Living Standards Measurement Study Working Paper No. 86 Public Disclosure Authorized Poverty and Inequality during Unorthodox Adjustment

The Case of Peru, 1985-90 Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized LSMS Working Papers

No. 15 MeasuringHealth as a Componentof Living Standards No. 16 Proceduresfor Collectingand Analyzing Mortality Data in LSMS No. 17 The LaborMarket and SocialAccounting: A Frameworkof Data Presentation No. 18 Time Use Dataand the Living StandardsMeasurement Study No. 19 The ConceptualBasis of Measuresof HouseholdWelfare and TheirImplied Survey Data Requirements No. 20 StatisticalExperimentation for HouseholdSurveys: Two CaseStudies of Hong Kong No. 21 The Collectionof PriceData for the Measurementof Living Standards No. 22 HouseholdExpenditure Surveys: Some Methodological Issues No. 23 CollectingPanel Data in DevelopingCountries: Does It Make Sense? No. 24 Measuringand Analyzing Levelsof Livingin DevelopingCountries: An AnnotatedQuestionnaire No. 25 The Demandfor UrbanHousing in the IvoryCoast No. 26 The C6ted'Ivoire Living StandardsSurvey: Design and Implementation No. 27 The Roleof Employmentand Earningsin Analyzing Levelsof Living:A GeneralMethodology with Applicationsto Malaysiaand Thailand No. 28 Analysis of HouseholdExpenditures No. 29 The Distributionof Welfarein Cdted'Ivoire in 1985 No. 30 Quality,Quantity, and SpatialVariation of Price:Estimating Price Elasticities from Cross-Sectional Data No. 31 Financingthe Health Sector in Peru No. 32 InformalSector, Labor Markets, and Returnsto Educationin Peru No. 33 WageDeterminants in COted'Ivoire No. 34 Guidelinesfor Adapting the LSMS Living StandardsQuestionnaires to LocalConditions No. 35 The Demandfor MedicalCare in DevelopingCountries: Quantity Rationingin Rural C6ted'Ivoire No. 36 LaborMarket Activity in C6te d'Ivoireand Peru No. 37 HealthCare Financing and the Demandfor MedicalCare No. 38 WageDetenninants and SchoolAttainment amongMen in Peru No. 39 The Allocationof Goodswithin the Household:Adults, Children,and Gender No. 40 The Effectsof Householdand CommunityCharacteristics on theNutrition of PreschoolChildren: Evidencefrom Rural Cated'Ivoire No. 41 Public-PrivateSector Wage Differentials in Peru, 1985-86 No. 42 The Distributionof Welfarein Peru in 1985-86 No. 43 Profitsfrom Self-Employment:A CaseStudy of COted'Ivoire No. 44 The Living StandardsSurvey and PricePolicy Reform: A Study of Cocoaand CoffeeProduction in Cdte d'Ivoire No. 45 Measuringthe Willingnessto Payfor SocialServices in DevelopingCountries No. 46 NonagriculturalFamily Enterprises in Cated'Ivoire: A DescriptiveAnalysis No. 47 The Poorduring Adjustment:A CaseStudy of C6ted'Ivoire No. 48 ConfrontingPoverty in DevelopingCountries: Definitions, Information, and Policies No. 49 SampleDesigns for the Living StandardsSurveys in Ghanaand Mauritania/Plansde sondage pour lesenquetes sur le niveau de vie au Ghanaet en Mauritanie No. 50 FoodSubsidies: A CaseStudy of PriceReform in Morocco(also in French, 50F)

(Listcontinues on the inside back cover) Poverty and Inequality during Unorthodox Adjustment

The Case of Peru, 1985-90

I The Living Standards Measurement Study

The Living Standards Measurement Study (LsMs)was established by the World Bank in 1980 to explore ways of improving the type and quality of house- hold data collected by statistical offices in developing countries. Its goal is to foster increased use of household data as a basis for policy decisionmaking. Specifically, the LsMSis worldng to develop new methods to monitor progress in raising levels of living, to identify the consequences for households of past and proposed gov- ernment policies, and to improve communcations between survey statisticians, an- alysts, and policymakers. The LSMSWorking Paper senes was started to disseminate intermediate prod- ucts from the LSMS.Publications in the series include critical surveys covering dif- ferent aspects Df the ISMSdata collection program and reports on improved methodologies for using Living Standards Survey (TSS)data. More recent publica- tions recommend specific survey, questionnaire, and data processing designs, and demonstrate the breadth of policy analysis that can be carried out using LSSdata. LSMS Working Paper Number 86

Poverty and Inequality during Unorthodox Adjustment

The Case of Peru, 1985-90

Paul Glewwe and Gillette Hall

The World Bank Washington, D.C. Copyright 0 1992 The International Bank for Reconstruction and Development/THE WORLD BANK 1818 H Street, N.W. Washington, D.C. 20433,U.S.A.

All rights reserved Manufactured in the United States of America First printing February 1992

To present the results of the Living Standards Measurement Study with the least possible delay, the typescript of this paper has not been prepared in accordance with the procedures appropriate to formal printed texts, and the World Bank accepts no responsibility for errors. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author(s) and should not be attributed in any manner to the World Bank, to its affiliated organizations, or to members of its Board of Executive Directors or the countries they represent. The World Bank does not guarantee the accuracy of the data included in this publication and accepts no responsibility whatsoever for any consequence of their use. Any maps that accompany the text have been prepared solely for the convenience of readers; the designations and presentation of material in them do not imply the expression of any opinion whatsoever on the part of the World Bank, its affiliates, or its Board or member countries concerning the legal status of any country, territory, city, or area or of the authorities thereof or concerning the delimitation of its boundaries or its national affiliation. The material in this publication is copyrighted. Requests for permission to reproduce portions of it should be sent to the Office of the Publisher at the address shown in the copyright notice above. The World Bank encourages dissemination of its work and will normally give permission promptly and, when the reproduction is for noncommercial purposes, without asking a fee. Permission to photocopy portions for classroom use is not required, though notification of such use having been made will be appreciated. The complete backlist of publications from the World Bank is shown in the annual Index of Publications, which contains an alphabetical title list (with fuillordering information) and indexes of subjects, authors, and countries and regions. The latest edition is available free of charge from the Distribution Unit, Office of the Publisher, Department F, The World Bank, 1818 H Street, N.W., Washington, D.C. 20433,U.S.A., or from Publications, The World Bank, 66, avenue d'Iena, 75116Paris, France.

ISSN: 02534517

Paul Glewwe is an economist in the Welfare and Human Resources Division of the Population and Human Resources Department of the World Bank. Gillette Hall is a Ph.D. candidate at Cambridge University in the Faculty of Economics.

Libraryof CongressCataloging-in-Publication Data

Glewwe, Paul, 1958- Poverty and inequality during unorthodox adjustment: the case of Peru, 1985-90 / Paul Glewwe and Gillette Hall. p. cm. - (Living standards measurement study, ISSN 0253-4517 86) Includes bibliographical references. ISBN0-8213-2060-2 1. Poor-Peru-Lima. 2. Cost and standard of licing-Peru-Lima. 3. Consumption (Economics)-Peru-Lima. 4. Structural adjustment (Economicpolicy)-Peru. 5. Peru-Economic policy. I. Hall, Gillette, 1962- . II. Title. m. Series: LSMSworking paper; no. 86. HC228.L5G58 1992 339.4'7'098525-dc2O 92-239 CIP ABSTRACT

This paper examines the change in poverty and inequality in Lima, Peru between 1985-86 and 1990. The data employed are from the 1985-86 and 1990 Living

Standards Measurement Surveys. The results are presented in the context of the

"unorthodox" macroeconomic policies which were undertaken between those years by the Peruvian government. However, no attempt is made to link specific macroeconomic policies to welfare outcomes. The paper presents a descriptive account of poverty and living conditions in Lima, and discusses some of the implications of the findings for social investment programs. The major findings are: 1. Between 1985-86 and 1990 the average household in Lima experienced a decline in per capita consumption of 55 percent. 2. Poverty, defined as the inability to cover the household's basic nutritional requirements, increased from 0.5% of the population in 1985-86 to 17.3% in 1990.

3. The poorest 20% of the population, especially the poorest 10%, suffered the most, experiencing declines in consumption of more than 60 percent. 4. Households headed by individuals with little or no education experienced greater declines in consumption than the better educated. Longer run social investment plans should prioritize education.

5. The rate of unemployment increased significantly, and by 1990 had become a distinct characteristic of the poor. Programs designed to increase low-wage employment opportunities would be self-targeted to the poorest households. 6. The provision of public services, particularly potable water and sewage services, deteriorated most significantly in the poorest areas of the city, among those who also suffered the greatest declines in per consumption. Public investment in public water and sanitation services is recommended in these areas. 7. Households headed by women are not found to have suffered greater declines in per capita consumption than male headed households. 8. Recently settled "pueblos jovenes" are not disproportionately populated by poor families. The poorest families are well- distributed throughout the three poorest regions of the city.

- v - ACKNOWLEDGEMENTS

We would like to thank Kalpana Mehra for excellent research assistance.

Useful comments on earlier versions of this paper were given by Karen Cavenaugh, Emmanuel Jimenez, Polly Jones, Ricardo Lago, Izumi Ohno, Francisco Sagasti and

Jacques van der Gaag. We are very grateful to the staff of Cuanto, S.A., Lima, Peru, whose collaboration made possible the LSMS survey work in 1990.

- vi - FOREWORD

The World Bank's structural adjustment policies have been criticized in recent years for failure to adequately protect the living standards of the poor. The real issue is whether alternative policies could have done a better job of protecting the poor while reviving economic growth. This paper examines an alternative set of policies adopted in Peru from 1985 to 1990, and the findings are quite sobering; not only did the economy as a whole deteriorate, but the policies also failed to protect the poor. This paper is part of a broader program of research in the Population and Human Resources (PHR) Department on the extent of poverty in developing countries and on policies to reduce poverty. This research program is located in the Welfare and Human Resources Division. The data used here are from the Peru

Living Standards Survey, which is one of the Living Standards Measurement Study (LSMS) household surveys which the World Bank has implemented in many developing countries. Aside from the findings in this study for Peru, one of the other objectives of this and similar work using LSMS data is to demonstrate the need for and usefulness of household data collection efforts in other developing countries.

Ann 0. Hamilton Director Population and Human Resources Department

- vii -

TABLE OF CONTENTS

I. Introduction ...... 1

II. Peruvian Economic Policy, 1980-90 ...... 3 The Peruvian Economy in the Early 1980s ...... 3 The Formulation of Peruvian Economic Policy, 1985-90 ...... 4 III. Living Standards Data and Their Use in Assessing Household Welfare . 8

The Peru Living Standards Surveys ...... 8 Using the LSMS Data to Construct an Indicator of Household Welfare ...... 10

IV. Changes in the Distribution of Consumption from 1985-86 to 1990 . . . 15

Changes in the Overall Distribution of Consumption Expenditures . . 15 Changes in Consumption Expenditures by Areas within Lima . . . . . 18 Changes in Consumption Expenditures by Head of Household Characteristics ...... 20 Other Indicators of Living Standards ...... 26 V. Poverty in Lima from 1985-86 to 1990 ...... 38

Measuring the Incidence of Poverty in Lima ...... 38 Implications of the Study for Social Program Targeting ...... 42 VI. Conclusion ...... 48 VII. Appendices

Survey Format, 1985-86 and 1990 ...... 51 Construction of Price Deflators ...... 52 Calculation of Household Consumption ...... 58 Changes in Real Expenditures in Lima, 1985-86 to 1990, by Expenditure Decile, Unadjusted ...... 60 Use of Household-Specific Price Deflators ...... 61 List of Geographic Areas ...... 65

VIII. References ...... 67

LIST OF TABLES

Table 1. Index of Basic Indicators, Peru: 1980, 1985, and 1990 . . . . 6 Table 2. Mean Monthly Consumption Expenditures in Lima, 1985-86 and 1990 ...... 13

Table 3. Changes in Real Expenditures in Lima, 1985-86 to 1990, by Expenditure Decile ...... 16 Table 4. Changes in Real Monthly Expenditures Per Capita, 1985-86 to 1990, by Area ...... 19 Table 5. Changes in Per Capita Monthly Expenditures by Characteristics of Household Head, All Lima ...... 21

- ix - Table 6. Changes in Per Capita Monthly Expenditures by Characteristics of Household Head, Old Lima ...... 22 Table 7. Distribution of Population by Characteristics of Household Heads, by Quintile, All Lima, 1985-86 ...... 26

Table 8. Distribution of Population by Characteristics of Household Heads, by Quintile, All Lima, 1990 ...... 27 Table 9. Characteristics of Household Heads by Quintile, Old Lima, 1990 28 Table 10. Distribution of Population by Housing Characteristics, All Lima 1985-86, by Quintile ...... 29 Table 11. Housing Characteristics, All Lima, 1990, by Expenditure Quintile ...... 30

Table 12. Distribution of Population by Housing Characteristics, All Lima, 1985-86, by Area ...... 33 Table 13. Distribution of Population by Housing Characteristics, All Lima, 1990, by Area ...... 34 Table 14. Change in Distribution of Durable Good Ownership, 1985-86 to 1990, 'All Lima', by Quintile ...... 36 Table 15. Change in Distribution of Durable Good Ownership, 1985-86 to 1990, 'All Lima', by Area ...... 37

Table 16. Poverty Lines Based on Monthly Real Expenditures ...... 39

Table 17. Incidence of Poverty in All Lima by Area, 1985-86 and 1990 . 42

x I. Introduction

The 1980s were not a prosperous decade for most developing countries. The majority of these countries experienced periods of economic stagnation, if not outright decline. For many of these countries, adapting to the new, less promising external environment has required a shift in macroeconomic policies. For most countries, this shift has entailed the implementation of structural adjustment programs recommended and partially financed by the World Bank. A few, however, have experimented with alternative programs in the hopes of reducing the perceived economic and social costs of structural adjustment. World Bank stabilization programs have recently been the topic of much evaluation and discussion in terms of their social and distributional consequences (Addison and Demery, 1985, 1986; Cornia, Jolly and Stewart, 1986;

Kanbur, 1987; Kakwani, Makonnen and van der Gaag, 1990) and macroeconomic performance (Killick, 1984; Taylor, 1988; Conway, 1991). However, very little assessment exists of the performance of alternative "heterodox" programs, mostly because until recently, the required data did not exist. In this paper we examine the case of Peru, a country where structural adjustment was deliberately avoided between 1985 and 1990, to see both how the economy fared and, most importantly, whether the poor did indeed benefit from the government's policy decisions. Two unusually rich household level data sets, one for 1985-86 and another for 1990, provide a unique opportunity to evaluate the evolution of living standards over the period in which the alternative program was implemented. The discussion is limited to Lima, the capital city of Peru, because although the

1985-86 data cover the entire country, the 1990 data are for Lima alone. The two main findings elaborated in this paper can be summarized as

follows. First, between 1985-86 and 1990 the average household in Lima experienced a decline in per capita consumption of over 50 percent. Second, those who were poor in 1985-86 suffered the greatest declines in consumption and

- 1- the greatest deterioration in living conditions, rendering the distribution of welfare more unequal over the five year period in question.

The paper is organized as follows. Section II provides an overview of

Peru's economic policy and performance from 1980 to 1990. Section III introduces the data from the two Living Standards Surveys used in the study. Section IV presents results on the changes in consumption levels and living conditions in

Lima between 1985-86 and 1990, while Section V focuses on the changes in poverty levels in Lima and the implications of the study for the purposes of social program targeting. The last section summarizes the findings and concludes the paper.

-2- II. Peruvian Economic Policy, 1980-90

The Peruvian Economy in the Early 1980s

The incoming government in July 1985 inherited an economy which had not weathered the debt crisis well. Despite two stabilization programs undertaken between 1981 and 1985, the rate of inflation soared, more than doubling each year

after 1982; real output declined a total of 27% during this period. The impending crisis was heightened by a steady drop in gross domestic investment, which fell by 62% over these same years.' Domestic policy between 1980 and 1985 consisted mostly in the drastic

reduction of real government expenditures (which were reduced even as a percentage of GDP), with corresponding drops in real public sector wages (56%),

the real minimum wage (44%), and social program financing (20%). The maintenance

of a freely floating exchange rate -- which depreciated 4100% between 1980 and 1985 -- may have contributed to the relatively steady increase in the volume of exports, but real export revenues declined over the same period due to a falling

real terms of trade. The economic conditions prevailing by 1985 were the result of a combination of international and domestic factors. Certainly the banker's panic ignited by the Mexican default in 1982 had negative ramifications for smaller Latin American

countries like Peru. Although the Peruvian government had been meeting debt payments on schedule (and even early) through 1983, private sector loans from

international capital markets became virtually unobtainable by the end of 1982. Between 1982 and 1985 total net capital inflow (public and private) fell by 78%,

while net capital flows from private sources were negative in 1984 and 1985. The drop in demand for Peru's exports resulting from the world recession produced a

50% decline in the unit value of exports between 1980 and 1985, so that while

'Sources for the macroeconomic figures quoted in this paper include Cuanto S.A. (Lima, Peru), United Nations, International Monetary Fund, and World Bank estimates.

-3- export production increased, total revenue from exports still registered a decline of 24% over the same period.

The Formulation of Peruvian Economic Policy, 1985-90

In July of 1985, the incoming APRA (Alianza Popular Revolucionaria Americana) government designed a "heterodox" stabilization program based on reduced foreign debt payments, a price freeze, and economic reactivation. The new government planned to redirect funds from foreign debt payments and the accumulation of foreign exchange reserves toward "reactivation" of the economy, giving priority to the needs of the poorest segments of the population via wage increases, jobs programs, and investments in education and health:

"A good part of the $3.7 billion due to be paid abroad ... has instead been assigned to priority investments for education, health, agriculture and transportation projects." --President Alan Garcia, New York Times, August 31, 1986

The basic policy therefore consisted of a unilaterally declared ceiling on foreign debt payments (10% of annual export earnings), and reactivation of the economy through government expenditure of the savings thus generated.

The theory behind this reactivation program was that a sharp initial stimulus to consumer demand, particularly to that of small-scale farming peasants and shanty town entrepreneurs, would provide an impetus for growth while the modern mining and manufacturing sectors would produce import-substitutes and exports, generating foreign exchange with which to later recommence servicing the debt. Postponing the payment of debt service was considered crucial to engineering this recovery, funding the demand-stimulating policies. The first reactivation program, introduced in October, 1985, was highly stimulatory to consumer demand, establishing a trend which would continue through August, 1988. Major policies included an increase in the minimum wage, interest- free loans to public sector employees, preferential interest rates for highland farmers, jobs programs in city slums, and subsidized production of basic foods

- 4 - to be sold in major cities. Most key prices were frozen, including the dollar exchange rate, while interest rates and the payroll tax were reduced.

Under the February 1986 program, the minimum wage was again raised and the sales tax reduced by 50% to insure continued growth in consumer demand. To encourage production, price controls were relaxed and subsidized financing was made available to manufacturers. While these policies did encourage economic growth in the short run, they proved to be unsustainable by leading to very strong inflationary pressures and deep budget deficits. This unsustainability was evident by mid-1987, when the final reactivation program was implemented. In the face of mounting inflation, rapidly declining Central Bank reserves and a growing public sector deficit, the government committed itself to maintaining real wage increases. Interest rates, already negative in real terms, were further reduced. Tax credits and exemptions were granted to manufacturers who added workshifts or increased installed capacity, yet at the same time taxes on "luxury" consumption goods were raised and key prices such as gasoline and electricity were increased to raise government revenues. The bank exchange rate accessible to the general public was devalued, but preferential exchange rates were granted for importers of food, drugs and "non-competing" import goods. A series of similar policy adjustments such as (nominal) wage increases and import subsidies continued until September,

1988. At this point, the fiscal deficit had grown to 16% of GDP, while reserves had fallen 60% (to $500 million) in one year. Monthly inflation reached 21%, while the black market for dollars flourished, offering a rate six times that of the bank rate. In September, 1988, the government announced "El Paquetazo", a severe readjustment of prices representing a 114% increase in the consumer price index. Ad hoc adjustment, composed mainly of price increases without wage increases and a rapid decline in production, continued throughout the last two years of the Garcia administration. The annual rate of inflation (December-December) was 1,720% in 1988, 3400% in 1989, and the monthly rate of inflation was still

- 5 - accelerating when the change of government occurred in late July, 1990.2 From 1987 to 1990 the total decline in production is estimated to have been 20%, and real wages on average are thought to have fallen 75% over the same period.

While early evaluations of the Peruvian Heterodox program were somewhat positive (Taylor, 1988; Thorp, 1988), recent appraisals point to the severe deterioration in virtually all indicators of macroeconomic performance, summarized in Table 1, which had occurred by 1990 (Dornbusch and Edwards, 1990;

Lago, 1991; Paus, 1991). These authors also attempted to explore the social ramifications of Peruvian economic policy between 1985 and 1990 by analyzing economic aggregates (such as GNP per capita and the real wage) coupled with earlier analyses of income distribution in Peru. However, without recent micro- level data on household income or expenditure, this approach has involved a fair amount of guesswork.

Table1: Indexof BasicIndicators, Peru: 1980,1985, and 1990

Indexof AnnuaL 1980 1985 1990

GDPper capita 100 87 70

AverageReal minimum wage,Lima 100 54 21

ConsumerPricesaa 100 3,474 40,216,592

Exports(S)b 100 76 83

Net InternationalReserves' 100 89 -13

a. June of eachyear, through June 1, 1990. b. Estimatedfrom data throughSeptember 1990. c. June of eachyear.

2 The monthly rate of inflation was 63% in July, 1990. Accumulated inflation for 1990 through July was 854%, which represents an annualized rate of 3852%.

- 6 - This paper analyzes household level data which document the change in socio-economic conditions which occurred in Lima between 1985 and 1990. The results of the two surveys show an average drop in consumption of over 50% during the period in question, which is without doubt one of the most rapid and severe peace-time deteriorations in living standards ever recorded (compared even to the case of Ghana, where national income declined 35% over a period of 12 years, the recent Peruvian experience is exceptional). The paper draws from the survey data a variety of indicators which together characterize the extent and geographic incidence of poverty in Lima during June and July of 1990, and assesses the implications for the design and targeting of future social programs in Lima.

-7- III: Living Standards Data and Their Use in Assessing Household Welfare

Two Living Standards Surveys have been conducted in Peru. The first, known as the 1985-86 Peru Living Standards Survey (PLSS), took place between June,

1985, and July, 1986, and provides data for 5,000 households nationwide.3 The second survey, referred to as the 1990 Peru Living Standards Survey, was conducted in June and July, 1990, and covers 1,500 households in metropolitan Lima only. In this section the data are described, differences between the 1985- B6 and 1990 survey data are discussed, and the indicator of household welfare uased in this paper, adjusted per capita consumption expenditures, is defined and presented.

The Peru Living Standards Surveys

The 1985-86 Peru Living Standards Survey is based on the World Bank's Living Standards Measurement Study (LSMS) household survey methodology. For idetailsof that methodology and how it was implemented in Peru, see Grootaert and

Arriagada (1986). The 1985-86 survey sample covered 5,000 households from all areas of Peru. The sample for the second survey includes the same 1,000 dwellings in Lima covered in the first survey (1985-86), and an additional 500 dwellings in the outlying areas of Lima (pueblos jovenes) settled after 1985.

Thus the 1990 sample as a whole, hereafter referred to as 'All Lima', represents the geographical population distribution of Lima in June, 1990. The 1990 sample can also be limited to only those areas of Lima which were already settled by 1985, referred to as 'Old Lima', which includes only the 1,000 dwellings sampled in both survey years (1985-86 and 1990). Of these dwellings, 800 were inhabited by the same family in both years. Thus the surveys provide a set of panel data that allows the change in living standards between 1985 and 1990 to be evaluated at the household and individual level. The regions sampled only in 1990 at the outskirts of metropolitan Lima are hereafter referred to as 'New Lima'. These

3In Peru this survey is known as the "Encuesta Nacional de Hogares sobre Medicion de Niveles de Vida" (ENNIV). -8- regions were oversampled by a factor of 2 to provide a data set large enough to be analyzed independently of the whole. This will allow the characteristics specific to these new settlements to be identified.4 The 1990 survey follows the 1985-86 questionnaire format almost exactly, and thus total consumption is defined in precisely the same manner in both survey years. The manner in which the survey results are used to measure changes in household welfare will be detailed in sub-section B, below. See Appendix A for an outline of the survey format. For details on how results from the 1985-86 questionnaire were used to measure household welfare in Peru, see Glewwe (1988). There are two aspects of the 1990 Living Standards Survey in Lima which must be borne in mind when working with the data and assessing the results. First, and most importantly, the survey was conducted during a period of extremely high inflation. The increase in the price level ranged between 1% and 5% Per day during the survey period. This rendered it difficult for survey respondents to recall the nominal value of purchases made previously, even if purchased only a short while before. This situation was dealt with by reducing the period of time over which some expenditures were reported (the "recall period"), and by recording the month in which large, infrequent expenditures were made (e.g. school tuition, household furnishings). The time period over which the survey was conducted was brief (8 weeks) in order to minimize the change in the price level between the first and last interview dates. In analyzing the results, extreme care was taken to construct deflators as sensitive as possible to the changing price level (see Appendix B), generating a different deflator for each type of expenditure (based on the recall period) and each household (based on the date of interview). Regardless of all efforts to provide accurate data, the reader is advised to bear in mind the rapidly changing price level in Peru during the 1990 survey and the difficulty this represents to the derivation of real or 'constant' values for expenditures during that period.

4 Analysis of the survey data from 'New Lima' and of the panel data is not undertaken in great depth in this paper, and remain topics for further research.

- 9 - The second factor to be stressed is more qualitative in nature, but nonetheless highly significant. During the past five years, terrorism and the generally volatile environment in Peru has served to isolate the upper classes to an even greater extent from the rest of society. While generally the response level to the 1990 survey was quite high (of households located, 94% agreed to be interviewed), interviewers found that some families in the wealthy area of Lima were extremely resistant to the survey, a problem which had not occurred in 1985- 86. The problem was dealt with by replacing households which refused to be interviewed, first with preselected sample alternates, then with other households in the same geographic segment selected at random by the field supervisor. For those households in upper class neighborhoods where interviews actually took place, many interviewers felt, contrary to their experience generally, that respondents in these households were understating actual levels of expenditures, earnings and savings. One interviewer was told explicitly "if you insist, I will answer that question; but I certainly will not tell you the truth." All of this suggests that the estimated level of consumption expenditures for the highest percentiles of the population may be significantly undervalued in the siurveyresults provided here. While there is no quantitatively valid method of correcting for this underestimation, those who interpret these results ought to keep this caveat in mind. specifically, systematic under-reporting of expenditures and assets by the wealthiest decile would cause the overall drop in consumption levels reported in this paper to be overestimated, and the degree of inequality in the distribution of consumption across deciles to be underestimated.

Ursing the LSMS Data to Construct an Indicator of Household Welfare

Standard microeconomic theory states that welfare levels of households are dletermined by their consumption of goods and services. If one assumes that each household has the same utility function, then household consumption, measured in monetary terms, serves an indicator of welfare. For the most part, consumption is equal to expenditures on the goods and services consumed, but there are three

- 10 - exceptions in the Peru Living Standards Survey (PLSS) data: the value of food and non-food items received free of charge from employers, the value of durable goods currently used by the household but purchased in previous years, and the value of housing which has already been paid for (owner-occupied housing). Two more difficulties complicate the matter: households have different sizes and compositions, and inflation has always been very high in Peru. This subsection briefly describes the way each of these potential problems was handled in calculating per capita consumption figures for Peru (for details, see Appendix

C) and presents some basic results. Turning to the components of consumption not captured by household expenditures, data on the value of food and non-food items received from employers are explicitly collected in both the 1985-86 and the 1990 surveys, so incorporating these values into explicit expenditures is straightforward. Regarding durable good ownership, the "use value" of these goods was computed for both years by calculating the real rate of depreciation for each type of good (from information obtained in the 1985-86 survey) and multiplying it by the current value of the good as declared by the household. To this one ought to add the opportunity cost of ownership in terms of forgone investment earnings, but since inflation was rampant in both years real interest rates have been assumed to be zero in both years, which implies no such cost (even if one were to assume a positive real rate of interest, the effect on the results presented here would be virtually imperceptible). Finally, in both years the value of owner-occupied housing was calculated by regressing the estimated rental value (as given by each household that owned its own dwelling) of such housing on housing characteristics such as number of rooms, type of construction materials and location within Lima. For all dwellings, whether rented or owned, the regression results were then used to estimate the value of the housing. These three components of household consumption were added to explicit household expenditures to create a total household consumption variable, which will henceforth be referred to as total household consumption expenditures.

- 11 - If each household had the same composition of individuals, household consumption expenditures would result in the same ranking of their welfare levels as would the underlying (but unobservable) utility function assumed by economic theory. Consumption expenditures thus represent a money-metric measure of utility. However, households have very different compositions, and small children have smaller needs for food and some other items relative to adults.

Further, there may be economies of scale in consumption for certain goods (for example, two people can listen to a radio at the same time). In the applied economics literature, the method for dealing with this problem involves the construction of household equivalence scales, the sum of which over household members is used to divide total expenditures to get an "equivalence scale adjusted" indicator of living standards (cf. Deaton and Muellbauer, 1980, 1986).

There are several methods for using household level data to estimate these equivalence scales, but all are open to criticism. The solution adopted here is the same as that used in Glewwe (1988); based on estimates of equivalence scales in other countries, all adults over the age of 18 are given a weight of one, while children aged 13-17, 7-12 and 0-6 are given weights of 0.5, 0.3 and 0.2. respectively. "Adjusted per capita consumption expenditures" are simply total household consumption expenditures divided by the sum of these weights. Using the same equivalence scale allows the consumption numbers presented here to consistently account for changes in household composition between the two survey years. We will examine the data briefly to see whether the results are sensitive to equivalence scale adjustment. Finally, a word regarding the monetary values for expenditures presented in the following analysis. Unless otherwise indicated, all monetary values (for both the 1985-86 and 1990 data) in this paper are given in terms June 1, 1990,

Intis. The method used to derive a real value in terms of June 1, 1990, Intis for expenditure data from the 1990 survey is given in Appendix B of this paper, as is the method used to express the 1985-86 survey data in terms of its real value on June 1, 1990.

- 12 - Table 2 provides some basic estimates of consumption expenditures for both surveys, introducing the reader to the data before discussing them in detail in

Section IV. The first column in Table 2 provides estimates of food and total per capita expenditures in Lima in 1985-86 valued in terms of June, 1985, prices.5

Both equivalence-scale adjusted and ordinary per capita figures are given -- the former are higher because household consumption expenditures are being divided by a lower figure (each child representing only a fraction of one adult). The second column in Table 2 presents the 1985-86 figures valued at June 1, 1990,

Intis, while the third and fourth columns present 1990 figures for 'Old Lima' and 'All Lima, respectively (recall that the latter includes regions of Lima settled

since 1985). Of course, the most striking aspect of Table 2 is the sharp decline in

consumption expenditures in Lima from 1985-86 to 1990 (see the percentage figures in parentheses in columns 3 and 4). This decline will be discussed in greater detail in the next section. In terms of the discussion of above, two results in

Table2: Mean MonthlyConsumption Expenditures in Lima,1985-86 and 1990

(1) (2) (3) (4) 1985-86 1985-86 1990 1990 (1985prices) (1990prices) Old Lima All Lima

WithoutEquivalence Scales FoodConsumption Per Capita 273.4 2510.7 1334.6 1298.5 (Changesince 1985-86) (-46.8%) (-48.3%) TotaLConsumption Per Capita 625.9 5747.5 2821.5 2664.4 (Changesince 1986-86) (-50.9%) (-53.6%)

With EquivalenceScales Food ConsumptionPer Capita 379.1 3480.9 1771.3 1757.0 (Change since 1985-86) (-49.1%) (-49.5%)

Total ConsumptionPer Capita 846.7 7774.4 3679.7 3531.7 (-52.7%) (-54.6%)

Notes: Figuresin column1 are vaLuedin constantJune 1985 Intis per month($1 12 Intis). Figuresin cotumns2-4 are valued in thousandsof constant June 1, 1990 Intis per month ($1 = 50,000 Intis). "1990 Old Lima" onLy includes regions of Lima which existed in 1985-86,white "1990 ALl Lima" includesregions which have been settled since 1985-86.

5 All figures in column 1 are about 10% higher than those given in Glewwe (1987) because the earlier paper mistakenly counted some individuals as household members who were only temporary visitors.

- 13 - Table 2 are worth noting. First, the use of household equivalence scales has very little impact on the magnitude of the decline in consumption expenditures relative to that resulting from the per capita (unadjusted by equivalence scales) figures. This implies that the figures to be presented in this paper are relatively insensitive to assumptions made concerning equivalence scales. Second, although 'All Lima' is somewhat poorer than 'Old Lima', the difference is very small relative to the overall decline in consumption expenditures that has occurred since 1985-86.

At this point the preliminaries regarding the data have been completed. Sections IV and V, which are the heart of this paper, will examine the data in greater detail.

.

- 14 - IV. Changes in the Distribution of Consumption from 1985-86 to 1990

This section examines both the 1985-86 and the 1990 expenditure data from Lima, Peru, to see how Peruvians at different consumption levels fared during the intervening 5 year period. The first subsection will examine changes in the overall distribution of consumption expenditures by deciles. The second subsection will examine changes in the different districts (areas) of Lima. The third will examine the welfare of different social groups as defined by head of household characteristics, and the fourth will discuss several other determinants of household welfare resulting from the survey data.

Changes in the Overall Distribution of Consumption Expenditures

In order to examine changes in the distribution of consumption expenditures from 1985-86 to 1990, the population of Lima was divided into ten expenditure deciles for both surveys, where the first decile contains the poorest 10% of the population as defined by equivalence scale adjusted per capita consumption expenditures, the second contains the next poorest 10%, etc. and the tenth contains the wealthiest 10%. Table 3 presents the mean levels of adjusted per capita food and total consumption expenditures for each decile in both 1985-86

and 1990. The percentage decline in food and total per capita expenditures for

each group is given, as well as the fraction of total expenditures devoted to purchases of food (in parentheses below the value of total expenditures). The

table compares Lima in 1985-86 with both 'All Lima' and 'Old Lima' in 1990, but the main findings hold in both cases.

Several conclusions can be drawn from Table 3. The most striking is that all groups experienced declines in the value of real food and total consumption

that left them at about one half of their 1985-86 levels, and for some groups the decline was even sharper. The decline in average (equivalence scale adjusted) per capita consumption for 'All Lima' was 55% (53% in 'Old Lima'), which

indicates a devastating fall in living standards in only 5 years. Another result

which is also striking is that the poorest decile seems to have lost the most,

- 15 - Table 3: Changes in Real Expendituresin Lima, 1985-86 to 1990, by ExpenditureDeciLe

All Lima 1985-86 All Lima 1990 % Chanoe Old Lima 1990 % Change Decile Food TotaL Food Total Food Total Food Total Food Total

1 1220.4 2258.6 517.4 848.9 -57.6 -62.4 501.3 843.1 -58.9 -62.7 (.540) (.609) (.595)

2 1653.6 3181.1 877.2 1345.2 -47.0 -57.7 855.3 1347.9 -48.3 -57.6 (.520) (.652) (.635)

3 2109.3 3808.8 1051.3 1731.9 -50.2 -54.5 1018.0 1770.7 -51.7 -53.5 (.554) (.607) - (.575)

4 2386.7 4386.9 1177.0 2015.2 -50.7 -54.1 1175.0 2070.7 -50.8 -52.8 (.544) (.584) (.567)

5 2632.8 5164.7 1326.7 2349.7 -49.6 -54.5 1328.5 2417.9 -49.5 -53.2 (.510) (.565) (.549) cn 6 3094.4 6098.9 1532.7 2739.6 -50.5 -55.1 1477.6 2812.7 -52.2 -53.9 (.507) (.559) (.525)

7 3567.3 7128.5 1693.1 3218.5 -52.5 -54.0 1661.6 3322.8 -53.4 -53.4 (.500) (.526) (.500)

8 4070.1 8669.9 2103.1 3970.8 -48.3 -54.2 2133.0 4142.3 -47.6 -52.2 (.469) (.530) (.515)

9 4984.0 11,451.5 2614.0 5311.0 -47.6 -53.6 2600.3 5578.0 -47.8 -51.9 (.435) (.492) (.466)

10 9110.4 25,657.8 4763.5 11,796.0 -47.7 -54.0 4983.4 12,546.0 -45.3 -51.1 (.355) (.404) (.397)

ALL Lima 3480.9 7774.4 1757.0 3531.7 -49.5 -54.6 1771.3 3679.7 -50.9 -52.7 (.448) (.497) (.481)

Notes: All figures are in thousandsof June 1, 1990 Intis per capita per month. On June 1, 1990 S1 = Intis 50,000. Figures in parenthesesrepresent the share of food in total expenditures. having experienced declines of 58% and 62% of food and total consumption respectively. Further, in terms of total consumption, the second poorest decile experienced the next largest decline, a drop of 58%. Thus the evidence from these two surveys indicates that the distribution of consumption expenditures became more unequal during the Garcia years, despite the rhetoric put forth by the Garcia administration that it was taking exceptional efforts to protect the poor. This holds whether or not one looks at 'All Lima' or only 'Old Lima', and whether one makes adjustments for equivalence scales or takes a simple per capita estimate of total expenditures (see Appendix D for unadjusted figures). Recall from Section III that although the possible bias in the data due to under- reporting of expenditures by wealthy households cannot be detected, it may mean that the average drop in consumption above is overestimated, while the regressive change in the distribution of consumption expenditures may be underestimated.

The overall finding (Tables 2 and 3) that real expenditures declined substantially from 1985-86 to 1990 might also be subject to question due to the method used to calculate an appropriate price deflator for this period (see Appendix B for details). However, in all deciles the share of total expenditures devoted to food rises substantially. This suggests that living standards did decline dramatically, since food shares tend to rise as families become poorer and, of course, the measurement of food shares during the two survey periods does not depend on the price deflator.

Finally, one may question whether these results are an artifact of over- simplification in accounting for changes in both the level of prices and in relative prices. To investigate this, a specific price index was calculated for each household according to shares of food and non-food expenditures in total household consumption. The results differ only slightly from those given here.

In particular, when consumption values are deflated using the new price indices all deciles still experience drops in consumption expenditures to about one half of 1985 levels, and the poorest decile experiences the largest decline. Details of these calculations are given in Appendix E.

- 17 - Changes in Consumption Expenditures by Areas within Lima

Table 4 presents means of both food and total expenditures, as well as the food share, for 9 different areas of Lima for both 1985-86 and 1990. The geographic demarcation of each area is given in Appendix F. For all areas of the city the declines in average food and total expenditures were very large, but not all regions suffered equally. The wealthiest area of Lima according to both food and total expenditures was the 'Estrato Alto' ('Upper Class'). Households in this area suffered the smallest declines in expenditures, and the share of food in total expenditures for these households remained low relative to all other areas and the city average. The second wealthiest area, 'Centro 3', experienced slightly larger declines in both food and total expenditures. The next three areas ranked in terms of 1985-86 total expenditures are

'Centro 1', 'Oeste' and 'Centro 2'. Of these three areas, 'Oeste' apparently suffered the smallest declines, losing less than 50% of food and total consumption per capita. 'Centro 1' was slightly better off than 'Centro 2' in 1985-86 in terms of average per capita total consumption. However, while both areas experienced roughly the same decline in total consumption in percentage terms, the decline in 'Centro 2' brought the absolute level of total consumption in that area to levels approximating those in Callao in 1990, a notably poorer area in 1985-86. Expenditures on food in these two areas fell by roughly fifty percent, bringing food consumption in 'Centro 2' below the absolute level in

Callao and some of the apparently even poorer areas (in terms of total expenditures). While this sharp drop in food expenditures may be due in part to price differentials across areas, it is evident that a particularly rapid cLeterioration in living standards has occurred in 'Centro 2' relative to other aLreas. The remaining four areas, 'Callao' and the Conos 'Norte', 'Sur', and 'Este' (the Northern, Southern and Eastern Cones of the city), are those in which significant geographic expansion has taken place since 1985-86 due to migration.

- 18 - Table4: Changesin RealMonthly Expenditures Per Capita,1985-86 to 1990,by Area

Lima 1985-86 Lima 1990 % Chanresince 1985-86 Food Food Food Total Share Food TotaL Share Food Total

Sur Old regions 3,337.6 6,425.7 0.519 1,668.8 2,801.3 0.596 -50.0 -56.4 ALL regions ------1,699.3 2,785.6 0.610 -49.1 -56.6 Norte Old regions 3,002.1 6,430.9 0.467 1,453.0 2,788.9 0.521 -51.6 -56.6 ALL regions ------1,456.5 2,698.8 0.540 -51.5 -58.1 Este OLd regions 3,110.8 7,625.7 0.428 1,441.8 2,576.6 0.560 -53.7 -64.5 ALL regions ------1,490.3 2,546.1 0.595 -52.1 -65.0 Callao Old regions 3,211.0 6,849.0 0.469 1,744.8 3,760.8 0.464 -45.7 -45.1 All regions ------1,809.3 3,798.7 0.476 -43.6 -44.5

Oeste 3,342.2 7,804.9 0.428 2,001.8 4,425.8 0.452 -40.1 -43.3 Centro1 3,461.7 8,177.5 0.423 1,723.3 4,054.3 0.427 -50.0 -50.4 Centro 2 3,474.6 7,491.0 0.464 1,577.2 3,726.8 0.422 -54.6 -50.2 Centro3 4,335.0 11,550.4 0.377 3,003.4 6,501.6 0.462 -31.0 -43.7 EstratoAlto 5,388.0 16,934.5 0.318 3,832.7 9,960.7 0.385 -28.9 -43.0

LiSma old regions 3,480.9 7,774.4 0.448 1,771.3 3,679.7 0.481 -49.1 -52.7 All regions ------1,757.0 3,531.7 0.497 -49.5 -54.6

Notes: All figuresare in thousandsof June 1, 1990Intis per capitaper month. On June 1, 1990 S1 = Intis50,000. Rowsfor "All regions"in 1985-86are bLankbecause "ALL regions"includes regions whichdid not exit in 1985. See Text,Section 111.

As the survey sample was extended in 1990 to include these newly populated regions, results are given in Table 4 for the 'Old' regions in each area which were surveyed again in 1990, and for the area population as a whole ('All' regions) in 1990. The most noticeable result is that while the incorporation of the newly settled regions consistently lowers the area mean food and total consumption values, it does so by very small and arguably statistically insignificant amounts. This indicates that while average living standards in the newly settled regions are low, they are not significantly worse than in each area as a whole. The implication for targeting efforts is that the poorest members of the population are not found in any great majority in the new settlements;

- 19 - therefore, targeting programs specifically to these new settlements may not be most efficient. The largest percentage drops in total expenditures occurred in the Conos

Norte, Este and Sur. This finding is broadly consistent with that in the previous section, which concluded that the poorest groups appear to have suffered the most in the economic decline from 1985-86 to 1990. It should be noted that the deterioration of living standards was most severe in the Cono Este. This area, where expenditure levels were higher on average than in Callao and the other Conos in 1985-86, had become the poorest in terms of total consumption by 1990. Callao seems to have fared slightly better than the three Conos, experiencing a smaller percentage decline in both total and food expenditures.

Changes in consumption Expenditures by Head of Household Characteristics

Tables 5 and 6 examine changes in consumption expenditures from 1985-86 to 1990 when households are divided into groups based on the gender, education and employment characteristics of the head of household. Table 5 shows the change in mean total consumption per capita in Lima as whole between 1985-86 and 1990, while Table 6 examines the change only in the 'Old' regions of Lima. The figures, which represent mean total consumption, have again been adjusted for each household's composition of children and adults. The percent of the total population living in households where the head displays the given characteristic is provided in the column to the right of total mean consumption in each year.

Table 6 has been provided to illustrate that when broken down by head of household characteristics, households in 'old Lima' fared marginally but consistently better than those in 'All Lima'. The following discussion will refer to Table 5 unless a significant difference between the two tables requires discussion.6

6 Note also that, as in Table 3, the results shown in both Table 5 and 6 remain essentially unchanged when price deflators based on each household's consumption patterns are used. For details see Appendix D.

- 20 - Table 5: Changesin Per CapitaMonthLy Expenditures by Characteristicsof HousehoLdHead, ALL Lima

ALL Lima 1985-86 ALL Lima 1990 PercentChange in Mean Expenditures(X of Pop) Mean Expenditures(% of Pop) Expendituressince 1985

Sex MaLe 7,943.2 (86.6) 3,613.6 (85.4) -54.5 FemaLe 6,681.0 (13.4) 3,012.2 (14.6) -54.9 Education LeveL None 4,288.5 (2.8) 1,770.7 (3.5) -58.7 Primary 5,677.6 (37.1) 2,324.4 (32.6) -59.1 SecondaryGeneraL 7,145.7 (35.4) 3,209.8 (44.1) -55.1 SecondaryTechnicaL 7,087.5 (5.3) 3,798.2 (2.3) -46.4 University 15,112.2 (15.5) 6,945.7 (12.9) -54.0 OtherPost-Secondary 7,634.3 (3.9) 4,665.0 (4.6) -38.9 EmnLoverof Head Goverrment 9,474.3 (19.1) 4,155.0 (14.6) -56.1 Private 7,604.0 (35.2) 3,321.2 (34.4) -56.3 PrivateHome 3,931.5 (1.3) 1,782.4 (0.7) -54.7 Setf-Employed 7,126.7 (36.3) 3,466.2 (36.4) -51.4 Occupationof Head Agriculture 6,430.0 (3.7) 3,189.4 (2.1) -50.4 SaLes/Services 7,532.4 (27.8) 3,259.3 (30.3) -56.7 Industry/Crafts 5,858.5 (37.3) 2,793.3 (34.7) -52.3 White Collar 11,307.8 (23.0) 5,195.3 (19.8) -54.1 UnempLoved 8,098.5 (2.9) 2,763.5 (5.1) -65.9 Retired 7,495.9 (4.9) 3,733.3 (6.9) -50.2

ALL Lima 7,774.4 100 3,531.7 100 -54.6

Notes: PopuLationpercentages do not add to 100due to missinginformation for 0.3%of observationsin 1985-86 and 1.8% in 1990. The mean expenditurelevel for thosewith SecondaryTechnicaL training becomes 6252.4 for All Lima, 1990,if one outlyingvalue is incLudedin the calculations.This would representa -11.8%change in expendituressince 1985.

Gender

It is first of all important to note that in 'All Lima' the position of female-headed households relative to male-headed households changed very little over the five year period in question. In both 1985-86 and 1990, mean consumption levels for female-headed households were approximately 84% of those of their male counterparts. The number of female-headed households increased only slightly, from 13.4% to 14.6% of the population. This finding is exactly the same for 'Old Lima'(Table 6), indicating that female-headed households are distributed equally throughout the established and the newly settled regions of the city. The general finding is that although female-headed households had consistently lower consumption levels than those headed by males, they were

- 21 - Table 6: Changes in Per Capita Monthly Expendituresby Characteristicsof HouseholdHead, Old Lima

ALL Lima 1985-86 Old Lima 1990 Percent Change in Mean Expenditures (X of pop) Mean Expenditures (% of pop) Expenditures since 1985

Sex Male 7,943.2 (86.6) 3,784.8 (85.4) -52.3 Female 6,681.0 (13.4) 3,062.7 (14.6) -54.2

Education Level None 4,288.5 (2.8) 1,784.2 (3.3) -58.4 Primary 5,677.6 (37.1) 2,324.0 (33.0) -59.1 Secondary General 7,145.7 (35.4) 3,309.6 (41.0) -53.7 Secondary Technical 7,087.5 (5.3) 3,836.7 (2.5) -45.9 University 15,112.2 (15.5) 7,238.9 (15.0) -52.1 Other Post-Secondary 7,634.3 (3.9) 4,775.3 (5.2) -37.4

Emtloyer of Head Government 9,474.3 (19.1) 4,310.3 (15.9) -54.5 Private 7,604.0 (35.2) 3,576.2 (30.3) -53.0 Private Home 3,931.5 (1.3) 1,785.8 (0.7) -54.6 SeLf-EmpLoyed 7,126.7 (36.3) 3,637.4 (36.7) -48.9

Occupation of Head Agriculture 6,430.0 (3.7) 3,697.1 (1.5) -42.5 SaLes/Services 7,532.4 (27.8) 3,349.7 (29.9) -55.5 Industry/Crafts 5,858.5 (37.3) 2,874.2 (30.7) -50.9 White ColLar 11,307.8 (23.0) 5,386.0 (22.3) -52.4

Unemploved 8,098.5 (2.9) 2,727.5 (5.4) -66.3

Retired 7,495.9 (4.9) 3,810.6 (8.9) -49.2

Notes: Populationpercentages do not add to 100 due to missing informationfor 0.3% of observationsin 1985-86 and 1.6% in 1990. The mean expenditure LeveL for those with Secondary Technicaltraining becomes 6580.7 for ALL Lima, 1990, if one outlying value is incLudedin the caLcuLations. This would representa -7.1% change in expendituressince 1985. apparently able to maintain their relative economic position. Thus they were not any more vulnerable to drops in living standards during the recent recession than were their male-headed counterparts.

Education

Table 5 shows that in Lima, households headed by someone with no education at all or with only primary education experienced the largest declines in expenditures relative to those with higher levels of education. Expenditures for households in these two categories fell by roughly equivalent amounts, 58.7% and

59.1%, respectively. Those with no education were significantly poorer than those with primary education even in 1985-86, and in 1990 represented a small

(3.5%) but very poor category of households in Lima. More generally, households

- 22 - in both of these categories exhibited mean expenditure levels well below other categories even in 1985-86. The percentage declines in expenditures experienced by these two groups were larger than for any other population group classified by head of household characteristic (with the sole exception of the unemployed).

The education level attained by the head of household therefore appears to be a significant indicator of poverty for the purposes of social program targeting in

Lima, both in terms of the household's relative position at any given time, and in terms of its vulnerability to declines in living standards over time.

Households headed by someone with secondary general or university level education fared only slightly better in terms of the percentage decline in average consumption levels, but maintained absolute consumption levels higher than any other educational category in both survey years. Households headed by those with secondary technical (-46.4%) or other post secondary education levels (-38.9%) exhibit falls in consumption which are significantly smaller in comparison to other categories and to the city average (-54.6). Note that households where the head has secondary technical training represent a small and apparently declining population group (from 5.3% of the population in 1985-86 to 2.3% in 1990). The decline in expenditures experienced by households in this category is the smallest compared to all other education categories except 'other post-secondary', suggesting that technical education helps reduce vulnerability to severe welfare loss. This finding is supported by an earlier study, based on the 1985-86 survey data (Arriagada, 1989). The fact that those with higher education and technical training also began the period with higher consumption levels than average is consistent with the overall finding in Table 3 that the distribution of expenditures became more unequal from 1985-86 to 1990.

Emiolovment

Classifying households according to the employer of the head casts more light on what happened in Peru between 1985-86 and 1990. Households headed by the self-employed, the largest category of households (34% in both years), experienced a (moderately) smaller reduction in expenditure levels than average.

- 23 - For these households, expenditures fell an average of 51.4% in 'All Lima' and 48.9% in 'Old Lima'. Further, the percentage of self-employed household heads fell slightly in the bottom 2 quintiles and rose in the wealthier 2 quintiles. However, in 1990 as in 1985-86, the fraction of households headed by the self- employed was larger in the poorer quintiles than in the wealthier quintiles. Households headed by those employed by the government or in the private sector experienced larger declines than the self-employed, of 56.1 and 56.3% in 'All Lima' respectively. It is also significant to note that a very small percentage of those in poorest quintile are government employees, both in 1985-86 (9.8%) and 1990 (9.4%). Finally, it should be noted that while households headed by an individual employed in a private home are indistinguishable from the rest in terms of the percentage drop in expenditures, these households began the period with expenditures of roughly half of the population average. Households in this category represent a very small and very poor segment of the total population (0.7% in 1990). Categorizing households by the economic sector in which the head is eimployed (Occupation of Head, Table 5) adds very little to the analysis except to reveal that those working in agriculture appear to have suffered slightly simaller expenditure declines than average, especially in 'Old Lima' (Table 6). In Lima, of course, this group represents a very small percentage of the economically active population, which is also apparently declining (from 3.7% in 1985-86 to 2.1% in 1990 in 'All Lima'). The proportion of the population living in households where the head is employed in the sales/services sector has increased; however, this is also the category for whom expenditures have deaclined the most (-56.7%) since 1985-86. Thus the only sector which appears to have expanded in terms of economic activity is at the same time the one for which average consumption levels have declined most precipitously (except for the unemployed). It may be conjectured that this sector absorbed some of those heads of households who lost or left jobs in other occupations. Persons living in households where the head was or became unemployed experienced by far the largest drop in consumption of any category (-65.9%), and

- 24 - expanded from 2.9% to 5.1% of the population. Further, absolute expenditure levels for these households were lower than for all employment categories except for those employed in a private home. This situation is in sharp contrast with the economic status of the unemployed in Lima in 1985-86, when households headed by the unemployed had higher expenditure levels than all other occupational categories except white collar employees. Turning to tables 7, 8, and 9 further illuminates the change in economic status of the unemployed. These tables present the distribution of the population within expenditure quintiles broken down by these same head of household characteristics. They point to a particularly significant change in employment levels between 1985-86 and 1990. Whereas in 1985-86 heads of household who were not working in any occupation were found in roughly equal proportion across quintiles, by 1990 this was not at all the case. The unemployment rate increased at a rapid pace in the lower quintiles, more than tripling in the bottom quintile, and doubling in the second quintile. Conversely, in the upper two quintiles the rate of unemployment was roughly the same in the two survey years. This suggests that unemployment rose substantially between 1985-86 and 1990, and that the bulk of these new unemployed are found among the most poor. It may be conjectured that in 1985-86 the unemployed were often those who were financially able to be selective as to their type of employment, whereas by 1990 the body of unemployed was largely composed of those in poorer deciles who simply could not find work.

To summarize, the best indicators of poverty in terms of characteristics of the head of household appear to be level of education and type of employer.

Households headed by someone with no education and/or employed in private homes were consuming 50% less per capita than the city average in 1990, which itself had fallen by roughly half,since 1985-86. Those least vulnerable to declines in consumption levels were those with secondary technical or post-secondary (non- university) training. Those hardest hit by the recessionary conditions prevailing after 1987 appear to have been the unemployed (more specifically, those who become unemployed between 1985-86 and 1990), who were heavily

- 25 - Table 7: Distributionof Populationby Characteristicsof HousehoLdHeads, by Quintile,All Lima,1985-86

QuintiLe Quintile AdjustedMean 1 Quintite Quintite Quintile 5 Per Capita (Poorest) 2 3 4 (WeaLthiest) Expenditures

SeK of Head MaLe 84.2 85.3 85.7 86.6 91.3 7,943.2 FesaLe 15.8 14.7 14.3 13.4 8.7 6,681.0 Educationof Head None 5.4 5.0 2.0 1.2 0.5 4,288.5 Primary 58.3 43.9 40.6 27.5 15.3 5,677.6 SecondaryGeneraL 28.0 38.0 38.9 41.1 30.9 7,145.7 SecondaryTechnical 1.8 6.2 4.5 9.7 4.1 7,087.5 University 3.4 4.1 10.1 15.5 44.5 15,112.2 OtherPost-Secondary 6.5 2.8 3.9 5.1 4.7 7,634.3 EmpLoyerof Head Goverrment 9.8 12.6 18.3 24.9 29.8 9,474.3 Private 41.6 35.3 34.2 32.3 32.5 7,604.0 PrivateHome 3.7 1.2 0.8 0.5 0.0 3,931.5 SeLf-EmpLoyed 40.0 43.5 36.7 32.4 29.0 7,126.7 Occupationof Head Agriculture 2.3 4.7 5.9 3.7 1.8 6,430.0 Sales/Services 34.6 27.3 24.2 23.4 29.2 7,532.4 Industry/Craft 47.6 49.9 38.6 35.3 15.1 5,858.5 WhiteCollar 10.6 10.6 21.2 27.3 45.1 11,307.8 Unemployed 2.1 3.2 3.0 2.8 3.6 8,098.5 Retired 2.5 4.3 6.2 7.2 4.6 7,495.9

Notes: CoLumnsmay not add to 100due to missinginformation for 1.6%of observations.Figures represent the percentageof the popuLationwithin each auintilein househoLdswhere the head dispLaysthe given characteristic.

represented in the poorest two quintiles in 1990 and whose consumption per capita fell by 65.9% on average. Note however that even in 1990, households headed by the unemployed represented a small percentage of the population in the poorest quintile (7.8%). This suggests that while addressing unemployment will benefit some of the poor, other complementary programs are required to reach the numerous poor households not headed by the unemployed.

Other Indicators of Living Standards

This subsection discusses the change in living standards observed in Lima between 1985-86 and 1990 based on housing conditions and ownership of durable goods across quintiles and geographical areas of the city. The indicators for

housing point graphically to a widespread deterioration in physical living

- 26 - Table 8: Distributionof Populationby Characteristicsof HouseholdHeads, by Quintile,All Lima, 1990

Quintile Quintile AdjustedMean 1 Quintile Quintite QuintiLe 5 Per Capita Percent Change (Poorest) 2 3 4 (WeaLthiest)Expenditure from 1985-86

Sex of Head MaLe 83.6 84.0 83.3 90.1 87.6 3,613.6 -54.5 Female 16.4 16.0 16.7 9.9 12.4 3,012.2 -54.9

Education of Head None 7.9 4.8 2.8 0.7 0.9 1,770.7 -58.7 Primary 55.9 39.8 31.2 24.8 12.2 2,324.4 -59.1 Secondary General 32.5 41.2 51.4 49.9 38.8 3,209.8 -55.1 SecondaryTechnicaL 2.1 2.0 3.1 2.2 2.5 6,252.4 -11.8 University 1.1 7.7 8.8 14.1 37.1 6,945.7 -54.0 Other Post-Secondary 0.5 4.5 2.7 8.4 8.5 4,665.0 -38.9

EmaLover of Head Goverment 9.4 11.5 13.7 18.9 22.4 4,155.0 -56.1 Private 35.3 28.3 34.6 35.6 29.6 3,321.2 -56.3 Private Home 1.6 0.9 1.2 0.1 0.0 1,782.4 -54.7 Self-Employed 39.4 40.7 35.5 33.5 33.4 3,466.2 -51.4

Occupation of Head AgricuLture 2.2 2.8 0.2 1.5 2.8 3,189.4 -50.4 Sales/Services 36.2 26.4 32.5 29.1 26.5 3,259.3 -56.7 Industry/Craft 44.2 36.5 35.0 32.2 17.1 2,793.3 -52.3 White Collar 4.3 16.5 17.7 26.0 39.6 5,195.3 -54.1

UnempLoyed 7.8 7.8 4.9 2.6 3.1 2,763.5 -65.9

Retired 4.9 9.6 7.7 7.8 8.9 3,733.3 -50.7

Notes: CoLumnfsmay not add to 100% due to missing informationfor 1.8% of observations. Figures representthe percentageof the population within each quintile in househoLds where the head displays the given characteristic.

conditions in metropolitan Lima between 1985-86 and 1990, which was most severe

in the Conos Sur, Este and Norte. Patterns in durable good ownership reflect

fairly similar levels of access to basic goods across all quintiles, but to

increasingly skewed ownership of luxury goods such as color televisions and cars.

Because these findings do not depend directly on price data, they are independent of the deflators used in this paper. They therefore serve to confirm that living standards declined substantially between 1985-86 and 1990, and that the sharpest

decline occurred among the poorest population quintiles.

Housing Conditions

Tables 10 and 11 in this section compare housing conditions in Lima in

1985-86 with 'All Lima' 1990 broken down by quintile, and top and bottom decile.

- 27 - Table 9: Characteristicsof HouseholdHeads by Quintile, Old Lima, 1990

Quintile Quintile Adjusted Mean 1 Quintile Quintile Quintile 5 Per Capita Percent Change (Poorest) 2 3 4 (Wealthiest) Expenditures from 1985-86

Sex of Head Mate 83.9 84.2 81.2 91.3 86.6 3,784.8 -52.3 Female 16.1 15.3 18.8 8.7 13.4 3,062.7 -54.2

Education of Head None 6.5 6.1 3.3 0.0 1.1 1,784.2 -58.4 Primary 54.5 40.3 33.2 26.1 9.7 2,324.0 -59.1 Secondary General 34.4 37.6 47.4 47.7 36.8 3,309.6 -53.7 Secondary Technical 3.2 2.2 3.8 1.7 3.6 6,580.7 -7.1 University 1.3 9.4 8.5 15.3 40.4 7,238.9 -52.1 Other Post Secondary 0.0 4.4 3.8 9.1 8.3 4,775.3 -37.9

EmoLoyer of Head Government 9.1 12.5 14.1 20.1 22.7 4,310.3 -54.5 Private 35.6 23.3 32.0 33.3 27.4 3,576.2 -53.0 Private Home 1.7 0.9 1.4 0.0 0.0 1,785.8 -54.6 SeLf-EmpLoyed 40.0 41.6 35.7 32.6 34.1 3,637.4 -49.0

Occupationof Head Agriculture 2.0 1.2 0.0 1.2 3.0 3,697.1 -42.5 Sales/Services 37.7 26.1 33.0 27.4 25.6 3,349.7 -55.3 Industry/Craft 43.8 33.9 32.7 29.0 15.8 2,874.2 -50.9 White Collar 4.1 17.5 18.0 29.2 40.5 5,386.0 -52.4

Unemployed 7.5 8.6 5.1 3.0 3.3 2,727.5 -66.3

Retired 4.9 12.0 8.8 9.1 9.9 3,810.6 -49.2

Notes: CoLumns may not add to 100% due to missing informationfor 2.0% of observations. Figures representthe percentageof the populationwithin each quintile in householdswhere the head displays the given characteristic.

A further category, top and bottom 5 percent, has been added to the tables for

1990. The inclusion of this category highlights distinct characteristics of the poorest segment of the population of Lima, and makes clear the particularly

skewed nature of living conditions in the city by emphasizing the markedly better living conditions of the population in the top 5 percent of expenditure levels. Tables 12 and 13 display the same characteristics for the population broken down by geographic area. These tables point to strong regional differences and to the

rapid deterioration in physical living conditions between 1985-86 and 1990. Comparing Tables 10 and 11, it is evident that home ownership pertains

rather equally to all expenditure groups in both 1985-86 and 1990. This

characteristic is therefore not 'highly significant in locating the poorest households, particularly since after 1985 land titles were [ssued sporadically

- 28 - Table10: Distributionof Populationby HousingCharacteristics, ALL Lima 1985-86,by Quintile

QuintiLe Quintile 1 Quintile Quintile Quintile 5 All Bottom10% ALL 2 3 4 ALL Top 10% Lima

Typeof Home Single famiLy BuiLding 64.6 66.5 63.7 57.8 62.7 63.4 65.3 62.7 Multi-famiLy BuiLding 12.5 11.8 15.6 14.5 11.6 6.8 3.5 11.6 Apartment 5.2 6.4 9.4 12.8 15.6 22.8 26.5 14.4 Quinta 9.4 7.9 7.1 11.6 7.6 5.5 4.1 7.8 Shack 8.3 6.9 4.2 3.2 2.5 1.5 0.6 3.5 Howoccupied owned 62.0 61.3 57.0 50.4 53.6 56.0 58.9 55.3 Rented 20.0 22.2 24.8 29.9 29.9 31.7 31.4 28.3 Invasion 9.0 6.1 2.2 4.0 3.1 0.9 0.6 3.0 Other 9.0 10.4 16.1 15.7 L3.4 11.4 9.1 13.3 Materialof Walls Brick, Cement 72.0 72.6 68.7 75.5 83.5 88.0 92.0 78.8 Adobe 12.0 12.7 18.3 11.3 11.0 7.3 6.3 11.6 Wood 8.0 5.2 3.0 3.6 0.0 1.5 0.6 2.4 Straw Mat 4.0 5.2 4.8 4.4 3.8 1.8 0.6 3.8 Sugar Cane 1.0 1.9 3.9 1.5 1.0 1.2 0.6 1.8 Stone,Mud, Other 3.0 2.4 1.3 3.6 0.7 0.3 0.0 1.6 Sourceof Water In Home 67.0 68.9 68.3 74.4 76.6 88.6 93.7 76.6 Truck 7.0 8.0 6.1 4.0 5.8 1.8 0.6 4.8 Public Standpipe 3.0 3.8 5.2 3.6 4.5 1.2 0.6 3.5 In Building 17.0 14.1 13.0 13.5 11.0 6.2 3.4 11.1 WelL/Canal 3.0 3.3 3.5 1.8 0.7 2.0 1.7 2.1 HaveSewage SYstem 88.0 90.6 91.3 93.4 94.5 98.8 100.0 94.2 Sourceof Light Electricity 90.0 92.0 96.1 94.5 94.8 98.2 100.0 95.4 Gas/Kerosene 7.0 6.1 3.0 3.6 2.7 0.6 0.0 3.0 Candle 3.0 1.9 0.9 1.8 2.1 1.2 0.0 1.6 CookingFuel Used Electricity 0.0 0.5 1.3 1.5 3.1 15.2 21.7 5.1 Gas 14.0 13.7 16.1 31.0 41.2 59.5 62.9 35.2 Kerosene 84.0 84.9 80.9 66.1 52.6 24.0 15.4 58.0 Coal/Wood 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.1

Notes: Figures represent the percentage of population within each cuintile in households displaying the givencharacteristic. Columns may not add to exactly100% due to rounding,and to responseswhich have not been listeddue to insignificantpercentage values. Ownershipby "Invasion"indicates that occupantsdo not possessiona titleof landownership.

by the government (for political purposes) in some regions of the Conos Norte, Este and Sur which had been recently settled by invasion. Correspondingly, the incidence of occupation by invasion apparently increased dramatically between

1985-86 and 1990, especially in the bottom 2 quintiles. It is therefore likely that many who reported ownership as defined by the questionnaire (possessing a

land title) had in fact invaded the land. Renting a home declined across all

- 29 - Table 11: Housing Characteristics,ALL Lima, 1990, by ExpenditureQuintile

QuintiLe QuintiLe 1 5 Bottom Bottom QuintiLe Quintile Quintile Top Top ALL 5% 10% ALL 2 3 4 ALL 10% 5% Lima

Type of Hom Single famiLy Building 76.1 77.6 75.8 70.8 71.0 71.5 75.2 73.4 73.5 72.9 MuLti-familyBuiLding 6.6 6.2 7.4 8.0 9.0 4.8 2.0 1.7 2.2 6.2 Apartment 3.1 2.0 3.9 6.1 6.3 9.8 15.0 19.5 20.5 8.2 Quinta 2.8 2.2 1.6 4.8 7.2 7.5 4.9 3.6 3.5 5.2 Shack 11.4 12.0 11.5 10.3 6.5 6.4 2.8 1.7 0.3 6.9

How Occupied Owned 53.7 58.6 52.7 52.9 59.3 57.4 62.9 63.8 67.8 56.0 Rented 10.9 7.9 10.5 16.9 17.4 17.5 21.8 22.0 22.7 16.8 Invasion 26.6 24.4 23.4 23.1 16.6 15.2 9.1 7.4 1.9 17.5 Other 8.7 9.1 12.3 7.2 6.6 9.8 6.1 6.8 7.6 9.4

Material of WaLLs Brick, Cement 67.0 66.2 69.8 72.3 77.0 78.9 90.0 93.1 95.1 77.6 Adobe 8.4 6.0 6.8 8.1 9.2 10.7 4.1 2.8 2.4 7.8 Wood 6.1 6.3 6.0 5.7 3.9 2.8 1.8 1.3 0.0 4.0 Straw Mat 15.3 18.2 14.3 10.6 6.5 5.6 2.4 1.4 0.1 7.9 Sugar Cane 0.0 0.8 1.3 2.5 3.0 1.8 1.1 1.1 2.2 1.9 Stone, Mud, Other 3.1 2.5 1.8 3.2 3.4 1.9 1.7 0.2 0.2 0.8

Average Number of Rooms per Person 0.3 0.4 0.5 0.5 0.8 0.9 1.4 1.7 2.0 0.9 Source of Water In Home 52.0 57.3 52.8 61.8 75.2 74.4 85.9 91.3 98.6 70.0 Truck 21.1 20.0 17.1 14.1 10.3 9.2 7.9 4.4 0.3 11.7 Public Standpipe 19.5 15.2 16.7 10.3 6.1 7.3 2.4 0.9 0.8 8.6 In Building 6.9 4.9 8.l 6.3 5.5 8.0 2.6 2.0 0.0 6.1 Well/Canal 0.5 2.5 4.9 6.4 1.9 0.9 1.1 1.3 0.3 3.0

Hours of Public Water Service Per DaY None 21.6 22.4 22.3 21.5 13.2 10.3 9.1 6.4 0.6 15.5 1-3 29.2 23.5 24.5 17.3 19.7 15.6 15.5 11.3 11.4 18.5 4-6 15.6 13.3 16.9 17.3 23.0 27.2 20.7 22.0 28.1 21.0 7-12 14.2 21.7 18.4 24.5 25.7 29.9 33.1 36.8 44.3 26.3 13 or more 19.4 19.0 17.8 18.9 18.5 16.6 21.3 23.5 15.6 18.6

Have Sewage System 60.1 63.5 57.0 62.9 71.8 77.1 88.5 93.2 97.9 71.5

Source of Light ELectricity 85.2 87.1 84.6 86.0 92.6 92.7 96.9 98.6 99.5 90.5 Gas/Kerosene 12.8 9.7 10.6 10.8 6.1 5.1 2.7 1.3 0.5 7.1 Candle 0.9 2.7 4.0 3.2 1.0 1.9 0.3 0.1 0.0 2.1

Cooking Fuel Used ELectricity 0.0 0.0 0.0 0.0 0.5 2.6 8.8 15.0 24.0 2.4 Gas 8.7 11.7 12.0 31.8 43.0 51.4 67.7 72.2 69.7 41.2 Kerosene 90.6 87.8 87.1 67.5 55.8 45.1 23.3 12.8 6.3 55.7 CoaL/Wood 0.6 0.3 0.8 0.5 0.0 0.0 0.0 0.0 0.0 0.3

See notes, Table 10.

- 30 - quintiles, but by unequal proportions: renting by 1990 became a distinct

characteristic of the wealthier expenditure groups.

The quality of homes deteriorated significantly for households in the bottom expenditure quintile (representing the poorest 20% of the population), where the incidence of housing made of "estera" (straw mats) approximately trebled between 1985-86 and 1990. Turning to public services, the number of households with water service in-home decreased consistently in the lower quintiles between 1985-86 and 1990. The glaringly uneven distribution of public

services is also evident. In 1990 a full 98.6% of households with expenditures

in the top 5% of the distribution had public water service in their homes, while

this service was available to roughly half of those in the bottom 5% of the

distribution. The situation is similar for access to public sewage systems;

97.9% of those in the highest expenditure category had a sewage system in their homes, while only 60.1% of those in the lowest category had this service. For 1990 an additional category of information is available, detailing the average hours of water service available to those served by the public system. This question was added because after 1985-86 public water services deteriorated, so that by 1990 some areas were receiving little or no water at all from public

pipes. From the results it is clear that in 1990 the lower expenditure groups were receiving the least water from public services, with 21% in the poorest quintile receiving no water at all.

There is one further characteristic of the lower quintiles which is noteworthy. A vast proportion of households at these lower expenditure levels

depend on kerosene for cooking fuel--in 1990, 90.6% of households in the bottom

5% of the distribution in terms of household expenditures were cooking with kerosene. Furthermore, it is clear from Table 11 that the usage of kerosene increases distinctly as expenditure levels drop. This is a significant factor to recall when considering the taxation or subsidization of kerosene, which will

be discussed further in Section V. Tables 12 and 13 present these same housing characteristics as distributed

within the 9 geographical areas of Lima. They confirm the findings resulting

- 31 - from Tables 10 and 11, highlighting in particular the geographical areas where public services deteriorated most significantly between 1985-86 and 1990. In the

Cono 'Este', the area with the lowest per capita expenditure levels in 1990

(Table 4), the provision of public services was also consistently the poorest in 1990 (although levels in the Cono Sur and Norte were only slightly better): 33% of households in the Cono 'Este' were not served by public water pipes, 20% had no electricity, and 48% had no sewage system. On the other hand, the deterioration since 1985-86 in the level of public services provided appears to have been most acute in the Cono 'Norte', where in 1985-86 the percentage of households with water in-home, with access to a sewage system and with electricity was slightly higher than in the other two Conos. This was no longer the case in 1990, where the Cono 'Sur' surpassed the Cono 'Norte' in the provision of both water in-home and electricity.

Durable Good Ownership

one convenient and intuitively appealing dimension of living standards is household ownership of various durable goods. Tables 14 and 15 provide this information for both 1985-86 and 1990, by quintile and by area of Lima respectively. The durable goods considered are radios, refrigerators, cars, bicycles, color and black-and-white televisions and gas stoves.

For Lima as a whole, ownership of the above mentioned durable goods was quite stable. The sole exception is the ownership of radios, which has increased

substantially for all expenditure groups. Refrigerator and car ownership declined slightly in all quintiles, but changes in the ownership of other goods vary somewhat by quintile. The extent to which durable goods are found primarily in households at

certain income levels can be useful for purposes of identifying the poor. Generally speaking, in Lima radios and black and white televisions are found in

the same proportion among the different quintiles. By contrast, the other five durable goods are more likely to be found among the better off households. The

most stark contrasts are apparent in the ownership of cars, color televisions and

- 32 - Table 12: Distributionof Population by Housing Characteristics,ALl Lima, 1985-86,by Area

Sur Norte Este Oeste CaLLao Centro 1 Centro 2 Centro 3 Estrato Alto

Tyoe of Home Single Family Building 86.3 84.4 86.4 36.8 86.3 35.4 31.1 52.9 39.4 Multi-Family Building 3.0 6.2 5.2 18.9 13.0 6.2 21.0 20.7 10.6 Apartment 0.0 3.3 3.1 18.9 9.2 22.9 31.5 18.4 43.9 Quinta 0.6 0.5 1.6 24.5 1.5 35.4 14.8 8.0 6.1 Shack/Other 10.1 5.7 3.1 0.9 2.3 0.0 1.6 0.0 0.0

How occupied Owned 68.9 59.9 68.8 36.6 62.0 41.7 31.6 55.5 62.1 Rented 6.7 11.2 16.4 53.6 26.3 41.7 53.2 36.7 28.8 Invasion 8.3 2.6 1.9 0.0 0.7 2.1 4.9 0.0 0.0 Other 16.1 21.1 13.0 9.8 10.9 14.6 10.3 7.8 9.1

Material of Walls Brick, cement 73.6 86.6 87.9 66.1 84.7 66.7 74.9 68.9 84.8 Adobe 0.5 2.6 8.7 31.2 2.9 33.3 16.0 28.9 13.6 Wood 4.7 5.2 0.5 0.9 1.5 0.0 2.3 2.2 0.0 Straw Mat 16.6 3.4 2.9 0.0 1.5 0.0 1.1 0.0 0.0 Sugar Cane 0.5 0.0 0.0 1.8 8.8 0.0 3.0 0.0 1.5 w Stone, Mud, Other 4.1 2.1 0.0 0.0 0.7 0.0 2.7 0.0 0.0

Source of Water In Home 72.0 72.4 68.1 79.5 81.7 97.9 76.8 81.1 72.0 Truck 10.9 13.4 5.3 0.0 1.5 0.0 0.0 0.0 10.9 Public Standpipe 2.6 0.9 10.1 0.0 3.6 0.0 5.3 0.0 2.6 In BuiLding 9.3 7.8 5.3 18.7 12.4 2.1 16.0 18.9 9.3 Well/Canal 1.0 2.6 9.6 0.0 0.0 0.0 0.4 0.0 1.0

Have Sewage System 83.9 97.4 89.9 96.4 96.3 100.0 97.3 96.7 98.5

Source of Lisht Electricity 93.8 95.7 90.3 97.3 97.8 100.0 96.6 96.7 97.0 Gas/Kerosene 4.1 3.0 4.3 2.7 0.7 0.0 3.0 2.2 3.0 Candle 2.1 1.3 4.8 0.0 1.5 0.0 0.4 1.1 0.0

Cooking Fuel Used Electricity 0.0 0.4 4.8 1.8 2.9 10.4 5.7 12.2 31.8 Gas 16.6 30.6 25.1 56.2 46.0 52.1 36.9 40.0 53.0 Kerosene 82.4 67.7 67.6 40.2 50.4 33.3 55.1 45.6 15.1 Coal/Wood 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0

Note: Figures representthe percent of the populationwithin each area living in households displaying the given characteristic. Table 13: Distributionof Populationby Housing Characteristics,ALL Lima, 1990, by Area

Sur Norte Este Oeste CalLao Centro 1 Centro 2 Centro 3 Estrato Alto

Type of Home SingLe FamiLy Building 80.1 91.5 83.6 42.9 75.7 34.4 48.9 59.1 49.6 Multi-Family Building 3.0 1.2 1.4 12.2 8.3 1.8 15.9 21.9 7.0 Apartment 0.8 0.5 1.2 11.7 8.6 23.9 26.6 9.7 36.0 Quinta 0.4 1.2 0.3 30.0 1.4 37.4 8.4 8.2 7.5 Shack/Other 15.6 5.6 13.8 3.2 6.0 2.4 0.1 1.1 0.0

How Occupied Owned 51.5 55.3 63.9 52.1 74.8 61.3 44.8 59.5 57.5 Rented 3.6 6.1 8.1 46.4 17.5 38.6 33.9 32.3 37.3 Invasion 37.5 28.3 18.4 0.0 1.3 0.0 6.9 1.8 2.6 Other 7.4 10.3 9.6 1.5 6.4 0.0 13.4 6.4 2.6

Material of Walls Brick, cement 75.9 78.1 78.8 76.2 76.7 82.2 78.2 69.2 86.4 Adobe 0.9 6.7 3.1 21.6 1.9 11.0 15.5 29.0 7.0 Wood 4.9 6.4 3.4 0.0 5.6 0.0 2.5 1.8 2.6 Straw Mat 16.7 7.7 13.5 0.0 6.0 0.0 0.5 0.0 0.0 Sugar Cane 0.0 0.5 0.2 2.2 9.0 4.3 3.2 0.0 3.9 Stone, Mud, Other 1.5 0.6 1.1 0.0 0.8 2.4 0.0 0.0 0.0

Average Number of Rooms per Person 0.7 0.8 0.7 1.2 0.8 1.4 0.9 1.3 1.6 Source of Water v In Home 64.0 61.3 58.1 89.8 80.3 97.5 79.3 79.6 81.6 Truck 18.0 14.2 27.9 0.0 2.2 0.0 0.0 0.0 0.0 PubLic Standpipe 8.6 18.3 5.6 5.0 8.3 0.0 5.4 0.0 0.0 In Building 1.3 2.8 2.4 5.2 7.8 2.4 14.2 20.4 18.4 Well/Canal 6.8 2.6 5.3 0.0 0.9 0.0 1.1 0.0 0.0

Hours of Public Water Service Per daY None 26.5 17.7 33.9 1.5 3.6 0.0 1.1 0.0 0.0 1-3 32.9 19.9 17.2 9.9 7.3 8.0 19.8 10.0 8.3 4-6 12.6 20.4 14.5 25.6 33.4 39.9 29.5 15.4 14.9 7-12 7.1 25.0 22.1 45.7 41.2 39.3 28.3 27.2 53.1 13 or more 20.9 17.0 12.3 17.4 14.6 12.9 21.3 47.3 23.7

Have Sewage System 61.7 65.0 57.9 87.8 80.8 100.0 83.7 86.4 84.6 Source of Light Electricity 93.4 84.6 80.9 100.0 93.9 100.0 95.3 100.0 100.0 Gas/Kerosene 4.8 11.2 13.7 0.0 5.7 0.0 3.9 0.0 0.0 Candle 1.7 3.5 4.6 0.0 0.4 0.0 0.8 0.0 0.0

Cooking Fuel Used Electricity 0.4 0.0 0.7 3.0 0.0 10.4 3.0 3.2 32.9 Gas 27.0 33.3 31.9 70.5 58.6 62.0 44.7 58.8 52.6 Kerosene 72.4 65.2 66.7 26.5 41.4 25.8 51.8 36.2 14.5 Coal/Wood 0.1 1.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0

Note: Figures represent the percent of the populationwithin each area Living in househoLds displaying the given characteristic. gas stoves. In 1990, only 2% of the population in the poorest quintile lived in households with cars, compared to 37% for the wealthiest quintile. The analogous figures for color televisions are 5% and 67%, and for gas stoves they are 16% and

74%. Further, no household in the poorest 5% of the population owed either a car or a color television in 1990.

One fact which is not reflected in Tables 14 and 15 is that the vast majority of durables (90%) were acquired before or during 1988, which implies that when the economic decline began in late 1988 households coped by delaying purchases of durable goods. This and other strategies of dealing with large drops in household incomes will be examined in a separate paper; Section V will examine patterns in durable good ownership for the purposes of targeting social programs.

- 35 - Table 14: Change in Distributionof Durable Good ownership,1985-86 to 1990, 'ALL Lima,' by Quintile

QuintiLe QuintiLe 1 Quintite Quintile Quintite 5 Durable Goods Year Bottom 5% Bottom 10% ALL 2 3 4 ALL Top 10% Top 5%

Radio 1990 82.5 81.2 80.3 83.2 84.4 88.9 90.6 91.0 95.3 1985 67.0 65.0 65.6 61.2 61.7 57.8 64.7 68.6 67.8

Refrigerator 1990 46.6 42.6 43.1 50.1 65.3 71.1 84.6 91.1 94.9 1 1985 30.4 43.0 48.2 56.1 67.4 75.5 87.4 93.5 97.0 w Car 1990 0.0 0.0 2.1 4.3 6.9 20.6 36.9 46.8 62.1 1985 0.0 1.9 3.4 5.0 7.9 18.8 45.1 55.6 67.0

Bike 1990 4.4 6.7 6.8 19.1 23.3 31.8 40.8 45.3 53.6 1985 9.3 7.0 10.2 18.2 16.2 26.1 35.5 36.6 31.6

Color TV 1990 0.0 4.0 5.0 19.2 34.0 46.6 67.3 81.3 93.2 1985 1.7 6.6 9.0 15.1 27.5 40.3 68.6 75.4 82.9

Black & white TV 1990 68.4 67.6 73.9 72.8 73.6 72.4 66.8 61.5 57.9 1985 68.1 73.5 73.7 80.5 70.8 74.5 67.9 62.5 61.4

Gas stove 1990 17.2 15.4 16.3 41.8 52.7 62.5 73.9 78.0 78.5 1985 20.9 23.4 26.6 35.1 48.2 62.9 70.2 70.6 64.6 Table 15: Change in Distributionof DurabLe Good Ownership,1985-86 to 1990, All Lima, by Area

Durable Goods Year Sur Norte Este Oeste Callao Centro 1 Centro 2 Centro 3 Estrato ALto

Radio 1990 84.6 86.5 80.6 84.9 86.9 87.7 86.2 94.3 91.2 1985 60.5 57.5 66.9 67.6 62.1 70.4 58.3 63.7 70.0

Refrigerator 1990 53.0 57.7 53.4 81.9 68.1 86.5 67.1 75.6 92.1 1985 54.5 66.0 62.7 77.2 68.1 90.0 67.2 75.8 84.8

Car 1990 9.9 7.2 10.4 28.0 14.8 20.2 11.0 34.0 54.4 1985 8.3 10.0 8.4 25.0 18.2 25.4 18.7 25.1 53.6

Bike 1990 17.3 22.1 19.4 34.7 24.3 44.8 26.1 34.0 47.4 1985 14.3 19.8 22.9 23.2 28.8 31.4 14.1 31.5 33.8

BLack & white TV 1990 73.5 74.9 65.2 77.2 80.3 65.0 70.8 68.1 59.6 1985 74.1 73.0 78.1 73.6 73.8 67.3 73.2 70.1 63.5

CoLor TV 1990 17.3 24.3 24.8 67.7 36.6 63.8 44.5 50.5 83.8 1985 19.7 23.0 27.1 49.2 32.3 51.8 32.8 48.8 70.0

Gas stove 1990 35.0 44.4 35.9 78.7 71.8 69.3 52.5 58.1 66.7 1985 33.2 49.8 41.5 66.4 59.7 63.6 50.0 50.7 52.8 V. Poverty in Lima from 1985-86 to 1990

The previous section examined changes in the overall distribution of consumption in Lima between 1985-86 and 1990. This section focuses on those households whose consumption is so low that they can be categorized as poor.

Subsection A discusses how the increase in poverty since 1985-86 may be quantified using several definitions of an absolute poverty line. Subsection B examines some distinct characteristics of the poor, focusing on how extremely poor groups may be identified for targeting purposes.

Measuring the Incidence of Poverty in Lima

There is no official or widely accepted poverty line in Lima. This subsection will examine several different methods of deriving such a line. To keep the analysis simple, a standard head-count definition of aggregate poverty will be applied. In other words, poverty will be measured in terms of the fraction of the total population that are classified as poor. Households are classified as poor if their per capita consumption levels, adjusted for household composition as explained in Section III, fall below the particular poverty line in question. The reader should bear in mind that any definition of poverty necessarily involves certain value judgments regarding the adequacy of a particular consumption level. However, it will be evident from the discussion below that the increase in poverty in Lima was very large between 1985-86 and 1990 over a fairly wide range of possible definitions of poverty. Table 16 presents figures on the extent of poverty in Lima in both years, using six different definitions of the poverty line. The first two poverty lines are based on the cost of a minimum basket of food for a family of six in Lima7 .

The daily cost of this basket per family in June 1, 1990 Intis is 154,416, which implies a cost of 4,632,480 Intis per month. The family is assumed to consist of two adults and four children. Adjusting the resulting per capita value of the

7Source: Cuanto S.A., Lima, Peru. This food basket (Canasta Basica) provides 2,168.8 Kcal and 62.3 grams of protein per capita per day.

- 38 - Table 16: Poverty Lines Based on Monthly Real Expenditures

Value of Poverty Line, Percent of PoouLation BeLow PovertY Line in: Poverty Line June 1, 1990 Intis ALL Lima 1985-86 ALL Lima 1990 OLd Lima 1990 New Lima 1990

1. Food Expenditures 1,447,650 12.7 54.7 55.8 49.3 per capita below 'Basic Needs' Food Basket

2. Total Expenditures 1,447,650 0.5 17.3 17.2 17.9 per capita below 'Basic Needs' Food Basket

3. TotaL Expendituresbelow 1,033,000 0.03 7.8 7.8 7.4 1985 Minimum Wage

4. TotaL Expendituresbelow 2,066,000 2.7 36.9 35.2 46.0 1985 Minimum Wage x 2

5. Total Expendituresbelow 695,900 0.0 2.6 2.6 2.4 1990 Minimun Uage

6. TotaL Expendituresbelow 1,391,800 0.5 15.7 15.8 15.6 1990 Minimum Waae x 2 food basket for household composition as in Section III, we assume that two of the children are age 6 or younger, the third is 7-12 years old and the fourth is

13-17 years old. Thus the number of adult equivalents in the family is 3.2 [2(1)

+ 2(0.2) + 0.3 + 0.5]. This implies a poverty line of 1,477,650 Intis per adult equivalent. Taking into account each household's composition of adults and children, if one categorizes as poor any household for whom per capita total consumption is less than this figure (so that even if all the household's money was spent on food it could not purchase the minimum amount of food required for each of its members) only 0.5% of the population of Lima was poor in 1985-86. However, if one categorizes as poor the households where per capita food expenditures are less than this amount, 12.7% of Lima's population was poor in 1985-86. The other four poverty lines are defined in relation to the minimum wage in Lima. While generally there is little theoretical justification for using prevailing minimum wage rates to determine a poverty line, in Peru there is an implicit understanding that minimum wage earnings should be adequate enough to prevent households from being in poverty. Thus we take the same family of six, with the same number of equivalent adults (3.2), and impose two different assumptions: a) if the household's total expenditures per month are below the minimum wage, then the household is poor; and b) if total expenditures for the household are below two minimum wages (i.e. the family has two wage earners) then the household is considered poor. The first assumption implies a poverty line of 1,033,000 Intis using the real 1985 monthly minimum wage (evaluated in June 1, 1990 Intis), while the second implies a poverty line twice as high, of 2,066,000 Intis. Finally, using the actual monthly minimum wage prevailing on June 1, 1990, the corresponding poverty lines are 695,000 and 1,391,000 Intis.

Note that the June 1990 minimum wage is 33% lower than the June 1985 minimum wage in real terms, which implies a less generous definition of poverty and consequently a reduced incidence of poverty. Turn now to the results in Table 16. Using the poverty line derived from the minimum basket of goods, only 0.5% of the population in Lima had total

- 40 - monthly expenditure levels below the amount required to purchase the basket in 1985-86. However, by 1990 this figure had risen to 17.3%, a 35-fold increasel Alternatively, if the poverty line is determined by the amount households were

actually spending on food in both years, 12.7% were not purchasing an adequate basket in 1985-86 while 54.7% were not in 1990. According to this determination

of the poverty line, the incidence of poverty quadruples between 1985-86 and

1990. In other words, in 1985-86 one out of every 8 residents of Lima were poor, but by 1990 more than half were poor. Similarly dramatic increases in poverty

are found using the minimum wage definition of poverty. Table 17 examines the incidence of poverty in each of the nine geographic areas of the city in both 1985-86 and 1990. The table shows the percent of

Lima's total population living in each area in 1990, and the percent of the

population in each area for whom 1) per capita total expenditures were below the value of the basic needs basket ('Total Expenditures Below Value of Basket') and b) per capita food expenditures were below the value of the basket ('Actual Food Consumption Below Basket'). According to this table the Cono Este exhibits

marginally but consistently the greatest incidence of poverty in 1990: in this area, 23.2% of the population could not purchase the basic needs basket if they spent all their money on food, and 60% of the population spent less on food than the basic needs basket would require. Further, the increase in the incidence of

poverty since 1985-86 was greater in the Cono Este than in any other area. This finding is consistent with Tables 4, 12 and 13, which show that per capita expenditure levels and housing characteristics declined most severely in the Cono Este. Further examination of Table 17 shows that the three Cono's (Sur, Norte,

and Este) and Centro 1 had by far the greatest proportion of people in severe

poverty -- people for whom total expenditures were below the value of the basic

needs basket -- but that the actual amount spent on food was below the value of the basic needs basket in households ranging across all areas of the city. To summarize this subsection, for a wide range of poverty lines the incidence of poverty increased tremendously in Lima from 1985-86 to 1990. This

is the case regardless of whether one looks only at the areas that were settled

- 41 - Table 17: Incidenceof Poverty in ALL Lima by Area, 1985-86 and 1990

Total Expenditures % of Total BeLow Value of Basket ActuaL Food ConsumptionBelow Basket Area Population 1985-86 1990 1985-86 1990

Sur (17.4%) 0.2 20.4 16.0 53.4

Norte (22.0%) 0.4 22.8 11.8 59.6

Este (18.8%) 0.1 23.2 11.8 60.0

Oeste (6.3%) 0.0 5.5 8.8 48.9

CalLao (10.8%) 0.1 9.2 11.6 57.6

Centro 1 (2.6%) 0.0 0.0 18.2 50.9

Centro 2 (22.0%) 1.8 19.8 14.9 59.6

Centro 3 (6.3%) 0.0 1.4 11.6 33.3

Estrato Alto (3.6%) 2.3 4.0 4.9 26.3

ALL Lima (100.0%) 0.5 17.3 12.7 54.7

Notes: Basket refers to "Basic Needs Basket," as in TabLe 12. Figures refers to the percent of the popuLation in each area beLow the given poverty Line.

before 1985 ('Old Lima') or one adds those areas settled since that time ('All Lima'). In fact, across the entire distribution of households in Lima expenditures declined so dramatically that virtually any poverty line would show a large increase in the "head-count" measure of poverty, that is, the proportion of the population with expenditures below the given poverty line. This in turn implies that poverty indices which take into account the depth of poverty (the

"gap" between the poverty line and each household's actual expenditures), including distributionally sensitive indices, will also show large increases in poverty regardless of how the poverty line is defined (cf. Foster and Shorrocks, 1988). The next subsection explores the use of these survey results in targeting social programs.

Implications of the Study for Social Program Targeting

The major findings of the study with regard to social program targeting fall into the following four categories: targeting by geographical area, by head

- 42 - of household characteristics, by housing conditions, and by the ownership of durable goods. Each of these categories will be discussed separately.

Targeting by Geographic Area

The survey shows that in 1990 the three poorest areas in terms of per capita consumption and access to basic public services were the Conos Este, Norte and Sur, where 74% of those in the poorest quintile (and 58% of the population in Metropolitan Lima) live. It is also in these areas where the largest drops

in per capita consumption were experienced after 1985 (Table 4), and where public services deteriorated most severely (Tables 12 and 13), indicating that these already poor areas were the hardest hit economically between 1985 and 1990. A surprising result of the 1990 survey is that the poorest families are fairly well distributed within each of these three areas, that is, they are not more heavily concentrated in the pueblo jovenes (i.e. 'New Lima') established after 1985

(Table 4). The most severe decline in living standards appears to have occurred in the cono Este. In this area per capita consumption levels were higher than in the other two Conos and in Callao in 1985-86; however by 1990 this area had become the poorest in terms of per capita consumption (Table 4). This observation is supported by the fact that in the Cono Este, the provision of basic public

services (water, sewage, electricity) also deteriorated most sharply (Tables 12 and 13), and the incidence of poverty according to variously defined poverty lines increased most rapidly (Table 17). This suggests that social investment, particularly in the improvement of public services such as potable water, would be well directed to these three Conos, and is most urgently required in the Cono

Este.

Targeting by Head of Household Characteristics

The head of household characteristics most significant for the purposes of identifying the poorest families in Lima are education level and employment

- 43 - category. Households headed by an individual with very little or no education, and/or who was a servant in another home were the poorest in terms of per capita consumption in 1990 (Table 5). Further, those with no education or some primary level schooling experienced the largest drops in consumption levels between 1985-

86 and 1990 (-59%). Those with technical training from secondary schools were better able to protect themselves from deterioration in consumption levels (-11%) than other households, and those employed by the government maintained levels of consumption significantly higher than the city average in both 1985-86 and 1990.

Households headed by someone with a university degree and/or a white collar employee were the wealthiest in both survey years. The self-employed experienced a drop in consumption which was slightly smaller than average (-51%), indicating that to some extent their real income generating capacity was better than that of employees. The unemployed experienced the largest drop in consumption levels between 1985-86 and 1990 (-66%), and by 1990 were heavily represented in the bottom 2 quintiles (Table 8). It is significant to note that gender of the head of household is not a particularly good indicator of changes in poverty between

1985-86 and 1990: per capita consumption in female headed households was virtually the same proportion to that of male headed households (84% and 83%) in both survey years. This indicates that while female headed households are poorer on average than their male counterparts in Lima, it would be inappropriate to single them out as a particularly susceptible group to deterioration in living standards due to the economic events of 1985-1990.

This breakdown of households by characteristics of the head reveals that education is the single best indicator of poverty in Peru today. It suggests that the poorest households may be located according to the education level of the head of the household, and that programs to improve educational facilities - - particularly those providing technical training -- and to keep children in school represent social investment programs with potentially very high medium to long run returns. It also makes clear the skewed nature of unemployment in Peru, and suggests that during subsequent recessions those who lose their jobs will be among the poorest population groups. Thus during future stabilization programs

- 44 - some type of employment generation for the very poor in urban areas is recommended, perhaps along the lines of public work programs in South Asia (cf.

Ravallion, 1991).

Targeting by Housing Conditions

Housing characteristics in Peru are useful in terms of determining the

general orientation of social investment programs and the impact of commodity taxes.' However they are of little use in identifying households which are eligible for specific program benefits. While there are several housing characteristics which are more common among the poorest quintile in Lima than in wealthier quintiles (living in a house made of straw mats, having access to water only from a public standpipe, and lacking electricity in the home), most of the

poor do not share these characteristics. For example, while it is true that most

households living in straw mat dwellings are poor, only 14% of households in the poorest quintile live in such dwellings (Table 11). Cooking with kerosene is the only characteristic shared by a very large proportion of households in the poorest quintile (87%); however, this characteristic is relatively common to

households in the second, third and fourth quintiles as well. Thus attempting to locate the poor according to the characteristics of their dwelling is not a

highly effective targeting strategy in Lima. Housing characteristics can be used to identify priorities for social investment programs, both in terms of the type of programs to be implemented and

the geographic areas of the city in which they ought to be concentrated. Most notably, access to potable water and sewage services deteriorated significantly

in Lima between 1985-86. This deterioration was most significant in the Conos Sur, Norte and Este, where up to 33% of the population was receiving no water

from public pipes (Table 13). Investment in the provision of water and sewage services in these three geographic areas would constitute a significant improvement in living standards, reaching 74% of the population in the poorest quintile. Finally, housing characteristics provide some information regarding

the impact of commodity taxes. The heavy dependence of the poor on kerosene for

- 45 - cooking, which declines steadily as expenditure levels increase, indicates that the burden of taxation on this commodity would fall most heavily on the poorest quintile. On the other hand, taxes on electricity would fall on the rich and poor equally.

Tarpetino by Durable Good OwnershiD

There are two ways in which information on durable good ownership may be useful for designing policies which target benefits to the poor. First, if poverty is strongly correlated with ownership patterns, and those patterns can be observed for individual households, ownership of durable goods can be used as atcriterion for inclusion or exclusion from certain benefits or programs. It is important to note that this would require that the government determine whether individual households own certain durable goods or not, which may difficult and costly. Second, patterns in durable good ownership can help determine whether commodity taxes and subsidies are progressive or regressive, both for the durable goods themselves and for items which are close complements to durable goods (e.g. gasoline for motor vehicles). Turning to the first type of targeting policies, if one could identify households with cars, gas stoves and color televisions, this information could in theory be used as criteria for excluding households from receiving direct benefits, or participating in certain programs, intended for the poor. In practice the application of this targeting method depends on the ease with which ownership of each good can be identified -- for example -- if accurate records of automobile purchases are already kept by the government then automobile ownership can be readily used to determine program eligibility. Given ownership patterns in Lima in 1990 (Table 14), commodity taxes on cars, gas stoves and color televisions are quite feasible ways to raise ,governmentrevenues without adversely impacting the very poor, assuming that they

are not likely to buy such expensive items in the near future. Certainly subsidies on such goods, whether implicit or explicit, discriminate against the

poor. Further, complementary goods such as gasoline for cars and natural gas for

- 46 - gas stoves are also candidates for commodity taxes. If car. run on gasoline while public transport depends primarily on diesel fuel, as has been the case in Peru, then the poorest households in Lima would not be directly affected by an increase in gasoline prices. Finally, it should be noted that non-durable goods as well as durables can be taxed, and a conclusive study of the incidence of commodity taxation on different income groups should examine all goods at once.

In conclusion, the survey results highlight three basic priorities for social investment in Lima: public services (potable water and sewage systems), education, and employment. These services can best be targeted by geographic area; the most urgent needs for improved services are in the Conos Norte, Este and Sur, where 74% of those in the poorest quintile reside. More precise identification of households qualifying as beneficiaries of direct transfer programs is difficult. While there are some physical characteristics more prevalent in poorer households, only a fraction of the poorest households display each characteristic. And while ownership of some goods is useful in distinguishing the poor from the non-poor, there is a great incentive to falsify ownership information once the criteria for program participation is known. Furthermore, in Peru as in many other countries, records of ownership of these goods are not well kept by the government. The most appropriate programs under these circumstances may be those which are self-targeting. For instance, in Lima a jobs program offering minimal wages will only attract those who are unemployed and willing to perform manual tasks. Given that the bulk of the unemployed are those from the poorest households, such a jobs program would be self-targeted to the most poor.

- 47 - VI. Conclusion

This paper has investigated the change in poverty levels in Lima, Peru between 1985-86 and 1990. The investigation is based on household level survey

data collected in those years. According to a comparison of the real value of household consumption in 1985-86 and 1990, the average household experienced a 55% drop in living standards in the intervening five year period. Poverty, defined as the inability to cover the household's basic nutritional requirements,

increased from 0.5% of the population in 1985-86 to 17.3% in 1990. During these

same years, the Peruvian government was experimenting with a policy of economic

reactivation in an attempt to encourage rapid growth and an improvement in living standards. While the specific links between the macroeconomic policies implemented between 1985 and 1990 and poverty are not explored in this paper, clearly, the hoped for improvement in living standards did not materialize and

in fact, the incidence of poverty in Lima increased dramatically. The two most important conclusions of this paper are as follows. First, for Lima as whole real consumption levels fell by slightly more than one half between 1985-86 and 1990, which is without doubt one of the worst economic performances in modern history. Second, the poorest 20% of the population, especially the poorest 10%, suffered the most, experiencing declines in consumption of 60% or more. Thus the incidence of poverty in this city increased

dramatically, and the distribution of consumption become more unequal. Further investigation provides a clearer picture of what happened between

1985 and 1990. First, households headed by individuals with relatively low levels of education experienced greater declines in consumption levels than the

better educated, which suggests that one possible reason that the poor suffered the most is because the returns to human capital were more stable at higher

levels of education. In terms of identifying the poorest households, the single

best indicator is the level of educational attainment of the head of household. This indicates that longer run social investment plans would do well to prioritize education. Second, unemployment rose significantly and became a

- 48 - distinct characteristic of the poor. Thus programs designed to increase employment opportunities will be automatically targeted to the poor. Third, the provision of public services, particularly potable water and sewage services, deteriorated across the city but most severely in the Conos Este, Norte and Sur.

These three areas were also the poorest in terms of per capita consumption levels, and experienced the greatest declines in average consumption levels between 1985-86 and 1990. In 1990, 74% of those in the poorest quintile lived within their boundaries. Thus programs targeted to these three areas, in particular investment in the construction and rehabilitation of public water and sanitation services, are recommended. Some further results stand out regarding possible schemes to target public assistance programs to the poor. Within the three Conos, the newly-settled pueblos jovenes ('New Lima') are not disproportionately populated by poorer families; the poor are found throughout the old and new regions of these three areas. In addition, households headed by women did not suffer greater declines in consumption levels than male-headed households between 1985-86 and 1990. The survey results thus indicate that targeting programs to either the new settlements or to female-headed households could be relatively ineffective methods of reaching the poorest segments of the population. It should be recalled that the survey results cover Lima alone, where only

30% of the population of Peru currently lives. No recent survey information is available for other areas of the country.8 From the 1985-86 survey we do know that only 3% of the poorest 10% of the population of Peru lived in Lima at that time, while 48% of these poor lived in the rural mountains. If it is true that households in rural areas are more self-sufficient, consuming what they themselves produce, then they may have been less vulnerable to the economic decline from 1985-86 to 1990. Consequently, the overall distribution of welfare in Peru may have become more equal between 1985-86 and 1990.

8 Survey work in rural areas began in September, 1991, with results scheduled for availability after January 1, 1992.

- 49 - Perhaps the most important lesson to be drawn from these conclusions is that great care must be taken in designing macro policies which address the twin problems of economic stagnation and poverty. "Unorthodox" alternatives to structural adjustment, while designed to encourage rapid growth, may not succeed in this endeavor, and may in fact hurt the poor more than the general population. Further, household surveys can provide a vehicle for assessing the changes in living standards which accompany particular macro policies, and for pointing out priorities for social investment as an integrated component of the adjustment process in the future.

- 50 - APPENDIX A: Survey Format, 1985-86 and 1990

The information collected in each section of the 1990 questionnaire, also collected in 1985-86, is as follows:

Section 1: Age, sex, marital status and relationship to head of household of all household members; Section 2: Physical characteristics of the dwelling and maintenance expenditures; Section 3: Education level attained and current educational expenditures for each member;

Section 4: Incidence of illness and current health care expenditures for each member; Section 5: Type(s) of employment and current earnings for each member age 6 and over;

Section 6: Place of birth, date of and reason for move to Lima for all members; Section 7: Type of enterprise owned and run by the household, current earnings and expenditures; Section 8: Household consumption of semi-durables, ownership of durables; Section 9: Household consumption of food; Section 10: Other income sources, transfer payments to and from the household;

Section 11: Household savings and credit, current liquidity holdings.

The three major alterations to the 1990 questionnaire are as follows:

1) The entire section on agricultural production was omitted in 1990 since the survey in that year covered only metropolitan Lima, where there is little agricultural activity; 2) In the household enterprise section, income and expenditure data were obtained for only the most significant enterprise in 1990, as opposed to the (up to) three enterprises covered in the 1985-86 questionnaire; this was done to reduce the amount of time required to conduct each interview. 3) The section covering income transfers to and from the household was reduced to a small number of questions in 1990, also for the purposes of brevity.

The most important data for assessing the welfare levels of households are obtained from sections 2, 3, 4, 8 and 9, which provide the data on expenditures.

The manner in which this assessment was done is explained in subsection B.

- 51 - APPENDIX B: Construction of Price Deflators

Peru experienced very high rates of inflation from 1985 to 1990. The two surveys discussed in this paper were conducted from July 1985 to July 1986 and from June to July 1990. During each of these data collection periods, the price level also rose substantially. This requires one to construct price indices to make comparisons of real purchasing power of households both across the two surveys and within each survey. Controlling for price increases within the 1985 survey was done by Glewwe (1988), the result being that all figures in that paper were given in terms of June 1985 Intis. For details see that paper. In this paper all figures, unless otherwise noted, are given in terms of thousands of May

26-June 1 Intis. This appendix explains two things: 1) the calculation of the price index which compares purchasing power in the month of June 1985 to that in the week of May 26-June 1, 1990,9 and 2) the method used to control for price inflation which occurred during the 1990 survey. Appendix E presents some evidence on whether alternative ways of measuring price inflation would significantly alter the findings of this paper.

Comparing the 1985-86 data with 1990 data

A price index with a base of June, 1985 was used to construct an "inflator" allowing the 1985-86 data, which was originally deflated to an average real value for the month of June, 1985, to be expressed in terms of June 1, 1990 Intis. The only inflation data for Lima available consistently throughout this period (June,

1985 - July, 1990) is the monthly rate of change in consumer prices. This information was used to derive a monthly index of prices in Lima from June 1, 1985 to June 1, 1990. Since the 1985-86 survey data is expressed as an averaae value for the month of June 1985, a geometric mean was taken of the index values for two periods: 1) June 1, 1985 to June 1, 1990, and 2) July 1, 1985 to June 1, 1990, which resulted in an inflator of 9,182,446. All expenditures from the

9In the text we refer to May 26-June 1, 1990 Intis as June 1, 1990 Intis.

- 52 - 1985-86 deflated survey data were multiplied by this value to derive a real value in terms of June 1, 1990 Intis. The monthly inflation data used was from Cuanto,

S.A. in Lima, Peru. Monthly inflation data was also available from the Instituto

Nacional de Estadistica (INE), and from Apoyo, S.A. The data from these two sources both resulted in slightly higher estimates of inflation between June 1985 and June 1990. Therefore the most moderate estimate of inflation, that provided by the data from Cuanto, S.A., was selected to avoid any overestimation of the drop in expenditure levels between 1985-86 and 1990 due simply to alternative methods of collecting price data.

The 1990 Survey Deflators

During the weeks in which the 1990 survey was conducted, the consumer prices in Lima rose daily at a rate ranging from 1 to 5 percent. The comparison of real expenditures across households required the construction of three deflators: 1) a fifteen day deflator, applied to all monetary values for household expenditures reported for the 15 days up to and including the interview date; 2) a monthly deflator, applied to monetary values referring to monthly expenditures (electricity and water bills, for example); and 3) a three month deflator, for expenditures on services and semi-durables made in the three months prior to the interview. These deflators allow all nominal expenditure values collected in the 1990 survey to be standardized according to an index where the price level on June 1, 1990, equals 1.00. The inflation data used in the calculation of these indices was collected by Cuanto, S.A., Lima, Peru.

The Fifteen Day Deflator

This deflator was applied to monetary values referring to total expenditures for food and other frequently purchased goods during the 2 weeks prior to (15 days up to and including) the interview. Daily inflation rates have only been calculated in Peru since June 30, 1990, whereas data collection began

- 53 - June 12, 1990. Therefore, weekly rates of inflation were used to construct a distinct deflator for each possible date of interview as follows.

A weekly price index (P) was calculated for 10 weeks, beginning two weeks before the first interview at week 1 (the week of May 26-June 1) and ending with the final week of interviews, week 10. The inflation rate on which this index is based measures the total change in the price level from Saturday to Friday.

Weekly Price Index 1

June 1 - August 3, 1990

Week Price Index Inflation Rate (%) (end of week) Week 1 (May 26-June 1, 1990) 1.00 Week 2 1.11 11.2 Week 3 1.21 8.9 Week 4 1.33 10.1 Week 5 1.52 14.1 Week 6 1.73 14.0 Week 7 1.93 11.5 Week 8 2.17 12.4 Week 9 2.50 15.1 Week 10 3.19 27.6

The 15 day deflator was then determined for each possible interview day, by taking a weighted average of price inflation on each of the 15 days prior to (up to and including) the interview date. If these days are denoted as (n), then the 15 day deflator can be expressed as:

15 day deflator = (P1 x n1) + (P2 x n2) + (P3 X n3) (n+ + n+ n3 = 15)

where (P1) represents the price index for week 1, (nl) represents the number of days in the fifteen day period prior to the interview which fall in week 1, and so on. Using the 15 day deflator, all monetary values are deflated to the price level prevailing on June 1, 1990.

- 54 - The Monthly Deflator

This deflator applies to monthly household expenditures such as gas and water payments. Each household reports the last monthly amount paid, and the month (M) in which the payment was made. The deflator corresponding to each month (M) is calculated based on an index of weekly inflation where the price level on June 1, 1990 equals 1.00. An average of this index is then calculated for the weeks falling in each calendar month, resulting in the following price index which is also the relevant deflator for each month:

Monthly Price Index

January - July, 1990

Month (1990) Price Index (P) Corresponding Weekly Price Indices

January .26 .24 .25 .26 .28 February .34 .30 .33 .35 .36 .39 March .46 .41 .45 .48 .50 April .62 .55 .62 .66 .68 May .87 .74 .82 .87 .92 1.00 June 1.29 1.11 1.21 1.33 1.52 July 2.08 1.73 1.93 2.17 2.50

The Three Month Deflator

This deflator standardizes monetary values corresponding to total expenditures for services and semi-durable goods purchased during the thirteen weeks up to and including the week of interview. Each respondent is asked to identify the calendar month in which the expenditure was made, or if the good was purchased several times, the month in which the bulk of the expenditure was made.

This month is referred to as (N) in the discussion that follows.

The deflator was again calculated based on weekly rates of inflation. A weekly price level index (P) was constructed for the period March 10-July 27, again setting the price level during the week of May 26-June 1 to 100:

- 55 - Weekly Price Index 2

March 16 - June 1, 1990

Week Price Index (P) Inflation Rate Week 1 (March 10-16, 1990) .45 Week 2 .48 5.3 Week 3 .50 5.9 Week 4 .55 9.8 Week 5 .62 12.8 Week 6 .66 5.2 Week 7 .68 3.3 Week 8 .74 8.5 Week 9 .82 11.9 Week 10 .87 5.2 Week 11 .92 5.7 Week 12 (May 26-June 1, 1990) 1.00 9.1 Week 13

Week 21 (weeks 13-21 take the same values as weeks 2-10, Weekly Price Index 1, representing June 1 - August 3, 1990)

For each good a household had purchased in the last three months a corresponding 3 month deflator was calculated, based on this price index, the week of interview, and the month (M) . The deflator is expressed as a weighted average of the price indices for each of the 13 weeks prior to and including the week of interview, assigning a total weight of .5 to the weeks falling in month (M) and a weight of .5 to the other weeks in the relevant 13 week period. Thus for a good purchased in June by a household interviewed in the week of June 9-15 (week

14), the 3 month deflator is calculated as follows:

3 month deflator = [13 +14 x (.5)] + P2 x ( 5)], 2 11 where (M) is June. The subscripts indicate the week corresponding to each price index (P). Note that the thirteen weeks up to and including the week of interview are weeks 2-14. Because the good was purchased in June, weeks 13 and 14 together receive a weight of (.5) in calculating the deflator. The weight (.5) assigned to the price indices corresponding to the weeks in month (M) was selected rather arbitrarily, but is probably a reasonable assumption. It accounts for the fact that (M) may refer to the month in which some but not all expenditures on the item in question took place. The impact of this assumption on the results was tested and proved to be minor; changing the

- 56 - weights even to the extremes of 1 for month (M) and 0 for the other weeks did not significantly change the results.

- 57 - APPENDIX C: Calculation of Household Consumption

As stated in Section III of the paper total household consumption consists of a) explicit expenditures (from Sections 2, 8 and 9 of the questionnaire), b) the estimated use value of durable goods, and c) imputed rent estimates based on housing characteristics. The value of explicit expenditures is easily calculated once appropriate price deflators are derived (cf. Appendix II), but calculation of the use value of durable goods and imputed rents is more involved. This appendix explains these calculations for the 1990 data; see Glewwe (1988) for details regarding the 1985-86 data. The calculation of the use value of durable goods is based on estimated rates of depreciation (in terms of value, not just physical condition) of each good. To estimate the depreciation rate, both the real value of the good when purchased and at the time of interview is required. Unfortunately, the former was not collected in the June, 1990, because respondents could not remember exact prices at the time of purchase due to the rapidly changing price level. Therefore, depreciation rates based on the 1985-86 data (see Glewwe, 1988) were used. The rate of depreciation was multiplied py the nominal value of each good

(at the time of the interview) as reported by the household. These values were then deflated according to the date of interview, applying the fifteen day deflator (cf. Appendix A) thus expressing the value of each durable good in terms of June 1, 1991, Intis.

Imputed rents were calculated as follows. All households which rented their dwellings reported the value of their rental payments, while households owning their homes reported the value at which they estimated their homes could be rented. Both of these values were deflated to June 1, 1991 Intis, according to the date of interview, again using the 15 day deflator. These two deflated variables were regressed on various characteristics of dwellings, such as the number of rooms, the materials used to construct the dwelling and the location within Lima. Standard procedures were used to control for sample selectivity in each regression but no significant selectivity was found. The regression in

- 58 - which the dependent variable was the rental value estimated by home owners fit the data better (in terms of the R-squared coefficient) and was thus used to assign a rental value to all the households in the sample. Since the dependent variable was deflated before the regression was estimated, no further deflation was needed.

- 59 - APPENDIX D

TableD: Changesin RealExpenditures in Lima,1985-86 to 1990,by ExpenditureDeciLe, Unadjusted

ALL Lima 1985-86 ALl Lima 1990 PercentChange Old Lima 1990 PercentChange 'DeciLe Food Total Food TotaL Food Total Food Total Food TotaL

1 868.6 1,524.8 360.3 578.6 -58.5 -62.0 354.8 582.2 -59.1 -61.8 (.568) (.623) (.609) 2 1,156.3 2,108.0 605.5 921.8 -47.6 -56.3 606.5 954.1 -47.5 -54.7 (.548) (.657) (.636) 3 1,406.0 2,622.6 732.4 1,180.9 -47.9 -55.0 733.2 1,246.8 -47.8 -52.5 (.536) (.620) (.588) 4 1,620.8 3,113.7 859.4 1,448.7 -47.0 -53.5 854.5 1,525.7 -47.3 -51.0 (.520) (.593) (.560) 5 1,930.1 3,700.5 957.8 1,702.3 -50.4 -54.0 989.3 1,789.3 -48.7 -51.6 (.522) (.563) (.553) 6 2,240.8 4,308.5 1,089.3 2,027.8 -51.2 -52.9 1,118.5 2,160.4 -50.1 -49.9 (.520) (.537) (.518) 7 2,529.5 5,155.5 1,354.0 2,448.6 -46.5 -52.5 1,339.0 2,603.8 -47.1 -49.5 (.491) (.553) (.514) 8 2,951.0 6,483.4 1,456.4 3,028.0 -50.6 -53.3 1,559.3 3,209.5 -47.2 -50.5 (.455) (.481) (.486) 9 3,593.3 8,559.5 2,015.8 4,021.5 -43.9 -53.0 2,050.6 4,267.6 -42.9 -50.1 (.420) (.501) (.480) 10 6,845.3 20,007.8 3,613.6 9,225.4 -47.2 -53.9 3,759.0 9,912.7 -45.1 -50.4 (.342) (.392) (.379)

ALL 2,510.7 5,747.5 1,303.7 2,656.4 -48.1 -53.8 1,334.6 2,819.8 -46.8 -50.9 Lima (.437) (.491) (.472)

Notes: ALL figuresare in thousandsof June 1, 1990Intis per capitaper month. On June 1, 1990$1 Intis 50,000. Figuresin parenthesesrepresent the shareof food in totaLexpenditures.

- 60 - APPENDIX E: Use of Household-Specific Price Deflators

Given that prices increased dramatically in Peru between 1985 and 1990, and particularly because the prices of different items rose by different amounts, one may question whether using the same price deflator for all households may affect the results. In particular, if the cost of living for poorer households rises by a different amount compared to that of wealthier households, should not different price deflators be used when examining declines in real expenditures by quintiles or deciles? Theoretically speaking, this objection is valid; one should use different deflators for different households when relative prices have changed. However, in practice this may or may not affect one's results. This appendix examines key results in the text to see whether they change when household-specific price deflators are used. In general, we find that there are some small changes but the general findings remain the same. As conventionally defined (cf. Deaton and Muellbauer, 1980), price deflators such as the familiar Paasche and Laspeyres indices can be different for different households only if the shares of expenditures on different items vary across households and the prices of these different items rise by different amounts. Perhaps the most obvious example of the former is that poorer households tend to spend a larger fraction of total expenditures on food, relative to wealthier households. This is also the case in Lima, Peru (cf. Table 3), and we also find that food prices rose by lower amounts than did non-food prices from 1985 to 1990.1O To see whether this change in relative prices brings about substantially different price indices for households at different expenditure levels, each household was given a specific price index defined as the weighted shares of the food and non-food price indices where the weights were each household's food and

loSetting food and non-food price indices equal to 1.0 for June, 1985, the food price index stood at 6,566.7 on June 1, 1990, compared to 12,068.5 for non- food prices. However, by the end of July, when the last interviews of the 1990 survey were taking place, the food index stood at 52,098.5, compared to 62,448.6 for the non-food index.

- 61 - Table El: Means of Household-SpecificDeftators, by ExpenditureQuintiLes

Quintile Deflator Relative Index

1 9,609.8 100.0

2 9,807.9 102.1

3 9,617.2 100.1

4 9,707.0 101.1

5 10,130.3 105.4

Notes: Deflator is indexed at June 1985 = 1.0. Relative index is normalized at the Quintile 1 deflator.

non-food budget shares." As can be seen from Table E.1, for households in the first four quintiles the average household-specific price deflators are quite similar, while that of the wealthiest quintile is about 5% higher than that of some of the other quintiles (because these households have, on average, the smallest food shares and non-food prices rose more than food prices). This suggests that using household specific deflators would have only a relatively small effect on the results presented in the text of the paper, but to cross- check this result we present some results comparable to those from Tables 3 and 7 in the text in Tables E.2 and E.3. Table E.2 shows the decline in real total expenditures by decile in "All Lima" from 1985-86 to 1990 (expenditure numbers are in 1985-86 Intis). As before, we find that the poorest decile experiences the steepest decline, and in fact if we group the households by quintiles (not shown here) we also find that the poorest quintile experiences the steepest declines. It is interesting to note that the wealthiest decile experiences the second-largest decline - this simply reflects the fact that these households have a higher price index because

"A more elaborate method which used price indices and budget shares for each of 61 items was also used. However, it became difficult to work with because although one has price indices on housing for renters (which in Lima rose at a very low relative rate between 1985 and 1990 due to rent control), it was not at all clear what price index to use for (imputed) expenditures on housing by home owners, who represent more than half of the population. This led us to simply aggregate all expenditures into food and non-food.

- 62 - TableE2: Declinesin Real TotalPer Capita(Adjusted) Expenditures in Lima,by Decile

DeciLe 1985-86Expenditures 1990 Expenditures PercentageDecline

1 246.0 114 -53.6 2 346.4 184 -46.9 3 414.8 227 -45.3 4 477.7 265 -44.5 5 562.4 308 -45.2 6 664.2 356 -46.4 7 776.3 415 -46.7 8 944.2 499 -47.1 9 1,247.1 653 -47.6 10 2,794.2 1,356 -51.5

Notes: Figuresare in June,1985, Intis. Figures refer to percapita real monthly household expenditures after adjustmentfor each household'scomposition of adultsand children.

TableE3: Declinesin RealTotal Per Capita(Adjusted) Expenditures, by Characteristicsof HouseholdHead

1985-86Expenditures 1990Expenditures PercentageDecline

Sex of Head Male 865 456 -47.3 Female 727 391 -46.2 Educationof Head None 467 253 -45.8 Primary 618 304 -50.8 SecondaryGeneral 778 413 -46.9 SecondaryTechnical 772 675 -12.6 University 1,646 854 -48.1 OtherPost-Secondary 831 588 -29.2 Emnloverof Head Government 1,032 541 -47.6 Private 828 430 -48.1 PrivateHouse 428 238 -44.4 Self-Employed 776 433 -44.2 Occuoationof Head Agriculture 700 429 -38.7 Sales/Services 820 408 -50.2 Industry/Craft 638 365 -42.8 White Collar 1,231 646 -47.5 Unemoloyed 882 362 -59.0 Retired 816 467 -42.8

Notes: Figuresare in June,1985, Intis. Figuresrefer to percapita real monthly household expenditures after adjustmentfor eachhousehold's composition of adultsand children.

- 63 - they have, on average, the highest non-food budget shares. Still, the major findings hold; all households experienced vast declines in real income and the poorest households were hurt the most.

Table E.3 examines the decline in expenditures using household-specific deflators when households are grouped according to head of household characteristics. Again, the results change very little. Specifically, a) female-headed households were no more vulnerable to declines in consumption than male-headed ones; b) households headed by someone with a relatively level of education suffered more than those with better educated heads;12 c) households in which the head was self-employed suffered somewhat less than those whose head was an employee; and d) households in which the head was unemployed suffered the largest declines in real expenditures.

12 Although this is no longer true for households in which the head had no education at all, this small group accounts for only 2.9% of all households in Lima in 1990.

- 64 - APPENDIX F: List of Geographic Areas

For the purposes of analysis, metropolitan Lima was divided into the following nine geographic areas. Each area is comprised of the districts as listed below. The number of households surveyed in each area, selected according to the population distribution of Lima in 1990, is noted in parentheses.13

1. Cono Sur (2031 including Chorrillos, San Juan de Miraflores, Villa Maria del Triunfo, Villa El Salvador, and Lurin 2. Cono Norte (266) including Puente Piedra, Carabayllo, Comas, Independencia, and San Martin de Porres 3. Cono Este (2231 including San Juan de Lurigancho, El Agostino, San Luis, Ate, and Lurigancho 4. Callao (140) including Callao, La Perla, Bellavista, Carmen de la Legua, and Ventanilla 5. Oeste (961 including San Miguel, Magdalena del Mar, Magdalena Vieja, and Brena

6. Centro 1 (40l including Jesus Maria and Lince 7. Centro 2 (1891 including La Victoria, Lima, and Rimac 8. Centro 3 (651 including Santiago de Surco, Barranco, and Surquillo 9. Estrato Alto (591 including San Isidro, San Borja, and Miraflores

"3Since the newly settled regions ('New Lima') were oversampled by a factor of two, each household in 'New Lima' receives a weight of (.5) when counting the total number of households in each area.

- 65 -

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Cuanto, S.A. 1990. "Costo Diario de una Canasta de Alimentos que Satisface Requerimientos Nutricionales Recomendados por el Instituto Nacional de Nutricion". Documento de Trabajo. Lima, Peru. (Mimeo).

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Instituto Nacional de Estadistica. April 1988. Encuesta Nacional de Hoaares Sobre Medicion de Niveles de Vida ENNIV (1985-1986): Analisis de Resultados. Direccion General de Censos y Encuestas. Lima, Peru.

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Kanbur, Ravi. 1987. "Measurement and Alleviation of Poverty with an Application to the Effects of Macroeconomic Adjustment". IMF Staff Pacers, 34(l):60- 85.

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Ravallion, Martin. 1991. "Reaching the Poor through Public Employment: Arguments, Evidence and Lessons from South Asia". The World Bank Research Observer, volume 6, no. 2, July. World Bank, Washington, DC. Taylor, Lance. 1988. Varieties of Stabilization Experience: Towards Sensible Macroeconomics in the Third World. Clarendon Press. Oxford. Thorp, Rosemary. 1987. "Politica Economica y Planificacion de Largo Plazo en el Modelo Heterodoxo". Instituto Nacional de Planificacion. Lima, Peru (Mimeo).

United Nations. 1980-88. Estudio Economico de America Latina v el Caribe. New York. Webb, Richard and Graciela Fernandez Baca. 1990. Peru en Numeros 1990. Cuanto S.A. Lima, Peru. World Bank. 1990. World Development ReDort. Oxford University Press. New York.

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No. 51 ChildAnthropometry in Coted'Ivoire: Estimates from Two Surveys, 1985and 1986 No. 52 Public-PrivateSector Wage Comparisons and Moonlightingin DevelopingCountries: Evidence from C6ted'Ivoire and Peru No. 53 SocioeconomicDeterminants of Fertilityin Cote d'Ivoire No. 54 The Willingnessto Payfor Educationin DevelopingCountries: Evidence from Rural Peru No. 55 Rigiditedes salaires: Donnees micro6conomiques et macroeconomiquessur l'ajustementdu march6 du travaildans lesecteur moderne (in French only) No. 56 The Poor in Latin Americaduring Adjustment:A CaseStudy of Peru No. 57 The Substitutabilityof Publicand PrivateHealth Care for the Treatmentof Childrenin Pakhstan No. 58 Identifyingthe Poor:Is "Headship"a UsefulConcept? No. 59 LaborMarket Perfonnanceas a Detenninantof Migration No. 60 The RelativeEffectiveness of Privateand PublicSchools: Evidence from Two DevelopingCountries No. 61 LargeSample Distribution of SeveralInequality Measures: With Applicationto C6te d'Ivoire No. 62 Testingfor Significanceof PovertyDifferences: With Applicationto C6ted'lvoire No. 63 Povertyand EconomicGrowth: With Applicationto Cate d'Ivoire No. 64 Educationand Earningsin Peru'sInformal Nonfarm Family Enterprises No. 65 Formaland InformalSector Wage Determination in UrbanLow-Income Neighborhoods in Pakistan No. 66 Testingfor LaborMarket Duality:The PrivateWage Sector in Coted'Ivoire No. 67 DoesEducation Pay in the LaborMarket? The LaborForce Participation, Occupation, and Earnings of Peruvian Women No. 68 The Compositionand Distributionof Incomein C6ted'Ivoire No. 69 PriceElasticities from Survey Data:Extensions and IndonesianResults No. 70 EfficientAllocation of Transfersto the Poor:The Problemof UnobservedHousehold Income No. 71 Investigatingthe Determinants of HouseholdWelfare in C8ted'Ivoire No. 72 The Selectivityof Fertilityand the Determinantsof Human CapitalInvestments: Parametric and SemiparametricEstimates No. 73 ShadowWages and PeasantFamily Labor Supply: An EconometricApplication to the PeruvianSierra No. 74 The Action of Human Resourcesand Povertyon One Another:What We Have Yet to Learn No. 75 The Distributionof Welfarein Ghana,1987-88 No. 76 Schooling,Skills, and the Returns to GovernmentInvestment in Education:An ExplorationUsing Datafrom Ghana No. 77 Workers'Benefits from Bolivia'sEmergency Social Fund No. 78 Dual SelectionCriteria with Multiple Alternatives: Migration,Work Status, and Wages No. 79 GenderDifferences in HouseholdResource Allocations No. 80 The HouseholdSurvey as a Toolfor PolicyChange: Lessonsfrom the JamaicanSurvey of Living Conditions No. 81 Patternsof Aging in Thailandand Coted'Ivoire No. 82 DoesUndernutrition Respond to Incomesand Prices? Dominance Tests for Indonesia No. 83 Growthand RedistributionComponents of Changesin PovertyMeasure: A Decompositionwith Applicationsto Braziland Indiain the 1980s No. 84 MeasuringIncomefrom Family Enterprises with HouseholdSurveys No. 85 DemandAnalysis and Tax Reformin Pakistan The MForldBank

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