(Excerpts from Veena Dubal’s study in Dissent Magazine. The study is from US, but same processes of digital work are increasingly in play even in countries like India.)
In the late nineteenth and early twentieth centuries U.S. industrialists exploited women’s subordinate position in both the family and the labor market to develop and extend “homework.” Garment manufacturers distributed tasks to immigrant women living in crowded tenements, paying them by the piece, not the hour. This piecework was advertised as “pleasure,” where a woman might make supplemental income while talking with friends. In reality, women homeworkers labored for eight to ten hours a day finishing the majority of all garments produced in the United States. That work took place in between, during, and after unpaid domestic work, at rates that were roughly one half of what women factory workers made. Homework and piece pay in the garment industry were largely abolished by the global labor struggles that preceded the New Deal and the legal standardization of the minimum wage.
Silicon Valley capitalists have brought back piecework, using legal gray zones and digital machinery to accelerate the amount of work that goes unpaid. But, bedazzled by the technology and corporate narratives, few people have noticed. When venture-funded labor platform companies like Uber, Lyft, and Amazon Mechanical Turk (MTurk) rose in popularity during the Great Recession, they promised to provide a source of flexible work and “freedom for people of all walks of life,” as one Uber ad put it. In a time of high unemployment and stagnant wages, jobs that people could get by downloading software or creating a profile seemed like a magical solution for precarious lives. But the corporate assurances were deceptive. While the companies might have created new ways for people to earn income, workers in the gig economy today labor for longer and earn far less.
This shift back to an earlier era of U.S. capitalism has been disguised by a rhetoric of technological advancement and innovation. Indeed, instead of discussing how to regulate piecework as a resurgent industrial practice, much of the contemporary debate about the future of work focuses on impending automation and the growth of the alternative workforce. Pundits across the political spectrum claim both that automation is an existential threat to paid work and that a vast majority of the U.S. workforce consists, or will soon consist, of independent contractor workers. Neither claim is empirically true, but these related ideas have shaped the perceptions of policymakers, who have tended to favor responses that help companies to profit at the expense of workers’ lives.
The historical relationship between capitalism, workers, and machines suggests that automation does not make labor obsolete; it reorders it, often rendering it invisible. Time spent laboring that was once accounted for through wages and legal protections becomes unpaid and unprotected. Understanding this difference suggests different policy priorities, like regulation and enforcement of existing labor protections. Gig workers, for example, are central to the supply chain of work that makes vehicular automation possible, often conducting the most time-intensive labor. In interviews I conducted with ride-hail drivers—who drive for companies like Lyft and Uber—and data processors—who sort and categorize data through platforms like MTurk—workers described a variety of unpaid work that they must do.
Like women working in crowded tenements and finishing sweaters for a pittance, gig workers work in between, during, and after other forms of paid and unpaid work. The workers insist that the application of technology has increased the amount of labor that goes unremunerated, and exacerbated feelings of anxiety and uncertainty. When they are unable to attain promised or desired earnings, it feels like a personal failure. Black-box algorithms, meanwhile, use psychological inducements to manipulate the price per piece, encouraging workers to work harder for reduced wages. As gig workers tell us, these mechanisms bely corporate tales of flexibility and independence—the idea that digital pieceworkers are free to decide how to use their time to earn.
Jeff Bezos launched Amazon Mechanical Turk in 2005, unveiling a plan to provision “humans as a service” through a crowdsourcing labor platform. On MTurk’s website, requesters with data-related microtasks connect to an atomized and dispersed virtual workforce that competes for and completes the tasks. Individual workers are paid not for their time, but by the piece, each of which is called a Human Intelligence Task (HIT). Unlike the garment homeworkers of a century ago, today’s digital homeworkers have to spend time competing for assignments and they must accept the risk that requesters will reject their work and decline to pay for it. These workers, or “Turkers,” are treated as independent contractors, and neither the requesters nor the labor platform companies assume the legal responsibilities of an employer. Thus, Turkers—more than half of whom are based in the United States—do not have access to the minimum wage, overtime, or any safety net protections. Since the amount of payment for each task is typically a few cents (sometimes less than one cent), data homeworkers are compelled to work swiftly through a set of tasks for extraordinarily low and unpredictable wages.
While manufacturers in the previous century claimed it was impossible to measure the time that garment homeworkers spent laboring in order to pay them by the hour and not the piece, time laboring online can be meticulously accounted for. Turkers are even advised to install accessory scripts into their browser, which calculate how much money they will earn per hour if they move through batches of HITs at a particular speed. (Although paid by the task, gigging homeworkers still think of their time through the medium of the hourly wage.) The scripts operate as tools of self-management and time discipline—pushing workers without human supervisors to maintain an exacting speed in order to increase their income. But the scripts are also the only way that these workers can even attempt to approximate how much—or how little—money they will make on a given day or week.
Digital homeworkers are acutely aware of what these scripts don’t account for: how much time they spend looking for work and doing data-processing tasks (often central to artificial intelligence and automation shifts) that go unpaid. Janey, who lives in a small former mining town in Appalachia and has been a digital pieceworker for almost five years, expressed how profoundly frustrated she was at the functional logic of MTurk, which prevented her from predicting and calculating potential income. The insecurities and demands of digital homework nagged at her through the day and even into the night. Both her conscious and unconscious time was spent looking for work without compensation:
‘If I work 12–16 hours a day, I’ll make maybe $5/hour. That’s when there is work, but when you’re sitting in between jobs and you consider that time, when you’re just looking for work, then the hourly wage falls dramatically. There are so many of us now, and fewer quality jobs. Sometimes I wake up in the middle of the night just to see if I can grab some good requests. Most HITs are gone if you don’t click right away.’
Janey and her homeworking colleagues work long and unpredictable hours, far exceeding the traditional eight-hour shift, but without any overtime pay. When I asked Janey how she decided that she had worked enough in one day, she answered that it was only when she met her financial goals that she let herself rest.
‘If I need to make $50 to pay the rent, then I’ll work sixteen hours straight. Whatever I need to do. . . But then there are those times when you don’t get paid or your work is rejected . . . so you can’t predict the time or the money, really. But you do the best you can.’
As Janey articulates, the very logic of MTurk makes it impossible to make any meaningful wage calculation. While a number of studies have attempted to capture how much money workers make in the gig economy, with vigorous debates among economists on how the data should be interpreted, the larger point is that workers themselves cannot know or understand their income in relationship to time spent working or their own expenses. This lack of income predictability has great human cost for workers like Janey. But rather than address this, gig companies have used technology to manipulate it, leveraging worker anxiety to increase profits.
(This study has found similar unpaid work in other digital economy companies like Uber, Lyft. We have presented just one example.)
[Originally published in The Truth: Platform for Radical Voices of The Working Class (Issue 8 / December 2020)]
PRC, CPI (ML) 
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