A once-in-a-century pandemic has sparked an unprecedented crisis. With over 167 million cases and 3.5 million deaths recorded worldwide and entire economies in turmoil, the fallout has been felt by all—but unevenly so. Public attention has rightfully focused on curbing COVID-19’s spread and alleviating economic hardship.
But lurking behind the headlines of vaccines and new variants are predatory financial investors whose work has placed many workers at risk and exploited those very vulnerabilities to profit from the pandemic.
In a recent article in American Behavioral Scientist, we examine how the rise of U.S. “shadow banks”—less regulated, private credit intermediaries such as private equity, venture capital, and hedge fund firms—has impacted the course of hardship and inequality during the crisis. These shadow banks play an instrumental role in how executives manage companies, which has important ramifications for societal responses to crises, the wellbeing and livelihoods of workers, and inequality throughout the labor market.
Although many studies have shown that inequalities have widened in many ways ever since the outbreak of the COVID-19 pandemic, much of the discussion has focused on objective measures, such as numbers of confirmed cases and deaths, access to healthcare and medical treatment, or unemployment/poverty. Less attention has been paid to inequality consequences in connection with subjective experience.
Objectively, studies have shown that chances of physical as well as economic survival are not evenly distributed across class, racial and ethnic groups, as well as residential areas and regions, with the disadvantaged vulnerable to more severe consequences. Subjectively, however, it is unclear whether disadvantaged population groups also bear more psychological burdens.
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.