuela

Foster. Greer.

T'hortieckerFirst MeLhod Second Method Head-count Index Poveny-gqp a = 2

No Transfer Perfea targeting 67.51 80.7- 73.55

Uniforrr Transfer 29.4- 27.5 25.5

Mapa de la Pobreza 35.45 26.3- 19.51

Mean per Capita Income 36.6- 29.6- 24.01

FGT a = 0, 1 36.8 30.9- 25.9

FGT a = 2 36.2- 31.3' 26.8'

Poverty Mimization 34.9' 35.a' 33.71

Perfea Targeting Uniform Transfer -38.3 -5.553 -52.0-

Maps de la Pobreza -32.4- -56.4' -57.2'

Mean per Capita Income -31.2' -53.2' -53.2'

FGT a = 0. l -31.0' -51.9 -51.6'

FGT a = 2 -31.6' -51.6' -50.7'

Poverty Minimization -32.9' -48.1' -44.6'

Uniform Transfer Map% de la Pobreza 6.0' -1.2 4.1'

Mean per Capita Income 7.2' 2.2' -1.5

FGT a = 0, 1 7.4' 3.5' 0.4

FOT as = 2 6.8' 3.9' 1.4

Poverty Minimization 5.5' 7.6' 8.5'

Mapa de la Pobrcza Mean per Capita Income 1.2 3.4' 4.6'

FGT a = 0. 1 1.4 4.7' 6.5'

FGT I = 2 0.8 5.0' 7.5'

Poverty Minimizaton -0.5 8.8'e 14.5

Mean per Capita Encome FGT . = 0. 1 0.2 1.3 1.9

_FGT a = 2 -0.4 1.7 2.9'

poverty Minimization -1.7 5.4' 9.9'

FPOT a 0.1 FGT . 2 -0.6 0.3 1.0

Poverty Minimizauon 1 -1.9 4.1 8.0

FGT a = 2 Poverty Minimization -1.3 3-7' 7.1'

Note:' Poverty differences arm significant at 5 percent level.

28

Annex IV

T Statistics for Testing Significance of Poverty Differences

Among Targeting Methods.

Jamaica

Fote, Greer,

First Method Second Method Head-count Index Poverty-gap a = 2

No Transfcr Perfect targeting 20.5' 28.0' 24.2'

Uniform Transfer 8.5* 8.9' 8.5*

Mean PCC. FGT a = 0, 1, 2 11.3 13.0' 12.4*

Poverty Minimization 11.41 14.6- 12.9'

Perfect Targeting Uniform Transfer -15.11 -24.8' -21.5'

Mean PCC. FGr a = 0. 1, 2 -11.6' -20.0' -17.1'

Poverty Minimization -11.5' -18.0 -16.6'

Uniform Transfer Mean PCC. FGT a = 0, 1 2 2.5- 4.1 4.10

Poverty Minimization 2.9' 5.8' 4.6'

Mean PCC, F(.r a = 0 1 2 Poverty Minimization 0.1 1.6 0.5

Note

I Poverty differences are significant at S percenl level.

29

Annex V

T Statistics for Testing Significance of Poverty DifferencesAmong Targeting Methods.

Mexico

Foster. Grseer,

First Method Second Method Head-count Index Poverty-gap a = 2

No Transfer Perfect targeting 23.1' 34.3 31.0'

Uniform Transfer 10.0 11 .0* 10.8'

State Level Targeting 12.5' 12.0* 11.1-

Municipal Level Targeting 13.7' 17.8- 17.7'

Locality Level Targeting 16.3' 21.3* 20.8'

Perfect Targeting Uniform Transfer -13.1' -24.0' -21.9'

State Level Targeting -10.6' -23.1- -21.6*

Municipal Level Targeting -9.5' -17.5' -15.5'

Locality Level Targeting -6.8* -14.0' -12.5*

Uniform Transfer State Level Targeting 2.5* 1.0 0.3

Municipal Level Targeting 3.6* 6.9' -7.2-

Locality Level Targeting 6.3' 10.4* 10.5.

StaLe Level Targeting Municipal Level Targeting 1.2 5.9' 6.9*

Lolity Level Targeting 3.8' 9.4' 10.2*

Municipal Level Targeting Locality Level Targeting 2.6' 3.6' 3.4'

aPoverty dlfferetscrs are significant at 5 Percent level.

30

Annex VI

T Statistics for Testing Significance of Poverty Differences

Among Transfer Schemes

Jamaica

Poster, Greer,

TlorbeckeFirt MeLhod Second Method Head-count Index Poverty-gap c = 2

Gcneral Food Subsidy Food Stamp Program 6.21 4.3- 2.1 '

Geographic Targeting (20% coverage) 4.2- 5.81 5.81

Geogruphic Targeting (30% coverage) -3.89 -6.4 -6.6*

_Geographic/Food Stamp Program -2.5 .2 -.6

Food Stamp Program Geographic Targeting (20% coverage) -1.9 1.5 3.7

Geographic Targeting (30% coverage) -2.4' -2.0w 4.5*

Geographic/Food Stamp Program -3.71 4.21 -2.7-

Geographic Targeting (20% coverage) Geographic Targeting (30% coverage) 0.4 | O5 -0.8

Geographic/Food Staup Program -1.3 -4.20 -2.9

Geographic Turgeting (30% coverage) Geographic/Food Stamp Program -3.6 | -6.2 | -7.21

i Note*Poverty differences acre significant at 5 percent level.

Distributors of World Bank PublicationsARGENTINA The Middle East Obrver ITALY PORTUCAL

Curlos Hirsch, SRL 41. Shenf Street Licosa Commisionaria Sansoru SPA Livrara Portu3al

Calena Guemes CaLro Via Duca Di Calabrna, 1/1 Rua Do Carmo 70-74

Florida 165, 4th Floor-O. 453/465 Casella Postale 552 1200 Lisbon

1333 Buenos Aires FINLAND 50125 Firenze

Akateeminen lijakauppa SAUDI ARABIA, QATARAUSTRALIA. PAPUA NEW GUINEA, P.O bo12 JAPAN linr Book Store

FIJI, SOLOMON ISLANDS, SF-00101 Helsinki 10 Eastern Book Service P.O. Box 3196

VANUATU, AND WESTERN SAMOA Hongo 3-Come, Bunkyo-ku 113 Rsyadh 11471

D.A Information Services FRANCE Tokyo648 Whitehorse Road World Bank Publications SINGAPORE. TAIWAN,

Mitcham 3132 66 avenue d'lkra KENYA MYANMARBRUNEI

Victoria 75116 Paru Africa Book Service (EA.) Ltd. Gower Asia Pamflc Pt. Ltd.Quaran House,-Mitangano, Stint Golden Wheel Building

AUSTRIA GERMANY P.O. Box 4,5245 41, Kallang Pudding, 004-03

Gerold and Co. INO-Verlag Nairobi Singapore 1334

Graben 31 PoppeLsdorfer Allee 55A-1011 Wien D-S300 Bonn 1 KOREA. REPUBLIC OF SOUTH AFRICA BOTSWANA

Pan Korea Book Corporation For sepia titLes;BANGLADESH HONC KONC, MACAO P.O. Box 101, Kwangwhamun Oxiord Universty Press

Micrno IndustriesDevelopment Asia 2000 Ltd. Seoul Southern Africa

AssistAnce Society (MIDAS) 4648 Wyndham Stret P.O. Box 1141

HouseS, Road 16 Winunog Cane Korean Stock Book Cente Cape Town 8000

Dhanmondi R/Aresa 2nd Floor P.O. Box 34Dhaka 1209 Central Hong Kong Yeoetdo For sucrwption orders;

Seoul Lntemational Subrppdon Service

Bsmch ofces; HUNGARY P.O. Box 41095

PineView IstFloor FoundatonforMarketEconomy MALAYSIA C-aighall

100 Agrabad Commercil Area Dombovan Ut 17-19 University of Malaya Cooperative Johannesburg 2024

Chittagong4100 H-1117 Budapest Bookshop, LimitedP.O. Box 1127, Jalan Panti Baru SPAIN

BELCIUM INDIA 59700 Kuaia Lumpur Mundi-Prensa Libros S.A.

Jean De Lannoy Allied Publishers Privte Ltd. Castello 37

Av. du Rot 202 751 Mount Road MEXICO 29001 Madnd

1060 Brussels Madras - 600 002 INFOTECApartado Postal 22460 Lirerla Internaional AEDCS

CANADA B mw-h oliczs 14060 Tlalpan, Mexico D.F Consell de Cent, 391

Le Diffuseur IS JN. Heredia Marg 08009 Barcelona

151A Boul. de Mortagne Ballard Estae NETHERLANDS

Boucherville, Quebec Bombay - 400 038 De LindeboomilnOr-PubLikaties SRI LANKA AND THE MALDIVES

J4B SE6 P.O. Box 202 LAke House Bookahop

13/14 Aaf Ali Road 7480AEHuaksbergen P.O. Box244

Renouf Publishing Co New Delhi -110002 1003 Sir Chittaopalam A.

1294 Algoma Road NEW ZEALAND GardinerMawatha

Ottawa Ontario 17ChittaranjanAvenue EBSCONZLtd. Colombo2

K B 3W3 Calcutta - 703 072 Private Mail Bag 99914New Market SWEDEN

CHILE Jayadeva Hostel Building Auckland For snte titls;

Invertec IGT S-A, 5th Main Road, Gandhinagar Fntties FackbokstoretLget

Av. Santa Maria 6400 Bangalore - S0 009 NIGERIA Regenngsgat n 12, Box 16356

Edificio INTEC, Of 201 Universty Prmn Limited S-103 27 Stockholm

Santiago 3-5-1129 Kachiguda Three Crowns Building Jericho

Cross Road Private Mail Bag 5095 For subcrtofts orders;CHINA Hyderabad -500027 Ibadan Wennergren-WiLiamsAB

China Financial &c Economic P. O Box 1305

Publishing House Prarthana Flats, 2nd Floor NORWAY S-171 25 Solna

8 Da Fo Si Dong Jie Near Thakore Baug, Navrangpura Narvesen Information CenterBeijing Ahmedabad - 380 009 Book Department SWITZERLAND

P.O. Box 61235 Etterstad Forsingic titles;

COLOMBIA Pabala House N-0602 Oslo 6 Libraine Payot

Inloenlace Ltda. 1frA Ashok Marg Case postale 3212

Apartado Aereo 34270 Lucknow - 226 001 PAKISTAN CH- 1002 Lausanri

Bogota D.E. Mirza Book Agency

Cantral Bazaar Road 65, Shahrah-e-Quaide.Azam For suiac,roe aordenCOTE D'IVOIRE 60 Ba)aj Nagar P.O. Box No. 29 Libraite Pyot

Centre d'Edition et de Diffusion Nagpur 440 010 Lahone 34000 Service des Abonnements

Afncaines (CEDA) Case postale 3312

04 B. P. 41 INDONESIA PERU CH 1002 Lausanne

Abid1an 04 Plateau Pt Indira Lurited Editonal Desarrollo SAJalan Borobudur20 Apartado 3824 THAILAND

CYPRUS P.O. Box 18 Lunal Central Department Store

Center of Applied Research Jakarta 10320 306 Silom RoPd

Cvpn.s College PHIUPPINES Bwigkok

6, Diogenes Street Engomi IRAN Intemataonal Book Center

P.O. Box 2006 Kowhkb Publishers Suite 1703, Cityland 10 TRINIDAD & TOBAGO, ANTIGUA

Nicos,a P0. Box 19573-511 Condominium Tower I BARBUDA, BARBADOS,

Tehran Ayala Avenue, H.V. dela DOMINICA. GRENADA, GUYANA,DENMAR e Costa Extensmon JAMAICA, MONTSERRAT, ST.

Sarrif-cist.,tteratur IRELAND Makati. Metro Manila KIlTTS & NEVIS ST. LUCIA,

Rosenoerns Al1e 11 Covernnnent Supplies Agency ST. VINCENT & GRENADINES

DK-1970 FedernksbergC 4-5 Harcourt Road POLAND Systematics Studies Unut

DubDlin 2 International Publishing Service #9 Wias StreetDOMINICAN REPUBUC Ul. rliekna 31/37 Cure?e

Ed.toru Taller. C. por A. ISRAEL 0o-o77 War,rwa Tnrnidad, West Indim

Restauricion e Isabel la CaOlica 309 Yozmot Literature Ltd.Aparudo de Correos 2190 Z-l PO Boo 56053 For -cr t orde: UNITED KINGDOM

Santo Domingo Tel Aviv 61610 [PS Journals Microinfo Ltd.Ul. Okrrzna 3 P.O Box 3

EGYPT, ARAB REPUBUCOF 02-916 Warvwa Alton. Hampshire CG34 2PG

Al Ahnam England

Al Glaa StreetCairn

LSMS Working Papers (continued)

No. 63 Kakwani, Poverty and Economic Growth: With Application to C6te d'Ivoire

No. 64 Moock, Musgrove, and Stelcner, Education and Earnings in Peru's Informal Nonfarm Family Enterprises

No. 65 Alderman and Kozel, Formal and Informal Sector Wage Determination in Urban Low-Income Neighborhoods in Pakistan

No. 66 Vijverberg and van der Gaag, Testingfor Labor Market Duality: The Private Wage Sector in C6te d'lvoire

No. 67 King, Does Education Pay in the Labor Market? The Labor Force Participation, Occupation, and Earnings of PeruvianWomen

No. 68 Kozel, The Composition and Distribution of Income in Cote d'Ivoire

No. 69 Deaton, Price Elasticitiesfrom Survey Data: Extensions and Indonesian Results

No. 70 Glewwe, Efficient Allocation of Transfers to the Poor: The Problem of Unobserved Household Income

No. 71 Glewwe, Investigating the Determinants of Household Welfare in Cote d'Ivoire

No. 72 Pitt and Rosenzweig, The Selectivity of Fertility and the Determinants of Human Capital Investments: Parametric andSemiparametric Estimates

No. 73 Jacoby, Shladow Wages and Peasant Family Labor Supply: An Econometric Application to the Peruvian Sierra

No. 74 Behrman, The Action of Human Resources and Poverty on One Another: What We Have Yet to Learn

No. 75 Glewwe and Twum-Baah, The Distribution of Welfare in Ghana, 1987-88

No. 76 Glewwe, Schooling, Skills, and the Returns to Government Investment in Education: An Exploration Using DatafromGhana

No. 77 Newman, Jorgensen, and Pradhan, Workers' Benefitsfrom Bolivia's Emergency Social Fund

No. 78 Vijverberg, Dual Selection Criteria with Multiple Alternatives: Migration, Work Status, and Wages

No. 79 Thomas, Gender Differences in Household Resource Allocations

No. 80 Grosh, The Household Survey as a Toolfor Policy Change: Lessonsfrom the Jamaican Survey of Living Conditions

No. 81 Deaton and Paxson, Patterns of Aging in Thailand and COte d'lvoire

No. 82 Ravaillon, Does Undernutrition Respond to Incomes and Prices? Dominance Testsfor Indonesia

No. 83 Ravaillon and Datt, Growth and Redistribution Components of Changes in Poverty Measure: A Decomposition withApplications to Brazil and India in the 1980s

No. 84 Vijverberg, Measuring Incomefrom Family Enterprises with Household Surveys

No. 85 Deaton and Grimard, Demand Analysis and Tax Reform in Pakistan

No. 86 Glewwe and Hall, Poverty and Inequality during Unorthodox Adjustment: The Case of Peru, 1985-90

No. 87 Newman and Gertler, Family Productivity, Labor Supply, and Welfare in a Low-Income Country

No. 88 Ravaillon, Poverty Comparisons: A Guide to Concepts and Methods

No. 89 Thomas, Lavy, and Strauss, Public Policy and Anthropometric Outcomes in C6te d'lvoire

No. 90 Ainsworth and others, Measuring the Impact of Fatal Adult Illness in Sub-Saharan Africa: An Annotated HouseholdQuestionnaire

No. 91 Glewwe and Jacoby, Estimating the Determinants of Cognitive Achievement in Low-Income Countries: The Case of Ghana

No. 92 Ainsworth, Economic Aspects of Child Fostering in Cote d'Ivoire

No. 93 Lavy, Investment in Human Capital: Schooling Supply Constraints in Rural Ghana

No. 94 Lavy and Quigley, Willingness to Payfor the Quality and Intensity of Medical Care: Low-Income Households in Ghana

No. 95 Schultz and Tansel, Measurement of Returns to Adult Health: Morbidity Effects on Wage Rates in Cote d'Ivoire and Ghana

No. 96 Louat, Grosh, and van der Gaag, Welfare Implications of Female Headship in Jamaican Households

No. 97 Coulombe and Demery, Household Size in Cote d'lvoire: Sampling Bias in the CILSS

No. 98 Glewwe and Jacoby, Delayed Primary School Enrollment and Childhood Malnutrition in Ghana: An Economic Analysis

The World Bank l W

Headquarters European Office Tokyo Office 1

1818 H1 Street, N.W. 66, avenue d'16na Kokusai BuildingWashington, D.C. 20433, U.S.A. 75116 Paris, France 1-1, Marunouchi 3-chome

Chiyoda-ku, Tokyo 100, Japan oTelephone: (202) 477-1234 Telephone: (1) 40.69.30.00 iFacsimile: (202) 477-6391 Facsimile: (1) 40.69.30.66 Telephone: (3) 3214-5001Telex: MCI 64145 WORLDBANK Telex: 640651 Facsimile: (3) 3214-3657

MCI 248423 WORLDBANK Telex: 26838Cable Address: INTBAFRAD

WASHINGTONDC

0

0

14EA ~ ~

0

ISBN 0-8213-2666-X