Small‐sample bias in synthetic cohort models of labor supply
研究合成队列模型在女性劳动供给估计中的小样本偏差，使用当前人口调查数据和蒙特卡洛模拟发现，每组需数千观测值才能忽略小样本问题，且抽样误差会导致收入弹性估计严重偏低。
Abstract 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.