Task Allocation and On-the-job Training

Mariagiovanna Baccara, SangMok Lee and Leeat Yariv
Additional contact information
SangMok Lee: Washington University in St. Louis
Leeat Yariv: Princeton University

Working Papers from Princeton University, Department of Economics, Center for Economic Policy Studies.

Abstract: We study dynamic task allocation when service providers' expertise evolves. Clients arrive sequentially seeking service. Seniors provide superior service but entail waiting in a queue, which progresses at a speed dependent on their volume. Juniors o§er service without wait and become seniors with experience. We show that clients choose senior service too frequently, generating longer waits and little training relative to the social optimum. Welfare gains from centralization are greater for larger institutions, better training technologies, and lower waiting costs. Finally, monitoring the seniors' queue increases welfare but may decrease training. Methodologically, we explore a matching setting in which agents' types are endogenous, and illustrate the usefulness of queuing theory techniques.

Keywords: Dynamic Matching; Training-by-Doing; Market Design (search for similar items in EconPapers)
JEL-codes: J22 (search for similar items in EconPapers)
Date: 2020-10
New Economics Papers: this item is included in nep-des
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Downloads: (external link)
http://lyariv.mycpanel.princeton.edu//papers/TaskAllocation.pdf

Related works:
Journal Article: Task allocation and on-the-job training (2023)
Working Paper: Task Allocation and On-the-job Training (2021)
Working Paper: Task Allocation and On-the-job Training (2021)
Working Paper: Task Allocation and On-the-job Training (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:cepsud:270

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