The Effects of the Mating Market, Sex, Age, and Income on Sociopolitical Orientation : Insights from Evolutionary Theory and Sexual Economics Theory.
Francesca R LubertiKhandis R BlakeRobert C BrooksPublished in: Human nature (Hawthorne, N.Y.) (2020)
Sociopolitical attitudes are often the root cause of conflicts between individuals, groups, and even nations, but little is known about the origin of individual differences in sociopolitical orientation. We test a combination of economic and evolutionary ideas about the degree to which the mating market, sex, age, and income affect sociopolitical orientation. We collected data online through Amazon's Mechanical Turk from 1108 US participants who were between 18 and 60, fluent in English, and single. While ostensibly testing a new online dating website, participants created an online dating profile and described people they would like to date. We manipulated the participants' popularity in the mating market and the size of the market (i.e., the number of ideal partners in the market) and then measured participants' sociopolitical attitudes. The sociopolitical attitudes were reduced to five dimensions via Principal Components Analysis (Sociosexuality, Benevolent Sexism, Wealth Redistribution, Nonconforming Behaviors, and Traditional Family Values). Both manipulations affected attitudes toward wealth redistribution but were largely not significant predictors of the other dimensions. Men reported more unrestricted sociosexual attitudes, and more support for benevolent sexism and traditional family values, than women did, and women supported wealth redistribution more than men did. There was no sex difference in accepting nonconforming behaviors. Younger people and people with lower incomes were more liberal than older people and people with higher incomes, respectively, regardless of sex. Overall, effects were largely not interactive, suggesting that individual differences in sociopolitical orientation may reflect strategic self-interest and be more straightforward than previously predicted.
Keyphrases
- mental health
- health insurance
- polycystic ovary syndrome
- physical activity
- health information
- middle aged
- gene expression
- dna methylation
- electronic health record
- machine learning
- type diabetes
- insulin resistance
- big data
- adipose tissue
- pregnancy outcomes
- hiv testing
- men who have sex with men
- pregnant women
- artificial intelligence
- data analysis