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Thompson says America must "unchoose" policies that have led to mass incarceration

Axinn says new data on campus rape will "allow students to see for themselves the full extent of this problem"

Frey says white population is growing in Detroit and other large cities


Susan Murphy to speak at U-M kickoff for data science initiative, Oct 6, Rackham

Andrew Goodman-Bacon, former trainee, wins 2015 Nevins Prize for best dissertation in economic history

Deirdre Bloome wins ASA award for work on racial inequality and intergenerational transmission

Bob Willis awarded 2015 Jacob Mincer Award for Lifetime Contributions to the Field of Labor Economics

Next Brown Bag

Monday, Oct 5 at noon, 6050 ISR
Colter Mitchell: Biological consequences of poverty

Attrition Bias in Economic Relationships Estimated with Matched CPS Panels

Archived Abstract of Former PSC Researcher

Neumark, David, and Genevieve Kenney. 2004. "Attrition Bias in Economic Relationships Estimated with Matched CPS Panels." Journal of Economic and Social Measurement, 29(4): 445-472.

Short panel data sets constructed by matching individuals across monthly files of the Current Population Survey (CPS) have been used to study a wide range of questions in labor economics. But because the CPS does not follow movers, these panels exhibit significant attrition, which may lead to bias in longitudinal estimates. The Survey of Income and Program Participation (SIPP) uses essentially the same sampling frame and design as the CPS, but makes substantial efforts to follow movers. We therefore use the SIPP to construct "data-based" rather than "model-based" corrections for bias from selective attrition. The approach is applied to two questions that have been studied with CPS data - union wage differentials and the male marriage wage premium. The evidence suggests that in many applications the advantages of using matched CPS panels to obtain longitudinal estimates are likely to far outweigh the disadvantages from attrition biases, although we should allow for the possibility that attrition bias leads the longitudinal estimates to be understated.

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