A Statistical Model for the Analysis of Mobility Tables as Weighted Networks with an Application to Faculty Hiring Networks

Mobility of individuals between a wide variety of geographic locations, social positions, or roles is frequently analysed in the social sciences. In recent research, mobility has increasingly been conceptualised as a network. For example, residential mobility, when individuals move between neighbourhoods of a city, can be understood as a network in which neighbourhoods are nodes that are tied by counts of mobile individuals that move from one neighbourhood to another. Understanding mobility as a network allows to apply concepts and methods from the network analyst's toolbox. However, the statistical modelling of such weighted networks in which ties can have individual attributes remains difficult. In this article we propose a statistical model for the analysis of mobility tables conceptualised as networks, combining properties from log-linear models and exponential random graph models (ERGMs). When no endogenous patterns are modelled, it reduces to a classic log-linear model for mobility tables. When modelling endogenous patterns but ignoring individual attributes, the model can be understood as an ERGM for weighted networks in which tie weights denote counts. Making use of special constraints of mobility networks, the model offers a parsimonious way to deal with weighted ties. Going beyond classical ERG modelling, the proposed approach can additionally incorporate tie characteristics that represent individual attributes of mobile people. The model is applied to two cases of faculty hiring networks---linking current departments of faculty members with their PhD granting institution---in history and computer science in the US.

‘Primary’ Factors in Intergenerational Class Mobility in Europe: Results from the Application of a Topological Model

There is little consensus in past research regarding the sources of cross-national variation in relative rates of intergenerational class mobility. We argue for the importance of distinguishing between ‘primary’ factors that explain why inequalities in relative chances of mobility exist in the first place, and ‘secondary’ factors that explain variation in these chances. Our main aim is to identify primary factors. We follow Erikson and Goldthorpe in developing a topological model of the endogenous mobility regime which we then apply to class mobility tables for 30 European nations. The model claims that inequalities in relative class mobility chances derive from three kinds of effect: those of class hierarchy, class inheritance and status affinity. When applied to all nations together, the model accounts for the very large part of the total association between class origins and destinations. Clear differences, however, show up between the mobility regimes of men and of women: gender is a secondary factor. When the model is applied separately to nations in the high fluidity and low fluidity sets that we distinguish, we find that the effects of the primary factors identified by our model strengthen in a consistent way from the former set to the latter, although it seems likely that different secondary factors may operate in offsetting ways. Finally, when the model is applied to the groups of nations that we distinguish within the high and low fluidity sets, few differences in the strengths of the various effects show up, but those that do are highly concentrated in post-socialist nations and can be related to secondary factors of a specific kind associated with particular features of their transitions to some form of capitalist democracy.

Mapping the Social Class Structure: From Occupational Mobility to Social Class Categories Using Network Analysis

This article develops a new explorative method for deriving social class categories from patterns of occupational mobility. In line with Max Weber, our research is based on the notion that, if class boundaries do not inhibit social mobility then the class categories are of little value. Thus, unlike dominant, theoretically defined class schemes, this article derives social class categories from observed patterns in a mobility network covering intra-generational mobility. The network is based on a mobility table of 109 occupational categories tied together by 1,590,834 job shifts on the Danish labour market 2001–2007. The number of categories are reduced from 109 to 34 by applying a new clustering algorithm specifically designed for the study of mobility tables (MONECA). These intra-generational social class categories are related to the central discussions of gender, income, education and political action by providing empirical evidence of strong patterns of intra-generational class divisions along these lines.