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 iPhones who weren't going to have kids anyway??
Why definitely not? And why definitely iPhones or Smart Phones or whatever?
As a rule of thumb, if you look at something for 3 minutes and have some obvious questions, the scientists that looked at it for several years of their life in great detail might have had those same obvious questions as well.
> As a rule of thumb, if you look at something for 3 minutes and have some obvious questions, the scientists that looked at it for several years of their life in great detail might have had those same obvious questions as well
This does not mean that just because they had those obvious questions that they were properly resolved. Human history has a long track record of people who knew better but chose to ignore. In science there is an incredible pressure to have positive results rather than negative ones (IE nobody would care or know about this study if the title was "we looked and iphone doesn't explain 33-52% of fertility decline"
This study treats ATT doing market research and progressive rollout through prioritized markets as a "natural experiment".
We could at least agree it's specifically chosen population, whatever ATT marketing dept had in mind when they planned the rollout.
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.
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.
Let me get this straight, I believe one needs to read a paper to get it straight.
But I fully understand your knee-jerk reaction. That was my reaction when I read the title too. However, it seems to be a surprisingly well-thought analysis where all your points are answered (controlled).
If I read it more thoroughly I'll likely find flaws on the statistical methods. But it's not like the authors didn't have common sense.
Edit: unfortunately, enough people had the same knee-jerk reaction to get this thread flagged. We really need a way to vouch a thread.
"Table 1 documents that treated counties (those with >90% AT&T 3G coverage) are substantially more urban, White, Republican-leaning, and affluent than control counties. To address this imbalance, we apply the entropy-balancing reweighting of Hainmueller (2012), which solves for the entropy-minimizing set of control-county weights that equalize the treated and reweighted-control means of a specified set of covariates."
1) A causes B
2) B causes A
3) C causes both B and A (in some order)
4) your correlation figure is bullshit (hence not counted in the 3 options, but certainly with news these days, it must be mentioned)
A famous way to illustrate where this goes wrong is to show a map which libraries that loaned out Harry Potter books, and a map of where poodles got raped. Very high correlation, and obviously an example of the 3rd option.
(obviously both were caused by population density, which leads to both library creation and poodle-related crimes. And probably non-poodle-related crimes)
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.
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.
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.
Plenty has been written about how any technological innovation leads to massive societal changes no one could foresee, and no one could avoid, but only analyze in retrospect.
Smartphones massively reduce the barriers to entry for self-directed career-based, social, political and educational activity (plus entertainment, but gambling addicts have differing fertility patterns so lesser degrees of it are useless to study). Outside observers may consider the real-world quality of such activities to be low, but the activity they enable people to do in their many brief lulls of free time between different daily tasks do fit into those buckets more than anything else. And the cumulative effect of all of them is to delay life milestones.
[0] https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6676839
Also, why is this flagged?
About time we revisted this: https://tylervigen.com/spurious-correlations
The emphasis on "it takes a village" seems strangely misplaced, as well. It presumes that culture is so malleable that a shift in government policy can change the culture overnight. Have East Germans completely abandoned their supposed previous culture of taking care of relatives/neighbors children after the fall? If there was a genuine cultural difference in communally raising children, and that was meant to improve fertility rates, you would expect to see higher fertility rates reflected regardless of the current government policy.
LMAO does the author really take themselves seriously as they type that.
This author has no understanding of statistical methods. This sort of article is the reason why people distrust science. Not because the scientific method is flawed, but rather because nonsense like this get published.
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 people are subject to human problems. But there’s nothing else in the history of the world with a better track record than science. I feel like the problem is that some politicians spread FUD and prey on people’s insecurities, and unfortunately it tends to work, disproportionately on people with less resources. The problem isn’t science at all; the problem is people and politics.
"The problem isn’t science at all; the problem is people and politics."
Agree completely.
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.)
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.
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?
"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.