A Lost European Culture, Pulled From Obscurity:

The little-known culture is being rescued from obscurity in an exhibition, “The Lost World of Old Europe: the Danube Valley, 5000-3500 B.C.,” which opened last month at the Institute for the Study of the Ancient World at New York University. More than 250 artifacts from museums in Bulgaria, Moldova and Romania are on display for the first time in the United States. The show will run through April 25.

At its peak, around 4500 B.C., said David W. Anthony, the exhibition’s guest curator, “Old Europe was among the most sophisticated and technologically advanced places in the world” and was developing “many of the political, technological and ideological signs of civilization.”

The Horse, the Wheel, and Language: How Bronze-Age Riders from the Eurasian Steppes Shaped the Modern World is a very interesting book. One of the problems with pre-literate civilizations is that they’re only accessible via archaeology, which is a field averse to system-building or theorizing. But it is likely from what we know of pre-literate cultures which Europeans encountered that lots of stuff happened. Perhaps ancient DNA will help resolve some of these questions, at least establishing whether peoples or just pots were on the move.

A friend of mine pointed me to an interesting weblog, Here in Glitner. From the “About” page:

Reflections from my life as a Muslim, perspectives on Islam in my true life as a non-Muslim. I was a Muslim woman, a Muslim wife, a Muslim mother, a Muslim sister. I wore hijab, abstained from pork, obeyed my husband, studied quran and sunnah, and avoided all forbidden and doubtful things as much as I could. And then, slowly, from the blip of one thought to a full-blown realization more than five years later, I emerged into my true life, into reality, and realized my atheism. As you will read, if you go back to the start, it took a long time – roughly two years, to sort out my lifestyle, the life I was living, my family, and my beliefs. This blog mixes in old journal entries from those times with my thoughts on Islam from the perspective of a kafir – an infidel.

The friend is an ex-Muslim as well, though they keep that information to themselves because of negative experiences. By some definitions I’m an “ex-Muslim,” insofar as I identified as a Muslim before the age of eight, at which point I realized I was basically what would be termed an “atheist” (I didn’t know that word at that point). But I never had a coherent supernatural world view. Though before the age of eight I could parrot the general cosmology imparted from Islam, my genuine understanding of the world was totally naturalistic. I had always had a deep interest in evolution and astronomy, and even when I wasn’t a self-conscious atheist God had no place in my model of the cosmos. Nor am I culturally Muslim, as my social network is almost exclusively non-Muslim (and mostly irreligious to boot). Though I can repeat suras I was taught as a child, I never grew up in a world where Islamic material civilization was prominent in any way. In other words, my lack of connection with my “ancestral religion” has had almost no psychic or social cost, and I do not have any personal history of rupture with a tradition which accompanies apostasy. My shedding of a Muslim identity as a child was plainly superficial, as I had never evinced a deep interest in religion, and generally dreaded the boredom of Islamic holidays.
That is why I am fascinated by the weblogs of both converts and apostates, though naturally I have more affinity with the latter. The psychological experiences are in a sense deeply alien to what I am familiar with. I suspect it is analogous to never having been drunk. The mental shock of going from a world filled with supernatural agents to one without, or vice versa, must be jarring. But from what I can tell most religious people take great solace in their personal beliefs, so losing such an anchor might be analogous to a hangover.

This article pointed me to this interesting paper, Rapid adaptive evolution of northeastern coyotes via hybridization with wolves:

The dramatic expansion of the geographical range of coyotes over the last 90 years is partly explained by changes to the landscape and local extinctions of wolves, but hybridization may also have facilitated their movement. We present mtDNA sequence data from 686 eastern coyotes and measurements of 196 skulls related to their two-front colonization pattern. We find evidence for hybridization with Great Lakes wolves only along the northern front, which is correlated with larger skull size, increased sexual dimorphism and a five times faster colonization rate than the southern front. Northeastern haplotype diversity is low, suggesting that this population was founded by very few females moving across the Saint Lawrence River. This northern front then spread south and west, eventually coming in contact with an expanding front of non-hybrid coyotes in western New York and Pennsylvania. We suggest that hybridization with wolves in Canada introduced adaptive variation that contributed to larger size, which in turn allowed eastern coyotes to better hunt deer, allowing a more rapid colonization of new areas than coyotes without introgressed wolf genes. Thus, hybridization is a conduit by which genetic variation from an extirpated species has been reintroduced into northeastern USA, enabling northeastern coyotes to occupy a portion of the niche left vacant by wolves.

Here is a figure which shows the distribution of mtDNA lineages geographically:

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Wet phases in the Sahara/Sahel region and human migration patterns in North Africa:

The carbon isotopic composition of individual plant leaf waxes (a proxy for C3 vs. C4 vegetation) in a marine sediment core collected from beneath the plume of Sahara-derived dust in northwest Africa reveals three periods during the past 192,000 years when the central Sahara/Sahel contained C3 plants (likely trees), indicating substantially wetter conditions than at present. Our data suggest that variability in the strength of Atlantic meridional overturning circulation (AMOC) is a main control on vegetation distribution in central North Africa, and we note expansions of C3 vegetation during the African Humid Period (early Holocene) and within Marine Isotope Stage (MIS) 3 (≈50-45 ka) and MIS 5 (≈120-110 ka). The wet periods within MIS 3 and 5 coincide with major human migration events out of sub-Saharan Africa. Our results thus suggest that changes in AMOC influenced North African climate and, at times, contributed to amenable conditions in the central Sahara/Sahel, allowing humans to cross this otherwise inhospitable region.

More details from the discussion:

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I’ve been crunching some county-by-county data at one of my other weblogs:
Where the fat folks live
Diabetes and obesity
The white vote for Obama, by county & correlates
Are over-leveraged counties seeing an increase in food stamp usage?

Since The New York Times put up the csv file which they used to generate their maps of food stamp usage, I thought I’d look at the data a little closer. In particular, look at this graphic of change in food stamp usage by county (dark equals more usage):

I was curious about this part from the story below::

While use is greatest where poverty runs deep, the growth has been especially swift in once-prosperous places hit by the housing bust. There are about 50 small counties and a dozen sizable ones where the rolls have doubled in the last two years. In another 205 counties, they have risen by at least two-thirds. These places with soaring rolls include populous Riverside County, Calif., most of greater Phoenix and Las Vegas, a ring of affluent Atlanta suburbs, and a 150-mile stretch of southwest Florida from Bradenton to the Everglades.

Thanks to the Census I happen to have 2007 housing value and household income data. Also though it would be interesting to compare with obesity and diabetes rates. Scatterplots & correlations (r) below.

It does indeed seem that food stamp usage has been increasing in higher income and property value counties. The Census data I used above were collected between 2005-2007, during the height of the late great property bubble. But when I took the ratio of property value by income as a rough proxy for being over-leveraged it didn’t seem to add much.

When I took the partial correlation of home value and increase in food stamp usage controlling for income, it was only 0.11. Here are some other correlations controlling for income:

% on food stamps – obesity = 0.33
% on food stamps – diabetes = 0.44
% of whites on food stamps – white diabetes rates = 0.36
% of whites on food stamps – white obesity rates = -0.05

There’s an obvious correlation between black proportion in a county and food stamp utilization. r = 0.43. So using proportion of blacks as a control:

% on food stamps – obesity = 0.43
% on food stamps – diabetes = 0.51
% on food stamps – white diabetes rates = 0.43
% on food stamps – white obesity rates = 0.06
% on food stamps – median household income = -0.71

It does seem to be correct though that food stamp utilization has been shooting up in more affluent communities. But if it is true that well over 90% of those eligible in places like Missouri are already using food stamps, while only 50% of those eligible in California are, it makes a bit more sense. In wealthier communities likely more people go in and out of eligibility and so never need to make recourse. In contrast, in regions where people are immobile and poverty is chronic there isn’t as much scope to increase the program because most people who are eligible are already on it. That probably explains the triangular geometry of the scatterplot, very low on the affluence latter social services seem to have soaked up all eligible individuals, leaving little room for increase with the recession.

Note: Estimates are white obesity are based on state level variation. Estimates of white diabetes rates are based on national level variation. These two variables need to be appropriately down-weighted in terms of confidence of their accuracy, especially the second.

Update: By coincidence, a reader noted this similarity of maps this morning:

Ben points to the a new article in The New York Times, Across U.S., Food Stamp Use Soars and Stigma Fades. The county-by-county data are of interest. I’ve just snatched the csv file, which they made available. Andrew Gelman has a modest critique of the assertion that 50% of children are on food stamps at some point in their childhood. The variance in utilization rates of the program by region (50% in California vs. 98% in Missouri) of those eligible, as well as the near saturation of utilization in much of the Black Belt and highland South (the Appalachians and the Ozarks), implies to me that while in some American subcultures the program is seen as a stop-gap in others it is a background condition of life. A minimum income guarantee or grain dole basically. Also, I recently heard a radio interview with Kevin Concannon, an under secretary of agriculture. In response to criticism of misrepresentation of the results of reports of hunger in America his stance was basically “statistics, schmamistics.”

The reason that I’m fixating a bit on the issue of hunger in America is that we’re also told that there’s an “obesity epidemic” in this country, in particular among the lower classes. Often from the same policy elites who point to long lines at soup kitchens as evidence of a surfeit of food! To be hungry sometimes is uncomfortable, I know this personally, I am hungry sometimes. Though for me it has to do with the fact that I don’t think that the immediate response to hunger always has to be food to satiate the pangs (I don’t like to eat past a certain hour). Nutritional belt tightening isn’t necessarily a bad thing, remember that the Great Depression saw an increase in life expectancy.

A friend of mine who was looking at the distributions on obesity and diabetes wondered about their political correlations. To do that and add anything new it seems that it would be best to estimate the white vote for Barack Obama in 2008 by county. This is how I did it:

1) I looked at the exit polls for each state, which has breakdowns by race for each candidate.

2) Since the white vote probably varies more county-by-county than the minority vote, especially the back, I used the state level exit polls and assumed that the minority vote in every county could be predicted by the state level exit poll. So for example, in New York the exit poll suggest that 100% of blacks voted for Obama. So I would weight appropriately.

3) I also weighted by national turnout numbers. In other words, whites were a little overrepresented in the electorate, blacks equal to their demographic weight, and Asians and Latinos underrepresented. So:

% Obama in county = (White turnout)(White %)(White proportion) + (Black turnout)(White %)(Black proportion) + (Latino turnout)(Latino %)(Latino proportion) + (Asian turnout)(Asian %)(Asian proportion)

Many states did not have results for ethnic minorities in the exit polls, so the white vote estimate is identical to the real results in many counties (the correlation between my estimate and the real returns is on the order of 0.99-0.98 north of 85% or more non-Hispanic white). In places like Mississippi where most everyone is either black or white, we can probably be sure that blacks voted well in excess of 90% for Obama, I think the estimate for whites is probably pretty good. The main issue is with Latinos, who I suspect seem to vary quite a bit more than blacks (in fact, they probably tend to follow whites in voting except that they’re more Democratic all variables controlled (again, I had to discard some counties were negative proportions pop up because Latinos are more Republican locally than on the state level).

Fist some maps, then some correlations. Again, note that red is below and blue above whatever threshold I’m using (usually median).

For the correlations, “est” means my estimate. Reduce the confidence in those correlations accordingly, as my data analysis hasn’t gone through peer review! (until you comment)

Here are the summaries for Obama vote estimate:

1st quartile = 0.2240
median = 0.3591
mean = 0.3587
3rd quartile = 0.4754

Since Democratic votes are concentrated in a few highly populous counties the low proportions are not a surprise. Lots of counties with few people are anti-Obama.

White Obama Vote (est)- White Diabetes Rate (est) = -0.26
White Obama Vote (est)- White Obesity Rate (est) = -0.29
White Obama Vote (est)- White Birth Rate = -0.17
White Obama Vote (est)- College Degree = 0.42
White Obama Vote (est)- Median Household Income = 0.28
White Obama Vote (est)- Median Home Value = 0.40

(for whites ancestry are proportion of whites, i.e., Irish/White = Irish proportion)
White Obama Vote (est)- Origins in Britain & Ireland = -0.24
White Obama Vote (est)- English = 0.08
White Obama Vote (est)- Irish = 0.37
White Obama Vote (est)- Scots Irish = -0.13
White Obama Vote (est)- American = -0.50
White Obama Vote (est)- German = 0.38
White Obama Vote (est)- Scandinavian = 0.30

Partial correlations controlling for college degree rate:

White Obama Vote (est)- White Diabetes Rate (est) = -0.30
White Obama Vote (est)- White Obesity Rate (est) = -0.29
White Obama Vote (est)- White Birth Rate = -0.20
White Obama Vote (est)- Median Household Income = 0.00
White Obama Vote (est)- Median Home Value = 0.17
White Obama Vote (est)- American = -0.46
White Obama Vote (est)- German = 0.36

Partial correlations controlling for median household income:

White Obama Vote (est)- White Diabetes Rate (est) = -0.36
White Obama Vote (est)- White Obesity Rate (est) = -0.33
White Obama Vote (est)- White Birth Rate = -0.21
White Obama Vote (est)- Median Home Value = 0.30
White Obama Vote (est)- American = -0.52
White Obama Vote (est)- German = 0.35

The correlation between the white Obama vote and the proportion of blacks within a county is in the range of -0.30 to -0.40 (on the high end), even controlling for income and such (the blacker the county, the fewer whites voted for Obama). Interestingly when I control for black proportion the German correlation for voting for Obama drops a bit to 0.26, and the American correlation drops from the other direction, -0.39. Race can explain some, but definitely not all of these inter-ethnic differences in the white vote.

Poking through demographic data, a few things always seem to crop up:

1) Texas isn’t quite like the rest of the South. It is more Republican on the federal level than racial polarization into a white and black party would predict.

2) The Latino counties in Texas are hard to fit into a model which is derived from conditions in the rest of the country. They have lower morbidity and are somewhat more conservative than Latinos elsewhere (in fact, their morbidity is lower than whites in many regions of the country). I often have to discard these counties because estimates using state level parameters are weird (in the case of white voting patterns or diabetes rates, negative values).

3) There’s stuff going on in Appalachia which needs to be explored. I’m going to analyze Appalachian counties specifically in the near future. I had assumed that aside from outliers like Asheville Appalachia was relatively homogeneous. Not so.

Hunger here vs. hunger there:

There has been a fair amount of buzz lately (examples here, here, here, here) about “food insecurity” in the U.S. According to the Reuters headline, one in seven Americans is short of food. In looking into the data, what has surprised us is how different the meaning of “hunger” is when we’re talking about the U.S. vs. the developing world.

Developing-world hunger: 30% of children underweight

…

The “food insecurity” categories are derived from people’s answers to questions like “We worried about whether our food would run out before we got money to buy more” and “We couldn’t afford to eat balanced meals” (full list on pg 3). The details of the answers are found on page 45:

…

Note in particular the difference regarding children. In the developing world, as shown above, severe child hunger is rampant. In the U.S., even in “food insecure” families, it’s extraordinarily rare for children to go hungry even temporarily. And indeed, World Bank data estimates that 1.3% of U.S. children under 5 are “underweight” – less than the 2.3% that would be expected in a fully normal distribution.

On the one hand the poor supposedly live in “food deserts” and so get fat. On the other hand, there’s a lot of hunger in America. Something doesn’t make sense. As someone whose family is from Bangladesh I have seen plenty of hungry people face to face. They look really hungry. If you’re really chronically hungry you can’t mask it with a stiff upper lip, you just look starved out, and a bowl of rice with salt is a luxury. They’re really short too. When I went to Bangladesh in the late 1980s for a visit I was much taller than many adult beggars despite being a pre-teen, and I was always around the 50th percentile on the height distributions in elementary school.

The fact that fewer American children are very light than would be expected under a normal distribution is also interesting. Assuming weight is a quantitative trait, like height or IQ, one would expect the deviation from the normal distribution to produce a “fatter tail”, not an attenuated one.

Felines worked part-time this week due to the holiday….

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