This Time Is Different: How Big Data Has Left the Middle Class Behind

What if innovation is driving economic stagnation and inequality? That's the question Charles Leadbetter analyzes in "The Whirlpool Economy" over at the UK Innovation Foundation's Long+Short site. He makes key points about the current relationships between innovation and the economy but misses partly what may make the new technology of big data a particularly toxic driver of current economic inequality. That stagnation haunts the U.S. and especially Europe is a common observation, but as Leadbetter notes, it's a "a very strange one, for it comes at time when our lives are in the midst of incessant change, much of it brought on by what claim to be radical innovations."

In past periods of stagnation, he argues, "the economy stagnated because there was little underlying dynamism, few new ideas and limited opportunities for entrepreneurship." He nods in the direction of basic Keynesian analyses of the problem, such as from Larry Summers, who sees a deficit in demand driven by lower wages and austerity public policy.

But Leadbetter argues that current innovation itself is a key driver of stagnation since so much new innovation "is aimed at eliminating jobs and lowering costs":

The economy is creating jobs but many of them are low-productivity, low-pay service jobs. The result is that many young people find themselves doing work for which they are over-qualified: a quarter of all "entry level" jobs in London are filled by someone with a degree, quite possibly one they have paid for themselves with debts they may never pay off.

He argues that this problem of the automation of jobs and deskilling of middle-class households needs to be addressed by policy that raises wages and kickstarts the virtuous cycle of higher incomes, higher demand and higher production. And we need less "disruptive" innovation and more innovation that "generates new jobs and augments existing ones; while addressing the spiralling costs of things like energy, health and social care that matter to median-income households."

Leadbetter's argument is very on point as far as it goes, but what he doesn't fully address is why automation now is so different from past cycles of boom-and-bust. Analysts have been worrying about mass unemployment and impoverishment of the working classes at least since the Luddites in the early 19th century saw new textile technology endangering skilled textile jobs. The rise of mass production and the assembly line were seen as replacing skilled craft workers with only semi-skilled automatons working at the behest of the production line machinery. Yes, robotics threatens to add to the cycle of displacement, but new jobs not even imagined before were created in the past and will likely be created in the future; heck, IBM just announced that they intend to train 10,000 engineers in analyzing Twitter data as part of their business services division, a kind of job that didn't even exist in the past.

But the kind of "big data" jobs IBM is developing as part of this cycle of job destruction/creation may highlight what is different about this technological cycle and why new innovation is not being channeled into new income for middle-income families. In past rounds of technological job destruction, after the initial pain of unemployment and skills redeployment, workers would organize to demand a share of the new wealth created by the new machines, and consumers would benefit from lower prices.

However, with new "disruptive" technology today, designed to help corporate America profile workers and consumers to better increase corporate profits, the "wealth" being created is by its nature more of a zero-sum game. The industrial age created at least some degree of shared wealth where Henry Ford could argue that paying higher wages for workers would in turn create demand for his cars, but subprime mortgage companies profiling consumers to sell them bad loans depend on the immiseration of working families as their profit source.

What we have seen over the last 50 years is that as every recession disrupts and rearranges the economy, when growth does return, less and less of the income generated goes to middle-class families. This following chart by Levy Institute economist Pavlina R. Tcherneva highlights how where most increased wealth during economic recoveries went to the bottom 90 percent of the population immediately after World War II, each successive recovery has seen more and more going to the wealthiest 10 percent, to the point where the current recovery has seen the lower 90 percent actually losing income during a recovery -- an unprecedented event -- even as the income of the wealthiest Americas has soared.

There are no doubt a number of factors contributing to this dynamic, but as we argued in our initial report at Data Justice, "Taking on Big Data as an Economic Justice Issue," big data technology means that corporations know so much about every person that during every hiring decision, every sale to a consumer, and every loan to a family, they can increasingly extract the maximum profit from each of those transactions. This big data dynamic seems like a key story in the current rise in economic inequality.

More and more companies scan social media and administer personality tests before hiring anyone, and not only does this hurt many individual people, but it allows companies to use algorithms to decide how to systematically weed out people who might agitate on behalf of all employees for higher wages. With big data, the best way to defeat a drive to organize a union in a company's workplace is to never hire people willing to stand up to their boss in the first place.

At the time, data analysis allows companies to decentralize their operations around the globe and within the United States into far-flung locations and even spinning off most workers to be on their own as "independent contractors." As a New York Times writer described:

Just as Uber is doing for taxis, new technologies have the potential to chop up a broad array of traditional jobs into discrete tasks that can be assigned to people just when they're needed, with wages set by a dynamic measurement of supply and demand, and every worker's performance constantly tracked, reviewed and subject to the sometimes harsh light of customer satisfaction.

The result is a data-driven pressure to push down wages with workers so fragmented that they have less and less ability to act collectively to demand higher pay.

On the consumer side, companies like Google and Facebook collect ever-increasing reams of personal data. Companies then can place ads or target consumers with offers not just based on what those consumers may be interested in but using the profile and algorithms to estimate the maximum price the consumer is likely to pay. Offering different prices to different people for the same product or service -- what economists call price discrimination -- allows companies to maximizing their profit on each transaction. Researchers Rosa-Branc Esteves and Joana Resende found that average prices under the traditional regime of mass advertising were lower than with targeted online advertising. Similarly, researcher Benjamin Reed Shiller found that where advertisers know consumers' willingness to pay different prices, economic models show companies can use price discrimination to increase profits and raise prices overall, with many consumers paying twice as much as others for the same product.

Subprime mortgages were the extreme example of this, where predatory companies used algorithms to identify the most likely victims and offered them worse deals than they offered people with the exact same credit ratings who just knew enough to refuse the bad deals. Similarly, payday lending and other exploitive financial companies use big data profiling to extract the most profit possible from economically struggling families.

What is different, then, in this round of technology is not so much that it's changing our physical processes of production, although that is happening as well, but that it's changing the informational relationship between companies and the population. Big data converts increasing information inequality into economic inequality.

Taking on that big data power is therefore a key step in taking on the broader economic stagnation and inequality that has left the middle class behind in the current recovery.