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Task Allocation and On-the-job Training

Author

Listed:
  • Mariagiovanna Baccara

    (Washington University)

  • SangMok Lee

    (Washington University)

  • Leeat Yariv

    (Princeton University)

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.

Suggested Citation

  • Mariagiovanna Baccara & SangMok Lee & Leeat Yariv, 2021. "Task Allocation and On-the-job Training," Working Papers 2021-21, Princeton University. Economics Department..
    • Handle: RePEc:pri:econom:2021-21
    as

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

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    Cited by:

    1. Konstantin Samouylov & Olga Dudina & Alexander Dudin, 2023. "Analysis of Multi-Server Queueing System with Flexible Priorities," Mathematics, MDPI, vol. 11(4), pages 1-22, February.

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    More about this item

    Keywords

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

    JEL classification:

    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design
    • M53 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Training

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