My research examines meaning and structure at work. I examine how policy and technological change interact with existing workplace culture, and its dynamics of power, identity, and hierarchy. My current projects include:
Minimum wage increases in Chicago and Oakland restaurants: I examine the effects of minimum wage increases through a sociological lens, and find that workplace hierarchies and meanings shape the outcome of increases. This dissertation project uses a unique dataset of 90 interviews of restaurant owners, workers, and managers. I find that wage hierarchies were preserved, and other aspects of these workplaces were maintained despite financial costs to owners. While my findings are in line with economic research on minimum wage increases, I argue that a sociological approach reveals a wider and more pervasive effect of these increases. I argue that minimum wage increases have a largely positive effect, but that legislators should be more aware of the extent of their impact as they craft these policies. The dissertation also includes a descriptive analysis of restaurant industry trends using Census data.
Job quality and technological change: Drawing a year-long workplace ethnography, I examine the effects of computer system upgrades on office work. I find that job quality was reduced by the proliferation of troubleshooting work due to errors and bugs in the system. While these frustrations have been dismissed as temporary, the constant upgrading of computer systems and databases means that dysfunction is a constant in office work, rather than an aberration. Theorizing IT upgrades as formalization of existing practice, I argue that errors are an inevitable and consequential features of these new systems.
Automation of text analysis: A third strand of research look at the potential of automated text analysis in the social sciences. Sociologists have begun to work with machine learning, topic modeling, and other forms of “big data” analysis, but the strengths and limitations of these methods for looking at complex issues continues to hinder their acceptance. A collaboration with Laura Nelson, Derek Burk, and Leslie McCall re-examines McCall’s analysis of media coverage of income inequality using supervised machine learning (SML), topic modeling, dictionaries, and other methods. We find that SML methods are effective at reproducing hand-coded analysis, and discuss the specialized roles that topic modeling and dictionary methods can play. We argue that these tools should supplement traditional content analysis, and offer practical advice for researchers considering these methods.