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- All HBS Web (991)
- Faculty Publications (347)
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- All HBS Web (991)
- Faculty Publications (347)
- July 2016
- Article
Taxation, Corruption, and Growth
By: Philippe Aghion, Ufuk Akcigit, Julia Cagé and William R. Kerr
We build an endogenous growth model to analyze the relationships between taxation, corruption, and economic growth. Entrepreneurs lie at the center of the model and face disincentive effects from taxation but acquire positive benefits from public infrastructure.... View Details
Keywords: Endogenous Growth; Public Goods; Corruption; Crime and Corruption; Entrepreneurship; Taxation; Economic Growth
Aghion, Philippe, Ufuk Akcigit, Julia Cagé, and William R. Kerr. "Taxation, Corruption, and Growth." Special Issue on The Economics of Entrepreneurship. European Economic Review 86 (July 2016): 24–51.
- 2022
- Working Paper
Values as Luxury Goods and Political Polarization
By: Benjamin Enke, Mattias Polborn and Alex A Wu
Motivated by novel survey evidence, this paper develops a theory of political
behavior in which values are a luxury good: the relative weight voters place
on values rather than material considerations increases in income. The model
predicts (i) voters who are... View Details
Keywords: Political Polarization; Government and Politics; Moral Sensibility; Luxury; Values and Beliefs; Voting
Enke, Benjamin, Mattias Polborn, and Alex A Wu. "Values as Luxury Goods and Political Polarization." Working Paper, April 2022. (Revised April 2023.)
- May 2025
- Article
Imagining the Future: Memory, Simulation and Beliefs
By: Pedro Bordalo, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli and Andrei Shleifer
How do people form beliefs about novel risks, with which they have little or no experience? Motivated by survey data on beliefs about Covid we collected in 2020, we build a model based on the psychology of selective memory. When a person thinks about an event,... View Details
Bordalo, Pedro, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli, and Andrei Shleifer. "Imagining the Future: Memory, Simulation and Beliefs." Review of Economic Studies 92, no. 3 (May 2025): 1532–1563.
- Article
Thinking About Technology: Applying a Cognitive Lens to Technical Change
We apply a cognitive lens to understanding technology trajectories across the life cycle by developing a co-evolutionary model of technological frames and technology. Applying that model to each stage of the technology life cycle, we identify conditions under which a... View Details
Keywords: Technology; Transformation; Outcome or Result; Economics; Cognition and Thinking; Business Model; Forecasting and Prediction
Kaplan, Sarah, and Mary Tripsas. "Thinking About Technology: Applying a Cognitive Lens to Technical Change." Research Policy 37, no. 5 (June 2008): 790–805.
- February 2020
- Article
Being 'Good' or 'Good Enough': Prosocial Risk and the Structure of Moral Self-regard
By: Julian Zlatev, Daniella M. Kupor, Kristin Laurin and Dale T. Miller
The motivation to feel moral powerfully guides people’s prosocial behavior. We propose that people’s efforts to preserve their moral self-regard conform to a moral threshold model. This model predicts that people are primarily concerned with whether their... View Details
Keywords: Prosocial Behavior; Moral Sensibility; Decision Making; Risk and Uncertainty; Behavior; Perception
Zlatev, Julian, Daniella M. Kupor, Kristin Laurin, and Dale T. Miller. "Being 'Good' or 'Good Enough': Prosocial Risk and the Structure of Moral Self-regard." Journal of Personality and Social Psychology 118, no. 2 (February 2020): 242–253.
- 2012
- Working Paper
Prominent Job Advertisements, Group Learning and Wage Dispersion
By: Julio J. Rotemberg
A model is presented in which people base their labor search strategy on the average wage and the average unemployment duration of people who belong to their peer group. It is shown that, if the distribution of wage offers is not stationary so lower wage offers tend to... View Details
Rotemberg, Julio J. "Prominent Job Advertisements, Group Learning and Wage Dispersion." NBER Working Paper Series, No. 18638, December 2012.
- 27 Feb 2019
- HBS Seminar
David Robinson, Fuqua School of Business at Duke University
- 2019
- Working Paper
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media... View Details
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
- 2022
- Article
Efficiently Training Low-Curvature Neural Networks
By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
Standard deep neural networks often have excess non-linearity, making them susceptible to issues such as low adversarial robustness and gradient instability. Common methods to address these downstream issues, such as adversarial training, are expensive and often... View Details
Keywords: AI and Machine Learning
Srinivas, Suraj, Kyle Matoba, Himabindu Lakkaraju, and Francois Fleuret. "Efficiently Training Low-Curvature Neural Networks." Advances in Neural Information Processing Systems (NeurIPS) (2022).
Siyu Zhang
Siyu Zhang is a second-year doctoral student at HBS. Zhang joined Harvard Business School in 2020 as a Research Associate and has been working on macroeconomic forecasting projects. Prior to joining HBS, he was a Data Scientist at John Hancock, where he utilized... View Details
- October 2009 (Revised April 2010)
- Case
Societe Generale (A): The Jerome Kerviel Affair
By: Francois Brochet
This case illustrates the tension/balance that firms with complex and risky business models must consider in designing their internal controls. It describes the environment in which a derivatives trader engaged in massive directional positions on major European stocks... View Details
Keywords: Risk Management; Problems and Challenges; Complexity; Cost Management; Balance and Stability; Business Model; Design; Stocks; Crisis Management; Financial Markets; Consulting Industry; Europe
Brochet, Francois. "Societe Generale (A): The Jerome Kerviel Affair." Harvard Business School Case 110-029, October 2009. (Revised April 2010.)
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,... View Details
- 23 Aug 2013
- Working Paper Summaries
Waves in Ship Prices and Investment
Keywords: by Robin Greenwood & Samuel Hanson
- 2024
- Working Paper
How Inflation Expectations De-Anchor: The Role of Selective Memory Cues
By: Nicola Gennaioli, Marta Leva, Raphael Schoenle and Andrei Shleifer
In a model of memory and selective recall, household inflation expectations remain rigid when inflation is anchored but exhibit sharp instability during inflation surges, as similarity prompts retrieval of forgotten high-inflation experiences. Using data from the New... View Details
Gennaioli, Nicola, Marta Leva, Raphael Schoenle, and Andrei Shleifer. "How Inflation Expectations De-Anchor: The Role of Selective Memory Cues." NBER Working Paper Series, No. 32633, June 2024.
- Article
Beacon and Warning: Sherman Kent, Scientific Hubris, and the CIA's Office of National Estimates
By: J. Peter Scoblic
Would-be forecasters have increasingly extolled the predictive potential of Big Data and artificial intelligence. This essay reviews the career of Sherman Kent, the Yale historian who directed the CIA’s Office of National Estimates from 1952 to 1967, with an eye toward... View Details
Keywords: National Security; Analytics and Data Science; Analysis; Forecasting and Prediction; History
Scoblic, J. Peter. "Beacon and Warning: Sherman Kent, Scientific Hubris, and the CIA's Office of National Estimates." Texas National Security Review 1, no. 4 (August 2018).
- June, 2021
- Article
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Benjamin T. Leyden and Michael Luca
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant... View Details
Keywords: COVID-19; Lockdown; Reopening; Impact; Coronavirus; Public Health Measures; Mobility; Health Pandemics; Governing Rules, Regulations, and Reforms; Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Benjamin T. Leyden, and Michael Luca. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
- 2024
- Article
Learning Under Random Distributional Shifts
By: Kirk Bansak, Elisabeth Paulson and Dominik Rothenhäusler
Algorithmic assignment of refugees and asylum seekers to locations within host
countries has gained attention in recent years, with implementations in the U.S.
and Switzerland. These approaches use data on past arrivals to generate machine
learning models that can... View Details
Bansak, Kirk, Elisabeth Paulson, and Dominik Rothenhäusler. "Learning Under Random Distributional Shifts." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 27th (2024).
- Article
Ensembles of Overfit and Overconfident Forecasts
By: Y. Grushka-Cockayne, V.R.R. Jose and K. C. Lichtendahl
Firms today average forecasts collected from multiple experts and models. Because of cognitive biases, strategic incentives, or the structure of machine-learning algorithms, these forecasts are often overfit to sample data and are overconfident. Little is known about... View Details
Grushka-Cockayne, Y., V.R.R. Jose, and K. C. Lichtendahl. "Ensembles of Overfit and Overconfident Forecasts." Management Science 63, no. 4 (April 2017): 1110–1130.
- Research Summary
Social Networks and Unraveling in Labor Markets
This paper develops a model of local unraveling (or early hiring) in entry-level labor markets. Information about workers' productivity is revealed over time and transmitted credibly via a two-sided network connecting firms and workers. While employment starts only... View Details
- Article
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to... View Details
Keywords: Machine Learning; Marketing Applications; Knowledge; Technological Innovation; Core Relationships; Marketing; Applications and Software
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.