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State Dependence and Heterogeneity in Health Using a Bias Corrected Fixed Effects Estimator

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  • Jesus M. Carro
  • Alejandra Traferri

Abstract

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample sizes.

Suggested Citation

  • Jesus M. Carro & Alejandra Traferri, 2011. "State Dependence and Heterogeneity in Health Using a Bias Corrected Fixed Effects Estimator," Documentos de Trabajo 402, Instituto de Economia. Pontificia Universidad Católica de Chile..
    • Handle: RePEc:ioe:doctra:402
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    Cited by:

    1. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    2. Fernández-Val, Iván & Savchenko, Yevgeniya & Vella, Francis, 2017. "Evaluating the role of income, state dependence and individual specific heterogeneity in the determination of subjective health assessments," Economics & Human Biology, Elsevier, vol. 25(C), pages 85-98.
    3. Chris Muris & Pedro Raposo & Sotiris Vandoros, 2020. "A Dynamic Ordered Logit Model with Fixed Effects," Department of Economics Working Papers 2020-14, McMaster University.
    4. Stephen Hoskins & David W. Johnston & Johannes S. Kunz & Michael A. Shields & Kevin E. Staub, 2024. "Heterogeneity in the Persistence of Health: Evidence from a Monthly Micro Panel," Papers 2024-06, Centre for Health Economics, Monash University.
    5. Bartolucci, Francesco & Pigini, Claudia & Valentini, Francesco, 2023. "Testing for state dependence in the fixed-effects ordered logit model," Economics Letters, Elsevier, vol. 222(C).
    6. Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting individual effects in fixed effects panel probit models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1109-1145, July.
    7. Pigini, Claudia & Presbitero, Andrea F. & Zazzaro, Alberto, 2016. "State dependence in access to credit," Journal of Financial Stability, Elsevier, vol. 27(C), pages 17-34.
    8. Bo E. Honor'e & Chris Muris & Martin Weidner, 2021. "Dynamic Ordered Panel Logit Models," Papers 2107.03253, arXiv.org, revised Apr 2024.
    9. William H. Greene & Mark N. Harris & Bruce Hollingsworth, 2015. "Inflated Responses in Measures of Self-Assessed Health," American Journal of Health Economics, University of Chicago Press, vol. 1(4), pages 461-493, Fall.
    10. Lionel WILNER, 2019. "The Dynamics of Individual Happiness," Working Papers 2019-18, Center for Research in Economics and Statistics.
    11. Lucchetti, Riccardo & Pigini, Claudia, 2017. "DPB: Dynamic Panel Binary Data Models in gretl," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i08).
    12. repec:spo:wpecon:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    13. repec:hal:wpspec:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    14. Pedro Albarran & Raquel Carrasco & Jesus M. Carro, 2019. "Estimation of Dynamic Nonlinear Random Effects Models with Unbalanced Panels," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(6), pages 1424-1441, December.
    15. repec:hal:spmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    16. Chaudhuri, Kausik & Reilly, Kevin T. & Spencer, David A., 2015. "Job satisfaction, age and tenure: A generalized dynamic random effects model," Economics Letters, Elsevier, vol. 130(C), pages 13-16.
    17. Chrysanthou, Georgios Marios & Vasilakis, Chrysovalantis, 2018. "The Dynamics and Determinants of Bullying Victimisation," IZA Discussion Papers 11902, Institute of Labor Economics (IZA).
    18. Chappell, Henry W. & McGregor, Rob Roy, 2018. "Committee decision-making at Sweden's Riksbank," European Journal of Political Economy, Elsevier, vol. 53(C), pages 120-133.
    19. Francesco Bartolucci & Claudia Pigini, 2017. "Granger causality in dynamic binary short panel data models," Working Papers 421, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    20. Cubi-Molla, P. & Jofre-Bonet, M. & Serra-Sastre, V., 2013. "Adaptation to Health States: A Micro-Econometric Approach," Working Papers 13/02, Department of Economics, City University London.
    21. repec:spo:wpmain:info:hdl:2441/f6h8764enu2lskk9p2m9mgp8l is not listed on IDEAS
    22. Greene, William & Harris, Mark N. & Knott, Rachel & Rice, Nigel, 2023. "Reporting heterogeneity in modeling self-assessed survey outcomes," Economic Modelling, Elsevier, vol. 124(C).
    23. Fernández-Val, Iván & Savchenko, Yevgeniya & Vella, Francis, 2013. "Evaluating the Role of Individual Specific Heterogeneity in the Relationship Between Subjective Health Assessments and Income," IZA Discussion Papers 7651, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

    Dynamic ordered probit; fixed effects; self-assessed health; reporting bias; panel data; unobserved heterogeneity; incidental parameters; bias correction;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I19 - Health, Education, and Welfare - - Health - - - Other

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