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Task Allocation and On-the-job Training

Author

Listed:
  • Mariagiovanna Baccara

    (Washington University in St. Louis)

  • SangMok Lee

    (Washington University in St. Louis)

  • Leeat Yariv

    (Princeton University)

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.

Suggested Citation

  • Mariagiovanna Baccara & SangMok Lee & Leeat Yariv, 2020. "Task Allocation and On-the-job Training," Working Papers 270, Princeton University, Department of Economics, Center for Economic Policy Studies..
    • Handle: RePEc:pri:cepsud:270
    as

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    File URL: http://lyariv.mycpanel.princeton.edu//papers/TaskAllocation.pdf
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    References listed on IDEAS

    as
    1. Pietro Ortoleva & Evgenii Safonov & Leeat Yariv, 2021. "Who Cares More? Allocation with Diverse Preference Intensities," Working Papers 2021-10, Princeton University. Economics Department..
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Dynamic Matching; Training-by-Doing; Market Design;
    All these keywords.

    JEL classification:

    • J22 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Time Allocation and Labor Supply

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    This paper has been announced in the following NEP Reports:

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