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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.
  • 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.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 2447.

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Date of creation: 30 Sep 2000
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Handle: RePEc:wbk:wbrwps:2447

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Keywords: Statistical&Mathematical Sciences; Public Health Promotion; Health Monitoring&Evaluation; Scientific Research&Science Parks; Educational Sciences; Science Education; Scientific Research&Science Parks; Health Monitoring&Evaluation; Poverty Assessment; Statistical&Mathematical Sciences;

References

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  1. Smith, J-P & Thomas, D, 1997. "Migration in Retrospect : Remembrances of Things Past," Papers, RAND - Labor and Population Program 97-06, RAND - Labor and Population Program.
  2. Sean Becketti & William Gould & Lee Lillard & Finis Welch, 1985. "The Panel Study of Income Dynamics After Fourteen Years: An Evaluation," UCLA Economics Working Papers, UCLA Department of Economics 361, UCLA Department of Economics.
  3. Mark Rosenzweig & Andrew D. Foster, . "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Home Pages, University of Pennsylvania _068, University of Pennsylvania.
  4. Thomas, D. & Frankenberg, E. & Smith, J.P., 2000. "Lost But Not Forgotten Attribution and Follow-up in the Indonesian Family Life Survey," Papers, RAND - Labor and Population Program 00-03, RAND - Labor and Population Program.
  5. 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, vol. 36(3), pages 556-592.
  6. 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.
  7. Evangelos M. Falaris & H. Elizabeth Peters, 1998. "Survey Attrition and Schooling Choices," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 531-554.
  8. 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, University of Chicago Press, vol. 6(4), pages 472-92, October.
  9. 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.
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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, International Food Policy Research Institute (IFPRI) 1310, International Food Policy Research Institute (IFPRI).
  2. Liao, Wen-Chi, 2012. "Inshoring: The geographic fragmentation of production and inequality," Journal of Urban Economics, Elsevier, vol. 72(1), pages 1-16.
  3. Yamano, Takashi & Jayne, Thomas S., 2002. "Measuring The Impacts Of Prime-Age Adult Death On Rural Households In Kenya," Staff Papers, Michigan State University, Department of Agricultural, Food, and Resource Economics 11632, Michigan State University, Department of Agricultural, Food, and Resource Economics.
  4. Durr-e-Nayab & G. M. Arif, 2012. "Pakistan Panel Household Survey Sample Size, Attrition and Socio-demographic Dynamics," Poverty and Social Dynamics Paper Series, Pakistan Institute of Development Economics 2012:01, Pakistan Institute of Development Economics.
  5. 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, Elsevier, vol. 12(1), pages 23-45, February.
  6. Dillon, Andrew, 2011. "The Effect of Irrigation on Poverty Reduction, Asset Accumulation, and Informal Insurance: Evidence from Northern Mali," World Development, Elsevier, Elsevier, vol. 39(12), pages 2165-2175.

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