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Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys

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  • Michael S. Rendall
  • Bonnie Ghosh-Dastidar
  • Margaret M. Weden
  • Zafar Nazarov

Abstract

Within-survey multiple imputation (MI) methods are adapted to pooled-survey regression estimation where one survey has a larger set of regressors but fewer observations than the other. This adaption is achieved through: (1) larger numbers of imputations to compensate for the higher fraction of missing values; (2) model-fit statistics to check the assumption that the two surveys sample from a common universe; and (3) specificying the analysis model completely from variables present in the survey with the larger set of regressors, thereby excluding variables never jointly observed. In contrast to the typical within-survey MI context, cross-survey missingness is monotonic and easily satisfies the Missing At Random (MAR) assumption needed for unbiased MI. Large efficiency gains in estimates of coefficients for variables in common between the surveys are demonstrated in an application to sociodemographic differences in the risk of experiencing a disabling occupational injury estimated from two nationally-representative panel surveys.

Suggested Citation

  • Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Zafar Nazarov, 2011. "Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys," Working Papers WR-887-1, RAND Corporation.
  • Handle: RePEc:ran:wpaper:wr-887-1
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    References listed on IDEAS

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    1. Thomas DeLeire, 2001. "Changes in Wage Discrimination against People with Disabilities: 1984-93," Journal of Human Resources, University of Wisconsin Press, vol. 36(1), pages 144-158.
    2. 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.
    3. Rodgers, Willard L, 1984. "An Evaluation of Statistical Matching," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(1), pages 91-102, January.
    4. Oh, Joong-Hwan & Shin, Eui Hang, 2003. "Inequalities in nonfatal work injury: the significance of race, human capital, and occupations," Social Science & Medicine, Elsevier, vol. 57(11), pages 2173-2182, December.
    5. Michael Rendall & Ryan Admiraal & Alessandra DeRose & Paola DiGiulio & Mark Handcock & Filomena Racioppi, 2008. "Population constraints on pooled surveys in demographic hazard modeling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(4), pages 519-539, October.
    6. 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.
    7. Thomas DeLeire, 2000. "The Wage and Employment Effects of the Americans with Disabilities Act," Journal of Human Resources, University of Wisconsin Press, vol. 35(4), pages 693-715.
    8. Roderick J. A. Little & Donald B. Rubin, 1989. "The Analysis of Social Science Data with Missing Values," Sociological Methods & Research, , vol. 18(2-3), pages 292-326, November.
    9. Moriarity, Chris & Scheuren, Fritz, 2003. "A Note on Rubin's Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 65-73, January.
    10. Michael Rendall & Mark Handcock & Stefan Jonsson, 2009. "Bayesian estimation of hispanic fertility hazards from survey and population data," Demography, Springer;Population Association of America (PAA), vol. 46(1), pages 65-83, February.
    11. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    12. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    13. Elizabeth Tighe & David Livert & Melissa Barnett & Leonard Saxe, 2010. "Cross-Survey Analysis to Estimate Low-Incidence Religious Groups," Sociological Methods & Research, , vol. 39(1), pages 56-82, August.
    14. Vicki Freedman & Douglas Wolf, 1995. "A case study on the use of multiple imputation," Demography, Springer;Population Association of America (PAA), vol. 32(3), pages 459-470, August.
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    Cited by:

    1. Goerke, Laszlo & Pannenberg, Markus, 2013. "Keeping up with the Joneses: Income Comparisons and Labour Supply," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 80033, Verein für Socialpolitik / German Economic Association.
    2. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Elizabeth H. Baker & Zafar Nazarov, 2013. "Multiple Imputation for Combined-survey Estimation With Incomplete Regressors in One but Not Both Surveys," Sociological Methods & Research, , vol. 42(4), pages 483-530, November.

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