I am a PhD candidate at Stanford University, where I study economic theory and behavioral economics. My research explores how people learn, interact, and make decisions under uncertainty.

You can view my CV here and contact me at bldavies@stanford.edu.

Working papers

The value of conceptual knowledge
(with Anirudh Sankar)
abstract [+] We formalize what it means to have conceptual knowledge about a statistical decision-making environment. Such knowledge tells agents about the structural relationships among unknown, payoff-relevant states. It allows agents to represent states as combinations of features. Conceptual knowledge is more valuable when states are more “reducible”: when their prior variances are explained by fewer features. Its value is non-monotone in the quantity and quality of available data, and vanishes with infinite data. Agents with deeper knowledge can attain the same welfare with less data. This is especially true when states are highly reducible.

Learning about a changing state
abstract [+] 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.

Work in progress

How mechanistic explanations reshape learning and behavior: Evidence from a fertilizer choice experiment in Eastern Uganda
(with Anirudh Sankar and others)
abstract [+] 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 how mechanistic explanations affect learning and behavior in economic settings. 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.

Publications

Gender sorting among economists: Evidence from the NBER
Economics Letters, 2022
abstract [+] data/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
abstract [+] When COVID-19 struck, the New Zealand government had two choices: enter lockdown immediately or delay its decision. Delay would have enabled more information to emerge about health and economic dynamics, while preserving the option to act at a later date. However, delay may have destroyed the option to eradicate COVID-19. We model the government’s decision when faced with the uncertainty around health and economic dynamics generated by COVID-19. Our model captures both two-sided uncertainty and the dynamic consequences that flow from the government’s initial decision. Our analysis will help guide future policy decisions amid similarly complex uncertainties.

Research funding and collaboration
(with Jason Gush, Shaun C. Hendy, and Adam B. Jaffe)
Research Policy, 2022
abstract [+] data/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
abstract [+] data/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 and policy reports

Estimating sample paths of Gauss-Markov processes from noisy data
abstract [+] 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)
abstract [+] data/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.

Climate change adaptation within New Zealand's transport system
(with Anthony Byett and others)

Resting papers

Why do experts give simple advice?
abstract [+] An expert tells an advisee whether to take an action that may be good or bad. He may provide a condition under which to take the action. This condition predicts whether the action is good if and only if the expert is competent. Providing the condition exposes the expert to reputational risk by allowing the advisee to learn about his competence. He trades off the accuracy benefit and reputational risk induced by providing the condition. He prefers not to provide it—i.e., to give “simple advice”—when his payoff is sufficiently concave in the posterior belief about his competence.

Contracting with persuasive agents
abstract [+] I study a contracting game played by a recruitment agent and their client. The client pays the agent if they find a match. Search costs depend on market thickness, which the agent observes but the client does not. The agent can persuade the client to pay more by manipulating their beliefs about market thickness and prospective match qualities. Persuasion benefits the agent more when they can shirk than when competition prevents them from shirking.

Bundling and insurance of independent risks
(with Richard Watt)
abstract [+] Risky prospects can often by disaggregated into several identifiable, smaller risks. In such cases, at least two modes of insurance are available: either (i) the disaggregated risks can be insured independently or (ii) the aggregate risk can be insured as one. We identify (ii) as risk bundling prior to insurance and (i) as separate, or unbundled, insurance. We investigate whether (i) or (ii) is preferable among consumers, insurers and the insurance market as a whole using numerical simulations. Our simulations reveal that separate contracts provide the socially optimal form of insurance when the insurer is able to charge the profit-maximising premia and has perfect information. Under asymmetric information with respect to consumers’ risk aversion, we find that separation is again the dominant method of insurance in terms of the market share it represents.