Useless Majors or Small Majors?

November 7, 2011 at 9:55 am 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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Chain of Litigation Seven-inch heels, natural language processing, and sociology

34 Comments

  • 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.

  • [...] 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.


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