![]() ![]() In Seth’s dataset, he has a measure of support for gay marriage by US state, which correlates with searches for gay porn at 0.27 which is not nothing, but it is also not statistically significant. That’s over a 50% difference in gay porn searches in two US states. But if we look at the data available on his website “ finalgaydata3”, we see at the lowest extreme, 4.0% of porn searches are for gay porn in North Dakota compared to 6.4% in New York. In his book he notes that “there are more gay porn searches in tolerant states compared to intolerant states”, but he claims differences are small, after giving a selected comparison between states. In an interview with Vox, he says that “gay porn searches are about the same everywhere”, indicating there are about the same number of homosexuals in all states.Īlthough Seth was not lying here, he was not quite telling the truth either. ![]() Seth Stephens-Davidowitz, the author of “Everybody Lies”, does the same across US states. We would then want to see if positive social attitudes toward homosexuality then predicted more people choosing to watch gay porn. What we can do is compare searches for “porn” and “gay porn” across different places. We’ll have to try a method which is slightly more precise. It could be consistent with a substantial increase in the proportion of people who are homosexual, or a substantial decrease! Since the data we are given is in such a low resolution, rounded to the nearest percent we can’t tell what’s happened. Over the past 12 months, the number is still 4%. In America in the year 2004, gay porn represented 4% of searches that include the term porn. Google adds up the searches for both terms and then calculates the searches for “gay porn” as a percentage of searches for both terms. I compare two search terms “gay porn” and just “porn”. Ideally, we would just check whether more people are watching gay porn compared to straight porn over time using Google Trends. ![]() The trick to our test would be to use people’s internet search history. In questioning the veracity of the therapist’s conversion he asked “when your close your eyes and masturbate, what images come into your head?” We want to know what images people use for masturbation. The gay comedian, Stephen Fry, once did an interview with an ‘ex-gay’ therapist who offered ‘gay conversion therapy’. How do we find out whether more people are becoming homosexual if we cannot trust them to tell us the truth? The answer is obvious. So with that, let’s get to the point and look at the data. But the results didn’t exactly go the way I intended and as John Maynard Keynes said “When the facts change, I change my mind. This can be tested and falsified, which is what I expected to do. My key rebuttal to Caplan was going to be that if acquired homosexuality is true, then more people would be engaging in more gay sexual behaviour. But rehashing the whole debate is somewhat tedious - I recommend you read Michael Bailey et al’s review of the science around homosexuality if you want a careful review of the issue. For example, although the twin studies do find a substantial effect of the shared environment, they still use self-identification which may be impacted by closeting. In fact, I originally planned this blog post as a rebuttal, a ‘deboonking’ of Caplan’s position. Now I am quite sceptical of these arguments. ![]() The explosion in LGBT groups and the rising prestige of the movement allows and facilitates recruitment. Or at least any massive increase in gay identification is then more likely to indicate an environmental effect making people homosexual. Homosexuals are not reproducing and gay genes are surely ‘imploding’, suggesting any increase is likely caused by the environment. If the increased rates of homosexuality were explained by reduced ‘closeting’ then we should not see such a difference between older and younger generations alive today. The heritable influence on homosexuality (it’s around 30-40% in men) does not preclude there being any large societal effects that are changing over time. Caplan then follows the data with what I consider to be his four key points/arguments: ![]()
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