Job Prospects in the 50 Biggest US Cities

Via Matt Yglesias (via Ryan Avent), an interactive map of job listings per capita for US cities:



Washington, DC has far and away the most job listings - more than 132 per 1000 people. And Baltimore easily takes second, with more than 90. As Yglesias notes, "the metro DC economy is in better-than-average shape and I think that may have a distorting influence on how the hill and the press are seeing the national economic picture which continues to be very bleak despite the fact that the financial panic has ameliorated." The media centers of the US, however, aren't doing nearly so well: New York has less than 28 job listings per 1000 people, and LA has less than 24.

The techie cities of San Jose, Seattle, and Austin are all doing relatively well; the Rust Belt not so much - of Midwestern cities, only Milwaukee has more than 40 jp/k, and Detroit has the fewest of any city: less than 15. Miami is in second-worst shape, with just over 17. The full ranking of the 50 metros are listed with the map here.

More on the Geography of Drugs in the US

Some people wanted to see maps of the distributions of specific drugs, to which I say: very well! Here are some more maps derived from the SAMHSA report's own maps. First, weed:



Here's coke:



Here's painkillers:



Here's a heroin map, based on a different SAMHSA report (note this one shows "TEDS treatment admissions," rather than actual use):



And here's a map for meth (from a previous post; also shows admissions rather than drug use per se):



Some interesting stuff here. I wonder how it's possible for New York to be in the top quintile for cocaine use, and New Jersey to be in the bottom quintile. And I wonder why heroin is so heavily concentrated in the Northeast Corridor while that same region is practically meth-free. And I wonder why the Mountain West uses tons of drugs, but the Plains states not so much. And I wonder why Mississippians don't like to do drugs. I wonder many things.

There are more maps in the SAMHSA report, but again I warn you: their color scheme is profoundly misguided.

UPDATE: All right, one more. Here's overall drug use, non-marijuana division:

Drug Use in the United States

This report from the Substance Abuse and Mental Health Services Administration (SAMHSA) details drug use across the 50 states based on the 2006 and 2007 National Surveys on Drug Use and Health, which involved interviews with over 135,000 people around the country. It revealed a rather wide range of reported drug use between states, as you can see in this map, which is derived from a map in the report:



The report actually has its own maps, but I warn you that they are very ugly. That's why I had to make my own.

A couple things are striking about this map. The less surprising of these things is that there's a very wide range in the numbers of people who report using drugs in the past year - from 5.55% in North Dakota to 11.10% in Alaska. I should say: in itself this isn't surprising, but the particular pattern of the distribution does surprise me somewhat. I would expect higher rates of drug use in states with smaller rural populations, but this map seems to show that that's just a false intuition.

The other striking thing about the map - and another thing that challenges my prejudices - is that there doesn't seem to be any correlation between the wealth or human development of states and their level of drug use. Some high-development states, like New Jersey and Pennsylvania, have low levels of drug use; and some, like Massachusetts and Colorado, have high levels of drug use. And some low-development states, like Arkansas and Tennessee, have high levels of drug use, while others, like South Carolina and Alabama, have low levels of drug use. There do seem to be some regional trends - especially the high rates of drug use in the non-Mormon West - but a lot of variation within regions as well. All in all it just looks pretty random. What do you think - am I missing something here?

(Thanks to Nikolas Schiller for the link.)

UPDATE: Richard Florida has some interesting follow-up in a couple of posts; he uses actual statistics and stuff do dig into this data a bit more. (One tidbit: more people do drugs in Obama states! Republicans ought to appreciate that. Come to think of it, Democrats might appreciate that as well...)

The Happy Planet Index Redux

Not long ago, I did a post about the Happy Planet Index, a quantification of 'ecological efficiency' from the New Economics Foundation. Roughly speaking, it measures the satisfaction of basic human needs per unit of resource consumption, so that the "happiest" countries are those that achieve higher standards of living while minimizing environmental impacts.

Well, now the NEF has come out with a rather souped-up HPI 2.0, complete with think-tanky 64-page report. Here's the new map:




The NEF regards the equivocation of economic growth with human progress as a foolish fallacy, a position The Map Scroll heartily endorses. They make this observation in their report (pdf), which includes this fascinating paragraph:

For most of human history, economic growth was a minor phenomenon: a side effect, where it existed, of the pursuit of other goals. It only attained its quasi-mystical role when GDP was placed atop the podium of indicators with the development of the United Nations system of National Accounts in 1947. At that time, focusing on productivity growth made sense. Much of the world needed to be rebuilt following the war, and that required growing economies. Furthermore, economic growth helped avoid distributional debates. The rising voice of the working classes demanded more of the material cake. The only way elites could respond to that voice without having to give up anything themselves was by growing the cake.

Our needs have changed since then, but "systems carry their own momentum, and even the wealthiest countries still pursue economic growth as if they were still struggling to recover from the war."

And the report notes this: "once our basic material needs are met, more concumption tends to make little difference to our well-being." This should be obvious and common-sensical; the marginal utility of consumption or wealth decreases dramatically once our basic physiological and safety needs are met. But the negative environmental externalities of consumption only become very onerous when we're talking about further consumption; the man compensating for his low self-esteem and need for acceptance by driving his Hummer does far more damage to the planet than the Malian woman getting inoculations for her infant, though the latter's actions do much, much more to increase happiness and limit suffering. But of course, buying a Hummer contributes orders of magnitude more to economic growth than does getting inoculations. On one hand, this is tragic: all our Hummer-driving and cheeseburger-eating is destroying the planet, and at the same time isn't even contributing much to our collective well-being. On the other hand, it also represents an enormous opportunity: if we could just see this fact, and re-order our priorities in accordance with it, we have a lot of room to limit our negative impacts on the environment while maintaining, or even improving, our level of well-being.

So that's the insight behind the HPI, and it's reinforced by some of their findings. Life expectancy correlates with higher GDP/capita, but not perfectly; Cuba, which is much poorer than the US, has a life expectancy that's nearly as high. And, the report says, "the most important gains in terms of both life expectancy and life satisfaction occur over the first 10,000 pounds of GDP distribution - beyond that there is little systemic difference between nations." This is evident in the map of life satisfaction by country:



The country with the greatest value of "happy life years" (a combination of life expectancy and satisfaction) is Costa Rica, with a GDP/capita about one-fourth that of the wealthiest countries. Even countries like Vietnam and China do better than the fairly wealthy Portugal.

The measures are set against the ecological footprint, a measure of resources used per capita. This is measured in terms of global hectares; the world average is 2.1 global hectares per person. The poorest countries have the lowest gha consumption; the largest ecological footprint is Luxembourg's, at 10.2 gha. The US is third, at 9.4. Here's the map of ecological footprints:



There's a broad correlation between wealth and ecological footprint, but it's not like all wealthy countries are interchangeable on this metric. South Korea uses only 3.7 gha, and the Netherlands uses only 4.4.

The map at the top of this post assigns countries a valuation of 'good,' 'middling,' or 'bad' for each of the three components of the index: life expectancy, life satisfaction, and ecological footprint, with a further very bad category for countries with exceptionally large ecological footprints. Countries that score poorly on life ecpectancy and satisfaction, like many of the poorest countries in Africa, show up as red even though they have small footprints. Coutries with high life expectancy and satisfaction, but very large footprints, are also red.

Comparing happy life years to ecological footprint yields some interesting regional patterns:



Where you want to be on this chart is in the upper left-hand corner: high on the happy life years scale and low on the ecological footprint scale. Most of the countries that come closest to that ideal are Latin American, with a few East Asian and Middle Eastern countries in that group as well. Sub-Saharan Africa tends to be low on both scales, and Western nations tend to be high on both.

Overall, the report says, the world has a life expectancy of 68.3 years, a life satisfaction of 6.1, and an ecological footprint of 2.4, for an overall HPI score of 49 out of 100. In other words, as a global society we're overshooting our resource limits, and we're not even all that happy. Hopefully we'll learn to look to places like Costa Rica to see how to find a balance between our desire to lead happy and fulfilling lives and our need to preserve that same opportunity for future generations, rather than blithely drive our SUVs over the precipice of catastrophe. And when you frame it that way, the choice we ought to make seems obvious. If only we weren't humans, I'd feel pretty confident that we'd make the right one.

The Yuppie Map of San Francisco

Town Me, a community website for San Francisco, takes the always fascinating project of demographic sub-group cartography (and you may take it as a mark on my character, for good or bad, that that was a non-ironic use of the word 'fascinating') and cleverizes it. Here, for instance, is the Yuppie map of San Francisco:



What they've done here is take an arcane sociodemographic category and translated it into a more sensible terms. In this case, "Yuppies" represent "young professionals (ages 25-35) who make $100,000 or more." They've given a similar treatment to several other categories, which they describe as follows:

Cougars - Single or divorced women ages 35-50
Sugar Daddies - Single or divorced men ages 45-60
Starving Students - People ages 18-34 currently enrolled in college
Baby Momma - Female householder, no husband present, with children aged 18 or under
Baby Daddy - Male householder, no wife present, with children aged 18 or under
People Overextending Themselves on Rent - People who spend a lot on rent

The only thing I might change here would be that rather unmellifluous last category; maybe they could change it to "Housing Crises"? Or the "Upwardly Immobile"? Regardless, as a fan of the English language, I always glory to see arcane and abstruse semiotic formulations reconstituted as more quotidian, but invariably more vividly delineative, linguistic signifiers.

Touring the Tour de France

Like cycling? Like maps? The New York Times has a nice interactive map of the Tour de France course:



Stages are numbered; Wednesday's 5th stage is highlighted.

Clicking on a stage lets you view its profile, like this one of the Pyreneesian 8th stage:



Tour de France, if my language skills are not failing me, translates literally as "Tour of France." But France only amounts to 1/6 of the countries through which the Tour travels. Clearly the planning for the Tour suffered from an appalling oversight in this regard; must be embarrassing for the organizers...

The "Politicosphere"

I've posted before on a map of the Iranian blogosphere. But now I discover, via Matt Yglesias, that PoliticoSphere.net has such a map for the US:



The map represents the "612 most visible and influential websites and blogs." Each node represents a website, and the sizes of nodes are determined by number of inbound links. Colors represent ideological or issues orientation; here's what they mean:

Green - Environment and Energy
Pink - Feminism
Brown - Defense
Orange - Education
Light blue - Health Policy
Peach - International Affairs
Gray - Law
Red - Conservative
Blue - Liberal
Yellow - Infopros (sites like Huffington Post and TPM, as well as mainstream media sites)

At the PoliticoSphere site, you can click on nodes to show the corresponding sites' links to other sites. Unrelatedly, the map seems to be shaped like a hawk in flight or the nation of Kyrgyzstan.

The Changing Hardiness Zones of the US

From the Baltimore Sun's B'More Green blog comes news (via the Sun's Garden Variety blog) that the US Department of Agriculture is planning to revise its map of plant hardiness zones across the country by this fall. But the Arbor Day Foundation has already updated changes in hardiness zones from 1990 to 2006, which they show in their interactive map:



This shows the changes in zone classification over that time period:



Some isolated areas of the interior West and Midwest have actually warmed enough to move up two zones, while a few areas in the Southwest have actually gone down a zone. But you can infer from the streaked pattern that most areas, especially in the eastern two-thirds of the country, have warmed by the equivalent of about half a zone. That actually strikes me as a bit extreme; zones are classified by average annual low temperature, as per the scale on the left; so if I'm reading it right, a half-zone change would correspond to the average annual low being about 5 degrees F warmer in 2006 than it was in 1990. Is that really plausible? Average temperatures certainly haven't warmed by that much; but maybe the climate has changed in such a way that especially cold snaps are less common at the height of winter. I don't know.

NASA Creates Best Topographical Map of the World Yet

Via New Scientist, the most detailed and complete topographical map of the world ever produced has been created with data from NASA's Terra satellite.



Says NS:

The map incorporates more than 1 million digital images and covers 99 per cent of the globe, a substantial increase over previous maps, which surveyed just 80 per cent of the planet. The new map covers latitudes between 83° north and 83° south, resolving patches of land as narrow as 30 metres across – three times the resolution of the next best digital topographical map, which was made by the space shuttle Endeavour during an 11-day mission in 2000.

According to the LA Times' L.A. Now blog, "the resolution is so clear that you can plainly see Dodger Stadium and other landmarks in pictures of Los Angeles," viz. this one:



And, via The Daily Mail, which has several large images, here's Europe:



The full data set is online and available for free.

Agricultural Production in a Warming World

More on global warming, this time from Conor Clarke, who links to William Cline's study Global Warming and Agriculture: Impact Estimates by Country. Clarke reproduces two maps from that study; this one shows "the change in agricultural productivity (by 2080) taking into account the potential benefits of 'carbon fertilization' (the increase in yield that occurs in a carbon rich environment"):



And this one shows the same without projecting carbon fertilization benefits:



Says Clarke:

The basic points of Cline's book are that, by the end of the 21st century, (1) climate change will lead to a slight decline in global agricultural productivity; and (2) climate change will lead to a giant decline in agricultural productivity in Africa, South America and India...

As a sidenote, I think it's important to recognize that deep brick color falling over most of Africa, South Asia and Latin America -- all places where agricultural productivity will fall by more than 25% -- actually hides big differences. For example, Cline reports that the southern regions of India would experience potential output declines of 30-35%, while northern regions would experience declines of 60%.

These maps, besides being delightfully Mondrianesque, illustrate beautifully (if that's the right word) the extent to which the business end of the global warming Howitzer is aimed squarely at the developing world (though the souther half of the UScould have some tough times ahead as well. The forecast for South Asia, which has enormous populations and is not that far removed from historically experiencing famine, and which could be among the most catastrophically inundated by rising seas starting near the end of the century, is especially distressing.

By contrast, under a favorable 'carbon fertilization' scenario much of the developed world actually comes out ahead (again, with the exception of the southern US, as well as much of Australia). China - around which the future track of global warming increasing hinges - also does rather well in the favorable scenario, and only somewhat poorly in the non-fertilization scenario. (By the way, as Clarke notes, "The effects of carbon fertilization are very uncertain, and depend crucially on the availability of other resources -- water for irrigation, say -- that will also be affected by global warming... [But] even if carbon fertilization yields large benefits, Cline estimates a decline in global agricultural productivity.)

As always with climate projections, there is a lot of uncertainty involved here. Things might not turn out so bad in a given region, or they might turn out far worse; but it's worth noting that the consequences of global warming so far have tended to meet or exceed climate scientists' most pessimistic forecasts.

The Cost of Cap-and-Trade by State

So there's a bill to do something about global warming that's wending it's way through the US Congress; it's known as Waxman-Markey, after its two main sponsors. The bill would institute a cap-and-trade system that would limit CO2 emissions; if implemented, it would ultimately have some cost to consumers - about $175/annum for the average American household by 2020. But those costs wouldn't be distributed evenly, and Nate Silver has a map of how those per-household costs would break down by state:



Nate has all the gory methodological details in his post. I just want to make two points:

1) This bill is, by itself, inadequate, has gotten watered down considerably already, and will undoubtedly be further watered down in the Senate; and, indeed, I'd be shocked if it passed the Senate at all. But the way to think about it, I think, is as a contribution to a conditional chain: if the US government fails to do anything in the reasonably near future to fight global warming, then horrible catastrophe is inevitable; but if the US does pass even a weak bill, then an international agreement becomes more likely; and if that happens, then altering the energy-intensive development of China becomes a possibility; and if that happens, then we might be able to moderate the slew of catastrophic consequences that are gathering for the end of this century.

2) The United States is not really a democracy, not by modern standards. I'm not talking about all the corruption, the lobbying, and the tilting of the playing field toward special interests, though you could surely make a decent case for the non-democraticness of the US on those grounds alone.

What I'm talking about, though, is the US Senate. Wyoming, which has about half a million people, has two senators. And New York, which has about 19,000,000 people, also has two senators. Florida, which might well be drowned in a century or two by rising seas, has 18,000,000 people and two senators. West Virginia, which produces a lot of coal, has less than 2 million people - and two senators. You see where I'm going with this? The United States government, which was revolutionary and awesome back in the 18th Century, should no longer be considered to have a legislature that meets modern standards for representative democracy. I'm not the first to point this out, of course, but it really doesn't get the attention it deserves. I mean, the form of government of the US is obsolete: why isn't this a matter for public discussion? And of course, the skewing of representative democracy tends to pull in favor of rural areas, which tend to both use and produce more in the way of CO2-heavy fossil fuels, and against urban areas, which are more energy-efficient and more supportive of efforts to fight global warming. So, to the litany of insidious aspects of the global warming challenge, add this: the outmoded institutional structure of the United States government.

Ballparks of the Major Leagues

Via Bats Blog at the New York Times, flipflopflyball.com, by graphic designer Craig Robinson, has a bunch of infographics about baseball. Some of them are definitely maps. Some of them, like the following, are arguably maps:



The stadia of the major leagues. I don't know if these are to scale; it looks like they might be. But here's one thing I would like to see, have looked for, and cannot find: major league ballpark dimensions overlaid on each other at scale, so you could make direct comparisons. Maybe Mr. Robinson would be interested in such a project...

By the way, Robinson is also responsible for Atlas, Schmatlas, which I have a feeling many of you might appreciate.

Human Development in the United States, Part II

Going above and beyond the call of duty, Mark Sadowski has also created his own human development index. The Advanced Nation Human Development Index, as he calls it, is meant particularly to compare US states to other developmed economies. It uses a different scale than his application of the UN HDI methodology, and it results in a map which, though broadly similar, is not without differences from the map based on UN HDI methodology (note the relative positions of Louisiana and Indiana, for instance):



Here's Mark on the methodology he uses for his ANHDI:

On reflection, what I thought what was needed was not an index that is consistent with the UN, as it is designed for comparisons between developing nations, nor even an index based on US standards like the AHDP. What I thought was needed was an index that is designed to compare the US states to the advanced states of the world.

Thus I decided to construct my own index. I have decided to call it the Advanced Nation Human Development Index (ANHDI). I wanted it to be an index that was conceptually consistent with the UNHDI but that would compare the US states with other advanced nations in a manner that was intentionally challenging for the US.

Since it was clear that the life expectancy index was already challenging for the US I saw no need to use a different set of data for the health component. Life expectancy data for the US states and the advanced nations is from the year 2005 and comes from the AHDP and the UN HDI. For the education index it was also clear that there were a number of advanced nations that led on the gross enrollment index. Thus I decided to base the education index solely on combined enrollment data. Combined enrollment data for the US states is calculated in the same fashion as described previously. Combined enrollment data for the advanced nations comes from the UNHDI. All life expectancy data is for the year 2005.

The US easily leads almost the entire world in terms of GDP per capita but much of this lead is due to the fact that Americans simply work longer hours than people in other advanced nations. Thus it seemed to me that a more appropriate measure of standard of living would be GDP per hour worked or productivity. The OECD already computes such numbers for its thirty member nations and the most recent data available was for the year 2007. Productivity data for 20 additional nations was available from the Human Conference Board website. Unfortunately, although it was consistent with the OECD data in other respects, their PPP GDP data was not equivalent to OECD data. Since the most similar data to the OECD PPP GDP data is from the IMF, I adjusted the Human Conference Board’s productivity data using IMF data. I used 2007 data in order to be consistent with the OECD.

To come up with estimates of productivity for the US states I had a slightly greater challenge. The method for calculating productivity involves calculating GDP per employed person and then dividing that by the average number of hours worked per employed person. Employed person data used by the OECD (and the Human Conference Board) is not consistent with the usual data. For the US as a whole it is greater by a factor of 1.0529. To come up with an estimate of the state level employed person data that was equivalent to OECD data I multiplied state level employed person data taken from the Census Bureau by 1.0529.

State level data on the average number of hours worked per employed person is not released by the BLS. As a proxy I used state level average manufacturing work week data from the BLS website and adjusted the national level average hours worked per employed person estimated by the OECD. This should be a good estimate since manufacturing workers are a subset of all employed persons and casual inspection of the data reveals patterns that are consistent with expectations (e.g. Alaskans and Kansans probably do work longer hours). Again all data was for 2007 to be consistent with the OECD and the Human Conference Board.

The final step was to come up with formulas for each of the three index components. Like the UN HDI all three indices were computed as a ratio of differences. Unlike the UN HDI index I chose not to take the log of the standard of living data. This was because differences in standard of living among the advanced nations is not as great as among all nations and it also served my admitted purpose of making the index more challenging to the US states. I chose upper and lower bounds for each of the three data sets based on a little trial and error (again with this purpose in mind) and in the process I found it necessary to drop 19 nations for which I had productivity data because they performed below the minimum level of one or more of the indices. The overall ANHDI score is simply the average of the three indices.

Here are those formulas:

Life Expectancy Index = (life expectancy – 73)/(83-73)

Education Index = (combined enrollment rate – 76)/(113-76)

Economic Index = (GDP per hour worked as % of US – 42)/(143-42)

Mark calculates the ANHDI for other developed nations, which yields this ranking, with US states interposed:

1. Norway - .717
2. Australia - .709
Connecticut - .685
New York - .680
Hawaii - .666
Massachusetts - .660
DC - .639
California - .626
New Jersey - .617
Delaware - .603
3. Ireland - .598
4. France - .596
5. Netherlands - .588
Alaska - .588
6. Iceland - .578
7. Luxembourg - .573
Rhode Island - .564
Minnesota - .561
8. Belgium - .555
9. Sweden - .552
10. Spain - .552
11. New Zealand - .548
12. Finland - .537
Maryland - .532
Illinois - .531
Colorado - .525
13. Denmark - .521
Virginia - .514
New Hampshire - .511
Washington - .509
14. Canada - .503
Wyoming - .501
15. United States - .496
Texas - .493
Vermont - .491
16. Austria - .485
North Dakota - .483
Wisconsin - .481
17. United Kingdom - .476
Oregon - .474
Iowa - .473
Florida - .472
Nebraska - .471
New Mexico - .468
18. Switzerland - .468
Utah - .468
19. Japan - .467
20. Germany - .467
Pennsylvania - .465
Michigan - .463
21. Italy - .461
North Carolina - .455
22. Greece - .445
Arizona - .443
Kansas - .431
Nevada - .424
Georgia - .414
Ohio - .413
Maine - .412
23. Taiwan - .407
Louisiana - .403
South Dakota - .402
24. Singapore - .390
Indiana - .390
Missouri - .379
Idaho - .372
Montana - .363
25. Hong Kong - .353
Oklahoma - .351
Kentucky - .342
South Carolina - .330
Tennessee - .330
26. South Korea - .330
Arkansas - .320
27. Slovenia - .319
Alabama - .314
28. Portugal - .300
West Virginia - .299
Mississippi - .257
29. Barbados - .228
30. Czech Republic - .163
31. Slovakia - .098

Mark concludes:

Keeping in mind that productivity data was not available for more than fifty nations, nevertheless what I think the ANHDI shows is the following. There are at least 28 nations, mostly in Western Europe, East Asia and Oceania that perform at a level, by the standards of ANHDI, above the worst performing US state. In addition as a whole the US ranks 15th on the ANHDI, not only behind the twelve nations that lead it on the UNHDI, but also behind Spain, Denmark and Luxembourg. I hope it will be eye opening to Americans as they see how their country and their state compares with the other advanced nations of the world as well as the other states by this standard.

Huge thanks again to Mark for all his work on this topic. All the statistical work is entirely his, and goes way beyond anything I would be able to do.

Human Development in the United States, Part I

EDIT: The original post reflected an error in the data on education. The map below has been corrected.

The human development index, which I've discussed here before, incorporates measures of income, life expectancy, and educational attainment to quantify the overall development of countries. I've wanted to compare HDI ratings for US states to those for other countries, but it's been a surprisingly hard thing to do; no one seems to have used the formula for the UN's HDI and applied it to the states. But it strikes me as such an inherently interesting question: how do the levels of development of states compare to other countries? And how much variation is there in the development of different states relative to other countries? It seems to me like answering these questions would give us a more fine-grained understanding of how the US compares economically to other developed nations. But, despite various efforts to compare the development levels of states to each other, no one seems to have made the direct comparison between states and other countries.

But thanks to a reader of this blog, we can finally make such a comparison. Mark A. Sadowski has made an attempt to apply the UN's HDI formula for US states, and this map is based on his results:



Here's how Mark describes his methodology:

In constructing an UN HDI consistent index for the US states I did the following:

1) To calculate the life expectancy index I merely used the state level data from the AHDP website.
2) To calculate the education index I had come up with estimates of state level literacy and combined enrollment rates.
a) The last time the Census collected state level literacy statistics was 1970. The last time they collected it on a national level was 1979. This was because literacy was essentially universal by the 1970's in the United States. For the UN HDI, any nation that has literacy rates above 99% or that does not collect such stats is allotted a score of 0.99 for that component. If one looks at the 1970 state level stats you will observe that the lowest literacy rate was for Louisiana or 97.2%. Based on even the lowest rate of decreases in the rate of literacy it is clear that by 1990 all the states in the United States probably would have had a literacy rate of 99% or higher by the UN's low standards. Thus all the US states were assigned an adult literacy index of 99.0.
b) The combined educational enrollment rate turned out to be more of a problem. The AHDP website lists such data but it is not consistent with the data reported in the UN HDI report. It is lower by a factor of 0.93. I suspect that the problem is not with the numerator (total enrollment) but with the denominator (population in relevant age group). Thus I estimated the state level gross enrollment index by multiplying the combined enrollment rates listed at the AHDP website by a factor of 1.075.
3) To calculate the GDP index I took state level real GDP per capita data from BEA and multiplied it by the US GDP deflator for 2005 (1.13039) in order to convert it to nominal GDP.

What did I learn from this exercise? The biggest differences in HDI by the UN standards occurred because of the differences in longevity. Twenty-two of the US states (plus DC) max out on the GDP index. All perform well by the low educational standards of the UN education index. On the life expectancy index in general the US states did not perform very well.

(EDIT: Corrected the following bit too.)

So how do states compare to the other countries of the world on the HDI scale? Well, the top state in Mark's analysis is Hawaii at .973. By comparison, Iceland has the highest HDI in the world at .968, according to this table from the UN (pdf) (which uses data from 2006). Only five countries in the world are at or above .960. Thirty-six US states are at or above .940, which is the HDI of Germany; the United Kingdom has an HDI of .942. The states in this range are essentially comparable to the wealthiest nations of Western Europe.

At the lower end of the scale, though, it's a different story. Mark finds that six states have an HDI below .920: Louisiana (.919); Arkansas, Oklahoma, and Alabama (.918); West Virginia (.911); and Mississippi (.901). The nations in this HDI range are typically either small East Asian countries or Middle Eastern petrostates: they include Brunei, Kuwait, United Arab Emirates, and Bahrain. A few peripheral European and Mediterranean nations are in that ballpark as well, including Slovenia (.923), Cyprus (.912), Portugal (.900), and the Czech Republic (.897). All of these countries are wealthy by global standards; nonetheless, it's clear that there's a group of states in the Upland and Deep South that, unlike states in the northern and western United States, has not achieved a level of development comparable to the largest Western European economies - and, as Mark notes, this is mostly due to relatively short life expectancies.

Thanks to Mark for compiling this data; I'll have another post based on Mark's work in a bit.

The Slow Melt of Antarctica

A little while ago the New York Times' Andrew Revkin had a post about a study by David Pollard and Robert DeCanto that found that even in the worst case, global warming would lead to a collapse of the Antarctic ice sheet much more slowly than was previously thought. That process is illustrated in this video:



Says Revkin:

The bottom line? In this simulation, the ice sheet does collapse when waters beneath fringing ice shelves warm 7 to 9 degrees Fahrenheit or so, but the process — at its fastest — takes thousands of years. Over all, the pace of sea-level rise from the resulting ice loss doesn’t go beyond about 1.5 feet per century, Dr. Pollard said in an interview, a far cry from what was thought possible a couple of decades ago. He, Dr. DeConto and other experts on climate and polar ice stressed that when Greenland’s possible contribution to the sea level is added, there’s plenty for coastal cities to consider. But for Greenland, too, some influential recent studies have cut against the idea that momentous coastal retreats are likely anytime soon.

Over all, the loss of the West Antarctic ice from warming is appearing “more likely a definite thing to worry about on a thousand-year time scale but not a hundred years,” Dr. Pollard said.

Well, that's good. I have to say, though, that rising sea levels have never seemed like the scariest threat from global warming. Terrible for Bangladesh, yes, and a few other places around the world; but something that, even on the scale of hundreds of years, let alone thousands, is something to which we could adapt. The collapse of ecosystems, the desertification or aridification of productive agricultural land, and the resultant famine, mass migrations, and political instability, though - those processes will play out in a much faster, unpredictable, and destructive way.

Global Peace Index

Vision of Humanity has updated their Global Peace Index for 2009. The results:



Says Vision of Humanity:

The results of the Global Peace Index for 2009 suggest that the world has become slightly less peaceful in the past year, which appears to reflect the intensification of violent conflict in some countries and the effects of both the rapidly rising food and fuel prices early in 2008 and the dramatic global economic downturn in the final quarter of the year. Rapidly rising unemployment, pay freezes and falls in the value of house prices, savings and pensions is causing popular resentment in many countries, with political repercussions that have been registered by the GPI through various indicators measuring safety and security in society.

This is the third annual edition of the report which "is composed of 23 qualitative and quantitative indicators from respected sources, which combine internal and external factors ranging from a nation’s level of military expenditure to its relations with neighbouring countries and the level of respect for human rights." Three categories of criteria were used in calculating the index: "measures of ongoing domestic and international conflict, measures of safety and security in society and measures of militarization." Examples of measures of ongoing conflict include number of external and internal conflicts fought between 2002 and 2007, number of deaths from organized conflict, and relations with neighboring countries; examples of safety and security include political instability, levels of violent crime, and levels of disrespect for human rights; examples of militarization include military expenditure/GDP, volume of weapons shipments, and ease of access to small arms. You can get full details on the methodology here.

The index ranks 144 countries, though they irritatingly omit Kyrgyzstan, along with Turkmenistan, Niger, and several other countries. The full rankings are here. Here are the most and least peaceful, along with a few other countries I semi-arbitrarily deem important:

1. New Zealand
2. Denmark
2. Norway
4. Iceland
5. Austria
6. Sweden
7. Japan
8. Canada
9. Finland
9. Slovenia
11. Czech Republic
12. Ireland
16. Germany
22. Netherlands
30. France
35. United Kingdom
40. Bhutan
74. China
83. United States
85. Brazil
99. Iran
108. Mexico
118. Thailand
122. India
129. Nigeria
136. Russia
137. Pakistan
138. Chad
139. Democratic Republic of the Congo
140. Sudan
141. Israel
142. Somalia
143. Afghanistan
144. Iraq

I don't think it will surprise anyone that the developed countries of Europe top this list or that a number of African countries rank rather low. I'm a little bit surprised at how low a few countries in Asia rank, especially India and Thailand, and at how high some of the countries in Africa rank, frankly. But overall the rankings here seem pretty intuitive.

Bubbletowns

I've looked at this topic before, but this post by Richard Florida has a nice map, made by Scott Pennington, that shows the unevenness of the housing bubble across the metropolitan areas of the US:



The big cities of the East Coast, Florida, and the West in general had, to use a Greenspanism, the most "froth." But a number of regions were substantially spared from the housing bubble, especially places that most people don't want to live - the Rust Belt, smaller cities in the South, Texas... Actually, a lot of people want to live in Texas; it's one of the fastest growing states - a classic Sun Belt economy - so I'm not sure why it was one of the regions least affected by the housing bubble (with the moderate exception of Austin).

Note that this map uses housing price-to-wage ration, rather than the more common housing price-to-income ratio. Says Florida:

The housing price-to-wage ratio may provide a better gauge of housing bubbles. Income is a broad measure that includes wealth from stocks and bonds, interests, rents, and government transfers and other sources. Wages constitute a more appropriate gauge of a region's underlying productivity, accounting for remuneration for work actually performed.

Some of the results:

The housing-to-wage ratio also generates a number of surprises. Greater New York's ratio (9.4) was slightly higher than Las Vegas (9), and Greater DC..'s (8.7) slightly bested Miami (8.4). Boston (8.1) and Seattle (7.6) topped Phoenix (7.2). Chicago's (5.9) was higher than Tampa (5.6) or Myrtle Beach (5.5).

What regions seem to have avoided the bubble? The cream of the crop on the housing-to-wage ratio are Dallas (3.5), Houston (3.2), Pittsburgh (3), and Buffalo (2.8).

So yeah, if you wanted to avoid the worst of the housing bubble, you would have done well to locate in either the negative-growth Rust Belt, or the rapidly growing big cities of Texas. Color me mystified.

Murder and the City

The New York Times has another interactive map that presents an absurd amount of information, so I am duty-bound to post it here:



It's a grim inventory: every murder in New York City since 2003. This image shows the race of the victim; they also show age, sex, and weapon used, among other statistics. Every dot is a life snuffed out, and you can click on them for details.

Fun fact: murder rates in the Middle Ages were much higher than they are today. By like orders of magnitude. This is from a paper by Manuel Eisner:



So, you know... none of that claptrap about "the good ol' days"...