social mobility

The staff at How Much recently visualized summaries from a Federal Reserve analysis showing how much a college degree can matter for your net worth. It turns out education can really pay…if you’re white.

This illustrates an important sociological point. When we talk about structural inequality, critics often note that we shouldn’t disregard individuals’ efforts to work and earn a better life. Getting a college degree is one of the centerpieces of this argument. These gaps show it’s not that effort doesn’t matter at all, but that inequality in social conditions means those efforts yield wildly different outcomes.

Want to read more on higher education and America’s wealth gap? Check out Tressie McMillan Cottom’s Lower Ed, Thomas Shapiro’s Toxic Inequality, and Dalton Conley’s Being Black, Living in the Red.

Evan Stewart is an assistant professor of sociology at University of Massachusetts Boston. You can follow his work at his website, on Twitter, or on BlueSky.

There was a great article in The Nation last week about social media and ad hoc credit scoring. Can Facebook assign you a score you don’t know about but that determines your life chances?

Traditional credit scores like your FICO or your Beacon score can determine your life chances. By life chances, we generally mean how much mobility you will have. Here, we mean a number created by third party companies often determines you can buy a house/car, how much house/car you can buy, how expensive buying a house/car will be for you. It can mean your parents not qualifying to co-sign a student loan for you to pay for college. These are modern iterations of life chances and credit scores are part of it.

It does not seem like Facebook is issuing a score, or a number, of your creditworthiness per se. Instead they are limiting which financial vehicles and services are offered to you in ads based on assessments of your creditworthiness.

One of the authors of The Nation piece (disclosure: a friend), Astra Taylor, points out how her Facebook ads changed when she started using Facebook to communicate with student protestors from for-profit colleges. I saw the same shift when I did a study of non-traditional students on Facebook.

You get ads like this one from DeVry:

Although, I suspect my ads were always a little different based on my peer and family relations. Those relations are majority black. In the U.S. context that means it is likely that my social network has a lower wealth and/or status position as read through the cumulative historical impact of race on things like where we work, what jobs we have, what schools we go to, etc. But even with that, after doing my study, I got every for-profit college and “fix your student loan debt” financing scheme ad known to man.

Whether or not I know these ads are scams is entirely up to my individual cultural capital. Basically, do I know better? And if I do know better, how do I come to know it?

I happen to know better because I have an advanced education, peers with advanced educations and I read broadly. All of those are also a function of wealth and status. I won’t draw out the causal diagram I’ve got brewing in my mind but basically it would say something like, “you need wealth and status to get advantageous services offered you on the social media that overlays our social world and you need proximity wealth and status to know when those services are advantageous or not”.

It is in interesting twist on how credit scoring shapes life chances. And it runs right through social media and how a “personalized” platform can never be democratizing when the platform operates in a society defined by inequalities.

I would think of three articles/papers in conversation if I were to teach this (hint, I probably will). Healy and Fourcade on how credit scoring in a financialized social system shapes life chances is a start:

providers have learned to tailor their products in specific ways in an effort to maximize rents, transforming the sources and forms of inequality in the process.

And then Astra Taylor and Jathan Sadowski’s piece in The Nation as a nice accessible complement to that scholarly article:

Making things even more muddled, the boundary between traditional credit scoring and marketing has blurred. The big credit bureaus have long had sidelines selling marketing lists, but now various companies, including credit bureaus, create and sell “consumer evaluation,” “buying power,” and “marketing” scores, which are ingeniously devised to evade the FCRA (a 2011 presentation by FICO and Equifax’s IXI Services was titled “Enhancing Your Marketing Effectiveness and Decisions With Non-Regulated Data”). The algorithms behind these scores are designed to predict spending and whether prospective customers will be moneymakers or money-losers. Proponents claim that the scores simply facilitate advertising, and that they’re not used to approve individuals for credit offers or any other action that would trigger the FCRA. This leaves those of us who are scored with no rights or recourse.

And then there was Quinn Norton this week on The Message talking about her experiences as one of those marketers Taylor and Sadowski allude to. Norton’s piece summarizes nicely how difficult it is to opt-out of being tracked, measured and sold for profit when we use the Internet:

I could build a dossier on you. You would have a unique identifier, linked to demographically interesting facts about you that I could pull up individually or en masse. Even when you changed your ID or your name, I would still have you, based on traces and behaviors that remained the same — the same computer, the same face, the same writing style, something would give it away and I could relink you. Anonymous data is shockingly easy to de-anonymize. I would still be building a map of you. Correlating with other databases, credit card information (which has been on sale for decades, by the way), public records, voter information, a thousand little databases you never knew you were in, I could create a picture of your life so complete I would know you better than your family does, or perhaps even than you know yourself.

It is the iron cage in binary code. Not only is our social life rationalized in ways even Weber could not have imagined but it is also coded into systems in ways difficult to resist, legislate or exert political power.

Gaye Tuchman and I talk about this full rationalization in a recent paper on rationalized higher education. At our level of analysis, we can see how measurement regimes not only work at the individual level but reshape entire institutions. Of recent changes to higher education (most notably Wisconsin removing tenure from state statute causing alarm about the role of faculty in public higher education) we argue that:

In short, the for-profit college’s organizational innovation lies not in its growth but in its fully rationalized educational structure, the likes of which being touted in some form as efficiency solutions to traditional colleges who have only adopted these rationalized processes piecemeal.

And just like that we were back to the for-profit colleges that prompted Taylor and Sadowski’s article in The Nation.

Efficiencies. Ads. Credit scores. Life chances. States. Institutions. People. Inequality.

And that is how I read. All of these pieces are woven together and its a kind of (sad) fun when we can see how. Contemporary inequalities run through rationalized systems that are being perfected on social media (because its how we social), given form through institutions, and made invisible in the little bites of data we use for critical minutiae that the Internet has made it difficult to do without.

Tressie McMillan Cottom is an assistant professor of sociology at Virginia Commonwealth University.  Her doctoral research is a comparative study of the expansion of for-profit colleges.  You can follow her on twitter and at her blog, where this post originally appeared.

We have become more aware that Americans’ chances of upward economic mobility have for decades been a lot lower than Americans imagined, that being poor or rich can last generations. Efforts to explain that lock-in have pointed to several patterns, from the intergenerational inheritance of assets (or debt, as the case may be) to intergenerational continuity in child-rearing styles (say, how much parents read to their children). In such ways, the past is not really past.

Increasingly, researchers have also identified the places – the communities, neighborhoods, blocks – where people live as a factor in slowing economic mobility. In a post earlier this year, I noted a couple of 2008 studies showing that growing up in poor neighborhoods impaired children’s cognitive skills and reduced their chances to advance beyond their parents. In this post, I report on further research by NYU sociologist Patrick Sharkey (here and here) suggesting that a bad environment can worsen the life chances not only of a child, but that of the child’s child, an unfortunate residential patrimony.

Consider the ways that the immediate environment shapes a child’s development. It does so physically. Air and soil pollution, noise, and traffic, for example, measurably affect children’s health, stress, and cognitive development. Local institutions and resources, such as the policing, quality of the schools, availability of health services, food options, parks, and so on matter, as well. And the social environment may matter most of all. Growing up in a community with gangs, dangerous streets, discouraging role models, confused social expectations, and few connections to outsiders commanding resources is a burden for any child. Just getting by day-to-day can be a struggle. (In a pair of studies, Sharkey found that a violent crime occurring near black children’s homes in the days before they took a standardized test reduced their scores on the test, presumably because of anxiety and distraction.)

In their research on historical effects, Sharkey and co-author Felix Elwert used a survey that has followed thousands of American families since 1968 (the PSID). The researchers know much about the adults in the survey, including where they lived when they were around 16, about the children they had and where those children lived around the age of six. The researchers also have the results from cognitive tests administered to those children in 2002.

Sharkey and Elwert found that living in a neighborhood where 20 percent or more of the residents are poor — many other things being held constant (including the parents’ education, health, and attitudes) — seems to lower the test scores of children. And so does having a parent who grew up in such a neighborhood. The effect on children of living in a poor neighborhood and having parents who had also are substantially greater than the effect of only the second generation living in a poor neighborhood. Moreover, the children of two generations of poor neighborhoods do much worse than those of two generations who managed to stay out of poor neighborhoods (over half a standard deviation worse). For technical reasons, these statistical results probably underestimate the real effect of neighborhood poverty on scores.

What appears to have happened is this: Survey respondents in the first generation who grew up in poor neighborhoods ran higher risks than other respondents, on average getting less education and worse jobs, if any, and bearing more physical, social, and psychological problems. Not surprisingly, they tended to end up in poor neighborhoods as adults. When this first generation became parents, they commonly passed on some of their personal disadvantages, such as weak reading skills, to their own children. And they also passed on their places, raising the second generation in poor neighborhoods, which further hampered their children. In this way, Sharkey and Elwert argue, neighborhood problems dragged down (at least) two generations.

No discussion of neighborhood effects can ignore the racial dimension, because the residential segregation of blacks has been and, though reduced, continues to be extreme: 41 percent of the African-American parent-child pairs in the study grew up in poor neighborhoods in both generations; only 2 percent of white families did. Poor whites were less likely to live in concentrated areas of poverty and are more likely to get out of them if they did. The weight of the past is much heavier for some than others.

Claude Fischer is a sociologist at UC Berkeley, is the author of Made in America: A Social History of American Culture and Character. This post originally appeared at Made in America and was re-posted on the Boston Review BR Blog.