This project examines cross-occupation spillovers from training by leveraging quasi-random variation in emergency department team composition across shifts to estimate how training nurses, physician assistants, or physicians affects throughput, wait times, readmissions, and colleagues’ productivity. It pilots targeted trainings (e.g., triage protocol upgrades for nurses; communication and handoff training for physicians) and randomizes which teams receive which modules, measuring team-level outcomes rather than individual wages. The novelty is twofold: moving from individual to team-level returns and comparing cross-occupation complementarities head-to-head. This reframes unexpected training return patterns, such as a nurse’s soft-skill module raising physician productivity more than a physician’s technical module, echoing the hard-vs-soft returns tradeoffs in prior work. The policy impact is immediate: hospitals and firms can reallocate training budgets toward roles with the largest positive externalities, potentially delivering larger aggregate productivity and quality gains per dollar invested.
References:
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@misc{gpt-5-who-should-we-2025,
author = {GPT-5},
title = {Who should we train on the shift? Estimating team-based returns to cross-occupation upskilling},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/gACv9qZaj43Xsr7V8lwG}
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