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[OQSPS] Inequality across prefectures in Japan is different

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(2015-Dec-18, 18:38:52)ljzigerell Wrote: Hi Emil,

Thanks for the comments.

I like your idea of adjusting the variables for population density. Something else might be to conduct the analysis on only the high population or high population density prefectures and then on only the low population or low population density prefectures. Maybe also conduct the analyses on only the northern prefectures and then on only the southern prefectures. These will be only exploratory analyses, but these disaggregated analyses might provide a sense of what it is about the Japan prefectures that makes them different from other countries in terms of the S factor.


Re. running analysis on groups divided by latitude or population (density). This is a typological approach (subgroup analysis), which decreases sample size quite markedly. As you say, it is also purely exploratory and conclusions would not be drawn with much certainty.

Note that adjusting for population density is fairly ad hoc. Population density data was available (or readily calculateable from area and population) in many prior studies but no such correction was made. Usually, population density has a fairly strong positive loading because cities tend to be higher in S; urbanicity often has a strong positive loading.

However, I went ahead. I tried 6 controls: population density, log population density, sqrt pop. density, population, log population, sqrt population. Log/sqrt versions were used to create more equality in the data which had very large differences between prefectures.

The results are attached. I have sorted the loadings by population density log. As can be seen, this control apparently solves most of the problems!

   

It even fixes the indicator sampling reliability which increases to 94% |r|>.50. The others were also improved, but not quite as much.
Standard.0.5 Pop. density.0.5 Pop. density (log).0.5 Pop. density (sqrt).0.5
0.533 0.577 0.933 0.899
Population.0.5 Population (log).0.5
0.780 0.816

   

What about criteria analysis?

   

Jensen's method

   

So, with the correction, the results become like those in all other countries, more or less. Apparently, Japan is weird in so far as population density almost entirely makes the S factor indiscernible. Now this makes me wonder how controlling for population density in the other analyses affects results. I guess someone could re-analyze all the prior studies.


Quote:It's correct that some of the variables are not clearly desirable or undesirable (e.g., marriage), and some might even have complicated desirabilities (e.g., too little and too much are both undesirable). But I'm wondering whether the finding of the lack of an S factor across the Japan prefectures might be strengthened by a model that included only those factors that are clearly desirable or undesirable, reflecting the idea that -- if there is no S factor across Japan prefectures in that model -- then it's really clear that there is no S factor there.

My problem with this general approach is that it requires me to make these decisions about variables that are clearly desirable and not.


Quote:Along those lines, the Mexico and Brazil S factor studies seemed to have a higher percentage of variables that were more clearly desirable or undesirable, and the Mexico and Brazil studies respectively had only 21 and 32 variables, so maybe an analysis with the 20 to 30 most obviously desirable and undesirable variables from Japan would be more convincing and provide a more even comparison. The analyses that you have already reported suggest that Japan is different than Mexico and Brazil in terms of the S factor, but I think it would strengthen the analyses to rule out the larger number of variables in the Japan analysis as a source of the difference between the Mexico/Brazil and Japan studies.

The general approach is including whatever suitable variables I can find. Some datasets simply have more suitable variables than others. I would rather reserve in depth studies of variable composition across studies to be delayed until someone does a large meta or mega-analysis. I do not at present have time for this.


Quote:Something else to consider is whether a relatively low variation in some variables might make measurement error a larger problem in this dataset. For example, income per person might need adjusted for cost of living in a prefecture, and divorce rate might need adjusted for marriage rates: in the 2013 data, Shiga-ken and Nara-ken have the same divorce rate (1.64) but the marriage rate is 5.27 in Shiga-ken and 4.44 in Nara-ken, so it's possible that a higher percentage of persons get divorced in Nara-ken and thus that the 1.64 divorce rate in Nara-ken is worse than the 1.64 divorce rate in Shiga-ken. Maybe a divorce-to-marriage ratio might be a better measure than individual marriage and divorce rates. (I'm assuming that the divorce rate is measured per 1,000 persons and not per 1,000 married persons.)

Once one gets started on the "lets correct variables for this or that", and "make new measures from existing variables", it quickly escalates. Due to time constraints, I rather forego this option (for now). In my defense, I have shared all the data and code, so if someone thinks this is worthwhile doing, they are more than welcome. :)


Quote:One way to avoid post-hoc coding biases is to identify ahead of time in general terms the obviously-desirable-or-undesirable variables that should be included in an S factor analysis, and then limit the variables in the main analyses to that set of pre-identified variables, such as measures of health, crime, unemployment, education, income, and dependency. Something like percentage farmers in the Brazil study would not fit in one of those categories, so it's not necessary to consider whether percentage farmers is a desirable or undesirable measure. Infrastructure reflects another set of variables in your S factor studies; this would be a good variable for a cross-national study, but I'm not sure that is always a good idea for subnational studies if, for instance, the quality of infrastructure in a subnational region largely reflects decisions made at the national level.

One could do this, but as I mentioned above, usually I take whatever I can find that's suitable. Often this means that there is some overlap across studies. The benefit of including all kinds of variables is that it is more exploratory. Of course, conclusions based on such exploratory research should be somewhat restrained, but I think it is a worthwhile trade-off.


Quote:Hope this is helpful. It's a really interesting study.

Well, it looks I need to add a new section, re-do the abstract and discussion. :)

---

I have rewritten parts of the paper to fit the new results. Have a look. Changes to abstract, robustness section and discussion (+ title). Code and data also updated.
https://osf.io/zfw38/
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Messages In This Thread
[OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-17, 20:39:57
RE: [OQSPS] Inequality across prefectures in Japan is different - by ljzigerell - 2015-Dec-18, 06:40:56
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-18, 07:43:46
RE: [OQSPS] Inequality across prefectures in Japan is different - by ljzigerell - 2015-Dec-18, 18:38:52
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-23, 06:06:58
RE: [OQSPS] Inequality across prefectures in Japan is different - by ljzigerell - 2015-Dec-23, 09:31:09
RE: [OQSPS] Inequality across prefectures in Japan is different - by Kenya Kura - 2015-Dec-23, 19:09:56
RE: [OQSPS] Inequality across prefectures in Japan is different - by Kenya Kura - 2015-Dec-21, 17:43:59
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-23, 11:17:58
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-23, 15:57:40
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2015-Dec-23, 23:05:40
Proofreading - by Emil - 2015-Dec-27, 22:08:17
RE: [OQSPS] Inequality across prefectures in Japan is different - by bpesta22 - 2016-Jan-25, 18:27:51
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2016-Jan-25, 22:23:50
RE: [OQSPS] Inequality across prefectures in Japan is different - by bpesta22 - 2016-Jan-29, 19:56:06
Reply to Pesta - by Emil - 2016-Jan-30, 11:50:54
RE: [OQSPS] Inequality across prefectures in Japan is different - by NoahCarl - 2016-Apr-04, 16:44:02
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2016-Apr-05, 23:29:41
RE: [OQSPS] Inequality across prefectures in Japan is different - by NoahCarl - 2016-Apr-06, 12:00:22
RE: [OQSPS] Inequality across prefectures in Japan is different - by Emil - 2016-Apr-06, 19:18:35
 
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