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- 2023
- Working Paper
Words Can Hurt: How Political Communication Can Change the Pace of an Epidemic
By: Jessica Gagete-Miranda, Lucas Argentieri Mariani and Paula Rettl
While elite-cue effects on public opinion are well-documented, questions remain as
to when and why voters use elite cues to inform their opinions and behaviors. Using
experimental and observational data from Brazil during the COVID-19 pandemic, we
study how leader...
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Keywords:
Elites;
Public Engagement;
Politics;
Political Affiliation;
Political Campaigns;
Political Influence;
Political Leadership;
Political Economy;
Survey Research;
COVID-19;
COVID-19 Pandemic;
COVID;
Cognitive Psychology;
Cognitive Biases;
Political Elections;
Voting;
Power and Influence;
Identity;
Behavior;
Latin America;
Brazil
Gagete-Miranda, Jessica, Lucas Argentieri Mariani, and Paula Rettl. "Words Can Hurt: How Political Communication Can Change the Pace of an Epidemic." Harvard Business School Working Paper, No. 24-022, October 2023.
- September 2023
- Exercise
Irrationality in Action: Decision-Making Exercise
By: Alison Wood Brooks, Michael I. Norton and Oliver Hauser
This teaching exercise highlights the obstacle of biases in decision-making, allowing students to generate examples of potentially poor decision-making rooted in abundant and unwanted bias. This exercise has two parts: a pre-class, online survey in which students...
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Brooks, Alison Wood, Michael I. Norton, and Oliver Hauser. "Irrationality in Action: Decision-Making Exercise." Harvard Business School Exercise 924-007, September 2023.
- August 2023
- Article
Can Security Design Foster Household Risk-Taking?
By: Laurent Calvet, Claire Célérier, Paolo Sodini and Boris Vallée
This paper shows that securities with a non-linear payoff design can foster household risk-taking. We demonstrate this effect empirically by exploiting the introduction of capital guarantee products in Sweden from 2002 to 2007. The fast and broad adoption of these...
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Keywords:
Financial Innovation;
Household Finance;
Structured Products;
Stock Market Participation;
Finance;
Innovation and Invention;
Household;
Personal Finance;
Risk and Uncertainty;
Behavior;
Market Participation
Calvet, Laurent, Claire Célérier, Paolo Sodini, and Boris Vallée. "Can Security Design Foster Household Risk-Taking?" Journal of Finance 78, no. 4 (August 2023): 1917–1966.
- 2023
- Working Paper
How People Use Statistics
By: Pedro Bordalo, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon and Andrei Shleifer
We document two new facts about the distributions of answers in famous statistical problems: they are i) multi-modal and ii) unstable with respect to irrelevant changes in the problem. We offer a model in which, when solving a problem, people represent each hypothesis...
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Bordalo, Pedro, John J. Conlon, Nicola Gennaioli, Spencer Yongwook Kwon, and Andrei Shleifer. "How People Use Statistics." NBER Working Paper Series, No. 31631, August 2023.
- June 2023
- Article
Amplification of Emotion on Social Media
By: Amit Goldenberg and Robb Willer
Why do expressions of emotion seem so heightened on social media? Brady et al. argue that extreme moral outrage on social media is not only driven by the producers and sharers of emotional expressions, but also by systematic biases in the way people that perceive moral...
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Goldenberg, Amit, and Robb Willer. "Amplification of Emotion on Social Media." Nature Human Behaviour 7, no. 6 (June 2023): 845–846.
- 2023
- Working Paper
Auditing Predictive Models for Intersectional Biases
By: Kate S. Boxer, Edward McFowland III and Daniel B. Neill
Predictive models that satisfy group fairness criteria in aggregate for members of a protected class, but do not guarantee subgroup fairness, could produce biased predictions for individuals at the intersection of two or more protected classes. To address this risk, we...
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Boxer, Kate S., Edward McFowland III, and Daniel B. Neill. "Auditing Predictive Models for Intersectional Biases." Working Paper, June 2023.
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider...
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Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- 2023
- Working Paper
Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs
By: Jacqueline N. Lane, Karim R. Lakhani and Roberto Fernandez
Competence development in digital technologies, analytics, and artificial intelligence is increasingly important to all types of organizations and their workforce. Universities and corporations are investing heavily in developing training programs, at all tenure...
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Keywords:
STEM;
Selection and Staffing;
Gender;
Prejudice and Bias;
Training;
Equality and Inequality;
Competency and Skills
Lane, Jacqueline N., Karim R. Lakhani, and Roberto Fernandez. "Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs." Harvard Business School Working Paper, No. 23-066, April 2023. (Accepted by Organization Science.)
- 2023
- Working Paper
Applications or Approvals: What Drives Racial Disparities in the Paycheck Protection Program?
By: Sergey Chernenko, Nathan Kaplan, Asani Sarkar and David S. Scharfstein
We use the 2020 Small Business Credit Survey to study the sources of racial disparities in use of the Paycheck Protection Program (PPP). Black-owned firms are 8.9 percentage points less likely than observably similar white-owned firms to receive PPP loans. About 55% of...
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Chernenko, Sergey, Nathan Kaplan, Asani Sarkar, and David S. Scharfstein. "Applications or Approvals: What Drives Racial Disparities in the Paycheck Protection Program?" NBER Working Paper Series, No. 31172, April 2023.
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in...
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Keywords:
Racial Disparity;
Paycheck Protection Program;
Measurement Error;
AI and Machine Learning;
Race;
Measurement and Metrics;
Equality and Inequality;
Prejudice and Bias;
Forecasting and Prediction;
Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- 2022
- Working Paper
Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence
By: Maya Balakrishnan, Kris Ferreira and Jordan Tong
Even if algorithms make better predictions than humans on average, humans may sometimes have “private” information which an algorithm does not have access to that can improve performance. How can we help humans effectively use and adjust recommendations made by...
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Keywords:
Cognitive Biases;
Algorithm Transparency;
Forecasting and Prediction;
Behavior;
AI and Machine Learning;
Analytics and Data Science;
Cognition and Thinking
Balakrishnan, Maya, Kris Ferreira, and Jordan Tong. "Improving Human-Algorithm Collaboration: Causes and Mitigation of Over- and Under-Adherence." Working Paper, December 2022.
- 2022
- Working Paper
Slowly Varying Regression under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022.
- 2022
- Working Paper
Confidence, Self-Selection and Bias in the Aggregate
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
The influence of behavioral biases on aggregate outcomes like prices and allocations depends in part on self-selection: whether rational people opt more strongly into aggregate interactions than biased individuals. We conduct a series of betting market, auction and...
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Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Confidence, Self-Selection and Bias in the Aggregate." NBER Working Paper Series, No. 30262, July 2022.
- 2022
- Chapter
Redirecting Rawlsian Reasoning Toward the Greater Good
By: Joshua D. Greene, Karen Huang and Max Bazerman
In A Theory of Justice, John Rawls employed the ‘veil of Ignorance’ as a moral reasoning device designed to promote impartial thinking. By imagining the choices of decision-makers who are blind to biasing information, one might see more clearly the organizing...
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Greene, Joshua D., Karen Huang, and Max Bazerman. "Redirecting Rawlsian Reasoning Toward the Greater Good." Chap. 15 in The Oxford Handbook of Moral Psychology, edited by Manuel Vargas and John M. Doris, 246–261. Oxford, UK: Oxford University Press, 2022.
- June 2022
- Article
The Use and Misuse of Patent Data: Issues for Finance and Beyond
By: Josh Lerner and Amit Seru
Patents and citations are powerful tools for understanding innovation increasingly used in financial economics (and management research more broadly). Biases may result, however, from the interactions between the truncation of patents and citations and the changing...
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Lerner, Josh, and Amit Seru. "The Use and Misuse of Patent Data: Issues for Finance and Beyond." Review of Financial Studies 35, no. 6 (June 2022): 2667–2704.
- May 2022 (Revised April 2023)
- Case
LOOP: Driving Change in Auto Insurance Pricing
By: Elie Ofek and Alicia Dadlani
John Henry and Carey Anne Nadeau, co-founders and co-CEOs of LOOP, an insurtech startup based in Austin, Texas, were on a mission to modernize the archaic $250 billion automobile insurance market. They sought to create equitably priced insurance by eliminating pricing...
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Keywords:
AI and Machine Learning;
Technological Innovation;
Equality and Inequality;
Prejudice and Bias;
Growth and Development Strategy;
Customer Relationship Management;
Price;
Insurance Industry;
Financial Services Industry
Ofek, Elie, and Alicia Dadlani. "LOOP: Driving Change in Auto Insurance Pricing." Harvard Business School Case 522-073, May 2022. (Revised April 2023.)
- 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.)
- Article
Fighting Bias on the Front Lines
By: Alexandra C. Feldberg and Tami Kim
Most companies aim for exceptional customer service, but too few are attentive to the subtle discrimination by frontline employees that can alienate customers, lead to lawsuits, or even cause lasting brand damage by going viral.
This article presents research... View Details
This article presents research... View Details
Keywords:
Customer Service;
Customer Focus and Relationships;
Service Delivery;
Diversity;
Prejudice and Bias;
Organizational Change and Adaptation
Feldberg, Alexandra C., and Tami Kim. "Fighting Bias on the Front Lines." Harvard Business Review 99, no. 6 (November–December 2021): 90–98.
- 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.
- September 2021
- Article
Gender Stereotypes in Deliberation and Team Decisions
By: Katherine B. Coffman, Clio Bryant Flikkema and Olga Shurchkov
We explore how groups deliberate and decide on ideas in an experiment with communication. We find that gender biases play a significant role in which group members are chosen to answer on behalf of the group. Conditional on the quality of their ideas, individuals are...
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Keywords:
Gender Differences;
Stereotypes;
Teams;
Economic Experiments;
Gender;
Prejudice and Bias;
Groups and Teams;
Perception
Coffman, Katherine B., Clio Bryant Flikkema, and Olga Shurchkov. "Gender Stereotypes in Deliberation and Team Decisions." Games and Economic Behavior 129 (September 2021): 329–349.