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TitleChild Schooling and the Measurement of Living Standards
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In the case of the supply-constrained model, knowing the shape of a series

of typical demand curves means knowing how an increase in supply of schooling

would affect enrollment rates among various groups in the population.

The figures indicate that two types of data must be collected: (a)

data on household characteristics and, for a schooling study, on the amounts

of schooling per child in households; and (b) data on the economic environment

of households, i.e. on prices of goods, on labor market conditions which

affect wages, and for a study of schooling demand, on the "price" of schooling.

We first consider the resulting implications for sample design and then

discuss the data needs corresponding to the variables in mcre detail.

Implications for Sample Design

In most cross-section household data sets, data on the market

environment is either absent altogether, or if present, includes no variation

across households because all households are in a small geographical area

within which prices are identical. (Changes over time in market conditions

for one set of households in a small geographical area would also work but

panel data on households is even more rare than variation in the prices

faced in a cross-section.) Yet as discussed in the outline of the model, it

is the exogenous price variables which are most important if empirical work is

to have any policy relevance.

There are several ways to get around this problem, which can be

thought of in terms of three types of data sets, in all of which the household

is the principal unit of observation. For simplicity, we call the three

the self-contained special survey; the aggregation - potential survey; and the

mixed bag. The principal difference among the three is in the source for the

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exogenous variables which are not specific to households, but which

represent in various forms the economic environment of the household.

These are the constraints the household faces which are most likely

to embody policy levers.

For the "mixed bag" approach, data on price differences across areas

are obtained from a different source and matched to the household data. This

obviously requires that the households sampled come from different areas. The

more areas they come from, the greater the variance is likely to be in 'prices,"

but also the greater the task of compiling and matching. Often househoLd

samples are based on large clusters of households from a limited number of

areas (e.g. from a few villages, or from several cities). On the one hand,

this makes the task of matching easier; on the other hand, it limits the

amount of variation in the price data.

The major advantage of matching data sets is that it allows for the

use of macroeconomic information not only at a point in time (e.g. the tlime

of the household survey), but over time. For example, trends in wage rates

at the local level, or changes in the past in the availability of schooLs,

in their "quality' within communities, can be related to current stocks -

of years of schooling of children. This approach in the long run could allow

for incorporation of what are usually large amounts of already published data

on trends, into an analytic framework. It does require that sampling design

for the household survey be done bearing in mind the physical boundaries which

have been used in aggregations of data in the past, e.g. regional accounts.

Households then need to have a code which links them to the geographic area

for which macroeconomic information is available.

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LSMS Working Papers (continued)

No. 28 Analysis of Household Expenditures

No. 29 The Distribution of Welfare in Cote 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

No. 35 The Demand for Medical Care in Developing Countries: Quantity Rationing in Rural C6te d'Ivoire

No. 36 Labor Market Activity in C6te 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 Cote d'Ivoire

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

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

No. 46 Nonagricultural Family Enterprises in C6te d'Ivoire: A Descriptive Analysis

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

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