This paper uses the control function to develop a framework for testing for selection bias. The idea behind our framework is if the usual assumptions hold for matching or IV estimators, the control function identifies the presence and magnitude of potential selection bias. Averaging this...

This paper examines both theoretically and empirically whether using ordinary least squares (OLS) multivariate regression models to estimate average treatment effects under experimental designs is justified by the Neyman model for causal inference. The paper finds that estimated standard errors...

The aim of this paper is to study the differential effects of the public programs that provide financial aid to small and medium enterprises during times of crisis in Spain. We use different methods to control for potential selection bias. Our results show that the effects of these programs are...

Paper presented at the fall 2007 Association for Public Policy Analysis and Management conference held in Washington, DC.

This paper investigates how effective safety education reduces workforce risks by using an establishment-level dataset in South Korea. We focus on identification and estimation of causal effects. Since provision of safety education depends on establishment’s decision, the main variable of...