Publications
Publications
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- All HBS Web
(2,455)
- Faculty Publications (289)
- September 2022
- Article
The Limits of Inconspicuous Incentives
By: Leslie K. John, Hayley Blunden, Katherine Milkman, Luca Foschini and Bradford Tuckfield
Managers and policymakers regularly rely on incentives to encourage valued behaviors. While incentives are often successful, there are also notable and surprising examples of their ineffectiveness. Why? We propose a contributing factor may be that they are not... View Details
- Article
All Eyes on Them: A Field Experiment on Citizen Oversight and Electoral Integrity
By: Natalia Garbiras-Díaz and Mateo Montenegro
Can information and communication technologies help citizens monitor their elections? We analyze a large-scale field experiment designed to answer this question in Colombia. We leveraged Facebook advertisements sent to over 4 million potential voters to encourage... View Details
- July 2022
- Article
The Passionate Pygmalion Effect: Passionate Employees Attain Better Outcomes in Part Because of More Preferential Treatment by Others
By: Ke Wang, Erica R. Bailey and Jon M. Jachimowicz
Employees are increasingly exhorted to “pursue their passion” at work. Inherent in this call is the belief that passion will produce higher performance because it promotes intrapersonal processes that propel employees forward. Here, we suggest that the pervasiveness of... View Details
- June 2022 (Revised July 2022)
- Technical Note
Causal Inference
This note provides an overview of causal inference for an introductory data science course. First, the note discusses observational studies and confounding variables. Next the note describes how randomized experiments can be used to account for the effect of... View Details
- June 2022
- Article
Conservatism Gets Funded? A Field Experiment on the Role of Negative Information in Novel Project Evaluation
By: Jacqueline N. Lane, Misha Teplitskiy, Gary Gray, Hardeep Ranu, Michael Menietti, Eva C. Guinan and Karim R. Lakhani
The evaluation and selection of novel projects lies at the heart of scientific and technological innovation, and yet there are persistent concerns about bias, such as conservatism. This paper investigates the role that the format of evaluation, specifically information... View Details
- 2022
- Article
How to Choose a Default
By: John Beshears, Richard T. Mason and Shlomo Benartzi
We have developed a model for setting a default when a population is choosing among ordered choices—that is, ones listed in ascending or descending order. A company, for instance, might want to set a default contribution rate that will increase employees’ average... View Details
- 2022
- Working Paper
Are Experts Blinded by Feasibility?: Experimental Evidence from a NASA Robotics Challenge
By: Jacqueline N. Lane, Zoe Szajnfarber, Jason Crusan, Michael Menietti and Karim R. Lakhani
Resource allocation decisions play a dominant role in shaping a firm’s technological trajectory and competitive advantage. Recent work indicates that innovative firms and scientific institutions tend to exhibit an anti-novelty bias when evaluating new projects and... View Details
- 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... View Details
- 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... View Details
- 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... View Details
- 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
- 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... View Details
Keywords: Salary; Inequality; Managers; Career Concerns; Pay Transparency; Wages; Equality and Inequality; Perception; Behavior
- 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... View Details
- 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... View Details
- 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... View Details
- 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... View Details
- 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... View Details
Keywords: Time; Subjective Well Being; Administrative Costs; Friction; Poverty; Well-being; Money; Perception; Kenya
- 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... View Details
- 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... View Details
- 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... View Details