IQ vs. Per Capita GDP by State (US)

I made you some more graphs.

I was originally going to use La Griffe du Lion’s Smart Fraction Theory to calculate this, but then I discovered that it doesn’t make any practical difference, so went with the simpler metric of IQ.

We have a correlation, but it’s not huge. There are a few states that seem like obvious outliers–the two states with the highest GDP per cap were Alaska (oil) and Delaware (tax haven of some sort.) Among under-performers, I speculate that Maine is being held back by geography (it’s really cold.) California has a low average IQ, but an abnormally wide IQ range, due to the presence of Stanford and Silicon Valley and the like, while West Virginia may have the opposite problem of an unusually narrow IQ range (it also has the problem of being in the mountains.) In these two cases, if I could actually calculate the smart fraction instead of using Griffe’s assumption of Gaussian distribution around the average, I’d probably get a more accurate result.

I decided to try running the regression again without the states with obvious external factors–California, Hawaii, Nevada, Alaska, West Virginia, Delaware, Maine, and Vermont–like tourism, climate, gambling, or oil. I did not eliminate outliers that did not have (potentially) clear reasons for their under- or over- performance (for example, I have no idea why Idaho should do worse than Wyoming. I also left in Louisiana, whose over-performance may be due to having a significant port and/or tourism.)

Potential conclusions:

1. Random chance matters. An oil boom in your area, nice beaches, or a long, harsh winter can push a state (or country) into wealth or poverty.
2. I suspect that redistribution strategies (ie, welfare) prevent states from dropping below a certain level, hence the near-flat line around $32,000. (Outliers at Mississippi and W. Virginia.)
3. All else held equal, IQ matters.

Sources: Wikipedia, List of US States by GDP Per Capita; List of Average IQ by State (I found these same numbers elsewhere, so I suspect they’re reliable.)

Some statistical notes

Source: The Atlantic

However, The Atlantic article notes that, “the significance of these figures may be hugely overblown. “Everybody who’s remotely professionally involved in this kind of stuff knows that beyond about 10, 15, 20 years, [population estimates] are basically useless,” says Dr. Sean Fox of the University of Bristol in the U.K.”

Personally, I’d still be worried.

 

1. Rare events / things are likely to be over-represented in survey results due to random chance, if the chance of randomly picking that option among the survey items is higher than the chance of it occurring in real life. For example, let’s suppose I hand out 1000 surveys with three options to select from:

1. Heterosexual
2. Homosexual
3. Asexual

Then chances are I will end up with an over-representation of asexuals. In real life, asexuality is rare–a British survey estimates it at about 1% of the British population, so I expect to get about 10 surveys marked asexual. But let’s suppose some people decide to just fill my survey out completely at random because they’re just here for the free M&Ms, or they’re not paying very good attention and mark the wrong box, or I accidentally make a mistake while tallying up the numbers. Then the chances of randomly ticking “asexual” are 33%. If 1% of responses are randomly incorrect, then I will get an additional 3.3 or so asexuals–that is, I will over-estimate the asexual population by about 33%. If 3% of responses are incorrect, then fully half of my reported asexuals aren’t asexual at all.

This problem will only get worse if there are two rare categories you can select on my survey. Suppose you can also select your race:

1. White
2. Black
3. Hispanic
4. Anything else

And we’re doing this survey in Comanche, TX, where Whites are 80%, Blacks are 1%, Hispanics are about 17%, and everyone else is about 2%.

The statistical odds of a black asexual in Comanche, TX, assuming these are independent variables, are therefore around 0.01%–in other words, we probably shouldn’t find any, so let’s hand out our survey to 10,000 people so we have a reasonable chance of finding one. (You know, pretending that Comanche has 10,000 people.)

If you’re filling this survey out randomly for the M&Ms, you’ve got a 25% chance of marking black and a 33% chance of marking asexual, for an 8.3% chance of marking both. If 1% of people do this, then we should see about 8 black asexuals–about 8 times as many as we ought to see.

A prominent real life demonstration of this effect was Pat Buchanan’s performance in the 2000 election in Florida. Voters had a close to 33% chance of randomly voting Buchanan if they mis-poked the ballot, but only 0.4% of people nationwide voted for Buchanan. This resulted in a large over-counting of votes for Buchanan.

Pop Palm Beach= 1.135 million * 51.3% voting rate = 582,255 voters. 0.4% of that is 2,329 votes. But if 1%–5,822–of those voters vote randomly, that’s another 1,921 votes for Buchanan. If the difference between winning and losing in Palm Beach comes down to less than 2,000 votes, then random chance, not democracy, is casting the deciding vote.

If your error rate goes above 1%, things obviously get even worse.

(To his credit, Pat Buchanan freely admitted that his anomalously high numbers in Palm Beach were probably due to people getting mixed up about the ballot.)

 

2. The black (African American) IQ score distribution may be wider and/or less normal than claimed.

The number of high-scoring blacks does not line up with the expected number of high-scoring blacks based on IQ distribution estimates. Pumpkin Person does a good breakdown of the math on this one, in their post, “Are too many U.S. blacks scoring high on IQ tests?“

disgust vs. aggression vs. fertility (part 4 of a series)

(See also: Part 1, Yes, Women Think Male Sexuality is Disgusting; Part 2, Is disgust Real? and Part 3, Disney Explains Disgust.)

So I made some graphs:

Log. graph of homicide rates vs. fertility rates

Graph of homicide rates vs fertility rates (data from the Wikipedia)

Take your pick. They’re the same graph, except the top graph has the homicide rate on a log scale and the bottom on a linear scale, because Honduras’s homicide rate is kind of off the charts.

I couldn’t fit all of the labels onto the graphs, but since most of the countries group by region, you can still figure out roughly where most of them go. The lower left hand corner, low fertility and low homicide, is filled with dots from East Asia and Western Europe. The central-middle-bottom section, of moderate birthrates and below-average homicide rates, has the Muslim countries. The far right–high fertility and medium violence–is mostly African. (Note that Angola and Gambia’s labels have been footnoted at the bottom of the graph.) And the upper left–high violence but moderate fertility–is Latin America. (The high homicide rates are one of the reasons I oppose unfettered Latin American immigration.)

While there is a positive relationship between violence and fertility, this is clearly not the whole story. Just eyeballing the log graph, it looks like a curved line would fit the data better than a straight line, but that’d require polynomial or quadratic regressions and we don’t really need to get into that much detail.

Rather, I suspect that just considering cleanliness explains the graphs: cleaner people are more easily disgusted by other people, and so have fewer children than their aggression levels would predict.

I haven’t found great data on overall “cleanliness” or “disgust rates,” but it appears that Latin Americans are some of the world’s cleanest people:

From the Atlantic, “How Often People in Various Countries Shower“

Brazilians shower nearly 12 times a week, and Mexicans wash their hair more than anyone else.

By contrast, the French and Russians are positively disgusting. (I’m giving India and China a pass here due to limited running water in their countries.)

Here’s another graph, showing similar numbers:

My personal experience with East Asians (Japanese, Taiwanese, etc.,) also suggests that they are cleaner overall than Americans. These countries appear to have been hit with a double-whammy–very clean and low aggression–driving down the interest in sex so far that 60% of girls aged 16-19 claim to be uninterested in sex or even despise it, and even 40-50% of married people haven’t had sex in the past month. They’ve even got a phrase for it, “sex disgust syndrome.”

I don’t find Japanese fertility rates concerning, just because Japan really does need fewer people and the sex-inclined Japanese will quickly replace the non-sex inclined ones, so the issue will work itself out naturally. (And at any rate, I am not really in any position to go telling the Japanese what to do with their own society.)

In our own society, though, I am concerned that decreased monogamy (more “hooking up” via apps like Tinder and Grindr, etc.) is leading to more, not less female concern about male sexual aggression, with all sorts of unfortunate side effects.

(See also: Part 1, Yes, Women Think Male Sexuality is Disgusting; Part 2, Is disgust Real? and Part 3, Disney Explains Disgust.)

I made some graphs for you

Graph of homicide rates vs fertility rates (data from the Wikipedia)

 

Log. graph of homicide rates vs. fertility rates

I tried to label as many points as I could, but the dense cluster in the lower left was too much of a pain to label. Most of it’s the Euro countries.

We’ll discuss these later.