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MANKIND QUARTERLY 2019 60.2 243-255
243
Smart and Poor, or Rich and Dull? A U.S. County-Level
Analysis of the Relationship between IQ and Presidential-
Election Voting Behavior
Emil O. W. Kirkegaard*
Ulster Institute for Social Research, London, UK
Bryan J. Pesta
Cleveland State University, USA
*Corresponding author email: emil@emilkirkegaard.dk
A small research stream exists which focuses on relationships
between political ideology (inferred from voting behavior) and the
intelligence of geopolitical sub-divisions such as the 50 U.S. states.
With U.S. state-level data, IQ scores positively predict votes cast for
Democrats, but only when controlling for state racial composition.
Here, however, we explore the relationship between IQ and voting
behavior at the level of U.S. counties (approx. n = 3,100). We find
that county-level IQ weakly predicts more votes cast for Republicans
(r’s .07 to .13) and less votes cast for Democrats (r’s -.10 to -.14).
These small relationships are also found in multiple regression
analyses, even when demographic data, social status, and
population density appear as covariates (3 - 4% points more votes
cast for Republicans per standard deviation of IQ). The effect of
general social status was opposite that of intelligence (6 - 9% points
less votes cast for Republicans per standard deviation of social
status), which is surprising considering the very strong positive
correlation (r = .77) between IQ and social status. Additionally, racial
homogeneity by itself predicts voting Republican; however, when
other variables are present in the regression model, homogeneity
predicts voting Democrat. Results indicate that aggregate-level
relationships between intelligence and voting outcomes are more
complex than previously thought.
Key Words: USA, Voting, Counties, Democrat, Republican,
Intelligence
MANKIND QUARTERLY 2019 60.2
244
The relationship between cognitive ability and political opinion has received
increased research attention in recent years. Perhaps unsurprisingly, most of this
work has focused on voting behavior within the United States, examining mean
cognitive ability levels of Republican versus Democrat voters, and self-identified
liberals versus conservatives (Caplan & Miller, 2010; Carl, 2014a,b, 2015a,b;
Ganzach, 2016, 2017, 2018; Ganzach, Hanoch & Choma, 2019; Kemmelmeier,
2008; Kirkegaard, Bjerrekær & Carl, 2017; Lewis & Bates, 2018; Ludeke &
Rasmussen, 2018; Meisenberg, 2015; Onraet et al., 2015; Oskarsson et al.,
2015). In this literature, usually no large gaps exist between the mean IQs of
supporters of different parties. When smaller gaps are found, however, the center
parties are somewhat favored by those with higher IQs. In the United States, only
very small differences exist between the mean IQs of Republican versus
Democrat voters. The direction of this effect depends both on which covariates
(race, education, income, etc.) appear in the models, and on which time period
the data are from.
When studies use one-dimensional scales with self-placement, sometimes
small IQ advantages (about 2-3 IQ points) are seen for “liberals” (in the left-wing,
U.S. sense) over conservatives. Moderates, independents, and centrists usually
score lower than both liberals and conservatives by about four to five IQ points.
This finding could be related to the fact that moderates are generally not strongly
interested in politics, and IQ correlates with political interest and participation (r =
.20 to .30; Deary, Batty & Gale, 2008; Kirkegaard & Bjerrekær, 2016). In the case
of voters and nonvoters, the gap is about 5-10 IQ points (Carl, 2019).
If one instead opts for more fine-grained measures of political ideology, then
more complex patterns emerge. One common approach splits political opinions
into two scales that are theoretically independent, though not necessarily
empirically uncorrelated: economic liberalism / freedom, and social liberalism /
freedom (Nolan chart). The former is concerned with the role of the state in the
economy (e.g., how much tax, which tax forms, government interventions,
regulations, etc.). The latter is concerned with various social and cultural
freedoms (e.g., gay marriage, drug legalization/regulation, prostitution, etc.). The
two scales, though, are usually found to be somewhat correlated. For example,
Carl (2015a) reported a positive correlation of r = .36 between the two scales in a
large U.S. sample, but Kirkegaard, Bjerrekær and Carl (2017) found only a
correlation of .07 in a smaller sample of approximately 250 Danes. This is
somewhat unexpected because at the party level, the two dimensions are often
negatively correlated in Western countries, such that parties favoring less
government influence in the economy also favor many limits on social behavior,
and vice versa. However, this finding seems to depend on political context
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
245
because the pattern is apparently reversed in China and many ex-communist,
Eastern European countries (de Regt, Mortelmans & Smits, 2011; Malka et al.,
2014, 2019; Pan & Xu, 2017).
Aside from individual-level analyses, some studies look at voting behaviors
across geo-political divisions of nations. Carl (2018) analyzed data from the
United Kingdom at two levels: 11 regions and 372 local authorities. He found that
economic liberalism was positively related to the mean IQs of the units (r = .70
and .33 for regions and local authorities, respectively). However, Carl (2018) also
found that IQ’s relationship to social liberalism was only weakly positive (r = .21
and .12, for regions and local authorities, respectively). Thus, it appears that the
strength but not the direction of the association strongly depends on the level of
analysis.
For the United States, both Pesta (2017), and Pesta and McDaniel (2014)
explored state-level relationships between “well-being”, IQ, and voting behaviors
for all presidential elections held this century. They found in all elections
nonsignificant, bivariate correlations between state IQ and votes cast for either
Republicans or Democrats. However, when race variables (i.e., percent White,
Black, or Hispanic) also appeared in the regression equations, state IQ
moderately-to-strongly predicted votes cast for Democrats.
For the USA, data also exist for voting behaviors at the county level (N =
approximately 3,100 counties). However, no published studies have looked at
U.S. county-level relationships between IQ and voter behavior. Hence, the
purpose of the present study is to close the gap in this literature by conducting
such a study.
Data and methods
Intelligence
We coded data from the Stanford Education Data Archive (SEDA; Reardon
et al., 2018), available at https://cepa.stanford.edu/seda/overview. SEDA
comprises a massive amount of cognitive testing data from many sources,
including NAEP and state tests that have been normed to the same scale. Data
are available at the county level for the years 2009-2015. The scores correlated
.77 on average between these years. We therefore averaged the scores across
years and subjects tested (language and math) to produce a single best estimate
for each county. Note that our measure correlated .86 with IQ estimates from a
previous study (Kirkegaard, 2016), which was based on partially overlapping
data. The IQ variable was standardized to a 0/1 scale (mean/SD). Figure 1 shows
a map of the distribution of average intelligence across counties in the United
States.
MANKIND QUARTERLY 2019 60.2
246
Figure 1. Map of average county intelligence in the United States (Hawaii and
Alaska not shown but included in all analyses), n = 3,085. The holes represent
missing data.
Political outcomes
The New York Times (2016) published voting results by U.S. county for the
2008, 2012, and 2016 presidential elections. They reported the percent of votes
cast in each county for the Democrat, Republican, Libertarian and Green Party
candidates. The Green Party, however, had missing data for about 500 counties
where the Green Party candidate did not run. Figure 2 shows the distribution of
percent Democrat votes by U.S. county.
Covariates
We used the extensive set of covariates compiled by Kirkegaard (2016).
These were compiled by merging various public U.S. surveys, with the majority
of the data coming from the American Community Survey (ACS,
https://www.census.gov/programs-surveys/acs). We also used Kirkegaard’s
(2016) scores for the general socioeconomic status (SES) factor (S factor). This
variable is a composite formed by factor analyzing 27 diverse indicators of well-
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
247
being (e.g. teen birth rate, proportion with at least a bachelor’s degree, smoking
rate, income Gini coefficient). The variable was standardized to a 0/1 scale
(mean/SD). Finally, we computed a racial homogeneity score for each county
based on the probability that two randomly chosen persons will be from the same
race or ethnic group (known as the Simpson or Herfindahl index).
Figure 2. Map of the percent Democrat vote in the 2016 election by U.S. county
(Hawaii and Alaska not shown but included in all analyses), n = 3,111. The holes
represent missing data.
Spatial data
As an additional control variable, we used publicly-available spatial data
(shapefile) from https://catalog.data.gov/dataset/tiger-line-shapefile-2017-nation-
u-s-current-county-and-equivalent-national-shapefile. This file contains the
borders of all US counties. We computed population density using the spatial data
and the population counts in the covariate datafile. The density variable was
extremely skewed, so the log10 value was taken and normality was achieved.
The study was analyzed in R (3.6.1). All code and data are available for reuse
in the supplementary materials file. The R notebook is available at
MANKIND QUARTERLY 2019 60.2
248
https://rpubs.com/EmilOWK/248961. We used the square root of population size
as weights in line with previous research (Fuerst & Kirkegaard, 2016). We also
outputted the unweighted versions, which are available in the supplementary
materials file. Results here were very similar to those found when using weights.
Analysis
Table 1 shows bivariate correlations between all study variables. It can be
seen that there are strong correlations between many of them. Of particular
interest are moderate negative correlations between intelligence and percent
Democrat voting, and the reverse for percent Republican voting. Third party data
were not available for 2008 and 2012 (and these were normalized too for
Democrat/Republican vote share), but in 2016 the correlations between vote
share for these and IQ were positive as well.
Table 1. Correlation matrix of main variables for ~3100 US counties. Weighted
by square root of population size.
IQ
S
White
Black
Hisp.
Asian
Amer.
Other
Homog.
S 0.77 1.00
White 0.53 0.37 1.00
Black
-0.46
-0.52
-0.58
1.00
Hispanic -0.25 -0.08 -0.67 -0.09 1.00
Asian
0.13
0.32
-0.41
0.01
0.25
1.00
Amerindian -0.23 -0.17 -0.18 -0.10 -0.03 -0.06 1.00
Other -0.02 0.12 -0.20 -0.08 0.02 0.52 0.18 1.00
Homogeneity 0.37 0.20 0.86 -0.56 -0.49 -0.49 -0.08 -0.28 1.00
Dem16 frac -0.11 0.07 -0.65 0.45 0.33 0.53 -0.01 0.19 -0.58
Dem12 frac
-0.14
0.02
-0.54
0.39
0.23
0.44
0.03
0.18
-0.43
Dem08 frac
-0.10
0.06
-0.47
0.33
0.20
0.43
0.03
0.18
-0.37
Rep16 frac 0.07 -0.14 0.61 -0.38 -0.33 -0.53 -0.01 -0.22 0.55
Rep12 frac 0.13 -0.04 0.52 -0.37 -0.23 -0.45 -0.03 -0.19 0.42
Rep08 frac 0.09 -0.08 0.45 -0.31 -0.20 -0.43 -0.03 -0.18 0.34
Green16 frac 0.09 0.35 -0.07 -0.25 0.13 0.38 0.16 0.44 -0.11
Libert16 frac 0.29 0.46 0.23 -0.48 0.05 0.03 0.18 0.19 0.18
Pop. density 0.18 0.23 -0.34 0.28 0.16 0.49 -0.25 0.10 -0.45
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
249
Dem16
frac.
Dem12
frac.
Dem08
frac.
Rep16
frac.
Rep12
frac.
Rep08
frac.
Green16
frac.
Libert16
frac.
S
White
Black
Hispanic
Asian
Amerindian
Other
Homogeneity
Dem16 frac
1.00
Dem12 frac
0.96
1.00
Dem08 frac 0.93 0.98 1.00
Rep16 frac
-0.99
-0.94
-0.92
1.00
Rep12 frac
-0.95
-1.00
-0.99
0.94
1.00
Rep08 frac
-0.92
-0.98
-1.00
0.92
0.98
1.00
Green16 frac 0.45 0.47 0.49 -0.51 -0.50 -0.51 1.00
Libert16 frac -0.01 0.00 0.06 -0.08 -0.03 -0.07 0.47 1.00
Pop. density
0.64
0.54
0.52
-0.62
-0.53
-0.51
0.17
-0.07
Results from bivariate analysis are of course possibly confounded with other
factors, and we see strong correlations between intelligence and other variables
in the table, especially general social status (S), and many of the demographic
variables. Thus, multivariate analysis is warranted to clarify possibly causal
relationships. Table 2 shows the regression results.
The results show multiple things of interest. First, despite the very strong
correlation between intelligence and general social status (r = .77, cf. Table 1), we
see that they have opposite signs in the regression models. Furthermore, IQ is a
positive predictor for Republicans, but negative for others, including third parties,
though the effect size for them is quite small. The effect size for Republicans
against Democrats is sizable at 2.5 to 4.3% points gain / loss per standard
deviation increase in county IQ. Moreover, social status is a quite potent predictor
of votes cast for Democrats, with an effect size of about 6 to 7% points, and -6 to
-9% points for Republicans. The effects of demographic variables are also
substantial. The percent of Blacks within a county predicts more Democrat votes
which is unsurprising because Blacks generally vote about 90% Democrat
(www.ropercenter.cornell.edu/how-groups-voted-2016). The effect size is
approaching ±1% point, meaning that a 1% point increase in the Black population
results in about a 1% point increase in the percent of votes cast for Democrats.
MANKIND QUARTERLY 2019 60.2
250
Table 2. Regression model results for U.S. county-level presidential elections. N
= 3,058-3,059. Intelligence and S are standardized (0/1 mean/SD), demographic
memberships are proportions (i.e. 0-1 range). S = general social status based on
up to 27 indicators. Weighted by the square root of population size. Values in
parentheses are standard errors. * = p < .01.
Outcome
Dem16
Dem12
Dem08
Rep16
Rep12
Rep08
Green16
Lib16
Predictor
IQ
-0.025
(0.004)*
-0.039
(0.004)*
-0.038
(0.004)*
0.033
(0.004)*
0.042
(0.004)*
0.041
(0.004)*
-0.003
(0.000)*
-0.002
(0.000)*
S 0.072
(0.003)*
0.061
(0.004)*
0.058
(0.004)*
-0.091
(0.004)*
-0.064
(0.004)*
-0.060
(0.004)*
0.003
(0.000)*
0.009
(0.000)*
Black 0.978
(0.025)*
0.878
(0.028)*
0.768
(0.028)*
-0.956
(0.027)*
-0.855
(0.029)*
-0.747
(0.028)*
-0.004
(0.001)*
-0.018
(0.003)*
Hisp. 0.579
(0.019)*
0.485
(0.022)*
0.432
(0.021)*
-0.600
(0.021)*
-0.480
(0.022)*
-0.421
(0.022)*
0.004
(0.001)*
0.015
(0.002)*
Asian
1.221
(0.067)*
1.154
(0.075)*
1.129
(0.074)*
-1.115
(0.072)*
-1.200
(0.077)*
-1.180
(0.075)*
0.009
(0.004)
-0.097
(0.008)*
Amer.
0.912
(0.043)*
0.906
(0.049)*
0.847
(0.048)*
-0.979
(0.046)*
-0.898
(0.050)*
-0.835
(0.049)*
0.027
(0.003)*
0.052
(0.005)*
Other 0.603
(0.154)*
0.802
(0.172)*
0.623
(0.169)*
-0.958
(0.163)*
-0.758
(0.176)*
-0.573
(0.172)*
0.119
(0.010)*
0.260
(0.018)*
Homog. 0.365
(0.019)*
0.480
(0.022)*
0.479
(0.021)*
-0.377
(0.021)*
-0.482
(0.022)*
-0.486
(0.022)*
0.005
(0.001)*
0.003
(0.002)
Dens. 0.085
(0.003)*
0.077
(0.003)*
0.074
(0.003)*
-0.084
(0.003)*
-0.075
(0.004)*
-0.072
(0.003)*
0.001
(0.000)*
0.000
(0.000)
R2 adj. 0.70 0.53 0.48 0.67 0.51 0.46 0.34 0.39
Hisp., Hispanic; Amer., Amerindian; Homog., homogeneity; Dens., density.
For Hispanics, Amerindians (Native Americans), and Others, it works about
the same way despite the fact that these groups have sizable proportions who
vote Republican. Curiously, the percent of Asians in a county has an effect size
above the ±1% point, which may seem impossible. This suggests that Asians
convert nearby voters to Democrats away from Republicans. We currently have
no non-speculative explanation for this effect.
Next, homogeneity, defined as the chance that two randomly picked persons
are from the same racial group, strongly predicts votes cast for Democrats. Thus,
it appears that (assuming causality), holding the other covariates constant,
increasing the diversity share of a county leads to fewer Democrat votes and more
Republican votes. This is surprising because the correlation between
homogeneity and Democrat votes is strongly negative at r = -.37 to -.58 (cf. Table
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
251
1). There is also a very strong suppression effect at play here, which has been
found previously for these kind of data (Pesta, 2017; Pesta & McDaniel, 2014).
Suppression probably results from opposite-signed direct and indirect paths,
where some of the indirect effect is mediated by another predictor in the model.
Finally, the results for third parties were quite weak in comparison and have only
limited interest, so are not discussed further.
Discussion
Intelligence is indeed related to voting outcomes at the U.S. county level.
Specifically, we found that higher intelligence predicts support for non-Democrat
parties in bivariate analysis. When covariates are introduced, this relationship is
still found but only for the Republicans versus third parties. These results are
interesting considering that previous studies, using state-level data, found that
higher IQ predicted more votes for Democrats (Pesta, 2017; Pesta & McDaniel,
2014), whereas we find the opposite here.
Furthermore, individual level results generally find little relationship between
intelligence and voting for either Democrats or Republicans, especially when
covariates are present (Ganzach, 2016; Meisenberg, 2015). Conversely, here,
the effect size for votes cast for Republicans was not trivial. Specifically, a one
standard deviation increase in intelligence was associated with 3-4% points more
support for the Republican candidate. On the other hand, general social status
(S) predicted support for the Democrat candidate at an even greater magnitude,
6-7% points. The opposite signs of the two predictors is surprising considering
that they are very strongly positively correlated (r = .77). Also of interest was that
racial homogeneity predicted Republican support in bivariate analysis (r = .34 to
.55), but predicted Democrat support in multiple regression (betas -0.38 to -0.48).
The reversal of direction of predictors and the contrast to prior studies are curious
and perhaps alarming since they seem to suggest that suppression effects are
particularly strong in this area of research, and that aggregation paradoxes
(Simpson’s paradox) — where results at one level of analysis are inconsistent in
direction with those at a different level — are perhaps common.
An interesting trend seen in the model comparisons is that the model fits
seemingly increased over time, from about .46-.48 in 2008 to .67-.70 in 2016.
This indicates that voting behavior has become more predictable from county-
level data, which suggests that political polarization of demographic groups is
increasing. There is in fact good evidence for such increased polarization, which
mainly results from the left moving further left, while the right remaining
approximately the same (Goldberg, 2019a, 2019b; Kaufman, 2019). However,
caution is advised because we only have three elections in the dataset, and
MANKIND QUARTERLY 2019 60.2
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because some of the covariate data are closer to 2016 than to 2008 in age. While
the social variables are available for many decades, the currently available
intelligence data only span 2009 to 2015, so it is not now possible to go back
further in time to cover additional elections at the U.S. county level.
Limitations and Conclusion
First, we did not have data disaggregated by race for voting behavior, so it
was not possible to see how effects might differ specifically by the race of the
voter. Second, the study was cross-sectional, so causality can be backwards for
the variables we considered. Longitudinal data are available for some of the
variables, so they could be used in a future study to better answer causality
questions. Of course, studies of this kind suffer from potential omitted variable
bias, and this may have affected our findings. Finally, we must be careful not to
commit the ecological fallacy here. The effects we observed using aggregate-
level data may not also be found at the level of the individual voter. Aggregate-
level results often differ from individual-level results (see, e.g., Robinson, 1950)
Nonetheless, we found that at the U.S. county level, intelligence predicts
votes cast for Republicans, and this effect persists even when various controls
are added in regression models. The pattern we report here is exactly opposite
that reported by both Pesta (2017), and Pesta and McDaniel (2014), who found
that intelligence predicted votes cast for Democrats (after controlling for race),
albeit using U.S. state-level data. These authors also reported very strong
suppression effects, which we found here as well. Why results differ for U.S.
counties versus states is a puzzle that future research should seek to solve.
Supplementary materials
See https://osf.io/rh4da/, and for code output, see https://rpubs.com/
EmilOWK/248961.
References
Caplan, B. & Miller, S.C. (2010). Intelligence makes people think like economists:
Evidence from the General Social Survey. Intelligence 38: 636-647.
Carl, N. (2014a). Cognitive ability and party identity in the United States. Intelligence 47:
3-9.
Carl, N. (2014b). Verbal intelligence is correlated with socially and economically liberal
beliefs. Intelligence 44: 142-148.
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
253
Carl, N. (2015a). Cognitive ability and political beliefs in the United States. Personality
and Individual Differences 83: 245-248.
Carl, N. (2015b). Does intelligence have a U-shaped relationship with leftism? Intelligence
49: 159-170.
Carl, N. (2018). IQ and political attitudes across British regions and local authorities.
Intelligence 69: 169-175.
Carl, N. (2019). Comparing EU knowledge among leave and remain voters: A replication
study. SocArXiv: 15 Sept. 2019.
de Regt, S., Mortelmans, D. & Smits, T. (2011). Left-wing authoritarianism is not a myth,
but a worrisome reality. Evidence from 13 Eastern European countries. Communist and
Post-Communist Studies 44(4): 299-308.
Deary, I.J., Batty, G.D. & Gale, C.R. (2008). Childhood intelligence predicts voter turnout,
voting preferences, and political involvement in adulthood: The 1970 British Cohort Study.
Intelligence 36: 548-555.
Fuerst, J. & Kirkegaard, E.O.W. (2016). Admixture in the Americas: Regional and national
differences. Mankind Quarterly 56: 255-373.
Ganzach, Y. (2016). Cognitive ability and party identity: No important differences between
Democrats and Republicans. Intelligence 58: 18-21.
Ganzach, Y. (2017). Cognitive ability and party affiliation: The role of the formative years
of political socialization. Intelligence 61: 56-62.
Ganzach, Y. (2018). Intelligence and the rationality of political preferences. Intelligence
69: 59-70.
Ganzach, Y., Hanoch, Y. & Choma, B.L. (2019). Attitudes toward presidential candidates
in the 2012 and 2016 American elections: Cognitive ability and support for Trump. Social
Psychological and Personality Science 10: 924-934.
Goldberg, Z. (2019a, June 6). America’s white saviors. Tablet Magazine. Retrieved from
https://www.tabletmag.com/jewish-news-and-politics/284875/americas-white-saviors
Goldberg, Z. (2019b, July 25). Democrats are turning immigration into a moral ultimatum.
Tablet Magazine. Retrieved from https://www.tabletmag.com/jewish-news-and-
politics/288500/democrats-immigration-moral-ultimatum
Kaufman, E. (2019). WHITESHIFT: Populism, Immigration and the Future of White
Majorities. Penguin Books.
Kemmelmeier, M. (2008). Is there a relationship between political orientation and
cognitive ability? A test of three hypotheses in two studies. Personality and Individual
Differences 45: 767-772. https://doi.org/10.1016/j.paid.2008.08.003
MANKIND QUARTERLY 2019 60.2
254
Kirkegaard, E.O.W. (2016). Inequality across US counties: An S factor analysis. Open
Quantitative Sociology & Political Science. Retrieved from http://openpsych.net/
OQSPS/2016/05/inequality-across-us-counties-an-s-factor-analysis/
Kirkegaard, E.O.W. & Bjerrekær, J.D. (2016). The OK Cupid dataset: A very large public
dataset of dating site users. Open Differential Psychology. Retrieved from
https://openpsych.net/paper/46
Kirkegaard, E.O.W., Bjerrekær, J.D. & Carl, N. (2017). Cognitive ability and political
preferences in Denmark. Open Quantitative Sociology & Political Science 1(1). Retrieved
from https://openpsych.net/paper/51
Lewis, G.J. & Bates, T.C. (2018). Higher levels of childhood intelligence predict increased
support for economic conservatism in adulthood. Intelligence 70: 36-41.
Ludeke, S.G. & Rasmussen, S.H.R. (2018). Different political systems suppress or
facilitate the impact of intelligence on how you vote: A comparison of the U.S. and
Denmark. Intelligence 70: 1-6.
Malka, A., Soto, C.J., Inzlicht, M. & Lelkes, Y. (2014). Do needs for security and certainty
predict cultural and economic conservatism? A cross-national analysis. Journal of
Personality and Social Psychology 106: 1031-1051.
Malka, A., Lelkes, Y. & Soto, C.J. (2019). Are cultural and economic conservatism
positively correlated? A large-scale cross-national test. British Journal of Political Science
49: 1045-1069.
Meisenberg, G. (2015). Verbal ability as a predictor of political preferences in the United
States, 1974–2012. Intelligence 50: 135-143.
Onraet, E., van Hiel, A., Dhont, K., Hodson, G., Schittekatte, M. & De Pauw, S. (2015).
The association of cognitive ability with right-wing ideological attitudes and prejudice: A
meta-analytic review. European Journal of Personality 29: 599-621.
Oskarsson, S., Cesarini, D., Dawes, C.T., Fowler, J.H., Johannesson, M., Magnusson,
P.K.E. & Teorell, J. (2015). Linking genes and political orientations: Testing the cognitive
ability as mediator hypothesis. Political Psychology 36: 649-665.
Pan, J. & Xu, Y. (2017). China’s ideological spectrum. Journal of Politics 80(1): 254-273.
Pesta, B.J. (2017). A U.S. state-level analysis of the presidential election in 2016: IQ,
race, and well-being emerge as mutually-suppressed predictors. Open Differential
Psychology 1(1). Retrieved from https://openpsych.net/paper/54
Pesta, B.J. & McDaniel, M.A. (2014). State IQ, well-being and racial composition as
predictors of U.S. presidential election outcomes. Intelligence 42: 107-114.
KIRKEGAARD, E.O.W & PESTA, B.J. SMART AND POOR, OR RICH AND DULL?
255
Reardon, S.F., Ho, A.D., Shear, B.R., Fahle, E.M., Kalogrides, D. & DiSalvo, R. (2018).
Stanford Education Data Archive (Version 2.1). Retrieved from
http://purl.stanford.edu/db586ns4974.
Robinson, W.S. (1950). Ecological correlations and the behavior of individuals. American
Sociological Review 15: 351-374.
