This paper investigates how to test for nonresponse selection bias in wage functions induced by missing income information. We suggest an “easy-to-implement” approach which requires information on interviewer IDs and the interview date rather than hard-to-get interviewer characteristics.

This paper investigates how to test and correct for nonresponse selection bias induced by missing income information when estimating wage functions. The novelty is to use the variation in interviewer-specific response rates as exclusion restriction within the framework of a sample selection model.

Item nonresponse is often dealt with through imputation. Marginal imputation, which consists of treating separately each variable requiring imputation, generally leads to biased estimators of parameters (e.g., coefficients of correlation) measuring relationships between variables. Shao and Wang...

We consider a general sample selection model where unit and item nonresponse simultaneously affect a regression relationship of interest, and both types of nonresponse are potentially correlated. We estimate both parametric and semiparametric specifications of the model. The parametric...