Studying 5.6 million biomedical science articles published over three decades, we reconcile conflicts in a longstanding interdisciplinary literature on scientists' life-cycle productivity by controlling for selective attrition and distinguishing between research quantity and quality. While research quality declines monotonically over the career, this decline is easily overlooked because higher "ability" authors have longer publishing careers. Our results have implications for broader questions of human capital accumulation over the career and federal research policies that shift funding to early-career researchers - while funding researchers at their most creative, these policies must be undertaken carefully because young researchers are less "able" on average.

Intelligent tutoring systems are known for providing customized learning opportunities for thousands of users. One feature of many systems is differentiating the amount of practice users receive. To do this, some systems rely on a threshold of consecutive correct responses. For instance, Khan Academy used to use ten correct in a row and now uses five correct in a row as the mastery threshold. The present research uses a series of randomized control trials, conducted in an online learning platform (eg., ASSISTments.org), to explore the effects of different thresholds of consecutive correct responses on learning. Results indicate that despite spending significantly more time practicing there is no significant difference on learning between two, three, four, or five consecutive correct responses. This suggests that systems, and MOOCS, can employ the simple rule of two or three consecutive correct responses when determining the amount of practice provided to users.

We draw on a recombinant view of innovation, where being in a new location and/or multiple locations leads to exposure to novel combinations of ideas that increase the creativity of top scientists.Using a rich, unique dataset we helped assemble, we estimate the empirical relationship between being in a new location and/or multiple locations and the expected interval before an eventual Nobel laureate (ENL) commences their prize-winning work.We find that being in a new location and in multiple locations are substantially and significantly associated with a shorter expected interval before ENLs commence their prize-winning work.

The pandemic of coronavirus disease 2019 (COVID-19) has severely affected the higher education system since Spring 2020. During the periods of school closures and limited in-person activities, engineering students and faculty experienced challenges in remote learning and mentoring activities. Funded by the National Science Foundation (NSF), this study surveyed 668 faculty and 3,385 undergraduate/graduate students in engineering from 94 institutions in 36 states across the country. Our findings indicated that several faculty and student subgroups were disproportionately negatively affected by the ongoing COVID-19 pandemic. Specifically, women engineering faculty and students encountered more challenges in transitioning to remote teaching and learning. Students with disabilities and those whose households experienced a loss of income during the COVID-19 pandemic faced more remote learning challenges, perceived less instrumental mentoring support, and were more likely to delay their graduate dates. Our structural equation modeling results showed that mentoring support could mitigate remote learning challenges and academic disruption for engineering students. The study underscores the importance of mentoring support during the crisis of a pandemic.

Studying 5.6 million biomedical science articles published over three decades, we reconcile conflicts in a long-standing interdisciplinary literature on scientists' life-cycle productivity by controlling for selective attrition and distinguishing between research quantity and quality. While research quality declines monotonically over the career, this decline is easily overlooked because higher ability authors have longer publishing careers. Our results have implications for broader questions of human capital accumulation over the career and federal research policies that shift funding to early-career researchers-while funding researchers at their most creative, these policies must be undertaken carefully because young researchers are less able on average.

Marketing and strategy researchers have often studied how organizations navigate multiple identities in relation to category spanning but extant literature pays less attention to understanding how individuals do so. Moreover, current econometric approaches only scratch the surface with respect to addressing the impact of multiple identities in professional settings. As a model domain to study labor market returns when individuals have more than one identity, we focus on interdisciplinary dissertators in the United States since evidence shows clear uptrends in dissertators engaging multiple professional identities and unclear trends in their outcomes. Our novel estimation method leverages a two-step process to characterize salaries of interdisciplinary dissertators as functions of the identities (academic fields) they acquire as graduate students. We estimate a first-stage regression of log earnings for monodisciplinarians on field dummies and respondent characteristics. After capturing the estimated field coefficients, we then regress log earnings for interdisciplinarians on linear and non-linear functions of these coefficients. Our estimates robustly reject the hypothesis that interdisciplinarians receive a salary premium. We also find evidence that the academic market, but not other employment sectors, particularly compensates researchers based on their primary discipline, an outcome that challenges emphases on interdisciplinarity. While our findings for interdisciplinarians point to the primary identity holding predominant importance for doctoral graduates in the United States, our two-step method provides a framework for parsing and estimating the varied impacts of multiple identities across a wide range of contexts.

Using unique, new, matched UMETRICS data on people employed on research projects and Author-ity data on biomedical publications, this paper shows that National Institutes of Health funding stimulates research by supporting the teams that conduct it. While faculty-both principal investigators (PIs) and other faculty-and their productivity are heavily affected by funding, so are trainees and staff. The largest effects of funding on research output are ripple effects on publications that do not include PIs. While funders focus on research output from projects, they would be well advised to consider how funding ripples through the wide range of people, including trainees and staff, employed on projects.

We study the effects of peer gender composition in STEM doctoral programs on persistence and degree completion. Leveraging unique new data and quasi-random variation in gender composition across cohorts within programs, we show that women entering cohorts with no female peers are 11.7 percentage points less likely to graduate within 6 years than their male counterparts. A 1 standard deviation increase in the percentage of female students differentially increases women’s probability of on-time graduation by 4.4 percentage points. These gender peer effects function primarily through changes in the probability of dropping out in a PhD program’s first year. © 2022 The University of Chicago. All rights reserved.

The diffusion and use of new ideas is critical for producing innovations and realizing their potential.We explore how characteristics of innovators and potential adopters affect the adoption of important, new scientific ideas in networks.Using rich data on biomedical researchers and their networks, natural language processing, and a novel two-way fixed effects strategy, we find that new ideas introduced by female scientists are under-utilized for two reasons.First, female innovators are less connected than men.Second, at short network distances, researchers (especially men) adopt women's ideas less.Similar gaps hold for underrepresented racial and ethnic minorities.

This paper investigates the local impact of biomedical research on mortality in the USA.Causally estimating the marginal value of biomedical research is challenging due to a lack of micro data linking health outcomes to plausibly exogenous variation in research.We create a new linkage between a research database (PubMed) and administrative death records that enables research to be related to mortality at the geographic, disease and time level.We then estimate the marginal impact of biomedical research on mortality using hospital market (HRR) level shocks to research activity by disease.Our identification strategy builds on the literature on the dissemination of knowledge, specifically that of local knowledge spillovers.By utilizing variation across diseases, time and distance from research we control for additional trends relative to the current literature.Our results show that an additional research publication on average reduces local mortality from a disease by 0.35%.Our results also provide novel evidence that there are health benefits to the local communities (local spillovers) in which biomedical research is conducted.