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Estimating expenditure impacts without expenditure data using asset proxies

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  • Martin Wittenberg

    () (School of Economics, University of Cape Town)

Abstract

When asset indices are used in regressions the coefficients obtained are typically difficult to interpret. We show how lower bounds on expenditure effects can be extracted, if the relationship between the assets and expenditure can be calibrated on an auxiliary data set.

Suggested Citation

  • Martin Wittenberg, 2009. "Estimating expenditure impacts without expenditure data using asset proxies," SALDRU Working Papers 29, Southern Africa Labour and Development Research Unit, University of Cape Town.
    • Handle: RePEc:ldr:wpaper:29
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    References listed on IDEAS

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    1. 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.
    2. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
    3. Wittenberg, Martin, 2007. "Testing for a common latent variable in a linear regression," MPRA Paper 2550, University Library of Munich, Germany.
    4. Darren Lubotsky & Martin Wittenberg, 2006. "Interpretation of Regressions with Multiple Proxies," The Review of Economics and Statistics, MIT Press, vol. 88(3), pages 549-562, August.
    5. Popkin, Barry M., 1999. "Urbanization, Lifestyle Changes and the Nutrition Transition," World Development, Elsevier, vol. 27(11), pages 1905-1916, November.
    6. Christina Paxson & Norbert Schady, 2007. "Cognitive Development among Young Children in Ecuador: The Roles of Wealth, Health, and Parenting," Journal of Human Resources, University of Wisconsin Press, vol. 42(1).
    7. Deon Filmer & Lant Pritchett, 2001. "Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 115-132, February.
    8. 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.
    9. Chou, Shin-Yi & Grossman, Michael & Saffer, Henry, 2004. "An economic analysis of adult obesity: results from the Behavioral Risk Factor Surveillance System," Journal of Health Economics, Elsevier, vol. 23(3), pages 565-587, May.
    10. David McKenzie, 2005. "Measuring inequality with asset indicators," Journal of Population Economics, Springer;European Society for Population Economics, vol. 18(2), pages 229-260, June.
    11. Boyle, Michael H. & Racine, Yvonne & Georgiades, Katholiki & Snelling, Dana & Hong, Sungjin & Omariba, Walter & Hurley, Patricia & Rao-Melacini, Purnima, 2006. "The influence of economic development level, household wealth and maternal education on child health in the developing world," Social Science & Medicine, Elsevier, vol. 63(8), pages 2242-2254, October.
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    Citations

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    Cited by:

    1. Nic Baigrie & Katherine Eyal, 2014. "An Evaluation of the Determinants and Implications of Panel Attrition in the National Income Dynamics Survey (2008-2010)," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 39-65, March.
    2. Martin Wittenberg & Murray Leibbrandt, 2017. "Measuring Inequality by Asset Indices: A General Approach with Application to South Africa," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 63(4), pages 706-730, December.
    3. Martin Wittenberg, 2011. "The Weight of Success: The Body Mass Index and Economic Well-being in South Africa," SALDRU Working Papers 65, Southern Africa Labour and Development Research Unit, University of Cape Town.

    More about this item

    Keywords

    asset index; proxy variables; attenuation; obesity;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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