James M. Zumel Dumlao

James M. Zumel Dumlao

PhD Student, University of Michigan School of Information

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publications

The effect of reviewer geographical diversity on evaluations is reduced by anonymizing submissions

Published in SocArXiv (R&R at Science), 2024

Preprint We study how geographical diversity among research evaluators affects the success of research producers. If evaluators favor work from their own countries (homophily), then producers from countries well-represented in the evaluator pool will benefit from homophily more often (differential access to homophily), resulting in a “geographical representation bias.” We test if this bias exists in science publishing using peer review data on 205K submissions to 60 journals published by the Institute of Physics Publishing. We find evidence of both homophily and differential access to homophily. Reviewers from the same country as the corresponding author are 4.78 percentage points more likely to review positively compared to other reviewers of the same manuscript. Authors from countries well-represented in the reviewer pool (e.g. USA, China, India) are 8-9 times more likely to be evaluated by same-country reviewers and benefit from homophily. Exploiting a policy shock that led to some papers being reviewed anonymously shows that anonymization causally reduces country homophily to a statistically non-significant level and, consequently, reduces representation bias. Geographical representation bias may be widespread, benefitting authors from wealthier countries that historically produced more research and have greater representation in the evaluator pool. Anonymization is an attractive tool for reducing this bias.

Recommended citation: Zumel Dumlao, James M., and Misha Teplitskiy. 2024. "Lack of Peer Reviewer Diversity Advantages Authors from Wealthier Countries." SocArXiv. May 6. doi:10.31235/osf.io/754e3. https://osf.io/preprints/socarxiv/754e3

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teaching

Teaching Assistant

ECON 311: Intermediate Microeconomics, University of San Francisco, Economics Department, 2020

Graded 5 problem sets for 34 students taught by Prof. Mario Muzzi.