Endogenous Matching and Applications to the On-Demand Economy (1)

William Zame
University of California, Los Angeles (UCLA)

Efficient functioning of the on-demand economy requires not only that workers and tasks be properly matched, but also that workers be provided incentives to work hard. Accomplishing these objectives is especially problematic when — as is usually the case — the firm/platform that matches workers to tasks and makes payments does not observe the characteristics of the workers, so that there is both adverse selection and moral hazard. These talks will first provide some background on two-sided matching models (which were invented by Shapley and Shubik in the 1970’s and have since been used in an enormous range of applications, from marriage markets to housing markets to labor markets to international trade) and then present new work showing how endogenous (non-random) matching addresses both adverse selection and moral hazard in the on-demand economy.

Presentation (PDF File)

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