计量经济分析 = Econometric analysis

The relationship between household wealth and educational enrollment of children can be estimated without expenditure data. A method for doing so - which uses an index based on household asset ownership indicators - is proposed and defended in this paper. In India, children from the wealthiest households are over 30 percentage points more likely to be in school than those from the poorest households, although this gap varies considerably across states. To estimate the relationship between household wealth and the probability that a child (aged 6 to 14) is enrolled in school, Filmer and Pritchett use National Family Health Survey (NFHS) data collected in Indian states in 1992 and 1993. In developing their estimate Filmer and Pritchett had to overcome a methodological difficulty: The NFHS, modeled closely on the Demographic and Health Surveys, measures neither household income nor consumption expenditures. As a proxy for long-run household wealth, they constructed a linear asset index from a set of asset indicators, using principal components analysis to derive the weights. This asset index is robust, produces internally coherent results, and provides a close correspondence with data on state domestic product and on state level poverty rates. They validate the asset index using data on consumption spending and asset ownership from Indonesia, Nepal, and Pakistan. The asset index has reasonable coherence with current consumption expenditures and, more importantly, works as well as - or better than - traditional expenditure-based measures in predicting enrollment status. The authors find that on average a child from a wealthy household (in the top 20 percent on the asset index developed for this analysis) is 31 percent more likely to be enrolled in school than a child from a poor household (in the bottom 40 percent). This paper - a product of Poverty and Human Resources, Development Research Group - is part of a larger effort in the group to inform educational policy. The study was funded by the Bank`s Research Support Budget under the research project Educational Enrollment and Dropout (RPO 682-11).

Estimating Wealth Effects without Expenditure Data or Tears: With an Application to Educational Enrollments in States of India

This paper has an empirical and overtly methodological goal. The authors propose and defend a method for estimating the effect of household economic status on educational outcomes without direct survey information on income or expenditures. They construct an index based on indicators of household assets, solving the vexing problem of choosing the appropriate weights by allowing them to be determined by the statistical procedure of principal components. While the data for India cannot be used to compare alternative approaches they use data from Indonesia, Nepal, and Pakistan which have both expenditures and asset variables for the same households. With these data the authors show that not only is there a correspondence between a classification of households based on the asset index and consumption expenditures but also that the evidence is consistent with the asset index being a better proxy for predicting enrollments--apparently less subject to measurement error for this purpose--than consumption expenditures. The relationship between household wealth and educational enrollment of children can be estimated without expenditure data. A method for doing so - which uses an index based on household asset ownership indicators- is proposed and defended in this paper. In India, children from the wealthiest households are over 30 percentage points more likely to be in school than those from the poorest households.

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Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application to Educational Enrollments in States of India

Offering a unifying theoretical perspective not readily available in any other text, this innovative guide to econometrics uses simple geometrical arguments to develop students' intuitive understanding of basic and advanced topics, emphasizing throughout the practical applications of modern theory and nonlinear techniques of estimation. One theme of the text is the use of artificial regressions for estimation, reference, and specification testing of nonlinear models, including diagnostic tests for parameter constancy, serial correlation, heteroscedasticity, and other types of mis-specification. Explaining how estimates can be obtained and tests can be carried out, the authors go beyond a mere algebraic description to one that can be easily translated into the commands of a standard econometric software package. Covering an unprecedented range of problems with a consistent emphasis on those that arise in applied work, this accessible and coherent guide to the most vital topics in econometrics today is indispensable for advanced students of econometrics and students of statistics interested in regression and related topics. It will also suit practising econometricians who want to update their skills. Flexibly designed to accommodate a variety of course levels, it offers both complete coverage of the basic material and separate chapters on areas of specialized interest.

Estimation and Inference in Econometrics

Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

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Constructing socio-economic status indices: how to use principal components analysis.

Very few demographic surveys in developing countries have gathered information on household incomes or consumption expenditures. Researchers interested in living standards therefore have had little alternative but to rely on simple proxy indicators. The properties of these proxies have not been analyzed systematically. We ask what hypotheses can be tested using proxies, and compare these indicators with consumption expenditures per adult, our preferred measure of living standards. We find that the proxies employed in much demographic research are very weak predictors of consumption per adult. Nevertheless, hypothesis tests based on proxies are likely to be powerful enough to warrant consideration.

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Edmundo Paredes

Papers

Measuring living standards with proxy variables