The Nonparametric Identification of Treatment Effects in Duration Models

Abbring, Jaap H.; Berg, Gérard J. van den

doi:10.1111/1468-0262.00456

Cited by 545 publications

(735 citation statements)

References 44 publications

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“…2 This allows identifying the causal effects of the health shock on the disability and employment status without exclusion restrictions or strong functional form restrictions. Abbring and Van den Berg (2003) and Heckman and Navarro (2007) provide extensive discussions on the identification of causal effects in event-history models and discrete-time discrete-choice models, respectively. We return to identification issues later.…”

Section: Theoretical Background and The Empirical Modelmentioning

confidence: 99%

“…We refer to Appendix A for a more elaborate, but intuitive explanation of the identification of our model. Abbring and Van den Berg (2003) provide a formal discussion of identification in event history models.…”

Section: Empirical Specificationmentioning

confidence: 99%

“…The three endogenous variables of the model are related via unobserved components that remain fixed across time. Identification of this kind of models has been discussed extensively by Abbring and Van den Berg (2003) and Heckman and Navarro (2007).…”

Section: Introductionmentioning

confidence: 99%

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Health shocks, disability and work

2016

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• Exploits unscheduled hospitalizations as unanticipated health shocks in event-history model.• Health shocks substantially increase disability risk, but no direct effect on employment.• Finds sizeable (local average treatment) effect of onset of disability on employment.• Effects larger for men and low-educated workers. a b s t r a c t a r t i c l e i n f o This paper focuses on the relation between health shocks and the onset of a disability and employment outcomes. We estimate an event-history model using data from the British National Child Development Study (NCDS), where accidents causing a unscheduled hospitalization are the measure for unanticipated health shocks. Our results show that experiencing such a health shock substantially increases the likelihood of the onset of a disability, while it does not have direct effects on employment at later ages. This finding is used to simulate the causal effects of the onset of a disability on later employment outcomes. These simulations show that about two-third of the association between disability and employment can be explained by the causal effect of the onset of a disability on employment. The remaining one-third is selection. For men and lower-educated workers the association is mainly explained by the causal effect, while for women selection is more important.

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Section: Theoretical Background and The Empirical Modelmentioning

confidence: 99%

Section: Empirical Specificationmentioning

confidence: 99%

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Health shocks, disability and work

2016

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“…Given that we do not have the precise dates for these windfalls, we cannot implement a timing of events framework à la Abbring and van den Berg (2003), and rely on the exogeneity of windfall wealth gains as key identifying strategy. Section (2) outlined the reasons for the stationarity of the optimal search strategy in our framework: workers compare their current job to job offers and home production opportunities arriving at a Poisson rate.…”

Section: Job Quality Search and Wealthmentioning

confidence: 99%

Wealth effects on job preferences

Haywood

2016

Labour Economics

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“…Their proposal does, however, not allow for the actual estimation of a treatment effect. Also related to our work is the large literature on parametric modelling for the estimation of a treatment effect on survival times, see, e.g., Robins (1999), Hernán, Brumback and Robins (2001) and Abbring and van der Berg (2003). For instance, Robins and co-authors also use a potential outcome framework although focusing on parametric modelling of the data generating mechanism.…”

Section: Introductionmentioning

confidence: 99%

Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences

Luna

Johansson

2010

Journal of Statistical Planning and Inference

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. Terms of use: Documents in Non-Parametric Inference for the Effect of a Treatment on Survival Times with Application in the Health and Social Sciences Xavier de Luna Per Johansson D I S C U S S I O N P A P E R S E R I E SIZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. In this paper we perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. Two such studies are discussed: a heart transplant program and a study of Swedish unemployed eligible for employment subsidy. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is time between the entrance in the study and treatment. This can be done only when sufficient data is available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of waiting time. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D. B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study. ...

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The Nonparametric Identification of Treatment Effects in Duration Models

Cited by 545 publications

References 44 publications

Health shocks, disability and work

Health shocks, disability and work

Wealth effects on job preferences

Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences

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