scholarly journals The roles actors play in policy networks: Central positions in strongly institutionalized fields

2021 ◽  
pp. 1-23
Author(s):  
Karin Ingold ◽  
Manuel Fischer ◽  
Dimitris Christopoulos

Abstract Centralities are a widely studied phenomenon in network science. In policy networks, central actors are of interest because they are assumed to control information flows, to link opposing coalitions and to directly impact decision-making. First, we study what type of actor (e.g., state authorities or interest groups) is able to occupy central positions in the highly institutionalized context of policy networks. Second, we then ask whether bonding or bridging centralities prove to be more stable over time. Third, we investigate how these types of centrality influence actors’ positions in a network over time. We therefore adopt a longitudinal perspective and run exponential random graph models, including lagged central network positions at t1 as the main independent variable for actors’ activity and popularity at t2. Results confirm that very few actors are able to maintain central positions over time.

2019 ◽  
Vol 121 (10) ◽  
pp. 1-32
Author(s):  
David Diehl ◽  
Robert A. Marx

Background/Context Research on the patterns of philanthropic funding of charter schools has largely focused on the behavior of major foundations. This work has documented how the once diffuse giving by these major foundations has become increasingly concentrated on a small number of jurisdictional challengers in the form of charter schools, charter management organizations, and intermediary organizations. Purpose The current study examines whether this convergence in giving has spread to the entire network of foundations giving to charter-school-related organizations. We do so by extending current work and focus on the broader institutional field that includes the interactions between major foundations, smaller foundations, and grantees over time. Moreover, we look to see, if such a field-wide convergence is present, whether there is evidence consistent with the institutional process of isomorphism in which low-status foundations match the giving strategy of higher status ones. Research Design We test for these dynamics using exponential random graph models (ERGMs), a hypothesis-testing framework for network analysis. More specifically, we analyze the funding ties among 809 foundations that gave grants to California charter schools and charter-school-related organizations between 2003 and 2014, as available through the Foundation Directory Online. We constructed multiyear windows to examine funding ties between foundations and recipients, using organizational characteristics, such as foundation type, foundation year, professionalization, foundation size, organizational type, and location, and endogenous features of the network as independent variables. Findings Results indicate centralization of giving over time, as larger and newer foundations began practicing more targeted giving and the most connected recipients were involved in a disproportionate number of funding ties. We also found evidence consistent with institutionalization, as foundations with professional staffs played a larger role in giving, and smaller foundations increasingly engaged in behavior similar to their larger peers over time. Finally, we found evidence for the consistent effect of propinquity: We observe co-funding and co-receiving ties between foundations and grantees in geographical proximity to each other. Conclusions This work examines the network dynamics of charter school philanthropic giving and provides evidence for the centralization and institutionalization of the field. In turn, this may create inequity in funding for charter schools because it may be more difficult for smaller or less ideologically popular organizations to penetrate the field. Policy makers should be aware of these forces and should take them into account when making budgetary and funding decisions.


Social Forces ◽  
2019 ◽  
Vol 98 (4) ◽  
pp. 1829-1858 ◽  
Author(s):  
Kevin Lewis ◽  
Andrew V Papachristos

AbstractGang members frequently refer to street life as a “game” (or “The Game”): a social milieu in which status is lost or won by the way individuals and groups manage their reputations. Like other games, successfully participating in the street game may demand adherence to certain rules, such as the willingness to violently redress threats, the avoidance of “weak” behaviors, and the protection of one’s allies. This paper draws on detailed police records of violent exchanges among gangs in Chicago to ascertain which rules of the game in fact contribute to the relative social standing of groups. Specifically, we use exponential random graph models to identify the underlying micro-arrangements among gangs that collectively generate macro-level patterns of homicide. Findings illuminate a large and diverse array of generative mechanisms based on gangs’ attributes and structural positions. However, these mechanisms vary depending on which two gangs are at hand; provide evidence of a contested hierarchy with few intergroup alliances; and are surprisingly inconsistent over time. As all gangs engage in local and ongoing struggles for dominance—and as the rules constantly change—the street game is continually played but never truly won.


2019 ◽  
Vol 57 (2) ◽  
pp. 872-905
Author(s):  
Sarah Galey-Horn ◽  
Sarah Reckhow ◽  
Joseph J. Ferrare ◽  
Lorien Jasny

We show how policy makers converged to support similar reforms on a major educational issue: teacher effectiveness. Our study demonstrates the importance of idea brokers—actors that facilitate connections between preferences in policy networks and promote consensus around new policy ideas. Our study is based on analysis of testimony from 200 Congressional hearings from 2001 to 2015. We use discourse network analysis to examine network ties based on policy preferences expressed in hearings. We visualize policy networks, identify brokers, and estimate exponential random graph models to examine policy changes between the Bush and Obama administrations. We show how idea brokerage is associated with a convergence of policy preferences around teacher effectiveness among a coalition of political actors.


2021 ◽  
Vol 64 ◽  
pp. 225-238
Author(s):  
George G. Vega Yon ◽  
Andrew Slaughter ◽  
Kayla de la Haye

2017 ◽  
Vol 7 (3) ◽  
pp. 505-522 ◽  
Author(s):  
Stefan Wojcik

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.


2020 ◽  
Vol 31 (5) ◽  
pp. 1266-1276 ◽  
Author(s):  
Julian C Evans ◽  
David N Fisher ◽  
Matthew J Silk

Abstract Social network analysis is a suite of approaches for exploring relational data. Two approaches commonly used to analyze animal social network data are permutation-based tests of significance and exponential random graph models. However, the performance of these approaches when analyzing different types of network data has not been simultaneously evaluated. Here we test both approaches to determine their performance when analyzing a range of biologically realistic simulated animal social networks. We examined the false positive and false negative error rate of an effect of a two-level explanatory variable (e.g., sex) on the number and combined strength of an individual’s network connections. We measured error rates for two types of simulated data collection methods in a range of network structures, and with/without a confounding effect and missing observations. Both methods performed consistently well in networks of dyadic interactions, and worse on networks constructed using observations of individuals in groups. Exponential random graph models had a marginally lower rate of false positives than permutations in most cases. Phenotypic assortativity had a large influence on the false positive rate, and a smaller effect on the false negative rate for both methods in all network types. Aspects of within- and between-group network structure influenced error rates, but not to the same extent. In "grouping event-based" networks, increased sampling effort marginally decreased rates of false negatives, but increased rates of false positives for both analysis methods. These results provide guidelines for biologists analyzing and interpreting their own network data using these methods.


2016 ◽  
Vol 46 ◽  
pp. 11-28 ◽  
Author(s):  
S. Thiemichen ◽  
N. Friel ◽  
A. Caimo ◽  
G. Kauermann

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