I am a PhD candidate in the economics department at Stanford University. I study economic theory and behavioral economics. My research explores how people learn, interact, and make decisions under uncertainty.
I am on the 2025–26 academic job market.
CV: here
Email: bldavies@stanford.edu
References:- Matthew Jackson (co-advisor): jacksonm@stanford.edu
- Arun Chandrasekhar (co-advisor): arungc@stanford.edu
- Steven Callander: sjc@stanford.edu
Research
My research papers are listed below and on Google Scholar.
Job market paper
The value of conceptual knowledge (with Anirudh Sankar)
Abstract [-] Paper We study the instrumental value of conceptual knowledge when making statistical decisions. Such knowledge tells agents how unknown, payoff-relevant states relate. It is distinct from the statistical knowledge gained from observing signals of those states. We formalize this distinction in a tractable framework used by economists and statisticians. Conceptual knowledge is valuable because it empowers agents to design more informative signals. It is more valuable when states are more “reducible”: when they can be explained with fewer common concepts. Its value is non-monotone in the number of signals and vanishes when agents have infinitely many signals. Agents who know more concepts can attain the same payoffs with fewer signals. This is especially true when states are highly reducible.
Abstract [-] Paper We study the instrumental value of conceptual knowledge when making statistical decisions. Such knowledge tells agents how unknown, payoff-relevant states relate. It is distinct from the statistical knowledge gained from observing signals of those states. We formalize this distinction in a tractable framework used by economists and statisticians. Conceptual knowledge is valuable because it empowers agents to design more informative signals. It is more valuable when states are more “reducible”: when they can be explained with fewer common concepts. Its value is non-monotone in the number of signals and vanishes when agents have infinitely many signals. Agents who know more concepts can attain the same payoffs with fewer signals. This is especially true when states are highly reducible.
Working papers
How mechanistic explanations reshape learning and behavior: Evidence from a fertilizer choice experiment in Eastern Uganda (with Anirudh Sankar, Robert Dulin, Vesall Nourani, Jess Rudder, Abraham Salomon, and Godfrey Taulya)
Abstract [-] Paper Mechanistic explanations—descriptions of a system through the causal interactions of its parts—play a key role in human cognition and scientific progress. Despite their importance, we lack systematic evidence on whether and how mechanistic explanations help lay decision-makers interpret information in complex economic environments. We evaluate the causal impact of including mechanistic explanations in an information intervention: public demonstrations of fertilizer use for smallholder tomato farmers in Eastern Uganda. In all demonstrations, extension officers showcased the impact of a recommended fertilizer recipe. In the treatment group, officers also explained the mechanisms underlying the recipe’s effects—introducing the language of macronutrients and the causal processes linking nutrients, soil features, and plant growth. We collect detailed data on beliefs and behaviors from 797 farmers in a lab-in-the-field experiment. Treated farmers are better able to generalize from mechanisms to update beliefs about the returns to fertilizers, substitute and arbitrage among fertilizers based on nutrient content, and exhibit better understanding of the principles of nutrient and soil science. In an incentivized fertilizer application task, they achieved 9% higher simulated profits by selecting more agronomically sound fertilizer recipes, without increasing costs.
Abstract [-] Paper Mechanistic explanations—descriptions of a system through the causal interactions of its parts—play a key role in human cognition and scientific progress. Despite their importance, we lack systematic evidence on whether and how mechanistic explanations help lay decision-makers interpret information in complex economic environments. We evaluate the causal impact of including mechanistic explanations in an information intervention: public demonstrations of fertilizer use for smallholder tomato farmers in Eastern Uganda. In all demonstrations, extension officers showcased the impact of a recommended fertilizer recipe. In the treatment group, officers also explained the mechanisms underlying the recipe’s effects—introducing the language of macronutrients and the causal processes linking nutrients, soil features, and plant growth. We collect detailed data on beliefs and behaviors from 797 farmers in a lab-in-the-field experiment. Treated farmers are better able to generalize from mechanisms to update beliefs about the returns to fertilizers, substitute and arbitrage among fertilizers based on nutrient content, and exhibit better understanding of the principles of nutrient and soil science. In an incentivized fertilizer application task, they achieved 9% higher simulated profits by selecting more agronomically sound fertilizer recipes, without increasing costs.
Learning about a changing state
Abstract [-] Paper A long-lived Bayesian agent observes costly signals of a time-varying state. He chooses the signals’ precisions sequentially, balancing their costs and marginal informativeness. I compare the optimal myopic and forward-looking precisions when the state follows a Brownian motion. I also compare the myopic precisions induced by other Gaussian processes.
Abstract [-] Paper A long-lived Bayesian agent observes costly signals of a time-varying state. He chooses the signals’ precisions sequentially, balancing their costs and marginal informativeness. I compare the optimal myopic and forward-looking precisions when the state follows a Brownian motion. I also compare the myopic precisions induced by other Gaussian processes.
Peer-reviewed publications
Gender sorting among economists: Evidence from the NBER
Economics Letters, 2022
Abstract [+] Paper Code Preprint
Economics Letters, 2022
Abstract [+] Paper Code Preprint
COVID-19, lockdown and two-sided uncertainty (with Arthur Grimes)
New Zealand Economic Papers, 2022
Abstract [+] Paper
New Zealand Economic Papers, 2022
Abstract [+] Paper
Research funding and collaboration (with Jason Gush, Shaun C. Hendy, and Adam B. Jaffe)
Research Policy, 2022
Abstract [+] Paper Code Preprint
Research Policy, 2022
Abstract [+] Paper Code Preprint
Relatedness, complexity and local growth (with David C. Maré)
Regional Studies, 2021
Abstract [+] Paper Code Preprint
Regional Studies, 2021
Abstract [+] Paper Code Preprint