They do a few checks for this in the article. For example, one of their subsamples is Union Army veterans, and they detect the same trend within those cohorts (0.03 C lower per decade), which used the same instrumentation, as when comparing across the subsamples (e.g., Union Army vs today).

I don't think there is any claim that the same instrumentation is used for the Union Army cohort -- even basic details like whether the temperature is taken orally or axially: "Whether the temperatures were taken orally or in the axilla is unknown; both methods were employed in the 19th century although oral temperature was more common".

That's true, but they do make the more abstract assumption that any bias isn't associated with birth date within cohort. I think the idea is that if there were bias due to instrumentation, it would have to be somehow systematically related to birth date within the cohort.

This is possible if there were some systematic shift in instrumentation with measurement year, but it would be blunted by the extent to which different ages were being sampled at each year.

I agree though that it would be nice to have some information on instrumentation over time and how it relates to temperature measurement.

It's worth noting that the results have practical implications regardless of the explanation of the observed trends, though, which is it's commonly assumed normal body temperature has certain characteristics, without recognizing that that might have shifted in significant ways over time.

It's interesting to me personally because I've often observed my body temp when I'm feeling fine is usually just above 36 C. That doesn't sound like much but it becomes a bigger deal when deciding whether or not I have a significant fever. Some of that might just be individual variation, but this is suggesting there's cohort trends within that too.

Paper is not really environmentally unfriendly. Those are farmed trees. No one is turning virgin rainforest into paper. The shipping is far and away the worst part of that.

On the other hand, ereaders use electricity. Tablets use more. So not much of an environmental angle here.

Streaming revenue is about the same as box office revenue, so the disparity isn't as big. But still, that's a lot of gaming revenue.

He was added as a foreword author after the main author had already died, just FYI.

A lot of people I know at various departments are switching their undergrad stats/econometrics classes from Stata to R. That's the beginning of the end of Stata.

That matters, but I don't think that's happening until all of the big graduate-level metrics textbooks get R versions. And even then, at least a few papers are going to run into trouble with older reviewers who are used to seeing work done in Stata and don't trust anything else.

Yes, and it's also non-trivial to write R code that matches your textbook's answer if your textbook used Stata. You have to do things like look up which specific variant of the sandwich estimator Stata uses for robust standard errors, so you can tell R to match that.

In Stata's defense: It helps that Stata is actually really good for the "running regressions" part. In particular, it gets robust standard errors right without much extra work in complex cases that would require a lot of additional code in Python or R.

R wins easily for data visualization and scripting, though. It's also much better as a skill you can "take with you". If you end up working in industry, you may not be able to expense a Stata license, but you'll almost certainly be able to use R (although maybe not RStudio).

You don't even want to expense a Stata license. Stata is the worst thing I've ever had to use. Maybe as a person who can't program it makes sense, but as a professional developer almost everything about Stata is non-intuitive, confusing, and stupid. Also the only thing to go on is their stupid pdf manual. Finding real people on the internet who actually use it is almost impossible.

Some people attach more emotional value to items like that. Some don't. I did for a long time. After a lot of big changes I kind of stopped hording things as much and it was very freeing.

Not an article, but the Seth McFarlane Cosmos has an episode that's largely about Clair Patterson.

Thanks! I'll check it out.

> This dearth of reaction to such a critical work...

It's a five year old book, of course no one is talking about it specifically anymore. (And when it came out, everyone was talking about it.)

And "nobody is talking about inequality" is ridiculous. I've been to three major conferences in the last six months and there were multiple whole sessions on inequality. And not just income inequality. Educational, environmental even. It's not the only thing economists talk about, but far from "no one" is looking at it.

Yes, that paragraph is very, very bad, because it's not people of equal education that would have equal wages, but equal productivity---which will vary depending on what firm you're at! Not just because of sorting and poaching of talent, but some firms' production and capital structure will be such that the marginal worker will be more productive.

That makes no sense, equivalent workers should get paid equivalent wages regardless of their firms’ productivities. Apple doesn’t pay more for aluminum than a soda can maker.

It doesn't but it would continue buying for longer if the price went significantly and permanently up. But the point of this whole thread is that labor isn't just another commodity like aluminum. It's a special, weird thing and the market in it is just different and probably less "efficient" than the markets for metals and grain. The question isn't "how can we best model this using our existing theory?" It's "what is the best way to model this?", so if the existing theory isn't very helpful, we should look for new ones that are better.