Selection Bias: More than a Female Phenomenon

Abstract

When a regression model is estimated using a censored subset of observations, coefficient estimates may be biased. Censored regression models have a long history in biometrics, engineering and other areas of applied statistics. The interest of economists in these models was stimulated by Tobin’s work on durable goods consumption in the late 1950s. It was Heckman’s publication of a simple two-step procedure for estimating censored regression models, however, that led to their widespread usage in applied econometric studies. Although this is not necessarily the best method for estimating all censored regression models, it has certain attractive properties. An understanding of this method is vital to the proper interpretation of the wealth of applied studies based on this approach. Also, valuable insights into the basic nature of sample selection problems can be gained from the formulation of the censored regression model popularized by Heckman.We begin by exploring the estimation problems resulting from censoring and from certain properties of Heckman’s two-step estimation method. Procedures are developed for assessing the nature and extent of problems resulting from censoring; these procedures are then applied in an empirical analysis of the wage rates and hours of work of individuals in 10 different demographic groups using data from the Panel Study of Income Dynamics. One of our findings is that estimation using censored data can lead to bias and other related problems even when the degree of censoring is slight.KeywordsOrdinary Little SquareWage RateOrdinary Little Square RegressionTobit ModelOrdinary Little Square EstimateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.