Register data are known for their large sample size and good data quality. The measurement accuracy of variables highly depends on their high importance for administrative processes. The education variable in the IAB employment sub-sample is an example for information that is gathered without a...

We provide a comparison of return to schooling estimates based on an influential study by Angrist and Krueger (1991) using two stage least squares (TSLS), limited information maximum likelihood (LIML), jackknife (JIVE), and split sample instrumental variables (SSIV) estimation. We find that the...

Register data are known for their large sample size and good data quality. The measurement accuracy of variables highly depends on their high importance for administrative processes. The education variable in the IAB employment sub-sample is an example for information that is gathered without a...

We provide a comparison of return to schooling estimates based on an influential study by Angrist and Krueger (1991) using two stage least squares (TSLS), limited information maximum likelihood (LIML), jackknife (JIVE), and split sample instrumental variables (SSIV) estimation. We find that the...

This paper provides an expression for the bias of the OLS estimator of the schooling coefficient in a simple static wage-schooling model where earnings persistence is not accounted for. It is argued that the OLS estimator of the schooling coefficient is biased upward, and the bias is increasing...

This paper provides an expression for the bias of the OLS estimator of the schooling coefficient in a simple static wage-schooling model where earnings persistence is not accounted for. It is argued that the OLS estimator of the schooling coefficient is biased upward, and the bias is increasing...

College graduates tend to earn more than non-graduates but it is difficult to ascertain how much of this empirical association between wages and college degree is due to the causal effect of a college degree and how much is due to unobserved factors that influence both wages and education (e.g....

In this paper, I present a general modeling framework for nonparametric models with endogenous regressors and heterogeneity. I show that many existing models in the literature can be derived from a structural equation with unobserved heterogeneity by imposing constancy assumptions on the first...

Consider an observed binary regressor D and an unobserved binary variable D*, both of which affect some other variable Y. This paper considers nonparametric identification and estimation of the effect of D on Y , conditioning on D* = 0. For example, suppose Y is a person's wage, the unobserved D...

This paper proposes a linear categorical random coefficient model, in which the random coefficients follow parametric categorical distributions. The distributional parameters are identified based on a linear recurrence structure of moments of the random coefficients. A Generalized Method of...