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.