Many centralized matching markets are preceded by interviews between the participants. We study the impact on the final match of an increase to the number of interviews  one side of the market can participate in . Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the NRMP match have switched to a virtual format. This has drastically reduced the cost to applicants of accepting interview offers . However, the reduction in cost is not symmetric since applicants, not programs,  bore most of the costs of in-person interviews. We show that if doctors are willing to accept more interviews  but the hospitals do not increase the number of interviews they offer, no doctor will be better off and potentially many doctors will be harmed. This adverse consequence results from a mechanism we describe as interview hoarding. We prove this analytically and characterize optimal mitigation strategies for special cases. We use simulations to extend the insights from our analytical results to more general settings.
Many centralized matching markets are preceded by interviews between the participants. We study the impact on the final match of an increase in the number of interviews for one side of the market . Our motivation is the match between residents and hospitals where, due to the COVID-19 pandemic, interviews for the 2020-21 season of the National Residency Matching Program were switched to a virtual format. This drastically reduced the cost to applicants of accepting interview invitations . However, the reduction in cost was not symmetric since applicants, not programs, previously bore most of the costs of in-person interviews. We show that , starting from a situation where the final matching is stable, if doctors can accept more interviews , but the hospitals do not increase the number of interviews they offer, then no doctor is better off and many doctors are potentially harmed. This adverse consequence is the result of what we call interview hoarding. We prove this analytically and characterize optimal mitigation strategies for special cases. We use simulations to extend these insights to more general settings.
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