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Overcoming Bias Commenter's avatar

If I was going to name the given factors they would be (1) HA = health awareness, (2) EFF= expensive family formation EFF, (3) and C = conscientiousness.

HA is the least interesting to me from a modeling perspective because it appears to be significantly "cultural" in a kind of geographically arbitrary way. However, from the perspective of "changing your mind and behaviors to get a better outcome" it seems like health awareness is the place to focus.

EFF makes sense in terms of being liberal/educated/urbanized and geographically it appears to be happening in areas where the cities are crammed together or pushed against a border, a great lake, or an ocean. If "housing costs" were taken into account I'd expect it to show up as a factor because as housing costs go up, family formation is more expensive, new humans are harder to make, more attention is paid to investing in the relatively less numerous kids, and you need a paying job in order to afford to stay there during retirement.

C looks like a difference between "ice people" and "sun people" to me. The issues that contribute to the "conscientiousness" label are church, crime, and school completion. I wouldn't be surprised if the geographic distributions have a lot to do seasonal affective disorder and snow (neither of which are available to contribute to the factor, but I'd predict that they would be part of it if they were available, another good factor would be per capita hours of air conditioning).

Louisiana and Mississippi are cheap places with sun people. California is expensive with sun people. Montana is cheap with ice people. The only combination that doesn't exist is top quartile in both expense and coldness, but Wisconsin and Michigan are examples that are close to that combination.

Interpreting EFF and C as opposite ends of the same "farmer-forager" axis seems sloppy to me. States like Montana and California fit the single axis model with their opposite extremes, but the states that are high or low in both EFF and C (like Michigan or Mississippi) give lie to the single axis model.

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Overcoming Bias Commenter's avatar

When you put up something like those maps, how about doing it so that the key isn't unreadably small?

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