About

Eleonora Patacchini is the Stephen and Barbara Friedman Professor in the Department of Economics at Cornell University. She specializes in applied economics and applied statistics. Her recent research focuses on the empirical analysis of behavioral models of strategic interactions for decision making. Her work has been profiled in Bloomberg, PBS Newshour, The Independent, The Economist, and The New York Times. Currently, she is Co-Editor of the Journal of Urban Economics. Her research interests include social networks, education, gender, race, immigration, crime, culture, climate change, urban planning, innovation, experimental methods, policy evaluation, and computational economics.

Research topics

• Political Science
• Computer Security
• Computer Science
• Law
• Econometrics
• Psychology
• Economics
• Artificial Intelligence
• Machine Learning
• Mathematics
• World Wide Web
• Microeconomics
• Social psychology
• Statistics
• Criminology
• Public economics
• Engineering
• Mathematical economics
• Algorithm

Selected publications

• Dynamic Choices with Social Interactions

  The Economic Journal · 2025-10-14

  articleSenior author

  Abstract We introduce the study of dynamic, forward-looking equilibrium choices in environments characterised by social interactions. Agents’ preferences capture inter-temporal links—such as habits and addictions—as well as attitudes to conform to a social reference group. We characterise equilibrium behaviour as a system of linear non-stationary Markovian policy rules, for each individual and in each time period. We then derive conditions for the identification of the parameters of the dynamic social interaction model with panel data. We finally illustrate the empirical implications of the model by bringing it to data in the context of adolescents’ smoking behaviour, where addiction effects are arguably a first-order concern. Indeed, we find strong evidence for the dynamic effects that we emphasise in this paper. In our empirical implementation, peer effect estimates for a misspecified static model typically used in the extant literature are approximately six times larger than those of a dynamic specification

  PublisherDOI

• An Algorithmic Approach to Reducing Gender Discrimination: Evidence from a Large-Scale Experimenton Tracking Decisions

  AEA Randomized Controlled Trials · 2025-02-13

  datasetSenior author
  PublisherDOI

• Low-rank bilinear autoregressive models for three-way criminal activity tensors

  arXiv (Cornell University) · 2025-05-02

  preprintOpen accessSenior author

  Criminal activity data are typically available via a three-way tensor encoding the reported frequencies of different crime categories across time and space. The challenges that arise in the design of interpretable, yet realistic, model-based representations of the complex dependencies within and across these three dimensions have led to an increasing adoption of black-box predictive strategies. While this perspective has proved successful in producing accurate forecasts guiding targeted interventions, the lack of interpretable model-based characterizations of the dependence structures underlying criminal activity tensors prevents from inferring the cascading effects of these interventions across the different dimensions. We address this gap through the design of a low-rank bilinear autoregressive model which achieves comparable predictive performance to black-box strategies, while allowing interpretable inference on the dependence structures of reported criminal activities across crime categories, time and space. This representation incorporates the time dimension via an autoregressive construction that accounts for spatial effects and dependencies among crime categories through a separable low-rank bilinear formulation. When applied to Chicago police reports, the proposed model showcases remarkable predictive performance and also reveals interpretable dependence structures unveiling fundamental crime dynamics. These results facilitate the design of more refined intervention policies informed by the cascading effects of the policy itself.

  PublisherOA PDFDOI

• Tax Professionals and Tax Evasion

  Journal of the European Economic Association · 2025-12-11

  articleSenior author

  Abstract Using unique data covering the entire population of sole proprietorships in Italy with their respective audit files, we examine the role of tax advisors in tax compliance. Exploiting quasi-random variation in audit policy, we document that tax advisors act as information hubs, gathering privileged information on the auditing policy from their activities and incorporating it into the tax return strategy of their clients. The heterogeneity in tax advisors’ willingness to serve this role establishes a market for intermediated tax evasion, in which taxpayers sort themselves on the basis of the tax advisors’ tolerance for it.

  PublisherDOI

• An Algorithmic Approach to Reducing Gender Discrimination: Evidence from a Large-Scale Experimenton Tracking Decisions

  AEA Randomized Controlled Trials · 2025-02-13

  datasetSenior author
  PublisherDOI

• Special issue on Immigration Economics at <i>Journal of Economic Geography</i>

  Journal of Economic Geography · 2025-01-01

  article1st authorCorresponding
  PublisherDOI

• Spillovers in Criminal Networks: Evidence from Co-Offender Deaths

  SSRN Electronic Journal · 2024-01-01

  articleOpen access
  PublisherDOI

• Unobserved Contributions and Political Influence: Evidence from the Death of Top Donors

  National Bureau of Economic Research · 2024-07-01

  reportOpen accessSenior author

  It has long been observed that there is little money in U.S. politics compared to the stakes.But what if contributions are not fully observable or non-monetary in nature and thus not easily quantifiable?We study this question with a new data set on the top 1000 donors in U.S. congressional races.Since top donors do not randomly support candidates, we propose an identification strategy based on information about top donors' deaths and the observed variations in candidates' performance after these events.The death of a top donor significantly decreases a candidate's chances of being elected in the current and future election cycles.Moreover, it affects the legislative activities of elected candidates.These effects do not depend on top donors' monetary contributions to a candidate but on their prominence and their total contributions during the election campaign.

  PublisherOA PDFDOI

• Closing the Gender Gap: Promoting Labour Market Participation

  SSRN Electronic Journal · 2024-01-01

  articleOpen accessSenior author
  PublisherDOI

• Spillovers in criminal networks: Evidence from co-offender deaths

  2024-12-06

  report

  We study spillover effects within criminal networks by leveraging the deaths of co-offenders as a source of causal identification. We find that the death of a co-offender significantly reduces the criminal activities of other network members. These spillover effects display a decaying pattern: offenders directly linked to a deceased co-offender experience the most significant impact, followed by those two steps away, and then those three steps away. Moreover, we find that the death of a more central co-offender leads to a larger reduction in aggregate crime, underlining the importance of network position in shaping spillover effects. We also provide evidence suggesting that the loss of a co-offender shrinks the future information set of offenders, which can influence their perceived probability of being convicted and consequently their criminal behavior. Our findings highlight the importance of accounting for spillover effects in designing more effective strategies for crime prevention.

  PublisherDOI

Frequent coauthors

• Yves Zénou

  Monash University

  471 shared

• Marco Battaglini

  138 shared

• Thierry Verdier

  114 shared

• Valerio Leone Sciabolazza

  94 shared

• Alberto Bisin

  New York University

  92 shared

• Claudia Olivetti

  Dartmouth Hospital

  37 shared

• Edoardo Rainone

  Bank of Italy

  35 shared

• Pierre M. Picard

  35 shared

Awards & honors

• Stephen and Barbara Friedman Professor in the Department of…