Task allocation and on-the-job training

Mariagiovanna Baccara, SangMok Lee and Leeat Yariv

Journal of Economic Theory, 2023, vol. 207, issue C

Abstract: We study dynamic task allocation when providers' expertise evolves endogenously through training. We characterize optimal assignment protocols and compare them to discretionary procedures, where it is the clients who select their service providers. Our results indicate that welfare gains from centralization are greater when tasks arrive more rapidly, and when training technologies improve. Monitoring seniors' backlog of clients always increases welfare but may decrease training. Methodologically, we explore a matching setting with endogenous types, and illustrate useful adaptations of queueing theory techniques for such environments.

Keywords: Dynamic matching; Training-by-doing; Market design (search for similar items in EconPapers)
JEL-codes: C02 C61 C78 D02 J22 L23 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
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)
Working Paper: Task Allocation and On-the-job Training (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:207:y:2023:i:c:s0022053122001776

DOI: 10.1016/j.jet.2022.105587

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