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- 2025
- Working Paper
Tax Planning, Illiquidity, and Credit Risks: Evidence from DeFi Lending
By: Lisa De Simone, Peiyi Jin and Daniel Rabetti
This study establishes a plausible causal link between tax-planning-induced illiquidity and credit risks in lending markets. Exploiting an exogenous tax shock imposed by the Internal Revenue Service (IRS) on cryptocurrency gains, along with millions of transactions in... View Details
Keywords: Taxation
De Simone, Lisa, Peiyi Jin, and Daniel Rabetti. "Tax Planning, Illiquidity, and Credit Risks: Evidence from DeFi Lending." Working Paper, February 2025.
- 2025
- Article
Emotion Regulation Contagion Drives Reduction in Negative Intergroup Emotions
By: Michael Pinus, Yajun Cao, Eran Halperin, Alin Coman, James J. Gross and Amit Goldenberg
When emotions occur in groups, they sometimes impact group behavior in undesired ways. Reducing group’s emotions with emotion regulation interventions can be helpful, but may also be a challenge, because treating every person in the group is often infeasible. One... View Details
Keywords: Emotion Contagion; Emotion; Emotion Regulation; Groups and Teams; Emotions; Conflict and Resolution
Pinus, Michael, Yajun Cao, Eran Halperin, Alin Coman, James J. Gross, and Amit Goldenberg. "Emotion Regulation Contagion Drives Reduction in Negative Intergroup Emotions." Art. 1387. Nature Communications 16 (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
Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India
By: Shawn Cole, Grady Killeen, Tomoko Harigaya 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: Performance Evaluation; Technology Adoption; Measurement and Metrics; Analytics and Data Science; Agriculture and Agribusiness Industry; India
Cole, Shawn, Grady Killeen, Tomoko Harigaya, and Aparna Krishna. "Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India." Harvard Business School Working Paper, No. 25-035, January 2025.
- 2025
- Working Paper
The Hidden Costs of Working Multiple Jobs: Implications for Spending Behavior and Wellbeing
By: Paige Tsai and Ryan W. Buell
Problem definition: Amidst inflation, rising costs of living, an explosion in remote and gig working opportunities, and an increase in the part-time labor mix in economies around the world, it is becoming evermore commonplace for
people to earn labor income... View Details
Keywords: Behavioral Operations; Employee Behavior; Job Design and Levels; Personal Finance; Well-being; Happiness; Satisfaction; Wages
Tsai, Paige, and Ryan W. Buell. "The Hidden Costs of Working Multiple Jobs: Implications for Spending Behavior and Wellbeing." Harvard Business School Working Paper, No. 25-036, January 2025. (Revised March 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.
- 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.
- September 2024
- Article
Backstage Matters: Collective Energy and Information Sharing on Global Teams
By: Wenjie Ma, Leslie A. Perlow and Eunice Eun
It is well documented that information sharing – which is central to team effectiveness – is complicated by cultural and geographical factors. However, little is known about the process of information sharing between subgroups within global teams. Building on Goffman’s... View Details
Ma, Wenjie, Leslie A. Perlow, and Eunice Eun. "Backstage Matters: Collective Energy and Information Sharing on Global Teams." Academy of Management Discoveries 10, no. 3 (September 2024): 463–487.
- September 2024
- Article
Investing in the Next Generation: The Long-Run Impacts of a Liquidity Shock
By: Patrick Agte, Arielle Bernhardt, Erica M. Field, Rohini Pande and Natalia Rigol
How do poor entrepreneurs trade off investments in business enterprises versus children's human capital, and how do these choices influence intergenerational socio-economic mobility? To examine this, we exploit experimental variation in household income resulting from... View Details
Agte, Patrick, Arielle Bernhardt, Erica M. Field, Rohini Pande, and Natalia Rigol. "Investing in the Next Generation: The Long-Run Impacts of a Liquidity Shock." American Economic Review 114, no. 9 (September 2024): 2792–2824.
- 2024
- Working Paper
The Operational Impact of Customer Location in On-Demand Services
By: Natalie Epstein, Santiago Gallino and Antonio Moreno
The rapid growth of on-demand delivery services, particularly in the food and grocery sectors, has driven the expansion of hyperlocal fulfillment centers (FCs). This paper uses data from an on-demand grocery delivery platform in Latin America to assess how customer... View Details
Keywords: Consumer Behavior; Logistics; Geographic Location; Service Delivery; Quality; Retail Industry; Latin America
Epstein, Natalie, Santiago Gallino, and Antonio Moreno. "The Operational Impact of Customer Location in On-Demand Services." Working Paper, September 2024.
- 2024
- Working Paper
Determinants of Top-Down Sabotage
By: Hashim Zaman and Karim R. Lakhani
We investigate the conditions that motivate managers to impede the growth of talented subordinates due to fears of future competition for their own positions. Our research expands on existing tournament and contest theory literature that considers peer-to-peer sabotage... View Details
Keywords: Succession Planning; Organizational Hierarchy; Compensation; Promotions; Tournaments; Talent and Talent Management; Organizational Structure; Employee Relationship Management; Performance Evaluation; Organizational Culture; Management Skills
Zaman, Hashim, and Karim R. Lakhani. "Determinants of Top-Down Sabotage." Harvard Business School Working Paper, No. 25-007, August 2024. (Revised December 2024.)
- August 2024
- Case
Scaling Seven Starling
By: Ryan W. Buell and Carin-Isabel Knoop
Seven Starling, a maternal mental health startup, is scaling its digital clinic model. Seven Starling addresses perinatal mental health challenges by providing licensed therapists, peer support, and medication to mothers across five states, with a hybrid care model... View Details
Keywords: Business Model; Business Startups; Health Care and Treatment; Growth and Development Strategy; Mission and Purpose; Health Industry
Buell, Ryan W., and Carin-Isabel Knoop. "Scaling Seven Starling." Harvard Business School Case 625-046, August 2024.
- 2024
- Article
Crucibles, Multiple Sensitive Periods, and Career Progression
By: Prithwiraj Choudhury, Sunasir Dutta, Hise O. Gibson and Eric Lin
We study the effects of crucible experiences along multiple sensitive periods on career progression. While prior literature has hinted that individuals can be imprinted during multiple sensitive periods, not just during the early career, there has been scant attention... View Details
Keywords: Military Service; Personal Development and Career; Transformation; Power and Influence; Learning; Human Capital
Choudhury, Prithwiraj, Sunasir Dutta, Hise O. Gibson, and Eric Lin. "Crucibles, Multiple Sensitive Periods, and Career Progression." Academy of Management Proceedings (2024).
- August 2024
- Article
How Do Copayment Coupons Affect Branded Drug Prices and Quantities Purchased?
By: Leemore S. Dafny, Kate Ho and Edward Kong
Drug copayment coupons to reduce patient cost-sharing have become nearly ubiquitous for high-priced brand-name prescription drugs. Medicare bans such coupons on the grounds that they are kickbacks that induce utilization, but they are commonly used by... View Details
Keywords: Prescription Drugs; Coupons; Impact; Health Care and Treatment; Markets; Price; Spending; Pharmaceutical Industry; United States
Dafny, Leemore S., Kate Ho, and Edward Kong. "How Do Copayment Coupons Affect Branded Drug Prices and Quantities Purchased?" American Economic Journal: Economic Policy 16, no. 3 (August 2024): 314–346.
- 2024
- Working Paper
Smaller than We Thought? The Effect of Automatic Savings Policies
By: James J. Choi, David Laibson, Jordan Cammarota, Richard Lombardo and John Beshears
Medium- and long-run dynamics undermine the effect of automatic enrollment and default savings-rate auto-escalation on retirement savings. Our analysis of 401(k) plans incorporates the facts that employees frequently leave firms (often before matching contributions... View Details
Choi, James J., David Laibson, Jordan Cammarota, Richard Lombardo, and John Beshears. "Smaller than We Thought? The Effect of Automatic Savings Policies." Working Paper.
- August 2024
- Article
The Labor Market Effects of Loan Guarantee Programs
By: Jean-Noël Barrot, Thorsten Martin, Julien Sauvagnat and Boris Vallée
We investigate the labor market effects of a loan guarantee program targeting French SMEs during the financial crisis. Exploiting differences in regional treatment intensity in a border discontinuity design, we uncover a central trade-off for such interventions. While... View Details
Barrot, Jean-Noël, Thorsten Martin, Julien Sauvagnat, and Boris Vallée. "The Labor Market Effects of Loan Guarantee Programs." Review of Financial Studies 37, no. 8 (August 2024): 2315–2354.
- July 2024
- Article
A (Dynamic) Investigation of Stereotypes, Belief-Updating, and Behavior
By: Katherine B. Coffman, Paola Ugalde Araya and Basit Zafar
Many decisions—such as what educational or career path to pursue—are dynamic in nature, with individuals receiving feedback at one point in time and making decisions later. Using a controlled experiment, with two sessions one week apart, we analyze the dynamic effects... View Details
Keywords: Feedback; Beliefs; Stereotypes; Self-assessment; Gender Gap; Gender; Equality and Inequality; Perception; Decision Choices and Conditions
Coffman, Katherine B., Paola Ugalde Araya, and Basit Zafar. "A (Dynamic) Investigation of Stereotypes, Belief-Updating, and Behavior." Economic Inquiry 62, no. 3 (July 2024): 957–983.
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- July 2024
- Article
Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings
By: Jason B. Liu, Robert S. Kaplan, David W. Bates, Mario O. Edelen, Rachel C. Sisodia and Andrea L. Pusic
This article describes the strategies that leaders at the Mass General Brigham (MGB) health system have used in launching a standardized patient-reported outcome measure (PROM) collection program in 2012, a major step in the value-based transformation of health care.... View Details
Keywords: Patient-reported Outcomes; Value Based Health Care; Health Care and Treatment; Transformation; Outcome or Result; Organizational Change and Adaptation; Performance Improvement; Health Industry
Liu, Jason B., Robert S. Kaplan, David W. Bates, Mario O. Edelen, Rachel C. Sisodia, and Andrea L. Pusic. "Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings." NEJM Catalyst Innovations in Care Delivery 5, no. 7 (July 2024).
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.