Strategic Management Journal 40(9)
(with Rembrand Koning)
We conduct a field experiment at an entrepreneurship bootcamp to investigate whether interaction with proximate peers shapes a nascent startup team’s performance. We find that teams whose members lack prior ties to others at the bootcamp experience peer effects that influence the quality of their product prototypes. A one-standard-deviation increase in the performance of proximate teams is related to a two-thirds standard-deviation improvement for a focal team. In contrast, we find that teams whose members have many prior ties interact less frequently with proximate peers, and thus their performance is unaffected by nearby teams. Our findings highlight how prior social connections, which are often a source of knowledge and influence, can limit new interactions and thus the ability of organizations to leverage peer effects to improve the performance of their members.
Working Paper (November, 2018)
(with Anuj Kumar)
We analyze whether widespread online access to school quality information affected economic and social segregation in America. We leverage the staged roll-out of GreatSchools.org school ratings across America from 2006-2015 to answer this question. Across a range of outcomes and specifications, we find that the mass availability of school ratings has accelerated divergence in housing values, income distributions, education levels, as well as the racial and ethnic composition across communities. Affluent and more educated families were better positioned to leverage this new information to capture educational opportunities in communities with the best schools. An unintended consequence of better information was less, rather than more, equity in education.
Research Policy, 48(9)
(with Rembrand Koning)
When do conversations lead people to generate better ideas? We conducted a field experiment at a startup bootcamp to evaluate the impact of informal conversations on the quality of product ideas generated by participants. Specifically, we examine how the personality of an innovator (openness to experience, capturing creativity) and the personalities of her randomly assigned conversational peers (extroversion, measuring willingness to share information) affects the innovator’s ideas. We find that open innovators who spoke with extroverted peers generated significantly better ideas than others at the bootcamp. However, closed individuals produced mediocre ideas regardless with who they spoke, suggesting limited benefits of conversations for these people. More surprisingly, open individuals, who are believed to be inherently creative, produced worse ideas after they spoke with introverted peers, suggesting individual creativity’s dependence on external information. Our study demonstrates the importance of considering the traits of both innovators and their conversational peers in predicting who will generate the best ideas.
(with Sampsa Samila and Alexander Oettl)
Long-term collaborations are crucial in many creative domains. Although there is ample research on why people collaborate, our knowledge about what drives some collaborations to persist and others to decay is still emerging. In this paper, we extend theory on third-party effects and collaborative persistence to study this question. We specifically consider the role that a third party’s helpful behavior plays in shaping tie durability. We propose that when third parties facilitate helpfulness among their group, the collaboration is stronger, and it persists even in the third’s absence. In contrast, collaborations with third parties that are nonhelpful are unstable and dissolve in their absence. We use a unique data set comprising scientific collaborations among pairs of research immunologists who lost a third coauthor to unexpected death. Using this quasi-random loss as a source of exogenous variation, we separately identify the effect of third parties’ traditional role as an active agent of collaborative stability and the enduring effect of their helpful behavior—as measured by acknowledgments—on the persistence of the remaining authors’ collaboration. We find support for our hypotheses and find evidence that one mechanism driving our effect is that helpful thirds make their coauthors more helpful.
Strategic Management Journal, 40(3), 331-356
(with Aaron Chatterji, Solene Delecourt and Rembrand Koning)
Why do some entrepreneurs thrive while others fail? We explore whether the advice entrepreneurs receive about managing their employees influences their startup’s performance. We conducted a randomized field experiment in India with 100 high-growth technology firms whose founders received in-person advice from other entrepreneurs who varied in their managerial style. We find that entrepreneurs who received advice from peers with a formal approach to managing people—instituting regular meetings, setting goals consistently, and providing frequent feedback to employees—grew 28% larger and were 10 percentage points less likely to fail than those who got advice from peers with an informal approach to managing people, two years after our intervention. Entrepreneurs with MBAs or accelerator experience did not respond to this intervention, suggesting that formal training can limit the spread of peer advice.
“Social_Networks_and_Careers“, in Social Networks at Work, D.J. Brass and S.P. Borgatti (eds.) S.I.O.P. Frontiers Book Series.
Social networks affect a range of career outcomes including job search, promotion and wage determination. Networks also affect major career transitions, including entry into entrepreneurship and exit into retirement. Across a range of studies, individuals are found to use their networks to deal with two perennial problems they face in labor markets and organizations: the scarcity of information and the absence of trust. I review the literature with an eye towards understanding which features of a person’s networks help them solve these problems at different career stages. I conclude by considering how the rising importance of information technology will affect the networks-career link moving forward.
Management Science, 61(10), 2536-2547.
(with Surendrakumar Bagde)
Much research suggests that social networks affect individual and organizational success. However, a strong assumption underlying this research is that network structure is not reducible to the individual attributes of social actors. In this article, we test this assumption by examining whether interacting with random peers causes exogenous growth of a person’s network. Using three years of network data for students at an Indian college, we evaluate the effect of peers on network growth. We find strong evidence that interacting with random, but well-connected, roommates causes significant growth of a focal student’s network. Further, we find that this growth also implies an increase in how close an actor moves to a network’s center and whether that actor is likely to serve as a network bridge. Fundamentally, our results demonstrate that exogenous factors beyond individual agency—i.e., random peers—can shape network structure. Our results also provide a useful model for causally identifying the determinants of network structure and dynamics.
Organization Science, 26(6), 1665-1681
(with John-Paul Ferguson and Rembrand Koning)
Prior work has considered the properties of individual jobs that make them more or less likely to survive in organizations. Yet little research examines how a job’s position within a larger job structure affects its life chances and thus the evolution of the larger job structure over time. In this article, we explore the impact of technical interdependence on the dynamics of job structures. We argue that jobs that are more enmeshed in a job structure through these interdependencies are more likely to survive. We test our theory on a quarter century of personnel and job description data for the nonacademic staff of one of America’s largest public universities. Our results provide support for our key hypotheses: jobs that are more enmeshed in clusters of technical interdependence are less likely to die. At the same time, being part of such a cluster means that a job is more vulnerable if its neighbors disappear. And the “protection” of technical interdependence is contingent: it does not hold in the face of strategic change or other organizational restructurings. We offer implications of our analyses for research in organizational performance, careers, and labor markets.
American Sociological Review, 78(6), 1009-1032.
(with Surendrakumar Bagde)
In this article we examine how social capital affects the creation of human capital. Specifically, we study how college students’ peers affect academic performance. Building on existing research, we consider the different types of peers in the academic context and the various mechanisms through which peers affect performance. We test our model using data from an engineering college in India. Our data include information about the performance of individual students as well as their randomly assigned roommates, chosen friends, and chosen study-partners. We find that students with able roommates perform better, and the magnitude of this roommate effect increases when the roommate’s skills match the student’s academic goals. We also find that students benefit equally from same- and different-caste roommates, suggesting that social similarity does not strengthen peer effects. Finally, although we do not find strong evidence for independent friendship or study-partner effects, our results suggest that roommates become study-partners, and in so doing, affect performance. Taken together, our findings demonstrate that peer effects are a consequential determinant of academic achievement.
Social Networks, 34(4), 506-514.
In this paper, we propose the application of a semi-parametric statistical methodology called Group-Based Developmental Trajectory Analysis to studying the dynamics of social networks. We begin with a discussion of theoretical problems in network analysis that may benefit from this approach. Next, we describe the methodology and how it can be applied to dyadic network data as well as aggregated node level data. We then demonstrate the methodology by analyzing the Newcomb Fraternity and the van de Bunt student data sets. Finally, we conclude with a discussion of potential directions for further research.