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      • September 2025
      • Article

      Sticky Capital Controls

      By: Miguel Acosta-Henao, Laura Alfaro and Andrés Fernández
      There is much ongoing debate on the merits of capital controls as effective policy instruments. The differing perspectives are due in part to a lack of empirical studies that look at the intensive margin of controls, which in turn has prevented a quantitative... View Details
      Keywords: Capital Controls; Macroprudential Policies; Stickiness; Intensive; (S, S) Costs; Capital; Management; Macroeconomics; Governance Controls; Mathematical Methods
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      Acosta-Henao, Miguel, Laura Alfaro, and Andrés Fernández. "Sticky Capital Controls." Art. 104104. Journal of International Economics 157 (September 2025).
      • 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
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      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).
      • July 2025
      • Article

      Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers

      By: Mengjie Cheng and Shunyuan Zhang
      The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers' reputations. We collected a novel dataset of user-generated YouTube videos created by prominent English-speaking influencers in... View Details
      Keywords: Reputation; Mathematical Methods; Marketing Reference Programs; Social Media; Brands and Branding
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      Cheng, Mengjie, and Shunyuan Zhang. "Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers." Management Science 71, no. 7 (July 2025): 5910–5932.
      • 2025
      • Working Paper

      Productivity Beliefs and Efficiency in Science

      By: Fabio Bertolotti, Kyle R. Myers and Wei Yang Tham
      We develop a method to estimate producers’ productivity beliefs in settings where output quantities and input prices are unobservable, and we use it to evaluate allocative efficiency in the market for science. Our model of researchers’ labor supply shows that their... View Details
      Keywords: Performance Productivity; Perception; Research
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      Bertolotti, Fabio, Kyle R. Myers, and Wei Yang Tham. "Productivity Beliefs and Efficiency in Science." Harvard Business School Working Paper, No. 25-063, June 2025.
      • 2025
      • Working Paper

      Trade and Industrial Policy in Supply Chains: Directed Technological Change in Rare Earths

      By: Laura Alfaro, Harald Fadinger, Jay Schymik and Gede Virananda
      Trade and industrial policies, while primarily intended to support domestic industries, may unintentionally stimulate technological progress abroad. We document this mechanism in the case of rare earth elements (REEs)—critical inputs for manufacturing at the knowledge... View Details
      Keywords: Industrial Policy; Global Value Chains; Directed Technological Change; Input-output Linkages; Innovation; Trade; Metals and Minerals; Technological Innovation; Supply Chain; Technology Industry
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      Alfaro, Laura, Harald Fadinger, Jay Schymik, and Gede Virananda. "Trade and Industrial Policy in Supply Chains: Directed Technological Change in Rare Earths." Harvard Business School Working Paper, No. 25-059, May 2025.
      • May–June 2025
      • Article

      Branch-and-Price for Prescriptive Contagion Analytics

      By: Alexandre Jacquillat, Michael Lingzhi Li, Martin Ramé and Kai Wang
      Contagion models are ubiquitous in epidemiology, social sciences, engineering, and management. This paper formulates a prescriptive contagion analytics model where a decision maker allocates shared resources across multiple segments of a population, each governed by... View Details
      Keywords: COVID-19; Mathematical Methods
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      Jacquillat, Alexandre, Michael Lingzhi Li, Martin Ramé, and Kai Wang. "Branch-and-Price for Prescriptive Contagion Analytics." Operations Research 73, no. 3 (May–June 2025): 1558–1580.
      • May–June 2025
      • Article

      Slowly Varying Regression Under Sparsity

      By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
      We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
      Keywords: Mathematical Methods; Analytics and Data Science
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      Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research 73, no. 3 (May–June 2025): 1581–1597.
      • 2025
      • Book

      The Experimentation Machine: Finding Product–Market Fit in the Age of AI

      By: Jeffrey J. Bussgang
      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
      Keywords: AI; Founder; Startup; AI and Machine Learning; Technology Adoption; Business Startups; Entrepreneurship; Market Entry and Exit
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      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
      Keywords: Mathematical Methods; Infrastructure; Information Infrastructure
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      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
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      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

      By: Liangzong Ma, Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
      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
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      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

      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
      Keywords: Employees; Compensation and Benefits; Satisfaction; Value; Research
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      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
      Keywords: AI and Machine Learning; Mathematical Methods; Analytics and Data Science
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      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
      Keywords: Innovation & Entrepreneurship; Innovation and Management; Entrepreneurial Finance; Entrepreneurship; Business Model; Business Startups; Agreements and Arrangements; Science-Based Business; Commercialization
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      Krieger, Joshua Lev, and Jim Matheson. "Xtalic." Harvard Business School Case 825-103, December 2024.
      • December 2024
      • Article

      Respect for Improvements and Comparative Statics in Matching Markets

      By: Scott Duke Kominers
      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
      Keywords: Market Entry and Exit; Marketplace Matching; Mathematical Methods; Market Design
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      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
      Keywords: AI and Machine Learning; Refugees; Geographic Location; Employment
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      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
      Keywords: Econometrics; Casual Inference; Marketing; Economics; Advertising; Mathematical Methods
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      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
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      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
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      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
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      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.
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