IDEAS home Printed from https://ideas.repec.org/p/koc/wpaper/1220.html
   My bibliography  Save this paper

Rescaled Additively Non-ignorable (RAN) Model of Attrition and Substitution

Author

Listed:
  • Insan Tunali

    (Department of Economics, Koç University)

  • Emre Ekinci

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

  • Berk Yavuzoglu

    (Department of Economics, University of Wisconsin-Madison)

Abstract

We modify 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 endogenous attrition and substitution behavior that can emerge when multiple visits to the same unit are planned. 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) of 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 using reflation factors expressed as parametric functions of the states occupied in subsequent rounds. Unlike standard weighting schemes, our method produces a unique set of corrected joint probabilities that are consistent with the margins used for computing the published cross-section statistics. Inference about the nature of the bias is implemented via Bootstrap methods. Our empirical results show that attrition/substitution in HLFS is a statistically and substantially important concern.

Suggested Citation

  • Insan Tunali & Emre Ekinci & Berk Yavuzoglu, 2012. "Rescaled Additively Non-ignorable (RAN) Model of Attrition and Substitution," Koç University-TUSIAD Economic Research Forum Working Papers 1220, Koc University-TUSIAD Economic Research Forum.
  • Handle: RePEc:koc:wpaper:1220
    as

    Download full text from publisher

    File URL: http://eaf.ku.edu.tr/sites/eaf.ku.edu.tr/files/erf_wp_1220.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bhattacharya, Debopam, 2008. "Inference in panel data models under attrition caused by unobservables," Journal of Econometrics, Elsevier, vol. 144(2), pages 430-446, June.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    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. 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.
    2. Xavier d'Haultfoeuille & Stefan Hoderlein & Yuya Sasaki, 2013. "Nonlinear difference-in-differences in repeated cross sections with continuous treatments," CeMMAP working papers CWP40/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Harding, Matthew & Lamarche, Carlos, 2019. "A panel quantile approach to attrition bias in Big Data: Evidence from a randomized experiment," Journal of Econometrics, Elsevier, vol. 211(1), pages 61-82.
    4. 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.
    5. Sasaki, Yuya, 2015. "Heterogeneity and selection in dynamic panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 236-249.
    6. Christophe Bell'ego & David Benatia & Vincent Dortet-Bernardet, 2023. "The Chained Difference-in-Differences," Papers 2301.01085, arXiv.org, revised May 2024.
    7. 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.
    8. Laurent Davezies & Xavier d'Haultfoeuille, 2013. "Endogenous Attrition in Panels," Working Papers 2013-17, Center for Research in Economics and Statistics.
    9. Rene Segers & Philip Hans Franses, 2014. "Panel design effects on response rates and response quality," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 1-24, February.
    10. 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.
    11. 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.
    12. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute of Labor Economics (IZA).
    13. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of Income Dynamics," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 251-299.
    14. Markus Frölich & Martin Huber, 2014. "Treatment Evaluation With Multiple Outcome Periods Under Endogeneity and Attrition," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1697-1711, December.
    15. Simon Calmar Andersen & Louise Beuchert & Phillip Heiler & Helena Skyt Nielsen, 2023. "A Guide to Impact Evaluation under Sample Selection and Missing Data: Teacher's Aides and Adolescent Mental Health," Papers 2308.04963, arXiv.org.
    16. Joachim Inkmann, 2010. "Estimating Firm Size Elasticities of Product and Process R&D," Economica, London School of Economics and Political Science, vol. 77(306), pages 384-402, April.
    17. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute for the Study of Labor (IZA).
    18. Das, M., 2004. "Simple estimators for nonparametric panel data models with sample attrition," Journal of Econometrics, Elsevier, vol. 120(1), pages 159-180, May.
    19. 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.
    20. Seik Kim, 2013. "Wage Mobility of Foreign-Born Workers in the United States," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 628-658.

    More about this item

    Keywords

    attrition; substitution; selectivity; short panel; rotating sample frame; labor force survey.;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:koc:wpaper:1220. 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: Sumru Oz (email available below). General contact details of provider: https://edirc.repec.org/data/dekoctr.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.