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Matching information

Author

Listed:
  • Chade, Hector

    (Department of Economics, Arizona State University)

  • Eeckhout, Jan

    (Department of Economics, University College London and GSE-UPF)

Abstract

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.

Suggested Citation

  • Chade, Hector & Eeckhout, Jan, 2018. "Matching information," Theoretical Economics, Econometric Society, vol. 13(1), January.
    • Handle: RePEc:the:publsh:1820
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    References listed on IDEAS

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    Cited by:

    1. Evan Sadler & Benjamin Golub, 2021. "Games on Endogenous Networks," Papers 2102.01587, arXiv.org, revised Feb 2024.
    2. Elias Bouacida & Renaud Foucart & Maya Jalloul, 2024. "Decreasing Differences in Expert Advice," Working Papers 408394204, Lancaster University Management School, Economics Department.

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    More about this item

    Keywords

    Assortative matching; teams; diversification; correlation;
    All these keywords.

    JEL classification:

    • C78 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Bargaining Theory; Matching Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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