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LSMS Working Papers

No. 24 Adeasu ring and Analyzing Leiels of Living in Developzng Coxniies: An Annotated Questionnaire

No. 25 7he Demandfor Urban Housing in the Ivory Coast

No. 26 The Cted'Iwire Living Standards Surve: Design and Imploeenttion

No. 27 TheRol of Empfoyment and Earnings in Analyzing Lvels of Living: A General Mehodology withApplications to Maysia and Thailand

No.28 Analysis of Househod Expenditures

No.29 7The Distribtion of Welfare in C8te d'Ivoire in 1985

No.30 Quality, Quantity, and Spatial Variaton of Price: Estiating Price Elasticitiesfrom Cross-SectionalData

No.31 Financing the Halth Sector in Peru

No.32 Informal Sector, Labor Markes, and Returns to Education in Peru

No.33 Wage Detrmiats in Cite d'oire

No.34 Guidelines for Adapting the LSMS Living Standards Questiomzaires to local Conditions

No.35 The Demandfor Medald are in Developing Countries: Quantity Rahoning in Rural GOted'loire

No.36 Labor Market Actity in Cote d'lvowire and Peru

No.37 Health Care Financing and the De and for Medical Care

No. 38 Wage Determinants and School Attainmt among MA in Peru

No. 39 The Alloation of Goods witkin the Household Adults, Chidren, and Gender

No.40 The Effects of Household and Community Charctristics on the Nutrition of Preshool Children:Evidencefrom Rural COted'Ivoire

No. 41 Public-Prkvte Sector Wage Doifrentials in Peru, 1985-86

No.42 The Distribution of Wdfare in Peru in 1985-86

No.43 Profits fm Self-Employment: A Case Study of COte d'lvoire

No.44 77T Liing Standards Survey and Price Policy Refjrw A Study of Cocoa and Coffee Production inC6ted'Ivoire

No.45 Measuring the Wiilingness to Payfor Social Services in Develping Countries

No.46 Nonagrmcultural Fmnily Enterprses in Cot d'Ivoire- A Descriptive Analysis

No.47 The Poor during Adjushment: A Case Study of Cote d'lvoire

No.48 Confronting Poverty in Developing Countries. Definitin, Information,and Policis

No. 49 Sanple Designs forthe Living Standards Surmeys in Ghana and MAuritanialPlans desondagEpour les enquites sur e nivewu de vieau Ghana et en Mauritanie

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

No. 51 Child Anthropmetry in CGte d'Iwvire Estimatesfromn Tzw Surveys, 1985 and 1986

No. 52 Public-Pivate Sector Wage Comparrsons and Moonlighting in Dveloping Countrus: Evidencefomn C6ted'wize and Peru

No.53 Soioenomic Determiants of Fertility in COted'lvoire

No.54 The Wilingnss to Pay for Education in Devdeloping Countries Evidencefrnn Rural Peru

No. 55 Rigiditddes salai- Donnes microiconom iques et macro6moniques sur 'justement du marchidu travad dans k secteur moderne (in French ordy)

No. 56 The Poor in Ltin America during Adjustment: A Case Study of Peru

No-57 7he Substitutaity of Public and Prir.ate Health Carefor ffit Treatment of Children in Pakistan

No.58 Identifying the Poor Is 'Headship' a Useful Concept?

(List continues on the inside back cover)

Welfare Implications of Female Headshipin Jamaican Households

The Living Standards Measurement Study

The living Stardards Measurement Study (Lsms) was established by theWorld Bank in 1980 to explore ways of improving the type and quality of house-hold data ollected by statistical offices in developing countries Its goal is to fosterincreased use of household data as a basis for policy deiion g S l,the lSVMS is working to develop new methods to mnt progress m raisng levelsof living, to identify the consequences for households of past and proposed gov-ernment policies, and to imnprove commumcations between survey statisticians, an-alysts, and poilicymakers.

The iREs Working Paper seres was started to diseminate intermediate prod-ucts from the LwS Publications in the seies include criticl surveys coverng dif-ferent aspects of the L%ws data collection program and reports on improvedmethodologies for using Lving Standards Survey (Ls) data. More recent publica-tions reommend specific survey, questionr and data processing degns, anddemonstrate the breadth of policy analysis that can be carried out using Ls data.

ISk Worlkng PaperNumber%

Welfare Inplications of Female Headshipin Jamaican Households

FrWdic LouatMargaret E Grosh

Jacques van der Gaag

I

The World BankWas,ington, D.C

Copyright 0 1993The International Bank for Reconsuctionand Development/THE WORLD BANK1818 H Street, N.W.Washington, D.C. 20433, USA.

All rights reservedManufactured in the United States of AmericaFirst printing May 1993

To present the results of the Living Standards Measurement Study with the least possible delay, thetypesaipt of this paper has not been prepared in accordance with the procedures appropriate to formalprinted texts, and the Worid Bank accepts no responsibility for errors.

The findings, interpretations, and condlusions 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 affiliaed oraizations, or to membersof its Board of Executive Directoxs or the countries fiey represent. The World Bank does not guarantee theaccracy of the data induded in this publication and accepts no responsibility whatsoever for anyconsequence of their use. Any maps that accompany the text have been prepared solely for the convemenceof readers; the designations and presentation of material in them do not imply the expression of anyopiuion whatsoever on the part of the World Bank, its afffliates, or its Board or member countiesconcerning the legal status of any country, territory, city, or area or of the authorities thereof or conceringthe delimitation of its boundaries or its national affiliation.

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 Warld Bankencourages dissenation of its work and wil normally give permission promptly and, when thereproduction is for noncommercial purposes, without asking a fee. Permission to copy portions forcassroom use is granted through the Copyright Clearance Center, 27 Congress Street, Salem, Massachusetts01970, USA.

The complete backlist of publiations from the World Bank is shown in the annual Inde of Pubibatims,whch contains an alphabetical title list (with full ordering information) and indexes of subjects, authors,and counties and regions. The latest edition is available free of charge from the Dastribion Unit, Office ofthe Publisher, Department F, The World Bank, 1818 H Street, N.W., Washingtom, D.C 20433, USA., or fromPublications, The World Bank 66, avenue d`Iena, 75116 Paris, France.

SSN: 0253-4517

Frdric:c Lauat is an economist for the Soc2&6 Gdn&rale in Paris, France. Margaret E Grash is an econamistin the Poverty and Human Resources Diviion in the Policy Research Departmnt of the World Bank.Jacques van der Gaag is the division chief of the Human Resources Operations Division in the LatinAmerican and the Carnbbean-Country Departnent m of the World Bank.

Librazy of Congress Catalogig-Publicatton Data

Louat, Frederic F.Welfare implicatios of female headship inJamaican households /

Fr6&dic Louat, Margaret E Ch, Jacques van der Gaag.p. can - (ISMS working paper, ISSN 0253-4517; no. 96)

Includes bibliographical references.ISBN 0-8213-2384-91. Women heads of households-4amaica-Economic conditions.

2 Poor women-Jamaica. L Crash, Margaret E. IL Gaag, J. vander. Title IV. Seres.HQ1517L68 1993306W59097292-dc2O 93-21834

CIP

v

Abstract

In this paper we first compare the economic status of male- and female-headed households. We thenanalyze differences in the use of resources (time and money) between the two groups. Finally, we focuson the relative well-being of the children in these households.

Our findings show that poverty and female headship are weakly linked. For instance, if we draw apoverty line that labels 10% of the Jamaican population as poor, 9.0 percent of people living in male-headed households are poor versus 11.1 percent of people living in female-headed households. Thisresult is based on per capita consumption as the welfare indicator. If other indicators are used, or povertymeasures other than the head count index, the differences become even smaller. If the main cause ofconcern for female-headed households is the expectation that female headship is highly correlated withpoverty, then this concem can be put to rest.

Ihe study finds some evidence of small differences in resource use between the two types of households.Labor force participation data indicate that female heads are more likely to work in the market place thanwomen with similar characteristics who are the spouses of male heads of households. Again, thedifferences are small: on average 64.5 percent versus 57.9 percent.

The analyses of household expenditures shows that female-headed households spend no more on food thando male headed households. However when looking at more detailed food expenditures, the differencesare more pronounced. For instance, female headships appear to be associated with spending on higherquality food items such as meat, vegetables, milk and other dairy products.

Perhaps the most important question answered in this paper is to what extent female headship influenceschild welfare. The resuts show that children in female-headed households have, by an large, equalaccess to social services and equally good welfare outcomes as chfldren in male-headed households.

vi

This paper owes much to many. We would lilke to ackmowledge he coaions, n terms of critiqueand saggestions, of the particMpants of th seminas organized by the PERPA and LATHR divions ofthe World Bank, and the Population Cwuncil in New York, and the Iutrnatonal Food Policy ResearchInstitute. Special thans go the Badbar Diallo for typing numerous vrsions of the manuscript and toNatalie Leboucha for computational supporL

U

vii

Foreword

Policymakers concerned with amelioration of poverty have singled out fenale-headedhouseholds as one of the key target groups deserving intensified attention. This paper examines theissue of female-beadship and household welfare in a specific context - Jamaica. In so doing itiliustrates how systematc analysis of the issue should be done in other countries, and the importanceof comprehensive, disaggregated data in examining the link between headship and welfare.

This paper is part of a broader program of research in the Population and Human Resources(PHR) Departnent on the extent of poverty in developing ccunries and on policies to reduce poverty.The research progra. is located in the Poverty Analysis and Policy Division. The data used here arefrom the Jamaican Survey of Living Conditions, which is one of the Living Standards MeasurementStudy (LSMS) household surveys which the World Bank has implemented in many developingcountries. All three aud:ors have helped the Jamaicans implement their survey and have at differenttimes been part of the Povety Analysis and Policy Division.

Ann 0. HamiltonDirector

Population and Human Resources DeparLment

ix

Table of Contents

L. Introduction IThe Budget Constraint 1The Time Constraint 3Different Preferences 4Summary 4

II. The Data S

m. Female-headship in Jamaica: Who, How Many, Where? 6How Prevalent are Female-headed Households? 6Why Are Females Heads of Households? 6What Do We Mean by Headship? 8How are Female-headed Households Different? 10

IV. Poverty, Welfare Distribution, and Female-headship 17Consumption Measures of Welfare 17The Distribution of Welfare in Jamaica isHow Does the Consumption Distribution Vary by Gender of

Household Head? 18Poverty Measures and Female-headed Households 24The Probability of Being Poor: Multivariate Analysis 26Female-headship as a Targeting Indicator 28Conclusion 30

V. Differential Resource Use 31Tnime Use 31Consumption Patterns 32

VI. Children's Acces to Social Services 37Health Services 37Education 38Nutrition Programs 39Summary 41

VI. Child Welfare Outcomes 43Health 43Nutrition 44Education 48Summary 50

VIII. Discussion 51

Bibliography 54

Annexes 57

x

List of Tables

Table 111-1: Location of Households and Gender of Head 10Table 111-2: Household Size and Household Composition by Gender of Head 1 ITable J11-3: Characteristics of Household Heads by Gender of Head 12Table 1114: Distribution of Heads by Labor Force Status and Gender of Head 14Table 111-5: Characteristics of Female-headed Households by Labor Force Status of Head 15Table 111-6: Distribution of Individuals 15-64 by Labor Force and Gender of Head 15Table 111-7: Total Number of Household Members for each Labor Force Status, by

Gender of Head 16Table IV-1: Comparison Between Male-headed Households and Female-headed Households:

Per Capita and per Adult Equivalent (Adjusted) Consumption, Accordingto various Household Categories 19

Table IV-2: Determinants of Households' Welfare as Defined by Per Capita Consumption 23Table IV-3: Poverty and Female Headship 25Table IV4: Probit Models of Probability of Being Poor 26Table IV-5: Elasticity of Poverty Measures by Gender of Household Head 27Table IV-6: Female Headship as a Targeting Tool 29Table IV-7: Poverty Outcomes with Transfers 29Table V-1: Non-Employment Income and Estimated Female Labor Force Participation

for Women 15-64 32Table V-2: Non-Employment Income Received by Male and Female-headed Households 32Table V-3: Consumption Shares by Gender of Household Head 34Table VA4: Food Item Shares and Female Headship 35Table VI-1: Children's Access to Health Care 38Table VI-2: Enrollment Rates by Gender of Child and Household Head 39Table VI-3: Estimated Probilities of Enrollment of 13-19 Year Olds Based on Probit

Regression 40Table VI-4: Children's Access to Nutrition Programs 41Table VII-1: Health Status of Children Age 04 43Table VII-2: Estimated Probability of Children's Diarrhea 44Table VII-3: Gender of Head and Nutritional Status of Children Age 0-4 46Table VII4: Gender Dummies in Nutritional Status of Children Age 0-4 Regressions 47Table Vll-5: Percentage of Repeaters Among Children Enrolled in Schools 48Table V11-6: Percentage of Children with Full Attendance During the Previous Week,

Among Children Enrolled in School 49Table VII-7: Enrollment in High Schools by Gender of Child and Household Head 50

List of Figures

Figure E-I: Identifying Female-headed Households 7Figure 111-2: Income, Age and Education in Female-headed Households with Partners 9Figure IV-1: Distribution of Household Consumption per Capita and per Adult Equivalent 20Figure IV-2: Distribution of per Capita Consumption, by Region 20Figure IV-3: Distribution of per Capita Consumption, Male vs. Female-headed Households 21

xi

AnnexI: Calculation of Per Capita Consumption 57I: The Estimation of the Distribution of Consumption Expendiures 58m: Technical Annex on Poverty Measures 59IV: Full Regression Results 61

I. Introduction

Female-headed households are of possible policy concern because, from what we know about the broaderissues of gender and welfare, we expect female-headed households to be less well off than otherhouseholds. First, with a woman as the main income earner and women's less favorable labor marketoutcomes, we would expect female-headed households to be poorer, and perhaps to be more vulnerableto recession, including those induced by macroeconomic adjustment policies. Second, because theysupport the family through income-generating activities, female heads of household may face tighter timeconstraints on non-market activities that are important to their children's welfare.

A relatively new strand of the gender and welfare literature counters this reasoning from female-headship to poverty to poor child welfare outcomes. It indicates that women use resources differentlyfrom men, so that resources in the hands of women will improve children's welfare outcomes more thanresources controlled by men. Since female heads of household presumably have fuill control over theirresources, their virtuous efficiency may produce higher child welfare outcomes than when a man headsthe household. The strength of the forces, and the ultimate outcome in the welfare of female-headedhouseholds is an empirical question.

This paper is designed to be a systematic empirical analysis of the welfare of female-headedhouseholds. We have chosen Jamaica as the country to study because female-headship is prevalent andof policy concern in Jamaica, and because we have access to good household survey data for Jamaica

The first section of the paper sets the conte for the empirical work in the paper. It brieflysketches the literature on the factors and hypotheses to be investigated. Section II describes the data setused for the empirical examination of female-headship in Jamaica. Section m asks the questions 'Whatis female headship? Is it important in terms of numbers?" Section IV examines the link between femaleheadship and poverty. It asks the questions "Are female-headed households poorer than male-headedhouseholds? By how much?" Section V asks 'Do female-headed households spend their resourcesdifferently than male-headed households?" Section VI examines the access of chfldren from female-headed households to social services. Section VHI com pares the welfare outcomes of children fromfemale-headed households with those of their counterparts in male-headed households. Together, thesesections ask Do female-headed households warrant extra government attention?" Section VIII concludes.

The three hypotheses that guide the design of this study (ower income, more severe timeconstraints and differential resource use) have their empirical counterparts in most of the literature onwomen in developing countries. We will briefly sketch this literature and, where possible, includeprevious studies on Jamaica on these issues.

The Budget Constraint

The notion tat female-headed households wil be poorer than male-headed households is based on theevidence that women's income and wealth are less th those of men. The sibtation is neatly summarizedin Commonwealth Secretriat (1989, pg. 2).

"...women account for half of the world's pion, perjbrm wthw ds of the hoursworked (though are recorded as working only one-tird of thse hours), receive one-ttof the world 's income, and have onehundredh of the world's propery registered in theirname.

2

With such disadvantaged heads upon whom to rely for sustenance, female-headed households are at riskof poverty.

The reasons that women's earnings are lower than men's are roughly divisible into three classes -- lesser human capital, discrimination, and less physical or financial capital.

To take the last of these first, women have less access to financial capital and land ownership thanmen. The small literature on access to credit shows that women may have less access to credit than men(see Berger and Buvinic [19891, for a review). The reasons for differential access are consonant withthe rest of the female-headed household literature. Women's tight time constraints may make it difficultfor them to go through the applications procedures. Women's lower levels of literacy make it moredifficult to apply for credit or to know where to apply. he de ju and de facto differences in property,tenure and title rights may limit their collateral. Cultural constaints may limit their ability to joinnetworks that can help overcome the barriers to credit, their ability to travel, to act independently, etc.Finally, discrimination in the review of credit applications may be a factor.

Human capital is built both by formal schooling or training and by job-related experience.Women's lesser human capital will tend to produce earnings lower than men's even in the absence ofdiscrimination. The female-male gap in formal schooling is catalogued in World Bank (1990). In 1987,in the world, girls' enrollment rates as a percent of boys' rates were 84 percent at the primary level and79 percent at the secondary level. This varied somewhat by region and very greatly by country. Chad,Guinea, and Rwanda all had ratios of under 50 percent As a region Sub-Saharan Africa was the worst,with averages rates of 77 percent at the primary level and 50 percent at the secondary level. LatinAmerica and the Caribbean showed the highest participation of girls relative to boys of all regions, at 96percent for the pdrimary level and 110 percent at the secondary level. Across the board, girls'participation relative to boys' increased markedly since 1970. Thus the extent to which women face ahandicap in human capital is falling.

Women's participation in labor market activities is relatively low. In the world as a whole,women's participation in the labor force is only 56 percent of that for men (UNDP, 1990). This ofcourse varies by region. In East and Southeast Asia, the ratio is 71 percent, in the Middle East andNorth Africa it is only 24 percent. In Latin America and the Caribbean it is 36 percent. These aggregatefigures do not take into account that women more frequently withdraw from the labor market and/or workpart time, which will fiurther reduce the accumnlation of human capital through work experience.

In Jamaica, however, lesser human capital among women is not the problem that it is in somecountries. Primary school enrollment is universal for both boys and girls. At the secondary Ievel, girls'enrollment rates are higher than boys (see Table VI-2). For adults who have completed their education,women also fare slightly better than men (see Table m-3).

Jamaican women's participation in the labor force is 80 percent of that of men (STATIN, 1990).It is also interesting to note that women are well represented in some of the high-paying occupations.For example, 8.2 percent of working women are in the professional, technical and administrative groupwhile only 4.9 of working men are so classified. Similarly 15.2 percent of worling women are inexecutive, managerial and related occupations while only 7.6 percent of working men are so classified.

Even when women's human capital is equivalent to men's, gnder discrimination in the labormarket may lower their earnings markedly. Psacharopoulos gLal.'s (ongoing) comparative study

3

quanifies this phenomenon. Case studies for several Latin American countries have used employmentsuvey data to decompose male-female earnings differentials into the part due to different human capitalcharacteristics and hours worked, and the part which is due to differential rewards to the same humancapital, that is, discrimination. Studies for Argentina, Bolivia, Brazil, Colombia, Ecuador and Venezuelashow that differences in education, experience and hours worked account for only about one third ofmale-female earnigs differentials. The other two thirds unexplained difference is about twice that foundin industrialized countries. Buvinic (1990)surveys within-sector and within-job comparisons in male-female earnings and further documents the phenomenon of discrimination.

For Jamaica, Scott (1990) decomposes the differences in men's and women's earnings controllingfor education, experience, and hours worked, and correcting for selectivity bias by modeling women'sdecision to participate in the labor force based on location and household and personal characteristics.Working women earn only 58 percent of what men earn even though women work only six percent fewerhours per week than men. The average level of education among working women is higher than that ofmen by onehalf of a year. Scott concludes: wWomen 's higher [hwnan capitall endowment is offset bythe vey strong effect of differen valuations of male and female labor in the marketplace. Theunexplained [discriminatory] portion of the differenid is so strong that it not only explains the wholewage gap bu negates the effect of women hving higher endowments. In short, wage differentials inJamaica are nota function of different levels of hwnan capital between men and women but are, unTead,due to the pricing mechanis m "(pg. 14).

Whether the evidence of male/female earnings differentials implies that female-headed householdswill be worse off than male-headed households depends upon household structure and the sharing ofresources within the family. If the female-headed household is a nuclear family from which the maleprincipal breadwinner who formerly shared all his income altruistically, is now removed and sends noremittances or child support, then clearly the female-headed household will be worse off. The conceptis, however, somewhat complicated.

Female-headed households may have other adult members, who can contribute to the householdincome. Furthermore, the household may receive remittances either from the absent man or from otherrelatives. In addition, even if a man is present, he may not share all his income with the family and, ofcourse, he is a consumer. The net effect on the household's welfare of his absence, as a contributor andconsumer, given the various coping mechanisms the household may adopt, is not a priori clear.

The Tihne Constraint

If the female head of household is pressed for money, she is also likely to be pressed for time. Women,in general, spend longer hours on the combination of income generating activities (be they market orhome-based) and domestic chores which contribute to household welfare. IDB (1990) summarizes theissues:

"According to 1984 stuies made by IL) n different countries of Latin America, the followingphenmna have been observed: (a) women i the workforce put in two fidl work shifts - one at homeand one on the job; (2) the increase in family income generated by their remnerated work may haveallowed the women in some sectors to contract ouside help for some of their tough household tasks, buttiWs help has not significany lightened women's household work load; and (3) in the households in whichthe regional economic crisis has signified an increase in the household work load, the male familymembers hae not increased their particlpation in hous,rhold chores accordingly . 2171. '

4

When female-headship implies that the woman must increase her income-generating activities,she naturally has less time for welfare-producing domestic activities. Children of female heads ofhouseholds may have to substitute for her domestic labor, or complement her eamings, thereby reducingthe time available for their schooling. The general health and nutritional levels of the family may declineas the activities which safeguard them are supplanted by more immediately urgent income-generatingactivities. Tlis would result in lower welfare outcomes for children now, and reduce their earningspotential in the future thereby transmitting poverty to the new generation.

As we shall see in Section m, Jamaican female heads of household do work more in the marketthan other women, and hence face this time pressure.

Different Preferences

Even if female-headed households are doubly constrained by low income and too little time to carry outall their tasks, the priority that they give to their children's welfare may help safeguard welfare. Dwyerand Bruce (1988) review a number of studies in three veins of this literature. One set of studies inseekdng to explain child welfare outcomes, notes that women's income, more than men's is an importantdetminant. A second set of studies shows that more of women's incomes than men's goes to everydaysubsistence and nutrition. A third set of work shows that women devote a higher and more constant shareof income to family as opposed to personal needs.

In Jamaica, Horton and MIller [no date] investigate gender differences in expenditure patternsusing a sample of 145 households. The sample was not drawn randomly, but rather so that it wouldinclude primarily low income households, and from families who were willing to make the large timeco.mmitment required to fill out daily questionnaires over 8 months in exchange for a mondlly cashremuneration equal to about 25 percent of sample mean expendiure. The households in the sample wereindeed poorer than the national mean, had larger household size, and were more often headed by womenthan expected.

Horton and Miller first analyze broad expenditure shares, but find no differences by gender.They then disaggregate within the food share. They look at how the item's share in the food budgetchanges with total consumption and compute an index of dietary quality based on eight micronutrients.The authors find that while female-headed households do not consume more calories per capita, they doconsume foods that rank higher on the index of quality and have more nutrients. Thus, Horton andMiller's work shows some weak support for the idea that female-headed households more than male-headed households use their resources in ways that will benefit child welfare. We will therefore replicatesome of their work with our much larger and more representative data set in Section V.

Summary

The received wisdom from the literature on women and welfare predicts that female-headed householdswill be relatively poor and time constrained. Their children may or may not be deprived depending onwhether female-headed households' use of resources is sufficiendy child-focused to offset the time andincome constraints that may be tighter in female-headed households. It is from this background that weproceed to a systematic empirical analysis of female-headship, poverty, and child welfare in Jamaica.The next section describes the data used for the anaIysis.

5

II: The Data

The data used for this paper come from the November 1989 round of the Survey of Living Conditions(SLC) and the linked October 1989 Labour Force Survey (LFS). The SLC is an integrated bouseholdsurvey instituted by the Jamaican government to provide a basis for the analysis of poverty and socialsector policies and programs.' The LFS contains the household roster and a brief set of information onlabor force information. The SLC has modules on health, education, nutritional status, housing, distanceto social services, and participation in government social programs. Detailed information on remittancesand households expenditures, including the value of gifts and home-produced food items, is collectedfor use as the general welfare measure for the family. A general description of the Jamaican Survey ofLiving Conditions is found in Grosh (1991).

The LFS has a nationally representative sample of 6000 households. The November 1989 SLCrevisited two thirds of the LFS sample Qi.e. about 4000 households) about a month after the firstinterview. The two data sets were then merged for the analysis presented here. The merge was slightlyiinperfec Therefiore, for issues supported solely by the SLC, the full SLC data set was used. WhereLabor Force data were needed, the smaller subset of successful matches between the two surveys is used.The merge was completed for 95 percent of individuals and 93.5 percent of household heads. Noapparent selectivity bias is implied; the percent of female-headed households in the matched andunmatched sets is the same.

The SLC is based on the World Bank's Living Standard Measurement Study surveys (see Glewwe,1990). In the course of adapting the survey to the constraints of a single interview, some features of astandard LSMS were omitted. Those most relevant to this paper are detail on the time use of householdmembers in home production and maintenance activities, and more detailed labor force and incomeinformation.

6

m. Female-headship in Jamaica: Who, How Many, Where?

How Prevalent are Fenale-headed Households?

Female-headed households comprise 42 percent of all households in Jamaica. This very high rate ofprevalence is important to the understanding of the phenomenon in Jamaica. With female-headshipaffecting nearly half of society, any disadvantage suffered by such households will have repercussionsfor the welfare of society as a whole, not just for a small, fringe group. Hence, the study of female-headship is important. On the other hand, because of female-headship's very commonness, it may be thatit has ceased to denote a small, homogenous segment of society with common problems amenable tocommon solutions. If, in their prevalence, female-headed households have become very heterogeneous,or hard to differentiate from male-headed households, it may no longer be that the gender of the headis an important feature of a household. With this in mind, let us look at what female-headship means inJamaica.

Why Are Females Heads of Households?

Overall, 42 percent of households in Jamaica are headed by women. Of these, three quarters (or 31percent of all households) are headed by women who are in the oldest generation present in the householdand who do not have a spouse or partner in the household. These conform to the most commonpercepton of female-headship. In nearly all of the other quarter of female-headed households, the womanwho heads the household belongs to the oldest generation in the household but does have a spouse orpartner present. In only three percent of all households are there members of a generation older than thatof the head. Even within that group, the portion of female heads with and without spouses/partners issimilar (see Figure EI-1).

As noted, one quarter of female heads of household have a partner present in the household. Incontrast, 59 percent of male heads of household have partners present. The definition of partner hereis very broad, including legal and common-law marriage as well as 'visiting unions" that do not implypermanency.2

2Ihe questionnaire first asked for union status, and then asked specifically if the partner was in thehousehold. Thus some non-resident partners, especially those in visiting relations, are excluded here.

7

Figr 111-1 Idiyhig FundabeadKd Households-

(23X)~ ~ ~ ~ 2

896

_ d Hiea isml(542) mo~54Hed is sinlt (n

Z9 Ned - not ve_fth a spouse I6

Needisf e Had is teneed belongl to Le tthe oldest gner-31) _ prntitn of thehoucehold. ).

! I el~~~~~~~~~leadIs role

Nlee Livw witth a

16661

I X44X)' _ ll~~~~~ead is fewtealt ~~~~~~~~~~~~~390_

223861 Ra is imei54 Read is singte Cro

rtrper pr esen)Only pawc pf t is faimei

99 the head LS in 3Z_the househotd _36

NOW td liv wf th * 3 r ed sote. __ _ Q~~~~~~~~spouse J Pater |ead'ls parents Car U' -" ' le s fewle

pwnt-in-lo) am _-' auto, ofsd the household. ,,Redis let |

X~~~~~~~~~~~ 11kad is single Cno '-I105 CX .Hprnr pi esent H .

B8oth parets of||{-|4edis felet |the head lfve in I11

-the househotid H4r ' |gHed lves mfth a| '- Uspou" / partner %edi l

The wtI nubers of haLmbotds in the spte is the ffrst nubeor. lhe percentate of ell households iS the 'Mr fnparendlthes.

8

The common notion of female-headship suppose. that if the woman is in union, the male will beconsidered the head of household. Yet the reverse happens in a quarter of the Jamaican female-headedhouseholds. To explore this, we compared the partners' labor force status, income, age and educationallevel in those households where females with partners were declared to be the head (see Figure IH-2).In these households, the woman had the higher income3 in 39 percent of cases. In 12 percent of casesshe had a lower income but was older.' In 9 percent of cases she had a lower income, and was younger,but had a higher education.' In 40 percent of cases, in spite of earning less, being younger, and beingless well educated, the female member of the union was declared as head. Thus, in ninety percent ofcases of female-headship there is a readily apparent reason why the woman might be declared as head(either she has no partner, or has a higher income, is older, or better educated than he). In the remainingten percent of cases of iemale heads (Gust 4% of all households), the reason is less clear. The ownershipof the dwelling, or the length or strength of relationship with the partner probably influences theassignment of headship.

What Do We Mean by Headship?

The measure of headship used is the declaration of the household member who was the respondent onthe Labor Force Survey'. Because of limitations in the data set, we cannot systematically examinewhether the declared head of household contributes more dollars or more hours of work to the familywelfare than the non-head of household,7 much less what sort of influence they have on decision-making.Rosenhouse (1989) shows that using a definition of headship based on hours of market labor providedalmost doubles the percentage of Peruvian households classified as female-headed. Fox and Paes doBarros (1990) show that in Brazil there is virtually no difference. They also show that use of an incomecriterion rather than reported headship raises female headship only slightly.

'By at least 10 percent.

'By at least two years.

5By at least two years.

61n order to determine whether the assignment of headship was dependent upon the gender of therespondent we compared the gender of the respondent and the declared head for households withpartnered heads. Male respondents reported a male head in 94% of cases, whereas female respondentsreported male heads in only 67% of cases.

'Although we did make use of individual income in describing the status of female heads vis-a-vistheir resident partners, the Labor Force Survey's income questions do not contain enough detail tosupport thorough work on relative incomes, especially for the self-employed. Furthermore, hours ofwork in labor force activities are collected only fo: broad ranges of hours. Without exact values andwithout information on household chores, a redefinition of headship based on labor contribution in notpossible.

Figure 111-2: Income, Age and Education In Female-Headed Households with Partners

66 (13,)

95 he has equal or H ( e c ldr higher Incoso _ 119 _*esrrd

109 O2X*eI l X)_both work _(X0S5X) 5

(14%1 Inam 29

32 ha7 smor_ ~~~~~he is ole _

43 hha eout or (8)3hi8e r irncomc _ 1 h nre edul

neither works _(X(19X) 31

she has higher(ax) Incoews8 assoe

with l10ie he has equal or 1 25%o)

| Partrars l ~12S he works ON fo < x400 and _<XOM1X she doesn't 7

she has higher1(2X) lncom

62 she worksand

(ISX)l he doesn't

The absolute nmber of households In the surpte Is the first number. The percentage of femle headed househotds with partr,ers Is In parentheses.

10

Now are Femaebheaded Households Different?

Lmcation. Female-headed households are somewhat more common in urban areas than in rural areas -46 percent as opposed to 39 percent (see Table rn-i).

Size. Female-headed households are larger dhan male-headed households. Their average size is4.4 members, as opposed to 3.9 (see Table 1-2). The smaller average size for male-headed householdsstems partly from the greaer tendency of men to live in single person households. Of al single personhouseolds, two tlirds are men. These account fbr one quarter of male-headed households. Thedifferences at the other end of the scale are less marked. 28 percent of female-headed households haverore than six members, and 25 percent of male-headed households pass this threshold.

Table M-1: LAcation of Households and Gender of Head

Gender of die Head

male Femnle

= - - - -.-- - .,.-. 7... ., ...... ... ;.

Kingstn M.A. 54.2 45.8

Other Towns 53.5 46.5

Rural Areas 61.0 39.0

AlU Country 57.7 42.3

Strucue. Female-headed households have slighty more children, both in terms of the averagenumber of children per household, and in terms of the percent of the household members who arechildren. Itere is an average 0.7 children age 0-5 in fmale-eaded households, but only 0.5 in male-headed households (see Table M-2). Extending the range to chidren 0-10 doubles the mnubers, butmaintains the patterns.

ii

Table m-2: Household Sue and Household Compostion by Gender of Head

Gender of the Head

Male Female

.HOUSEHOLD'.SIZE . ._-_-=_

Single Person 24.4 12.1

Medium: 2 - 5 51.2 60.4

Large: 6 & + 24.6 27.5

HOUSEHOLD STRUCTURE

Mean Size 3.9 4.4

No. Of Children 0 - 5 0.5 0.7

No. of Children 0 - 10 0.9 1.3

% of Members 0 -5 8.4 11.7

%ofMembers 0-10 16.4 23.5

Union Status. The union status of the heads of female-headed households is quite different fromthat of the heads of male-headed households. It would seem to imply less stability of union, and thereforeof responsibilities and resources for the household. Female-headed households are less than half as likelyas male-headed households to be in legal marriage -20 as opposed to 44 percent. They are also lesslikely to be in common-law marriages -15 as opposed to 22 percent. They are half again as likely tobe in 'visiting unions', three times as likely to be widowed, and twice as likely to be divorced. Finally,they are almost twice as likely to never have been married (see Table IH-3).

12

Table m-3: Characteristics of Household HEeads by Gender of Head

GENDER OF THE ALLHEAD COUNTRY

lMale Female

;UNION STATUS '- .-;.-_-,:-

Married 43.5 20.0 33.3

Common law 22.1 15.2 19.0

Visiting 10.3 16.1 12.7

Casual 3.0 2.4 2.7

Widowed 5.0 18.2 10.5

Divorced 0.5 1.2 0.8

Never Married 15.6 26.9 20.2

AGE OF THE HEAD _ E_ _

15 -29 15.2 15.4 15.3

330-39 21.5 22.0 21.7

40 - 49 19.9 15.9 18.2

50 - 59 16.3 14.3 15.5

60& + 27.1 32.4 29.3

i IEDUCATION OF THE D _ -_-_-_- -- _- _-_-

None 2.3 1.5 2.0

Some Primary 10.0 10.2 10.1

Primary Completed 62.0 61.8 61.9

Some Secondary 10.3 11.3 10.7

Seoondary Completed 14.8 14.0 14.5

Unknown 0.7 1.1 0.9

13

Age of Head. Female heads of household are somewhat more likely to be over age 60 than aremale heads of household -32 percent as opposed to 27 percent. They are correspondingly slightly lesslikdy to be in the prime earning range of 40-60. They are not any likelier than male heads of householdto be under 40 (see Table 1-3).

Education. There are no systematic differences in the education status of male and female headsof households. Differential earnings are therefore unlikely to be due to inferior levels of formal educationon the part of the female heads (see Table m-3).

Labour Force Status of Head of Household. The labor force status of the head of householddiffers markedly by the sex of the head of the household. While 79 percent of male heads work, only50 percent of female heads work (see Table m 1-4). Female heads of household are two and a half timesas likely to be unemployed, and three times as likely to be out of the labor force. Of the third of femaleheads who are not in the labor force, over ninety percent give as the reason that they 'do not want towork'. When working, female heads of household work fewer hours. This implies, of course, lowerearnings but more time for domestic chores. This issues will be taken up in greater depth in Section V.

Given the stereotype of the beleaguered female head striving to support her family, the prevalenceof women not in the labor force is somewhat surprising. We therefore explore three possibleexplanations. First, since headship is almost always the privilege of the oldest generation, it may be thatfemale heads of household who are out of the labor force have adult children in the household. Second,these women may also be among those who had a partner in the household. Third, those women withmany young children would presumably find it more difficult to work and chose not to if they can findsustenance from other sources.

In fact, none of these explanations is borne out by the data. The out-of-the-labor force femaleheads of household were only slightly more likely to have adult children in the household than the femaleheads of household in the labor force -37 percent as opposed to 34 percent (see Table m-5). They wereless likely to have partners in the household -21 percent as opposed to 26 percent. They were less thanhalf as likely to have young children -11 percent as opposed to 24 percent for children aged 0-5 and17 percent as opposed to 40 percent for children age 0-10.

14

Table m-4: Distribution of Heads by Labor Force Status and Gender of Head

SEX OF THE HEAD

MALE FEMALE

COMPONENT OF THE LABOR FORCE]. -

Employed 78.7 50.0

Unemployed 4.1 10.2

Out of Labor Fore 10.5 32.0

Too Young 0.6 0.9

Unknown 6.1 6.9

dREASFOR NOT WORNG

Does not Want 90.6 93.1

Illness 6.0 3.3

At School Full-time 1.7 0.6

Not Prpared 1.3 0.0

Pregnancy 0.0 0.8

Stay w/Children or Relatives 0.0 1.9

Home Duties 0.0 0.4

Other 0.4 0.0

--- HOURS WORKEDDR LAST WEEK!

32 Hrs. or Less 6.5 15.3

33-40 Hrs. 43.7 47.8

41-49 His 35.8 28.7

49 His. & More 14.0 8.1

15

Table E-5: Characteistics of Female-headed Households by Labor Force Status of Head

Female Head in Female Head Outthe Labor Force of the Labor Force

% Frequency Overall 60.2% 32.9%

HOUSEHOLD STRUCTURE l

% with adult child in hh. 33.5 37.3

% with partner in hh. 26.1 21.2

9 with young child (0-5) 23.9 11.3

% with young child (0-10) 40.2 16.9

Labor Force Status of All Household Menbers. The difference in labor force status of allmembers of the household is less marked than differences in the household heads. Half of theworking age members of female-headed households work, while 61 percent of working age membersof male-headed households work (see Table I*-6). In female-headed households, 15 percent of theworking age members are unemployed and 24 percent are out of the labor force. In male-headedhouseholds, 10 percent are unemployed, and 21 percent out of the labor force.

Table m-6: Distribution of Individuals 15-64 by Labor Force Status and Gender of Head

SEX OF THEHEAD

MAIE jFEMALE

COMPONENT OF THE LABOR FORCE

Employed 61.4 51.3

Unemployed 10.3 15.5

Out of Labor Force 20.8 24.3

Too Young 0.5 0.7

Unknown 7.1 8.3

16

Depeadent-Earner Ratio. 'he combination of differences in age structure in the household andin labor force status of those of working age combine to result in different dependent-to-worker ratios.In female-headed households the ratio is 2.0 as contrasted to 1.4 for male-headed households (see Tablem-7).

Table m-7: Total Number of Households Members for each Labor Force Status, by Gender of Head

SEX OF THE HEAD

MALE FEMALE

Total Number of Employed Persons in the HH. 1.6 1.4

Number of Unemployed Persons in dte M 0.3 0.4

Number of HR. Members out of die Labor Force 0.7 0.9

Number of HH Members too Young toWork 1.1 1.5

Number of HH. Members with Unknown Activity 0.2 0.2

Number of Dependents per Worker 1.4 2.0

Summary. Female-headed households have less stable unions, are bigger, have more children,and have both heads and other members who are less likely to be working. All of these factors may leadto households to be poorer than male-headed households. Poverty and female-headship is therefore takenup in the net section.

17

IV: Poverty, Welfare Distrbution, and Female-headship

One of the fundamental reasons that female-headship is of concern is the tendency for female-headshipand poverty to go hand in hand. This section examines the relationship between poverty and female-headship in some detail.

Consumpeion Measures of Welfare

We use consumption as the measure of welfare rather than income for twu reons. The first istheoretical - welfare levels of households are raised by the goods and services they consume, not bythe income available for consumption. The second reason is more practical and also more decisive -income data are more prone to errors than consumption data. This is true in general, and especiallyof the data set used for this paper, which had a very sparse income module with no probing oreliciting of different sources of income.

The aggregation of consumption expenditures' used here is comprehensive. The data setincludes about 150 questions on direct household expenditures. The value of gifts received is soughtfor most items, and the value of home-produced food for sixteen items. The use value of consumerdurables and the rental value of owner-occupied housing are imputed (see Appendix A for technicaldetails).

The utility level reached by a household of a given total consumption level will depend uponthe household's composition. The simplest way of reflecting this is to divide total householdconsumption by the number of members. We refer to this as per capita consumption and will use itas the main indicator of welfare for this paper.

Per capita measures, though simple to calculate and interpret, are a somewhat inaccurateadjustment for household composition. Additional household members, particularly children, are less'costly', in the sense of requiring less additional consumption to maintain the welfare level of thehousehold, relative to the initial cost of attaining that welfare level in a household composed of Esingle person or a childless couple. This idea is supported by both common sense and economicreasoning. Clothing and other items can be handed down from older to younger children, durablegoods such as radios and refrigerators can be enjoyed by additional members at no extra cost and,even in the case of food, children consume less than adults. The method for adjusting for thisphenomenon is the esfimation of adult equivalence scales. These measure the 'cost' of additionalhousehold members in terms of fractions of adults (see Deaton and Muellbauer, 1980, Chapter 8).

Using adult equivalence scales, we calculate adjusted per capita consumption in order to testwhether some of the results in the paper are robust to the definition of welfare. The equivalent scalesused are those calculated by Deaton and Muellbauer (1986) for Sri Lanka and Indonesia and used byGlewwe (1987a and 1987b) for Cote d'Ivoire and Peru. They assign a weight of .2 to children 0-6years, of .3 to children 7-12 years and a weight of .5 to children 13-18 years. Persons over age 18have a weight of one.

'For convenience we refer to consumption expenditures (both implicit and explicit) simply asexpenditures througbout the paper.

18

The Distribution of Welfare in Jamaica

The welfare distribution in Jamaica is shown in Figure IV-1. The height of the curve shows theprobability that a person will have that level of consumption. The area under the curve sums to one.The mean per capita consumption level is J$6359 per year. The mean adjusted consumption level isJ$8805. The distribution of consumption is very skewed. The Gini coefficient for per capitaconsumption is 0.43. This is relatively high compared to many other countries.

The differences in consumption levels by region are quite marked, as shown in Figure IV-2. Themodal per capita consumption level for rural households is only about J$2000 per year, while forhouseholds in Kingston, it is nearly J$7000.'

How Does the Consumption Distribution Vary by Gender of Household Head?

The density function. Plotting the density function for male- and female-headed households is revealing.The upper tail of the function (above about J$7000) is longer and thicker for male-headed households thanfor female-headed households (see Figure IV-3). Thus among the wealthy, male-headed householdspredominate. At the lower end of the function (below about J$2500) the distributions are more similar.The higher number of observations at the wealthy end of the spectrum for male-headed households pullup their mean and create large differences in per capita consumption. When poverty is the concern, thefocus should only be on the lower end of the distribution, where the differences are less marked. Indeed,at very low levels of consumption, below J$1000, male-headed households predominate.

Mean per capita consumption. A simple compaison of mean per capita consumption betweenfemale- and male-headed households leads to the strong conclusion that female-headed households arepoorer than male-headed households. The differences are large in economic terms and statisticallysignificant. Overall, male-headed households have per capita consumption levels which are about aquarter higher than those of female-headed households (see Table IV-1). In Kingston, the difference isespecially marked, with male heads having a premium of 42 percent over their female counterparts.

9In general, one would want to control for regional prices variations before drawing conclusions aboutrelative welfare levels from this comparison. Because Jamaica is small and has a good transport network,when the SLC was designed the expected regioDal price variation was so small that the decision was takennot to collect regional price data. The price levels for 38 items are reported for each area in theConsumer Price Index (STATIN, 1990). For these, prices in the Kingston Metropolitan Area average5 percent higher than in Other Towns, and 6.7 percent higher than in Rural Areas. Adjusting theexpenditure figures for regional price variations would therefore result in only small changes in theirmagnitudes. The conclusion that rural areas are very poor would remain unchanged.

Vt

Table IV - t: Comparison Between Mate-headed louseholds and Femakeheaded Households:Per Capit and per Adult Equivaleut (Adjusted) Consumption, According to Various Hlousehold Calegorles

kMBER OF INDIVID. MEAN HOUSEHOLD SIZE MEAN PER CAP. CONS. RATIO T-TEST MEAN ADJUSTED CONS. RATIO T TEST

CATEGORY Nale-hdud Fem.-hded Halethded Fm.-hded Malehded Fem.*hded PCM/PCf of hypoth Nalt hded fm.ehded ACm/ACf of hypothhouseh. hotuseh . househ . hotuseh . househ . househ. househ . househ.(NM) (N) (So) (60 (Pc) (Paf) PcwPcf (Acm) (Acf) AcmAcf

ALL JANAICA 8566 7063 3.87 4.35 7,012 5,564 1.26 14.43 9,369 8,113 1.15 10.27

KINGSTON 2159 2191 3.64 4.36 10,920 7,667 1.42 13.51 14,143 11,045 1.Z8 10.87OTHER TOAS 1352 1264 3.68 4.03 8,135 6,300 1.29 6.73 10,622 9,081 1.17 5.06RURAL AREAS 505S 3588 4.03 4.48 5,043 4,015 1.26 11.02 6,996 5,977 1.17 8.40

SINGLE-PERSO 546 197 1.00 1.00 16,982 13,433 1.12 1.35 16,982 13,433 1.12 1.41|

ONE POTENTIAL WORKER 672 1373 3.03 3.38 6,571 5,948 1.10 2.52 9,175 10,120 0.91 .2.55exclud. *Inhle-personhouseholds

ONE POTENTIAL UCaKER 480 1243 3.50 3.62 6,008 5,787 1.04 0.83** 9,640 10,395 0.93 -1.70*with child (children)

ONE POTENTIAL YORKER 182 675 4.14 4.12 5,202 5,117 1.02 0.23** 9,377 10,152 0.92 -1.10**I/ child. 05 yrs old

ONE POTENTIAL UORKER 252 789 3.82 4.05 5,629 4,964 1.13 1.85** 9,735 9,676 1.01 0.09**w/ child. 6-10

ONE POTENTIAL WORKER 320 812 3.n 4.10 5,923 5,355 1.11 1.90' 9,509 9,665 0.98 0.32**W child. 11-17

TWO POTENTIAL WORRS 2814 1868 4.60 4.88 6,564 5,225 1.26 9.37 10,430 8,340 1.25 9.75with child (children)

*asmmmbaidpsnbIU h t":diwa'mh m Id $4 ss5 doS M1

M ) 1)1 iN.qdM Ka 1h 5.Iyt.14 A*Mad emumon I do .a..m pu a"A .quwd eaPitsi fa IdW*4ba Va*m.Ini-h04yon .M :0.2 7.t2pyn aM s:063

1.17 fan i .s .a ms I 1.0

4) 'radd wut1-.pnm d. IImd 64 yon. aiM.

20

Figure N : Drbuion of Houshold Conumpuonper Capit and per Adult Equiabnt

5I.

3 2 4 S 5 10 12 14 18 12 " 22 24 E 2S

Pe CaPIL - Per Ainlt eq.

Figurs nl2: DIbutlon of per Capit Conuunpfloui,by Regian

2000

x

16

0 2 4 S U 10 12 14 16 18 20 22 2426 21

-- Khpbn -06mw Towni -- Rurd Ar

21

FigureV- a: DIutribftom of pmr Capit ConumptlonMal. vs. Femsb-md HousholdS

IS 1" j I 0I'

v 0 206|1124@22"

x~~~

s1o

50

o 2 4 S S 10 12 14 1 18 20 2224 2521imminAt of J I Juw

- M.boH.ded MH - - Fb Hindio NHH

Using mean adjusted consumption shows that male-headed households are wealthier than thefmale-headed households by about 15 percent on average (see Table IV-1, right-hand panel). ForKingon, the difference is 28 percent. The difference are less than found when using unadjusted meanper capita consumption because female-headed households are more likely to have more children.Nonetheless, the differences are still economically sizable, and stadstically significant.

When households with only one potential worker and children of various ages are compared,average welfare differences are not significant. 'le difficulty of single-handedly raising and providingfor children is apparently as great for male heads as for female heads in similar circumstances. Whenthere are no children or when two potential workers are present, welfare differences are substantial. Thushousehold structure is an important factor in understanding household welfare levels.

Multivariate Analysis. Just as part of the difference in mean per capita consumption isaccounted for by the difference in age structure between male- and female-headed households, otherfactors that tend to be correlated with headship can influence the welfare level of the household. To sortout the impact of these, we use multivariate regression analysis. This tells us the individual influenceof each of the included variables, holding all other included variables constant.

The analysis shows that female headship has an independent and negative influence on the welfarelevel of the household. The results presented in Table IV-2 imply that female-headed multipersonhouseholds have per capita consumption levels 11 percent lower than male-headed multiperson householdswith similar structure, human capital characteristics, location, etc. Single person households have percapita consumption levels about half again as high as multiperson households with otherwise similarcharacterstics. Among single person households, men are about 5 percent better off than women.

22

The other factors we included in the multivariate analysis are standard and the results much asexpected. The age, educational level, and occupation of the head of household are included because theyare important determinants of the head's earnings. An extra year of education for the head raiseshousehold per capita consumption by about 2.5 percent, and extra year of age by about one percent. Thearea is included to account for regional variations in living standards, which are marked. Per capitaconsumption is 48 percent higher in Kingston than in rural areas, and 29 percent higher in other townsthan in rural areas. The number of children is included to control for household structure. The morechildren, and the younger they are, the lower is household welfare. Fmally, stability of union status ispositively associated with welfare. Households with legal marriages have welfare levels that are higherthan those with other union staus, except for the widowed and divorced.

Now that we have looked at the overall distnbution of welfare and seen how female-headedhouseholds fit into it, let us turn to the low end of the distnbution and focus on poverty.

TaMb IV.2 Detau1an of Hou1hlds' Wdta a Dd.b by Per Capda Cowupdta

DEPRNDENT VARIABLE: LN wew capita Camusuption); mi 84.86, dd. dev. 93.71.

UNIVERSE: ALL HOUSEHOLDS

......... <I §S i , kl ...... ..

DUMMY VARIABLES | CONTINMUS VARIABLES

Mle Head .01 3.04 48.3 17.2Feale Head -.11 -3.94 .42 .49 AJ of HWd Squared/100 40.02 4.09 26.51 17.93Oanemmbor.aa .47 12.22 .14 .34 Yn of Bsdcad- Had .02 2.92 7.3 2.3Omo rembcefemal .42 8.25 .05 .22 Yn of EdAacao Squamd/100 .0O 2.68 98.2 S3.9Head ln lepl manas omitd .34 .47 Yn of Edition a Max. In hh. .04 6.87 1.31 5.9Hsd In como.lew mu*lage ..22 -7.33 20 .40 No. of Chideco 0-5 -.17 -14.59 56 .91Head Ihltor cauwal unlo -.17 -4.74 S .36 No.of Clidren6-10 __ l.62 54 _ 4Head widowad or dioreed -.03 -.75 .11 .31 Nao of Chidm&a 11-18 -.13 *13.51 .77 1.12Head ever wawed -.AS -5.49 .20 .40 No. of Pnoaus with Poorlukh -.10 -3.81 .15 .41Kinson .19 6.32 .28 .45 Conatr 8.38 70.27.

Olier towf laued = .18 .39 SUMMARY STATISTICS:

Rurul emas -.29 .10.26 .53 .50Head Not Woriting .-- - Nuter of Oba loom - 3536g Head Not Wouidag omlucd .32 .47 P aF(3, 3513) - .57Head SeltfEpkyked Asticultum -.17 -5.49 .23 .42 Pueb >P - 0.0000

- - M~~~~~~Ajusud 12 - 0.4926Head Self-EBnlcyd Other .00 .65 .18 .38

Head Prote/f m ln/C ledSae 32 8.95 .14 .35

H"d Odier Sector .02 .65 .13 .34

24

Poverty Measures and Female-headed Households

In the discussion of poverty, we use two poverty lines - J$1669 per capita household consumption, andJS3005 per capita household consumption. These correspond to the poorest 10 and 30 percent of thepopulation respectively. The 30 percent relative poverty line is approximately the same as the absolutepoverty line derived for Jamaica in Gordon (1989).

We use the Foster-Greer-Thorbecke family of poverty indices (see Annex 3). As the valueassigned to the parameter a increases, the sensitivity to income inequality of the Foster-Greer-Tihorbeckeindex increases. When a=O, the FGT index reduces to the headcount index-that is, the proportion ofpersons who fall below the poverty line. When a= 1, the FGT index reduces to the poverty gap - thatis, the average amount by which a poor person's income falls short of the poverty line. When a=2, theFGT index is sensitive to the distribution of welfare among the poor, with the highest weight on thepoorest of the poor. (See Annex m for details.)

Using the lower poverty line and per capita consumption, we find 9.04 per cent of people livingin male-headed households to be poor, compared with 11.11 per cent in female-headed households (TableIV-3). The difference is statistically significant (t=4.28). The other two measures do not differsignificaly by headship. When we use adjusted consumption as the welfare indicator none of thedifferences are significant for the 10 percent poverty line. For the more generous poverty line (J$3005),however, all comparisons show a higher incidence of poverty among female-headed households. Thus,though the results show a mixed picture, they are by and large consistent with the notion that female-headship is correlated with poverty. Next we will use a multivariate technique to determine whetherheadshipper se has an independent influence Gn the probability of being poor or whether other household

-haracteristics, correlated with headship, are responsible for our findings.

Table 1V-3: Poverty and Feunae Headslp

10% Poverty Line 30% Poverty LineMale Female t value of Male Female t value ofWelfare Measure Head Head difference Head Head differ

Mean per Capita Consumption $7,012 $5,564 14.43 $7,012 $5,564 14.43Mean P.C. Consumption of the poor $1,230 $1,277 4.90 $1,959 $1,992 6.04FaT a = 0 (Head Count) 9.04 11.11 4.28 28.08 32.35 5.79FGP a - I (Poverty cap) 2.38 2.61 1.59 9.77 10.91 3.64Fara - 2 0.S97 0.911 0.21 4.57_[ 5.08 2.80

1,pw , . , , , N E .................... > ..... .Mean Adjusted Per Capita Consumption $9,369 8,113 10.27 $9,369 $8,113 10.27Mean Adjusted P.C. Consumption of die poor $1,931 $1,969 1.34 $3,034 $3,043 2.43FGT a - 0 (Head Count) 9.79 10.26 0.97 29.2 31.0 2.47FOT a - I (Poverty cap) 2.S9 2.57 0.16 9.78 10.3 1.81FGT a - 2 0.95 0.93 0.30 4.51 4.69 1.02

26

The Probability of Being Poor: Mufivarlate Analys

Using a probit equation, we estimate the probability of falling within the group of poor using the twopoverty lines (10 and 30 percent poorest) and the two measures of consumption (per capita consumptionand adjusted per capita consumption). The results indicate that, just as for consumption, female-headshiphas an independent impact, increasing the probability of being poor (see Table IV4). However, thequantative effect is minuscule: the results translate into a 1.4 percentage point higher probability of beingpoor for female-headed households, holding all other variables constant at the sample mean.'°Furthermore, the effect holds only for the lower poverty line and per capita expenditures as the measureof consumption. At the higher poverty line or using adjusted per capita expenditures, female headshipdoes not raise the probability that a household will be poor. In these estimations, we controlled for thearea, rurality, family structure and health status of the household, and the union status, age, education,and sector or work of the head of household. Ihe impact of these variables is very similar to the onespresented in Table IV-2 regarding the deter_min of welfare. Full estimation results are presented inAnnex 4.

Table IV-4: Probit Models of Probabifity ofBeing Poor

------ -Emd Hbip Dummy

-Dcjx=d6*:V~~~~~~~~~~~~~iajcao-.- - - .--- ---.-. .

10% Poverty Lhis-, Capita xpend 26 1.37 -.02-10%. ;overty Lino-A . S:.Cd '." .81. ::,':,_'42. ; ,, _ ._. !.

30s Pbvoty i.r .2-9

-e Apeni f =.: -u rcruuo reni............SS=: ,S--30% :.-0-k- - - .0 :-- - ~~~131 .19

------. --- : . - . . - - - -

Wris is for dte 10 percent poverq line nd per capita consumption, the case for which the result ismore pronounced (Table IV-1).

Table IV-S: E bdky of Poverty Mmureby Gender of Household Head

FEMALE-HEADED HOUSEHOLDS MALE-HEADED HOUSEHOLDS

Value of 81nck wr 8Sowit Value of Lki* wn B=icitpove Meawigty t PavOty mean witPoveq Mealut Measure Consumpdon bnequalkly Measrt Consumplion /aquk

POTa -O 11.1 -2.7 6.2 9.0 3.1 10.0PUTa - I 2.6 -3.3 10.9 2.4 -2.3 13.2POT a - 2 0.9 -3.7 15.4 0.4 -3.3 19.0

POT a - 0 32.4 -1.3 1.1 28.1 -1.1 1.7PGT a - 1 10.9 -2.0 3.5 9.8 -1.9 4.8POT a - 2 5.1 -2.3 5.7 4.6 -2.3 7.7

Para -o 10.3 -2.8 ; .9 9.8 -2.8 7.1PGTa - I 2.6 -3.0 9.3 2.6 -2.8 | 10.7Par@a - 2 0.9 -3.S 13.6 0.9 -3.S | 16.1

FGT a - ° 31.0 | 1.4 | 1.1 29.2 |-1.4 | 1.4|FC:T a - ' 10.3 -2.0 3.3_ 9.8 _ _0 4.1PGT& 1 2 4.7 -2.4 3.4 4.5 _ -2.3 6.6

28

Finally we estimated how changes in the level and distribution of consumption in Jamsica wouldchange the poverty index (following Kakwani [1990aJ). The elasticity of poverty with respect to themean consumption level shows how poverty would change if the whole welfare distribution were shiftedup or down by a uniform amount, e.g. if the mean consumption rose but the Gini coefficient stayed thesame. For Jamaica, a change in mean consumption level is not systematically different for female- andmale-headed households (see Table IV-5). That is, in periods of growth, poverty among female-headedhouseholds will decrease in proportion to that of male-headed households. Conversely, during periodsof falling consumption levels such as occurred with structural adjustment in the mid-1980s, female-headedhouseholds are apparently not more vulnerable."1

The elasticity of the poverty measure with respect to the distribution of welfare -hows howpoverty would change if the total resources in the economy were constant but their distribution amonghouseholds changed, e.g. if the mean consumption were constanc, but the Gini coefficient fell. ForJamaica, the change in poverty caused by a change in inequality is less in femalk-headed households thanin male headed-households (see Table IV-5). That is, they are somewhat protected against furtherskewing of the income distribution, but would gain proportionately less if overall inequality were reduced.

Female-headship as a Targeting Indicator

Governments concerned with poverty will try to target the benefits of some of their social programsspecifically to the poor. Because it is difficult and expensive to assess the households' welfare levelsaccurately, many programs are designed to reach persons or households with characteristics that areknown to correlate strongly with poverty. In spite of the difficulties in defining female-headship, it issometimes used as a criterion for eligibility of such programs (e.g. the United States' Aid to Familieswith Dependent Children and the Honduran Single Mothers' School Coupon). It is therefore interestingto assess how well the gender of the head of household will do as a proxy for welfare in targetingprograms.

When using a proxy for poverty, one is concerned with two types of errors: errors of exclusion,that is the failure to reach some of the poor, and errors of inclusion, the leakage of some of the benefitsto the non-poor. Here we have tested the use of two proxy indicators - residence in a female-headedhousehold and residence in rural areas - for both the 10 and 30 percent poverty lines based on per capitaconsumption.

The results show that in Jamaica, the gender of the head of household is not a useful proxy forincome in targeting. If residence in a female-headed household were used as the targeting criterion withthe poverty line set to capture either the poorest 10 or the poorest 30 percent of t!" populatiun, just halfof the poor would be reached. In contrast, if residence in a rural area were used as the targetingcriterion, only 13.1 percent of those below the lower poverty line would be exluNded, and only 20.3percent of those below the higher line (see Table PV-). In all cases the errors of inclusion remain high;even for rural targeting, 56.8 per cent of the benefit would accrue to the non-poor will benefit using the

lIlt should be emphasized that in this conclusion, the growth or depression are assumed not to changethe distribution (or structure) of the economy, which may run counter to fact. If so, the change inpoverty will need to consider both the elasticity with respect to the mean and the elasticity with respectto the distribution.

29

higher poverty line. Note that the results for targeting on female headship are very close to what onewould expect from targeting a random sample of the population. For a 10% poverty line, nearly 90%of benefits given to female-headed households accrue to the non-poor. For the 30% povertq line, two-thirds of the benefits given to female-headed households accrue to the non-poor.

Table IV-6: Famale Bleadshp a a Tureing Tool

Proxy Indicator for Poverty

Female Headship Resident in Rurl Area

Targeting Outcome 10% 30% 10% 30%Poverty Poverty Poverty PovertyLine Line Line Line

Errors of Exclusion 49.6% 51.3% 13.1% 20.3%

Errors of Inlusion 88.1% 67.6% 84.3% 56.S%

The relative merits of using female-headship or rural residence as a targeting criteria areillustrated by setting up a simple hypothetical government program. Suppose that the government hasa fixed budget to help the 3860 households in our sample, say JS 811,000 (about four tines the amountreceived by these households from the food stamp program). If the program gives the benefits equallyto all persons in female-headed households, each person will receive JS 115. Alternatively the programcould dispense l$ 96 to all persons in rural households. The latter will reduce poverty more than theformer for more combinations of a and poverty lines (see Table IV-7).

Table IV-7: Poverty Outcomes wnth Transrers

Befor After Trasfer to individuals in:

Program FHH Rurd

10% Poverty LineFGT a = 0 9.98 9.29 8.91

a= 1 2.48 2.17 2.03a= 2 0.90 0.76 0.68

:30%Poverty Linc -

FGTa = 0 30.01 29.12 29.98a = 1 10.28 9.74 9.54a= 2 4.80 4.45 4.28

Note: Program budget is J$ 811,000, distributed to aU individualsin feade-headed households (3$115 per persn) or to dal individualsin rurl households (JS 96 per person).

30

Conduion

The simplest compaison of mean per capita consumption levels between male- and female-headedhouseholds shows female-headed households to be markedly worse off than male-headed bouseholds.Adjusting ependitures for household composition greatly lessens the force of the conclusion. Introducingcontrols for other household charteristics, and for the human capital characteristics of the head showsthat the gender of the household head itself has a still smaller impact on household consumption levels.When focusing on poverty itself, the differences between male- and female-headed households diminishstill further. Differences by gender of the head of household in both poverty measures and the probabilityof being poor are sensitive to the poverty line set, the welfare measure used, and the poverty measureused.

While most of our results indicate that the probability of being poor is somewhat higher forfemale headed-households, the difference with male-headed households is small. Indeed, targeting socialprograms to female-headed households will not be a successful way of reaching the poor; better proxiesfor poverty are available. If the main cause for concern for female-headed households is the expectationthat female headship is highly correlated with poverty, then this concer can be put to rest in Jamaica.

31

V: Differentia Resource Use

So far we have explored whether the economic resources available to female-headed households differfrom those in male-headed households. In this section we will concentrate on how the households usethe resources available to them. There are two types of resources: time and money.'2

TIme Use

In the stereotypical male-headed household, the male head works for income and the female partnerspends time on household activities that produce welfare for the family, and especially for the children.The stereotypical female head of household must manage to both provide income and carry out welfare-enhancing household activities. With only one person filfilling both roles, the time constraint is likelyto imply fewer hours devoted to one or the other of the important activities. We already saw that femaleheads are considerably less likely to be employed than their male counterparts (50 percent versus 79percent, see Table M-4). Female heads of households' income-generating act vities are thus lessened.If there is also a difference in labor force-participation among women of woLing age (15-64) betweenwomen who are heads and who are partners of heads, then female-headed households would beconstrained in their welfare generating household non-market activities more than male-headedhouseholds. We estimated a stndard labor force participation equation (see, for instance, Deaton andMuellbauer, 1980, Chapter 11) that shows how participation is determined by age, education, numbersof workers in the household, health status, region, non-earned income and headship. The results of thelast two variables are of particular interest

Female headship does have an independent effect on labor participation, but the impact is small.The estimaton results" icply that for a woman with average characteristics, but without any non-earmedincome, the probability of being in the labor force is 64.5 percent if she is the head of the householdversus 57.9 percent if she is the spouse of the head.

We would expect that the need for female heads to work would be lessened by higher remittancesreceived by female heads of household. This is borne out by the data. Non-earned income does reducelabor force participation. (see Table V-i). Moreover, households with a female head receive more in theform of non-earned income (see Table V-2) - JS1567 per household as opposed to J$1134.

We will look at the implications for chfldren's welfare of this relatively small but statisticallysignificant difference in time use between female heads and other women in Section VI, but first we willlook at the use of the second resource: money. In particular, we will analyze to what extent male- andfemale-headed households show different consumption patterns.

%n the remainder of this paper we will use a standard neo-classical model of household behavior asour analytical framework. This model is presented in Annex 4.

"See Annex 4 for fill estimation results.

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Table V-1: Non-Employment Imn and Es_mad Fanme Labor Fore adcaipatioinfor Women 1544"

Female FemleHeds Spouscs

No Oduer- iome 64.5 57.9

Domestic Remittaces - $150 63.5 56.9Remittance from abroad = $650Food Stamps = $60

Domestic Remittances = $300 62.6 56.0Remittances fom abroad = $1300Food Samps =120 _

Domestic Reamittces = $450 61.7 55.0Remittan fom abroad = $1950Food Samps = $180

Table V-1: Non-Employment Income Received by Maleand Fane-headed owsebolds

Female-baded Male-headedHiousehlolds Households

D1made Remittan J$212. JS136.Reminanie fiomn abroad 703. 582.Food Sftmps 61. 41.lstun 62. 92.Cbld Supp 398. 153.O.her 131. 130.

Total J$lS67 J$1134

Consumption Pattens

First, we looked at broad consumption categories of food, housing, daily expendiures, durable goods,non-consumption expenditures and other consumption exendiures by quintile and gender of thehousehold head.' There is litde difference by gender of the household head (see Table V-3). The

'For full esfimation results see Annex 4.

"'Daily expenditures' are frequently purchased items such as food and bevecages consumed awayfrom home, coal, kerosene, wood, personal care items and tobacco products. Non-consumptionexpenditures are for insurance, taxes, loan payments, charble donations, transfers to relatives

33

similarities are especially marked in the poorest quintile, where consumption differences might have themost marked impact on child welfare. These results are confirmed by a regression of the foodshareon the logarithum of total expenditures, the squared value of this variable, the number of children, adultmales and adult females in the household and regional dummy variables. Female headship does not havean independent effect on the total food share. (See Annex 4, Table A4-6 for complete estimation results).

When we look at food expenditures in more detail, however, we find differences in thecombinion of foods purchased. Expenditures on such higher quality food items as meat, vegetables,milk and other dairy products tend to be higher in female headed households, all other things equal.Expenditures on alcoholic beverages are lower. (See Table VA4, and Annex 4 for details) These resultsare in broad agreement with those of Horton and Mi'ler.

the household, and expendiures on weddings and funerals. Other oDnsumption expenses cover a wholegamut of items for the household, clothing, tasport, inment, etc.

'6 The food share includes the value of explicit purchases plus food produced at home or received asgifts.

Table V-3: Consumptlon Share by Gender of Household Head

CONSUMPTION QUINILUS USIG PEt CAPrIA CONSUMPTIONALL

aENDER OF HEAD | ENDER OF HEAD GENDEIR OF HEAD GENDER OF HEAD GENDEt OF HEAD GENDER OF HEADI_________ FEMALE MAEI FEMALE MALE FMALS E . FEMALE MALE FEMALE MALI FEMALEFOOD 4S 47.0 45.2 429 424 311 319 . 340.4RWr _ '1i. 11.9 IJJ II 133 12.4 14 14 1 35.3 17.5 14.1 13.9DALY EWPSND

15.1 15.2 15.9 16.S 17.4 17.5 19.1; 17.3 193 16.4 18.0 16.7SEMI.DURABLECONSUMOIN 213 21.6 21.6 23.1 21.7 23.9

f-U;2, 24.0 25.7 26.2 23.2 24.0NON-CONSUMFIONECPENDrrURES 0.7 07 13 1.5 2.3 6 1. 2.5 8.0 5.5 4.2 2.6DURABL GOODS ....

IA.4 1.1 1.7 1.6 2.3 2.1 ±.7 : 3.2 3.4 33 2.6 2.4TOTAL 100 100 100 100 100 100 100 100 100 10 100 100

35

Tibia V4: Food Ite Saar. md Fae.e Heumip

FEMALE EADW I| COOElFICIE!NT |I TSrAMS'lC

Ois an FM. .23 |12

Plour -.14 -1.19

Brea& -.42 -1.56

Sur -.01 0.03

Rice .02 0.08

Pouhy -.32 -1.04

Soup, .29 3.89

Yama -.62 1.36

Conmewa .04 0.48

Milk .96 2.13

Fish -.46 -1.29

Otr ceak -0.50 1.57

Fnut -.23 0.97

Vegtablae 1.13 4.20

Aoholic Bveage -4.12 -11.71

Condiments .40 2.69

Non-Aboholic Bevam -.18 |0.85

Other}Fbood .71 2.02

OtherDairy Producs 1.13 3.93

Meat 1.64 3.31

Total Food Show .01 0.96

36

We also lookred at the share of expenditues for so called child goods (shoes, clotiing and lodthigmateria for children, baby food and education expenditure) for households with children. The meanJOI of household expediture do wot vary significantly by gender of household head - from 1$ 1637in female-headed households to J$ 1684 in male-headed households (t=0.58). When we estimated dieshare equations for children's goods, female-headship did matter. After controlling for the households'expenditure level, region and household structure, the share of children's goods in total consumption ishigher for female-headed households though the difference is tiny - 4.7% on average rather than 4.3%in male-headed households (see Annex 4 for full esimation results).

Finally, we looked at expenditure for domestic help which we expeted to be especially high forfemale-headed households. The results came as a surprise: 18 percent of male-headed householdsreported such expenditures versus only 12 percent of female-headed households. Average expenditurelevels were JS485 and J$294, respectively. Of course, part of this result stems from the fact that moremale headed households are in the upper tail of the income distribution. But it is only among the poorest10 percent of the populaton that femaIe-headed households are more likely to spend on domestic helptha other households. Among the poor, however, the numbers are very low: 0.37 percent of maleheaded households versus 2.49 percent of female heads.

In sum, we do find evidence that female heads use their time differently than other women andthat consumption patterns differ between male- and female-headed households. The difrerences, however,are so small that they are unlikely tD have a discernable impact on child welfare. This issue will be takenup in the next section.

37

VI: Children's Access to Social Services

Tbis section explores the children's access to social services by looking for differences by gender ofhousehold and gender of the child. If statistically significant differences are found in average access toa service, then multivariate techniques are presented to learn what causes the difference"7.

Health Services

Preventive Care. There are no significant differences in children's access to preventive care by genderof the household head, whether all children are considered together, or the analysis is performedseparately for girls or boys. This is true using as the definition of preventive care the percentage ofchildren for whom clinic visits for preventive care were reported in the six months preceding the survey,coverage for individual vaccines, or complete immunizaon coverage. In only one of the eighteen cellstested was a significant difference found, hardly a systematic result (see Table VI-1).

Curative Care. For those children who were reported to be ill in the four weeks prior to thesurvey period, just over half of them received medical care (see Table VI-1). Again, there were nosignificant differences in access by gender of the head of household or by gender of the child.

171n fact, multivaiate techniques were used throughout, but are not reported here to simplify thepresentation.

38

Tabic VI-1: Children's Access to Health Care

Children of Both Sexes Boys Girls

Male Female Male | Female Male FemaleHead Head Head j Head Head J Head

% receiving t stat _receiving t stat % receiving t stat

Preventive 20.2 | 18.5 0.84 19.5 18.1 0.51 20.9 J 18.9 0.68

VaccinationsL i =_=

> /3 OPV 92.4 91.4 0.54 92.3 91.2 0.52 92.4 91.7 0.35

>3 DPT 92.7 91.8 0.61 92.3 91.3 0.50 93.1 92.3 0.39

BCG 97.6 97.1 0.51 97.0 98.7 1.53 98.2 95.5 1.96

Measles 90.7 90.6 0.07 90.5 90.5 0.00 90.9 90.6 0.11

Fun 83.8 84.1 0.16 84.3 83.4 0.32 83.2 84.9 0.57Immunizat.on

Consultation 54.9 53.0 0.39 51.0 53.0 0.29 58.7 53.1 0.82IWhen Child

(Last 4

1/ Vaccination coverage is figured for children 1-4 yeaws old. Use of preventive care is for childnC 0-5 years old.

* Significant at 5% level.

Education

Enrollment in Secondary Education. hzzause enrollment of primary age children in Jamaica is nearlyuniversal, we concentrate on the enrollment of secondary age chiUdren. Just over half of chUdren fromage 13-19 are enrolled in school. Children mostly stay in school to age 14, but after that, enrollmentrates drop sharply with age. This is partly the result of drop-outs, and partly the result of a multi-trackschool system. Only the highest quality schools are intended to hold students until they have completed13 years of schooling. The lowest track of secondary ends after only nine years of schooling.

As apparent from Table VI-2, girls of secondary age have higher enrollment rates than boys.Compared to the gender of the child, the difference by gender of the household head are smaller. Weexplore these differences furither using probit models to predict the probability of enrollment for varioushousehold characteristics.

39

Table VJ-2: Enrobuent Rat. by Gender or Child and Household Head

_____________ ~~Boys Gizrl-s _ _ _

Male Female t Male Female tAge of Child Ed Head Statistic Head Ead Statistic

7-12 Yeus 98.8 98.8 0.10 98.0 99.1 1.51

13-19 Yeas 52.8 52.0 0.30 60.1 57.2 l.00

Controlling for other factors, the gender of the head of household and of the student aresignificant determinants of enrollment (in both the substantive and the statistical sense). Girls are morelikely to be in school than boys, regardless of the gender of the head of household. Boys appear to bebetter off in female-headed households, but girls' enrollment rates are higher in male-headed households.(see Table VI-3). Although this may be of some concem, girls in female-headed households are stillnoticeably more likely to be in school than boys. Keeping boys in school, regardless of gender of thehousehold head, is of higher policy concern than keeping children in female-headed households in school.

The other factors controlled for in isolating the effect of the sex of the child and household headwere the age of the child, the family welfare level and the education of the parents. All are importantdeterminans of the probability of eaollment. An additional year of education for the father mattersslightly more than an additional year of education for the mother in raising the chance that the child willbe enrolled. This is, again, a result somewhat the reverse of that expected.

Nutrifion Prograns

Both of the large nutrition programs in Jamaica are tied to use of other sevices. School feedingprograms obviously reach only those in school. lhe food stamps program is tied to the public health caresystem. The access to nutrition programs may, therefore, follow the same pattern as found for the useof health and education services.

40

Table VI-3: Estimated Probabilities of Enrolhnent of 13-19 Year OldsBased on Probi± Regression'

Boys Girls

male Femade m ale FemaleHead Head Head Head

Age13 98.4 98.9 99.4 99.3

14 94.8 96.1 97.9 97.3

1S 75.8 79.7 86.3 84.1

16 59.8 64.3 73.6 70.4

17 24.0 28.2 37.8 34.2

18 6.7 8.6 13.5 11.5

19 4.0 5.2 8.7 7.3

Per Capita Consump.ion1669 41.6 46.8 57.3 53.6

3005 46.1 51.3 61.7 58.0

4000 49.3 54.5 64.7 61.1

6000 55.6 60.7 70.4 67.0

8000 61.4 66.3 75.4 72.3

12000 71.4 75.7 83.2 80.7

School Feeding. Jamaica has two main school-feeding programs. The Nutribun programdistributes daily to schools centrally prepared portions of milk and a bun or cake. The program isconcentrated in urban areas or schools with good road transport to allow the daily delivery of theproducts. The "traditional' program consists of subsidies and commodities distributed to schools whichprepare cooked meals on the school grounds. The allowances are usually less than necessary to delivera good meal every school day. Although the Nutnbun program also has some problems achieving itsdaily delivery goals, on average it feeds children more regularly. Some schools also have snacks,breakasts, or other forms of food available under a variety of community and state-supported programs.

Overall, slightly over half of the enrolled children aged 7-19 receive some kind of school feeding(see Table VIA4). There is no difference in access to some kind of school feeding by gender of the headof household, or of the child. Children in female-headed households are more likely to be in the

"'For full estimation results see Annex 4.

41

Nutribun program than children in male-headed households, who are more likely to be in the cooked mealprogram.' 'is is most likely due to the fact that as shown in Table M-1, female-headship is higherin urban areas than rural areas, where Nutnbuns are the prevalent form of scbool feeding, whereascooked meals are served in mral schools where female-headed households are less concentrated.

Table VI-4: Children's Access to Nutrition Programs

Both Sexes BS B Girls

Program Male Female t Male Female t Male Female tHead Head Statistic Head Head Statistic Head Head Statistic

Schiol Feeding - Children 7-19 Eurolled in School . = _ _

Nutribums 26.5 31.3 3.06 27.5 29.8 1.08 25.5 32.7 3.26

Cooked Meal 21.0 17.2 2.82 21.2 17.3 2.09 20.8 17.2 1.89

Odher 5.S 4.3 2.09 5.5 4.3 1.15 6.1 4.2 1.80

None 46.5 47.2 0.38 45.7 48.4 1.16 47.5 46.0 0.63

lF6od-Stamps - Children 0-4

14.0 12.5 0.94 12.7 13.1 10.17 15.4 11.8 1.50

ood Stams. The food stamps program has two parts, a means-tested portion for poorhouseholds, and a maternal-child portion. The maternal-child portion is linked to public primary healthcenters. Any child under age five is eligible for food stamps upon presentation of the child's birthcertificate at a pnmary health center when a food stamp officer is present to conduct the registration,which is done on a limited number of days per month. The food stamps are then collected once everytwo monfs at the health center. Participation in the program therefore requires traveling to the clinicand standing in line periodically. Where time constraints are particularly severe, this may discourageparticipation. At the time of the November 1989 SLC, the child's allotment of food stamps was J$20per month.

Overall, about 13 percent of children age 0-5 received food stamps in November 1989 (see TableVI-4). There were no significant differences in receipt by gender of the child, or by gender of the headof household.

Summary

We have considered children's access to preventive and curative health services, primary and secondaryeducation, school feeding programs, and food stamps. No differences in access were found for healthcare. For secondary school enrollments, we found that female-headship marginally increased the chancesof boys, but did produce lower enrollments for girls. Overall, however, girls rates exceed those of boys.For nutrition program-, the gender of the head of household produced no difference in access either to

9These conclusions hold for aU children age 7-19 and for enrolled children 7-12 as well.

42

food stamps or school feeding, though it was correlated with the type of school feeding program received,probably indirectly through location.

We have found little evidence thatJamaican children in female-headed households fare any worsein terms of access to social services than children in male-headed households. This is perhaps notsurprising since economic welfare is usually a strong deteminan of social service use, and female-headship and povety are not strongly linked in Jamaica. Any barriers to access that might be imposedby the time oDnstraint faced by female-headed households seem to be minor or, at least, to have beensurmounted.

We have so far focused on the social service-provided inputs to producing good child welfareoutcomes. In the next section we will look at the actual welfare outcomes themselves.

43

VU: Child Welfare Outcomes

One of the most compelling reasons to be concerned over the issue of female-headship is the possibilitythat, if they face tighter income and time constraints, these may imply poor child welfare outcome. Thiswould be undesirable not only for the present welfare loss, but because the early child welfare outcomesare also strong influences on the later human capital upon which the children will have to rely to earntheir livelihood later in life. In this section, we focus on child welfare outcomes.

Health

Children get sick more often than adults. Their susceptibility to illness is increased if they aremalnourished or if their families are unaware of, too busy, or too poor io practice basic health practicesthat help prevent and treat common intestinal and respiratory upsets.

Diarrhea arnong Under Fives. Diarrhea is one of the most common childhood maladies.Preventive measures such as infant breastfeeding, access to potable water or boiling impure water,cleaning vegetables adequately, and good personal hygiene are effective in decreasing diarrhea'soccurrence. Proper feeding practices and rehydration can shorten its duration and minimize its impacton overall health status when children do have diarrhea. The incidence of diarrhea is therefore a goodindicator of how effectively the household produces health.

Overall, about 6 percent of children under five years of age had had an episode of diarrhea inthe two weeks prior to the survey. From the means, it would seem that children in female-headedhouseholds have diarrhea more often than children in male-headed households. Considering both sexesof children together, about 8 percent of children in female-headed households had diarrhea in the twoweeks prior to the survey, while the figure was only 5 percent for children in male-headed households(see Table VII-1). Given the alarming nature of this result, we examine it further using a probitregression to predict the probability that a child will have had a recent episode of diarrhea based on anumber of factors.

Table VlI-1: Health Status of Children Age of 0-4

Both Sexes Boys Girls

Repoting Male Female t male Female : Male Female tHead Head Stat Head Head Stat Head Head Stat

Diarrhea in 4.82 7.68 2.35 4.61 7.65 1.81 5.04 7.71 1.50last 2 weeks I_I_I

Ilness in 26.9 25.5 0.61 25.2 24.9 0.08 28.8 26.2 0.79last 4 wes ._ _

44

Table Vl-2: EstiFated PlobabiIily of Children's Diarrhea*

Boys Girls

Male FemAle Male FemaleAge in Months Head Head Head Head

0-11 5.1% 8.7% 5.6% 8.1%

12-23 7.0 11.7 7.8 10.9

24-35 2.4 4.6 2.8 4.2

36-47 3.1 5.7 3.5 5.3

48-59 0.8 1.6 0.9 1.5

*For fiull estimation results see Annex 4.

In the multivariate probit, neither the gender of the child, nor the gender of the head of thehousehold is a significant determinant of the probability that the child will have diarrhea. It wouldtherefore seem that the difference in mean probabilities of diarrhea found using only bivariate techniquesis explained by the correlation with headship and the age of the children. After controlling for age ofthe child, neither the per capita consumpdon variables, nor parental education matter. Family structurehas some influence, though the pattern is not clear. A lower number of adults in the household is weaklycorrelated with diarrhea. Presumably with fewer adult hours in which to provide child care and to"produce' health, the child may be more susceptible. Although the number of young children in thehousehold is not a significant determinant, more children age 6-10 is positively associated with theincidence of diarrhea. (see Annex 4).

The most important determinant of the probability of diarrhea is the age of the child. Theprobability rises from zero to two years, when it peaks, which is consistent with results found in manycountries, as weaning has taken place for almost all children by this stage with its concomitant exposureto food and water borne organisms to which the child has not built up resistance (see Table VH-2).

Incidence of Illness among Children 04. Next we consider all reported illness among children0-4 years of age reported in the month prior to the survey. About a quarter of children were reportedas having been ill (see Table VII-1). Looking at the mean incidence, there are no significant differencesin the incidence of all illness among children in female-headed households vis-a-vis those in male-headedhouseholds.

Nutrition

Nutritional status is a very sensitive and important indicator of child welfare. Malnourished children aremore susceptible to illness and are less able to respond to stimuli and learn in either formal or informal

45

settings. If their growth is reduced, they will have less muscle-mass with which to perforn work asadults (McGuire and Austin, 1987).

Here we consider three measures of nutritional status. Weight for age is a general summarymeasure. Low weight for height, or wasting, is an indicator of acute malnutrition. Low height for age,or stunting, is a cumulative indicator of chronic or repeated episodes of malnutrition which disturb achild's growth pattern. The reference norms used here are from the United States' National Center forHealth Statistics.

Each child's height and weight have been converted to its z score - the number of standarddeviatons of the reference norm specific for age and sex. We present both the scores as an indicator ofsatisfactory development, and the percentage of children who fall below the cut-off points and areconsidered malnourished.'

Of the thirty-six combinations of definitions of nutritional status, gender of child, and gender ofthe head of the household, thirty-four show no significant differences by gender of headship (see TableVU-3). For the other two - the z score for height for age for both sexes of children and moderatewasting for girls - children in female-headed households appear to fare less well than children in male-headed households.

In order to explore these results further, we performed regressions on the z score for weight forage, height for age, and weight for height. The female-headship dummy was not significantly differentfrom zero in any case. Aside from the gender of child and head combinations, we controlled for percapita consumpdon, age of the child in years, area, household age structure, parents' education, andunion status of the head of household. In general, the results were quite inaccurate, with right hand sidevariables predicting less than four percent of the variation in nutritional status, presumably because wewere missing the important factors of the parents' heights and weights (see Table VII-4).

2 Children are moderately wasted if their weight for height is from 70 to 80 percent of the referencenorm, and severely wasted if their weight for height is below 70 percent of the norm. Children aremoderately stunted if their height for age is from 85 to 90 percent of the reference norm, and severelystunted if their height for age is below 85 percent of the reference norm. By World Health Organizationstandards, children are classified as moderately malnourished if their weight for age is between 60 and80 percent of the reference norm, and severely malnourished if their weight is below 60 of the referencenorm. By the Gomez standards, children are mildly malnourished if their weight for age is between 75and 90 percent of the reference norm, moderately malnourished at 60 to 75 percent of the norm, andseverely malnourished below 60 of the norm.

46

Table VII-3: Gender of Head and Nutritional Status of Children Age 0-4

Both Sexes Boys Girls

Male Female t Male | Female | t Male Fedale tHand EHead | Stat Hed Head Sta.t Head Head stt

Weight fbr Age - -- - - - -

zScore -0.14 -0.29 1.8 -0.11 -0.28 1.4 -0.18 -0.3 1.2

mild malnut-Gomez 25.2 25.8 0.29 25.9 26.7 0.24 24.3 24.9 0.17

mod. mlnut-Gomez 1.8 3.2 1.81 1.7 2.4 0.77 1.8 3.9 1.73

sev. malnut- Gomez 1.0 0.5 1.14 1.2 0.7 0.68 0.8 0.3 1.00

mod. malnut-WHO 5.8 7.0 1.00 6.5 6.6 0.07 5.0 7.3 1.36

sv.malut-WHO 1.0 0.5 1.14 1.2 0.7 0.68 0.8 0.3 1.00

Hgh .-.for A- - .-.. .. ,.

zScore -0.05 1-0.251 2.1* 1 .01 .28 1.9 I -0.13 -0.22 .9

modeat estlmting 1.4 1.8 T063 1.4 2.0 0.57 1.3 1.6 0.30

sevee stuting 1.1 1.5 0.68 1.2 1.5 0.34 1.1 1.6 0.64

Weifor Hei '.,-'g.-.h-,-','t

z Score -0.14 |-0.13 -0.13 -0.19 -0.16 -0.37 |-0.08 | .1 |.1S

modewasting 1.11 1.8 1.07 1.71 1.0 10.88 1Q5 2.6 2.33*

severe wasting 1.4 1.0 0.67 1.7 1.2 0.55 j1.1 .8 0.38

47

Table VHI-4: Gender Dnmies ii Nutritional Stabs of Children 04 Regrons

DUMMY VARIABLE REGRESSIONWEIGHT FOR WEIGHT FOR HEIGHT FOR

AGE HEIGHT AGE

Boy-Male Headed Household Omitted Omitted Omitted

Boy-Female Headed Household -.09 .09 .27(.81) (.81) (-1.88)

Girl-Male Headed Household -.10 -.10 -.15________________________ (-.99) (-99) (1.18)

Girl-Female Headed Household -.11 -.11 .24(-.93) (1.10) (-1.70)

Adj. R2 .08 .03 -.01

Note The z score for die child's height/weight is the dependent variable. Al regressionshave the same regressors: a constant per capita consumption; dummies for the 4 combinationsof gender of child and head; dummies for age in years; dummies for area number of membersof the household 0-5, 6-10, 11-18, and adults; the education in years of the mother and father,and dummies for heads union satus

t statistcs are given in parethesis.

For full regression resuts see Annex 4.

48

Education

In lieu of a measure of cognitive achievement which is not yet available2' we use three measures ofeducation outcome - repetition, daily attendance and enrollment in the most exclusive of the tracks ofsecondary school.

Repetition. Repetition is a sign of poor educational performance. It is costly to the state to haveto provide instruction for repeaters, and costly to the family to have to support a student through repeatedyears in school.

Table VII-5 shows the prevalence of repetition among secondary students in Jamaica. The tablesuggests that prevalence is more linked to rurality and age than to the gender of student or of householdhead. Only among teenage boys it there a significant difference by gender of the head of household.Boys of age 13-19 in female-headed households repeat less than those in male-headed households.

Probit analysis controlling for area, gender of student and head, age of student, per capitaconsumption, parental education and presence in the household, access to transport, and type of school,confirm the notions. The influence of per capita consumption and of rurality is strong (see Annex 4).For boys, those in female-headed households repeat less often than do those in male-headed households.For girls, gender of the household head is insignificant.

Table Vl-S: Percentage of Repeaters among Children Enrolled in Schoosl

SEX OF THE PERSON

_____ BOY GIRL l

Male Female t stat Male Female t statHead Head Head Head

All Children 6.0 5.3 0.67 5.8 5.6 0.26

Kino M.A. 4.0 4.6 0.33 6.3 3.6 1.28

Odher Towns 8.6 4.1 1.59 7.1 6.6 0.18

Rural reas 6.2 6.1 0.06 5.3 6.4 0.68

Age of te Stdentiyes _ -_ _

7-12 Years 5.1 [ 6.3 0.87 5.5 6.0 0.35

13 -19 Years 7.5 3.6 T 2.2-i 6.4 T 4-9 0.86

2Note that cognitive achievement tests for children enrolled in school were part of the November1990 Survey of Living Conditions, for which data are currendy being processed.

49

Table VII-6: Percentage of Children with Full Attendance During the PreviousWeek, Among Children Enrolled In School

Sex of the Student

Boys Girls

Male Female Male FemaleHead Head Head Head

% % % %All Children 86.4 87.1 88.2 87.9

Area-

Kingston M.A. 93.2 91.8 95.5 94.3

Other Towns 87.6 89.1 86.3 90.1

Rural Areas 83.4 83.8 86.3 829

Age of the Student, in Years --

7-12 years 85.4 86.6 87.9 86.4

13-19 Years 88.0 88.1 88.8 90.3

Nce: Fuflaimeadace is defined as not having mised a school day duig the week preceding the mamy

DailyAttendance. Low daily attendance is common in Jamaica. It is an education policy concernbecause it significantly reduces the educational contact time of the absent children. Furthermore, havingto accommodate the pervasive absenteeism can reduce the speed at which the teachers can advancethrough the year's curriculum for the whole class, thereby lowering the overall quality of education ofeven those students who do attend regularly.

Overall, for the sample of enrolled students in primary and secondary schools, about 85 percentof students reported having attended all official schools days in the week preceding the survey (See TableVII-6). In predicting which children are likely to be absent, neither the sex of the child nor of the headof household matters. This is suggested by the bivariate analysis, and is confirmed by a probit regressioncontrolling for age of the student, per capita consumption, area, distance to public transport, parentaleducation and presence in the household, and the availability of school lunches.

Enrollment in High Schools. The secondary school system is divided into several tracks. Threeare general curriculum, the others vocational. The three general tracks differ significantly in quality.Placement in the tracks is determined by performance on the Common Entrance Exam which moststudents take at the end of the sixth grade. All of the schools are state schools, so that the tuition is nota constraint to entrance into the better tracks. The constraints are the student's test score and thegeographic access (The higher track schools are not accessible from some runad areas).

so

Table VII-7: Enrollment in High Sdhools by Gender of Child and Household Head

-_____ Boys _ Girls |

Male Female t Male Female tHead Head Stat Head Head Stat

Among Children 13-19 10.9 14.9 2.13 21.5 18.0 1.55

Among Secondary Students 22.8 30.0 2.00 38.6 34.1 1.17

Girls are enrolled in 'high schools', the most exclusive track of the Jamaica secondary system,markedly more than boys (see Table VII-7). Differentiating by the gender of the head of householdshows that among boys, those in female-headed households are enrolled in high school significantly moreoften than those in male-headed households. Among girls, the differences by gender of household headare not significant.

Probit analysis controlling for gender of the student and head, age of the student, per capitaconsumption, parental education and presence in the household, and access to transport confirms theresult (see Annex 4).

summar

Using a variety of measures, we have explored children's health status, nuritonal status, and educationperformance. Again, there is little support for the hypothesis that children in female-headed householdsare disadvantaged. These children are faring well in the present. The current welfare outcomes are alsogood indicators of the formation of human capital on which they will rely in their adult, productive lives.They should fare well in the futre as well.

The result that the welfare outcomes of children in female-headed households are not below thoseof children in male-headed households is consistent with what we have learned in previous sections ofthe paper - that poverty and headship are not strongly linked, that female-headed households use theirresources in ways that in some cases are more child-oriented than male-headed households, and that theirchildren have equal access to social services.

51

VII: Discussion

Review. We started with the premise that female-headed households were doubly burdened by tighterincome and time constraints than male-headed households. These constraints could lower the access tosocial services important to child welfare, and lower child welfare outcomes. A countervailing influencecould be a strong preference on the part of decision makers in female-headed households to use theirresources in ways beneficial to children.

We did a comprehensive empirical exploration of these hypotheses. Poverty and female-headshipappear to be linked, but not strongly enough to make headship a useful targeting indicator. Female headsof households work more in the workplace, but the difference in participation rates is only 6 or 7percentage points. The foodshare in female-headed households is not larger than in male-headedhouseholds. Significant differences do exist among expenditures; female headed households, by andlarge, seem to buy a more nutritious food basket. The results on children's access to social services andchild welfare outcomes show very few significant differences between the two types of households.Although in a few cases the gender of headship is statisticaily significant, in some of these cases theinfluence is favorable and in others unfavorable, so there is not a systematic case that headship affectchild welfare outcomes.

The results in These results may seem surprising, or out of step with the main body offemale-headship literature. They are not, however, unique. In Ghana, for example, for households withchildren under age 15 and using per capita adult equivalence measures of consumption, female-headedhouseholds have higher mean welfare levels than male-headed households (Lloyd and Brandon, 1991).In Peru, the same welfare measures show no differences in welfare levels between households that declarefemale heads and those that declare male heads (Rosenhouse, 1989). In Cote d' Ivoire, female headedhouseholds have, on average, higher welfare levels than their male-headed counterparts (Glewwe, 1989a).The sensitivity of the link between poverty and female-headship to definitions and methods shown forseveral Asian countries in Visaria (1980).

Moreover, to show that female-headship is not a hardship to household welfare in Jamaica is notto dismiss concern for female-headship in other countries. Jamaica, after all, has a long history ofwomen's access to education, and to the early passage of equitable family and labor law (World Bank,1989). Furthermore, while feminist may find that social customs constrain Jamaican women's actionsin ways that they find undesirable, it must be recognized that the social inequalities are several orders ofmagnitude less than in societies where customs such as purdah or non-Islamic variants prevail. TheJamaican story can be interpreted to show that the actions that are usually advocated to protect women'swelfare and that have been more fully implemented in Jamaica than in many countries have, indeed,resulted in better welfare outcomes for female-headed households than found in many other countries.

The Limitations. While we have been, perhaps, more than usually comprehensive in ourtreatment of monetary welfare for the household and health, mntrition and education outcomes forchildren, there are some issues we have not been able to address, largely for want of data. First, we havenot looked at the welfare of the women themselves. Is producing these adequate child welfare outcomesstraing them in unacceptable ways? Is their leisure, health, or happiness suffering? We camot say.Women's welfare is, of course, an important outcome in its own right, but may also be important in the

52

sustainability of children's welfare outcomes. Second, we have not been able to address the dynamicsof household formation. What causes a household to be female-headed? If women only formindependent households when they can guarantee themselves and their children a minimum level ofwelfare, there may be potential female-headed households sheltering within large extended families orwith males to whom the women are tied more by necessity than choice. These households arepresumably worse off than independent households, but we don't measure it. Third, how do femaleheads of households cope? We have shown that the differences in market work, remittances andexpenditure patterns are small. The answer may lie in differential patterns of time use for homeproduction, corwmunity participation and leisure, though data for such analyses are scarce. Part of theexplanation may be the fairly well developed social sector infrastructure in Jamaica that providesrelatively easy access to basic services such as primary education, primary health care, immunization andnutrition services.

Policy Lmplications. Our investigation of poverty, welfare and female-headship in Jamaica hastwo strong policy implications. First, programs concerned with poverty should concentrate on povertyand not be distracted by female-headship. Female-headship is not a usefil targeting proxy for povertyin Jamaica. Similarly, programs concerned with child welfare need not be especially concerned forchildren in female-headed households. The programs should focus on other issues of access such asensuring service in rural areas, to children of vulnerable ages, and to the poor.

The second policy implication is for those concerned with women's issues in Jamaica. The workpresented here suggests that issues specifically of female-headship are not the first priority. Rather thefocus should be on issues that potentially affect all women. By ensuring equitable treatment of womenin labor and capital markets and in family law, female heads of household will of course benefit, but sowill women who live in households headed by men.

Methodologcal Implications. Perhaps the strongest lesson from our work is not for thoseinterested in social policy in Jamaica, but for those concerned with female headship in other countries.Much of the gender and welfare literature uses relatively simple statistical analysis. Indeed, much of

it is based not on empirical analysis of full household data sets, but on strings of reasoning built onsummary staistics from secondary data sources.' These techniques are not sufficient, and may producemisleading results.

Our analysis of poverty and female-headship is a good example of the importance of full-scaleanalysis of primary data. Looking only at mean consumption levels led to the conclusion that female-headed households were very much poorer than male-headed households. Examination of the wholewelfare distribution showed that among the poor, female-headed households were not so overrepresented.Multivariate analysis also contributed to the understanding of the determinants of poverty. Finally, atargeting analysis showed that despite the impression created by differences in mean welfare, female-headship is not a viable targeting indicator.

Further work in the field of female-headship should seek high standards of statistical rigor anddata quality. This implies, of course, the need for household survey data that is recent, publicly

2The bodies of literature fostered by the Population Council and by the International Center forResearch on Women are two important exceptions to this criticism.

53

available, and sufficiently comprehensive in scope to cover the range of issues relevant to poverty, childwelfare and female-headship.

A second methodological implication has to do with the usefulness of the concept of beadship.It is a very blunt instrument to use in chipping away at the intricacies of gender, intra-householdbargaining, and welfare. This is particularly true when, as in this paper, headship is used as reportedin a survey rather than as a more rigorously defined concept based in indicators of the relativewbargainingw position of the various members of the household. To what extent the results are influencedby the fact that female headed households in Jamaica consdtute a large heterogenous group, rather thanbeing a small fringe group suffering from stigma and open discimination, as is the case in many othersocieties, is also an unanswered question. Our results should be interpreted with those limitations inmind.

54

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Bolles, A. Lynn. (1986) 'Economic Crisis and Female-Headed Households in Urban Jamaica in Nashand Safa, eds. Women and Change in Latin America Bergin and Garvey Publishers.Massachusetts.

Buvinic, Mayra. (1990) 'Women and Poverty in Latin America and the Caribbean: A Primer for PolicyMakers", processed. IDB, Washington

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Dwyer, Daisy and Judith Bruce. (1988) A Home Divided: Women and Income in the Third WorldStanford University Press. Stanford.

Folbre, Nancy. (undated) "Mothers on Their Own: Policy Issues for Developing Countries" processed.International Center for Research on Women. Washington.

Folbre, Nancy. (1991) 'Women on Their Own: Global Patterns of Female Headship" Gallin andFerguson, eds. The Women and International Development Annual, Vol. 2, Westview Press.Boulder.

Foster, James, Joel Greer and Erik Thorbecks (1984), "A Class of Decomposable Povery Measures.-Econometrical Vol. 52, pp. 761-765.

Glewwe, Paul. (1990) "Improving Data on Poverty in the Third World: The World Bank's LivingStandards Measurement Study" Policy, Research and External Affairs Worldng Paper 416, TheWorld Banl. Washington.

Glewwe, Paul. (1987a) "The Distnbution of Welfare in Cote d'Ivoire 1985" Living StandardsMeasurement Study Working Paper No. 29. The World Bank. Washington.

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Glewwe, Paul. (1987b) IThe Distribution of Welfare in Peru in 1985-86" Living Standards MeasurementStudy Working Paper No. 42. The World Bank. Washington.

Gordon, Derek. (1989) *ldentifying the Poor: Developing a Poverty Line for Jamaica" Jamaican PovertyLine Project Working Paper No. 3, Planning Institute of Jamaica. Kingston, Jamaica.

Grosh, Margaret E. (1991) The Household Survey as a tool for Policy Change: Lessons from theJamaican Survey of Living Conditions' Living Standards Measurement Study Working PaperNo. 80, The World Bank. Washington.

Horton, Susan and Barbara Diane Miller. (undated) "The Effect of Gender of Household Head on FoodExpenditure: Evidence from Low Income Households in Jamaica" processed. Departnent ofEconomics, University of Toronto. Toronto.

International Development Bank. (1990) 'Working Women in Latin America" Economic and SocialProgress in Latin America 1990. pp. 205-256. International Development Bank. Washington.

Kakwani, Nanak. (1990a) "Poverty and Economic Growth: With Application to Cote d'Ivoire" LivingStandards Measurement Study Working Paper No. 63. The World Bank. Washington.

Kakwani, Nanak (1990b) *Te-'-ig for Significance of Poverty Differences: With Application to Coted'Ivoire- Living Stan.Jrds Measurement Study Worldng Paper No. 62. The World Bank.Washington.

Lloyd, Cynthia and Stacy Brandon. (1991) "Women's Role in the Maintenance of Households: Povertyand Gender Inequality in Ghana", processed, Population Council, New York

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McGuire, Judith S. and James Austin. (198) 'Beyond Survival: Children's Growth for NationalDevelopment" Assignment Children. 1987. No. 2, UNICEF, New York.

Moser, Caroline. (1989) "Gender Planning in the Third World: Meeting Practical and Strategic GenderNeeds' World Develment vol. 17, No. 11, pp. 1799-1825. Pergamon Press. Oxford.

Paes do Barros, Ricardo and Louise Fox. (1990) "Female Headed Households, Poverty, and the Welfareof Children in Urban Brazil" processed. The World Bank. Washington.

Rosenhouse, Sandra. (1989) "Identiing the Poor: Is 'Headship' a Useful Concept?" Living StandardsMeasurement Study Working Paper No. 58. The World Bank. Washington.

Psachapoulos, George and Zafiros Tzannatos. (1991) Female Employment and Pay in Latin America:ARegional Study processed. The World dBank. Washington.

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Statistical Institute of Jamaica. (1990) Consumer Price Indices: New Series Januara 1989-October 1990Statistical Istitute of Jamaica. Kingston.

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57

Annex I: Calculation of Fler Capita Consumption

The welfare measure used in this paper is a comprehensive measure including foodconsumption (46 items purchased, 16 items received as gift or home produced); daily expenditures(fuel, tobacco, foods consumed away from home); consumption expenditures (41 items coveringhousehold expenses for clothes, household furnishings and supplies, transport, etc.); non-consumptionexpenditures (insurance, taxes, weddings, donations, outgoing transfers); home repair costs; rent(actual or imputed); and the use value of durable goods.

Different recall periods are used for different items. The values from them were annualized.For some of these items, two recall periods are used on the questionnaire. In all cases the longperiod was used in the annualization process. For example, for food items, the expenditure in the lastweek and in the last four weeks is asked. The value for the last four weeks was multiplied by 13 toget the annual expenditure for that item.

The imputed rent calculation is based on the regression of renters' rent payment oncharactistics of their dwellings (housing materials, size, availability of services, etc.). Then forhouseholds that own their dwelling, the dwelling's characteristics are valued from the results of therenters' regression. This synthetic value of imputed rent is used for owner-occupied dwellings. Forrenters, the actual rent paid is used.

For each of the fifteen durable goods, the average depreciation rate is figured from theinformation on age, value at purchase, and current value. This depreciation rate is then multiplied bythe current reported value to derive the use value for the current year.

Finally the household's total consomption is divided by the number of household members.

58

Annex II: Ibe Estimation of theDlstribution of Consumption

Expenditures

The non-parametric estimation of the density function of per capita consumption was compiledwith the kernel method. The estimated probability of consumption level x is given by

AX) (h (1)hni., h

where: n is the number of observations,h is a smoothing parameter (or 'bandwidth');K is a kernel function which satisfies the condition

f K(x)dx=1 (2)

The estimation process consists of constructing a series of "bumps' centered at eachobservation. The width of each is determined by the smoothing parameter h. The sum of the bumpsgives the esimator (for more detail, and illustrating pictures, see Silverman, 11986]).

In this paper, we used a Gaussian functional form of the kernel:

K(x)= -C 2 (3)

Silverman (1986) shows that the value of h which minimizes the mean integrated square error(MISE) of the estimator for a Gaussian kernel is: ch q,, = 1.06 'I, where a is the sample standarddeviation.

The non-parametric estimation was computed with the software Gauss. The computerprogram is a modified version of the program used by Deaton (1989). In contrast to Deaton ourprogram computes weighted density; that is each household observation is weighted by the householdsize. Consequently, the density function is related to the distribution of individuals rather than to thedistribution of households. This feature is particularly important since we compare subgroups (femalehousehelds vs. male households) where the mean household size is significantly different.

We used a total of 100 points (value of x in formula [1]). The graphs are truncated forJS15,000 since we are interested mainly in the lower part of the distribution. The value of the densityis multiplied by 10b.

59

Annex m: Tedhnical Annex oil Poverty Measures

The poverty measures used in this paper are the Foster, Greer and Thorbecke (FGT) familyof additively separable poverty measures. Their formula is:

P I (z-x) n:(

WhereN: Total number of households in the samplez : poverty line 7in J$ per year)x;: welfare variable (per capita consumption)n1: number of individuals in household in: total number of individualsIj: dummy variable equal to 1 if xc <z (poor)

We used the three usual values of a:

a = 0 Head-count measurea = 1 Poverty gapa = 2 Distributionally sensitive FGT measures

The elasticities of the poverty measures with respect to mean consumption and to inequality(Gini coefficient) were computed as follows:

Elasticity with respect to mean consumption:

a[]?,-, - PJ] ()lp=. (2PI

Elasticity with respect to inequality:

P. 2 n P + l (3)

Where x is the mean per capita consumption of the population. See Kaliwani (1990a) fordetails.

60

In the particular case of a = 0 cannot be computed with formula [11]. Te elasticity ofthe head-count measure was computed as:

P0

where f(z) is the probability density function of consumption x at x =z.

f(z) was estimated from Kakwani (1990,b) specification of the Lorenz curve.

Similarly, cannot be computed from general formula (3). It was computed as:

z

61

Annex IV: Full Regression Results

Analytical Framework

Table A4-1: Probability of Being in Poorest 10% of Per Capita Consumption DistributionTable A4-2: Probability of Being in Poorest 10% of Adjusted Per Capita Consumption DistributionTable A4-3: Probability of Being in Poorest 30% of Per Capita Consumption DistributionTable A4-4: Probability of Being in Poorest 30% of A'Justed Per Capita Consumption DistributionTable A4-5: Probability of Participating in the Labor ForceTable A4-6: Food Shares RegressionTable A4-7 Share of Children Goods in Total ConsumptionTable A4-8: Probability of Being EnrolledTable A4-9: Probability of Having Diarrhea in the Two Weeks Preceeding the SurveyTable A4-10: Z Score for Weight for AgeTable A4-1 1: Z Score for Height for AgeTable A4-12: Z Score for Weight for HeightTable A4-13: Probability of Repetition in SchoolTable A4-14: Probability of Full Attendance in Reference School WeekTable A4-15: Probability of Being in High SchoolTable A4-16: Probability of Being in High School

62

Analytical Framework and Regression Results

The multivariable analyses reported in the following tables can all be thought off as beingderived from the same general analytical framework. The base is the well-known neo-classical modelof household behavior developed for the study of household consumption patterns:

Let U be a household welfare or utility function, defined over as a set of K commoditiesdenoted by vector X. The household is assumed to select X so as to maximize U, given abudget constraint:

max UU (x, .. ,x,) (1)x

Ksubjectto E pjX; = C (2)

i-I

where p, is the price of a unit of good i", xi is the quantity of good Yi purchased and C istotal expenditures or "income'.

The result is a set of demand equations that give the optimal purchase of each commodity as afunction of all prices, pi, and the total available budget, C. As follows:

X F 1= p ,.--, pK , C)

X= F1 (1 ,--., px , C) (3)

x- = Fx (PI,..., P,C)

A more realistic model will take the differences in 'needs' among household into account asresulting, for instance, from differences in household size and composition. Moreover, in most cross-section analyses all households are assumed to face the same prices, so the set of demand equations(3), reduces to a set of Engel curves:

x, = F, ( C; D)x2= F2 (C; D) (4)

x= FK ( C; D)

where D represents household size and composition, as well as other relevant householdcharcteristics. Throughout the paper we are focussing on one specific household characteristic:female headship, represented by a dummy variable which takes the value I if the household head isfemale and which is 0 (zero) otherwise.

Table A4-6 presents the complete estimation results of the set of equations given in (4).Table V-4 in the text summarizes these results of the effect of female headsbip.

63

The general famework given in (1) and (2) can easily be expanded to study labor supplybehavior. For instance, allow one of the commodities x, say xK, to represent leisure, and extend thebudget constraint as follows:

K-1E p3x1 = y+wA (5)i-1

L+A =T

where L = leisure timeA = work timeT = total time availablew = the price of time (the wage rate)y = non-labor income (such as transfers).

In order to maximize their utility over consumption and le sure, households will spend theirtotal resources, y+wA, on commodities x1, i = 1, K - 1. In order to be able to do that they have towork A hours, leaving them L = T - A hours of leisure. The labor force participation equation forfemales presented in Table A4-S can be derived from this general framework (See Deaton andMuellbauer, 1980, chapters 4 and 11 for further elaborations).

A more significant extension of the model presented in (1) and (2) can be used to develop theframework for such "commodities" as health or education. For example, if the commodity of interestis health, one of the xi's in the utility function will represent health and the model needs to beexpanded with a health production function. Some of the arguments in this production function couldbe the same as the "regular" commodities already included in X (e.g. certain types of food). Timeinputs are also likely to be important Prices' should be interpreted in a broad way, to include allosts incurred by the household (e.g. the cost of caring for a sick family member, or the travel time

and expenses associated with visiting a health clinic).

The resulting "demand for health" equation will show health to be a function of all prices, aswell as of income and endowments that are exogenous to the household. Behrman and Deolalikar(1988) present this theoretical framework in its most elaborate form. In most empirical studies(including ours) some prices are represented by their determinants. For instance, the "time cost" ofobtaining health care is represented by the distance to the health clinic, and "the price of +;...e 'rwage rate) is represented by the age and education level of the individual.

All remaining results presented in the combination of this Annex can be thought off as derivedfrom this framework, with school enrollment, achievement, immunization, doctor visits, etc., being ineach case, the conunodity of interest. In all cases we include 'income" (that is, total per capitaexpenditures) as an explanatory variable. This is, strictly speaking, Lilcorrect since, as shown inmodel (5), income is endogenous, resulting from labor supply decisions. We ignore this complicationhere. Consequently, all results should be interpreted as conditional upon the household's income.The estimation results regarding the variable of interest, female headship, ire presented in the textwhen the effect is statistically significant. Full estimation results follow.

.

DUMMVY VARIABLES CONTINUOUS VARIABLES . -

Male Head omited . _ A_e of Head -.03 .tO 483 1720|Femal Head .26 2.37 .42 .49 Age of Head Squared/100 .03 2.22 26.51 17.93Ono Member-Male -.48 -2.03 .14 .34 Yri of Education - Head .01 -31 7 230Ono Member-Femal -.23 -.87 .0S .22 Yre of Edu. Sq.- Head/100 .18 -1.03 9S.2 53.90Hed In Legal Marriage onmitted - .34 .47 Yre of Edu. Max. In HH -.06 -2.72 131 5.90Hud in Common-law .20 1.72 .20 .40 Number of Children 0-5 24 6.24 .56 .91Head In Visting/Casual Union .14 .84 .15 .36 Number of Children 6-10 .21 4.90 .54 .84H"d Widowed or Divoreed .17 1.22 .11 .31 Number of Cbildren ll-18 .25 7.72 .77 1.12Hud Never Married -.05 -.35 20 .40 No. of Peraona with Poor Heah .17 2.04 .15 .41VUngston -.61 -2.83 28 .45 Conat .1.58 -3.23Other Towns omitted . 18 .39 SUMMARY STATISTICS

Rural rea .52 3.90 53 .50 Number of Obsrvtions - 3S36Head Not Working omitted - 32 ~~~~~~~~~Chi' (23) - 433.85Hud Not Working onutkd _32 47 Prmb > ChI- 0.000

Hud Self-Employed Agriculture .38 3.47 23 .42 Ls Likelthood - -582.44

Hed Self-Employed Oher .07 .54 18 .38

Hed PtafesdA/MmlnlCtdcal/sale -.86 -2.32 14 .35

Hed Other Setor .05 .28 13 .34

Note: Te probabiity of beig poor retft to individuals. Th eathmto Is based on household oberviton.

Tabhs A4-2: Probi: RaeSsaulon Resuks

DEPENDENT VARIABLEt PROBABILITY OF BRING IN POOREST 10% OF ADJUTD PER CAPITA CONSUMTON DISTRIDUTMONI m .0, gd. d.w. J6.

UNIVERSI; ALL HOUSEHOLDS

DUMMY VARIABLES CONTINUOUS VARIABLES

Ml Head o|lual AgeofI ud -.00 .05 48.3 17.2

FemaleHead .08 .81 .42 .49 Ageof HudSquared/100 .01 1.00 26.5 17.93

One Member-Male -.40 -2.49 .14 .34 Yeare of Educaton - Hud -.04 -1.61 73 230One Menmber-Pemak. -.06 _ .34 OS .22 Yeo of Education Squared/100 .16 .00 93.2 53.90

HeAd In L<eGl Marriae omrtid _ .34 .47 Yrs of Education - Max. In HH -.02 -1.14 1.31 5.90

Hud la Comrmonlaw Marnige .39 .3.62 .20 .40 No. of ChUldrn 0.5 .03 .70 .56 .91

Head In ViaItWng/CawaI Union .21 1.36 .15 .36 No. of Children 6-10 .04 .15 .54 .14

HeadWldowedIDIvorced .04 .30 .11 .31 No. ofChildrenaI 11 .16 4.95 .77 1.12

Head Never Maried .22 1.S .20 .40 No. of Pemons with Poor Healh .13 2.45 .15 .41

Kingson -.47 -2.31 .23 .45 Cond nA .1.95 -4.44 _ .Other Towns omitted .1. .39 SUMMARY STATISTICS

Rurel Ae .50 4.33 .53 .50 Number of Obaervdons * 3536Chi*i (23) - 356.06

Head Not Working omitted =32 .47 Pmb > ChiP- 0.000Head Self-Emiloyed Agriculture .27 2.85 .23 .42 Log ULkelihood - -763.42

Head Self.Eayloyed Othr -.09 -.74 .11 .36

Head Protoaa/Admin/Ciedcal/Siae -*65 -2.62 .14 .35

Head OMar Setor .03 .17 .13 .34

Noto: Te proahtUky of btiK pow rffen to indhivdu Tie eMtmadati Is baued on houseold obeAvatdo.

Table A4-3: Probit Regreaalon Results

DEPENDENT VARIABLEs PROBABILITY OF BEING IN POOREST 30% OF PER CAPITA CONSUMPTION DISTRIBUTION; mean t1, std. dev. .41.

UNiVERSE: ALL HOUSEHOLDS

DUMbfY VARALES __ CONTINiUOUS VARIABLES

Me Head onsirvi = = Age of Head -.02 140 48130 17.20Female Head .11 1.63 .42 .49 Ago of Head Squared/100 .02 2.03 26.51 17.93Or. Membet-Mae -.73 -S.35 .14 .34 Yean of Education -Had -.06 .2.99 7.30 2.30One Member-Female -.39 -2.31 0S .22 Years of Educaton Squared/lOG .03 33 98.20 53.90Hud In Legal MeMASge onited .34 .47 Yean of Educaion- Max. In HH -.07 4.72 1.31 5.90Head In Common-law Margle .30 -3,60 .20 .40 Number of Children 0-5 .30 10.10 .56 .91Head to Vitin or Cul Union .23 2.12 15 .36 NumberofCbii cd6-I0 .22 6.75 54 .14Head Wowed or Divocced .09 .90 .11 .31 Number ofChildren 1-1a .22 3.55 .77 1.12Head Never Marred 15 1.62 .20 .40 Nunber of Petona wh Poor Heah .23 3.65 IS A41ing_oo -.54 -5.45 .21 .45 Conain -.55 -1.64 Other Town 0cafted .1. .39 SUIMMAY STATISTICS

Rural Anas -50 6.13 .53 J0 Number of Observationa - 3536Chi, (23) - 1011.39Hesd Not Wong omc- - 32 .47 Pob > ChP- 0.000

Heed Sklf-sployed Ag4culure .26 3.31 .23 .42 Lag Ukelihood m -1314.39Head Self.EmployedOdher .14 1.59 13 33

Head ProfeAu/Mmin/Ckiecal/Sels -.43 -3.23 .14 35Head Otbr Secto .13 .1.12 .13 34

Noteh Te probabilty of being poor reen to indicldualh. U. esimation Is based on bousehold ebuevalon.

Table A44: Pmbit iReRaulon Result

DEPENDENT VAIUABLE. PROBABILITY OF BEING IN POOREST 30% OF ADJUSMED PER CAPITA CONSUMPIlON DISTRIBUTION; m. .24, std. dw. .43.UNIVER3S: ALL HOUSEHOLDS

DUMMY VARIABLES . CONntiNUOUS VARIABLES _

Mae Hud omaued ASg of Head -.02 -1.69 4830 17.2014na )luHd .09 131 .42 .49 A of Hud Squar.d/l00 .03 2.80 2651 17.93One MAenbu-Mae -.44 -4.00 .14 .34 Yenotf Educadoan-Head -.07 -335 7.20 230One MemberFemale -.11 -.30 .05 .22 Yau ofEducadooSquamd/100 -.12 .1.30 9.20 53.90H"d Ia laaal Manla oatted .34 .47 Yearsl ofducaa - Maz. ia HH -.05 -336 lIJ 5.90Head I Commae- w Manhae 30 3.35 .20. .40 Number of aliidreo 05 .11 3.13 .56 .91

Had to V ldg oa Caam Ur .02 .13 A1 .36 NuwiberefChIldn 6410 .04 1.36 _4 .U4Ho" Wiowe orDinved .05 .55 .11 31 NumberofChLIdreaL-I 1 .16 6.62 .77 1.12Hud Never Madod .21 2.49 .20 .40 Nuw'e otfetoawia Poaet Hefh .17 2.13 .15 Al1Klgi -.47 -4.96 .28 .45 C_om -.21 .69

aObeg ToWe o mlid _I .39 StUMAURY SrATISTICSRunl Au .45 6.12 .53 .50 Nuaierof Obueevdoa - 3536

Rad Ngo Woiag* omued .32 .47 PR(b > t- 12.45

Head Slf.Employed Aghekuku .32 4.32 .23 .42 LIg lkellbad - -1538.97

Head 81-EImployed Wu .05 .63 .16 .38

Hed Pu(bIMAdIn/ChlaUlake -.46 -3.70 .14 .35

Head Ow Sectcr -.07 -.72 .13 .34

Note: The probiltly of being poor rtof to ldIviduals. The ealtloa Is based on housebold obse,vloba.

.

Table A4-S: Probit Rogresion Result

|DIlP1NDLNr VARIABLE: PROBABILITY OF PARTICIPATING IN THE LABIOR FORCEI mea .60, dtd. dev. .49.

UNWERSE: FEMALE HEADS OF HOUSEHOLDS AND FEMALE SiPOUSElS, AGE 15-64:~ t1 .- o CS t a M ean S d, y. l ... .Std. . ... ...- '''.

Male Head omitted . . Domestic Remittances (OOO'S OF J$) -.02 -.42 .13 6.58Female Head .l7 2.59 .S2 .50 iaternatlonal Remitnces (000'S OF IS) -.04 -2.13 .56 19.02Kingston .01 .13 .31 .46 Food Stamps (O0O'S OF JS) .02 .11 .05 1.47Other Towns omitted .18 .39 Inters (OOO'S OF J$) .15 1.66 .07 8.28Rural .12 1.S6 .SI .50 Child Support (OOO'S OF JS) .02 .78 .30 12.05Number of Children 0-5 -.01 -.29 .36 .81 Other Uneaend Income (OOO'S OF J$) -.01 1.33 .08 8.66Number of Children 6-10 .03 .86 .70 .89 Age .1S 7.92 40.60 12.20Number of Children 11 18 -.00 -.13 .99 1.19 Age Squared/100 -.18 -8.11 17.98 10.39 0%Number of Aduls -.04 -1.00 2.49 1.31 Number of Employed Persons In Household -.05 -1.04 .70 1.06Yea of Education-Head .01 .67 7.56 2.29 Own Health Poor -.32 -6.78 .41 .71Yea of Education Squared/100 .14 1.66 Number of Persons with Poor Hath -.55 -4.09 .10 .35

=___=___=_ Consant -2.73 -6.48 =

SUMMRY STATISTICS

Number of Obsrvations - 2189Chi1 (20) - 203.32Prob > Chit M 0.000L,, LIkelibood - 1372.12

Table 4-6: Pood Sbae Regreaaloue

to laFeale Tell Toala Number Number Number Dummy DummyHeadAbip EzpeaCilureA Expeadiure of Adull of Aduh of for for Rutal"atoo Comm Dummy (000's of Ji) (OO0 of 3J) emale Mal chidreti Klagac Ameas Adj. Rs mue

Oils ad Fas 13.50 0.23 -5.05 0.00 0.05 0.52 0.03 0.03 0.02 0.09 5.74(.44) (1.51) (1.3) (0.04) (1.14) (2.62) (1.15) (0.65) (0-.1)ll"ar L".99 0.14 -0.94 .0.01 0.13 0.17 0.2s 0.13 0.1 0.19 26.-35) (.1.12) (1.47) (.0.31) (4.95) (4.74) (14.12) (1.3) (2.33)Breads 41.73 40.42 .6.02 0.24 0.01 .0.01 0.13 .0.03 0.17 0.05 6.56(5.90) (0.156) (.4.14) (3.27) (0.13) (4.12) (4.51) (4.12) (0.9)utaw 53.77 0.01 46.21 0.26 0.06 0.10 0.26 0.16 0.54 0.1u 4.21(9.09) (0.03) (-.09) (5.33) (1.69) (2.14) (10.67) (1.26) (43I)Rke 7.65 0.02 40.46 .0.09 0.19 0.14 0.21 0.61 a.2m 0.07 4.5(1.52) (0.06) (4.44) (.1.61) (3.19) (2.5) (7.57) (3.93) (.132)Poukay 46.46 .0.32 5.66 0.13 0.21 0.22 0.34 0.55 025 0.17 4.74(5.72) (-1.04) .. 4) (1.54) (2-22) (2.30) (7.36) (am) (1.1)go" 0.03 0.29 0.32 .0.02 .0.01 0.06 .0.01 -O.2 0.06 0.03 0.94(5.16) (3.93) (4.16) (2.62) (4.4) (.1.17) (0.3) (3.-9) (0.02)Yaom 2.1 .0.63 1.64 .0.15 .0.30 0.30 0.16 *1.12 130 0.11 4.9(.24) (1.86) (.90) (.1.60) (2.90) (3.03) 0.22) (4.11) (7.13)Corsea I4.50 0.04 -2.96 0.12 0.01 0.06 0.4 0.11 0.05 0.03 07(6.26) (0.48) (.5.53) (4.76) (I. 1) (2.44) (2.7) (1.55) (0.71)Mlk .43.63 0.96 10.36 0.46 .0.03 0.00 -0.27 0.05 4.10 0.06 I1.97M. (2.15) (4.27) (.3.72) (4.21) (4.-04) (4.03) (4.14) (4-30)Fisb 443 0.46 -7.15 .0.37 0.03 .0.07 4.21 *1.07 .0.56 0.01 3.36(4.71) (.1.29) (.3.72) (3.76) (0.24) (4.71) (4.4) (-3.72) (.2.17)OGe ceeas 30.21 .0.50 -5.34 0.29 -0.04 .0.13 00 -. 49 1.21 0.02 5J(3.66) (1.57) (3.15) (3.31) (4.43) (.1.44) (4.00) (-5.92) (5.17)Falia *-10.55 0.23 2.17 -0.07 40.16 .0.12 .0.27 0.17 -0.03 0.03 5.53(.1.7) (0.97) (1.70) (.1.03) (.2.21) (l.71) (-7.77) (0.39) (41.5)Vgdablea 16.34 1.13 *2.67 0.16 4.25 0.03 -0.17 0.00 -0.75 0.03 5.62(2.32) (4.2) (11.4) (2.21) (.2.99) (0.42) (.4.33) (.0.02) (.3.91)Aeabolio Beverages .20.90 -4.12 4.74 .0.21 0.32 .0,62 4.37 0.5 -0.42 0.07 2.61(-2.27) (11.71) (2.50) (.11) (3.97) (.5.96) (-7.21) (0.52) (1.60)Coodlmeau -13.09 0.40 2.56 40.09 40.09 -0.03 -0.10 0.16 .0.19 0.06 2.74(-3.33) (3.69) (3.13) (.2.26) (.2.01) (.1.39) (-4.64) (-1.34) (.1.80)Noo-AlcoboleBDeveragea -13.02 0.23 2.33 40.07 .0.06 4.14 -0.19 40.09 0.02 0.03 2.09(-2.29) (4.84) (3.00) (.1.23) (4-.83) (.2.16) (.5.19) (4.50) (0.12)Oaser Foods -33.77 0.71 3.03 .0.36 4.23 .0.06 0.47 1.57 0.05 0.06 6.17(-4.32) (2.02) (4.27) (-3.76) (-2,13) (4.59) (9.16) (5.57) (0.25) 5tiber Dairy Producu -1630 1.13 3.60 -0.12 4.32 4.13 4.10 0.53 4.40 0.07 7.24(.2.19) (3.93) (2.32) (-1.47) (-3.70) (-1.59) (-2.43) (2-30) (-1.90)Mea -11.10 1.64 1.44 0.07 4.22 0.17 -0.22 -1.15 0.33 0.06 10.28(4.s5) 3.31) (0.54) (0.53) (-1.43) (1516) 0.3.06) (-2.37) (0.90)

ToWal Food Shbae 0.01 0.01 0.16 4.01 0.01 0.01 0.02 0.05 0.04 .29 40.04(0.04) (0.96) (3.91) (4.16) (3.62) (4.22) (13.49) (0.39) (6.2)

Mes .47 9.35 97.65 .94 1.29 1.35 .29 .54Sid. Dev, .49 .32 15.97 1.41 1.47 2.02 .45 .50

Nole: sm i l psteAheAls Me I Values.

Table A4-7: OLS Regression Results

DEPENDENT VARIABLE: SHARE OF CHILDREN GOODS IN TOTAL CONSUMPTION; mean 4.95; std. dev. 4.27.

UNIVERSE: ALL HOUSEHOLDS

Vasiable coefficient T v*luea MeaI Std. D:v. Variable CoefficienT t valus Mean SSd. D

DUMMY VARIABLES CONTINUOUS VARIABLES

Male Head omitted - In (Per Capita Expenditure) 5.97 2.22 10.05 .72Female Head .59 1.82 1.48 .50 In (Per Capita Expenditure)1 -.31 .11 101.47 14.47Kingston 1.29 .25 .29 .45 Number of Adult Women -.3S .08 1.16 1.57Other Towns omitted - Number of Adult Men -.16 .08 1.39 1.66Rural -.03 .23 .55 .50 Number of Children 0-5 .99 .08 .87 1.00

Number of Children 6-10 .73 .09 .82 .92

________ _ Number of Children 11 18 .60 .07 1.17 1.20

__________ ________ Constant -25.18 11.09

__________ ________ ______ SUMMARY STATISTICS

______________ _____=___=_ Number of Observations = 2465F (10,2455) - 33.08Prob > P .000Adjusted R2 = .115

I~~~ . = -

! ~~~~~~~~~~~~~~~~~Table A4-8: Probit Regreuon Results0

DEPNDET VRIBLE PRBAITCOF BEING ENROLLED; mean j S,td.D~. d offcv. JO.a ea 8dDe.UNIVERSE: CHILDREN AGE 13-19

DUMMIY VARIABLES CONTINUOUS VARIABLES

3oy-Ma1e Headed Household omited Per Capita Expenditure .09 7.7S 5532 4730Boy-Femal Headed Houhold .13 1.28 .24 .43 Per Capiu Expenditure - Squared x 10' .99 -4.58 5297S 199104Old-Female Heeded Household .30 2.90 .24 .43 Father - Yeam of Edcadon .04 4.1! 5.75 3_t2Oli-Maeb Heuded Housold .40 4.17 .24 .43 Mother - Yen of Edueetion .03 2.24 6.66 3.28Ap 13 onIed .12 .33 Miles to Bus Stop

__03_-2_17 1_2S 2 _74

Age 14 -.52 -2.31 .s .36 Constant 1.24 5.40Age 15 -1.45 -7.00 14 34

Age 16 -1 91_ _9__ 1

Age 17 -2.85 -13.94 .15 .36

Age 18 -3.64 -16,86 .14 .3S

Age 19 -3.90 -17.57 .14 .35 __

Kingston omittod - .28 .45 __OtherTowns -18 -170 .18 .38 _

Rural -.05 -.61 5S .50

Mother in Household 22 2 7S .65 .48 SUMMMY STATISTICS

Father in Household 04 _ 47 .39 .49 Number of Observations -2488

ChO (18) = 1618.32- - -_ Prob > Chi = 0.00

_______ Log Likelihood = -900.49

==- - - _

Table A4-9: Pobit Regresslon Results

DEPENDENT VARABLE: PROBABILITY OF HAVING DIARRHEA IN THE TWO WEEK PRECEEDING THE SURVEY; mean .063, dId. dew. .U2.UNIVERSE: CHLDREN UNDER AG ES

Vad~b1. ~ ., CoefteE~ t vekaeu MeM ~taL bev. VaA. .. ff .i .... ui en id DvDUMMY VARIAILE CONTNUOUS VARIABLES

Boy - Mu Headed Houehold oniluted . _ Yer of Education o Mother -. 00 -.06 8.43 2.36Boy -Pnalb Hcded Household .29 1.69 .26 .44 Yeo of EducAtion -Father -.02 -157 6.96 3.30Oid - Female Headed Houshold .24 1.46 .24 .43 Pcr Capits Expeaditure .07 .99| aw Male Headed Houshold .OS .33 .24 .43 Per Capia Expeaditum- Squared -.01 -1.53Age 0-1 monha omitted Nunber of Children 0.5 .04 .74 2.06 1.15Age I yaer .77 3.57 .19 .39 Numberof Childrn 6-10 .15 2.94 .94 1.04A Zge2year _ _ .94 4.48 .20 .40 Number of Chidn 1-18 -.0') -.98 1.10 1.29Age3yeaf .44 I.96 .20 .40 Number of Adults -.08 -2.09 2.99 1.67Age4year .JS 2.52 .20 .40 Co asa -1.75 -4.39K.goa omited ' .26 .44

OthrTown_ -.21 -1.25 .17 .37 _ =

| Rund _ -.33 -2.34 .57 .50

Head In Legal Marriage omied . .38 .48

Head In Common bw Marriage -.30 -2.19 .30 .46 _

Head In visltlng or cral union -.54 -2.58 .11 .31 SUMMARY STATISTICS

eH"d Widowed or Divorced -.53 -2.21 .09 .28 Number of Observadoas - 1530ChI2 (11) - 70.26HeadNever Marded -.16 -.90 .13 .33 Pwb > ChP - 0.00Log ULkelihood - -323.59

I~~~ * m == _- ,

Table A4-10: 01. Regrssion Results

DEPENDENT VARIABLE: Z SCORE FOR WEIGI!T FOR AGE; mean -.22, std. dev. 1.51.

UNIVERSE: CHILDREN UNDER AGE FIVE

DUMMY VARIABLE CONTINUOUS VARIABLES

Boy - Male Headed Household omitted Per Capita Consumption .04 3.40 4708 3898Boy - Female Headed Household _.09 -.81 .26 .44 Number of Childrn 0-5 -.04 -1.14 2.06 1.15Girl - Female Headed HousehoW -.11 -.93 .24 .43 Number of Children 6-10 -.02 .0.61 .94 1.04Girl - Male Headed Household -.10 -.99 .24 .43 Number of Children 11-18 -.05 -1.65 1.10 1.29Age 0-12 Months omined - Number of Adults 05 1_87 2_99 1_67Age I year 1.11 9.30 .19 39 Yea of Education 00 43 286

____________________ ______ ~~~~~~1Mother____

Age 2 year .13 1.07 .20 40 Years of Education - .00 .0 6.96 3.80Father

Ago 3 year .09 .74 .20 .40 Constant -.62 -2.78Age 4 year .02 .13 .20 .40

Kingston omitted .26 44

Other Towns .05 .46 .17 .37

Rural .07 .76 .57 .50 _

Head in Legal Marriage omitted .38 .48Head in Common-law Marriage -.01 -0.07 .30 .46Head in visiting or casual union -.11 -.75 .11 .31 SUMMAY STATISTICS

Head Widowed or Divorced .02 .13 .09 .29 Number of Observations - 1549Head Never Married -.13 -0.94 .13 .33 F(20,1528) = 7.75

- ~~~Prob .Z F = 0.00Adjusted RI - .08

, ~ n=.,

.

Table A4-11: OLS Regresion Raults

DEPENDENT VARIABLE: Z SCORE FOR HEIGHT FOR AGE; mean -.17, std. dev. 1.81.

UNIVERSE: CHILDREN UNDER AGE FIVE

Variable : ' 6 :'.,; . ' '1Coffici Ia v. i i t ean: Sd.y.DUMMY VARIABLES _ CONTIUOUS VARIABLESBoy - Male Headed Household omitted = _ Per Capita Consumption .02 1.76 4708 3898Boy - Female Headed Household -.27 -1.88 .26 .44 Number of Children 0-5 .01 A6 2.06 1.15Girl - Female Headed Household -.24 -1.70 .24 .43 Number of Children 6-10 -.05 -.97 .94 1.04Girl -Male Headed Household -.IS -1.18 .24 .43 Number of Children 11-18 -.06 -I.S4 1.10 1.29Age 0-12 Months omitted - Number of Aduls .04 1.19 2.99 1.67Age 1 .46 3.09 .19 39 Year of Education - .02 .92 8.43 2.86| _______________________________ ____________ M oth err____________ ______MotheAge 2 -.13 -.89 .20 40 YewaofhEducation- -.00 -.23 6.96 3.80

Age 3 .11 .77 .20 .40 Constant -.26 -.93 .Agp 4 .06 .41 20 .40 _

Kingston omitted .26 .44 _

Other Towns .03 .23 .17 .37 _I~~~~~~ . -. I

Rural .16 -1.28 S7 .50Head in Legal Marriage omitted .38 .48 SUMMARY STATISTICSHead in Common-law Marriage .01 .09 .30 .46 Number of Observations

____________ _a "- , 1528) - 2.17Head in Visiting or Casual Union -.14 -.81 .11 .31 Prob > P = 0.00t _______________________ -- Adjusted R' -. 01Head in Widowed or Divorces .19 1.05 .09 .28

Head Never Married -.09 -.52 .13 .33

Table A4-12: OLS Rcgreion Results

DEPENDENT VARIABLE: Z SCORE FOR WEIGHT FOR HEIGHT; mean -.13, std. dev. 1.28.

UNIVERSE: CHILDREN UNDER AGE FIVEy .... 1..-11:0s * ............. .%>t l--

DUMMY VARIABLES CONTINUOUS VARIABLES

Boy - Malo Headed Household omitted - - - Per Capita Consumption .02 1.99 4708 3898Boy - Female Headed Household .04 .37 .26 .44 Number of Children 0-5 -.04 -1.26 2.06 1.15Girl -Femca Haded .11 1.10 .24 .43 Number of Children 6-10 .03 .78 .84 1.04Household

Girl - Male Headed Household .07 .81 .24 .43 Number of Children 11-18 .01 -.23 1.10 1.29Age 0-12 Months omitted Number of Adults .03 1.S3 2.99 1.67Age 1 .71 6.87 .19 .39 Years of Education - -.00 -.30 8.43 2.86

-_______________ _ -_-_----___M other

Age 2 .29 2.85 .20 .40 Years of Education - .01 -.61 6.96 3.80 >_________________________ ~~~~~ ~~~ ~~~Father __ _ __ _

Age 3 12 1.17 .20 .40 Constant -.72 -3.69 - -

Age 4 .09 .86 .20 .40

Kingston omitted .26 .44

Other Towns .10 .98 .17 .37 =

Rural .22 2.S4 .57 .50 -

Head in Legal Marriage omitted .38 .48 SUMMAY STATISTICS

Head in Common-law Marriage .02 .30 .30 .46 Number of Observations = 1543Head in visiting or casual union .10 .77 .11 .31 F (20, 1S52) 3.66________________ ______ a - ~~~~Prob > F = 0.00Head widowed or divorced .00 .02 .09 .28 Adjusted R2 - -.03

Head Never Married .03 .23 .13 .33

Table A4-13: Probit Regrssion Results

[DEPENDENT VARIABLE: PROBALITY OF REPETITION IN SCHOOL; mean .058, ntd. dev. .233.

UNIVERSE: CHILDREN AGE 13-19

X Y Wt'l"'S'o''fi'cn".t'' Y" ale M' SW' DIv.. .. ra ec ' au M':1 Si' ' Dt 1.DUMMY VARIABLES _ _ CONTINUOUS VARIABLES

Boy-Male Headed Household omitted - .28 Per Capita Expenditure .05 2.84 5702 5011Boy-Female Headed Household -.44 -2.30 .23 .42 Per Capita Expenditure - Squared -.00 -1.27 - -Girl-Female Headed Household -.24 -1.36 .24 .43 Father - Years of Education .00 .15 6.11 3.73Girl-Male Headed Household -.05 -.35 .25 .44 Mother - Years of Education -.02 -.99 7.05 3.15Ago 13 omited .22 .41 Miles to Bus itop .01 .71 1.27 2.60Age 14 -.21 -1.15 .27 4S Constant -1.79 -4.60Age 15 .09 .52 .20 .40 _

Age 16 -.10 -.SI .20 .40

Age 17 .34 1.57 .08 .27Age 18 1.00 3.37 .02 .15Age 19 .00 .00

Kingston omitted 2S 4ASOther Towns .43 2.26 .17 .38Rural .38 2.26 .55 .50 Private School omitted SUMMARY STATISTICS

- - Number of Observations - 1310Public School -.05 -.18 .96 .30 ChP (18) = 41.8SMother in Household .02 -.14 69 46 Log Likelihood = .26920

Father in Household -.21 -1.42 .41 .49* AlU of the 14 children of age 19 have a value zero for the depedent variable.They have been dropped from this regression.

Table A4-14: Prbbi Rasso Rh |

DEPENDENT VARIABLE: PROBALITY OF FULL ATTENACE IN RFENE SCHOOL WEEK mte 89, st. de. Jil.

UNIVEPRSB: CHILDREN IN SCHooI, AG8 13 19

~ .SA,Ey, 'hi 4A~ 6 Cofiiot V*U IeM8. DUMMY VARIABLECS CONTINUOUS VARLABLES

IoyMAIb HNeded HH omitted . .28 . Psi Capita Expenditur -.01 -.24 5702 5011Doy-Feomb Headed HH -.06 -.42 .23 .42 Per Capitu ExpenditireiSqured .00 .76 . -

OidIFemalo Headed HH .02 .15 .24 .43 Fther - Ymn of Education .02 1,35 6.11 3.73aid-Male Haded HH .04 .32 .2S .44 Mother - Yeaz of Education .01 .M 7.05 3.15AP 13 omited .22 .41 MUes to Dusop -.05 .3.03 1.27 2.60Ag 14 *.08 -.61 .27 .45 Cooam 1.03 3.07

APis .20 1.30 .20 .40 =Ap 16 -.02 -.17 .20 .40 ____ _

Ap 17 -.0S 2-.41 0 .27 _

A_s_S .OS .18 .02 .15-

AP 19 _ .32 .62 .00 .00 _

Kingstonandted =2 U 45 II OtherTowns -.14 -.89 .17 i3 ___ _ _=

Rum) .30 -2.11 .55 .50

Fe School Lunch -.61 .1.50 SUMMARY STATLIICS

Paid School Lunch .36 2.34 Nun*r of Obsewaom - 1280

Mother la Houehoid .03 .31 .69 .46 al (18) - 41.86- ~~~~~~~~~~Ptob >. CM' a-0.00Father in Household .14 .1.18 .41 ,49 Lg oL od - 43132

Private School omited

PubUi School .22 .94 .96 .30_~ - __

Table A4-15: Probi Regresion Results

DEPENDENT VARIABL.:; PROBALITY OF BEING IN HIGH SCHOOL; mean .30, rtd. dev. .459.

UNIVERSE: CHILDREN AGE 13-19 - ENROLLED IN SECONDARY SCHOOLS

lhl~~~Of~1 I auaMai st,d, ,Vrlb1 t.ol DUMMY VARIABLES _ CONTINUOUS VARIABLESBoy-Male Headed Household omi#d .28 Per Capita Expendture .10 9.19 5702 5011Boy-Female Headed Houswehold .24 2.19 .23 .42 Per Caph Expenditure - Squared/1000 -.97 -4.30 . .Girl-Peale Headed Household .31 2.87 .24 .43 Father - Year of Education .01 1.45 6.11 3.73Girl-Maie Headed Household .45 4.71 .25 .44 Mother Ye of Education .01 1.11 7.05 3.15Ag 13 omitted .22 .41 Miles to Bus dop -.04 -2.27 1.27 2.60Age 14 -.07 -.72 .27 .45 Constdan -1.54 -9.42 .Agp 15 .12 1.23 .20 .40

Age 16 .15 1.55 .20 .40 coAge 17 .33 2.28 .08 .27

Age18 _.90 3.54 .02 .15 _ _Age 19 .06 0.17 .00 .00

Kingston omitted . .2s .45 .Other Towns .26 2.56 .17 .38

Rurl -.14 -1.53 .55 .50 = =Mother in Household -.02 -0.28 .69 .46 SUMMAY STATISTICSFather In Houwhold .06 .68 .41 .49 Number of Observations = 1681

Chis (18) - 381.7-_ a Prob > Chp' =0.00

Log Lilelihood - -900.82

Table A4-16: Probk Regression Resuls

DEPENDENT VARIABLB: PROBALITY OF BEING IN HIGH SCHOOL; mean .16, std. dew. 367. |

UNIVERSB: CHILDREN AGE 13-19-- , 3,~~~~~~~~~~~~~~K~i; W i

DUMMIY VARIABLES CONTINUOUS VARIABLES

Boy-Mak Headed HH omitted Per Capita Expendiure .09 8.65 5532 4730Boy-Female Headed HH .23 2.18 .24 .43 Per Capita zxpnd. - SquaredIooo -.88 -4.39 52975 199104Girl-Female Headed HH .39 3.67 .24 .43 Father - Yean of Education .02 2.07 5.75 3.82Girl-Mate Headed HH .49 5.23 .24 .43 Mother- Yean of Education .01 1.10 6.66 3.28Age 13 omitd - .12 .33 Miles to Bus stop -.03 -1.85 1.28 2.74Age 14 -.08 -.70 .15 .36 Constant -1.55 -9.21 =

Age 15 -.02 -.14 .14 .34 ___

Age 16 -.12 -1.09 .17 .37

Age 17 -.57 -4.62 .15 .36

Age 18 -1.03 -7.0S .14 .35

Age 19 -1.70 -8.36 .14 .3S

Kingston omited - .28 4S

Other Towns .01 .S .18 .38

Rural -.23 -2.68 SS .50Mother in Household .10 1.27 .6S .48 SUMMARY STATISTICS

Father In Household .02 .24 .39 .49 Number of Observations - 2488ChP (18) - 381.7

-_ Pmb > ChP = 0.00Log Likelihood - -906.23

I~~ . = -- - -

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LSMS Working Papers (continued)

No. 59 Labor Market Performance as a DEterminant of Migration

No. 60 The Relative Effectiveness of Prwate and Public Schools: Evidencefrmn Two Developing Countries

No. 61 Large Sample Distribution of Seueral Inequality Measures: With Application to Cte d'lwire

No. 62 Testing for Significance of Poverty Differences: With Application to Cote d'lwire

No. 63 Poverty and Economic Growth: With Application to Cote d'lvoire

No. 64 Education and Earnings ini Peru's Informal Nonfarm Family Enterprises

No. 65 Formal and Informal Setor Wage Determination in Urban Low-Income Neighborhoods in Pakistan

No. 66 Testing for Labor Market Duality: The Private Wage Sector in Cote d'luoire

No.67 Does Education Pay in the Labor Market? The Labor Force Participation, Occupation, and Earningsof Peruvian Women

No. 68 The Composition and Distribution of Income in C6te d'lwire

No. 69 Prwce Elasticities from Survey Data: Extensions and Indonesian Results

No. 70 Efficient Allocation of Transfers to the Poor The Problem of Unobserved Household Incno

No. 71 Investigating the Detenninants of Housdeold Welfare in C6te d'lwire

No. 72 The Selectivity of Fertility and the Determinants of Human Capital Investments: Parametricand Semiparametric Estimates

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

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

No. 75 Te Distribution of Welfare in Gkana, 1987-88

No. 76 Schooling, SkiUls, and the Returns to Government Investment in Education: An Exploklion UsingData from Ghana

No. 77 Workers' Benefits fron Bolivia's Emergency Social Fund

No. 78 Dual Sdection Critenia mth Multiple Alternatives: Migration, Work Status, and Wages

No. 79 Gender Differences in Household Resource Allocations

No. 80 The Household Surveyasa ToolforPolicy0Change: Lessonsfromn the Jamaican Surveyof livingConditions

No.81 PatternsofAgingin ThailandandC6ted'Ivoire

No. 82 Does Undernutrition Respond to Incomes and Prices? Domninance Tests for Indonesa

No. 83 Growth and Redistribution Components of Changes in Poverty Measure A Decompoition withApplications to Brzil and India in the 1980s

No. 84 Measuring Income fron Family Enterprises with Household Surveys

No. 85 Demand Analysis and Tax Reform in Pakistan

No. 86 Poverty and Inequality during Unorthodox Adpstment: The Case of Peru, 1985-0

No.87 Family Prductivity, Labor Supply, and Wdfare in a Low-Income Country

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

No. 89 Public Policy and Anthrpomnetric Outcmes in Cote d'Ivire

No. 90 Mearing the Impact of Fatal Adult Illness in Sub-Sahan Africa: An Annotated HouseholdQuestionnaire

No. 91 Estimating the Detmninants of Cognitie AcvemEnt in Low-Income Countri The Case of Ghan

No. 92 Emnomic Aspects of Child Fostering in Coe d'lvoire

No. 93 Invesfrnent in Human Caitl: Sdcooling Supply Constmints in Rural Ghana

No. 94 Willingness to Payfor the Quality and Inknsity of Medical Carec Low-Income Househols in Ghana

No. 95 Measurement of Returns to Adult Healtk Morbidity Effects on Wage Rates in Ce d'Ivoire andGhana