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Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India*

Abstract

Using data from India, we estimate the relationship between household wealth and children’s school enrollment. We proxy wealth by constructing a linear index from asset ownership indicators, using principal-components analysis to derive weights. In Indian data this index is robust to the assets included, and produces internally coherent results. State-level results correspond well to independent data on per capita output and poverty. To validate the method and to show that the asset index predicts enrollments as accurately as expenditures, or more so, we use data sets from Indonesia, Pakistan, and Nepal that contain information on both expenditures and assets. The results show large, variable wealth gaps in children’s enrollment across Indian states. On average a “rich” child is 31 percentage points more likely to be enrolled than a “poor” child, but this gap varies from only 4.6 percentage points in Kerala to 38.2 in Uttar Pradesh and 42.6 in Bihar.

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This research was funded in part through World Bank research support grant (RPO 682-11). We would like to thank Harold Alderman, Zoubida Allaoua, Gunnar Eskeland, Jeffrey Hammer, Keith Hinchliffe, Valerie Kozel, Alan Krueger, Peter Lanjouw, Marlaine Lockheed, Berk Ozler, and Martin Ravallion for valuable discussions and comments, including some on an earlier version of this paper (Filmer and Pritchett 1998). The findings, interpretations, and conclusions expressed here are entirely those of the authors. They do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent.

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Filmer, D., Pritchett, L.H. Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India*. Demography 38, 115–132 (2001). https://doi.org/10.1353/dem.2001.0003

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Keywords

  • Instrumental Variable
  • Consumption Expenditure
  • Household Wealth
  • Asset Index
  • Poor Quintile