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Attrition In Longitudinal Household Survey Data: Some Tests For Three Developing-Country Samples

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  • Alderman, Harold
  • Behrman, Jere R.
  • Kohler, Hans-Peter
  • Maluccio, John A.
  • Watkins, Susan Cotts

Abstract

Longitudinal household data can have considerable advantages over much more widely used cross-sectional data. The collection of longitudinal data, however, may be difficult and expensive. One problem that has concerned many analysts is that sample attrition may make the interpretation of estimates problematic. Such attrition may be particularly severe in areas where there is considerable mobility because of migration between rural and urban areas. Many analysts share the intuition that attrition is likely to be selective on characteristics such as schooling and that high attrition is likely to bias estimates made from longitudinal data. This paper considers the extent of and implications of attrition for three longitudinal household surveys from Bolivia, Kenya, and South Africa that report very high per-year attrition rates between survey rounds. Our estimates indicate that (1) the means for a number of critical outcome and family background variables differ significantly between attritors and nonattritors; (2) a number of family background variables are significant predictors of attrition; but (3) nevertheless, the coefficient estimates for “standard” family background variables in regressions and probit equations for the majority of the outcome variables considered in all three data sets are not affected significantly by attrition. Therefore, attrition apparently is not a general problem for obtaining consistent estimates of the coefficients of interest for most of these outcomes. These results, which are very similar to results for developed economies, suggest that for these outcome variables—despite suggestions of systematic attrition from univariate comparisons between attritors and nonattritors, multivariate estimates of behavioral relations of interest may not be biased due to attrition.

Suggested Citation

  • Alderman, Harold & Behrman, Jere R. & Kohler, Hans-Peter & Maluccio, John A. & Watkins, Susan Cotts, 2000. "Attrition In Longitudinal Household Survey Data: Some Tests For Three Developing-Country Samples," Papers 16423, FCND Discussion Papers.
    • Handle: RePEc:ags:fcnddp:16423
    DOI: 10.22004/ag.econ.16423
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    References listed on IDEAS

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    8. Fletschner, Diana & Peterman, Amber & Santos, Florence & Savath, Vivien, 2013. "Can government-allocated land contribute to food security? Intrahousehold analysis of West Bengal’s microplot allocation program:," IFPRI discussion papers 1310, International Food Policy Research Institute (IFPRI).
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    10. Shahidur R. Khandker & Douglas F. Barnes & Hussain A. Samad, 2013. "Welfare Impacts of Rural Electrification: A Panel Data Analysis from Vietnam," Economic Development and Cultural Change, University of Chicago Press, vol. 61(3), pages 659-692.
    11. Yamano, Takashi & Jayne, Thomas S., 2002. "Measuring the Impacts of Prime-age Adult Death on Rural Households in Kenya," Food Security Collaborative Working Papers 55152, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    12. Justin S. White & Hana Ross, 2015. "Smokers' Strategic Responses to Sin Taxes: Evidence from Panel Data in Thailand," Health Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 127-141, February.
    13. David Lawson & Andy Mckay & John Okidi, 2006. "Poverty persistence and transitions in Uganda: A combined qualitative and quantitative analysis," Journal of Development Studies, Taylor & Francis Journals, vol. 42(7), pages 1225-1251.
    14. Ueyama, Mika, 2007. "Mortality, mobility, and schooling outcomes among orphans: Evidence from Malawi," IFPRI discussion papers 710, International Food Policy Research Institute (IFPRI).
    15. Yamano, Takashi & Jayne, Thomas S., 2002. "Measuring the Impacts of Prime-age Adult Death on Rural Households in Kenya," Food Security Collaborative Working Papers 55152, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    16. Jonathan H. Conning & Pedro Olinto & Alvaro Trigueros, 2000. "Managing Economic Insecurity in Rural El Salvador: The role of asset ownership and labor market adjustments," Department of Economics Working Papers 2001-09, Department of Economics, Williams College.
    17. Stampini, Marco & Davis, Benjamin, "undated". "Discerning transient from chronic poverty in Nicaragua: measurement with a two period panel data set," ESA Working Papers 289096, Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA).
    18. Daniel Suryadarma, 2010. "Labor Market Returns, Marriage Opportunities, or the Education System? Explaining Gender Differences in Numeracy in Indonesia," CEPR Discussion Papers 644, Centre for Economic Policy Research, Research School of Economics, Australian National University.
    19. Dillon, Andrew, 2011. "The Effect of Irrigation on Poverty Reduction, Asset Accumulation, and Informal Insurance: Evidence from Northern Mali," World Development, Elsevier, vol. 39(12), pages 2165-2175.
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    21. Durr-e-Nayab & G. M. Arif, 2012. "Pakistan Panel Household Survey Sample Size, Attrition and Socio-demographic Dynamics," Poverty and Social Dynamics Paper Series 2012:01, Pakistan Institute of Development Economics.
    22. Glick, Peter & Sahn, David, 2005. "Intertemporal female labor force behavior in a developing country: what can we learn from a limited panel?," Labour Economics, Elsevier, vol. 12(1), pages 23-45, February.

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