Heterogeneity versus duration dependence with competing risks

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  • Author: Richard Robb, Halina Frydman and Andrew Robertson
  • Date: 09 October 2018
  • Copyright: Image copyright of Patrick Rhodes

Two hypotheses can explain the declining probability of gaining employment as an unemployment spell wears on: heterogeneity of the unemployed versus duration dependence. The nonparametric tests developed in the literature for testing duration dependence would not account for the fact that an unemployment spell can terminate in other ways than employment. The nonparametric tests developed in this paper extend, under certain conditions, those tests to competing risks. In a recent paper, published in Applied Stochastic Models in Business and Industry, the authors illustrate their test using US unemployment data in which we find little consistent evidence for duration dependence.

The full paper is available below:

Heterogeneity versus duration dependence with competing risks: an application to the labor market

Richard Robb, Halina Frydman and Andrew Robertson

Applied Stochastic Models in Business and Industry, Volume 33, Issue 5, September/October 2017, pages 465-475

thumbnail image: Heterogeneity versus duration dependence with competing risks

In March 2013, 629 people entered the U.S. Current Population Survey as unemployed. In each of the following 3 months, the proportion of unemployed individuals who found jobs declined: 18.8% in April, 17.0% in May, and 7.7% in June. There are two explanations for why the probability of finding a job goes down as an unemployment spell persists: (i) heterogeneity (the surviving population contains the least-employable individuals) or (ii) duration dependence (each individual’s chance of employment decreases over time, e.g., as skills deteriorate or stigma builds). This paper develops a very simple nonparametric test to detect duration dependence in discrete data. The test accounts for competing risks—besides finding employment, some individuals drop out of the labor force or go missing from the survey. This test can be applied to a wide variety of problems where an event becomes less likely over time, such as epidemiology (does the incidence of a particular disease decrease with age because individuals in the surviving population inherited a genetic resistance to that disease or because they have developed stronger immune systems over time?) and insurance (do drivers become less likely to file their first insurance claims as time passes because those who remain are naturally cautious or because they have grown skilled with practice?).

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