Research
Vertesi, J. A., & Enriquez, D. (2025). The Ghost of Middle Management: Automation, Control, and Heterarchy in the Platform Firm. Sociologica, 19(1), 13–35. https://doi.org/10.6092/issn.1971-8853/16415
In an effort to attend to the distinct organizational form of algorithmic management, we interrogate the arrangement of platform labor through the lens of the post-bureaucratic organization instead of that of the industrialized factory. Prior studies of gig workers rely heavily on sociological accounts of factory labor, but we posit that gig economy platforms represent a heterarchical organizational form, marrying the logics of industrial control induced by computational systems with the logics of post-bureaucracy inherited from flattening firms and downsizing middle management. In a technique we describe as automation by omission, we show how middle-managerial roles and responsibilities are excised entirely from the platform firm, how the vestigial traces of such roles are only imperfectly replaced by technical systems, and how “situated” managerial tasks essential to post-bureaucratic organizations are picked up by the worker, uncompensated.
Enriquez, D. (2025). Publishing publicly available interview data: an empirical example of the experience of publishing interview data. Front. Sociol., 05 June 2024. Sec. Sociological Theory, Volume 9 - 2024 https://doi.org/10.3389/fsoc.2024.1157514
In September 2021 I made a collection of interview transcripts available for public use under a CreativeCommons license through the Princeton DataSpace. The interviews include 39 conversations I had with gig workers at AmazonFlex, Uber, and Lyft in 2019 as part of a study on automation efforts within these organizations. This article documents my thought process and step-by-step design decisions for designing a study, gathering data, masking it, and publishing it in a public archive. Importantly, once I decided to publish these data, I determined that each choice about how the study would be designed and implemented had to be assessed for risk to the interviewee in a very deliberate way. It is not meant to be comprehensive and cover every possible condition a researcher may face while producing qualitative data. I aimed to be transparent both in my interview data and the process it took to gather and publish these data.
Data Set: Delivery Gig Worker Interviews on Automation at Work
These data include 39 structured interview transcripts. Each case is someone who worked at the time for Uber, UberEats, Lyft, and/or Amazon Flex (Amazon’s contractor delivery service). These data were collected between July and September 2019. All but one of the interviews occurred over the phone. My questions are focused on the structure of their gig work jobs and the technology they used at work or expected to use at work in the future. I included a description of the data, the recruitment methods, and the discussion guide in this ReadMe file.
Enriquez, D., & Goldstein, A. (2020). COVID-19’s Socioeconomic Impact on Low-Income Benefit Recipients: Early Evidence from Tracking Surveys. Socius, 6. https://doi.org/10.1177/2378023120970794 (Original work published 2020)
Using novel survey data samples of Supplemental Nutritional Assistance Program (SNAP) recipients and U.S. Census Bureau Household Pulse Survey data, the authors examine the incidence of COVID-19-induced hardships among low-income/benefits-eligible households during the early months of the crisis. Food insecurity and debt accrual grew more prevalent between from April to June 2020, and job losses compounded. Although the magnitude of racial differences varies across indicators and data sources, Black respondents fared consistently worse than non-Hispanic whites in both survey data sets, and Latinx respondents fared worse than whites in the Household Pulse Survey. These results provide early systematic evidence on the impact of the COVID-19 crisis on poor Americans and racial disparities therein.
Vertesi, J. A., Goldstein, A., Enriquez, D., Liu, L., & Miller, K. T. (2020). Pre-Automation: Insourcing and Automating the Gig Economy. Sociologica, 14(3), 167–193. https://doi.org/10.6092/issn.1971-8853/11657
This paper examines a strategic configuration in the technology, logistics, and robotics industries that we call “pre-automation”: when emerging platform monopolies employ large, outsourced labor forces while simultaneously investing in developing the tools to replace these workers with in-house machines of their own design. In line with socioeconomic studies of imagined futures, we elaborate pre-automation as a strategic investment associated with a firm’s ambitions for platform monopoly, and consider Uber, Amazon Flex and Amazon Delivery Services Partnership Program drivers as paradigmatic cases. We attempt detection of firms' pre-automation strategies through analysis of patenting, hiring, funding and acquisition activity and highlight features of certain forms of gig work that lay the infrastructural foundations for future automation.
Enriquez, D. (2020). The Freelance Penalty: Income Variation and Job Structure of High-Skill Freelance Workers in the United States. The Federal Reserve, Atlanta, and Princeton University
Popular media portraits of high-skill freelancers often focus on the entrepreneurial full-time freelancers, who are well positioned with their professional networks to earn high incomes and control their time. This portrait, however, ignores the freelancers who compete in this market as part-time small freelancers or workers who only occasionally freelance but generation a lot of income from a few projects. This article focuses on the differences in time structured into the freelance role: there are Full-Time, Part-Time and Occasional Freelancers who balance different risks and professional commitments. Though there some important exceptions, most Full-Time Freelancers earn several thousand dollars less yearly than their full-time employed workers as well as the part-time and occasional freelancers in the same occupation. The Full-Time Freelancers who are the exceptions to this trend tend to be well-positions and visible in their industry, thus they more closely resemble small businesses competing in their market rather than decoupled employees seeking a foothold in the labor market.