Results for “education”
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Competency-based education comes to Wisconsin (hi future)

From The Chronicle:

Later this year Wisconsin’s extension system will start a competency-based learning program, called the Flexible Option, in which students with professional experience and training in certain skills might be able to test out of whole courses on their way to getting a degree.

Competency-based learning is already famously used by private institutions like Southern New Hampshire University and Western Governors University, but Wisconsin will be one of the first major public universities to take on this new, controversial form of granting degrees. Among the system’s campuses, Milwaukee was first to announce bachelor’s degrees in nursing, diagnostic imaging, and information science and technology, along with a certificate in professional and business communication. UW Colleges, made up of the system’s two-year institutions, is developing liberal-arts-oriented associate degrees. The Flex Option, as it’s often called, may cost the Wisconsin system $35-million over the next few years, with half of that recovered through tuition. The system is starting with a three-month, all-you-can-learn term for $2,250.

If done right, the Flex Option could help a significant number of adults acquire marketable skills and cross the college finish line—an important goal in Wisconsin, which lags behind neighboring states in percentage of adults with college diplomas. There are some 800,000 people in the state who have some college credits but no degree—among them Wisconsin Gov. Scott Walker, who dropped out of Marquette University. He had pushed the university system to set up the Flex Option early last year, when he was considering inviting Western Governors to the state to close a statewide skills gap in high-demand fields like health care, information technology, and advanced manufacturing.

The article seems to suggest that some professors in the Wisconsin system are opposed to this innovation.  Developing…

The high price of land in Singapore, educational Morlocks edition

At N.T.U., a group of researchers has spent the past year gathering available data on the university’s surface topography and subsurface geology.

The preliminary survey, completed late last month, found that the campus, which is in western Singapore, offers opportunities for underground space development. Extensive investigations indicated that rock strata 20 to 30 meters, or 66 to 98 feet, below the surface, are suited for cavern construction with spans as wide as 20 meters wide.

“In the long term, the university may need to go underground” to accommodate projected increases in the student population, said Zhao Zhiye, one of four researchers who worked on the study.

…Designed for both learning and socializing, the learning complex — a group of interconnected caverns — would include the university’s main library, a museum, study rooms, cafeterias and conference halls. The sports hall, beneath the existing university sports complex, would house basketball, badminton and table tennis courts, swimming pools and spectator stands.

There is more here, and here is the previous installment in the series.

President Obama’s new higher education plan

Here is one summary:

“Early Thursday, he released a plan that would:

  • Create a new rating system for colleges in which they would be evaluated based on various outcomes (such as graduation rates and graduate earnings), on affordability and on access (measures such as the percentage of students receiving Pell Grants).
  • Link student aid to these ratings, such that students who enroll at high performing colleges would receive larger Pell Grants and more favorable rates on student loans.
  • Create a new program that would give colleges a “bonus” if they enroll large numbers of students eligible for Pell Grants.
  • Toughen requirements on students receiving aid. For example, the president said that these rules might require completion of a certain percentage of classes to continue receiving aid.”

There is another summary here.

So far I don’t get it.  There seems to be plenty of information about colleges, and I doubt if a federal rating system would improve on those ratings already privately available.  To the extent that federal system became focal, the incentives to game and scheme it would become massive, and how or whether to punish the gamers, if and when they are caught, would be a political decision.  I don’t see that as healthy.

Given that previous educational subsidies mostly are converted into higher rates of tuition and thus captured by the school, the second plank would simply boost the subsidy to high performing colleges.  There are plenty of ways to do that and in any case it doesn’t seem to help today’s marginal students, who probably cannot do well in those environments in any case.  Furthermore colleges with high graduate earnings are very often those located in or near high-paying cities.  Should we be subsidizing on that basis?  Should we be giving colleges an incentive to identify and deny admission to potential lower earners?  Do we really want the federal government helping to crush humanities majors?  And I don’t see that the kind of rating system under discussion here is measuring actual value added, ceteris paribus of course.

I am not opposed to tougher requirements for aid recipients, but again there is a danger of gaming.  For instance the aid recipients might simply choose easier classes and majors and aid-hungry colleges might very well accommodate them and make things as easy as they need to.

On the third plank, I don’t think the problem is that Pell Grant recipients cannot get into a good enough college.  The problem, insofar as there is one, relates to how well they do once they show up, given what is often inadequate preparation.  Encouraging now-rejecting colleges to accept them will if anything lure them into environments they are not capable of handling.

I would find it helpful if this proposal would outline the core, underlying theory of market failure in higher education, and then how these ideas would fix it.  It is difficult for me to put that argument together in my mind.  I do get the intuitive reason why “aid should be tied to outcomes.”  But presumably students, who already have by far the most at stake in choosing a college, already allocate their own dollars and aid dollars on the basis of outcomes.  If that process isn’t broken, this plan seems to address a pseudo-problem.  If that process is broken (misguided students?), we need to know whether this plan really will fix the kink in the system.  For instance if students cannot right now choose the schools offering the best expected outcomes for them, this plan seems to work mighty hard to get the schools to do the choosing for them, but in reality only ends up putting the students into tougher and less appropriate institutions.  Can you spell “remedial”?  In any case, under these assumptions, it would seem to be the students who need the fixing, not the schools.  And so on.

I do like this part:

Further, the administration is promising to issue “regulatory waivers” for “high-quality, low-cost innovations in higher education, such as making it possible for students to get financial aid based on how much they learn, rather than the amount of time they spend in class.”

Overall the ideas here strike me as underdeveloped in terms of logic.  Perhaps the plan will have positive effects simply through the “bully pulpit” medium.

The future of higher education in Alabama?

The video that was posted online appeared to be a tour of the spa area at some swanky new hotel.

There were cascading waterfalls into hot and cold pools. There was an arcade section. A smoothie bar. Flat-screen TVs adorned every open space. There were lockers the members at Augusta National would find acceptable.

This was luxury, no doubt. But it was not at a hotel.

Instead, this shaky video tour was of the inside of a college football team’s training and lounge area. Specifically, it is the training, weight room and lounge area within the Mal Moore Athletic Complex on the campus of the University of Alabama.

Pricetag: $9 million. (And that’s just for the upgrades. The original facility, which opened in 2005, cost about $50 million.)

The host school, the University of Alabama, raised tuition seven percent last year.

Competition in higher education

When undergraduate students at Southern Methodist University peruse their course catalogs this fall, several listings may strike them as odd.

First, the courses will be taught entirely online—an option that Southern Methodist has never before offered to undergraduates.

Second, the courses will be taught by professors at other universities—including Emory University, the University of Notre Dame, and Washington University in St. Louis, among others.

Southern Methodist, along with Baylor University and Temple University, plans to announce on Tuesday that it will allow undergraduate students to take online courses from other colleges for credit.

The courses, offered through the online-education company 2U, will come from a consortium of colleges participating in 2U’s Semester Online program, which is focused on undergraduate education at selective institutions.

Southern Methodist, Baylor, and Temple will be “affiliates” of the program, meaning they will not produce courses but will list certain courses developed by other members of the consortium and will grant “elective credit”—that is, general-education credit—to students who pass.

Participating in the 2U consortium as an affiliate will allow Southern Methodist to see how well online courses work for its students without committing resources to building its own, said Stephanie Dupaul, associate vice president for enrollment management at the university.

Here is more, via Phil Hill.

In California, however, plans for for-credit MOOCs in public universities have been put on hold.

Higher education in Greece

From a recent article:

“He says his name is George but declines to give his last name. He’s 29 years old, holds a master’s degree in economics, and has been unemployed for a year and a half, not counting the five months he worked as a street cleaner.

“It’s more difficult for the highly qualified,” he says. “The market thinks we will cost too much.” He’s applying for a position as a secretary, a job that requires a high school degree. For a couple of minutes, he and Stratigaki discuss whether his education will be an asset or a liability, and then their names are called.”

The article is here, sad throughout.  For the pointer I thank George Hawkey.

For-profit education is better than we thought

From today’s Inside Higher Ed, there is now a revised version (pdf) of the Kevin Lang and Russell Weinstein paper which was very critical of the returns from for-profit education.  The new results are more like this:

The two economists, who were not available for comment, apparently tweaked their methodology and came to a different conclusion about the relative value of credentials earned at for-profits.

“We find no statistically significant differential return to certificates or associates degrees between for-profits and not-for-profits,” they wrote in the paper, which was released last month.

Certificate holders from for-profits tended to fare slightly worse in the job market, according to the study, while associate degrees from for-profits were worth slightly more than those from nonprofit institutions. Hence no clear winner emerged.

The revised paper still included some worrisome findings about for-profits. Those colleges are typically more expensive than their nonprofit counterparts, particularly community colleges. For-profits charged an average of $6,300 more in annual tuition for certificate programs, according to the study’s sample, and $6,900 more per year for associate degrees.

“The return on investment is undoubtedly lower at for-profits,” the paper said.

At least this time around, the real world falls in line (somewhat) with the theory.

How much of education and earnings variation is signalling? (Bryan Caplan asks)

On Twitter Bryan asks me:

Would you state your human capital/ability bias/signaling point estimates using my typology?

He refers to this blog post of his, though he does not clearly define the denominator there: is it percentage of what you spend on education or explaining what percentage of the variation in lifetime earnings?  I’ll choose the latter and also I’ll focus on signaling rather than trying to separate out which parts of human capital are from birth and which are later learned.  My calculations are thus:

1. I don’t wish to count “credentialed” occupations, where you need a degree and/or license, but you are reaping rents due to monopoly privilege.  It’s neither human capital nor signaling (it’s not about intrinsic talent), though you could argue it is a kind of human capital in rent-seeking.  In any case, let’s focus on private labor markets without such barriers.

2. Most capital and resource income is due to factors better explained by human capital theories or due to inheritance.  That is more than a third of earnings right there.  Note that the higher is inequality, the less the signaling model will end up explaining.  That is one reason why the signaling model has become less relevant.

3. Depending on job and sector, what you’ve signaled, as opposed to what you know, explains a big chunk of wages in the first three to five years of employment.  Within five years (often less), most individuals are earning based on what they can do, setting aside credentialism as discussed under #1.  Here is my earlier post on speed of employer learning.

Keep in mind that everyone’s wages change quite a bit over their lifetime and that is mostly not due to retraining (i.e., changes in the educational signal) in the formal sense, as most people stop formal retraining after some point.  The changes are due to employer estimates of skill, modified by bargaining power.  In this sense all theories are predominantly human capital theories, whether they admit it or not.

To be generous, let’s give Bryan the full first five years of income based on signaling alone, out of a forty year career.  And let’s say that on average wages rise at the rate of time discount (not true as of late, but a simplifying assumption and I think Bryan believes in a claim like this anyway.)

How much of income is explained by signaling?  I’m coming up with “1/8 of 2/3,” the latter fraction referring generously to labor’s share in national income.  That will fall clearly under ten percent, but recall I’ve inserted some generous assumptions here.

Bryan wants to call me “a signaling denialist,” yet I see signaling as still very important for understanding some aspects of the labor market.  But it’s far from the main story for the labor market as a whole, especially as you move into the out years.

That all said, this “decomposition” approach may obscure more than it illuminates.  Let’s consider two parables.

First, imagine a setting where you need the signal to be in the game at all, but after that your ingenuity and your personal connections explain all of the subsequent variation in income.  Depending what margin you choose, the contribution of signaling to later income can be seen as either zero percent or one hundred percent.  Signaling won’t explain any of the variation of income across people with the same signal, yet people will compete intensely to get the signal in the first place.

Second, in a basic signaling model there are two groups and one dimension of signaling.  That’s too simple.  A signaling model implies that a worker is paid some kind of average product throughout many years, but of course the reference class for defining this average product is changing all the time and is not, over time, based on the original reference class of contemporaneous graduating peers.  For the purposes of calculating your wage based on a signal, is your relevant peer group a) all those people who got out of bed this morning, b) all those people in the Yale class of 2012, or c) all those who have been mid-level managers at IBM for twenty years?  This will change as your life passes.

So there’s usually a signaling model nested within a human capital model, with the human capital model determining the broader parameters of pay, especially changes in pay.  The employer’s (reasonably good but not perfect) estimate of your marginal product determines which peer group you get put into, if you choose to invest in additional signals (or not).  The epiphenomena are those of a signaling model, but the peer group reshufflings over time are ruled by something else.  Everything will look like signaling but again over time signaling won’t explain much about the variation or evolution in wages.

Seeing the relevance of those “indeterminacy” and “nested” perspectives is more important than whatever decomposition you might cite to answer Bryan’s query.

Differential Pricing in University Education

Traditionally universities have charged every student the same tuition/price regardless of major. Under budget pressure, however, differential pricing is becoming more common. Differential pricing is tending to reduce the peculiar cross-subsidies that currently exist as pointed out in a new working paper by Kevin Stange (earlier version):

Higher education in the United States is heavily subsidized, both through direct support for institutions by state governments and private donors, and through federal and state support
directly to students. There are also substantial differences in the extent of subsidization across
institutions and sectors, with students at selective private institutions more heavily subsidized
than those at less selective institutions(Winston 1999). Less commonly noticed, however, is that
there are also large cross-subsidies between students within the same institutions due to the
conventional practice of charging similar tuition fees to all undergraduate students regardless of
the cost of instructing them. The cost of instruction differs tremendously between upper and
lower division coursework and across programs even within institutions. For instance, recent
analysis of cost data from four large state post-secondary systems (Florida, Illinois, New York‐
SUNY, and Ohio) indicated that upper division instruction costs approximately 40% more per
credit hour than lower division instruction, and that upper-division engineering, physical science,
and visual/performing art was approximately 40% more costly than the least costly majors
(SHEEO, 2010). In fact, an earlier but more extensive cost study found that more than three-fourths of the variance in instructional cost across institutions is explained by the disciplinary
mix within an institution (U.S. Department of Education 2003). The consequence is that lower division students subsidize upper-division students and students in costly majors are subsidized
by those in less expensive ones.

This pattern of cross-subsidization generally runs counter to differences in post-schooling
earnings and ability to pay. Lower division includes many students who eventually drop out,
while students that have advanced to upper division are more likely to graduate and earn more.
Engineering, science, and business majors tend to earn more and have higher returns than
education and humanities majors, even after controlling for differential selection of major by
ability (Arcidiacono 2004).

I have argued for targeting education subsidies to the majors that are most likely to have the greatest positive spillovers. Differential pricing moves prices closer to costs which opens up the possibility for more rational pricing but notice that it can in some cases move prices away from optimal subsidy levels.

Hat tip: Dubner at Freakonomics.

Is the labor market return to higher education finally falling?

Peter Orszag considers that possibility in his recent column.  About one in four bartenders has some kind of degree.  Orszag draws heavily on this paper by Beaudry and Green and Sand, which  postulates falling returns to skill.  It’s one of the more interesting pieces written in the last year, but note their model relies heavily on a stock/flow distinction.  They consider a world where most of the IT infrastructure already has been built, and so skilled labor has not so much more to do at the margin.  This stands in noted contrast to the common belief — which I share — that “IT-souped up smart machines” still have a long way to go and are not a mature technology.  You can’t hold that view and also buy into the Beaudry and Green and Sand story, unless you think we have suddenly jumped to a new margin where machines build machines, with little help from humans.

Rather than accepting “falling returns to skill,” I would sooner say that education doesn’t measure true skill as well as it used to.

The more likely scenario is that the variance of the return to having a college education has gone up, and indeed that is what you would expect from a world of rising income inequality.  Many people get the degree, yet without learning the skills they need for the modern workplace.  In other words, the world of work is changing faster than the world of what we teach (surprise, surprise).  The lesser trained students end up driving cabs, if they can work a GPS that is.  The lack of skill of those students also raises wage returns for those individuals who a) have the degree, b) are self-taught about the modern workplace, and c) show the personality skills that employers now know to look for.  All of a sudden those individuals face less competition and so their wages rise.  The high returns stem from blending formal education with their intangibles (there is also more pressure to get an advanced degree to show you are one of the privileged, but that is another story.)

This polarization of returns — among degree holders — explains both why incomes are rising at the top end, and why the rate of dropping out of college is rising too.  At some point along the way in the college experience, lots of students realize they won’t be able to “cross the divide,” and the degree alone won’t do it for them.  They foresee their future tending bar and act accordingly.

Too many discussions of the returns to education focus on the mean or median and neglect the variance and what is likely a recent increase in that variance.

The GMU/UVa wage disparity and the signalling model of education

It’s a well-known fact — well-known around GMU that is — that GMU graduates earn higher average salaries than do UVA grads (direct link here), that is for four year undergrads in their first year of employment.

It’s not just that UVa is in decline, or that some of them end up richer later in life.  Or others may use their wealthier parents to live in Williamsburg, Brooklyn and avoid direct employment.  A major reason for the wage discrepancy is simply that a disproportionate chunk of GMU students are likely to get jobs in the relatively high-paying Washington, D.C. area.

OK, so how does this relate to the broader ongoing debate over the signaling theory of education and wages?

It is widely accepted that UVa is a more exclusive school than GMU by the usual standards.  Yet here we see labor markets “seeing through” those credentials, and paying more to the GMU graduates.  In other words, labor markets are seeing that GMU students are, on average, “less exclusive by origin but will have a higher marginal product very quickly.”

The signaling model, in its simplest, most stripped down form, assumes that employers cannot judge the marginal products of individual new hires but instead pay them according to their credentials.  Yet here we have a case where employers seem quite willing to make a judgment about marginal product and indeed that is a judgment which contradicts data on exclusivity of academic origins.  Once you postulate that employers are willing to make estimates of individual marginal products which differ from the rankings that might be given by “raw ability,” the signaling model is  less applicable.  I don’t want to claim that the wages converge exactly on marginal products, but the credentials clearly are just one factor of many.  Employer judgments of expected marginal products are not dominated by credentials, and you can imagine that after having a worker for a year or two the credentials are even less important as a means of judging prospective marginal product.

Another way to put this point is that the speed of employer learning is in fact fairly rapid, and some of it happens before the job even starts.

How to make the rate of return on higher education negative

They’re signing up as we speak for a two-year degree course in heavy metal music (believed to be the first of its kind), which begins in September in a college in Nottingham.

…The degree organisers are loftily talking up the course by using terms such as “culture” and “context”. They point out that you can study music at Oxford, Cambridge or any other university, but that this “genre” degree is unique.

“Heavy metal is an extremely technical genre of music and its study is a rising academic theme,” they say. Metal is “seriously studied in conservatoires in Helsinki”, has classical music roots, and leading axe-men such as Joe Satriani incorporate the works of Paganini in their oeuvre.

Wow, Paganini.  Get this:

“It’s a degree, so it will be academically rigorous,” said Mr Maloy [the sequence designer].

And why Nottingham?:

Not only was Earache Records, a heavy metal-focused record label, founded in the city, but additionally, the region’s Download Festival appeals to over 75,000 rock and metal fans on an annual basis.

The course fees are £5,750 a year.  Here is a bit more information.

Online Education Trumps the Cost Disease

In a large, randomized experiment Bowen et al. found that students enrolled in an online/hybrid statistics course learned just as much as those taking a traditional class (noted earlier by Tyler). Perhaps even more importantly, Bowen et al. found that the online model was significantly less costly than the traditional model, some 36% to 57% less costly to produce than a course using a traditional lecture format. In other words, since outcomes were the same, online education increased productivity by 56% to 133%! Online education trumps the cost disease!

Bowen et al. caution that their results on cost savings are speculative and it is true that they do not include the fixed costs of creating the course (either the online course or the traditional course) so these cost savings should be thought of as annual savings in steady-state equilibrium. The main reason these results are speculative, however, is that Bowen et al. only considered cost savings from faculty compensation. Long-run cost reductions from space savings may be even more significant, as the authors acknowledge.

Bowen et al. also do not count cost savings to students. Based on my work with Tyler at MRUniversity, I argued in Why Online Education Works that students in online course can learn the same material in less time. Consistent with this, Bowen et al. found:

…that hybrid-format students took about one-quarter less time to achieve essentially the same learning outcomes as traditional-format students.

A 25% time-savings is significant. Moreover, the 25% time-savings figure is in itself an underestimate of savings since it does not include the time savings from not having to drive to class, for example.

Online education even in its earliest stages appears to be generating large improvements in educational productivity.