Collective Risk Shift: Commodifying Social Inequalities with Risky Healthcare, Risky Retirement, and Risky Credentialing

The Mass Transformation of Other People’s Risk Into Profit” reminded me of Tressie McMillan Cottom’s writing on “risk shift” in “Lower Ed”, “Where Platform Capitalism and Racial Capitalism Meet”, and “The University and the Company Man”.

Lower Ed refers to credential expansion created by structural changes in how we work, unequal group access to favorable higher education schemes, and the risk shift of job training, from states and companies to individuals and families, exclusively for profit.

Yale political scientist Jacob S. Hacker says the new economy marks both an economic change and an ideological change, each characterized by the “great risk shift” of corporate responsibility to workers and families.

Source: Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy | The New Press

The so-called 1099 workforce represents a collective risk shift from firms to individuals (Cottom 2017; Hacker 2008) that extends beyond employees to obfuscating the idea of employee altogether. Digital technologies abet that risk shift through the sociopolitical regime of platform capture. That platform capture effectively transforms workers into independent contractors.

Source: Where Platform Capitalism and Racial Capitalism Meet: The Sociology of Race and Racism in the Digital Society

In his 2006 book, The Great Risk Shift, Jacob Hacker explores “the new economic insecurity and the decline of the American dream” by measuring the shift of risk from corporations to individuals. He focuses on three trends: the erosion of company-paid pensions, the declining value of corporate-subsidized health benefits, and the use of layoffs to manage company bottom lines. To take the example of pensions: the National Institute on Retirement Security reports that in 1975, 88 percent of private sector employees had a pension plan wherein the company guaranteed benefits, but by 2005 that number was 33 percent. Rather than eating the cost of the company man’s inevitable aging, the private sector shifted the costs of old age onto individual workers, replacing pensions with individual worker-funded investment accounts like 401(k)s and the security of the organization with the volatility of the stock market.

In The Two Income Trap: Why Middle Class Parents are Going Broke, Elizabeth Warren and Amelia Warren Tyagi describe how this shift of risk to workers has changed our family lives, with rising child care and education costs driving middle-class families into economic crisis. The continuing downward pressure on wages has made things look even worse today than in 2004, when the book was first published: Warren and Tyagi didn’t consider the extra costs borne by families paying not only for their children’s tuition but their own further education, in order to stop the decline in their wages.

For the rest of us, the prescription for insecurity is more college, but colleges do not know what work to prepare us for. In the 1950s the labor market presented us with a social contract, and higher education responded. But the economic forces that brought us the great risk shift killed the company man. For those of us looking for economic security who are not fortunate or able enough to be fast-tracked into the good jobs, there isn’t much college can do.

Source: The University and the Company Man

We can’t endure the great risk shift, as noted by Sarah Kendzior in “The View from Flyover Country”.

Failure, in an economy of extreme inequalities, is a source of fear. To fail in an expensive city is not to fall but to plummet. In expensive cities, the career ladder comes with a drop-off to hell, where the fiscal punishment for risk gone wrong is more than the average person can endure. As a result, innovation is stifled, conformity encouraged. The creative class becomes the leisure class – or they work to serve their needs, or they abandon their fields entirely.

People go to college because not going to college carries a penalty. College is a purchased loyalty oath to an imagined employer. College shows you are serious enough about your life to risk ruining it early on. College is a promise the economy does not keep – but not going to college promises you will struggle to survive. In an entrenched meritocracy, those who cannot purchase credentials are not only ineligible for most middle-class jobs, but are informed that their plight is the result of poor “choices”. This ignores that the “choice” of college usually requires walking the road of financial ruin to get the reward – a reward of employment that, in this economy, is illusory. Credentialism is economic discrimination disguised as opportunity.

Source: The View from Flyover Country | Sarah Kendzior | Macmillan

“Risky credentialing” is explored by both “The View from Flyover Country” and “Lower Ed”.

Hacker identifies two major areas where American workers feel these effects: healthcare and retirement. He calls the shift from corporate responsibility for workers through pensions and health insurance to personal responsibility “risky healthcare” and “risky retirement.” To that I would add, “risky credentialing,” or Lower Ed. Declining investment in social insurance programs that, by design, diffused the individual risk of old age or health episodes exacerbates the risks associated with sickness and old age. In the same way, declining investment in public higher education exacerbates the risks associated with labor market shocks. As social insurance policies like healthcare and pensions declined, so too did public investment in higher education. Traditional colleges shifted more of the cost of a credential (or risk) to students and families with more loans and fewer grants offered, even as steep price discounting fought to hold individual costs down.

The risk for changing jobs and moving up the professional ladder has shifted to individual workers across race, class, and gender. That risk makes credentials valuable only insofar as those credentials are easy to start, easy to fit into complex lives, and easy to pay for. For-profit colleges nail that trifecta for millions of people who are similarly vulnerable in this new economy of risk shift, declining job tenure, and insecurity.

Source: Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy | The New Press

Today, creative industries are structured to minimize the diversity of their participants – economically, racially and ideologically. Credentialism, not creativity, is the passport to entry.

One would suspect that a college student who can pay $22,000 to work 25 hours a week for free in one of the most expensive cities in the world needs little help making connections. But that misconstrues the goal of unpaid internships: transforming personal wealth into professional credentials. For students seeking jobs at certain policy organizations, the way to get one’s foot in the door is to walk the streets paved in gold. In the post-employment economy, jobs are privileges, and the privileged have jobs.

What they are defending is a system in which wealth is passed off as merit, in which credentials are not earned but bought. Aptitude is a quality measured by how much money you can spend on its continual reassessment.

Namely, they have raised the price of the credentials needed to participate in the new meritocracy by such dramatic measures that it locks out a large part of the population while sending nearly everyone else into debt.

Source: The View from Flyover Country | Sarah Kendzior | Macmillan

A broad theme I take away is the commodification of inequality by shifting risk.

Lower Ed is, first and foremost, a set of institutions organized to commodify social inequalities…

Source: Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy | The New Press

More selections on risk shift from Lower Ed:

we have a labor market where the social contract between workers and the work on which college has previously relied has fundamentally changed and makes more workers vulnerable.

The new economy makes one overriding demand of education: constantly and consistently retrain millions of workers, quickly and at little to no expense for the employer. The new economy is marked by four characteristic changes to the relationships that underpin our social contract: people are frequently changing jobs and employers over their working lifetimes (job mobility); firms place greater reliance on contract, term, and temporary labor (labor flexibility); there is less reliance on employers for income growth and career progression (declining internal labor markets); and workers are shouldering more responsibility for their job training, healthcare, and retirement (risk shift).

Risk shift for those with good jobs means greater competition for less stability but still high status. Risk shift for those with bad jobs means more of the same poor labor market outcomes and fewer ways to work one’s way into a good job.

This risk shift has created an ascendant new work contract that provides fewer buffers to help workers navigate life shocks.

But as one river, these streams flow through a single valley—a time trap where the risk shift of educational costs outstrip social insurance programs like affordable childcare, the viability of investment vehicles like education savings accounts, and employer security like promotions and wage increases. For millions of people, the time trap makes a for-profit college your only practical choice for labor market entry, stability, or mobility.

Now Senator Elizabeth Warren and Amelia Warren Tyagi called this the “middle-class squeeze” in their book The Two-Income Trap. Hacker calls this the “risk shift.” Sociologist Arne Kalleberg, in his book on good jobs and bad jobs, talks about the “hollowed out middle” class jobs.8 Of course, poor people and the working poor have long felt this squeeze, absorbed this risk, and stared down the gulf between themselves and their dreams. Essentially, even those with good jobs don’t feel like those jobs buy the same quality of life as they once did. They are right.

Source: Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy | The New Press

Algorithms: Opinions Embedded in Math … and Ed-tech

A much more accurate definition of an algorithm is that it’s an opinion embedded in math.

So, we do that every time we build algorithms — we curate our data, we define success, we embed our own values into algorithms.

So when people tell you algorithms make thing objective, you say “no, algorithms make things work for the builders of the algorithms.”

In general, we have a situation where algorithms are extremely powerful in our daily lives but there is a barrier between us and the people building them, and those people are typically coming from a kind of homogenous group of people who have their particular incentives — if it’s in a corporate setting, usually profit and not usually a question of fairness for the people who are subject to their algorithms.

So we always have to penetrate this fortress. We have to be able to question the algorithms themselves.

We live in the age of the algorithm – mathematical models are sorting our job applications, curating our online worlds, influencing our elections, and even deciding whether or not we should go to prison. But how much do we really know about them? Former Wall St quant, Cathy O’Neil, exposes the reality behind the AI, and explains how algorithms are just as prone to bias and discrimination as the humans who program them.

Source: The Truth About Algorithms | Cathy O’Neil – YouTube

Follow the video up with Cathy O’Neil’s book “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy”. Here are some selected quotes from the introduction.

And then I made a big change. I quit my job and went to work as a quant for D. E. Shaw, a leading hedge fund. In leaving academia for finance, I carried mathematics from abstract theory into practice. The operations we performed on numbers translated into trillions of dollars sloshing from one account to another. At first I was excited and amazed by working in this new laboratory, the global economy. But in the autumn of 2008, after I’d been there for a bit more than a year, it came crashing down.

The crash made it all too clear that mathematics, once my refuge, was not only deeply entangled in the world’s problems but also fueling many of them. The housing crisis, the collapse of major financial institutions, the rise of unemployment- all had been aided and abetted by mathematicians wielding magic formulas. What’s more, thanks to the extraordinary powers that I loved so much, math was able to combine with technology to multiply the chaos and misfortune, adding efficiency and scale to systems that I now recognized as flawed.

If we had been clear-headed, we all would have taken a step back at this point to figure out how math had been misused and how we could prevent a similar catastrophe in the future. But instead, in the wake of the crisis, new mathematical techniques were hotter than ever, and expanding into still more domains. They churned 24/ 7 through petabytes of information, much of it scraped from social media or e-commerce websites. And increasingly they focused not on the movements of global financial markets but on human beings, on us. Mathematicians and statisticians were studying our desires, movements, and spending power. They were predicting our trustworthiness and calculating our potential as students, workers, lovers, criminals.

This was the Big Data economy, and it promised spectacular gains. A computer program could speed through thousands of résumés or loan applications in a second or two and sort them into neat lists, with the most promising candidates on top. This not only saved time but also was marketed as fair and objective.

Yet I saw trouble. The math-powered applications powering the data economy were based on choices made by fallible human beings. Some of these choices were no doubt made with the best intentions. Nevertheless, many of these models encoded human prejudice, misunderstanding, and bias into the software systems that increasingly managed our lives. Like gods, these mathematical models were opaque, their workings invisible to all but the highest priests in their domain: mathematicians and computer scientists. Their verdicts, even when wrong or harmful, were beyond dispute or appeal. And they tended to punish the poor and the oppressed in our society, while making the rich richer.

I came up with a name for these harmful kinds of models: Weapons of Math Destruction, or WMDs for short.

Equally important, statistical systems require feedback- something to tell them when they’re off track. Statisticians use errors to train their models and make them smarter. If Amazon. ​ com, through a faulty correlation, started recommending lawn care books to teenage girls, the clicks would plummet, and the algorithm would be tweaked until it got it right. Without feedback, however, a statistical engine can continue spinning out faulty and damaging analysis while never learning from its mistakes.

Many of the WMDs I’ll be discussing in this book, including the Washington school district’s value-added model, behave like that. They define their own reality and use it to justify their results. This type of model is self-perpetuating, highly destructive- and very common.

In WMDs, many poisonous assumptions are camouflaged by math and go largely untested and unquestioned.

This underscores another common feature of WMDs. They tend to punish the poor. This is, in part, because they are engineered to evaluate large numbers of people. They specialize in bulk, and they’re cheap. That’s part of their appeal. The wealthy, by contrast, often benefit from personal input. A white-shoe law firm or an exclusive prep school will lean far more on recommendations and face-to-face interviews than will a fast-food chain or a cash-strapped urban school district. The privileged, we’ll see time and again, are processed more by people, the masses by machines.

Needless to say, racists don’t spend a lot of time hunting down reliable data to train their twisted models. And once their model morphs into a belief, it becomes hardwired. It generates poisonous assumptions, yet rarely tests them, settling instead for data that seems to confirm and fortify them. Consequently, racism is the most slovenly of predictive models. It is powered by haphazard data gathering and spurious correlations, reinforced by institutional inequities, and polluted by confirmation bias. In this way, oddly enough, racism operates like many of the WMDs I’ll be describing in this book.

Source: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (pp. 2-3, pp. 6-8). Crown/Archetype. Kindle Edition.

For how this fits into education, read Weapons of Math Destruction along with Lower Ed and Paying the Price.

Lower Ed shows exploitation of vulnerable. Paying the Price tells how we fail them. Weapons of Math Destruction outlines tools we made to do it.

Source: Kyle Johnson on Twitter

Indeed. These three great books provide a systems view of higher education and its intersections with tech and algorithms. Below, I excerpt from their introductions and book blurbs, provide chapter lists, and select a handful of tweets from authors Tressie McMillan Cottom, Sara Goldrick-Rab, and Cathy O’Neil. They are all active on Twitter and well worth a follow.

Source: Lower Ed, Paying the Price, and Weapons of Math Destruction – Ryan Boren

See also Safiya Umoja Noble’sAlgorithms of Oppression: How Search Engines Reinforce Racism”.

This book is about the power of algorithms in the age of neoliberalism and the ways those digital decisions reinforce oppressive social relationships and enact new modes of racial profiling, which I have termed technological redlining. By making visible the ways that capital, race, and gender are factors in creating unequal conditions, I am bringing light to various forms of technological redlining that are on the rise. The near-ubiquitous use of algorithmically driven software, both visible and invisible to everyday people, demands a closer inspection of what values are prioritized in such automated decision-making systems. Typically, the practice of redlining has been most often used in real estate and banking circles, creating and deepening inequalities by race, such that, for example, people of color are more likely to pay higher interest rates or premiums just because they are Black or Latino, especially if they live in low-income neighborhoods. On the Internet and in our everyday uses of technology, discrimination is also embedded in computer code and, increasingly, in artificial intelligence technologies that we are reliant on, by choice or not. I believe that artificial intelligence will become a major human rights issue in the twenty-first century. We are only beginning to understand the long-term consequences of these decision-making tools in both masking and deepening social inequality. This book is just the start of trying to make these consequences visible. There will be many more, by myself and others, who will try to make sense of the consequences of automated decision making through algorithms in society.

Part of the challenge of understanding algorithmic oppression is to understand that mathematical formulations to drive automated decisions are made by human beings. While we often think of terms such as “big data” and “algorithms” as being benign, neutral, or objective, they are anything but. The people who make these decisions hold all types of values, many of which openly promote racism, sexism, and false notions of meritocracy, which is well documented in studies of Silicon Valley and other tech corridors.

Source: Algorithms of Oppression: How Search Engines Reinforce Racism (Kindle Locations 162-177). NYU Press. Kindle Edition.