Rotation group bias and the persistence of misclassification errors in the Current Population Surveys
提出一个通用错误分类模型，解释当前人口调查中不同轮换组报告劳动力统计数据的差异，发现回答不仅取决于真实值还受之前回答影响，并据此给出比官方更高但比忽略持续性的估计更低的美国失业率新估计。
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.