Community Research and Development Information Service - CORDIS



Project ID: 339186
Funded under: FP7-IDEAS-ERC
Country: Spain

Mid-Term Report Summary - RISK AND DIVERSITY (Labor Market Risk and Skill Diversity: Implications for Efficiency, Policy, and Estimation)

The grant proposal was structured around two main themes:
1. Labor Market Risk.
1.1. Unemployment Risk. In this project we have shown that sorting of low asset workers into low productivity jobs occurs under a condition closely related to Decreasing Relative Risk Aversion. The calibrated the infinite horizon economy shows that the high asset holders find a job with a probability that is 18% lower. We find that higher benefits are beneficial for consumption smoothing but deter the entry of firms and change the probability of job finding.
1.2. Matching Stochastic Types. In this line of the project, we analyze matching markets with stochastic characteristics. We show that in addition to complementarity in the match output function, the conditions for assortative matching now also depend on the properties of the stochastic order of the distributions of the agents’ ex post characteristics. We provide two applications of the model: one analyzes mismatch in the labor market for executives, and the other decomposes the sources of increased inequality of married household into marital and stochastic sorting.
2. Skill Diversity
2.1. QBTC vs SBTC. The main contribution is to consider an allocation problem in which there is not just the matching of qualities but also of quantities. This allows for the interpretation of assortative matching in large firms. The second part of the project is now in full development, which aims to estimate this model using matched employer-employee data. This will allow us to quantify the relative importance of Quantity Biased Technological Change (QBTC) versus Skill Biased Technological Change (SBTC).
2.2. Information as Skill. This project analyzes the optimal allocation of experts to teams, where experts differ in the precision of their information, and study the assortative matching properties of the resulting assignment. The main insight is that in general it is optimal to diversify the composition of the teams, ruling out positive assortative matching. This diversification leads to negative assortative matching when teams consist of pairs of experts. Related to this work is the work on matching skilled workers where there are spillovers between the competing teams, which analyzes spillovers in the allocation of agents to teams.
2.3. Skill Diversity across Locations. This part of the project is currently being started, building on the early results from earlier work.

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