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National Intelligence and Economic Growth: A Bayesian Update

September 2022
Mankind Quarterly 63(1)

DOI:10.46469/mq.2022.63.1.2

Authors:

George Francis

Emil O. W. Kirkegaard

Ulster Institute for Social Research

Since Lynn and Vanhanen's book IQ and the Wealth of Nations (2002), many publications have evidenced a relationship between national IQ and national prosperity. The strongest statistical case for this lies in Jones and Schneider's (2006) use of Bayesian model averaging to run thousands of regressions on GDP growth (1960-1996), using different combinations of explanatory variables. This generated a weighted average over many regressions to create estimates robust to the problem of model uncertainty. We replicate and extend Jones and Schneider's work with many new robustness tests, including new variables, different time periods, different priors and different estimates of average national intelligence. We find national IQ to be the "best predictor" of economic growth, with a higher average coefficient and average posterior inclusion probability than all other tested variables (over 67) in every test run. Our best estimates find a one point increase in IQ is associated with a 7.8% increase in GDP per capita, above Jones and Schneider's estimate of 6.1%. We tested the causality of national IQs using three different instrumental variables: cranial capacity, ancestry-adjusted UV radiation, and 19 th-century numeracy scores. We found little evidence for reverse causation, with only ancestry-adjusted UV radiation passing the Wu-Hausman test (p < .05) when the logarithm of GDP per capita in 1960 was used as the only control variable.

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MANKIND QUARTERLY 2022 63.1 9-78

National Intelligence and Economic Growth: A Bayesian

Update

George Francis*

Independent Researcher, United Kingdom

Emil O.W. Kirkegaard*

Independent Researcher, Denmark

* Addresses for contact: george.t.francis@protonmail.com and

emil@emilkirkegaard.dk

Since Lynn and Vanhanen’s book IQ and the Wealth of Nations (2002),

many publications have evidenced a relationship between national IQ and

national prosperity. The strongest statistical case for this lies in Jones and

Schneider’s (2006) use of Bayesian model averaging to run thousands of

regressions on GDP growth (1960-1996), using different combinations of

explanatory variables. This generated a weighted average over many

regressions to create estimates robust to the problem of model

uncertainty. We replicate and extend Jones and Schneider’s work with

many new robustness tests, including new variables, different time

periods, different priors and different estimates of average national

intelligence. We find national IQ to be the “best predictor” of economic

growth, with a higher average coefficient and average posterior inclusion

probability than all other tested variables (over 67) in every test run. Our

best estimates find a one point increase in IQ is associated with a 7.8%

increase in GDP per capita, above Jones and Schneider’s estimate of

6.1%. We tested the causality of national IQs using three different

instrumental variables: cranial capacity, ancestry-adjusted UV radiation,

and 19th-century numeracy scores. We found little evidence for reverse

causation, with only ancestry-adjusted UV radiation passing the Wu-

Hausman test (p < .05) when the logarithm of GDP per capita in 1960 was

used as the only control variable.

Key Words: Human capital, National IQ, Economic growth, Bayesian

model averaging, Intelligence, Smart fraction

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

Much research has found that national IQ is associated with per capita GDP

and economic growth, suggesting cognitive skills are important for prosperity.

However, it is unclear exactly how robust this relationship is and how important

this theory is compared to alternative theories. Merely reporting regression

models is not sufficient. Researchers can choose what variables to employ and

what models to report that best support their own theories. Moreover, only so

many variables can be included in a model, allowing for few comparisons

between national IQ and other explanatory variables.

Jones and Schneider (2006) came closest to addressing this problem. They

performed Bayesian model averaging of economic growth, running thousands of

regressions with different explanatory variables, including national IQ, and then

took a weighted average of these results. National IQ was in 96% of the models

after the models were weighted according to how well they fitted the data,

suggesting national IQ is very good at predicting economic growth. Unfortunately,

Jones and Schneider did not report results for other variables making any firm

comparisons between national IQ and other variables difficult. Furthermore,

recent literature on Bayesian model averaging and economic growth has found

the results to be very sensitive to minor changes in the data and priors used

(Bruns & Ioannidis, 2020; Ciccone & Jarociński, 2010; Rockey & Temple, 2016).

Just as with regression models, it is not sufficient to only present one or a few

Bayesian model averages, rather a large range should be used if we are to be

confident in the results.

In this paper, we replicate and extend Jones and Schneider’s (2006) work by

employing national IQ in Bayesian model averaging. We standardize our

variables and present the results for all variables possible to compare national IQ

to rival theories of economic growth. We use a long series of stress tests from the

Bayesian model averaging literature and others we have come up with. This

includes resampling, different datasets, different priors, and different time periods.

This is to see if national IQ is robust or whether Jones and Schneider’s results

were merely a fluke from the sensitivity of Bayesian model averaging.

We also review the prior literature on causality and use instrumental variables

to test this. GDP per capita at the start of the observation period is used as a

“fixed regressor” in all regressions, meaning it is not a variable we test in this

paper. We use the term fixed regressor not in the traditional way it is used in time

series econometrics, but rather to mean that it is forced into every regression that

is model averaged, ensuring its posterior inclusion probability (PIP) is always

equal to one.

Compared to all other tested explanatory variables, we find national IQ to be

the most important predictor of economic growth. In every set of stress tests, we

find national IQ to have the largest average coefficient and the largest posterior

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

inclusion probability. Our results suggest each IQ point increases GDP per capita

by 7.8% compared to Jones and Schneider’s 6.1% estimate. We extend the use

of Bayesian model averaging to re-evaluate many questions in the national IQ

literature. We test smart fraction theory — whether the mean IQ or that of another

part of the IQ distribution best predicts economic growth, as well as rival

psychological explanations of economic growth, finding national IQ to dominate

them all. We also test whether national IQ affects economic growth through an

exogenous model, an endogenous one, or a Nelson-Phelps technology diffusion

model.

In section 1 we summarize the literature on how national IQ can function as

a measure of human capital. In section 2 we review the issue of model uncertainty

and how Bayesian model averaging has tried to respond to this problem. In

section 3 we explain our Bayesian model averaging methodology. In section 4 we

explain what data we use in this paper. This includes which national IQ measures

are used, such as the World Bank’s Harmonised Learning Outcomes, what

measures of GDP we use, what datasets of control variables we employ, and the

additional variables we employ in stress tests. In section 5 we present our main

results using Bayesian model averaging to test the robustness of national IQ and

Smart Fraction theory. Section 6 discusses the problem of causality and tests this

with instrumental variables. In section 7 we review the general limitations of our

methodology. In section 8 we draw our conclusions and describe their

implications for policy and the future of the world economy. In an online

supplement we discuss our results in the context of endogenous and exogenous

models and test the Nelson-Phelps technology diffusion model.

Section 1: National IQ as a Measure of Human Capital

Economists have empirically tested the role of human capital in causing

economic growth since at least Mankiw, Romer and Weil (1992). Typically studies

have used years in education, such as Barro and Lee’s years in education

measure (1993), or enrollment rates in education as in Sala-i-Martin (1997) and

Sala-i-Martin, Doppelhofer and Miller (2004). However, despite the correlation

between the level of education and economic growth, increases in education have

sometimes been found to have no relationship with economic growth (Hamilton

& Monteagudo, 1998) or even a negative relationship (Pritchett, 2001), calling

into question whether variation in education is a satisfactory indicator of variation

in human capital.

An alternative approach to measuring human capital as a predictor of

economic growth has been to use measures of human intelligence or attained

academic ability rather than amount of schooling — focusing on educational

‘output’ (Hanushek & Woessmann, 2015), rather than any supposed ‘inputs’. If

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

education is ineffective at improving ability (see Caplan, 2018 for a review), or is

heterogeneous in its quality, or simply only one of many causes of variation in

human ability, then a direct measurement of a people’s cognitive ability may

better measure their human capital. If more capable countries tend to have more

education, then it may be confounded with human ability explaining education’s

apparently spurious relationship with growth.

Although this output-based approach to human capital had been suggested

by Barbara Lerner (1983), its relationship with economic output was only tested

and supported in regression models by Hanushek and Kim (1995) and Lynn and

Vanhanen (2002). Lynn and Vanhanen created “national IQs” using samples of

psychometric IQ tests from countries around the world to create average national

IQs to test their effect on GDP. By contrast, Hanushek and Kim used the results

of recurrent international student assessments in mathematics and reading to

create measures of education quality to predict economic growth.

Many further studies have been published studying economic growth with

data from student assessments such as PIRLS, TIMMS and the OECD’s

Programme for International Student Assessment (PISA) (e.g. Angrist et al.,

2019, 2021; Hanushek & Woessmann, 2015; Lim et al., 2018) and IQ tests (e.g.

Christainsen, 2020; Meisenberg, 2012; Ram, 2007, Weede & Kämpf, 2002),

finding these variables to have positive and significant coefficients. Furthermore,

some studies have used both student assessments and national IQs or even

combined them (Rindermann, 2008, 2018). Both student assessments and

national IQs appear to measure the same construct, cognitive ability

(Rindermann, 2006, 2007), as they correlate at 0.8 or more at the national level

(Meisenberg & Lynn, 2011). For the sake of ease, we refer to all measures of

national average cognitive ability as ‘national IQ’, rather than just average IQ

scores from nationally representative samples.

There are strong theoretical reasons for supposing that IQ would make an

appropriate measure of human capital. For a start, intelligent people tend to be

more productive workers. Psychologists have found smarter workers are more

productive in their occupations (Gottfredson, 1986, 1997). One IQ point is

associated with approximately 1% higher wages (Behrman et al., 2004; Bishop,

IQ stands for intelligence quotient and was originally invented to measure cognitive

ability by dividing scores by the test taker’s age, hence the term ‘quotient’. The term is

somewhat of a misnomer when used in ‘national IQ’ because modern IQ tests do not

divide by the test taker’s age and they are designed for individuals rather than nations.

Nevertheless, it has become commonplace to refer to national cognitive ability as

national IQ, so we follow this practice.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

1989; Cawley et al., 1997; Grosse et al., 2002; Neal & Johnson, 1996; Zax &

Rees, 2002).

It is clear that individual differences in intelligence could cause large

individual differences in income. However, the magnitude of differences in the

average intelligence of nations also implies large differences of wealth between

nations. For example, in Lynn’s original IQ dataset (Lynn & Vanhanen, 2002), the

UK has a national IQ of 100 whilst Brazil has an IQ of 82, more than a standard

deviation (15 points) lower than the UK.

Figure 1. A ‘level 3’ difficulty question asked on PISA.

The magnitude and importance of national IQ differences may be more

clearly intuited by looking at the national results from individual questions on

intelligence tests. Take the question in Figure 1 from the OECD’s 2012 maths

PISA test given to 15-year-olds (accessed from: https://www.oecd.org/pisa/test-

2012/). It involves reading a table providing details regarding different cars. The

test subject had to find the car with the smallest engine capacity. This question is

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

considered to be of ‘level 3 difficulty’ by the OECD. Despite the ease of this task,

only 55% of OECD students scored level 3 or above. 80% of Singaporeans

achieved level 3 or above compared to only 18% of Mexicans, 13% of Brazilians,

and 8% of Indonesians. Inability to read tables would make any sort of analytical

work impossible and would even make simple tasks, such as reading a train

timetable, difficult for the majority of people in many countries. Given the

extraordinary differences in average cognitive ability between countries, we ought

to consider whether it could affect gross domestic product.

IQ’s effect on individual productivity is not the only explanation for why it

predicts economic growth. Jones and Schneider (2010) calibrated an IQ-

augmented Cobb-Douglas production function to model log GDP per worker in

the year 2000, using the 1% estimate of IQ’s effect on earnings. They found this

estimate of IQ’s effect could only explain 16% of the variation in national earnings,

whilst in a simple correlation (Lynn & Vanhanen, 2006) IQ can explain 58% of the

variation and each additional national IQ point is associated with 6.7% higher per

capita earnings. This “IQ paradox” suggests substantial reverse causation or

externalities, with intelligent people not fully internalizing the benefits of their

intelligence. We discuss the issue of causality in the ‘Causality’ section of this

paper.

Potential causes of externalities arising from IQ include its association with

free-market opinions (Carl, 2014a, 2015), with greater knowledge of economics

(Caplan & Miller, 2010), with lower time preference and more saving (Jones &

Podemska-Midluch, 2010; Kirkegaard & Karlin, 2020; Shamosh & Gray, 2008;

Yeh et al., 2021), with higher levels of social trust (Carl, 2014b; Carl & Billari,

2014), with cooperation in public goods games (Al-Ubaydli et al., 2016; Putterman

et al., 2012) and the prisoner's dilemma (Proto et al., 2019), national IQ’s

association with institutional quality (Jones & Potrafke, 2014; Kanyama, 2014;

Potrafke, 2012) and the prevalence of O-ring production functions (Jones, 2013).

For an overview of the mechanisms by which IQ could create externalities see

Jones (2016) and Anomaly and Jones (2021).

Section 2: Modelling Uncertainty

In growth modelling with IQ, and regression modelling generally, researchers

are faced with the problem of model uncertainty. Model estimates are dependent

on what variables are included, meaning reported significant results from a subset

of all possible models may just be an artefact. For example, with 50 potential

explanatory variables, there are  possible models, which is greater than one

quadrillion. How can we be sure the few models presented in a paper, or even an

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

entire literature, are representative and that the results are not just the result of

data mining?

Model uncertainty implies that regression results could be coincidental, and

it also allows researchers to ‘p-hack’ their results with specification searching —

only presenting the models which best support their theories. In addition, journals

may prefer only to publish statistically significant results rather than null findings.

The economics literature seems to have been affected by specification searching,

as p and t values in published findings are less likely to be marginally insignificant

than would be expected by chance (Brodeur et al., 2016; Bruns et al., 2019; Vivalt,

2019). This indicates that economists are biased, consciously or not, and slightly

change their methodology until their results are statistically significant. This

should make us skeptical of the reported results from merely a few models.

Bayesian model averaging can potentially provide more accurate results free

from biases arising from selective model reporting (Zeugner & Feldkircher, 2015).

This methodology was first employed to study economic growth by Fernandez et

al. (2001) and Sala-i-Martin et al. (2004), although previous papers had attempted

similar methods by creating summary statistics from running many (Levine &

Renelt, 1992) or millions of regressions (Sala-i-Martin, 1997a,b).

This method involves running a large sample of possible models with

different explanatory variables and fixing a prior probability of each model being

the ‘true model’ before looking at the data. Then using Bayes theorem, a posterior

model probability is calculated for each model based on their marginal likelihoods.

The coefficients of explanatory variables are then calculated by weighting

coefficients in individual models by the model’s posterior probability. Furthermore,

a posterior inclusion probability (PIP) is calculated for each explanatory variable

which represents the sum of the posterior model probabilities of the models in

which the covariate is included. As such the PIP represents the explanatory

power of a variable and can be crudely understood as the probability of a variable

being in the ‘true model’ or the probability that a variable’s true coefficient is non-

zero. Moreover, by comparing the posterior inclusion probability of a variable to

its prior inclusion probability we can see whether the data tends to increase or

decrease our impression of whether a variable is a robust predictor. Full details

of Bayesian model averaging are given in the Methodology section.

To test how robust national IQ is as a predictor of economic growth, Jones

and Schneider (2006) used this variable in Bayesian model averaging of the

growth rate in GDP per capita. In addition to national IQ, they employed the 21

variables considered robust in Sala-i-Martin’s (1997a,b) paper ‘I just ran two

million regressions’. Jones and Schneider found that national IQ had an extremely

high posterior-inclusion probability of 96%. By comparison, the top three

performing variables in Fernandez and Lay’s (2001) study using Sala-i-Martin’s

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

entire dataset had PIPs of 99.5% (fraction Confucian), 99.5% (life expectancy)

and 94% (equipment investment). A 1 point increase in national IQ was

associated with a 0.11% increase in the GDP growth rate.

The results of Jones and Schneider (2006) provide strong support for the

relationship between economic growth and national IQ and suggest the significant

results of other studies are not the result of poorly specified models, whether by

coincidence or various forms of bias. Unfortunately, Jones and Schneider do not

provide the results for all the control variables, meaning that fifteen years on we

still do not have a clear picture of how important national IQ is relative to other

variables — both in the size of its coefficient and its posterior inclusion probability.

Does IQ have the largest effect size? Are there many or any variables as robust

as national IQ? We hope to answer these questions.

The Bayesian modelling literature

Since Fernandez et al. (2001), Bayesian model averaging has been widely

applied to the study of economic growth, with focus on things such as the

jointness of growth determinants (Doppelhofer & Weeks, 2009; Ley & Steel,

2007), growth in specific regions (Cuaresma et al., 2009, 2013; Masanjala &

Papageorgiou, 2008; Próchniak & Witkowski, 2013a) and testing particular

theories of economic growth (Durlauf et al., 2008; Égert, 2015; Eris & Ulasan,

2013; Horvarth, 2011; Próchniak & Witkowski, 2013b). However, despite the

strong results of Jones and Schneider (2006), no one has employed national IQ

with Bayesian model averaging since.

Despite the popular use of Bayesian modelling, some researchers have

found the results to be extremely fragile to minor differences in methodology and

data. If the results of Bayesian modelling are unstable or unreliable, we may not

have confidence that Jones and Schneider’s (2006) results on IQ will replicate.

Ciccone and Jarocinski (2010) in their paper Determinants of Economic Growth:

Will Data Tell? re-ran the Bayesian modelling of Sala-i-Martin et al. (2004) using

different updates of the Penn World Tables (PWT versions 6.0, 6.1 and 6.2),

which provides the national accounting data for variables such as GDP. They

found that minor changes in the national accounting data were enough to

substantially alter the results of Bayesian model averaging. An extreme example

of this was the ‘Investment Price’ variable, which moved from having a posterior

inclusion probability of 98% to 2% depending upon the PWT dataset used. Thus

variables that could appear to be robust predictors of economic growth may not

be robust to measurement error.

Bruns and Ioannidis (2020) tested the sensitivity of Bayesian modelling using

multiple time periods from 1960 to 2010. They found the inferences on growth

determinants were unstable across time periods. The posterior inclusion

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

probabilities of determinants were evenly distributed across early time periods

preventing identification of which control variables were important. However,

Ioannidis and Bruns find more recent time periods show less even distributions

of posterior inclusion probabilities suggesting improvements in measurement

could cause Bayesian modeling to find more stable results.

In response to concerns about the sensitivity of Bayesian model averaging

some researchers have suggested methods to improve BMA. Feldkircher and

Zeugner (2009) proposed the use of flexible priors on model coefficients that

allow for ‘data-adaptive shrinkage’. Feldkircher and Zeugner (2012) show that the

use of these priors makes Bayesian modelling more robust to changes in the

Penn World Tables. Likewise, Rockey and Temple (2016) recommend the use of

fixed regressors to improve the stability of results, particularly the use of GDP per

capita in the starting year and regional dummies. However the use of flexible

priors and fixed regressors leads to PIPs being more evenly distributed making it

difficult to determine which variables are important.

In overview, Bayesian model averaging appears to be either very sensitive,

or it fails to discriminate between variables (Bruns & Ioannidis, 2020; Rockey &

Temple, 2016), resulting in ‘robust ambiguity’ with failure to find support for growth

regressors, except for GDP per capita in the starting year. Given that national IQ

has only been tested with Bayesian modelling by Jones and Schneider (2006), it

is reasonable to question whether it will replicate given the apparent conclusion

of robust ambiguity. To see whether national IQ’s posterior inclusion probability

is too sensitive to be robust, we run many sets of Bayesian model averaging,

using different datasets, different time periods, different versions of the Penn

World Tables, different priors, different fixed regressors, new potential confounds

of IQ, different national IQs, and resampling methods such as bootstrapping and

Jackknife resampling. In applying all the robustness tests in the BMA growth

literature and more, we ensure that our research employs the most challenging

set of tests in the literature so far. If national IQ performs the best in all these

tests, with the highest average PIP and coefficient in each test, we will consider

it to be the most powerful predictor of economic growth in the literature.

Ciccone and Jarocinski (2010) asked in the title of their paper Determinants

of Economic Growth: Will Data Tell?. We question whether the right data, national

IQ, is being used.

Section 3: Methodology

To perform Bayesian modelling averaging (BMA), we use the BMS package

in R. We draw heavily from the package’s tutorial (Zeugner & Feldkircher, 2015)

in explaining the methodology.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

To model growth we can use a linear regression model of the following structure:

   

  󰇛󰇜

 󰇝󰇞

 is our dependent variable, the average rate of growth in nations. is a

constant,  the regression coefficients, and ε is the normal IID error term

variance . With many plausible control variables󰇝󰇞to employ as  but with

a limited sample size, simply employing all variables would not be feasible.

BMA deals with the problem of model uncertainty by estimating models for

many possible combinations of  󰇝󰇞 and then taking a weighted average

over all of them. The models are weighted by their posterior model probability

󰇛󰇜 which can be derived from Bayes’ Rule:

󰇛󰇜 󰇛󰇜󰇛󰇜

󰇛󰇜 󰇛󰇜󰇛󰇜

󰇛󰇜󰇛󰇜





󰇛󰇜 is the model prior and 󰇛󰇜 is the marginal likelihood of model

. 󰇛󰇜 denotes the integrated likelihood which is the same for all models.

The posterior model probability is thus proportional to the marginal likelihood of

the model times the prior model probability.

The marginal likelihood of model is given by

󰇛󰇜  󰇛󰇜󰇛󰇜

where 󰇛󰇜 is the likelihood of model  and 󰇛󰇜 is the

prior density of the coefficients of model .

The key results we are interested in are the variable posterior inclusion

probabilities (PIPs) and the variable coefficients. The PIPs indicate what percent

of the posterior model mass is made up of models including regressor .

IID normality of error terms is certainly a strong assumption, nevertheless there has

been little discussion of its importance in the growth literature and the statistical

package we employ relies on this assumption. As such, we follow common practice by

keeping the assumption.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

To be precise the PIP of  is the sum of posterior model probabilities over

those models where  is included.

󰇛 󰇜 󰇛󰇜



 󰇝    󰇞

 is an indicator function that is 1 if regressor  is included in model j and

0 otherwise.  is the set of models that include .

How should we interpret PIPs? Sala-i-Martin et al. (2004) suggest comparing

the posterior inclusion probability to the prior inclusion probability. If the PIP is

larger than the prior inclusion probability, we can say that the data has updated

our priors in favor of variable . Sala-i-Martin suggests we use this as a

threshold for ‘significance’.

󰇛󰇜󰇛󰇜󰇛󰇜





The model weighted density function for coefficients is represented above.

󰇛󰇜 represents the density function of coefficientgiven the data

in model , whilst󰇛󰇜 represents the marginal likelihood of model .

This explains the core mechanics of Bayesian modeling, but we need to elicit

what the model priors are to calculate the results. To perform BMA well we should

choose priors that are non-informative so that the priors have little impact on

posterior inference so we let the data come to its own conclusions rather than

forcing our own priors upon it. The standard method has been to follow Fernandez

et al. (2001) in assigning a ‘g-prior’ (Zellner, 1986) on  and improper priors on

 the constant term and  the error variance. Improper priors are those that are

evenly distributed over their domain - complete prior uncertainty:

󰇛󰇜 

󰇛󰇜

 󰇛󰇛



󰆒󰇜󰇜

The key prior is the one on the regression coefficients. We assume a prior

mean of 0 on the coefficients. The variance structure is defined according to

Zellner’s g. This means we start with an expectation of all coefficients being equal

to 0 and our confidence in this prior is given by the term g. A small g represents

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

high certainty that the coefficients are near zero whilst a large one means we are

very uncertain that the coefficients are zero. A large g tends to concentrate

posterior model probabilities on a few best-fitting models, known as the

‘supermodel effect’ (Feldkircher & Zeugner, 2009), potentially making BMA

oversensitive to small changes in the data (Feldkircher & Zeugner, 2015), whilst

a small g can systematically underestimate all coefficients leading to ambiguous

results.

Various values for g have been suggested. A brief explanation of popular g’s

are given in Table 1. Unfortunately there is no consensus about which value of g

is best. For a full review of different g-priors see Feldkircher and Zeugner (2009).

We take the Unit Information Prior as our standard in this paper, but use the rest

as a robustness test. The value of the Unit Information Prior is in its simplicity. It

sets g equal to the number of observations available, thus linking our confidence

in our regression results to the sample size — the information available. The

Uniform Information Prior has been used widely in Bayesian model averaging

such as in the papers of Feldkircher and Zeugner (2012) and Rockey and Temple

(2016).

Table 1. Descriptions of g-priors used.

g-prior

Description

Uniform

Information

Prior (UIP)

  , Sets g according to the amount of information available

which is the number of observations. This causes the Bayes factors

to behave according to the Bayesian Information Criterion (BIC)

(Kass & Wasserman, 1995).

Risk Inflation

Prior (RIC)

  , Calibrates priors for model selection based upon the

Risk Inflation (Zeugner & Feldkircher, 2015)

Benchmark

Prior (BRIC)

  󰇛󰇜, Fernandez et al. (2001)

Hannan-Quinn

Criterion (HQ)

  󰇛󰇜 (Zeugner & Feldkircher, 2015)

Local Empirical

Bayes (EBL)

Estimates a separate g for each model using its marginal

likelihood. (Zeugner & Feldkircher, 2015)

Hyper g prior

(Hyper)

Uses a hyperprior distribution on g. Adjusts the posterior

distribution to reflect the data’s signal-to-noise ratio, reducing the

sensitivity of BMA (Feldkircher & Zeugner, 2009).

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

In addition to providing a g-prior, we need to choose a model prior. We use

binomial model priors, putting a fixed prior inclusion probability ofon each

variable which in turn determines the priors for each model. As such the prior

probability of a model size  is

󰇛󰇜 󰇛 󰇜

 represents the inclusion probability of each regressor and is the number

of regressors included in . In growth modeling, a model size of seven is

considered standard (Barro & Sala-i-Martin, 2003; Jones & Schneider, 2005;

Sala-i-Martin et al., 2004). As such we adjust  in each set of regressions to

ensure the expected model size is 7. This means    where  is the total

number of explanatory variables in our dataset. However, when fixed regressors

are used in addition to GDP per capita in the starting year, we increase the

expected model size to ensure our tested variables always have the same prior

probability of inclusion across tests of different fixed regressors.

Alternative model priors are possible. One approach is to make  random as

done by Ley and Steel (2009) and Bruns and Ioannidis (2020). This is to reduce

the effect of possibly fixing the wrong model priors. Another approach is to set 

equal to 0.5 resulting in uniform model priors. A problem with this approach is that

it centres the expected model size at , overweighting the more numerous

‘large’ models which can cause overfitting. Fernandez et al. (2001) and Jones

and Schneider (2006) use uniform model priors for example. Crucially, Jones and

Schneider only consider models of size 7 meaning there is no overweighting of

large models. Our approach of considering models of all sizes allows our results

to be influenced by the strongest models regardless of their size. Given the

simplicity of setting an expected model size of seven, we use this as standard

practice. Uniform model priors and random model priors are used as a robustness

test.

There are two other important methodological differences between our

research and Jones and Schneider (2006). In their approach they use the

maximum possible sample size allowed in each regression. This means the

regression models are not being employed on the same observations making

them less comparable. In our approach we remove observations that cannot be

used in every possible regression model. Nevertheless, both approaches are

affected by selection bias into the sample.

Another difference is that Jones and Schneider (2006) enumerate all their

possible models and take a Bayesian average of them. They are able to do this

because they keep three regressors fixed, which Sala-i-Martin (1997) considered

to be particularly strong variables, ensuring there were fewer possible models.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

We employ the same fixed variables only in our robustness test examining the

effect of different fixed variables. We avoid this approach to ensure we do

consider a wider population of possible models. To do this we only take a sample

of all possible models rather than enumerating all our possible models which

would take too long. To do this we use Markov Chain Monte Carlo sampler as is

standard practice in the prior literature (eg. Bruns & Ioannidis, 2020; Fernández,

Ley & Steel, 2001b). We use 200,000 iterations of models after a burn in phase

of 100,000 iterations of models. Due to time constraints, we used a tenth of the

iterations in each Bayesian model average within our bootstrapping approach

which involved 1,000 Bayesian model averages. We use 20,000 iterations of

models after a burn in phase of 10,000 iterations of models. By comparison, the

default settings for the BMS package, which is designed with the application to

economic growth in mind, uses 3,000 models after a burn in phase of 1,000

models.

A final note regarding methodology is necessary. In every BMA run we

include the logarithm of GDP per capita in the starting year as a fixed regressor.

This is because prior literature has consistently found a strong negative effect of

the starting level of the logarithm of GDP per capita (eg. Barro & Sala-i-Martin,

2003, p. 496, p. 521) dubbed the advantage of backwardness. This has

theoretical roots in neoclassical growth models in which diminishing marginal

returns to investment allows poorer countries to grow faster. Given the high

correlation of IQ and other variables with GDP in the starting year, any models

that do not include GDP would bias national IQ’s coefficient downwards as it takes

on some of the effect of the advantage of backwardness. Given this potentially

large co-dependency of GDP per capita and other explanatory variables, we

follow common practice by including it as a fixed regressor.

Section 4: Data

National IQ

National intelligence can be measured in two ways. One way is the

psychometric method, administering IQ tests to more-or-less representative

samples within each country. These results were collected by Richard Lynn and

co-authors (Lynn & Vanhanen, 2002, 2012; Lynn & Becker, 2019). These scores

are adjusted for changes in IQ over time, the Flynn effect, assuming these

changes are linear and the same across countries. The UK’s score is then set to

100, and one standard deviation in IQ amongst British people is set to 15. This is

the ‘Greenwich mean IQ’.

The Lynn national IQ data derived from IQ tests have been criticized by

Wicherts et al. (2010a,b). The critics suggest that scores from Sub-Saharan

countries are implausibly low, do not use representative samples, and may be

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

unduly deflated by cultural factors, poor nutrition, and poor education. Lynn and

Meisenberg (2010a) have responded to these critiques defending the quality of

the national IQ scores. Lynn and Meisenberg argued many of the studies

Wicherts et al. used to show higher IQ for Sub-Saharan Africans are

unrepresentative because they are based on university students. David Becker

later went back to the sources and recalculated the national IQs with a more

rigorous set of conditions for inclusion (Lynn & Becker, 2019). These scores

correlated very well with Lynn and Vanhanen’s (2002) original IQ scores and

support many of the very low scores for the most underdeveloped nations.

Moreover, Lynn and Vanhanen’s (2002) IQ scores for Sub-Saharan Africans are

consistent with the results of the Southern and Eastern Africa Consortium for

Measuring Education Quality (SACMEQ) assessment (Sandefur, 2018;

Thompson, 2016).

Sample representativeness is a serious concern for using national IQs as a

measure of human capital. Other critiques are not necessarily so important for

economics research. Whilst psychologists may be concerned that culture,

nutrition and education quality may mask a nation’s ‘true’ or potential intelligence,

economists are interested in the actual, phenotypic ability that determines the

cognitive human capital of workers. These environmental factors may be

important for reverse causality, which we discuss in the Causality section of the

paper.

To avoid specific critiques based on the psychometric national IQ datasets,

we also repeat our analysis using a second set of cognitive measures: student

assessment tests. This includes the PISA, TIMSS and PIRLS tests which are

regularly given to students in a large range of countries testing proficiency in

mathematics, the native language, and science. As a measure of educational

output without pretensions of measuring ‘IQ’, these scores avoid criticism that

they do not in fact measure intelligence. Nonetheless, they appear to measure

the same cognitive human capital that IQ tests measure because their scores

correlate highly (r > .9) with different updates of Lynn’s national IQs (Lynn &

Meisenberg, 2010b; Meisenberg & Lynn, 2011). This strong relationship between

test scores collected by intergovernmental organizations and Lynn’s

psychometric national IQs strongly supports the validity of psychometric IQs.

Furthermore, student assessments have been popular amongst economists,

having been employed in various research (Hanushek & Woessmann, 2012,

2015) and used to create human capital measures for the World Bank (Angrist et

al., 2019, 2021; Lim et al., 2018).

Student assessment tests were available at the time Jones and Schneider

(2006) performed their Bayesian model averaging, but sample sizes were very

small. Since then, many more countries have had their school children take part

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

in these standardized assessments, allowing larger samples to be used in the

study of economic growth (e.g. Hanushek & Woessmann, 2015). Thus in our

replication of Jones and Schneider, we employ various measures of cognitive

human capital derived from student assessments to avoid criticism unique to

Lynn’s national IQ data. Furthermore, given the discussed sensitivity of Bayesian

model averaging, it is important to see whether minor variations in national IQ

data could alter the results.

Table 2. National IQ data.

Name

Citation

Notes

Becker,

psychometric

Lynn &

Becker

(2019)

Column ‘E’ of the ‘FAV’ tab, version 1.3.3 of the national IQ

dataset (https://viewoniq.org/). This variable recreates Lynn &

Vanhanen’s psychometric IQs, with different methodology

and selection criteria, weighting samples by their quality and

size.

Becker,

psychometric/SAS

Lynn &

Becker

(2019).

A simple average of Becker’s psychometric and student

assessment IQ scores.

Becker, SAS

Lynn &

Becker

(2019)

Mean IQs calculated from PISA, TIMSS and PIRLS tests.

Hanushek &

Woessmann, SAS

Hanushek &

Woessmann

(2012).

A ‘cognitive skills’ measure created from 12 different student

assessment tests from 1964-2003.

L&V 2002,

Psychometric

Lynn &

Vanhanen

(2002)

The original set of psychometric national IQs, standardized to

British IQ, adjusting for a constant Flynn effect

L&V 2012,

Psychometric

Lynn &

Vanhanen

(2012)

Updates Lynn & Vanhanen’s (2002) national IQ scores with

additional samples and countries.

Rindermann,

Psychometric/SAS

Rindermann

(2018)

Weighted average of psychometric scores and student

assessment scores, putting twice as much weight on SAS,

missing data extrapolated from International Mathematics

Olympiad

Rindermann, SAS

Rindermann

(2018)

Student assessment scores from a range of tests

standardized across tests and time.

World Bank HLOs

(Harmonized

Learning

Outcomes), SAS

Angrist et al.

(2021)

Student assessment results standardized across different

tests. World Bank does not provide an average of these

scores across time so we only used HLOs from 2015.

https://datacatalog.worldbank.org/dataset/harmonized-

learning-outcomes-hlo-database

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

For this study, we picked a wide range of publicly available popular

psychometric, student assessment (SAS) and mixed measures of national IQ. A

list and brief description of these variables are presented in Table 2. Our standard

national IQ is the mixed SAS and psychometric score of Rindermann (2018). This

measure has the largest sample size of all the national IQs because it uses a

wide range of student assessments and national IQs. This measure also ensures

a larger sample size for each country making IQ estimates more accurate. Finally,

the data puts a greater weight (3 to 1) on student assessment scores due to their

larger sample sizes, making it preferable to mixed national IQ scores from

Becker’s national IQ dataset. The rest of our selected national IQs are used as a

robustness test.

A correlation matrix for our national IQ variables is shown in Table 3. As in

the prior literature, cognitive skills whether measured via IQ tests, student

assessments or a mixture correlate extremely well. The lowest correlation seen

between any two measures is .83.

Table 3. National IQ correlation matrix; P = psychometric, S = school

achievement, H&W = Hanushek & Woessmann, L&V = Lynn & Vanhanen, R =

Rindermann, HLO = Harmonized Learning Outcomes (World Bank), mean r =

mean correlation with the other measures.

Becker

P&S

Becker

H&W,

L&V

2002 P

L&V

2012 P

P&S

R, S

HLO,

mean

Becker, P

0.95

0.86

0.83

0.88

0.89

0.87

0.84

0.87

Becker,

P&S

1.00

0.98

0.92

0.96

0.97

0.96

0.93

0.95

Becker, S

1.00

0.93

0.92

0.96

0.97

0.94

H&W, S

1.00

0.87

0.92

0.95

0.89

0.91

L&V 2002, P

1.00

0.95

0.94

0.92

0.88

0.90

L&V 2012, P

1.00

0.98

0.96

0.94

R, P&S

1.00

0.99

0.94

0.95

R, S

1.00

0.92

0.94

HLO, S

1.00

0.91

Control variables

We employ two different datasets of control variables in this paper. The first

is the dataset created by Sala-i-Martin, Doppelhofer and Miller (2004) used in

their Bayesian modeling, hereinafter named SDM. SDM first found variables that

predicted economic growth in the prior literature. From this group, only select

variables were included in the dataset if they had values sufficiently close to the

year 1960. Unfortunately, some variables start in 1965. This was to reduce the

problem of endogeneity. To ensure a large sample size, SDM then selected

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

variables that would maximize the product of the sample size and the number of

variables. This left SDM with 67 explanatory variables.

SDM studied the determinants of growth for the period 1960-1996. To

incorporate more recent GDP data, we employ the growth rate from 1960-2010

and use new figures for log GDP per capita. SDM’s GDP data came from version

6 of the Penn World Tables (PWT) whereas ours come from PWT 10 (Feenstra

et al., 2015), variable RGDPe, to provide log GDP per capita in the starting year

and the growth period. RGDPe is expenditure-side real GDP at chained PPPs, to

compare relative living standards across countries and over time.

The SDM dataset has two key advantages. The first is its large number of

control variables and large sample size. The second advantage is that it is a well-

tested and well-respected dataset having been used in many subsequent papers

using Bayesian modeling to study economic growth (eg. Ciccone et al., 2010;

Doppelhofer & Weeks, 2009). This limits criticism regarding our choice of control

variables and ensures we could not commit ‘data dredging’ — specifically

designing a dataset to prove our hypotheses.

We could have used the dataset created by Sala-i-Martin (1997a,b) or the

subsection of it which was first used for Bayesian modeling by Fernandez et al.

(2001). The subsection takes only variables found to be ‘important’ and well-

performing in Sala-i-Martin’s (1997a,b) model averaging method and other

variables from the Sala-i-Martin data which did not reduce the sample size. This

subsection is also a popular dataset used in other papers on Bayesian modelling

(e.g. Horvarth, 2011; Ley & Steel, 2007). Jones and Schneider (2006) use a

subset of the Sala-i-Martin dataset, in a similar fashion to Fernandez et al. (2001),

but take only the significant variables.

We think the SDM dataset is superior to the Fernandez et al. and Jones and

Schneider dataset. Excluding plausible control variables, which model averaging

has previously found little support for, would undermine our goal of testing

national IQ against all plausible variables and theories. The rejection of the

excluded variables could represent a false negative, which may perform well

under different specifications.

The second dataset we use is the dataset used by Bruns and Ioannidis

(2020), which is itself intended to recreate a dataset similar to SDM. Hereafter the

modified Bruns and Ioannidis data is labelled the BI dataset. In their paper, Bruns

and Ioannidis test the stability of predictors across different time frames. This

restricts their variables to those that are available regularly for different periods.

This property of the dataset makes it easy for us to test the effect of national IQ

in different time periods from 1960 to 2010: 20-year time periods starting in years

divisible by 5. When we use this dataset, all control variables except national IQ

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

are taken from the starting year of the growth period. This is a substantial

limitation of our method which is discussed further in the Causality section.

Furthermore, Bruns and Ioannidis take many of their variables from recent

editions of the Penn World Tables. This allows us to easily perform a sensitivity

test using different editions of the Penn World Tables. Bruns and Ioannidis

attempt to further remove any endogeneity by removing variables describing

countries in the duration of the growth period, such as a socialist dummy, average

inflation rate, and proportion of time the country spends at war. Despite this, Bruns

and Ioannidis still employ a variable measuring growth in the terms of trade (that

is change in exports divided by imports), which we remove in our dataset.

Ioannidis and Bruns use time-invariant geographic variables used by SDM,

but which originally come from the Gallup et al. (2001) geography dataset.

Because the geographic variables contain the same missing variables, it would

not reduce our sample size to include additional ones. As such, to test national

IQ against a larger body of variables and thus plausible theories, we include

additional time-invariant geographic variables from the Gallup et al. (2001)

geography dataset.

A full list of all the variables employed in these datasets is given in Tables 11

and 12 of the Appendix.

Additional explanatory variables

To impose discipline on the estimation methodology, we have not added any

additional control variables to the SDM dataset, except NIQ. But we also do this

because these datasets have been optimized to maximize the sample size. If we

carelessly add additional variables, the efficiency of our Bayesian model

averaging could be severely affected. With Rindermann’s national IQ, the SDM

dataset has 82 observations and the BI dataset has 43 observations. These are

not sample sizes that should be decreased further unnecessarily. Thus to test the

effect of additional variables we use these as a single and separate test.

Additional variables included are given in Table 4.

The first type of variables we add are psychological ones. These are social

trust, time preference, and kinship intensity. Social trust is measured by how a

nation’s population responds to the following question from the World Values

Survey: “Generally speaking would you say that most people can be trusted or

that you can't be too careful in dealing with people?” Many economists have found

a positive relationship between a nation’s level of social trust and their GDP, see

Bjørnskov (2017) for a review of this literature. A trusting, or perhaps a

trustworthy, society can reduce the transaction costs of business and reduces the

risk of theft and rent-seeking in business (Algan & Cahuc, 2010, 2013). Intelligent

individuals (e.g. Carl & Billari, 2014) and intelligent societies (Rindermann, 2008)

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

tend to be more trusting. This suggests social trust might mediate national IQ’s

effect or that it might confound IQ’s effect on economic growth. Roth (2009) found

that with national fixed effects, social trust had a negative association with

economic growth. Later, Carl (2014) found social trust no longer predicted GDP

when IQ was controlled for. We replicate this test by including social trust as a

variable.

Table 4. Extra variables.

Variable name

Source and description

Social trust

Measure of self-reported social trust; from Carl (2014) and derived

from the World Values Survey.

Time preference

Time preference measure derived from surveys and correlated

variables such as credit risk; from Rieger et al. (2021).

Kinship Intensity

Index

Measure of ‘kinship intensity’ from Schulz et al. (2019)

Economic

freedom

Fraser Institute’s Economic Freedom of the World index (Murphy

& Lawson, 2018)

UV radiation

Taken from Andersen et al. (2021)

Time preference or patience refers to how individuals value consumption

across different time periods. Less intelligent individuals tend to have a larger

time preference (Mischel et al., 1972, 1989; Watts et al., 2018), preferring smaller

rewards today over larger ones in the future. At the level of nations, time

preference has been estimated using the Global Preference Survey (Falk et al.,

2018) in which individuals are asked about how they would make trade-offs

between cash prizes given at different points in time. Time preference may

influence economic growth through higher savings and investment (Jones, 2010,

2012). Unsurprisingly, national IQ correlates with savings rates (Jones, 2010) and

time preference measures (Kirkegaard & Karlin, 2020) at the national level. When

Karlin and Kirkegaard tested national IQ and time preference as predictors of

national welfare, they found time preference was statistically insignificant when

IQ was included. Like social trust, time preference represents a potential mediator

or confound of national IQ so we include it as an additional variable.

Joseph Henrich (2020) has suggested that marriage patterns have played a

key role in determining the prosperity of the West. Rules created by the Catholic

Church discouraged Europeans from marrying their relatives, and therefore

exposure to the Catholic Church is associated with non-cognitive psychological

differences today (Schulz et al., 2019). This reduction of inbreeding is thought to

have reduced the intensity of kin-based institutions and allowed for individualism

which has driven innovation and capitalism. Henrich (2020) found individualism

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

to be associated with patents per capita. We employ the Kinship Intensity Index,

which measures the presence of cousin-marriage preferences, polygamy, co-

residence of extended families, clan organization, and community endogamy

(Schulz et al., 2019). As a novel variable to indicate psychological differences, it

is a natural rival to national IQ.

As mentioned in Section 2, smarter individuals tend to support a free market

(Carl, 2014a, 2015; Kirkegaard et al., 2017). Smarter nations tend to have a freer

economy which may mediate national IQ’s effect on GDP (Christainsen, 2020;

Rindermann & Thompson, 2011). The SDM dataset includes an old “Degree of

Capitalism” measure (Hall & Jones, 1999). However, the variable is not very

sophisticated (Christainsen, 2020) and is ordinal rather than continuous. To

improve upon this we employ the Economic Freedom Index created by the Fraser

Institute (Murphy & Lawson, 2018). In our test of additional variables, we add this

to the BI dataset and replace the Degree of Capitalism variable with the Economic

Freedom Index in the SDM dataset.

National variations in cognitive ability have often been explained as an

evolutionary adaptation to the challenges of cold winters (Frost, 2019; Lynn,

1987; Rushton, 1995). The theory fits the data with groups further from the

equator having larger cranial capacity (Kanazawa, 2008) and skin color having

the strongest relationship, of all variables, with national IQ (Templer & Arikawa,

2006). We discuss Cold Winters theory in greater depth in the Causality section.

If the theory is wrong, national IQ might only predict growth due to geographic

confounding. After all, many economists have found strong relationships

between absolute latitude (distance from the equator) and GDP per capita (e.g.

Nordhaus, 2006). Absolute latitude is already in the SDM and BI datasets, but to

better test IQ against possible climatic confounding, we also include UV radiation

as one of our extra variables.

Section 5: Results

Before using any of our robustness tests or additional variables, we ran

Bayesian model averaging with the BI and SDM datasets with Rindermann’s

national IQ scores. As expected, national IQ performs extremely well with a

posterior inclusion probability of 1. Its absolute coefficient is the largest of all

variables tested, not including log GDP per capita in 1960 because it is used as

a fixed regressor in all regressions. The SDM dataset has the larger number of

explanatory variables (81) and observations (69), whereas our BI dataset has 63

variables and 63 observations.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

In the SDM dataset (Figure 2), national IQ has an absolute coefficient of 1.4,

implying a one standard deviation increase in national IQ is associated with a 1.4

percentage-point increase in the growth rate. In the BI dataset, national IQ has a

somewhat smaller coefficient of 1.2. Without standardizing our independent

variables, one national IQ point is associated with 0.09 percentage point higher

growth per year with the BI data and 0.11 percentage points in the SDM dataset.

If we interpret our results through the framework of an exogenous model, each

additional national IQ point is associated with a 6.5% larger GDP per capita in the

BI data and 7.8% in the SDM data. This compares with a previous estimate of

6.4% from Jones and Schneider (2006). Details of these calculations and

discussion of different growth models (exogenous, endogenous and the Nelson-

Phelps technology diffusion model) can be found in the online supplement.

Some other variables do perform well, with posterior inclusion probabilities

higher than their prior probabilities. In the SDM dataset there are five other

variables that pass this test: fraction of GDP in mining, primary school enrolment,

fraction of population living in the tropics, fraction of the country in the tropics,

trade openness. However, only the first three of these variables had posterior

inclusion probability greater than 0.5 indicating they are more likely to be included

in the best model than not. Moreover even of these variables the highest

coefficient is less than half of national IQ’s coefficient indicating IQ is substantially

more important than even best performing competitors.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 2. SDM data main results

The BI dataset has four other variables with higher PIPs than priors (Figure 3).

These are life expectancy, exports of primary goods as a percentage of GDP,

average distance to rivers, and average distance to the coast. The failure of

primary school enrolment to have a higher PIP than prior in the BI dataset should

make us skeptical of its robustness despite its good performance in the SDM

dataset.

An important difference between our results and Sala-i-Martin et al.'s (2004)

results is that their East Asian dummy had the highest PIP, and fraction Confucian

was the 9th best variable. These variables do not perform well in our results. This

is likely due to the fact East Asian countries have high national IQs, and Sala-i-

Martin et al.’s variables were mostly proxies for high IQ. Likewise the Sub-

Saharan African dummy variable was the 10th best variable for Sala-i-Martin et al.

(2004). This suggests much of the regional effects found in prior studies might be

spurious due to being confounded with national IQ.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 3. BI data main results.

Results with additional variables

To test whether national IQ’s apparent success is due to omitted variable

bias, we ran the same Bayesian model averaging but with popular variables that

are related to or confounded with intelligence as represented in Table 4. National

IQ’s posterior inclusion probability falls from 1 to 0.96 in both datasets. The results

have barely changed indicating that national IQ’s predictive powers cannot be

explained by possible confounds.

UV radiation, time preference, social trust, and economic freedom did not

have a higher PIP than prior (Figures 4 & 5). This replicates the findings of Carl

(2014) and Kirkegaard and Karlin (2020) who respectively tested whether social

trust and time preference could explain national IQ’s relationship with economic

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

growth. The failure of UV radiation and latitude to robustly predict economic

growth suggests that IQ’s relationship with growth is not due to spatial

autocorrelation. The Fraser Institute’s Economic Freedom Index’s PIP is lower

than its prior in both datasets. This contrasts with Christainsen (2020) and Weede

and Kämpf (2002), who find that economic freedom is statistically significant in

their growth regressions which use national IQ. Nonetheless, this result is not

unexpected because degree of capitalism has shown inconsistent results in

studies using rigorous model averaging methods. For example, Sala-i-Martin

(1997) found that the variable was robust whilst Sala-i-Martin et al. (2004) found

the degree of capitalism to have the third lowest PIP out of 67 tested variables.

Note: Dashed line denotes the prior inclusion probability

Figure 4. SDM results with extra variables.

Only one of our extra variables, the Kinship Intensity Index, has a higher

posterior than prior probability. Moreover this result only occurs in the BI dataset.

It has a posterior inclusion probability of 0.20 which is not large but double the

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

prior inclusion probability. Given that we have added four extra variables into two

different datasets, we should probably expect at least one of the variables to

perform well once by chance. Nonetheless, the result is consistent with Joseph

Henrich’s hypothesis that societies with lower kinship intensity tend to become

more prosperous (Henrich, 2020).

Note: Dashed line denotes the prior inclusion probability

Figure 5. BI results with extra variables

Smart fraction theory

So far we have only considered the effect of average national IQ on economic

growth, however national populations do not just differ in the means but in their

entire distributions. An important question is whether the smartest fraction of a

country’s IQ distribution plays a more important role than the mean.

This is a plausible theory on many grounds. Psychologists such as Terman

(1916) and Jensen (1980, p. 114) have argued that because exceptional

achievement is created by the brightest, they will have the largest effect on

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

societies. Many previous studies looking at economic development and economic

growth have found larger effects of the 95th percentile IQ than the 5th percentile

or the mean level (Rindermann, 2012, 2018; Rindermann, Kodila-Tedika &

Christainsen, 2015).

A particular problem with this research is that the mean IQ and IQ of the top

5% correlate highly, even at r = .98 in Rindermann, Kodila-Tedika and

Christainsen (2015). With such multicollinearity it is difficult to differentiate the

effect of the average IQ and that of the cognitive elite. Kirkegaard (forthcoming)

responds to this issue by regressing the top 5% IQ on the mean IQ and taking the

residuals as a measure of how smart the brightest in the nation are compared to

what one would expect based on the mean ability. In regressions both the mean

and residualized elite IQs were statistically significant predictors of national

welfare across many variables.

Note: Dashed line denotes the prior inclusion probability

Figure 6. Smart fraction results using BI data.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

We test Kirkegaard’s smart fraction measure in the SDM and BI datasets

(Figures 6 & 7). In this test we use Rindermann’s (2018) student assessment IQ

scores rather than his combined psychometric and student assessment scores

because the measurement of the nation’s smartest 5% of students comes only

from the results of student assessments.

Note: Dashed line denotes the prior inclusion probability

Figure 7. Smart fraction results using SDM data.

In general the Smart Fraction variable performed poorly, with a PIP of 0.18

in the BI dataset and 0.08 in the SDM dataset. The result for the BI dataset was

marginally above the prior inclusion probability of 0.14. Moreover, the estimated

coefficient in the BI dataset was 0.03, implying a standard deviation increase in

the elite IQ, over and above what is expected from the mean IQ, only increases

economic growth by 0.03% a year. Despite performing well in a few regression

models of the general socioeconomic factor for nations (Kirkegaard and Carl, in

review), we find smart fraction theory has little explanatory power for economic

growth. Kirkegaard and Carl (in review) note that measurement error is largest in

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

the tails of a distribution, suggesting our measure of the smart fraction may be

weak. Use of residuals to test smart fraction theory should be re-evaluated in the

future when further samples allow better estimations.

A more general problem for smart fraction theory is that it is not clear how

the smart fraction should be defined. In the work of Rindermann (2018) the smart

fraction is defined as how smart the smartest 5% of a country is. However, in one

formulation of smart fraction theory (La Griffe du Leon, 2002) the variable of

concern is the fraction of the population of a ‘smart’ IQ of 105 necessary for

complex work. Our residualized approach compounds this problem. If the IQ of

the top 5% is higher than what would be expected of the mean IQ, then our

measure also tells us that the median IQ is lower than the mean. In other words,

our residualized approach to measuring Rindermann’s conception of the smart

fraction may be negatively related to La Griffe du Lion’s measure of the smart

fraction. An alternative operationalization of smart fraction theory may be

necessary.

Alternate national IQs

So far we have used Rindermann’s (2018) national IQs as our standard. This

was because by combining student assessment scores with national IQs we

attain data on more countries and have larger samples for the countries included.

Here we try different national IQs. Given the sensitivity of Bayesian model

averaging it is possible that even slight changes in national IQ scores due to

measurement issues could create substantial differences in results.

In the SDM dataset, the posterior inclusion probability is greater than the prior

probability for every national IQ score employed, with two thirds performing

extremely well with a PIP equal to one (Figure 8).

There is no obvious difference in the PIPs between student assessment

scores such as Hanushek and Woessmann’s (2012) and the World Bank’s

Harmonised Learning Outcomes, and the psychometric IQ scores such as Lynn

and Vanhannen’s (2002, 2012). Nonetheless the coefficients for the student

assessment scores tend to be lower, although the situation is reversed in David

Becker’s data with student assessments outperforming psychometric IQ. It should

be noted that the observations available were different for each IQ score meaning

the coefficients are not perfectly comparable. In the BI dataset, national IQ’s

coefficients and PIPs are smaller, but still robust (Figure 9). This should be

expected given that even with the use of Rindermann’s psychometric and SAS

national IQs, the sample size is only 43.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 8. Results of different NIQs using SDM data.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

Note: Dashed line denotes the prior inclusion probability

Figure 9. Results of different NIQs using BI data

MANKIND QUARTERLY 2022 63.1 9-78

The IQ data with the largest coefficient on growth is also the oldest — Lynn

and Vanhanen’s (2002) dataset. This is perhaps particularly surprising given the

scale of criticism this data has been given methodologically. In particular critics

suggested the scores could be biased against Sub-Saharan African countries

because their scores were so low. However, in our results Sub-Saharan Africa

has a negative coefficient on growth. The complete results are available in the

online supplement. If Sub-Saharan Africa was not inherently bad for growth and

Richard Lynn’s national IQ scores underestimated the human capital of Sub-

Saharan Africans, then the Sub-Saharan African binary variable should have a

positive coefficient. However, the estimated coefficient is negative. These results

support Garett Jones’s (2012) comments on Lynn’s national IQ scores “If national

average IQ estimates are indeed ’biased’, they appear to be biased in favor of

productivity growth.”

Penn World Tables

Ciccone and Jarociński (2010) found that using different editions of the Penn

World Tables leads to radically different results, suggesting the relevant economic

data is simply not good enough to reliably find the best explanatory variables.

Using national IQ as an explanatory variable with the BI dataset we replicate their

test. The BI dataset is used because a substantial number of its variables come

from recent editions of the Penn World Table allowing us to easily use different

versions of the same variables.

Across the five most recent editions of the Penn World Tables (PWT) we find

remarkably similar results. National IQ has a PIP of 1 regardless of what Penn

World Table is used. National IQ’s coefficient ranges from 1.1 to 1.3 (Figures 10

& 11). The coefficient is typically larger in older versions of the Penn World

Tables. This is consistent with prior research finding that older editions of the

Penn World Tables tend to provide more accurate measures of GDP, with

stronger correlations to proxies such as light intensity (Johnson et al., 2013;

Pinkovskiy & Sala-i-Martin, 2016). This may imply that better measures of GDP

would further increase our estimate of national IQ’s effect.

The consistency of national IQ in the face of measurement error and different

observations from different editions of the Penn World Tables further supports the

idea that national IQ is an extremely robust predictor of economic growth.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 10. Results of different PWTs using BI data.

Figure 11. National IQ’s coefficient on economic growth using different PWT

data.

MANKIND QUARTERLY 2022 63.1 9-78

Fixed regressors

Employing regional dummies as fixed regressors has been found to reduce

the sensitivity of Bayesian model averaging to measurement error in the Penn

World Tables (Rockey & Temple, 2016). Many explanatory variables show

greater variation across regions than within them. This means that regressions

with or without different regional dummies can find radically different results for

many explanatory variables. Because of this, Rockey and Temple (2016)

recommend using regional dummy variables alongside the logarithm of GDP per

capita as fixed regressors. This may be particularly important to diminish omitted

variable bias for national IQ given there are large regional variations in IQ scores.

We reran our Bayesian model averaging with regional dummies in every

regression. We also tried the fixed regressors utilized by Sala-i-Martin (1997) and

Jones and Schneider (2006). These are primary school enrolment in 1960, life

expectancy in 1960, and the logarithm of GDP per capita in 1960. We do this not

only to allow our results to be comparable with Jones and Schneider’s, but also

to stress test national IQ in case it is confounded with life expectancy or

education, showing co-dependency with these variables. After all, better health

and education may increase national intelligence.

In all variations of fixed regressors, national IQ still has a posterior inclusion

probability of 1 (Figures 12 & 13). Although national IQ’s coefficient is larger when

regional dummies are used in the BI dataset, it is slightly smaller under this

scenario in the SDM dataset. For the use of Sala-i-Martin’s fixed regressors, the

situation is reversed with a larger coefficient in the SDM dataset and a lower one

in the BI dataset. We can conclude that fixed regressors do not substantially alter

our results. Moreover, the effect of IQ is not due to regional confounding.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability. LGDPpc fixed regressors

include log GDP per capita. Continental fixed regressors include log GDP per capita and

regional dummies. SDM fixed regressors include life expectancy, primary school

enrolment and log GDP per capita.

Figure 12. Results of different fixed regressors using BI data.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability. lGDPpc fixed regressors include

log GDP per capita. Continental fixed regressors include log GDP per capita and regional

dummies. SDM fixed regressors include life expectancy, primary school enrolment, and

log GDP per capita.

Figure 13. Results of different fixed regressors using SDM data.

MANKIND QUARTERLY 2022 63.1 9-78

Different priors

In Bayesian model averaging we had to specify priors on model probabilities

and on the variance of coefficients — the ‘g prior’. This means approaching the

data with different prior expectations can alter the posterior conclusions. If IQ

really is robustly associated with economic growth, it should perform well under

all reasonable priors. So far we have only used one set of priors. We created

model probabilities based on the number of variables they included, assuming a

fixed probability of the inclusion of any variable such that the expected model size

was 7. We let the g prior be equal to the number of observations, calibrating our

certainty in coefficient sizes to the amount of information we could supply with our

Bayesian model averaging.

To test whether our results are robust to different priors, we employ all

possible combinations of model priors and g-priors within the BMS package. In

our results presented in Figures 14 and 15, the first part of the legend indicates

what model priors were used, Random meaning model priors drawn from a beta

distribution, Uniform meaning all models have the same prior, and Fixed which

we have already employed. The second part of the legend provides the acronym

for the g-prior used. The names, acronyms and brief explanations for these g-

priors are provided in the Methodology section. Further details of these priors can

be found in Feldkircher and Zeugner (2009) and Zeugner and Feldkircher (2015).

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 14. Results of different priors using BI data.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 15. Results of different priors using SDM data.

MANKIND QUARTERLY 2022 63.1 9-78

Subsampling

In prior literature on the sensitivity of BMA in growth modelling, different data

has been found to substantially change the results due to both measurement error

and different sampling resulting from different datasets (Rockey & Temple, 2016).

So far we have only studied whether national IQ is robust to using different Penn

World Tables data and different national IQs, but we have not studied the issue

of sampling in isolation. With sampling bias our results so far may be inaccurate,

and even with random sampling our results may be coincidental due to outliers.

To pursue this issue further we use Bayesian model averaging again by

resampling our data with two methods: bootstrapping, and jackknife resampling.

Thus we are performing observation sampling and then model sampling

sequentially. Although resampling and weighting observations has been

recommended to improve the robustness of Bayesian model averaging with

economic growth (Doppelhofer & Weeks, 2011), no one has yet tried it. To

perform this resampling most effectively, we use the SDM dataset of controls

because its larger sample size allows us to present the results of a wider range

of possible combinations of observations.

In bootstrapping we randomly resample our observations. In this process the

same observation may be picked more than once for the new sample. We

resample our observations 1,000 times. We then perform Bayesian model

averaging upon each resample. Our initial attempt to do this ran into dummy

variable traps that had homogenous values for certain variables making the

regressions impossible. To solve this problem we sorted the variables by the

number of unique values they had and deleted the variables with fewest unique

values, one by one, until running all our regressions was feasible. This meant we

had to reduce the number of variables from 68 to 43. Although we could have

kept all variables and ignored failed regressions, that would be limiting the

resampling, ‘baking in’ sampling bias.

We use the box plot shown in Figure 16 to present the distribution of the PIPs

and coefficients estimated from this method.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 16. Bootstrap sampling results using SDM data.

MANKIND QUARTERLY 2022 63.1 9-78

Of our tested variables, GDP per capita in 1960 and national IQ had the

highest median PIP of 1. Primary school enrollment in 1960 was a close

competitor with a median PIP of 0.99. However, national IQ performed the best

with the first quartile of its PIPs being equal to 0.98, whilst primary education’s

lower quartile PIP was at 0.88. National IQ had the lowest interquartile range of

PIPs at 0.02. The next smallest interquartile range was for primary education at

0.12, six times larger than national IQ’s PIP interquartile range. National IQ’s

median coefficient was the largest for all variables tested at 1.07. primary school

enrolment had a coefficient of 0.69. This was the second largest coefficient, but

it was still only 64% the size of national IQ’s coefficient. Political Rights, a

democracy index with higher values indicating greater levels of democracy

(Barro, 1991), was the worst performing of all the variables with PIPs greater than

priors. It should be noted its coefficient on economic growth was negative,

suggesting the result may have been a fluke. Summary statistics for PIPs and

coefficients of variables with a median PIP higher than the prior inclusion

probability (0.14) are given in Tables 5 and 6.

Notably, PIPs range from at least 0.2 to 1 for all trialed variables. This is

testament to the sensitivity of Bayesian model averaging of economic growth to

sampling, suggesting that reporting one or a few BMAs with the same data or

variables is not sufficient to be confident in one’s results. Nonetheless, the fact

national IQ is the best performing in terms of its median coefficient, median PIPs,

first quartile PIP, and interquartile range of PIP in the face of this sensitivity,

suggests it is the most robust predictor of economic growth.

Table 5. Summary statistics of posterior inclusion probabilities.

Variable

Min.

1st

quartile

Median

Mean

3rd

quartile

Max.

Inter-

quartile

range

Political rights

0.00

0.05

0.20

0.39

0.79

1.00

0.74

Openness

0.00

0.07

0.24

0.35

0.58

1.00

0.51

Hydrocarbon

deposits

0.01

0.09

0.28

0.40

0.72

1.00

0.63

Investment

price

0.00

0.06

0.45

0.51

0.98

1.00

0.92

% tropical

population

0.01

0.36

0.88

0.69

1.00

0.64

Population

density

0.01

0.10

0.96

0.65

1.00

0.90

Primary

schooling

0.00

0.88

0.99

0.86

1.00

0.12

GDP per capita

1.00

0.00

National IQ

0.02

0.98

1.00

0.90

1.00

0.02

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

Table 6. Summary statistics of variable coefficients.

Variable

Min.

1st

quartile

Median

Mean

3rd

quartile

Max.

Inter-

quartile

range

Political rights

-1.45

-0.35

-0.06

-0.19

-0.01

0.32

0.34

Openness

-2.28

0.01

0.07

0.13

0.20

0.97

0.19

Hydrocarbon

deposits

-0.42

0.01

0.06

0.12

0.21

0.68

0.20

Investment

price

-0.55

-0.30

-0.11

-0.15

0.00

0.53

0.29

% tropical

population

-1.19

-0.54

-0.39

-0.36

-0.12

0.19

0.42

Population

density

-0.39

0.04

0.69

0.60

0.99

2.81

0.95

Primary

schooling

-0.42

0.50

0.69

0.65

0.84

1.67

0.34

GDP per capita

-2.38

-1.71

-1.54

-1.38

-0.36

0.33

National IQ

-1.18

0.82

1.07

0.98

1.24

2.57

0.42

Jackknife resampling, otherwise known as the leave-one-out cross

validation, involves removing each observation separately and then running BMA

on all the resulting subsamples. This method focuses on the effect of each one-

removed observation allowing us to check if there are any influential observations

skewing our estimates of national IQ’s PIP or coefficient. An advantage of this is

that it allows us to keep all variables from the SDM dataset employed without

running into rank deficient models. However, the subsamples are more similar to

the original dataset than the resamples from the bootstrapping method, making

the jackknife a less rigorous check on the effects of resampling.

We find the PIP of national IQ to be 1 in all jackknife samples (Figure 17),

meaning no individual observations are skewing the inclusion probability of

national IQ. We find the coefficient of national IQ ranges from 1.3 to 1.4 with a

median value of 1.4. Regardless of which observations are removed we find large

robust coefficients for national IQ.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 17. Jackknife sampling results using SDM data.

MANKIND QUARTERLY 2022 63.1 9-78

Time periods

As the BI dataset is set up for many different time periods, we re-ran our

analysis on 20-year periods from 1960 to 2010. All explanatory variables, except

national IQ and geographic variables, were given for the starting year of the

growth period studied. National IQ had a higher PIP than prior in every single time

period. Of the seven time periods studied we only found one, 1975-1995, where

national IQ had a posterior inclusion probability less than 0.50. This indicates that

national IQ consistently predicts economic growth, with its high performance not

being the coincidental result of any particular time period. It is the best performing

tested variable in all but two time periods, 1975-1995 and 1980-2000. In these

time periods fertility has a higher PIP than national IQ, with a negative coefficient.

This is surprising given that fertility has a lower PIP than prior in three of our

subperiods and in our main 1960-2010 period. We suggest this may be

coincidental due to fertility’s strong negative correlation with national IQ

(Meisenberg, 2009).

Bruns and Ioannidis (2020) were the first to test Bayesian model averaging

across different time periods. They found no tested variable was robust across all

time periods. They suggested this supported the view of ‘robust ambiguity’, that

statistical modeling of economic growth is unable to identify strong explanatory

variables with the exception of GDP per capita in the starting year. Their

conclusion was in the title of their paper Different Time Different Answer. Our

results contradict Bruns and Ioannidis because national IQ is supported in every

one of our time periods. It is not the case that Bayesian model averaging cannot

identify strong explanatory variables. Rather, economists have failed to use the

variable that matters the most.

In the shorter time periods national IQ’s coefficients are between 0.4 and 1.4,

typically smaller than our estimate in the SDM dataset of 1.4 for the 1960-2010

time period (Figures 18 and 19). GDP per capita’s coefficient is also smaller and

more variable, ranging from around -0.7 to -2.5 compared to our SDM result of -

1.4.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 18. NIQ’s PIPs and coefficients under different time periods using BI data.

MANKIND QUARTERLY 2022 63.1 9-78

Note: Dashed line denotes the prior inclusion probability

Figure 19. Results under different time periods using BI Data.

MANKIND QUARTERLY 2022 63.1 9-78

Section 6: Causality

Whilst we have found national IQ to be extremely robust in its relationship to

economic growth, the causality of this relationship can be questioned. This

problem has two parts: to what extent can economic growth cause increase in

national IQ scores, and to what extent do changes in national IQ scores represent

real changes in ‘intelligence’ with the same causal effect on GDP.

The scores from psychometric tests are compiled from many different time

periods mainly in the second half of the 20th century, and most of the student

assessment scores are even more recent. If there is much reverse causality from

growth to test scores, this could explain national IQ’s strong relationship with

growth.

Large increases in IQ scores in the 20th century, known as the Flynn effect,

support the possibility of reverse causality. For example, IQ scores in East Asia

have risen rapidly (e.g. te Nijenhuis et al., 2012). The national IQ scores we use

adjust for the Flynn effect by assuming it is the same in all countries, but if Flynn

effects are heterogeneous, our estimated coefficients could be upwards biased

due to reverse causality. On the other hand, if Flynn effect changes in national IQ

are in some way ‘hollow’ and have a smaller effect on GDP, then this could put a

downwards bias on estimates.

An important step in disentangling the problem of causality was the study of

Rindermann and Becker (2018) which found significant correlations between

some lags of the Flynn effects in countries and the rate of economic growth.

However, the paper only studied 27 countries and various biases could be driving

the results. For example, the Flynn effect and economic growth could be

confounded by a third factor, as national level fixed effects were not employed. If

the Flynn effect and economic growth move in parallel, then even with lags it may

be difficult to identify which factor is causing the other. This is because lagged

variables do not always avoid ‘simultaneity bias’ (Reed, 2015). Moreover, their

reported correlations do not provide us with an easily interpretable effect of Flynn

effects on economic growth because they use rates of change in national IQs to

predict economic growth with time periods of differing length for different

observations. Furthermore, it is not clear from the results that it is not genetic

changes rather than Flynn effects that are associated with economic growth.

A general problem for arguing that Flynn effects drive growth is the failure of

increases in education to predict growth in fixed effects analysis (e.g. Hamilton &

Monteagudo, 1998; Pritchett, 2001). A meta-analysis of quasi-experimental

studies suggests years in education do increase IQ test scores (Ritchie & Tucker-

Drob, 2018) meaning that if increases in IQ scores affect growth, so should

education. We suggest that Flynn effects may be a hollow ‘inflation’ in test scores

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

that do not affect growth. After all, whilst education increases test scores, it does

not appear to affect general intelligence (Ritchie et al., 2015) or processing speed

(Ritchie et al., 2013). General intelligence g refers to the latent factor which IQ

tests try to measure (Spearman, 1904). This interpretation of Flynn effects on

economic growth also concords with the Jensen effect (Rushton, 1998) whereby

correlations between outcomes and IQ are strongest on more ‘g-loaded’ IQ tests

which are also more heritable. Many studies also support this interpretation by

finding Flynn effects appear to only exist on specific IQ tests rather than on the

general factor of intelligence (e.g. Jensen, 1998; Must et al., 2003; Rushton,

1998; te Nijenhuis & van der Flier, 2013; Woodley & Madison, 2013). However,

some scientists have found Flynn effects represent a Jensen effect on fluid rather

than crystallized measures of IQ (Colom et al., 2001). An important caveat to this

line of thought is that whilst the gains from education may be hollow, other

hypothetical causes of IQ increases, such as nutrition, may have “real” effects on

intelligence.

An approach to remove possible reverse causality is to create national IQ

scores that were created before the period of economic growth studied or very

close to it, since future economic growth cannot plausibly alter past intelligence

scores. This approach has been used in a few studies such as Christainsen

(2020), Rindermann (2018) and Hanushek and Woessmann (2015). These

studies find past test scores have the same coefficient on future growth as

contemporary test scores have on past results. However, there are some

limitations to this approach. The sample sizes are often much smaller.

Christainsen (2020) had the largest sample size of 45 countries using this method

whilst the other papers have often had substantially smaller samples. The small

sample sizes may reduce the accuracy of modelling and possibly introduce bias

and range restriction from missing values.

Intelligence may have a causal effect despite Flynn effects if national levels

of intelligence are path dependent, which could be caused by genetics. Under

such a theory societies that start more intelligent grow more and continue to

perform more highly in measures of human capital, whether or not the increases

in human capital measures are actually determining economic growth. This path

dependence would allow us to estimate the effect of intelligence on economic

growth, regardless of when intelligence is measured. For example, although

Meisenberg and Woodley (2013) found the student assessment scores of low-IQ

countries were catching up with high-IQ countries between 1995 and 2009, the

rank order of different regions remained the same.

Strong path dependence in human capital has been shown by Baten and Juif

(2014). They use age heaping measures of numeracy from 1820 and compare

them with student assessment scores from the second half of the twentieth

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

century, as used by Hanushek and Woessmann (2012). Innumerate people are

less likely to be able to calculate or remember their age so they typically give

rounded figures for their age, leading to ages on gravestones, censuses, and

documents ‘heaping’ at ages that are multiples of five or ten. From the degree of

‘heaping’ an index for numeracy can be created. When Baten and Juif (2014)

found the age heaping scores had a statistically significant relation with more

recent student assessment scores, they did not report a simple correlation.

Kirkegaard (2015) found Lynn’s 2012 IQ scores had a correlation between 0.52

with an age heaping index in 1890 and 0.85 with an age heaping index from 1800.

This approach has a few limitations because the ceiling effect of numeracy is very

strong in later cohorts (i.e., there is almost no heaping), and because the set of

included countries varies across cohorts.

Path dependence can be found more generally in the ‘deep roots’ literature

on economic growth. Comin, Easterly and Gong (2010) in their paper Was the

Wealth of Nations Determined in 1000 BC find a strong relationship between

development throughout history, such as a correlation of r = .71 between

‘migration adjusted technology level’ in 1500 AD and log per capita income in

2002. Similar results have been found by Putterman and Weil (2010) and

Spolaore and Wacziarg (2013) who diplomatically state that “The evidence

suggests that economic development is affected by traits that have been

transmitted across generations over the very long run.”

Given strong path dependence in human capital and economic growth, it

should be no surprise that contemporary IQ scores can predict past prosperity far

further back than the twentieth century. For example, Lynn and Vanhanen (2012)

find national IQ has a Spearman correlation with GDP per capita greater than .70

for 2003, 1870, and 1700.

Strong path dependence in human capital and GDP and correlations

between them support the idea that human capital has played a large role in

determining prosperity throughout history, but it is still vulnerable to collider bias

with a confounding variable determining both GDP and human capital. We

suggest genetic differences between populations are what determines human

capital and thus GDP. Despite the controversy surrounding the issue of race and

intelligence, when intelligence researchers are surveyed anonymously, 85%

believe genetics plays a role in the Black-White IQ gap in the USA (Rindermann

et al., 2020). Although we do not have the space in this paper to discuss racial

differences in intelligence within America, if genes affect intelligence differences

within the United States it is likely that they have some effect across the globe.

Genetic differences could also explain variation in the Flynn effect, with

genetically smarter populations being faster to learn (eg. te Nijenhuis et al., 2012).

For example, African Americans score lower than East Asians in IQ tests in

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

America despite similar environments. As expected, East Asian countries have

seen large Flynn effects whilst sub-Saharan countries appear to have

experienced none (Wicherts et al., 2010c).

Various evidence suggests the national variation in intelligence may be

genetic in origin. Piffer (2015, 2019, 2020, 2021) finds educational polygenic

scores, trained on predicting educational attainment in white populations,

correlate with national IQs at r > .90. Nevertheless, there are concerns about the

transracial validity of these polygenic scores. Economists have found genetic

distance between countries is associated with various outcomes including

economic growth (eg. Saha & Mishra, 2020). Whilst this literature uses genetic

distance as proxy for cultural distance, assuming that ideas and technology

diffuse faster across similar groups, it also has obvious implications for the

possibility that genetic variation in intelligence could mediate differences in

economic growth. Moreover, genetic distance correlates with national IQ (Becker

& Rindermann, 2016; Kodila-Tedika & Asongu, 2016). IQs correlate with cranial

size (r = .26; Pietschnig et al., 2015), and national IQs have been found to

correlate with cranial size in a sample of ten countries (r = .91; Rushton, 2010),

supporting a biological origin for national differences in intelligence. Furthermore,

cranial capacity is substantially genetic with a heritability of around 90% in early

adulthood (Batouli et al., 2014).

Quasi-experimental evidence from variation in wealth and environment also

suggests genetics may be the cause of variation in national IQ. For example,

countries that are or become rich due to oil wealth attain no higher IQs than their

poorer genetically similar neighbors (Christainsen, 2013; Jones & Schneider,

2009). Christainsen (2013) set out to estimate environmental effects through

regressing national IQ on measures of environment such as education and

malnutrition. He found that regional dummy variables dominated the regression

relative to environmental variables, suggesting ancestry has a much larger effect

on IQ than socioeconomic environment.

A popular explanation for why nations and peoples differ in their intelligence

is Cold Winters Theory (Frost, 2019; Lynn, 1987; Rushton, 1995). This theory

supposes that the challenges of cold winters and the necessary preparation for

the seasons is cognitively demanding such that humans are selected for

intelligence further from the equator and in colder environments. The theory has

repeatedly been rediscovered by scholars throughout history such as Alfred

Russell Wallace (1864), Arthur Schopenhauer (2000, p. 159) and Sa’id al-

Andalusi who was born in the year 1029 (Lewis & Lewis 1990, pp. 47-48). The

theory fits the data as groups with higher intelligence and higher cranial capacity

tend to have evolved in colder environments further from the equator (Kanazawa,

2008). The strongest cold winter correlate of national IQ appears to be skin

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

reflectiveness suggesting UV radiation may best capture the cold winter effect

(Templer & Arikawa, 2006). Furthermore, behavioral ecologists have

independently found the same pattern within non-human primates (Navarrete et

al., 2016), birds (e.g. Roth et al., 2010; Sol et al., 2010) and other species (Gillooly

& McCoy, 2014; Jiang et al., 2015), giving the theory parsimony. In particular, the

evidence from birds shows that the relationship between absolute latitude or

winter temperature to brain size is only present in non-migrating birds, a

prediction of the cold winters theory that is difficult to explain otherwise.

To test whether genetic or path-dependent variation in intelligence causes

growth, rather than the reverse, we use instrumental variable estimation.

Appropriate instrumental variables are ones that should influence national

intelligence but have no other association with economic growth, allowing us to

isolate the direct effect of national IQ on economic growth. In the first stage the

instrumental variables model national IQ and predicted national IQs are taken. In

the second stage, growth regressions are run using predicted national IQs rather

than actual IQs. The predicted values of national IQ represent the effect of the

instrumental variables directly on national IQ and any possible indirect effect

through economic growth. Crucially, however, the predicted national IQs are

unaffected by exogenous changes in GDP during the growth period studied.

We employ two further statistical tests in this approach. Firstly we use the

Wu-Hausman endogeneity test. This is a test of whether the OLS and IV

coefficient estimates significantly differ, indicating the existence of endogeneity in

the OLS regression model. Because we expect our IV estimates to identify a

causal estimate of the effect of prior cognitive ability on economic growth, a

significantly different OLS estimate would indicate that endogeneity biases

estimates of IQ’s effect on economic growth. Furthermore, we employ the Weak

Instruments Test. This is an F test comparing the second stage regression with

and without the instrument. If the regression does not perform significantly better

with the instrument, this suggests it is weak. To perform instrumental variable

estimation we use the R package ivreg (Fox et al., 2021). Further details of our

statistical tests can be found in the textbook Econometric Analysis (Greene,

1993).

We are not the first to employ instrumental variables for measures of national

cognitive ability. Previously, measures of educational institutions and school

quality (Hanushek & Woessmann, 2015) have been used as instrumental

variables. For example, these instrumental variables include the level of private

school competition, the existence of exit exams, and relative teacher pay. They

found no evidence of reverse causation with the Wu-Hausman endogeneity test.

However, these instrumental variables are taken from times during the growth

period studied. This makes these variables unsuitable for identifying causality

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

because educational institutions may be influenced by changes in growth or

changes in intelligence. In our instrumental variable approach we only use

measures taken before the period of growth to represent the deep root causes of

intelligence. Christainsen (unpublished) has used regional dummies as

instruments for national IQ. Given it is unpublished, we refrain from discussing

the results.

We employ three instrumental variables separately. Firstly we use numeracy

measures created with age heaping, which was collected from a range of samples

by Joerg Baten (2015). We standardize age heaping scores across all time

periods in the 19th century and then take an average. This tests whether human

capital’s relationship with growth is path dependent, allowing us to predict

economic growth with national IQs from any time period. Our second instrumental

variable is cranial capacity. This is an estimate from Beals et al. (1984) which

uses a sample of skulls from 124 ethnic groups and then imputes estimated

cranial capacity for all areas of the planet. From these scores David Becker (2019)

estimated cranial capacities for countries, adjusting for population density and

migration (See column FW in the ‘NAT’ tab of version 1.3.3 of the national IQ

dataset at https://viewoniq.org/?page_id=9). Given the prior support for Cold

Winters theory, we also use UV radiation by country adjusted for migration post-

1500. This measure is obtained from Andersen et al. (2021), who estimate the

average level of UV radiation nations’ ancestors had in 1500. The cranial capacity

and UV measures thus allow us to test whether biological factors determine the

wealth of nations. A correlation matrix of our instrumental variables with

Rindermann’s national IQ scores is provided in Table 7.

For control variables we use log GDP per capita in the starting year. We also

perform the same IV estimation with the control variables from the model with the

highest posterior model probability (15%) in our Bayesian model averaging with

the SDM dataset of controls. These variables are Tropical Population Percent,

Primary School Enrollment in 1960, and Fraction of GDP in Mining. Initial

regressions before employing IV estimation are in Table 8 and our IV estimates

are in Table 9.

Table 7. Correlation matrix of NIQ and instruments

Age

heaping

Ancestry-adjusted UV

radiation

Cranial

capacity

National IQ

0.69

-0.79

0.54

Age heaping

1.00

-0.69

0.39

Ancestry-adjusted UV

radiation

1.00

-0.68

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

Table 8. OLS models of economic growth; * p < 0.05, ** p < 0.01, *** p < 0.001

Variables

Model 1

Model 2

Intercept

-0.66

(0.84)

2.34*

(1.05)

national IQ

0.14***

(0.01)

0.11***

(0.01)

Log GDPpc 1960

-1.06***

(0.14)

-1.31***

(0.13)

Fraction of GDP in mining

5.80***

(1.22)

Primary school enrolment

1.46**

(0.44)

Tropical population percent

-0.93**

(0.21)

Observations

104

Adjusted 

0.63

0.76

Table 9. Instrumental variable models of economic growth; * p < 0.05, ** p < 0.01,

*** p <0.001

Instrumental variable

Cranial capacity

Ancestry-

adjusted

UV radiation

Age heaping

(19th century

numeracy)

Model

Intercept

-0.60

(0.89)

1.78

(2.16)

-0.91

(0.89)

1.25

(1.56)

1.29

(0.96)

3.28*

(1.52)

National IQ

0.13***

(0.02)

0.13***

(0.04)

0.17***

(0.03)

0.14***

(0.03)

0.14***

(0.02)

0.11***

(0.03)

Log GDPpc 1960

-1.02***

(0.23)

-1.36***

(0.21)

-1.31***

(0.17)

-1.27***

(0.19)

-1.39***

(0.16)

Fraction of GDP in

mining

5.95***

(1.32)

6.07***

(1.28)

4.98***

(1.12)

Primary school enrolment

1.28

(0.74)

1.12

(0.57)

1.31

(0.92)

Tropical population %

-0.81

(0.531)

-0.69

(0.41)

-0.86*

(0.37)

Observations

104

101

Weak instruments test

p-value

0.00

0.01

0.00

Wu-Hausman p-value

0.84

0.77

0.02

0.33

0.74

0.98

Our initial estimates find one IQ point increases economic growth by 0.14%

without controls and 0.11% with controls, which was the same estimate our

Bayesian model averaging produced. In our IV estimates all models pass the Wu-

Hausman test except when ancestry-adjusted UV is employed (p < .05) without

control variables in model 3. In model 3, national IQ’s coefficient is 0.17 which is

significantly larger than the OLS estimate of 0.11. If there is reverse causation, it

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

causes us to underestimate the effect of national IQ. In the other instrumental

variable estimations, national IQ’s coefficient is similar to the OLS estimate. A

likely explanation for model 3 is that ancestry-adjusted UV may not be exogenous

because its geographic or environmental confounds may have an independent

effect on growth. This problem might be solved by using control variables such

as Tropical Population. In all regressions our instrumental variables passed the

Weak Instruments test, meaning they have a robust relationship with national IQ.

The failure to reject the null in the Wu-Hausman test is suggestive of there

being little reciprocal causation, but it is not proof. It merely suggests OLS

estimates are close to the true causal effect size, assuming our instruments only

covary with GDP growth due to their effect on intelligence.

Overall our instrumental variable estimates found results consistent with the

OLS and BMA methods. We conclude that reverse causality does not play a

substantial role in distorting estimated coefficients of national IQ on GDP growth.

Section 7: Limitations

The Bayesian model averaging method attains more reliable estimates by

reducing researcher degrees of freedom in choosing explanatory variables.

Nonetheless many researcher degrees of freedom still existed in this study such

as the choice of dataset and the choice of time period. Furthermore, BMA creates

more researcher degrees of freedom by requiring the specification of prior

probabilities. In this study we have tried using a sample of plausible

methodologies as robustness tests to see whether the sensitivity of BMA is

distorting our results. We held our method constant whilst making one change to

our method at a time as a robustness test. It is possible that different combinations

could have produced different results. Nonetheless, given the breadth of tests

performed and the consistency of national IQ to have the highest average PIP

and coefficient in all the tests gives us strong reasons for supposing national IQ

is the best predictor of economic growth.

A more challenging problem would be if our results were systematically

biased. This could occur through sample bias and range restriction. Our data will

not be missing at random as it is likely that poorer countries are less likely to have

data available from the 1960s. This may undermine the power of variables

competing with national IQ. It is likely that the worst socialist and authoritarian

countries would not have sufficient economic statistics to be in our sample. For

example, North Korea was not an observation in any of our models. Were the

statistics available we might have found stronger results for economic and

political freedom indexes, when in fact their posterior inclusion probabilities were

systematically lower than their prior inclusion probabilities. As the world develops,

more data should be released from countries allowing national IQ to be tested

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

with large samples. However, regional levels of prosperity have consistently had

a strong relationship with regional IQs (Fuerst & Kirkegaard, 2016; Lynn et al.,

2018). Given that there are no sample biases for within-country studies, we

should be skeptical to think that sample bias plays any substantial role in the high

performance of national IQ to predict growth.

A related surprising result from Bayesian model averaging is how poorly

popular theories of economic growth perform. As mentioned, popular institutional

measures such as democracy and economic freedom appear to have negligible

or even negative effect sizes. A possible reason for this is that our variables may

be poor quality measures, for example there are various difficulties in measuring

institutions (see Glaeser et al., 2004). Furthermore, many of the variables tested

will change over time meaning economic growth might relate to their average

values over a time period rather than their initial values. This problem should be

reduced in the subperiod analysis we have performed. Yet many popular

variables, such as the Polity 2 democracy index, still perform poorly in the

subperiod analysis.

An important question for judging our control variables is how to interpret

posterior inclusion probabilities. The very low PIPs of rival explanatory variables

might suggest that only national IQ and few other variables matter. Alternatively,

the rival variables might have a small but real effect on economic growth which

we are unable to distinguish due to the low sample size. With only one planet of

nations, of which we only have a limited sample, regression methods only have

sufficient degrees of freedom to distinguish the largest effects on economic

growth. Whilst it is certainly plausible that variables apart from education, IQ and

natural resources do influence growth, their effects may be subtle and more

suited to historical rather than statistical analysis.

Section 8: Conclusion

Of our tested variables, national IQ consistently has the largest coefficient

and the largest posterior inclusion probability, suggesting it is the most robust

predictor of economic growth. This replicates the finding of Jones and Schneider

(2006) showing that national IQ has a high posterior inclusion probability in

Bayesian model averaging. We found that IQ’s effect was robust under many

tests such as the use of different data, different fixed regressors, different time

periods and resampling methods. Prior literature which did not use national IQ

found that growth modeling led to ‘robust ambiguity’ without clear indications of

which variables really matter. Our results contradict robust ambiguity findings

because there is strong consistent support for national IQ. The methods, sample

size and data quality were not insufficient to find powerful causes of the wealth of

nations, rather the best explanatory variable was not being used by economists.

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

We also applied Bayesian model averaging to study more niche issues in the

national IQ literature. We found potentially confounding variables and rival

psychometric variables, but these did not explain away national IQ’s relationship

with economic growth. Using updated measures of smart fractions we found that

only the average IQ of nations was robustly predictive of economic growth, rather

than the intelligence of the ‘elite’ section of the intelligence distribution.

In interpreting our results, we discussed the prior literature suggesting that

human capital differences between nations are deep-rooted and possibly of

biological origin. To support this hypothesis we used 19th-century numeracy

measures, cranial capacity, and ancestry-adjusted UV radiation as instrumental

variables for national IQ. Endogeneity was only found (p < .05) in one of the six

models. This was when ancestry-adjusted UV radiation was used as an

instrument with only the logarithm of GDP per capita as a control variable. This

result disappeared when additional controls were employed. We suggested the

endogeneity found in this regression represented UV radiation having geographic

confounds that could affect economic growth. Overall our IV methodology could

not find strong evidence for reciprocal causation.

Our findings have substantial implications for government policy and the

future of economic growth. The poor evidence for smart fraction theory suggests

only small effects from having an intelligent elite. This weakens the case for

policies, such as Paul Romer’s charter cities, ‘state building’ and imperialism,

which attempt to employ highly educated smart people from Western countries to

design or run key institutions in developing countries. The finding may also

suggest immigration can lower per capita GDP. If a high IQ country takes in lower

IQ immigrants the new average may determine the prosperity of the society, even

if the intelligence of the native elite remains the same. Moreover the finding makes

the ‘migration-ability paradox’ (Rindermann, 2018, p. 422) worrisome. When less

intelligent countries send their smartest people to intelligent countries, this can

lower the average IQ of both nations. Under Smart Fraction theory the less

intelligent nation might lose whilst the more intelligent nation may be relatively

unaffected. However, when the average national IQ is what matters, both senders

and receivers of migrants may be made worse off.

National IQ appears to be the most important factor in determining the GDP

of a nation, yet it is deeply path-dependent with 19th-century numeracy measures

having an effect on GDP similar to more recent student test scores. Moreover,

the strong correlates of national IQ with the genetic polygenic scores and cranial

capacity suggest biology determines which human capital paths nations are on.

The inequality of countries may be fatalistically determined in our genes.

To find policies that can increase economic growth, economists, scientists,

governments and the private sector should study and test the effectiveness of

FRANCIS, G. & KIRKEGAARD, E.O.W. INTELLIGENCE AND ECONOMIC GROWTH

policies to increase national intelligence. Our results provide new evidence

supporting Cattell’s (1937a,b) calls for nations to develop strategies to increase

their intelligence. For example, Cattell recommended an ‘intelligence department’

of the state devoted to measuring and improving national intelligence over time.

With embryo selection and gene editing, humanity now has powerful and

consensual tools to increase national intelligence genotypically. See Anomaly

and Jones (2020) and Anomaly (2020) for a discussion of the ethics and policy

implications of genetic engineering.

If genes determine GDP, we must expect future economic growth to fall.

Since Charles Darwin (1871), scientists have observed dysgenics — the less

intelligent having more children and doing so faster than others. See Dutton and

Woodley (2018) for a review of this literature. More recently genetic data from the

United States (Beauchamp, 2016), United Kingdom (Hugh-Jones & Abdellaoui,

2021) and Iceland (Kong et al., 2017) show polygenic scores for educational

attainment to be declining. After accounting for unexplained variance in the

educational polygenic scores in the Icelandic data, Dutton and Woodley (2018)

estimated that IQ was falling by 0.8 points per decade. We can crudely

extrapolate our finding that each IQ point increases GDP per capita by 7.8% to

estimate the effect of dysgenics on GDP in one hundred years’ time. If we could

stop the current dysgenics of 0.8 points per decade, then GDP will be

     higher in 2122 than under our current dysgenic

trajectory. The power of genetics to determine prosperity paints a bleak picture of

our future.

Online Supplement: The appendix is available at https://osf.io/4x38f/, as are

Figures 1-19 from this paper.

Acknowledgements: We would like to thank the attendants of the London

Conference of Intelligence 2021 for their feedback on an early presentation of this

paper. We would particularly like to thank Gregory Christainsen for his

discussions with the authors. All errors and omissions are our own.

FRANCIS, G. & KIRKEGAARD, E.O.W.    INTELLIGENCE AND ECONOMIC GROWTH 
67 
 
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Prison Populations: The International Mystery

Article

Full-text available

Jun 2023
MANKIND QUART

At first glance international prison rates, measured by prisoners per 100,000 population, appear to be uncorrelated with national intelligence and with racial diversity, which contradicts both a priori expectations and individual level observations that higher intelligence (IQ) generally reduces criminality, although GDP per capita shows a small positive correlation. We present a solution to this mystery by first breaking prison rates into three components: crime rates (proxied by homicide rates), effectiveness at catching and imprisoning criminals, and sentencing length. We then employ a path analysis to first estimate the effect of national IQ and racial diversity on GDP per capita and income inequality. Finally, we estimate both the direct and indirect association of intelligence and diversity with our prison rate components. Consistent with previous literature we find that national IQ increases GDP per capita and reduces income inequality, whilst racial diversity raises income inequality. GDP per capita in turn is correlated with lower homicide rates, higher effectiveness and longer sentencing length, whilst inequality is not significantly associated with any outcome variable, after controlling for racial diversity. While the association between national IQ and prison rate components appears to be entirely indirect, through the channel of national income, racial diversity is directly associated with higher homicide rates and is weakly positively correlated with sentencing length. This evidence suggests that the opposing effects of raising effectiveness and reducing homicide rates provides an explanation for the lack of correlation between national IQ and prison rates.

Honesty, Intelligence, and Race

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Full-text available

Jul 2023

Research shows that honesty correlates positively with intelligence. Similarly, there are racial differences in honesty, with Europeans being more honest than various other ethnic groups. It is currently unknown to what degree race differences in intelligence can explain the differences in honesty. We investigated this question using data from the National Longitudinal Study of Youth 1997 (NLSY97), a large American longitudinal dataset. We replicate prior findings that honesty correlated with measures of intelligence (r = .38, 95% CI [.34, .41]) and that Blacks (d = -0.67, 95% CI [-.76, -.59]) and Hispanics (d = -0.4, 95% CI [-.50, -.31]) are less honest than Whites, and this holds whether honesty is measured by self-reports, interviewer-reports or by parent-reports. In addition, race differences in honesty remained between Blacks and Whites but not between Whites and Hispanics after controlling for intelligence. Differences between Blacks and Whites but not Whites and Hispanics were noticeably lower in self-reports (Blacks: d = -0.18 [-0.24, -0.11], Hispanics: d = -0.24 [-0.31, -0.17]) than parent-reports (Blacks: d = -0.43 [-0.52, -0.35], Hispanics: -0.24 [-0.33, -0.15]) and interviewer-reports (Blacks: d = -0.7 [-0.75, -0.64], Hispanics: -0.3 [-0.36, -0.25]). Cross-national comparisons were made using national IQ data and Hofstede’s cultural dimensions. Bayesian model averaging suggests that Hofstede’s individualism dimension (β = .64, PIP = 100%), national IQs (β = .25, PIP = 73.6%), and masculinity (β = -.35, PIP = 100%) predict differences in honesty between countries. Parking violations per diplomat were only predicted by national IQs (r = -0.28, p < .001), given that no other variable reached a posterior inclusion probability above 0% besides national IQs. Implications and theories concerning these findings are discussed.

Deep roots of admixture-related cognitive differences in the USA

Article

Full-text available

Jul 2023

Introduction: There are well-known cognitive ability differences between socially-identified racial/ethnic groups in the United States. Ameliorating these differences is considered a top grand challenge for the American social sciences. However, reducing these achievement gaps requires a better understanding of the nature of these group differences and also of the mechanisms by which these differences are intergenerationally transmitted. As a result, it is necessary to understand how cognitive differences relate to admixture among admixed groups. Recent studies show a linear positive relationship between European ancestry and cognitive ability in admixed African-European-Amerindian descent groups. Objectives: This study attempts to determine if the association between admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. Methods: First, we use the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the associations between cognitive ability, socially identified-race, genetically-predicted color, and genetic ancestry among Puerto Ricans, and non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to see if we can trace ancestry-associated cognitive differences back to the 19th and early 20th century by taking advantage of early census distinctions by blood and also by using age-heaping-based numeracy as a proxy for cognitive ability. Results: In the ABCD sample, we find that European ancestry is positively associated with cognitive ability within race/ethnic groups (rs =.05 to.47; r(weighted)-average =.10). In the census data, among African Americans and American Indians but not among Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and that this holds when we subset data by age, sex, and literacy status. Conclusions: In the 19th and early 20th century, European admixture was associated with numeracy among African Americans and Native Americans. To better understand these associations a systematic review of 20th century admixture studies is called for.

Deep roots of admixture-related cognitive differences in the USA?

Article

Full-text available

Jun 2023

This study attempts to determine if the association between admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we use the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the associations between cognitive ability, socially identified-race, genetically-predicted color, and genetic ancestry among Puerto Ricans, and non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to see if we can trace ancestry-associated cognitive differences back to the 19th and early 20th century by taking advantage of early census distinctions by blood and also by using age-heaping based numeracy as a proxy for cognitive ability. In the ABCD sample, we find that European ancestry is positively associated with cognitive ability within race/ethnic groups (_r_s =.05 to.47; _r_weighted-average =.10). In the census data, among African Americans and American Indians but not among Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and that this holds when we subset data by age, sex, and literacy-status. The implications of these findings are discussed.

Deep roots of admixture-related cognitive differences in the USA?

Article

Full-text available

May 2023

This study attempts to determine if the association between admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we use the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the associations between cognitive ability and parental-reported race, genetically-predicted color, and genetic ancestry among Puerto Ricans, and non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to see if we can trace ancestry-associated cognitive differences back to the 19th and early 20th century by taking advantage of early census distinctions by blood and also by using age-heaping based numeracy as a proxy for cognitive ability. In the ABCD sample, we find that European ancestry is positively associated with cognitive ability within race/ethnic groups. In the census data, among African Americans and American Indians but not among Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and that this holds when we subset data by age, sex, and literacy-status. The implications of these findings are discussed.

Deep roots of admixture-related cognitive differences in the USA?

Article

Full-text available

May 2023

This study attempts to determine if the association between admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we use the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the associations between cognitive ability and parental-reported race, genetically-predicted color, and genetic ancestry among Puerto Ricans, and non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to see if we can trace ancestry-associated cognitive differences back to the 19th and early 20th century by taking advantage of early census distinctions by blood and also by using age-heaping based numeracy as a proxy for cognitive ability. In the ABCD sample, we find that European ancestry is positively associated with cognitive ability within race/ethnic groups. In the census data, among African Americans and American Indians but not among Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and that this holds when we subset data by age, sex, and literacy-status. The implications of these findings are discussed.

Deep roots of admixture-related cognitive differences in the USA

Article

Full-text available

Apr 2023

This study attempted to determine if the association between genetic admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we used the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the association between cognitive ability and parental-reported race, genetically-predicted color, and genetic ancestry among Puerto Ricans, non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to trace ancestry-associated cognitive differences back to the early 19th and early 20th century among African American, American Indian, and Puerto Rican groups by using age-heaping-based numeracy as a proxy for cognitive ability. In the ABCD sample, we found that European ancestry is positively associated with cognitive ability within race/ethnic groups. In the census data, among African Americans and American Indians but not Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy and this holds when we subset by age, sex, and literacy status. The implications of these findings are discussed.

Deep roots of admixture-related cognitive differences in the USA

Article

Full-text available

Apr 2023

Deep roots of admixture-related cognitive differences in the USA

Article

Full-text available

Apr 2023

Deep roots of admixture-related cognitive differences in the USA

Article

Full-text available

Apr 2023

This study attempted to determine if the association between genetic admixture and cognitive ability among African, European, and Amerindian descent groups in the USA holds across a large time period. First, we used the large and nationally representative Adolescent Brain Cognitive Development Study (ABCD) sample to examine the association between cognitive ability and parental-reported race, genetically-predicted color, and genetic ancestry among Puerto Ricans, non-Hispanic Whites, Blacks, and American Indians in the 21st century. Second, we use the 1850 to 1930 US censuses to trace ancestry-associated cognitive differences back to the early 19th and early 20th century among African American, American Indian, and Puerto Rican groups by using age-heaping-based numeracy as a proxy for cognitive ability. In the ABCD sample, we found that European ancestry is positively associated with cognitive ability within race/ethnic groups. In the census data, among African Americans and American Indians but not Puerto Ricans, we find that greater apparent European admixture is associated with higher numeracy, and this holds when we subset by age, sex, and literacy status. Therefore, we established that admixture is associated with cognitive ability in the 21st century as it was in the 19th and early 20th century. The implications of these findings are discussed.

Smart Fraction Theory: A Comprehensive Re-evaluation

Article

Full-text available

Dec 2022
Comp Sociol

Previous studies have found that the ability of a country’s cognitive elite is generally more predictive than the average ability. However, these studies have relied on sub-optimal methods. Here, the authors tested smart fraction theory, as it is known, using a pre-residualization approach, which obviates the problem of collinearity. For outcome variables, they utilised the 51 indicators of the Social Progress Index, as well as 6 economic variables. Like in previous research, the authors operationalized the ability of the intellectual class as the 95th percentile score. Consistent with smart fraction theory, they found evidence that the ability of the intellectual class does influence outcomes over and above that of the average ability. For the 93 countries with available data, average ability explained 57% of the variation in country performance. Adding the 95th percentile score increased this to 66%. The authors discuss their findings in light of the existing literature.

Divergent selection on height and cognitive ability: evidence from Fst and polygenic scores

Article

Full-text available