why universities can’t be the primary site of political organizing

This is not a political publication, but I am definitely interested in discussing campus issues in this space, and I would like to take a second and lay out some reasons why Amber A’Lee Frost is correct that the university can’t be the key site of left-wing (or any other) organizing. (If you think that idea’s a strawman, I invite you to read the Port Huron Statement.)

Please note that this is a series of empirical claims, not normative ones. I’m not saying it would be good or bad for campus to be the key site of a given movement’s organizing strategy. I’m saying that it’s not going to work, for good or bad.

There’s not a lot of people on campus. There’s a lot of universities out there, and you could be forgiven for overestimating the size of the student population. But NCES says there’s only about 20 million students, grad and undergrad, enrolled in degree-granting post-secondary institutions. There’s also about 4 million people who work in those institutions. Back of the envelope that means that there’s about 7.5% of the American population regularly on campus in one capacity or another, setting aside questions of online-only education. Is 7.5% nothing? Not at all. It’s a meaningful chunk of people. But even if all of them were capable of being politically organized – which of course is far from the truth – you’re still leaving out the vast majority of the adult population.

Campus activism is seasonal. You aren’t going to hear a lot about campus protests for a few months. Why? Because of summer break. Vacation is notoriously hard on student protest groups. Why did the “campus uprising” of a few years ago fizzle out? In large measure because of Christmas break – the spring semester wasn’t nearly as active as the fall – and then summer break. Activism requires momentum and continuity of practice, and the regularity of vacation makes that quite difficult. Organizations that are careful and have strong leadership in place can take steps to adjust for this seasonal nature, but there’s just always going to be major lulls in campus organizing according to the calendar. And politics happens year-round.

College students are an itinerant population. Speaking of continuity of practice, campus political groups constantly have to replace membership and leadership because students (we hope) will eventually graduate. Again, that problem can be ameliorated with hard work and forethought by these groups, but it’s very difficult to have consistent strength of numbers and a coherent political vision when you’re seeing 100% turnover in a 5-6 year span.

Town and gown conflicts can make local organizing difficult. Sadly, many university towns are sites of tension and mutual distrust between the campus community and the locals. The degree of these tensions varies widely from campus to campus, and they can be ameliorated. In fact, making attempts to heal those divides can be the best form of campus activism. But it’s the case that the complex conflicts between colleges and the towns in which they’re housed will often make it difficult to build meaningful solidarity across the campus borders, which often serve as an invisible wall of attention and community.

Students are too busy to devote too much time to organizing. 70% of college students work. A quarter have dependent children. These students must also do all of the necessary work of being students. We should be realistic and fair with their time and recognize that a majority of students will not be able to engage politically for many hours out of the week.

College students have a natural and justifiable first-order priority of getting employed. Everyone who works is of course at risk of having professional repercussions for their political engagement, but college students perhaps have a unique set of worries about being publicly politically active, particularly in the era of the internet. Nowadays, we’re all constantly building an easily-searchable, publicly-accessible archive of the things we once thought and did. This is particularly troublesome for those who have not yet gotten their first jobs and have yet to build the kind of social capital necessary to feel secure in their ability to get work with a controversial political past. It’s my impression that a lot of college students are inclined to be political but who feel that they simply can’t risk it, and that’s a fear that we should respect given the modern job market.

College activism can either be a low-stakes place where students learn and grow safely, or an essential site of organizing – but it can’t be both. Oftentimes, when campus activists make mistakes (such as forcing a free yoga class for disabled students to be shut down because yoga is “cultural appropriation”), defenders will say, hey, they’re just college kids – they need a chance to screw up, to make mistakes, to be free to fail. And there’s some real truth to that. The problem is that this attitude cannot coexist with the idea that campus has to be a central site or the central site of left-wing political organizing. If what happens on campus is crucial to the broader left movement, it can’t then be called not worth worrying about; if campus organizing is a space that is largely free of consequences for young activists, then it can’t be a space where essential political work gets done. These ideas are not compatible.

Organize the campus’s workforce according to labor principles. None of this means that organizing shouldn’t take place on campus; it absolutely should. But like Frost I think that the left is far too fixated on what happens in campus spaces, likely because these spaces are some of the only areas where the left appears to hold any meaningful power. Student activists should be encouraged to engage politically in order to learn and grow, but we should not imagine that they are the necessary vanguard of the young left, given that only a third of Americans ever gets a college degree. Meanwhile, we absolutely must continue to organize the campus as a workplace. (For the record, Frost is a member of a campus union, as am I.) But that organization takes place according to labor principles, not according to any special dictates of academic culture. And this returns to Frost’s basic thesis: it is the organization of labor, not of students, that must be the primary focus and goal of the American left.

correlation: neither everything nor nothing

via Overthinking

One thing that everyone on the internet knows, about statistics, is this: correlation does not imply causation. It’s a stock phrase, a bauble constantly polished and passed off in internet debate. And it’s not wrong, at least not on its face. But I worry that the denial of the importance of correlation is a bigger impediment to human knowledge and understanding than belief in specious relationships between correlation and causation.

First, you should read two pieces on the “correlation does not imply causation” phenomenon, which has gone from a somewhat arcane notion common to research methods classes to a full-fledged meme. This piece by Greg Laden is absolute required reading on correlation and causation and how to think about both. Second, this piece by Daniel Engber does good work talking about how “correlation does not imply causation” became an overused and unhelpful piece of internet lingo.

As Laden points out, the question is really this: what does “imply” mean? The people who employ “correlation does not imply causation” as a kind of argumentative trump card are typically using “imply” in a way that nobody actually means, which is as synonymous with “prove.” That’s pretty far from what we usually mean by “implies”! In fact, using the typical meaning of implication, correlation sometimes implies causation, in the sense that it provides evidence for a causal relationship. In careful, rigorously conducted research, a strong correlation can offer some evidence of causation, if that correlation is embedded in a theoretical argument for how that causative relationship works. If nothing else, correlation is often the first stage in identifying relationships of interest that we might then investigate in more rigorous ways, if we can.

A few things I’d like people to think about.

There are specific reasons that an assertion of causation from correlation data might be incorrect. There is a vast literature of research methodology, across just about every research field you can imagine. Correlation-causation fallacies have been investigated and understood for a long time. Among the potential dangers is the confounding variable, where an unknown variable is driving the change in two other variables, making them appear to influence one another. This gives us the famous drownings-and-ice cream correlation – as drownings go up, so do ice cream sales. The confounding variable, of course, is temperature.1 There are all sorts of nasty little interpretation problems in the literature. These dangers are real. But in order to have understanding, we have to actually investigate why a particular relationship is spurious. Just saying “correlation does not imply causation” doesn’t do anything to actually improve our understanding. Explore why, if you want to be useful. Use the phrase as the beginning of a conversation, not a talisman.

Correlation evidence can be essential when it is difficult or impossible to investigate a causative mechanism. Cigarette smoking causes cancer. We know that. We know it because of many, many rigorous and careful studies have established that connection. It might surprise you to know that the large majority of our evidence demonstrating that relationship comes from correlation studies, rather than experiments. Why? Well, as my statistics instructor used to say – here, let’s prove cigarette smoking causes cancer. We’ll round up some infants, and we’ll divide them into experimental and control groups, and we’ll expose the experimental group to tobacco smoke, and in a few years, we’ll have proven a causal relationship. Sound like a good idea to you? Me neither. We knew that cigarettes were contributing to lung cancer long before we identified what was actually happening in the human body, and we have correlational studies to thank for that. Blinded randomized controlled experimental studies are the gold standard, but they are rare precisely because they are hard, sometimes impossible. To refuse to take anything else as meaningful evidence is nihilism, not skepticism.

Sometimes what we care about is association. Consider relationships which we believe to be strong but in which we are unlikely to ever identify a specific causal mechanism. I have on my desk a raft of research showing a strong correlation between parental income and student performance on various educational metrics. It’s a relationship we find in a variety of locations, across a variety of ages, and through a variety of different research contexts. This is important research, it has stakes; it helps us to understand the power of structural advantage and contributes to political critique of our supposedly meritocratic social systems.

Suppose I was prohibited from asserting that this correlation proved anything because I couldn’t prove causation. My question is this: how could I find a specific causal mechanism? The relationship is likely very complex, and in some cases, not subject to external observation by researchers at all. To refuse to consider this relationship in our knowledge making or our policy decisions because of an overly skeptical attitude towards correlational data would be profoundly misguided. Of course there’s limitations and restrictions we need to keep in mind – the relationship is consistent but not universal, its effect is different for different parts of the income scale, it varies with a variety of factors. It’s not a complete or simple story. But I’m still perfectly willing to say that poverty is associated with poor educational performance. That’s the only reasonable conclusion from the data. That association matters, even if we can’t find a specific causal mechanism.

Correlation is a statistical relationship. Causation is a judgement call. I frequently find that people seem to believe that there is some sort of mathematical proof of causation that a high correlation does not merit, some number that can be spit out by statistical packages that says “here’s causation.” But causation is always a matter of the informed judgment of the research community. Controlled experiments are the gold standard in that regard, but there are controlled experiments that can’t prove causation and other research methods that have established causation to the satisfaction of most members of a discipline.

Human beings have the benefit of human reasoning. One of my frustrations with the “correlation does not imply causation” line is that it’s often deployed in instances where no one is asserting that we’ve adequately proved causation. I sometimes feel as though people are trying to protect us from mistakes of reasoning that no one would actually fall victim to. In an (overall excellent) piece for the Times, Gary Marcus and Ernest Davis write, “A big data analysis might reveal, for instance, that from 2006 to 2011 the United States murder rate was well correlated with the market share of Internet Explorer: Both went down sharply. But it’s hard to imagine there is any causal relationship between the two.” That’s true – it is hard to imagine! So hard to imagine that I don’t think anyone would have that problem. I get the point that it’s a deliberately exaggerated example, and I also fully recognize that there are some correlation-causation assumptions that are tempting but wrong. But I think that, when people state the dangers of drawing specious relationships, they sometimes act as if we’re all dummies. No one will look at these correlations and think they’re describing real causal relationships because no one is that senseless. So why are we so afraid of that potential bad reasoning?

Those disagreeing with conclusions drawn from correlational data have a burden of proof too. This is the thing, for me, more than anything. It’s fine to dispute a suggestion of causation drawn from correlation data. Just recognize that you have to actually make the case. Different people can have responsible, reasonable disagreements about statistical inferences. Both sides have to present evidence and make a rational argument drawn from theory. “Correlation does not imply causation” is the beginning of discussion, not the end.

I consider myself on the skeptical side when it comes to Big Data, at least in certain applications. As someone who is frequently frustrated by hype and woowoo, I’m firmly in the camp that says we need skepticism ingrained in how we think and write about statistical inquiry. I personally do think that many of the claims about Big Data applications are overblown, and I also think that the notion that we’ll ever be post-theory or purely empirical are dangerously misguided. But there’s no need to throw the baby out with the bathwater. While we should maintain a healthy criticism of them, new ventures dedicated to researched, data-driven writing should be greeted as a welcome development. What we need, I think, is to contribute to a communal understanding of research methods and statistics, including healthy skepticism, and there’s reason for optimism in that regard. Reasonable skepticism, not unthinking rejection; a critical utilization, not a thoughtless embrace.


you learn by being taught

Forgive the relative quiet lately; I’ve been enjoying my birthday weekend and then catching up on a ton of work. There’s a bunch of good things coming this week, including the return of book reviews after a brief (and unplanned) break.

This morning I spoke to an entire public high school, where I was invited to discuss being a product of public schools, higher ed, and success. It was very funny for me to be asked, though flattering – as I told the kids today, I would never think of myself casually as a success. Who ever thinks that way, beyond the wealthy and the deluded? But it was flattering and fun. I told them that there was no great wisdom in life, just a series of decisions before you, and hopefully with time the perspective to be able to choose better from worse. And, because I think this is important, I told them that they needed to cultivate a sense of “good enough” in their lives. At that age, they are being told constantly that they should pursue their dreams. But very few of us get what we’ve dreamed of, and those who have often find it’s far less grand than they’d imagined. So I told them to learn and experience and enjoy and to figure out how to live in the essential disappointment of human life.

It wasn’t as much of a bummer as it sounds!

I have been reflecting on the value of teachers. I have been accused a lot, lately, of not believing that teachers matter. That’s the opposite of the truth, really. I just think that this notion of casting the value of teachers in purely quantitative terms is a mistake, and a very recent one. The entire history of the Western canon, from Socrates to Aquinas to Locke to Dewey to Baldwin, contains arguments against this reduction. But this fight, to define what I mean and what I don’t against the tide, is a fight I suspect I will always have to keep fighting, and I intend to.

Our culture celebrates autodidacts. It talks constantly of “disrupting” education. It insists always that we need to radically reshape how we teach and learn. It treats as heroic the rejection of teachers and traditional mentorship. The self-help aisle of the bookstore abounds with writers who insist that they truly learned by rejecting the typical method of education and became, instead, self-taught, self-made. It’s an unavoidable trope.

What amazes me about my own education is just how far that is from the truth for me personally. I’ve learned, over decades, how I learn. It’s pretty simple: teachers teach me. That was true in kindergarten and it’s true now that I have my doctorate. I can’t tell you how often I have found myself feeling lost and ignorant, only to have patient, kind teachers take me through the familiar processes of modeling and repetition that are cornerstones of education. I think back to my graduate statistics classes, where I often feel like the slowest person in class, but where I always ended up getting there, thanks to steady and reassuring teaching. When I don’t get what I need from class, I’d go to office hours, or I’d go to the statistics help room, where brilliant graduate students eagerly shared knowledge and experience with me. None of this is fundamentally any different than when Mrs. Gebhardt taught me to cut shapes out of paper or when Mr. Shearer taught me simple algebra or when Mr. Tucci taught me to read poetry or when Dr. Nunn taught me to write a real research paper. The process is always the same, and in every case, I have succeeded not through rejecting the authority of teachers but by accepting their help, by recognizing their superior knowledge and letting them use it to enrich my life.

Is that a contradiction of what I’ve said about the limited ability of teachers to control the outcomes of their students? I don’t think so. The question is, do you want us to have a fuller and more humane vision of what it means to learn? I do.

They say that great men see farther than others by standing on the shoulders of giants. I think most of us are enabled to see as far as others because others have collectively reached their hands down and pulled us up.

another notch in the belt

It’s my birthday today. Wasn’t that long ago that I was part of a vanguard of young writer types. What the hell happened?

This project’s about three months old now, and I gotta tell you guys: I haven’t had this much fun writing in ages. It’s been better than I could have hoped. Thanks for coming along.

I woke up one day to find that my life had gotten pretty damn good. My job’s not perfect, but it’s still pretty great. I miss teaching, and I’d love to be in a position where I had some motivation to get peer reviewed stuff published. But I’m working at a great college with a gorgeous campus in a system I admire immensely. It’s part of my job to stay on top of the research literature, so I’m reading books and articles at a good clip. Polyani said that a scholar is someone who lives with the questions, and I do, and that’s enough. Very few people get that opportunity. It’s a privilege.

It’s also a privilege to live in this city. The other day I was walking home, cutting through Prospect Park right after dusk. I came to the Long Meadow, which a few hours before had been absolutely packed with people picnicking and jogging and flying kites and walking dogs. For a brief moment I found it utterly empty, not another soul in sight, alone in one of the most popular parks in the city. And I knew in that moment that it was all for me.

Study of the Week: Feed Kids to Feed Them

Today’s Study of the Week is about subsidized meal programs for public school students, particularly breakfast. School breakfast programs have been targeted by policymakers for awhile, in part because of discouraging participation levels. Even many students who are eligible for subsidized lunches often don’t take advantage of school breakfast. The reasons for this are multiple. Price is certainly a factor. As you’d expect, price is inversely related to participation rates for school breakfast. Also, in order to take advantage of breakfast programs, you need to arrive at school early enough to eat before school formally begins, and it’s often hard enough to get teenagers to school on time just for class. Finally, there’s a stigma component, particularly associated with subsidized breakfast programs. It was certainly the case at my public high school, where 44% of students were eligible for federal school lunch subsidies, that school breakfast carried class associations. At lunch, everybody’s eating together, but students at breakfast tended to be poorer kids – which in turn likely makes it less likely that students will want to be seen getting school breakfast.

The study, written by Jacob Leos-Urbel, Amy Ellen Schwartz, Meryle Weinstein, and Sean Corcoran (all of NYU), takes advantage of a policy change in New York public schools in 2003. Previously, school breakfast had been free only to those who were eligible for federal lunch subsidies, which remains the case in most school districts. New York made breakfast free for all students, defraying the costs by raising the price of unsubsidized lunch from $1.00 to $1.50. They then went looking to see if the switch to free breakfast for all changed participation in the breakfast program, looking for differences between the three tiers – free lunch students, reduced lunch students, and students who pay full price. They also compared outcomes from traditional schools to Universal Free Meal (UFM) schools, where the percentage of eligible students is so high that everyone in the school gets meals for free already. This helped them tease out possible differences in participation based on moving to a universal free breakfast model. They were able to use a robust data set comprising results from 723,843 students from 667 schools, grades 3–8. They also investigated whether breakfast participation rates were associated with performance in quantitative educational metrics.

It’s important to say that it’s hard to really get at causality here because we’re not doing a randomized experiment. Such an experiment would be flatly unethical – “sorry, kid, you got sorted into the no-free-breakfast group, good luck.” So we have to do observational studies and use what techniques we can to adjust for their weaknesses. In this study, the authors used what’s called a difference in difference design. These techniques are often used when analyzing natural experiments. In the current case, we have schools where the change in policy has no impact on who receives free breakfast (the UFM schools) and schools where there is an impact (the traditional schools). Therefore the UFM schools can function as a kind of natural control group, since they did not receive the “treatment.” You then use a statistical model to compare the change in the variables of interest for the “control” group to the change for the “treatment” group. Make sense?

What did the authors find? The results of the policy change were modest, in almost every measurable way, and consistent across a number of models that the authors go into in great detail in the paper. Students did take advantage of school breakfast more after breakfast became universally free. On the one hand, students who paid full price increased breakfast participation by 55%, which is a large number; but on the other hand, their initial baseline participation rates were so low (again because breakfast participation is class-influenced) that they only ate on average 6 additional breakfasts a year. Reduced price and free were increased by 33% and 15%, respectively – the latter particularly interesting given that those students did not pay for breakfast to begin with. Still, that too only represents about 6 meals over the course of a year, not nothing but perhaps less than we’d hope for a program with low participation rates. The only meaningful difference in models seems to be when they restrict their analysis to the small number (91) of schools where less than a third of students are eligible for lunch subsidies, in which case breakfast participation grew by a substantially larger amount. The purchase of lunches, for what it’s worth, remained static despite the price increase.

There’s a lot of picking apart the data and attempting to determine to what degree these findings are related to stigma. I confess I find the discussion a bit muddled but your money may vary. The educational impacts, also, were slight. They found a small increase in attendance, but this result was not significant, and no impact on reading and math outcomes.

These findings are somewhat discouraging. Certainly we would hope that moving to a universal program would help to spur participation rates to a greater degree than we’re seeing here. But it’s important to note that the authors largely restricted their analysis to the years immediately before and after the policy change, thanks to the needs of their model. When broadening the time frame by a couple years, they find an accelerating trend in participation rates, though the model is somewhat less robust. What’s more, as the authors note, decreasing stigma is the kind of thing that takes time. If it is in fact the case that stigma keeps students from taking part in school breakfast, it may well take a longer time period for universal free breakfast to erode that disincentive.

I’m also inclined to suspect that the need to get kids to school early to eat represents a serious challenge to the pragmatic success of this program. There’s perhaps good news on the way:

Even when free for all, school breakfast is voluntary. Further, unlike school lunch, breakfast traditionally is not fully incorporated into the school day and students must arrive at school early in order to participate. Importantly, in the time period since the introduction of the universal free breakfast policy considered in this paper, New York City and other large cities have begun to explore other avenues to increase participation. Most notably, some schools now provide breakfast in the classroom.

Ultimately, I believe that making school breakfast universally free is a great change even in light of relatively modest impacts on participation rate. We should embrace providing free breakfast to all students regardless of income level out of the principle of doing so, particularly considering that fluctuations in parental income might make kids who are technically ineligible unable to pay for breakfast. In time, if we set up this universal program as an embedded part of the school day, and work diligently to erase the stigma of using it, I believe more and more kids will begin their days with a full stomach.

As for the lack of impacts on quantitative metrics, well – I think that’s no real objection at all. We should feed kids to feed them, not to improve their numbers. This all dovetails with my earlier point about after school programs: if we insist on viewing every question through the lens of test scores, we’re missing out on opportunities to improve the lives of children and parents that are real and important. Again, I will say that I recognize the value of quantitative academic outcome in certain policy situations. But the relentless focus on quantitative outcomes leads to scenarios where we have to ask questions like whether giving kids free breakfast improves test scores. If it does, great – but the reason to feed children is to feed children. When it comes to test scores and education policy, the tail too often wags the dog, and it has to stop.