The present paper estimates and decomposes the employment effect of innovation by R&D intensity levels. Our micro-econometric analysis is based on a large international panel data set from the EU Industrial R&D Investment Scoreboard. Employing flexible semi-parametric methods - the generalised...

Nonlinear estimation of the gravity model with Poisson-type regression methods has become popular for modelling international trade flows, because it permits a better accounting for zero flows and extreme values in the distribution tail. Nevertheless, as trade flows are not independent from each...

A new bandwidth selection method that uses different bandwidths for the local linear regression estimators on the left and the right of the cut-off point is proposed for the sharp regression discontinuity design estimator of the average treatment effect at the cut-off point. The asymptotic mean...

The present paper studies the relationship between R&D investment and firm productivity growth by explicitly accounting for non-linearities in the R&D-productivity relationship and inter-sectoral firm heterogeneity. In order to address these issues, we employ a two step estimation approach, and...

The present paper estimates and decomposes the employment effect of innovation by R&D intensity levels. Our microeconometric analysis is based on a large international panel data set from the EU Industrial R&D Investment Scoreboard. Employing flexible semi-parametric methods - the generalised...

We provide a novel evidence about the innovation-employment nexus by decomposing it by R&D intensity in a continuous setup and relaxing the linearity assumption. Using a large international firm-level panel data set for OECD countries and employing a flexible semi-parametric method – the...

When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is...

Average treatment effects estimands can present significant bias under the presence of outliers. Moreover, outliers can be particularly hard to detect, creating bias and inconsistency in the semi-parametric ATE estimads. In this paper, we use Monte Carlo simulations to demonstrate that...

We consider identification and estimation of nonseparable sample selection models with censored selection rules. We employ a control function approach and discuss different objects of interest based on (1) local effects conditional on the control function, and (2) global effects obtained from...

The impact of measurement error in explanatory variables on quantile regression functions is investigated using a small variance approximation. The approximation shows how the error contaminated and error free quantile regression functions are related. A key factor is the distribution of the...