The UCLA Department of Statistics and the Center for Social Statistics presents:

Contemporary research on occupational mobility, i.e. how people move between jobs, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex, ethnicity, age, education or class origin and how they get access to jobs of different wages, working conditions, desirability, skill profiles and job security. Consequently, observations in occupational mobility tables are understood as independent of one another, which allows the use of a variety of well-developed statistical models. As opposed to these “classical” approaches focussed on individual and occupational characteristics, I am interested in modelling and understanding endogenously emerging patterns in occupational mobility tables. These emergent patterns arise from the social embedding of occupational choices, when occupational transitions of different individuals influence each other. To analyse these emergent patterns, I conceptualise a disaggregated mobility table as a network in which occupations are the nodes and connections are made of individuals transitioning between occupations.

In this paper, I present a statistical model to analyse these weighted mobility networks. The approach to modelling mobility as an interdependent system is inspired by the exponential random graph model (ERGM); however, some differences arise from ties being weighted as well as from specific constraints of mobility tables. The model is applied to data on intra-generational mobility to analyse the interdependent transitions of men and women through the labour market, as well as to understanding the extent to which clustering in mobility can be modelled by exogenously defined social classes or through endogenous structures.