Analyzing Movements over Time in Employment Status and Welfare Participation while Controlling for Seam Bias using SIPP

Joint with: Xianghong Li and Lara Shore-Sheppard

Economists and policymakers have long been interested in the determinants of employment and welfare dynamics among less-educated women. Estimating duration models on longitudinal data allows for time changing factors that may differentially impact entries and exits from various

labor market states to be identified. However, in using such longitudinal data, researchers must confront particular data-quality issues. In this paper we develop a parametric approach to address seam bias, a common source of reporting errors in longitudinal surveys, in a duration

model setting. “Seam bias” refers to the tendency for a much larger fraction of transitions to be reported as occurring at the end of the reference period than would be expected to occur by chance. We apply this approach to the analysis of transitions between employment and nonemployment, and transitions between participation and non-participation in welfare, among lesseducated

single mothers using the Survey of Income and Program Participation (SIPP). We discuss identification of the model, and show that the model is identified without restricting the duration dependence. We compare results from our approach to those obtained following the

standard approach in applied work of using only the observations from the last month of the reference period. We find that the standard approach leads not only to a loss of statistical power, but also to biased estimates due to the omission of short spells and the incorrect measurement of spell length.

For more information, contact Petra Todd.