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Attrition in longitudinal household survey data - some tests for three developing-country samples

Author

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

Abstract

For capturing dynamic demographic relationships, longitudinal household data can have considerable advantages over more widely used cross-sectional data. But because the collection of longitudinal data may be difficult and expensive, analysts must assess the magnitudes of the problems, specific to longitudinal, but not to cross-sectional data. One problem that concerns many analysts is that sample attrition may make the interpretation of estimates problematic. Such attrition may be especially severe where there is considerable migration between rural, and urban areas. And attrition is likely to be selective on such characteristics as schooling, so high attrition is likely to bias estimates. The authors consider the extent, and implications of attrition for three longitudinal household surveys from Bolivia, Kenya, and South Africa that report very high annual attrition rates between survey rounds. Their estimates indicate that: 1) the means for a number of critical outcome, and family background variables differ significantly between those who are lost to follow-up, and those who are re-interviewed. 2) A number of family background variables are significant predictors of attrition. 3) Nevertheless, the coefficient estimates for standard family background variables in regressions, and probit equations for the majority of outcome variables in all three data sets, are not significantly affected by attrition. So attrition is apparently 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 those for industrial countries, suggest that multivariate estimates of behavioral relations may not be biased because of attrition. This wold support the collection of longitudinal data.

Suggested Citation

  • Alderman, Harold & Behrman, Jere R. & Kohler, Hans-Peter & Maluccio, John A. & Cotts Watkins, Susan, 2000. "Attrition in longitudinal household survey data - some tests for three developing-country samples," Policy Research Working Paper Series 2447, The World Bank.
  • Handle: RePEc:wbk:wbrwps:2447
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    References listed on IDEAS

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    1. Jeffrey E. Zabel, 1998. "An Analysis of Attrition in the Panel Study of Income Dynamics and the Survey of Income and Program Participation with an Application to a Model of Labor Market Behavior," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 479-506.
    2. Duncan Thomas & Elizabeth Frankenberg & James P. Smith, 2001. "Lost but Not Forgotten: Attrition and Follow-up in the Indonesia Family Life Survey," Journal of Human Resources, University of Wisconsin Press, pages 556-592.
    3. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    4. Sean Becketti & William Gould & Lee Lillard & Finis Welch, 1985. "The Panel Study of Income Dynamics After Fourteen Years: An Evaluation," UCLA Economics Working Papers 361, UCLA Department of Economics.
    5. Jere R. Behrman & Yingmei Cheng & Petra E. Todd, 2004. "Evaluating Preschool Programs When Length of Exposure to the Program Varies: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 108-132, February.
    6. Becketti, Sean, et al, 1988. "The Panel Study of Income Dynamics after Fourteen Years: An Evaluatio n," Journal of Labor Economics, University of Chicago Press, vol. 6(4), pages 472-492, October.
    7. Smith, J-P & Thomas, D, 1997. "Migration in Retrospect : Remembrances of Things Past," Papers 97-06, RAND - Labor and Population Program.
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    Citations

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    Cited by:

    1. Santos, Florence & Fletschner, Diana & Savath, Vivien & Peterman, Amber, 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).
    2. Dillon, Andrew, 2008. "Access to irrigation and the escape from poverty: Evidence from Northern Mali," IFPRI discussion papers 782, International Food Policy Research Institute (IFPRI).
    3. 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.
    4. 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.
    5. 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.
    6. Liao, Wen-Chi, 2012. "Inshoring: The geographic fragmentation of production and inequality," Journal of Urban Economics, Elsevier, vol. 72(1), pages 1-16.
    7. Santos, Florence & Fletschner, Diana & Savath, Vivien & Peterman, Amber, 2014. "Can Government-Allocated Land Contribute to Food Security? Intrahousehold Analysis of West Bengal’s Microplot Allocation Program," World Development, Elsevier, vol. 64(C), pages 860-872.
    8. 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.
    9. 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.

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