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Rotation group bias and the persistence of misclassification errors in the Current Population Surveys

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  • Shuaizhang Feng
  • Yingyao Hu
  • Jiandong Sun

Abstract

We develop a general misclassification model to explain the so-called “Rotation Group Bias (RGB)” problem in the Current Population Surveys, where different rotation groups report different labor force statistics. The key insight is that responses to repeated questions in surveys can depend not only on unobserved true values, but also on previous responses to the same questions. Our method provides a framework to understand why unemployment rates in rotation group one are higher than those in other rotation groups in the CPS, without imposing any a priori assumptions on the existence and direction of RGB. Using our method, we provide new estimates of the U.S. unemployment rates, which are much higher than the official series, but lower than previous estimates that ignored persistence in misclassification.

Suggested Citation

  • Shuaizhang Feng & Yingyao Hu & Jiandong Sun, 2022. "Rotation group bias and the persistence of misclassification errors in the Current Population Surveys," Econometric Reviews, Taylor & Francis Journals, vol. 41(9), pages 1077-1094, September.
    • Handle: RePEc:taf:emetrv:v:41:y:2022:i:9:p:1077-1094
    DOI: 10.1080/07474938.2022.2091361
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