Small-sample bias in synthetic cohort models of labor supply
This paper investigates small-sample biases in synthetic cohort models (repeated cross-sectional data grouped at the cohort and year level) in the context of a female labor supply model. I use the Current Population Survey to compare estimates when group sizes are extremely large to those that arise from randomly drawing subsamples of observations from the large groups. I augment this approach with Monte Carlo analysis so as to precisely quantify biases and coverage rates. In this particular application, thousands of observations per group are required before small-sample issues can be ignored in estimation and sampling error leads to large downward biases in the estimated income elasticity. Copyright © 2007 John Wiley & Sons, Ltd.
Year of publication: |
2007
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Authors: | Devereux, Paul J. |
Published in: |
Journal of Applied Econometrics. - John Wiley & Sons, Ltd.. - Vol. 22.2007, 4, p. 839-848
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Publisher: |
John Wiley & Sons, Ltd. |
Saved in:
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