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Social Networks and Occupational Structure

This project is concerned with understanding how occupations influence the social structure of inequality. Few things are more influential in determining the social, economic and cultural milieus of individual lives than are occupations. Indeed, sociology has long recognised that relationships between occupations themselves provide key clues to the structure of social inequalities. This project contributes to that understanding by conducting two extended analyses of patterns of social interactions between the incumbents of occupations, and by relating those analyses to underlying research questions concerning change over time in occupational structures, and the impact of educational expansion across birth cohorts.

The project applies two alternative methods of analysis to data on the social connections held by the incumbents of occupational positions. These are Social Network Analysis (SNA) and Social Interaction Distance analysis (SID), methods which have not hitherto been brought together. Applying these two approaches to data on detailed occupational positions provides exciting new evidence on the nature of occupational structures and social inequalities.

The two methodologies have very different backgrounds. Social Interaction Distance analyses have a long and well established history. Paul Lambert supports the CAMSIS (Cambridge Social Interaction and Stratification Scales) website (www.camsis.stir.ac.uk), the principle resource for undertaking and distributing the results of SID analyses of occupational data both for the UK and internationally. These resources are widely exploited by social scientists from across the world.

Social Network Analysis is an exciting and emerging approach to analysing social structures. To our best knowledge, SNA has not previously been used to explore wide-scale structures of social interactions between occupations. The contribution of SNA to understanding occupational structure focuses upon identifying important 'routes' between socially connected occupations. To give a stylised example, in a contemporary UK town it might be noted that residents of a large council estate have very few voluntary social interactions with residents of a nearby large private estate. Analysing the occupational connections held by the respective residents might, however, highlight a few key occupations where members of the two groups have much more frequent social contacts (say, jobs in the protective services). An SNA analysis can highlight such particular points of connection, with implications for policy proposals on social inclusion and social justice.

Historical and contemporary datasets are utilised from across the globe. We are interested in observing the trends and processes which help develop, and reinforce occupational stratification and explore how these emerge across different societies. We embrace other statistical methods, such as multilevel modelling, in our analyses and attempt to generate a new methodological toolkit for analysing social inequalities and occupational advantage.