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How to Adjust for Nonignorable Nonresponse: Calibration, Heckit or FIML?

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  • Johansson, Fredrik

    ()
    (Department of Economics)

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    Abstract

    When a survey response mechanism depends on the variable of interest measured within the same survey and observed for only part of the sample, the situation is one of nonignorable nonresponse. Ignoring the nonresponse is likely to generate significant bias in the estimates. To solve this, one option is the joint modelling of the response mechanism and the variable of interest. Another option is to calibrate each observation with weights constructed from auxiliary data. In an application where earnings equations are estimated these approaches are compared to reference estimates based on large a Swedish register based data set without nonresponse.

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    File URL: http://uu.diva-portal.org/smash/get/diva2:39109/FULLTEXT01.pdf
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    Bibliographic Info

    Paper provided by Uppsala University, Department of Economics in its series Working Paper Series with number 2007:22.

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    Length: 25 pages
    Date of creation: 22 Aug 2007
    Date of revision:
    Handle: RePEc:hhs:uunewp:2007_022

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    Postal: Department of Economics, Uppsala University, P. O. Box 513, SE-751 20 Uppsala, Sweden
    Phone: + 46 18 471 25 00
    Fax: + 46 18 471 14 78
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    Web page: http://www.nek.uu.se/
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    Keywords: Earning equations; Nonignorable response mechanism; Calibration; Selection; Full-information maximum likelihood;

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    References

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    1. Edin, P.-A. & Fredriksson, P., 2000. "LINDA - Longitudinal INdividual DAta for Sweden," Papers 2000:19, Uppsala - Working Paper Series.
    2. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," Review of Economic Studies, Wiley Blackwell, vol. 70(1), pages 33-58, January.
    3. Johansson, Fredrik & Klevmarken, Anders, 2006. "Explaining the size and nature of response in a survey on health status and economic standard," Working Paper Series 2006:2, Uppsala University, Department of Economics.
    4. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    5. Daniel S. Hamermesh & Stephen G. Donald, 2004. "The Effect of College Curriculum on Earnings: Accounting for Non-Ignorable Non-Response Bias," NBER Working Papers 10809, National Bureau of Economic Research, Inc.
    6. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
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    Cited by:
    1. Adrián Rubli, 2012. "La importancia de corregir por el sesgo de selección en el análisis de las brechas salariales por género: un estudio para Argentina, Brasil y México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 1-36, November.

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