Arbeitspapier

Nonseparable Sample Selection Models with Censored Selection Rules: An Application to Wage Decompositions

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from integration over ranges of values of the control function. We provide conditions under which these objects are appropriate for the total population. We also present results regarding the estimation of counterfactual distributions. We derive conditions for identification for these different objects and suggest strategies for estimation. We also provide the associated asymptotic theory. These strategies are illustrated in an empirical investigation of the determinants of female wages and wage growth in the United Kingdom.

Sprache
Englisch

Erschienen in
Series: IZA Discussion Papers ; No. 11294

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
Thema
sample selection
nonseparable models
control function
quantile and distribution regression

Ereignis
Geistige Schöpfung
(wer)
Fernández-Val, Iván
van Vuuren, Aico
Vella, Francis
Ereignis
Veröffentlichung
(wer)
Institute of Labor Economics (IZA)
(wo)
Bonn
(wann)
2018

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Fernández-Val, Iván
  • van Vuuren, Aico
  • Vella, Francis
  • Institute of Labor Economics (IZA)

Entstanden

  • 2018

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