scholarly journals Networks of Global Social Policy Diffusion: The Effects of Culture, Economy, Colonial Legacies, and Geographic Proximity

2021 ◽  
pp. 1-37
Author(s):  
Ivo Mossig ◽  
Michael Windzio ◽  
Fabian Besche-Truthe ◽  
Helen Seitzer

AbstractThe introductory chapter to the volume by Mossig, Windzio, Seitzer and Besche-Truthe defines the core concepts, such as diffusion and contagion, and gives an example of an application diffusion and contagion in epidemiology. The most important underlying functions, namely the logistic density and cumulative logistic density function, are explained, followed by a very brief introduction to the core concepts of event history analysis. In the network diffusion model, contagion, or, in other words, the adoption of information or innovation, is based on the concept of exposure which will be elaborated in this chapter. Finally, after describing and visualizing the four different networks and their correlations, exponential random graph models are used to analyze structural and substantive properties of these networks. The introduction concludes with a brief overview of the chapters.

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.


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.


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

Sign in / Sign up

Export Citation Format

Share Document