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- 2021
- Working Paper
BEAT Unintended Bias in Personalized Policies
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" attributes....
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- June 2021
- Article
The Role of Beliefs in Driving Gender Discrimination
By: Katherine B. Coffman, Christine L. Exley and Muriel Niederle
While there is ample evidence of discrimination against women in the workplace, it can be difficult to understand what factors contribute to discriminatory behavior. We use an experiment to both document discrimination and unpack its sources. First, we show that, on...
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- May–June 2021
- Article
How to Close the Gender Gap
By: Colleen Ammerman and Boris Groysberg
Most companies say they’re committed to advancing women into leadership roles. What they may fail to recognize, though, is that systemic barriers are holding women back. As a result, women remain disadvantaged at every stage of their employment and underrepresented in...
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- 2021
- Working Paper
Venture Capital's 'Me Too' Moment
By: Sophie Calder-Wang, Paul A. Gompers and Patrick Sweeney
In this paper, we document the historically low rate of hiring of women in the venture capital sector. We find that the high-profile Ellen Pao v. Kleiner Perkins gender discrimination trial had dramatic treatment effects. In difference-in-differences regressions, we...
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- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on...
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- March 2021
- Teaching Plan
The Black New Venture Competition
Black entrepreneurs encounter many unique obstacles when raising capital to start and grow a business, some stemming from deep systemic discrimination. During their second year at Harvard Business School (HBS), MBA students Kimberly Foster and Tyler Simpson decided to...
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Keywords:
Analytics;
Startup;
Start-up;
Startup Financing;
Financing;
Startups;
Start-ups;
Business And Community;
Business And Society;
Business Growth;
Discrimination;
Women;
Women-owned Businesses;
African Americans;
African-american Entrepreneurs;
African-american Investors;
African-American Protagonist;
African-American Women;
Early Stage Funding;
Early Stage;
Innovation ＆ Entrepreneurship;
Innovation Competitions;
Entrepreneurial Financing;
Business Plan;
Business Startups;
Diversity;
Gender;
Race;
Entrepreneurship;
Venture Capital;
Small Business;
Leadership;
Technology;
Competition
- September 2020
- Case
The Black New Venture Competition
Black entrepreneurs encounter many unique obstacles when raising capital to start and grow a business. During their second year at Harvard Business School (HBS), MBA students Kimberly Foster and Tyler Simpson decided to do something to make a difference for...
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Keywords:
Startup;
Start-up;
Startup Financing;
Startups;
Start-ups;
African-American Protagonist;
African-american Entrepreneurs;
African-american Investors;
African-Americans;
African-American Women;
Black Leadership;
Black Inventors;
Black Entrepreneurs;
Harvard Business School;
Harvard;
Business And Society;
Early Stage Funding;
Early Stage Finance;
Technology Entrepreneurship;
Discrimination;
Technology Ventures;
Entrepreneurial Finance;
Entrepreneurial Financing;
Business Plan;
Business Startups;
Business Ventures;
Financing and Loans;
Business Growth and Maturation;
Diversity;
Gender;
Race;
Entrepreneurship;
Venture Capital;
Small Business;
Leadership;
Technology;
Competition;
Technology Industry
- September 2020
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
E-commerce;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Data and Data Sets;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised December 2020)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...
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- September 2020 (Revised April 2021)
- Exercise
Artea (B): Including Customer-level Demographic Data
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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- September 2020 (Revised April 2021)
- Exercise
Artea (C): Potential Discrimination through Algorithmic Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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- September 2020 (Revised March 2021)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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- September 2020 (Revised April 2021)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
＂Marketing Analytics＂;
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analytics;
Data Analysis;
E-commerce;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting"
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- September 2020 (Revised March 2021)
- Supplement
Spreadsheet Supplement to Artea Teaching Note
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to Artea Teaching Note 521-041. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...
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- 2020
- Working Paper
The Salary Taboo: Privacy Norms and the Diffusion of Information
By: Zoë B. Cullen and Ricardo Perez-Truglia
The limited diffusion of salary information has implications for labor markets, such as wage discrimination policies and collective bargaining. Access to salary information is believed to be limited and unequal, but there is little direct evidence on the sources of...
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- May 2018
- Supplement
Abe on Womenomics, part 1: Introductions & Japan's Corporate Culture: Excerpt from Opening Address to the World Assembly of Women, Tokyo, August 28, 2015
By: Boris Groysberg
This video supplement part one of a lightly edited 2015 speech by Japanese prime minister Shinzo Abe in which he describes Womenomics--policies and aspirations to promote greater economic participation by Japan's women, thereby promoting economic growth, greater...
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Keywords:
Gender Equality;
Japan;
Leadership;
Government-business Relations;
Shinzo Abe;
Economic Growth;
Aging Society;
Womenomics;
Abenomics;
Labor Market Discrimination;
Workplace Culture;
Women And Leadership;
Change Management;
Leading Change;
Gender;
Business and Government Relations;
Growth and Development;
Employment;
Working Conditions;
Japan
- May 2018
- Supplement
Prime Minister Shinzo Abe – Speech on Womenomics in Japan: Opening Address to the World Assembly of Women, Tokyo, August 28, 2015
By: Boris Groysberg
This video supplement is a lightly edited 2015 speech by Japanese prime minister Shinzo Abe in which he describes Womenomics--policies and aspirations to promote greater economic participation by Japan's women, thereby promoting economic growth, greater work/life...
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Keywords:
Gender Equality;
Japan;
Leadership;
Government-business Relations;
Shinzo Abe;
Economic Growth;
Aging Society;
Womenomics;
Abenomics;
Labor Market Discrimination;
Workplace Culture;
Women And Leadership;
Change Management;
Leading Change;
Gender;
Business and Government Relations;
Growth and Development;
Employment;
Working Conditions
- January 2018 (Revised June 2018)
- Teaching Note
Womenomics in Japan
By: Boris Groysberg and David Lane
Teaching Note for HBS No. 417-002.
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Keywords:
Gender Equality;
Japan;
Leadership;
Government-business Relations;
Shinzo Abe;
Economic Growth;
Aging Society;
Womenomics;
Abenomics;
Labor Market Discrimination;
Workplace Culture;
Women And Leadership;
Change Management;
Working Conditions;
Leading Change;
Gender;
Business and Government Relations;
Growth and Development;
Employment;
Japan
- Article
Repairing the Damage: The Effect of Price Knowledge and Gender on Auto-Repair Price Quotes
By: Meghan Busse, Ayelet Israeli and Florian Zettelmeyer
In this paper we investigate whether sellers treat consumers differently on the basis of how well informed consumers appear to be. We implement a large-scale field experiment in which callers request price quotes from automotive repair shops. We show that sellers alter...
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