Imagine that you are deciding whether to release a person on bail, grant a consumer a loan, or hire a job candidate. Now imagine your method of making this decision involves using data to algorithmically predict how people will behave—who will skip bail, default on the loan, or be a good employee. How will you know if the way you determine  outcomes is fair?

In recent years, computer scientists and others have done a lot to try to answer this question. The flourishing literature on “algorithmic fairness” offers dozens of possibilities, such as testing whether your algorithm predicts equally well for different people, comparing outcomes by race and sex, and assessing how often predictions are incorrect.

Think of a dangerous job. One where workers experience daily risk and suffering. Where the accidental burn, cut, and blood is to be expected— maybe even mundane. Perhaps what comes to mind is a firefighter barreling into a fiery building, a meat packing plant worker who trims sides of beef, or a police officer in a foot chase with an armed suspect.

What likely did not come to mind are the folks who whipped up the plate of palak paneer you dined on last Saturday or baked the croissant you nibbled this morning: chefs, cooks, and other restaurant kitchen workers. But, data from the U.S. Bureau of Labor Statistics finds that workers at dining establishments—about 2.3 million people in the United States— experience comparatively more pain and injury than those of other professions, including the stereotypically dangerous ones I mentioned earlier (see table below).

Many social settings are gender-segregated: At the workplace, in higher education, and in friendship cliques, women and men typically encounter peers of their own gender. This separation slows down efforts toward gender equality because women and men get access to different resources through their social networks and engage in gender-typed behaviors and activities. But much less is known about gender segregation in civic life. Voluntary associations, such as sports clubs, community associations, or leisure groups, are often viewed as places that bring communities together and equalize access to social resources. However, previous research suggests – often based on highly simplified figures – that voluntary associations are segregated along gendered lines as well: Women and men are usually involved in different types of associations and perform different voluntary work, often matching broader gender stereotypes and extending traditional patterns of labor division to community life. For example, while women pull together in school- or care-related organizations, men more often meet each other in sports clubs and local political parties. 

“The surest way to get a player to come back to the game is to make it a habit, a part of their life.”

 — A game designer and product manager

Much of consumption, which includes the acquisition, appreciation, and use of goods and services, is habitual. On any typical day, millions of people engage in various activities available on their Apple or Android smartphones, communicate with friends and family through social networks such as Twitter, and use popular streaming services such as Netflix and Spotify to entertain themselves. Reinvigorated by the rise of social media, digital platforms, and artificial intelligence, and recently jolted by the COVID-19 pandemic, the recurrent and habitual transactions of individual consumers with provider firms are a mainstay of the market economy.

Survey researchers and pundits alike frequently lament the fact that citizens know too little about politics. Furthermore, public opinion polling consistently suggests that women appear to be less knowledgeable about politics than men. But how can we measure political sophistication in the first place? And more importantly, how does our measurement approach impact our conclusions about citizen competence about politics? The conventional approach to measure political sophistication in surveys focuses on multiple-choice questions assessing people’s ability to recall information about political institutions and officeholders. (For instance, the American National Election Study routinely asks questions such as, “Do you happen to know which party currently has the most members in the U.S. Senate?”) These types of factual knowledge questions—which have been used in scores of surveys for academic research and polling—have important limitations. For instance, determining a comprehensive set of knowledge questions is far from trivial since it involves strong assumptions about what information is necessary for people to be politically competent. Furthermore, knowledge questions vary in difficulty across demographic segments and can therefore introduce systematic measurement error when comparing groups of respondents.

After the birth of their first child, different sex couples often opt for a gendered labor division, with mothers assuming more of the primary responsibility for childcare, which often leads to a reduction in their salaried working hours, while fathers continue full-time employment. In my research, I analyzed the role financial aspects play when new parents negotiate their labor division. What role do childcare costs play when parents decide whether to work outside the home or take care of their children by themselves? Are mothers more likely to reduce their paid working hours and take on more unpaid work than fathers, because, on average, they earn less?

To answer these questions, I traced the financial decisions of 54 parents (27 couples) living in Switzerland. While most of the couples belonged to the middle-class, their income configurations varied: before childbirth, the man earned more than the woman did in 12 couples.  In 9 couples, both parents earned the same.  In 6 couples, the woman earned more than the man did. Each parent was interviewed once before and once or twice in the first two years after the birth of their first child. This longitudinal data with more than 130 in-depth interviews provided a detailed picture of how the couples navigated their financial decisions.

Why did Unilever enter the market for plant-based alternatives to meat? Why was BP engaged in solar panel manufacturing? And why did Patagonia enter the organic food market?

Usually market entry decisions are ascribed to superior resources or capabilities: companies enter markets where they can leverage their knowledge, technology, or expertise to gain an advantage. But of the three instances mentioned above, only one is largely consistent with this story. What accounts for market entry decisions when companies do not have the requisite resources and capabilities?

A first step to solving this puzzle is to acknowledge that the markets these companies have entered—markets for organic food, solar energy, and plant-based meat alternatives—are all instances of so-called “moral markets”: sectors that emerge not just to create economic value, but to explicitly offer market-based solutions to social and environmental problems.

Diversity, equity and inclusion (DEI) policies aim to improve the experiences of marginalized groups in organizations, yet a large body of research finds that these policies are often ineffective and might even backfire. One previously explored reason is that they can activate biases, generating opposition to policies from majority groups. In a recently published article, I identified another reason why DEI policies might fail, which I label the “Equality Policy Paradox.”

Key Finding: The Equality Policy Paradox

I found that managers who voice the most support for improving equality might also be, contradictorily, the least likely to implement DEI policies. This Equality Policy Paradox may come about because often the managers who are most supportive of DEI policies are themselves members of marginalized groups. As such, they experience career barriers, such as discrimination, that make it difficult for them to practically support DEI policies without harming their own careers.

Navigating the entrepreneurial landscape is a challenging feat for women. The hurdles they face are well-documented, which range from equitable access to funding and other resources, to widespread discrimination from venture capitalists and investors. Crunchbase, a data provider, reveals that in 2020, a mere 2.3% of global venture capital was allocated to female entrepreneurs. But the issues are not limited to the funding stage. Post-entry, a systemic performance gap persists. As an example, Crunchbase also reports that of the 120 new entrants to its “Unicorn Board” that year, only 10 were founded by women.

This inequity is far from new or surprising. Research has consistently highlighted the patterns of unfair distribution of venture capital. However, even after succeeding in raising venture capital, we still observe a persistent performance disparity between startups led by men and women. Why? Are there any other hidden challenges that female entrepreneurs face, hampering their success?