...cognitive skills—intelligence quotient scores, math skills, and the like—have only a modest influence on individual wages, but are strongly correlated with national outcomes. Is this largely due to human capital spillovers? This paper argues that the answer is yes. It presents four different channels through which intelligence may matter more for nations than for individuals: (i) intelligence is associated with patience and hence higher savings rates; (ii) intelligence causes cooperation; (iii) higher group intelligence opens the door to using fragile, high-value production technologies; and (iv) intelligence is associated with supporting market-oriented policies.
Plots of mean IQ and per capita real Gross Domestic Product for groups of 81 and 185 nations, as collected by Lynn and Vanhanen, are best fitted by an exponential function of the form: GDP = a 10^b\(IQ), where a and b are empirical constants. Exponential fitting yields markedly higher correlation coefficients than either linear or quadratic. The implication of exponential fitting is that a given increment in IQ, anywhere along the IQ scale, results in a given percentage in GDP, rather than a given dollar increase as linear fitting would predict. As a rough rule of thumb, an increase of 10 points in mean IQ results in a doubling of the per capita GDP.
....In their book, IQ and the Wealth of Nations, Lynn and Vanhanen (2002) present a table listing for 81 nations the measured mean IQ and the per capita real Gross Domestic Product as of 1998 (their Table 7.7). They subsequently extend this to all 185 nations, using estimated IQs for the 104 new entries based chiefly on IQ values for immediate neighbors (their Table 8.9). In both cases they observe a significant correlation between IQ and GDP, with linear correlation factors R^2 = 0.537 for the 81-nation group and 0.389 for 185 nations. McDaniel and Whetzel have extended the examination of correlations to quadratic fitting in a paper that demonstrates the robustness of these correlations to minor variations in individual IQ values (McDaniel & Whetzel, in press). But an even stronger correlation is found if the fitting is exponential rather than linear or quadratic.
It peeves me when scatterplots of GDP per capita versus something else use a linear scale—do they actually think the difference between $30k and $20k is anywhere near as important as that between $11k and $1k? And yet hardly anybody uses logarithmic scales.
Likewise, the fit looks a lot less scary if you write it as ln(GDP) = A + B*IQ.
Yes, Dickerson does point out that his exponential fit is a linear relationship on a log scale. For example, he does show a log-scale in figure 3 (pg3), fitting the most reliable 83 nation-points on a plot of log(GDP) against mean IQ in which the exponential fit looks exactly like you would expect. (Is it per capita? As far as I can tell, he always means per capita GDP even if he writes just ‘GDP’.) Figure 4 does the same thing but expands the dataset to 185 nations. The latter plot should probably be ignored given that the expansion comes from basically guessing:
In their book, IQ and the Wealth of Nations, Lynn and Vanhanen (2002) present a table listing for 81 nations the measured mean IQ and the per capita real Gross Domestic Product as of 1998 (their Table 7.7). They subsequently extend this to all 185 nations, using estimated IQs for the 104 new entries based chiefly on IQ values for immediate neighbors (their Table 8.9).
I dunno. I’ve given it a try and while it’s easy enough to reproduce the exponential fit (and the generated regression line does fit the 81 nations very nicely), I think I screwed up somehow reproducing the smart fraction equation because the regression looks weird and trying out the smart-fraction function (using his specified constants) on specific IQs I don’t get the same results as in La Griffe’s table. And I can’t figure out what I’m doing wrong, my function looks like it’s doing the same thing as his. So I give up. Here is my code if you want to try to fix it:
lynn <- read.table(stdin(),header=TRUE,sep="")
Country IQ rGDPpc
Argentina 96 12013
Australia 98 22452
Austria 102 23166
Barbados 78 12001
Belgium 100 23223
Brazil 87 6625
Bulgaria 93 4809
Canada 97 23582
China 100 3105
Colombia 89 6006
Congo 65 822
Congo 73 995
Croatia 90 6749
Cuba 85 3967
CzechRepublic 97 12362
Denmark 98 24218
Ecuador 80 3003
Egypt 83 3041
EquatorialGuinea 59 1817
Ethiopia 63 574
Fiji 84 4231
Finland 97 20847
France 98 21175
Germany 102 22169
Ghana 71 1735
Greece 92 13943
Guatemala 79 3505
Guinea 66 1782
HongKong 107 20763
Hungary 99 10232
India 81 2077
Indonesia 89 2651
Iran 84 5121
Iraq 87 3197
Ireland 93 21482
Israel 94 17301
Italy 102 20585
Jamaica 72 3389
Japan 105 23257
Kenya 72 980
Lebanon 86 4326
Malaysia 92 8137
MarshallIslands 84 3000
Mexico 87 7704
Morocco 85 3305
Nepal 78 1157
Netherlands 102 22176
NewZealand 100 17288
Nigeria 67 795
Norway 98 26342
Peru 90 4282
Philippines 86 3555
Poland 99 7619
Portugal 95 14701
PuertoRico 84 8000
Qatar 78 20987
Romania 94 5648
Russia 96 6460
SierraLeone 64 458
Singapore 103 24210
Slovakia 96 9699
Slovenia 95 14293
SouthAfrica 72 8488
SouthKorea 106 13478
Spain 97 16212
Sudan 72 1394
Suriname 89 5161
Sweden 101 20659
Switzerland 101 25512
Taiwan 104 13000
Tanzania 72 480
Thailand 91 5456
Tonga 87 3000
Turkey 90 6422
UKingdom 100 20336
Uganda 73 1074
UnitedStates 98 29605
Uruguay 96 8623
WesternSamoa 87 3832
Zambia 77 719
Zimbabwe 66 2669
em <- lm(log(lynn$rGDPpc) ~ lynn$IQ); summary(em)
Call:
lm(formula = log(lynn$rGDPpc) ~ lynn$IQ)
Residuals:
Min 1Q Median 3Q Max
-1.6124 -0.3866 -0.0429 0.3363 2.0311
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.77760 0.51848 3.43 0.00097
lynn$IQ 0.07876 0.00583 13.52 < 2e-16
Residual standard error: 0.624 on 79 degrees of freedom
Multiple R-squared: 0.698, Adjusted R-squared: 0.694
F-statistic: 183 on 1 and 79 DF, p-value: <2e-16
# plot
plot (log(lynn$rGDPpc) ~ lynn$IQ)
abline(em)
# an attempt at La Griffe
erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
sf <- function(iq) ((69321/2) * (1 + erf(((iq - 108)/15) / sqrt(2))))
# check for sigmoid
# plot(c(85:130), sf(c(85:130)))
lg <- lm(log(lynn$rGDPpc) ~ sf(lynn$IQ)); summary(lg)
Call:
lm(formula = log(lynn$rGDPpc) ~ sf(lynn$IQ))
Residuals:
Min 1Q Median 3Q Max
-2.5788 -0.6857 0.0678 1.0521 1.5901
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.705620 0.126152 69.01 <2e-16
sf(lynn$IQ) 0.000121 0.000102 1.19 0.24
Residual standard error: 1.13 on 79 degrees of freedom
Multiple R-squared: 0.0175, Adjusted R-squared: 0.0051
F-statistic: 1.41 on 1 and 79 DF, p-value: 0.239
# same plotting code
(In retrospect, I’m not sure it’s even meaningful to try to fit the sf function with the constants already baked in, but since I apparently didn’t write it right, it doesn’t matter.
Hm, one thing I notice is that you look like you’re fitting sf against log(gdp). I managed to replicate his results in octave, and got a meaningful result plotting smart fraction against gdp.
My guess at how to change your code (noting that I don’t know R):
sf <- function(iq,c) ((69321/2) * (1 + erf(((iq - c)/15) / sqrt(2))))
lg <- lm(lynn$rGDPpc ~ sf(lynn$IQ,108)); summary(lg)
That should give you some measure of how good it fits, and you might be able to loop it to see how well the smart fraction does with various thresholds.
I can’t tell whether that works since you’re just using the same broken smart-fraction sf predictor; eg. sf(107,108) ~> 32818, while the first smart fraction page’s table gives a Hong Kong regression line of 19817 which is very different from 33k.
Hmmm. I agree that it doesn’t match. What if by ‘regression line’ he means the regression line put through the sf-gdp data?
That is, you should be able to calculate sf as a fraction with
sf <- function(iq,c) ((1/2) * (1 + erf((iq-c)/(15*sqrt(2)))))
And then regress that against gdp, which will give you the various coefficients, and a much more sensible graph. (You can compare those to the SFs he calculates in the refinement, but those are with verbal IQ, which might require finding that dataset / trusting his, and have a separate IQ0.)
Comparing the two graphs, I find it interesting that the eight outliers Griffe mentions (Qatar, South Africa, Barbados, China, and then the NE Asian countries) are much more noticeable on the SF graph than the log(GDP) graph, and that the log(GDP) graph compresses the variation of the high-income countries, and gets most of its variation from the low-income countries; the situation is reversed in the SF graph. Since both our IQ and GDP estimates are better in high-income countries, that seems like a desirable property to have.
With outliers included, I’m getting R=.79 for SF and R=.74 for log(gdp). (I think, I’m not sure I’m calculating those correctly.)
Trying to rederive the constants doesn’t help me, which is starting to make me wonder if he’s really using the table he provided or misstated an equation or something:
R> sf <- function(iq,f,c) ((c/2) * (1 + erf((iq-f)/(15*sqrt(2)))))
R> summary(nls(rGDPpc ~ sf(IQ,f,c), lynn, start=list(f=110,c=40000)))
Formula: rGDPpc ~ sf(IQ, f, c)
Parameters:
Estimate Std. Error t value Pr(>|t|)
f 99.64 3.07 32.44 < 2e-16
c 34779.17 6263.90 5.55 3.7e-07
Residual standard error: 5310 on 79 degrees of freedom
Number of iterations to convergence: 4
Achieved convergence tolerance: 8.22e-06
If you double 34779 you get very close to his $69,321 so there might be something going wrong due to the 1⁄2 that appears in uses of the erf to make a cumulative distribution function, but I don’t how a threshold of 99.64 IQ is even close to his 108!
(The weird start values were found via trial-and-error in trying to avoid R’s ‘singular gradient error’; it doesn’t appear to make a difference if you start with, say, f=90.)
Most importantly, we appear to have figured out the answer to my original question: no, it is not easy. :P
So, I started off by deleting the eight outliers to make lynn2. I got an adjusted R^2 of 0.8127 for the exponential fit, and 0.7777 for the fit with iq0=108.2.
My nls came back with an optimal iq0 of 110, which is closer to the 108 I was expecting; the adjusted R^2 only increases to 0.7783, which is a minimal improvement, and still slightly worse than the exponential fit.
The value of the smart fraction cutoff appears to have a huge impact on the mapping from smart fraction to gdp, but doesn’t appear to have a significant effect on the goodness of fit, which troubles me somewhat. I’m also surprised that deleting the outliers seems to have improved the performance of the exponential fit more than the smart fraction fit, which is not what I would have expected from the graphs. (Though, I haven’t calculated this with the outliers included in R, and I also excluded the Asian data, and there’s more fiddling I can do, but I’m happy with this for now.)
> sf <- function(iq,iq0) ((1+erf((iq-iq0)/(15*sqrt(2))))/2)
> egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)
> summary(nls(rGDPpc ~ egdp(IQ,iq0,m,b), lynn2, start=list(iq0=110,m=40000,b=0)))
Formula: rGDPpc ~ egdp(IQ, iq0, m, b)
Parameters:
Estimate Std. Error t value Pr(>|t|)
iq0 110.019 4.305 25.556 < 2e-16 ***
m 77694.174 26708.502 2.909 0.00486 **
b 679.688 1039.144 0.654 0.51520
Residual standard error: 4054 on 70 degrees of freedom
> gwe <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,99.64)); summary(gwe)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 99.64))
Residuals:
Min 1Q Median 3Q Max
-10621.6 -2463.1 442.6 1743.4 12439.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1345.7 886.6 -1.518 0.133
sf(lynn2$IQ, 99.64) 40552.7 2724.1 14.887 <2e-16 ***
Residual standard error: 4241 on 71 degrees of freedom
Multiple R-squared: 0.7574, Adjusted R-squared: 0.7539
F-statistic: 221.6 on 1 and 71 DF, p-value: < 2.2e-16
> opt <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,110)); summary(opt)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 110))
Residuals:
Min 1Q Median 3Q Max
-11030.3 -1540.1 -416.8 1308.6 12493.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 676.4 731.5 0.925 0.358
sf(lynn2$IQ, 110) 77577.0 4869.6 15.931 <2e-16 ***
Residual standard error: 4025 on 71 degrees of freedom
Multiple R-squared: 0.7814, Adjusted R-squared: 0.7783
F-statistic: 253.8 on 1 and 71 DF, p-value: < 2.2e-16
> his <- lm(lynn2$rGDPpc ~ sf(lynn2$IQ,108.2)); summary(his)
Call:
lm(formula = lynn2$rGDPpc ~ sf(lynn2$IQ, 108.2))
Residuals:
Min 1Q Median 3Q Max
-11014.0 -1710.0 -196.9 1396.3 12432.9
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 362.6 748.0 0.485 0.629
sf(lynn2$IQ, 108.2) 67711.5 4258.5 15.900 <2e-16 ***
Residual standard error: 4031 on 71 degrees of freedom
Multiple R-squared: 0.7807, Adjusted R-squared: 0.7777
F-statistic: 252.8 on 1 and 71 DF, p-value: < 2.2e-16
> em <- lm(log(lynn2$rGDPpc) ~ lynn2$IQ); summary(em)
Call:
lm(formula = log(lynn2$rGDPpc) ~ lynn2$IQ)
Residuals:
Min 1Q Median 3Q Max
-1.12157 -0.34268 -0.00503 0.29596 1.41540
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.812650 0.446308 1.821 0.0728 .
lynn2$IQ 0.089439 0.005051 17.706 <2e-16 ***
Residual standard error: 0.4961 on 71 degrees of freedom
Multiple R-squared: 0.8153, Adjusted R-squared: 0.8127
F-statistic: 313.5 on 1 and 71 DF, p-value: < 2.2e-16
Most importantly, we appear to have figured out the answer to my original question: no, it is not easy. :P
And inadvertently provided an object lesson for anyone watching about the value of researchers providing code...
The value of the smart fraction cutoff appears to have a huge impact on the napping from smart fraction to gdp, but doesn’t appear to have a significant effect on the goodness of fit, which troubles me somewhat. I’m also surprised that deleting the outliers seems to have improved the performance of the exponential fit more than the smart fraction fit, which is not what I would have expected from the graphs.
My intuition so far is that La Griffe found a convoluted way of regressing on a sigmoid, and the gain is coming from the part which looks like an exponential. I’m a little troubled that his stuff is so hard to reproduce sanely and that he doesn’t compare against the exponential fit: the exponent is obvious, has a reasonable empirical justification. Granting that Dickerson published in 2006 and he wrote the smart fraction essay in 2002 he could at least have updated.
[edit] Sorry, it looks like the formatting for my code is totally ugly.
You need to delete any trailing whitespace in your indented R terminal output. (Little known feature of LW/Reddit Markdown code blocks: one or more trailing spaces causes the newline to be ignored and the next line glommed on. I filed an R bug to fix some cases of it but I guess it doesn’t cover nls or you don’t have an updated version.)
I don’t understand your definition
egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)
sf(iq,iq0) makes sense, of course, and m presumably is the multiplicative scale constant LG found to be 69k, but what is this b here and why is it being added? I don’t see how this tunes how big a smart fraction is necessary since shouldn’t it then be on the inside of sf somehow?
But using that formula and running your code (using the full dataset I posted originally, with outliers):
R> erf <- function(x) 2 * pnorm(x * sqrt(2)) - 1
R> sf <- function(iq,iq0) ((1+erf((iq-iq0)/(15*sqrt(2))))/2)
R> egdp <- function(iq,iq0,m,b) (m*sf(iq,iq0)+b)
R> summary(nls(rGDPpc ~ egdp(IQ,iq0,m,b), lynn, start=list(iq0=110,m=40000,b=0)))
Formula: rGDPpc ~ egdp(IQ, iq0, m, b)
Parameters:
Estimate Std. Error t value Pr(>|t|)
iq0 102.08 4.89 20.88 < 2e-16
m 37108.87 9107.73 4.07 0.00011
b 1140.94 1445.76 0.79 0.43241
Residual standard error: 5320 on 78 degrees of freedom
Number of iterations to convergence: 7
Achieved convergence tolerance: 5.09e-06
My intuition so far is that La Griffe found a convoluted way of regressing on a sigmoid, and the gain is coming from the part which looks like an exponential. I’m a little troubled that his stuff is so hard to reproduce sanely and that he doesn’t compare against the exponential fit: the exponent is obvious, has a reasonable empirical justification. Granting that Dickerson published in 2006 and he wrote the smart fraction essay in 2002 he could at least have updated.
I emailed La Griffe via Steve Sailer in February 2013 with a link to this thread and a question about how his smart-fraction model works with the fresher IQ/nations data and compares to Dickerson’s work. Sailer forwarded my email, but neither of us has had a reply since; he speculated that La Griffe may be having health issues.
In the absence of any defense by La Griffe, I think Dicker’s exponential works better than La Griffe’s fraction/sigmoid.
he doesn’t compare against the exponential fit: the exponent is obvious, has a reasonable empirical justification.
The theoretical justifications are entirely different, though. It seems reasonable to me to suppose there’s some minimal intelligence to be wealth-producing in an industrial society, and the smart fraction estimates that well and it predicts gdp well. But, it also seems reasonable to treat log(gdp) as a more meaningful object than gdp.
It’s also bothersome that the primary empirical prediction of the smart fraction model (that there is some stable gdp level that you hit when everyone is higher than the smart fraction) is entirely from the extrapolated part of the dataset, and this doesn’t seem noticeably better than the exponential model, whose extrapolations are radically different.
Granting that Dickerson published in 2006 and he wrote the smart fraction essay in 2002 he could at least have updated.
Yeah; I’m curious what they’d have to say about the relative merits of the two models. I’ll see if I can get this question to them.
You need to delete any trailing whitespace in your indented R terminal output.
Fixed, thanks!
but what is this b here and why is it being added?
It’s an offset, so that it’s an affine fit rather than a linear fit: the gdp level for a population with no people above 108 IQ doesn’t have to be 0. Turns out, it’s not significantly different from zero, but I’d rather discover that than enforce it (and enforcing it can degrade the value for m).
But, it also seems reasonable to treat log(gdp) as a more meaningful object than gdp.
I’m not entirely sure… For individuals, log-transforms make sense on their own merits as giving a better estimate of the utility of that money, but does that logic really apply to a whole country? More money means more can be spent on charity, shooting down asteroids, etc.
It’s also bothersome that the primary empirical prediction of the smart fraction model (that there is some stable gdp level that you hit when everyone is higher than the smart fraction) is entirely from the extrapolated part of the dataset, and this doesn’t seem noticeably better than the exponential model, whose extrapolations are radically different.
The next logical step would be to bring in the second 2006 edition of the Lynn dataset, which increased the set from 81 to 113, and use the latest available per-capita GDP (probably 2011). If the exponential fit gets better compared to the smart-fraction sigmoid, then that’s definitely evidence towards the conclusion that the smart-fraction is just a bad fit.
Yeah; I’m curious what they’d have to say about the relative merits of the two models. I’ll see if I can get this question to them.
I’d guess that he’d consider SF a fairly arbitrary model and not be surprised if an exponential fits better.
It’s an offset, so that it’s an affine fit rather than a linear fit: the gdp level for a population with no people above 108 IQ doesn’t have to be 0. Turns out, it’s not significantly different from zero, but I’d rather discover that than enforce it (and enforcing it can degrade the value for m).
Why can’t the GDP be 0 or negative? Afghanistan and North Korea are right now exhibiting what such a country looks like: they can barely feed themselves and export so much violence or fundamentalism or other dysfunctionality that rich nations are sinking substantial sums of money into supporting them and fixing problems.
For individuals, log-transforms make sense on their own merits as giving a better estimate of the utility of that money, but does that logic really apply to a whole country?
The argument would be that additional intelligence multiplies the per-capita wealth-producing apparatus that exists, rather than adding to it (or, in the smart fraction model, not doing anything once you clear a threshold).
Why can’t the GDP be 0 or negative?
There’s no restriction that b be positive, and so those are both options. I wouldn’t expect it to be negative because pre-industrial societies managed to survive, but that presumes that aid spending by the developed world is not subtracted from the GDP measurement of those countries. Once you take aid into account, then it does seem reasonable that places could become money pits.
The argument would be that additional intelligence multiplies the per-capita wealth-producing apparatus that exists, rather than adding to it (or, in the smart fraction model, not doing anything once you clear a threshold).
That’s the intuitive justification for an exponential model (each additional increment of intelligence adds a percentage of the previous GDP), but I don’t see how this justifies looking at log transforms.
There’s no restriction that b be positive, and so those are both options. I wouldn’t expect it to be negative because pre-industrial societies managed to survive
The difference would be a combination of negative externalities and changing Malthusian equilibriums: it has never been easier for an impoverished country like North Korea or Afghanistan to export violence and cause massive costs they don’t bear (9/11 directly cost the US something like a decade of Afghanistan GDP once you remove all the aid given to Afghanistan), and public health programs like vaccinations enable much larger populations than ‘should’ be there.
That’s the intuitive justification for an exponential model (each additional increment of intelligence adds a percentage of the previous GDP), but I don’t see how this justifies looking at log transforms.
GDP ~ exp(IQ) is isomorphic to ln(GDP) ~ IQ, and I think log(dollars per year) is an easier unit to think about than something to the power of IQ.
[edit] The graph might look different, though. It might be instructive to compare the two, but I think the relationships should be mostly the same.
It’s worth pointing out that IQ numbers are inherently non-parametric: we simply have a ranking of performance on IQ tests, which are then scaled to fit a normal distribution.
If GDP ~ exp(IQ), that means that the correlation is better if we scale the rankings to fit a log-normal distribution instead (this is not entirely true because exp(mean(IQ)) is not the same as mean(exp(IQ)), but the geometric mean and arithmetic mean should be highly correlated with each other as well). I suspect that this simply means that GDP approximately follows a log-normal distribution.
I suspect that this simply means that GDP approximately follows a log-normal distribution.
This doesn’t quite follow, since both per capita GDP and mean national IQ aren’t drawn from the same sort of distribution as individual production and individual IQ are, but I agree with the broader comment that it is natural to think of the economic component of intelligence measured in dollars per year as lognormally distributed.
...We find substantial impacts of salt iodization. High school completion rose by 6 percentage points, and labor force participation went up by 1 point. Analysis of income transitions by quantile shows that the new labor force joiners entered at the bottom of the wage distribution and took up blue collar labor, pulling down average wage income conditional on employment. Our results inform the ongoing debate on salt iodization in many low-income countries. We show that large-scale iodized salt distribution had a targeted impact, benefiting the worker on the margin of employment, and generating sizeable economic returns at low cost...The recent study by Feyrer et al. (2013) estimates that Morton Salt Co.’s decision to iodize may have increased IQ by 15 points, accounting for a significant part of the Flynn Effect, the steady rise IQ in the US over the twentieth century. Our estimates, paired with this number, suggest that each IQ point accounts for nearly one tenth of a point increase in labor force participation.
If, in the 1920s, 10 IQ points could increase your labor participation rate by 1%, then what on earth does the multiplier look like now? The 1920s weren’t really known for their demands on intelligence, after all.
And note the relevance to discussions of technological unemployment: since the gains are concentrated in the low end (think 80s, 90s) due to the threshold nature of iodine & IQ, this employment increase means that already, a century ago, people in the low-end range were having trouble being employed.
Social science research has shown that intelligence is positively correlated with patience and frugality, while growth theory predicts that more patient countries will save more. This implies that if nations differ in national average IQ, countries with higher average cognitive skills will tend to hold a greater share of the world’s tradable assets. I provide empirical evidence that in today’s world, countries whose residents currently have the highest average IQs have higher savings rates, higher ratios of net foreign assets to GDP, and higher ratios of U.S. Treasuries to GDP. These nations tend to be in East Asia and its offshoots. The relationship between national average IQ and net foreign assets has strengthened since the end of Bretton Woods.
...And time preference differs across countries in part because psychometric intelligence, a key predictor of patient behavior, differs persistently across countries (Wicherts et al., 2010a,b; Jones and Schneider, 2010)....John Rae (1834) provides a precursor of the approach presented here: Chapter Six of his treatise (cited in Becker and Mulligan, 1997, and Frederick et al., 2002) focuses on individual determinants of savings, including differences in rates of time preference, while his Chapter Seven draws out the cross-country implications...A recent meta-analysis of 24 studies by Shamosh and Gray concluded: “[A]cross studies, higher intelligence was associated with lower D[elay] D[iscounting]...” Their meta-study drew on experiments with preschool children and college students, drug addicts and relatively healthy populations: With few exceptions, they found a reliable relationship between measured intelligence and patience. And recent work by economists (Frederick, 2005; Benjamin et al. 2006; Burks et al, 2009; Chabris et al., 2007) has demonstrated that low-IQ individuals tend to act in a more “behavioral,” more impulsive fashion when facing decisions between smaller rewards sooner versus larger rewards later.
Here’s another one: “National IQ and National Productivity: The Hive Mind Across Asia”, Jones 2011
Above link is dead. Here is a new one
http://mason.gmu.edu/~gjonesb/JonesADR
“Exponential correlation of IQ and the wealth of nations”, Dickerson 2006:
It peeves me when scatterplots of GDP per capita versus something else use a linear scale—do they actually think the difference between $30k and $20k is anywhere near as important as that between $11k and $1k? And yet hardly anybody uses logarithmic scales.
Likewise, the fit looks a lot less scary if you write it as ln(GDP) = A + B*IQ.
Yes, Dickerson does point out that his exponential fit is a linear relationship on a log scale. For example, he does show a log-scale in figure 3 (pg3), fitting the most reliable 83 nation-points on a plot of log(GDP) against mean IQ in which the exponential fit looks exactly like you would expect. (Is it per capita? As far as I can tell, he always means per capita GDP even if he writes just ‘GDP’.) Figure 4 does the same thing but expands the dataset to 185 nations. The latter plot should probably be ignored given that the expansion comes from basically guessing:
Is it easy to compare the fit of their theory to the smart fraction theory?
I dunno. I’ve given it a try and while it’s easy enough to reproduce the exponential fit (and the generated regression line does fit the 81 nations very nicely), I think I screwed up somehow reproducing the smart fraction equation because the regression looks weird and trying out the smart-fraction function (using his specified constants) on specific IQs I don’t get the same results as in La Griffe’s table. And I can’t figure out what I’m doing wrong, my function looks like it’s doing the same thing as his. So I give up. Here is my code if you want to try to fix it:
(In retrospect, I’m not sure it’s even meaningful to try to fit the
sf
function with the constants already baked in, but since I apparently didn’t write it right, it doesn’t matter.Hm, one thing I notice is that you look like you’re fitting sf against log(gdp). I managed to replicate his results in octave, and got a meaningful result plotting smart fraction against gdp.
My guess at how to change your code (noting that I don’t know R):
That should give you some measure of how good it fits, and you might be able to loop it to see how well the smart fraction does with various thresholds.
(I also probably should have linked to the refinement.)
I can’t tell whether that works since you’re just using the same broken smart-fraction
sf
predictor; eg.sf(107,108)
~> 32818, while the first smart fraction page’s table gives a Hong Kong regression line of 19817 which is very different from 33k.The refinement doesn’t help with my problem, no.
Hmmm. I agree that it doesn’t match. What if by ‘regression line’ he means the regression line put through the sf-gdp data?
That is, you should be able to calculate sf as a fraction with
And then regress that against gdp, which will give you the various coefficients, and a much more sensible graph. (You can compare those to the SFs he calculates in the refinement, but those are with verbal IQ, which might require finding that dataset / trusting his, and have a separate IQ0.)
Comparing the two graphs, I find it interesting that the eight outliers Griffe mentions (Qatar, South Africa, Barbados, China, and then the NE Asian countries) are much more noticeable on the SF graph than the log(GDP) graph, and that the log(GDP) graph compresses the variation of the high-income countries, and gets most of its variation from the low-income countries; the situation is reversed in the SF graph. Since both our IQ and GDP estimates are better in high-income countries, that seems like a desirable property to have.
With outliers included, I’m getting R=.79 for SF and R=.74 for log(gdp). (I think, I’m not sure I’m calculating those correctly.)
Trying to rederive the constants doesn’t help me, which is starting to make me wonder if he’s really using the table he provided or misstated an equation or something:
If you double 34779 you get very close to his $69,321 so there might be something going wrong due to the 1⁄2 that appears in uses of the
erf
to make a cumulative distribution function, but I don’t how a threshold of 99.64 IQ is even close to his 108!(The weird start values were found via trial-and-error in trying to avoid R’s ‘singular gradient error’; it doesn’t appear to make a difference if you start with, say,
f=90
.)Most importantly, we appear to have figured out the answer to my original question: no, it is not easy. :P
So, I started off by deleting the eight outliers to make lynn2. I got an adjusted R^2 of 0.8127 for the exponential fit, and 0.7777 for the fit with iq0=108.2.
My nls came back with an optimal iq0 of 110, which is closer to the 108 I was expecting; the adjusted R^2 only increases to 0.7783, which is a minimal improvement, and still slightly worse than the exponential fit.
The value of the smart fraction cutoff appears to have a huge impact on the mapping from smart fraction to gdp, but doesn’t appear to have a significant effect on the goodness of fit, which troubles me somewhat. I’m also surprised that deleting the outliers seems to have improved the performance of the exponential fit more than the smart fraction fit, which is not what I would have expected from the graphs. (Though, I haven’t calculated this with the outliers included in R, and I also excluded the Asian data, and there’s more fiddling I can do, but I’m happy with this for now.)
And inadvertently provided an object lesson for anyone watching about the value of researchers providing code...
My intuition so far is that La Griffe found a convoluted way of regressing on a sigmoid, and the gain is coming from the part which looks like an exponential. I’m a little troubled that his stuff is so hard to reproduce sanely and that he doesn’t compare against the exponential fit: the exponent is obvious, has a reasonable empirical justification. Granting that Dickerson published in 2006 and he wrote the smart fraction essay in 2002 he could at least have updated.
You need to delete any trailing whitespace in your indented R terminal output. (Little known feature of LW/Reddit Markdown code blocks: one or more trailing spaces causes the newline to be ignored and the next line glommed on. I filed an R bug to fix some cases of it but I guess it doesn’t cover
nls
or you don’t have an updated version.)I don’t understand your definition
sf(iq,iq0)
makes sense, of course, andm
presumably is the multiplicative scale constant LG found to be 69k, but what is thisb
here and why is it being added? I don’t see how this tunes how big a smart fraction is necessary since shouldn’t it then be on the inside ofsf
somehow?But using that formula and running your code (using the full dataset I posted originally, with outliers):
I emailed La Griffe via Steve Sailer in February 2013 with a link to this thread and a question about how his smart-fraction model works with the fresher IQ/nations data and compares to Dickerson’s work. Sailer forwarded my email, but neither of us has had a reply since; he speculated that La Griffe may be having health issues.
In the absence of any defense by La Griffe, I think Dicker’s exponential works better than La Griffe’s fraction/sigmoid.
The theoretical justifications are entirely different, though. It seems reasonable to me to suppose there’s some minimal intelligence to be wealth-producing in an industrial society, and the smart fraction estimates that well and it predicts gdp well. But, it also seems reasonable to treat log(gdp) as a more meaningful object than gdp.
It’s also bothersome that the primary empirical prediction of the smart fraction model (that there is some stable gdp level that you hit when everyone is higher than the smart fraction) is entirely from the extrapolated part of the dataset, and this doesn’t seem noticeably better than the exponential model, whose extrapolations are radically different.
Yeah; I’m curious what they’d have to say about the relative merits of the two models. I’ll see if I can get this question to them.
Fixed, thanks!
It’s an offset, so that it’s an affine fit rather than a linear fit: the gdp level for a population with no people above 108 IQ doesn’t have to be 0. Turns out, it’s not significantly different from zero, but I’d rather discover that than enforce it (and enforcing it can degrade the value for m).
I’m not entirely sure… For individuals, log-transforms make sense on their own merits as giving a better estimate of the utility of that money, but does that logic really apply to a whole country? More money means more can be spent on charity, shooting down asteroids, etc.
The next logical step would be to bring in the second 2006 edition of the Lynn dataset, which increased the set from 81 to 113, and use the latest available per-capita GDP (probably 2011). If the exponential fit gets better compared to the smart-fraction sigmoid, then that’s definitely evidence towards the conclusion that the smart-fraction is just a bad fit.
I’d guess that he’d consider SF a fairly arbitrary model and not be surprised if an exponential fits better.
Why can’t the GDP be 0 or negative? Afghanistan and North Korea are right now exhibiting what such a country looks like: they can barely feed themselves and export so much violence or fundamentalism or other dysfunctionality that rich nations are sinking substantial sums of money into supporting them and fixing problems.
The argument would be that additional intelligence multiplies the per-capita wealth-producing apparatus that exists, rather than adding to it (or, in the smart fraction model, not doing anything once you clear a threshold).
There’s no restriction that b be positive, and so those are both options. I wouldn’t expect it to be negative because pre-industrial societies managed to survive, but that presumes that aid spending by the developed world is not subtracted from the GDP measurement of those countries. Once you take aid into account, then it does seem reasonable that places could become money pits.
That’s the intuitive justification for an exponential model (each additional increment of intelligence adds a percentage of the previous GDP), but I don’t see how this justifies looking at log transforms.
The difference would be a combination of negative externalities and changing Malthusian equilibriums: it has never been easier for an impoverished country like North Korea or Afghanistan to export violence and cause massive costs they don’t bear (9/11 directly cost the US something like a decade of Afghanistan GDP once you remove all the aid given to Afghanistan), and public health programs like vaccinations enable much larger populations than ‘should’ be there.
GDP ~ exp(IQ) is isomorphic to ln(GDP) ~ IQ, and I think log(dollars per year) is an easier unit to think about than something to the power of IQ.
[edit] The graph might look different, though. It might be instructive to compare the two, but I think the relationships should be mostly the same.
It’s worth pointing out that IQ numbers are inherently non-parametric: we simply have a ranking of performance on IQ tests, which are then scaled to fit a normal distribution.
If GDP ~ exp(IQ), that means that the correlation is better if we scale the rankings to fit a log-normal distribution instead (this is not entirely true because exp(mean(IQ)) is not the same as mean(exp(IQ)), but the geometric mean and arithmetic mean should be highly correlated with each other as well). I suspect that this simply means that GDP approximately follows a log-normal distribution.
This doesn’t quite follow, since both per capita GDP and mean national IQ aren’t drawn from the same sort of distribution as individual production and individual IQ are, but I agree with the broader comment that it is natural to think of the economic component of intelligence measured in dollars per year as lognormally distributed.
“Salt Iodization and the Enfranchisement of the American Worker”, Adhvaryu et al 2013:
If, in the 1920s, 10 IQ points could increase your labor participation rate by 1%, then what on earth does the multiplier look like now? The 1920s weren’t really known for their demands on intelligence, after all.
And note the relevance to discussions of technological unemployment: since the gains are concentrated in the low end (think 80s, 90s) due to the threshold nature of iodine & IQ, this employment increase means that already, a century ago, people in the low-end range were having trouble being employed.
A 2012 Jones followup: “Will the intelligent inherit the earth? IQ and time preference in the global economy”