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Mental illness and the left

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Mental illness and the left

Abstract and Figures

It has been claimed that left-wingers or liberals (US sense) tend to be more mentally ill than right-wingers or conservatives. This potential link was investigated using the General Social Survey. A search found 5 items measuring one's own mental illness in different ways (e.g."Do you have any emotional or mental disability?"). All of these items were associated with left-wing political ideology as measured by self-report. These results held up mostly in regressions that adjusted for age, sex, and race. For the variable with the most data, the difference in mental illness between "extremely liberal" and "extremely conservative" was 0.39 d. This finding is congruent with numerous findings based on related constructs.
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Mental Illness and the Left
Emil O. W. Kirkegaard1
Ulster Institute for Social Research, London, UK
It has been claimed that left-wingers or liberals (US sense) tend to more often suffer from
mental illness than right-wingers or conservatives. This potential link was investigated using
the General Social Survey cumulative cross-sectional dataset (1972-2018). A search of the
available variables resulted in 5 items measuring one's own mental illness (e.g., ”Do you
have any emotional or mental disability?”). All of these items were weakly associated with
left-wing political ideology as measured by self-report, with especially high rates seen for the
“extremely liberal” group. These results mostly held up in regressions that adjusted for age,
sex, and race. For the variable with the most data (n = 11,338), the difference in the mental
illness measure between “extremely liberal” and “extremely conservative” was 0.39 d.
Temporal analysis showed that the relationship between mental illness, happiness, and
political ideology has existed in the GSS data since the 1970s and still existed in the 2010s.
Within-study meta-analysis of all the results found that extreme liberals had a 150%
increased rate of mental illness compared to moderates. The finding of increased mental
illness among left-wingers is congruent with numerous findings based on related constructs,
such as positive relationships between conservatism, religiousness and health in general.
Key Words: Mental illness, Mental health, Happiness, Life satisfaction, Political ideology,
Left-wing, Liberalism, Right-wing, Conservatism
It has been reported that left-wingers or liberals (US sense) tend to more often suffer from mental
illness than right-wingers or conservatives (Bullenkamp & Voges, 2004; Duckworth et al., 1994;
Guhname, 2007; Howard & Anthony, 1977; Kelly, 2014; Unorthodox Theory, 2020). This
suggestion is consistent with other research showing that religiosity predicts both mental and
physical health (AbdAleati et al., 2016; Cotton et al., 2006; Dutton et al., 2018; Moreira-Almeida et
al., 2006; Seeman et al., 2003; VanderWeele, 2017), given the known strong relationship between
political conservatism and religiousness (Koenig & Bouchard Jr., 2006; Ludeke et al., 2013).
Furthermore, political conservatism has been found to be associated with longevity (Kannan et al.,
2019).
In a recent series of tweets, Lemoine (2020) analyzed data from the Slate Star Codex (SSC)
2020 reader survey2 (n = 8,043; Alexander, 2020), and showed that self-rated political ideological
position (1-10 scale) and self-rated far-left labels were related to mental health. Since his work was
not published, we reprint this figure showing the relation to the self-placement scale in Figure 1.
1 Email: emil@emilkirkegaard.dk
2 The Slate Star Codex is a popular blog by Scott Alexander, a Silicon Valley-based philosopher and
psychiatrist. The reader survey is a public survey composed by Alexander and colleagues and was freely
available to take via a link from his blog. https://slatestarcodex.com/
Figure 1. Self-reported mental health and self-reported political label. Reproduced from Lemoine
(2020).
However, the SSC survey is far from representative, being mainly limited to readers of a
particular blog that attracts mainly European-descent, highly intelligent readers (Karlin, 2018).
Thus, there was a need to replicate the analysis in more representative samples. Hence, the aim of
the article was to examine the links between mental health and political ideology in the General
Social Survey (GSS, https://gss.norc.org/), a public access large-scale survey with relevant data.
Data
We used data from the cumulative cross-sectional file 1972-2018 (release 1) available for public
use at https://gssdataexplorer.norc.org/pages/show?page=gss%2Fgss_data. This has a total
sample size of 64,814, but not all items (questions) were asked in every wave, or given to all
respondents in each wave. We searched the database for items relating to mental illness.3 Five
items were found with at least a sample size of 1,000, shown in Table 1. Four of these were binary/
dichotomous, and one was numeric. Political ideology was measured by a 1-7 scale going from
extremely liberal to extremely conservative, which was available for all subjects. Figure 2 shows the
distribution of political ideology by survey year.
Table 1. Survey questions concerning mental health.
Question Response n Years
3 We used search terms such as “mental”, “illness”, “disorder”, and “depression”.
Do you have any emotional or mental
disability?
Yes/no 2,777 2006
Now thinking about your mental health,
which includes stress, depression, and
problems with emotions, for how many days
during the past 30 days was your mental
health not good?
Numeric [0-30] 11,338 2002, 2004,
2006, 2010,
2012, 2014,
2016, 2018
First, thinking about health related matters,
did any of the following happen to you since
[12 months ago]? Underwent counseling for
mental or emotional problems.
Yes/no 2,345 1991, 2004
Have you ever felt you had a mental health
problem?
Yes/no 1,053 1996
Have you personally ever received
treatment for a mental health problem?
Yes/no 1,413 2006
Figure 2. Distribution of political ideology by survey year.
Moderates are the largest group at roughly 40% of the population. People with extreme views
represent approximately 10% of the population in 2018 but only about 5% in 1975. Thus, there is a
long running tendency towards more ideological extremism, at least insofar as this self-report
measure is concerned. The increase in recent years is consistent with the pattern from other
sources about the Great Awokening (Goldberg, 2019; Kaufman, 2019; Winegard & Winegard,
2018; Yglesias, 2019).
Results
First, we plotted the average of each outcome by political ideology and sex. We included a split by
sex because sex relates to politics4, and sex relates to mental illness (women higher), and ignoring
sex would thus lead to some confounding. Figures X-X show the results.
Figure 3a. Mental health outcomes by political ideology and sex: treatment for mental health. Error
bars are 95% analytic confidence intervals.
4 Sex/gender relates to politics in that women vote more for left-wing parties. This, however, is not a
constant finding through history. For research, see e.g. (Abendschön & Steinmetz, 2014; Edlund & Pande,
2002; Inglehart & Norris, 2000). However, even before women voted for left-wing parties (roughly, prior to
the 1970s), their influence on politics was to increase state spending on welfare, i.e. a left-wing/big
government influence (Abrams & Settle, 1999; John R. & W. Kenny, 2015) . Evidence for this comes from
countries and subnational governments that implemented women’s suffrage (vote) at different times.
Figure 3b. Mental health outcomes by political ideology and sex: ever had mental health problem.
Figure 3c. Mental health outcomes by political ideology and sex: counseling last year.
Figure 3d. Mental health outcomes by political ideology and sex: days in poor health.
Figure 3e. Mental health outcomes by political ideology and sex: emotional/mental disability.
So we see that for mental illness outcomes, left-wing political ideology, in particular “extremely
liberal”, predicts worse mental health. The results also hold across sexes, though the sample sizes
are not large for the extreme groups, and the predictor did not always have p<.05 in a regression
model. Are the results large enough to care about? One approach is calculating Cohen’s d values
for the group gaps. This can only properly be done when the outcome data is continuous. Table 2
shows the results for the “days of poor mental health” variable with which we have the most data
and which is a continuous outcome.
Table 2. Cohen’s d gaps for “days of poor mental health last 30 days” by political ideology.
Positive values indicate more days of poor health compared to the “extremely conservative” group.
Extremely
liberal Liberal
Slightly
liberal Moderate
Slightly
conservative Conservative
Liberal 0.21
Slightly liberal 0.25 0.04
Moderate 0.20 -0.01 -0.05
Slightly conservative 0.28 0.07 0.03 0.08
Conservative 0.36 0.15 0.12 0.16 0.09
Extremely conservative 0.39 0.18 0.14 0.19 0.11 0.03
For the two most extreme groups, the gap is of moderate size, at least as measured by this
single item. Another way to quantify it, is to convert political ideology into numeric form (1-7) and
correlate it with the variable (Pearson correlation). This produces a correlation of only .07 (p = 3e-10).
Hence, overall, the relationship between the two is quite weak and reaches a notable size only for
extreme groups.
To examine whether age was a confound, we fit a regression model for each outcome, logistic
models for the dichotomous and OLS for the continuous (days of poor health). Each model
included political ideology, age (as a natural spline), and sex as predictors, without any interactions
as the sample size did not provide sufficient statistical power. For each model, we projected the
predicted levels of mental illness from the models using the ggeffects package’s ggpredict()
function (Lüdecke, 2018), shown in Figures 4a-d.
Figure 4a. Model projections of mental health by political ideology, controlling for age and sex
(covariates are held at average values): treatment for mental health. Error bars are 95% prediction
intervals.
Figure 4b. Model projections of mental health by political ideology, controlling for age and sex
(covariates are held at average values): ever had mental health problem.
Figure 4c. Model projections of mental health by political ideology, controlling for age and sex
(covariates are held at average values): counseling last year.
Figure 4d. Model projections of mental health by political ideology, controlling for age and sex
(covariates are held at average values): days in poor health.
Figure 4e. Model projections of mental health by political ideology, controlling for age and sex
(covariates are held at average values): emotional/mental disability.
As before, the results show that there is some relationship between mental health and political
ideology such that left-wingers have worse outcomes. The confidence intervals are fairly wide,
however. For people reporting having an emotional mental disorder or not, there was seemingly no
pattern except that “extremely liberal” reported this higher than everybody else. As a robustness
test, we ran models on whites only to avoid any potential confound with race and mental illness
(Coleman et al., 2016; Maura & Weisman de Mamani, 2017). However, the results were very
similar and are not shown here. Full statistical output and code can be found in the supplementary
materials (https://osf.io/fhpxm/).
Reverse indicators: happiness and political ideology
Another option is to use reverse items that instead of measuring mental illness measure the
opposite, happiness or high life satisfaction. The GSS has two items asking about happiness based
on the questions: 1) “Taken all together, how would you say things are these days would you say
that you are very happy, pretty happy, or not too happy?”, and 2) “If you were to consider your life
in general, how happy or unhappy would you say you are, on the whole? [from completely unhappy
to completely happy, 7 levels]”. The sample sizes for these were 60,054 and 2,444, respectively.
Figures 5a and b show the mean happiness level by sex and political ideology.
Figure 5a. Mean happiness level by sex and political ideology: would you say that you are very
happy, pretty happy, or not too happy? N = 60,054. Error bars are 95% analytic confidence
intervals.
Figure 5b. Mean happiness level by sex and political ideology: how happy or unhappy would you
say you are, on the whole? N = 2,044.
We see the opposite pattern, i.e. that more conservative groups have higher happiness levels
on both questions. In terms of Cohen’s d, the effect sizes between the extreme groups are 0.20
and 0.56, respectively; in terms of Pearson correlations, they are .06 and .11, respectively. It’s
unclear why the second should produce substantially larger gaps when the questions are quite
similar.
Temporal pattern
Two of the items used have been asked for many years (bad days per month and happiness
question 1), allowing for analysis of any temporal pattern. Because large samples are required to
detect weak patterns, the waves of data were grouped at the decadal level. Figure 6 shows the
results.
Figure 6. Temporal pattern in relationship between political ideology, mental illness and
happiness. Error bars are 95% analytic confidence intervals.
The results show that the relationships persist across all waves of data, beginning in the 1970s
and into the end of the 2010s. Cohen’s d was also calculated for the same comparisons shown in
the figure. Although there were some differences across decades, they were all numerically
consistent in direction and probably not significantly different in most cases if formally tested. The
full numerical output can be found in the statistical output.
Party affiliation as alternative political measure
At the request of a reviewer, we replicated the main result in Figure 3 using the party affiliation
question. This question is similar to the political ideology one, but instead asks people which party
they are affiliated with, ranging from strong Democrat to strong Republican. There is also an option
for third parties, but we removed this due to incompatibility with the other results, as well as limited
sample size which makes the results too imprecise to interpret. Figure 7 shows the results.
Figure 7. Party affiliation and mental illness measures. Error bars are 95% analytic confidence
intervals.
With the four binary measures, the pattern is roughly the same as the prior analyses. However,
for the continuous outcome (days poor health), we see a non-monotonic pattern such that
Independents actually score higher. Even in this subanalysis, however, strong Republicans are
quite far below their counterpart.
Meta-analysis
The individual estimates of mental illness measures are somewhat unreliable owing to the small
number of persons in the extreme groups, and the low base rates. To overcome this limitation, we
can meta-analyze the findings from Table 3a-e. Is this the correct table? The main issue here is
that they are not on the same scale (dichotomous vs. quasi-continuous), and even those on the
same scale have different base rates. Thus, to facilitate comparison, the data were converted into
relative risk (RR) relative to the moderates.5 Bootstrapped standard errors were computed by
resampling individuals and then computing all downstream statistics 1000 times. This was done
because it was unclear how to calculate analytic error bars for these data, which were converted
into RR and originating from different scales, as well as having some degree of non-independence
between values due to multiple questions asked in the same wave in some cases. Figure 8 shows
the results.
5 One could also have computed them relative to the overall mean. The latter would remove any changes
related to the changing of the distribution of political ideology, as was shown to exist in Figure 2.
Figure 8. Meta-analysis of mental illness indicators on relative risk scale compared to moderates.
Error bars are 95% bootstrapped confidence intervals. The error bar for moderates has 0 height
before it was the reference group. Total sample sizes shown at the bar bottoms.
The results confirm the general pattern from before, namely that there is a strongly elevated
risk for mental illness among the extreme liberals (+150%), a small increase among the liberals and
slightly liberals (+29 to 32%), and somewhat lower rates among conservatives and extreme
conservatives (-17 to 24%). Breaking the pattern, slightly conservatives had a marginally increased
rate (+6%). A variant of this analysis was also carried out by including the happiness metrics
reverse-coded. This produced materially the same pattern, but was weaker since the happiness
items had a weaker relationship with political ideology than the mental illness variables had.
Discussion
The present study investigated a large dataset of representative adult Americans to see whether
there was a relationship between political ideology and mental health. Prior research and media
claims had indicated these variables were related such that left-wing ideology was associated with
worse mental health (Bullenkamp & Voges, 2004; Duckworth et al., 1994; Guhname, 2007; Howard
& Anthony, 1977; Kelly, 2014; Lemoine, 2020; Unorthodox Theory, 2020). The results of the
present study are in line with previous claims, in particular concerning people who reported being
“extremely liberal”, though this is a small minority of persons in the study (about 5% in 2018, cf.
Figure 2). It is notable that the question based on the largest sample size (n = 11,338, spanning the
years 2002-2018, days of poor mental health last 30 days) showed one of the most consistent
patterns, both in the simple averages by sex and when adjusting for age. The effect size between
the two extreme groups was 0.39 d, thus of moderate size. As both variables are single item
measures which have limited reliability, the true score effect size would perhaps be around 0.50
assuming about 0.70 test-retest reliability (Kim & Abraham, 2016; Littman et al., 2006; Spörrle &
Bekk, 2014). On the other hand, the correlation between the variables is only .07 (p = 3e-10).
So, is the effect large enough to care about? It may depend on whether one is interested in
people with extreme political views, roughly in the top 10% of extremism (5% on either side, cf.
Figure 2). There is evidence that most political discourse and activism is done by highly interested,
generally intelligent people (Kalmoe, 2020), who also tend to be more ideologically consistent and
thus more represented among the extreme groups (Kalmoe, 2020). Thus, one might expect that
among such people, the left-wing political activists would tend to be more mentally ill than the
equally extreme right-wing political activists.
With regards to etiology, this kind of cross-sectional study is not highly informative. Both mental
illness and political ideology are substantially heritable (Brikell et al., 2018; Hatemi et al., 2014;
Kirkegaard, 2018; Neumann et al., 2016), and both are moderately to strongly related to broader
personality differences (Fatke, 2017; Ksiazkiewicz & Friesen, 2019). A good start would be doing a
multivariate behavioral genetic study to assess the degree to which the relations are due to
common genetic variance or shared environmental (which includes upbringing). Based on prior
findings (Kirkegaard, 2018), it is unlikely that the shared environment contributes substantially to
the relationship.
However, even finding common genetic variation would not necessarily be terribly informative
regarding causality. It is possible that mentally ill people select into extreme left-wing views, or that
being in extreme left-wing contexts results in mental illness (i.e. left-wing contexts promote mental
illness). It is also likely that both are caused to some degree by other factors not measured or even
mentioned here. With regard to overlap between contexts, it is well-known that academics lean
extremely to the left (especially the softer fields), and show high rates of mental illness (Duarte et
al., 2015; Kinman & Wray, 2013; Langbert, 2018). There are also reports of increasing rates of
mental illness among students and PhD students in particular (Levecque et al., 2017; Puthran et
al., 2016; Rotenstein et al., 2016; Twenge et al., 2010).
This suggests that there is perhaps something about being in university that is causal for
mental illness and probably also encourages people with poor mental health to self-select into it. A
promising route would be to look at people who were somehow randomized to attend college or
not, or to become a PhD student or not, perhaps as a result of a lottery for scholarships. This would
remove the possibility of self-selection, and thus be informative about the degree of causality from
university attendance or employment to mental illness. It would also be informative to locate
datasets with longitudinal data between the variables to see if mental illness in childhood or teens
predict later entry into academic employment, thus suggesting self-selection rather than causal
effects of the academic environment. Similarly, one could do a longitudinal study to see if people
leaving academia tend to become happier.
Limitations and suggestions
The study has a number of limitations. First, since we were limited to single items, there is a
question of whether these tap into the construct of mental illness properly (construct validity).
Generally, research on single item measures of mental health find that they are useful (Ahmad et
al., 2014). Some prior research on the topic employed stronger methods such as looking at the
voting pattern of people who are institutionalized or hospitalized for mental illness, and also found a
left-wing association (Bullenkamp & Voges, 2004; Duckworth et al., 1994; Howard & Anthony,
1977; Kelly, 2014). Thus, construct validity does not seem a plausible issue. As the reliability of
single items is usually estimated to be around .70, the observed relationships would be somewhat
stronger if adjusted for this measurement error. For instance, the correlation of .07 between bad
mental health days and political ideology would become .09, while the Cohen’s d between extreme
liberals and extreme conservatives of 0.39 would become 0.56.
Second, the sample sizes were not always sufficient to estimate differences with confidence for
the extreme groups. We used all the available data as of this time. Our use of meta-analysis across
items uses the available data in an optimal way to increase statistical certainty. Research should
attempt to find large surveys that use better measures of mental illness, and include a measure of
political ideology. Another alternative is to scrape data from political activists online (e.g. Twitter)
and examine it for indicators of mental illness (Coppersmith et al., 2014, 2018; Nadeem, 2016;
Reece et al., 2017). A third option would be to sample persons already known to have severe
mental illness issues and examine their political views, as already done in a number of small
studies of voting behavior among patients in mental health facilities (Bullenkamp & Voges, 2004;
Duckworth et al., 1994; Howard & Anthony, 1977; Kelly, 2014). This could be done via the internet
since various online survey companies allow targeting of specific subgroups, such as those with a
diagnosis of mental illness.
Third, a thorny issue is whether there is measurement invariance by group. In the case of
single items, one cannot conduct measurement invariance (MI) testing since differential item
functioning (DIF) tests rely upon other items to estimate their parameters. One reason to believe
there might be measurement bias is that left-wing political views are on the whole more friendly
disposed towards people with mental illness (Gonzales et al., 2017; Parcesepe & Cabassa, 2013),
and thus may be more willing to seek help, get diagnosed, get treatment, and even admit their
problems to themselves (Alexander, 2020). This sort of measurement bias could be examined
using standard methods such as multi-group confirmatory factor analysis (MGCFA), and DIF,
especially if one used a heterogeneous set of items or tests. A number of studies have examined
other groups where one might expect measurement bias exists and found it lacking (ethnic groups
Hoe & Brekke, 2008; natives vs. immigrants Iliceto et al., 2013; across sexes T.-H. Wu et al., 2015).
Finally, one might look at objective or other-ratings of mental illness.
Among objective indicators, the suicide rate could be useful as it represents a concrete action
that is difficult to misinterpret. Various research shows that conservative and religious people have
much lower suicide risk, suggesting lower rates of mental illness among conservative and religious
people (Stack & Wasserman, 1992; A. Wu et al., 2015). However, the same argument could be
made here that suicides are a faulty indicator because Abrahamic religions have laws against
them, which result in fewer suicides despite equal rates of mental illness. While measurement
invariance issues cannot at present be ruled out, they seem implausible considering that every
available indicator examined has the same direction of effect.
Fourth, similar to the use of a single item to measure mental illness, the use of a single item to
measure political ideology is questionable. Various factor analytic studies of politics find that one
can in general not easily summarize the views of the general population into a simple one-
dimensional scale like typically done in studies, including the present (Carl, 2015; Carmines et al.,
2012; Feldman & Johnston, 2014; Kalmoe, 2020; Kirkegaard et al., 2017; Swedlow, 2008). Many
researchers advocate two- or three-dimensional approaches. It is likely that the pattern found in this
study will turn out to be more complicated if such measures were used. In order to keep the
analyses simple here, such more advanced measurements were not attempted, but left for future
studies. At the request of a reviewer, we did conduct an extra analysis using the party affiliation
measure, and this showed weaker relationships than the political ideology measure, and in one
case, a different non-monotonic pattern (Figure X). The reasons for this are unknown but deserve
investigation.
Supplementary materials
Study analysis code, full statistical output (R notebook), and data are available at
https://osf.io/fhpxm/.
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