Publications

2017
Blitz Discussions

Of Margins and Modalities

2015
Working Paper

Measurement Errors of Expected-Return Proxies and the Implied Cost of Capital

By: Charles C.Y. Wang

Despite their popularity as proxies of expected returns, the implied cost of capital's (ICC) measurement error properties are relatively unknown. Through an in-depth analysis of a popular implementation of ICCs by Gebhardt, Lee, and Swaminathan (2001) (GLS), I show... View Details

Keywords: Measurement and Metrics; Cost of Capital; Investment Return

SSRN

27 Feb 2018
First Look

First Look at New Research and Ideas, February 27, 2018

December 2017 Journal of Creative Behavior In Pursuit of Everyday Creativity By: Amabile, Teresa M. Abstract—Creativity researchers have long paid careful attention to individual creativity, beginning with studies of well-known geniuses View Details

Keywords: Sean Silverthorne

01 Aug 2020
News

"In a Hurry from a Young Age"

Keywords: women in business; gender bias; healthcare; change leadership; gender diversity; workplace diversity; New Zealand

21 Apr 2010
Working Paper Summaries

Why Do Firms Use Non-Linear Incentive Schemes? Experimental Evidence on Sorting and Overconfidence

Keywords: by Ian Larkin & Stephen Leider

October 2019
Case

Harlem Capital: Changing the Face of Entrepreneurship (A)

By: George Serafeim and David Freiberg

Jarrid Tingle and Henri Pierre-Jacques had spent the summer between their first and second years of their Harvard Business School MBA program fund raising for their start-up venture capital (VC) firm, Harlem Capital Partners. Harlem Capital was founded upon the... View Details

Educators

Article

Learning from Potentially Biased Statistics: Household Inflation Perceptions and Expectations in Argentina

By: Alberto Cavallo, Guillermo Cruces and Ricardo Perez-Truglia

When forming expectations, households may be influenced by perceived bias in the information they receive. In this paper, we study how individuals learn from potentially biased statistics using data from both a natural experiment and a survey experiment during a... View Details

Keywords: Inflation Expectations; Bayesian Estimation; Inflation and Deflation; Information; Household; Behavior; Argentina

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2016
Working Paper

Who Pays for White-Collar Crime?

By: Paul Healy and George Serafeim

Using a proprietary dataset of 667 companies around the world that experienced white-collar crime, we investigate what drives punishment of perpetrators of crime. We find a significantly lower propensity to punish crime in our sample, where most crimes are not reported... View Details

Keywords: Crime; Gender Bias; Women; Women Executives; Corruption; Legal Aspects Of Business; Firing; Human Capital; Human Resource Management; Prejudice and Bias; Crime and Corruption; Judgments; Law Enforcement; Human Resources; Corporate Governance; Gender

SSRN

2017
Blitz Discussions

Of Margins and Modalities

December 2020 (Revised April 2021)
Teaching Note

Women Entrepreneurs and Tech Ecosystems: One City, Two Realities, and Four Diverse Women

By: Rosabeth Moss Kanter and Joyce J. Kim

Four diverse women entrepreneurs launched their ventures in a thriving entrepreneurial ecosystem that was part of a shift to a creative technology-driven economy for Miami. Although Miami was rated the #1 U.S. city for startups in 2017, the region contained structural... View Details

Keywords: Women; Racism; Black Entrepreneurs; Entrepreneurship; Diversity; Gender; Race; Prejudice and Bias; Innovation and Invention; City; Culture; Miami

November 2024
Article

Preference Externality Estimators: A Comparison of Border Approaches and IVs

By: Xi Ling, Wesley R. Hartmann and Tomomichi Amano

This paper compares two estimators—the Border Approach and an Instrumental Variable (IV) estimator—using a unified framework where identifying variation arises from “preference externalities,” following the intuition in Waldfogel (2003). We highlight two dimensions in... View Details

Keywords: Econometrics; Casual Inference; Marketing; Economics; Advertising; Mathematical Methods

October 2020 (Revised April 2021)
Case

Women Entrepreneurs and Tech Ecosystems: One City, Two Realities, and Four Diverse Women

By: Rosabeth Moss Kanter and Joyce J. Kim

Four diverse women entrepreneurs launched their ventures in a thriving entrepreneurial ecosystem that was part of a shift to a creative technology-driven economy for Miami. Although Miami was rated the #1 U.S. city for startups in 2017, the region contained structural... View Details

Keywords: Female Entrepreneur; Entrepreneurial Ecosystems; Inclusion; Innovation ＆ Entrepreneurship; Racism; Sexism; Start-up; Entrepreneurship; Business Startups; Diversity; Gender; Race; Prejudice and Bias; Innovation and Invention; City; Culture; Miami

Educators

2024
Working Paper

Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift

By: Matthew DosSantos DiSorbo and Kris Ferreira

Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,... View Details

Keywords: AI and Machine Learning; Decision Choices and Conditions

Forthcoming
Chapter

Racism, Causal Explanations, and Affirmative Action

By: Theresa K. Vescio, Amy Cuddy, Faye Crosby and Kevin Weaver

BOOK ABSTRACT: In recent decades, research in political psychology has illuminated the psychological processes underlying important political action, both by ordinary citizens and by political leaders. As the world has become increasingly engaged in thinking about... View Details

Keywords: Prejudice and Bias; Race; Complexity

Forthcoming
Article

Human-Algorithm Collaboration with Private Information: Naïve Advice Weighting Behavior and Mitigation

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... View Details

Keywords: AI and Machine Learning; Analytics and Data Science; Forecasting and Prediction; Digital Marketing

July 2019
Teaching Note

Arlan Hamilton and Backstage Capital

By: Laura Huang

Teaching Note for HBS No. 419-029. Frustrated by an inability to convince existing venture capital firms to invest in companies led by women, people of color, and LGBT founders, Arlan Hamilton started her own firm, Backstage Capital, in 2015. Hamilton understood the... View Details

Keywords: Black Leadership; LGBTQ; People Of Color; Entrepreneurship; Mission and Purpose; Venture Capital; Corporate Social Responsibility and Impact; Identity; Personal Characteristics; Prejudice and Bias; Social Issues; Diversity; Gender; Race

28 Jul 2020
Research & Ideas

Racism and Digital Design: How Online Platforms Can Thwart Discrimination

Having uncovered the scope of discrimination taking place against Black guests and hosts on Airbnb, researcher Michael Luca and his colleagues put together a toolkit to aid managers in recognizing View Details

Keywords: by Kristen Senz; Travel; Entertainment & Recreation; Service

21 Mar 2012
Op-Ed

Finding the Right Jeremy Lin Storyline

around his identity. Is he an underdog or is he privileged? Does he fit in with the NBA or doesn't he? Here are some of the stories we miss in the storylines we do embrace. The Basketball is Black Story: If the story is one of ethnic View Details

Keywords: by Lakshmi Ramarajan; Entertainment & Recreation

September 2016
Article

Whitened Résumés: Race and Self-Presentation in the Labor Market

By: Sonia K. Kang, K. A. DeCelles, András Tilcsik and Sora Jun

Using interviews, a laboratory experiment, and a résumé audit study, we examine racial minorities’ attempts to avoid anticipated discrimination in labor markets by concealing or downplaying racial cues in job applications, a practice known as "résumé whitening."... View Details

Keywords: Prejudice and Bias; Selection and Staffing; Job Search; Race

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