Abstract
This paper suggests that skill accumulation through past work experience, or "learning-by-doing" (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.
| Original language | English |
|---|---|
| Pages (from-to) | 1498-1520 |
| Number of pages | 23 |
| Journal | American Economic Review |
| Volume | 92 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2002 Dec 1 |
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All Science Journal Classification (ASJC) codes
- Economics and Econometrics
Cite this
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Learning-by-doing as a propagation mechanism. / Chang, Yongsung; Gomes, Joao F.; Schorfheide, Frank.
In: American Economic Review, Vol. 92, No. 5, 01.12.2002, p. 1498-1520.Research output: Contribution to journal › Article
TY - JOUR
T1 - Learning-by-doing as a propagation mechanism
AU - Chang, Yongsung
AU - Gomes, Joao F.
AU - Schorfheide, Frank
PY - 2002/12/1
Y1 - 2002/12/1
N2 - This paper suggests that skill accumulation through past work experience, or "learning-by-doing" (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.
AB - This paper suggests that skill accumulation through past work experience, or "learning-by-doing" (LBD), can provide an important propagation mechanism in a dynamic stochastic general-equilibrium model, as the current labor supply affects future productivity. Our econometric analysis uses a Bayesian approach to combine micro-level panel data with aggregate time series. Formal model evaluation shows that the introduction of the LBD mechanism improves the model's ability to fit the dynamics of aggregate output and hours.
UR - http://www.scopus.com/inward/record.url?scp=0040081819&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0040081819&partnerID=8YFLogxK
U2 - 10.1257/000282802762024601
DO - 10.1257/000282802762024601
M3 - Article
AN - SCOPUS:0040081819
VL - 92
SP - 1498
EP - 1520
JO - American Economic Review
JF - American Economic Review
SN - 0002-8282
IS - 5
ER -