Self-selected migration presents one potential explanation for why observed returns to a college education in local labor markets vary widely even though U.S. workers are highly mobile. To assess the impact of self-selection on estimated returns, this paper first develops a Roy model of mobility and earnings where workers choose in which of the 50 states (plus the District of Columbia) to live and work. Available estimation methods are either infeasible for a selection model with so many alternatives or place potentially severe restrictions on earnings and the selection process. This paper develops an alternative econometric methodology which combines Lee's (1983) parametric maximum order statistic approach to reduce the dimensionality of the error terms with more recent work on semiparametric estimation of selection models (e.g., Ahn and Powell, 1993). The resulting semiparametric correction is easy to implement and can be adapted to a variety of other polychotomous choice problems. The empirical work, which uses 1990 U.S. Census data, confirms the role of comparative advantage in mobility decisions. The results suggest that self-selection of higher educated individuals to states with higher returns to education generally leads to upward biases in OLS estimates of the returns to education in state-specific labor markets. While the estimated returns to a college education are significantly biased, correcting for the bias does not narrow the range of returns across states. Consistent with the finding that the corrected return to a college education differs across the U.S., the relative state-to-state migration flows of college- versus high school-educated individuals respond strongly to differences in the return to education and amenities across states.

This paper studies alternative empirical strategies for estimating the effects of organizational design practices on performance, as well as the factors which determine organizational design, in a cross-section of firms. In particular, we propose an approach for estimating the parameters of an “organizational design production function.” Further, we identify consistent tests for two classes of hypotheses: first, that some sets of organizational design practices are mutually complementary; and second, that adoption patterns are consistent with static optimization of the organization’s profit. We develop an economic model where multiple organizational design practices are endogenously determined. The model includes exogenous variation in the costs and returns to each of the individual practices, which is the source of the heterogeneity among organizations. In many empirical applications, some of these variables will be unobserved to the econometrician. The model is used to evaluate how different econometric strategies can be interpreted under alternative assumptions about the economic and statistical environment. Of particular interest are a set of results which demonstrate that, under plausible hypotheses about the joint distribution of the unobservables, different reduced-form approaches used in the existing literature to test

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This paper presents estimates of the changing distribution of wages that are robust to possible selection effects. We find convincing evidence of an increase in overall inequality, changes in the “return ” to education and increases in inequality within age and education groups. On the other hand we find that the increase in the relative wages of women may have been driven by selection. 1 Introduction and

In a sample selection or treatment effects model, common unobservables may affect both the outcome and the probability of selection in unknown ways. This paper shows that the distribution function of potential outcomes, conditional on covariates, can be identifiedgivenanobservedvariableVthat affects the treatment or selection probability in certain ways and is conditionally independent of the potential outcome equation error terms. Selection model estimators based on this identification are provided, which take the form of either simple weighted averages or GMM or two stage least squares. These estimators permit endogenous and mismeasured regressors. Empirical applications are provided to estimation of a firm investment model and a returns to schooling wage model. Portions of this paper were previously circulated under other titles including, ”Two Stage Least Squares Estimation

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In this paper we introduce a test for the normality assumption in the sample selection model. The test is based on a generalization of a semi-nonparametric maximum likelihood method. In this estimation method, the distribution of the error terms is approximated by a Hermite series, with normality as a special case. Because all parameters of the model are estimated both under normality and in the more general specification, we can test for normality using the likeli-hood ratio approach. This test has reasonable power as is shown by a simulation study. Finally, we apply the generalized semi-nonparametric maximum likeli-hood estimation method and the normality test to a model of car ownership and car use. The assumption of normal distributed error terms is rejected and we provide estimates of the sample selection model that are consistent.

A semiparametric two stage estimation method is proposed for the estimation of sample se-lection models which are subject to Tobit-type selection rules. With randomization restrictions on the disturbances of the model, all the regression coefficients in the model are in general identifiable without exclusion restrictions. The proposed estimator is shown to be y'n-consistent and asymp-totically normal. Some Monte Carlo results to demonstrate its finite sample performance are also provided. JEL classification number: 211