With the collapse various North African regimes there has been a great concern about the migration of people from the southern shore of the Mediterranean to the northern. The of the reasons for this concern is that there is an imbalance in population growth. So I thought I’d review some of the data on Mediterranean nations over time using Google Data Explorer.

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A questioner below was curious if vocabulary test differences by ethnic and region persist across income. There’s a problem with this. First, the INCOME variable isn’t very fine-grained (there is a catchall $30,000 or greater category). Second, it doesn’t seem to control for inflation. But, there is a variable, DEGREE, which asks the highest level of education attained. I used this to create a “college” and “non-college” category (i.e., do you have a bachelor’s degree or not). Because of sample size considerations I removed some of the ethnic groups, but replicated the earlier analysis.

Below are two tables. One shows the mean vocab score for region and ethnicity (for whites) for those without college educations, and another shows those with college educations. I decided to generate a correlation over the two rows, even though it sure isn’t useful as a quantitative statistical measure because of the small number of data points. Rather, I just wanted a summary of the qualitative result. The short answer is that the average vocabulary difference seems to persist across educational levels (the exception here is the “German” ethnicity).

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Mike the Mad Biologist has a post up, A Modest Proposal: Alabama Whites Are Genetically Inferior to Massachusetts Whites (FOR REALZ!). The post is obviously tongue-in-cheek, but it’s actually an interesting question: what’s the difference between whites in various regions of the United States? I’ve looked at this before, but I thought I’d revisit it for new readers.

First, I use the General Social Survey. Second, I use the WORDSUM variable, a 10 question vocabulary test which has a correlation of 0.70 with general intelligence. My curiosity is about differences across white ethnic groups by region. To do this I use the ETHNIC variable, which asks respondents where their ancestors came from by nation. I omitted some nations because of small sample size, and amalgamated others.

Here are my amalgamations:

German = Austria, Germany, Switzerland

French = French Canada, France

Eastern Europe = Lithuania, Poland, Hungary, Yugoslavia, Russia, Czechaslovakia (many were asked before 1992), Romania

Scandinavian = Denmark, Norway, Sweden, Finland (yes, I know that Finland is not part of Scandinavia, Jaakkeli!)

British = England, Wales, Scotland

Next we need to break it down by region. The REGION variable uses the Census divisions. You can see them to the left. I combined a few of these to create the following classes:

Northeast = New England, Middle Atlantic

Midwest = E North Central, W North Central

South = W S Central, E S Central, South Atlantic

West = Pacific, Mountain

The key method I used is to look for mean vocabulary test scores by ethnicity and religion. I also later broke down some of these ethnic groups by religion. Finally, all bar plots have 95 percent confidence intervals. This should give you a sense of the sample sizes for each combination.

First let’s break it down by race/ethnicity and compare it by region to get a reference:

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The above results are from an Ipsos MORI from last summer. Please note, the opinions above are restricted only to those who asserted a religious affiliation. Obviously in Saudi Arabia this is irrelevant, as nearly the whole population has a religious affiliation. But it is important in Japan, because there nearly 2 out of 3 individuals in the survey reported no religion, so these are results from the minority who reported having an affiliation (mostly Buddhist). As they say, read the whole thing. Here are some conclusions I drew from these data:

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We’ll be talking about Iran a lot in the near future in the United States. I doubt we’ll invade the country (thank god). But one thing I think needs to be emphasized: on social issues Iran is more “progressive” than many of our close allies in the region, like Saudi Arabia, and one of the more progressive nations in the region. This is neither here nor there in the domain of geopolitics, but to convince a public about something it is often necessary to make a cartoon or caricature the enemy. I think it is important to remember though that aside from Israel our closest allies in the region are techno-feudal monarchies like Saudi Arabia, not those nations, like Iran, which have made a more thorough accommodation with modernity out of necessity (because oil can’t support the whole economy). It also reminds us that labels like “Islamic Republic” may not be totally useful.

As a gauge of modern outlook, as understood in the West, I poked around the World Values Survey. The results are for wave 4, around ~2000. The question asked was: A wife must always obey her husband. Possible answers:
- Agree strongly
- Agree
- Neither agree or disagree
- Disagree
- Strongly disagree

Below are two tables with nations which responded to this question. I stratified by sex and educational level of respondents. The sample sizes are in the “Total” column. The other numbers are percentages, summed along the rows to 100%. There are some surprises, but I’ll let the data speak for itself….

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In the post below I stumbled upon a weird datum. Kuwait’s total fertility is now below 3. The average estimates seem to be ~2.5 or so. This surprised me, as my impression was that Gulf Arab petroleum based states tended to encourage pro-natalism. This was both a matter of ideology, and also because the small and wealthy native populations lived off rents, and had not had to modify their neo-medieval ideologies to foster productivity driven economic growth. But perhaps Kuwait is an anomaly? Well, it turns out that the Saudi fertility rate is now below 3 as well. Again, depending on which numbers you trust a value of ~2.5 seems plausible. In 1980, at the peak of OPEC’s power and a period when Saudi Arabia was flush with incredible per capita wealth the fertility rate was north of 7.0. But even in the mid-1990s Saudi Arabia’s fertility remained a robust 5.0. Obviously one has to account for the fact that some of the “Arab” nations are not very Arab. The UAE has huge South Asian and Persian populations, not to mention all other sorts. So its fertility of 1.80 can be chalked up to its unique demographics. But would you have guessed that Lebanon’s fertility rate is now the same as Finland’s?

Below the fold is a chart which shows the trends among Arab nations and Finland over the past 40 years. The shading of the bars is proportional to life expectancy.

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Argentina has famously gone from being the 10th wealthiest to the 60th wealthiest nation in the world over the past 100 years. But I was curious about the trends for its neighbors as well in a world context.

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I wanted to clarify a few issues with the Census’ American Community Survey. These data come from the interval of 2006-2008, and they allowed me to query the proportional of various Latino/Hispanic groups who identified as white. I knew in the aggregate that the majority of America’s Latinos identified as white, but I was curious about two things:

1) The variation in white identification by group (by national origin)

2) The variation in white identification of Mexican Americans by selected states

Results below. There are stories in these data….

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With the current economic malaise in the developed economies and the rise of the “B.R.I.C.s” you hear a lot about “China” and “India.” There is often a tacit acknowledge that China and India are large diverse nations, but nevertheless in a few paragraphs they often get reduced to some very coarse generalizations. What’s worse is when you compare China and India to nations which simply aren’t on their scale. For example, over at Brown Pundits there is sometimes talk about India vs. Bangaldesh/Pakistan/Nepal/Sri Lanka. The problem is that the appropriate comparison are specific Indian states, not the whole nation. Uttar Pradesh, the largest Indian state in population, is actually in the same range as Bangladesh and Pakistan. Similarly, when comparing social metrics in Bangaldesh vs. India, one should focus on culturally similar regions, such as the state of West Bengal, not the sum average of India as a nation.

Similarly, we look at frenetic Chinese growth and worry about how they are “leaving us behind” (from an American perspective). But do take a step back to wonder how much the Chinese are leaving the Chinese behind?

Below are two charts which show the yawning chasm within these mega-nations on the scale of states (at a finer grain the variation is even greater). First a rank order of Chinese provinces by GDP PPP, with comparable nations interspersed within. PPP values shouldn’t be taken too literally, and the Chinese data seem to overestimate the values on a province level basis by 10-15%. But you get the general picture.

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I recently noted that the SDA Archive has an American Community Survey interface. The ACS has huge sample sizes because the US government can afford to do extensive surveys. And naturally you find some really interesting facts. For example, there’s a variable which tells you about the presence of grandchildren in the household. In some nations this wouldn’t be a big deal, but in the USA it is not too common. You can also look at this by state, which is what I did. Then I compared the total proportion to those limited with college degrees or higher. No surprise, those with college degrees tended to be less likely to have grandchildren living at home…. But can you guess which state has the highest proportion of grandchildren living at home? And which states deviate from the trendline? Surprises to me….

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One of the things I really hate are unqualified linear projections. They’re so useless most of the time. A science fiction magazine will give you more insight about the future than the United Nations population projection for the year 2100. This is just as much of an issue when it comes to American Census demographic projections. As I’ve noted before population projections of the coming non-Hispanic white minority 2040 to 2050 are sensitive to the assumptions behind the basic parameters. The logic of the projection is crystal clear and airtight, but just because a certain set of assumptions holds today, does not mean that those assumptions will hold indefinitely (though the Census projections are much more plausible than the United Nations projections because two generations are so much more strongly impacted by by the inertia of current conditions that four generations). In the 18th and 19th century white Americans, and especially the Anglo-Saxon founding stock, were a highly fertile folk. They took over the American Southwest and the Northwest in large part due to their demographic assault. In New England the 30,000 of 1650 became the 700,000 in 1790 in large part due to fertility rates on the order of 7 per woman! Today no one would expect that Anglo-Saxon Americans would be so fertile, let alone the New Englanders who were prominent in the population control movements of the 20th century. In the 17th and 18th century the Jews of Eastern Europe were a highly prolific group, and the gentile majority in places like Poland viewed the waxing of the proportion of this minority with great suspicion. Today no one views the Ashkenazi Jews as demographic engines, though in places like Israel the fecund Haredi have now helped close the “birth gap” with the Arab population, as its fraction of the Jewish population keeps increasing. I can give you other “counter-intuitive” examples from the recent past, but a little history goes a long way in teaching suspicion (e.g., in the Balkans in the late 19th century rural Christian populations had much higher fertility than urban Muslim ones).

These sorts of reversals are not inexplicable. Fertility shifts occur, sometimes within a generation or two. This is why Thomas Malthus turned out to be wrong: he didn’t predict the demographic transition. But we shouldn’t be complacent and assume we’ve reached the “end of history” when it comes to fertility transitions. In the early 20th century there was great terror in the American elite due to the immigration of what would later be termed “ethnic whites,” in particular Jews and Southern Europeans. And yet the Jewish proportion of the American population peaked in the late 1940s at ~5%. What about the other groups? The General Social Survey has large sample sizes for some ethnic groups, so I decided to look there.

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In the comments below a weird fact came to light: it does not seem that liberal/Democrat reduced skepticism toward astrology vs. conservatives/Republicans can be explained just by a secularization, and therefore diminished Christian orthodoxy. There are two reasons for this. First, on a priori grounds most people are religious, liberals and conservatives. The difference between the religious and irreligious on this issue would have to be rather large, and the different apportionment across ideology to be striking, for it to drive the division which seems so robust. Second, within the results it seems rather clear that the gap between liberals and conservatives is most evident amongst the religious of both! In other words, secular liberals and conservatives tend to agree (and be skeptical) in relation to astrology. While religious conservatives are skeptical of astrology, as one would expect from orthodox conservative Christians, religious liberals are not. The table below shows some results.

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Someone on twitter was curious about GOP attitudes toward astrology. I left the party breakdown out of the previous post because ideology accounts for most party differences. In other words, conservatives are more skeptical of astrology than liberals, and Republicans more than Democrats, but the second result just seems to emerge from the Republican’s greater conservatism.

Why are independents so gullible? It probably has to do with their lower average intelligence (this goes for moderates too). So I simply limited the sample to those with at least bachelor’s degrees to control for intelligence:

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At Culture of Science there’s a little discussion about whether acceptance of evolution indicates intelligence. Looking at the GSS data there doesn’t seem to be a strong causal relationship when you control for other variables. But there is a correlation. That correlation can be explained by the fact that, for example, people who are Biblical literalists tend to be duller than those who are not, and Biblical literalists don’t accept evolution (in fact, I’ve seen evidence that very intelligence Biblical literalists are more Creationist than their duller co-religionists, probably because they’re more coherent in their beliefs).

With that, I’ll leave you with a screenshot of the results for WORDSUM, a 10 word vocabulary test, against acceptance or rejection of human evolution from other organisms (note that the numbers below the proportions are weighted sample sizes):

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Married at age 21

A few years ago the president of Iran, Mahmoud Ahmadinejad, asserted that his nation did not have gays as they did in the West. What Ahmadinejad seems to have meant is that a public gay identity does not exist in Iran. He has to be aware that homosexual behavior is not unknown in his nation. More generally Ahmadinejad’s comments brought up the issue of men having sex with men throughout the Middle East before marriage. This is a taboo topic in much of the region, so getting good quantitative data seems pretty much impossible. But today PLoS Medicine came out with a paper with a result which suggests that the anecdotes of relatively widespread homosexual behavior in the Middle East are not totally unfounded or unrepresentative (the journalist Hugh Pope has indicated that Middle Eastern men have sometimes assumed he would naturally be open to sexual propositions because he was a Westerner. He grew a mustache to discourage such inquiries) . The paper is about HIV, Are HIV Epidemics among Men Who Have Sex with Men Emerging in the Middle East and North Africa?: A Systematic Review and Data Synthesis. Here’s the figure which jumped out at me:

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