Matching information

• 成果类型: Article
• 署名作者: Chade, Hector; Eeckhout, Jan
• 署名单位: Arizona State University; Arizona State University-Tempe; University of London; University College London; Pompeu Fabra University; ICREA; Barcelona School of Economics
• 刊物名称: THEORETICAL ECONOMICS
• ISSN/ISSBN: 1555-7561
• DOI: 10.3982/TE1820
• 发表日期: 2018-01-01
• 页码: 377-414
• 关键词: Assortative matching; teams; diversification; correlation

摘要

We analyze 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. And when experts' signals are conditionally independent, all teams have similar precision. We also show that if we allow experts to join multiple teams, then it is optimal to allocate them equally across all teams. Finally, we analyze how to endogenize the size of the teams, and we extend the model by introducing heterogeneous firms in which the teams operate.