Optimal Team Formation Under Asymmetric Information

Ashwin Kambhampati, C. Segura-Rodríguez
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Abstract

How does a profit-maximizing manager form teams and compensate workers in the presence of both adverse selection and moral hazard? Under complete information, it is well known that any complementarity in characteristics implies that positive assortative matching is productively efficient. But, under asymmetric information, we uncover the problem of disassortative incentives: incentive costs may increase in assortativity. Profit maximization thus prescribes either random or negative assortative matching, both productively inefficient, when complementarities are weak and e ort costs are high enough. When this is the case, the manager may instead prefer to dele-gate matching, allowing workers to sort themselves into teams. Our results shed light on recent empirical work documenting patterns of non-assortative matching inside of firms.
信息不对称条件下的最优队形
在逆向选择和道德风险并存的情况下,利润最大化的管理者如何组建团队并对员工进行补偿?众所周知,在完全信息条件下,任何特征的互补性都意味着正分类匹配是有效的。但是,在信息不对称的情况下,我们发现了非配性激励的问题:激励成本可能会增加配性。因此,当互补性较弱且工作成本足够高时,利润最大化要求的要么是随机匹配,要么是负分类匹配,两者的生产效率都很低。在这种情况下,经理可能更倾向于授权匹配,允许员工将自己分成团队。我们的结果揭示了最近的实证工作,记录了公司内部的非分类匹配模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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