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Distribution Regression in Duration Analysis: an Application to Unemployment Spells


  • Miguel A. Delgado
  • Andr'es Garc'ia-Suaza
  • Pedro H. C. Sant'Anna


This article proposes estimation and inference procedures for distribution regression models with randomly right-censored data. The proposal generalizes classical duration models to a situation where slope coefficients can vary with the elapsed duration, and is suitable for discrete, continuous or mixed outcomes. Given that in general distribution regression coefficients do not have clear economic interpretation, we also propose consistent and asymptotically normal estimators for the average distribution marginal effects. Finite sample properties of the proposed method are studied by means of Monte Carlo experiments. Finally, we apply our proposed tools to study the effect of unemployment benefits on unemployment duration. Our results suggest that, on average, an increase in unemployment benefits is associated with a nonlinear, non-monotone effect on the unemployment duration distribution and that such an effect is more pronounced for workers subjected to liquidity constraints.

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  • Miguel A. Delgado & Andr'es Garc'ia-Suaza & Pedro H. C. Sant'Anna, 2019. "Distribution Regression in Duration Analysis: an Application to Unemployment Spells," Papers 1904.06185,
  • Handle: RePEc:arx:papers:1904.06185

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    References listed on IDEAS

    1. Stute, W., 1993. "Consistent Estimation Under Random Censorship When Covariables Are Present," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 89-103, April.
    2. Victor Chernozhukov & Ivan Fernandez-Val & Siyi Luo, 2018. "Distribution regression with sample selection, with an application to wage decompositions in the UK," CeMMAP working papers CWP68/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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