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- 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.
- 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.
- 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–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.
- 2024
- Working Paper
Winner Take All: Exploiting Asymmetry in Factorial Designs
By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers and practitioners have embraced factorial experiments to simultaneously test multiple treatments, each with different levels. With the rise of technologies like Generative AI, factorial experimentation has become even more accessible: it is easier than ever... View Details
Keywords: Factorial Designs; Fisher Randomizations; Rank Estimators; Employer Interventions; Causal Inference; Mathematical Methods; Performance Improvement
DosSantos DiSorbo, Matthew, Iavor I. Bojinov, and Fiammetta Menchetti. "Winner Take All: Exploiting Asymmetry in Factorial Designs." Harvard Business School Working Paper, No. 24-075, June 2024.
- June 2024
- Article
The Monitoring Role of Social Media
By: Jonas Heese and Joseph Pacelli
In this study, we examine whether social media activity can reduce corporate misconduct. We use the staggered introduction of 3G mobile broadband access across the United States to identify exogenous increases in social media activity and test whether access to 3G... View Details
Keywords: Corporate Misconduct; Twitter; Corporate Accountability; Mobile and Wireless Technology; Social and Collaborative Networks
Heese, Jonas, and Joseph Pacelli. "The Monitoring Role of Social Media." Review of Accounting Studies 29, no. 2 (June 2024): 1666–1706.
- April 29, 2024
- Editorial
Stemming the Ripple Effect of Untreated Mental Illness: A Prescription for Change: Reimagining U.S. Healthcare
By: Lidia Moura and Susanna Gallani
Moura, Lidia, and Susanna Gallani. "Stemming the Ripple Effect of Untreated Mental Illness: A Prescription for Change: Reimagining U.S. Healthcare." Psychology Today (website) (April 29, 2024).
- April 2024 (Revised July 2024)
- Case
Market Dynamics and Moral Dilemmas: Novo Nordisk’s Weight-Loss Drugs
By: Joseph L. Badaracco, Tom Quinn and John Schultz
Danish pharmaceutical company Novo Nordisk was owned by a charitable foundation, and since its founding in the 1920s had focused on producing insulin to treat diabetes. In 2017, however, it released Ozempic, a diabetes treatment with the revolutionary side effect of... View Details
Keywords: Cost vs Benefits; Decisions; Judgments; Values and Beliefs; Global Strategy; Health Care and Treatment; Patents; Growth and Development Strategy; Growth Management; Product Positioning; Supply and Industry; Supply Chain; Corporate Social Responsibility and Impact; Mission and Purpose; Philanthropy and Charitable Giving; Opportunities; Social Issues; Equality and Inequality; Pharmaceutical Industry; Health Industry; Denmark; United States; Europe; China; India; Middle East; North Africa
Badaracco, Joseph L., Tom Quinn, and John Schultz. "Market Dynamics and Moral Dilemmas: Novo Nordisk’s Weight-Loss Drugs." Harvard Business School Case 324-114, April 2024. (Revised July 2024.)
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- 2023
- Working Paper
Design-Based Inference for Multi-arm Bandits
By: Dae Woong Ham, Iavor I. Bojinov, Michael Lindon and Martin Tingley
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online decision-making. Consequently, there is an increased desire to use the available... View Details
Ham, Dae Woong, Iavor I. Bojinov, Michael Lindon, and Martin Tingley. "Design-Based Inference for Multi-arm Bandits." Harvard Business School Working Paper, No. 24-056, March 2024.
- March 2024
- Article
Medicare Price Negotiation and Pharmaceutical Innovation Following the Inflation Reduction Act
By: Matthew Vogel, Pragya Kakani, Amitabh Chandra and Rena M. Conti
The Inflation Reduction Act (IRA) requires Medicare to negotiate lower prices for some medicines with high Medicare spending. Using historical data from public and proprietary sources to apply the IRA's negotiation criteria retrospectively, we identify all drugs that... View Details
Keywords: Policy; Government Legislation; Health Care and Treatment; Negotiation; Price; Pharmaceutical Industry
Vogel, Matthew, Pragya Kakani, Amitabh Chandra, and Rena M. Conti. "Medicare Price Negotiation and Pharmaceutical Innovation Following the Inflation Reduction Act." Nature Biotechnology 42, no. 3 (March 2024): 406–412.
- 2023
- Working Paper
'De Gustibus' and Disputes about Reference Dependence
By: Thomas Graeber, Pol Campos-Mercade, Lorenz Goette, Alexandre Kellogg and Charles Sprenger
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines the implications of heterogeneity in gain-loss attitudes for such tests. In experiments on labor supply and exchange behavior we measure gain-loss attitudes and then... View Details
Graeber, Thomas, Pol Campos-Mercade, Lorenz Goette, Alexandre Kellogg, and Charles Sprenger. "'De Gustibus' and Disputes about Reference Dependence." Harvard Business School Working Paper, No. 24-046, January 2024.
- January 2024
- Article
Population Interference in Panel Experiments
By: Kevin Wu Han, Guillaume Basse and Iavor Bojinov
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
Han, Kevin Wu, Guillaume Basse, and Iavor Bojinov. "Population Interference in Panel Experiments." Journal of Econometrics 238, no. 1 (January 2024).
- 2023
- Working Paper
Money, Time, and Grant Design
By: Kyle Myers and Wei Yang Tham
The design of research grants has been hypothesized to be a useful tool for
influencing researchers and their science. We test this by conducting two thought
experiments in a nationally representative survey of academic researchers. First,
we offer participants a... View Details
Myers, Kyle, and Wei Yang Tham. "Money, Time, and Grant Design." Harvard Business School Working Paper, No. 24-037, December 2023.
- December 2023
- Teaching Note
Buurtzorg
By: Ethan Bernstein and Tatiana Sandino
Teaching Note for HBS Case No. 122-101. As co-founders of home nursing company Buurtzorg, Jos de Blok and Gonnie Kronenberg prized both self-management and organizational learning. Buurtzorg’s 10,000 nurses across 950 neighborhood nursing teams in the Netherlands were... View Details
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which... View Details
Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a... View Details
Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
Keywords: AI and Machine Learning; Performance Improvement; Small Business; Decision Choices and Conditions; Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.