White nationalism before Trump

Political parties are messy coalitions that are pretty much constantly changing. They involve various demographic and interest groups with a hodgepodge of policy preferences. When we look at periods in which the policies and priorities of the parties change substantially, we often see that some people cross party lines when voting and, if the changes last, even flip their party identities.

It seems relatively clear that the U.S. is now in one of these periods of heightened change. The Republican party has been relatively more opposed to civil rights for minorities since the 1960s, and relatively more opposed to immigration since the 1990s, and relatively more suspicious of Muslims since the 2000s. But these kinds of white nationalist positions haven’t been the party’s main focus. The candidacy of Donald Trump has changed that, at least for the moment.

This Republican shift opens up risks and opportunities for both major parties. Before Trump, Republicans were more likely than Democrats to hold white nationalist views—combining conservative positions on immigration, race, Islam, and related areas—but there have remained substantial internal differences within both parties. So it seems likely that this presidential election will involve some shifts in prior voting patterns.

To get specific, here I’ll look at white nationalist views within the parties in the period immediately prior to Trump, who announced his candidacy in June of 2015. I’m using Pew political data from February 2013 to May 2015. I created an overall measure of white nationalist views based on a couple dozen individual survey questions—the measure is based primarily on opinions on immigration and secondarily on opinions regarding equal rights for blacks, and also contains a few items on views on Muslims, terrorism, and free trade. It’s a combined profile of the essential core of Trump’s campaign.

So, before Trump, who were the Democrats and who were the Republicans for whom this white nationalist agenda had the most and least appeal? Who, that is, should we expect to be most and least likely to cross their old party lines in this election given the new change in policy focus?

I first split the sample into Democrats (including Democratic leaners) and Republicans (including Republican leaners). I then explored the demographic predictors (in each side separately) of positions on my overall white nationalism measure. Based on this analysis, I divided both sides’ supporters into five subgroups, shown in the chart below.

PewPWN2(Technical notes: Sample size is 23,667. Results are weighted. “White” is non-Hispanic white. For ease of visual interpretation, I defined the top 20% of the overall sample on the policy scale as “White Nationalist” and the bottom 20% as “Multiculturalist,” with the other quintiles represented by the three middle categories.)

The big themes: Ethnicity, education, and age

By initially splitting the sample into Democrats and Republicans, there’s already a lot of demographic information built in here. The pre-Trump Republicans are more likely to be whites who are Evangelical, wealthier, and/or men (especially whites with middle-of-the-road levels of education). The pre-Trump Democrats are more likely to be non-whites or whites who are non-Christian, poorer, and/or women (especially whites in these categories who have more education).

When we look within these coalitions at white nationalist views, the main divisions involve ethnicity, education, and age. In both parties before Trump, we see splits such that non-whites, those with more education, and younger folks were less likely to favor white nationalist policies than older whites with less education.

On the Democratic side before Trump, those already most opposed to white nationalist policies were the college-educated, atheists and agnostics, Millennials, and non-whites. But when we look at the group to which none of these apply, we actually we a Democratic group that contains more folks supportive of white nationalist policies than opposed.

On the Republican side before Trump, those who were most likely to oppose white nationalist policies were blacks, Hispanics, Muslims, Buddhists, and Hindus, followed by young folks, followed by Jews and Mormons, followed by those who either have college degrees or are wealthier. The remaining group are the bulk of Republicans, who showed the most support for white nationalist policies before Trump—these are mostly older, less-educated, non-rich, white Christians.

Sometimes the obvious idea goes a long way

So what accounts for Trump’s appeal? What accounts for the recent spate of general election polls showing him doing particularly poorly with minorities, and weaker-than-usual with college-educated whites, but with tremendous appeal to less-educated whites?

Commentators have proposed a number of ideas, but there’s an obvious core here that shouldn’t be forgotten. Before Trump announced his candidacy, the kind of white nationalist agenda he has emphasized was already most popular among less-educated and older whites and least popular among the better educated, minorities, and younger folks. Trump hasn’t completely reorganized the parties’ policy positions, but he has introduced a new prioritization of white nationalist policies. So this will likely lead to some shifts in prior voting patterns.

There’s more going on in this election than just this change in white nationalist policy focus, of course. But sometimes the basics aren’t that complicated. Some people are supporting Trump—even some Democrats—because he’s pushing an agenda they already supported. And some people are opposing Trump—even some Republicans—because he’s pushing an agenda they already opposed.

On discrimination and political correctness

It’s really hard to try to understand what’s driving the debates over discrimination and political correctness without becoming overwhelmed by the powerful desire to paint one’s own political side as reasonable and one’s opponents’ side as corrupt or duped or unthinking. Yet it remains possible to analyze the political conflict without assuming that a huge chunk of the public is unhinged—it’s not pretty or emotionally satisfying, but it’s possible.

The basics

Let’s start with a couple of fundamental points. First, group-based discrimination harms some but benefits others. Ta-Nehisi Coates, for example, writes of plunder—calling attention to the fact that America’s history of racial discrimination hasn’t just been about holding blacks back, but about whites benefiting from subjugated blacks’ labor and property (including various examples from slavery to the recent aggressive marketing of high-interest subprime mortgages to blacks). In addition, of course, a frequent context for discrimination involves competition over college admissions and employment, where someone’s success almost necessarily implies someone else’s disappointment.

A second basic point is that there’s no such thing as a neutral system for allocating general social status or specific desirable positions. We can try to lessen the prevalence of group-based discrimination, but we’re not replacing it with nothing at all or with something handed down by God or Nature. In the past half-century, the main competing allocation regime has been test-based and education-based meritocracy. Gaining desirable positions is now less about race, religion, gender, and so on, and more about how well one does on tests and one’s educational pedigree. Like any allocation regime, meritocracy picks winners and losers in potentially controversial ways—in college admissions, for example, there are constant arguments over the proper role of standardized tests (though we could go much deeper and wonder, e.g., why the people who are already the most knowledgeable should be preferentially admitted into publicly subsidized learning institutions). And, like any allocation regime, meritocracy is subject to its own pathologies and entrenchments.

Relative winners and losers from discrimination and meritocracy

The key to arriving at a view of the non-craziness of public opinion on discrimination and political correctness is to compare the typical winners and losers from the old group-based allocation regime and the new meritocratic allocation regime. The group-based regime primarily favors whites, Christians (particularly Protestants), men, the native-born, and heterosexuals at the expense of others. The meritocratic regime primarily favors good test takers and the highly educated.

The biggest winners from the old group-based regime as compared with the meritocratic regime, then, are whites, Christians, and so on, but specifically those who are poor test takers and poorly educated. In contrast, the biggest winners from the meritocratic regime as compared with the group-based regime are good test takers and the highly educated, but specifically those who are non-white, non-Christian, and so on.

Reviewing a wide range of evidence on public opinion, we see patterns that are consistent with this basic breakdown. At high levels of meritocratic competence (i.e., among those with good test-taking ability and more education), folks really tend to prefer meritocratic policies over group-based ones, and are particularly opposed to group-based discrimination against people with their own group-based features. At lower levels of meritocratic competence, people tend to oppose group-based discrimination against people in their own groups, while favoring group-based discrimination against people not in their own groups—so, for example, immigrant Christians with less education are typically pro-immigrant but supportive of religious discrimination.

Political correctness is about penalizing the coordinating signals of discrimination

Group-based conflict often involves an important set of social signals. I’m talking here not just about group-based discrimination in its traditional sense, but more broadly—for example, this applies to conflicts between political partisans, rival colleges, rival nations, and so on. In group-based conflict, people use demeaning labels for out-groups, tell jokes painting out-group members as stupid and ugly, make wild categorical statements (“They’re bringing crime. They’re rapists…”), display relevant symbols of in-group loyalty (flags and whatnot), and related phenomenon. These signals serve important coordinating functions.

One way to think about political correctness is that, at its core, it’s an effort to disrupt these kinds of coordinating signals, specifically with regard to group-based discrimination on the basis of, at a minimum, race, ethnicity, religion, gender, disability, and sexual orientation. This effort primarily involves imposing tangible costs on discriminatory signalers. Distinguished professors and heads of major companies can be fired for a sexist joke, a racist text, or an underling’s racist email.

Like any complex system of social guidelines, the boundaries of political correctness are fuzzy. There are paradigmatic violations—e.g., blatant instances of racism, anti-Semitism, sexism, or homophobia—but also lots of grey areas. These grey areas exist both in terms of what counts as offending actions (e.g., debates over micro-aggressions) and what kinds of groups political correctness should cover (e.g., whether something like weight discrimination should be treated like racism and sexism). The very serious consequences of violating norms of political correctness make such boundary issues particularly important. There are also concerns that, like any blunt instrument of power, political correctness can be distorted to serve other agendas—becoming, say, a generalized tool to silence conservative political views or a way to penalize criticisms of Israel.

A multi-sided fight over discrimination and political correctness

Overall, one should expect stronger levels of support for political correctness from those who simultaneously gain under meritocracy but lose under group-based discrimination—people with higher levels of meritocratic competence (better test takers with more elite educations) who also have traditionally subordinate group identities (e.g., non-whites, non-Christians, women, gays and lesbians). On the other side, one should expect stronger levels of opposition to political correctness from people who combine lower levels of meritocratic competence with traditionally dominant group identities (white, Christian, male heterosexuals).

But matters grow more complex when we consider non-paradigmatically situated folks. One set are people who combine lower levels of meritocratic competence with a mix of dominant and subordinate group identities—think, for example, of less-educated people who are black Christians, Latino immigrant Christians, white non-Christians, and so on. At lower education levels, these folks tend to express pro-discrimination views regarding areas where they’re in dominant groups (so, e.g., black heterosexual Christians at lower education levels often favor school prayer and oppose gay rights) but also anti-discrimination views regarding areas where they face discrimination (e.g., these same black heterosexual Christians often have strongly liberal views on racial issues). One shouldn’t expect such cross-pressured opinion-holders to support generalized notions of political correctness—instead, their support and opposition will typically be more selective.

Other non-paradigmatic folks combine high levels of meritocratic competence with largely dominant group identities—highly educated white guys, mostly. So, you know, just to throw out some random names: Ross Douthat, Jonathan Chait, Conor Friedersdorf, Jon Haidt, and so on. Here, we might see people with a genuine commitment to meritocratic rules, but who also can be leery of the reach of political correctness. This will be particularly true when they think that proponents of political correctness seek to extend its reach into a kind of anti-meritocratic burden specifically on white males, conservatives, and so on. It’s really just the same general tendency that produced political correctness in the first place—namely, folks who do well under meritocratic rules are particularly sensitive to discriminatory threats against people with their own characteristics.

Explanatory space

Here, I’m trying to make sense of the rough battle lines—why the professoriate, elite media, and big business have generally embraced very strong anti-discriminatory norms, why these norms are particularly opposed by many less-educated white Christians, why some folks are strongly committed to anti-discriminatory norms but also express growing concerns over the reach of political correctness, and so on. One key, I think, is to consider a broad range of non-crazy personal interests.

But I understand that this isn’t what most people want to hear. What they want to hear is that their own side in this multi-sided fight is rational and just, while their opponents are unbalanced and immoral. They want to hear about the outrageous anecdotes that prove their opponents’ madness.

Yet I hope there’s space to attempt non-hand-waving explanatory work, to try to figure out what might lead people to give offense and to be offended, to situate these conflicts within wider psychological and political theories. These explanatory attempts might not securely embrace one’s preferred moral story, but neither do they prevent such stories. They don’t prevent anyone from defining and defending their own political positions.

Evolutionary psychology and modern fertility

The recent history of evolutionary approaches to human mating and fertility has been one of increasing focus on trade-offs and strategic diversity. I think that lots of people think that evolutionary approaches are mostly about universal human behaviors, but that spectacularly misses the main thrust of the past few decades of research.

The big trend has been in the development and adoption of life-history perspectives, with a special focus on quantity-quality trade-offs. The ideas here involve how reproducing organisms string together sequences of life events (or life histories) that are about maximizing both offspring quantity and offspring quality (i.e., the offspring’s later reproductive prospects). Life-history theory analyzes how time spent mating competes with time spent parenting, how having lots of kids might shorten one’s lifespan, how having more offspring might diminish the quality of each offspring, and related concerns.

With quantity-quality trade-offs come strategic diversity. Both across species and within species, some might shoot for more quantity at the expense of quality, or more quality at the expense of quantity. These days, lots of folks talk broadly about this kind of diversity in terms of fast and slow patterns. As applied to humans, the main idea is that harsher environments encourage faster strategies—emphasizing earlier childbearing and more kids at the expense of investments in quality—while stable, plentiful environments encourage slower strategies.

There have also been real strides in examining sexual and mating variability. Particularly notable advances have come from the conceptual and empirical development of sociosexuality, including the idea of strategic pluralism. This involves the cad/dad dilemma, that is, how women face a trade-off between mating with cads who have good genes or with dads who are good providers. And, again, with trade-offs come strategic diversity. Various features of environments and individuals factor into whether women in a given population, or different women within a population, might lean more toward the good provider or the good genes side of the trade-off.

Notice that strategic pluralism involves not a quantity-quality trade-off, but a quality-quality trade-off. It’s about two different classes of benefits that might help offspring to later become more successful reproductively—the genetic advantages that come from a physically attractive father vs. the material advantages that come from a provisioning father. As to how this relates to quantity trade-offs, some current evolutionary work has become confused. As I explained in a prior post, it is now common to suppose that unrestricted sociosexuality is a component of fast patterns while restricted sociosexuality is a component of slow patterns. In fact, sociosexuality and fast/slow patterns are empirically distinct at the individual level (in the modern U.S., anyway) and probably are more likely to line up restricted/fast vs. unrestricted/slow (rather than the predicted restricted/slow vs. unrestricted/fast) when comparing different human societies.

In my post from a couple of days ago, I discussed this as a 3D rich-sexy-lots trade-off. This is (as though matters aren’t already complicated enough) a quality-quality-quantity trade-off, where attempting to simultaneously maximize both quality aspects (i.e., the Sex and the City pattern) leads to an especially big hit to quantity.

And then I talked yesterday about how serious diversity can arise through goals-and-hints psychological processes. Here, people don’t just inherit specific behavioral patterns or detailed sets of if-then decision rules, but rather have a range of evolved goals and hints and then individually seek to come up with their own behavioral strategies to achieve these goals in the context of whatever local features are relevant.

So don’t expect any easy answers here.

Getting specific

Evolutionary approaches have made advances in sorting through the kinds of developmental regularities, decision rules, and goals-and-hints that drive human behavior generally. There have also been advances in thinking through trade-offs and strategic diversity, and how similar goals being pursued by different individuals in different environments might lead to patterns of divergence across and within populations.

But to analyze a given population at a given time requires a simultaneous focus on the real-world implications of a range of particulars. One needs to focus on how different kinds of individuals in different situations have different realistic solution pathways in achieving a range of at-times-conflicting goals.

With fertility patterns, we have a sense of what some of these particulars are. Is someone’s local circumstance one in which kids often die? One in which kids specifically without fathers often die? One in which kids are useful for things other than one’s own reproduction (e.g., as agrarian laborers)? What’s the local operational sex ratio? What are the mix of features in young local people that lead to mating success? How do local people achieve those features? What are the individual-difference factors that constrain different individuals’ realistic options for pursuing various pathways to social status or mating success?  What are the realistic prospects for stable parenting relationships? How much does relative wealth and social status rely on women’s employment? And so on.

So we might look at modern America, and get specific. Kids don’t often die. They devour dramatically more resources than they produce. Extended formal educations are the primary pathway to enhanced resources and social status, but are costly and seriously delay fertility. Social norms and birth control technology allow for lots of non-reproductive sex, reducing the costs of trying to figure out ways to turn shorter-term relationships into longer-term ones. Divorce is relatively low-cost. Dense populations present lots of mating alternatives. The African American population has a particularly imbalanced operational sex ratio, with substantially fewer living, non-incarcerated young men than young women. High income disparity, declining male incomes, and lower marriage rates imply a greater need for female labor market participation to achieve relative wealth. And so on.

To start understanding modern American patterns, one needs to combine a general sense of the relevant evolutionary goals with lots of the specific factors. Then one can start to map out how differently situated individuals might develop different pathways with different trade-offs in their pursuit of widely shared evolved goals.

This is what I had in mind in my earlier post posing a hypothetical about giving advice to an American teenager who wants lots of rich, sexy offspring. I traced through some thoroughly modern hurdles she would face in terms of education, her own income, and the support of a helpful partner. I ignored individual differences, but these are crucial as well. Is she someone whose family background and cognitive abilities give her an easier path to succeeding in higher education and the professional workforce? Do the young men who are realistically available to her often develop stable, higher-earning patterns that make them useful husbands? To what extent does she have personal features that make her attractive as a long-term partner? What are her available family and community resources that might affect various trade-offs? Within any potentially reproductive relationship, what are the realistic prospects regarding relationship stability and how would (additional) children affect that? What are their sources of income and how would (additional) children affect that? And so on.

There’s just a lot we don’t yet know about how these complicated processes interact. But the current tools give us a sense of the larger patterns. I wrote in an earlier post, for example, about the kind of lifestyle profiles that emerge from combining notions of fast/slow life-history with restricted/unrestricted sociosexuality, such that we can think about fast/restricted patterns, slow/restricted patterns, fast/unrestricted patterns, and slow/unrestricted patterns. This isn’t enough, of course, but it’s something, especially when we notice that the slow/unrestricted pathway seems to be the main driver of low modern American fertility.

I’ve been focusing on modern America in these discussions because my experience is mostly with modern American data. And my conclusion is that we know some interesting things, but in part because the puzzle involves so many complex specifics, we’ve got a way to go. Given the importance of specifics, I’d urge caution in extrapolating into different times and places. For example, the U.S. saw wild ups and downs in fertility just from the 1910s to the 1970s. Explaining 1930s America will require details that make sense of declining overall fertility along with extremely wide individual variance; explaining 1950s America will require details that make sense of fertility rates that rose incredibly quickly and broadly.

Similarly, various other countries require their own explanations. Right now, for example, both Germany and Japan have spectacularly low fertility rates. Figuring out each will require researchers who understand evolutionarily grounded goals-and-hints processes but who also have detailed understandings of social status, mating, and fertility trade-offs that are specific to modern Germany and specific to modern Japan.

Yeah, look, this stuff is hard. It’s slow work that often becomes more difficult the more one knows.

Strategic flexibility with evolved goals-and-hints

Evolutionary psychology is based on a recognition that past selection pressures have left their mark in substantial ways on modern human minds. It stems, in part, from two observations, both of which are at once obvious and profound. First, like any current species, our genetic material was inherited disproportionately from those members of past populations who were more successful at producing genetic descendants. And second, genes have major roles in building and maintaining human limbs, livers, spleens, bladders, blood, and bones—and they also have major roles when it comes to our brains. Or, to use a recurring phrase, evolution doesn’t stop at the neck.

But how does evolution affect the mind? It’s clear that human minds are not just collections of instincts. It’s also clear that human minds aren’t generalized fitness-calculating supercomputers with a single embedded goal of maximizing future genetic representation.

It’s complicated. The detailed workings of human brains are unfathomably complex. For most of us mortals, it’s about glimpsing a range of insights and then relying on metaphors that hopefully get us in the ballpark.

Artificial intelligence

Most computer programs do what they do with wholly pre-programmed code. There are sets of elaborate instructions that fully define what the program does in various situations. If A, then do B; if C, then do D; and so on. There’s an awful lot that such programs can do. But over the years it became painfully apparent that there’s also an awful lot that such programs are awful at—managing something that looks vaguely like animal locomotion over uneven terrain, for example, or understanding natural language, or producing output that appears reasonably intelligent in freestyle interactions.

These days, the most sophisticated programs have aspects of machine learning—they’re programs that are programmed to figure stuff out, rather than programs that are limited to responses that have been built in. And some of the most intriguing use various forms of artificial neural networks, structures based (very roughly) on brains like ours. These systems have ways of taking inputs and processing them into outputs, ways of comparing their outputs against desired and undesired outcomes, rules to make modifications to their processing operations based on whether their current outputs are close to or far away from desired targets, and lots of time to wade through various kinds of inputs, making processing adjustments as they go. They’re not handed a solution to their problem; they figure out a solution to their problem. Often these systems work better when they’re given hints, where, instead of searching the whole of an abstract processing terrain, they’re given biases to go look over there, to try certain kinds of answers first, to pay closer attention to certain kinds of inputs, and so on.

We could call this kind structured learning a goals-and-hints approach. There’s a basic architecture that is capable of self-modifying based on feedback, and it’s told what its goals are, given hints about how best to proceed, and interacts with its relevant environment over time in ways that lead to improved performance based on its ongoing modifications. Along the way, it develops its own sub-goals and sub-hints, moves that help it reach its higher-level goal in the specific context of its local environment.

Goals-and-hints processing has some very interesting features. For example, when two instances of the same program (with the same goals and hints) are given substantially different inputs, they’re likely to reach different processing solutions—each one is making it up as they go, and different environments might imply different ways of maximizing the same goal. Even when different instances of the same program have very similar inputs and reach very similar processing solutions, it’s important to keep in mind that each made up its own solution.

In humans, we see lots of goals-and-hints processing. Learning how to drive a car, for example. There are various implicit and explicit goals—don’t drive off the road, don’t run into other cars or people, follow traffic laws, and so on—and various hints from driving instructors. But mastery is ultimately about getting behind the wheel and having one’s brain make ongoing subtle adjustments to figure out over time how to perform competently.

Humans also take goals-and-hints processing to another level. We often engage in virtual interactions with our environments, imagining how things might turn out for us under various hypothetical circumstances. We observe how others succeed or fail in achieving goals we share. We pay special attention to stories, both real and fictional, that contain possible lessons for our own lives. We use these lessons to modify our own strategic moves.

The point here is that there are various ways for evolution to leave its mark on human minds. There could be basic developmental regularities that lead, most of the time, to certain common behavioral tendencies. There could be various complex sets of if-then rules that normally get built into our cognitive architecture. There could be combinations of developmental regularities and decision rules, where various developmental contingencies tend to lead to predictably varied sets of complex decision rules.

But we’ve also got a huge, flexible cortexes packed with massive neural networks just waiting to self-modify in whatever environment they happen to find themselves, guided by some set of specified goals-and-hints. Evolution can select for various social goals-and-hints (mostly housed in the non-cortical systems that handle basic emotional and motivational evaluations), letting each individual’s cortex work out its own detailed strategies in the context of that individual’s local situation. Some of these goals could be more specific and some could be more general.

Human brains are very complex amalgams of lots of different kinds of mechanisms—developmental contingencies, decision rules, goals-and-hints processing, and other variants, all inter-woven in complex ways. Further, brains are highly bureaucratic—they contain multiple departments with different tasks and competing recommendations, often operating without a clear organizational hierarchy. The various departments are themselves driven by complex amalgams of mechanisms. There’s a lot going on in there.

Modern minds with Stone Age goals-and-hints

I’ve done evolutionary work mostly on topics not to be discussed in polite company—politics, religion, and sex. Other than in my dissertation, though, I haven’t been very explicit about how I think these studies fit into the broader context of evolutionary psychology. Here it is: When it comes to complex social patterns, I’ve never thought that evolution’s impact on the human mind is only or even primarily in terms of regularly developing Pleistocene if-then decision rules (though I think we probably have lots of mechanisms that roughly fit this description). In addition to decision rules, I think about human behavior in terms of goals-and-hints processing, where we have widely shared goals and hints, but each individual works out his or her own strategies for achieving these goals given the specifics features of one’s self and one’s situation. When I see something like Kenrick’s pyramid of fundamental motives, for example, I don’t take these just as guides to identifying very particular evolved goals, but also to thinking about how some goals might be relatively broad—the sorts of goals that don’t just drive specific behaviors, but drive goals-and-hints processes that individuals use to invent and re-invent behavioral strategies based on local details.

And so, I’ve looked at people’s contrasting views on abortion and marijuana legalization, the recent rise of liberal-conservative ideology, and modern politics generally. I’ve looked at modern church attendance (explicitly saying that I don’t think the current individual-difference patterns have all that much to do with ancient individual-difference patterns). I’ve looked at the non-reproductive sex of college kids, at speed dating, at how college educations and cash incomes affect fertility.

I see these as evolutionary studies not because they are signs of Stone Age minds, but because modern minds are still organized around deep motivations regarding domains such as resources, protection, affiliation, social status, mating, and parenting. Humans confront these old social problems through complex mixtures of more specific and more general motivating mechanisms. Using goals-and-hints processing, individuals might figure out, for example, how the legality of abortion or marijuana (even if these are historically novel phenomena) relates to their self-determined context-dependent strategies in achieving old mating goals. (By “self-determined” here, I certainly don’t mean that social environments are irrelevant—far from it. An absolutely central part of humans’ goals-and-hints operations involves paying close attention to what other relevant people are doing.) They might figure out how the competition between meritocratic and discriminatory policies affects their self-determined context-dependent strategies in achieving old goals regarding social status, even if the modern concept of meritocracy didn’t arise until the 20th century. They might take into account the realistic usefulness of modern higher education, birth control, and religious participation in developing their self-determined context-dependent strategies to achieve old goals regarding mating and fertility.

This doesn’t mean, though, that any of these strategies optimize a hypothetical highest-level evolutionary goal. Even with powerfully flexible goals-and-hints processing, human evolutionary history might have set insufficient parameters—the wrong goals, or the wrong hints, or the wrong basic learning architecture—to regularly lead to optimal solutions for genetic propagation within a given environment. There are, after all, relatively clear examples of modern technologies that seem to suck lots of folks into plainly maladaptive behaviors by imitating evolutionarily valuable stimuli—OxyContin, heroin, potato chips, soft drinks, online computer games, and so on. Evolved psychological mechanisms in their more specific and more general forms might or might not be roughly adaptive in a given environment.

This post is part of my ongoing series on modern low fertility. I took this detour into deep thoughts because I wanted to give a sense of why I don’t think modern fertility patterns are (just) about how a detailed batch of ancient decision rules got tripped up by modern environments. I think that humans often deeply integrate modern (even novel) environmental conditions into their behavioral strategies. While there are old decision rules and old goals-and-hints driving the processes, individuals develop their own behavioral strategies over their lifetimes, strategies developed specifically in the context of their local (even novel) conditions. This may or may not lead on average to genetically optimal solutions, but it often means that the introduction of something like college educations or the pill or modern religion isn’t going to fundamentally alter the nature of what humans are doing—these new inventions might be tools employed or avoided in the service of ancient goals. Or, again, they might not. As always, these are empirical questions.

Rich, Sexy, Lots: A modern 3D life-history trade-off

In response to the evolutionary puzzle of modern low fertility, I’ve suggested that the patterns in various times and places can require their own explanations—so, for example, in the U.S., high 19th century fertility likely requires a different mix of explanations than high 1950s fertility, and low 1930s fertility likely requires a different mix of explanations than low 21st century fertility. I’ve suggested that the explanations for 21st century fertility in the U.S. probably can’t rely on the view that women generally don’t actually want to have kids, because even young women in college usually really do.

In my previous post, I discussed a three-way trade-off for women that is relevant to modern low fertility—it’s not the answer to everything, but it’s a piece of the puzzle. This complex modern trade-off involves having children who are likely to end up being relatively rich, having children who are likely to end up being physically attractive and sexy adults, and having lots of children. Call it the rich-sexy-lots trade-off.

This trade-off is in three dimensions. There are pretty straightforward rich/lots trade-offs—for example, getting a college education typically increases wealth but also delays childbearing, women’s careers often go on a less-lucrative path when they have more children, having more kids decreases one’s investments in each kid, and so on. But there are also trade-offs involving sexy.

Women’s sexy conundrum involves a theme that has been popular in women’s fiction for centuries, the old cad/dad dilemma—how to balance preferences for promiscuous men with preferences for reliable husbands. One of Jane Austen’s recurring plot points is the short-term appeal but long-term risk of sexy men. The Wickhams, Willoughbys, and Henrys of Austen’s early-19th-century world are attractive and immediately charming, but turn out to be dishonest seducers, lovers-and-leavers, and cheaters—in short, not great husband material. More recently, Sex and the City is at heart a story about modern professional women navigating mating terrain where Wickhams, Willoughbys, and Henrys are particularly sought after, with the best of them being hard to find and harder to tame. And very low fertility follows in the modern story—two of the main characters end up with one child each, and the other two have none.

The 3D trade-off gets real

Sex and the City is fiction, of course. But it’s not too far off the mark on similar women’s fertility outcomes. It’s well known that low modern fertility is particularly concentrated among women with the most education. That’s related to the rich-lots part of the trade-off. But what’s not as well known is that sexy is also a big deal—within educated women, very low modern fertility is concentrated among the more sexually adventurous.

In the chart below, I show completed fertility averages for the Baby Boom generation of American women (born from 1946 to 1964). I’ve plotted their average number of children according to two features—how much education the women got, and how many men they slept with since age 18. It’s basically the rich-sexy-lots trade-off in a single chart. The very highest fertility (with averages in the 2.5 to 3 children range) is among those women who combine little education with more monogamous sexual histories. And the very lowest fertility (with averages around 1 child) is essentially the Sex and the City profile.

GSSEdSexFert(Technical notes: The sample size is 3,588. Results are weighted. Number of children was capped at 8. Education had a floor of 9 and a cap of 19. Number of male sex partners since age 18 was capped at 15. The displayed results are from a regression equation predicting number of children with education and sex partners, including, to show non-linear trends, squared terms for both as well as the interaction between the two predictors.)

Not only is fertility lower as both education and sex partners rise, but there is an interaction as well. Number of sex partners doesn’t have as much to do with fertility among the less educated as it does among the college educated.

Back in college

In an earlier post, I described my sample of over 1,300 young women at six normal American universities. There we saw that most really wanted to get married in their mid-20s to faithful sorts of guys, and to have two or more kids beginning in their late-20s.

To see how sociosexual differences related to their marriage and fertility plans, I created a Sex and the City variable combining various items on how they feel about casual sex and how much they party and hook up. (For those of you who follow these things, it’s an expanded version of Sociosexual Orientation Inventory, adding three items about recent non-intercourse hook ups, getting drunk, and enjoying wild parties.)

This sociosexual variable was basically uncorrelated with how many children these young women eventually wanted to have and with how important having kids was to them—on the whole, the college women who were more abstinent and sociosexually restricted didn’t want children any more or less than unrestricted Sex and the City types did.

But sociosexual differences did relate to how important getting married was to them and the age at which they’d prefer to get married. Restricted women thought marriage was a bit more important and saw themselves getting married a bit earlier than unrestricted women.

And there were differences in what they wanted from future partners. As I described previously, I had almost half the sample rate a long list of features in terms of how desirable the features would be in a future partner. Some of these ratings varied significantly between more restricted and more unrestricted college women. Restricted women gave higher average ratings than unrestricted women to faithful-and-nice traits in future mates: someone you trust; who wouldn’t cheat on you; who’s kind; will be with you forever; has good morals; is patient. On the other hand, unrestricted women gave higher average ratings than restricted women to Sex and the City traits in future mates: has an exciting personality; has many friends; has an active social life; will have a high income; is physically attractive. It’s in large part an inherent sociosexual trade-off for women—and an enduring theme in stories especially popular among young women—namely, it’s tough to find men who combine faithful and sexy.

So how does sociosexuality end up relating to differences in fertility? These college women mostly started off looking to end up in a similar fertility neighborhood, but intending to take different roads with different kinds of guys. And, it appears, the nice-guy road to Fertville ends up being the more reliable one. But, again, it’s a trade-off—nice guys are less likely to father sexy sons.

Are people who open restaurants trying to lose money?

A big chunk of new restaurants—perhaps as many as 60%—fail within a year. So what’s up with people opening restaurants? Do they not care about making money? Are they trying to go bust?

Obviously, there’s sense in which phrasing the questions this way misses the point. They’re not trying to go bust. They’re trying to establish successful restaurants, something that would be really cool if it happened. But it’s an especially tough market. In short, they’re pursuing a high-risk high-reward venture.

We could ask similar kinds of questions about why any young person would ever put in lots of time and energy trying to be a professional athlete or a popular musician. Or about people who borrow a ton of money to go to college. Or about politicians or military officers who have extramarital affairs. Or about venture capitalists or stock-pickers. High-risk high-reward stuff happens a lot.

Now, saying that something is a high-risk high-reward strategy doesn’t mean that it’s rational in any specific case. People have limited knowledge and might genuinely misjudge their chances. They might be suckers. But they might also in some cases have real shots at getting things that they want—things that lots of people might want under the right circumstances—even if those things are difficult to pull off and potentially very costly when they don’t work out. There’s no general mystery in the fact that some people in some circumstances attempt things that have big potential upsides when they succeed and also have big potential downsides when they don’t.

Maybe the Sex and the City pattern is in this high-risk high-reward category. The big potential upside is something many young women would love to end up with—rich, sexy offspring. (The heroine’s prize in fairy tales, after all, is often both a prince and charming.) The big potential downside is something many young women want to avoid—having no offspring. Does that make it in some sense a rational strategy? Clearly not in all cases, but it sometimes works. Life is competitive and risky; it has trade-offs.

Again, I don’t want to say that the rich-sexy-lots trade-off fully explains the evolutionary puzzle of modern low fertility. It doesn’t. Indeed, it might not really hold together on closer inspection. But I’m proposing it as a possible piece of the puzzle that has received insufficient attention. The very lowest fertility rates these days are particularly concentrated among educated-and-unrestricted women. And it doesn’t look like they generally start off wanting very low fertility. Recognizing the complexity of the real-world trade-offs at least gets us closer.

So far in these fertility posts I haven’t been very explicit about the big picture. I’ve been busy getting some key empirical themes out. I’ll do a couple of concluding posts in this series that try to draw some of this together—that try to give a more general sense of how I think we might integrate evolutionary work on strategic pluralism and life-history trade-offs, while paying closer attention to the specifics of a given population’s actual circumstances and trade-offs.

The social scientist as advice columnist

For those of you who do, dabble in, or devour social science, I have an odd scenario that I want you to take seriously. Imagine that you receive an anonymous request from an American teenager that reads as follows:

I’m a young woman, and I’ve decided that what I really want is to have my own children, the more the better. More than that, though, I really want to have children who will end up being popular, good-looking, sexy, successful, rich adults—the kinds of adults that other adults will really want to have sex and have families with. Being realistic, what should I do?

Imagine that you don’t know whether she’s rich or poor, in a big city or a small town, good looking or plain, athletic, nerdy, brilliant, boring. You know nothing other than what she wrote above. And imagine that you resist the urge to refer her to a qualified therapist. That is, take the request at face value and actually try to give a data-based answer without letting your personal feelings get in the way. What should you say?

Here’s my shot at the basics. As a first move, I’d separate it into three questions: How do women have lots of kids? How do women have kids who are likely to become physically attractive, sexy adults? And how do women have rich kids? None of these questions is particularly hard to answer, assuming we’re just trying to say, in general, how a young American woman can make it statistically more likely to have things turn out this way.

How women have lots of kids

This one is quite easy: Women who have lots of their own kids usually start having kids when they’re pretty young. In general, women these days are in their peak able-to-make-their-own-babies years in their 20s. By age 39 or 40, their likelihood of having their own children has dropped to about half of its peak, and by age 44 it’s pretty rare for a woman to be able to have her own biological children. (Women can still get pregnant, just not typically with their own eggs—a large portion of modern stories of women having children past their early 40s involve in vitro fertilization using younger women’s eggs.)

The typical American woman over the past century who had 4 or more children had her first at age 19; the typical woman who ended up with 3 children started at age 21. Sure, we can point out that it’s possible for women to have lots of kids after waiting longer. But our task here is to lay out the path that is typical, that is most likely. And, for women, the path to lots of kids usually begins with a pregnancy in her teens to early 20s.

For most women, if they’re just trying to have a lot of kids—without considering more—it’s a simple recipe: Start having unprotected sex, get pregnant, have the baby, give your body some time to recover, and repeat. Most young women are capable of producing children. Most young men are happy to help out, if all we’re talking about is sex. Problem solved.

How women have sexy kids

The next part is how to have kids that are likely to grow up to be attractive, sexy adults—the kinds of people other people really want to hook up with. For women, there’s more good news. The on average, in general, statistical answer is not complicated: When she’s having sex that is likely to get her pregnant, she should do so with attractive, sexy guys from attractive, sexy families. Good looks, after all, have a lot to do with genes (and so does sociosexuality). People who are good looking and sexy are more likely to have good-looking, sexy offspring. It isn’t a guarantee, of course. But, again, we’re giving advice on the plan that makes these things more likely than not.

Combine the “how to have lots of kids” plan for women with the “how to have sexy kids” plan for women, and it’s something like: Start having unprotected sex early with guys who are sexy, get pregnant, have the baby, give your body some time to recover, and repeat. Mission accomplished.

If this were a movie, I’d now say something unintentionally foreboding, like: “Huh, this isn’t going to be so hard after all.” And right then, of course, all hell would break loose.

How women have rich kids

The first thing to note in figuring out how women have rich kids is that these women (with rich kids) tend to be rich themselves. There’s more to it, of course, but let’s start here: Statistically speaking, who are the wealthier women?

There are at least three big predictors of women’s average wealth outcomes these days: education, marriage, and work. If we’re talking about the typical situation—ignoring the improbable, just-get-lucky scenarios—wealthier women have a lot of education, they marry guys with a lot of education, the guys work full time pretty much constantly from college to retirement, the women work full time for some relatively long period of their adult lives, and they stay married.

Here are some simplified numbers. If we want to predict an American woman’s yearly household income while she’s in her 40s, this gets us in the typical range. Start with $29,000. Add $37,000 if she’s married. Add another $29,000 if either she or her husband has a 4-year college degree, and add another $29,000 if they both have college degrees. Voila. The result is in the neighborhood of that group’s median household income. The lowest group—at $29,000—are unmarried women without college degrees. The highest group—at $124,000—are college-educated women married to college-educated men.

Of course, degrees and spouses don’t magically produce higher incomes; they typically do so through work. People with more education are more likely to be employed and they usually make more money when they are employed. The kinds of men who get married also tend to be the kind of guys who stay employed full time in higher paying careers.

Further, it isn’t just getting married that produces wealth, but staying married. Never-married and divorced women in their 40s, for example, have yearly household incomes that don’t differ very much on average. Divorced women are more likely than the never-married to have some support from exes, but this is frequently offset by the fact that divorced women are also more likely to have children, which is associated with lower work rates and less income. Over time, the real economic payoff for women from marriage occurs when they stay married for a long time, taking advantage year after year of the lower costs of a shared home and building up joint assets.

So how does a woman have richer kids? A big part of the answer is that she should get lots of education, work, get married to a guy with lots of education who will work, have fewer children, and stay married. Unfortunately, our earlier advice on how women can have lots of kids and sexy kids now gets derailed utterly by a range of serious contradictions.

One problem: Women who have lots of kids tend to start having them in their teens and early 20s. But women who get lots of education and marry guys with lots of education rarely have children in their teens and early 20s.

A second problem: Women with lots of young children are less likely to work full time outside the home, even when they’re unmarried and could really use the money. Women without a lot of education who have young children are especially unlikely to be able to find better jobs.

A third problem: Part of having richer kids is having fewer kids, because the parents are able to concentrate their resources more. This puts further tension between the goals of having lots of children and having richer children.

But wait there’s more. How do women stay married? People who get married later are more likely to stay married. Those who don’t have children before they marry are more likely to stay married. People who don’t sleep with lots of people are more likely to stay married—and sleeping with lots of people relates pretty strongly to being physically attractive. Now, how is a woman supposed to have lots of sexy children if she waits so long to get married and have kids, and then marries a less-attractive, sexually reserved guy?

Sorry, kid

Our project of giving social-science advice to a young woman who wants lots of rich, sexy kids is now, umm, screwed. Should she start having kids early or wait until later? Well, starting early is the major pathway to having lots of kids. But on the other hand, people who marry and have children young get less education, so they end up with less-wealthy children on average. Also, getting married young leads to higher divorce rates, and having children before marriage also leads to high divorce rates. And higher divorce rates lead to less-wealthy children.

Should she partner with a sexier guy or a less-sexy guy? Well, sexier guys produce sexier offspring. But on the other hand, sexier people are less likely to stay married, which, again, leads to poorer children. In particular, people who marry at younger ages need to be rather sociosexually reserved to avoid really high divorce rates these days.

Should she work outside the home or not? Paid work increases the family’s wealth, which leads to richer kids. But on the other hand, women with less education and more kids have a significantly harder time finding and maintaining higher-paying jobs.

In the end, there’s no simple advice for the young woman who wants lots of rich, sexy offspring. It turns out that the three parts of the goal—the lots, the rich, and the sexy—all conflict in serious ways. Trade-offs are everywhere. Indeed, these kinds of trade-offs could help us make progress in the puzzle of modern low fertility that I’ve been discussing in these last few posts (here, here, and here). More to come.

What college women want

Since the 1970s, fertility rates in the U.S. have been pretty low. The causes are complex, but it’s clear that one of the major themes involves education. If we look at the completed childbearing of recent American women (based on General Social Survey data), those who didn’t finish high school averaged around 3 children, those who finished high school but didn’t get a 4-year college degree averaged around 2.2 children, and women with 4-year degrees averaged around 1.6 children.

In fact, around four in ten college-educated women had no children or only one child in the end, compared with around one in four women without 4-year degrees. These kinds of very low fertility outcomes are particular evolutionary puzzles.

Most notably, what’s the deal with college-educated women? One possibility is that lots of women generally don’t really want to have any children (or more than one) these days—maybe it’s something about how college-educated women are better able to control their actual outcomes to match these general preferences for very low fertility. Another possibility is that the sorts of women who get college educations in the first place also tend to be the sorts of women who disproportionately prefer very low fertility.

There are other possibilities, of course, but in this post I’ll focus on these two. Both predict that, if you ask college women about their family plans, large numbers of them will say that they would prefer very low fertility. Is this true?

To birth, or not to birth—that is the question

I’ve given various surveys to college students over the years, surveys that often included questions about what they wanted in terms of future mates, marriage, and children. I’ve ended up with a few thousand respondents from a nice range of normal American universities (Penn, ASU, UNC, UMich, UHouston, and UCF), as well as samples from a couple of countries in Europe and a couple in Asia. The combined sample includes over 1,300 women ages 18 to 23 (most were 18 or 19) at American universities. It’s not a random sample—it’s a convenience sample, mostly of undergrads taking big psych courses—but there’s no reason to think that, on the whole, it would be a seriously warped representation of these schools in the early 21st century.

So, in college, how many children did they see themselves eventually having? The average was 2.5. This is actually the same as the recent average completed fertility for American women who never went to college at all, and it’s almost a full child more than the average completed fertility of college graduates.

The charts below give a more detailed view, contrasting the desired number of children for the sample of American university women with the actual fertility of college-educated women ages 42 and higher. So, for example, less than 9% of the university sample said they wanted either no children or only one child, while among college-educated women in recent years, the actual outcome was around 42%.

GSSColFert(Technical notes: The college-student sample size is 1,349. The GSS sample size is 1,239; results are weighted.)

Given the enormous contrast between the expectations of college students and the outcomes of college graduates, perhaps the student numbers are fundamentally unserious—perhaps they say they want kids, but it’s not actually something that’s important to them. The surveys asked about this, having the students rate how important eventually having children was to them. Over 60% of women rated it a 7 on a scale that maxed out at 7. The average for the group as a whole was 6.1, with only 7% rating it below the scale’s mid-point.

Results were even more impressive for wanting to get married. Over two-thirds of women gave the importance of eventually getting married a maximum 7 out of 7, and the sample average was 6.4.

Perhaps they imagine all this happening in some very distant future that need not be taken seriously any time soon? No, that’s not it. When asked at what age they’d like to get married, the typical female student said 25. The middle 80% of the sample said they wanted to get married between the ages of 23 and 28. And when did they want their first child? The middle 80% said between the ages of 25 and 30.

But do they understand that these fairy tales of marriage and children typically require, you know, a prince? Maybe they don’t imagine princes, but they do image the sorts of guys who do well as married fathers. For almost half the sample, I asked them to rate 32 possible characteristics in a future partner. I threw the kitchen sink at it: physical attractiveness, income, likes to travel, has lots of friends, is dependable, ambitious, creative, laid back, educated, thoughtful, and so on, and so on. And here are the top six most highly rated things that these women want: Someone who you trust; who wouldn’t cheat on you; who’s stable; kind; will be with you forever; and—oh, yes, wait for it—would be a good parent. That’s their top six. In college. Around age 19. In the 21st century.

Come on—they’ve got to be, like, religious conservative nuts or something, right? Actually, not even a quarter were attending religious services more than once a month. About half had had multiple non-intercourse hook-up partners in the past few years, about half imagined they’ve have multiple intercourse partners over the next few years, and almost two-thirds said they got drunk at least once in a typical month. All these numbers are roughly typical of college kids these days. They’re not as routinely wild as many media accounts claim, but neither are they routinely cloistered—in reality, college kids are a sociosexually diverse lot.

No, really, humans typically want to reproduce

In my recent posts, I’m exploring the evolutionary puzzle of modern low fertility (I started here and here). There have occasionally been views from evolutionary thinkers (including some big names) that essentially come down to this: Humans evolved to seek out sex and to take care of children when they arrive, but aren’t really naturally inclined to seek reproduction per se; so, when you introduce modern birth control, and let people have sex without ever having kids, that’s what tons of them will do.

There’s just very little in the data to suggest that this is a plausible account. For example, when sociologists talk to low-education teens who are “at risk” for pregnancy, they often hear back that these teens understand about birth control but want to have children. And, though it’s an unpopular finding on both sides of the aisle, it turns out that neither abstinence programs nor comprehensive sex ed programs typically have much effect on actual behavior, including actually using birth control. And, as we just saw above, young college women—who overwhelmingly don’t want children right now and also are generally knowledgeable about modern birth control—nonetheless typically have strong and explicit desires to eventually have children.

So the puzzle is coming into focus. What we need is an account of why young women generally want to have children these days, yet (1) they don’t want very many and (2) they often end up falling short of even their relatively modest target numbers. Again, I’m not promising a tidy answer—this stuff is really hard—but I’ll try to get us closer. More to come.

A century of American fertility

Let’s start with a pop quiz. Here are two questions about total fertility rates (that is, about the rate at which women in a given population in a given year were having children, taking into account the ages of the population’s women). First question: For the years 1907, 1937, 1957, and 1967, in which of these years did American women have the highest fertility rate? Second question: In which of these years did they having the lowest fertility rate? (Thirty seconds—good luck.)

These aren’t trick questions, but they’re ridiculously hard. I’m not sure that even people relatively knowledgeable about fertility trends would typically get both answers right off the tops of their heads.

The answer to the highest rate question is that the Baby Boom isn’t called a Boom for nothing. The Baby Boom is generally measured as running from 1946 to 1964. And peak Boom was from 1956 to 1960, when the total fertility rate was about 3.6. (A total fertility rate of 3.6 in a given year means that, if a woman over the course of her life had children at the rate that women on average were having children that year—if she had as many in her 20s as women in their 20s had that year on average, and as many in her 30s as women in their 30s had that year on average, etc.—she would end up giving birth to 3.6 children total.) The Baby Boom was an astounding time period. You have to go back to the 1800s to find higher American total fertility rates than those of peak Boom.

The answer to the lowest rate question is that the Great Depression isn’t called Great for nothing. I suspect that lots of people would have guessed that fertility rates were lower in 1967 than in 1937. After all, the pill came on the market in 1960 and by 1965 became the most used form of birth control. With such a strong tool at their disposal, surely American women in 1967 would have fewer children than those pill-less primitives of the 1930s. But it wasn’t even close. The total fertility rate was around 2.5 in 1967, compared with a mere 2.1 in 1937. A great depression indeed.

Way back in the day

People who think about historical fertility generally agree that the macro-story of the past two or three hundred years in (currently) developed societies is that, yes, lots of people used to have lots of kids. These were times when these societies were largely agrarian and poor, where there was little formal education and mortality rates generally (and child mortality rates specifically) were much higher. Then we see periods of transition in which these societies became industrialized and richer, formal education increased and mortality rates fell—and birth rates also fell. Which of these are causes and which are effects are matters of debate.

The point about mortality rates makes clear that it’s one thing to compare fertility rates in terms of how many babies women are giving birth to, but it’s another to compare reproductive rates in terms of how many surviving offspring women are having. So, for example, the total fertility rate in the U.S. was falling substantially from the mid-1870s to the mid-1920s—it went from around 4.5 in 1875 to around 2.8 in 1925. But this was also a time of greatly increasing child survival rates. If we adjust for child survival to age ten, the adjusted total fertility rate (i.e., how many kids a hypothetical population-typical woman would have that would survive to age ten) declines much less dramatically from around 3.1 in 1875 to 2.5 in 1925. Put another way, a woman had to have 4.5 births in 1875 to end up with 3.1 ten year olds on average, but over the next half-century this kind of gap narrowed considerably.

Below I show this adjusted total fertility rate (i.e., the production of ten year olds rather than the production of babies) in the U.S. for each decade from the 1880s to the first half of the 2010s. This takes the issue of child mortality off the table and lets us see something about the approximate real family sizes women were ending up with on average. Here we see the slow decline of the late-19th and early-20th century, the great plunge of the Depression years, the mind-boggling baby-mania of the 1950s, the colossal crash of the 1970s, the slight rebound of the 1990s and 2000s (which was partly due to increased immigration), and the lesser plunge of our recent Great Recession.

AdjTFR(Technical note: This combines historical data from the Social Security Administration (from Chart 10 here—you can click on that chart to get a data table), updated with more recent World Bank data.)

These historical data tell a pretty complex story—a roller coaster ride with deep dips and steep climbs. But that’s just the tip of the iceberg.

Not all periods of low fertility are the same

From the chart above, 1930s fertility looks a whole lot like 2000s fertility. In both periods the adjusted total fertility rate was a bit over 2. Yet it turns out that fertility patterns in these time periods were really very different.

The next chart (below) is an eyeful, but worth the strain. Here, instead of thinking about how things would turn out for an average hypothetical woman, we look at how things turned out for different actual women. The bottom axis isn’t the year they were having babies, but the year in which the women were themselves born. These data show how many kids American women actually ended up having based on the women’s year of birth. It’s from General Social Survey data, which allowed me to produce reasonable estimates for women born from 1907 to 1965.

The key is that I’m showing not average fertility, but rather what percentages of women ended up giving birth to 0 (the black line), 1 (yellow), 2 (grey), 3 (orange), and 4 or more (the blue line) children. Looked at in this way, it’s clear that the average-level similarity between the 1930s and recent years masks very different patterns. The early years of this chart are mostly those women having kids (or not) in the 1930s, and, for them, it’s a feast-or-famine pattern. Relative to later women, these Depression-era women include lots of 0s and 1s and a moderately sized 4+ group, but relatively few 2s and 3s. Flash forward to women born in the 1960s, and now there are a ton of 2s, but actually fewer 0s and 1s than for Depression-era women. In between, of course, are the mothers from the Baby Boom years, mothers born mostly in the 1920s and 1930s—for them, it’s a straightforward story of high fertility as the 0s and 1s plunge and the 3s and 4-or-mores rocket up.

GSSComFert(Technical notes: The total sample size is 16,820. Results are weighted. To smooth out the results, estimates for each cohort year are based on a combination of the five surrounding cohort years.)

Puzzling over low modern fertility

This post is part of a series I started yesterday on the evolutionary puzzle of low fertility in modern developed societies. Here, I want to put this modern puzzle in wider historical context.

Mainly, it’s important to understand that there isn’t just one fertility puzzle here. And there won’t be one simple answer. This isn’t about how women used to have a ton of kids and now for the first time they don’t—because of the pill, or college, or whatever. This isn’t about how there have never been many childless women and now there are. This isn’t about how people have high fertility when they’re poor and low fertility when they’re rich (or else the Baby Boom would have occurred during the Depression years).

Instead, there are lots of puzzles with their own complex pieces. High-fertility patterns in the 19th century will have different explanations compared with high-fertility patterns in the 1950s. Low-fertility patterns in the 1930s will have different explanations compared with the low-fertility patterns in the past few decades. I’m going to focus on these most recent decades in these posts. Evolutionary researchers can and should look for general themes and for developmental and psychological mechanisms that are widely applicable over human history—but the unique, complex, situation-dependent outcomes that are plainly apparent just in the past century of American fertility tell us that we must also pay close attention to context and contingencies. And the modern tools of evolutionary psychology help us to do just that. More to come.

Some evolutionary puzzles

Critics of evolutionary psychology often point to a number of standard puzzles. If the driving force of human behavior is genetic replication, then why are there homosexuals, and why do people adopt others’ children, and why are fertility rates so low in modern developed societies?

These are all real puzzles. But they can’t somehow render evolutionary approaches irrelevant. Really, just flip it around. Does anyone have a satisfying way to explain, without resorting to some kind of evolutionary account, why the vast majority of young adults want to have—and the large majority in fact end up having—their own biological kids? If modern life in developed countries is about selfish pleasures, or consumerism, or going to interesting places, or intellectual enrichment, or whatever, then why have kids at all?

Especially for women. I mean, seriously—the idea here is that they’re going to grow little humans inside their bodies, which I hear is often a very uncomfortable thing to do. Then they’re going to squeeze a wriggling, wailing watermelon out of their lady parts (or perhaps have a physician literally slice open their abdomens to remove it).

Then the new parents will spend years losing sleep, cleaning up feces, fretting and obsessing, all while surrounded by some of the worst music, videos, and books in human history, material that will be listened to and relistened to, watched and rewatched, read and reread in a soul-squashing spectacle of mind-melting monotony. And it’s expensive, both in terms of mothers’ lost income and the money pit of pregnancy, basic necessities, childcare, Disney World, sports, summer camps, cell phones, cars, college, and so on. All to produce beings for which many parents would literally lay down their own lives if needed.

People don’t have kids to maximize modern pleasures; they have kids because they’re powerfully motivated to bear the specific burdens of reproduction. It’s in fact a triumph of evolutionary psychology that it’s perhaps the only non-hand-waving way to make broad sense of the rather extreme sacrifices of parenting.

Yet, no doubt, there are puzzles. Some are bigger deals than others.

Minor puzzles: Homosexuality and adoption

Homosexuality is an evolutionary puzzle, but a minor one. First, same-sex behaviors are not limited to humans, so this isn’t something about how humans have broken the mold of evolution. Second, the most theoretically puzzling forms of human homosexuality are rare, and at any rate just a small fraction of a larger puzzle of non-reproductive lives.

If we look at Baby Boomers, for example, according to U.S. General Social Survey data of those ages 42 and older (i.e., past the ages when people typically become first-time parents), only 0.8% of men and 0.4% of women—less than 1% of each—report having had sex only with members of their own sex since age 18 and not having had any children. Even if these are on the low side given underreporting, it’s still the case that a paradigmatically homosexual life-history is pretty rare. By comparison, about 3% of both men and women reported having had sex with both men and women and having had children.

If we focus on Boomers who didn’t end up having any children, while the vast majority have had sexual partners, only around 15% of childless men and 13% of childless women report ever having had a same-sex partner since age 18—that is, the large majority of childless Baby Boomers have had exclusively heterosexual adult sexual histories. What this suggests is that, even when focused on the puzzle of childlessness, same-sex patterns just aren’t the major theme.

Adoption presents a similar story, though I don’t have exact numbers at my fingertips. If we focus on de facto adoption and not just legal adoption—that is, cases in which folks who are not the biological parents of children nonetheless assume the ordinary responsibilities of childrearing for those children, whatever the legal category of the relationship—the large majority of cases are ones in which the caregiver is either a close biological relative of the child (e.g., a grandparent) or is a romantic partner of one of the biological parents. Neither situation is an evolutionary puzzle. Further, for the rarer cases in which there is no preceding biological nexus between adopting parents and their children, it seems clear that such cases often follow from circumstances in which couples would have preferred conceiving their own children but couldn’t.

Both homosexuality and adoption present interesting issues that should be explored. But neither represents some kind of fundamental challenge to evolutionary perspectives on modern humans. In fact, there are large and growing bodies of research on both topics from an evolutionary perspective, research that often spots important themes that others miss.

A bigger puzzle: Low modern fertility

A bigger puzzle is that it’s apparent that folks are just not having that many kids in developed countries these days. And they could be having a ton. Rich societies have never been richer. Child mortality rates are as low as they’ve ever been. Developed societies have in place more-and-less-generous welfare systems that, at a minimum, aren’t generally going to let (many) of their children starve or go without basic medical care or education. If we are evolved creatures bent on maximizing the head-count of our surviving offspring, there’s never been a better time to cry Nature! and let slip the dogs of fertility.

In an often-quoted phrase in evolutionary circles, back in the 1980s Vining called low modern fertility “the central theoretical problem of human sociobiology.” It’s not that it’s an existential threat to evolutionary psychology, but it is a tough nut.

Recent approaches suggest potential ways to get one’s arms around the puzzle. Advances in life-history theory and sociosexuality, for example, have introduced various ideas involving complex and contingent sexual and reproductive strategies, though I haven’t seen much work systematically applying these advances to the specifics of low modern fertility.

That’s a gap I’m going to try to help fill in. This is the first in a series of posts I’ll do on the low-fertility puzzle. It won’t lead to The Big Answer to Everything. But I’ll make progress. To help give focus to the puzzle, I’ll provide some background on how the modern U.S. differs from the past century, and I’ll show where within the modern U.S. very low fertility rates do and do not appear. I’ll offer some suggestions about how to think through and expand existing notions of life-history trade-offs in the specific context of modern developed societies. It won’t be everything, but it really will be something.

White Christians are declining, but remain a majority of voters

The U.S. has passed a major milestone in political demographics. At probably every point in its history, a majority of resident adults were white Christians. But around 2014 that changed.

The relative decline of white Christians comes from two sources—the increasing non-white population and the decreasing proportion of whites who are Christian. In the past few years, in fact, net migration has slowed, making the primary driver of the trend the rapid rise of non-Christian whites. The chart below is based on Pew political surveys from 2013 and 2015. In just this 2-year period, white Christians declined from 52% to 48% of the adult resident population. And while the non-white percentage increased a small amount from around 30% to 31%, the percentage of white non-Christians increased substantially from 18% to 21%.

PewWC(Technical notes: The number of respondents was 14,104 in 2013 and 17,518 in 2015. Results are weighted. “White” means non-Hispanic white and includes some individuals with missing information and mixed white/native background. The “registered” and “non-registered” percentages are estimates based on responses to Pew’s registration item; the accompanying bars are weighted party identification totals—Democrat, independent leaning Democrat, independent with no lean, independent leaning Republican, and Republican.)

White Christians retain their registration majority, for now

While white Christians are no longer a majority of the resident adult population, they’re still a majority of those registered to vote. The chart shows estimates of the percentage registered for each group based on Pew’s self-report measure. When the math is worked out, while white Christians were 52% of the adult resident population in 2013, they were over 56% of those registered to vote; and while white Christians declined to only 48% of the population in 2015, their percentage of those registered dropped to only a bit under 54%.

White Christians maintained their registration advantage in a couple of ways. First, a substantially larger portion of whites are eligible to vote, both because relatively more Hispanics and Asians are non-citizen immigrants and because felon voting bans disproportionately affect blacks. Second, both non-white and non-Christian adults are substantially younger than white Christians on average—while the typical white Christian is 52, the typical non-white is 39 and the typical white non-Christian is 40. Indeed, part of the relative registration advantage of white non-Christians in 2015 stems from the fact the non-white and non-Christian registration rates declined substantially from 2013 to 2015 (which relates to the unusually low turnout in the 2014 midterms), yet the registration rate for white Christians remained almost as high in 2015 as it was in 2013.

Projecting forward, how do things look for this year’s election? As far as I can tell, white Christians might end up being about half of those registered later this year. This assumes that non-white and non-Christian registration rates go back up to their 2013 levels, and that white Christians continue their relative population decline at the same pace that we saw from 2013 to 2015—assumptions that may or may not hold.

The Democratic majority continues to emerge, but the patterns are always changing

The relative number of white Christian voters has been crucial in recent elections because they form the core of the Republican coalition. As shown in the chart, registered white Christians have a heavy tilt towards Republicans, registered white non-Christians have an equally heavy tilt towards Democrats, and registered non-whites favor Democrats enormously.

Assuming that the current patterns in partisan preferences continue, this suggests an emerging Democratic majority. The major caveat is that the non-white/non-Christian coalition has been far more likely to turn out in presidential than midterm elections. Also, of course, each election presents unique circumstances—for example, a financial crisis under a Republican president surely contributed to Obama’s large victory margin in 2008.

But can we assume that the current patterns in partisan preferences will continue? Probably not fully. Party coalitions are in constant flux, though at some times in quicker and more obvious ways than at other times. This year—with a particular contrast on issues of immigration, internationalism, and discrimination—we might see more college-educated whites heading away from Republicans as less-educated whites head towards it. This could weaken somewhat the religious divides within whites, as some college-educated Christians move to the left and some less-educated non-Christians move right. This would still predict an increasing Democratic majority on the whole, but one built on somewhat altered foundations.

A key unknown in all this is how voter participation patterns might change. As shown in the chart, non-registered folks tend to show more mixed partisan preferences than those in their demographic groups who are registered. This could be because political participation itself encourages side-taking; but it could also be in part because people with mixed liberal/conservative policy preferences are less likely to vote when given a choice between fully liberal and fully conservative candidates. If we move away somewhat from liberal vs. conservative parties and more towards minority/cosmopolitan vs. white nationalist parties, we should probably expect the current patterns of voter participation to evolve to some degree—those whose views are a better fit to the new alignment might increase their participation while those who find themselves newly politically homeless might become somewhat more likely to stay home on election day.

But then, of course, future elections will bring new coalitional pressures, as elections often do. Recent patterns give rise to a certain set of current expectations, but those patterns are constantly changing.