Useless Majors or Small Majors?
November 7, 2011 at 9:55 am gabrielrossman 34 comments
| Gabriel |
The WSJ has a very interesting table of the unemployment and wage distributions for various majors. There’s lots to talk about, particularly the STEM/humanities/social/vocational divide, but one thing that struck me was that the highest and lowest unemployment rates were dominated by tiny majors. In general, small populations tend to have more widely varying outcomes just as a function of standard error, which is why you should always ignore headlines about big jumps in the crime rate for small towns. Anyway, I downloaded the data, generated some plots, and yup, it’s your classic funnel.
Here’s unemployment by rank popularity. Because low rank means popular, the funnel is backwards.
WSJ only provides rank, but I approximated raw size as the inverse of log rank plus 1 and this gives us the typical funnel.
Moral of the story, don’t change your major from clinical psych to actuarial science just yet. On the other hand, nursing, elementary education, and general education really do appear to be real deal outliers of low unemployment.
Here’s the code.
insheet using ~/Documents/codeandculture/majors.txt, clear drop v7 gen unemploymentpercent_real=real(subinstr(unemploymentpercent,"%","",.)) twoway scatter unemploymentpercent_real popularity, xtitle(Rank Order Popularity) ytitle(% Unemployed) graph export majors_unemployed_rank.png, replace gen size=1/(log(1+popularity)) corr unemploymentpercent_real popularity size lab def size 0 "Obscure" 2 "Ubiquitous" lab val size size twoway (scatter unemploymentpercent_real size), xlabel(#2, labels angle(forty_five) valuelabel) xtitle(Approximate Raw Size) ytitle(% Unemployed) graph export majors_unemployed_size.png, replace *have a nice day
Also, here’s the data in plain text:
Major Field Unemployment Percent 25th % Earnings Median % Earnings 75th % Earnings Popularity ACCOUNTING 5.4% $41,000 $61,000 $94,000 3 ACTUARIAL SCIENCE 0.0% $52,000 $81,000 $116,000 150 ADVERTISING AND PUBLIC RELATIONS 6.1% $36,000 $50,000 $74,000 41 AEROSPACE ENGINEERING 3.6% $60,000 $84,000 $111,000 105 AGRICULTURAL ECONOMICS 1.3% $30,000 $57,000 $99,000 122 AGRICULTURE PRODUCTION AND MANAGEMENT 3.0% $32,000 $48,000 $71,000 75 ANIMAL SCIENCES 5.7% $26,000 $40,000 $60,000 67 ANTHROPOLOGY AND ARCHEOLOGY 6.9% $30,000 $40,000 $60,000 55 APPLIED MATHEMATICS 4.1% $52,000 $71,000 $100,000 131 ARCHITECTURAL ENGINEERING 5.8% $50,000 $71,000 $96,000 140 ARCHITECTURE 10.6% $37,000 $60,000 $85,000 33 AREA ETHNIC AND CIVILIZATION STUDIES 5.7% $34,000 $48,000 $76,000 66 ART AND MUSIC EDUCATION 4.2% $32,000 $41,000 $51,000 48 ART HISTORY AND CRITICISM 6.9% $33,000 $45,000 $71,000 81 ASTRONOMY AND ASTROPHYSICS 0.0% $56,000 $62,000 $101,000 170 ATMOSPHERIC SCIENCES AND METEOROLOGY 1.6% $40,000 $68,000 $101,000 146 BIOCHEMICAL SCIENCES 7.1% $30,000 $48,000 $80,000 87 BIOLOGICAL ENGINEERING 6.8% $39,000 $60,000 $94,000 126 BIOLOGY 5.6% $35,000 $51,000 $76,000 14 BIOMEDICAL ENGINEERING 5.9% $45,000 $68,000 $101,000 137 BOTANY 6.9% $26,000 $40,000 $55,000 147 BUSINESS ECONOMICS 5.0% $44,000 $71,000 $101,000 80 BUSINESS MANAGEMENT AND ADMINISTRATION 6.0% $38,000 $56,000 $85,000 1 CHEMICAL ENGINEERING 3.8% $60,000 $86,000 $117,000 49 CHEMISTRY 5.1% $39,000 $59,000 $85,000 36 CIVIL ENGINEERING 4.9% $55,000 $76,000 $101,000 32 CLINICAL PSYCHOLOGY 19.5% $25,000 $40,000 $61,000 168 COGNITIVE SCIENCE AND BIOPSYCHOLOGY 4.5% $36,000 $43,000 $91,000 167 COMMERCIAL ART AND GRAPHIC DESIGN 8.1% $31,000 $45,000 $69,000 21 COMMUNICATION DISORDERS SCIENCES AND SERVICES 3.3% $32,000 $41,000 $50,000 98 COMMUNICATION TECHNOLOGIES 6.7% $33,000 $50,000 $73,000 89 COMMUNICATIONS 6.3% $35,000 $50,000 $81,000 7 COMMUNITY AND PUBLIC HEALTH 4.1% $31,000 $46,000 $70,000 110 COMPOSITION AND SPEECH 7.7% $30,000 $40,000 $61,000 99 COMPUTER ADMINISTRATION MANAGEMENT AND SECURITY 9.5% $39,000 $52,000 $75,000 114 COMPUTER AND INFORMATION SYSTEMS 5.6% $44,000 $62,000 $86,000 31 COMPUTER ENGINEERING 7.0% $58,000 $81,000 $102,000 47 COMPUTER NETWORKING AND TELECOMMUNICATIONS 5.2% $35,000 $53,000 $76,000 97 COMPUTER PROGRAMMING AND DATA PROCESSING 6.2% $39,000 $55,000 $84,000 121 COMPUTER SCIENCE 5.6% $50,000 $77,000 $102,000 10 CONSTRUCTION SERVICES 5.4% $49,000 $65,000 $101,000 76 COSMETOLOGY SERVICES AND CULINARY ARTS 7.3% $26,000 $41,000 $60,000 115 COUNSELING PSYCHOLOGY 5.2% $23,000 $34,000 $42,000 133 COURT REPORTING 4.9% $36,000 $55,000 $81,000 151 CRIMINAL JUSTICE AND FIRE PROTECTION 4.7% $36,000 $50,000 $73,000 13 CRIMINOLOGY 5.2% $35,000 $50,000 $71,000 92 DRAMA AND THEATER ARTS 7.1% $28,000 $40,000 $60,000 45 EARLY CHILDHOOD EDUCATION 4.1% $28,000 $37,000 $45,000 50 ECOLOGY 5.2% $31,000 $43,000 $60,000 109 ECONOMICS 6.3% $42,000 $69,000 $108,000 16 EDUCATIONAL ADMINISTRATION AND SUPERVISION 0.0% $41,000 $65,000 $89,000 171 EDUCATIONAL PSYCHOLOGY 10.9% $28,000 $35,000 $51,000 156 ELECTRICAL AND MECHANIC REPAIRS AND TECHNOLOGIES 8.4% $30,000 $44,000 $68,000 134 ELECTRICAL ENGINEERING 5.0% $60,000 $86,000 $111,000 17 ELECTRICAL ENGINEERING TECHNOLOGY 5.5% $42,000 $65,000 $91,000 65 ELEMENTARY EDUCATION 3.6% $32,000 $40,000 $49,000 8 ENGINEERING AND INDUSTRIAL MANAGEMENT 9.2% $50,000 $71,000 $98,000 127 ENGINEERING MECHANICS PHYSICS AND SCIENCE 6.5% $40,000 $67,000 $101,000 132 ENGINEERING TECHNOLOGIES 5.3% $40,000 $60,000 $91,000 117 ENGLISH LANGUAGE AND LITERATURE 6.7% $32,000 $48,000 $75,000 11 ENVIRONMENTAL ENGINEERING 2.2% $54,000 $67,000 $90,000 144 ENVIRONMENTAL SCIENCE 5.0% $40,000 $52,000 $76,000 60 FAMILY AND CONSUMER SCIENCES 5.1% $30,000 $40,000 $58,000 29 FILM VIDEO AND PHOTOGRAPHIC ARTS 7.3% $30,000 $45,000 $71,000 54 FINANCE 4.5% $44,000 $65,000 $101,000 12 FINE ARTS 7.4% $28,000 $44,000 $65,000 22 FOOD SCIENCE 6.9% $34,000 $71,000 $101,000 129 FORESTRY 3.1% $38,000 $50,000 $73,000 104 FRENCH GERMAN LATIN AND OTHER COMMON FOREIGN LANGUAGE STUDIES 5.9% $32,000 $48,000 $67,000 43 GENERAL AGRICULTURE 3.0% $28,000 $44,000 $68,000 71 GENERAL BUSINESS 5.3% $38,000 $59,000 $91,000 2 GENERAL EDUCATION 4.2% $31,000 $41,000 $53,000 9 GENERAL ENGINEERING 5.9% $47,000 $73,000 $101,000 24 GENERAL MEDICAL AND HEALTH SERVICES 5.8% $35,000 $50,000 $71,000 74 GENERAL SOCIAL SCIENCES 8.2% $34,000 $50,000 $74,000 68 GENETICS 7.4% $33,000 $71,000 $99,000 163 GEOGRAPHY 6.1% $40,000 $54,000 $81,000 62 GEOLOGICAL AND GEOPHYSICAL ENGINEERING 0.0% $56,000 $73,000 $101,000 166 GEOLOGY AND EARTH SCIENCE 5.7% $41,000 $60,000 $93,000 73 GEOSCIENCES 3.2% $36,000 $52,000 $71,000 153 HEALTH AND MEDICAL ADMINISTRATIVE SERVICES 4.3% $36,000 $51,000 $76,000 63 HEALTH AND MEDICAL PREPARATORY PROGRAMS 5.2% $40,000 $60,000 $86,000 130 HISTORY 6.5% $34,000 $50,000 $81,000 18 HOSPITALITY MANAGEMENT 5.8% $32,000 $49,000 $71,000 38 HUMAN RESOURCES AND PERSONNEL MANAGEMENT 6.6% $40,000 $55,000 $85,000 40 HUMAN SERVICES AND COMMUNITY ORGANIZATION 6.9% $29,000 $38,000 $50,000 77 HUMANITIES 8.4% $30,000 $45,000 $62,000 118 INDUSTRIAL AND MANUFACTURING ENGINEERING 5.6% $50,000 $75,000 $100,000 59 INDUSTRIAL AND ORGANIZATIONAL PSYCHOLOGY 10.4% $45,000 $62,000 $81,000 135 INDUSTRIAL PRODUCTION TECHNOLOGIES 3.1% $46,000 $67,000 $91,000 82 INFORMATION SCIENCES 5.9% $48,000 $71,000 $95,000 69 INTERCULTURAL AND INTERNATIONAL STUDIES 6.6% $32,000 $50,000 $71,000 100 INTERDISCIPLINARY SOCIAL SCIENCES 6.3% $31,000 $40,000 $50,000 96 INTERNATIONAL BUSINESS 8.5% $38,000 $52,000 $87,000 72 INTERNATIONAL RELATIONS 5.8% $40,000 $57,000 $93,000 79 JOURNALISM 7.0% $34,000 $50,000 $79,000 25 LANGUAGE AND DRAMA EDUCATION 5.0% $32,000 $41,000 $50,000 58 LIBERAL ARTS 7.6% $32,000 $48,000 $71,000 20 LIBRARY SCIENCE 15.0% $23,000 $36,000 $49,000 159 LINGUISTICS AND COMPARATIVE LANGUAGE AND LITERATURE 10.2% $30,000 $44,000 $70,000 90 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS 4.2% $47,000 $71,000 $96,000 44 MARKETING AND MARKETING RESEARCH 5.9% $40,000 $59,000 $90,000 6 MASS MEDIA 6.9% $32,000 $46,000 $69,000 35 MATERIALS ENGINEERING AND MATERIALS SCIENCE 7.7% $57,000 $84,000 $105,000 136 MATERIALS SCIENCE 4.7% $65,000 $81,000 $106,000 161 MATHEMATICS 5.0% $42,000 $63,000 $95,000 28 MATHEMATICS AND COMPUTER SCIENCE 3.5% $55,000 $91,000 $151,000 158 MATHEMATICS TEACHER EDUCATION 3.4% $34,000 $42,000 $56,000 108 MECHANICAL ENGINEERING 3.8% $60,000 $81,000 $106,000 23 MECHANICAL ENGINEERING RELATED TECHNOLOGIES 6.6% $38,000 $65,000 $87,000 123 MEDICAL ASSISTING SERVICES 2.9% $34,000 $51,000 $71,000 95 MEDICAL TECHNOLOGIES TECHNICIANS 1.4% $44,000 $58,000 $74,000 51 METALLURGICAL ENGINEERING 3.9% $50,000 $86,000 $110,000 152 MICROBIOLOGY 5.2% $40,000 $60,000 $86,000 94 MILITARY TECHNOLOGIES 10.9% $81,000 $86,000 $126,000 173 MINING AND MINERAL ENGINEERING 4.3% $71,000 $101,000 $121,000 162 MISCELLANEOUS AGRICULTURE 3.7% $31,000 $46,000 $67,000 160 MISCELLANEOUS BIOLOGY 5.3% $31,000 $50,000 $69,000 125 MISCELLANEOUS BUSINESS & MEDICAL ADMINISTRATION 5.3% $35,000 $52,000 $81,000 64 MISCELLANEOUS EDUCATION 3.7% $33,000 $46,000 $65,000 61 MISCELLANEOUS ENGINEERING 7.4% $42,000 $71,000 $91,000 106 MISCELLANEOUS ENGINEERING TECHNOLOGIES 6.0% $45,000 $65,000 $91,000 88 MISCELLANEOUS FINE ARTS 16.2% $26,000 $40,000 $49,000 164 MISCELLANEOUS HEALTH MEDICAL PROFESSIONS 3.3% $35,000 $45,000 $62,000 93 MISCELLANEOUS PSYCHOLOGY 10.3% $30,000 $45,000 $71,000 120 MISCELLANEOUS SOCIAL SCIENCES 3.8% $38,000 $52,000 $85,000 143 MOLECULAR BIOLOGY 5.3% $32,000 $50,000 $76,000 124 MULTI-DISCIPLINARY OR GENERAL SCIENCE 4.6% $36,000 $55,000 $81,000 26 MULTI/INTERDISCIPLINARY STUDIES 5.5% $34,000 $42,000 $50,000 107 MUSIC 5.2% $30,000 $45,000 $67,000 37 NATURAL RESOURCES MANAGEMENT 6.9% $36,000 $50,000 $71,000 78 NAVAL ARCHITECTURE AND MARINE ENGINEERING 1.7% $60,000 $96,000 $117,000 145 NEUROSCIENCE 7.2% $34,000 $52,000 $76,000 154 NUCLEAR ENGINEERING 4.1% $65,000 $96,000 $138,000 149 NUCLEAR INDUSTRIAL RADIOLOGY AND BIOLOGICAL TECHNOLOGIES 2.2% $47,000 $64,000 $81,000 142 NURSING 2.2% $48,000 $60,000 $80,000 4 NUTRITION SCIENCES 6.4% $35,000 $51,000 $71,000 101 OCEANOGRAPHY 3.3% $40,000 $50,000 $79,000 148 OPERATIONS LOGISTICS AND E-COMMERCE 4.7% $45,000 $65,000 $97,000 102 OTHER FOREIGN LANGUAGES 6.4% $32,000 $45,000 $76,000 111 PETROLEUM ENGINEERING 4.4% $83,000 $127,000 $178,000 138 PHARMACOLOGY 0.0% $48,000 $60,000 $101,000 169 PHARMACY PHARMACEUTICAL SCIENCES AND ADMINISTRATION 3.2% $78,000 $105,000 $121,000 53 PHILOSOPHY AND RELIGIOUS STUDIES 7.2% $30,000 $42,000 $65,000 42 PHYSICAL AND HEALTH EDUCATION TEACHING 4.5% $34,000 $46,000 $59,000 39 PHYSICAL FITNESS PARKS RECREATION AND LEISURE 4.8% $33,000 $45,000 $61,000 27 PHYSICAL SCIENCES 2.5% $36,000 $51,000 $68,000 157 PHYSICS 4.5% $39,000 $68,000 $101,000 70 PHYSIOLOGY 4.6% $30,000 $48,000 $68,000 113 PLANT SCIENCE AND AGRONOMY 2.7% $28,000 $42,000 $71,000 85 POLITICAL SCIENCE AND GOVERNMENT 6.0% $38,000 $57,000 $91,000 15 PRE-LAW AND LEGAL STUDIES 7.9% $32,000 $45,000 $69,000 91 PSYCHOLOGY 6.1% $30,000 $43,000 $65,000 5 PUBLIC ADMINISTRATION 6.9% $36,000 $50,000 $78,000 112 PUBLIC POLICY 2.2% $47,000 $65,000 $101,000 141 SCHOOL STUDENT COUNSELING 0.0% $18,000 $20,000 $42,000 172 SCIENCE AND COMPUTER TEACHER EDUCATION 5.0% $36,000 $47,000 $58,000 116 SECONDARY TEACHER EDUCATION 3.8% $35,000 $43,000 $59,000 57 SOCIAL PSYCHOLOGY 8.8% $32,000 $45,000 $60,000 155 SOCIAL SCIENCE OR HISTORY TEACHER EDUCATION 3.0% $35,000 $45,000 $58,000 83 SOCIAL WORK 6.8% $30,000 $39,000 $51,000 30 SOCIOLOGY 7.0% $33,000 $45,000 $67,000 19 SOIL SCIENCE 4.9% $43,000 $64,000 $81,000 165 SPECIAL NEEDS EDUCATION 3.6% $34,000 $42,000 $50,000 52 STATISTICS AND DECISION SCIENCE 6.9% $50,000 $76,000 $108,000 128 STUDIO ARTS 8.0% $25,000 $37,000 $57,000 84 TEACHER EDUCATION: MULTIPLE LEVELS 1.1% $30,000 $38,000 $48,000 86 THEOLOGY AND RELIGIOUS VOCATIONS 4.1% $25,000 $38,000 $54,000 46 TRANSPORTATION SCIENCES AND TECHNOLOGIES 4.4% $42,000 $68,000 $98,000 56 TREATMENT THERAPY PROFESSIONS 2.6% $40,000 $62,000 $81,000 34 UNITED STATES HISTORY 15.1% $30,000 $50,000 $96,000 139 VISUAL AND PERFORMING ARTS 9.2% $20,000 $36,000 $52,000 103 ZOOLOGY 6.7% $33,000 $55,000 $81,000 119
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gabrielrossman
pkremp
1.
Cosma Shalizi | November 7, 2011 at 10:20 am
The plot is very suggestive, but wouldn’t the natural check on this be to see if the unemployment levels equalize when looking over multiple years? I suppose we’d really want to look at deviations from a global average for each year.
2.
Alex McClung | November 8, 2011 at 12:58 pm
Hi Gabriel,
I enjoyed your post! I wonder if there is an expectation that the WSJ will feature data here on recent college grads (or recent grads vs. others) since this table accompanies their “Generation Jobless” series? I tried to look into it (shameless plug – I wrote a post about it), and I think they are using 25-64 year olds from the 2009 American Community Survey.
3.
gabrielrossman | November 16, 2011 at 1:56 pm
good question, and a good post. obviously from a practical perspective we’re more interested in recent cohorts (actually, future cohorts but there’s an obvious data problem). i don’t really know how you’d go about getting that data. the two projects i know of that do work on college majors and the economy are steve brint’s project on curriculum and also http://snaap.indiana.edu/. the second one is limited just to arts majors but in some ways that’s the single most interesting group.
4.
Maurice Walshe | November 21, 2011 at 5:41 am
Or majors where a greater proportion of grads are women ? nursing, elementary education, and general education have much greater bias towards females.
5.
gabrielrossman | November 21, 2011 at 6:23 am
I think the fact that sector is the driving issue, not gender.
6.
mikey | November 21, 2011 at 5:43 am
Easily the most impenetrably technical thing I’ve ever read. But then I am a philosophy graduate.
7.
gabrielrossman | November 21, 2011 at 6:57 am
Here’s the simple version. If you flip a coin twice, you’ll get two heads reasonably often. If you flip a coin 100 times, you’ll basically never get all heads. It doesn’t mean that the coin you flipped twice is a “heads-biased” coin whereas the coin you flipped 100 times is a “fair” coin. By a similar principle, you want to take any statement about a small sample with a grain of salt. This shows up as a funnel graph if you plot an outcome against sample size for multiple subsamples.
8.
nathansnow | November 27, 2011 at 5:52 am
Though if you flip a coin enough times you will eventually get a string of 100 heads in a row. #Alchian #Stats is limited
9.
gabrielrossman | November 27, 2011 at 6:50 am
I dunno, I’m comfortable saying that .5 ^ 100 is “basically never.” This is one of those cases where there’s a difference between “big” and “infinite.” Similarly, somewhere Dawkins shows how with any large but finite number of monkeys w typewriters you’re extremely unlikely to ever get Shakespeare.
10.
Dylan | December 9, 2011 at 8:53 am
As a holder of a PhD in philosophy and a teacher of introductory critical thinking, I think that this post is exactly the kind of thing that philosophers should understand. I think I’ve brought this post up in class at least twice already!
11.
parksejin | November 22, 2011 at 3:08 pm
Please don’t insult your friends with a degree in philosophy. This article involves only the basics of statistics and a little bit of stata code, which I, a philosophy graduate, can easily comprehend.
12.
Trent Neilson | November 21, 2011 at 6:09 am
Do any patterns emerge if results are consolidated into broad fields; physical sciences, biological sciences, etc?
13.
gabrielrossman | November 21, 2011 at 6:34 am
Good question as that would solve the small population issue I’m describing. You’d really want to know the exact sample sizes for each major in order to do so, rather than just approximating the sample sizes as a function of rank. If you click the link to the WSJ, there’s on option to filter the list by broad field and take a look at unemployment rates. Unfortunately you can’t really trust their coding since they call “political science” a “science” but not the other social sciences, apparently because it has the word “science” in it. Somebody else may have recoded the data better if you find it, or care to do it yourself, please post a link in the comments.
14.
conchis | November 23, 2011 at 3:33 am
Cue Andrew Gelman: why not use a multi-level model?
15.
Hamish Atkinson (@HamboGlider) | November 21, 2011 at 7:53 am
OK, so it’s a funnel – that’s to be expected. But let’s not bury our heads in the funnel and ignore data that might carry some useful information.
Quite apart from random variation in unemployment being less for common majors, the reason some majors are popular is that there is a lot of demand for careers requiring those majors.
But, the primary driver of unemployment is supply and demand.
If there are more people graduating with a given major than there is demand for, the unemployment rate for that major will be high and vice versa for majors in demand.
What your funnel plot does show very well is that it is much harder to predict whether or not you will be unemployed if you pick an uncommon major. Many students starting a 3-4 year degree now might well choose to study school counseling instead of psychology on the basis of the unemployment rate in this data. But in 3 years time, this could easily lead to a glut of trained school counselors and a corresponding unemployment rate, accompanied by a shortage in clinical psychologists, military technologists and fine artists. (Although I expect the economy would cope with that latter).
This is unlikely to happen with nursing, education or business majors because of the inertia caused by the large numbers of students already doing those majors and the correspondingly large demand for them.
So, much more interesting would be the trends. Do the rarer majors indeed oscillate wildly from one 3 year period to the next (confirming the random nature of what the funnel plot is showing)? Of the more stable majors, are any trending upwards or downwards in a consistent manner?
16.
Hamish Atkinson (@HamboGlider) | November 21, 2011 at 7:59 am
Or is it just that some subjects are just much less interesting to study, thus demand outstrips supply? Hands up if you’re an actuarial science major that’s been laid recently…
17.
TallDave | November 22, 2011 at 9:19 am
Thanks, an interesting graph.
Many people commented a while back that based on the numbers, the sample size of the entire WSJ survey itself does not appear to be very good. (IIRC, it originated from Census.)
18.
Cliff | November 22, 2011 at 9:32 am
This is a useless post. It says almost nothing about whether certain majors have more or less unemployment than others (which by the way, must be true). Instead, it says “small sample sizes have high variance”. Well, we knew that and it is not actually relevant to the question at hand.
19.
gabrielrossman | November 22, 2011 at 10:35 am
You may know that but many people don’t and even when people know things in the abstract it may not occur to them that it applies in a particular case.
20.
Roger Sweeny | November 22, 2011 at 9:34 am
This is old data. I have some experience with recently graduated nurses and elementary school teachers. The nurses get jobs, though it may not be the kind they want. Most of the elementary ed graduates don’t. The surplus of graduates over openings is staggering. I suspect it will be even worse four years from now.
21. Okaaaay, But | feed on my links | November 22, 2011 at 12:27 pm
[...] This was an interesting take: The WSJ has a very interesting table of the unemployment and wage distributions for various majors. There’s lots to talk about, particularly the STEM/humanities/social/vocational divide, but one thing that struck me was that the highest and lowest unemployment rates were dominated by tiny majors. In general, small populations tend to have more widely varying outcomes just as a function of standard error, which is why you should always ignore headlines about big jumps in the crime rate for small towns. Anyway, I downloaded the data, generated some plots, and yup, it’s your classic funnel. [...]
22.
DW | November 22, 2011 at 12:27 pm
Check out what happens when you put earnings on the y axis:
http://webtrough.wordpress.com/2011/11/22/okaaaay-but/
23.
andrew fischer lees | November 25, 2011 at 9:59 pm
Do you know if the median income data contains income=0 for all of the unemployed people with that major? Or, is the income data only for those earning more than $0?
If the former, then it seems like the median data is the only graph needed (leaving aside risk aversion)
24.
gabrielrossman | November 26, 2011 at 6:20 am
I’m afraid I don’t know if median income excludes the unemployed in these figures. You might be interested to know that some of Steve Brint’s work shows that growth in enrollment by major tracks number of jobs, not income. So by that light college students seem to have an expressed preference contrary to what you’re suggesting.
25. Assorted links — Marginal Revolution | November 22, 2011 at 12:30 pm
[...] 5. Graphing college majors vs. unemployment rates. [...]
26.
Eric Larson | November 22, 2011 at 3:46 pm
Hamish,
Ahem.
27.
zbicyclist | November 23, 2011 at 6:54 am
This statistical pattern is certainly true: small entities tend to show up at the top and bottom. But there are real differences here in the small majors — the data isn’t random.
Similarly, the small Chicago suburbs of Kenilworth (rich) and Ford City (poor) are legitimately rich and poor, although they show up on the extreme rankings partly because they are small and therefore more homogeneous. One good indicator might be the tendency of the major to have a consistently high/low unemployment rate over time (although a rush to get into the field could change any low rate).
28.
gabrielrossman | November 23, 2011 at 7:04 am
Agreed. I’m not saying there’s no effect of major on employment but only that we can’t take data about small majors at face value. Looking for trends over time (or for that matter, across similar majors) would mitigate the problem.
29. College Majors, Earnings, and Damned Statistics | Adam Shrugged | November 27, 2011 at 1:23 pm
[...] many reasons but I thought I’d focus on some of the pitfalls of this type of data. The blog Code and Culture has already taken a stab at this with a couple of useful scatter-plot charts such as this [...]
30.
RG | November 27, 2011 at 10:13 pm
Who would have thought that occupations supported almost exclusively by state bureaucracies would have the lowest unemployment percentages?
31. Graduate Incomes and Getting Better Data | HESA | November 30, 2011 at 4:14 am
[...] Street Journal) trying to point graduates to the “right” disciplines in a tight labour market. As others have pointed out in fact, a lot of the highest disciplinary rates – the ones that really attract attention – [...]
32.
Andy | December 27, 2011 at 3:55 pm
(Cute “have a nice day” comment
)
33.
gabrielrossman | December 27, 2011 at 6:27 pm
That’s actually functional. Some languages won’t execute a line that lacks an EOL so I always put a trivial comment at the end of my scripts to ensure that the last real command executes.
34.
Andy | December 28, 2011 at 3:27 am
Ah should have guessed.