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The iPhone explains 33–52% of fertility decline among women aged 15–44

nber.org

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Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#51

Post by virgilp »

FabHK wrote: post #26
virgilp wrote: post #23

Earlier quoted context omitted.

That's for the good studies. Let's not pretend that all published studies are honest. Unfortunately it is quite reasonable to be skeptical about extraordinary claims such as this one.

It is reasonable to be skeptical, absolutely. But responding to a study like this with "haha what about if only rich people got iPhones" or "bro don't you know that correlation does not imply causation" is juvenile.

To be fair, their control variables treat the first objection (wealth), not the second (brand preference; and yeah there's some correlation but one doesn't imply the other)

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#52

Post by dahart »

hodder wrote: post #48
dahart wrote: post #44

Earlier quoted context omitted.

What exactly is the problem with that sentence? Are you sure the problem isn’t that you don’t understand what it means? Here are a few links explaining the terms: https://pmc.ncbi.nlm.nih.gov/articles/PMC7384548/ https://en.wikipedia.org/wiki/Poisson_regression https://lost-stats.github.io/Model_Estimation/Research_Desig... I don’t know why people distrust science. Sure, it’s not perfect, and scientists, like all peo…

I know what a linear regression is and how to examine event studies, lol. What you don't understand is that the author is leaning on linguistics to insinuate strong evidence of causation where it doesn't exist. If this was a quant in finance, they'd be out the door in days. "The problem isn’t science at all; the problem is people and politics." Agree completely.

> the author is leaning on linguistics

Please elaborate. I haven’t used entropy balancing or difference in differences, but those articles explain that their purpose is to try to tease out causation. What - exactly - is the linguistic trick, if they actually did use an entropy balanced Poisson regression and difference of differences?

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#53

Post by pbhjpbhj »

ilija139 wrote: post #13
onlyrealcuzzo wrote: post #9

So... Let me get this straight. Because people who had iPhones during the AT&T exclusive period has less kids... They think there is no other possibly explanation besides the iPhone, because they looked at similar groups on different networks and in different areas that didn't yet have coverage for iPhones? It definitely couldn't have been due to richer people having iPhones and having less kids, or people preferring…

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 ha…

>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.

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#54

Post by wavemode »

virgilp wrote: post #46
wavemode wrote: post #25

Earlier quoted context omitted.

The 5th option is random chance. That often results from p-hacking. In a world of infinite variables, if you look hard enough you are guaranteed to eventually find two completely unrelated variables that correlate with each other over a statistically significant period of time.

That's the 4th option

I guess it could be? I interpreted what the parent commenter wrote like "the variables aren't actually correlated" (which definitely does happen sometimes)

Whereas my point is moreso when, the variables really are correlated but it's purely due to random chance. Not bullshit, per se, just bad luck (or possibly, p-hacking).

(Though the solution to both is the same - you shouldn't trust a study until it's been independently replicated on new data.)

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#55

Post by phyzix5761 »

There's another interesting paper that talks about how in 2007 teens shifted their socializing to the smartphone. This led to a large reduction in teen fertility, as teens would spend more time communicating digitally rather than in person[0].

[0] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6676839

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#57

Post by macNchz »

browningstreet wrote: post #30
bel8 wrote: post #5

I'll bite. Maybe it's not the iPhone itself, but social media. I have seen studies about damages that social media can cause in behaviours. This might be one of them. Smartphones are the catalyst to social media consumption as we know. Like people contantly on their phones everywhere instead of interacting with other people, for example.

Social media wasn't the thing it is today during the AT&T exclusive period.

That's the thing that jumped out to me here—iPhones themselves and everything you could actually do with one during that period were way different from today. There were effectively no endless feeds of content to consume, the phone screens were way smaller and less vibrant, push notifications only came out about halfway through.

I was age 19-23 during that period (in the "highest impact" age group from the article), and I think I used my phone more for coordinating in-person social activity than anything else at the time. Additionally on that—iPhones were not widespread in my cohort at the time, even at an expensive private college with many students from upper income families.

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#58

Post by ilija139 »

pbhjpbhj wrote: post #53
ilija139 wrote: post #13

Earlier quoted context omitted.

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 ha…

>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.

The iPhone was the only smartphone in 2007, and the one that they could track based on AT&T coverage.

Re: The iPhone explains 33–52% of fertility decline among women aged 15–44

#59

Post by hodder »

dahart wrote: post #52
hodder wrote: post #48

Earlier quoted context omitted.

I know what a linear regression is and how to examine event studies, lol. What you don't understand is that the author is leaning on linguistics to insinuate strong evidence of causation where it doesn't exist. If this was a quant in finance, they'd be out the door in days. "The problem isn’t science at all; the problem is people and politics." Agree completely.

> the author is leaning on linguistics Please elaborate. I haven’t used entropy balancing or difference in differences, but those articles explain that their purpose is to try to tease out causation. What - exactly - is the linguistic trick, if they actually did use an entropy balanced Poisson regression and difference of differences?

Entropy balancing cannot fix unobserved confounders.

"Teasing out causation" is exactly why this methodology fails. You are confusing the intended purpose of a statistical tool with its real-world validity. No one is questioning what an Entropy-Balanced Poisson Regression or a Synthetic Difference-in-Differences model is designed to do.

The issue is that the authors have profoundly violated the mathematical assumptions required for these tools to actually function. Throwing high-level econometric terms into an abstract does not make the underlying logic scientific, but rather acts as a linguistic tuxedo on a fundamentally broken causal claim.

If you cannot see through economic (and other) confounders that invalidate their approach and their biased statements, I cannot help you. This isn't science. Getting an LLM to run an SDID model and spit out a result doesn't = science.

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