Although costly signals can take many forms, according to several researchers ( Boone, 1998 ; Kantner & Vaughn, 2012 ; Smith & Bliege Bird, 2003 ), they must meet the following criteria: (1) individuals vary in some underlying, unobservable attribute that is relevant to others, (2) individuals can convey information about this attribute through a signal, and (3) the cost of sending the signal is correlated with the underlying attribute, and thus signaling tends to honestly advertise signalers’ underlying qualities. Binge drinking appears to meet these criteria: (1) people vary in unobservable attributes of relevance to others, such as income, intrasexual competitive ability, and heritable fitness, (2) binge drinking can convey information about these attributes, and (3) binge drinking is costlier for individuals who are lower in these attributes.

Some individuals are more likely to binge drink than others, including those between the ages of 18 and 34 ( Centers for Disease Control and Prevention, 2017 ). Mating competition also tends to be stronger at these younger ages ( Kruger & Schlemmer, 2009 ). One reason why excessive drinking is particularly prevalent among young adults may be that binge drinking is motivated by a suite of evolved human mating strategies that include the proclivity to compete ( Hone & McCullough, 2015 ), and binge drinking may represent a costly signal that communicates one’s underlying qualities ( Vincke, 2017 ). Displaying conspicuous traits or behaviors that are costly in terms of time, energy, and resources can relay underlying qualities of the signaler to the perceiver ( Bliege Bird & Smith, 2005 ). Because the signals are costly, organisms with lower underlying qualities would be less capable of producing and maintaining the signals.

Binge drinking can be considered a type of risk-taking behavior in part due to the negative outcomes associated with binge drinking. Binge drinking can not only result in diminished cognitive and psychomotor abilities ( Brumback, Cao, & King, 2007 ) but can also weaken the immune system ( Afshar et al., 2015 ). A single binge drinking episode (e.g., five shots of vodka) has been shown to weaken the immune system within 20 min ( Afshar et al., 2015 ). The ability to withstand these detrimental effects could signal “vigor” and advertise mate quality. This idea is related to Zahavi’s (1975) handicap principle, where females of many species prefer males that display exaggerated traits that are costly to maintain and develop; hence, these traits serve as “honest” signals. Indeed, occasional drinkers and frequent drinkers were rated as significantly more attractive than nondrinkers, especially in short-term mating contexts ( Vincke, 2016 ). Additionally, a recent review of the evolution of human sex-specific cognitive abilities by Geary (2017) identifies alcohol as a stressor/intoxicant that compromises the building, maintenance, and expression of sexually selected traits. That is, condition-dependent, sexually selected traits that support sexual competition (e.g., visuospatial competencies in men) and are fully developed and function correctly under favorable conditions are compromised by alcohol consumption. Thus, the ability to function despite consuming alcohol (i.e., to “hold your liquor”), especially among men, might signal that the binge drinker is of sufficient quality to cope with the harmful consequences of alcohol.

We developed sex-specific predictions regarding the impact of sex ratio on binge drinking. First, we predicted that male binge drinking rates would increase with OSR. Male-biased sex ratios decrease men’s ability to obtain mates ( Balshine-Earn, 1996 ); hence, if binge drinking increases with mating competition, then it should intensify when the population is male biased. Conversely, male binge drinking rates should be lower in lower OSR environments because female-biased sex ratios would facilitate male short-term mating opportunities ( Moss & Maner, 2016 ). In other words, when there are ample opportunities for men to pursue successful short-term mating, men would not need to engage in costly binge drinking or other similar risk-taking behaviors.

R statistical analysis software was used (Version 3.6.1; R Core Team, 2017 ). We tested whether OSRs influence binge drinking rates using linear mixed-effects modeling, conducted using the “lme4” (Version 1.1-21; Bates, Mächler, Bolker, &Walker, 2015 ) and “lmerTest” (Version 3.1-0; Kuznetsova, Brockhoff, & Christensen, 2017 ) in R. Four separate models were conducted for male and female binge drinking rates. In our first two models, male and female binge drinking rates were entered as dependent variables, OSR (i.e., total number of unmarried men to women in the county) was entered as an independent variable, and opposite-sex binge drinking rate (i.e., male binge drinking rate when the dependent variable is female binge drinking rate, and vice versa) and the state to which each county belonged were entered as covariates. The inclusion of years (2009–2012) was random and entered as a random intercept. Allowing OSR and opposite-sex binge drinking rates to vary across each year using random slopes resulted in convergence issues; hence, we included only random intercepts. In our third and fourth models, we ran the same analyses, replacing the OSR variable with OSR for the age groups: 20–29, 30–39, 40–49, and 50+ as independent variables. Full output, model specifications, and scripts can be found in the Supplementary materials.

Following previous studies ( Griskevicius et al., 2012 ; Kruger & Schlemmer, 2009 ), we obtained archival data on the OSR (measured as the ratio of adult unmarried men to unmarried women) across available counties in the United States between 2009 and 2012 from the American Community Survey ( U.S. Census Bureau, 2012 ). Given that some unmarried women can be unavailable to mate due to pregnancy or breastfeeding, this measure comprises a proxy measure of the true OSR. We used the OSR data to compare to rates of binge drinking reported by Dwyer-Lindgren et al. (2015) . Although Dwyer-Lindgren et al. (2015) provided binge drinking data for every county in the United States from 2002 to 2012, we used only data from 2009 to 2012 for comparison with available OSR data because OSRs for all counties across the United States were unavailable prior to 2009. We selected all counties reported in the American Community Survey as the target sample for this investigation, yielding data from 3,143 U.S. counties across 50 states, as well as Washington, D.C. The American Community Survey reported total number of unmarried men and women as well as age-specific group categories of unmarried men and women (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64, 65–74, 75–84, and ≥85 and over). In order to explore age-specific relationships while limiting the total number of tests and producing bins of more equal size, we combined these OSR data into the following age groups: 20–29, 30–39, 40–49, and 50+, as well as computing the overall OSR. We aimed to use the categorized OSR groups to test the relationships between the OSR of specific age groups and binge drinking rates, given that young adults aged 18–34 are most likely to engage in binge drinking in the United States ( Centers for Disease Control and Prevention, 2017 ).

Estimates of the prevalence of binge drinking between 2009 and 2012 in all U.S. counties were obtained from the Institute for Health Metrics and Evaluation ( Dwyer-Lindgren et al., 2015 ). Binge drinking was defined as the consumption of more than four alcoholic drinks for women and five drinks for men on a single occasion at least once in the past 30 days ( Dwyer-Lindgren et al., 2015 ).

There was also an effect of age-specific OSR on female binge drinking rates. The OSR of the 20–29 age-group (β = −.06, SE = .01, t = −9.13, p < .001) and the OSR of 50+ (β = −.07, SE = .01, t = −5.99, p < .001) significantly predicted female binge drinking rates, but the OSR of the 30–39 age-group (β = −.01, SE = .01, t = −0.29, p = .775) and the OSR of the 40–49 age-group (β = −.01, SE = .01, t = −1.36, p = .175) did not. Here, the covariate, male binge drinking rate, also significantly predicted female binge drinking rates (β = .70, SE = .01, t = 96.17, p < .001). For this model, the marginal R 2 was 83.1% and the conditional R 2 was 83.4%.

There was an effect of the age-specific OSR on male binge drinking rates. The OSR of the 20–29 age-group (β = .03, SE = .01, t = 5.13, p < .001), the OSR of 40–49 age group (β = .01, SE = .01, t = 2.44, p = .015), and the OSR of 50+ (β = .06, SE = .01, t = 5.75, p < .001) significantly predicted male binge drinking rates, but the OSR of other groups did not (OSR of the 30–39 age-group: β = −.01, SE = .01, t = −.31, p = .754). The covariate, female binge drinking rate, also significantly predicted male binge drinking rates (β = .63, SE = .01, t = 96.16, p < .001). The marginal R 2 for this model was 85.1%, and the conditional R 2 was 85.5%.

There was an effect of overall OSR on both male binge drinking rates (β = .04, SE = .01, t = 6.93, p < .001) and female binge drinking rates (β = −.05, SE = .01, t = −9.18, p < .001). In both analyses, opposite-sex binge drinking rate was a significant predictor (male binge drinking rates: β = .63, SE = .01, t = 97.61, p < .001; female binge drinking rates: β = .70, SE = .01, t = 97.62, p < .001). The marginal R 2 , the proportion of variance explained by the fixed factors, was 84.8% for male binge drinking and 82.6% for female binge drinking. Using “MuMIn” package (Version 1.43.6; Bartoń, 2018 ), we calculated the conditional R 2 , the proportion of variance explained by both fixed and random factors. The conditional R 2 was 85.2% for male binge drinking and 83.0% for female binge drinking. We used “ggplot2” package (Version 3.2.0; Wickham, 2016 ) and plotted the overall effect of OSR on male and female binge drinking rates ( Figure 1 ).

Discussion

We were interested in examining whether the varying sex ratios across U.S. counties influenced binge drinking rates, particularly among young adults. Specifically, we predicted higher male binge drinking rates and lower female binge drinking rates in environments with a male-biased OSR. In line with our predictions, our results indicate that a higher overall OSR is associated with higher male binge drinking rates across counties. The opposite relationship was observed with female binge drinking rates. In other words, a greater abundance of unmarried males compared to unmarried females was associated with higher binge drinking rates among men but lower binge drinking rates among women (Figure 1). Second, we found that counties with higher male binge drinking rates also had higher female binge drinking rates, perhaps due to the common influence of unmeasured social variables on both sexes and/or the influence of men and women on each other. For example, a social network analysis study by Lorant and Nicaise (2015) found that being socially tied (friendship, working with, partying with, or roommate) to binge drinkers increases the frequency of one’s binge drinking acts. Third, we found that the OSR of the youngest (20–29) and the oldest (50+) age groups predicted overall binge drinking rates in sex-specific directions.

Age-Specific OSR and Female Binge Drinking Rates For women, we found that the higher the OSR of ages 20–29 (i.e., relatively higher abundance of unmarried males compared to unmarried females), the lower the rates of female binge drinking (Figure 2). In other words, higher binge drinking rates in women were more likely to be observed in female-biased environments. Women who are in their reproductive prime might engage in binge drinking to competitively gain short-term mating opportunities in environments where men are scarce. As such, lower OSR environments are associated with higher rates of female promiscuity (Kenrick et al., 2003; Schmitt, 2005), despite the fact that women tend to be more sexually restricted than men (Buss & Schmitt, 1993; Puts et al., 2015; Schmitt, 2005). No significant association was observed between female binge drinking rates and OSR of older age groups (30–39 or 40–49). However, there was a significant negative correlation between female binge drinking rates and OSR in women of ages 50+ (Figure 2). It is expected that older married women and women with long-term partners are more likely to invest in both parenting of existing progeny and long-term relationships (Hughes & Aung, 2017), rather than short-term mating opportunities. However, older unmarried women might be more likely to engage in greater mating effort, including risk-taking behaviors such as binge drinking, in order to attain a mate in female-biased environments. In line with this reasoning, older women tend to show less restricted sociosexual behaviors (Meskó, Láng, & Kocsor, 2014) and are less likely to regret engaging in casual sex than younger women (Kennair, Bendixen, & Buss, 2016). As in male binge drinking, unmarried women in the 50+ age-group may be more likely to employ a short-term mating strategy that involves binge drinking because it is more likely that they (a) have never have been interested in long-term mating (and hence remain unmarried at later ages) and/or (b) are once again interested in short-term mating after divorce and/or raising children to an age of independence. In addition, the OSR of the 50+ age-group may serve as an indicator of environments in which binge drinking is more likely for younger women, perhaps because 50+ unmarried women prefer these environments. We emphasize that our age-related explanations are speculative, as we utilized data on binge drinking rates across age groups; to our knowledge, age-specific binge drinking rates at the county level are not available.

Alternative Explanations Alternatively, binge drinking might be a consequence, rather than a form, of intense mating competition. When mating opportunities are limited in unfavorable OSR environments, it is possible that the reward derived from binge drinking may compensate for the absence of reward derived from mating. This phenomenon has been demonstrated in fruit flies; male fruit flies that were deprived of sexual access to females increased ethanol intake, which increased Neuropeptide F levels associated with the reward system (Shohat-Ophir, Kaun, Azanchi, Mohammed, & Heberlein, 2012). On the other hand, male fruit flies exposed to ample mating opportunities decreased their ethanol intake. In humans, we might also predict that men and women in environments with fewer mating opportunities would pursue other rewarding behaviors such as those associated with drug and alcohol use. However, we found associations between male binge drinking rates and the OSR only in the youngest and oldest age groups. These findings seem more consistent with the hypothesis that binge drinking functions as a form of mating competition than with the notion that binge drinking is compensatory, which would seemingly hold true for men and women in all age groups. Finally, when one demographic group is relatively more prevalent, it may have a greater influence on local patterns of behavior. For example, if young men as a group have a greater proclivity to binge drink, then a higher proportion of young men locally may result in more binge drinking behavior in this and other demographic groups. When there are more young men, there may be more binge drinking in general because younger men are more likely to binge drink. In addition, binge drinking behaviors in young men might influence the binge drinking rates of others (e.g., young women, older men, etc.). The former explanation is less likely since the binge drinking rates obtained from Dwyer Lindgren et al. (2015) are already age adjusted. Although the latter view is possible, it does not explain why we did not observe similar correlations between male binge drinking rates and the OSR across age groups.