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All HBS Web
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- Faculty Publications (169)
- 2022
- Working Paper
Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina
By: Sebastian Calonico, Rafael Di Tella and Juan Cruz Lopez Del Valle
Many medical decisions during the pandemic were made without the support of causal evidence obtained in clinical trials. We study the case of nebulized ibuprofen (NaIHS), a drug that was extensively used on COVID-19 patients in Argentina amidst wild claims about its...
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Keywords:
COVID-19;
Drug Treatment;
Health Pandemics;
Health Care and Treatment;
Decision Making;
Outcome or Result;
Argentina
Calonico, Sebastian, Rafael Di Tella, and Juan Cruz Lopez Del Valle. "Causal Inference During A Pandemic: Evidence on the Effectiveness of Nebulized Ibuprofen as an Unproven Treatment for COVID-19 in Argentina." NBER Working Paper Series, No. 30084, May 2022.
- Article
How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that...
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Keywords:
Difference In Differences;
Staggered Difference-in-differences Designs;
Generalized Difference-in-differences;
Dynamic Treatment Effects;
Mathematical Methods
Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395. (Editor's Choice, May 2022; Jensen Prize, First Place, June 2023.)
- April–June 2022
- Other Article
Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'
There has been a substantial discussion in various methodological and applied literatures around causal inference; especially in the use of machine learning and statistical models to understand heterogeneity in treatment effects and to make optimal decision...
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Keywords:
Causal Inference;
Treatment Effect Estimation;
Treatment Assignment Policy;
Human-in-the-loop;
Decision Making;
Fairness
McFowland III, Edward. "Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters'." INFORMS Journal on Data Science 1, no. 1 (April–June 2022): 21–22.
- 2022
- Working Paper
Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment
Hybrid work is emerging as a novel form of organizing work globally. This paper reports causal evidence on how the extent of hybrid work—the number of days worked from home relative to days worked from the office—affects work outcomes. Collaborating with an...
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Keywords:
Hybrid Work;
Remote Work;
Work-from-home;
Field Experiment;
Employees;
Geographic Location;
Performance;
Work-Life Balance
Choudhury, Prithwiraj, Tarun Khanna, Christos A. Makridis, and Kyle Schirmann. "Is Hybrid Work the Best of Both Worlds? Evidence from a Field Experiment." Harvard Business School Working Paper, No. 22-063, March 2022.
- Article
Corruption and Firms
By: Emanuele Colonnelli and Mounu Prem
We estimate the causal real economic effects of a randomized anti-corruption crackdown on local governments in Brazil using rich micro-data on corruption and firms. After anti-corruption audits, municipalities experience an increase in the number of firms concentrated...
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Colonnelli, Emanuele, and Mounu Prem. "Corruption and Firms." Review of Economic Studies 89, no. 2 (March 2022): 695–732.
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless...
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Keywords:
Causal Inference;
Partial Interference;
Synthetic Controls;
Bayesian Structural Time Series;
Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- 2023
- Working Paper
Fintech to the (Worker) Rescue: Access to Earned Wages, Financial Health and Employee Turnover
By: Jose Murillo, Boris Vallée and Dolly Yu
Using novel data from a Mexican FinTech firm, we study the usage by workers of earned wages access, an innovative financial service offered by firms to their employees as a benefit. We find usage to be significant and concentrated towards the end of the pay cycle. We...
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Keywords:
Fintech;
Present Bias;
Earned Wage Access;
Wages;
Employees;
Retention;
Well-being;
Mexico
Murillo, Jose, Boris Vallée, and Dolly Yu. "Fintech to the (Worker) Rescue: Access to Earned Wages, Financial Health and Employee Turnover." Working Paper, 2023.
- March 2022
- Article
How Much Does Your Boss Make? The Effects of Salary Comparisons
By: Zoë B. Cullen and Ricardo Perez-Truglia
The vast majority of the pay inequality in an organization comes from differences in pay between employees and their bosses. But are employees aware of these pay disparities? Are employees demotivated by this inequality? To address these questions, we conducted a...
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Keywords:
Salary;
Inequality;
Managers;
Career Concerns;
Pay Transparency;
Wages;
Equality and Inequality;
Perception;
Behavior
Cullen, Zoë B., and Ricardo Perez-Truglia. "How Much Does Your Boss Make? The Effects of Salary Comparisons." Journal of Political Economy 130, no. 3 (March 2022): 766–822.
- 2022
- Working Paper
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...
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Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Working Paper, March 2022.
- 2022
- Working Paper
Consumer Reviews and Regulation: Evidence from NYC Restaurants
By: Chiara Farronato and Georgios Zervas
We investigate the informativeness of hygiene signals in online reviews, and their effect on consumer choice and restaurant hygiene. We first extract signals of hygiene from Yelp. Among all dimensions that regulators monitor through mandated restaurant inspections, we...
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Keywords:
Restaurants;
Reviews;
Hygiene;
Yelp;
Regulation;
Food;
Governing Rules, Regulations, and Reforms;
Consumer Behavior
Farronato, Chiara, and Georgios Zervas. "Consumer Reviews and Regulation: Evidence from NYC Restaurants." NBER Working Paper Series, No. 29715, February 2022.
- 2022
- Working Paper
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...
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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?" NBER Working Paper Series, No. 29735, February 2022.
- 2022
- Article
Alleviating Time Poverty Among the Working Poor: A Pre-Registered Longitudinal Field Experiment
By: A.V. Whillans and Colin West
Poverty entails more than a scarcity of material resources—it also involves a shortage of time. To examine the causal benefits of reducing time poverty, we conducted a longitudinal feld experiment over six consecutive weeks in an urban slum in Kenya with a sample of...
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Keywords:
Time;
Subjective Well Being;
Administrative Costs;
Friction;
Poverty;
Well-being;
Money;
Perception;
Kenya
Whillans, A.V., and Colin West. "Alleviating Time Poverty Among the Working Poor: A Pre-Registered Longitudinal Field Experiment." Art. 719. Scientific Reports 12 (2022).
- 2022
- Working Paper
Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms
By: Dany Bahar, Prithwiraj Choudhury, Sara Signorelli and James M. Sappenfield
Leveraging a unique dataset merging patent data with all work-related migration reforms that took place in 15 countries over 26 years, we show that reforms discouraging inventor mobility decrease the patenting of MNE subsidiaries within a country, while reforms...
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Keywords:
Migration;
Technology;
Policy Evaluation;
Patents;
Information Technology;
Immigration;
Policy;
Collaborative Innovation and Invention;
Globalization
Bahar, Dany, Prithwiraj Choudhury, Sara Signorelli, and James M. Sappenfield. "Talent Flows and the Geography of Knowledge Production: Causal Evidence from Multinational Firms." Harvard Business School Working Paper, No. 22-047, January 2022. (Revised December 2022.)
- 2021
- Working Paper
Limits to Bank Deposit Market Power
By: Juliane Begenau and Erik Stafford
Claims about the market power of bank deposits in the banking literature are numerous and far reaching. Recently, a causal narrative has emerged in the banking literature: market power in bank deposits, measured as imperfect pass-through of short-term market rates on...
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Keywords:
Bank Deposits;
Market Power;
Net Interest Margin (NIM);
Banks and Banking;
Interest Rates;
Risk and Uncertainty
Begenau, Juliane, and Erik Stafford. "Limits to Bank Deposit Market Power." Harvard Business School Working Paper, No. 22-039, November 2021.
- Article
A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects
By: Edward McFowland III, Sandeep Gangarapu, Ravi Bapna and Tianshu Sun
We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility maximizing context, public...
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Keywords:
Prescriptive Analytics;
Heterogeneous Treatment Effects;
Optimization;
Observed Rank Utility Condition (OUR);
Between-treatment Heterogeneity;
Machine Learning;
Decision Making;
Analysis;
Mathematical Methods
McFowland III, Edward, Sandeep Gangarapu, Ravi Bapna, and Tianshu Sun. "A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects." MIS Quarterly 45, no. 4 (December 2021): 1807–1832.
- Winter 2021
- Article
Can Staggered Boards Improve Value? Causal Evidence from Massachusetts
By: Robert Daines, Shelley Xin Li and Charles C.Y. Wang
We study the effect of staggered boards (SBs) using a quasi-experiment: a 1990 law that imposed an SB on all Massachusetts-incorporated firms. The law led to an increase in Tobin's Q, investment in CAPEX and R&D, patents, higher-quality patented innovations, and...
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Keywords:
Staggered Board;
Entrenchment;
Life-cycle;
Tobin's Q;
Innovation;
Profitability;
Investor Composition;
Governing and Advisory Boards;
Investment;
Innovation and Invention;
Institutional Investing;
Value
Daines, Robert, Shelley Xin Li, and Charles C.Y. Wang. "Can Staggered Boards Improve Value? Causal Evidence from Massachusetts." Contemporary Accounting Research 38, no. 4 (Winter 2021): 3053–3084.
- December 2021
- Article
Seeing Oneself as a Valued Contributor: Social Worth Affirmation Improves Team Information Sharing
By: Julia Lee Cunningham, Francesca Gino, Dan Cable and Bradley Staats
Teams often fail to reach their potential because members’ concerns about being socially accepted prevent them from offering their unique perspectives to the team. Drawing on relational self and self-affirmation theory, we argue that affirmation of team members’ social...
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Keywords:
Social Worth Affirmation;
Relational Identity;
Self-affirmation;
Information Sharing In Teams;
Concerns About Social Acceptance;
Groups and Teams;
Identity;
Relationships;
Knowledge Sharing
Cunningham, Julia Lee, Francesca Gino, Dan Cable, and Bradley Staats. "Seeing Oneself as a Valued Contributor: Social Worth Affirmation Improves Team Information Sharing." Academy of Management Journal 64, no. 6 (December 2021): 1816–1841.
- November 5, 2021
- Article
Leaders: Stop Confusing Correlation with Causation
By: Michael Luca
We’ve all been told that correlation does not imply causation. Yet many business leaders, elected officials, and media outlets still make causal claims based on misleading correlations. These claims are too often unscrutinized, amplified, and mistakenly used to guide...
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Keywords:
Behavioral Economics;
Data Analysis;
Organizations;
Decision Making;
Analytics and Data Science;
Analysis;
Learning
Luca, Michael. "Leaders: Stop Confusing Correlation with Causation." Harvard Business Review Digital Articles (November 5, 2021).
- 2021
- Working Paper
COVID-19, Government Performance, and Democracy: Survey Experimental Evidence from 12 Countries
By: Michael Becher, Nicholas Longuet Marx, Vincent Pons, Sylvain Brouard, Martial Foucault, Vincenzo Galasso, Eric Kerrouche, Sandra León Alfonso and Daniel Stegmueller
Beyond its immediate impact on public health and the economy, the COVID-19 pandemic has put democracy under stress. While a common view is that people should blame the government rather than the political system for bad crisis management, an opposing view is that...
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Keywords:
COVID-19 Pandemic;
Government Performance;
Democracy;
Health Pandemics;
Government and Politics;
Crisis Management;
Public Opinion
Becher, Michael, Nicholas Longuet Marx, Vincent Pons, Sylvain Brouard, Martial Foucault, Vincenzo Galasso, Eric Kerrouche, Sandra León Alfonso, and Daniel Stegmueller. "COVID-19, Government Performance, and Democracy: Survey Experimental Evidence from 12 Countries." NBER Working Paper Series, No. 29514, November 2021. (Revise and resubmit requested, The Journal of Politics.)
- November 2021
- Article
Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective
By: Iavor Bojinov, Ashesh Rambachan and Neil Shephard
In panel experiments, we randomly assign units to different interventions, measuring their outcomes, and repeating the procedure in several periods. Using the potential outcomes framework, we define finite population dynamic causal effects that capture the relative...
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Keywords:
Panel Data;
Dynamic Causal Effects;
Potential Outcomes;
Finite Population;
Nonparametric;
Mathematical Methods
Bojinov, Iavor, Ashesh Rambachan, and Neil Shephard. "Panel Experiments and Dynamic Causal Effects: A Finite Population Perspective." Quantitative Economics 12, no. 4 (November 2021): 1171–1196.