More than just climate: Income inequality and sex ratio are better predictors of cross-cultural variations in aggression

Abstract

Van Lange et al. argue that variations in climate explain cross-societal variations in violence. We suggest that any approach seeking to understand cross-cultural variation in human behavior via an ecological framework must consider a wider array of ecological variables, and we find that income inequality and sex ratio are better predictors than climate of cross-societal variations in violence.

Full text

Commentary on Target article: Van Lange, Rinderu, & Bushman, in press
Abstract: 59 words
Main text: 975 words
References: 445 words
Total Word Count: 1616 words
Title: More than just climate: Income inequality and sex ratio explain unique variance in
qualitatively different types of aggression
Authors: Jaimie Arona Krems & Michael E. W. Varnum
Institution: Arizona State University
Mailing Address: Department of Psychology
Arizona State University
950 S. McAllister St.,
Tempe, AZ 85287-1104
Phone: (480) 965-7598
Emails: jaimie.krems@asu.edu; mvarnum@asu.edu
In press, Behavioral and Brain Sciences

ABSTRACT
Van Lange, Rinderu, and Bushman argue that variations in climate explain cross-societal
variations in violence. We suggest that any approach seeking to understand cross-cultural
variation in human behavior via an ecological framework must consider a wider array of
ecological variables, and we find that income inequality and sex ratio are better predictors than
climate of cross-societal variations in violence.

Van Lange, Rinderu, and Bushman (VR&B) present a model wherein ecological variations
(climate) predict cross-societal variation in aggression. We agree with VR&B that an ecological
framework can provide ultimate explanations for such variation in aggression as well as a wide
variety of human behaviors. However, any approach seeking to understand cross-cultural
variation in human behavior via an ecological framework would do well to consider a wider
array of theoretically-relevant ecological variables (e.g., Grossmann & Varnum 2015; Varnum
& Grossmann, in press).
To illustrate our point, we draw on established work in behavioral ecology, evolutionary
biology, and animal behavior to identify two ecological features that might play an equal if not
larger role than climate in explaining cross-cultural variation in the frequency of two types of
aggression (e.g., Clutton-Brock & Parker 1995; Daly & Wilson, 2001; Emlen & Oring 1977;
Fischer 1930; Griskevicius et al. 2012). Here, we focus on income inequality (a marker of the
distribution of resources in an ecology) and adult sex ratio (the adult male-to-female ratio in an
ecology) as predictors of homicide (typically male-male violence) and rape (typically male-
female violence).
Income inequality has been touted as arguably the best predictor of variability in violence across
nations, with greater inequality linked to greater (typically male-perpetrated) violence (e.g.,
Daly & Wilson 2001; Kenrick & Gomez Jacinto 2013; Ouimet 2012; Wilson & Daly 1997).
Because males experience greater fitness variance, they engage in more risky, frequent, and
intense intrasexual competition (e.g., violence) for status and related mating opportunities.
Income inequality is thought to exacerbate male mating competition, and thus male violence, by
increasing the perceived benefits of high-risk competition, perhaps especially when lower-risk
routes to status are unavailable and/or yield unsubstantial gains (e.g., Daly & Wilson 2001).
Compared to income inequality, the link between biased sex ratios and violence is equivocal;
previous research has found variously that male-biased sex ratios are associated with more, less,
or no differences in violence (e.g., see Schacht & Mulder, 2014). There may be a stronger case,
however, for linking male-biased sex ratios to higher rates of sexual violence against women.
Previous work has argued that more males means more male mating competition, increasing the
likelihood that some males resort to rape to obtain mating opportunities and/or to intimate
partner violence to prevent the loss of existing relationships (e.g., Messner & Blau 1987;
D’Alessio & Stolzenberg 2010; Thornhill & Palmer 2000; Trent & South 2012).
We gathered archival data on 2009-2010 income inequality (World Bank 2015), 2010 adult sex
ratio (the ratio of males to females, aged 15 to 64; Central Intelligence Agency 2016), and
climate (total climatic demands; Van de Vliert 2013). Zero-order correlations are presented in
Table 1. We tested the relative contribution of these ecological factors to cross-societal
variations in rates of intentional homicide (World Bank 2016) and rape (United Nations Office
on Drugs and Crime 2011), in the year 2010, using multiple regression analyses in which all
three predictors were simultaneously entered and only countries with data on all 3 predictors
and the DV were included. Data for rates of intentional homicide and all three predictors were
available for 87 countries, and data for rates of rape and all three predictors were available for
39 countries (all data available at
https://dataverse.harvard.edu/dataverse/Krems_Varnum_2016_BBS_Commentary).

Table 1
Income Inequality, Adult Sex Ratio, Climatic Demands, Intentional Homicide Rates, and Rape
Rates: Correlations
Variables
1
2
3
4
5
1. Income Inequality
-
2. Adult Sex Ratio
.009
(n = 95)
-
3. Climatic Demands
-.548***
(n = 95)
-.025
(n = 189)
-
4. Homicide Rate
.476***
(n = 87)
-.096
(n = 144)
-.233**
(n = 143)
-
5. Rape Rate
.401**
(n = 39)
.208†
(n = 58)
-.149
(n = 58)
.174
(n = 54)
-
†p < .12, * p < .05, ** p ≤ .01, *** p ≤ .001
Multiple regression analysis revealed that the strongest predictor of homicide rates was income
inequality, β = .402, p = .001. Male-biased sex ratios, β = -.176, p = .070, were marginally
associated with homicide rates, but climatic demands were not, β = -.139, ns. 1 Income
inequality was also the strongest predictor of rape rates, β = .571, p = .003. Male-biased sex
ratios were also a significant predictor of rape rates, β = .363, p = .018, but climatic demands
were not, β = .219, ns.
Drawing on the cross-disciplinary literatures dealing with ecology, we identified two ecological
features with established theoretical and empirical links to violence. These features were
comparatively better predictors of cross-societal variation in homicide and rape rates than was
climate. These findings are consistent with the idea that we can use ecology to understand cross-
societal variations in aggression, however they illustrate the importance of considering multiple
ecological dimensions in such models.
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1 In additional analysis of the homicide data we used Cook’s D to identify data from any nations
that might be exerting undue influence. Using the conventional cut-off point of 4/n, we
identified potential outliers—Maldives, Honduras, Jamaica, El Salvador, Lesotho—and ran an
additional regression model excluding them. Income inequality remained a significant predictor
of intentional homicide rates, β = .743, p < .001. Adult sex ratio, β = -.117, ns, and climate, β =
.076, ns, were not significant predictors in this model. We also used Cook’s D to identify
outliers in the rape data — Botswana, Sweden, Jamaica, Columbia—and ran an additional
regression model excluding them. Income inequality, β = .493, p = .026, and adult sex ratio, β =
.375, p = .032, remained significant predictors of rape rates. Climate remained non-significant,
β = .082, ns.
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World Bank (2015). Income inequality [rates ascertained for 2009, 2010]. Retrieved from
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World Bank (2016). Intentional homicide [rates ascertained for 2010]. Retrieved from
http://databank.worldbank.org/data/reports.aspx?source=2&series=VC.IHR.PSRC.P5&c
ountry=.