A Positive Analysis of Targeted Employment Protection Legislation

In many countries, Employment Protection Legislation (EPL) establishes less strict dismissal procedures for specific groups of workers. This paper builds a simple matching model with heterogeneous workers in order to analyze this feature of EPL. We use the model to analyze the effects of reforms targeted at lowering the firing costs of a particular group of workers, and compare the results with those stemming from a comprehensive reform that reduces firing costs for all workers. The model is calibrated for the Spanish economy, where an important reform of this kind took place in 1997. Overall, our results point out that EPL reforms achieve the largest reduction in unemployment when they are targeted to workers with lower and more volatile productivity.Publicad

A MATCHING MODEL OF CROWDING-OUT AND ON-THE- JOB SEARCH (WITH AN APPLICATION TO SPAIN)

This paper considers a matching model of heterogenous and jobs which includes on-the-job search. High-educated workers transitorily accept unskilled jobs and continue to search for skilled jobs. We study the implications of this model for the unemployment rates of high and low-educated workers, for the share of mismatched workers and wage nequality both within and between skill groups. The model is used to shed light on the Spanish experience following a large educational upgrading since the mid-eighties.

Measuring the vulnerability of the Uruguayan population to vector-borne diseases via spatially hierarchical factor models

We propose a model-based vulnerability index of the population from Uruguay to vector-borne diseases. We have available measurements of a set of variables in the census tract level of the 19 Departmental capitals of Uruguay. In particular, we propose an index that combines different sources of information via a set of micro-environmental indicators and geographical location in the country. Our index is based on a new class of spatially hierarchical factor models that explicitly account for the different levels of hierarchy in the country, such as census tracts within the city level, and cities in the country level. We compare our approach with that obtained when data are aggregated in the city level. We show that our proposal outperforms current and standard approaches, which fail to properly account for discrepancies in the region sizes, for example, number of census tracts. We also show that data aggregation can seriously affect the estimation of the cities vulnerability rankings under benchmark models.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS497 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org