We propose a simple test that uses information on workers’ mobility, wages and firms’ profits to identify the sign and strength of assortative matching. The basic intuition underlying our empirical strategy is that, in the presence of positive (negative) assortative matching, good workers...

The study of properties of mean functionals of random probability measures is an important area of research in the theory of Bayesian nonparametric statistics. Many results are known by now for random Dirichlet means but little is known, especially in terms of posterior distributions, for...

The paper proposes a new nonparametric prior for two-dimensional vectors of survival functions (S1,S2). The definition we introduce is based on the notion of Lévy copula and it will be used to model, in a nonparametric Bayesian framework, two-sample survival data. Such an application will yield...

The present paper provides a review of the results concerning distributional properties of means of random probability measures. Our interest in this topic has originated from inferential problems in Bayesian Nonparametrics. Nonetheless, it is worth noting that these random quantities play an...

In this paper I provide estimates of the impact of immigration on native wage and employment levels (rather than on wage inequality which has been the focus of the literature). I use variation within 2-digit industries across regions using Austrian panel data from 1986 to 2004 for...

We use a unique data set providing administrative information on earnings by skill-level (blue collars, white collars), on the local stock of human capital and on several firm’s characteristics, including balance sheet data, to investigate the size of localized human capital externalities in...

Administrative data from INPS (Italian Institute for Social Security) on Italian high tenure workers job-histories (15 years, from 1985 to 1999) is used to quantify the temporal pattern of the effect of displacement on workers’ earnings, employment and wages. Moreover, I distinguish different...