Correct. The data is suggestive, but does not make for very strong evidence of poisoning. Personally, I think the case for increased osteoporosis is strong for a variety of reasons, but we need information to answer if this was because of a mass-poisoning. .
Yeah, I think a lot of anecdata point to the fact that a pretty significant portion of the clergy are gay. The most interesting question in my view is how and why that portion might rise to very high levels ~80% once you get to Rome.
That's true, but the probability mass function for total sibship size we estimate here would left-shifted if the non-survivors are absent. The reported family sizes would be smaller in kind.
I might be wrong that this would neutralize the effect you're pointing out, but I think it does.
A sufficiently high band in the CCP could work.
I can't tell if this is a joke, but it's very funny even if it's not.
Aren't non-surviving children as likely to make a cardinal higher in pregnancy order as they are to make him lower in that order?
It would be nice if you had the sexes of the siblings, since it's supposedly only the older brothers that count, though I don't really expect that to change anything. I wanted to do that but given the Ablaza et al. results where the effect exists for all older siblings, I decided it wasn't worth the drop in power.
I think Greg Clark has stated his new book will claim none of the observed status-related birth order effects commonly cited actually exist in a dataset with sufficient size and resolution.
Maybe my next substack post will be trying to analyze how the expose equilibrium changes as a function of the percent_gay parameter.
Thanks for the thoughtful comment. I'll try to address these remarks in order. You state
They also use overall mortality (Web Table 10), which is what I was trying to reproduce and screenshotted. The significance figures aren't really different than those for the regressions broken down by mortality cause (Web Table 15), but the rate ratios for the all cause mortality ratios are clearly smaller in the disaggregated regressions because people die from other stuff. I mostly ignored the rate ratio sizes and focused on the significances here, but agree the most powerful effects probably result from the disaggregated regressions.
This is a fundamental misunderstanding of how controls work and what they're supposed to do. Just yesterday Cremieux wrote a pretty good piece on this very topic. The authors include these controls with little thought to the underlying causal mechanism. Their only remarks on them at all in the supplemental material are
This isn't necessarily true at all. Consider one of the controls: Health expenditure as a percent of GDP. Is that a confounder, influencing USAID spend levels and mortality rates simultaneously? Is it a collider, caused by increased or decreased USAID spend and mortality changes? In the former case, yes, go ahead and control for it, but if it's the latter, it screws up the analysis. Both are plausible. The authors consider neither.
I did apparently miscount the controls. It's unclear why their own spec on page 13 miscounts them as well.
You mention the Monte Carlo simulations aren't comparable. This is a fair, and I really like the explanation. I didn't really touch on that aspect of this analysis in the post, but you've persuaded me I'm making a cheap point.
"Also it makes no sense to say "some of the choices they make to strengthen the result end up being counterproductive".
This was unclear, and I regret it. I meant counterproductive in the sense of complicating the analysis. I'm still not clear how they got such strong, consistent results. My suspicions is careful control selection as alluded to above.