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.

Freakodevelopment

Well, Justin Wolfers at Freakonomics has now chimed in on the the HDI debate. He refers to Andrew Gelman's observation that the fake data I used in my post on the HDI of US states pretty much just amount to a ranking of states by income under a fancy name. Wolfers adds this observation:

Given this debate, I wondered whether Gelman’s critique might also apply to the U.N.’s original cross-national Human Development Index, so I downloaded the latest data. The graph below compares a country’s ranking on the human development index with its ranking on average income. The correlation between the two is even stronger — a massive 95 percent! For all but a handful of countries, your ranking on average income is the same as your ranking on this multi-dimensional index.

Interesting. I'll talk about this in a moment, but first I want to address what Wolfers says about yrs. truly:

Some commentators have been comparing the scores of individual states on the state-based index with the international index, which yields newsworthy bites, like “Mississippi has an H.D.I. level roughly on par with that of Turkey.” But the two indices aren’t comparable. Dig deep into the methods used to construct the state-based index, and you’ll find that not only are the inputs different, but so are the formulae.

Aha! What Wolfers is ignoring is that I was comparing fake data for the US states to real data for countries. But they were on the same scale! It was an apples-to-apples comparison - it just happens that some of my apples were imaginary.

Okay, back to Wolfers' substantive point: I believe I disagree! It is the case that his chart ends up showing a rather straight, clumpy diagonal line, meaning that most countries' ranks for HDI are quite similar to their ranks for GDP per capita. But it's also obvious that there are a handful of big outliers. GNQ appears to be ranked in the 110s for HDI but in the 20s for GDP/capita. CUB is in the 40s for HDI but in the 80s for GDP. QAT is in the top 5 for GDP but in the 30s for HDI, etc. But more than that: the general correlation doesn't seem to hold as well when you zoom in on this chart. For instance, look at the lower-left corner, where all the wealthiest and most developed countries are clustered. Within that group, there seems to be wide divergence from the overall trend that GDP/capita rank directly correlates with HDI rank. Or look farther up the diagonal line: RUS and ALB (presumably Russia and Albania) both appear to follow the general pattern of a strong correlation which Wolfers sees in this data. But RUS appears to be ranked in the 50s for GDP and in the 70s for HDI, whereas ALB look to be right around 70 for HDI, but is way to the right on the GDP scale, somewhere in the 90s. That's a significant difference!

Wolfers concludes, "For all the work that goes into the Human Development Index, it just doesn’t tell you much that you wouldn’t learn from simple comparisons of G.D.P. per capita. But you do get the veneer of something broader, with a normatively loaded name for this index." But the difference between RUS and ALB is big, and if we didn't have HDI, we wouldn't have the vocabulary (or as much vocabulary, at least) to talk about that difference. Or about the difference between a rich petro-state and a Scandinavian democracy. Or about the consequences of Cuba's form of government on its people's standard of living. All Wolfers' chart demonstrates, really, is that rich countries are almost certain to have high HDI ranks, poor countries are almost certain to have low HDI ranks, and middle-income countries are almost certain to have middling HDI ranks. But we already knew that! Wolfers doesn't acknowledge that, with only a slightly more fine-grained look at the HDI numbers, we can find a ton of valuable information that we can't get from simply looking at GDP/capita. The HDI has more than a "veneer of something broader." It gives us the vocabulary to talk about development in terms of something other than just GDP; it allows us to contribute other values to our understanding of what it means to achieve development.

Is Part of the United States in the Third World?

EDIT WITH HUGE DISCLAIMER: The US HDI is not at all comparable to the world HDI. The data which this map represent are not, in fact based on the American Human Development Project; and the AHDP's data are not, in fact, suitable for making international comparisons - they were specifically designed with the American context in mind. So consider this a sort of interesting thought exercise, but go to the AHDP's website for the real data. They also have some very nice maps of their own.
___________________

A little while ago I asked if the United States was becoming a third world country. The purpose of that post was to point out that the US had rates of income inequality that were totally out of line with other developed countries, but would have been typical for countries in the developing world.

But there's a much more direct measure of the actual level of development of a country: the human development index. The HDI combines measures of various social indicators, including life expectancy, literacy, education, and per capita GDP, to measure overall human development, which "refers to the process of widening the options of persons, giving them greater opportunities for education, health care, income, employment, etc." By this measure, the United States ranks rather high - 15th out of all countries, with an HDI of .950, according to this table, which is based on 2006 data. But the HDI of individual states varies quite a bit. Here is a map from Wikipedia of states by their human development index score:



This map is based on numbers from this table, which come from the American Human Development Report. It gives a good sense of regional patterns of human development in the US and the comparative relationship of states to each other. But the numbers in the abstract don't tell us much; to see what these numbers mean, we need to compare them to other countries. And when we do that, we see that the HDI of many states are comparable to some of the most developed countries in the world. However, other states have HDI scores well outside the range of the developed economies of Europe and Asia.

To illustrate the point, I am now going to make a long list. These are the 76 top countries ranked by human development index score, with the 50 states interposed to show their relative level of development, based on the two tables linked above:

1. Iceland - .968
2. Norway - .968
3. Canada - .967
4. Australia - .965
5. Ireland - .962
Connecticut - .962
Massachusetts - .961
New Jersey - .961
District of Columbia - .960
Maryland - .960
Hawaii - .959
New York - .959
6. Netherlands - .958
7. Sweden - .958
New Hampshire - .958
Minnesota - .958
Rhode Island - .958
California - .958
Colorado - .958
Virginia - .957
Illinois - .957
8. Japan - .956
9. Luxembourg - .956
10. Switzerland - .955
11. France - .955
Vermont - .955
Washington - .955
Alaska - .955
12. Finland - .954
Delaware - .953
13. Denmark - .952
Wisconsin - .952
14. Austria - .951
Michigan - .951
15. United States - .950
Iowa - .950
Pennsylvania - .950
16. Spain - .949
17. Belgium - .948
18. Greece - .947
Nebraska - .946
19. Italy - .945
20. New Zealand - .944
21. United Kingdom - .942
22. Hong Kong - .942
Kansas - .941
23. Germany - .940
Arizona - .939
North Dakota - .936
Oregon - .935
Maine - .932
Utah - .932
Ohio - .932
24. Israel - .930
Georgia - .928
Indiana - .928
25. South Korea - .927
North Carolina - .925
26. Slovenia - .923
27. Brunei - .919
28. Singapore - .918
Texas - .914
29. Kuwait - .912
30. Cyprus - .912
Missouri - .912
Nevada - .911
31. United Arab Emirates - .903
32. Bahrain - .902
South Dakota - .902
33. Portugal - .900
34. Qatar - .899
Florida - .898
35. Czech Republic - .897
Wyoming - .897
New Mexico - .895
36. Malta - .894
Idaho - .890
37. Barbados - .889
Montana - .885
38. Hungary - .877
39. Poland - .875
40. Chile - .874
41. Slovakia - .872
42. Estonia - .871
South Carolina - .871
43. Lithuania - .869
44. Latvia - .863
45. Croatia - .862
46. Argentina - .860
47. Uruguay - .859
48. Cuba - .855
49. Bahamas - .854
50. Costa Rica - .847
51. Mexico - .842
52. Libya - .840
53. Oman - .839
54. Seychelles - .836
55. Saudi Arabia - .835
56. Bulgaria - .834
57. Trinidad and Tobago - .833
58. Panama - .832
59. Antigua and Barbuda - .830
60. Saint Kitts and Nevis - .830
61. Venezuela - .826
62. Romania - .825
63. Malaysia - .823
64. Montenegro - .822
65. Serbia - .821
66. Saint Lucia - .821
Kentucky - .820
67. Belarus - .817
Tennessee - .816
Oklahoma - .815
Alabama - .809
68. Macedonia - .808
69. Albania - .807
70. Brazil - .807
71. Kazakhstan - .807
72. Ecuador - .807
73. Russia - .806
Arkansas - .803
74. Mauritius - .802
75. Bosnia and Herzegovina - .802
Louisiana - .801
West Virginia - .800
Mississippi - .799
76. Turkey - .798

As you can see, there's a number of states, mostly in the Northeast but some in the Midwest and West, that are as highly developed as just about anywhere in the world. Other states are more similar to the Asian Tiger countries or the more marginal areas of Western Europe. Still others are most comparable to some of the emerging economies of Eastern Europe or the Petrostates of the Middle East.

And then there is a group of Southern States that is a good jag farther down the list. These eight states - Kentucky, Tennessee, Oklahoma, Alabama, Arkansas, Louisiana, West Virginia, and Mississippi - form a core region where human development index scores are well below the HDIs of any other country that would clearly be considered "highly developed." Among the nations that have a higher HDI than each of these states are Cuba, Mexico, Libya, Bulgaria, Panama, Malaysia, Montenegro, and Serbia. Four of these states rank below Albania, which has a per capita GDP of $6,000. In terms of human development, this clutch of states in the Upland and Deep South is well outside of the mainstream of developed economies.

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 Weird Politics of the Underdeveloped South

That map of states by human development index score reminded me of something. Remember this New York Times map of voting shifts from 2004 to 2008?



The bluer counties shifted more towards the Democrats in the presidential elections from 2004 to 2008, and red counties shifted more towards the Republicans. The country as a whole shifted about 9.7% more Democratic; but one region stands out for having a lot of counties that actually went more Republican in 2008 - and it sure looks like it correlates pretty strongly with what I described yesterday as the underdeveloped core: the eight states with human development index scores well outside the mainstream for other developed economies. Those states all went for John McCain in 2008, just like they all went for Bush in 2004 and 2000 (though Bill Clinton did pretty well in the region in his two elections). They're not the most Republican states (though Oklahoma's close to the top of that list), but they all seem to be moving towards the Republicans, even as most of the rest of the country moves toward the Democrats.

If anything, this correlation is even more striking when you make the apples-to-apples comparison of state HDI vs. state voting shift from 2004 to 2008.



This shows the voting shift towards the Democrats from 2004 to 2008. The scale is set so that red states shifted less toward the Democrats than the nation as a whole (even though most of them shifted somewhat toward the Democrats) and blue states shifted more toward the Democrats than the nation as a whole. Again, the vote shift in the underdeveloped core was less toward the Democrats than in any other region; five of the 8 states actually shifted toward the Republicans - the only states to do so. Based on Dave Leip's US Election Atlas, here are the states that moved the least toward the Democrats, with their percentage change in the Democratic margin:

1. Arkansas, -10.09
2. Louisiana, -4.12
3. Tennessee, -0.79
4. West Virginia, -0.25
5. Oklahoma, -0.15
6. Massachusetts, +0.65
7. Arizona, +1.99
8. Kentucky, +3.64
9. Alaska, +4.01
10. Alabama, +4.04
11. Mississippi, +6.52

Massachusetts was the home state of the Democrat in 2004, and Arizona and Alaska were the home states of the Republican presidential and vice-presidential candidates in 2008. If you take out those three states, the top 8 states that shifted the least toward the Democrats were precisely those eight states that constitute the underdeveloped core. Does that seem like an odd correlation to you? The states that seem to be moving towards the Republicans are exactly those that have the lowest human development index scores.

One possible explanation for this would hold if Republicans were generally increasing their vote share among poorer people: if that were so, it would be most evident in the poorest states. But according to this compilation of exit poll data, that's not the case; lower income voters moved about as much toward the Democrats as the country as a whole.

Other people have explained the relatively strong Republican showing in this region as a phenomenon of Appalachia or the Upland South. But that doesn't account for the pattern of voting shifts in the Deep South. Some moron also argued that the areas of Republican improvement in 2008 should best be conceptualized as those parts of the South where there are few blacks. But that wouldn't account for the fact that Republicans did well relative to 2004 in some states with lots of blacks, like Louisiana and Mississippi, and not as well in some other states with large black populations, like Georgia and North Carolina. The pattern of areas of relative Republican improvement and the states with very low HDI scores makes for a much tidier correlation.

This is a bit hard to figure out. I mean, it's not like the Republicans are avowedly interested in addressing poverty or issues of human development in any direct way. And it's not as if they're popular among lower income people. Yet here they are making inroads in the one region of the country where levels of human development diverge widely from the norms of the developed world. The only explanation I can think of is that, in areas with lower levels of human development, traditionalist values have a firmer hold, and Republican appeals to those values have been paying off in the underdeveloped South. But it still seems odd that such values would swamp material concerns for voters in the one region of the country where the material standard of living really isn't up to snuff.

Some More Stuff From the American Human Development Project

The American Human Development Project has some other tools beyond their Human Development Index that help to make sense of the geography of development in the US, including maps that break their data down by congressional district (pdf). Here's a map of overall human development by CD:



And here's their map for life expectancy:



There are also maps for income and education level. And, too, they have something called a Common Good Forecaster, which lets you set up conditional scenarios for counties around the country to see how changes in educational attainment would affect things like like expectancy, median income, and community involvement. For instance, if everyone in Cook County Illinois, where Chicago is located, got a high school degree, the average income would rise from $36,171 to $37,364, and the murder rate would drop from 12.5 per 100,000 to 11.3.

And then there is the well-o-meter. In the past I've expressed doubt about the idea that you could measure "human development" at the level of the individual, as the concept of development would seem to be the sort of thing that pertains to a community or society as a whole. But the AHDP more or less lets you calculate just such a measure ("your own human development level"). Answer a series of questions, like "Have all four of your grandparents lived past the age of 80?" and "Are you happy?"; then they spit out a number. I got a 5.69 - slightly below Maryland and well below Asian males, but slightly better than the Midwest.



Now if only I could raise taxes to fund my health care and education, I could really get my human development on...

In Which I Get Debunked at Fivethirtyeight and Set Off A Minor Media Firestorm

Well, well. So my post comparing HDI scores of US states to foreign countries has gone a bit viral. It got picked up by Catherine Rampell at Economix, a New York Times blog; then by Richard Florida, of all people, posting at Andrew Sullivan's blog; and then a bunch of other places. I have to say, though, most folks seem less interested in my insightful analysis that Mississippi is kinda like Albania than in the Wikipedia map I used for the post:



Well, so Andrew Gelman, posting at fivethirtyeight.com, got ahold of the map, and it seems to have irked him. Says Gelman:

Is Alaska really so developed as all that? And whassup with D.C., which, according to the table, is #4, behind only Connecticut, Massachusetts, and New Jersey? I know about gentrification and all that, but can D.C. really be #4 on any Human Development Index worth its name?

Time to look behind the numbers.

Gelman goes on to bring his statistical chops to bear on the data, which came from this table from Wikipedia, and his analysis is frankly a bit beyond my pay grade. The upshot is, he's an expert, and he's dubious about the data. So I decided to look into the matter a bit more.

The Wikipedia page that lists HDI by state claims that the data come from the American Human Development Project. So I contacted them to find out if the numbers from the Wikipedia page were reliable. Kristen Lewis, a Co-Director of the project, emphasized that the AHDP's HDI index - though calaculated based on measurements of education, health, and income, just like the UN's index - uses, as she put it, "different indicators that serve as more reliable and meaningful proxies in the U.S. context." She went on to say:

To avoid creating the impression that our index was comparable to the UNDP global HDI published every year that ranks all the world's countries, we used a different scale. Rather than 0 to 1, we used 1 - 10. In addition, we said in several places that our index was not directly comparable to the UN index.

We still wanted to make international comparisons, but we did so in our book by using more discrete indicators. So for instance, we compared our incarceration rate to those of other countries, noting that ours was higher than those of China or Russia; we compared our infant mortality rate, noting that in parts of Mississippi, the infant death rate was on par with those of Libya and Thailand, etc.

I have no idea who created that table in Wikepedia and what methodology they used to convert our scale to the UN scale. We have data tables on our website where the person could have gotten the LIEX by state; I'm not sure what he or she used for income, but if they used median personal earnings, it's not comparable to the UN scale and if they used state GDP, they would run into the problems described above; and in terms of education, he or she may well have used school enrollment, which we have, but I don't know what they would combine it with as we don't have literacy by state and, again, the educational attainment figures would not be comparable.

So there you have it. Who knows where the numbers on which the above map is based came from? It seems, to the extent that anyone drew their data from the AHDP report, their methodology must necessarily have been flawed, since the AHDP data are incompatible with the data the UN uses for their HDI, and the table used for the map above used values based on the UN's scale.

However, the AHDP does have its own maps of their data, to wit:



Note that the HDI presented here is, as Lewis points out, on a 1-10 scale, rather than the UN's 0-1 scale. Note also that this map closely mirrors the Wikipedia map; indeed, the ordering of the states is nearly identical. But the ratios of HDI between the states are not. For instance, look at the bottom 15 or so states on the AHDP list (pdf):

36. Missouri - 4.54
36. Nevada - 4.54
38. South Dakota - 4.53
38. Wyoming - 4.53
40. New Mexico - 4.49
41. Idaho - 4.37
42. Montana - 4.34
43. South Carolina - 4.27
44. Kentucky - 4.12
45. Tennessee - 4.10
46. Oklahoma - 4.02
47. Alabama - 3.98
48. Arkansas - 3.86
49. Louisiana - 3.85
50. West Virginia - 3.84
51. Mississippi - 3.58

There does seem to be something of a "long tail" - several states seem to be pretty significant outliers from the national median. But the conclusion I had drawn based on the Wikipedia table was that there was a core of eight states - the bottom eight on this list - that were relatively close to each other on the development scale but which were collectively far below not just the other states in the US, but just about anywhere else in the developed world. But based on the AHDP table, there is not such a clear break - more of a gentle downward slope as you get towards the tail end of the distribution. And while it may or may not be the case that the level of development of these states are well outside the mainstream of other developed countries, there's no way to tell based on this data alone.

For Gelman's part, he tongue-in-cheekily proposes another metric for levels of development of US states:



Gelman reflects:

Why do I have such strong feelings about this? It's probably a simple case of envy, that this little bit of index-averaging has probably received more publicity than all of my life's research put together, envy that it has received so much funding. I'm sure they all have had good intentions, but I think something went wrong, at least with this part of the project.

But maybe I'm thinking about this all wrong: these folks are clearly doing well, so maybe I should emulate them. I'll start by making maps of everything ranked by state, and we'll see how that goes.

To that I can only say: map away, sir!

So, to sum up, I have a couple points. First: it is really danged difficult to find a measure of HDI by US state that can be compared to other countries. But it's such an inherently interesting - even important - question. The US is a huge country, diverse in every way. To really understand our place in the world, we need more fine-grained data than national scale measurements provide. That's what makes the AHDP cool. And it's nice to be able to make intra-US comparisons between states. But it would be fascinating to be able to compare states, in as close to an apples-to-apples way as possible, to other countries. It was curiosity about such comparisons that led me to write the original post, and it was probably a similar sort of curiosity that led whoever drew up the map on Wikipedia to do so. But in all the vast, vast internets, I can find no such comparisons. So, social scientists, what's the hold-up? Is West Virginia more developed than Serbia? The people want to know!

Second: good lord, but things do get a life of their own on the internets, don't they? Please note that the American Human Development Project has done some really nice work. I mean, their report has a foreword by Amartya Sen, for crissakes! The greenish map above is not their responsibility, but the brownish one IS theirs. And I encourage everyone to go check it out at their site; there is a ton of interesting information there.

Another Measure of Human Development for US States

All right, forgive me, but it seems that I'm not quite done with this whole human development index thing. I've come across another effort, in addition to the American Human Development Project's, to apply a measure similar to the United Nations' HDI, to measure the human development of the US states. First, here is the AHDP's map of their version of HDI by state:


The other attempt to measure HDI by state is by Jeremy R. Porter of Rice University and Christopher W. Purser of Mississippi State in this 2008 paper. And they've come up with some slightly different results from the AHDP. Here is a map of HDI by state that I made based on data found in their paper, "Measuring Relative Sub-National Human Development: An Application of the United Nations' Human Development Index in the US".



The scales are different, obviously, but you can still see the general ordering of the states by HDI. And it's interesting to see what sort of differences can result from slightly different methodologies. The broad patterns here are about the same: in both studies the least developed states are all in the South, with the most developed states clustering in the Northeast, the Upper Midwest, and parts of the West. But there are some real differences. California is a top-ten state in the AHDP, but is 18th in the Porter and Purser paper. Wyoming is in the second-lowest quintile in the AHDP, but it shoots to #8 in Porter and Purser. Virginia is somewhere in the teens in the AHDP, but #33 in Porter and Purser. And Georgia, which is close to the middle in the AHDP, drops to 44th in Porter and Purser (out of 48: P & P don't list Alaska and Hawaii).

There are a couple of general trends that could describe some of the differences between the two studies: P & P's methodology seems to slightly favor the interior West; and the least developed region in the AHDP is the Upland South plus the lower Mississippi Valley states, whereas in P & P it's the Deep South that comes out at the bottom. (In both cases, Mississippi is the least developed state.)

So what are the differences in methodology that have led to these different outcomes? Well... I don't know. But you can compare the differences for yourself. Kristen Lewis, a Co-Director of the AHDP, told me about their methodology in general terms. It uses measures of income, education, and health, just like the UN HDI, but adapted for the US context:

Income: using the US GDP per capita would assign everyone the same income, which is far from reality and obviously not helpful for making comparisons among groups. A state GDP would not make that much sense, as economies are increasingly regional, with people living and working and selling their goods and services across state borders all the time (think of the NY-NJ-CT area, or DC-MD-VA-DE). Using personal earnings tied income to actual people and allowed us to disaggregate by state, congressional district, racial/ethnic group, and even gender (this is why we went with median personal earnings rather than household earnings). Thus we use median personal earnings for full and part time workers aged 16 and up.

Education: the global HDI uses literacy and school enrollment as their proxies for knowledge. In countries where one in five or one in three adults cannot read and where huge numbers of school-aged children are not in school, these are excellent proxies. In the US, however, we need a more demanding standard than literacy - just being able to read doesn't in and of itself allow for a life of freedom, opportunity, choices. Of course, functional illiteracy is still a problem here - but that group is well captured in our "less than high school degree" category. Also, data on literacy is not collected in the same way or down to the same level as the educational attainment data we have from ACS. THus we use a combination of degree attainment for the population of adults over 25 – HS, BA, and graduate degree – and school enrollment for the population aged 3-24.

Health: we also use life expectancy. We calculated life expectancy from mortality data, and ours is actually the first published source of state and congressional district-level life expectancy. We calculated LIEX from county death data collected by the Centers for Disease Control and Prevention.

As for Porter and Purser, here is part of the description of methodology in their paper:

Data for this project were acquired from a number of sources. First, data on literacy was obtained from the National Institute for Literacy (NIFL)1. As proposed by Lind (1992) there are five different types of literacy (NIFL 1998). The data used in this study reflect those in each county that are in the lowest literacy group (level 1 literacy). Those in this literacy category (level 1) would have minimal literacy skills and would be relatively disadvantaged in relation to the average individual in the U.S (NIFL 1998. For the creation of the final scale the variable was reverse coded so that a high score was desirable. Second, the data on those within the county with a bachelors degree was obtained from the census bureau2. This was substituted for the percent enrollment due to the low variation in enrollment rates at the county level (extremely high percent of students enrolled in high school in the U.S. with low variation) and is considered to be a proxy for the measure. In relation to the health component, data pertaining to the average life-expectancy of each county was obtained from the National Center for Health Statistics (NCHS)3.

Finally, data pertaining to the per capita personal income at the county level were obtained from the Bureau of Economic Analysis (BEA)4 via the CA 1-3 table at the county level and was used as a proxy for the GDP of the county. The per capita personal income variable was as a proxy for the county level GDP based on ancillary analyses that showed it to be an adequate substitute, as evidenced by the fact that, at the state level, the Gross State Product (GSP) and the State Personal Income per capita (SPI) correlate at the .001 significance level with a coefficient of .998. Region and metropolitan status were obtained from the U.S. Census Bureau and Economic Research Services (ERS), respectively as a classifiers for spatial description.

Make of that what you will. Personally, I find it counter-intuitive that New Mexico would be more highly ranked than Arizona, and that Georgia - anchored by the major metropolitan region of Atlanta - would rank in the bottom five and below states like Arkansas and Kentucky, which is what the Porter and Purser paper proposes. But then (with apologies to David Foster Wallace) the sum of my expertise in this area could be inscribed with a magic marker on the rim of a shot glass.

By the way, Porter and Purser have a couple of maps of their own, including this one of relative development at the county level:



Florida appears to be dripping.

Human Development and the US-Mexico Border

Andrew Sullivan links to a map from the 2009 Human Development Report, which uses HDI, the Human Development Index, as a measure of the general level of development for jurisdictions on both sides of the US-Mexico border:



As Steven Taylor notes:

What is interesting is that the lowest HDI county on the US side (Starr County Texas) is higher than the highest HDI municipality in Mexico (i.e., Mexicali).

This is, of course, likely not a shock to anyone paying even a modicum of attention to the situation. Still, it continues to underscore that fundamental aspect of this situation: it is the disparity of wealth between the two countries that continues to create the synergy of migration over the border. As I keep saying: any policy that ignores this fact will fail. As such, calls for massive deportations or that assumes it is possible to stop migration over the border is naught more than fantasy. “Seal the border!” is a slogan, not a viable policy.

That's true. It also points up what ought to be an obvious truth about immigration from Mexico and other relatively poor countries to the United States: it is comprised mostly of individuals who are driven by lack of economic opportunity to leave their homeland in order to exchange their labor for money. That many people feel so threatened by this class of people, which is already among the most powerless in society, has always baffled me.

Also: as long-time readers of this blog know, I like nothing better than using HDI for various countries as a frame of reference for apprehending the significance of HDI ratings for various sub-national jurisdictions! And so, here are selected HDI-comparable nations (based on this table (pdf) from the same organization) for each of the five HDI ranges indicated on the map (with countries listed in ascending order of HDI):

.636-.700 - Morocco, Botswana, South Africa, Tajikistan, Vanuatu, Kyrgyzstan, Guatemala, Nicaragua

.701-.765 - Uzbekistan, Honduras, Egypt, Vietnam, Mongolia, Bolivia, Indonesia, Philippines, El Salvador, Algeria, China, Georgia

.766-.830 - Dominican Republic, Jordan, Belize, Tonga, Ukraine, Thailand, Peru, Turkey, Kazakhstan, Brazil, Serbia, Malaysia, Venezuela

.831-.895 - Panama, Bulgaria, Oman, Mexico, Costa Rica, Cuba, Argentina, Lithuania, Chile, Hungary, Malta

.896-.950 - Czech Republic, Portugal, UAE, Singapore, Slovenia, South Korea, Israel, Germany, UK, Italy, Belgium, United States

Is Part of Italy in the Third World?

I wanted to see if other wealthy countries were similar to the US in having a region within their borders that doesn't really live up to the standards of human development that are generally found in the developed world. Italy has the 19th highest HDI score in the world, slightly below that of the US; but like the United States, it's known for the discrepancy between its wealthy and industrialized North and its poorer and more agrarian South. So I looked at the human development index scores of the regions of Italy, found in this paper (pdf); here's what they show.



Here are the specific HDI values.

Piedmont - .919
Emilia Romagna - .910
Marches - .909
Latium - .907
Tuscany - .907
Friuli Venezia Giulia - .906
Valle d'Aosta - .905
Liguria - .904
Umbria - .902
Lombardy - .901
Veneto - .901
Abruzzo - .900
Trentino Alto Adige - .896
Molise - .894
Basilicata - .883
Sardinia - .881
Calabria - .872
Apulia - .868
Sicily - .864
Campania - .857

Now, there's a bit of a complication here. For reasons I can't figure out, the authors of this paper are using HDI numbers for regions that would imply an overall HDI for Italy far below its "official" HDI (in 2006) of .945. So if anything, this data must be understating the level of development in Italian regions, relative to the numbers I used for US states. Nonetheless, the numbers are still useful for showing the relative levels of development of the regions of Italy. And even if these (evidently low) HDI numbers are taken at face value, it's clear that the variance between Italian regions is far less than that between states in the US, where the range is between .799 for Mississippi and .962 for Connecticut - a spread of .163. In Italy, the difference between Campania (.857) and Piedmont (.919) is only .062.

Furthermore, no region in Italy is close to as underdeveloped as the states of the underdeveloped core of the US. Again, even comparing these apparently low numbers to other countries finds that the least developed region of Italy - Campania - is comparable Uruguay or Cuba, above countries like Mexico and Bulgaria, and well above the underdeveloped core of the US, the top HDI of which goes to Kentucky, at .820. And of course if the Italian numbers were projected upward to fall in line with an overall Italian HDI of .945, even Campania would be at or near .900 - comarable to Portugal or the Czech Republic and completely leaving the underdeveloped core of the US South in the dust.

In short: no, part of Italy is not in the Third World.

(By the way, that paper documents that the north, which generally has the highest per capita GDPs in Italy, slips a bit, and the central regions improve, when you look at HDI. For example, Valle d'Aosta, Trentino Alto Adige, and Lombardy have the three highest per capita GDPs, but are only ranked 6th, 13th, and 9th, respectively, among Italian regions in terms of HDI; whereas Marches and Tuscany, ranked 11th and 10th in terms of GDP, jump to 2nd and 3rd in terms of HDI.)

More on the Happy Planet Index

As promised, here's more on the Happy Planet Index. This is from their map of Europe (note that it uses a different scale than the world map for the sake of intra-regional comparison):



Note that the European HPI is calculated differently than the world HPI: the Euro version uses carbon footprint as the denominator in the index, whereas the global HPI uses overal ecological footprint (details here). (And can I make a modest suggestion to the folks at the New Economics Foundation? If you have two indexes - one for Europe and another for the world - that don't use the same variables, perhaps you shouldn't use the same name for those two indexes.) As is just about always the case, the Scandinavians lead the way, followed by Italy, Spain, and a few others. (Presumably they aren't leaders in a global sense, though; Europe, though thriftier than the US, still consumes a lot by global standards.)

Now back to the global HPI. As I mentioned in the previous post, I like the concept behind this index. As with the Human Development Index, it seeks to take a broader measure of well-being than can be obtained by simply looking at cumulative economic activity. In particular, it assigns a value to ecosystems and the life of the planet which, being that which sustains us, is of some importance. Another way to put this is that the HPI is an economic indicator which incorporates certain external costs - the costs of economic activity which are not paid by those directly involved in a given transaction. Economists - especially those legions that have come out of the University of Chicago - for some reason tend to be incredibly myopic about such things. I don't know why; I guess they find mathematical models more elegant than the real world, with all its knotty complications, but those models don't do so well at taking into account the big picture - the social and environmental consequences of economic activity.

So I appreciate the effort here. But at first glance some of the results seemed counter-intuitive, e.g., the "happiest planet" countries being located in Central America. As a commenter said in the previous post, "The real question is Mexico. American companies move to Mexico to avoid our environmental laws, most Mexicans are quite poor, and while the government isn't necessarily mistreating them, drug cartels evidently are, but Mexico is ecologically efficient?" My concerns were along similar lines - it seems that these countries are subject to considerable ecological exploitation. But actually, the HPI accounts for this in their measure of ecological footprint:

The ecological footprint measures how much land area is required to sustain a given population at present levels of consumption, technological development and resource efficiency, and is expressed in global-average hectares (gha). The largest component elements of Footprint are the land used to grow food, trees and biofuels, areas of ocean used for fishing, and ­ most importantly ­ the land required to support the plant life needed to absorb and sequester CO2 emissions from fossil fuels.

Footprint takes account of the fact that in a global economy people consume resources and ecological services from all over the world. Therefore, a Chiquita plantation in Costa Rica will not count towards Costa Rica’s Footprint, but rather towards the Footprint of those countries where the bananas are consumed. For this reason, a country’s Footprint can be significantly larger than its actual biocapacity. The Footprint of a country is thus best understood as a measure of its consumption, and its worldwide environmental impact.

That seems sensible. And it helps to explain the situation for countries like Mexico, where the ecological costs of a lot of industrial and agricultural activity are borne by the US and Canada (as far as the HPI is concerned, at least!), Mexico's NAFTA buddies which are the destination for the lion's share of Mexican goods. But this means that the maps of HPI aren't reflective of the ecological health or sustainability of practices in a country; they're more like a measure of countries' responsibility for ecological costs (which in the real world may often be borne in countries with some of the highest HPI scores).

And for all that, most of the low-consuming countries of Africa still score very low on the HPI:



Not only do they not consume much, their consumption contributes disproportionately little to their life expectancy and well-being.

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.