WORDSUM is a variable in the General Social Survey. It is a 10 word vocabulary test. A score of 10 is perfect. A score of 0 means you didn’t know any of the vocabulary words. WORDSUM has a correlation of 0.71 with general intelligence. In other words, variation of WORDSUM can explain 50% of the variation of general intelligence. To the left is a distribution of WORDSUM results from the 2000s. As you can see, a score of 7 is modal. In the treatment below I will label 0-4 “Dumb,” 5-7 “Not Dumb,” and 8-10 “Smart.” Who says I’m not charitable? You also probably know that general intelligence has some correlation with income and wealth. But to what extent? One way you can look at this is inspecting the SEI variable in the GSS, which combines both monetary and non-monetary status and achievement, and see how it relates to WORDSUM. The correlation is 0.38. It’s there, but not that strong.
To further explore the issue I want to focus on two GSS variables, WEALTH and INCOME. WEALTH was asked in 2006, and it has a lot of categories of interest. INCOME has been asked a since 1974, but unfortunately its highest category is $25,000 and more, so there’s not much information at the non-low end of the scale (at least in current dollar values).
Below you see WEALTH crossed with WORDSUM. I’ve presented columns and rows adding up to 100%. Then you see INCOME crossed with WORDSUM. I’ve just created two categories, low, and non-low (less than $25,000 and more). Additionally, since the sample sizes were large I constrained to those 50 years and older for INCOME.
| Wealth & Intelligence (2006) | |||||
| Columns = 100% | |||||
| Less than $40 K | $40-$100 K | $100-$250 K | $250-$500 K | More than $500 K | |
| Dumb | 22 | 14 | 12 | 13 | 5 |
| Not Dumb | 55 | 65 | 63 | 57 | 48 |
| Smart | 23 | 22 | 25 | 31 | 47 |
| Row = 100% | |||||
| Less than $40 K | $40-$100 K | $100-$250 K | $250-$500 K | More than $500 K | |
| Dumb | 50 | 13 | 18 | 16 | 4 |
| Not Dumb | 32 | 16 | 24 | 18 | 10 |
| Smart | 29 | 11 | 20 | 20 | 20 |
| Income & intelligence (2000-2008), age 50 and above | |||||
| Columns = 100% | |||||
| Low | Not Low | ||||
| Dumb | 32 | 11 | |||
| Not Dumb | 50 | 50 | |||
| Smart | 18 | 39 | |||
| Row = 100% | |||||
| Low | Not Low | ||||
| Dumb | 58 | 42 | |||
| Not Dumb | 32 | 68 | |||
| Smart | 17 | 83 | |||
Of those with low income, about 1 out of 5 are smart. And of those who are smart, 1 out of 5 are poor. Remember, this is for those above the age of 50, not college students. I thought perhaps retirees might be skewing this. Constraining it to 50-64 changes the results some in a significant fashion. 1 out of 5 poor remain smart, but only 1 out of 10 of the smart are poor. As for the rich dumb, you have to look to wealth. It is notable to me that there’s a big drop off at more than $500,000 dollars in wealth. And, a large fraction of those with wealth in the $100,000 to $500,000 are dumb. I think we might be seeing the 2000s real estate boom.
In any case, I began to think of this after a recent post by the quant-blogger Audacious Epigone, Average IQ by occupation (estimated from median income). This is what he did:
…It’s not supposed to be an exact measure of IQ by profession by any means, as it is based entirely on average annual income figures. In other words, it’s an income table with the values converted to IQ scores….
…the following table estimates average IQ scores by occupation solely on the basis of the Career Cast mid-level income figures. The median salary (of a paralegal assistant) is taken to correspond to an IQ of 100. One standard deviation is assumed to be 15 IQ points….
You can see the full list at the Audacious Epigone‘s place, but here’s a selection I found of interest:
| Occupation | Estimated IQ from median income |
| Surgeon | 234 |
| Physician | 161 |
| CEO | 148 |
| Dentist | 140 |
| Attorney | 128 |
| Petroleum engineer | 126 |
| Pharmacist | 126 |
| Physicist | 125 |
| Astronomer | 125 |
| Financial planner | 123 |
| Nuclear engineer | 121 |
| Optometrist | 121 |
| Aerospace engineer | 120 |
| Mathematician | 120 |
| Economist | 117 |
| Software engineer | 117 |
| School principle | 116 |
| Electrical engineer | 115 |
| Web developer | 115 |
| Construction foreman | 115 |
| Geologist | 114 |
| Veterinarian | 114 |
| Mechanical engineer | 113 |
| Biologist | 111 |
| Statistician | 111 |
| Architect | 111 |
| Chemist | 109 |
| Stockbroker | 109 |
| Registered nurse | 107 |
| Historian | 107 |
| Philosopher | 106 |
| Accountant | 106 |
| Farmer | 105 |
| Zoologist | 104 |
| Author | 103 |
| Undertaker | 103 |
| Librarian | 103 |
| Anthropologist | 103 |
| Dietician | 102 |
| Archeologist | 102 |
| Physiologist | 102 |
| Teacher | 102 |
| Police officer | 101 |
| Actor | 101 |
| Electrician | 100 |
| Paralegal | 100 |
| Plumber | 100 |
| Clergy | 98 |
| Social worker | 97 |
| Carpenter | 97 |
| Machinist | 96 |
| Nuclear decontamination technician | 96 |
| Welder | 95 |
| Roofer | 95 |
| Bus driver | 95 |
| Agricultural scientist | 95 |
| Typist | 94 |
| Travel Agent | 93 |
| Butcher | 92 |
| Barber | 90 |
| Janitor | 90 |
| Maid | 88 |
| Dishwasher | 88 |
Off the top of my head, I would say that the highest disjunction in the low income direction would be clergy. This is especially true for Roman Catholic and mainline Protestant denominations in the United States, which have moderately stringent educational prerequisites for their clerics. I assume that the biggest in the other direction are surgeons and medical doctors, who enter a market where there’s less and less real price signalling, where labor controls the supply of future labor, as well as well influencing the range of services that competitive professions (e.g., nurses) can provide.
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