Sharique Hasan

Uttara Ananthakrishnan, Sharique Hasan, Anuj Kumar (2025), Gentrification and Racial Distrust in Communities: Evidence from 911 Calls”, Management Science, 71 (1), pp. 708-730.

Abstract: The prevalence of racial bias in policing has long concerned social scientists and policymakers. This article studies a predecessor mechanism that constitutes an important source of policing bias in American society: calls by individuals to the police to investigate “suspicious” behaviors, often involving neighbors. We construct a novel data set of more than 39 million 911 calls across 14 U.S. cities from 2011 to 2020. These data, obtained through the digitization initiatives of local governments, provide us with a unique opportunity to study neighborhood-level trust and social cohesion and demonstrate how changes to a neighborhood’s composition lead to systematic increases in the prevalence of “unfounded” suspicion calls to the police. Across a range of specifications, the proportion of unfounded suspicion calls increases as more non-Black residents move into neighborhoods with historically high levels of Black residents. This relationship is exacerbated in gentrifying neighborhoods and those with public spaces that enable more contact between community members. However, we also find some evidence that Black leadership and public support of Black citizens in communities mitigate the association between non-Black residents and the proportion of unfounded 911 calls. We discuss our results and implications for future research and policy.

Sharique Hasan and Anuj Kumar (2024), Who captures the value of organizational ratings?: Evidence from public schools, Strategy Science, 9 (3).

Abstract: Ratings of organizations and firms have become ubiquitous. These ratings, often produced by intermediaries (including private and public organizations), are designed to aid consumers and other stakeholders in their decision making while guiding rated organizations toward performance improvement or compliance. In doing so, these intermediaries introduce new information to markets. However, disparities may exist in the ability to strategically capture the value from such ratings, often due to differential access to complementary assets among stakeholders. Consequently, this differential ability can lead to outcomes contrary to the rating institutions’ intentions. Reflecting on this dynamic, we analyze how widespread access to a prevalent type of rating—school performance information, often produced to enhance transparency and equity in educational access—has affected existing economic and social disparities in America. We leverage the staged rollout of GreatSchools.org school ratings from 2006 to 2015 to answer this question. Across various outcomes and specifications, we find that the availability of school ratings has accelerated the divergence in housing values, income distributions, education levels, and racial and ethnic composition across communities. Affluent and more educated families were better positioned to strategically leverage this new information to capture educational opportunities in communities with top schools. The uneven benefits we observe highlight how ratings can unintentionally deepen existing inequalities, thereby complicating their intended impacts.

Ines Black, Sharique Hasan, Rembrand Koning (2023), Hunting for talent: Firm-driven labor market search in the United States, Strategic Management Journal.

Abstract: Research Summary We analyze firm-driven labor market search, where firms “hunt” for talent rather than rely on workers to apply for vacancies. We leverage three approaches. We develop a model of firm-driven search and derive equilibrium conditions under which firms use this channel. We test our model's predictions using two data sources. Data from a nationally representative survey of 10,000 workers shows that the percentage hired through recruiting has increased from 4.9% in 1991 to 14.3% in 2022. This share is larger for higher-skilled workers and those with online profiles on LinkedIn. We complement this analysis with data on the near universe of online job postings from 2010 through 2020. Consistent with our model and worker survey evidence, we find firms that demand higher-skilled workers or operate in labor markets with heavy LinkedIn use are more likely to “hunt for talent.” Managerial Summary We study the phenomenon of “hunting” for talent, where firms fill open positions by searching for workers and inviting them to a recruiting process, rather than relying on workers to apply directly. We find that the percentage of workers hired through hunting has increased from 4.9% in 1991 to 14.3% in 2022. We propose that firms that rely more on high-skilled workers and/or operate within industries with a higher share of available candidates with online profiles are more likely to hunt for their talent. We find support for this conjecture using two data sets, documenting the worker and firm side of the labor market. Data from a nationally representative survey of 10,000 workers shows they are more likely to have been “hunted” by their employer if they work in an occupation that requires more skills, or if their industry has more candidates with online profiles. Moreover, data on US-wide job postings over the past decade shows that firms in need of highly skilled workers are more likely to invest in outbound recruiting capabilities.

Rembrand Koning, Sharique Hasan, Aaron Chatterji (2022), Experimentation and startup performance: Evidence from A/B testing, Management Science, 68 (9), pp. 6434-6453.

Abstract: Recent scholarship argues that experimentation should be the organizing principle for entrepreneurial strategy. Experimentation leads to organizational learning, which drives improvements in firm performance. We investigate this proposition by exploiting the time-varying adoption of A/B testing technology, which has drastically reduced the cost of testing business ideas. Our results provide the first evidence on how digital experimentation affects a large sample of high-technology start-ups using data that tracks their growth, technology use, and products. We find that, although relatively few firms adopt A/B testing, among those that do, performance improves by 30%–100% after a year of use. We then argue that this substantial effect and relatively low adoption rate arises because start-ups do not only test one-off incremental changes, but also use A/B testing as part of a broader strategy of experimentation. Qualitative insights and additional quantitative analyses show that experimentation improves organizational learning, which helps start-ups develop more new products, identify and scale promising ideas, and fail faster when they receive negative signals. These findings inform the literatures on entrepreneurial strategy, organizational learning, and data-driven decision making.