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Multivariate Decomposition for Hazard Rate Models

Author

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  • Powers, Daniel A.

    (University of Texas at Austin)

  • Yun, Myeong-Su

    (Inha University)

Abstract

We develop a regression decomposition technique for hazard rate models, where the difference in observed rates is decomposed into components attributable to group differences in characteristics and group differences in effects. The baseline hazard is specified using a piecewise constant exponential model, which leads to convenient estimation based on a Poisson regression model fit to person-period, or split-episode data. This specification allows for a flexible representation of the baseline hazard and provides a straightforward way to introduce time-varying covariates and time-varying effects. We provide computational details underlying the method and apply the technique to the decomposition of the black-white difference in first premarital birth rates into components reflecting characteristics and effect contributions of several predictors, as well as the effect contribution attributable to race differences in the baseline hazard.

Suggested Citation

  • Powers, Daniel A. & Yun, Myeong-Su, 2009. "Multivariate Decomposition for Hazard Rate Models," IZA Discussion Papers 3971, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp3971
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    References listed on IDEAS

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    Cited by:

    1. YAMAGUCHI Kazuo, 2014. "Decomposition of Gender or Racial Inequality with Endogenous Intervening Covariates: An extension of the DiNardo-Fortin-Lemieux method," Discussion papers 14061, Research Institute of Economy, Trade and Industry (RIETI).
    2. Oertel, Simon, 2014. "Density imprints and organisations’ survival chances: a question of organisational ancestors," Journal of East European Management Studies, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 19(1), pages 81-105.
    3. Daniel A. Powers & Hirotoshi Yoshioka & Myeong-Su Yun, 2011. "mvdcmp: Multivariate decomposition for nonlinear response models," Stata Journal, StataCorp LP, vol. 11(4), pages 556-576, December.
    4. Weihua An & Adam N. Glynn, 2021. "Treatment Effect Deviation as an Alternative to Blinder–Oaxaca Decomposition for Studying Social Inequality," Sociological Methods & Research, , vol. 50(3), pages 1006-1033, August.
    5. Bowblis, John R. & Yun, Myeong-Su, 2010. "Racial and Ethnic Disparities in the Use of Drug Therapy," IZA Discussion Papers 4853, Institute of Labor Economics (IZA).
    6. Dieudonne Ndaruhuye Muhoza & Pierre Claver Rutayisire & Aline Umubyeyi, 2016. "Measuring the success of family planning initiatives in Rwanda: a multivariate decomposition analysis," Journal of Population Research, Springer, vol. 33(4), pages 361-377, December.
    7. Antonella Biscione & Dorothée Boccanfuso & Raul Caruso & Annunziata Felice, 2022. "The innovation gender gap in transition countries," Economia Politica: Journal of Analytical and Institutional Economics, Springer;Fondazione Edison, vol. 39(2), pages 493-516, July.
    8. García, A., 2016. "Oaxaca-Blinder Type Counterfactual Decomposition Methods for Duration Outcomes," Documentos de Trabajo 014186, Universidad del Rosario.
    9. Rocha, Vera & Carneiro, Anabela & Varum, Celeste, 2015. "What explains the survival gap of pushed and pulled corporate spin-offs?," Economics Letters, Elsevier, vol. 126(C), pages 127-130.
    10. Woojin Chung & Monica Das Gupta, 2007. "The Decline of Son Preference in South Korea: The Roles of Development and Public Policy," Population and Development Review, The Population Council, Inc., vol. 33(4), pages 757-783, December.
    11. Salas-Ortiz, A., 2021. "Explaining the ethnic gaps in COVID-19 outcomes in Mexico," Health, Econometrics and Data Group (HEDG) Working Papers 21/20, HEDG, c/o Department of Economics, University of York.
    12. Goldenstein, Jan & Hunoldt, Michael & Oertel, Simon, 2019. "How optimal distinctiveness affects new ventures' failure risk: A contingency perspective," Journal of Business Venturing, Elsevier, vol. 34(3), pages 477-495.

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    More about this item

    Keywords

    decomposition; piecewise constant exponential model; Poisson regression; hazard rates;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • J13 - Labor and Demographic Economics - - Demographic Economics - - - Fertility; Family Planning; Child Care; Children; Youth

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