This article examines the stratifying effects of economic classifications. We argue that in the neoliberal era market institutions increasingly use actuarial techniques to split and sort individuals into classification situations that shape life-chances. While this is a general and increasingly pervasive process, our main empirical illustration comes from the transformation of the credit market in the United States. This market works as both as a leveling force and as a condenser of new forms of social difference. The U.S. banking and credit system has greatly broadened its scope over the past twenty years to incorporate previously excluded groups. We observe this leveling tendency in the expansion of credit amongst lower-income households, the systematization of overdraft protections, and the unexpected and rapid growth of the fringe banking sector. But while access to credit has democratized, it has also differentiated. Scoring technologies classify and price people according to credit risk. This has allowed multiple new distinctions to be made amongst the creditworthy, as scores get attached to different interest rates and loan structures. Scores have also expanded into markets beyond consumer credit, such as insurance, real estate, employment, and elsewhere. The result is a pattern of cumulative advantage and disadvantage with both objectively measured and subjectively experienced aspects. We argue these private classificatory tools are increasingly central to the generation of “market-situations”, and thus an important and overlooked force that structures individual life-chances. In short, classification situations may have become the engine of modern class situations.
The second is “Data Visualization in Sociology” and will appear in next year’s Annual Review of Sociology. Here’s the abstract for that one:
Visualizing data is central to social scientific work. Despite a promising early beginning, sociology has lagged in the use of visual tools. We review the history and current state of visualization in sociology. Using examples throughout, we discuss recent developments in ways of seeing raw data and presenting the results of statistical modeling. We make a general distinction between those methods and tools designed to help explore datasets, and those designed to help present results to others. We argue that recent advances should be seen as part of a broader shift towards easier sharing of the code and data both between researchers and with wider publics, and encourage practitioners and publishers to work toward a higher and more consistent standard for the graphical display of sociological insights.
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