Economic inequality drives female sexualization

Abstract

Sexual selection plays a crucial role in understanding the diversity of both physiological and behavioral traits of animals and in recent years has made significant contributions to explaining the patterning of inter- and intrasexual competition in humans (1). With increasing interest in the active role of females as competitors (2), evolutionary social scientists are examining more closely variation in women’s reproductive strategies (e.g., ref. 3). Blake et al. (4) take this exercise into new empirical territory: the posting of sexy selfies (defined as sexualized self-portrait photographs). Given the conventional view among social and psychological scientists that female sexualization is a direct product of male dominance and gender inequality, the preeminence of sexy selfies in parts of the world where women are least oppressed poses a puzzle that the authors seek to solve. Their alternative proposal is that rising economic inequality promotes status competition among women, manifest through the posting of sexy selfies. The authors reason that, insofar as income inequality breeds status competition and anxiety (5), it strengthens incentives for women to compete in the sexual marketplace—more specifically, to display their physical traits to both enhance their mate value among potential partners and compete with other women. The findings bear out the authors’ expectations. Looking at the patterning of the entire population of sexy selfies published (and geolocated) from Twitter and Instagram during a 1-mo period in 2016 (and controlled for many factors that predict postings more generally), they find that the prevalence of sexy selfies is greatest in environments characterized by highly unequal incomes. This replicates across city- and county-level analyses within the United States and across 113 nations. Furthermore, focusing on what is perhaps a more subtle form of female competition—expenditures in women’s beauty salons and clothing stores—the authors find similar patterning within US cities and counties. By … [↵][1]1Email: mborgerhoffmulder{at}ucdavis.edu. [1]: #xref-corresp-1-1