Knowledge that Transforms

We study the impact of a student’s ordinal rank in a high school cohort on educational attainment several years later. To identify a causal effect, we compare multiple cohorts within the same school, exploiting idiosyncratic variation in cohort composition. We find that a student’s ordinal rank significantly affects educational outcomes later in life. Students with a higher rank are significantly more likely to finish high school and to attend college. Exploring potential channels, we find that students with a higher rank have higher expectations about their future career, as well as a higher perceived intelligence.

Algorithmically recommending workers to employers for the purpose of recruiting can substantially increase hiring: in an experiment conducted in an online labor market, employers with technical job vacancies that received recruiting recommendations had a 20% higher fill rate compared to the control. There is no evidence that the treatment crowded out hiring of nonrecommended candidates. The experimentally induced recruits were highly positively selected and were statistically indistinguishable from the kinds of workers employers recruit “on their own.” Recommendations were most effective for job openings that were likely to receive a smaller applicant pool.

Social networks are important for finding jobs, but which ties are most useful? Granovetter has suggested that “weak ties” are more valuable than “strong ties,” since strong ties have redundant information, while weak ties have new information. Using 6 million Facebook users’ data, we find evidence for the opposite. We proxy for job help by identifying people who eventually work with a pre-existing friend. Using objective tie strength measures and our job help proxy, we find that most people are helped through one of their numerous weak ties but a single stronger tie is significantly more valuable at the margin.

This paper estimates peer effects in a university context where students are randomly assigned to sections. While students benefit from better peers on average, low-achieving students are harmed by high-achieving peers. Analyzing students’ course evaluations suggests that peer effects are driven by improved group interaction rather than adjustments in teachers’ behavior or students’ effort. Building on Angrist’s research, we further show that classical measurement error in a setting where group assignment is systematic can lead to a substantial overestimation of peer effects. However, when group assignment is random—like in our setting—peer effect estimates are biased toward zero.