2015-Dec-18, 06:40:56

Very interesting.

1. In terms of presentation, the patterns in Figure 3 are not obvious because -- to assess whether a particular variable reflects the expected pattern -- a reader must read the variable label and then consider whether the expected loading would be positive or negative. It might make the figure easier to interpret to instead plot the inverse of undesirable variables such as divorce so that the expected loadings are positive for all variables; then the reader can simply check whether the point for the variable is to the left or right of zero.

2. It might be worth considering suspicions about the accuracy of Japan's reported abortion rates and suicide rates (e.g., https://www.guttmacher.org/pubs/journals/25s3099.html, http://www.japantimes.co.jp/news/2013/02...nN3SFJRKDk). I'm not sure that misreporting would vary by prefecture, but combining homicides and suicides into an unnatural death measure might avoid at least part of any measurement problems with the suicide measure.

3. It's not clear that some of the variables are necessarily desirable. Museums and libraries negatively correlate with population, which might reflect big cities having fewer-but-bigger libraries; if so, it's not obvious that having a lot of small libraries is better than having fewer big libraries with more amenities.

4. I did not see this pattern in the Mexico S factor study, but it appears that the S factors for the Japan prefectures in Figure 4 correlate fairly highly with population or at least population density. I think that all but one of the top 15 prefectures by population have an S factor above zero, and the two prefectures of those 15 with the lowest S factors (Hokkaidō, and Niigata-ken) have the lowest population density of those 15. Moreover, all but one of the bottom 15 prefectures by population have an S factor below zero, and the one prefecture with an S factor in that set above zero (Kagawa-ken) has the highest population density of that set. I'm not sure what -- if anything -- to make of that, though.

1. In terms of presentation, the patterns in Figure 3 are not obvious because -- to assess whether a particular variable reflects the expected pattern -- a reader must read the variable label and then consider whether the expected loading would be positive or negative. It might make the figure easier to interpret to instead plot the inverse of undesirable variables such as divorce so that the expected loadings are positive for all variables; then the reader can simply check whether the point for the variable is to the left or right of zero.

2. It might be worth considering suspicions about the accuracy of Japan's reported abortion rates and suicide rates (e.g., https://www.guttmacher.org/pubs/journals/25s3099.html, http://www.japantimes.co.jp/news/2013/02...nN3SFJRKDk). I'm not sure that misreporting would vary by prefecture, but combining homicides and suicides into an unnatural death measure might avoid at least part of any measurement problems with the suicide measure.

3. It's not clear that some of the variables are necessarily desirable. Museums and libraries negatively correlate with population, which might reflect big cities having fewer-but-bigger libraries; if so, it's not obvious that having a lot of small libraries is better than having fewer big libraries with more amenities.

4. I did not see this pattern in the Mexico S factor study, but it appears that the S factors for the Japan prefectures in Figure 4 correlate fairly highly with population or at least population density. I think that all but one of the top 15 prefectures by population have an S factor above zero, and the two prefectures of those 15 with the lowest S factors (Hokkaidō, and Niigata-ken) have the lowest population density of those 15. Moreover, all but one of the bottom 15 prefectures by population have an S factor below zero, and the one prefecture with an S factor in that set above zero (Kagawa-ken) has the highest population density of that set. I'm not sure what -- if anything -- to make of that, though.