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Rescaled Additivity Non-Ignorable (RAN) Model of Generalized Attrition

Author

Listed:
  • Emre Ekinci

    () (Department of Business Administration, Universidad Carlos III de Madrid)

  • Insan Tunah

    () (Department of Economics, Koc University)

  • Berk Yavuzoglu

    () (Department of Economics, Nazarbayev University)

Abstract

We augment the Additively Non-ignorable (AN) model of Hirano et. al. (2001) so that it is suitable for data collection efforts that have a short panel component. Our modification yields a convenient semi-parametric bias correction framework for handling selective non-response that can emerge when multiple visits to the same unit are planned. Selective non-response can be due to attrition, when initial response is followed by nonresponse (the commonly studied case), as well as a phenomenon we term reverse attrition, when initial nonresponse is followed by response. Accounting for reverse attrition creates an additional identification problem, which we circumvent by rescaling. We apply our methodology to data from the Household Labor Force Survey (HLFS) in Turkey, which shares a key design feature (namely a rotating sample frame) with popular surveys such as the Current Population Survey and the European Union Labor Force Survey. The correction amounts to adjusting the observed joint distribution over the state space (inactive, employed, unemployed in our example) using reflation factors expressed as parametric functions of the states occupied in the initial and subsequent rounds. Our method produces a unique set of corrected joint probabilities that are consistent with externally obtained marginal distributions (in our case published official statistics). The linear additive version has a closed form solution, a feature which renders our method computationally attractive. Our empirical results show that selective attrition/reverse attrition in HLFS-Turkey is a statistically and substantially important concern.

Suggested Citation

  • Emre Ekinci & Insan Tunah & Berk Yavuzoglu, 2017. "Rescaled Additivity Non-Ignorable (RAN) Model of Generalized Attrition," Working Papers 1702, Nazarbayev University, Department of Economics, revised Mar 2017.
  • Handle: RePEc:naz:wpaper:1702
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    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. Abowd, John M & Zellner, Arnold, 1985. "Estimating Gross Labor-Force Flows," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 254-283, June.
    3. 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.
    4. 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.
    5. Golan, Amos & Judge, George & Robinson, Sherman, 1994. "Recovering Information from Incomplete or Partial Multisectoral Economic Data," The Review of Economics and Statistics, MIT Press, vol. 76(3), pages 541-549, August.
    6. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, vol. 144(2), pages 430-446, June.
    7. Heng Chen & Marie-Hélène Felt & Kim Huynh, 2014. "Retail Payment Innovations and Cash Usage: Accounting for Attrition Using Refreshment Samples," Staff Working Papers 14-27, Bank of Canada.
    8. 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.
    9. Stasny, Elizabeth A, 1988. "Modeling Nonignorable Nonresponse in Categorical Panel Data with an Example in Estimating Gross Labor-Force Flows," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 207-219, April.
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