WSJ’s Student Loan Coverage Improves: More Facts, Fewer Deadbeats

And not just facts, neutral facts, which is how reporting is supposed to be. I’ve criticized The Wall Street Journal‘s student loan coverage, but its most recent article on the topic, “U.S. to Forgive at Least $108 Billion in Student Debt in Coming Years,” is a start in the right direction.

Okay, the title could use some work. More accurately, it should be something like: “GAO Projects U.S. Will Forgive $108 Billion in Student Loans in Coming Years.” It’s 76 characters, which is too long for most SEO-obsessed editors, but it doesn’t characterize a possibility as a certainty.

Conversely, the WSJ neglects to cite another GAO study on the subject of student debtors’ earnings. Its data are nearly two years old, but they show that 72 percent of people on income-sensitive repayment plans were earning $20,000 annually or less. Not even 10 percent of IBR and PAYE participants (157,000) made more than $40,000 per year.

Thus, the WSJ’s reasoning still follows a shaky line of reasoning:

(1) IBR participants’ debts are high,

(2) High debts are only feasible for grad students taking out Grad PLUS loans,

(3) Graduates tend to find jobs with high incomes and have low unemployment rates,

(4) So the benefits of IBR go to high-income people.

The prior GAO study pokes holes in (3) and (4). Income is the independent variable, not debt, and incomes are low. Still, the WSJ’s reporting this time inserts enough adverbs to qualify these claims that I’m going to give this an earned “C.” There is no grade inflation on this blog.

Oddly, in its haste to cover the GAO’s attacks on the government’s accounting for student loans, the WSJ neglects to include immanent compensating factors that will raise student debtors’ incomes: tax cuts, stimulus, job growth, a harried Fed, and 3-4 percent growth in the near future. Things will rapidly get better for America’s student debtors.

Gini Coefficients: How to Calculate Them in Excel and What The F They Mean

Social scientists and wonk readers are familiar with Gini coefficients, and while I’ve used them a few times myself, I have no idea what they really mean.

Okay, fine, I know what a Lorenz curve is, and I can explain it mathematically. But ultimately any understanding of the Gini coefficient is inherently relative, e.g. “They say .3 is good for income distribution, but anything above .5 is bad. The U.S. is .45, so that’s bad … ish.” It doesn’t say anything about the actual distribution.

Indeed, the first two Wonkblog posts I found in a brief Internet search describe the Gini coefficient essentially as a (well-known) measure of inequality. No details.

Vox is slightly better: “[I]t’s an incredibly abstract idea that’s difficult to verbally describe. [Thanks, Vox.] The advantage to using a gini coefficent is that in principle it summarizes all the information about the distribution of income and thus facilitates easy comparisons.”

Easy comparisons? To what? Other Gini coefficients? Whatever.

Still, aside from eyeballing a bunch of Lorenz curves—fun fact: I’m going to make you eyeball a bunch of Lorenz curves today—a Gini coefficient is totally undescriptive because it’s a decimal without any units.

This post tries to remedy that, but first, per the title here’s a big ol’ array formula for calculating Gini coefficients in MS Excel. The source is Excel and UDF Performance Stuff. I chose the Angus Deaton version and improved on it by replacing the ROW formulae with COUNTIFs because those account for blank cells in the data. It also avoids the pitfall of negative data numbers, which mess up Lorenz curves. However, as an array formula, it must be entered with CTRL-SHIFT-ENTER otherwise it won’t work. Behold:

((COUNTIF(Datarange,”>=0″)+1)/(COUNTIF(Datarange,”>=0″)-1)-2/(COUNTIF(Datarange,”>=0″)*(COUNTIF(Datarange,”>=0″)-1)*AVERAGEIF(Datarange,”>=0″))*SUM((IF(Datarange>=0,RANK(Datarange,Datarange))+((COUNT(Datarange)+1-IF(Datarange>=0,RANK(Datarange,Datarange,0))-IF(Datarange>=0,RANK(Datarange,Datarange,1)))/2))*IF(Datarange>=0,Datarange)))/(COUNTIF(Datarange,”>=0″)/(COUNTIF(Datarange,”>=0″)-1))

*Where “Datarange” is something like, “$B$1:$B$1000”.

The Web site contains a few other methods, but this one is the most comprehensive and isn’t limited to 4,000 data points. I’ve tested this version with hand-cranked Ginis, that is, sorting the data by size, creating a cumulative sum of them (the Lorenz curve), calculating a bunch of trapezoids among them to find the area under the Lorenz curve, adding those up, subtracting them by equivalent triangle of equality, and then dividing them by that triangle.

So what does the Gini coefficient mean? In other words, what kind of distribution can one imagine when provided with a given Gini number?

It’s a damn good question that I figured out an answer to. I created a bunch of columns in Excel with a bunch of RAND() formulae, gathered some statistics, and then averaged the results. The RAND() formula produces a random number from 0 to 1. The more of them you use, the bigger, more robust the distribution you get. A .1 is as likely as a .9, etc. The mean and median will be .5. But what’s the Gini coefficient of a large number of random numbers within a set range?

Answer: One third.

This is huge for me because now I know when someone says that the Gini coefficient for the income distribution in some country is .333333, I can visualize that it’s equivalent to a random income distribution. Because one third is such a round number, I had a hunch that taking each RAND() formula to different powers would yield other round Gini coefficients, so, here’s a table of Gini coefficients as a power of each data point from a random distribution. (And yes, this blog endorses the divisible properties of the number 12.) I’m adding similar information for the resulting Lorenz curves. It’s quite intriguing—and quite tedious because it takes a few minutes for my computer to spit out the numbers. But this blog wouldn’t be anything if it didn’t hog some clock cycles:

DATA POINTS SHARE OF MAXIMUM AT GIVEN GINI COEFFICIENT
POWER GINI MINIMUM 10TH PERCENTILE 25TH PERCENTILE MEDIAN 75TH PERCENTILE 90TH PERCENTILE MAXIMUM MEAN
1/12 .040 48.9% 82.5% 89.1% 94.4% 97.6% 99.1% 100.0% 92.3%
2/12 .077 24.2% 68.0% 79.4% 89.1% 95.3% 98.3% 100.0% 85.7%
3/12 .111 12.1% 56.1% 70.7% 84.1% 93.1% 97.4% 100.0% 80.0%
4/12 .143 06.1% 46.3% 63.0% 79.4% 90.9% 96.6% 100.0% 75.0%
5/12 .173 03.1% 38.2% 56.1% 74.9% 88.8% 95.7% 100.0% 70.6%
6/12 .200 01.6% 31.5% 50.0% 70.7% 86.7% 94.9% 100.0% 66.7%
7/12 .226 00.8% 26.0% 44.5% 66.8% 84.6% 94.0% 100.0% 63.2%
8/12 .250 00.4% 21.4% 39.7% 63.0% 82.6% 93.2% 100.0% 60.0%
9/12 .273 00.2% 17.7% 35.3% 59.5% 80.7% 92.4% 100.0% 57.2%
10/12 .294 00.1% 14.6% 31.5% 56.2% 78.8% 91.6% 100.0% 54.6%
11/12 .315 00.1% 12.0% 28.1% 53.0% 76.9% 90.8% 100.0% 52.2%
12/12 .334 00.0% 09.9% 25.0% 50.0% 75.1% 90.0% 100.0% 50.0%
16/12 .400 00.0% 04.6% 15.7% 39.7% 68.3% 86.9% 100.0% 42.9%
20/12 .454 00.0% 02.1% 09.9% 31.5% 62.1% 83.9% 99.9% 37.6%
24/12 .500 00.0% 01.0% 06.3% 25.0% 56.4% 81.0% 99.9% 33.4%
30/12 .555 00.0% 00.3% 03.1% 17.7% 48.9% 76.9% 99.9% 28.6%
36/12 .600 00.0% 00.1% 01.6% 12.5% 42.4% 72.9% 99.9% 25.1%
42/12 .636 00.0% 00.0% 00.8% 08.9% 36.8% 69.2% 99.9% 22.3%
48/12 .666 00.0% 00.0% 00.4% 06.3% 31.9% 65.6% 99.9% 20.1%
54/12 .692 00.0% 00.0% 00.2% 04.4% 27.6% 62.3% 99.9% 18.2%
60/12 .714 00.0% 00.0% 00.1% 03.1% 24.0% 59.1% 99.8% 16.7%
66/12 .733 00.0% 00.0% 00.0% 02.2% 20.8% 56.1% 99.8% 15.4%
72/12 .749 00.0% 00.0% 00.0% 01.6% 18.0% 53.2% 99.8% 14.3%
84/12 .777 00.0% 00.0% 00.0% 00.8% 13.5% 47.9% 99.8% 12.6%
96/12 .799 00.0% 00.0% 00.0% 00.4% 10.2% 43.1% 99.7% 11.2%
120/12 .833 00.0% 00.0% 00.0% 00.1% 05.8% 35.0% 99.7% 09.1%
144/12 .856 00.0% 00.0% 00.0% 00.0% 03.3% 28.3% 99.6% 07.7%
240/12 .909 00.0% 00.0% 00.0% 00.0% 00.3% 12.3% 99.4% 04.8%

And here is the same information for the Lorenz curves.

CUMULATIVE DATA POINTS SHARE OF MAXIMUM AT GIVEN GINI COEFFICIENT (LORENZ CURVES)
POWER GINI MINIMUM 10TH PERCENTILE 25TH PERCENTILE MEDIAN 75TH PERCENTILE 90TH PERCENTILE MAXIMUM MEAN
1/12 .040 00.0% 08.2% 22.3% 47.2% 73.3% 89.2% 100.0% 48.0%
2/12 .077 00.0% 06.8% 19.8% 44.5% 71.5% 88.5% 100.0% 46.2%
3/12 .111 00.0% 05.6% 17.7% 42.0% 69.8% 87.7% 100.0% 44.4%
4/12 .143 00.0% 04.6% 15.7% 39.7% 68.2% 86.9% 100.0% 42.9%
5/12 .173 00.0% 03.8% 14.0% 37.4% 66.6% 86.2% 100.0% 41.4%
6/12 .200 00.0% 03.1% 12.5% 35.3% 65.0% 85.4% 100.0% 40.0%
7/12 .226 00.0% 02.6% 11.1% 33.4% 63.5% 84.7% 100.0% 38.7%
8/12 .250 00.0% 02.1% 09.9% 31.5% 62.0% 83.9% 100.0% 37.5%
9/12 .273 00.0% 01.8% 08.8% 29.7% 60.5% 83.2% 100.0% 36.4%
10/12 .294 00.0% 01.5% 07.8% 28.1% 59.1% 82.5% 100.0% 35.3%
11/12 .315 00.0% 01.2% 07.0% 26.5% 57.7% 81.8% 100.0% 34.3%
12/12 .334 00.0% 01.0% 06.2% 25.0% 56.3% 81.1% 100.0% 33.3%
16/12 .400 00.0% 00.5% 03.9% 19.8% 51.2% 78.3% 100.0% 30.0%
20/12 .454 00.0% 00.2% 02.5% 15.8% 46.5% 75.6% 100.0% 27.3%
24/12 .500 00.0% 00.1% 01.6% 12.5% 42.3% 73.0% 100.0% 25.0%
30/12 .555 00.0% 00.0% 00.8% 08.9% 36.7% 69.3% 100.0% 22.3%
36/12 .600 00.0% 00.0% 00.4% 06.3% 31.8% 65.8% 100.0% 20.0%
42/12 .636 00.0% 00.0% 00.2% 04.4% 27.5% 62.4% 100.0% 18.2%
48/12 .666 00.0% 00.0% 00.1% 03.1% 23.9% 59.3% 100.0% 16.7%
54/12 .692 00.0% 00.0% 00.0% 02.2% 20.7% 56.3% 100.0% 15.4%
60/12 .714 00.0% 00.0% 00.0% 01.6% 18.0% 53.4% 100.0% 14.3%
66/12 .733 00.0% 00.0% 00.0% 01.1% 15.6% 50.7% 100.0% 13.4%
72/12 .749 00.0% 00.0% 00.0% 00.8% 13.5% 48.1% 100.0% 12.6%
84/12 .777 00.0% 00.0% 00.0% 00.4% 10.2% 43.3% 100.0% 11.2%
96/12 .799 00.0% 00.0% 00.0% 00.2% 07.6% 39.1% 100.0% 10.1%
120/12 .833 00.0% 00.0% 00.0% 00.1% 04.3% 31.7% 100.0% 08.4%
144/12 .856 00.0% 00.0% 00.0% 00.0% 02.4% 25.7% 100.0% 07.2%
240/12 .909 00.0% 00.0% 00.0% 00.0% 00.3% 11.2% 100.0% 04.6%

And as promised, some Lorenz curves:

gini-coefficients-at-given-powers-of-random-distribution

Ooooh, the colors.

And for a fun digression, here’s a Lorenz curve of U.S. earnings.

lorenz-curve-of-u-s-earnings-2015

(Source: Social Security Administration)

The data aren’t presented in a way that can be properly sorted, so I can’t calculate the area under the curve, but at the median, the Gini coefficient is probably about .5. At the 90th percentile, it’s more like .8. I wouldn’t be surprised if U.S. income polarization is worse than .45.

So, now you know how to estimate a Gini coefficient in MS Excel, and if someone throws Gini numbers at you, you can look at the tables to get a sense of what they mean in terms of the maximum data point. It’s still a number without any units, but at least you can see the relationships among the data points more clearly.

Good News: Legal Services Industry Grew 2.0 Percent in 2015

Since I started writing here more than six years ago, it’s always been bad news for the legal services industry. Dwindling output, year in, year out. This time, no longer. We have growth: 2.0 percent in 2015.

gdp-and-legal-services-industry-value-added-1000s-chained-2009

(Source: Bureau of Economic Analysis (BEA))

And yes, thanks to an alert reader I can now show the BEA’s complete GDP-by-industry dataset going back to 1963! We can now see that if the legal services industry had maintained its mid-20th century growth rate it would be nearly double its current size. Imagine how much better law practice would be. You might think there’d be a need for more law schools to meet the demand.

Arguably, the government’s definition of the industry or its composition has changed over the decades as it has for other industries, but I doubt it. It’s mostly lawyers’ offices. Undeniably, though, the typical product of the legal services industry has changed. I’d bet that the weighted-average hour of legal work is very different now than in 1975. Even so, it’s still possible to give a dollar figure of how much stuff private practice lawyers are producing.

…And it ain’t much. The legal services industry produced less in 2015 than in 2012, 1995, and 1988. There’s room for a lot of growth. The sector peaked in 2008, and since then it’s shrunk more than 20 percent.

The other caveat is that the legal services industry’s growth this year is mostly attributable to the gross operating surplus (what goes to firm owners, partners, solos) as opposed to employee compensation, which better indicates budding demand for new lawyers. The breakdown is: gross operating surplus, +1.5 percent; taxes on production and imports, +0.5 percent; and compensation of employees, +0.0 percent.

Yeah. You read that right. 0.

However, compensation has shaved off growth since 2007, so maybe a zero year isn’t so bad. Here’s the chart of the industry’s components, which still only goes back to 1987:

components-of-legal-services-industry-real-value-added

Compensation of employees in the legal services industry peaked in 2003 at $121 billion (2009 $). Now it’s $97 billion, a similar 20 percent decline.

Finally, although the legal services sector did well in 2015, the rest of the economy did better: GDP grew 2.6 percent, of which 1.9 percent went to compensation of employees. Things still look better for non-law.

Finally, legal services as a share of household expenditures grew for the first time in thirteen years.

legal-services-share-of-household-consumption-expenditures

At its maximum, households spent $99.5 billion on lawyers in 2003. Now it’s $87.9 billion, down 11.7 percent.

I’ve written elsewhere that the legal services sector can’t shrink forever into nothing. It’s like estimates of the year Japan’s population reaches zero. So we were bound to have some good years. What we need is evidence of sustained growth, especially in employee compensation. Instead, that’s not going anywhere, but at least it’s not falling anymore.

LSAT Tea-Leaf Reading: September/October 2016 Edition

It only took the LSAC 40 days since the September/October LSAT administration to update its Web site, which is a noticeable improvement over June and February. Frankly, I’m finding that more interesting than the actual numbers: 33,563 for September/October, up 1.0 percent from last year.

no-lsat-takers-4-testing-period-moving-sum

Because of the rise in test takers, The four-period moving sum budged up 0.3 percent to 106,030. Essentially, the trend is flat, but I thought it would continue falling because of the New York Times article a few months back. I think it’s safe to say that if the Times can’t discourage people from law school, then the low-hanging fruit of easily dissuaded potential applicants has been exhausted.

Still, we don’t know anything about people who don’t choose to take the LSAT because they think it’s a bad idea. I question whether that’s a logically valid category. On the whole, we can expect another poor haul for law schools. Maybe others will consider going the Indiana Tech route.

Speaking of Indiana Tech, I recommend reading J-Dog’s gloating on the subject. He earned the privilege.

Indiana Tech Accused of ‘Bait and Switch’

By students of the soon-to-be-closed law school? NO! By a lawyer representing an aggrieved faculty member, according to Fort Wayne’s News-Sentinel:

[Indiana Tech’s board of director’s] decision “throws into chaos the lives and academic plans of the student body. The law school’s tuition is just under $20,000. You don’t have to be a lawyer to be repulsed by this outrageous bait and switch.”

I predict very few lawyers are repulsed by Indiana Tech’s decision. I’m not the first to opine on it, but Indiana Tech School of Law’s demise benefits humanity. It was never fully enrolled, only one of its twelve graduates passed the bar, and at last the board saw the writing in the blue book. Whether it will herald more law school closures is debatable. I think many of its peers will see Indiana Tech as an Icarus rather than a bellwether.

If I were cynical, I’d suspect Indiana Tech knew it was going to fail and used its provisional accreditation as a sword to rescue its students from the ignominy of starting their legal educations over at a more sound ABA school than a shield against total failure.

Otherwise, I have very little to say on this subject, except to remind everyone of those bygone days five years ago when Indiana Tech School of Law was a glint in its board’s eyes—and its Feasibility Committee was warning that there would be an attorney shortage so unbearable that we’d have to import foreign lawyers. Seriously, it was that dishonest.

Now Indiana Tech’s president, Arthur Snyder, concedes, “Over the course of time it has become apparent that the significant decline in law school applicants nationwide represents a long term shift in the legal education field, not a short-term one.”

Many voices warned Indiana Tech not to open a law school. It ignored them and made a $20 million mistake. But don’t expect it to apologize to its students for its hubris—they’re the ones who really paid.

Half of States to See Decline in Lawyer Surpluses

In August, the National Association for Law Placement verified a trend that appeared in ABA data several months earlier: Despite the falling supply of law school graduates, demand for their work stubbornly refuses to materialize. In fact, the number of graduates who found work as lawyers fell far more than the number of unemployed graduates, suggesting that either many graduates failed the bar or that new lawyer jobs are much more transitory than they appear.

But if the short term trend indicates fewer lawyers in the future, what about the long-term outlook?

Fortunately, the Bureau of Labor Statistics updated its biennial state-level occupational employment projections. These data include an estimate of the number of lawyer positions (not people who are lawyers) out there in 2014, a prediction of how many there will be in 2024, and the projected number of annual lawyer job openings. This last figure can be compared to the number of new law licenses issued courtesy of the National Conference of Bar Examiners (NCBE) or law school graduates (from the ABA) to give the “lawyer surplus” and the “law graduate surplus,” respectively.

There are a few reasons to calculate two surplus ratios rather than one. For the lawyer surplus, the NCBE’s number of new law licenses includes many duplicates—people who become licensed in more than one jurisdiction—but it helps track people who obtain licenses on motion to places where few people sit for the bar, e.g. Washington D.C. Meanwhile, the law graduate surplus measures discrete individuals, but it excludes people who go to non-ABA-accredited law schools and not everyone who graduates from an ABA law school finds jobs as lawyers.

The two surpluses permit comparisons among states’ legal markets to show which parts of the country might provide better opportunities for new lawyers, but they are not a direct proxy for the typical number of people seeking job openings.

First, here’s a table of the state-level occupational employment information for the 2014-24 period compared to the 2012-22 period. The “STATES” row is the sum of the data from the state-level employment information, including the District of Columbia but excluding Puerto Rico, but the “U.S.A.” row is from the national projections provided by the BLS late last year. The STATES row and the Bureau of Economic Analysis regions below only include jurisdictions that reported in both time periods to ensure relevant comparisons.

STATE/BEA REGION NO. EMPLOYED LAWYERS LAWYER EMPLOYMENT PROJECTIONS ANNUAL LAWYER GROWTH RATE
2012 2014 2022 2024 2022 2024
Alabama 7,040 7,050 7,710 7,410 180 140
Alaska 1,020 1,070 1,010 1,020 20 20
Arizona 11,740 9,630 14,160 11,870 430 370
Arkansas 4,420 4,720 4,940 5,360 120 130
California 87,400 91,900 97,300 102,700 2,390 2,420
Colorado 15,800 15,800 19,280 19,270 600 600
Connecticut 9,390 12,620 10,080 13,080 220 230
Delaware 3,400 3,540 3,700 3,660 80 60
District of Columbia 33,460 38,920 35,040 41,480 690 830
Florida 51,860 59,400 61,310 68,400 1,930 1,770
Georgia 19,520 18,160 23,220 19,690 680 420
Hawaii 2,460 2,410 2,580 2,500 50 40
Idaho 2,700 N/A 2,820 N/A 60 N/A
Illinois 34,810 35,840 38,400 37,950 920 740
Indiana 7,680 9,450 8,810 10,520 240 250
Iowa 4,450 4,340 5,050 4,880 130 120
Kansas 4,950 5,090 5,610 5,570 150 130
Kentucky 5,600 9,490 6,450 10,640 300 250
Louisiana 9,310 9,180 10,490 9,730 270 190
Maine 2,930 3,170 3,010 3,210 60 50
Maryland 14,800 11,690 16,330 13,370 390 360
Massachusetts 22,640 22,100 24,590 23,080 560 420
Michigan N/A 17,900 N/A 19,230 N/A 400
Minnesota 12,550 12,640 13,080 13,340 260 260
Mississippi 3,220 3,760 3,460 4,030 80 80
Missouri 12,620 12,470 14,410 13,160 380 250
Montana 2,270 2,550 2,530 2,830 60 70
Nebraska 4,060 3,910 4,430 4,400 100 110
Nevada 5,640 6,030 6,260 7,880 150 270
New Hampshire 2,280 2,010 2,380 2,070 50 40
New Jersey 24,150 24,520 26,390 25,140 610 420
New Mexico 3,830 3,810 3,980 3,830 80 60
New York 82,220 90,830 88,680 99,020 1,960 2,150
North Carolina 14,810 16,020 17,500 17,870 510 420
North Dakota 1,540 1,740 1,680 1,790 40 30
Ohio 21,160 20,180 23,480 21,290 570 410
Oklahoma 9,260 9,480 10,270 10,290 250 220
Oregon 5,070 8,250 5,830 9,440 160 240
Pennsylvania 31,260 31,240 34,700 32,960 840 630
Puerto Rico 4,440 4,420 5,040 4,500 130 70
Rhode Island N/A 4,210 N/A 4,460 N/A 90
South Carolina 7,140 7,220 7,950 7,670 200 150
South Dakota 1,400 980 1,540 1,080 40 20
Tennessee 8,010 7,990 10,520 8,690 380 200
Texas 49,350 51,420 60,090 63,140 1,800 1,920
Utah 5,890 5,310 7,470 6,360 250 180
Vermont 2,030 1,940 2,150 1,990 40 30
Virginia 20,430 21,860 23,030 24,150 590 550
Washington 16,290 17,290 20,070 18,940 670 430
West Virginia N/A N/A N/A N/A N/A N/A
Wisconsin 9,330 9,620 10,740 9,940 290 170
Wyoming 1,050 1,160 1,170 1,130 30 20
STATES (EXCL. P.R.) 711,540 749,800 802,860 827,820 20,800 18,870
U.S.A. (EXCL. P.R.) 759,800 778,700 834,700 822,500 19,650 15,770
New England 39,270 41,840 42,210 43,430 930 770
Mideast 189,290 200,740 204,840 215,630 4,570 4,450
Great Lakes 72,980 75,090 81,430 79,700 2,020 1,570
Plains 41,570 41,170 45,800 44,220 1,100 920
Southeast 151,360 164,850 176,580 183,640 5,240 4,300
Southwest 74,180 74,340 88,500 89,130 2,560 2,570
Rocky Mountains 25,010 24,820 30,450 29,590 940 870
Far West 117,880 126,950 133,050 142,480 3,440 3,420

Superficially, some states seem to have created many new lawyer jobs between 2012 and 2014. For example, it’s doubtful that Kentucky’s and Oregon’s legal markets grew by more than 60 percent in just two years, or that South Dakota’s contracted by 30 percent. The only state whose large swing may be plausible is Nevada’s. Its lawyer job count grew by about 7 percent since 2012, but its 10-year outlook rose by 25 percent with a corresponding 80 percent surge in projected annual job openings. On average, annual job openings sank by 12 percent among jurisdictions that reported in both periods while excluding Puerto Rico. Only 10 states and the District of Columbia had higher annual job growth rates than in 2012. The rate of decline in annual job growth for all jurisdictions that reported in both years and excluding Puerto Rico is 9 percent, which is less alarming than the BLS’s 20 percent drop for the whole country.

Offsetting the slowdown in lawyer job growth is somewhat greater losses in bar admits and law school graduates, 13 percent and 14 percent, respectively. The result is that 24 states and the District of Columbia have smaller lawyer and law graduate surpluses in 2014 than 2012. Overwhelmingly, the cause in these jurisdictions is modest annual job growth projections coupled with strong losses in new graduates and new lawyers. Here’s the full table.

# STATE/BEA REGION NO. ABA LAW SCHOOL GRADS NO. BAR ADMITS RATIO ABA GRADS TO ANNUAL LAWYER JOBS RATIO BAR ADMITS TO ANNUAL LAWYER JOBS
2013 2015 2013 2015 2013 2015 2013 2015
1 Wyoming 78 73 161 198 2.60 3.65 5.37 9.90
2 North Dakota 75 79 267 219 1.88 2.63 6.68 7.30
3 Alaska 0 0 130 140 0.00 0.00 6.50 7.00
4 New Hampshire 107 70 250 272 2.14 1.75 5.00 6.80
5 Puerto Rico 662 569 491 458 5.09 8.13 3.78 6.54
6 New Jersey 859 585 3,386 2,586 1.41 1.39 5.55 6.16
7 New Mexico 114 112 287 292 1.43 1.87 3.59 4.87
8 Massachusetts 2,391 2,164 2,411 1,981 4.27 5.15 4.31 4.72
9 Hawaii 108 111 206 188 2.16 2.78 4.12 4.70
10 South Dakota 73 63 121 93 1.83 3.15 3.03 4.65
11 Wisconsin 485 447 843 781 1.67 2.63 2.91 4.59
12 Missouri 883 700 1,034 1,051 2.32 2.80 2.72 4.20
13 New York 5,007 4,105 10,251 8,867 2.55 1.91 5.23 4.12
14 Washington 654 579 1,353 1,759 0.98 1.35 2.02 4.09
15 Maryland 600 537 1,742 1,382 1.54 1.49 4.47 3.84
16 Tennessee 501 533 1,011 741 1.32 2.67 2.66 3.71
17 Minnesota 942 723 1,028 939 3.62 2.78 3.95 3.61
18 Vermont 203 163 151 108 5.08 5.43 3.78 3.60
19 Illinois 2,278 2,036 3,184 2,525 2.48 2.75 3.46 3.41
20 Louisiana 936 822 533 630 3.47 4.33 1.97 3.32
21 South Carolina 442 335 598 494 2.21 2.23 2.99 3.29
22 Alabama 427 351 503 454 2.37 2.51 2.79 3.24
23 Pennsylvania 1,703 1,418 2,241 1,927 2.03 2.25 2.67 3.06
24 Utah 292 258 499 548 1.17 1.43 2.00 3.04
25 Iowa 328 263 416 356 2.52 2.19 3.20 2.97
26 Maine 96 78 183 145 1.60 1.56 3.05 2.90
27 Mississippi 377 274 305 232 4.71 3.43 3.81 2.90
28 District of Columbia 2,181 1,916 3,120 2,389 3.16 2.31 4.52 2.88
29 Georgia 1,085 931 1,377 1,205 1.60 2.22 2.03 2.87
30 Ohio 1,474 1,088 1,444 1,172 2.59 2.65 2.53 2.86
31 Michigan 2,206 1,606 1,248 1,082 N/A 4.02 N/A 2.71
32 Kansas 324 255 393 340 2.16 1.96 2.62 2.62
33 Nebraska 249 245 316 285 2.49 2.23 3.16 2.59
34 North Carolina 1,429 1,422 1,091 1,072 2.80 3.39 2.14 2.55
35 California 5,184 4,392 7,008 6,150 2.17 1.81 2.93 2.54
36 Indiana 834 764 675 625 3.48 3.06 2.81 2.50
37 Oregon 527 427 659 574 3.29 1.78 4.12 2.39
38 Connecticut 541 477 680 530 2.46 2.07 3.09 2.30
39 Virginia 1,440 1,277 1,590 1,252 2.44 2.32 2.69 2.28
40 Montana 81 82 204 158 1.35 1.17 3.40 2.26
41 Arizona 640 705 906 835 1.49 1.91 2.11 2.26
42 Arkansas 275 255 302 268 2.29 1.96 2.52 2.06
43 Rhode Island 174 129 201 175 N/A 1.43 N/A 1.94
44 Colorado 437 439 1,217 1,125 0.73 0.73 2.03 1.88
45 Kentucky 422 395 668 463 1.41 1.58 2.23 1.85
46 Florida 3,190 2,718 3,476 3,177 1.65 1.54 1.80 1.79
47 Texas 2,323 2,075 3,836 3,346 1.29 1.08 2.13 1.74
48 Delaware 279 170 148 99 3.49 2.83 1.85 1.65
49 Oklahoma 468 380 463 350 1.87 1.73 1.85 1.59
50 Nevada 132 131 343 321 0.88 0.49 2.29 1.19
N/A Idaho 117 106 231 212 1.95 N/A 3.85 N/A
N/A West Virginia 130 125 274 242 N/A N/A N/A N/A
STATES (EXCL. P.R.) 43,474 37,423 63,010 54,644 2.09 1.98 3.03 2.90
U.S.A. (EXCL. P.R.) 46,101 39,389 64,964 56,355 2.35 2.50 3.31 3.57
New England 3,338 2,952 3,675 3,036 3.59 3.83 3.95 3.94
Mideast 10,629 8,731 20,888 17,250 2.33 1.96 4.57 3.88
Great Lakes 5,071 4,335 6,146 5,103 2.51 2.76 3.04 3.25
Plains 2,874 2,328 3,575 3,283 2.61 2.53 3.25 3.57
Southeast 10,524 9,313 11,454 9,988 2.01 2.17 2.19 2.32
Southwest 3,545 3,272 5,492 4,823 1.38 1.27 2.15 1.88
Rocky Mountains 888 852 2,081 2,029 0.94 0.98 2.21 2.33
Far West 6,605 5,640 9,699 9,132 1.92 1.65 2.82 2.67

Of the 22 states that produced higher lawyer surpluses than before, all but three showed steep declines in annual lawyer job creation, nearly all of them over 25 percent. Washington State stands out in particular because it admitted 30 percent more lawyers while its lawyer market is expected to produce 36 percent fewer jobs annually. On the other hand, it has 12 percent fewer graduates in 2015 than 2013 and some growth in lawyer employment, so there are reasons to believe its outlook isn’t so bad. Other states tell similar stories.

The BLS’s methodology distinguishes jobs created by economic growth from those created by replacement of people leaving the occupation. The annual number of positions created by growth is measured by simply taking the difference between the predicted number of employed lawyers in 2024 and 2014, and then dividing that by ten. The annual number of jobs created by replacement can be found by subtracting the number of jobs created by growth from the number of jobs created annually. Consequently, it’s possible to explore which category of jobs states think will (or won’t open up). Consistent with the BLS’s national-level employment projections, state governments predominantly predict jobs created by economic growth will plummet while jobs created by vacancies will fall at a smaller rate.

Notably, among states that reported employment data for 2012 and 2014, the cumulative number of annual openings (18,870) is much higher than the BLS’s more dour prediction (15,770). This suggests that the BLS is much more pessimistic about lawyer job growth than state governments are. Specifically, about 41 percent of lawyer job openings will be created by growth according to the state projections as opposed to 28 percent as reported by the BLS. Hopefully the former will pan out for new graduates who pass the bar.

Overall, it’s good news that lawyer surpluses are falling, even if it isn’t a widespread phenomenon and not due to a bright future for the legal profession. It’s unclear why state governments and the BLS are so pessimistic about lawyer job growth compared to two years ago. The ultimate cause may be due to predictions of slow job growth in general and not lawyer jobs specifically. Although that development is discouraging, the crash in law students is compensating for it, meaning fewer graduates will struggle to find work.

WSJ Has No Idea Who Benefits From IBR/PAYE/REPAYE/ETC

A hypothetical: Jill and Jack live in the same town. Jill has many healthy habits but is a nurse who spends time around infected people, Jack less so. The town is hit with a case of spectrox toxaemia, a dangerous disease. The government offers to immunize people. Jill decides to be immunized; Jack does not. Jill does not get sick; Jack does. So, epidemiologists, did Jill not contract spectrox toxaemia because she was immunized or because of her healthy habits (or luck)?

If you’re The Wall Street Journal, the answer is her habits. Most of us would believe otherwise, given how dangerous spectrox toxaemia is and Jill’s contact with its victims.

Likewise, this line of reasoning animates the WSJ’s opinion of the government’s income-sensitive repayment programs for student debtors, which it claims benefit higher-debt people with better credit scores than lower-debt people who don’t. It’s unintuitive, if you’re the WSJ apparently, but it makes more sense to those of us familiar with the student debt system.

Here’s how it works: People who take out lots of debt might not in fact have the incomes to repay them, so they choose an income-sensitive repayment because the alternative is … Default! Thus, looking at how much they borrow is less important than looking at how much they’re paid.

Last year, in fact, the Government Accountability Office explored this topic and found that most people in income-sensitive repayment programs were earning less than $20,000 annually. So the Jills aren’t so different from the Jacks after all.

Sure, if there were no IBRs/PAYEs/REPAYEs/ETCs, then these Jills with good borrowing habits would be more likely to take deferments and forbearances, but their debts would still not be repaid. That’s because debts that can’t be repaid will not be repaid, no matter what someone’s credit score or how much they borrowed. What matters is what they earn, and college graduates don’t earn much these days.

And if you think the Jills have too much debt, then the problem isn’t IBR/ICR/REPAYE/ETC, it’s that the government lends too much money to people for degrees they don’t need.