In many countries, important features of municipal government (such as the electoral system, mayors' salaries, and the number of councillors) depend on whether the municipality is above or below arbitrary population thresholds. Several papers have used a regression discontinuity design (RDD) to...

Parametric regression models are often not flexible enough to capture the true relationships as they tend to rely on arbitrary identification assumptions. Using the UK Labor Force Survey, the authors estimate the causal effect of national minimum wage (NMW) increases on the probability of job...

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...

Big Data refers to data sets of much larger size, higher frequency, and often more personalized information. Examples include data collected by smart sensors in homes or aggregation of tweets on Twitter. In small data sets, traditional econometric methods tend to outperform more complex...

Centralized school assignment algorithms must distinguish between applicants with the same preferences and priorities. This is done with randomly assigned lottery numbers, nonlottery tie-breakers like test scores, or both. The New York City public high school match illustrates the latter, using...

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...

Understanding the mechanisms through which treatment effects come about is crucial for designing effective interventions. The identification of such causal mechanisms is challenging and typically requires strong assumptions. This paper discusses identification and estimation of natural direct...

Understanding the mechanisms through which treatment effects come about is crucial for designing effective interventions. The identification of such causal mechanisms is challenging and typically requires strong assumptions. This paper discusses identification and estimation of natural direct...