John Cochrane on the university endowment tax

John Cochrane:

How can we credibly proclaim that we, universities, provide the true public good and deserve subsidies, but the rest of you get lost? Do we not look just a little hypocritical if when a tax reform is announced, we jump in line with the rest of them to demand our pork back?…

Moreover, if indeed universities provide useful public goods — and they do, and I  include low tuition for low-income students and basic research — surely how that activity is subsidized matters. There are good and bad ways to subsidize anything…

Usually, giving a lot of money to a large opaque and competition-protected bureaucratic institution that does a lot of things, to spend as it wishes, does not produce the outcome you want. Usually, funding that subsidy by giving a franchise such as a little monopoly or the unique opportunity to run a tax-shielded hedge fund does not produce the outcome you want.

When you want  a public good from such institutions, conservative principles usually suggest that the subsidy be transparent, annually appropriated, reviewed, and given for the activity you want. Give federal scholarships to the students, chosen by federal rules, and studying things that taxpayer representatives find useful. Support research through competitive grants. Perfect? No. But a lot better than counting on tax-subsidized endowment profits to go where you want them to go.

Otherwise, you tend to get big administrative bureaucracies, sports and recreation programs, bloated faculties with high salaries, low teaching loads and a lot of silly research, and a few crumbs to the worthy students…

Robert Kelchen on higher ed data in 2017

Robert Kelchen

It was a great year for data on higher education outcomes. The release of the College Scorecard in 2015 was a big step forward for researchers, policymakers, and the public—providing the first comprehensive institutional-level data on earnings and student loan repayment rates. (And the Department of Education recently signed a five-year agreement to keep getting earnings data from Treasury, allaying the fears of some about data in the Trump administration.) This year also saw the long-awaited release of graduation rates for Pell Grant recipients and part-time/transfer students via the Integrated Postsecondary Education Data System.

But the data release that stole the show in 2017 was from the Equality of Opportunity Project, a tremendous and well-funded collaboration by several top economists. With a well-coordinated release in the New York Times, the team made available its college-level data on the percentage of students from lower-income families who reached higher income quintiles by their early 30s. This highlighted the good work of many moderately-selective public and private nonprofit colleges, as well as the incredible share of super-wealthy students at Ivy League institutions. (The dataset also has marriage rates by college, which I had fun playing around with.) One caution: since the data come from tax records, some colleges are aggregated in strange ways. Be mindful of that when using this great dataset.

Siddhartha Mukherjee on a common post hoc abuse of statistical analysis

Siddhartha Mukherjee:

then a second instinct takes over: Why not try to find the people for whom the drug did work?…

But it’s also a treacherous seduction…

Perhaps the most stinging reminder of these pitfalls comes from a timeless paper published by the statistician Richard Peto. In 1988, Peto and colleagues had finished an enormous randomized trial on 17,000 patients that proved the benefit of aspirin after a heart attack. The Lancet agreed to publish the data, but with a catch: The editors wanted to determine which patients had benefited the most. Older or younger subjects? Men or women?

Peto, a statistical rigorist, refused — such analyses would inevitably lead to artifactual conclusions — but the editors persisted, declining to advance the paper otherwise. Peto sent the paper back, but with a prank buried inside. The clinical subgroups were there, as requested — but he had inserted an additional one: “The patients were subdivided into 12 … groups according to their medieval astrological birth signs.” When the tongue-in-cheek zodiac subgroups were analyzed, Geminis and Libras were found to have no benefit from aspirin, but the drug “produced halving of risk if you were born under Capricorn.” Peto now insisted that the “astrological subgroups” also be included in the paper — in part to serve as a moral lesson for posterity. I’ve often thought of Peto’s paper as required reading for every medical student.


Jonathan Haidt on the decline of liberal arts education

Jonathan Haidt:

Today’s identity politics . . . teaches the exact opposite of what we think a liberal arts education should be. When I was at Yale in the 1980s, I was given so many tools for understanding the world. By the time I graduated, I could think about things as a utilitarian or as a Kantian, as a Freudian or a behaviorist, as a computer scientist or as a humanist. I was given many lenses to apply to any given question or problem.

But what do we do now? Many students are given just one lens—power. Here’s your lens, kid. Look at everything through this lens. Everything is about power. Every situation is analyzed in terms of the bad people acting to preserve their power and privilege over the good people. This is not an education. This is induction into a cult. It’s a fundamentalist religion. It’s a paranoid worldview that separates people from each other and sends them down the road to alienation, anxiety and intellectual impotence. . . .

Is moving to free college good for the health of higher education?

There is much talk about moving to free college in the US. What impact would that have? Some evidence of possible consequences is provided in The End of Free College in England: Implications for Quality, Enrolments, and Equity by Richard Murphy, Judith Scott-Clayton, and Gillian Wyness:

This paper examines the consequences of charging tuition fees on university quality, enrolments, and equity. To do so, we study the English higher education system which has, in just two decades, moved from a free college system to one in which tuition fees are among the highest in the world. Our findings suggest that England’s shift has resulted in increased funding per head, rising enrolments, and a narrowing of the participation gap between advantaged and disadvantaged students. In contrast to other systems with high tuition fees, the English system is distinct in that its income-contingent loan system keeps university free at the point of entry, and provides students with comparatively generous assistance for living expenses. We conclude that tuition fees, at least in the English case supported their goals of increasing quality, quantity, and equity in higher education.

WSJ reports that Americans losing faith in the value of college

Josh Mitchell and Douglas Belkin of the Wall Street Journal report that Americans are losing faith in the value of college, as represented in these survey results:
The most shocking results to me are that 30% of college degree holders do not think a college degree is worth it, as do over half of men and over half of 18-34 year olds.

Andrew Gelman and Hal Varian on Instrumental Variables

Andrew Gelmen

The trick: how to think about IV’s without getting too confused

Suppose z is your instrument, T is your treatment, and y is your outcome. So the causal model is z -> T -> y. The trick is to think of (T,y) as a joint outcome and to think of the effect of z on each. For example, an increase of 1 in z is associated with an increase of 0.8 in T and an increase of 10 in y. The usual “instrumental variables” summary is to just say the estimated effect of T on y is 10/0.8=12.5, but I’d rather just keep it separate and report the effects on T and y separately…

If there’s any problem with the simple correlation, I see the same problems with the more elaborate analysis–the pair of correlations which is given the label “instrumental variables analysis.” I’m not opposed to instrumental variables in general, but when I get stuck, I find it extremely helpful to go back and see what I’ve learned from separately thinking about the correlation of z with T, and the correlation of z with y. Since that’s ultimately what instrumental variables analysis is doing.

Hal Varian adds

You have to assume that the only way that z affects Y is through the treatment, T. So the IV model is
T = az + e
y = bT + d
It follows that
E(y|z) = b E(T|z) + E(d|z)
Now if we
1) assume E(d|z) = 0
2) verify that E(T|z) != 0
we can solve for b by division. Of course, assumption 1 is untestable.

An extreme case is a purely randomized experiment, where e=0 and z is a coin flip.

Scott Alexander on what passes for socialism these days

Scott Alexander

Most of the policies being mooted by the supposedly socialist left today – Medicare-for-all, better social safety nets, et cetera – are well within the bounds of neoliberalism – ie private property and capitalist economies should exist, but the state should help poor people. “Socialism” should be reserved for systems that end private property and nationalize practically everything. I’m worried that people will use the success of neoliberal systems in eg Sweden to justify socialism, and then, socialism having been justified, promote actual-dictionary-definition socialism. To a first approximation, Sweden is an example of capitalists proving socialism isn’t necessary; Venezuela is an example of socialism actually happening.