This paper estimates a wage equation with three high-dimensional fixed effects, using a longitudinal matched employer-employee dataset covering virtually all Portuguese wage earners over a little more than two decades. The variation in log real hourly wages is decomposed into different...

We use matched employer-employee data and firm balance sheet data to investigate the importance of firm productivity and firm labor market power in explaining firm heterogeneity in wage formation. We use a linear regression model with one interacted high dimensional fixed effect to estimate...

This study is an empirical assessment of the impact of the drug decriminalization policy followed by Portugal in July, 2001. We investigate especially the impact of the policy change on the price of illicit drugs. The analysis is performed using a difference-in-differences approach and a...

This paper investigates the impact of drug decriminalization in Portugal using the Synthetic Control Method. The applied econometric methodology compares Portuguese drug-related variables with the ones extracted from a convex combination of similar European countries. The results suggest that a...

We use matched employer-employee data and firm balance sheet data to investigate the importance of firm productivity and firm labor market power in explaining firm heterogeneity in wage formation. We use a linear regression model with one interacted high dimensional fixed effect to estimate...

Taking as our point of departure a model proposed by David Card (2001), we suggest new methods for analyzing wage dispersion in a partially unionized labor market. Card's method disaggregates the labor population into skill categories, which procedure entails some loss of information....

The paper constructs an asymmetric information model to investigate the efficiency and equity cases for government mandated benefits. A mandate can improve workers' insurance, and may also redistribute in favor of more "deserving" workers. The risk is that it may also reduce output. The more...