About

Frederico Finan is a professor in the Haas Business and Public Policy Group at UC Berkeley. He received his PhD in Agriculture and Resource Economics from UC Berkeley in 2006. Prior to his current position, he was an Assistant Professor of Economics at UCLA and has held various consulting roles, including with the World Bank Group and the International Food Policy Research Institute. His research interests include applied microeconomics, development economics, and political economy, with a focus on understanding the functioning of government institutions, corruption, and state capabilities. He is an affiliate of the Bureau for Research and Economic Analysis of Development (BREAD), and a research fellow at IZA and the National Bureau of Economic Research (NBER). Finan has contributed to the academic field through numerous publications and has been involved in various external service roles, including as an affiliate and research fellow at several economic research centers. His work emphasizes the analysis of political selection, government audits, and the personnel economics of the state, among other topics.

Research topics

• Sociology
• Computer Security
• Economics
• Computer Science
• Political Science
• Business
• Law
• Demographic economics
• Operations research
• Political economy
• Microeconomics
• Engineering

Selected publications

• Learning about Treatment Effects with Prior Studies: A Bayesian Model Averaging Approach

  ArXiv.org · 2026-01-14

  articleOpen access1st authorCorresponding

  We establish concentration rates for estimation of treatment effects in experiments that incorporate prior sources of information -- such as past pilots, related studies, or expert assessments -- whose external validity is uncertain. Each source is modeled as a Gaussian prior with its own mean and precision, and sources are combined using Bayesian model averaging (BMA), allowing data from the new experiment to update posterior weights. To capture empirically relevant settings in which prior studies may be as informative as the current experiment, we introduce a nonstandard asymptotic framework in which prior precisions grow with the experiment's sample size. In this regime, posterior weights are governed by an external-validity index that depends jointly on a source's bias and information content: biased sources are exponentially downweighted, while unbiased sources dominate. When at least one source is unbiased, our procedure concentrates on the unbiased set and achieves faster convergence than relying on new data alone. When all sources are biased, including a deliberately conservative (diffuse) prior guarantees robustness and recovers the standard convergence rate.

  PublisherOA PDF

• Learning about Treatment Effects with Prior Studies: A Bayesian Model Averaging Approach

  arXiv (Cornell University) · 2026-01-14

  preprintOpen access1st authorCorresponding

  We establish concentration rates for estimation of treatment effects in experiments that incorporate prior sources of information -- such as past pilots, related studies, or expert assessments -- whose external validity is uncertain. Each source is modeled as a Gaussian prior with its own mean and precision, and sources are combined using Bayesian model averaging (BMA), allowing data from the new experiment to update posterior weights. To capture empirically relevant settings in which prior studies may be as informative as the current experiment, we introduce a nonstandard asymptotic framework in which prior precisions grow with the experiment's sample size. In this regime, posterior weights are governed by an external-validity index that depends jointly on a source's bias and information content: biased sources are exponentially downweighted, while unbiased sources dominate. When at least one source is unbiased, our procedure concentrates on the unbiased set and achieves faster convergence than relying on new data alone. When all sources are biased, including a deliberately conservative (diffuse) prior guarantees robustness and recovers the standard convergence rate.

  PublisherDOI

• On-the-Job Training for Politicians: Evidence from Brazil

  AEA Randomized Controlled Trials · 2025-10-20

  dataset
  PublisherDOI

• On-the-Job Training for Politicians: Evidence from Brazil

  AEA Randomized Controlled Trials · 2025-10-20

  dataset
  PublisherDOI

• Brokering Votes With Information Spread Via Social Networks

  The Review of Economic Studies · 2025-08-21 · 2 citations

  articleOpen access

  Abstract Politicians rely on political brokers to buy votes throughout much of the developing world. We investigate how social networks facilitate these vote-buying exchanges. Our conceptual framework suggests brokers should be particularly well-placed within the network to learn about non-copartisans’ reciprocity in order to target transfers effectively. As a result, parties should recruit brokers who are central among non-copartisans. We combine village network data from brokers and citizens with broker reports of vote buying, allowing us to use broker and citizen fixed effects. We show that networks diffuse information about citizens to brokers who leverage it to target transfers. In particular, among those citizens who are not registered to their party, brokers target reciprocal citizens about whom they can learn more through their network, and these citizens are more likely to support the brokers’ party. Moreover, recruited brokers are significantly more central than other citizens among non-copartisans, but not among copartisans. These results highlight the importance of information diffusion through social networks for vote buying, broker recruitment, and ultimately for political outcomes.

  PublisherOA PDFDOI

• Climate Politics in the United States

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Combating Political Corruption with Policy Bundles

  Journal of Political Economy · 2025-02-24 · 3 citations

  article1st authorCorresponding

  In this paper, we develop a dynamic model of politicians who can engage in corruption. The model provides important insights into the determinants of corruption and how to design policies to combat it. We estimate the model using data from Brazil to measure voters’ willingness to pay for various commonly proposed anticorruption policies, such as increasing audit probabilities, raising politicians’ wages, and extending term limits. We document that while audit policies effectively reduce corruption, a multipronged approach that bundles an audit policy with other policies can achieve much higher welfare gains.

  PublisherDOI

• Malfunctioning Democracies: Understanding Accountability Failures in Developing Countries

  National Bureau of Economic Research · 2025-09-01

  reportOpen accessSenior author

  This chapter examines why democracies in the developing world frequently underperform in providing effective governance.We argue that these shortcomings stem from weaknesses in accountability mechanisms, which leave governments vulnerable to corruption, clientelism, and elite capture.Our framework distinguishes three accountability channels: vertical (citizens' control over politicians), horizontal (checks and balances across state institutions), and diagonal (oversight by media and civil society).We synthesize the recent theoretical and empirical literature to assess how each channel operates, the conditions under which it succeeds, and why it often fails.A central finding is that accountability institutions rarely fail on their own; instead, they are actively undermined by political actors seeking to preserve rents and entrench power.This dynamic weakens electoral competition, erodes judicial independence, and curtails media freedom, producing a mutually reinforcing cycle of weak accountability.Additionally, we argue that sustainable reforms cannot be achieved by strengthening any single channel in isolation.Since vertical, horizontal, and diagonal accountability are interdependent, effective reform requires bolstering all three simultaneously.We conclude by discussing the implications of this perspective for future research, including the role of new technologies, political polarization, and candidate selection in reshaping accountability in developing democracies.

  PublisherDOI

• Climate Politics in the United States

  National Bureau of Economic Research · 2025-08-01

  report
  PublisherDOI

• Decoupling Taste-Based versus Statistical Discrimination in Elections

  National Bureau of Economic Research · 2025-05-01 · 1 citations

  reportOpen access
  PublisherDOI

Frequent coauthors

• Claudio Ferraz

  University of British Columbia

  76 shared

• Ernesto Dal Bó

  48 shared

• Élisabeth Sadoulet

  41 shared

• Alain de Janvry

  University of California, Berkeley

  39 shared

• Laura Schechter

  16 shared

• Johanna Rickne

  15 shared

• Robert Tetlow

  Federal Reserve

  14 shared

• Maurizio Mazzocco

  11 shared

Education

• Ph.D., Agriculture and Resource Economics

  UC Berkeley

  2006

Awards & honors

• National Science Foundation, Research Grant (PI) 2009
• Senate Grant 2007
• Latin American Studies Grant 2007
• NICHD Pilot Grant, Population Research Center 2007
• NICHD Pilot Grant, California Center for Population Research…