An official website of the United States government
Although much attention is accorded to star performers, this paper considers the extent to which stars, themselves, benefit from the contribution of their collaborators (the constellation). By considering stars, constellations, and the synergies between them, we address a key question: To what extent is collaboration performance driven by the great individual or by great constellations? We introduce a novel approach that uses a matching model to uncover the complementarities driving collaboration formation. We use formal value-capture theory to estimate the relative contribution of stars and constellations to joint value creation. Analyzing a sample of academic research collaborations, we document that stars’ relative contribution exceeds that of their constellations in less than 15% of collaborations, although constellations provide a greater relative contribution in 9%. In most collaborations, neither party dominates: Innovation is a collective endeavor driven equally by the star and the constellation. Joint value creation and relative contribution are explained by the subtle interplay between complementarities in joint work and the substitutability of collaborative parties in the market. Joint value creation increases with the strength of complementarities between parties in a match. Relative value creation, and hence dominance, increases with the substitutability of one’s collaborative partner. Interestingly, joint value creation is greatest in collaborations where both stars and constellations offer bundles of rare attributes and where neither the star nor the constellation dominates. This paper was accepted by Olav Sorenson, organizations. Funding: D. Mindruta gratefully acknowledges funding from the HEC Foundation and from the French National Research Agency (ANR) “Investissements d’Avenir” (LabEx Ecodec/ANR-11-LABX-0047). J. Bercovitz and M. Feldman gratefully acknowledge funding from the Science of Science Approach to Analyzing and Innovating the Biomedical Research Enterprise (SCISIPBIO) program of the U.S. National Science Foundation (NSF) [Grant 1934875]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2021.01969 .
more »
« less
Social Interactions and Legislative Activity
Abstract We develop a model of social interactions, as well as strategic interactions that depend on such social activity, and use it to measure social complementarities in the legislative process. Our model allows for partisan bias and homophily in the formation of relationships, which then impact legislative output. We use it to show how increased electoral competition can induce increased social behavior and the nonlinear effects of political polarization on legislative activity. We identify and structurally estimate our model using data on social and legislative efforts of members of each of the 105th–110th U.S. Congresses (1997–2009). We find large spillover effects in the form of complementarities between the efforts of politicians, both within and across parties. Although partisanship and preference differences between parties are significant drivers of socializing, our empirical evidence paints a less polarized picture of the informal connections of legislators than typically emerges from legislative votes alone.
more »
« less
- Award ID(s):
- 2018554
- PAR ID:
- 10372574
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Journal of the European Economic Association
- ISSN:
- 1542-4766
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Recent work on legislative politics has documented complex patterns of interaction and collaboration through the lens of network analysis. In a largely separate vein of research, the field experiment—with many applications in state legislatures—has emerged as an important approach in establishing causal identification in the study of legislative politics. The stable unit treatment value assumption (SUTVA)—the assumption that a unit’s outcome is unaffected by other units’ treatment statuses—is required in conventional approaches to causal inference with experiments. When SUTVA is violated via networked social interaction, treatment effects spread to control units through the network structure. We review recently developed methods that can be used to account for interference in the analysis of data from field experiments on state legislatures. The methods we review require the researcher to specify a spillover model, according to which legislators influence each other, and specify the network through which spillover occurs. We discuss these and other specification steps in detail. We find mixed evidence for spillover effects in data from two previously published field experiments. Our replication analyses illustrate how researchers can use recently developed methods to test for interference effects, and support the case for considering interference effects in experiments on state legislatures.more » « less
-
Abstract The use of two‐dimensional images to teach students about three‐dimensional molecules continues to be a prevalent issue in many classrooms. As affordable visualization technologies continue to advance, there has been an increasing interest to utilize novel technology, such as augmented reality (AR), in the development of molecular visualization tools. Existing evaluations of these visual–spatial learning tools focus primarily on student performance and attitude, with little attention toward potential inequity in student participation. Our study adds to the current literature on introducing molecular visualization technology in biochemistry classrooms by examining the potential inequity in a group activity mediated by AR technology. Adapting the participatory equity framework to our specific context, we view equity and inequity in terms of access to the technological conversational floor, a social space created when people enter technology‐mediated joint endeavors. We explore three questions: What are the different ways students interact with an AR model of the potassium channel? What are salient patterns of participation that may signify inequity in classroom technology use? What is the interplay between group social dynamics and the introduction of AR technology in the context of a technology‐mediated group activity? Pairing qualitative analysis with quantitative metrics, our mixed‐methods approach produced a complex story of student participation in an AR‐mediated group activity. The patterns of student participation showed that equity and inequity in an AR‐mediated biochemistry group learning activity are fluid and multifaceted. It was observed that students who gave more explanations during group discussion also had more interactions with the AR model (i.e., they had greater access to the technological conversational floor), and their opinion of the AR model may have greater influence on how their group engage with the AR model. This study provides more nuanced ways of conceptualizing equity and inequity in biochemistry learning settings.more » « less
-
Abstract Living in a rapidly changing environment can alter stress physiology at the population level, with negative impacts on health, reproductive rates, and mortality that may ultimately result in species decline. Small, isolated animal populations where genetic diversity is low are at particular risks, such as endangered Virunga mountain gorillas (Gorilla beringei beringei). Along with climate change‐associated environmental shifts that are affecting the entire population, subpopulations of the Virunga gorillas have recently experienced extreme changes in their social environment. As the growing population moves closer to the forest's carrying capacity, the gorillas are coping with rising population density, increased frequencies of interactions between social units, and changing habitat use (e.g., more overlapping home ranges and routine ranging at higher elevations). Using noninvasive monitoring of fecal glucocorticoid metabolites (FGM) on 115 habituated Virunga gorillas, we investigated how social and ecological variation are related to baseline FGM levels, to better understand the adaptive capacity of mountain gorillas and monitor potential physiological indicators of population decline risks. Generalized linear mixed models revealed elevated mean monthly baseline FGM levels in months with higher rainfall and higher mean maximum and minimum temperature, suggesting that Virunga gorillas might be sensitive to predicted warming and rainfall trends involving longer, warmer dry seasons and more concentrated and extreme rainfall occurrences. Exclusive use of smaller home range areas was linked to elevated baseline FGM levels, which may reflect reduced feeding efficiency and increased travel efforts to actively avoid neighboring groups. The potential for additive effects of stress‐inducing factors could have short‐ and long‐term impacts on the reproduction, health, and ultimately survival of the Virunga gorilla population. The ongoing effects of environmental changes and population dynamics must be closely monitored and used to develop effective long‐term conservation strategies that can help address these risk factors.more » « less
-
The increased social media usage in modern history instigates data collection from various users with different backgrounds. Mass media has been a rich source of information and might be utilized for countless purposes, from business and personal to political determination. Because more people tend to express their opinions through social media platforms, researchers are excited to collect data and use it as a free survey tool on what the public ponders about a particular issue. Because of the detrimental effect of news on social networks, many irresponsible users generate and promote fake news to influence public belief on a specific issue. The U.S. presidential election has been a significant and popular event, so both parties invest and extend their efforts to pursue and win the general election. Undoubtedly, spreading and promoting fake news through social media is one of the ways negligent individuals or groups sway societies toward their goals. This project examined the impact of removing fake tweets to predict the electoral outcomes during the 2020 general election. Eliminating mock tweets has improved the correctness of model prediction from 74.51 percent to 86.27 percent with the electoral outcomes of the election. Finally, we compared classification model performances with the highest model accuracy of 99.74634 percent, precision of 99.99881 percent, recall of 99.49430 percent, and an F1 score of 99.74592 percent. The study concludes that removing fake tweets improves the correctness of the model with the electoral outcomes of the U.S. election.more » « less
