Managing Diversity and Inclusion at Yelp

Michael  Luca; Joshua  Schwartzstein; Gauri  Subramani

Case | HBS Case Collection | August 2017

Managing Diversity and Inclusion at Yelp

by Michael Luca, Joshua Schwartzstein and Gauri Subramani

Abstract

This case explores the industry-wide lack of employee diversity in the technology sector and Yelp’s decision to take a leadership position in identifying strategies to increase diversity. The goal of the case is to provide an opportunity for students to develop a framework for understanding the factors that might lead to a less diverse workforce and for evaluating approaches to increase diversity. The case opens in 2014, when Yelp hired Rachel Williams into a newly created position—the Head of Diversity and Inclusion. Rachel was tasked with developing strategies to ensure that Yelp was attracting and retaining a diverse, productive workforce and creating a welcoming environment for all employees. The case puts students in Rachel’s footsteps upon being hired and tasks them to propose a set of changes for Yelp to make in order to increase the diversity of the workforce.

Keywords: Diversity; Employees; Leading Change; Strategy; Organizational Culture; Technology Industry;

Language: English

Format: Print

9 pages

Educators Purchase

Citation:

Luca, Michael, Joshua Schwartzstein, and Gauri Subramani. "Managing Diversity and Inclusion at Yelp." Harvard Business School Case 918-009, August 2017.

We present a framework for analyzing “model persuasion,” where persuaders influence people’s beliefs by proposing models (likelihood functions) that suggest how to organize past data (e.g., on investment performance) to make predictions (e.g., about future returns). Receivers are assumed to find models more compelling when they better explain the past. A key tradeoff persuaders face is that models that better fit the data induce less movement in receivers’ beliefs. Model persuasion sometimes makes the receiver worse off than if he interprets data through the lens of a default model. The receiver is most misled by persuasion when there is a lot of publicly available data that is open to interpretation and exhibits randomness, as this gives the persuader “wiggle room” to highlight false patterns. Even when the receiver is exposed to the true model, the wrong model often wins because it better fits the past. Competition more broadly pushes persuaders towards overfitting available data and as a result tends to neutralize the data by leading receivers to view it as unsurprising. With multiple receivers, a persuader is more effective when receivers share similar priors and default interpretations. We illustrate with examples from finance, politics, and law.

Chapter | Handbook of Behavioral Economics | 2019

Behavioral Economics and Health-Care Markets

Amitabh Chandra, Benjamin Handel and Joshua Schwartzstein

This chapter summarizes research in behavioral health economics, focusing on insurance markets and product markets in health care. We argue that the prevalence of choice difficulties and biases leading to mistakes in these markets establish a special place for them in economic analysis. In addition, we argue that while the behavioral health-economics literature has done a better job documenting consumer-choice mistakes in insurance and treatment choices than explaining why those mistakes occur, it is clear that we should not ignore these mistakes in our analyses. We document evidence showing that consumers leave lots of money on the table in their insurance-plan choices, sometimes thousands of dollars. This is true both when consumers make active choices (e.g., they do not have a default plan) and when they make passive choices (e.g., they have a default plan). We discuss the implications of this body of work for the design and regulation of insurance markets, including the interaction between consumer choice difficulties or biases and adverse selection. We then document evidence on consumer mistakes in health-care utilization and treatment choices, especially in response to changes in prices such as copayments and deductibles. We show how choice difficulties or biases may lead patients to respond to such increases in patient cost-sharing by reducing demand for high-value care, muddying the traditional argument that the price elasticity of demand for medical care meaningfully captures the degree of moral hazard. We conclude with directions for future research.

Keywords: Behavioral economics; Consumer Behavior; Economics; Health Care and Treatment; Insurance; Markets;

Citation:

Chandra, Amitabh, Benjamin Handel, and Joshua Schwartzstein. "Behavioral Economics and Health-Care Markets." In Handbook of Behavioral Economics: Foundations and Applications 2, edited by B. Douglas Bernheim, Stefano DellaVigna, and David Laibson. Amsterdam: Elsevier/North-Holland, 2019. View Details

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This case explores how neuroscientist Mariam Chammat helped set up the first behavioral insights team at the center of the French government, and encouraged French administrations to innovate and create policy initiatives based on psychological theories of influence and persuasion. Students are asked to assess 35 projects ripe for behavioral intervention and pick the winning proposals.

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Managing Diversity and Inclusion at Yelp

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Managing Diversity and Inclusion at Yelp

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