Collaborate to Compete: An Empirical Matching Game under Incomplete Information in Rank-Order Tournaments

June 05 2019

Status: Revise and resubmit at Marketing Science
Coauthors: Tat Chan (Wash U) and Yijun Chen (Wash U)


This paper studies the collaboration of talents in rank-order tournaments. We use a structural matching model with unobserved transfers among participants to capture the differentiated incentives of participants behind collaborations, with specific focus on incorporating incomplete information and competition in the matching game. We estimate our model using data from a leading data science competition platform and recover the heterogeneous preferences of participants that determine whether and with whom they form teams. Using model parameters, we conduct policy experiments to investigate how the collaboration efficiency is affected by the incomplete information and competitive pressure on the platform. Our results provide implications on how firms could better align individual incentives to foster and improve collaborations.

Keywords: Matching game, Collaboration, Incomplete information

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