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An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of income Dynamics

  • John Fitzgerald
  • Peter Gottschalk
  • Robert Moffitt

By 1989 the Michigan Panel Study on Income Dynamics (PSID) had experimented approximately 50 percent sample loss from cumulative attrition from its initial 1968 membership We study the effect of this attrition on the unconditional distributions of several socioeconomic variables and on the estimates of regression coefficients for those variables We provide a statistical framework for conducting tests for attrition bias that draws a sharp distinction between selection on unobservables and on observables and that shows that weighted least squares can generate consistent parameter estimates when selection is based on observables that are endogenous Our empirical analysis shows that attrition is highly selective and is concentrated among lower socioeconomic status individuals We also show that attrition is concentrated among those with more unstable earnings marriage and migration histories holding fixed the level of those variables Nevertheless we find that the absolute magnitude of the selection is not large and it is moderated by regression-to-the-mean effects from attrition on transitory components Consequently despite the large amount of attrition the PSID has remained roughly representative through 1989

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 379.

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Date of creation: 1998
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Handle: RePEc:jhu:papers:379
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  1. Nijman, T.E. & Verbeek, M.J.C.M., 1992. "Non-response in panel data : The impact on estimates of a life cycle consumption function," Other publications TiSEM 3c661e33-2cd1-47f1-a7d9-3, Tilburg University, School of Economics and Management.
  2. Duncan, Greg J & Hill, Daniel H, 1989. "Assessing the Quality of Household Panel Data: The Case of the Panel Study of Income Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 441-52, October.
  3. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, vol. 47(1), pages 153-61, January.
  4. Heckman, James J. & Robb, Richard Jr., 1985. "Alternative methods for evaluating the impact of interventions : An overview," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 239-267.
  5. Imbens, G. & Lancaster, T., 1991. "Efficient Estimation and Stratified Sampling," Papers 9145, Tilburg - Center for Economic Research.
  6. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
  7. Imbens, Guido W & Lancaster, Tony, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 655-80, October.
  8. James J. Heckman, 1989. "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: The Case of Manpower Training," NBER Working Papers 2861, National Bureau of Economic Research, Inc.
  9. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-73, March.
  10. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 1-14, February.
  11. MaCurdy, Thomas E., 1982. "The use of time series processes to model the error structure of earnings in a longitudinal data analysis," Journal of Econometrics, Elsevier, vol. 18(1), pages 83-114, January.
  12. Bound, John, et al, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-68, July.
  13. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
  14. Ridder, Geert, 1992. "An empirical evaluation of some models for non-random attrition in panel data," Structural Change and Economic Dynamics, Elsevier, vol. 3(2), pages 337-355, December.
  15. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
  16. John M. Abowd & David Card, 1986. "On the Covariance Structure of Earnings and Hours Changes," NBER Working Papers 1832, National Bureau of Economic Research, Inc.
  17. 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.
  18. Van den Berg, G J & Lindeboom, M & Ridder, G, 1994. "Attrition in Longitudinal Panel Data and the Empirical Analysis of Dynamic Labour Market Behaviour," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 9(4), pages 421-35, Oct.-Dec..
  19. Nijman, Theo & Verbeek, Marno, 1992. "Nonresponse in Panel Data: The Impact on Estimates of a Life Cycle Consumption Function," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(3), pages 243-57, July-Sept.
  20. 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 472-92, October.
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