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:(EDIT: Corrected the following bit too.)
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.
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.
31 comments:
This is an interesting analysis question, but I think that in some cases, we're hiding some of our great variances in development between metropolitan and non-metro America.
If there was a way to do this analysis on the county level, I think you'd see some much greater variety.
You undoubtedly would. But to be a coherent concept, 'development' needs to refer to communities of a sufficiently large scale. In the extreme case, you might want to talk about the level of development of individual neighborhoods, or even individual households; but that's really stretching the concept past the breaking point. US states mostly have populations in the millions, and they constitute a level of government at which significant policy decisions are made, so I think it makes sense to talk about development at the state level. Though it might also be reasonable to do a study of HDI for metropolitan or megaregional areas.
Having said that, this post has a map of relative development at the county level.
If we no longer report literacy, I wonder if we no longer ask about it. Is it possible that literacy rates in the US are not what they once were? Anecdotes exist of students entering college who are functionally illiterate. I don't know how or how often this actually happens, but given the increasing population of immigrants from Latin America (Mexico has a literacy rate of 91%, Guatemala 69%), many of whom are not adequately counted by the census in the first place, one wonders if we can really claim 99% literacy if we haven't actually measured it in thirty years
Chachy,
This really drove home the power of maps for me. I had the data of course, but seeing it in map form made me notice an interesting anomaly. All but ten of the US states performed at a level less than the US UN HDI average (0.950), and if one were to compute a weighted average of the state scores it would be less than the US average. There is a logical reason for this of course: the GDP per capita index is capped at $40,000 for 22 states (and DC), as it was for the US as a whole.
Thanks for doing this post. I'm looking foreward to the next one.
Chachy,
I think there is a mistake in methodology. Namely, the author is using states' nominal GDP per capita when he should have used PPP (purchasing-power parity) measures.
This would reduce best-ranking states' score (since they are expensive places) and increase worst-ranking states' score (for they are cheaper).
Gus,
I was thinking about your point and thanks to your comment I noticed a simple spreadsheet error in the calculation of the education index. The averages in the map reflect the AHDP combined enrollment and are not adjusted to be equivalent to the UN HDI. I've emailed Chachy a corrected Excel file. I hope he can post a correction. This personally is very frustrating because I had been looking foreward to this post and I'm sorry to have put Chachy through all this grief. It also means my previous comment is incorrect. Nearly half of the US states do score at the US level or higher.
However, let me respond to your comment in the meantime. First, the UN standard for literacy is rather low, lower than functional literacy. And the standard practice for the UN HDI is to assume 99% or higher literacy when such data is not collected.
However, your point about immigrant literacy is quite interesting. In 1970 immigrants made up only 4.7% of the US population and there were only 800,000 Mexican born immigrants in the US. As of 2007 immigrants made up 12.6% of the US population and there were 11.7 million Mexican born immigrants living in the US (including illegals).
The Center for Immigration Studies has a good website on immigration data. The educational level of immigrant populations is often quite different from the country of origin. For example India has an adult literacy rate of about only 60% but about 80% of Indian immigrants in the US have a college degree. Five immigrant populations stand out in terms of low educational attainment (10% or less with a college degree and 38% or more with less than a high school diploma): Dominicans, El Salvadorians, Guatemalans, Hondurans and Mexicans. I would speculate that the literacy rate of these groups is about the same as their country of origin. The literacy rate in Dominica, El Salvador and Honduras is 87%, 80% and 80% respectively. Dominicans are concentrated in NY and NJ. Guatamalans are concentrated in CA. Mexicans and El Salvadorians are concentrated in CA and TX.
Assuming that their literacy rates are the same as the country of origin then these populations are likely to push the overall literacy rate down significantly in four states: AZ, CA, NV and TX. For example I estimate that about 1.7% of Californians are illiterate immigrants from one of these countries, more than any other state. For the sake of argument let's assume that another 0.5% of Californians are US born illiterates. Then CA's illiteracy rate is perhaps 2.2%.
How much might that affect CA's overall score and rank? Literacy is two thirds of the education index which in turn is one third of the overall average. CA's average score would move down by 0.003. It's overall rank would fall from 4th to tied for 8th place with NH. AZ, NV and TX would similarly be affected but to much less of a degree.
It would be possible to make such an adjustment but lacking any actual data that would be of course a very crude estimate.
Diego,
I did find an error (see my previous comment) but not the one you are suggesting. Purchasing Power Parity (PPP) equalizes the purchasing power of different currencies in their home countries for a given basket of goods. Since all the US states use the dollar it is by definition both unnecessary and impossible. A similar situation prevails in the Eurozone. No adjustment for PPP is possible between Slovakia and Luxembourg for example.
I'm not saying your point isn't valid of course. Cost of living should matter and that is the reason that PPP is used instead of exchange rates. But what I was intending to do with this exercise was to come up with estimates of state level HDI consistent with UN methods. Since the UN does not adjust for cost of living in the Eurozone I see no need to do it for the US.
I'm mystified by these maps, because they seem mostly drive by per capita GDP.
Yet the reason GDP is so much higher in places like California, Massachusetts, and New Jersey, for example, is that the high cost of living in those places, especially for housing and taxes, drives a need for high pay to get anyone to live there. The high pay creates a high GDP, since GDP is driven in the main by personal consumption and government spending, which is driven entirely by personal income.
Having traveled over a great deal of this country, I've never had the impression that because Wall Street bankers are really highly paid, that NYC is somehow better developed than Mississippi or Montana.
Just because some banker around New York or San Francisco pays $20 million for a 20 room mansion while you can buy the same house in the heartland for under $1 million, its doesn't follow that the the coastal area is "better developed" than the heartland because one banker is making 20 times as much as another.
Andrew,
The GDP component of the UN HDI is capped at $40,000 per capita and 22 states and DC had their GDP per capita truncated because of that. Similarly literacy, by the low standards of the UN, is two thirds of the education index and that is more or less universal in the US. Most of the differences in UN HDI between the states consequently occur because of differences in life expectancy. In particular the states in the Upland and Deep South lag far behind the rest of the nation by this measure.
This is great discussion. Reminds me of a really good graduate seminar class.
Mark - nice analysis on immigrants and literacy. I still wonder if we are measuring literacy at all in the U.S. I'm curious about these varying standards and whether literacy in general in the U.S. is declining as a whole, immigration aside. We imagine this to be impossible, but is it? This is a separate issue from the map, and which standard to use is up for grabs.
Andrew - There are serious impediments to measuring geographical variation in poverty in the U.S. because data is generally collected in such a way as to ignore cost of living variations. Nevertheless, it is generally accepted by scholars that poverty in the U.S. is concentrated in the deep south and Appalachia. While there are poor people everywhere, and plenty of homeless on the streets of New York, it is likely that a higher proportion of residents of the deep south are poorer in any monetary terms (including relative ones) than the rest of the U.S. Your point is important though, and there needs to be a lot more work done to figure out just what the real disparity is. All of this just points up how hard it is to really measure a Human Development Index for any kind of regional comparison.
I once heard a presentation from a scholar of geographic variation in U.S. poverty, and I almost asked her about this cost of living issue, but I didn't have the guts, or perhaps didn't want to embarrass her. I'm not sure she had a good answer, either.
Gus,
I'm glad you're delighted. I'm enjoying this too. Your initial comment really made me think. Also, it takes some of the pain out of my spreadsheet error to hear some positive feedback.
I have no educated comments on your other thoughts however. My intuition is that we are perhaps (oddly) becoming a more numerate society at the same time that our literacy (by whatever standard) is in decline.
Thanks, Mark - fixed the map and the post.
Mark A. Sadowski,
first of all, thank you very much for this work. No matter how much criticism I can make about it, it is an excellent job.
Now, you say: "Purchasing Power Parity (PPP) equalizes the purchasing power of different currencies in their home countries for a given basket of goods."
In fact, that is not right. PPP data corrects for different price levels, even in the same currency zone. That's why Eurostat gives different PPP corrections for different Eurozone countries.
I don't know how the UN calculated PPP data for HDI, but it is legitimate to ask about the real value of the index if no price-level correction is made.
Anyway, thank you once again.
Diego,
First of all thanks for constructive criticism. That which doesn't kill us can only make us stronger.
I thought I was familiar with Eurostat methodology. If you think I am wrong in saying that there is no cost of living correction made in the Eurozone please point me in the right direction.
I know Eurostat makes a PPS (Purchasing Power Standards) correction but it is my impression this only applies to the 11 EU states that don't use the Euro.
Again, thanks for the kind comments and criticism.
Mark A. Sadowski,
Eurostat makes different price corrections for different European countries, be they in the eurozone or not. It is straightforward that a country like France is far more expensive than, say, Greece; so Eurostat converts euros into local PPS and concludes Greece's capital region (Attiki) is richer than France's average, despite it being poorer in nominal terms.
This is a very important correction, since European funds are allocated according to PPS local wealth.
You just have to compare nominal GDP per capita and PPS GDP per capita for different Eurozone countries and you'll see the different price corrections in action.
Though not so familiar with other stats sources, I'm pretty sure that's the way the IMF (and possibly WB) stats are made.
Diego,
I finally managed to confirm what you are saying. PPP is computed for the Eurozone by the Eurostat-OECD PPP Programme. The methodological manual is available here:
http://epp.eurostat.ec.europa.eu
/cache/ITY_OFFPUB/KS-BE-06-002
/EN/KS-BE-06-002-EN.PDF
Essentially they use Germany as the standard for purchasing power parity and make adjustments in terms of the "German Euro." Frankly I'm surprised that they do this and very embarrassed that I didn't know.
What this means of course is that this map does not reflect PPP and so to be consistent with the UN HDI that should be incorporated. It turns out that the BEA has recently been using BLS data to construct something they call "regional price parities" (RPP). So far they appear to have only done it at the state level for the years 2005 and 2006. Ultimately this may become a part of their regular database. You can find the information here:
http://www.bea.gov/scb/pdf/2008/11%20November/1108_spotlight_parities.pdf
What I can do is adjust the data for 2005 RPP (in fact I'll do that today as I'm curious how things will change) and if Chachy is interested we could create yet another map. This also could have implications for the subsequent post.
Mark:
Just pinning most of the discrepancies on life expectancy doesn't tell us much either. On a worldwide scale, populations are not particularly mobile, with relatively little immigration compared to populations as a whole.
With US states, not true. Its quite possible that the population of people who retires to warmer climates in the south from New England and New York is a self-selecting group that is less healthy than people who stay behind. This would boost life expectancy in origin states and supress it in receiver states.
Also, its well known that black American have a lower life expectancy than white Americans. The south has a higher concentration of black Americans than New England, so we wouldn't be surprised that aggregate life expectancy is different. But is life expectancy of New England whites vastly different from Mississippi whites, and New England blacks from Mississippi blacks?
It would also be enlightening to know if the life expectancy variance within racial groups between states is caused by occupational variations, such as the preponderance of people in agriculture, mining, and manufacturing down south vs. office occupations in the northeast. Or is it a case of infant mortality incidence causing much of the difference, where difference makes no difference to those who live past 5?
Mark:
Regarding GDP, even capping it does not do justice to the huge differences in living standards between states and regions. $50-$100K can buy a really nice house in many rural parts of the country. I can't think of much you could get for that in California or metro-New York to Boston. Also, the costs of food, utilities, gasoline, medical care, and other necessities vary wildly between high cost high "GDP" regions and low cost ones.
The biggest drain on the appalachian/southern low GDP regions vs. high GDP regions is the uniform cost of cars around the country. Having to buy a new car or truck is a much bigger deal in the poorer areas than the wealthier ones. But that is probably the only area of sacrifice due to lower incomes. And it probably explains the prevelance of luxury brands in high GDP areas vs. low GDP areas. Not too many Mercedes and BMW's and Lexi in Mississippi, while new Jersey and Connecticut are overwhelmed with them.
However, having to "make do" with a Lincoln instead of getting a Mercedes doesn't tell me that one area is "more developed" than another.
Andrew,
Actually, with respect to the effects of migration I would argue that retirement havens benefit from the inflow. Both Arizona and Florida have longer than average life expectancies and I suspect that is partially the result of their importing of relatively healthy retirees.
As to race's role in life expectancy that is absolutely true. African Americans have both higher infant mortality rates and lower life expectancies post infancy even when you correct for other factors. Asian Americans on the other hand have longer life expectancies and I suspect that that is a major reason why Hawaii leads the nation in life expectancy. But while this may partially explain why the Deep South has such low life expectancies it does not account for the similarly poor performance found in the Uplands.
I have corrected the data for RPP (cost of living) per Diego's suggestion and will probably email it to Chachy later today. Perhaps he can do another post on UN HDI in the near future. What I found by doing that was that 35 of the states max out on the income component when corrected for RPP. This only serves to increase the importance of life expectancy in explaining the differences in UN HDI.
In fact that was one of the points of this post from my perspective. UN HDI is not a very useful index in trying to establish a relative level of development in the United States because it essentially devolves to a measurment of life expectancy due to the low standards of the income and education components. That is why I set out to create an Advanced Nation Human Development Index (ANHDI) described in the subsequent post. That too is a work in progress. I have incorporated RPP into that as well thanks to Diego's persistance in raising the issue, and similarly hope Chachy can do another post on RPP adjusted ANHDI.
In fact when corrected for RPP, GDP per hour worked, or productivity, in many Northeastern states falls to the national average. Nevertheless, many of those states continue to lead the nation in ANHDI because of their performance on life expectancy and combined enrollment. Similarly, the when corrected for RPP, GDP per hour worked in the Deep South and the Uplands rises to the national average. but they still lag the nation in ANHDI because of their poor performance on life expectancy and combined enrollment.
Well, poor humand development (education) is associated with Republican voting areas. No surprise there. Do we need any more evidence that Republicans are the party of ignorance?
I wonder is this map is still valid before the crisis...
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