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18 - Autologistic Actor Attribute Model Analysis of Unemployment: Dual Importance of Who You Know and Where You Live
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
- Print publication:
- 19 November 2012, pp 237-247
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Summary
Unemployment: Location and Connections
Persistent regional unemployment disparities have been characterized as a major cause of regional decay and impose significant costs on communities (Bill, 2005; Mitchell & Bill, 2004). Macroeconomic explanations for the persistence of unemployment often revolve around economic factors, including spatial changes in the skill requirements of jobs, migration of jobs to the suburbs, persistent demand constraints, wage differentials, low labor mobility and related structural impediments, and variations in the distribution of industries across space (see reviews, for example, in Ihlanfeldt and Sjoquist (1998) and Ramakrishnan and Cerisola (2004)). Outside traditional macroeconomic explanations of unemployment at the local area level (e.g., suburb), explanations draw on theories of residential segregation (Cheshire, Monastiriotis, & Sheppard, 2003; Hunter, 1996), which suggest that similar educational background and socioeconomic status along with housing market factors play a substantial role in determining how people are distributed across geographic space. Over time, these differences may become more pronounced as people sort further along lines of race and income (Bill, 2005). Cheshire et al. argued that where people live does not drive inequality but rather determines geographic location of inequality:
Where people live and the incidence of segregation and ultimately of exclusion, mainly reflects the increasing inequality of incomes. So if either the incidence of unemployment rises and/or if the distribution of earning becomes more unequal then social segregation intensifies…the poor are not poor, isolated and excluded for the reason which makes them poor. They are not poor because of where they live; rather they live where they do because they are poor. (2003, 83–84)
22 - Modeling Social Networks: Next Steps
- Edited by Dean Lusher, Swinburne University of Technology, Victoria, Johan Koskinen, University of Manchester, Garry Robins, University of Melbourne
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- Book:
- Exponential Random Graph Models for Social Networks
- Published online:
- 05 April 2013
- Print publication:
- 19 November 2012, pp 287-302
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Summary
In this final chapter, we review the progress made in modeling social networks using exponential random graph models (ERGMs) and identify some important questions that remain to be addressed, along with possible ways of approaching them. We also consider the development of ERGMs in the context of broader modeling developments for social networks.
Distinctive Features of ERGMs
As the preceding chapters demonstrate, ERGMs offer four features that are likely to be attractive to social scientists who are interested in deepening our understanding of social networks and the social processes associated with them.
First, and perhaps most important, ERGMs conceptualize social networks as the outcome of social processes that are dynamic, interactive, and local. They are therefore well aligned with many contemporary theoretical views about the evolution of social networks (e.g., Emirbayer, 1997), even though there are a variety of distinct views about the precise social mechanisms involved (e.g., Jackson, 2008; Pattison, Robins, & Kashima, 2008; Rivera, Soderstrom, & Uzzi, 2010; Snijders, 2006). ERGMs give expression to propositions about the outcomes of the dynamic, interactive, local processes that drive network formation and allow us to quantify and assess the observable regularities in social network structure implied by these propositions. Moreover, these assessments are set within a clear statistical framework. Even though there may not necessarily be a fine-grained match between hypothesized theoretical mechanisms and the form of an ERGM, or the means to explore model assumptions in detail (as we discuss later in the chapter), ERGMs nonetheless provide a valuable new capacity to demonstrate potential links between hypothesized network processes and observable network regularities. In brief, they provide social scientists with access to a statistical approach for exploring regularities in social networks and describing these regularities with greater precision.