Artikel
Time distributed difference-in-differences estimates of return to training
This work is devoted to estimating the individual return to worker's professional training. The research is based on the personnel records of Russian metallurgical enterprise (2006-2010). The main factors that distinguish this paper from others are the following: (I) We focused on the internal labour market, concluding that it has common peculiarities of wage setting concerned with training as an open labour market. (II) We show that mobility-friendly training programs give high returns, and not only in transition economies. (III) We suggest controlling for mobility by choosing a corresponding control group. (IV) We use a robust new specification that is reactive to different dynamics of the dependent variable in treated and control groups in difference-in-differences estimates. (V) We compared three different kinds of training and our conclusions could have practical application. The best way to raise personal earnings is on-the-job training. The internal mobility caused by retraining courses has the same impact on workers as if they lacked retraining. The wages of workers trained in the same field grow randomly for a few months before and after training. Nevertheless it is difficult to prove the causal effect of this kind of training on wage growth.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Cogent Economics & Finance ; ISSN: 2332-2039 ; Volume: 5 ; Year: 2017 ; Issue: 1 ; Pages: 1-12 ; Abingdon: Taylor & Francis
- Klassifikation
-
Wirtschaft
- Thema
-
earnings function
Mincerian type equation
difference-in-differences
treatment effect
personnel records
panel data
internal labour market
training
retrain courses
return to training
- Ereignis
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Geistige Schöpfung
- (wer)
-
Aistov, Andrey
- Ereignis
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Veröffentlichung
- (wer)
-
Taylor & Francis
- (wo)
-
Abingdon
- (wann)
-
2017
- DOI
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doi:10.1080/23322039.2017.1300978
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Artikel
Beteiligte
- Aistov, Andrey
- Taylor & Francis
Entstanden
- 2017
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