We use a Regression Discontinuity Design (RDD) to evaluate the impact of cost-sharing on the use of health services. In the Italian health system, individuals reaching age 65 and earning low incomes are given total exemption from cost-sharing for health services consumption. Since the...

This paper presents evidence on intra-household retirement externalities by assessing the causal effect of spousal retirement on various health behaviors and health status across 19 European countries. We identify partner's and own retirement effects by applying a fuzzy regression discontinuity...

We use a Regression Discontinuity Design (RDD) to evaluate the impact of cost-sharing on the use of health services. In the Italian health system, individuals reaching age 65 and earning low incomes are given total exemption from cost-sharing for health services consumption. Since the...

We study the impact that participation in the Erasmus program produces on a number of labor market outcomes. By implementing a Fuzzy Regression Discontinuity Design, we show that participating in the international mobility program positively affects the probability of being employed three years...

We study the impact that participation in the Erasmus program produces on a number of labor market outcomes. By implementing a Fuzzy Regression Discontinuity Design, we show that participating in the international mobility program positively affects the probability of being employed three years...

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different bootstrapping procedures. First, the bootstrap...

We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error terms that are arbitrarily correlated within known clusters but uncorrelated across them. The...

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. On the other hand, semiparametric and nonparametric methods, which are not restricted by parametric assumptions, require more data...

Classical parametric estimation methods applied to nonlinear regression and limited-dependent-variable models are very sensitive to misspecification and data errors. This sensitivity addressed by the theory of robust statistics which builds upon parametric specification, but provides methodology...