Working Paper: NBER ID: w29312
Authors: Mariagiovanna Baccara; Sangmok Lee; Leeat Yariv
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: task allocation; on-the-job training; centralization; discretionary procedures; queueing theory
JEL Codes: C02; C61; C78; D02; J22; L23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| speed of client arrivals (C69) | centralized task allocation (H77) |
| centralized task allocation (H77) | service quality (L15) |
| speed of client arrivals (C69) | service quality (L15) |
| centralized task allocation (H77) | welfare gains (D69) |
| backlog of clients (L84) | immediate service provision (I38) |
| backlog of clients (L84) | future expertise development (O29) |
| improved monitoring (E01) | service quality (L15) |
| improved monitoring (E01) | training scope (M53) |