The Fourth Industrial Revolution has significantly changed the world through big data, artificial intelligence, and other forms of automation. Hence, the workplace is increasingly fraught by technological disruptions and consequent loss of long-term employment security for all generations. Even educated Millennials who are popularly considered as digital natives are not spared the anxiety of automation and rapidly changing requirements for new skill sets.
How can Millennials best adapt to a transforming world and prepare themselves for a vastly unsettling future of work? My new book Shaping the Futures of Work: Proactive Governance and Millennials aims to provide answers to these questions. Why Millennials? I study their careers because they are currently in large numbers in the workforce.
Is worker ownership the way to eliminate workplace inequalities?
While organizational scholars here and elsewhere have long focused on the range of mechanisms that create and maintain a variety of social inequalities within workplaces, the context of capitalism and investor-owned firms minimizing worker voice and power is generally treated as a given.
However, an alternative form of enterprise exists: worker cooperatives, businesses owned and democratically controlled by their workers. Although worker cooperatives are still a small proportion of U.S. enterprises, an estimated 4,700 workers in as many as 1000 worker cooperatives produced over US$238 million in revenue in 2020. Indeed, as part of the anti-inequality activism that arose from the Great Recession, worker cooperative numbers have essentially doubled in the last decade across multiple industrial sectors, increasingly with the support of unions and local municipalities, and have shown great resilience during the pandemic years. Under conditions of worker ownership and control, we might assume resistance to and disruption of the kinds of class, ethnoracial, and gender inequalities that have been central to these social movements and their organizations.
Corporate managers used to track stores through weekly or monthly sales records, phone calls, and in-person visits. Today, software creates near-constant – but not necessarily meaningful – communication between store managers and their senior corporate counterparts.
Metrics like “sales per hour,” which captures a store’s sales revenue per labor-hour, now drive moment-to-moment corporate decisions about staffing. These just-in-time scheduling practices try to match the sales volume to workers on the clock at any moment.
The goal is to minimize labor costs. Yet, the result may not be higher profits.
In January 21 of 2016, Bloomberg Businessweek published a cover story titled “Why Doesn’t Silicon Valley Hire Black Coders”. Vauhini Vara followed a cohort of Black computer science students enrolled at Howard University located in Washington, D.C., one of the oldest historically Black universities in the United States. Even after a Google engineer upgrades the curriculum, students in this cohort are denied opportunities to work full time in Silicon Valley. Vara informs the reader that “although 20 percent of all black computer science graduates attend a historically black school … the Valley wasn’t looking for those candidates”.
In this same year, Reveal’s Center for Investigative Reporting analyzed the diversity reports of Silicon Valley technology firms. It found that Black employees made up no more than 2 percent of the 23 companies, who had released their figures. Eight of the twenty-three companies that provided their demographics including Google, Twitter, Square and 23andMe, did not report a single Black woman in an executive role. In a separate study conducted by The Ascend Foundation, a pan-Asian foundation, found that the number of Black and Latinx women had actually declined between 2014 and 2017.
The 1970s and 1980s marked a disaster for the U.S. labor movement. Gone was nearly one out of three members in the private sector, once the heart of organized labor. Today unions represent six percent of corporate employees, the same as in 1929.
Facing slow extinction, leaders of large unions and their federations sought to rebuild. It led to prolonged membership campaigns like Justice for Janitors and the creation of an organizing-oriented union federation, Change to Win. There was experimentation with new tactics, one of which was the leveraging of union pension assets to restore labor’s power.
My new book, Labor in the Age of Finance: Pensions, Politics, and Corporations, examines the financial turn. It came on the heels of a shareholder revolt led by public pension plans from blue states and cities, the vanguard being the giant California Public Employees’ Retirement System (CalPERS). Whereas once most stock was directly owned by households, post-1980 financialization transferred ownership to a relatively small group of institutional investors, including pension funds.
History shows that the standards by which societies judge economic activity change over time. As these moral frameworks evolve—or devolve—many of the changes make their way into law. For example, modern anti-trust law is grounded in the widely accepted belief that monopolies depress competition and growth and encourage unscrupulous behavior.
However, in the sixteenth and early seventeenth centuries, the state explicitly sought to protect large trade monopolies, which were commonly regarded as good for trade. The slow transformation of the moral status of monopoly over the seventeenth and eighteenth centuries figured prominently in a larger cultural transformation, which might be thought of as the shift from a moral economy to a political economy, and ushered in the birth of classical economics. Appreciating how and why this shift occurred reveals interesting links between power, political representation, and economic theory. It may also allow us to recover some important moral ideas about exchange that were lost along the way.
Twenty years ago, a financial trader was still usually a human being, either sharing a trading pit along with dozens or hundreds of other sweaty human bodies, or sitting at a computer terminal, talking into a telephone and buying and selling with keyboard and mouse. A decade later, digital algorithms had made decisive inroads into trading, but those algorithms still mostly ran on conventional computer systems. Nowadays, a trader is very often a specialised silicon chip known as an FPGA, or field-programmable gate array, such as the large, square chip at the centre of this photograph, coated with white paste that had held a cover in place.
The FPGAs that do so much of today’s trading are mainly to be found in about two dozen anonymous, warehouse-like buildings in and around Chicago, New York, London, Frankfurt and other major global financial centres. To walk through one of these computer datacentres is to listen to the hum of tens of thousands of computer servers in row upon endless row of metal cages and to glimpse the incomprehensible spaghetti of cables that interconnect the machines packed into those cages. When I first did so, in October 2014, I was still struggling to find a way of understanding the complex new world of ultrafast trading algorithms that was evolving.
What determines the number on your paycheck? When asked, the vast majority of U.S. workers list their own individual performance as a key factor. Large majorities of pay-setters – senior management, human resource directors, and others directly involved in setting compensation rates – likewise believe workers’ individual performance is very important.
I know, because in a series of surveys conducted over the last few years I asked average workers and pay-setters about their ideas about how our wages and salaries are determined. No matter how I posed the question, no factor garnered as much support as individual performance when it comes to our understandings of pay determination in the modern economy.
Social science historians have long recognized that war was the rule rather than the exception in early modern Europe. The so-called “Second One Hundred Years’ War”, for example, pitted France and Britain against each other in at least six major confrontations between 1688 and 1815.
The motivations behind these armed conflicts were manifold: religious rifts, dynastic interests, territorial expansion, and commercial rivalry.
But these wars had political implications that are felt to this day. Following Charles Tilly’s dictum, state-making was inseparable from war-making during this period. Armies and navies were costly. To pay for their services, taxes had to be raised.
Scholars of work and labor do not often analyze labor coercion these days. It is considered a bit passé, and is simply taken as a given that economic coercion undergirds labor relations in capitalist economies. With this implicit foundation in place, the primary story of work and labor in contemporary scholarship is one of precarity: the instability, insecurity, and low wages of gig work, temp work, freelancing, day labor, adjunct work, just-in-time work, and more.
But precarity does not characterize the work lives of all workers, and economic coercion is not the only power dynamic that shapes labor relations. In my new book Coerced: Work Under Threat of Punishment, I identify a different form of labor coercion, one in which employers’ power does not stem from their control over workers’ wages (e.g., through their ability to hire, fire, promote, and demote workers). Rather, it stems from their control over workers’ “status” and all of the rights, privileges, and opportunities—economic and otherwise—that such status confers.