Current Circumstances Cast a Shadow on Past Decisions

Put positively: Current circumstances illuminate past decisions.

I guess it works.

I flipped through Access Group’s and Gallup’s jointly produced report, “Life After Law School” (pdf), which surveyed a panel of 7,000 recent and not-so-recent law-school graduates of some Southeastern law schools. I don’t have much time or interest in picking apart the study, but I observe that the seven schools it chose are a pretty broad range, from older, established Vanderbilt to recent Elon. There aren’t any for-profits though.

I thought I’d illustrate some of “Life After Law School”‘s tables. There are times when tables are helpful, e.g. lists of law schools, and times when we prefer charts, like when we want to see trends. “Life After Law School” is a time for trends.

Here’s how graduates answered, “If I could do it again, I’d still get a law degree.”

If I could do it over again

“These results could reflect the lack of time more recent graduates have had to realize the value of their law degree or their greater difficulty in finding a good job after graduation.”

You can see growing dissatisfaction with law school among recent graduates.

And here’s, “My degree from LAW SCHOOL was worth the cost.”

My law degree

Again, growing dissatisfaction over the decades. I’ll not chart the report’s debt table because it doesn’t break the numbers down by approximate graduation year. Time is what we’re talking about.

So here’s a question: Do graduates become more satisfied with law school over time, or is this a phenomenon unique to recent grads? Predictably, Access Group believes the former; it says so in the introduction. “While [metrics of near-term earnings and job placement of recent graduates] have merit, they do not provide a holistic view of graduates’ lives or the broader benefits that legal education provides.” If the long-term picture looks good, then we can discount the experiences of recent graduates.

Alternatively, factors outside law schools and law degrees affect people’s job outcomes and happiness. For example, if demand for legal services stagnates, and universities keep opening law schools, and the costs rise without quantified benefits, then we should expect more people to be dissatisfied with law school.

Thus, “Life After Law School” echoes the After the JD study, whose own authors misinterpreted their results, treating survey responses as evidence of legal education’s value rather than the respondents’ perception of their legal educations’ values. Current circumstances feed into perceptions of past decisions. As always, the question is, were there better alternatives to law school when people chose to attend. Recent graduates’ jaundiced perception of law school indicates they believe there were better alternatives in hindsight. But that’s a question Access Group won’t ask.

Record 16 Law Schools Didn’t Report 2015 Graduate Debt to U.S. News

…And for some reason U.S. News didn’t rank law schools by debt, generously offering me an opportunity to do so in its stead.

Each year U.S. News lists the amount of debt law schools’ recent graduates took out. Importantly, the figure excludes accrued interest, which can be quite considerable. However, these figures are probably the best estimate of the cost of attendance at a particular law school, presented in a comparable form. The ABA does not publicize graduate debt in the 509 information reports, making U.S. News an unfortunately necessary source.

Here’s the debt table. A recurring problem in U.S. News‘ debt data is law schools that misreport their graduating students’ annual debt as opposed to their cumulative debt, which is what the magazine asks for. Thus, I include last year’s numbers for illustration and encourage ridicule of law schools that cannot follow basic directions.

# SCHOOL 2014 DEBT 2015 DEBT PCT. CHANGE
1. Thomas Jefferson 172,445 172,726 0.2%
2. Columbia 154,076 168,627 9.4%
3. New York University 147,744 166,022 12.4%
4. San Francisco 154,321 162,434 5.3%
5. John Marshall (Chicago) 143,518 162,264 13.1%
6. California Western 151,197 162,260 7.3%
7. New York Law School 166,622 161,910 -2.8%
8. Florida Coastal 162,785 160,942 -1.1%
9. Georgetown 150,529 160,606 6.7%
10. American 159,316 160,274 0.6%
11. Vermont 156,713 156,710 0.0%
12. Miami 143,845 155,796 8.3%
13. Northwestern 163,065 155,796 -4.5%
14. Tulane 140,965 153,606 9.0%
15. Harvard 137,599 149,754 8.8%
16. Pacific, McGeorge 140,517 149,470 6.4%
17. Fordham 140,577 149,058 6.0%
18. Pepperdine 145,525 148,959 2.4%
19. Widener (Commonwealth) 148,496
20. Whittier 151,602 148,316 -2.2%
21. Loyola (CA) 147,701 148,035 0.2%
22. Virginia 132,182 146,907 11.1%
23. Charleston 147,031 146,230 -0.5%
24. California-Berkeley 143,546 144,981 1.0%
25. Pennsylvania 130,002 144,153 10.9%
26. Santa Clara 136,990 144,130 5.2%
27. Golden Gate 146,288 143,740 -1.7%
28. Michigan 132,473 142,572 7.6%
29. Lewis and Clark 127,064 140,025 10.2%
30. Catholic 144,737 139,803 -3.4%
31. Syracuse 129,249 139,753 8.1%
32. Suffolk 120,993 138,724 14.7%
33. Mercer 125,301 138,575 10.6%
34. Marquette 134,533 138,549 3.0%
35. Barry 149,175 138,410 -7.2%
36. Detroit Mercy 132,245 137,047 3.6%
37. Widener (Delaware) 139,229 136,992 -1.6%
38. Seattle 131,414 136,889 4.2%
39. George Washington 141,346 136,662 -3.3%
40. California-Hastings 129,178 135,886 5.2%
41. Baylor 116,666 135,817 16.4%
42. San Diego 128,477 135,433 5.4%
43. Southern California 137,163 134,673 -1.8%
44. Ave Maria 132,236 134,071 1.4%
45. Willamette 136,099 133,318 -2.0%
46. Loyola (IL) 134,968 133,052 -1.4%
47. Seton Hall 128,100 133,000 3.8%
48. Stanford 128,137 132,970 3.8%
49. Denver 132,083 132,158 0.1%
50. DePaul 125,895 131,148 4.2%
51. Duke 125,406 131,073 4.5%
52. Valparaiso 132,010 131,024 -0.7%
53. Stetson 148,394 130,079 -12.3%
54. Penn State (Penn State Law) 129,772
55. Chicago 144,695 129,636 -10.4%
56. Elon 132,444 128,407 -3.0%
57. Northeastern 134,918 127,406 -5.6%
58. California-Irvine 102,891 125,473 21.9%
59. Gonzaga 121,281 125,347 3.4%
60. Hofstra 143,646 125,300 -12.8%
61. Albany 130,184 125,157 -3.9%
62. Pace 122,180 124,823 2.2%
63. Southern Methodist 124,617 124,723 0.1%
64. Loyola (LA) 117,892 124,143 5.3%
65. Samford 124,106 124,106 0.0%
66. Nova Southeastern 136,450 123,798 -9.3%
67. Roger Williams 128,543 123,332 -4.1%
68. Notre Dame 111,310 122,822 10.3%
69. Yale 117,093 122,796 4.9%
70. St. Mary’s 116,782 122,560 4.9%
71. Western State 120,350 122,315 1.6%
72. George Mason 126,723 121,910 -3.8%
73. South Texas 113,748 121,767 7.0%
74. Oklahoma City 121,476 121,607 0.1%
75. Emory 108,690 121,278 11.6%
76. Cardozo, Yeshiva 121,644 119,294 -1.9%
77. California-Los Angeles 121,066 118,874 -1.8%
78. Illinois 89,852 118,731 32.1%
79. Creighton 126,586 117,980 -6.8%
80. Penn State (Dickinson Law) 123,842 116,717 -5.8%
81. Capital 113,434 116,283 2.5%
82. Dayton 113,486 115,740 2.0%
83. St. John’s 111,959 115,666 3.3%
84. Campbell 90,065 115,128 27.8%
85. Chicago-Kent, IIT 119,884 115,040 -4.0%
86. Maryland 102,183 114,493 12.0%
87. Vanderbilt 122,327 114,447 -6.4%
88. California-Davis 93,498 113,765 21.7%
89. St. Louis 128,764 113,070 -12.2%
90. Boston College 97,006 112,439 15.9%
91. Baltimore 114,725 112,008 -2.4%
92. Washington 105,990 111,003 4.7%
93. Villanova 95,297 110,792 16.3%
94. Richmond 110,380 110,665 0.3%
95. William and Mary 98,487 110,140 11.8%
96. Washington and Lee 109,422 110,067 0.6%
97. Washington University 111,345 109,232 -1.9%
98. Brooklyn 114,953 108,942 -5.2%
99. New Hampshire 121,469 108,896 -10.4%
100. William Mitchell 110,738 108,678 -1.9%
101. District of Columbia 73,175 108,095 47.7%
102. Drake 108,857 107,679 -1.1%
103. Hamline 113,173 107,359 -5.1%
104. Colorado 116,280 107,080 -7.9%
105. Oregon 105,900 106,540 0.6%
106. Arizona State 97,431 106,426 9.2%
107. Indiana (Indianapolis) 96,651 106,114 9.8%
108. Case Western Reserve 131,724 105,854 -19.6%
109. Duquesne 107,496 104,623 -2.7%
110. Pittsburgh 103,461 104,484 1.0%
111. Texas A&M [Wesleyan] 103,500 104,200 0.7%
112. Chapman 148,429 103,956 -30.0%
113. North Carolina 92,475 102,828 11.2%
114. Massachusetts — Dartmouth 105,415 102,603 -2.7%
115. Ohio Northern 107,764 102,414 -5.0%
116. Boston University 107,850 102,329 -5.1%
117. Texas 100,868 102,101 1.2%
118. St. Thomas (MN) 100,401 101,950 1.5%
119. Arizona 95,533 100,902 5.6%
120. Drexel 91,915 100,362 9.2%
121. Maine 101,749 99,617 -2.1%
122. Wake Forest 107,532 97,550 -9.3%
123. Quinnipiac 119,956 97,335 -18.9%
124. Missouri (Kansas City) 96,639
125. Ohio State 97,021 96,253 -0.8%
126. Lincoln Memorial 95,495
127. Florida International 89,815 95,331 6.1%
128. Toledo 96,924 94,295 -2.7%
129. Cleveland State 89,879 93,865 4.4%
130. Michigan State 95,494 93,245 -2.4%
131. Regent 125,158 93,142 -25.6%
132. Minnesota 104,733 92,179 -12.0%
133. Indiana (Bloomington) 89,785 91,020 1.4%
134. Southern Illinois 90,727
135. Louisiana State 89,471 90,609 1.3%
136. Houston 88,664 87,602 -1.2%
137. Temple 97,323 86,999 -10.6%
138. SUNY Buffalo 76,010 86,970 14.4%
139. Louisville 90,195 86,880 -3.7%
140. Southern University 77,067 86,708 12.5%
141. Washburn 83,396 86,621 3.9%
142. Georgia 88,825 86,515 -2.6%
143. Iowa 92,373 86,373 -6.5%
144. West Virginia 84,727 85,063 0.4%
145. Rutgers-Camden 85,054
146. Rutgers-Newark 89,507 85,054 -5.0%
147. South Carolina 84,590 85,006 0.5%
148. Northern Kentucky 82,989 84,714 2.1%
149. Wisconsin 79,373 84,650 6.6%
150. Florida 82,410 84,580 2.6%
151. Cincinnati 76,663 82,988 8.3%
152. Tulsa 99,305 82,954 -16.5%
153. Oklahoma 81,789 82,818 1.3%
154. Wayne State 79,881 82,397 3.1%
155. Florida State 80,375 82,102 2.1%
156. Nevada 99,678 81,579 -18.2%
157. Missouri (Columbia) 67,289 81,149 20.6%
158. Kansas 74,890 80,884 8.0%
159. Montana 71,216 79,304 11.4%
160. Utah 78,725 79,124 0.5%
161. Akron 82,322 78,575 -4.6%
162. Northern Illinois 77,182 77,975 1.0%
163. Kentucky 76,746 77,793 1.4%
164. Memphis 78,030 77,752 -0.4%
165. City University 82,415 77,751 -5.7%
166. Wyoming 67,087 77,421 15.4%
167. Alabama 69,440 74,921 7.9%
168. Texas Tech 84,272 74,673 -11.4%
169. Mississippi 73,003 71,330 -2.3%
170. New Mexico 71,029 69,366 -2.3%
171. Connecticut 70,139 69,195 -1.3%
172. North Dakota 64,818 69,058 6.5%
173. Arkansas (Little Rock) 69,532 68,960 -0.8%
174. Liberty 69,475 68,667 -1.2%
175. Tennessee 66,201 66,939 1.1%
176. Georgia State 69,822 66,637 -4.6%
177. Arkansas (Fayetteville) 63,541 64,901 2.1%
178. Brigham Young 54,203 62,423 15.2%
179. Nebraska 62,985 58,744 -6.7%
180. South Dakota 78,963 57,170 -27.6%
181. Belmont 56,225
182. Hawaii 56,266 54,988 -2.3%
183.

~14

Cornell 135,000 53,680

155,025

-60.2%

14.8%

184. Mississippi College 130,700 38,213 -70.8%
185.

~156

Idaho 92,732 32,114

81,993

-65.4%

-11.6%

186. North Carolina Central 58,061 27,972 -51.8%
187. Howard 24,021 23,197 -3.4%
188. St. Thomas (FL) 140,808 -100.0%
189. Western New England 130,124 -100.0%
190. Touro 154,855 -100.0%

And per this post’s title, here’s the List of Shame: Law schools that chose not to submit their graduates’ debt information to U.S. News, along with their last-reported figures and the year in which they reported them. Thanks to the gainful employment rule, I was able to track down median graduate debt at three for-profits. As I am merciful, I exclude the three Puerto Rico law schools from this count. Nevertheless, 16 non-reporting law schools is a record.

  • Arizona Summit [Phoenix] – $178,263 [2015, median, for-profit]
  • Atlanta’s John Marshall – $161,910 [2015, median, for-profit]
  • Charlotte – $145,834 [2015, median, for-profit]
  • Touro – $154,855 (2014)
  • Thomas (FL) – $140,808 (2014)
  • Western New England – $130,124 (2014)
  • New England – $132,246 (2013)
  • Faulkner – $122,187 (2013)
  • Southwestern – $147,976 (2012)
  • WMU Cooley – $122,395 (2012)
  • Appalachian – $114,740 (2012)
  • La Verne – $112,628 (2012)
  • Texas Southern – $99,992 (2012)
  • Florida A&M – $96,934 (2011)
  • Concordia – NEVER
  • Indiana Tech – NEVER

Yes, Indiana Tech was provisionally accredited just days before the U.S. News rankings came out, but too bad. All accredited law schools are judged equally!

These 16 law schools account for 3,169 graduates out of 39,390, or 8 percent of the total.

Overall, weighted-average private law-school graduate debt rose from $131,195 to $132,207 $132,985 (1%). For public law schools, debt rose from $93,985 to $94,700 $95,047 (1%). The weights are the percent of graduates who took out debt per U.S. News multiplied by the number of graduates according to the 509 information reports.

Interestingly, the unweighted average private law-school graduate debt, which is what is commonly reported, fell by 1.5 0.7 percent; it rose by 1.4 2.1 percent at public law schools. Be wary of lazy reporting: The number of students at high-debt schools matter.

Other thoughts:

  • Last year I didn’t think S. News would report Hamline’s numbers, but it did. Good on U.S. News.
  • Fluctuations: District of Columbia (+48%), Illinois (+32%), and Campbell (+28%); Quinnipiac (-19%), Case Western (-20%), Regent (-26%), South Dakota (-28%), and Chapman (-30%).
  • Big raspberries to North Carolina Central (-51.8%), Cornell (-60.2%), Idaho (-65.4%), and Mississippi College (-70.8%). Please report data better.

That’s all for now.

Click to read the 2014 edition or the 2013 edition of this post.

[Update, 2016-03-28: The University of Idaho College of Law corrected its debt data.]

[Update, 2016-03-31: Cornell Law School corrected its debt data.]

How Much Is (Nonfinancial Corporate) Land in the U.S.A. Worth?

You wouldn’t know it from my writing, but I’ve been wading into real-estate mapping and assessment this year. For reasons I’ll discuss in future posts, I’m researching land-residual vs. building-residual assessment methodologies. Naturally, my initial Internet searches into building-residual assessments led to Georgist writings, and with good reason. Land-residual assessment, which subtracts land values from the total price, invariably leads to absurd results.

One source on the topic is Michael Hudson, whom long-time readers will recall as the economist who coined the phrase, “Debts that can’t be repaid won’t be.” Hudson gave a speech about the demerits of land-residual assessment in 2001, which he republished in 2010. He repeated a claim I first read in the superb collection The Losses of Nations: In 1993, the government valued nonfinancial corporate land at -$4 billion, and as a result, it stopped publishing economy-wide real-estate data.

1994 is the last year for which [the Fed] has estimated economy-wide land values. The problem was that the Fed discovered that its methodology produced nonsensical results – a negative value of $4 billion for all land owned by nonfinancial corporations for the year 1993. This number resulted from imputing land values by subtracting the estimated replacement cost of buildings from overall property market prices. This “land residual” method left little room for land value, for replacement values continue their rise even when overall market prices decline, as periodically occurs. In such downturns the replacement value absorbs nearly all the market value of corporately owned real estate.

It’s a damning accusation, but it’s also untrue. The Fed never stopped tracking nonfinancial corporate real-estate data. Perhaps it changed its source, but in the age of FRED, all this information is readily available. In fact, one can find three releases of the supplemental tables to the Fed’s Financial Accounts of the United States between 1997 and 1998 that indicate a residual nonfinancial corporate land value of -$4.6 billion, which appears to be what Hudson discovered. Starting with the June 1998 release, however, the residual land value for 1993 rose to $21.1 billion and ultimately to $25.5 billion when 1993 last appears (June 2000 release).

You can chalk Hudson’s mistake up to the migration of data to the Internet, or any other reason really. I don’t think it’s bad faith on his part, just bad luck. Obviously, though, there isn’t a clear conspiracy by Fed statisticians to cease reporting data that made it look bad.

Indeed, if anything, the reported data vindicate Hudson and make the financial accounts look worse: In 1996 and 2009-2010 the final residual nonfinancial corporate land value fell below zero—far lower than -$4.6 billion. More disturbingly, it’s skyrocketed since 2010.

See for yourself.

Nonfinancial Corporate Real-Estate Value

(Source: Federal Reserve (NCBEMVQ027S, RCSNNWMVBSNNCB, RCVSRNWMVBSNNCB), BEA fixed-investment price indexes (Table 1.1.9.), my calculations)

(I deflated residential and nonresidential structures by their respective BEA price indexes and then estimated the land value by keeping it in proportion to assessed fair-market real-estate values. It’s crude but “accurate,” I think.)

So at the beginning of 2010, the entire species missed out on the best real-estate deal ever: $566.83 billion (current dollars) to anyone willing to take all nonfinancial corporate land along with it. Just last week the Fed valued it at $4 trillion. And here you thought you missed out on speculating on Bitcoin.

So what should we make of this?

Are the Fed’s estimates merely imprecise or inaccurate? Imprecision just means that the land value is off by about a few trillion dollars one direction or the other. When the land value is negative, that’s just fuzzy math that ought to be improved.

By contrast, inaccuracy suggests government estimates of either the total real-estate values or the structures is systemically flawed. These properties might be worth far more than their assessed values. For instance although it’s a nonprofit, Brooklyn Law School’s 2 Pierrepont Street dormitory was ridiculously under-assessed at $3.88 million when the school sold it for $35 million. Alternatively, as I think Hudson tends to argue, the overall real-estate prices are correct but too much of their price increases are imputed to structures. He comments persuasively that “Building prices seem to be responsible for the rise in real estate prices, while land prices are held responsible for their decline.” The implication is that commercial land owners can depreciate land value.

I tend to think both inaccurate assessments of fair-market and structure values are at work, but the former is the bigger culprit. Regardless, Hudson is correct that encouraging local governments to adopt building-residual assessment methodologies would prevent absurd numbers from appearing in the financial accounts of the United States.

Robots Won’t Take Your Profs’ Jobs

I’m going to weave a few themes together for you today; it’ll make sense by the end.

We begin with a friend’s comment last week about robots taking everyone’s jobs. I called him on the lump-of-labor fallacy—there isn’t a fixed amount of work to be done in an economy and therefore technology only creates jobs. You can argue the fallacy as much as you like, but don’t talk about robots taking our jobs until you’re aware of it.

I wrote about robots in the past, when Paul Krugman popularized it in December 2012. I’ve revisited it and found an interesting exchange between Sandwichman and Nick Rowe that I missed last year.

To summarize: Sandwichman argued that the lump-of-labor fallacy is really Say’s Law in disguise. Say’s Law is to me a confusing, contentious tautology that evades a concise rendition. My crack? An economy’s production supplies it with sufficient purchasing power to consume that production. Thus, under normal circumstances there can be no general surpluses, including labor. Keyensians, including Krugman, reject the strict use of Say’s Law but for some reason still point at the lump-of-labor fallacy.

Rowe countered that technology’s impact depends on people’s preferences and money. People can simply consume more of what they make, or the central bank needs to give them more money to increase their consumption. I didn’t like some parts of Rowe’s model, but his last, parenthetical paragraph closes the issue perfectly: Technology is only a problem if it displaces workers from land.

I’m starting to think that maybe just about all productivity advances substitute for land and not labor, which is good. The converse is rare, e.g. Dutch disease scenarios where technology makes it easier and more profitable to extract oil than pay workers to make stuff. The workers don’t get the benefits, unlike the landowners, and they can’t leave the country. The land question precedes and supersedes any discussion of technology.

Theme number two is “cost disease,” the explanation of higher college tuition costs on lack of productivity improvements in lecturing. The illustration for cost disease is a string quartet, which takes the same quantity of labor to produce as ever. Cost disease came up twice in the legal-education context in the last few weeks. Once by a dean claiming that scambloggers ignore it, and again by a study pointing at federal student lending as the fuel for higher college tuition, aka the Bennett hypothesis.

I chewed on these two ideas while at … the Saint Paul Chamber Orchestra, which was performing Aaron Copeland’s Appalachian Spring with some other stuff for padding. It was a real treat, and right at the finale of Mozart’s Piano Concerto No. 24 in C minor*, it all came together. It was a really rewarding feeling.

(* Mozart only composed one other piece in a minor key. I have absolutely no ear to tell keys, but it was lovely.)

So, what does last year’s lump-of-labor discussion tell us about cost disease?

We can set up a model just as Rowe did for Sandwichman, but instead of labor hours, as a good Georgist I’ll use land. 60 people live and work on 60 hectares; 30 grow apples and 30 grow bananas, one each of everything. (Numbers divisible by 12 are always good.) Nobody wants their own type of product, so they trade for the other. Someone stumbles on an apple-growing process that doubles productivity. One of three things happens:

(a) The apple growers each double their output, leaving the bananas constant. 30 hectares grows 60 apples, 30 hectares grows 30 bananas. The ratio of apples to bananas doubles to 2:1, but bananas’ share of the output has fallen to one third. The apple growers really want those bananas.

(b) Banana growers really want their apples, so 20 apple growers double their output, but 10 apple growers switch to banana cultivation. 20 hectares creates 40 apples, and 40 hectares creates 40 bananas. This situation creates an equilibrium for the ratio of apples to bananas, 1:1.

(c) Same as (b), but the 10 hectares shifted to banana production go to a third commodity. This situation is essentially identical to (a), since bananas are what we care about.

Cost disease says that higher education is like situation (a) (and (c)). Productivity “enables” people to satisfy their preferences for the same stuff when we want it to increase their purchasing power to demand new stuff. Here, the more productivity increases, the more income goes to the unproductive.

Now for the twist: If banana-production technology never improves, and people’s appetite for bananas doesn’t wane, we can say that the supply of bananas is inelastic—insensitive to changes in price. But that’s exactly what proponents of the Bennett hypothesis argue: Higher education is a positional good, so educators absorb money lent to students to buy it.

So what’s the difference between the Bennett hypothesis and cost disease? Formally, they’re the same, so the policy responses should be the same: Lending money to people to buy educations that don’t respond to price changes is no different than increasing their productivity, ergo don’t lend the money. Just as Sandwichman argued that Say’s Law is the lump-of-labor fallacy, so too is the cost disease really the Bennett hypothesis.

The function of cost disease, though, I think is different. It’s raised to neutralize the positional-goods argument implied by the Bennett hypothesis. It’s not that education is a rate race, they argue; rather, it’s that we can’t make the rat race better.

If that sounds like a non sequitur, it’s because it is, but with logic like that we needn’t worry about robots replacing the profs.

CBO Misleads on Household Formation?

Last year, the Congressional Budget Office reported in its “Budget and Economic Outlook” that better job prospects and easier access to mortgages would help accelerate household formation. At the same time it raised concerns that student loans were inhibiting people from buying houses.

I thought the CBO was living in a fantasy world about household formation, and soon after the Federal Reserve Bank of New York agreed with me. However, going by this year’s “Budget and Economic Outlook,” it looks like I could be wrong: Household formation started rising as the CBO predicted.

"Household formation is the change in the average number of households from one calendar year to the next."

“Household formation is the change in the average number of households from one calendar year to the next.”

(Page 163)

At first I thought, “Well, it might be the start of a trend, but I don’t see why it’ll continue.” But then I looked at the Census Bureau’s household data (Table 13a), which the CBO was clearly relying on. It turns out, when you look at the whole calendar year, and not just the average, household formation spiked until mid-2015, and then it collapsed.

YoY Change in Household Formation by Month

Household growth in 2015 could be a blip in either direction, but I’m curious how 2.2 million households would choose to form in December 2014. Seems like an awful time of year to do it. Even the CBO concedes household formation could be slower than it expects (page 54).

I think the CBO should’ve inspected the household data a little more closely before concluding that residential real-estate construction would contribute to economic growth. It said nothing about the vacancy rate, which I’ll look into when the Census Bureau updates those data.

NY Fed: Student Debt Delinquencies Still High in 2015

What started in 2012 just isn’t stopping. According to the Federal Reserve Bank of New York’s Housing Debt and Credit Report, the percent of student-loan balances that are 90+ days delinquent was about 11.5 percent at the end of 2015, about where it was a year ago. Delinquencies for all other household debts save credit-card debt fell last year:

Student-Loan Delinquencies (2015)

This year, the NY Fed declined to discuss all those bad student loans, unlike last year.

Between fourth quarter 2014 and and the end of 2015, all non-housing debt grew from $3.15 trillion to $3.37 trillion. Student-loan debt accounted for 31 percent of the $220 billion increase.

Meanwhile, looking through Department of Education data, only 51.74 percent of all $1.204 trillion in federal student loans are in active repayment. 21 percent are in deferment or forbearance, and 9.5 percent are in default. Of the $585.8 billion of direct loans in repayment, forbearance, or deferment, $188.2 billion are on IBR or PAYE. Nearly one-third of all direct loans in repayment are in one of these plans, about 15.6 percent of all student loans.

This just doesn’t end. Until it will.