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

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  • Johansson, Fredrik

    (Department of Economics)

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.

Suggested Citation

  • Johansson, Fredrik, 2007. "How to Adjust for Nonignorable Nonresponse: Calibration, Heckit or FIML?," Working Paper Series 2007:22, Uppsala University, Department of Economics.
    • Handle: RePEc:hhs:uunewp:2007_022
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Fredrik Johansson-Tormod & Anders Klevmarken, 2022. "Explaining the Size and Nature of Response in a Survey on Health Status and Economic Standard," International Journal of Microsimulation, International Microsimulation Association, vol. 15(1), pages 63-77.
    4. Edin, P.-A. & Fredriksson, P., 2000. "LINDA - Longitudinal INdividual DAta for Sweden," Papers 2000:19, Uppsala - Working Paper Series.
    5. 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.
    6. Jacob A. Mincer, 1974. "Schooling and Earnings," NBER Chapters, in: Schooling, Experience, and Earnings, pages 41-63, National Bureau of Economic Research, Inc.
    7. Jacob A. Mincer, 1974. "Schooling, Experience, and Earnings," NBER Books, National Bureau of Economic Research, Inc, number minc74-1, May.
    8. Mitali Das & Whitney K. Newey & Francis Vella, 2003. "Nonparametric Estimation of Sample Selection Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(1), pages 33-58.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    2. Richard Fabling & Arthur Grimes, 2015. "Over the Hedge: Do Exporters Practice Selective Hedging?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(4), pages 321-338, April.
    3. Ohlsson, Henry, 2007. "The legacy of the Swedish gift and inheritance tax, 1884–2004," Working Paper Series 2007:23, Uppsala University, Department of Economics.
    4. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).
    5. 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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    More about this item

    Keywords

    Earning equations; Nonignorable response mechanism; Calibration; Selection; Full-information maximum likelihood;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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