IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0220.html

Some searches may not work properly. We apologize for the inconvenience.

   My bibliography  Save this paper

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

Author

Listed:
  • John Fitzgerald
  • Peter Gottschalk
  • Robert Moffitt

Abstract

By 1989 the Michigan Panel Study on Income Dynamics (PSID) had experienced 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 several sets of regression coefficients. 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, even when they 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. Nevertheless, we find that these variables explain very little of the attrition in the sample, and that the selection that occurs is moderated by regression-to-the-mean effects from selection on transitory components that fade over time. Consequently, despite the large amount of attrition, we find no strong evidence that attrition has seriously distorted the representativeness of the PSID through 1989, and considerable evidence that its cross-sectional representativeness has remained roughly intact.

Suggested Citation

  • John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," NBER Technical Working Papers 0220, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0220
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0220.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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 455-473, March.
    2. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    3. Guido W. Imbens & Tony Lancaster, 1994. "Combining Micro and Macro Data in Microeconometric Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(4), pages 655-680.
    4. 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 421-435, Oct.-Dec..
    5. repec:cup:cbooks:9780521444606 is not listed on IDEAS
    6. 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.
    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    8. Heckman, J.J. & Hotz, V.J., 1988. "Choosing Among Alternative Nonexperimental Methods For Estimating The Impact Of Social Programs: The Case Of Manpower Training," University of Chicago - Economics Research Center 88-12, Chicago - Economics Research Center.
    9. Manski, C.F., 1990. "The Selection Problem," Working papers 90-12, Wisconsin Madison - Social Systems.
    10. 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.
    11. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    12. 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.
    13. Imbens, Guido W. & Lancaster, Tony, 1996. "Efficient estimation and stratified sampling," Journal of Econometrics, Elsevier, vol. 74(2), pages 289-318, October.
    14. 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 243-257, July-Sept.
    15. 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.
    16. 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.
    17. Becketti, Sean & Gould, William & Lillard, Lee & Welch, Finis, 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.
    18. 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.
    19. Abowd, John M & Card, David, 1989. "On the Covariance Structure of Earnings and Hours Changes," Econometrica, Econometric Society, vol. 57(2), pages 411-445, March.
    20. 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 441-452, October.
    21. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 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 345-368, July.
    22. repec:cup:cbooks:9780521444590 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nevo, Aviv, 2003. "Using Weights to Adjust for Sample Selection When Auxiliary Information Is Available," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 43-52, January.
    2. Emre Ekinci, 2009. "Dealing with Attrition When Refreshment Samples are Available: An Application to the Turkish Household Labor Force Survey," 2009 Meeting Papers 353, Society for Economic Dynamics.
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
    4. Shin, Jaeun & Moon, Sangho, 2006. "Fertility, relative wages, and labor market decisions: A case of female teachers," Economics of Education Review, Elsevier, vol. 25(6), pages 591-604, December.
    5. Nicoletti, Cheti, 2006. "Nonresponse in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 132(2), pages 461-489, June.
    6. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    7. Richard Dorsett, 2004. "Using matched substitutes to adjust for nonignorable nonresponse: an empirical investigation using labour market data," PSI Research Discussion Series 16, Policy Studies Institute, UK.
    8. Terence C. Cheng & Pravin K. Trivedi, 2015. "Attrition Bias in Panel Data: A Sheep in Wolf's Clothing? A Case Study Based on the Mabel Survey," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1101-1117, September.
    9. Marcel Das & Vera Toepoel & Arthur van Soest, 2011. "Nonparametric Tests of Panel Conditioning and Attrition Bias in Panel Surveys," Sociological Methods & Research, , vol. 40(1), pages 32-56, February.
    10. JM Abowd & Bruno Crépon & Francis Kramarz, 1997. "Moment Estimation with Attrition," Working Papers 97-35, Center for Research in Economics and Statistics.
    11. Yamana Kazufumi, 2020. "Monte Carlo Evidence on the Estimation Method for Industry Dynamics," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-12, January.
    12. Bryan S. Graham & Cristine Campos De Xavier Pinto & Daniel Egel, 2012. "Inverse Probability Tilting for Moment Condition Models with Missing Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(3), pages 1053-1079.
    13. Robert Moffitt & Sisi Zhang, 2018. "Income Volatility and the PSID: Past Research and New Results," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 277-280, May.
    14. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    15. Boudarbat, Brahim & Grenon, Lee, 2013. "Sample Attrition in the Canadian Survey of Labor and Income Dynamics," IZA Discussion Papers 7295, Institute of Labor Economics (IZA).
    16. Badi Baltagi & Seuck Song, 2006. "Unbalanced panel data: A survey," Statistical Papers, Springer, vol. 47(4), pages 493-523, October.
    17. Inkmann, J., 2005. "Inverse Probability Weighted Generalised Empirical Likelihood Estimators : Firm Size and R&D Revisited," Other publications TiSEM c39cff1f-16c1-4446-a83f-c, Tilburg University, School of Economics and Management.
    18. Inkmann, Joachim, 2001. "Accounting for Nonresponse Heterogeneity in Panel Data," CoFE Discussion Papers 01/03, University of Konstanz, Center of Finance and Econometrics (CoFE).
    19. Heng Chen & Marie-Hélène Felt & Kim P. Huynh, 2017. "Retail payment innovations and cash usage: accounting for attrition by using refreshment samples," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 503-530, February.
    20. Nail Kashaev, 2022. "Estimation of Parametric Binary Outcome Models with Degenerate Pure Choice-Based Data with Application to COVID-19-Positive Tests from British Columbia," University of Western Ontario, Departmental Research Report Series 20225, University of Western Ontario, Department of Economics.

    More about this item

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberte:0220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.