Virtual Library

Since the dawn of the digital age, decision-making in finance, employment, politics, health and human services has undergone revolutionary change. Today, automated systems—rather than humans—control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.

In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lays dying to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.

The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.

There is a wealth of evidence that disabled people experience far higher levels of unemployment and underemployment than non-disabled peers. Yet hitherto sociologists have paid scant attention to the structural causes of this issue. Drawing on a socio/political or social model of disability perspective this paper argues for a reconfiguration of the meaning of disability and work in order to address this problem. It is also suggested that such a strategy will make a significant contribution to the struggle for a fairer and equitable global society.

Labor historians describe Right to Work (RTW) as among the most consequential pushbacks against the early twentieth-century ascent of labor unions. Yet research on the economic consequences of RTW remains mixed, with nearly all research centered empirically and theoretically on the time surrounding RTW passage. In the current study, I use 41 waves of longitudinal data from the Panel Study of Income Dynamics between 1968 and 2019 to empirically and theoretically extend the mechanisms that link RTW and economic outcomes. First, following the vast majority of research on RTW, I show the demobilizing effects for labor following RTW passage: mean wages decline, wage inequality increases, and the union premium is halved in the middle and lower portion of the wage distribution. Second, I move theoretical focus beyond the time surrounding law enactment, arguing for a second-order effect whereby RTW institutionalizes a logic of polarized economic distributions and low labor power. To test this mechanism, I develop a novel strategy of comparison across respondents who are differentially mobile across state boundaries. I find individuals who cross RTW contexts experience a unique decline in mean wages and increase in wage variance, but this distinct trend can be explained by state-level institutional variation across RTW and non-RTW states. Thus, RTW is not only consequential in the periods around its passage, but also in establishing a long-standing, high-inequality regime. Results reveal multiple mechanisms by which RTW contributes to the long-run processes of union decline and broadly shared inequality growth.