About

Jesse M. Shapiro is the George Gund Professor of Economics and Business Administration at Harvard University. The page provides his professional title and affiliation but does not include specific details about his research focus, background, or key contributions.

Research topics

• Computer Science
• Computer Security
• Economics
• Management science
• Econometrics
• Mathematics
• Statistics
• Data science

Selected publications

• Data and Code for: “What is newsworthy? Theory and evidence"

  ICPSR Data Holdings · 2026-03-27

  datasetOpen accessSenior author

  We introduce a model in which a benevolent news outlet decides whether to report the realization of a state to a consumer, who pays a cost to receive it. A simple statistical rule, called a proper scoring rule, describes when the outlet should be more likely to report the realization. Using data from the US television news, we show that a particular scoring rule successfully predicts many salient features of news reporting. We show how to use this rule as a control variable to discipline tests of reporting bias, and we show that controlling for it matters in our applications.

  PublisherDOI

• Data and Code for: “What is newsworthy? Theory and evidence"

  ICPSR Data Holdings · 2026-03-27

  datasetOpen accessSenior author

  We introduce a model in which a benevolent news outlet decides whether to report the realization of a state to a consumer, who pays a cost to receive it. A simple statistical rule, called a proper scoring rule, describes when the outlet should be more likely to report the realization. Using data from the US television news, we show that a particular scoring rule successfully predicts many salient features of news reporting. We show how to use this rule as a control variable to discipline tests of reporting bias, and we show that controlling for it matters in our applications.

  PublisherDOI

• xtevent: Estimation and visualization in the linear panel event-study design

  The Stata Journal Promoting communications on statistics and Stata · 2025-03-01 · 6 citations

  articleCorresponding

  Linear panel models and the “event-study plots” that often accompany them are popular tools for learning about policy effects. We introduce the xtevent package, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al. (Forthcoming, Visualization, identification, and estimation in the linear panel event-study design [Cambridge University Press]). The package implements various procedures to estimate the underlying policy effects and allows for nonbinary policy variables and estimation adjusting for preevent trends.

  PublisherDOI

• Visualization, Identification, and Estimation in the Linear Panel Event-Study Design

  Cambridge University Press eBooks · 2025-10-31 · 1 citations

  book-chapterSenior author
  PublisherDOI

• xtevent: Estimation and Visualization in the Linear Panel Event-Study Design

  Working paper · 2024-08-01 · 4 citations

  reportOpen access

  Linear panel models and the "event-study plots" that often accompany them are popular tools for learning about policy effects.We introduce the Stata package xtevent, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al. (Forthcoming).The package implements various procedures to estimate the underlying policy effects, and allows for nonbinary policy variables and estimation adjusting for pre-event trends.

  PublisherOA PDFDOI

• Policy Effect Estimation and Visualization in Linear Panel Event-Study Designs: Introducing the xtevent Package

  2024-08-01

  reportOpen access

  Linear panel models and the "event-study plots" that often accompany them are popular tools for learning about policy effects. In this paper, we introduce the "xtevent" package for Stata, which enables the construction of event-study plots following the suggestions in Freyaldenhoven et al. (forthcoming). The package implements various procedures to estimate the policy effects that underlie the plots, and allows for non-binary policy variables and estimation adjusting for pre-event trends.

  PublisherOA PDFDOI

• Communicating Scientific Uncertainty via Approximate Posteriors

  National Bureau of Economic Research · 2024-01-01

  reportOpen accessSenior author

  Empirical researchers frequently rely on normal approximations in order to summarize and communicate uncertainty about their findings to their scientific audience.When such approximations are unreliable, they can lead the audience to make misguided decisions.We propose to measure the failure of the conventional normal approximation for a given estimator by the total variation distance between a bootstrap distribution and the normal distribution parameterized by the point estimate and standard error.For a wide class of decision problems and a class of uninformative priors, we show that a multiple of the total variation distance bounds the mistakes which result from relying on the conventional normal approximation.In a sample of recent empirical articles that use a bootstrap for inference, we find that the conventional normal approximation is often poor.We suggest and illustrate convenient alternative reports for such settings.

  PublisherDOI

• What is Newsworthy? Theory and Evidence

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Robust Content Moderation: Theory and Applications

  National Bureau of Economic Research · 2024-02-01 · 8 citations

  reportOpen accessSenior author

  A sender sends a signal about a state to a receiver who takes an action that determines a payoff.A moderator can block some or all of the sender's signal before it reaches the receiver.When the moderator's policy is transparent to the receiver, the moderator can improve the payoff by blocking false or harmful signals.When the moderator's policy is opaque, however, the receiver may not trust the moderator.In that case, the moderator can guarantee an improved outcome only by blocking signals that enable harmful acts.Blocking signals that encourage false beliefs can be counterproductive.

  PublisherDOI

• Pitfalls of Demographic Forecasts of US Elections

  National Bureau of Economic Research · 2024-10-01 · 2 citations

  reportOpen accessSenior author

  Many influential observers have forecast large partisan shifts in the US electorate based on demographic trends. Such forecasts are appealing because demographic trends are often predictable even over long horizons. We backtest demographic forecasts using data on US elections since 1952. We envision a forecaster who fits a model using data from a given election and uses that model, in tandem with a projection of demographic trends, to predict future elections. Even a forecaster with perfect knowledge of future demographic trends would have performed poorly over this period—worse even than one who simply guesses that each election will have a 50-50 partisan split. Enriching the set of demographics available does not change this conclusion. Slow demographic change, unstable group preferences, and strategic party responses all help to explain why demography has not been destiny in US politics.

  PublisherDOI

Recent grants

• Branding and Product Differentiation in Markets with Advertising

  NSF · $461k · 2013–2019

• The Causes and Consequences of Mass Media Content

  NSF · $358k · 2006–2009

• The Effect of SNAP Receipt on Household Behavior and Well-being: Evidence and Economic Interpretation

  NSF · $377k · 2017–2021

Frequent coauthors

• Matthew Gentzkow

  206 shared

• Justine Hastings

  Coventry University

  185 shared

• Simon Freyaldenhoven

  58 shared

• Christian Hansen

  University of Chicago

  58 shared

• Edward L. Glaeser

  National Bureau of Economic Research

  54 shared

• Isaiah Andrews

  Harvard University Press

  38 shared

• Michael Sinkinson

  Yale University

  30 shared

• Levi Boxell

  25 shared

Labs

• Jesse M. Shapiro LabPI

Education

• Ph.D., Economics

  Harvard University

  2005

• B.A., Economics

  University of California, Berkeley

  1999