Thursday, October 27, 2022

The Oath Keepers' national organization is unusual among groups conducting political violence in that they seem to behave as a business

Klinenberg, Danny, Selling Violent Extremism (October 5, 2022). SSRN:

Abstract: The Oath Keepers' national organization is unusual among groups conducting political violence in that they seem to behave as a business. Using leaked membership data, internal chat forums and publicly available articles posted to their website, I show that, unlike other far-right organizations, such as the Proud Boys, the Oath Keepers do not organize as a club. Rather, its behavior is better explained as a firm that adjusts the price of membership over time to maximize profit. I then estimate the Oath Keepers' price elasticity of demand for new membership using five membership sales between 2014 and 2018. I find the organization's demand is highly sensitive to changes in price. These results imply that political violence can be motivated by nonideological entrepreneurs maximizing profits under current legal institutions -- a chilling conclusion.

Keywords: Extremism, Applied microeconomics


In line with previous findings, especially Neuroticism, Extraversion, and Conscientiousness are genetically the most important personality traits for well-being

Unraveling the Relation Between Personality and Well-Being in a Genetically Informative Design. Dirk H. M. Pelt, Lianne P. de Vries, and Meike Bartels. European Journal of Personality, Oct 26 2022.

Abstract: In the current study, common and unique genetic and environmental influences on personality and a broad range of well-being measures were investigated. Data on the Big Five, life satisfaction, quality of life, self-rated health, loneliness, and depression from 14,253 twins and their siblings (age M: 31.82, SD: 14.41, range 16–97) from the Netherlands Twin Register were used in multivariate extended twin models. The best-fitting theoretical model indicated that genetic variance in personality and well-being traits can be decomposed into effects due to one general, common factor (Mdn: 60%, range 15%–89%), due to personality-specific (Mdn: 2%, range 0%–78%) and well-being-specific (Mdn: 12%, range 4%–35%) factors, and trait-specific effects (Mdn: 18%, range 0%–65%). Significant amounts of non-additive genetic influences on the traits’ (co)variances were found, while no evidence was found for quantitative or qualitative sex differences. Taken together, our study paints a fine-grained, complex picture of common and unique genetic and environmental effects on personality and well-being. Implications for the interpretation of shared variance, inflated phenotypic correlations between traits and future gene finding studies are discussed.


Using a large population sample of twins and siblings, the current study provides detailed insights into the genetic overlap between personality and a broad range of well-being measures. Given our large sample size, the present study was well-powered. Overall, our results are in line with the previous finding that especially Neuroticism, Extraversion, and Conscientiousness are genetically the most important personality traits for well-being (Hahn et al., 2013Røysamb et al., 2018Weiss et al., 2008). Furthermore, the heritability of the personality traits of ∼40–55% (Vukasović & Bratko, 2015) and well-being traits of ∼30%–40% (Bartels, 2015Nes & Røysamb, 2015) are comparable with previous meta-analyses.
Our results indicate that personality traits and well-being traits share considerable amounts of common genetic and environmental influences, yet that they are also influenced by their own domain-specific and trait-specific effects. Additive (vs. non-additive) genetic effects were more shared between personality traits and well-being traits, as no trait-specific additive effects were found after accounting for common effects. Non-additive genetic effects showed a greater variety in effects due to different sources. Below we discuss the results in relation to each of our three research questions in detail.

Genetic and Environmental Overlap Between Personality and Well-Being (RQ1)

Genetic and environmental effects shared between personality and well-being traits varied considerably across traits. Genetic effects due to the general, common factor ranged from 15% (Ag) to 89% (Ne) (Mdn: 60%). Genetic effects on the personality traits due to the personality-specific factor ranged from 0% (Ne) to 78% (Op) (Mdn: 2%). Genetic effects on the well-being traits due to the well-being-specific factor ranged from 4% (DEP) to 35% (SAT) (Mdn: 12%). Finally, trait-specific genetic effects ranged from 0% (SAT) to 65% (Co) (Mdn: 18%). Environmental effects were mostly trait-specific (Mdn: 68%, ranging from 26% for DEP to 91% for Op), and much less common (Mdn: 20%, ranging from 0% for Op to 72% for DEP) or domain-specific (Mdn: 9%, ranging from 2% for Ne, DEP, and LON to 43% for QOL). Of all personality traits, Neuroticism was most strongly related to well-being, and particularly strongly genetically related to depression and loneliness, in line with previous research (Abdellaoui et al., 2019Fanous et al., 2002Kendler et al., 2006Okbay et al., 2016Schermer & Martin, 2019). Because of its pivotal role, Neuroticism is sometimes included as a well-being trait (Baselmans et al., 2019a2019b). On the other hand, Openness, Agreeableness, and self-rated health appeared to mostly be genetically and environmentally distinct from the other traits.
Importantly, the percentages from the previous section are based on common genetic effects on personality and well-being once their respective shared variances have been taken into account. For example, Neuroticism showed the strongest bivariate genetic correlations with well-being traits, but also with the other personality traits. In the best-fitting theoretical model in which shared domain-specific variance was taken into account, it still showed the strongest overlap with well-being. Thus, genetic effects on Neuroticism and well-being were not due to the genetic overlap that Neuroticism shares with other personality traits, or the genetic overlap that well-being traits share with each other. The same was true for Conscientiousness and Extraversion. Earlier claims that these personality traits and well-being are influenced by cross-domain pleiotropic effects (Hahn et al., 2013Røysamb et al., 2018Weiss et al., 2008) thus seem to be robust.
Based on our results, it can be concluded that the genetic overlap between personality and well-being is quite large (Mdn: 60%). This is in line with a proposed (genetic) “covitality” factor (Figueredo et al., 2004Weiss & Luciano, 2015) influencing the variation in both personality and well-being ratings: the recovering of such an overarching factor in our best-fitting model supports this claim. Based on the substantial genetic overlap, it has previously been suggested that “happiness is a personality thing” (Weiss et al., 2008). Yet, without explicit modeling of the direction of causation, personality may be a well-being thing just as well as well-being may be a personality thing (Keyes et al., 2015). At the phenotypic level, both directions of causality may indeed be simultaneously operating (e.g., Soto, 2015Specht et al., 2013). However, the current study shows that shared genes will act as a confounder for these effects. Additional research on causality in which genetic confounding is taken into account is thus needed (Briley et al., 2018).
When these causal mechanisms become more clear, our results are informative for future intervention studies. Although both are relatively stable over the lifespan, well-being is thought to be more malleable than personality (Anusic & Schimmack, 2016) and several well-being interventions have proven to be successful (van Agteren et al., 2021). Again, genetic effects need to be taken into account, as they play a role in stability and change of both personality and well-being (Nes et al., 2006Pedersen & Reynolds, 1998). By gaining more insights into what (genetically) separates well-being from personality, it will become easier in the future to target interventions specifically at effects unique to well-being.
Our findings on common, domain-specific, and trait-specific effects have implications for molecular genetic studies. GWASs are designed to identify the genetic variants associated with a trait. Several GWASs on personality (De Moor et al., 2015Lo et al., 2017van den Berg et al., 2016Weiss et al., 2016) and well-being (Baselmans et al., 2019aOkbay et al., 2016Turley et al., 2018) have been published in recent years. Recently, multivariate methods have been developed to investigate the (latent) genetic structure underlying traits at a molecular genetic level and use this structure to find new genetic variants for the identified latent factors (Genomic SEM; Grotzinger et al., 2019). Our models can be used as input for such investigations. Ultimately, this should make it possible in the future to arrive at a clear picture of the variants that are uniquely associated with well-being and personality, or with both.
Based on our results, one could alternatively argue that, overall, personality and well-being are quite distinct (100%–60% = 40%). With regards to the overlap and distinction, we largely concur with Keyes and colleagues (2015) who noted that personality reflects how one functions in life, while well-being reflects how well one functions. Being both part of the process of functioning in life they have much in common, but they also differ in their role in this process. These differences and similarities are likely to be reflected in their genetic makeup.

The Influence and Interpretation of Domain-Specific Shared Variance

Although we fitted domain-specific factors mostly to control for domain-specific variance, our results can provide insights for the interpretation of these factors. In the CP models, we found that loadings of Neuroticism (∼ −.85), Extraversion (∼ .55), and Conscientiousness (∼ .46) on the common personality factor were sizeable, while loadings of Agreeableness (∼ .23) and Openness (∼ −.08) were low. We thus did not find strong support for a phenotypic common personality factor (referred to as the General Factor of Personality; van der Linden et al., 2016). At the same time, the domain-specific well-being factor was well-defined by all well-being traits in our CP models, with phenotypic loadings ranging from ∼.40 (self-rated health) to ∼ −.84 (loneliness). In addition, in the IP models, domain-specific effects were more pronounced for well-being compared to personality. These results provide evidence for a broad, general well-being factor underlying different well-being measures (e.g., Longo et al., 2016) and makes it plausible that this factor has a solid genetic basis (Bartels & Boomsma, 2009Baselmans & Bartels, 2018).
Nevertheless, the superior fit of IP (vs. CP) models implies that these common factors must be interpreted with caution. This finding indicates that they may not be the causal factors influencing their indicators, as the common and unique effects operate at the indicator level, and not at the common factor level (Franić et al., 2013). Yet, the existence of a latent construct cannot be proven or disproven based on the relative fit of IP over CP models alone. For example, IP models tend to fit better than CP models when fitting them to the facets underlying each of the Big Five factors (Franić et al., 2014Jang et al., 2002). Rather than dismissing the Big Five as constructs altogether, Jang et al. (2002) concluded that they “do not exist as veridical psychological entities per se, but rather they exist as useful heuristic devices that describe pleiotropic effects and the common influence of environmental factors on sets of individual facets.” (p. 99). Similarly, the common factors in the current study may be viewed as an organization of traits on which common genetic and environmental are operate, each of them also having their own unique influences. Ultimately, to answer the question what these common factors represent, multi-trait-multi-method (MTMM) studies based on ratings of personality and well-being (see Schimmack & Kim, 2020) in a genetically informative design are needed to accurately separate trait from method effects (Bartels et al., 2007Borkenau et al., 2001).
Although not providing clear evidence on its meaning, the current study can parsimoniously explain why controlling for the shared Big Five variance reduces their correlations with well-being (Kallio Strand et al., 2021Kim et al., 2018Schimmack & Kim, 2020). In the suboptimal CP models, the genetic and environmental correlations between the latent general well-being and general personality factor were much higher (1.00. .96, and .81, for ADE respectively) than in the IP models (.25, 1.00, and .50, respectively). If then, in the CP models, the common genetic effects on indicators are aggregated to a higher level in an unbalanced way (as is the case for the higher-loading Neuroticism, Extraversion, and Conscientiousness, compared to Openness and Agreeableness), then this will artificially lead to higher genetic correlations between the common factors. These stronger genetic correlations translate to the phenotypic level. Thus, when we control for the shared phenotypic personality variance, then we are haphazardly controlling for the “true” underlying genetic and environmental effects at the indicator level, reducing the correlations between the Big Five and well-being. Again, this hypothesis needs to be tested in the future using genetically informative MTMM studies.

Non-additive Genetic Effects (RQ2)

In line with previous work, significant amounts of non-additive variance were found to influence both personality and well-being, and their overlap (Bartels & Boomsma, 2009Hahn et al., 2013Keller et al., 2005). Non-additive genetic effects accounted for between 14% (depressive symptoms) to 95% (Agreeableness) of the total genetic variance in the traits (Table 4). In the Cholesky model, absolute non-additive genetic correlations ranged from .13 to .93 (Mdn: .47). This is important, for example, for future molecular genetic studies trying to identify the genes associated with personality and well-being, since the methods used in such studies often assume additive genetic effects (Visscher et al., 2017). The amount of non-additive variance present in traits is also important for theoretical reasons, as it is assumed to be indicative of the evolutionary pressures that have caused these traits to emerge (Penke et al., 2007Verweij et al., 2012).
With our current sample size, we had sufficient power to detect non-additive genetic effects (D), but this does not apply to all previous studies on this topic. We found that especially for D, traits differed in the amount of effects due to common, domain-specific, and trait-specific effects. This will obscure results when effects are aggregated to higher trait levels. For example, when one creates a general well-being scale from multiple scales that differ in their common and unique additive and non-additive effects, then the resulting general measure will be a cloudy mix of these different genetic effects. These findings stress the importance of modeling higher order factors (e.g., “general well-being”) as latent variables in twin designs, to uncover the nuances in their underlying genetic effects.

Sex Differences in Genetic and Environmental Effects (RQ3)

In our large sample, we found moderate to small mean sex differences on the Big Five. In line with previous studies (Costa et al., 2001Schmitt et al., 2008Weisberg et al., 2011), females scored higher on Neuroticism and Agreeableness, and somewhat higher on Conscientiousness. In contrast to other studies, we found no sex differences in Extraversion, which may be due to our focus on the Big Five factors rather than facets residing below the Big Five. Females tend to score higher on the facet Enthusiasm and males on Assertiveness (Costa et al., 2001Feingold, 1994Weisberg et al., 2011). At the aggregate factor level, these differences may have canceled each other out. Sex differences on well-being traits were generally small, with the largest effect found for depression, also replicating previous work (Batz & Tay, 2018Batz-Barbarich et al., 2018Eaton et al., 2012).
Given our large sample and similar results from previous studies (Bartels, 2015Keyes et al., 2010Røysamb et al., 2018South et al., 2018Vukasović & Bratko, 2015), it seems safe to assume that, at the aggregate level, the same genes influence personality and well-being for males and females, and to the same extent. This is important information for theoretical and practical reasons as it suggests that mean differences are probably due to non-shared environmental circumstances. These non-shared environmental exposures reflect idiosyncratic experiences that only a single twin within the same family experiences, making them more different from their siblings. This may include life events, differences in socialization, different opportunities, or specific gender roles (South et al., 2018). Our results further imply that in future gene finding studies, male-specific and female-specific genes for personality and well-being are unlikely to be found.
It is tempting to conclude that the mean sex differences on personality and well-being are completely unrelated to genetic differences. However, genes may still play a role through more subtle processes such as gene-environment interplay. For example, we investigated genetic and environmental influences independent of age effects by regressing them out from the traits. It may be that a sex by age interaction is present, implying that quantitative or qualitative sex differences are only apparent at specific ages (e.g., during adolescence). For instance, puberty seems to coincide with increases in mean levels of internalizing symptoms and with increases in its heritability, particularly in girls (Bergen et al., 2007Patterson et al., 2018). Future studies investigating genetic and environmental effects as a function of both age and sex are needed to confirm such processes for personality and well-being.
It is also possible that genetic differences exist between males and females, but that these are masked by unmodeled gene by environment interaction (GxE) effects. Traditional twin models assume that GxE is not present, that is, that genetic effects are similar across different environments and/or subgroups. This may not be the case; Nes et al. (2010b), for example, showed that the environmental exposure marriage influenced the heritability estimates of SWB. Importantly, these marriage effects differed across males and females. GxE effects may also explain why gender differences tend to be larger in more prosperous societies: possible genetic differences between males and females may be more easily expressed in developed countries (Schmitt et al., 2008). In our study, we investigated a sample from the Netherlands, a highly developed country with relatively equal opportunities for males and females. Within our egalitarian sample, the smaller amount of variance in opportunities and gender roles between males and females may have attenuated the expression of genetic sex differences. Future studies that explicitly model GxE effects for males and females, preferably across countries with different developmental standards, are thus needed.


There are limitations to this study. First, as this study was conducted in a single context, the Netherlands, results may not generalize to other contexts. The heritability estimates of personality traits have been found to differ across cultures (Jang et al., 199820022006). In addition, culture has been found to moderate mean well-being (Deaton, 2008) and mean personality (Schmitt et al., 2007) levels, and their associations (Kim et al., 20122018). Thus, future studies with samples from different countries are needed to investigate whether our results apply to other cultural contexts.
Second, the data used were cross-sectional in nature and we therefore cannot make claims about causal effects or temporal changes in personality and well-being. Nevertheless, our results can still be useful as they indicate that genetic confounding needs to be taken into account in future studies investigating associations between personality and well-being. The growing availability of polygenic scores (i.e., individuals’ genetic risk for a given trait based on the effect sizes from GWAS; Wray et al., 2014) will increasingly allow for this. A third important limitation is that all our trait measures were based on self-reports. It could therefore be the case that the common effects on the personality and well-being traits were partly driven by common method biases (CMB), such as response styles related to item keying, social desirability, or acquiescence, which have been found to be partially heritable (Kam et al., 2013Melchers et al., 2018). This mechanism is especially relevant for the common variance among personality traits, as it is proposed to mainly reflect CMB (Chang et al., 2012). Although this possibility cannot be completely ruled out, our findings suggest that such effects may be limited. This is because IP models fit better than CP models: if CMB would be driving the associations between variables, then it would probably have led to such strong correlations between the traits that phenotypic common factors would be more pronounced (and lead to improved fit). As mentioned previously, additional genetic research on the overlap between personality and well-being using multiple raters is needed, since such designs can control for rater-specific biases (Bartels et al., 2007Borkenau et al., 2001).
Fourth, although the (extended) CTD has proven to be a robust method for estimating the heritability of complex traits, it comes with its limitations (Røysamb & Tambs, 2016). First, the CTD only provides an omnibus (upper-limit) test of the total amount of genetic and environmental effects on traits, without identifying specific genes (or environments). Relatedly, in addition to GxE effects, gene-environment correlations (rGE) are assumed to be non-present (Verhulst & Hatemi, 2013). These limitations notwithstanding, the results from extended CTD designs can still be informative for subsequent gene finding studies (e.g., Lo et al., 2017) or investigations of gene-environment interplay (e.g., Krueger et al., 2008). Finally, assortative mating (when people with the same phenotype or genotype tend to mate more than expected at random chance levels) is also not accounted for. However, little assortative mating for personality and well-being is found previously (Luo, 2017).
Finally, in this study, we incorporated a wide range of related traits to cover the broader well-being domain. However, the scope could be expanded by including more traits such as happiness or self-esteem (Bartels & Boomsma, 2009Diener, 1984Hufer-Thamm & Riemann, 2021Hufer‐Thamm & Riemann, 2021), which were not available to us. In addition, different conceptualizations and measures of well-being exist, which include (combinations of) hedonic, eudaimonic, emotional, and social aspects (e.g., Keyes et al., 2015). On the personality side, alternatives to the Five-Factor Model exist, such as the HEXACO six-factor model (Ashton & Lee, 2001). These models may cover broader or slightly different aspects of personality and well-being, which in turn may lead to finding different shared and unique effects in relation to well-being. However, because of the large overlap between different conceptualizations of well-being (also genetically; Baselmans & Bartels, 2018), and different personality models (Ludeke et al., 2019), results will likely be highly similar to ours (see Keyes et al., 2015).

The Paradox of Wealthy Nations’ Low Adolescent Life Satisfaction can largely be attributed to higher learning intensity

The Paradox of Wealthy Nations’ Low Adolescent Life Satisfaction. Robert Rudolf & Dirk Bethmann. Journal of Happiness Studies, Oct 26 2022.

Abstract: Using PISA 2018 data from nearly half a million 15-year-olds across 72 middle- and high-income countries, this study investigates the relationship between economic development and adolescent subjective well-being. Findings indicate a negative log-linear relationship between per-capita GDP and adolescent life satisfaction. The negative nexus stands in stark contrast to the otherwise positive relationship found between GDP per capita and adult life satisfaction for the same countries. Results are robust to various model specifications and both macro and micro approaches. Moreover, our analysis suggests that this apparent paradox can largely be attributed to higher learning intensity in advanced countries. Effects are found to be more pronounced for girls than for boys.


We define learning intensity as the product of quantity and complexity of learning tasks completed by a student within a given time period, e.g., a school year. The amount of learning that happens in school is known to be positively correlated with the level of economic development of a country. Due to differing returns to education across nations, Becker et al. (1990) concluded that “societies with limited human capital choose large families and invest little in each member; those with abundant human capital do the opposite”. Hence, parental investment in education of their offspring is highest in high-income countries, and so are the expectations that teachers and parents have in the actual cognitive efforts that children exert (Becker et al., 1990; Mincer, 1984). Given the importance of education and the overall level of development, high-income countries also provide higher school quality (World Bank, 2017; 2021). According to the World Bank (2017), “37 million African children will learn so little in school that they will not be much better off than kids who never attended school”. The secular expansion of schooling and of cognitive effort over the twentieth century economic development processes of OECD nations have further been associated with generational gains in intelligence levels and growth in the human prefrontal cortex (Blair et al., 2005; Flynn 1984, 1987).

A growing body of literature documents declining levels of adolescent SWB between the ages 10 and 15 (Casas and González-Carrasco, 2019). If it is true that schoolwork pressure and test requirements increased during early teen age, it would be advisable to control for education-related factors (Wiklund et al., 2012). Comparing PISA 2015 and 2018 data, Marquez and Long (2021) find declining levels of adolescent life satisfaction in 39 out of 46 countries over time.