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Scarring effects of remaining unemployed for long-term unemployed school-leavers

Bart Cockx (UGent) and Matteo Picchio (UGent)
(2013) JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY. 176(4). p.951-980
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Organization
Abstract
This study investigates whether and to what extent further unemployment experience for youths who are already long-term unemployed imposes a penalty on subsequent labour market outcomes. We propose a flexible method for analysing the effect on wages aside of transitions from unemployment and employment within a multivariate duration model which controls for selection on observables and unobservables. We find that prolonging unemployment drastically decreases the chances of finding employment, but hardly affects the quality of subsequent employment. The analysis suggests that negative duration dependence in the job finding rate is induced by negative signalling and not by human capital depreciation.
Keywords
DURATION DATA, NONPARAMETRIC IDENTIFICATION, JOB SEARCH, YOUTH UNEMPLOYMENT, LABOR-MARKET, INSURANCE, EMPLOYMENT, MODELS, TIME, EARNINGS, Duration dependence, Employment quality, Scarring effect of unemployment duration, Selectivity, Wage in multivariate duration model

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Citation

Please use this url to cite or link to this publication:

MLA
Cockx, Bart, and Matteo Picchio. “Scarring Effects of Remaining Unemployed for Long-term Unemployed School-leavers.” JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 176.4 (2013): 951–980. Print.
APA
Cockx, B., & Picchio, M. (2013). Scarring effects of remaining unemployed for long-term unemployed school-leavers. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 176(4), 951–980.
Chicago author-date
Cockx, Bart, and Matteo Picchio. 2013. “Scarring Effects of Remaining Unemployed for Long-term Unemployed School-leavers.” Journal of the Royal Statistical Society Series A-statistics in Society 176 (4): 951–980.
Chicago author-date (all authors)
Cockx, Bart, and Matteo Picchio. 2013. “Scarring Effects of Remaining Unemployed for Long-term Unemployed School-leavers.” Journal of the Royal Statistical Society Series A-statistics in Society 176 (4): 951–980.
Vancouver
1.
Cockx B, Picchio M. Scarring effects of remaining unemployed for long-term unemployed school-leavers. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY. 2013;176(4):951–80.
IEEE
[1]
B. Cockx and M. Picchio, “Scarring effects of remaining unemployed for long-term unemployed school-leavers,” JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, vol. 176, no. 4, pp. 951–980, 2013.
@article{2988857,
  abstract     = {This study investigates whether and to what extent further unemployment experience for youths who are already long-term unemployed imposes a penalty on subsequent labour market outcomes. We propose a flexible method for analysing the effect on wages aside of transitions from unemployment and employment within a multivariate duration model which controls for selection on observables and unobservables. We find that prolonging unemployment drastically decreases the chances of finding employment, but hardly affects the quality of subsequent employment. The analysis suggests that negative duration dependence in the job finding rate is induced by negative signalling and not by human capital depreciation.},
  author       = {Cockx, Bart and Picchio, Matteo},
  issn         = {0964-1998},
  journal      = {JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY},
  keywords     = {DURATION DATA,NONPARAMETRIC IDENTIFICATION,JOB SEARCH,YOUTH UNEMPLOYMENT,LABOR-MARKET,INSURANCE,EMPLOYMENT,MODELS,TIME,EARNINGS,Duration dependence,Employment quality,Scarring effect of unemployment duration,Selectivity,Wage in multivariate duration model},
  language     = {eng},
  number       = {4},
  pages        = {951--980},
  title        = {Scarring effects of remaining unemployed for long-term unemployed school-leavers},
  url          = {http://dx.doi.org/10.1111/j.1467-985X.2012.01086.x},
  volume       = {176},
  year         = {2013},
}
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