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)
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.
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)
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.
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
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.
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.
Publications
Gender sorting among economists: Evidence from the NBER
Economics Letters, 2022
Paper Code Preprint I compare the co-authorship patterns of male and female economists, using historical data on National Bureau of Economic Research working papers. Men tended to work in smaller teams than women, but co-authored more papers and so had more co-authors overall. Both men and women had more same-gender co-authors than we would expect if co-authorships were random. This was especially true for men in Macro/Finance.
Economics Letters, 2022
Paper Code Preprint I compare the co-authorship patterns of male and female economists, using historical data on National Bureau of Economic Research working papers. Men tended to work in smaller teams than women, but co-authored more papers and so had more co-authors overall. Both men and women had more same-gender co-authors than we would expect if co-authorships were random. This was especially true for men in Macro/Finance.
COVID-19, lockdown and two-sided uncertainty (with Arthur Grimes)
New Zealand Economic Papers, 2022
Paper When COVID-19 struck, the government had two choices: enter lockdown immediately or delay its decision. Delaying would have allowed more information to emerge about health and economic impacts, and preserved the option to act later. However, delaying may also have destroyed the option to eradicate COVID-19. We model the government’s decisions under the health and economic uncertainty generated by COVID-19. Our model captures both two-sided uncertainty and the dynamic consequences that flow from the government’s initial decision.
New Zealand Economic Papers, 2022
Paper When COVID-19 struck, the government had two choices: enter lockdown immediately or delay its decision. Delaying would have allowed more information to emerge about health and economic impacts, and preserved the option to act later. However, delaying may also have destroyed the option to eradicate COVID-19. We model the government’s decisions under the health and economic uncertainty generated by COVID-19. Our model captures both two-sided uncertainty and the dynamic consequences that flow from the government’s initial decision.
Research funding and collaboration (with Jason Gush, Shaun C. Hendy, and Adam B. Jaffe)
Research Policy, 2022
Paper Code Preprint We analyze whether research funding contests promote co-authorship. Our analysis combines Scopus publication records with data on the Marsden Fund, the premier source of funding for basic research in New Zealand. We use fixed-effect models to analyze within-researcher-pair variation in co-authorship. Among pairs who ever co-authored or co-proposed, co-authorship was 13.8 percentage points more likely in a given year if they had co-proposed during the previous ten years than if they had not. This co-authorship rate was not significantly higher among funded pairs. However, when we increase post-proposal publication lags towards the length of a typical award, we find that funding, rather than participation, promotes co-authorship.
Research Policy, 2022
Paper Code Preprint We analyze whether research funding contests promote co-authorship. Our analysis combines Scopus publication records with data on the Marsden Fund, the premier source of funding for basic research in New Zealand. We use fixed-effect models to analyze within-researcher-pair variation in co-authorship. Among pairs who ever co-authored or co-proposed, co-authorship was 13.8 percentage points more likely in a given year if they had co-proposed during the previous ten years than if they had not. This co-authorship rate was not significantly higher among funded pairs. However, when we increase post-proposal publication lags towards the length of a typical award, we find that funding, rather than participation, promotes co-authorship.
Relatedness, complexity and local growth (with David C. Maré)
Regional Studies, 2021
Paper Code Preprint We derive a measure of the relatedness between economic activities based on weighted correlations of local employment shares. Our approach recognizes variation in the extent of local specialization and adjusts for differences in data quality between cities. We use our measure to estimate activity and city complexity, and examine the contribution of relatedness and complexity to urban employment growth in New Zealand. Relatedness and complexity are complementary in promoting employment growth in New Zealand’s largest cities, but do not contribute to employment growth in its smaller cities.
Regional Studies, 2021
Paper Code Preprint We derive a measure of the relatedness between economic activities based on weighted correlations of local employment shares. Our approach recognizes variation in the extent of local specialization and adjusts for differences in data quality between cities. We use our measure to estimate activity and city complexity, and examine the contribution of relatedness and complexity to urban employment growth in New Zealand. Relatedness and complexity are complementary in promoting employment growth in New Zealand’s largest cities, but do not contribute to employment growth in its smaller cities.
Technical notes
Estimating sample paths of Gauss-Markov processes from noisy data
Paper I derive the pointwise conditional means and variances of an arbitrary Gauss-Markov process, given noisy observations of points on a sample path. These moments depend on the process’s mean and covariance functions, and on the conditional moments of the sampled points. I study the Brownian motion and bridge as special cases.
Paper I derive the pointwise conditional means and variances of an arbitrary Gauss-Markov process, given noisy observations of points on a sample path. These moments depend on the process’s mean and covariance functions, and on the conditional moments of the sampled points. I study the Brownian motion and bridge as special cases.
Delineating functional labour market areas with estimable classification stabilities (with David C. Maré)
Paper Code We describe an unsupervised method for delineating functional labour market areas (LMAs) in national commuting networks. Our method uses the Louvain algorithm, which we extend to support top-down hierarchical LMA classification and estimable classification stabilities. We demonstrate our method using historical Census commuting data from New Zealand.
Paper Code We describe an unsupervised method for delineating functional labour market areas (LMAs) in national commuting networks. Our method uses the Louvain algorithm, which we extend to support top-down hierarchical LMA classification and estimable classification stabilities. We demonstrate our method using historical Census commuting data from New Zealand.