Because it does not appear to be posted anywhere else on the web, and because it was such a tedious slog to put together (a labor like this isn't going to just sit unexposed in an excel file!), the following table shows IQ by occupation as estimated from GSS wordsum scores. The mean wordsum score for native-born whites is assumed to correspond to an IQ of 100, with a standard deviation of 15.
Sample sizes are not sufficient for statistical significance for a host of job categories (as long as the respondent pool is plural, it is included), and the usual disclaimer about a ten question vocabulary test not correlating perfectly with IQ (Razib reports an r-value of .71) applies. Women tend to do slightly better on verbal measures of intelligence, while men do better on quantitative measures of the same. Calculated from wordsum scores, women enjoy a bit less than a 2 point IQ advantage over men, so heavily female occupations like teaching are slightly overstated and heavily male occupations like those involving engineering are similarly understated. To avoid issues of English language proficiency, only those born in the US are included. Because a perfect score translates to an IQ of 128, high-end wordsum scorers encounter an artificial ceiling on their purported IQ. So, to a much lesser extent, do low-end scorers, as missing all ten items corresponds to an estimated IQ of 53. Consequently, the table is merely suggestive, not definitive or statistically rigorous:
GSS variables used: ISCO88, WORDSUM, BORN(1)
Sample sizes are not sufficient for statistical significance for a host of job categories (as long as the respondent pool is plural, it is included), and the usual disclaimer about a ten question vocabulary test not correlating perfectly with IQ (Razib reports an r-value of .71) applies. Women tend to do slightly better on verbal measures of intelligence, while men do better on quantitative measures of the same. Calculated from wordsum scores, women enjoy a bit less than a 2 point IQ advantage over men, so heavily female occupations like teaching are slightly overstated and heavily male occupations like those involving engineering are similarly understated. To avoid issues of English language proficiency, only those born in the US are included. Because a perfect score translates to an IQ of 128, high-end wordsum scorers encounter an artificial ceiling on their purported IQ. So, to a much lesser extent, do low-end scorers, as missing all ten items corresponds to an estimated IQ of 53. Consequently, the table is merely suggestive, not definitive or statistically rigorous:
Occupation | IQ | n |
1. Mathematician | 117.1 | 4 |
2. Physician | 117.0 | 48 |
3. Geologist | 116.7 | 5 |
4. Meteorologist | 116.7 | 2 |
5. College professor | 115.9 | 89 |
6. Author | 115.1 | 58 |
7. Librarian | 114.6 | 28 |
8. Attorney | 114.5 | 102 |
9. Biologist | 113.7 | 8 |
10. Optometrist | 112.9 | 3 |
11. Statistician | 111.4 | 2 |
12. Computer systems analyst | 111.3 | 85 |
13. Judge | 111.0 | 3 |
14. Psychologist | 110.3 | 20 |
14. Actor | 110.3 | 20 |
14. Dentist | 110.3 | 16 |
14. Chemist | 110.3 | 11 |
18. Museum curator | 110.1 | 4 |
19. Clergyman | 108.9 | 38 |
20. Pharmacist | 108.7 | 11 |
21. Teacher | 108.1 | 297 |
22. Agronomist | 107.9 | 6 |
22. Electrical engineer | 107.9 | 46 |
24. Stockbroker | 107.8 | 31 |
25. Fine artist | 107.4 | 33 |
26. Physical therapist | 107.4 | 40 |
26. Sociologist | 107.4 | 4 |
28. Economist | 106.6 | 21 |
29. Mechanical engineer | 106.5 | 35 |
29. Architect | 106.5 | 26 |
31. Real estate agent | 105.7 | 80 |
32. Commercial airline pilot | 105.6 | 12 |
33. Dental hygienist | 105.5 | 33 |
34. Social worker | 105.0 | 109 |
35. Registered nurse | 104.9 | 238 |
36. Stenographer | 104.6 | 44 |
37. Government official | 104.1 | 77 |
37. Insurance agent | 104.1 | 77 |
37. Computer programmer | 104.1 | 41 |
40. Accountant | 104.1 | 168 |
41. Civil engineer | 104.0 | 23 |
42. Undertaker | 103.6 | 8 |
43. Jeweler | 103.2 | 5 |
44. Secretary | 103.1 | 430 |
45. Engineering technician | 102.6 | 55 |
46. Police officer | 102.5 | 81 |
47. Industrial machine repairer | 102.4 | 9 |
47. Photographic process worker | 102.4 | 6 |
49. Debt collector | 101.7 | 10 |
50. Sales representative | 101.6 | 134 |
50. Compositor/typesetter | 101.6 | 8 |
52. Fashion designer | 101.4 | 7 |
53. Photographer | 100.8 | 19 |
54. Receptionist | 100.8 | 104 |
55. Machine tool operator | 100.6 | 7 |
56. Veterinarian | 100.4 | 5 |
57. Communications equipment mechanic | 100.2 | 9 |
57. Broadcast technician | 100.2 | 9 |
59. Glazier | 99.3 | 3 |
60. Mail carrier | 99.1 | 68 |
61. Retail salesperson | 99.0 | 368 |
62. Telephone operator | 98.7 | 41 |
63. Dressmaker | 98.4 | 18 |
64. Bank teller | 97.8 | 67 |
65. Licensed practical nurse | 97.5 | 73 |
66. Plumber | 97.3 | 66 |
67. Maid | 97.1 | 107 |
68. Waiter/bartender | 96.5 | 289 |
69. Aircraft mechanic | 96.3 | 21 |
70. Barber | 96.2 | 117 |
71. Data entry clerk | 96.0 | 53 |
72. Carpet and tile installer | 95.8 | 143 |
72. Painter | 95.8 | 51 |
74. Child care worker | 95.4 | 181 |
75. Tool maker | 95.3 | 53 |
76. Telephone installer/repairer | 95.1 | 10 |
77. Security guard | 95.0 | 26 |
78. Farmer | 94.7 | 11 |
79. Bus driver | 94.6 | 45 |
79. Firefighter | 94.6 | 42 |
81. Insulation installer | 93.9 | 5 |
82. Cashier | 93.8 | 290 |
83. Furniture upholsterer | 93.7 | 13 |
84. Electrician | 93.6 | 66 |
85. Taxi driver | 93.5 | 76 |
86. Bookbinder | 93.0 | 5 |
87. Welder | 92.7 | 63 |
88. Automobile mechanic | 91.6 | 131 |
89. Dietitian | 91.5 | 9 |
90. Truck driver | 90.6 | 216 |
91. Railroad conductor | 90.4 | 7 |
91. Sailor | 90.4 | 3 |
93. Bricklayer | 90.3 | 22 |
94. Cook | 90.3 | 140 |
95. Construction worker | 90.0 | 139 |
96. Roofer | 89.3 | 16 |
97. Sheet metal worker | 88.5 | 44 |
98. Carpenter | 87.4 | 3 |
99. Janitor | 86.9 | 82 |
100. Drill-press operator | 86.7 | 2 |
101. Forklift operator | 85.8 | 42 |
102. Butcher | 84.3 | 21 |
103. Concrete worker | 82.9 | 8 |
103. Surveyor | 82.9 | 2 |
105. Shoe maker/cobbler | 79.6 | 6 |
106. Lumberjack | 75.3 | 8 |
GSS variables used: ISCO88, WORDSUM, BORN(1)
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