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Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India

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  • Filmer, Deon*Pritchett, Lant

Abstract

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.

Suggested Citation

  • Filmer, Deon*Pritchett, Lant, 1998. "Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India," Policy Research Working Paper Series 1994, The World Bank.
  • Handle: RePEc:wbk:wbrwps:1994
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    References listed on IDEAS

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    1. Jere Behrman & James C. Knowles, "undated". "How Strongly is Child Schooling Associated with Household Income?," CARESS Working Papres 97-22, University of Pennsylvania Center for Analytic Research and Economics in the Social Sciences.
    2. Glewwe, P. & Hall, G., 1995. "Who is Most Vulnerable to Macroeconomic Shocks? Hypotheses Tests Using Panel Data from Peru," Papers 117, World Bank - Living Standards Measurement.
    3. Lanjouw, Peter & Ravallion, Martin, 1995. "Poverty and Household Size," Economic Journal, Royal Economic Society, vol. 105(433), pages 1415-1434, November.
    4. Patrinos, Harry Anthony, 1997. "Differences in education and earnings across ethnic groups in Guatemala," The Quarterly Review of Economics and Finance, Elsevier, vol. 37(4), pages 809-821.
    5. Mark Montgomery & Michele Gragnolati & Kathleen Burke & Edmundo Paredes, 2000. "Measuring living standards with proxy variables," Demography, Springer;Population Association of America (PAA), vol. 37(2), pages 155-174, May.
    6. Jalan, Jyotsna & Ravallion, Martin, 1998. "Transient Poverty in Postreform Rural China," Journal of Comparative Economics, Elsevier, vol. 26(2), pages 338-357, June.
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