Task Allocation and On-the-job Training

Mariagiovanna Baccara, SangMok Lee and Leeat Yariv
Additional contact information
SangMok Lee: Washington University
Leeat Yariv: Princeton University

Working Papers from Princeton University. Economics Department.

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: D47 M53 (search for similar items in EconPapers)
Date: 2021-09
New Economics Papers: this item is included in nep-des and nep-hrm
References: View references in EconPapers View complete reference list from CitEc
Citations:

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 (2020)
Working Paper: Task Allocation and On-the-job Training (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2021-21

Access Statistics for this paper

More papers in Working Papers from Princeton University. Economics Department. Contact information at EDIRC.
Bibliographic data for series maintained by Bobray Bordelon ().