I am an economist and Senior Researcher at Microsoft, studying demand estimation, retail pricing, competition, and market design. My recent work has focused on the economics of cloud computing and on the use of Large Language Models for conducting market research.

Email: jamesbrand@microsoft.com, jamesbrandecon@gmail.com.

While at Microsoft, I’ve been able to work on research projects with four fantastic PhD interns: Avner Kreps (Northwestern), Rebekah Dix (MIT), Chinmay Lohani (Penn Econ), and Yihao Yuan (Wharton).

We often have openings for summer interns, so feel free to reach out if you’d like to work together.

  • In this paper I show that consumers in food stores and supermarkets/hypermarkets became significantly less price sensitive between 2006 and 2017. At the median, across thousands of stores and products in nine large categories, estimated own-price elasticities have declined by 25% over this period. I argue that these changes are likely due in part to improved supply chain management, which has led stores to offer a larger variety of goods which better match consumers’ individual preferences. I show that newer products are indeed more “niche” in this sense, and that other potential sources of rising differentiation including increases in quality and changes in consumer wealth play a smaller role. Markups implied by a monopolistic pricing rule suggest that the observed rise in differentiation was large enough to generate significant increases in firms’ markups absent any changes in pricing behavior or competition.

Estimating Productivity and Markups Under Imperfect Competition (Revision Requested, Journal of Econometrics)

  • This paper revisits the standard production function model and proposes an alternative identification and estimation procedure. Specifically, I argue that some of the assumptions of the standard production function model are inconsistent with the increasingly popular use of production function methods in the estimation of markups. I then show that the seminal nonclassical measurement error result in Hu and Schennach (2008) can be used to nonparametrically identify the production function under alternative assumptions which do not require specifying the demand firms face or any knowledge of firms’ input demand functions. I apply the intuition of this result to develop a GMM estimation procedure for the most practically relevant production function models, and explore the performance of the resulting estimates relative to workhorse methods in simulations.

  • This is a brief note describing an approach to flexible ("nonparametric") demand estimation when many products are observed by the econometrician but substitution between many pairs of products is weak. I outline a simple theoretical model and show that the approach works well on simulated data. This project is no longer a priority, and this note mainly serves as documentation for the associated Julia package NPDemand.jl (see "Code" or "Github" link above).

(“Resting” paper) What Makes Teachers Better? Evidence From a Long Panel of Classrooms

  • In this paper I study how teacher value-added is impacted by classroom experience measured by the number, size, and demographic composition of classes previously

    taught. First, I document significant heterogeneity in these measures, even among teachers with the same tenure in the profession. After 15 years of teaching, some teachers have been assigned 1,000 more students than their peers. I then present my central finding, that teachers who have taught more students, and in particular larger classes, have higher value-added than their less experienced peers on average. These results conflict with the literature on the importance of small classes for students, as they imply that assigning teachers larger classes early in their careers may benefit future students. Finally, I show that the interaction of the race and gender of teachers and those of the students they teach affect the rate at which teachers improve through this mechanism.

Other work

Contributed to Microsoft’s New Future of Work Report, 2023, which details many ways in which LLMs will and are changing the way people work, including the work of researchers in various fields.

Working Papers

Using GPT for Market Research (with Ayelet Israeli and Donald Ngwe)

  • Large language models (LLMs) have quickly become popular as labor-augmenting tools for programming, writing, and many other processes that benefit from quick text generation. In this paper we explore the uses and benefits of LLMs for marketing researchers and practitioners. In contrast to prior work, we focus on the distributional nature of LLM responses. We offer two sets of results. First, we show that the Generative Pre-trained Transformer 3 (GPT-3) model, a widely-used LLM, responds to sets of survey questions in ways that are consistent with economic theory and well-documented patterns of consumer behavior, including downward-sloping demand curves and state dependence. Second, we show that estimates of willingness-to-pay for products and features generated by GPT-3 are of reasonable magnitudes and match estimates from a recent study that elicited preferences from human consumers. We also offer preliminary guidelines for how best to query information from GPT-3 for marketing purposes and discuss potential limitations.