Recently I broke down the 2022-23 Faculty Compensation Survey to analyze how U.S. professors’ pay varies across institution types, institutional pay percentiles, and faculty ranks. However, there were two big hidden factors in the underlying report that made the results, and by extension my blog post, difficult to interpret:

1. Cost of living. Some of the cross-institutional differences in pay, especially within institution type, are surely due to regional differences in cost of living since it takes more to attract faculty to New York City than to Lincoln, NE (at least for financial reasons). 
2. Disciplinary differences. A redditor recently posted a different recent post to r/academia, where it got some critical discussion. It seemed that a great deal of the differences in opinion on the reasonableness of that simple simulation’s assumptions were based on discipline. Those in the humanities found it too rosy, while others I had shared it in other disciplines with found it a bit pessimistic. 

While we all know anecdotally or from public salary databases that some disciplines make more than others, I hadn’t previously come across any datasets laying this out clearly and systematically. The Chronicle of Higher Education’s data don’t address it either, like the underlying IPEDS data. So I’ve been keeping my eye out for a helpful data source. Good news: I found one!

But first, let’s discuss my priors.

My Priors

Coming in to this, I expected the professorial pay distribution would look something like this:

1. Fields that can make a lot of money outside academia.
2. Physical sciences.
3. Social sciences.
4. Humanities.
5. Glorified job training.
6. The Associate Vice Deanvost for Faculty Exploitation Initiative’s personal shoe shiner.
7. English professors.

Just kidding, English profs – I love you! And obviously the AVDFE has no need for a personal shoe shiner since all our department chairs are forced to constantly lick their boots. But the first 5 is basically what I thought I’d find coming in. 

Before you proceed, take a minute to set your own expectations. What do you think we will find? Whatever it is, I’m guessing there will be a few results that surprise you just as much as they did me.

The Government Has Good Data

BLS FTW

How can we get hard data on this question? Enter the Bureau of Labor Statistics’s Occupational and Employment Wage Statistics estimates. I’ll tell you about the May 2023 estimates (the most recent currently available). These tables are based on a survey of 1.1 million establishments employing 81 million individuals over three years included on a rolling basis, then estimates unobserved establishment-occupation combos’ wages based on their similarity to observed pairs. This enormous sample size enables it to estimate pay for more detailed occupations than are usually publicly available.

Now, I don’t want to bore you with the nerdy details, but…

THE NERDY DETAILS

Just kidding, I know you dorks have nothing better to do! Thus, you can geek out over the dataset’s documentation in all its pdf glory here, but for those of you too busy procrastinating on a conference submission deadline, let me note a few important issues.

Pay Measurement

For most occupations, they ask for the hourly wage or else estimate it based on the presumed number of hours worked annually (52 x 40 = 2,080). However, they specifically note that certain occupations such as teachers are paid annually but don’t work year-round, with the result that they only provide total annual pay estimates for these occupations (and don’t attempt to normalize it to full-time-equivalence or calculate hourly pay). To double check, I emailed BLS staff, and they confirmed this interpretation: “We request survey respondents to only report the annual salary for postsecondary teachers. We do not normalize those wages to represent 2,080 hours, because postsecondary teachers do not generally work year-round, full time.” Hence, part of the heterogeneity in pay by discipline will inevitably be due to differences in supplemental pay such as summer salary. 

What pay is included? To quote the documentation: “Wages for the OEWS survey are straight-time, gross pay, exclusive of premium pay. Base rate; cost-of-living allowances; guaranteed pay; hazardous-duty pay; incentive pay, including commissions and production bonuses; and tips are included. Excluded are overtime pay, severance pay, shift differentials, nonproduction bonuses, employer cost for supplementary benefits, and tuition reimbursements.” My interpretation is that supplemental summer funds as well as 9-month base pay will be included in this definition. However, I believe consulting pay and anything else not routed through one’s W2 job will not be included.

Finally, these aggregated data are top coded at $239,200. The data file just included a “#” symbol to signal that which I replaced with the topcode value. This only affects the 90th percentile statistics for Law and Medicine, so everything else should be fully accurate.

Included Workers

Just about all professors should be included here: “The OEWS survey includes all full- and part-time wage and salary workers in non-farm industries.” My interpretation is that everyone from adjuncts to full professors will be included here.

To double check, I emailed BLS again, and they clarified: “Part-time and full-time workers are counted the same in the OEWS estimates. However, annual wages for part-time are adjusted to their full-time equivalent by the respondent while reporting the data.“ So that means adjuncts with less than a full-time load will be included, but their total compensation will be scaled up to full time.

Occupational Details

Occupation in this report is measured at two relevant levels – broadly in categories that would roughly correspond to typical intra-university college distinctions such as engineering, life sciences, etc; and in detailed categories such as Physics or Geography that would roughly correspond to intra-college departmental differences. I’ll give you the data from both. 

Enough Already – Show Me the Data!

Fine, fine! There’s no need to be so pushy. As a first cut, I’ll look at this two different ways: by broad disciplinary differences (I’ll just refer to these as college-level differences) and by detailed disciplinary differences (I’ll call them departmental differences).

‘College’-Level Differences

Here’s the data!

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
All	101,750	48,210	82,270	177,180	1.71	2.15	3.68
Health	122,760	51,150	100,950	225,010	1.97	2.23	4.40
Engineering and Architecture	119,600	60,380	106,860	183,960	1.77	1.72	3.05
Business	113,240	48,580	97,130	211,230	2.00	2.17	4.35
Physical Sciences	105,600	52,900	94,060	174,330	1.78	1.85	3.30
Law, Criminal Justice, and Social Work	104,340	47,780	81,770	199,890	1.71	2.44	4.18
Life Sciences	101,440	51,330	84,260	169,410	1.64	2.01	3.30
Social Sciences	100,790	50,140	84,600	171,510	1.69	2.03	3.42
Math and Computer Science	100,060	49,150	83,300	171,020	1.69	2.05	3.48
Arts, Communications, History, and Humanities	93,950	47,990	79,960	163,400	1.67	2.04	3.40
Miscellaneous Postsecondary Teachers	85,400	40,800	71,800	146,070	1.76	2.03	3.58
Education and Library Science	81,230	40,100	74,550	130,150	1.86	1.75	3.25

First, let’s talk about the ‘all’ row. I have to admit that I find these data surprising. According to this, the mean total W2 compensation for postsecondary teachers is in the 6 figures at $101,750, with a median of $82,270 and a 10th/90th range of $48,210-$177,180. All of those numbers are higher than I expected. I don’t find it surprising because of the average, since that statistic is quite close to the full-time averages reported in the 2022-23 AAUP faculty compensation survey report (the report doesn’t actually report the grand mean, but it reports the average full-time salary for men [$117,830] and women [$96,903] and provides the percentage of full-time faculty that are men [51.8%] and women [48.2%], which if you do the math – and you know I did! – works out to an average of $107,743). What I find surprising is that the statistic in the table above supposedly includes all faculty, including tenured, tenure-track, full-time non-tenure track, and part-time adjuncts. Considering that the AAUP survey report also shows in Figure 5 that nearly half of all postsecondary faculty in their data are part-time, and shows in Table 15 the average part-time pay per course as $3,874, I am surprised that the grand mean is not significantly lower. After all, even if the average part-time faculty is stitching together 12 courses per year, that still implies mean pay of $46,488.

However, let’s proceed on the assumption that if these numbers are upwardly biased, they are at least similarly biased by discipline and ‘college’ which is how I describe the aggregations of disciplines in the table above. As you can see, there’s quite a bit of cross-college inequality: the mean health faculty makes about 50% more than the mean education/library science faculty, with a steady gradient in between. We could if we like group these rows into three tiers of mean pay:

1. Health, Engineering & Architecture, and Business
2. Physical Sciences, Law/CJ/SW, Life Sciences, Social Sciences, Math & Computer Sciences
3. Arts/Comm/History/Humanities, Miscellaneous, and Education & Libraries

By and large this makes intuitive sense based on the mental model that the capitalist tendrils into the ivory tower have implanted within us: Those in the top tier generally have the most lucrative non-academic employment options, while those at the bottom tier generally have the least lucrative non-academic employment options.

I think the intra-college inequality is pretty interesting here, too. I calculated three simple statistics here: the 90/10 ratio (the ratio of the 90th percentile to the 10th percentile of the pay distribution), the 50/10 ratio (the ratio of the median/50th percentile to the 10th percentile of the pay distribution), and the 90/50 ratio (you get the idea). Of note, the most highly paid colleges also have the highest intra-college 90/10 ratio. This makes a degree of sense since high mean pay colleges will often have a larger than typical rightward skew. There are two exceptions to that rule, however: Engineering is in the top college pay tier yet has the lowest 90/10 ratio (driven more but its low 90/50 ratio than its pretty average 50/10 ratio); and Law, Criminal Justice, and Social Work is in the middle college pay tier yet has an unusually high 90/10 ratio, driven particularly high by its very large 90/50 ratio. Presumably this is because Law professors make substantially more on average than Criminal Justice and Social Work professors.

‘Department’-Level Differences

And here’s the much longer ‘departmental’ data, which is mostly intuitive, but with a few surprises! Here we go.

As you can see, there’s an enormous spread in postsecondary teacher pay both across disciplines. The discipline with the highest average pay is Law (average $142,440, median $127,360) which pays more than double the lowest paid discipline (Career & Technical Education, average $68,300, median $62,060). 

One thing that’s very notable here is that the ‘floor’ of pay (10th percentile) is much more similar across disciplines than the ceiling. With a few exceptions, nearly every discipline features a 10th percentile in the $45k-55k range. Education & Career & Technical Education are exceptions on the low end with 10th percentiles below $40k. On the high end, Engineering & Architecture both have 10th percentiles over $60k. But this handful of disciplines aside, the low end of the discipline-specific pay distribution is very similar across disciplines.

You can’t say that about the ceiling, where the 90th percentiles for the highest-paid disciplines exceed the top-code of $239k, while Career & Technical Education’s 90th percentile is $105k. You won’t be broke on the latter anywhere in the country, but it is pretty discouraging as a ceiling. The rest of the 90th percentile statistics are pretty evenly distributed between $127k and $211k.

Between those extremes, there is a relatively smooth gradient in mean pay with the exception of a handful of break points, which I’ll use to divide the disciplines into tiers. They are:

• Tier 1: Elite Professions: Law, Health Specialties, and Economics. These disciplines all boast average pay of >$130k, median pay well into the 6 figures, and 90th percentiles at or approaching the top code. 
• Tier 2: Slightly Less Elite Professions: Engineering, Architecture, Business, Other Physical Sciences. These disciplines’ average pay is $112k-121k and all have medians in or very close to the 6 figures, with 90th percentile values at $174k+.
• Tier 3: Most Hard Sciences, Some Social Sciences, & Art. Physics, Computer Science, Political Science, Anthropology and Archaeology, Chemistry, Biology, Forest & Conservation Science, Environmental Science, Art/Drama/Music, Area/Ethnic/Cultural Studies. These fields all have average pay in the low 6 figures, median pay $80k+, and 90th percentiles mostly in the $165k-180k range.
• Tier 4: Other Social & Hard Sciences, Humanities, & Non-Elite Professions. Geography, Sociology, Agriculture, Math, Psychology, History, Communications, Philosophy/Religion, Family & Consumer, Foreign Language, Library, Recreation & Fitness, English, Nursing. These fields have average pay $86k-98k, median pay $76k-86k, and 90th percentiles $130k-177k. 
• Tier 5: Middle Class Professions. Criminal Justice & Law Enforcement, Social Work, Education, Career & Technical Education. These field have average pay $68k-83k, median pay $62k-75k, and 90th percentiles $105k-135k. 

Do My Priors Hold Up?

Now, let’s dig more into tier-specific details and cover within-disciplinary pay differences while we’re at it. This is where it really gets fun.

Tier 1: Elite Professions

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
Law	142,440	51,810	127,360	239,200	2.46	1.88	4.62
Health Specialties	134,440	52,300	105,650	239,200	2.02	2.26	4.57
Economics	133,650	58,060	115,300	221,170	1.99	1.92	3.81

News flash: You should have been a lawyer, doctor, or economist. These fields have the potential to make a lot of money outside the academy, so I suppose it’s not too surprising that they make more than the rest of us inside the academy. That’s one point for my priors.

If you had, though, you would face a pretty high range of within-field inequality, as these three fields sport three of the four highest 90/10 ratios of any discipline. Law sports the highest 90/10 ratio of any discipline (4.62), driven more by the low end (2.46 50/10) than the high end (90/50 2.26). Health Specialties is right behind with a 90/10 ratio of 4.57, though its inequality is concentrated on the high end. Economics features relatively lower inequality among its own ranks (though still 4th highest out of 38 overall), with a 90/10 ratio of 3.81 that’s about evenly divided between 50/10 and 90/50. Notably, Economics has one of the lowest 10th percentiles of any field, which is perhaps not surprising for the discipline that invented the concept of the reservation wage.

Tier 2: Slightly Less Elite Professions

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
Engineering	120,630	60,250	106,910	200,030	1.77	1.87	3.32
Architecture	114,900	60,610	105,770	174,390	1.75	1.65	2.88
Business	113,240	48,580	97,130	211,230	2.00	2.17	4.35
Other Physical Sciences	111,930	52,430	100,690	195,940	1.92	1.95	3.74

Here in tier 2, we’re still in disciplines that roughly fit my mental model priors. Engineers, architects, and business experts seem likely to have strong demand for their services outside the academy. And Other Physical Sciences (the BLS code is “Atmospheric, Earth, Marine, and Space Sciences”) is the first representative of the hard sciences category that I assumed came next. So far, so good.

In tier 2, business has the 3rd-highest 90/10 ratio at 4.35 (relatively evenly split between 50/10 and 90/50), while Architecture is one of the less unequal fields, driven by its combination of highest 10th percentile and a lower 90th percentile compared to others in this tier. Engineering and Other Physical Sciences line up in the middle.

Tier 3: Most Hard Sciences, Some Social Sciences, & Art

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
Physics	106,950	55,150	98,020	171,820	1.78	1.75	3.12
Computer Science	106,380	49,770	96,430	175,150	1.94	1.82	3.52
Political Science	104,910	51,610	93,810	172,910	1.82	1.84	3.35
Anthropology and Archeology	103,640	51,980	93,650	172,180	1.80	1.84	3.31
Chemistry	102,630	52,590	85,810	171,750	1.63	2.00	3.27
Biological Science	102,270	51,670	83,920	171,410	1.62	2.04	3.32
Forestry and Conservation Science	102,230	58,120	101,650	141,800	1.75	1.39	2.44
Environmental Science	100,910	51,280	88,410	166,150	1.72	1.88	3.24
Art, Drama, and Music	100,840	47,570	80,360	178,670	1.69	2.22	3.76
Area, Ethnic, and Cultural Studies	100,390	51,590	86,030	169,680	1.67	1.97	3.29

Until now things have lined up about how I expected, but this is where things start to get a little weird. Physics, Computer Science, Chemistry, and Biology? All makes sense. Poli Sci, Anthropology? Ok, but it’s surprising that their averages are higher than Chemistry and Biology. Then we have **checks notes** Art/Drama/Music and Area/Ethnic/Cultural Studies? WHAT?? That’s right – contrary to what I, probably you, and definitely your uncle would assume, those folks make roughly the average of all professors on average, significantly ahead in some cases of some field that we might have assumed made more.

These fields mostly have intra-disciplinary inequality on the below average side for all professors (3.68 90/10 ratio), with two exceptions: Art / Drama / Music professors have pretty high intra-disciplinary inequality, with a 3.76 90/10 ratio that’s pretty concentrated on the 90/50 (2.22) rather than 50/10 (1.69) side. And this tier also features our least unequal discipline: **drum roll** Forestry & Conservation Science! I don’t know who you had on your bingo card, but those folks weren’t on mine. Good for them.

Tier 4: Other Social & Hard Sciences, Humanities, & Non-Elite Professions

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
Geography	97,660	50,870	85,600	155,010	1.68	1.81	3.05
Sociology	97,580	49,900	82,670	169,920	1.66	2.06	3.41
All Other	96,570	45,930	79,870	177,050	1.74	2.22	3.85
Agricultural Sciences	95,610	49,240	85,260	149,660	1.73	1.76	3.04
Mathematical Science	95,320	48,740	81,020	165,880	1.66	2.05	3.40
Psychology	93,990	49,790	82,140	151,890	1.65	1.85	3.05
History	93,120	48,760	82,140	141,840	1.68	1.73	2.91
Social Sciences, All Other	91,710	43,890	77,750	170,180	1.77	2.19	3.88
Communications	90,950	48,060	79,910	160,060	1.66	2.00	3.33
Philosophy and Religion	89,680	48,450	79,930	137,310	1.65	1.72	2.83
Family and Consumer Sciences	89,630	48,410	78,410	146,020	1.62	1.86	3.02
Foreign Language and Literature	88,490	48,230	78,760	136,260	1.63	1.73	2.83
Library Science	88,190	53,660	80,310	131,310	1.50	1.64	2.45
Recreation and Fitness Studies	87,340	44,400	75,770	148,610	1.71	1.96	3.35
English Language and Literature	87,090	48,090	78,130	137,100	1.62	1.75	2.85
Nursing	86,530	49,120	80,780	130,320	1.64	1.61	2.65

My mental model priors mostly hold up in tier 4 as this is about where I expected the bulk of the non-econ social sciences and then humanities to show up. However, Math sitting there below Geography, Sociology, and Ag is a little surprising. And did you expect Nursing to have a lower average than English? I didn’t.

As with tier 3, intra-disciplinary inequality is comparatively low here. Sociology (3.41), Math (3.40), and Recreation & Fitness Studies (3.35) do stand out as a the three most unequal disciplines in this tier, which is sorta hilarious since Sociology could pretty fairly be described as very focused on inequality. Still, all disciplines in this tier have below-average 90/10 ratios, so those are just the highest of the pretty low. On the other end, Library Sciences come in particularly low at 2.45 90/10 ratio. 

Tier 5: Middle Class Professions

Occupation	Mean	P(10)	P(50)	P(90)	50/10	90/50	90/10
Criminal Justice and Law Enforcement	83,470	46,320	69,030	134,580	1.49	1.95	2.91
Social Work	80,840	46,790	75,020	127,760	1.60	1.70	2.73
Education	80,750	39,670	73,240	130,000	1.85	1.77	3.28
Career/Technical Education	68,300	39,120	62,060	105,200	1.59	1.70	2.69

My labels would work a little better if Nursing had been a bit closer to CJ & Law Enforcement here, but oh well. The four fields here are comparatively focused on job training for middle class professions, and they receive a corresponding low level of pay, roughly fitting my mental model in the glorified job training category.

These all also have low intra-disciplinary inequality, but Education’s 3.28 90/10 ratio is notably higher than the others’.

Closing Thoughts

By and large, disciplinary pay differentials fit what I expected: high-paying professions on the high end, low-paying ones on the low end, then with physical sciences, social sciences, and humanities lined up in the middle. Flouting this neat orderly mental model are those surprisingly well paid artistes, area/cultural folks, poli sci and anthropology geeks; and surprisingly poorly paid math nerds.

So what’s going on with the aberrations my priors? A couple thoughts:

1. Some fields with less active labor markets may have faculty that skew senior. So for instance if new Anthropology Ph.D.s are having trouble getting jobs, those that actually are Anthropology professors might be disproportionately full professors.
2. Some fields might be better represented at lower paying schools than others. For instance, you can’t really have any type of college or university without a math department, so math professors might be disproportionately employed at community colleges and directionals compared to a field like Area / Ethnic Studies.

Altogether, professors inhabit some highly variable economic realities both between and within disciplines. The AAUP faculty compensation survey results I’ve previously analyzed can help us make sense of the very high degree of intra-disciplinary pay differences, since schools vary enormously in typical faculty pay both between and within university types. The other hidden factors to unpack here are cost of living, productivity, negotiation skills, and luck. I don’t think I’ll be able to break all of those down, but cost of living is next on my list – stay tuned.

Replication Files

pay_by_discipline.do