What Do Editors Maximize? Evidence from Four Economics Journals

• 成果类型: Article
• 署名作者: Card, David; DellaVigna, Stefano
• 署名单位: University of California System; University of California Berkeley; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00839
• 发表日期: 2020-03
• 页码: 195-217
• 关键词: single-blind; favoritism; BIAS; BEHAVIOR; rewards; IMPACT; facts

摘要

We study editorial decisions using anonymized submissions matched to citations at four leading economics journals. We develop a benchmark model in which editors maximize the expected quality of accepted papers and citations are unbiased measures of quality. We then generalize the model to allow different quality thresholds, systematic gaps between citations and quality, and a direct impact of publication on citations. We find that referee recommendations are strong predictors of citations and that editors follow these recommendations closely. We document two deviations from the benchmark model. First, papers by highly published authors receive more citations, conditional on the referees' recommendations and publication status. Second, recommendations of highly published referees are equally predictive of future citations, yet editors give their views significantly more weight.