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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.

This paper provides a test for completeness in a class of nonparametric specification with an additive and independent error term. It is known that such a nonparametric location family of functions is complete if and only if the characteristic function of the error term has no zeros on the real line. Because a zero of the error characteristic function implies that of an observed marginal distribution, we propose a simple test for zeros of characteristic function of the observed distribution, in which rejection of the null hypothesis implies the completeness. This test is applicable to many popular setting, such as nonparametric regression models with instrumental variables, and nonclassical measurement error models. We describe the asymptotic behavior of the tests under the null and alternative hypotheses and investigate the finite sample properties of the proposed test through a Monte Carlo study. We illustrate our method empirically by estimating a measurement error model using the CPS/SSR 1978 exact match file.

This paper presents a novel self-report approach to identify a general causal model with an unobserved covariate, which can be unobserved heterogeneity or an unobserved choice variable. It shows that a carefully designed noninvasive survey procedure can provide enough information to identify the complete causal model through the joint distribution of the observables and the unobservable. The global nonparametric point identification results provide sufficient conditions under which the joint distribution of four observables, two in a causal model and two from surveys, uniquely determines the joint distribution of the unobservable in the causal model and the four observables. The identification of such a joint distribution including the unobserved covariate implies that the complete causal model is identified.