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Living StandardsMeasurement StudyWorking Paper No. 65 FILE COPY

Formal and Informal Sector Wage Determination in UrbanLow-Income Neighborhoods in Pakistan

Harold AldermanValerie Kozel

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LSMS Working Papers

No. 1 Living Standards Surveys in Developing Countries

No. 2 Poverty and Living Standards in Asia: An Overview of the Main Results and Lessons of SelectedHousehold Surveys

No. 3 Measuring Levels of Living in Latin America: An Overview of Main Problems

No. 4 Towards More Effective Measurement of Levels of Living, and Review of Work of the United NationsStatistical Office (UNSO) Related to Statistics of Levels of Living

No. 5 Conducting Surveys in Developing Countries: Practical Problems and Experience in Brazil, Malaysia,and the Philippines

No. 6 Household Survey Experience in Africa

No. 7 Measurement of Welfare: Theory and Practical Guidelines

No. 8 Employment Data for the Measurement of Living Standards

No. 9 Income and Expenditure Surveys in Developing Countries: Sample Design and Execution

No. 10 Reflections on the LSMS Group Meeting

No. 11 Three Essays on a Sri Lanka Household Survey

No. 12 The ECIEL Study of Household Income and Consumption in Urban Latin America: An AnalyticalHistory

No. 13 Nutrition and Health Status Indicators: Suggestions for Surveys of the Standard of Living in DevelopingCountries

No. 14 Child Schooling and the Measurement of Living Standards

No. 15 Measuring Health as a Component of Living Standards

No. 16 Procedures for Collecting and Analyzing Mortality Data in LSMS

No. 17 The Labor Market and Social Accounting: A Framework of Data Presentation

No. 18 Time Use Data and the Living Standards Measurement Study

No. 19 The Conceptual Basis of Measures of Household Welfare and Their Implied Survey Data Requirements

No. 20 Statistical Experimentation for Household Surveys: Two Case Studies of Hong Kong

No. 21 The Collection of Price Data for the Measurement of Living Standards

No. 22 Household Expenditure Surveys: Some Methodological Issues

No. 23 Collecting Panel Data in Developing Countries: Does it Make Sense?

No. 24 Measuring and Analyzing Levels of Living in Developing Countries: An Annotated Questionnaire

No. 25 The Demand for Urban Housing in the Ivory Coast

No. 26 The Cote d'Ivoire Living Standards Survey: Design and Implementation

No. 27 The Role of Employment and Earnings in Analyzing Levels of Living: A General Methodology withApplications to Malaysia and Thailand

No. 28 Analysis of Household Expenditures

No. 29 The Distribution of Welfare in C6te d'lvoire in 1985

No. 30 Quality, Quantity, and Spatial Variation of Price: Estimating Price Elasticities from Cross-sectional Data

No. 31 Financing the Health Sector in Peru

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

No. 33 Wage Determinants in C6te d'Ivoire

No. 34 Guidelines for Adapting the LSMS Living Standards Questionnaires to Local Conditions

(List continues on the inside back cover)

Formal and Informal Sector WageDetermination in Urban Low-Income

Neighborhoods in Pakistan

The Living Standards Measurement Study

The Living Standards Measurement Study (LSMS) was established by the WorldBank in 1980 to explore ways of improving the type and quality of household datacollected by statistical offices in developing countries. Its goal is to foster increaseduse of household data as a basis for policy decisionmaking. Specifically, the LSMSis working to develop new methods to monitor progress in raising levels of living,to identify the consequences for households of past and proposed governmentpolicies, and to improve communications between survey statisticians, analysts,and policymakers.

The LSMS Working Paper series was started to disseminate intermediate productsfrom the LSMS. Publications in the series include critical surveys covering differentaspects of the LSMS data collection program and reports on improvedmethodologies for using Living Standards Survey (LSS) data. More recentpublications recommend specific survey, questionnaire, and data processingdesigns, and demonstrate the breadth of policy analysis that can be carried out usingLSS data.

LSMS Working PaperNumber 65

Formal and Informal Sector WageDetermination in Urban Low-Income

Neighborhoods in Pakistan

Harold AldermanValerie Kozel

The World BankWashington, D.C.

Copyright © 1989The International Bank for Reconstructionand Development/THE WORLD BANK1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

All rights reservedManufactured in the United States of AmericaFirst printing September 1989

This is a working paper published informally by the World Bank. To present the results ofresearch with the least possible delay, the typescript has not been prepared in accordancewith the procedures appropriate to formal printed texts, and the World Bank accepts noresponsibility for errors.

The findings, interpretations, and conclusions expressed in this paper are entirely those ofthe author(s) and should not be attributed in any manner to the World Bank, to its affiliatedorganizations, or to members of its Board of Executive Directors or the countries theyrepresent. Any maps that accompany the text have been prepared solely for the convenienceof readers; the designations and presentation of material in them do not imply theexpression of any opinion whatsoever on the part of the World Bank, its affiliates, or itsBoard or member countries concerning the legal status of any country, territory, city, or areaor of the authorities thereof or concerning the delimitation of its boundaries or its nationalaffiliation.

The material in this publication is copyrighted. Requests for permission to reproduceportions of it should be sent to Director, Publications Department, at the address shown inthe copyright notice above. The World Bank encourages dissemination of its work and willnormally give permission promptly and, when the reproduction is for noncommercialpurposes, without asking a fee. Permission to photocopy portions for classroom use is notrequired, 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 ofPublications, which contains an alphabetical title list and indexes of subjects, authors, andcountries and regions; it is of value principally to libraries and institutional purchasers.The latest edition is available free of charge from the Publications Sales Unit, 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'1ena, 75116 Paris, France.

Harold Alderman is an economist at the International Food Policy Research Institute,Washington, D.C., and a consultant to the Welfare and Human Resources Division of theWorld Bank's Population and Human Resources Department. Valerie Kozel is an economistin the same department of the Bank.

Library of Congress Cataloging-in-Publication Data

Alderman, Harold, 1948-Formal and informal sector wage determination in urban low-income

neighborhoods in Pakistan / Harold Alderman, Valerie Kozel.p. cm. -- (LSMS working paper, ISSN 0253-4517 ; no.65)

Includes bibliographical references.ISBN 0-8213-1308-81. Wages--Pakistan. I. Kozel, Valerie, 1952- . II. Title.

III. Series.HD5070.5.A75 1989331.2'15'095492--dc20 89-22493

CIP

ABSTRACT

The paper examines the structure of urban labor markets in Pakistan,

focusing in particular on wage formation and returns to education for male

workers. Empirical work is based on a survey of 1,000 low-income urban

households administered in 1986 by the International Food Policy Research

Institute (IFPRI) and the Pakistan Institute of Development Economics

(PIDE). We identify three distinct segments of the labor market: the self-

employed, wage workers in the informal sector, and wage workers in the formal

sector, 85 percent of whom are government workers. An intuitively plausible

picture of individual job search and labor market participation decisions is

obtained using a nested multinomial logit model. The results of this model

are then used to estimate selectivity-corrected wage equations for the two

wage sectors -- formal and informal -- using a modification of an approach

described in Lee (1983) and Trost and Lee (1984).

The study found that the difference in average wages between the

formal and informal sector is primarily caused by differences in skills

(measured in terms of work experience and education), although there is an

appreciable premium on wages in the formal sector, particularly for college

educated men. A smaller although still significant premium was found for less

educated men. Further, we identified two distinct categories of economically

inactive individuals -- young, more educated men apparently waiting to find a

job in the formal sector rather than accepting less desirable employment in

the informal sector, and older, less qualified men who tend to live in

wealthier extended families receiving substantial remittances from within

Pakistan and abroad. In the latter case, unearned income helps these

individuals to remain outside the labor force. To be effective, employment

policy must address the very different kinds of constraints faced by

individuals in each of the categories.

- vii -

TABLE OF CONTENTS

Introduction .......... * ..**................. ........................l

A Polychotomous Model of Wages ....................................*..*....*..*....**. 3

Data and Context ..................................................9

Empirical Results.......-.............. .- o .... ...... 13

Labor Force Participation Equations ... ........ e.... 14Wage Equations. ........ ... . ..... ............ .e.21

Conclusions .. ..9-9 .....9...9..9...........99 ..99.....9 9......... . 25

References ....... .. ......... .. .......... 27

LIST OF TABLES

Table 1: Employment Activities, Weekly Hours Worked, and Wages ofIndividuals not enrolled in School, Males by Age........ 10

Table 2: Variable Means for Labor Supply Equations. .................12

Table 3: Labor Force Participation Models; Urban Pakistani Males,25 Years and Older.. .. ..... ..... 15

Table 4: Labor Force Participation Models; Urban Pakistani Males,10-25 Years Old..... .. . .. ..-. ... ...0.19

Table 5: Selectivity Corrected Wage Equations, Regulated andUnregulated Sectors. ...... o ............... o ..... 22

Table 6: Representative Wages Predicted in the Wage Equations.......24

INTRODUCTION

The analysis of wage formation and labor supply decisions must

confront the fact that labor markets are often not homogeneous. This may be

particularly true for developing countries in which a formal sector--often

regulated and unionized--exists in parallel to an informal wage-earning sector

as well as to a large group of self-employed traders and artisans. The

existence of qualitatively different sectors is not, however, in itself

evidence that skills are rewarded differently in different sectors; these

skills may themselves determine where employment will be. Nor are there

necessarily any a priori expectations about what direction wage differentials

may take if they occur. While nonmarket forces in the formal sector may raise

wages relative to informal sector, it may also be the case that the

government--which dominates the formal sector--pursues an equity-oriented wage

policy that compresses wages. Hence, Lindauer and Sabot (1983) found a

substantial wage premium for government employees in Tanzania while Corbo and

Stelcner (1983) found wages slightly higher in the private sector in Chile.

Regardless of the direction of the difference, its very existence

poses a problem. It is logical to expect workers to prefer the sector with

higher wages and some selection criteria--either by the employer based on

worker characteristics or employees based on their preferences and long-term

strategies--is likely operating. As recently reported by van der Gaag and

Vijverberg (1988), failure to account for the selection process within a

segmented market can result in misleading conclusions about the sign as well

as the magnitude of wage differences.

-2-

The study examines the structure of urban labor markets in Pakistan,

focusing in particular on wage formation and returns to education. Empirical

work is based on a survey of 1,000 low-income urban households administered in

1986. We identify three distinct segments of the labor market: the self-

employed, wage workers in the informal sector, and wage workers in the formal

sector (85 percent of whom are government workers). An intuitively plausible

picture of individual job search and labor market participation decisions is

obtained using a nested multinomial logit model. The results of this model

are then used to estimate selectivity-corrected wage equations for the two

wage sectors--formal and informal--using a modification of an approach

described in Lee (1983) and Trost and Lee (1984).

The underlying participation equations are in themselves of interest

because Pakistan has a relatively large proportion of economically inactive

males. It is important to determine whether these individuals represent the

educated unemployed with high reservation wages anticipating a formal sector

job, or unskilled workers unable (or unwilling) to enter the job market at any

level. Our results suggest that both explanations may be relevant for

different segments of the potential workforce--older inactive men tend to have

few skills while younger aged nonparticipants include a relatively high

proportion of skilled individuals.

The paper first describes the methodology used for model estimation,

and briefly overviews the urban employment situation in Pakistan. It then

presents empirical results and summarizes the study findings.

A POLYCHOTOMOUS MODEL OF WAGES

We use the familiar post-school human capital earnings function for

the basic wage model. The theoretical basis for the approach was initially

discussed in Becker (1965) and Mincer (1974). Differences in wage rates (W)

are assumed to depend on differences in human capital, measured in terms of

schooling completed (S) and age (A). Thus, ignoring sectoral wage

differences, for the moment, and dropping individual subscripts:

2

wi =So + s i + 82 Ai + 33Ai + Vi (1)

Such wage equations may suffer from selectivity biases for a variety

of reasons cited in the literature. A method proposed by Lee (1983; see also

Trost and Lee 1984) to correct for selectivity bias in a polychotomous

(multiple) choice situation is used in the analysis.

In such a model, information about the choice of labor force

participation used to correct for potential bias in the wage equation.

Moreover, the choice model is inherently of interest as it is a theoretically

consistent model of economic activity and provides insight about employment

choices. In the model used here, individuals are assumed to choose among

I (i = 1, ... , I), mutually exclusive and collectively exhaustive sectors of

economic activity,!' which include (1) working in the formal wage sector, (2)

working in the informal wage sector, (3) self-employment, and (4) not working

1/ For present purposes, the possibility of working concurrently in more thanone sector is not allowed for. This restriction is not unreasonable forurban Pakistan, as very few respondents in the survey report multiple jobholdings.

- 4 -

at all. In keeping with previous work (see, for example, Hill 1983; Trost and

Lee 1984; Khandker 1987), individual preferences are described by a well-

behaved utility function (U) whose arguments include leisure time (1), a

Hicksian composite commodity (C), and a vector of exogenous variables

affecting employment choices (R).

U = U(l, C; R) (2)

The function is maximized subject to time and income constraints.

The likelihood of being employed is a function of the market wage and

the individual's demand for leisure; if the wage offer (W.ii) in any sector i

for individual j exceeds the individual's shadow wage rate (Wrj), he or she

will participate in the labor market. Further, he or she will choose to work

in that labor market segment that yields the highest utility, where the

utility function is defined in terms of the market wage (W. ), exogenous or

nonwage income (Y.), the endowment of time (T.), and other exogenous variables

specific to the individual (Ri).

Vij = V(wip, Yj, Tj; R.) (3)

Yij is assumed to be a linear in parameters indirect utility function. It can

be decomposed into stochastic and nonstochastic elements, e.g.:

vij = 1i xij + 1iJ

where iXij represents the known or nonstochastic component for market sector

i and n.. is the (stochastic) disturbance term.:LJ

Following McFadden (1973, 1976, 1981) individuals are assumed to

choose outcome i from a set of K alternatives (k = formal wage, informal wage,

self-employed, inactive) if the utility associated with outcome i is higher

than for any other outcome. Thus, i is chosen if

Vi > Max Vk (k = 1, ... , K, i # k) (5)

Using the stochastic specification in (4),

Pi = Pr (Vi > Vk I k = 1, ... , K, i # k) (6)

= Pr (a.Xij . ni > 3kxkj + nkj ;

for all k = 1, ... , K, i # k)

= Pr (.i Xi j BkXkXk > nkj - lij )

If the ni's are distributed IID Gumbel, the difference in the stochastic

portions of the utility function (n k - iij ) has a logistic distribution and

model is multinomial logit. The choice probabilities can be computed in

closed form as:

exp (X.)

iji exp (8kXkj) (7)

k=1, ... , K

The model specified in (7) must conform to the independence from

irrelevant alternatives (IIA) property. Under IIA, the ratio of the

probability of choosing some alternative i to the probability of choosing k is

independent of the set of alternatives in the model. The condition is violated

if there is correlation between some of the unobserved or random components of

the indirect utility functions (for example, the nik's are not independent).

In such a case, alternatives can be partitioned into subsets within which IIA

holds, and a nested multinomial or GEV-type model used to account for between

subset IIA violations (McFadden, 1981; Amemiya 1981; Train 1986). It is

important to note that how one actually partitions the alternatives is deter-

mined by expected correlations within the error structure of the model, and

does not necessarily reflect an actual sequencing or hierarchy of decisions.

For example, we primarily considered two types of sequencing or nesting:

inactive a ctive in/ < ~~~aor force

(1)1

self- informal formalemployed wage wage

sector sector

(2)

informal formalj -- ~~~wage wage

sector sector

inactive self-employed

The first reflects one notion of an appropriate decision hierarchy--

individuals first decide whether to work, and then decide on the employment

sector. The second reflects another predicated on possible error term

correlations; excluded effects in discriminating between inactive individuals

may be are correlated with excluded effects for the self-employed,

particularly those self-employed individuals who work few hours in very

marginal activities. Note that the limiting case is a simple, non-nested,

four-way logit model.

The GEV model can be estimated sequentially or at a single pass using

full-information maximum likelihood. The former was chosen on the basis of

computational tractability, despite possible efficiency losses (Small and

Brownstone 1982). The model was estimated in two steps--a conditional logit

model is used to parametize the indirect utility function(s) for the lower

nest, and these results then used by means of an "inclusive value," for

example:

loge el$s/ )Xs (8)

to estimate the correct marginal probabilities in the upper-level nest. Note

that X is a scale parameter reflecting the degree of independence (thus

1 - X is a measure of correlation) between alternatives within a subset of

alternatives. To be consistent with random utility theory, X should lie in

the unit interval. If X = 1, the model collapses into the standard

multinomial logit model. A measure greater than one suggests specification

error or inappropriate decision sequencing (Ben-Akiva 1973, Hensher 1986).

The parameters from the sector choice model are used to estimate

selectivity-corrected wage equations for formal and informal sector employees

by means of an approach described in Lee (1983) and Trost and Lee (1984).

According to this method, the selectivity corrected wage equations are

estimated in two stages by first estimating the multiple-outcome labor

-8-

participation model and then using these results to compute a correction

factor to be used in estimating the OLS wage equations. The wage equation is

defined as:

Wi Yi Yi'Z + (a [3iX /J F (8:X) + (9)

where

=i si a ( [JifXI] / F[$8XJ - [,{i:X}J / F[B:X] ) (10)

Note that * is the standard normal density function, ai is the standard

deviation of pi (from [1]), and Pi is the correlation between1~~~~~~-

,pi and J (ni) (from L4]), where J = 0 F. Trost and Lee describe this

procedure for non-nested logit models, but it is easily applied to nested

models as well.

- 9 -

DATA AND CONTEXT

In most regions, the vast majority of adult males who are not

enrolled in school are in the labor force.21 In Pakistan, however, the male

labor force participation rate is surprisingly low, particularly among younger

age groups; according to the 1981 Population Census, some 17.7 percent of

urban males between the ages of 15 and 24 years were neither enrolled in

school nor in the labor force, and 12.6 percent of the men between the ages of

25 and 54 were likewise inactive. These numbers are substantiated by recent

Household Income and Expenditure Surveys (e.g. 1979 and 1984) and in the data

used for this study, a survey of 1,000 urban households residing in low-income

neighborhoods throughout Pakistan. The survey was conducted by the

International Food Policy Research Institute (IFPRI) and the Pakistan

Institute of Development Economics (PIDE) in 1986.31

As indicated in Table 1, the proportion of economically inactive men

in the sample is particularly high among younger age groups (10-14 years or

15-19 years of age, not presently enrolled in school). However, a surprising

proportion of men of "prime" working age are likewise inactive--some 11.4

percent of men between the ages of 20 and 54 are not in the labor force. With

the exception of the oldest group, whose members are past the mandatory

retirement age for many jobs, workers are equally as likely to be self-

employed as working for wages. The majority of wage employees are found in

21 For example, Lipton (1983) estimates that 95 percent of males between 15and 55 years of age are economically active.

3/ See Alderman et al. (1988) for details.

TABLE 1: Employment Activities, Weekly Hours Worked, and Wages of Individuals not enrolled in School, Males, by Age

Wage EmployeesSelf-Employed Informal Sector Formal Sector

Percent Average Percent Average Average Percent Average AverageNo, of Percent Self- Hours Earning Hours Wage Earning Hours Wage

Age Cases Inactive Employed Worked/Week Wages Worked/Week (Rs/Hour) Wages Worked/Week (Rs/Hour)

10-14 years 163 55.2 18.4 46.2 26.4 56.9 1.13 ...

15-19 years 276 32.9 35.1 44.1 29.3 53.1 2.67 2.6 45.1 2.83

20-24 years 349 15.8 40.1 46.3 32.6 55.9 3.82 11.5 47.7 6.45

25-34 years 493 9.7 44.6 51.2 31.6 56.5 4.08 14.0 50.3 6.20 i'

35-44 years 313 11.5 42.2 48.4 29.1 54.8 5.72 17.2 50.4 6.69

45-54 years 289 9.0 47.4 51.8 28.0 55.4 4.96 15.6 47.1 8.16

55-64 years 182 22.5 44.0 46.2 24.7 53.9 4.83 8.8 51.1 6.05

> 65 years 189 58.2 32.3 46.5 9.5 50.3 2.95 ... .....

Total 2,254 22.0 39.8 48.4 27.8 55.2 3.95 10.4 49.2 6.60

Source: IFPRI-PIDE Urban Survey, 1986.

the informal sector. Note that average wages peak somewhat earlier in the

informal sector and are also lower than in the formal sector,4/ and wage

workers in the informal sector typically work longer hours than those in the

formal sector.

Table 2 provides further information on the sample. Two clarifi-

cations are useful. First, the average household size for the sample is not

the average household size in urban Pakistan; the sample is of individuals

and, logically, there are more individuals from large households included in

the regressions. Second, the education level of the younger men in the sample

does not reflect country-wide averages in that age bracket: our study does

not include individuals currently in school, all of whom have at least a

primary and often middle school and secondary education.

4I The distinction between formal and informal sector used here is whether ornot the individuals contribute to the Employees' Provident Fund, amandatory savings program for regulated industries and governmentemployees. Eighty-six percent of formal sector employees are governmentworkers.

- 12 -

TABLE 2: Variable Means for Labor Supply Equations

Variable MeansVariable 10-25 Years Over 25 Years

Age in years 19.0 43.8

EducationNone 0.501 0.527Primary 0.213 0.166Middle 0.139 0.112Secondary 0.125 0.136University 0.022 0.059

Household demographicsHousehold sizea 9.80 9.17Head of household? 0.033 0.609Child of head? 0.786 0.276

Household and individual incomeNonlabor income (Rs/year) 2109.2 3277.4Receive remittances from abroad? 0.050 0.080Receive remittances from Pakistan? 0.088 0.109Nonlabor income net of remittances (Rs/year) 763.0 1089.9Wage rate (Rs/hour) 3.47 5.29Hours worked per week 36.1 42.3

Source: IFPRI-PIDE Urban Survey, 1986.

a Mean household size of individuals currently in the sample.

- 13 -

EMPIRICAL RESULTS

For purposes of estimation, the sample was partitioned by age into

two groups: (1) males, 10-25 years old, not presently enrolled in school; and

(ii) males, 25 years and older. The partitioning allows for age-related

differences in basic determinants of labor force participation (where we do

expect differences) and wages (where we do not necessarily expect

differences). Consider, for example, labor force participation: in general,

one expects to find a positive relationship between wages (and thereby

education) and work participation. However, well-educated younger men may

have to wait for some period of time to obtain a "good" job, and thus be

unemployed despite high levels of education. In contrast, well-educated older

men are less likely to be outside the labor force. In the first case, one is

likely to find a negative relationship between work participation and

education, while in the second, a positive relationship.

The results which are the principal concern of this study--the impact

of education on wages and the differences in wages across sectors--are

presented in Tables 5 and 6. Before discussing the wage equations, however,

it is useful to discuss the underlying selection process. Tables 3 and 4 show

nested and non-nested labor force participation models for adult men and young

men, respectively. In both cases, estimation results indicate that nesting is

appropriate: the coefficient for the inclusive value in the upper-level nest

lies between 0 and 1, and is significantly different than 1. Furthermore, the

sequencing structure studied was found superior to alternative structures.5/

5/ These tended to yield inclusive value coefficients outside the unitinterval. This indicates inappropriate sequencing.

- 14 -

A plausible interpretation of these results is that the errors in the

estimates of the decision to be self-employed or inactive are correlated.

This rejects IIA and can be interpreted as implying that individuals perceive

similarities between self-employment and unemployment that do not hold between

these sectors and the wage sectors.

Labor Force Participation Equations

Table 3 presents results of logit equations for labor participation

of adult males. The reported coefficients represent the difference in

probability of being in a particular labor category relative to the inactive

category. Of particular note in these equations is the impact of household

demographics, the strong but variable impacts of educational achievement, and

the importance of exogenous (or nonlabor) income. Regarding demographics:

age and gender composition tends to be negatively (but generally not signif-

icantly) correlated with labor force participation. Effects are strongest for

adult males--the greater the number of adult men living in the household, the

lower the probability that any particular man is in the labor force. Indivi-

duals who are not part of the direct household lineage--neither the household

head nor offspring of the head--are also less likely to be employed. These

cousins, siblings, and other relatives that make up the extended family

network are apparently benefiting from an internal support network, but the

direction of causality from unemployment to residence in the non-natal family

or vice-versa cannot be directly inferred from the cross-sectional data.

Regarding education and exogenous income effects: own education

(particularly at secondary and university levels) has a strong and significant

- 15 -

TABLE 3: Labor Force Participation Models; Urban Pakistani Males, 25 Years and Older

Base Model - Not Nested (Base = inactive, N = 261)Self-Employed Informal Wage Formal Wage

Variables (N=630) Sector (N=389) Sector (N=186)

Older than 55 years? -1.646 (8.5) -2.082 (8.9) -2.508 (7.0)

Education (base = none)Primary 0.326 (1.4) 0.496 (1.9) 0.997 (3.0)

Middle 0.311 (1.1) 0.501 (1.6) 1.248 (3.3)

Secondary school 0.300 (0.9) 0.601 (1.9) 2.196 (6.2)

College or university 0.111 (0.2) 0.634 (1.2) 3.222 (6.4)

Household demographics

Head of household? 1.192 (4.9) 1.066 (3.8) 1.118 (3.1)

Child of head? 1.345 (4.9) 1.353 (4.4) 0.559 (1.5)

No. females, 7-14 years -0.099 (1.4) -0.239 (2.8) -0.129 (1.3)

No. males, 7-14 years -0.005 (0.1) -0.004 (0.1) -0.154 (1.5)

No. adult females, 15+ years -0.029 (0.5) -0.010 (0.2) -0.057 (0.7)

No. adult males, 15+ years -0.135 (2.5) -0.215 (3.5) -0.085 (1.1)

Nonlabor income, excluding remittances

(in Rs/year) -0.000023 (1.3) -0.000068 (2.4) -0.000028 (1.1)

Remittance income (in Rs/year)

Revenue from abroad? -0.488 (1.7) -0.749 (2.3) -0.707 (1.8)

How much? ... ...

Receive from within Pakistan? -0.566 (2.1) -0.132 (0.5) -0.060 (0.2)

How much? ... ...

Location of householdLive in Punjab? -0.313 (1.8) -0.724 (3.9) 0.619 (2.7)

Inclusive value (x) ... ...

Intercept 1.252 (4.0) 1.353 (3.) -0.323 (0.7)

Model statisticsNumber of observations 1,466

Percent in each categoryInactive 17.8

Self-employed 43.0Unregulated wage sector 26.5Regulated wage sector 12.7

Log-likelihood value

Restricted (slopes = 0) -1882.6

Convergence -1649.0

x2 statistic 467.1

(continued)

- 16 -

Table 3 (continued)

aNested Model (Base = inactive, N = 261)

Lower Nest Upper Nest

Self-Employed Informal Wage Formal WageVariables (N=630) Sector (N=389) Sector (N=186)

Older than 55 years? -1.636 (8.4) -1.407 (5.7) -1.847 (5.1)

Education (base = none)Primary 0.347 (1.4) 0.384 (2.1) 0.661 (2.4)

Middle 0.456 (1.6) 0.440 (2.1) 0.924 (3.1)

Secondary school 0.193 (0.6) 0.467 (2.3) 1.836 (7.6)

College or university 0.157 (0.3) 0.625 (2.8) 3.004 (9.5)

Household demographics

Head of household? 1.175 (4.7) 0.880 (2.9) -0.124 (0.4)

Child of head? 1.431 (5.0) 1.061 (3.2) -0.484 (1.4)

No. females, 7-14 years -0.087 (1.1) -0.198 (3.1) -0.107 (1.3)

No. males, 7-14 years -0.020 (0.3) -0.008 (0.1) -0.187 (2.2)

No. adult females, 15+ years -0.051 (0.9) 0.002 (0.1) -0.079 (1.1)

No. adult males, 15+ years -0.151 (2.8) -0.129 (3.0) -0.131 (2.2)

Nonlabor incorme, excluding remittances

(in Rs/year) -0.000032 (1.7) -0.000059 (2.4) -0.000023 (1.0)

Remittance income (in Rs/year)Revenue from abroad? -0.488 (1.7) ... ...

How much? ... -0.000021 (1.7) -0.000000 (0.0)Receive from within Pakistan? -0.682 (2.5) ... ...

How much? ... -0.000017 (0.5) -0.000006 (0.2)

Location of household

Live in Punjab? -0.363 (2.1) -0.545 (4.3) -0.601 (3.5)

Inclusive value (x) ... 0.403 (2.9)Intercept 1.406 (4.4) ... .-

Model statistics

Number of observations 891 1,466

Percent in each category

Inactive 29.3 1 50.8Self-employed 70.7

Unregulated wage sector ... 26.5Regulated wage sector ... 12.7

Log-likelihood value

Restricted (slopes = 0) -538.8 -1343.8

Convergence -444.7 -1209.7

x2 statistic 188.4 268.2

Source: IFPRI-PIDE urban survey, 1986.

a Estimated sequence (2), p. 6:

per nest)

informal formal

(lower nest) wage wagesector sector

inactive self-

employed

- 17 -

effect on the likelihood of working in the formal wage sector, and a weaker,

although still generally significant, effect on the likelihood of working in

the informal wage sector. In short, adult men who are either inactive or

self-employed are generally less educated than men who are working for

wages. In addition, the relationship between exogenous income and work

participation is particularly interesting for Pakistan. Consistent with what

theory predicts, equations show the "standard" income effect, e.g. as

exogenous income (net of remittances) rises, work participation falls,

particularly participation in the informal sector (not surprisingly, formal

sector coefficients, while negative, are not statistically significant). An

alternative way of stating the same result is that the higher the income level

the higher an individual's reservation wage, hence lowering the likelihood

that he will be in the labor force. We have identified separate income

effects for domestic and foreign remittances, which constitute an important

source of income for many Pakistani households.61 Interestingly, individuals

living in households who receive remittances, particularly remittances from

abroad, are notably less likely to be employed than individuals living in

households who do not receive remittances.

These results of the modeling exercise suggest important consi-

derations concerning employment policy. The low participation rates of

Pakistani men appear to be primarily related to low education endowments

(implying low expected wages) and high levels of exogenous income (implying

high reservation wages), derived in large part from foreign remittances. Our

6/ According to the IFPRI/PIDE survey, two-thirds of nonlabor income isderived from remittances.

- 18 -

results suggest that Pakistan has a large pool of uneducated and likely

inexperienced adult men, often residing in an extended family, who are ill-

equipped to enter the labor force, let alone find a job should exogenous

incomes fall. This suggests that the recent decrease in labor migration to

the Middle East and the concomitant (and continuing) drop in remittances may

have a much stronger impact on wages and unemployment than anticipated; not

only will returning migrants attempt to enter the domestic workforce, but some

tens of thousands of previously inactive men may choose to enter the labor

force as well.

Table 4 shows nested and non-nested participation models for young

Pakistani men. While exogenous income effects are similar to those found for

older men, other coefficients suggest that a rather different employment

decision process typifies the younger cohort. In particular, better educated

young men are generally more likely to be unemployed than working in either

part of the informal sector (e.g. self-employed or the unregulated wage

sector), and less likely to be unemployed than working in the formal wage

sector. This lends support to our earlier contention that inactive young men

are likely to have different characteristics than inactive older men; many of

them are better educated than those in their age cohort presently working in

the informal sector, and evidently not willing to begin work until they find a

job in the formal sector.

Young men who are heads of household tend to work with greater

frequency than non-heads, likely due to economic necessity. In addition, the

probability of employment falls with increasing levels of the household head's

education. This may reflect future schooling aspirations (the son is outside

the labor force because he plans shortly to attend college or some other

- 19 -

TABLE 4: Labor Force Participation Models; Urban Pakistani Males, 10-25 Years Old

Base Model - Not Nested (Base = inactive, N = 249)Self-Employed Informal Wage Formal Wage

Variables (N=318) Sector (N=277) Sector (N=54)

Age in years? 0.391 (2.0) 0.268 (1.4) 2.023 (2.4)(Age)2 -0.0043 (0.8) -0.0018 (0.3) -0.042 (2.1)

Is individual > 20 years old? ...

Education (base = none)

Primary or middle school -0.001 (0.1) -0.207 (0.9) -0.166 (0.4)Secondary school -0.811 (2.5) -0.705 (2.1) 0.723 (1.6)College or university -1.344 (1.7) -1.218 (1.3) 2.388 (3.0)

Head of household? 1.324 (1.6) 1.650 (2.0) 1.260 (1.1)

Head's education (base = none)Literate, but less than secondary -0.404 (2.0) -0.103 (0.5) -0.593 (1.6)

Secondary or university -0.773 (2.2) -0.596 (1.6) -1.301 (2.1)

Nonlabor income, excluding remittances(in Rs/year) -0.000036 (1.4) -0.00023 (3.3) -0.00054 (1.0)

Remittance income (in Rs/year)Receive from abroad? -0.665 (1.6) -0.956 (2.1) -2.166 (2.3)Receive from within Pakistan? -0.450 (1.4) -0.477 (1.4) -1.036 (1.4)

Location of household

Live in Punjab? 0.188 (1.0) 0.205 (1.1) -0.553 (1.6)

Inclusive value (X) ... ...Intercept -5.076 (3.0) -3.774 (2.3) -24.195 (2.8)

Model statistics

Number of observations 898

Percent in each category

Inactive 27.7

Self-employed 35.4

Unregulated wage sector 30.9Regulated wage sector 6.0

Log-likelihood valueRestricted (slopes = 0) -1127.1

Convergence -999.1

x2 statistic 256.1

(continued)

- 2Q -Table 4 (continued)

aNested Model (Base = inactive, N = 249)

Lower Nest Upper Nest

Self-Employed Informal Wage Formal Wage

Variables (N=318) Sector (N=277) Sector (N=54)

Age in years? 0.415 (2.0) ... ...

(Age)2

-0.0050 (0.9) ... ...

Is individual > 20 years old? ... 0.668 (2.9) 1.338 (3.7)

Education (base = none)Primary or middle school 0.029 (0.1) -0.172 (1.0) 0.095 (0.2)

Secondary school -0.758 (2.3) -0.332 (1.3) 1.370 (3.4)

College or university -1.139 (1.4) -0.552 (0.7) 3.261 (4.5)

Head of household? 1.288 (1.5) 1.140 (2.6) 0.809 (0.9)

Head's education (base = none)

Literate, but less than secondary -0.429 (2.1) -0.152 (1.0) -0.523 (1.5)

Secondary or university -0.875 (2.4) -0.537 (1.7) -1.163 (1.9)

Nonlabor income, excluding remittances

(in Rs/year) -0.000034 (1.3) -0.00023 (3.3) -0.000045(0.8)

Remittance income (in Rs/year)

Receive from abroad? -0.758 (1.8) -0.838 (2.0) -2.007 (2.1)

Receive from within Pakistan? -0.408 (1.2) -0.398 (1.5) -0.831 (1.2)

Location of household

Live in Punjab? 0.201 (1.0) -0.161 (1.1) -0.742 (2.4)

Inclusive value (x) ... 0.402 (2.8)

Intercept -5.279 (3.0) ... -2.228 (6.3)

Model statistics

Number of observations 567 398

Percent in each category

Inactive 43.9 } 63.1

Self-employed 56.1

Unregulated wage sector , 30.9

Regulated wage sector ... 6.0

Log-likelihood value

Restricted (slopes = 0) -1127.1 -738.3

Convergence -999.1 -695.9

x2 statistic 256.1 84.8

Source: IFPRI-PIDE urban survey, 1986.

a Estimated sequence (2), p. 6:

per nest)

informal formal

(lower nest) ~Ilwage wagesector sector

inactive self-

employed

- 21 -

training) or represent yet another income effect. Interestingly, the head's

education has the strongest (negative) effect on the probability of being

employed in the formal sector, contrary to the positive effect of own

education.

Wage Equations

Table 5 presents selectivity-corrected wage equations. In the study,

schooling is represented by four dummy variables representing level completed

(primary, secondary, matric, university). This allows for different effects

of education at different levels. Age is used in lieu of work experience.

The age-based specification greatly simplifies estimates of returns to

education, one of desired outputs of the empirical work. In addition to

strict human capital variables, a regional dummy variable (residence in

Punjab) is included to account for labor market differences between urban

markets, and a dummy variable for large cities is included to control for

possible wage differences related to city (and labor market) size.

The percentage increase in wages attributable to higher education is

appreciable in both sectors and for both age groups. The most marked

difference between the regulated and unregulated wage sector is with regard to

the coefficient of post-secondary (e.g. college or university) education. The

wage increments attributable to (completing) post-secondary education are an

estimated 101 percent for adult men working in the unregulated wage sector, as

compared to 135 percent in the regulated sector, or roughly 7.1 and 9.7

percent, respectively, for every year of schooling completed. The effects of

secondary schooling are lower as expected. Moreover, they are quite similar

between the two sectors--47 percent and 49 percent for unregulated and

- 22 -

regulated sectors, respectively. Interestingly, returns to completing primary

or middle schooling are low and typically not significant. In terms of age

stratifications, younger men with secondary or post-secondary degrees

TABLE 5: Selectivity Corrected Wage Equations, Regulated and Unregulated Sectors

Unregulated Wage Sector Regulated Wage SectorVariable Men, 10-25 Years Men, > 25 Years Men, 10-25 Years Men, > 25 Years

Age in years 0.409 (4.9) 0.532 (2.8) -0.287 (0.6) 0.630 (2.7)

(Age)2 -0.0089 (4.0) -0.0005 (3.5) 0.0075 (0.7) -0.0006 (2.3)

Education (relative to none)

Primary school -0.055 (0.6) 0.082 (1.1) 0.334 (1.3) 0.064 (0.5)Middle school -0.009 (0.1) -0.009 (0.1) 0.055 (0.1) 0.260 (1.8)

Secondary school 0.323 (2.6) 0.384 (4.6) 0.675 (2.7) 0.402 (3.2)

College or university 0.546 (1.3) 0.698 (4.8) 1.088 (3.0) 0.858 (4.4)

Live in large city?a -0.010 (0.1) 0.212 (3.5) 0.109 (0.6) 0.127 (1.6)

bF -0.097 (0.6) 0.164 (1.1) -0.080 (0.5) -0.016 (0.1)

Intercept -3.652 (4.9) -0.222 (0.6) 3.413 (0.7) -0.165 (0.3)

Model Statistics

Number of observations 253 386 53 186R2 0.315 0.162 0.379 0.317

Source: IFPRI-PIDE urban survey, 1986.

a These include Karachi, Lahore, Rawalpindi, and Peshawar.

b Correction (Trost and Lee, 1984) for self-selection, where wage equation is:

w Ti Z - Cy x])/F(S. x) + e

i Yi i( F)

appear to do better in the regulated sector than do older men. While this

result for the 10-25 age bracket may be an artifact of the small sample, it is

- 23 -

consistent with the job search interpretation of the labor participation

equation for that group. Education effects in the unregulated sector were in

general found to be similar between age cohorts.

Table 6 shows estimated average wages for adult men aged 45 years and

30 years, by level of education. As expected, differentials between the

regulated and unregulated sectors increase with increasing levels of

education. For example, a 45-year-old man with primary education earns only 7

percent higher wages (4.67 Rs/hour as compared with 4.99 Rs/hour) in the

formal sector, while a college-educated 45- year-old man earns an estimated 28

percent higher wages (8.64 Rs/hour as compared with 11.04 Rs/hour).

If we compare Tables 1 and 6, it is clear that the difference in

average wages between regulated and unregulated sectors in Pakistan primarily

reflects differences in average levels of education between the sectors and

not differences in wages for equal skills, with the exception of the premium

for college graduates. This does not imply that the returns to individuals

are necessarily the same. The Employees Provident Fund, which is the

distinguishing characteristic of the formal sector, contributes 10 percent of

the individual's salary to a retirement fund. Moreover, as observed by van

der Gaag and Vijverberg (1988), other benefits such as paid vacations, health

insurance, and worker's compensation during illnesses, are more prevalent in

the formal sector.

- 24 -

TABLE 6: Representative Wages Predicted in the Wage Equationsa

W a g eEducation Unregulated Sector Regulated Sector

Age 45 None 4.30 4.68Primary 4.67 4.99Middle 4.26 6.07Secondary 6.32 6.99College or university 8.64 11.04

Age 30 None 3.10 3.69Primary 3.36 3.93Middle 3.07 4.78Secondary 4.55 5.51College or university 6.23 8.69

a Predictions based on results in Table 5 and use subsample averages for theTrost and Lee's correction and assume residence outside the largest cities.

- 25 -

CONCLUSIONS

Our results suggest that young, educated men in Pakistan's cities are

waiting to find employment in the formal sector rather than accepting a less

desirable job in the informal sector. Furthermore, this behavior makes good

sense; there appears to be an appreciable premium on wages in the formal

sector, particularly for college-educated males. Although wage premiums are

small for less educated men, (unmeasured) benefits are likely to be

substantially higher in the formal sector. There is also a higher probability

of finding employment in the formal sector as education levels increase. How-

ever, it is the difference in skills more than the premium itself which

accounts for the difference in average wages between the formal and informal

sectors.

Older men outside the labor force have very different characteristics

than younger men. In particular, they tend to be uneducated, likely have

little work experience, and often live in an extended family receiving

remittances from abroad. The likelihood of being (e.g. remaining) outside the

labor force is strongly influenced by exogenous or nonlabor income, which is

itself affected by remittance flows. Any significant drop in exogenous income

is likely to force some (previously inactive) men into the labor force, an

outcome which many of them are clearly ill-equipped to cope with. Thus, the

recent influx of workers returning from the Middle East may cause more severe

employment problems than previously anticipated. While the returning workers

will themselves likely find employment based on skills acquired in their

previous occupation, their remuneration will be far less than received in the

Middle East. The concomitant drop in household income may force previously

- 26 -

inactive and typically unskilled household members to seek, although not

necessarily find, employment.

Although not a formal test of market segmentation, the nesting

sequence which proved most suitable is plausible and consistent with a labor

market in which a number of self-employed individuals are marginalized in

petty trade and artisanal activities. While education explains entry into the

formal sector, household demographics and an alternative source of income

explains the difference in the probabilities of being inactive versus employed

in the informal sector. It was somewhat difficult to clearly distinguish

self-employed individuals from informal sector wage earners, particularly for

younger men. A more complete exploration of alternative nesting structures,

perhaps with detailed labor force histories and finer disaggregation of alter-

natives, may yield better insights. It is clear from our results, however,

that the urban labor market in Pakistan is far more heterogeneous than

conventional approaches typically allow for, and careful and more

disaggregated analyses can yield valuable insights regarding future employment

policy.

- 27 -

REFERENCES

Alderman, Harold, M. Ghaffar Chaudhry, and Marito Garcia. 1988. "Householdfood security in Pakistan: The ration shop system". Working papers onfood subsidies no. 4. Washington, D.C.: International Food PolicyResearch Institute, and Islamabad: Pakistan Institute of DevelopmentEconomics.

Amemiya, T. 1976. "Specification and estimation of a multinomial logitmodel". Technical Report No. 211, Institute of Mathematical Studies inthe Social Sciences, Stanford University.

Becker, Gary S. 1964. Human Capital. New York: Columbia University Press.

Ben-Akiva, Moshe. 1973. Structure of Passenger Travel Demand Models.Unpublished Ph.D. Thesis, Department of Civil Engineering, MassachusettsInstitute of Technology.

Corbo, Vittorio, and Morton Stelcner. 1983. "Earnings determination andlabour markets: Gran Santiago, Chile - 1978". Journal of DevelopmentEconomics, 12 (February/April): 251-266.

Government of Pakistan, Federal Bureau of Statistics. 1984. 1981 Populationcensus. Karachi.

_ 1984b. 1979 Household Income and Expenditure Survey. Karachi.

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Hensher, David. 1986. "Sequential and full information maximum likelihoodestimation of a nested logit model". Review of Economics and Statistics,68 (November): 657-667.

Hill, Anne M. 1983. "Female labor force participation in developing anddeveloped countries - consideration of the informal sector". Review ofEconomics and Statistics, 65(3):459-468.

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Lindauer, David, and Richard Sabot. 1983. "The public/private wage differ-ential in a poor urban economy". Journal of Development Economics, 12(February/April): 137-152.

- 28 -

Lipton, Michael. 1983. Labor and poverty.' World Bank staff working papersno. 616. Washington, D.C.: World Bank.

McFadden, D. 1973. "Conditional logit analysis of qualitative choicebehavior", in Frontiers in Econometrics (ed. P. Zarembka). New York:Academic Press.

McFadden, D. 1976. "Quantal choice analysis: A Survey". Annals of Economicand Social Measurement, 5:363-390.

McFadden, D. 1981. "Econometric models of probabilistic choice", inStructural Analysis of Discrete Data with Econometric Applications, (eds.Manski, C., and D. McFadden). Cambridge, Mass: MIT Press.

Mincer, J. 1974. Schooling, Experience, and Earnings. New York: NBER andColumbia University Press.

Sahn, David, and Harold Alderman. "The effects of human capital on wages andthe determinants of labor supply in a developing country". Journal ofDevelopment Economics (forthcoming).

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Trost, Robert, and Lung-Fei Lee. 1984. "Technical training and earnings: Apolychotomous choice model with selectivity". Review of Economics andStatistics, 66 (February): 151-166.

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LSMS Working Papers (continued)

No. 35 The Demand for Medical Care in Developing Countries: Quantity Rationing in Rural Cate d'7voire

No. 36 Labor Market Activity in Cote d'Ivoire and Peru

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

No. 38 Wage Determinants and School Attainment among Men in Peru

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

No. 40 The Effects of Household and Community Characteristics on the Nutrition of Preschool Children:Evidence from Rural Cote d'Ivoire

No. 41 Public-Private Sector Wage Differentials in Peru, 1985-86

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

No. 43 Profits from Self-Employment: A Case Study of CUte d'Ivoire

No. 44 The Living Standards Survey and Price Policy Reform: A Study of Cocoa and Coffee Production in CUted'Ivoire

No. 45 Measuring the Willingness to Pay for Social Services in Developing Countries

No. 46 Nonagricultural Family Enterprises in Cote d'Ivoire: A Descriptive Analysis

No. 47 The Poor during Adjustment: A Case Study of C6te d'Ivoire

No. 48 Confronting Poverty in Developing Countries: Definitions, Information, and Policies

No. 49 Sample Designs for the Living Standards Surveys in Ghana and Mauritania/Plans de sondage pour lesenquetes sur le niveau de vie au Ghana et en Mauritanie

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

No. 51 C6te d'Ivoire: Estimates for Child Anthropometry from Two Surveys, 1985 and 1986

No. 52 Public-Private Sector Wage Comparisons and Moonlighting in Developing Countries: Evidence fromCUte d'Ivoire and Peru

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

No. 54 The Willingness to Pay for Education in Developing Countries: Evidence from Rural Peru

No. 55 Rigidite de salaire: donn6e microeconomique et macroeconomique sur l'ajustement du marche du travaildans le secteur moderne (in French only)

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

No. 57 The Substitutability of Public and Private Health Care for the Treatment of Children in Pakistan

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

No. 59 Labor Market Performance as a Determinanbt of Migration

No. 60 The Relative Effectiveness of Private and Public Schools: Evidence from Two Developing Countries

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

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

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

No. 64 The Child-Bearing Family in Sub-Saharan Africa: Structure, Fertility, and the Future

The World Bank

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