Faculty & Research

• HBS Book

Trust is the most powerful force underlying the success of every business. Yet it can be shattered in an instant, with a devastating impact on a company’s market cap and reputation. How to build and sustain trust requires fresh insight into why customers, employees, community members and investors decide whether an organization can be trusted. Based on two decades of research and illustrated through vivid storytelling, Sandra Sucher and Shalene Gupta examine the economic impact of trust, the science behind it and conclusively prove that trust is built from the inside-out.

• Journal of Financial Economics 142, no. 1 (October 2021): 1-22.

With an overall lack of gender and ethnic diversity in the innovation sector documented in Gompers and Wang (2017), we ask the natural next question: Does increased diversity lead to better firm performances? In this paper, we attempt to answer this question using a unique dataset of the gender of venture capital partners’ children. First, we find strong evidence that parenting more daughters leads to an increased propensity to hire female partners by venture capital firms. Second, using an instrumental variable set-up, we also show that improved gender diversity, induced by parenting more daughters, improves deal and fund performances. These effects concentrate overwhelmingly on the daughters of senior partners than junior partners. Taken together, our findings have profound implications on how the capital markets could function better with improved diversity.

• Digital Initiative

Common regulatory perspective on the relationship between data, value, and competition in online platforms has increasingly centered on the volume of data accumulated by incumbent firms. This view posits the existence of "data network effects," where more data leads to product improvements, which in turn leads to additional users and more data. In particular, this has raised concerns around incumbent data advantage creating an insurmountable barrier to entry and leading to winner-take-all outcomes in online platforms. However, this perspective generally does not reflect the value of data in practical settings. More recent work across economics, management science, and engineering shows that there are a variety of factors that impact the value of data and that implications for competition are much more complex and subtle. The framework in this paper presents four key factors—data quality, scale and scope of data, and data uniqueness—that can influence the value that firms can derive from data.

• Featured Case

In fall of 2018, Germany’s chancellor Angela Merkel had logged significant successes. Germany was the largest exporter in the world, had maintained low unemployment through the 2008 financial crisis, and was gradually reforming its welfare state to meet future pension liabilities. Yet it still faced considerable challenges. Within Europe, the Eurozone financial crisis continued to hold down economic growth, and the burden of leadership to find a solution seemed to fall on Merkel’s shoulders. The refugee inflows of 2015-2016 had been slowed, but they had ignited a rise in political support for the far right in Germany and other European countries. And Britain was in the middle of a negotiated exit from the European Union. Could Germany sustain its recent economic success? Was Germany’s success compatible with a stable and prosperous Europe?

• Featured Case

The case follows President Linda Oubré (HBS MBA Class of 1984) as she seeks to move Whittier College towards a more diverse, equitable, and inclusive campus. In 2020, the student body of Whittier, a private liberal arts college in Los Angeles County, California, is slowly evolving to represent the growing diversity of the surrounding area. Roughly 70 percent of Whittier students are non-white, and 59 percent are female. The college’s leadership, however, remains largely white and male. Oubré, who had spent most of her previous career in business, was selected as the college’s president in 2018—the first black woman to serve in that role. Almost immediately upon stepping into the role, Oubré set her sights on diversifying the college’s staff, administration, and board of trustees; in 2020, the racial reckoning that swept across the United States only heightened the urgency of her task. Oubré knows that sweeping change is needed at Whittier. But change, of course, is hard. How can she continue to push for diversity among faculty, staff, board members, and students? And how can she galvanize support among all of the college’s many constituents?

• HBS Working Paper

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. This unintended discrimination is often caused by underlying correlations in the data between protected attributes and other observed characteristics used by the algorithm (or machine learning (ML) tool) to create predictions and target individuals optimally. Because these correlations are hidden in high dimensional data, removing protected attributes from the database does not solve the discrimination problem; instead, removing those attributes often exacerbates the problem by making it undetectable and, in some cases, even increases the bias generated by the algorithm. We propose the BEAT (Bias-Eliminating Adapted Trees) framework to address these issues.

• HBS Working Paper

Investors, advocacy groups, academics, and the 200 CEOs of the US Business Roundtable have asked corporations to take on an added purpose beyond a narrow pursuit of shareholder value. In response, many companies now issue ESG (Environmental, Societal, and Governance) reports. These reports, however, are so broad in scope that they fail to address the unique measurement challenges within each of ESG’s constituent components. Moreover, the breadth of ESG reporting allows corporations to gloss-over (implicit) moral tradeoffs when their actions improve one of the reported ESG metrics (such as GHG emissions from its truck fleet) but worsen performance for an unreported metric (indentured labor used to mine minerals for electric vehicles’ batteries). Many ESG reports selectively present only those non-financial metrics favorable to them. We propose improvements in ESG reporting by focusing on dimensions where broad societal agreement already exists about the preferred outcomes from corporate actions, such as reducing greenhouse gas (GHG) emissions and avoiding use of indentured labor in supply chains.