Council of Economic Advisors: College Pays. Grad School? Sh!

Or, “The Reality Behind AEI’s Reality Behind the Student Debt ‘Crisis'”

Speaking of student loans, I am directed to the American Enterprise Institute’s response to the Council of Economic Advisor’s (CEA’s), “Investing in Higher Education: Benefits, Challenges, and the State of Student Debt” (pdf).

Because I try to deliver early on my post titles rather than bury them, here’s the report’s chart on the crucial but under-emphasized dispersion of earnings by educational attainment for 35-44 year-olds with payroll incomes. (This cohort doesn’t seem so representative to me of recent student borrowers—and not in a good way, but that’s a different issue.)

CEA--State of Student Loans--Figure 5

Eyeballing the chart, more than a quarter of graduate-degree holders earn less than the median 4-year-degree holder in the same age bracket, and the bottom 25 percent of grads earn about $45,000 or less. The B.A.s earn between about $20,000 and $130,000 while the grads make roughly between $30,000 and $170,000. Graduates in between the 75th and 90th percentiles haul in nearly half the total difference. This wide dispersion cries for more analysis because graduate borrowing amplifies student debt loads. High debts and low incomes, even for this small group of debtors, tend to discredit the human capital hypothesis and the purpose of student lending.

But back to the reality behind the reality behind the- etc.

Critical readers should always be on their guards whenever someone characterizes the “student debt crisis.” Frequently it’s a strawman of the crisis writers want to discuss rather than how much of the unpayable debt will be written down in the future. In the AEI’s case, the crisis is, “[T]he macroeconomic impact of high debt levels.” Here, AEI takes this to mean the stock of $1.3 trillion of debt.

The AEI post turns to its education scholars, startlingly Jason Delisle, who perhaps has moved on from the New America Foundation. Delisle focuses first on the claim that “student debt is holding back the economy.” The CEA report attempts to discredit this position in six ways. One, student debt is not as big as the mortgage bubble (which I don’t think I’ve seen anyone argue for a few years now). Two, hardship today will be offset by the future productivity unleashed by education. Three, everyone borrowed student loans when the opportunity costs were lowest, so high debt levels are in step with the economy and not undermining it. Four, student debt is only slightly reducing homeownership among young people. Five, student loans only reduce auto debt for high-balance debtors. Six, student-loan debts reduce small-business formation and their incomes somewhat, but other factors are involved.

The study concludes, “Had the same students received an education without as many loans, the recovery would likely have been stronger, but not substantially so. Most individuals, and the economy as a whole, will benefit from the education made possible by student loans” (56).

In other words, the Obama administration is asking everyone to double down on its hope that all this education will pay off someday and the government won’t have to write down hundreds of billions of dollars in unpayable education debt, whether by forgiveness promises in repayment plans or new legislation. It’s a theme that crops up elsewhere in the report, and it suffers from two problems. One, higher education doesn’t correspond to higher aggregate incomes; rather it seems to be swapping high-school grads with college grads while keeping incomes flat. If college boosts incomes like video-game power-ups, then we’d expect exponential growth in aggregate incomes, but we’re not. And anyone who thinks the payoff will come later must explain why intervening variables aren’t involved, e.g. occupational differences, which would explain the wider earnings dispersions for the credentialed. The CEA gives us no confidence in its education bet.

Problem number two is that the report tends to side against studies produced by the Federal Reserve Bank of New York (especially those by Meta Brown, et al.) in favor of research producing more satisfying results. The impacts might be trivial, but the NY Fed found that youngish student debtors weren’t getting mortgages or were more likely to live with their parents than the unindebted (links buried here). Meanwhile, the report shoos away the Bennett hypothesis by claiming a lack of consensus, with the caveat that there may be some “administrative bloat” in colleges and universities. Consensuses are tough rhetorical animals to wrestle with and should require significant evidence to prove. A few studies here and there will not do it.

So back to AEI. When Delisle writes, “[Advocacy groups] say student debt is forcing people to delay things like buying a house, starting a family, all productive things. This report is pretty clear that isn’t the case,” he’s wrong. The report clearly concedes that student debt is negatively affecting the economy, albeit to a small degree, and thanks in part to wishing away contrary NY Fed studies and insisting that all the education will pay off someday.

To clarify, student debt is a notable if not primary contributor to a generational disaster dominated by the trade deficit or slack aggregate demand—and new student borrowing is declining—but the CEA report isn’t the source to show it. So that’s a strike against Delisle.

He asks:

Why are millions of borrowers flocking to enroll in a program [IBR, PAYE, REPAYE, etc.] that allows them to cap their student loan payments at a small share of their income if the return on an educational investment are large? Something seems amiss there. I’ve done a lot of work showing that the income-based repayment program is too generous as a result of Obama administration changes, which may explain this disconnect.

I’ve answered the first question already: There is no large, aggregate return to higher education. As to Delisle’s work on the changes to IBR, it’s never demonstrated that the programs are too generous because it’s based on lopsided, self-verifying hypotheticals. In fact, according to a GAO study, in 2014 only 2 percent of debtors in IBR or PAYE plans earned more than $80,000, so Delisle’s mythical IBR deadbeat is not a serious policy concern. Amusingly, Delisle’s reaction to the GAO study at the time was to blame debtors for not making enough money, gasping that they’d use IBR plans for long-term rather than short-term debt relief.

AEI then turns to resident scholar Andrew Kelly, who writes, “Lower interest rates [proposed by Democrats] won’t help folks with small balances who aren’t repaying nearly as much as they’ll help those with average or large balances, most of whom have no trouble repaying because they have the highest educational attainment!”

I have problems with the Warrenian interest-rate proposals too, but Kelly makes the frequent mistake of flipping the income and debt variables to conclude that high-balance debtors are deadbeats, even though the CEA report shows a wide income dispersion for graduate-degree holders.

I admit I didn’t give the CEA report a thorough read, but it looks like the AEI scholars didn’t either.

Office of Management and Budget: +$1.1 Trillion in Direct Loans by 2026

…Which is down from $1.4 trillion by 2025 as predicted last year.

Every year in July the Office of Management and Budget (OMB) publishes its Mid-Session Review of the budget, which includes the Federal Direct Loan Program, and projects its future. The federal government’s direct loans consist primarily of student loans, but there are a few other programs in there. However, federal direct loans do not include private student loans, but these are a small percentage of all student loans. Thus, the OMB’s measure is both over- and under-inclusive of all student debt, but it covers most of it.

The OMB classifies direct loan accounts as financial assets totaling $1.144 trillion in 2015. According to the office’s projections, by 2026 this figure will grow to $2.213 trillion—93 percent.

Projected Direct Loan Balances (OMB, Billions Current $)

(Source: OMB FY2017 Mid-Session Review (pdf))

As with previous years, the current direct loan balance is below the OMB’s past projections. For FY2012, it predicted the balance would be $1.363 trillion by 2015, $219 billion (19 percent) higher than what actually occurred. Even last year, the OMB’s estimate for 2015 was still high by 4 percent. Here are the OMB’s direct loan projections going back to FY2010.

Direct Loan Balance Projections (OMB Billions Current $)

Because the OMB expects GDP to grow as well over this time period (we’d have bigger problems than student loans if it didn’t), the ratio of direct loans to GDP will level off below 8 percent over the next decade.

The OMB’s measure of direct loans is the net amount owed to the government, and the annual changes to that amount are not the same as the amount lent out each year to students. The Department of Education tracks its lending, and I last discussed it here. As of 2015, fewer students were borrowing from the federal government, so lending appears to be declining. The newly implemented gainful employment rule might further reduce student lending as well. These factors may explain why the OMB’s projections keep falling short. Consequently, I don’t believe student debt will exceed $2 trillion.

No Libertarians, the ABA Does Not Control The Supply of Lawyers

Writing for Forbes, University of Chicago law professor Todd Henderson explains to us “Why Lawyer Salaries Are Skyrocketing.” Although he attributes most of the cause of the big-law salary hike to the libertarian red-tape boogeyman, Henderson opens the article with long-falsified supply-side reasoning.

On the supply side, the American Bar Association operates a state-approved cartel, which uses a licensing regime to artificially limit the supply of legal services. In a recent white paper, the White House came out against occupational licensing in general, and breaking the ABA cartel would be a good first step in addressing the staggering growth in lawyer pay.

The last time I recall encountering the “ABA attorney shortage” claim in any depth was two years ago when Michael Lind on Salon told us that that the ABA controls the supply of lawyers. Henderson’s argument though more predictably libertarian is nevertheless surprising because only a month ago The New York Times explored law-graduate underemployment in depth. The natural question is, how can Henderson discuss an attorney shortage while graduates a state away from him struggle to find work at far less pay?

In recent years bar-passage rates have played a role in graduate underemployment to some extent, but not all of the 5,004 unemployed or unsurveyed class of 2015 graduates failed the bar. Another 5,400 graduates were in JD-advantage jobs, which frequently includes positions that could be filled with people with less education. These graduates should be pushing lawyer pay down, and this is prior to any discussion of whether big law salaries should track inflation.

Then of course, there’s the fact that payroll lawyers’ incomes have been flat for quite a while.

10th to 90th Percentile Dispersion of Annualized OES Lawyer Incomes

From a business perspective, law firms could also take the same amount of money and substitute more new associates for the same (or less) pay to cover demand for their services. That is, if demand for their services is really an issue.

Then of course, there’s the ABA’s accrediting power, which a Department of Education panel threatened with a one-year suspension not because it’s refusing to accredit more law schools but because it’s accrediting law schools with insufficient regard to graduates’ employment outcomes.

Cleary other forces are responsible for the ~$20,000 big-law pay raise. I insist I’m not a biglawologist and other voices such as Steven Harper are vastly more credible than I am on the subject, but anyone who thinks ABA rules are choking lawyer supply doesn’t have much credibility when it comes to regulatory boogeymen either.

Let’s Shift St. Paul, Minnesota’s, Property Taxes Onto Land!

At last, I’ve found time to write! This will be the first in a while for a while. A few months back I threatened readers with posts on real-estate mapping and assessment, and I make good on my threats.

Background: When I moved to the Twin Cities, I joined Minnesota’s Common Ground chapter, and this year its intern introduced some of us to the black arts of geographic information system/science (GIS) software. GIS is a discipline full of jargon, like “vectors” and “table joins,” that I find silly, but they’re certainly more sensible than “JD Advantage.”

The purpose of our inculcation is to help Common Ground advocate for land-value tax districts in Minnesota cities as a pilot program toward enabling municipalities to adopt split-rate taxation like Pennsylvania’s. The goal is to observe the effects of removing taxes on improvements and replacing them on locations to encourage development. Connecticut enabled a pilot program like this in 2014 (and it’s being extended), so LVT of a kind is in the policy air. So far, Common Ground’s efforts have successfully resulted in the introduction of a bill in the Minnesota House of Representatives.

Oh, and I claim zero credit for any of this.

But I do have newfound technical skills to unleash on readers (actually, a lot of the work is done in MS Excel, which is old hat around here), so with the open source QGIS and the state’s wonderful MetroGIS’s database in hand, here’s how a property tax shift would affect St. Paul, Minn.—because I was born there, and it’s the city we started our training with.

Boom:

St Paul Shift Small

Click to enlarge.

What you’re seeing is the distribution of the percent changes in property taxes for each parcel, divided into negative changes (blue, decreased charges) and positive changes (red, increased charges), and excluding tax-exempt properties. Because more parcels’ property taxes would be cut by the shift (yay!) than raised, the number of blue/red parcels isn’t equal.

Here’s a histogram of the percent changes of property taxes by percentile for single-unit residential lots claimed as homesteads, which dominate the city’s land use by nearly two-thirds of all properties and are probably the most salient politically.

Distribution of Property Tax ChangesThe results for these properties is inauspicious. A bare majority, 52 percent, would get breaks, and the remaining homeowners would pay more by comparison.

I’ve included a macro table for reference below, but generally, the tax shift would move the property tax to residential parcels and off commercial lots. Vacant lots would pay more—as they should—but there aren’t many of them in St. Paul.

One of the biggest conceptual problems with estimating the effects of a land-tax shift is that the current property-tax system discriminates among property classes. Residential lots on average pay less than commercial lots—by design. Single-unit homesteaders pay on average 1.52 percent of their assessed values in property taxes, and commercial owners pay 4.24 percent. Meanwhile, vacant residential lots pay 8.08 percent, illustrating existing progressivity in the property-tax system.

Consequently, much of the effect of a property-tax shift is really just equalizing the tax rate on all parcels, eliminating discrimination among property classes. In fact, a land-value tax shift starting from a hypothetical flat property-tax rate that includes buildings is better for single-unit residential landowners. Unsurprisingly, Common Ground Minnesota explores what happens when residential parcels are treated differently than other types in its advocacy.

Let’s return to the map above. Comparing the top 10 percent of property-tax reductions to the top 10 percent of increases, the property-tax burden is lifted most from the northeast part of St. Paul and downtown, and moved to the southwest part of the city, which is mostly residential.

These results don’t fill me with warm fuzzies, but the underlying issue is not who would pay but who isn’t currently paying, i.e. it’s the land-value assessments. Minnesota requires properties to be assessed at their fair-market values. These estimates are fed into multiple formulae to arrive at properties’ tax capacities, and then tax authorities apportion levies against all properties based on these tax capacities. When properties are under-assessed, they receive a hidden tax break; when they’re over-assessed, they opposite is true. Between commercial landowners and homeowners, guess who gets the hidden breaks?

To illustrate why I think St. Paul’s real estate isn’t properly assessed, here’s a map of St. Paul’s land values per square foot, including only the bottom 10 percent of parcels and top five percent.

St Paul LVPSF

Click to enlarge.

I’ve zoomed the map to the southwest part of St. Paul, and while you can see that the downtown cluster includes much real estate in the top 1 percent by value, some of it streaks west along Grand Ave. There are a few other peculiar concentrations of 1 percent real estate: a few blocks south of St. Catherine University (St. Kate’s to the locals), and a few blocks south of the University of St. Thomas’ divinity school (the law school’s campus occupies a surprisingly large chunk of land in nearby downtown Minneapolis). Slightly less valuable real estate lies along University Ave. (where the light rail connects the Twin Cities) and the L-shape along Cretin Ave. and Ford Parkway.

Most of the top .01 percent is downtown (~$56 per square foot), but some of it is still in these areas. Naturally, you may be wondering why property owners with the most valuable land aren’t demanding their properties be rezoned so they can build office towers in western St. Paul. The answer is that much commercial real estate is under-assessed, and many parcels’ values are malapportioned between buildings and land. (I have an acquaintance who recently bought a decades-old house on a plot of land valued at a mere $5,000.)

Although Minnesota’s tax authorities go to great lengths to ensure assessments are fair, notably sales-ratio equalization estimates of numerous parcels, these methods only include properties that were sold in arms-length transactions. One ongoing problem I’ve identified is that commercial real estate transactions differ substantially from those of owner-occupiers. The wealthy simply buy land differently from the rest of the populace.

For example, on November 10, 2015, the First National Bank building sold for $37.25 million but its assessed value as of January was only $25.5 million. Why? Because according to the Ramsey County Assessor’s Office, it was a “Not Typical Market” transaction, so it was disqualified from the sales-ratio equalization analysis.

Here’s another fun one: A Walgreens on Larpenteur Ave. (which might actually be in Roseville) sold for $11.2 million in April 2013, but was excluded from the sales-ratio equalization study because of “unusual financing.” The same goes for the Walgreens on Ford Parkway, which sold for $13.9 million but was assessed at $3.2 million.

Other times sales that qualify for the sales-ratio equalization analysis still result in assessments that are below their sales prices, e.g. the lot on 240 4th St. East, which sold for $800,000 in March 2015 but was assessed at only $286,300 in 2016. The biggest offender I’ve found in my casual search is ten vacant lots along Dunlap St. that sold for $7.5 million and have been assessed at about $3,200 per parcel. You’d think qualified sales of vacant real estate would be assessed at something close to their sale prices, but they’re simply not. This results in large property-tax breaks for wealthy landowners and an increased burden on everyone else.

If I were a St. Paul homeowner (and I know a bunch), I would grab my pitchfork and march on the Ramsey County Assessor’s Office and demand the city’s land values, especially its commercial land, be properly assessed according to law. I’m confident that would shift some of the property-tax burden away from homeowners and onto downtown landowners without affecting their property values. Municipalities should also rely more on mass building-residual assessments to arrive at more accurate land (and building) values, echoing the negative corporate land values in 2009 that I wrote about a few months back. I believe better assessments would make land-value-only property taxes more attractive to single-unit homesteaders than the current system illustrates.

Appendix:

Here’s the macro table of what the LVT change would do to the majority of the city’s parcels.

Macro Tax Shift Table

Click to enlarge, if you dare.

********************

Change in Graduate Outcomes Driven by Small Jobs

My second cut at the class of 2015 employment data:

Comparing the law-school classes of 2015 to 2014 (and excluding our three Puerto Rico law schools), there were 3,772 fewer graduates, a decline of 8.7 percent. Four employment categories constituted nearly 90 percent to this change: bar-passage-required jobs (52%), JD-advantage jobs (13.1%), law-school-funded jobs (14.3%), and unemployed grads seeking jobs (9.5%).

Changes among the employment types accounted for 85 percent of the 3,772 fewer graduates. The four largest drivers were 2-10-lawyer practices (25.9%), business-and-industry jobs (23.6%), government jobs (11.6%), and public interest jobs (7.5%).

Finally, I looked at the distribution of graduates among the employment categories and statuses by their Gini coefficients. Some of these are more informative than others given the small number of grads that fit into some of them, e.g. the two dozen employed – undeterminable grads. There’s nothing unexpected here. Aside from solos and unknowns, outcome inequality at law firms increases with firms’ sizes. Federal clerkships are still doled out like income in a landlocked, kleptocratic, military dictatorship. Public interest jobs aren’t so easy to come by either, which casts some doubt on the willingness of grads to take them given their student loan burdens.

In all, I’m surprised so little of the decline is attributable to fewer unemployed grads. Instead, it appears that small-law and non-law jobs took much more of the hit. The change in how law-school-funded jobs are tallied distorts these results somewhat, and I look forward to years in which the employment criteria remain constant. At least the categories and statuses (mostly) added up correctly.

Here’s an analytic table I base these opinions on.

EMPLOYMENT CATEGORY NO. OF GRADS GRADS PCT. OF TOTAL PCT. CHANGE IN GRADS DISTRIBUTION OF CHANGE IN GRADS GINI COEFFICIENT
2014 2015 2014 2015 2015 2015 2014 2015
Employed – Bar Passage Required 26,794 24,832 62.0% 63.0% -7.3% 52.0% 0.30 0.32
Employed – JD Advantage 5,913 5,420 13.7% 13.7% -8.3% 13.1% 0.36 0.37
Employed – Professional Position 1,787 1,634 4.1% 4.1% -8.6% 4.1% 0.49 0.53
Employed – Non-Professional Position 600 537 1.4% 1.4% -10.5% 1.7% 0.57 0.54
Employed – Law School 1,577 1,037 3.7% 2.6% -34.2% 14.3% 0.73 0.79
Employed – Undeterminable 21 25 0.0% 0.1% 19.0% -0.1% 0.93 0.94
Employed – Pursuing Graduate Degree 693 649 1.6% 1.6% -6.3% 1.2% 0.44 0.50
Unemployed – Start Date Deferred 313 285 0.7% 0.7% -8.9% 0.7% 0.64 0.63
Unemployed – Not Seeking 553 494 1.3% 1.3% -10.7% 1.6% 0.54 0.57
Unemployed – Seeking 4,103 3,744 9.5% 9.5% -8.7% 9.5% 0.43 0.47
Employment Status Unknown 841 766 1.9% 1.9% -8.9% 2.0% 0.67 0.68
Total Graduates 43,195 39,423 100.0% 100.0% -8.7% 100.0% 0.27 0.29
EMPLOYMENT STATUS
Solo 902 653 2.1% 1.7% -27.6% 6.6% 0.51 0.53
2-10 7,657 6,680 17.7% 16.9% -12.8% 25.9% 0.34 0.33
11-25 1,875 1,737 4.3% 4.4% -7.4% 3.7% 0.37 0.39
26-50 1,036 942 2.4% 2.4% -9.1% 2.5% 0.42 0.44
51-100 799 807 1.8% 2.0% 1.0% -0.2% 0.46 0.46
101-250 1,090 952 2.5% 2.4% -12.7% 3.7% 0.50 0.52
251-500 1,082 1,059 2.5% 2.7% -2.1% 0.6% 0.66 0.66
501-PLUS 3,968 4,008 9.2% 10.2% 1.0% -1.1% 0.78 0.77
Unknown 247 254 0.6% 0.6% 2.8% -0.2% 0.80 0.83
Business Industry 6,608 5,718 15.3% 14.5% -13.5% 23.6% 0.35 0.36
Government 5,038 4,602 11.7% 11.7% -8.7% 11.6% 0.32 0.33
Public Interest 2,160 1,878 5.0% 4.8% -13.1% 7.5% 0.50 0.51
Federal Clerkship 1,275 1,222 3.0% 3.1% -4.2% 1.4% 0.67 0.70
State/Local Clerkship 2,056 2,007 4.8% 5.1% -2.4% 1.3% 0.57 0.58
Other Clerkship 37 125 0.1% 0.3% 237.8% -2.3% 0.93 0.86
Education 772 635 1.8% 1.6% -17.7% 3.6% 0.46 0.49
Unknown Employer Type 90 206 0.2% 0.5% 128.9% -3.1% 0.84 0.94
Total Employed by Type 36,692 33,485 84.9% 84.9% -8.7% 85.0% 0.29 0.30

That’s all.