Tag Archives: education

Free Market Reflexes

By James Kwak

I’ve been reading a lot about education recently, for reasons that are not worth going into here. I don’t know that much about the area, so I’ve been reading some background stuff and review articles, including a Hamilton Project white paper by Michael Greenstone, Adam Looney, and Paige Shevlin.

It’s pretty mainstream, self-professed “third way” stuff, with a heavy dose of measurement and performance evaluation. Basically they repeat over and over again that educational policies should be based on evidence and new programs should go through rigorous assessments. There are a fairly strong tilt toward market mechanisms and some idealistic naivete about practical problems (e.g., “One way to [improve accountability systems] is to develop tests that measure the skills children should learn”), but nothing too outrageous in substance.

The white paper, however, betrays a certain conceptual bias that I find disturbing, even in topical areas where it seems otherwise reasonable.

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Are Subsidized Student Loans Worth the Price?

By James Kwak

Previous guest blogger Anastasia Wilson has written a post on her own blog comparing the student loan racket (for-profit colleges help people take out lots of federally guaranteed student loans to pay for their tuition, then do a lousy job educating them, walking away with the money and leaving students to default) to the subprime loan racket. The flagbearer for this parallel is Steve Eisman, who has gone from shorting subprime mortgages to now shorting for-profit colleges.

In theory, for-profit colleges should not be able to do this. If too many of their former students go into default, the Department of Education is supposed to prevent their new students from taking out federally subsidized loans. (Since the government is ultimately underwriting these loans, it should have the power to make sure that the loans are being used to buy an education that will help borrowers pay back those loans.) But colleges have so far been able to get around the rules by pushing defaults outside the time period that matters for regulatory purposes, as described by the Chronicle of Higher Education.

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For Profit or For Students?

This guest post is contributed by Mark Paul and Anastasia Wilson. Both are members of the class of 2011 at the University of Massachusetts-Amherst.

For-profit colleges are expanding enrollments at a rapid pace, but it is questionable whether these revenue-seeking universities give adequate consideration to students’ welfare, retention/graduation rates, and overall economic well-being alongside their bottom line profits.

A new post by Judith Scott-Clayton, a professor at Columbia Teachers College and new weekly contributor to the New York Times Economix blog, explores the merits of for-profit colleges, arguing that in many ways these schools are more efficient at seeking funding opportunities for students and adopting new teaching technologies. These schools procure more Federal dollars per student and employ more cost-saving technologies, in the classroom and online, than their non-profit public and private competitors.

However, the real question is not a matter of efficiency, but instead concerns students and the taxpayers funding Federal loans and grants consumed by for-profits. Are the relative merits of profit-oriented schools, including their comparative advantage in securing Federal funding, being used to improve the return on investment for students or for their shareholders? On the macro level, does the growth of for-profit higher education promote new risks in the economy, as drop-out and loan default rates continue to increase?

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Bad Data

By James Kwak

To make a vast generalization, we live in a society where quantitative data are becoming more and more important. Some of this is because of the vast increase in the availability of data, which is itself largely due to computers. Some is because of the vast increase in the capacity to process data, which is also largely due to computers. Think about Hans Rosling’s TED Talks, or the rise of sabermetrics (the “Moneyball” phenomenon) not only in baseball but in many other sports, or the importance of standardized testing scores in K-12 education, or Karl Rove’s usage of data mining to identify likely supporters, or the FiveThirtyEight revolution in electoral forecasting, or the quantification of the financial markets, or zillions of other examples. I believe one of my professors has written a book about this phenomenon.

But this comes with a problem. The problem is that we do not currently collect and scrub good enough data to support this recent fascination with numbers, and on top of that our brains are not wired to understand data. And if you have a lot riding on bad data that is poorly understood, then people will distort the data or find other ways to game the system to their advantage.

Readers of this blog will all be familiar with the phenomenon of rating subprime mortgage-backed securities and their structured offspring using data exclusively from a period of rising house prices — because those were the only data that were available. But the same issue crops up in many different stories covering different aspects of society.

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How Are the Kids? Unemployed, Underwater, and Sinking

This guest post is contributed by Mark Paul and Anastasia Wilson. Both are members of the class of 2011 at the University of Massachusetts-Amherst.

In some cultures asking how the kids are doing is a colloquial way of asking how the individual is faring, acknowledging that the vitality of the younger generation is a good metric for the well-being of society as a whole. In the United States, the state of the kids should be an important indicator. Young workers bear the significant burden of funding intergenerational transfer programs and maintaining the structure of payments that flow in the economy. Today, the kids’ outlook is almost as bleak as the housing market; they are unemployed, underwater on student debt, and out of luck from a reluctant political system.

Currently, even after a slight boost in jobs growth, unemployment for 18-24 year olds [correction: should be 18-19 year olds] stands at 24.7%. For 20-24 year olds, it hovers at 15.2%. These conservative estimates, using the Bureau of Labor Statistics U3 measure, do not reflect the number of marginally attached or discouraged young workers feeling the lag from a nearly moribund job market.

The U3 measure also does not count underemployment, yet with only 50% of B.A. holders able to find jobs requiring such a degree, underemployment rates are a telling index of the squeezing of the 18-30 year old Millennial generation. While it appears everyone is hurting since the financial collapse, young adults bear a disproportionate burden, constituting just 13.5% of the workforce while accounting for 26.4% of those unemployed. Even with good credentials, it is difficult for young people to find work and keep themselves afloat.

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Beyond Crazy

By James Kwak

Daniel Hamermesh points out a Wall Street Journal article on how colleges and universities are trying to increase accountability and productivity by measuring costs and benefits quantitatively. The “star” example is Texas A&M, which created a report showing a profit-and-loss summary for each professor or lecturer, where revenues are defined as external grants plus a share of tuition (if you teach one hundred students, you are credited with ten times as much revenues as someone who teaches ten students).

Let’s not argue about whether our colleges and universities are doing a good job. Let’s not even argue about whether we need more transparency and accountability in higher education. Assuming we do, this is just about the most idiotic way of doing it that I could imagine. No, wait; there’s no way I could have imagined something this stupid.

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Why the Education Gap?

By James Kwak

Probably the most important and intractable economic problem we face is not restarting the economy after the financial crisis, but the decades-old problem of stagnant wages for the lower and middle classes and the consequent massive increase in income inequality. This is something that Raghuram Rajan brings up in the first chapter of Fault Lines, and, like many people, he points the finger at education. Citing (like everyone else) Claudia Goldin and Lawrence Katz, he writes (pp. 22-23),

“As agriculture gave way to manufacturing in the mid-1800s, the elementary school movement in the United States created the most highly educated population in the world. . . . The high school movement took off in the early part of the twentieth century and provided the flexible, trained workers who would staff America’s factories and offices. . . .

“Recent technological advances now require many workers to have a college degree to carry out their tasks. But the supply of college-educated workers has not kept pace with demand–indeed, the fraction of high school graduates in every age cohort has stopped rising, having fallen slightly since the 1970s.”

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The Perils of Studying Economics

By James Kwak

Patrick McGeehan at the New York Times recently wrote about a New York Fed study finding that studying economics makes you a Republican. The headline conclusion is that the more economics classes you take, the more likely you are to be a Republican. Majoring in economics or business is also more likely to make you a Republican. (See Table 2 in the original paper.) The study is based on thousands of observations of undergraduates at four large universities over three decades, so it is focused on undergraduate-level economics.

Studying economics also affects your position on several public policy issues. Of seven issues, economics courses were significantly associated with the five following positions (Table 6):

  • Tariffs are bad.
  • Trade deficits are not so bad.
  • The government should not cap oil prices in response to a supply shock.
  • Raising the minimum wage increase unemployment for low-wage workers.
  • Income distribution should not be more equal.

These are all pro-free market, anti-government intervention positions.

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Why Do Harvard Kids Head to Wall Street?

By James Kwak

That’s the title of a post a couple weeks ago by Ezra Klein, in which he interviewed a friend of his who went to Wall Street after Harvard. Having seen this phenomenon from a couple of different angles, I’d say the interview is right on. This is how Klein summarizes the central theme:

“The impression of the Ivy-to-Wall Street pipeline is that it’s all about the money. You’re saying that it’s actually more that Wall Street has constructed a very intelligent recruiting program that speaks to the anxieties of the students and makes them an offer that there’s almost no reason to refuse.”

When I graduated from college, I had no interest in investment banking or its close cousin, management consulting. But I went to McKinsey for reasons that were only slightly different than those of the typical Ivy League undergrad; after getting a Ph.D. in history, I discovered that I was unlikely to get a good academic job and was pretty much unqualified for anything else, and McKinsey was one of the few places that would hire me into a “good” job with no discernible qualifications (other than academic pedigree). Now that I’m at Yale Law School, where maybe 15% of students (my wild guess) come in wanting to be corporate lawyers but 75% end up at corporate law firms (first job after law school, not counting clerkships), I’m seeing it again.

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Rewarding Teacher Performance? Resist the Temptation to “Race to Nowhere”

This guest post is contributed by Kathryn McDermott and Lisa Keller. McDermott is Associate Professor of Education and Public Policy and Keller is Assistant Professor in the Research and Evaluation Methods Program, both at the University of Massachusetts, Amherst.

On March 29, the U.S. Department of Education announced that Delaware and Tennessee were the first two states to win funding in the “Race to the Top” grant competition.  A key part of the reason why these two states won was their experience with “growth modeling” of student progress measured by standardized test scores, and their plans for incorporating the growth data into evaluation of teachers.  The Department of Education has $3.4 billion remaining in the Race to the Top fund, and other states are now scrutinizing reviewer feedback on their applications and trying to learn from Delaware’s and Tennessee’s successful applications as they strive to win funds in the next round.

One of the Department’s priorities is to link teachers’ pay to their students’ performance; indeed, states with laws that forbid using student test scores in this way lost points in the Race to the Top competition.  A few months ago, James pointed out some of the general flaws in the pay-for-performance logic; here, our goal is to raise general awareness of some statistical issues that are specific to using test scores to evaluate teachers’ performance.

Using students’ test scores to evaluate their teachers’ performance is a core component of both Delaware’s and Tennessee’s Race to the Top applications.  The logic seems unassailable: everybody knows that some teachers are more effective than others, and there should be some way of rewarding this effectiveness.  Because students take many more state-mandated tests now than they used to, it seems logical that there should be some way of using those test scores to make the kind of effectiveness judgments that currently get made informally, on less scientific grounds.

The problem is that even if you accept the assumption that standardized tests convey useful information about what students have learned (which we both do, in general), measuring the performance gains (or losses) of students in a particular classroom is far more complicated than subtracting the students’ September test scores from their June test scores and averaging out the gains.  We’re concentrating on the statistical issues here; there are other obvious challenges in test-based evaluation, such as what to do for teachers who teach grade levels where students do not take tests and/or subjects without standardized tests.

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It’s Not That Easy

By James Kwak

Elizabeth Green (hat tip Ezra Klein) discusses the importance of teaching techniques. Here’s one key passage (at least for people like me):

“The testing mandates in No Child Left Behind had generated a sea of data, and researchers were now able to parse student achievement in ways they never had before. A new generation of economists devised statistical methods to measure the ‘value added’ to a student’s performance by almost every factor imaginable: class size versus per-pupil funding versus curriculum. When researchers ran the numbers in dozens of different studies, every factor under a school’s control produced just a tiny impact, except for one: which teacher the student had been assigned to.”

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Leading Indicator of Me

If I ever go to another school, you should run away from it as fast as you can. That is the practical implication of Felix Salmon’s post a few days ago rounding up arguments for why you should not get a Ph.D. in the humanities or go to law school.

Thomas Benton’s article, “Graduate School in the Humanities: Just Don’t Go,” nails the basic reasons why I went to UC Berkeley nineteen years ago: excitement in the subject, a history of high grades, the comforting structure of academia, romanticization of university life, and no practical application of academic skills. (See the six bullets halfway down the article.) When I left Berkeley in 1997, I could not get an academic job that I wanted … and the rest is history, I guess. If you do get a Ph.D. in the humanities these days, the numbers are even more heavily against you than they were then. First, American universities as a whole are shifting from tenure-track jobs to untenured adjunct positions; second, within universities, the jobs are shifting from the humanities into vocational fields like accounting and nursing. The ongoing bloodbath in state finances is only making things worse, since most of the good universities in the country are public.

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Long-Term Returns to Stimulus: Education

The fiscal stimulus debate is currently hampered by confusion over its objectives. On the one hand, one purpose of the stimulus is to generate economic activity quickly in order to boost aggregate demand and break the recessionary spiral we seem to be in. On the other hand, people rightly worry about the capacity of the government to spend large amounts of money quickly without wasting it, and argue that the money should be put to productive use, rather than paying people to dig holes and then fill them in again. (This is why you see (at least) two versions of criticism of the stimulus plan: on the one hand, the criticism is that the government is incapable of putting money to productive use; on the other hand, the criticism is that money for things like electronic health records will not be spent in time to have a short-term effect.)

My opinion is that both are valid purposes. There probably is a limit to the number of tens of billions of dollars the government can spend next month without wasting some of it. But given the projected duration of the output gap (the difference between potential and actual GDP, meaning that the economy is performing below its full-employment capacity), I think there is also value in programs that take several quarters to disburse their money – as long as those programs are also good investments.

One major area of spending is education, where the plan includes more than $150 billion in new spending over two years. While politicians (and economists) reflexively cite education as an area where investments can have positive long-term returns (through increases in productivity which increase GDP and our average standard of living), I wanted to see what empirical research there has been on this topic. There has been a lot of research on the impact on individuals’ earnings of additional education (this is a common example used in first-year statistics classes), but somewhat less on the impact on national economic growth.

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The Scariest Blog Post Ever

Seeking Alpha is perhaps the largest financial blog/blog aggregator around. And for at least a week now, one of their “most popular” posts has been The Scariest Chart Ever. Take a look. Then come back here.

The chart itself isn’t very scary. It shows that the amount borrowed by banks from the Federal Reserve – “Borrowings of Depository Institutions from the Federal Reserve” – has spiked from a trivial level (a few billion dollars) to several hundred billion. It sat at a trivial level because, in ordinary times, there is no reason for a bank to borrow at the discount window when it can borrow instead from another bank at a lower rate (since the Fed funds rate is usually lower than the discount rate). It has spiked up recently as a symptom of the credit crisis; basically, what the chart shows is that the Fed is doing its job of providing liquidity in a crisis.

What’s scary is that the author of the post claims that the chart shows “federal borrowing,” called it “the scariest chart ever,” and concluded, “Anyone still think there are not some rough patches down the road?” . . . and then this became the most popular post on the most popular financial blog in the world. And even though a few people (including me) tried to point out the basic error, the vast majority of the comments pile on to the idea that this chart shows a huge spike in government borrowing.

This is scary (actually, depressing might be a better word) for those of us who think that blogs (and the Internet in general) can serve a valuable purpose in disseminating useful information and allowing constructive discussion. It also points to the importance of financial education, although maybe this example is more about basic verbal education (read the title of the chart) and numerical education (read the numbers on the Y axis: if the chart says that government borrowing was a few billion dollars as recently as 2007, then there’s something wrong).

Update: I should point out that in general I think Seeking Alpha provides a useful service by aggregating information from a wide variety of blogs and using community techniques to filter through them. Among other things, they republish some articles that Simon and I write here. This example just shows that sometimes the community filtering technique produces weird results.