In my last post, I took issue with the PayScale college rankings, as well as with how economics reporters framed these rankings, citing their low calculated Return on Investment as evidence that these colleges “make” students poor. Jordan Weissmann has graciously responded to my critique. The Senior Vice President from PayScale itself has also responded in a comment on my post, defending the rankings as useful to high school seniors who later tell him, “I wish I had known this before.” I’m glad for the dialogue, and I think this issue connects to a larger debate on the value of higher education in general, as well as the relative value of different colleges, so I am going to keep the conversation going with a more extensive response.
Weissmann is not alone in his framing of PayScale’s rankings. While his headline at Slate reads “Which College Will Make You Poorest?” Derek Thompson at the Atlantic treads similar ground: “Which College—and Which Major—Will Make You Richest?” and later, “These U.S. Colleges and Majors Are the Biggest Waste of Money.” Walt Hickey goes with a more straightforward, but still uncritical view at 538: “Study Names Colleges with Best Return on Investment.”
My argument was first that salaries from art schools and engineering schools are not directly comparable, since students are not often choosing between Harvey Mudd (#1) and Maryland Institute College of Art (#1301). Chris Chabris mentions this point in his post criticizing 538′s coverage of the rankings. Second, I argued that PayScale and many journalists who wrote about the rankings, are overstating the role of the college in shaping its students salaries and understating other factors in what determines a students future salary, such as the role of selective admissions, constraints in the labor market and choices of the students themselves. Weissmann acknowledges various limitations (including selection bias) of PayScale’s methodology, but he doesn’t see these as reason enough to avoid phrasing like: “Which college will make you poorest?” or “Yes, the art school really is making students poorer if it delivers a negative ROI.”
The subhead for Weissmann’s recent defense of ranking colleges based on graduate salaries reads: “Academics might not like it, but schools should be held accountable.” Since I am the only named academic in the article, and my quote is framed as “Like many in higher education…” I can assume that Weissmann sees my criticism as based on my identity as an academic. True, I am an academic. I do feel solidarity with my fellow providers of higher education, whether they be at the University of Virginia (apparently THE best value! Wahoo!) or at arts schools like Maryland College of Art and Design, or Historically Black Colleges and Universities like Shaw and Fayetteville State University. However, my criticism of this particular set of rankings owes more to my training as a social scientist (and as a psychologist in particular) than to my allegiance with all institutions of higher education. Here’s why:
Claim a Cause? You’ve Got to Earn It
All this writing about salaries reminds me of another kind of earnings. When a scientist such as myself reads a headline with a claim that something causes something else, I ask whether the speaker has earned the right to make that claim. Can the data cash the check that the causal claim makes? A few examples: “Smoking causes cancer,” “miracle diet causes weight loss,” “stress causes ulcers,” and “vaccines cause autism.” While many educated people may start with a skeptical “correlation does not equal causation,” most scientists (especially social scientists) know that not all correlations are created equal, some studies are better than others. For a long time, the only good data on the harmful nature of smoking was epidemiological: those who smoked tended to have worse health than those who didn’t. Notice the lack of a causal claim, a study might conclude that a heavy smoker is 50 times more likely to develop lung cancer than a non-smoker, but it is still possible that smokers in general lead more stressful lives, exercise less, and have less healthy diets. In this case, rather than causing cancer, smoking is merely another marker of an unhealthy lifestyle. More recently studies have narrowed in on the biological mechanisms of smoking’s harmful effects on the body. What is it precisely that causes the harm? Is it the nicotine? the tar? What are the cellular mechanisms of the carninogens in cigarettes? These questions now have much more specific answers based on carefully controlled experiments, and even though there still seems to be some randomness to the overall pattern of carcinogens causing cancer (yes, some people can smoke for 60 years and not get cancer), we know a lot more about the biochemical effects of smoking.
But even without research into the cellular mechanisms, the epidemiological, correlational research was able to earn its causal claims. One landmark study compared British male doctors who smoked with …. British male doctors who did not smoke, and followed them for over 40 years. Keeping many other variables constant, this study still finds incredibly harmful effects of smoking.
A similar search has been conducted for a connection between vaccines and autism. Many children exhibit the first signs of autism spectrum disorders at around the same time they are vaccinated. Yet this is a misleading correlation. Epidemiological research would seem to be impossible. How can we compare levels of autism in non-vaccinators and vaccinators if everyone vaccinates? Except now that the fear has spread, we can. Populations that don’t vaccinate their children have the same incidence of autism as those populations who do vaccinate. Amazingly enough, however, populations who choose not to vaccinate DO have higher risk of an outbreak of whooping cough. A long search has concluded that vaccines do not cause autism, through both epidemiological work, as well as the search for biological mechanisms of harm.
Which brings us back to PayScale and the economics reporters. Have PayScale and these reporters earned their causal claims? Are these rankings actually useful to high school seniors deciding where to go to college, as Barnaby Dornfman comments on my previous post, or merely perceived to be?
I remain deeply skeptical.
The Median is not the Message*
*with apologies to Stephen Jay Gould
There are (at least) three classes of factors that could predict a high school student’s future salary. First, a student’s individual characteristics such as preferences, abilities, motivation and even demographic variables such as race and gender affect their future salary. Second, that person’s education and training of course influence what they earn. This is where college fits in, but it might also include high school preparation as well as out of school preparation. Third, students future salaries are dependent on the labor market they enter as they leave college. As many unemployed or underemployed PhD’s are now discovering, someone can have great achievements in the other two factors, but if there are few to no jobs, one’s salary is severely constrained. The PayScale rankings understate these factors in comparing salaries of graduates to the median salary for someone who has only a high school diploma.
The reporters who have covered the rankings, and to a lesser degree, PayScale itself, make two critical errors that undermine their causal claims. First, they gloss over the impact of student and labor market factors on salaries. Second, they also understate how inseparable colleges are from the students who attend them. The rhetorical effect of the apparently straightforward reporting on these rankings are a pair of bold pronouncements “Get thee to a college that doth begin with H-A-R-V, and drink thy fill from the trough of increased economic outcomes” and “But lo! if thy find thyself in Pellville Directional State University, fly fly away, and become a manager at Ye Olde Chipotle, for thy riches will grow more from avoiding useless, expensive learning than from seeking it.” The truth is a lot more complicated. Aggregate salaries depend far more on student characteristics present before they enter college and the labor market that they choose to compete in as they exit college, than what differentiates the colleges that choose to admit them.
There are a few ways to see this in the rankings themselves. I’ll admit that I don’t know much about the Colorado School of Mines, but what is making their students rich is not necessarily the school itself, but the students’ decision to become engineers in Colorado (or miners, maybe Clementine’s dad had it right after all!). When PayScale compares their salaries to what a median high school graduate makes, this isn’t the best comparison, given that these students have already decided to become engineers, it seems with a focus on energy and environment. Likewise, for the number 1 ranked school, (Harvey Mudd), before Harvey Mudd does any educating, it has selected certain students to admit, and some subset (approximately 30%) of those students have made a decision to attend a small engineering school in California. Of these students who choose to attend Harvey Mudd, 14% qualify for Federal Pell Grants and 46% qualify for any federal student loan aid. For these students, the average total federal student loan aid is around $6,400. I am certain that Harvey Mudd is a fantastic student experience, but I think it is fair to say that they enroll a student body that is privileged, motivated, ambitious, and focused on a high salary occupation from the moment they arrive on campus. These students have decided to become scientists or engineers before they entered Harvey Mudd, and it doesn’t make much sense to compare their return on investment to what the median high school graduate makes.
Perhaps to defenders of the value of these rankings, they are fine with the advice boiling down to: Want to make more money? Become an engineer, not an artist. Here is hard data on how poor you will be as an artist. Going to college won’t help you if you choose a low paying career. As I said before, I am dubious of the value of such advice, although PayScale Senior Vice President Barnaby Dorfman has experienced sending the PayScale rankings to many people who say “I wish I had known!” However, for the I-wish-I-had-known camp to improve economic outcomes by going into engineering, we need enough engineering jobs to accommodate increased labor supply. But this is not the case. There is no STEM skills gap. There is no labor shortage for STEM jobs. Perhaps a better way to improve outcomes would be to forgo college altogether, and, as Weissmann suggests, work in a Ford factory or manage a Chipotle. This assumes that the student choosing these jobs instead of college will make greater than the median salary of high school graduates, which Weissmann acknowledges may not be the case, due in part to “weaker skills.” Even though Weissmann and others briefly consider the other flaws in this model of salary prediction, I think it is worth it to drill down deeper.
The Worst Performers
What if we do this kind of analysis for Derek Thompson’s so-called “biggest wastes of money?” The students entering these colleges quite often already have a deck stacked against them, and not just in college choice. A student entering Shaw University, the oldest historically black college in the South, located in Raleigh, North Carolina, is making a hopeful investment in a college education. But comparing their future salary to the median high school graduate does not take into account the racial disparities in our current labor market. Further, as Raj Chetty and colleagues have shown, economic mobility is constrained not just by demography of the individual, but by geography as well. In his testimony submitted yesterday to the Senate Budget Committee, Chetty describes one pattern with this lack of mobility: “The first pattern we document is that upward income mobility is significantly lower in areas with larger African-American populations.” He also notes that if mobility is measured by a child’s chances of moving from having parents with the lowest quintile in income (poorest 1/5th of population) to themselves having upper quintile income, one of the “commuting zones” lowest in intergenerational economic mobility? You guessed it, Raleigh, North Carolina with 5%, considerably lower than San Francisco, San Jose and San Diego, California,, all with odds more than twice that. See the map he created below:
To connect geographical mobility to higher education, we must understand how far students are likely to travel for college. Most students stay close to home. Look closely at this data from the ACT. The median distance to college (for those who took the ACT) is 51 miles. But if you separate groups by test scores, those who score lowest, stay closest to home. If you score between 1 and 15 on the ACT, the median distance to college is 18 miles. The same pattern can be seen depending on parental characteristics. For students whose parents have no college education, the median distance they travel to go to college is 24 miles (parents with graduate degrees? median distance of 95 miles). Poor students are less physically mobile, which is influenced by, and contributes to, their lack of economic mobility.
Take My Advice, Don’t Try it Twice, if You’ve Got but 50 Cents
So the PayScale rankings would have you believe that students should choose higher ranked colleges, instead of those that “make them poor,” like art schools and HBCUs. The advice is not “Don’t be black, because structural racism will still hurt your progress” or “Don’t be poor in the south, because intergenerational economic mobility there is very low” or “don’t find value in art, because your society doesn’t support that,” but rather: “Watch out for these schools, they waste your money!”
Here is where I notice a difference between what I will call the neoliberal economic approach from a conservative one like Charles Murray’s. Neoliberals are often confident in the power of greater information to improve equality and efficiency, whereas conservatives are openly pessimistic that all students are intellectually capable enough to complete college. Here’s Charles Murray:
So even though college has been dumbed down, it is still too intellectually demanding for a large majority of students, in an age when about 50 percent of all high school graduates are heading to four-year colleges the next fall. The result is lots of failure.
Whereas Weissmann closes his original reporting of the PayScale rankings with this:
But it does reinforce why we so desperately need high-quality consumer information about higher education. In the past, the higher-ed lobby has stood in the way of allowing the Department of Education to track college graduates over the long term to keep tabs on their lifetime earnings—what’s known as a “unit-record system.” And as a result, we have to rely on less complete government surveys, or less-than-ideal crowdsourced databases like Payscale’s. As a result, some students are going into college financially blind, and they could be ending up poorer for it—literally
Notice another causal claim: the lack of data is what is causing students to go into college financially blind. What goes unsaid here is that if better data existed, then students and their parents would view that data and incorporate it into their model of how college works. Then, armed with better financial vision, they would made better financial decisions. This same confidence underlies the concern about undermatching, in which high achieving poor students choose to attend lower status institutions, when they would apparently have better outcomes at a wealthier, more prestigious college. This is a topic I have written about here and here. Sara Mayeux makes some excellent points about Yale’s tone-deaf and misguided efforts to address undermatching.
A common theme across the educational spectrum emerges. Yes, economic inequality has grown and states continue to disinvest in higher education, but a cheap, easy and effective way of improving students’ economic situation is through educating people who experience low workforce success (i.e. poor people) to help them make more informed decisions. The implication is clear: Low performing schools (whether colleges or urban public schools) don’t need resources, they need to be held accountable. Low performing graduates and their parents don’t need better economic opportunities, they need better data. Graduates of HBCUs don’t need laws and policies to counteract the persistent effects of racist housing programs, racist tax policy, racist hiring practices, racist underfunding of public K-12 schools, they need bourgious norms, invigorating moral culture, and better salary data. Apparently these students should be informed that an HBCU, which may once have been a proud part of familial identity, a cause of pride in black scholarship, a shining beacon of advancement through higher education, well, now big data science shows it offers a low ROI. Prospective students should be advised to go to a better school, or an engineering school, or work at a factory instead.
I know that Weissmann, PayScale, and everyone involved in this debate wants a better system of higher education in these United States of America. We all should want, to paraphrase Lincoln, a higher education system of the students, for the students, and chosen by the students. One that is accountable to the students. But in search of this goal, they are willing to forgive imperfections of their scale, convinced of the desperate need for accountability. But as a psychologist well versed in some of the shameful consequences of imperfect scales in psychology’s past, I am skeptical. Instead of progress and accountability, I see such rankings of colleges based on assumptions of privilege and status, devised by privileged tech boom MBA’s, for the software and energy engineers. I see this scale as one that does not encourage accountability, but merely reflects existing social inequities and blames society’s economic losers, just as existing social policies make their fates increasingly unavoidable. Don’t tell me you are holding colleges accountable, while holding your thumb on the scale.