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by ilija139 3 days ago
Not every rich person got an iPhone. The rich people without an iPhone did not had equal amount of less kids. There are two groups, one has an iPhone, the other has not. The assumption is that two groups are big enough to have equal amount of people from any other group that can explain the decline in fertility, i.e. equal amount of rich/poor, educated, etc. They can control for this because they know which people had access to the iphone based on the AT&T network coverage.

At the end of the abstract they state the likely explanation of this seemingly spurious correlation: > National-survey evidence on time use and sexual behavior is consistent with the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.
1 comments
pbhjpbhj 3 days ago
>the iPhone reducing in-person interactions, increasing pornography use, and reducing sexual frequency.

Why would iPhone _particularly_ do that? I can see greater social media use, greater access to porn, would do those things. But that's common to smartphones in general.

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ilija139 3 days ago
The iPhone was the only smartphone in 2007, and the one that they could track based on AT&T coverage.
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mike_hearn 3 days ago
It wasn't the only smartphone. And it's not like the internet appeared simultaneous with the iPhone. It was just a different way to get access to the same websites, back when it launched, most of which didn't support the iPhone's screen size anyway. And the only difference was that it made it easier to reach those websites ... outside. On the move. Where you are more likely to meet people.

Their causal explanation here does not ring true in all sorts of ways. Nobody was deciding not to get married and have kids because they were too busy watching porn on an iPhone in 2007, a time when video on smartphones barely worked. Social media predated the iPhone by years. And this practice of controlling for some ad-hoc list of things and then announcing causality is a major cause of spurious false positive claims in academic science. They really should know better than to make strong claims of causality in a case like this. Sociology just isn't capable of explaining things like this no matter how many stats you throw at it.

https://pmc.ncbi.nlm.nih.gov/articles/PMC4816570/

There's much better work out there on this topic that is less heavy on the cute regressions and more heavy on the historical analysis+common sense. It's often a lot more convincing.

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