Nicholas Brown

Research

Job Market Paper

Working Papers

Price distortions and hoarding: An experiment

Edgar Castro Cesar Martinelli Nicholas Brown · Revise & Resubmit, Journal Name

Abstract

We study how current and anticipated price distortions affect storage decisions in a two-period model. Both anticipated rationing and anticipated price liberalization create incentives to hoard, though for different reasons. We test these predictions in the laboratory using three treatments: a free market with rising costs, an anticipated price cap, and liberalization following a price cap. The model successfully predicts the broad patterns of shortages and prices across treatments. Under liberalization, shortages become increasingly severe as participants gain experience with the regulated market, and prices more than double once the cap is lifted. When a price cap is anticipated, however, participants store less than predicted and bid too little for storage, as if they underestimate the severity of future rationing. Both price-control regimes entail substantial welfare losses with respect to the free market, with liberalization producing the lowest surplus as rent-seeking rather than defensive storage becomes the dominant motive for hoarding.

Mass Reproducibility in Psychological Science

Brodeur, Abdul Baki, Fazio et al. (large scale coauthor)

Abstract

Concerns about the credibility and reproducibility of published findings have motivated numer- ous initiatives to improve transparency in psychology. In this article, we present evidence from a large-scale reproducibility initiative conducted in partnership between Psychological Science and the Institute for Replication (I4R). Our project systematically reproduces empirical results pub- lished in the journal between 2024 and 2025 using independent research teams. Through a com- bination of Replication Games and researcher-led initiatives, 67 independent teams examined 44% of the articles (i.e., 64 articles) published during our time frame, documenting data and code availability, checking for coding errors, and deviations from preregistration, and compu- tationally reproducing the main numerical results. We find that the majority of articles provide cleaned data and analysis scripts, though raw data and data-cleaning scripts are shared far less often. Computational reproducibility depends strongly on the starting point: 58.2% of articles are fully reproducible from final analysis data, but only 28.4% are fully reproducible from raw data. Teams identify coding errors or statistical reporting inconsistencies in 63% of articles, al- though most issues are minor; 40.5% of the articles flagged with coding or statistical reporting issues contain discrepancies with potentially major implications for estimates or interpretation. Preregistration is common (74.6% of articles), yet 84% of preregistered articles deviate from their pre-analysis plan, and nearly half of these deviations go unacknowledged. Our findings provide new insights into the reliability of empirical results in a leading psychology journal and highlight the value of structured mass reproducibility initiatives.

Work in Progress