Duration Dependence of Job-Finding Rates in Japan

2018 ◽  
pp. 169-186
Author(s):  
Akiomi Kitagawa ◽  
Souichi Ohta ◽  
Hiroshi Teruyama
Keyword(s):  
Duration Dependence ◽  
Job Finding

Statistical Discrimination and Duration Dependence in the Job Finding Rate

2018 ◽  
Vol 86 (4) ◽  
pp. 1631-1665 ◽  
Author(s):  
Gregor Jarosch ◽  
Laura Pilossoph
Keyword(s):  
Experimental Evidence ◽  
Labour Market ◽  
Statistical Discrimination ◽  
Unemployment Duration ◽  
Duration Dependence ◽  
Dynamic Selection ◽  
Job Finding ◽  
Large Decline

Abstract This article models a frictional labour market where employers endogenously discriminate against the long-term unemployed. The estimated model replicates recent experimental evidence which documents that interview invitations for observationally equivalent workers fall sharply as unemployment duration progresses. We use the model to quantitatively assess the consequences of such employer behaviour for job finding rates and long-term unemployment and find only modest effects given the large decline in callbacks. Interviews lost to duration impact individual job finding rates solely if they would have led to jobs. We show that such instances are rare when firms discriminate in anticipation of an ultimately unsuccessful application. Discrimination in callbacks is thus largely a response to dynamic selection, with limited consequences for structural duration dependence and long-term unemployment.

Mismatch

2007 ◽  
Vol 97 (4) ◽  
pp. 1074-1101 ◽  
Author(s):  
Robert Shimer
Keyword(s):  
Business Cycle ◽  
Labor Markets ◽  
Dynamic Model ◽  
Duration Dependence ◽  
Cycle Frequency ◽  
Separation Rate ◽  
The Business Cycle ◽  
Job Finding

This paper develops a dynamic model of mismatch. Workers and jobs are randomly allocated to labor markets. Each market clears, but some have excess (unemployed) workers and some have excess (vacant) jobs. As workers and jobs switch markets, unemployed workers find vacancies and employed workers become unemployed. The model is quantitatively consistent with the business cycle frequency comovement of unemployment, vacancies, and the job finding rate and explains much of these variables' volatility. It can also address cyclicality in the separation rate into unemployment and duration dependence in the job finding rate. The results are robust to some nonrandom mobility. (JEL E24, J41, J63, J64)

scholarly journals Job Seekers’ Perceptions and Employment Prospects: Heterogeneity, Duration Dependence, and Bias

2021 ◽  
Vol 111 (1) ◽  
pp. 324-363
Author(s):  
Andreas I. Mueller ◽  
Johannes Spinnewijn ◽  
Giorgio Topa
Keyword(s):  
Predictive Power ◽  
Search Behavior ◽  
Duration Dependence ◽  
Optimistic Bias ◽  
Job Seekers ◽  
Dynamic Selection ◽  
Duration Of Unemployment ◽  
Job Finding ◽  
Substantial Heterogeneity

This paper uses job seekers’ elicited beliefs about job finding to disentangle the sources of the decline in job-finding rates by duration of unemployment. We document that beliefs have strong predictive power for job finding, but are not revised downward when remaining unemployed and are subject to optimistic bias, especially for the long-term unemployed. Leveraging the predictive power of beliefs, we find substantial heterogeneity in job finding with the resulting dynamic selection explaining most of the observed negative duration dependence in job finding. Moreover, job seekers’ beliefs underreact to heterogeneity in job finding, distorting search behavior and increasing long-term unemployment. (JEL D83, E24, J22, J64, J65)

An easy way to create duration variables in binary cross-sectional time-series data

2020 ◽  
Vol 20 (4) ◽  
pp. 916-930
Author(s):  
Andrew Q. Philips
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Time Series Data ◽  
Series Data ◽  
Duration Dependence ◽  
Cross Sectional ◽  
Common Solution ◽  
The Common

In cross-sectional time-series data with a dichotomous dependent variable, failing to account for duration dependence when it exists can lead to faulty inferences. A common solution is to include duration dummies, polynomials, or splines to proxy for duration dependence. Because creating these is not easy for the common practitioner, I introduce a new command, mkduration, that is a straightforward way to generate a duration variable for binary cross-sectional time-series data in Stata. mkduration can handle various forms of missing data and allows the duration variable to easily be turned into common parametric and nonparametric approximations.

scholarly journals Job Search and Savings: Wealth Effects and Duration Dependence

2005 ◽  
Vol 23 (3) ◽  
pp. 467-489 ◽  
Author(s):  
Rasmus Lentz ◽  
Torben Tranæs
Keyword(s):  
Job Search ◽  
Wealth Effects ◽  
Duration Dependence

Bubbles, Stock Returns, and Duration Dependence

1994 ◽  
Vol 29 (3) ◽  
pp. 379 ◽  
Author(s):  
Grant McQueen ◽  
Steven Thorley
Keyword(s):  
Stock Returns ◽  
Duration Dependence

Time is Not A Theoretical Variable

2010 ◽  
Vol 18 (3) ◽  
pp. 293-294 ◽  
Author(s):  
Nathaniel Beck
Keyword(s):  
Time Series ◽  
Cross Section ◽  
Duration Dependence ◽  
Cross Section Data ◽  
Section Data ◽  
Data Analyst ◽  
Binary Time Series ◽  
Simple Cubic ◽  
Cubic Polynomial

Carter and Signorino (2010) (hereinafter “CS”) add another arrow, a simple cubic polynomial in time, to the quiver of the binary time series—cross-section data analyst; it is always good to have more arrows in one's quiver. Since comments are meant to be brief, I will discuss here only two important issues where I disagree: are cubic duration polynomials the best way to model duration dependence and whether we can substantively interpret duration dependence.

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