ASA speaks with sociologist Michèle Lamont at the 2016 ASA Annual Meeting on August, 2016, in Seattle, WA. Lamont talks about what it means to “do sociology,” how she uses sociology in her work, highlights of her work in the field, the relevance of sociological work to society, and her advice to students interested in entering the field.
ASA speaks with sociology graduate student Simone Kolysh at the 2016 ASA Annual Meeting on August, 2016, in Seattle, WA. Kolysh talks about what it means to “do sociology,” how she uses sociology in her work, highlights of her work in the field, the relevance of sociological work to society, and her advice to students interested in entering the field.
This study addresses inequality through resource distribution in Iranian provinces with the use of new data collected and compiled from various sources using multilevel modeling. The models compare predictions of the various resource distribution theories using Iran’s 31 provincial budgets over 10 years. This resource distribution study provides a rare look at inequality in a country that, to a large degree, prohibits such examinations.
Racial disparities persist throughout the employment process, with African Americans experiencing significant barriers compared to whites. This article advances the understanding of racial labor market stratification by bringing new theoretical insights and original data to bear on the ways social networks shape racial disparities in employment opportunities. We develop and articulate two pathways through which networks may perpetuate racial inequality in the labor market: network access and network returns.
Racial stratification is well documented in many spheres of social life. Much stratification research assumes that implicit or explicit bias on the part of institutional gatekeepers produces disparate racial outcomes. Research on status-based expectations provides a good starting point for theoretically understanding racial inequalities. In this context it is understood that race results in differential expectations for performance, producing disparate outcomes.
Most intergenerational mobility studies rely on either snapshot or time-averaged measures of earnings, but have yet to examine resemblance of earnings trajectories over the life course of successive generations. We propose a linked trajectory mobility approach that decomposes the progression of economic status over two generations into associations in four life-cycle dimensions: initial position, growth rate, growth deceleration, and volatility.
Women’s opportunities have been profoundly altered over the past century by reductions in the social and structural constraints that limit women’s educational attainment. Do social constraints manifest as a suppressing influence on genetic indicators of potential, and if so, did equalizing opportunity mean equalizing the role of genetics? We address this with three cohort studies: the Wisconsin Longitudinal Study (WLS; birth years 1939 to 1940), the Health and Retirement Study, and the National Longitudinal Study of Adolescent Health (Add Health; birth years 1975 to 1982).
We study the relationship between inter-class inequality and intergenerational class mobility across 39 countries. Previous research on the relationship between economic inequality and class mobility remains inconclusive, as studies have confounded intra- with between-class economic inequalities. We propose that between-class inequality across multiple dimensions accounts for the inverse relationship between inequality and mobility: the larger the resource distance between classes, the less likely it is that mobility from one to the other will occur.
Corporations gather massive amounts of personal data to predict how individuals will behave so that they can profitably price goods and allocate resources. This article investigates the moral foundations of such increasingly prevalent market practices. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithmic prediction in the U.S. consumer economy—to unpack the premise that predictive data are fair to use and to understand the conditions under which people are likely to challenge that moral logic.
Computational sociology leverages new tools and data sources to expand the scope and scale of sociological inquiry. It’s opening up an exciting frontier for sociologists of every stripe—from theorists and ethnographers to experimentalists and survey researchers. It expands the sociological imagination.