Remanufacturing Consent: How Algorithmic Management Repurpose Workplace Consent

Funded Research Proposal

In recent years, the number of workers in the US who show up to work by turning on an app on their smartphone has dramatically increased. Dubbed on-demand or “gig” workers, these individuals log onto digital platforms and depend on algorithms, rather than managers, to set pay rate, segment work tasks, and supervise and evaluate their actions while on the job. Researchers have sounded alarms about the “encompassing, instantaneous, interactive, and opaque” (Kellogg et al., 2020) nature of such algorithmic management, concluding that it traps workers in an “invisible cage” (Rahman 2021). And yet, many on-demand workers report enjoying management by algorithms, enthusiastically noting the freedom that comes with this type of app-based work. Indeed, data from the Federal Reserve and Bureau of Labor from the past five years indicate a tight labor market alongside an increase in the number of workers in the on-demand economy (Kaplan et al., 2021; Katz & Krueger, 2016; 2019), suggesting that on-demand workers may not experience the work to be as oppressive as some scholars have suggested (Rosenblat, 2018; Ravenelle, 2019; Shapiro, 2018). Moving beyond money as the sole explanatory mechanism of why individuals work hard at their jobs, especially under less-than-ideal conditions, this study takes seriously how on-demand workers describe and value finding freedom and choice in their jobs. In doing so, I identify the limitations of the “carrots and sticks” metaphor that scholars have long used to describe the production of consent in the traditional workplace. Instead, I examine how algorithmic management, in conjunction with the new work arrangements of the gig economy, creates consent through the notion of workers having increased choices—a form of consent that, I argue, is more pervasive, but also more fragile.Read More

Algorithmic Pricing and Transparency in the Gig Economy

Funded Research Proposal

Algorithms control pricing and match customers and workers in the gig economy. However, algorithms face several critiques: they lack transparency, can be biased, and can be inefficient. We empirically analyze these issues and show that algorithms lose efficiency from two sources: competition between platforms and misaligned worker incentives. We model workers’ strategic responses to variation in pricing and estimate counterfactuals on the effects of minimum wage and transparent pricing policies.Read More

Measuring Strategic Behavior by Gig Economy Workers: Multihoming and Repositioning

Funded Research Proposal

Using a structural model, we show that workers are highly heterogenous in their preferences for both multihoming and repositioning. We provide counterfactual estimates on the effects of proposed firm and regulatory policies aimed at multihoming and repositioning.Read More

The Peril of Pay Variability: Determinants of Worker Aversion to Variable Compensation in Lower-Wage Jobs

Working Papers

Uber. Upwork. TaskRabbit. The world of work is transforming and my research agenda attempts to identify and explain 1) how work is changing and 2) how these changes affect workers, especially those who are marginalized or vulnerable.Read More

Living from Paycheck to Paycheck: The Implications of Paycheck Dispersion for Gig Economy Workers

Funded Research Proposal

Despite the prevalence of paycheck dispersion, defined as fluctuations in the amount of pay they receive in return for their labor from paycheck to paycheck, in contemporary employment relationships, we know relatively little about its consequences for organizations.Read More

Behavioral and Operational Lens into Managing Flexible Workforce

Funded Research Proposal

On-demand or gig economy has been growing dramatically in the past decade and is starting to become an everyday feature of modern society. Although independent contractors have been around for centuries, recent technologies allow workers to quickly connect with customers online and create new work arrangements. Read More