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All HBS Web
(2,367)
- Faculty Publications (294)
- March–April 2022
- Article
Uncovering the Mitigating Psychological Response to Monitoring Technologies: Police Body Cameras Not Only Constrain but Also Depolarize
By: Shefali V. Patil and Ethan Bernstein
Despite organizational psychologists’ long-standing caution against monitoring (citing its reduction in employee autonomy and thus effectiveness), many organizations continue to use it, often with no detriment to performance and with strong support, not protest, from...
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Keywords:
Monitoring;
Transparency;
Polarization;
Body Worn Cameras;
Quasi Field Experiment;
Analytics and Data Science;
Employees;
Perception;
Law Enforcement
Patil, Shefali V., and Ethan Bernstein. "Uncovering the Mitigating Psychological Response to Monitoring Technologies: Police Body Cameras Not Only Constrain but Also Depolarize." Organization Science 33, no. 2 (March–April 2022): 541–570. (*The authors contributed equally to this manuscript.)
- 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.
- 2023
- Working Paper
Can Evidence-Based Information Shift Preferences Towards Trade Policy?
By: Laura Alfaro, Maggie X. Chen and Davin Chor
We investigate the role of evidence-based information in shaping individuals' preferences for trade policies through a series of survey experiments that contain randomized information treatments. Each treatment provides a concise statement of economics research...
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Alfaro, Laura, Maggie X. Chen, and Davin Chor. "Can Evidence-Based Information Shift Preferences Towards Trade Policy?" Harvard Business School Working Paper, No. 22-062, March 2022. (Revised May 2023. NBER Working Paper Series, No. 31240, May 2023)
- 2022
- Working Paper
Do Startups Benefit from Their Investors' Reputation? Evidence from a Randomized Field Experiment
By: Shai Benjamin Bernstein, Kunal Mehta, Richard Townsend and Ting Xu
We analyze a field experiment conducted on AngelList Talent, a large online search platform for startup jobs. In the experiment, AngelList randomly informed job seekers of whether a startup was funded by a top-tier investor and/or was funded recently. We find that the...
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Keywords:
Startup Labor Market;
Investors;
Randomized Field Experiment;
Certification Effect;
Venture Capital;
Business Startups;
Human Capital;
Job Search;
Reputation
Bernstein, Shai Benjamin, Kunal Mehta, Richard Townsend, and Ting Xu. "Do Startups Benefit from Their Investors' Reputation? Evidence from a Randomized Field Experiment." Harvard Business School Working Paper, No. 22-060, February 2022.
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords:
Algorithm Bias;
Personalization;
Targeting;
Generalized Random Forests (GRF);
Discrimination;
Customization and Personalization;
Decision Making;
Fairness;
Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 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
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.
- March 2022
- Article
Sensitivity Analysis of Agent-based Models: A New Protocol
By: Emanuele Borgonovo, Marco Pangallo, Jan Rivkin, Leonardo Rizzo and Nicolaj Siggelkow
Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such...
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Keywords:
Agent-based Modeling;
Sensitivity Analysis;
Design Of Experiments;
Total Order Sensitivity Indices;
Organizations;
Behavior;
Decision Making;
Mathematical Methods
Borgonovo, Emanuele, Marco Pangallo, Jan Rivkin, Leonardo Rizzo, and Nicolaj Siggelkow. "Sensitivity Analysis of Agent-based Models: A New Protocol." Computational and Mathematical Organization Theory 28, no. 1 (March 2022): 52–94.
- 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.
- March 2022
- Article
Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field
Identifying high-growth microentrepreneurs in low-income countries remains a challenge due to a scarcity of verifiable information. With a cash grant experiment in India we demonstrate that community knowledge can help target high-growth microentrepreneurs; while the...
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Keywords:
Microentrepreneurs;
Community Information;
Field Experiment;
Loans;
Entrepreneurship;
Developing Countries and Economies;
Financing and Loans;
Information;
Mathematical Methods;
India
Hussam, Reshmaan, Natalia Rigol, and Benjamin N. Roth. "Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field." American Economic Review 112, no. 3 (March 2022): 861–898.
(Online Appendix with Corrigendum—Thanks to Isabella Masetto, Diego Ubfal, and The Institute for Replication for identifying a minor coding error in the production of Table 4.)
(Online Appendix with Corrigendum—Thanks to Isabella Masetto, Diego Ubfal, and The Institute for Replication for identifying a minor coding error in the production of Table 4.)
- February 15, 2022
- Article
How Managers Can Build a Culture of Experimentation
By: Frank V. Cespedes and Neil Hoyne
Testing in business presents qualitatively different challenges than those in clinical trials and most scientific research. There are very few opportunities for randomized control experiments in a changing, competitive market. Yet, change and competition make testing a...
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Cespedes, Frank V., and Neil Hoyne. "How Managers Can Build a Culture of Experimentation." Harvard Business Review Digital Articles (February 15, 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
Measuring Time Use in Rural India: Design and Validation of a Low-Cost Survey Module
By: Erica Field, Rohini Pande, Natalia Rigol, Simone Schaner, Elena Stacy and Charity Troyer Moore
Time use data can help us understand individual labor supply choices, especially
for women who often provide unpaid care and home production. Although
enumerator-assisted diary-based time use data collection is suitable for
low-literacy populations, it is costly and...
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Keywords:
Time Use;
Household;
Rural Scope;
Developing Countries and Economies;
Time Management;
Analytics and Data Science;
Surveys
Field, Erica, Rohini Pande, Natalia Rigol, Simone Schaner, Elena Stacy, and Charity Troyer Moore. "Measuring Time Use in Rural India: Design and Validation of a Low-Cost Survey Module." NBER Working Paper Series, No. 29671, January 2022. (Revised September 2022.)
- Article
Megastudies Improve the Impact of Applied Behavioural Science
By: Katherine L. Milkman, Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Pepi Pandiloski, Yeji Park, Aneesh Rai, Max Bazerman, John Beshears, Lauri Bonacorsi, Colin Camerer, Edward Chang, Gretchen Chapman, Robert Cialdini, Hengchen Dai, Lauren Eskreis-Winkler, Ayelet Fishbach, James J. Gross, Samantha Horn, Alexa Hubbard, Steven J. Jones, Dean Karlan, Tim Kautz, Erika Kirgios, Joowon Klusowski, Ariella Kristal, Rahul Ladhania, Jens Ludwig, George Loewenstein, Barbara Mellers, Sendhil Mullainathan, Silvia Saccardo, Jann Spiess, Gaurav Suri, Joachim H. Talloen, Jamie Taxer, Yaacov Trope, Lyle Ungar, Kevin G. Volpp, Ashley V. Whillans, Jonathan Zinman and Angela L. Duckworth
Policy-makers are increasingly turning to behavioural science for insights about how to improve citizens’ decisions and outcomes. Typically, different scientists test different intervention ideas in different samples using different outcomes over different time...
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Milkman, Katherine L., Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Pepi Pandiloski, Yeji Park, Aneesh Rai, Max Bazerman, John Beshears, Lauri Bonacorsi, Colin Camerer, Edward Chang, Gretchen Chapman, Robert Cialdini, Hengchen Dai, Lauren Eskreis-Winkler, Ayelet Fishbach, James J. Gross, Samantha Horn, Alexa Hubbard, Steven J. Jones, Dean Karlan, Tim Kautz, Erika Kirgios, Joowon Klusowski, Ariella Kristal, Rahul Ladhania, Jens Ludwig, George Loewenstein, Barbara Mellers, Sendhil Mullainathan, Silvia Saccardo, Jann Spiess, Gaurav Suri, Joachim H. Talloen, Jamie Taxer, Yaacov Trope, Lyle Ungar, Kevin G. Volpp, Ashley V. Whillans, Jonathan Zinman, and Angela L. Duckworth. "Megastudies Improve the Impact of Applied Behavioural Science." Nature 600, no. 7889 (December 16, 2021): 478–483.
- 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.
- 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.
- Article
Using Fresh Starts to Nudge Increased Retirement Savings
By: John Beshears, Hengchen Dai, Katherine L. Milkman and Shlomo Benartzi
We conducted a field experiment to study the effect of framing future moments in time as new beginnings (or “fresh starts”). University employees (N=6,082) received mailings with an opportunity to choose between increasing their contributions to a savings plan...
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Keywords:
Choice Architecture;
Randomized Field Experiment;
Savings;
New Beginning;
Fresh Start;
Saving;
Retirement;
Behavior
Beshears, John, Hengchen Dai, Katherine L. Milkman, and Shlomo Benartzi. "Using Fresh Starts to Nudge Increased Retirement Savings." Organizational Behavior and Human Decision Processes 167 (November 2021): 72–87.
- 2021
- Article
Don't Get It or Don't Spread It: Comparing Self-interested versus Prosocial Motivations for COVID-19 Prevention Behaviors
By: Jillian J. Jordan, Erez Yoeli and David Rand
COVID-19 prevention behaviors may be seen as self-interested or prosocial. Using American samples from MTurk and Prolific (total n = 6,850), we investigated which framing is more effective—and motivation is stronger—for fostering prevention behavior intentions. We...
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Keywords:
COVID-19;
Prevention;
Prosocial Motivation;
Health Pandemics;
Behavior;
Motivation and Incentives
Jordan, Jillian J., Erez Yoeli, and David Rand. "Don't Get It or Don't Spread It: Comparing Self-interested versus Prosocial Motivations for COVID-19 Prevention Behaviors." Art. 20222. Scientific Reports 11 (2021).
- October 2021
- Article
Changing Gambling Behavior through Experiential Learning
By: Shawn A. Cole, Martin Abel and Bilal Zia
This paper tests experiential learning as a debiasing tool to reduce gambling in South Africa, through a randomized field experiment. The study implements a simple, interactive game that simulates the odds of winning the national lottery through dice rolling....
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Keywords:
Debiasing;
Experiential Learning;
Behavioral Economics;
Financial Education;
Learning;
Games, Gaming, and Gambling;
Behavior;
Decision Making
Cole, Shawn A., Martin Abel, and Bilal Zia. "Changing Gambling Behavior through Experiential Learning." World Bank Economic Review 35, no. 3 (October 2021): 745–763.
- Article
Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment
By: Thiemo Fetzer and Thomas Graeber
Contact tracing has for decades been a cornerstone of the public health approach to epidemics, including Ebola, severe acute respiratory syndrome, and now COVID-19. It has not yet been possible, however, to causally assess the method’s effectiveness using a randomized...
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Fetzer, Thiemo, and Thomas Graeber. "Measuring the Scientific Effectiveness of Contact Tracing: Evidence from a Natural Experiment." Proceedings of the National Academy of Sciences 118, no. 33 (August 17, 2021): 1–4.