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Small‐sample bias in synthetic cohort models of labor supply
Author(s) -
Devereux Paul J.
Publication year - 2007
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.938
Subject(s) - econometrics , current population survey , monte carlo method , statistics , sample (material) , context (archaeology) , sample size determination , cohort , sampling (signal processing) , population , economics , computer science , mathematics , demography , geography , chemistry , archaeology , filter (signal processing) , chromatography , sociology , computer vision
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.