About

Jeremy Magruder is the George W. and Elsie M. Professor of Food and Agricultural Resource Economics in the Department of Agricultural & Resource Economics at UC Berkeley. He studies the microeconomics of agricultural productivity and labor markets in developing countries. His research focuses on understanding the market frictions and constraints to employment and productivity growth that are prevalent in the developing world. These frictions include a heavy reliance on social networks as an intermediating institution, and much of his research examines how individuals use social networks to enhance and inhibit productive outcomes in development. He holds a Ph.D., M.Phil., and M.A. in Economics from Yale University, and a B.A. with high honors in Economics from Michigan State University.

Research topics

• Mathematics
• Computer Science
• Economics
• Business
• Industrial organization
• Marketing
• Microeconomics
• Statistics
• Geography
• Agricultural economics

Selected publications

• The impacts of mechanization and facilitating output market linkages on the adoption of irrigation in Rwanda

  AEA Randomized Controlled Trials · 2024-12-26

  datasetSenior author
  PublisherDOI

• The impacts of mechanization and facilitating output market linkages on the adoption of irrigation in Rwanda

  AEA Randomized Controlled Trials · 2024-12-26

  datasetSenior author
  PublisherDOI

• Labor Supply Complementarities in Urban Côte d‘Ivoire

  2024-02-28

  articleOpen access

  This data examines complementarities in labor supply: to what extent does a person’s desire to work at a firm depend on whether others in her social network also work at the firm? The researchers conducted two field experiments in urban Côte d’Ivoire. In the first experiment, job seekers are 16pp more likely to accept a formal full-time factory job if their network members also receive a job offer, and 15pp more likely to stay in that job four months later—but only if they will be employed in the same shift (rather than different shifts). These effects are driven by workers with long commute times, who can commute to work together. Consistent with this channel, in the firm’s administrative data, workers’ own attendance and turnover are predicted by the attendance and quits of co-commuting peers. In a second field experiment with a different firm, the researchers again randomize whether a worker’s network members are offered a job, whether they would be co-located with the worker, and job location—inducing exogenous variation in commute time. The researchers replicate the finding of complementarities in labor supply, but only in the case of long assigned commute times. These findings indicate that the social composition of one’s peers can have large impacts on labor supply, and suggest that one important mechanism is commuting costs—which are especially high in developing country cities. The results provide a novel explanation for key features of urban labor markets, including firms’ widespread use of referrals for hiring and persistent gaps in employment across social groups.

  PublisherOA PDFDOI

• How do digital platforms affect employment and job search? Evidence from India

  Journal of Development Economics · 2023-09-19 · 26 citations

  articleSenior author
  PublisherDOI

• Benchmarking Heterogeneous Programs

  AEA Randomized Controlled Trials · 2022-03-04

  dataset1st authorCorresponding
  PublisherDOI

• Factor Market Failures and the Adoption of Irrigation in Rwanda

  American Economic Review · 2022 · 60 citations

  Senior authorCorresponding
  • Economics
  • Agricultural economics
  • Geography

  Factor market failures can limit adoption of profitable technologies. We leverage a plot-level spatial regression discontinuity design in the context of irrigation use by farmers provided free access to water. Using irrigation boosts profits by 43–62 percent. Yet, farmers only irrigate 30 percent of plots because of labor costs. We demonstrate inefficient irrigation use, by showing farmers irrigating one plot reduce their irrigation use on other plots. This inefficiency is largest for smaller households and wealthier households, suggesting labor market frictions constrain use of irrigation. (JEL D24, O13, Q12, Q15, Q16)

  PublisherDOI

• Highly Powered Analysis Plans

  SSRN Electronic Journal · 2022-01-01

  articleOpen accessSenior author
  PublisherDOI

• Benchmarking Heterogeneous Programs

  AEA Randomized Controlled Trials · 2022-03-04

  dataset1st authorCorresponding
  PublisherDOI

• Highly Powered Analysis Plans

  National Bureau of Economic Research · 2022-03-01 · 3 citations

  reportOpen accessSenior author

  Formal analysis plans limit false discoveries by registering and multiplicity adjusting statistical tests. As each registered test reduces power on other tests, researchers prune hypotheses based on prior knowledge, often by combining related indicators into evenly-weighted indices. We propose two improvements to maximize learning within these types of analysis plans. First, we develop data-driven optimized indices that can yield more powerful tests than evenly-weighted indices. Second, we discuss organizing the logical structure of an analysis plan into a gated tree that directs type I error towards these high-powered tests. In simulations we show that researchers may prefer these "optimus gates" across a wide range of data-generating processes. We then assess our strategy using the community-driven development (CDD) application from Casey et al. (2012) and the Oregon Health Insurance Experiment from Finkelstein et al. (2012). We find substantial power gains in both applications, meaningfully changing the conclusions of Casey et al. (2012).

  PublisherDOI

• Can Network Theory-Based Targeting Increase Technology Adoption?

  American Economic Review · 2021 · 302 citations

  • Computer Science
  • Economics
  • Industrial organization

  Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low. (JEL O13, O18, O33, Q12, Q16)

  PublisherDOI

Frequent coauthors

• Niall Keleher

  26 shared

• Lori Beaman

  26 shared

• Lori G. Beaman

  University of Ottawa

  18 shared

• Florence Kondylis

  14 shared

• Sylvan Herskowitz

  14 shared

• John Loeser

  12 shared

• Maria Jones

  12 shared

• Caitlin Rowe

  9 shared