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...

Balances of Trade Since 1980

Whilst perusing the interwebs on HPI-related matters, I came across this Wikipedia map of cumulative current account balances from 1980 to 2008, based on IMF data:



This map indicates, more or less, the trade surplus or deficit for countries since 1980, in billions of dollars.

According to this, the top five net exporters since 1980 have been

1. Japan: 2,747.943 billion dollars US
2. China: 1,521.887
3. Germany: 1,047.328
4. Russia: 613.978
5. Switzerland: 596.977

The top five net importers have been

1. United States: -7,335.869
2. Spain: -773.443
3. United Kingdom: -695.155
4. Australia: -529.031
5. Mexico: -263.667

For 2007, the top twenty country net exporters included 6 European countries, 6 East Asian countries, and 8 members of OPEC. The top 16 net importers included 11 European countries (including Turkey) and 6 countries with a history of British colonization. But note that Ireland gets counted twice.

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

The New Economics Foundation has come up with something called the Happy Planet Index that categorizes countries based on their relative ecological efficiency. It doesn't actually have much to do with happiness, per se, and it's not to be confused with this map, but it's a snappy enough title. Here it is:



So what do they mean by ecological efficiency? And what do those colors indicate? Well, here's some of what they have to say:

The Happy Planet Index (HPI) is an innovative new measure that shows the ecological efficiency with which human well-being is delivered around the world. It is the first ever index to combine environmental impact with well-being to measure the environmental efficiency with which country by country, people live long and happy lives.

The Index doesn’t reveal the ‘happiest’ country in the world. It shows the relative efficiency with which nations convert the planet’s natural resources into long and happy lives for their citizens. The nations that top the Index aren’t the happiest places in the world, but the nations that score well show that achieving, long, happy lives without over-stretching the planet’s resources is possible. The HPI shows that around the world, high levels of resource consumption do not reliably produce high levels of well-being (life-satisfaction), and that it is possible to produce high levels of well-being without excessive consumption of the Earth’s resources. It also reveals that there are different routes to achieving comparable levels of well-being. The model followed by the West can provide widespread longevity and variable life satisfaction, but it does so only at a vast and ultimately counter-productive cost in terms of resource consumption.

Sound a bit vague to you? Here's the HPI described in somewhat more concrete terms:

The HPI reflects the average years of happy life produced by a given society, nation or group of nations, per unit of planetary resources consumed. Put another way, it represents the efficiency with which countries convert the earth’s finite resources into well-being experienced by their citizens.

Still dubious? Try this:



There: variables expressed in Greek. So you know it's serious.

Actually, the principle here seems like a good one to me; I'll have a bit more to say about it later. For now I only ask: could Guatemala and Honduras really be among the most "ecologically efficient" countries in the world? I don't know - I'm really just asking. But it does surprise me a bit.

Carbon Footprints, Transit Ridership and More

Via Good, the Housing and Transportation Affordability Index has some excellent maps for several dozen US metros that pertain to housing, transportation, and energy issues. This one shows CO2 emissions from auto use per capita in the New York City area:



The numbers go way, way down as you move towards the urban center. Of course, most people in New York City don't even own cars. But maybe a bit more surprisingly, the pattern is almost as striking in cities where sprawl is rampant. Here's Atlanta, for instance:



The gray lines are freeways and the black lines are Marta rail lines. It looks like per capita CO2 use is highest both along the Marta lines and near freeways; in the case of the latter, that's presumably because the sprawl is somewhat more dense near freeways. But, of course, the general rule is that the closer you are to the city center, the smaller your carbon footprint.

There are many, many more maps here on a number of variables pertaining to housing and transportation. To pick one at random, here's transit ridership as a percentage of workers in the Bay Area:



There's much more like this - average rents, gasoline expenses, travel time to work, etc.; it's a ton of information that's both fascinating and useful. And all in map form. If you're like me, in other words, this site has the potential to waste a tremendous amount of your time.

Made in America

Forbes has an interactive map of manufacturing in the United States:



Each of 369 metropolitan statistical areas in the US are represented by a pushpin; darker blue means a higher percentage of workers in manufacturing. Clicking on the pins will reveal the top five manufacturing industries by employment in a given metro area, like yea:



Says Forbes, of the map:

Patterns emerge. Some are expected (timber is big in the Pacific Northwest; cheese dominates Green Bay, Wisc., and auto manufacturing is the top industry in Detroit). Others reveal a shift in American manufacturing toward more lucrative high-tech products.

The steel industry, once a mainstay of the American economy, is now a top-five manufacturing employer in only seven U.S. metro areas. Pharmaceutical-related manufacturing, a dominant employer in 17 metro areas, is now the top industry in the Allentown-Bethlehem-Easton area of Pennsylvania and New Jersey, once home to one of the country’s largest steel plants. The semiconductor industry is among the top five manufacturing employers in 14 areas, Allentown among them.

And they still build ships in New Orleans. I did not know that!

Mapping Plankton

This report from Science Daily is interesting:

Researchers from Oregon State University, NASA and other organizations said today that they have succeeded for the first time in measuring the physiology of marine phytoplankton through satellite measurements of its fluorescence – an accomplishment that had been elusive for years.

With this new tool and the continued use of the MODIS Aqua satellite, scientists will now be able to gain a reasonably accurate picture of the ocean's health and productivity about every week, all over the planet...

"Until now we've really struggled to make this technology work and give us the information we need," said Michael Behrenfeld, an OSU professor of botany. "The fluorescence measurements allow us to see from outer space the faint red glow of tiny marine plants, all over the world, and tell whether or not they are healthy. That's pretty cool"...

The bottom map shows the global distribution of phytoplankton fluorescence.



They've already made some discoveries about the correlation between iron levels in the oceans and phytoplankton growth.

Some surprises are already in.

It was known, for instance, that parts of the equatorial Pacific Ocean, some regions around Antarctica and parts of the sub-Artic Pacific Ocean below Alaska were limited in production by the poor availability of iron. The newest data, however, show that parts of the northern Indian Ocean during the summer are also iron limited – a phenomenon that had been suggested by some ocean and climate models, but never before confirmed.

"Iron is often brought to the oceans by dust coming off terrestrial regions, and is a necessary nutrient that often limits the potential for marine phytoplankton growth," said Allen Milligan, an OSU assistant professor of botany and co-author of this study, which is being published in the journal Biogeosciences.

"If forces such as global warming, circulation changes or the growth of deserts change the amount of dust entering the oceans, it will have an impact on marine productivity," Milligan said. "Now we'll be able to track those changes, some of which are seasonal and some of which may happen over much longer periods of time. And we'll also be able to better assess and improve the climate models that have to consider these phenomena."

The Spread of Marriage Equality in the US

The LA Times has a nice interactive map of gay marriage rights by state that shows the many changes that have taken place over the past decade or so. Here's how things stood in 2000:



And here's where things stand today:



The scale represents the range of rights denied or afforded to gay couples, from "constitutional amendment ban[ning] gay marriage and other legal rights for gay couples" (dark red) to "domestic partnership legal" (pale green) to "gay marriage legal" (dark green). You can mouse ove states for details. They also have a timeline depicting the various votes, judicial rulings, and other events that have produced the civil rights hodgepodge depicted on this map.

On the face of it, it looks like there's been a lot of movement both for and against legal recognition for gay marriage; while some states have extended full marriage rights, others have retrenched with constitutional amendments banning the same. But I think it makes sense to see these as two sorts of steps that are actually fundamentally moving in the same direction. After all, prior to 2000 or so, marriage equality wasn't really seen as conceivable - not in the foreseeable future, at any rate. But then - possibly because of a genuine sense of a shifting ground in public sentiment - a bunch of states amended their constitutions. This was a defensive move which anticipated the possibility that gay people might be allowed to marry which, like most changes in social institutions, tended to freak out people with more traditionalist orientations. But even though the effect was that a bunch of anti-equality measures got written into law, this was a symptom of a general progressive movement on the issue of gay marriage.

In retrospect, I think you might even say that the big movement earlier this decade to amend state constitutions to explicitly ban gay marriage actually spurred further progressive movement. Those votes - many of which were orchestrated in an effort to get more people to come out and vote Republican in 2004 and 2006 - raised the profile of the issue, certainly, and perhaps placed a veneer of plausibility on the concept of gay marriage. After all, if it was a concept that had to be fought at the ballot box, it must have been a concept that needed to be taken seriously, right? And with several states approving gay marriage this year, following the high-profile loss of gay marriage rights in California last November, I wonder if all the efforts at thwarting gay marriage have just had the net effect of telescoping the timeline of the spread of marriage equality.

6/3 UPDATE: And New Hampshire officially becomes the 6th state to sanction gay marriage, and the third to do so legislatively, after Vermont and Maine. Rhode Island is the only New England state without marriage equality.

A Tale of Two Very, Very Different Cities

I've lately been listening to the KunstlerCast, a podcast about "the tragic comedy of suburban sprawl," featuring James Howard Kunstler. If you don't know Kunstler, he's a sort of one-man media empire of urbanist criticism of American cities and culture. I think his criticisms of suburban sprawl are generally spot-on, and while I'm sympathetic to his predictions about peak oil and the imminent (or nascent) decline and demise of the "happy motoring" culture that has characterized the US (and, increasingly, the world) over the past 60 years or so, I tend to find his predictions a bit horologically over-aggressive. (For instance, in a recent blog post he anticipated "that the current mood of public paralysis will dissolve in a blur of blood and spittle sometime between Memorial Day and July Fourth, even with Nascar in full swing, and the mushrooming ranks of the unemployed lost in raptures of engine noise and fried cornmeal. It doesn't take too many determined, pissed-off people to create a lot of mischief in a complex society." Watch your clocks - we've barely a month before the uprising begins.) But for my money, the somewhat overwrought pronouncements of doom are more than compensated for by the skill and undiluted vim with which he writes.

Anyways, the KunstlerCast has done a couple of episodes recently which have cleverly employed Google Street View. One was a Virtual Walking Tour of Paris. You can follow along with an embedded Street View player as Kunstler and Duncan Crary talk about what makes Paris such a pleasurable city to visit or to live in.



And, for a happy contrast, they also did an episode in which they toured Detroit. Poor, poor Detroit.



Detroit has been really done in by the decline of the auto industry in the US, of course. But it's also been a victim of clueless leadership, segregation, race riots, suburbanization, and the prioritization of the automobile over the human being in urban design. Now there are entire city blocks, even neighborhoods, once teeming, where most of the buildings are simply gone - razed by demolition crews or burned down (arson being a popular pastime in the Motor City). It's a city that's dying.

Anyway, it's a clever use of Street View, and if nothing else, Kunstler is an expert diagnostician; he does an excellent job of explaining, in detail, what is alienating and depressing about the project of suburban development in the US - and, for that matter, what is pleasant and ennobling about well-constructed cities like Paris.

Human Development Over Time: Maps from the UN

The site for the UN's Human Development Report has maps that show the (somewhat) shifting geography of development over the past few decades. Here are maps of HDI - broken into the conceptually nebulous but statistically precise categories of "low," "medium" and "high" development - for three selected years:





The main action seems to have been in Latin America, where several countries have gone from medium (.500-.800 on the HDI scale) to high (.800+), and in South Asia, where the entire Subcontinent has jumped the bar from low (<.500) to medium development.

But of course you notice how broad these categories are: high devlopment countries as of 2006 include everything from Ecuador to Denmark; middle income countries include both Thailand and Papua New Guinea. But you can click on countries to get a ton of details on demographics, health, education, and economics. You can also see HDI numbers for every hald-decade since 1975; China, for instance, has gone from .530 in 1975 to .777 in 2005.

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.

If You Like It Then You Shouldn't Put a Ring Road On It

Via Strange Maps, Thumb has this poster which overlays ring roads from around the world:



According to Thumb, "This poster is designed as a sort of calling card for Rice School of Architecture, located in Houston. We collected ring roads from 27 international cities and layered them all at the same scale. As it turned out, Houston has the largest system of those we surveyed. (Beijing was second)"

Strange Maps says that it's not entirely clear what is being referred to here as Houston's ring road. But it seems clear that the shape in the poster is that of the area contained by Highway 6 on the south and west sides of town and FM 1960 on the north side. The east side of this loop is a bit undefined, but it looks like it could be formed by Highway 146, which runs up the west side of Galveston Bay. Or maybe the east side of the "ring" is just open, represented by a notional north-south line that forms the right side of the poster.

Now, I can attest from personal experience: follow the route of this ring road and you will see nothing but the worst sorts of urban sprawl; it's truly a netherworld of placelessness, an interminable conurbation of strip malls and glass boxes which inspire the human spirit with nothing but alienation and loathing. I am sure that such is the case with most of the other US ring roads represented here.



As for the rest of the cities here, I can't say much; I've only been to a few of them, and never did I make a point of touring their ring roads. Is the spawning of endless sprawl by ring roads an international phenomenon? Maybe the very concept of the ring road is anti-urban: its function is to connect peripheral areas of the metropolis to each other, rather than to the central city. In the case of smaller rings, like Vienna's or Amsterdam's, the ring might be contained within an area that is basically urban; but for the larger rings, it seems inevitable that they'd promote auto-centric lower-density development - a.k.a. sprawl.

As noted above, Beijing has the second largest ring. I'd be especially interested in knowing what the character of development there is like. One thing that I'm curious about is the extent to which China is emulating the American style of urban development. China is, after all, adding cars to its roads at a furious rate. On the other hand, it seems to be adding to its mass transit infrastructure at an equally furious rate. And the other Chinese ring roads on this poster - Guangzhou and Tianjin - aren't obscenely huge. So, my legion of Chinese readers: what's the deal with urban growth there? Is it proceeding with a sensible consideration of the needs of a healthy urban environment? Or is it sprawling in the same sort of wasteful, inane, and crude patterns as the US has been for the last 60 years?

BART vs. Metrorail: How (and How Not) to Build a Subway

Yesterday's post reminded me of a question I've had occasion to ask myself: what's the deal with BART? That's the urban rail system in the San Francisco Bay area; it's always noted as one of the most significant urban rail systems in the US, it has a huge number of track-miles, and supposedly the 5th highest ridership in the country, and yet... I've been to the Bay Area several times, and BART just doesn't seem very present. It's not a great way for visitors to get around the city and you rarely come across stations unless you're seeking them out. So what gives?

Christof Spieler atIntermodality, the blog of Houston's Citizen's Transportation Coalition, may have the answer:



San Francisco's BART is on the left; DC's Metrorail is on the right. They're shown at the same scale, which reveals both the similarities and differences between the two systems. Says Spieler:

Rail transit projects don’t come with control groups — we can’t clone a section of a city, build two different rail lines, and compare the results. In this case, though, there’s an interesting comparison to be made between two remarkably similar rail systems.

San Francisco is in the 5th largest metropolitan area in the country. Washington DC is in the 4th largest. Both cities have old, urban cores with major employment centers surrounded by extensive post-war suburban development. In the 1960s, both decided to build a heavy rail system. And not only do those two systems use very similar technology, they are nearly the same length (104 miles vs. 106 miles).

There is one significant difference between San Francisco’s BART and Washington’s Metrorail, though: Washington has 2 1/2 times the ridership (902,100 average weekday boardings compared to 338,100.) Why? I’ve lived in both places, and I’ve ridden both systems. And I think the difference is that BART is primarily a suburban system while Metrorail, even though it serves the suburbs as well, is at its heart a urban system.

Spieler explains the difference:

BART serves the suburbs. Metrorail serves the suburbs and the urban core.
BART’s furthest station is 25 miles from downtown SF as the crow flies, across two small mountain ranges. Metrorail’s furthest station is 15 miles from the center of DC.
BART has a single line through the city of San Francisco. It serves Downtown and one urban neighborhood, the Mission... Metrorail has 5 lines through Washington, serving many neighborhoods in all parts of DC. Metrorail also serves many more suburban employment centers than BART does...

BART saves money by using existing rights of way; Metrorail maximizes ridership by puting lines where the transit demand is...Only the San Francisco subway, a two-station subway in Downtown Oakland and a three-station subway in Berkeley [don't use existing rights-of-way]. The latter — which serves the University of California along with Downtown Berkeley — was built only because the city contributed money; BART planners wanted to put the station a mile from the edge of the UC campus. Metrorail uses some existing rail lines and a Virginia freeway corridor, but the majority of the system is in subway alignments that serve neighborhoods and employment centers...

BART stations are where the cars are; Metrorail stations are where the people are. The vast majority of BART stations are car-oriented. The “typical” BART station is an elevated structure surrounded by park-and-ride lots in a low-density neighborhood. Over half of Metrorail stations, by contrast, don’t even offer parking. These stations serve employment centers (urban and suburban), universities, neighborhood crossroads, and residential areas...

Spieler posts a couple more maps to illustrate this last point:



I've used both systems, and there's just no competition: the Metrorail is a lot more convenient for getting around DC. And it actually extends significantly into the surrounding suburbs, so it's not like it's useless for commuters; but that seems to be the primary function of BART. And that's how you end up with systems of similar lengths, but one of which has 2 1/2 times the ridership of the other: one was built for people, the other was built for cars.

Actually, considering that pretty much everything we've done in the US to develop our cities since WWII has been designed primarily for the use of cars, rather than people, it's sort of miraculous that the DC system got built the way it did. Here's hoping that the designers of future mass transit systems are receptive to the lesson that the differing histories of BART and Metrorail provide.

Subways of the World, United (at the same scale)

One of my favorite maps that I've covered here has been this map from radical cartography showing the mass transit systems of North America at the same scale. Recently, via Matt Yglesias, I came across a similar project: this one is by Neil Freeman and it shows subway systems from around the world at the same scale. Here are some of the subway systems he's got, though there are many others.



I - an American who detests the standard mode of American urban development of the last 60 years - tend to think of European cities as walkable utopias with comprehensive mass transit systems. I don't know if that's quite a fair judgment, but one thing you notice from these maps is that, other than New York City, there are very few American cities with a subway system that forms a dense web like Paris' or Madrid's. Even Washington, which has a very good subway system, is sort of spread out, and then it goes downhill from there. But a bunch of cities in Europe and Asia have very dense systems, and it seems that that's what you need to have truly comprehensive rail mass transit: the ability to walk to the nearest Metro stop, and then to get from pretty much any point in a city to pretty much any other point on the subway. You can do that in New York City; I don't know if you can do it in any other city in North America.

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.

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.

Global Warming as Mephistophelean Challenge

Ezra Klein points to a study on global warming (pdf) from the Lancet and University College London. As Ezra notes, one of the many complications involved in global warming is that the countries most affected by it will be those that are least responsible for it. Look at these two maps, which Klein posts:



The top map is a cartogram with countries scaled to represent their cumulative CO2 emissions from 1950-2000; the bottom map shows the distribution of mortality as a result of global warming health consequences (in particular, malaria, malnutrition, diarrhea, and flood-related deaths). What's immediately obvious is that those who are most responsible for global warming - the US, Europe, Japan and, increasingly, China - will pay only a small fraction of the price; whereas the countries that makes the most negligible contributions to global warming, especially in Africa, will bear by far the largest burden.

Here's a thought experiment: see if you can imagine a problem that more exquisitely exploits the weaknesses of our nature as human beings than global warming. It's as if the problem were cooked up in the devil's own lab. Consider:

• It is our nature to look after ourselves. But as you can see here, the countries that will be least affected by global warming are those that are most responsible. They (we) have the least incentive to worry about the consequences.
• It is our nature to discount benefits that will only accrue in the future. That is, if we are offered $10 right now, we value that $10 more than ten bucks promised to us six months from now. (This is why pretty much everyone procrastinates: we value the present satisfaction of goofing off before working more than the future satisfaction of goofing off after working.) But global warming's greatest effects won't even be felt 6 months from now - they'll be felt more like a century from now.
• It is in our nature to care about problems that affect us directly, rather than those that affect us indirectly or not at all. But we'll all be dead before the greatest effects of global warming hit. And to the extent that the problems will arrive while we're still alive, they'll manifest in mostly indirect ways: as somewhat higher food prices, say, rather than actual illness or death.
• It is in our nature to want to accumulate wealth. But the way we've gone about accumulating wealth over the past century or two is causing global warming; and it's not at all clear that the continued accumulation of wealth - a.k.a. economic growth - is compatible with seriously confronting global warming.
• It is in our nature to respond to sudden and drastic change, rather than incremental change; frogs and boiling water and all that. Global warming is the ultimate boiling frog.
• It's in our nature to be able to comprehend tangible and local problems, rather than abstract and global problems. The level of conceptual analysis that's required simply to comprehend the problem of global warming goes beyond what most people employ in their daily lives. And mustering the intellectual will to actually weigh the consequences of ignoring or responding to the crisis, let alone actually taking action in response, is a whole order of magnitude more complex.
• It's in our nature to worry first about our physiological concerns, then about our safety, then about our material satisfaction, and only much later about our needs for things like living in a sustainable society, or having moral concerns for people not yet born. For any given person at any particular point in time, their physical and material needs are likely, in general, to lead to actions that contradict the actions that are called for by their abstract ethical concerns about future generations or people in distant lands; and since the physiological and material needs are more fundamental, there is every reason to expect them to win out.
• It's in our nature to have difficulty working together in large organizations. Yet global warming requires that we work together with an unprecedented degree of global cooperation.

That's just a partial list of the really fundamental aspects of human nature - a nature that has evolved over millions of years to deal with a very different set of problems - which seem almost perfectly unequipped to deal with a problem like global warming. This is why I'm not especially optimistic about our ability to come to grips with this problem before widespread calamity is ensured, and why I expect the people of the late 20th and early 21st Centuries will go down in history as the people who traded a healthy world for a pile of Big Macs and SUVs.

On the other hand, if we are able to meet the challenge, think of what that would mean: that we were able to overcome everything innately self-interested and short-sighted and stupid in our nature to retain a livable home for future generations. That, to me, seems like enough reason to keep trying.

Natural Disasters

Andrew C. Revkin points to a UN study on the risk of death from natural disasters around the globe. This is from a map based on the study:



Says Revkin:

Some experts on disaster preparation aren’t happy with some aspects of the report, and many feel there’s little substance behind international pledges to cut risks. But many told me this report is still a helpful window on the outsize vulnerability of a few places on the planet where the costs of calamity are highest. A prime factor contributing to increased vulnerability is urbanization, with about one billion people already crammed into what are euphemistically called “informal” settlements in and around cities, better known as slums, and 25 million more moving in each year. These communities are usually built on steep slopes, floodplains or other vulnerable spots. Another is ecological damage, like the loss of mangroves in Myanmar that appears to have allowed the flood surge there to propagate inland more readily. The report projects that human-caused climate change will progressively tip the odds toward more trouble. But it stresses that increasing resilience to disasters can help limit climate risks, as well, even as it reduces poverty and potentially boosts global security.

The 200-page report has a bounty of maps. Here's one of drought frequency:



And here's a detail from a map on fire danger:



There's lots more there, if browsing through UN-issued PDF reports is your kind of thing. They also have a mapping tool, with which you can zoom in on the risks for individual countries.