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- Faculty Publications (325)
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- All HBS Web (987)
- Faculty Publications (325)
- 2010
- Working Paper
Agglomerative Forces and Cluster Shapes
By: William R. Kerr and Scott Duke Kominers
We model spatial clusters of similar firms. Our model highlights how agglomerative forces lead to localized, individual connections among firms, while interaction costs generate a defined distance over which attraction forces operate. Overlapping firm interactions...
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Keywords:
Entrepreneurship;
Geographic Location;
Patents;
Labor;
Industry Clusters;
Industry Structures;
Relationships;
Competitive Advantage;
Technology Industry;
California
Kerr, William R., and Scott Duke Kominers. "Agglomerative Forces and Cluster Shapes." Harvard Business School Working Paper, No. 11-061, December 2010.
- 05 Jul 2006
- Working Paper Summaries
A Cross-Sectional Analysis of the Excess Comovement of Stock Returns
- 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...
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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.
- Forthcoming
- 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,...
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Bordalo, Pedro, Giovanni Burro, Katherine B. Coffman, Nicola Gennaioli, and Andrei Shleifer. "Imagining the Future: Memory, Simulation and Beliefs." Review of Economic Studies (forthcoming).
- 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....
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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.
- 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...
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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.
- 23 Aug 2013
- Working Paper Summaries
Waves in Ship Prices and Investment
Keywords:
by Robin Greenwood & Samuel Hanson
- 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...
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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.
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,...
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- 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...
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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).
- 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...
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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.)
- 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...
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- 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...
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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.
- 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, Michael Luca and Benjamin T. Leyden
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...
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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, Michael Luca, and Benjamin T. Leyden. "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.
- 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...
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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.
- 2021
- Working Paper
An Empirical Study of Time Allotment and Delays in E-commerce Delivery
By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays....
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Keywords:
Logistics;
E-commerce;
Mathematical Methods;
AI and Machine Learning;
Performance Productivity
Balakrishnan, M., MoonSoo Choi, and Natalie Epstein. "An Empirical Study of Time Allotment and Delays in E-commerce Delivery." Working Paper, December 2021.
- September 2010
- Article
Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?
By: Saravanan Kesavan, Vishal Gaur and Ananth Raman
Firm-level sales forecasts for retailers can be improved if we incorporate cost of goods sold, inventory, and gross margin (defined here as the ratio of sales to cost of goods sold) as three endogenous variables. We construct a simultaneous equations model, estimated...
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Keywords:
Sales;
Forecasting and Prediction;
Distribution;
Goods and Commodities;
Cost;
Public Sector;
Profit;
Mathematical Methods;
Data and Data Sets;
Retail Industry;
United States
Kesavan, Saravanan, Vishal Gaur, and Ananth Raman. "Do Inventory and Gross Margin Data Improve Sales Forecasts for U.S. Public Retailers?" Management Science 56, no. 9 (September 2010).
- February 2024
- Article
Representation and Extrapolation: Evidence from Clinical Trials
By: Marcella Alsan, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein and Heidi L. Williams
This article examines the consequences and causes of low enrollment of Black patients in clinical
trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is
more relevant for decision-making by physicians and patients when it...
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Keywords:
Representation;
Racial Disparity;
Health Testing and Trials;
Race;
Equality and Inequality;
Innovation and Invention;
Pharmaceutical Industry
Alsan, Marcella, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein, and Heidi L. Williams. "Representation and Extrapolation: Evidence from Clinical Trials." Quarterly Journal of Economics 139, no. 1 (February 2024): 575–635.
- 2024
- Working Paper
Consumer Inertia and Market Power
By: Alexander MacKay and Marc Remer
We study the pricing decisions of firms in the presence of consumer inertia. Inertia, which can arise from habit formation, brand loyalty, and switching costs, generates dynamic pricing incentives. These incentives mediate the impact of competition on market power in...
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Keywords:
Consumer Inertia;
Market Power;
Dynamic Competition;
Demand Estimation;
Consumer Behavior;
Markets;
Performance;
Competition;
Price
MacKay, Alexander, and Marc Remer. "Consumer Inertia and Market Power." Harvard Business School Working Paper, No. 19-111, April 2019. (Revised January 2024. Direct download.)
- Research Summary
Overview
Paul is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular,...
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