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A sample selection model for unit and item nonresponse in cross-sectional surveys

Author

Listed:
  • Giuseppe De Luca

    (University of Rome “Tor Vergata”)

  • Franco Peracchi

    () (University of Rome “Tor Vergata”)

Abstract

We consider a general sample selection model where unit and item nonresponse simultaneously affect a regression relationship of interest, and both types of nonresponse are potentially correlated. We estimate both parametric and semiparametric specifications of the model. The parametric specification assumes that the errors in the latent regression equations follow a trivariate Gaussian distribution. The semiparametric specification avoids distributional assumptions about the underlying regression errors. In our empirical application, we estimate Engel curves for consumption expenditure using data from the first wave of SHARE (Survey on Health, Aging and Retirement in Europe).

Suggested Citation

  • Giuseppe De Luca & Franco Peracchi, 2007. "A sample selection model for unit and item nonresponse in cross-sectional surveys," CEIS Research Paper 95, Tor Vergata University, CEIS.
  • Handle: RePEc:rtv:ceisrp:95
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    File URL: ftp://www.ceistorvergata.it/repec/rpaper/No-95.pdf
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    References listed on IDEAS

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    1. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2003. "Asking consumption questions in general purpose surveys," Economic Journal, Royal Economic Society, vol. 113(491), pages 540-567, November.
    2. John C. Ham, 1982. "Estimation of a Labour Supply Model with Censoring Due to Unemployment and Underemployment," Review of Economic Studies, Oxford University Press, vol. 49(3), pages 335-354.
    3. Cheti Nicoletti & Franco Peracchi, 2005. "Survey response and survey characteristics: microlevel evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 763-781.
    4. Erich Battistin & Raffaele Miniaci & Guglielmo Weber, 2003. "What Do We Learn from Recall Consumption Data?," Journal of Human Resources, University of Wisconsin Press, vol. 38(2).
    5. Gabler, Siegfried & Laisney, Francois & Lechner, Michael, 1993. "Seminonparametric Estimation of Binary-Choice Models with an Application to Labor-Force Participation," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 61-80, January.
    6. 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.
    7. Regina Riphahn & Oliver Serfling, 2005. "Item non-response on income and wealth questions," Empirical Economics, Springer, vol. 30(2), pages 521-538, September.
    8. Lee, Lung-fei, 1995. "Semiparametric maximum likelihood estimation of polychotomous and sequential choice models," Journal of Econometrics, Elsevier, vol. 65(2), pages 381-428, February.
    9. Joachim Winter, 2004. "Response bias in survey-based measures of household consumption," Economics Bulletin, AccessEcon, vol. 3(9), pages 1-12.
    10. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, pages 37-58.
    11. Agar Brugiavini & Tullio Jappelli & Guglielmo Weber, 2002. "The Survey on Health, Aging and Wealth," CSEF Working Papers 86, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    12. Gerfin, Michael, 1996. "Parametric and Semi-parametric Estimation of the Binary Response Model of Labor Market Participation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(3), pages 321-339, May-June.
    13. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    14. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 31(3), pages 129-137.
    15. Mark B. Stewart, 2004. "Semi-nonparametric estimation of extended ordered probit models," Stata Journal, StataCorp LP, vol. 4(1), pages 27-39, March.
    16. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    17. Whitney K. Newey, 2009. "Two-step series estimation of sample selection models," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 217-229, January.
    18. Leung, Siu Fai & Yu, Shihti, 1996. "On the choice between sample selection and two-part models," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 197-229.
    19. 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.
    20. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    21. repec:ebl:ecbull:v:3:y:2004:i:9:p:1-12 is not listed on IDEAS
    22. Newey, Whitney K, 1985. "Maximum Likelihood Specification Testing and Conditional Moment Tests," Econometrica, Econometric Society, vol. 53(5), pages 1047-1070, September.
    23. Melenberg, B. & van Soest, A.H.O., 1996. "Measuring the costs of children : Parametric and semiparametric estimators," Other publications TiSEM 1227b8b2-0575-4b5d-9bac-7, Tilburg University, School of Economics and Management.
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    Citations

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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. Maribel Jiménez, 2011. "Un Análisis Empírico de las No Linealidades en la Movilidad Intergeneracional del Ingreso. El caso de la Argentina," CEDLAS, Working Papers 0114, CEDLAS, Universidad Nacional de La Plata.
    3. Maribel Jimenez & Monica Jimenez, 2009. "La Movilidad Intergeneracional del Ingreso: Evidencia para Argentina," CEDLAS, Working Papers 0084, CEDLAS, Universidad Nacional de La Plata.
    4. Nicoletti, Cheti, 2008. "Multiple sample selection in the estimation of intergenerational occupational mobility," ISER Working Paper Series 2008-20, Institute for Social and Economic Research.
    5. Giuseppe De Luca, 2008. "SNP and SML estimation of univariate and bivariate binary-choice models," Stata Journal, StataCorp LP, vol. 8(2), pages 190-220, June.
    6. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," Working Paper Series of the German Council for Social and Economic Data 165, German Council for Social and Economic Data (RatSWD).
    7. Ivan Faiella, 2010. "The use of survey weights in regression analysis," Temi di discussione (Economic working papers) 739, Bank of Italy, Economic Research and International Relations Area.

    More about this item

    Keywords

    Unit nonresponse; item nonresponse; cross-sectional surveys; sample selection models; Engel curves.;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis

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