An Ad Hominem Argument Against GDP

Epistemic status: this is not the argument against GDP but it’s definitely underappreciated.

Suppose, you read about some study in the media and decide to check the study’s findings yourself. What do you do?

First, you can read the abstract of the paper and look at some summary statistics that the researchers provided. Most likely, you will realize that this is just another bullshit correlational study or that n is equal to like 10; sometimes, you will see that the effect is too large to be believable; occasionally, you will find that the summary statistics are actually impossible. You will definitely miss the dinosaur. Source.

Second, you can inspect the data itself. This way you can notice that, for example, the data looks kind of weird or, conversely, too normal; or you can notice that the researchers forgot to select some rows in Excel when analysing the data or let Excel interpret genes' names as dates.

Third, you can think about how the study’s data is defined, collected, and analyzed. Thus, you will understand how it’s possible that the US is the 10th most dangerous country in the world for women or that Sweden has the highest rape rate in the world; or you may come to understand how some fMRI analyses may feature up to 70% false positive rate; or how, for that matter, the entire quantitative scientific literatures can be complete bullshit. Likely, a consequence of treating statistics as a significance-producing machine.

Fourth, — this is where we start to ignore the data and move meta — you can look at the incentives people face when producing the data. Think about it for a while and you won’t be surprised to learn that countries routinely embellish their GDP data, PISA scores, and likely any other statistic of any consequence. When I interned at the Russian Government we analyzed the data on performance of some bureaucrats. I asked my boss if this data was used to promote those who do well, to which the answer was, “if we started using the data, everybody would just doctor it.”

Finally, you can look at the actual people who produce the data. Specific people collect, systemize, analyze and only then show it to us; and — even having incentives taken into account — it matters who these people are.

GDP estimation

I have precisely one idea: estimating GDP (and comparing estimates over time) is an incredibly complex process and it matters who creates these estimates.

So, let me ask you a question: who would you trust with producing US GDP estimates and the methodology for estimating GDP?

And after you have a personal answer to this question, let’s see

Who exactly creates GDP estimates

In the US — Bureau of Economic Analysis (BEA). Besides various national, regional, international, and industry accounts, BEA produces the methodology for GDP estimation and the estimates themselves.

But before looking more closely at BEA, let’s take a slight detour to academia.

I looked at all professors of Economics, excluding visiting and emeritus professors, at several US universities. For every professor, I looked at the university they got their PhD from and at the RePeC rank I used the June 2018 ranking. of that university. RePeC stands for “Research Papers in Economics” and their rankings are considered to be somewhat canonical. Note that only top 280 universities are ranked, so if the university is not in top 280, I wrote down its rank as 280.

Below are histograms for Harvard (rank 1), University of Southern California (rank 25), University of Minnesota (rank 61), and University of Pittsburgh (rank 89). Initially, I wanted to pick ranks 1, 25, 50, 75, but universities at ranks 50-60 are not American, so I picked rank 61. Then, ranks 86, 87 are again not American, rank 88 (CMU Tepper) shows large discrepancy in the number of faculty listed on RePeC and on CMU’s site, so on a semi-ad hoc basis I decided to look at rank 89.

We can see a clear, if somewhat weak, trend from the pictures — among Harvard professors more than 50% got their PhD either from Harvard or MIT (rank 2). Then, the median steadily declines to 13 (Yale), 19 (Michigan), and 22 (Northwestern).

What if we build a similar histogram for the Federal Reserve Bank of New York?

The data suggests that if New York Fed was an Economics Department, it would be solid — not the best — but a solid department. The median researcher there got their PhD from Yale.

BEA’s site lists 44 people in the Research at BEA section. Listed, to be precise. The page was removed after the BEA’s site redesign in mid-August 2018. Looking at the profiles, we can see that BEA hires quite a few Economics PhDs, and for 27 people we can see the universities that granted their PhDs:

The median researcher at BEA got their Economics PhD at University of Texas-Austin (rank 69). You can look at the full data I used in the appendix.

But BEA is not primarily a research institution and maybe looking at its researchers is not totally fair. So, let’s look at Economists at BEA.

According to, BEA has 483 employees (LinkedIn lists 348), among them 291 are listed as “Economists” (LinkedIn search for “Economist” at BEA returns 174 results). Of these 174 people:

I was able to get information about bachelor’s degrees for 88 people out of these 132. The ranking I use is from 2014 and sorts the US universities by their average SAT scores. Some example ranks:

The histogram for BEA is:

The median Economist at BEA got their Bachelor’s degree at Pennsylvania State University (rank 241, SAT 1175). Zero people finished a top 10 school. An identical argument could be made for most of the other countries' GDP estimates. Speaking of Russia — I don’t ever hear students at top universities talking about working for Rosstat.

See reddit discussion of this post here.

Why the constancy of the rate of growth, though?

I think it’s likely that this is significantly a result of the phenomenon described in this article:

To Elfin, however, who has a Harvard master’s diploma on his wall, there’s a kind of circular logic to it all: The schools that the conventional wisdom of the meritocracy regards as the best, are in fact the best–as confirmed by the methodology, itself conclusively ratified by the presence of the most prestigious schools at the top of the list. In 1997, he told The New York Times: “We’ve produced a list that puts Harvard, Yale and Princeton, in whatever order, at the top. This is a nutty list? Something we pulled out of the sky?”

According to sources close to the magazine, a bitter internal struggle broke out when it became clear that Caltech was going to come out on top in the late spring of 1999 after the rankings had been changed to count every category the same way. Fallows’ replacement Stephen Smith and new Special Projects Editor Peter Cary were both reportedly shocked to see that, under the new formula Graham had recommended, the conventional wisdom of the meritocracy would be turned upside down, and there were discussions about whether the rankings should be revised to change the startling results. (Morse and Cary both deny this.) Eventually, a decision was made to keep the new formula and U.S. News received a hefty dose of criticism from baffled readers. Morse declined to say how the formula has been changed for the rankings that will be printed on September 4th of this year. But if Caltech’s ranking drops and one of the three Ivies recovers its crown, read the small print carefully. Caltech’s advantage over the second ranked school last year was an astronomical seven points (more than the difference between #2 and #8). The methodology would have to be monkeyed with substantially to drop Caltech out of the top spot.

People expect sliding growth to be 2-3% and whenever it starts to systematically deviate from this, methodology might be gently adjusted to reflect the “““correct””” growth.

Common Objections

GDP estimation is basically accounting. You don’t need Harvard PhDs working on this problem

Nope. Definition of GDP is simple. Actually trying to calculate it is hard. Some example problems:

Even if the methodologies tell us how to estimate everything, there’s still a ton of tacit knowledge and ad hoc decisions one needs to make at every step. You can fuck up data analysis in a million of different ways. Also see 1 and 2.

I should add a note here: I neither ever worked on GDP estimation, nor ever worked near a government agency that did it. Most of my knowledge stems from reading Economic papers, BEA documents, and discussions with friends. If you do have good arguments either for estimating GDP being simple or against it — I would love to hear them.

All methodology is done by people at top universities anyways

Uh, maybe? But from my reading of BEA papers this doesn’t seem to be the case. If you actually know anything about this — do let me know.


Thanks to Anastasia Kuptsova for data visualization and valuable comments. Thanks to Maxim Alexeev for stimulating discussion. Thanks to Gleb Posobin and Nicholas Perry for helpful suggestions.


“This note examines mobile phone CPIs for 12 countries… Their CPIs vary wildly, ranging from no change (for Japan) to over 20 percent declines per annum (for New Zealand and the UK)" (a) (via)

An anonymous reader writes

As someone working in economic statistics, in the UK, this blogpost is on-the-money.

BEA researchers' PhD insitutions and institutions' RePeC rank

Minimum, median, and maximum are indicated by color.

Rank Institution Professors' / Researchers' PhD institutions RePeC ranks
1 Harvard University 1, 2, 2, 1, 2, 13, 1, 1, 5, 1, 2, 2, 1, 97, 2, 7, 19, 1, 1, 40, 1, 4, 4, 8, 3, 147, 3, 1, 2, 1, 13, 5, 2, 2, 1, 2, 1, 5, 2, 1, 1, 19, 35, 1, 2, 2, 5, 2, 3, 3, 20, 2, 2, 2, 5, 2, 22, 1, 22, 8
25 University of Southern California 18, 29, 10, 10, 10, 163, 21, 13, 113, 8, 12, 4, 8, 11, 13, 13, 280, 5, 33, 12, 58, 31, 96, 13, 147, 5, 21
New York Fed 65, 36, 20, 9, 20, 89, 2, 4, 11, 41, 3, 12, 61, 5, 9, 18, 254, 13, 2, 4, 32, 1, 3, 280, 3, 280, 13, 1, 16, 2, 16, 5, 70, 1, 3, 16, 16, 22, 61, 27, 211, 36, 13, 4, 2, 4, 27, 15, 147, 14, 16, 4, 46, 3, 4, 262, 3, 11, 2, 8
61 University of Minnesota 16, 5, 2, 12, 88, 88, 21, 22, 5, 1, 13, 42, 4, 8, 21, 19, 4, 21, 61, 89, 97
89 University of Pittsburgh 22, 114, 22, 9, 147, 41, 280, 14, 14, 16, 5, 61, 116, 129, 13, 27, 43, 13, 147, 9, 73, 22, 51, 13, 27, 38, 3, 12, 21
BEA 26, 16, 13, 13, 61, 83, 280, 69, 116, 240, 95, 13, 249, 15, 1, 21, 240, 240, 280, 240, 70, 4, 73, 186, 15, 30, 30

initials university RePeC rank (only top 280 are ranked)
AA Boston College 26
BA University of Pennsylvania 16
CA Yale University 13
MB Boston University 13
BB University of Minnesota 61
BC Indiana University 83
GC University of Cincinnati 280
AD University of Texas at Austin 69
DF Purdue University 116
MG American University 240
MG George Washington University 95
KH Yale University 13
MI University of Georgia 249
MK University of California-San Diego 15
RK Harvard University 1
WL University of California, Los Angeles 21
RM American University 240
BM American University 240
DR University of Kansas 280
RR American University 240
JS Johns Hopkins University 70
RS University of Chicago 4
PT Washington University in St. Louis 73
SW George Mason University 186
TY University of California, San Diego 15
DY University of California at Davis 30
WZ University of California at Davis 30

BEA economists' undergraduate institutions

initials undergrad institution rank
AB George Mason University 295
JH The College of William and Mary 47
RC Vassar College 35
AY University of California-San Diego 100
NP University of Illinois Chicago 388
BD The Catholic University of America 382
AR George Mason University 295
GL University of Rochester 57
MS University of Michigan 62
RO San Diego State University 451
LW University of California, Davis 188
TG Penn State University 241
BT Smith College 67
JD University of Maryland Baltimore County 188
LF The Catholic University of America 382
SM University of Maryland - College Park 81
DD University of California, Irvine 258
MK Binghamton University 99
EE DePaul University 166
RP University of Mississippi 410
RG Miami University 69
CN University of Wyoming 352
HH University of Maryland College Park 81
WM Lawrence University 102
ES University of Massachusetts Boston 611
CM Salisbury University 288
DH University of Maryland College Park 81
WP University of South Carolina 196
KA University of North Carolina at Greensboro 713
JH University of Louisville 301
BL Washington State University 725
LM University of Mary Washington 388
CR The Johns Hopkins University 26
MR Michigan State University 285
NM Arizona State University 301
EX Cornell University 28
JN University of Maryland 81
OK University of North Carolina at Greensboro 713
PW Pomona College 11
TM St. Norbert College 352
EM Virginia Polytechnic Institute and State University 180
SK University of Houston 334
NH Claremont McKenna College 29
DC James Madison University 301
EW Trinity University 128
NM Anderson University 546
DK University of Maryland Baltimore County 188
AF University of Virginia 55
CL University of Michigan 62
AD Loyola University New Orleans 301
SA Penn State University 241
MS George Mason University 295
KR East Carolina University 591
KW Kent State University 622
PC Bucknell University 91
DJ Saint Louis University 137
MS Wheaton College 166
BV University at Albany-SUNY 464
JF Washington State University 725
MG St. Mary’s College of Maryland 216
CS University of Illinois at Urbana-Champaign 102
FN St. Joseph’s College 388
MT James Madison University 301
AG University of Central Florida 237
SB Hawaii Pacific University 937
MH University of Pennsylvania 18
RM University of Oregon 382
JH The College of William and Mary 47
MC Oklahoma State University 301
PG Virginia Polytechnic Institute and State University 180
RT Youngstown State University 1090
EB John Carroll University 352
LM University of Maryland College Park 81
DB Frostburg State University 1049
LW Ohio University 388
JM Portland State University 622
AB University at Albany-SUNY 464
TH Morgan State University 1232
TP St. Mary’s College of Maryland 216
MF University of Maryland Baltimore County 188
MH University of California, Berkeley 42
JG University of Tampa 591
RN University of Michigan 62
CB University of Maryland College Park 81
CE Mount St. Mary’s University 509
LN Wellesley College 29
TM East Carolina University 591

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