Brief: Do trust levels predict sexual activity among the young?

I'm becoming more convinced that the decline in trust levels during the late 1980s may have a lot to do with the decline starting a few years later in all sorts of risky behavior. The logic is simple: the more (or less) trusting you are of others, the more (or less) risk you're willing to take in social life, and also exploiters will have a more (or less) easy time finding "suckers." We looked before at homicide rates. Now let's turn to sexual activity among young people.

For trust levels, I use the General Social Survey's question on whether you think others can be trusted, and restricted respondents to those from 18 to 25 years old. The GSS doesn't survey minors, so I used this age range for "young people." The time series for trust doesn't seem to vary wildly based on which age range you restrict it to, so it seems OK to use trust levels among young adults as a proxy for trust levels among adolescents. For risky sexual activity, I use the pregnancy rate among females ages 15 to 17 (here), which are yearly from 1972 to 2006; and the percent of high schoolers who have had sex before age 13 (here), which are bi-yearly from 1991 to 2007. The trust data are from 1972 to 2008, and coverage is usually bi-yearly or more frequent.

Here are the results. Trust levels are in blue and sexual activity measures in red.



Although the relationship is not perfect, there's a close match overall, and the timing looks like the trust level changes first, followed by a change in sexual activity. That fits with trust being the cause and risky behavior the effect. The correlation across years between trust and the pregnancy rate is +0.47, and between trust and the early sex rate it is +0.44. The Youth Risk Behavior Survey that I got the early sex rate data from has three other measures of teenage sexual activity, and the relationships are similar but not quite as strong.

The correlations with trust are: +0.15 for percent of high schoolers who'd had sex at least once in the past 3 months; +0.21 for the percent who've had 4+ partners in their life; and +0.23 for the percent who've ever had sex. * I also created an index of young sexual activity, which is just the sum of the fraction of students responding positively to each of the 4 questions. This is the expected number of "yes" answers that a high schooler would give while reading off the check-list of risky sex behaviors. The correlation between this index and trust is +0.25.

In general, I think these correlations are weaker just because there are fewer data -- 1991 to 2007, every other year -- than in the case of pregnancy rates. I suspect that the pre-1991 picture for the 4 behaviors surveyed in the YRBS would have looked highly similar to the teen pregnancy rate, so if we had those data, they would probably make the pattern even stronger. In any case, it seems clear that, while not the entire story, how trusting people are of one another plays a substantial role in how willing young people are to engage in risky sexual behavior. That's not too surprising if we think of trust as a form of insurance, but it's still something that's been completely overlooked as far as I know from the social science lit on these two topics that have previously been studied independently of each other.

If I can find good data on cross-national differences in number of lifetime sex partners, or other measure of promiscuity, I might use the national trust level data from the World Values Survey and turn this into a fuller post. It depends on how easy the former data are to get. Informally, though, my impression is that the low-trust countries are more sexually conservative, while the high-trust ones are more sexually liberal.

* The sexual activity correlations paired the sex activity variable and trust variable for the same year if possible (for 1991 and 1993), but since the data use different sets of alternate years after that, I paired the sex variable in a certain year with the trust variable in the following year.

GSS variables used: trust, age, year

Brief: Did third-wave feminism ruin Gen-X guys' sex lives?

There was a society-wide hysteria that reached a fever pitch in 1991, encompassing the rebirth of identity politics, feminism ("third-wave"), gay rights, and political correctness in general. The panic was still pretty high through 1994, although it died off afterward. Compared to second-wave feminism -- which focused more on equal pay, a lower housework burden, and so on -- third-wave feminism was much more focused on sexual harassment, rape, male libido, etc.

Was all that paranoia about all men being crypto-rapists just cheap talk, or did it have real consequences for the relation between the sexes? An easy way to test this is to look at how likely men were to remain virgins after graduating college through their 20s. I used the General Social Survey to focus on males aged 21 to 29 when they were surveyed. I grouped them into 3-year birth cohorts, except for the last one, where a tiny number of late-'80s males were lumped into the previous cohort just to make the sample sizes better.

Here is the percent of 20-something respondents who said they'd had 0 female partners since turning 18 -- which in context I take to mean 0 partners ever -- shown by the middle year of the 3-year cohort (or by 1986 for the '84-'89 cohort):


The long-term trend looks like between 6% and 7% fall into the 20-something virgin group. The early-'60s cohort is much lower, which makes sense because that cohort was completely unaffected by feminist hysteria. They were born too late to come of age during the counter-culture and too soon to come of age during the early-'90s hysteria. As I've pointed out elsewhere on this blog, the late '70s and early '80s were a remarkably non-ideological and hysteria-free period. However, those who came of age during the early-'90s hysteria show much higher rates of virginity in their 20s -- about double the long-term trend, or 5 to 6 percentage points higher. The effect even lasts through those who came of age in the mid-'90s, although their departure from the trend isn't so extreme. It isn't until those who were born in the early '80s, who came of age in the late-'90s or later, that the rate returns to the trend.

People often poo-poo the framework of generations -- there's so much variation within generations, not as much across generations, no sharp changes, etc. Well here's a very clear demonstration of an abrupt and gigantic change in a key rite of passage for males, and the timing is not mysterious given what we know about larger cultural changes afoot in the early-'90s. This also supports my approach to see generations as a cohort of vulnerable individuals who are struck by a huge but passing hysteria (a shock to the social system). There's some age range in which people are vulnerable to bearing the scars of the hysteria -- not younger or older -- and those indelibe impressions cause a lot of them to look similar to each other and different from other groups. That's why you can still tell who was coming of age during the counter-culture even though that was 40 years ago.

GSS variables used: numwomen, cohort, sex, age

Brief: Trust and crime

I've been thinking more about trust and its effects on all areas of social interaction and culture. It's at the root, really: if you don't trust others, you will try to get by on your own. Sociability requires a certain level of trust, and in a social species like ours there are gains to be had by interacting with others -- especially in a modern market economy where you can outsource so many things to others in the market instead of doing them yourself. You don't make your own shoes, grow your own food, manufacture your own computer, or gather your own news about the President. Sounds great -- why would you possibly withdraw from that sociability?

If you trust others, you're making yourself vulnerable to exploitation. The more trusting people there are in a population, the easier it will be for exploiters to thrive, and that will drive up their numbers. The exploiters may become so numerous, and their effects so offensive, that people start to withhold their trust lest they become the next statistic. That state-of-mind could show up in behavior by simply not venturing out into the public sphere where they'd be vulnerable, hiding out in the safer personal sphere.

But then when there are far fewer trusting people, that dries up the resource that exploiters had been thriving on. Now there are lots of them competing to exploit a shrinking number of trusting people. So that will drive down the numbers of exploiters. Eventually people will realize how safe things have become and extend their trust once again, which will in turn drive up the numbers of exploiters as before, and the cycle repeats.

This variant on a model of how hosts and parasites, or predators and prey, interact in ecology suggests looking at data on how trusting people have been over time, and what the crime rate has been like. Before I showed that people rationally respond to changes in the homicide rate by becoming more afraid when it's going up and less afraid when it declines, with about a 2-year lag. So we know that perceptions are affected by crime levels -- but could crime levels be affected by perceptions, i.e. how trustworthy you think other people are?

The General Social Survey asks respondents whether other people can be trusted, cannot be trusted, or that it depends. Here is a plot over time of the percent of respondents who said that other people can be trusted (in black), together with the homicide rate (in red):


Clearly the relationship is less stark than it was for fear resonding to crime, where the correlation within a year was +0.74. Still, the correlation here is +0.53, meaning that indeed higher crime rates are associated with higher levels of trust. And as in the case of fear and crime, that understates the strength of the relationship since there is a time lag. In particular, trust levels started falling steadily before the homicide rate did so. So the predicted relationship is there: crime rates are high throughout the '70s and '80s, and at some point in the mid-late-'80s, people have had enough and start trusting others less and less. With far fewer trusting people to exploit, criminals start scaling back or dying off: the homicide rate peaks in 1991 and starts falling steadily afterward.

We will have to wait a few more decades to see if the rest of the story pans out -- whether with such low crime rates, people will assume it's safe to start trusting people again, and whether those higher levels of trust (if they did happen) would be followed by a rise in the crime rate.

Criminologists and others debate what causes crime to go up or down -- tougher or more lenient punishment by the government, technology that makes it easier or harder to report crimes, etc. I have only a passing familiarity with the range of causes they discuss, but as far as I know, how trusting the average person is does not get much attention, although there may be some minority contingent that does look at it. Perhaps it is as simple as a rise in crime following an increase in the number of people who criminals would consider "suckers," and a decline in crime following a contraction in the numbers of "suckers," just as we see between a host species and a parasite species. The logic is hard to argue against, and the graph above shows that it has some modest empirical support as well.

GSS variables used: trust, year

Brief: Season of birth and signaling anger

The various questions in the General Social Survey that probe people's personalities -- for example, whether you consider yourself outgoing -- don't show strong differences in the responses by birth month. But what if we look at questions that focus more on people's actual behavior? I found one question that asks how much the person agrees with the statement, "When I'm angry, I let people know." Here is the percent who either strongly agree or agree by their birth month:


A majority in all groups signal their anger to others, but there is a very clear seasonal pattern, with this tendency increasing during the fall, winter, and spring birth groups, then falling during the summer group. Moreover, the change is not trivial -- 11.2 percentage points separate the low in August from the peak in April.

This could just be a reflection of genetics rather than environment, whereby anger-signaling mothers pass on their variants for the trait, but where they're more likely to conceive in the later part of summer. Still, I favor an environmental story where babies born into more stressful environments assume unconsciously that life is going to be tough, and so adopt a more defensive "don't mess with me" posture. Most such stress today will not come from parental strife or lack of basic nutrition but from the one source that we still don't have much control over -- pathogens that make us sick. That's why the flu season shows up pretty well in the graph.

There could also be a vitamin D story, where newborns produce less vitamin D during the less sunny months (sunlight hitting the skin is a necessary step), and that this somehow influences their personality or behavioral strategy. But without a clearer mechanism, I wouldn't put much emphasis on this -- why wouldn't the mother's vitamin D level matter, which would tend to go in the opposite direction? In any event, there's something neat to try to explain.

GSS variables used: showangr, zodiac

Brief: Intelligence and age at first birth

I've seen many pictures and tables showing that smarter people get married later and have fewer children. But what about when generation length -- how long do people wait until they actually have their first kid? I'm sure those data are out there, but I don't recall them off the top of my head, so they can't be too well known.

The General Social Survey asks what age you were when your first child was born, and also gives them a makeshift IQ test. Here is the relation between them:


Generation length increases as you look at smarter people, but it only becomes pronounced in the average-and-above range (6 to 10 words correct). Among those below-average in intelligence, there is no strong tendency toward longer or shorter generation length. So not only do below-average people reproduce more in terms of number of children, but they also reproduce faster. Over the course of a century, below-average people with a generation length of about 22.5 years will have had 4.4 generations, while the smartest people with a generation length of about 27 years will have had only 3.7.

To put this in an evolutionary context, think of a new selection pressure on both populations that was strong enough to cause a new mutant to reach fixation within 50 generations (a pretty strong pressure). It would take the below-average population 1125 years to get there, 225 years before the smartest group would get there (1350 years in total). These are not necessarily the *same* pressure for each group -- it doesn't matter what direction they are pushing adaptation in, just that the two pressures are pushing equally strongly on their respective populations. This huge split between smart and dull is pretty new, and who knows how long it will last, but over the next millennium we may see the duller people adapting at more impressive rates on a genetic level. Hopefully the smarties will figure out non-genetic ways to adapt just as impressively to their environment.

GSS variables used: wordsum, agekdbrn

Brief: More on measuring how aesthetic the zeitgeist has been over time

Returning to a previous attempt to measure how aesthetically minded people have been over time by using diversity in baby names, let's see if the frequency of relevant words in the newspaper of record agrees. I searched the NYT for "aesthetic" and "beautiful" back to 1852, and here are the results:






"Aesthetic" trends upward from the beginning through the early 1910's -- only some of which may have to do with Aestheticism in the arts -- and then declines until the mid-1940's, after which there is a gradual rise through the mid-1990's, and then an explosion from then until the present. "Beautiful" shows a pretty similar history, except for an initial decline during the 1850s (although the number of articles is much smaller in the beginning). It also has two apparent upward shocks: one in mid-1920's -- perhaps reflecting the 1925 world's fair in Paris that popularized Art Deco? -- and the other in the late 1970's. In the graph with moving averages, you can see how similar their trajectories have been. (They are also similar after 2000, but I left that stage out in order to better highlight the ups and downs in "aesthetic," which are hard to see against the recent explosion.)

They also agree well with the movements in the baby name diversity graph. Those data only go back to 1880, but there too we saw a rise from 1880 through the early 1910's, a slump through the mid-1940's, a moderate increase from then until the late 1980's, when it shot up even faster.

After the dizzying changes of industrialization during the latter half of the 19th C., and especially with the outbreak of WWI, many in the West became disillusioned with embracing global interconnectedness and cultural dynamism. Roughly from WWI through WWII, we became more inward-looking and focused on normalcy. We left that behind after winning WWII somewhat restored our faith in global relations and cultural change, and even more so during the most recent era of increased globalization.

There really does seem to be something about a cosmopolitan zeitgeist that makes people more concerned with aesthetic matters -- and clearly not just because the artists can now find mercantile patrons. It's not as though America in the Roaring Twenties wanted for wealthy donors. And a cynic would say that when the culture is becoming more cosmopolitan, status-seekers will try to one-up each other by showing off just how diverse their tastes are.

But I think it's more due to encountering new and exciting things from those other groups you're doing business with. And not only those across the world, but even from strange parts of your own country. Besides, it's not as if rich status-seekers are driving those trends in baby name diversity -- they're too small to count in data taken from social security cardholders. Ordinary people too get swept up in the age of aesthetics, as Virginia Postrel documents in The Substance of Style. To follow her most provocative example, Wal-Mart's website offers about five different streamlined styles of toilet brushes and as many different styles of toiletpaper holders. Wal-Mart shoppers are far below the elite in rank, and because hardly anyone will get the chance to see their brushed stainless steel toilet brush or abstract tulip-shaped toiletpaper holder, these items are unlikely to be only so much ammunition in the status war.

Just how permissive are the beliefs of non-heterosexuals?

Studies of gay and bisexual men show that they are more sexually permissive in their behavior, and likewise for bisexual women compared to straight or lesbian women. But sexual behavior is always constrained by the simple fact that it takes two. Why don't we look at their beliefs on sexually permissive behavior, which might give us a purer view of the differences between heterosexuals and non-heterosexuals.

(I collapse gay and bisexual males into a single non-heterosexual group, but preserve the three-part division among females, because that's what the findings on arousal argue for. Men are aroused either by male or female erotic images, but not by both; whereas some women truly are aroused by both.)

We will treat "sexual permissiveness" as a personality trait or preference that's too tough to measure precisely, but that we can investigate using La Griffe du Lion's method of thresholds. We imagine sexual permissiveness as a continuous trait -- some people score really low, others really high, and everything in between. At some point toward the high-end of this spectrum, we set a threshold and ask what percent of the various groups score above that threshold? We can then use this to figure out how far apart the average scores of the various groups are, assuming the trait to be normally distributed. Knowing that, we can also predict what ratio of one group to another we would expect at some extreme value of the trait -- far out into the right tail.

To identify a clearly high-end threshold for sexual permissiveness, consider what your beliefs are about two 14 to 16 year-olds having sex. The General Social Survey asks just such a question: is it always wrong, almost always wrong, sometimes wrong, or not wrong at all? As you look at higher values of sexual permissiveness, at some point you reach the value where the person is permissive enough to think that teenage sex is OK (maybe with provisos) rather than always wrong. Here is a picture to help see what I mean:


Along the spectrum of permissiveness, at the low end we find people who believe that oral sex is OK -- people whose value is to the left of that point are not even that permissive -- while farther to the right, we find people who are permissive enough to believe that teenage sex is OK. Farther to the right still, we find people with ever more permissive beliefs, perhaps going so far as to condone public unprotected sex. A group of people will have a distribution of values along this spectrum, and we'll assume it's normal for convenience and because most personality traits are made to be normal too. We also assume the various groups have the same variance.

By finding out what percent of some group lies to the right of our threshold point, we can work backward to determine what z-score this threshold represents for the group. Doing this for two groups, we can find the difference between the means of each. For example, if 16% of group A exceeds the threshold, while just 2.5% of group B exceeds the threshold, that implies that the threshold is +1 S.D. above A's mean and +2 S.D. above B's mean. That means that there is a difference of 2 - 1 = 1 S.D. between the two group's means, favoring A.

Furthermore, knowing how far apart the two groups are on average, we can extrapolate what the A-to-B ratio would be at an even further extreme value that we haven't even observed. Obviously there will be more A's than B's (if the two groups are the same size), but the question is by how much. These extreme value predictions are useful because we find extremes more fascinating than averages, and it may be harder to get honest answers out of people as we ask more and more extreme questions.

Now back to sexual permissiveness. The GSS allows two ways to figure out who is heterosexual or not. One is straightforward and asks what sex your sex partners have been over the last 5 years: only male, male and female, or only female. They also ask how many male (or female) partners you've had since age 18. If a male answers 0 partners, he's straight; if he answers 1 or more, he's not. You can do the same for females; bisexuals have to answer 1 or more partners for both questions. The sample sizes are a bit larger using the second method, but the results are very similar. So I'll show the graphs for both methods, but I'll only use the second one in applying the method of thresholds.

First, here are the graphs for male heterosexuals vs. non-heterosexuals, for the first and then second method:



Straight males are more likely to say teenage sex is always wrong, while gay males are more permissive -- although a bare majority of them still think it's always wrong. Next, the differences among females for the first and then second method:



Notice first that females overall are less permissive than males -- no surprise there. Lesbians are more permissive than straight females, but not nearly by the same gap as the one separating gay and straight males. However, there is a yawning chasm separating bisexual females not only from the other female groups but even from both male groups! This result about their beliefs is consistent with their sexual behavior, as surveys (including the GSS) routinely show that bisexual females lead over-sexed lives. They have a general "wild child" mindset, so it shouldn't be surprising that those who think teenage sex is always wrong are in the minority among bisexual women.

(I've looked at responses to this teenage sex question across all sorts of demographic groups, and aside from GSS respondents who are youngsters themselves, I haven't found a single group where the majority think it is OK, let alone one where one estimate puts them at over 60%. Very wild indeed.)

Comparing gay to straight males, we find a difference in means of 0.31 S.D. favoring gay males. Comparing lesbian to straight females, there is a 0.17 S.D. gap favoring lesbians. And comparing bisexual to straight females, there is a 0.68 S.D. gap favoring bisexuals. To make this more intuitive, let's pretend we were talking about height instead of sexual permissiveness. It's as if gay men were 0.92 inches "taller" than straight men on average; as if lesbians were 0.51 inches "taller" than straight women on average; and as if bisexual women were 2 inches "taller" than straight women on average. You might notice the gay-straight gap in the real world, probably not the lesbian-straight gap, but almost certainly you'd notice the bisexual-straight gap.

Now suppose we wanted to predict how much one group would predominate at an even more extreme value along the permissiveness spectrum. That might be holding the belief that it's OK to have unprotected sex in public -- or actually having done so. These extremes are harder to investigate directly because the events that would tip us off to people being there are incredibly rare, and asking people if they've ever had unprotected sex in public may not give us very reliable answers. So we turn to the method of thresholds and ask what ratio of non-heterosexuals to heterosexuals we predict to find at, say, the value of 3 S.D. above the heterosexual average.

Obviously this won't tell us what to expect in the real world since the straight and non-straight populations are very different in size. Still, we'll pretend they were the same size in order to highlight how different the various groups are. In a moment, I'll re-adjust these ratios to reflect the groups' real sizes in order to get better real-world predictions.

At the extreme value of 3 S.D. above the straight male mean, we expect a gay-to-straight ratio of 2.6 to 1. At the value of 3 S.D. above the straight female mean, we expect a lesbian-to-straight ratio of 1.7 to 1, and a bisexual-to-straight ratio of 7.6 to 1! Clearly the minds of heterosexuals and non-heterosexuals are not the same on average.

Correcting for the huge differences in population size between straights and non-straights, I've used the GSS to figure out what percent of the male and female population are gay, straight, or bisexual. For males, 96% are straight and 4% gay. For females, 96.4% are straight, 2% are lesbian, and 1.6% are bisexual. In the real world, then, we expect 9.2 straight males for every gay male at the extreme; 28.1 straight females for every lesbian; and 8 straight females for every bisexual female. The larger point remains, though: non-heterosexuals, especially bisexual women, are more permissive in the beliefs -- and so presumably in their actions -- than heterosexuals.

As an analogy, consider the fact that Ashkenazi Jewish people score much higher (about 1 S.D.) on IQ tests than other Europeans. Still, because Jews make up such a small fraction of the overall population (about 2%), it will still be the case that non-Jewish Europeans will outnumber Jews at extreme IQ levels. The first fact tells us that a representative person from the two groups will be pretty different, perhaps due to their ancestors facing different evolutionary selection pressures or due to having different hormone levels or whatever. The second fact tells us that we shouldn't let the first fact lead us to expect a majority of the unusual group at unusual levels, unless the two groups are similar in size.

GSS variables used: teensex, sex, sexsex5, nummen, numwomen