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- September 2025
- Article
Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India
By: Shawn Cole, Tomoko Harigaya, Grady Killeen and Aparna Krishna
This paper evaluates a low-cost, customized soil nutrient management advisory service in India. As a methodological contribution, we examine whether and in which settings satellite measurements may be effective at estimating both agricultural yields and treatment... View Details
Keywords: Measurement and Metrics; Mathematical Methods; Analytics and Data Science; Agriculture and Agribusiness Industry; India
Cole, Shawn, Tomoko Harigaya, Grady Killeen, and Aparna Krishna. "Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India." Journal of Development Economics 176 (September 2025).
- 2025
- Book
The Experimentation Machine: Finding Product–Market Fit in the Age of AI
Leverage AI to be a 10x Founder
Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, I reveal how AI is transforming the way startups find product-market fit and scale.... View Details
Today’s most successful founders know that the startups that learn the fastest will win. In The Experimentation Machine, I reveal how AI is transforming the way startups find product-market fit and scale.... View Details
Keywords: AI; Founder; Startup; AI and Machine Learning; Technology Adoption; Business Startups; Entrepreneurship; Market Entry and Exit
Bussgang, Jeffrey J. The Experimentation Machine: Finding Product–Market Fit in the Age of AI. Damn Gravity Media, 2025.
- 2025
- Working Paper
Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure
By: Jason Milionis, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers and Tim Roughgarden
We introduce the first formal model capturing the elicitation of unverifiable information from a party (the "source") with implicit signals derived by other players (the "observers"). Our model is motivated in part by applications in decentralized physical... View Details
Milionis, Jason, Jens Ernstberger, Joseph Bonneau, Scott Duke Kominers, and Tim Roughgarden. "Incentive-Compatible Recovery from Manipulated Signals, with Applications to Decentralized Physical Infrastructure." Working Paper, March 2025.
- March 2025
- Article
Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures
By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani
The literature on communities of practice demonstrates that a proven way for senior professionals to upskill
themselves in the use of new technologies that undermine existing expertise is to learn from junior
professionals. It notes that juniors may be better able... View Details
Keywords: Rank and Position; Competency and Skills; Technology Adoption; Experience and Expertise; AI and Machine Learning
Kellogg, Katherine C., Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon, and Karim R. Lakhani. "Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures." Art. 100559. Information and Organization 35, no. 1 (March 2025).
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
- 2025
- Working Paper
Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI
By: Rebecca Karp
Existing literature often separates research on the design of innovations from their implementation and use, neglecting the role of selection—how organizations choose which innovations to implement. Although scholars suggest scientific approaches for selecting novel... View Details
Keywords: Decision Choices and Conditions; Technology Adoption; Groups and Teams; Prejudice and Bias
Karp, Rebecca. "Crossing the Design-Use Divide: How Process Manipulation Shapes the Design and Use of AI." Harvard Business School Working Paper, No. 25-034, January 2025.
- 2025
- Working Paper
Home Sweet Home: How Much Do Employees Value Remote Work?
By: Zoë B. Cullen, Bobak Pakzad-Hurson and Ricardo Perez-Truglia
We estimate the value employees place on remote work using revealed preferences in a high-stakes, real-world context, focusing on U.S. tech workers. On average, employees are willing to accept a 25% pay cut for partly or fully remote roles. Our estimates are three to... View Details
Cullen, Zoë B., Bobak Pakzad-Hurson, and Ricardo Perez-Truglia. "Home Sweet Home: How Much Do Employees Value Remote Work?" NBER Working Paper Series, No. 33383, January 2025.
- 2025
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics 43, no. 1 (2025): 256–268.
- December 2024
- Case
Xtalic
By: Joshua Lev Krieger and Jim Matheson
This case study examines the commercialization efforts of Xtalic, a startup founded by MIT scientists based on their discovery of a novel material science method to protect metal substrates. The case focuses on the strategic decisions involved in bringing this... View Details
- December 5, 2024
- Article
A Consensus Definition of Creativity in Surgery: A Delphi Study Protocol
By: Alex Thabane, Tyler McKechnie, Phillip Staibano, Vikram Arora, Goran Calic, Jason W. Busse, Sameer Parpia and Mohit Bhandari
Introduction
Clear definitions are essential in science, particularly in the study of abstract phenomena like creativity. Due to its inherent complexity and domain-specific nature, the study of creativity has been complicated, as evidenced by the various... View Details
Clear definitions are essential in science, particularly in the study of abstract phenomena like creativity. Due to its inherent complexity and domain-specific nature, the study of creativity has been complicated, as evidenced by the various... View Details
Thabane, Alex, Tyler McKechnie, Phillip Staibano, Vikram Arora, Goran Calic, Jason W. Busse, Sameer Parpia, and Mohit Bhandari. "A Consensus Definition of Creativity in Surgery: A Delphi Study Protocol." PLoS ONE 19, no. 12 (December 5, 2024).
- December 2024
- Article
Respect for Improvements and Comparative Statics in Matching Markets
One of the oldest results in the theory of two-sided matching is the entry comparative static, which shows that under the Gale–Shapley deferred acceptance algorithm, adding a new agent to one side of the market makes all the agents on the other side weakly... View Details
Kominers, Scott Duke. "Respect for Improvements and Comparative Statics in Matching Markets." Journal of Mechanism and Institution Design 9, no. 1 (December 2024): 83–104.
- November–December 2024
- Article
Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing
By: Kirk Bansak and Elisabeth Paulson
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment... View Details
Bansak, Kirk, and Elisabeth Paulson. "Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing." Operations Research 72, no. 6 (November–December 2024): 2375–2390.
- November 2024
- Article
Preference Externality Estimators: A Comparison of Border Approaches and IVs
By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano
This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in... View Details
Ling, Xi, Wesley R. Hartmann, and Tomomichi Amano. "Preference Externality Estimators: A Comparison of Border Approaches and IVs." Management Science 70, no. 11 (November 2024): 7892–7910.
- 2024
- Working Paper
Pitfalls of Demographic Forecasts of U.S. Elections
By: Richard Calvo, Vincent Pons and Jesse M. Shapiro
Many 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... View Details
Keywords: Mathematical Methods; Voting; Political Elections; Trends; Forecasting and Prediction; Demographics
Calvo, Richard, Vincent Pons, and Jesse M. Shapiro. "Pitfalls of Demographic Forecasts of U.S. Elections." NBER Working Paper Series, No. 33016, October 2024.
- 2024
- Working Paper
Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python
By: Melissa Ouellet and Michael W. Toffel
This paper describes a range of best practices to compile and analyze datasets, and includes some examples in Stata, R, and Python. It is meant to serve as a reference for those getting started in econometrics, and especially those seeking to conduct data analyses in... View Details
Keywords: Empirical Methods; Empirical Operations; Statistical Methods And Machine Learning; Statistical Interferences; Research Analysts; Analytics and Data Science; Mathematical Methods
Ouellet, Melissa, and Michael W. Toffel. "Empirical Guidance: Data Processing and Analysis with Applications in Stata, R, and Python." Harvard Business School Working Paper, No. 25-010, August 2024.
- 2024
- Working Paper
The New Digital Divide
By: Mayana Pereira, Shane Greenstein, Raffaella Sadun, Prasanna Tambe, Lucia Ronchi Darre, Tammy Glazer, Allen Kim, Rahul Dodhia and Juan Lavista Ferres
We build and analyze new metrics of digital usage that leverage telemetry data collected by Microsoft during operating system updates across forty million Windows devices in U.S. households. These measures of US household digital usage are much more comprehensive than... View Details
Keywords: Mathematical Methods; Measurement and Metrics; Geographic Location; Behavior; Technology Adoption; Demographics
Pereira, Mayana, Shane Greenstein, Raffaella Sadun, Prasanna Tambe, Lucia Ronchi Darre, Tammy Glazer, Allen Kim, Rahul Dodhia, and Juan Lavista Ferres. "The New Digital Divide." NBER Working Paper Series, No. 32932, September 2024.
- August 2024
- Technical Note
Measuring Concentrated Ownership
By: Christina R. Wing, Everett Alexander and Justin Huang
Firms with strong governance practices exhibit lower control premiums due to reduced risks and more efficient operations. Conversely, poorly governed firms may exhibit higher control premiums as new owners anticipate the need for substantial governance improvements.... View Details
- 2024
- Working Paper
Modest Victims: Victims Who Decline to Broadcast Their Victimization Are Seen As Morally Virtuous
By: Nathan Dhaliwal, Jillian J. Jordan and Pat Barclay
What do people think of victims who conceal their victimhood? We propose that the decision to not broadcast that one has been victimized serves as a costly act of modesty—in doing so, one is potentially forgoing social support and compensation from one’s community. We... View Details
Dhaliwal, Nathan, Jillian J. Jordan, and Pat Barclay. "Modest Victims: Victims Who Decline to Broadcast Their Victimization Are Seen As Morally Virtuous." Working Paper, August 2024.
- 2024
- Working Paper
People, Practices, and Productivity: A Review of New Advances in Personnel Economics
By: Mitchell Hoffman and Christopher T. Stanton
This chapter surveys recent advances in personnel economics. We begin by presenting evidence showing substantial and persistent productivity variation among workers in the same roles. We discuss new research on incentives and compensation; hiring practices; the... View Details
Hoffman, Mitchell, and Christopher T. Stanton. "People, Practices, and Productivity: A Review of New Advances in Personnel Economics." NBER Working Paper Series, No. 32849, August 2024.
- July–August 2024
- Article
Disclosing Downstream Emissions
By: Robert S. Kaplan and Karthik Ramanna
An increasing number of companies are using the E-liability carbon-accounting method as an important tool for tracking progress toward reducing global emissions in their supply chains. The system does not require formal accounting for downstream emissions—those... View Details
Keywords: Carbon Emissions; Environmental Accounting; Corporate Accountability; Corporate Social Responsibility and Impact; Corporate Disclosure; Environmental Sustainability
Kaplan, Robert S., and Karthik Ramanna. "Disclosing Downstream Emissions." Harvard Business Review 102, no. 4 (July–August 2024): 124–133.