Making Good Men Harder to Find: How Changing Wealth Inequality Affects Marriage Patterns

Lots of things affect how hard it is to find a spouse. Local sex ratios are an example. For instance, in Alaska, where there are many more men than women, it is harder for a man to find a wife than a woman to find a husband. Something else that affects how hard it is for someone to find a spouse are preferences. If a person will only accept someone as a spouse who has characteristics that few people have, then it will hard for that person to find a spouse.

In a recent paper published in Evolution and Human Behavior, Robert Brooks and coauthors used computer simulation models to mimic how different wealth characteristics of individuals influence the outcome of marriage markets. Their models simulated a population of one million men and one million women encountering each other and pairing off if they met a partner who met their specification for wealth. All the simulated men and women had a wealth characteristic that was distributed normally in the population, with a certain mean and standard deviation. That is, some men and women were rich, some were poor and most were in between, with the amount of inequality measured by the standard deviation of wealth. The women had the greater requirements for a spouse—they would only pair with a man they encountered of equal or greater wealth than themselves. The men had the least requirements for a spouse—they would pair with a woman they encountered who was willing to pair with them.

In the computer simulations, these simplified “men” and “women” randomly encounter each other and pair off after they encounter someone who meets their wealth specification. Sometimes, the authors ran the simulation just once, and sometimes they repeated it, so if those individuals did not find a spouse in the first round they would have a chance in the second round. The more rounds (iterations) the greater the chances that each individual would find a spouse. 

In different simulations, the authors varied the gender gap in the average wealth of the men and women so in some simulations the men earned more than the women on average; in others, the women earned more than the men on average; and in others, the average wealth was the same for men and women. They found that after many rounds or iterations of the same simulation, when male and female average wealth was the same, high wealth men and low wealth women were most likely to find a spouse, while low wealth men and high wealth women were least likely to pair successfully. In the simulations where average male wealth was higher than average female wealth, high wealth men and low wealth women were again most likely to find a spouse; they also found that a larger proportion of both men and women ended up paired successfully. When average female wealth was higher than average male wealth, once again high wealth men and low wealth women were most likely to find a spouse, but a smaller proportion of both men and women ended up pairing successfully.

The authors also varied how much wealth inequality (as measured by the standard deviation of wealth) there was among the individual men and women in the simulated population. They found that when men had greater wealth than women, on average, greater wealth inequality among men and among women exacerbated the finding above. That is, it became less likely that men of below-average wealth and women of above-average wealth would pair successfully. High wealth inequality among men only also made this outcome more likely.

The point of all this was to find out how the circumstances of wealth inequality both between sexes and within sexes influence marriage outcomes, under the assumption that women prefer to pair with men of equal or higher wealth than themselves. This is not an unreasonable assumption, as there is a great deal of research in evolutionary psychology and in sociology that suggests that women are more interested than men in finding a spouse who make the same or more money than they do. The models were based on this very simple assumption and only considered the effects of wealth and wealth inequality on pairing outcomes. In real life, many other factors and circumstances influence actual marriages. 

Yet simplified models such as this can shed light on real life processes. They can show how seemingly unconnected things can be connected (in this case, gender gaps in wealth and wealth inequality with marriage rates and patterns). Indeed, results from the simulations predict actual marriage outcomes in the U.S. population quite well. For example, in the U.S. today with the circumstances of higher average male income and substantial income inequality, high income men are the most likely to ever marry, while high income women are the least likely to ever marry. The simulations also suggest that one of the reasons for the fall in marriage rates in the U.S. is the narrowing of the income gap between men and women. The results of the simulations lead to the prediction that as women’s educational attainment relative to men increases, and assuming that the wage gap between men and women narrows further, marriage rates will continue to decline in the United States. Increasing income inequality is likely to exacerbate this trend.

Rosemary L. Hopcroft is Professor Emerita of Sociology at the University of North Carolina at Charlotte. She is the author of Evolution and Gender: Why it matters for contemporary life (Routledge 2016), editor of The Oxford Handbook of Evolution, Biology, & Society (Oxford, 2018) and author (with Martin Fieder and Susanne Huber) of Not So Weird After All: The Changing Relationship Between Status and Fertility (Routledge, 2024).