Archives for posts with tag: map

If you know anything about me, you probably know that I watch the polls for national elections like a watch college football: obsessively. While polling is an imperfect science, it offers the best glimpse into an electoral result before people vote. Politicians dedicate massive resources to poll constituents, either by conducting polls in house or by contracting polling organizations. Polls often determine how and what a candidate will speak about in public addresses, or what promises might be made on the campaign trail. It should come as no surprise that Governor Mitt Romney has distanced the healthcare reform he implemented in Massachusetts from Obama’s healthcare reforms; after all, polls show a majority of Americans oppose Obama’s healthcare legislation.

While politicians usually keep their in house polling private, there is a wealth of polling data that is public and well organized. RealClearPolitics collects polling data from a wide variety of sources and nicely organizes them into polling averages for a particular race. Using multiple polls adds legitimacy and a degree of predictability to polls.

In this post I’d like to focus on the polling on the US Presidential election and the implications on the electoral geography in 2012. When I say electoral geography I am referring to the relative significance of certain US regions in presidential elections. The US uses the Electoral College system, which isn’t really as complicated or unfair as its maligned for being. Every state is given a number of Electoral College (EC) votes based on their total congressional delegation: the total number of Senators and Representatives. Every US State gets two Senators regardless of size, while Representatives are allotted based on population. Because most states (Nebraska and Maine allot some EC votes by congressional district) are winner-take-all, certain larger states are immensely important. In 2008, Obama turned many states that were considered “safe” Republican holds blue:

Most notably Obama turned Indiana, Virginia, North Carolina, and one congressional district in Nebraska blue. Obama’s victory was so sweeping that he could have lost California and New York and still won the Electoral College; but much has changed since 2008. It is looking increasingly unlikely that Obama can win many of these states that he turned blue four years ago, and persistent high unemployment has made Obama vulnerable in a number of states. All of this makes for an election with many more “swing states” than previous elections have had:

Polling comparing Obama to the Republican field for 2012 has been both extensive and varied in results. Most polls show that right now Obama would easily defeat any of the GOP contenders by both popular vote and Electoral College, with the exception of Mitt Romney. Because Romney is leading the Republican Primary right now and polls the closest to Obama, I will use polls comparing Romney to Obama as a basis for my electoral maps. With the exception of Missouri, every state I listed as a swing state on the map above was won by Obama in 2008. Instead of laboring over the polling in every state I will justify my decisions briefly here: Michigan, Minnesota, and Wisconsin aren’t “swing states”  because recent polls have Obama winning each state by at least 8% (the same margin that polls show Romney winning in Georgia).

So with 146 EC votes apparently up for grabs, one might ask why I titled this post “Why Ohio Means Everything,” surely 18 measly EC votes isn’t going to dictate who wins, right? Ohio’s significance as a key swing states derives from a historic fact: no Republican has ever won a presidential election without winning Ohio. One can counter that history does not determine the future; after all, no Democrat had won Virginia since 1964 before Obama broke that trend in 2008. But Ohio’s historic significance is less important here than its contemporary trends. Ohio and Pennsylvania share the distinction of being large swing states, but Pennsylvania’s demographics give it a slightly Democratic lean in national elections. For reference, the Cook Partisan Voting Index (PVI) shows Pennsylvania as D+2 and Ohio at R+1 (for perspective they rank Alabama at R+13 and Massachusetts at D+12). Part of this derives from the voting tendencies of the large metropolitan areas on both states. In 2004, Ohio’s Cleveland and Columbus metro areas supported John Kerry, while Cincinnati sided with George Bush. Pennsylvania’s large Philadelphia and Pittsburgh metro areas strongly supporting Kerry back then.  Bush ended up winning Ohio by 2% and losing Pennsylvania by 3% that year. Four years later both states would turn blue, with Obama winning Ohio by 4% and Pennsylvania by 10%. This trend is older than recent history: Ohio has not voted Democratic without Pennsylvania also doing so since 1948!

Interestingly, new polls actually show Obama doing better in Ohio than in Pennsylvania (it should be noted that polls in Penn. are a month older). Both RCP averages have Obama winning by 4.5% and 3.2% respectively (see previous links). Some have suggested that a recent referendum on collective-bargaining rights for public sector employees may have pushed Romney’s support down in Ohio. Were these polls to translate into wins for Obama, Romney’s chances to win the election in 2012 look much different:

For Romney to win, he would need to net 100 EC votes out of 108; Obama, on the other hand, could win by taking just ten. This makes the polls in the remaining swing states critical to Romney’s changes; here is a list of RCP averages in the remaining states:

Colorado (8/4 – 12/4): Obama +4.5%*

Florida (1/19 – 1/27): Romney +0.3%

Iowa (7/5 – 11/29): Obama +2.6%

Missouri (11/9 – 1/29): Romney +1.5%

Nevada (10/20 – 12/20): Obama +3%

New Hampshire (11/28 – 2/2): Obama +3.5%

New Mexico (6/23 – 12/12): Obama +11%*

North Carolina (12/1 – 1/11): Romney +2.6%

Virginia (12/11 – 1/18): Obama +1.7%

(note: *only one polling organization used, I was unable to find polls for Indiana.)

Even if Indiana went to Romney, his chances of winning 100 EC votes from these states is low. The problem doesn’t just come from larger swing states like Florida and Missouri, but from smaller ones as well. If Ohio goes blue, states as small as New Hampshire and Nevada could tip the EC advantage in Obama’s favor. Expect campaigning in the buckeye state to be brutal. Some speculate that the recent jobs report will alter the dynamics of the presidential race, in what ways remains to be seen. I, for one, plan on watching these polls obsessively over the next year; so much can happen between now and November!

(note: I haven’t written a blog in weeks because I’ve moved recently and have been working a great deal since then.)

One thing that is too often absent in the debate about immigration in developed countries has been the so-called “Brain Drain.” This term is used to describe the trend of mass emigration of skilled/highly educated workers (also called “human capital”) to a few high interest places. This trend has been documented both between countries and inside of countries. In the US, this has been described as “rural flight” while the international phenomenon continues to be called either “human capital flight” or simply the “brain drain.” Most developing countries see some form human capital flight, be it Ethiopia’s loss of doctors to Chicago or the high percentage of Chinese who study abroad and choose to stay abroad after graduating. While these countries lose human capital, many developed countries gain human capital as a result of this.

America and other developed countries benefit immensely from large amounts of highly educated individuals migrating to their shores:

(source)

The trend isn’t new, either. From the Chinese source we can see that even in the 1970s Chinese students were moving to the US with only a minority choosing to return. Similarly, the trend of educated individuals fleeing the countryside in America isn’t new; this map shows US migration from 1970 to 2000:

Something that’s always interested me when it comes to maps/graphs about the brain drain is they often break down either by country (which country is gaining and which is losing) or they only focus on recent statistics (who is moving where right now). Instead I would like to look at which parts of the US have the highest concentration of educated/skilled individuals. This is not intended to rank how intelligent parts of America are, or to malign the parts of the US where educational attainment is low. Instead I want to look at where the highest concentration of people with at least post-secondary education reside in the US. I decided to use the American Human Development Project in part because it allows you to measure these indicators by state but also by Congressional District (CDs). I think this is the best metric because US congressional districts have to be at least similar in population, only small states like South Dakota have wildly divergent congressional district populations. Of course there are other problems that can threaten congressional districts as units of measure: they can be gerrymandered to include disproportionate percentages of groups for political reasons. But while this could threaten the reliability of statistics on educational attainment, I don’t think it threatens the reliability of broader observations about educational attainment in the US.

Alright, now for my map and my methodology: I used a ranking of the top congressional districts in the US by percent of population with at least a Bachelor’s Degree. I made the threshold 32%, which produced 121 congressional districts. (You can find a table of this data here)

Many things can be noted from this so I will just list them first. The the states with the most CDs with at least 32% populations with a BA are: California with 18, New York with 13, and then Massachusetts with 8, several states then tie with 7. States not on the list include: Alaska, Arkansas, Delaware, Hawaii, Idaho, Iowa, Kentucky, Louisiana, Maine, Mississippi, Montana, N. Dakota, Nevada, New Mexico, Oklahoma, Rhode Island, S. Carolina, S. Dakota, West Virginia, and Wyoming.

What I found interesting was the heavy concentration in particular areas. Despite its smaller population, the Bay Area in California has nearly as many CDs (8) as the rest of the state (10). Massachusetts is similar in this regard: only two CDs in the state aren’t counted, the entire Boston Metropolitan Area is included. Massachusetts actually has more CDs on this list than Texas (with 3.8x as many people). Boston’s high concentration of respected universities is likely a factor in this, take a glance at this map I found on Radical Cartography of Boston:

Similarly, California’s Bay Area includes Standford University and UC Berkeley, which probably contributes to its high percentage of college graduates. But does my list adequately grasp which parts of the US have the highest concentration of skilled/educated workers? I decided that pruning this list down by making the criteria higher: what congressional districts have a 45% or higher population with at least a Bachelor’s Degree? lets see what we found (my updated list can be found here):

This pruning yields some interesting results. While many metropolitan areas were represented on the former map, this updated map excludes many large cities. only 4 CDs from southern states are listed, which makes them notable. The Research Triangle in North Carolina (a region where UNC, NC State, and Duke intersect) is represented, 2 CDs from the Atlanta metro area and one from Houston are included. The Boston-Washington DC corridor is well represented with 16 CDs, while California has 7. What’s notable about California is that when you raise the threshold to 45% it actually puts the Bay Area on top with 4 CDs, compared to the rest of the state’s 3.

Finally, I wanted to corroborate these findings with a final metric. We know now where the highest concentration of people with at least 4-year degrees are, but where do people who go beyond that tend to live? I took the 30 congressional districts with at least 18% or higher having a Master’s or Professional Degree (think medicine/law/engineering degrees). Here we find similar results with the previous graph:

Here we see that like in the other graph, the Boston-Washington corridor and California are leading the nation with 16 CDs and 7 CDs respectively. In addition to these dominant regions, Seattle, Houston, Chicago, Atlanta, and the NC Research Triangle appear to have to highest concentration of human capital.

Finding out where the largest concentration of human capital is a much easier question to answer than why that concentration exists in the first place. Here are some of my less informed ideas about these maps and why we find concentrations of human capital in a few places. One immediate assumption I had was that these elite regions must have the highest wages in the country and thus attract the most human capital as a result. But surprisingly, the top 30 districts didn’t directly correlate with higher wages. Comparing Income Index with % Master’s/professional degrees produced almost as many mismatches as it did hits. None of the Boston metro districts made it to the top 30 in Income Index and Seattle and 8 others were also not in the top 30 Income Index districts. I am by no means asserting that wages and education don’t correlate here, just that they don’t perfectly correlate this instance.

But some broad observations do appear to hold true: places with more than one elite university in near proximity tend to have more human capital (UC Berkeley+Stanford in the Bay Area, Boston’s plethora of top schools, The Research Triangle’s UNC+Duke, etc.). There also appears to be a based on type of industry for at least a few districts. Microsoft and Amazon.com are headquartered near Seattle and Boeing was headquartered there and still operates a large plant in the region. The importance of Silicon Valley cannot be understated: Facebook, Apple, Google, and Intel are all based there. Washington DC’s role as the epicenter of national political life and most federal agencies makes the region’s concentration of human capital almost inevitable (Imagine the human capital the CIA, FBI, NSA, DoD, DoS attract?). Finally, places of commerce like Houston and Chicago probably attract a great deal of human capital with a combination of incentives.

I think the question of critical importance for regions that lack these advantages in attracting human capital (be it a lack of quality universities or lucrative industries) is to attract human capital using alternative methods. According to one source, making housing attainable in the urban core where they say amenities/diversity/jobs/social life are usually concentrated. A compelling blog was written about attracting smart people to cities that rates cities by their potential and actual college graduates by sq/mile. Personally, I just hope that my city doesn’t get left behind in the zero-sum competition over human capital.

I found these cartograms from an article in the Telegraph and was immediately impressed. The cartograms originated here and use data from the Global Rural-Urban Mapping Project as to create the intriguing images. You can use the map in the previous link to look at any country’s population cartogram, here are a few that stood out to me:

First I would like to show three countries that had their national capitals moved from a heavily populated coastal city, to an inland location.

Istanbul’s historical significance cannot be understated. As Constantinople it was the seat of the Byzantine empire before becoming the capital of the Ottoman empire for centuries. But in 1923, after allies had occupied Istanbul at the close of WWI, newly independent Turkey moved its capital to Ankara.

Pakistan’s capital used to be in Karachi but was moved in 1960 to Islamabad. Perhaps this was to disperse the population of Pakistan more evenly, or to protect the government’s critical infrastructure from a naval attack.

For multiple centuries Rio de Janeiro was the capital of colonial and independent Brazil, until in 1960 when the capital was moved to the planned city of Brasilia.

Now I would like to look at examples of countries where populations tend to be focused in one place and are not evenly distributed.

Russia represents a interesting example of a nation having an East-West divide. Geographers often divide Russia along the Ural mountains, with the west often called “European Russia” and the east called “Asian Russia.” 78% of Russians live in the western part of Russia, in about a quarter of the country’s landmass.

Argentina’s population centers around its capital Buenos Aires and only small nothern cities like Mendoza and Cordoba figure.

This shows a population trend that’s been observed in the US (to a lesser degree): densely populated coastal cities and an “empty quarter” in the center. I say that this occurs to a lesser degree because the US has many large cities in the midwest and other inland locations:

Another great model for US population density comes from the Times article “Where we live.” The image was created by Joe Lertola:

The US has many big patches of population such as the Acela megaregion (AKA Bos-Wash) which includes everything between Boston and Washington DC. This appears to be the biggest patch, possibly followed by Southern California. Demographers and the like have tried to anticipate growth in the US by grouping large populations into megaregions like Bos-Wash and focusing on infrastructure and land reforms that accommodate these growth trends. One of the most obvious ones is the America 2050 initiative. This group puts out a map of what they consider to be the most important US megaregions in the next 50 years:

Here you can see that the US population isn’t quite as diffuse as the earlier cartogram would indicate. The same group estimates the population of Bos-Wash to be 49 million, or nearly a 5th of the total US population. Overall, this group says a majority of Americans live inside one these megaregions. By 2025 they predict that 75% of Americans will live in these megaregions. the regions themselves are loosely defined; for instance the “Texas Triangle” includes Oklahoma City despite it being nearly 200 miles away from Dallas or any other city in the megaregion. Similarly the “Front Range” region connects Albuquerque to Denver, a distance of 330 miles separates them. If the distance between large populations inside of these megaregions seems daunting, the distances between megaregions is an entirely different beast. Especially in the western US, megaregions are spread very far apart. Separating the “Front Range” from “Cascadia” (my megaregion : ) ) is nearly a thousand miles of mountainous frontier. Maybe the lesson from this map is that the US should focus more on connecting megaregions within themselves instead of paying to connect them with one another. This idea was actually adopted by Obama when he announced plans to build several high speed rail lines in the US. One of the proposed lines would connect Vancouver, BC to Eugene, OR with a high speed rail.

I think its important to note that models showing population trends can help convey the trends that might not be apparent to the casual observer. Once one sees how the population of a country is placed, one can start to ask how it affects policy regarding those trends.

When I was getting vaccinated for a trip to the Middle East, I was surprised to find the doctor asking me which part of Turkey I’d be visiting. She pulled out a map that looked something like this:

I was shocked because Turkey had to be the most developed country we would visit on that trip; how could they the only one where Malaria shots are necessary in certain parts. I felt even more shocked after I visited Istanbul, where its level of development seemed otherworldly compared to Damascus, Amman, Beirut, or even Jerusalem. They have a transit system that is efficient, people sort of obeyed traffic rules, and everything looked much better maintained. It felt like a European city, while the others felt like something else. I marveled at Istanbul’s unique mix of secularism  and development. Yet apparently for someone living in Diyarbakir, Malaria is a part of life.

What’s more startling is that Turkey’s HDI for 2010 is 0.679, behind Jordan and Tunisia and not far ahead of Algeria [source]. How could a city that seemed so modern be in a country less developed than resource-starved Jordan, who has some 13 miles of coastline and a mostly desertous landscape. Jordan’s GDP (PPP) per head in 2010 was $5,400 while Turkey’s is more than twice that at $12,300 [source]. There has been research from multiple good sources on the matter:

A great research publication called “Regional Disparities and Territorial Indicators in Turkey: Socio-Economic  Development Index (SEDI)” written by Metin ÖZASLAN, Bülent DINCER, and Hüseyin ÖZGÜR (found here) delves into this question with depth and authority I can’t match, so I’m going to just post some of their findings and briefly summarize them. They use 58 different indicators from myriad sources to measure provincial development and collate them into one index called the SEDI. Unfortunately this means that, like the HDI value from measureofamerica.org we cannot compare these values directly to other countries. Fortunately they do go into great detail in the article on their methodology and it appears to check out. Time for some cool maps thanks to this article.

Many things stand out. Most of Turkey’s most developed regions are in the western part of Turkey, with the lowest SEDI scored provinces all being in the east.Four Cities+suburbs stand out as the most developed provinces in Turkey: Istanbul, Izmir, Ankara, and Bursa. These provinces (and a province that includes suburbs of from Istanbul) have a combined population of 26.14 million, according to Turkstat. This means that of Turkey’s 73 million people, just over a 3rd live in these five most developed provinces [source]. This shouldn’t seem so troubling, when you look at the US (as we did in the previous blog) you can see its not so uncommon for states (especially ones with big cities) to score higher on development indicators. The problem with Turkey is it’s development curve among provinces is much steeper than the disparity among US states. A graph from the same source illustrates this effectively:

Istanbul’s score dwarfs the others, its more than four times that of 6th ranked Eskişehir province. The top five provinces themselves dwarf the remainder of the provinces.

Here is a map of the geographical regions of Turkey using the same source. I edited it to show which regions have above average and below average scores (note: mediterranean is almost perfectly at the average):

Now here is an unedited graph showing the regional SEDI scores from the same source:

This paints a picture of Turkey having three regions that this source claims, drive most economic growth in Turkey, with Marmara far outpacing the rest of the country. Meanwhile the two easternmost regions of Turkey experience the least amount of growth or development. This doesn’t perfectly coincide with the Malaria map I showed earlier, but I suspect that map was geared towards ease of use and probably wanted to include the entire southern border region to aid tourists traveling by land.

The next article I am going to use comes from the World Bank, titled “Turkey: Country Economic Memorandum Volume I – Main Report” it can be found here and the section I will cite begins on chapter 6, page 29 (41 in adobe). This article compares regional GDP per head variation among European countries and shows its findings in this graph:

Turkey ties with Belgium, a country known for its Flemish/Wallonian divide (a north/south divide in this case). I am surprised by the other results in this publication, as I had assumed Italy’s regional GDP variation would exceed the UK’s.

This article points out that from 1980 to 2000 Turkey’s regional disparity has either increased (gotten worse) or stayed the same. It points out that while Industrial activity has expanded in the Western half of Turkey, the East remains primarily employed by agriculture, and that hours/employee are considerably lower in the East. Finally, the article gives some explanations for why Turkey’s institutions might inhibit growth in the Eastern regions; it states that Turkey’s centralized planning and allocation of resources for things like infrastructure and public works projects gives local officials few options to raise their provinces from poverty and underemployment.

A few quick statistics can be found in this report from the European Commission titled “Second report on economic and social cohesion: Regional Features in Turkey” found here. It states:

“between east and west: two-thirds of the population were concentrated in the west of the country in half the land area, accounting for 82% of national GDP, and with GDP per head 23% above the national average (41% of the EU average). In the east, GDP per head was 53% of the national average, much the same as 10 years earlier”

One word that is missing from all of these articles is “Kurd” which is surprising because Kurds make up the largest ethnic minority in Turkey with 15 million living there, most of them are located in the Eastern part of Turkey. Here is a map I found from the University of Texas here that shows where most Kurds live:,

I found another, more recent map here that looks at recent elections results in Turkey in 2011:


It’s worth noting that Turkey has a unique 10% electoral threshold that prevents most Kurdish interest parties from electing members into parliament; the easiest way to circumvent this rule is to run candidates as independents.

There seems to be a strong correlation (using these two maps and the first map) between Kurds and low development. I am not trying to imply that Kurds don’t work as hard, but simply pointing out that like Appalachia and the Mississippi Embayment, the Kurdish region of Turkey appears to lag behind the rest of Turkey. Something I would be very interested in seeing is how a partition of Turkey that removed part Eastern Turkey from the rest would effect the HDI value Turkey currently enjoys. Using the 3 sources from the beginning of this article, it seems clear that Turkey’s Western half would benefit (at least in its HDI score) if its indicators were measured separately from the Eastern/Kurdish part. Of course the political ramifications of such an outcome would be significant. I’ll leave that debate for the citizens of Turkey, be it Kurds or Turks.

recently I wrote about the trend of low HDI scores in the US South and Appalachia. This time I want to focus on a metric found from the same source. The metric of life expectancy from birth is actually a good way of comparing US congressional districts to other countries.

The CIA World Factbook has a listing of most nations’ average life expectancy from birth here. It’s important to note that because this is an actual year-based estimate, the rankings for congressional districts and countries is tightly ranked, and misreporting statistics from developing countries is a possibility. I want to stress that these are averages so while someone living 2 years less might not seem like much, but this is the result of everyone in a district/country living longer or shorter lives. Some of these statistics will make you question the world.

While I’ll mention the regional disparity briefly, I’d like to focus on the comparison of life expectancy with certain US congressional districts (CDs) and other countries as well.

Here is a map of the bottom 100 CDs in the US:

Many things stand out compared to the HDI graph representing the bottom 100, though the US South+Appalachia region is similarly represented in this map.

Firstly, the West Coast does considerably better than the East Coast, with only a single CD making the list west of Texas. Second, cities in many Eastern States have lower comparative life expectancies than their HDI suggests. Regions of the Rust Belt including North Ohio and the Detroit metro area score poorly. The mid-Atlantic cities Philadelphia and Baltimore do poorly, but the rest of the Northeast does well.

This map shows the bottom 25 districts in the US, these districts only live 72 to to 75 years on average (I’ll provide a complete table of average life expectancy later).

Much like the bottom 25 districts by HDI, the bottom 25 in life expectancy are almost all inside the US South and Appalachian regions. The Mississippi embayment and the Kentucky-West Virginia border are the worst hit.

Here is a listing of the bottom 100 districts by age. But I’ve added a column for countries with similar life expectancies for the bottom 50. I got these numbers here and here.

West Virgini 3 72.9 Egypt 72.66
Kentucky 5 73.6  Thailand 73.6
Mississippi 2 73.6 Bulgaria 73.59
Alabama 4 74.3 Serbia 74.32
Pennsylvania 2 74.4 Mauritius 74.48
Oklahoma 2 74.5 Algeria 74.5
Pennsylvania 1 74.5 Colombia 74.55
Georgia 2 74.6 China 74.68
Alabama 3 74.7 Syria 74.69
Alabama 7 74.7 Cook Islands 74.7
Louisiana 7 74.8 Hungary 74.79
Arkansas 1 74.8 Tunisia 75.01
Tennessee 8 75.0 Lebanon 75.01
Tennessee 9 75.0 West Bank 75.01
Mississippi 3 75.0  Macedonia 75.14
North Caroli 1 75.0 Tonga 75.16
Louisiana 5 75.0 ” “
Arkansas 4 75.1 ” “
Georgia 1 75.1 ” “
Missouri 8 75.1 ” “
Alabama 1 75.1 ” “
Georgia 8 75.1 ” “
Mississippi 4 75.2 ” “
South Caroli 6 75.3 Lithuania 75.34
Florida 4 75.3 ” “
Louisiana 4 75.3 ” “
Mississippi 1 75.4 Antigua and Barbuda 75.48
Georgia 12 75.4 ” “
Arkansas 2 75.4 ” “
Michigan 13 75.4 ” “
Michigan 14 75.4 ” “
Kentucky 1 75.5 ” “
Maryland 7 75.5 ” “
Louisiana 6 75.5 ” “
District of Columbia 75.6 ” “
Louisiana 1 75.6 ” “
Oklahoma 4 75.6 ” “
South Caroli 5 75.7  Ecuador 75.73
Louisiana 3 75.7 Croatia 75.79
Alabama 2 75.7
Alabama 6 75.7
Tennessee 1 75.7
Virginia 9 75.7
West Virgini 2 75.9  Morocco 75.9
Louisiana 2 75.9
Tennessee 4 75.9
Oklahoma 5 76.0  Poland 76.05
North Caroli 7 76.0
Oklahoma 3 76.0
Oklahoma 1 76.0
Virginia 3 76.0
Alabama 5 76.1
Texas 1 76.1
Ohio 6 76.2
Kentucky 4 76.3
Illinois 12 76.3
South Caroli 3 76.3
Texas 13 76.4
Tennessee 7 76.4
Virginia 4 76.5
Georgia 3 76.5 Mexico 76.47
Texas 8 76.5
Michigan 5 76.6
Tennessee 6 76.6
Kentucky 3 76.6
Tennessee 3 76.6
Indiana 7 76.6
Texas 5 76.6
Michigan 11 76.7
North Caroli 10 76.7
Missouri 5 76.7
West Virgini 1 76.7
North Caroli 3 76.7
South Caroli 4 76.7
Texas 19 76.8
North Caroli 8 76.8
Texas 2 76.8
Georgia 11 76.9
Ohio 15 76.9
North Caroli 2 76.9
Maryland 3 76.9
Indiana 1 76.9
California 2 76.9
Georgia 10 76.9
Maryland 2 77.0
Ohio 17 77.0
Ohio 9 77.0
Missouri 4 77.1
North Caroli 5 77.1
Virginia 5 77.1
Kansas 4 77.1
Indiana 8 77.1
Wisconsin 4 77.2
Missouri 3 77.2
Tennessee 2 77.2
Ohio 10 77.2
Ohio 11 77.2
Tennessee 5 77.2
Indiana 6 77.2
Texas 14 77.2

Surprisingly, many countries perform better than US congressional districts. Eastern Kentucky has the same life expectancy of someone in Thailand, think about that for a second. Not only are parts of the US much lower than the US average, they’re actually much lower than most developed countries. The US  ranks 50th overall on the CIA World Factbook, th0ugh a number of meaningless micro-states and dependent territories distort this ranking somewhat. US life expectancy raises many important questions about access to healthcare and our dietary habits among other things.

Finally, I want to point out that some of these statistics are hard to accept. Jordan ranks higher than the Netherlands, for example. and Bosnia, despite its violent recent history has a higher life expectancy than Denmark. I’m not necessarily accusing these countries of outright dishonesty, but perhaps their methodology was vulnerable to inaccuracies. There are hundreds of thousands of Bedouin in Jordan, many of them weren’t born in hospitals so its possible that age estimates could be wrong. This isn’t the first time I’ve suspected this, in a much earlier blog on female literacy I found that the country of Georgia claims 100% literacy, despite having a GDP per capita lower than Syria, and a very rare and complicated language, in addition to smaller languages like Tsez being spoken. Take these statistics for what you will, its intriguing no less.

Unfortunately this isn’t a subject where I can compare countries and their regions to other countries. Instead, here is a selection of articles and related maps that deal with the problem of uneven growth across various counties. I want to stress that these graphs use different measurements from different time periods and thus cannot be compared with each other. I will provide links for the maps I use and give a brief summary of the research that corresponds with them.

The first example I would like to present is from a familiar source, the USA. here we can use a brilliant website designed by social/political scientists to display a variety of statistics relating to US development. using measureofamerica.org you can access an HDI map of the US that is divisible to the congressional district level. here is a state level map of the US using their latest dataset.

I got this map here it covers most US cultural regions that I accept with its omission of Appalachia NOTWITHSTANDING.

look at measure of america. wow here is the HDI of the US

this website is really interesting.

Look at what happens when you gauge obesity and diabetes in the USA:

This shows a tendency of Appalachia and southern US states to be have comparatively bad health. The West and New England do well here. How about Diabetes?

between these two maps, the unhealthiness of Appalachia stays strong. What are they doing wrong?

Diane Sawyer has some ideas about this

here are some other maps from Appalachian Regional Commission 

This shows college completion rates in the region and compares it to the US average.

Another troubled area represented on the HDI map is the US South. Breaking the region down by Congressional District allows us to look closer at regional disparity by showing disparity inside states.

Using the HDI data from our earlier source, lets see what the bottom 100 US Congressional districts look like:

It’s important to note that the bottom 100 districts is an arbitrary measure and many districts with similar HDI values were excluded. Nonetheless it includes the important bottom quintile with about 10 districts from the next lowest quintile. It also provides us with nearly a quarter of the 437 US congressional districts so I went with it. This map took a long time to make so please feel free to verify my findings here

In many states there are examples of urban poverty as well as rural/agrarian poverty being represented. In my region (Pacific Northwest) the eastern districts in Oregon and Washington are examples of rural poverty. NYC provides an intriguing example of urban poverty. The district NY-16 is one of the lowest HDI scores in the US, it sits nearly adjacent to NY-14, the district with the highest score in the US. The difference in the scores (8.79 vs 3.20) shows how geography can mean little when defining a region’s development.

But while NY-14 sticks out, it pales in comparison to the overwhelming poverty of the US South+Appalachia. To corroborate my view of the US South look to this wikipedia page

Of the 100 lowest HDI scores, this combined region contributes 59 districts (59%). When you count the bottom 50 this region contributes 30 (60%).

But when you count only the bottom 25 you get a staggering 20 Southern+Appalachian districts or 80% of the bottom 25. This map illustrates the disparity:


  Here’s a chart of the 100 lowest HDI scores and my Southern+Appalachian selections in red:

California

20

2.60

Kentucky   

5

2.82

West Virgini

3

3.16

New York

16

3.20

Texas

29

3.23

Missouri   

8

3.24

Oklahoma   

2

3.33

Mississippi

2

3.34

Alabama    

4

3.37

Arkansas   

1

3.39

Alabama    

7

3.46

Kentucky   

1

3.50

Tennessee  

4

3.50

Virginia   

9

3.50

Arkansas   

4

3.50

South Caroli

6

3.52

Louisiana  

5

3.52

North Caroli

1

3.53

Georgia    

2

3.55

Alabama    

3

3.61

Georgia    

12

3.66

Louisiana  

2

3.68

Tennessee  

8

3.69

California

34

3.69

Arizona

4

3.70

California

18

3.73

Texas

15

3.74

California

31

3.78

Texas

28

3.78

California

43

3.80

Illinois

4

3.80

Tennessee  

1

3.81

Pennsylvania

1

3.86

Florida    

3

3.86

Louisiana  

7

3.87

Texas

27

3.88

Texas      

1

3.89

Texas

30

3.90

Texas

13

3.92

Texas

20

3.92

Georgia    

1

3.93

Louisiana  

3

3.94

Michigan

13

3.95

Alabama    

2

3.95

Oklahoma

3

3.95

New Mexico

2

3.95

Ohio       

18

3.98

Louisiana  

4

3.99

Texas

9

3.99

Mississippi

1

3.99

Texas

19

4.01

Ohio       

6

4.04

Mississippi

4

4.04

Georgia    

8

4.06

Arkansas   

3

4.06

South Caroli

5

4.07

Alabama    

1

4.07

Tennessee  

9

4.08

Missouri   

4

4.09

North Caroli

7

4.09

Texas

18

4.10

Texas

11

4.10

California

47

4.11

California

2

4.11

Michigan

14

4.13

California

21

4.13

North Caroli

10

4.13

Tennessee  

3

4.13

North Caroli

2

4.14

Michigan

1

4.15

West Virgini

1

4.15

West Virgini

2

4.16

Texas

17

4.17

South Caroli

3

4.19

North Caroli

3

4.20

Texas      

5

4.20

Pennsylvania

12

4.22

Indiana

7

4.22

Missouri   

7

4.22

Mississippi

3

4.23

Washington

4

4.24

Virginia   

3

4.24

Oregon

2

4.26

Arizona

1

4.26

Nevada

1

4.26

Florida    

1

4.27

Kentucky   

2

4.27

Virginia   

5

4.27

Ohio

17

4.27

Oklahoma

4

4.28

New York

23

4.29

Pennsylvania

9

4.29

Indiana

8

4.30

Tennessee  

6

4.30

Texas

16

4.32

Illinois

17

4.32

Georgia    

9

4.32

Florida

23

4.32

North Caroli

8

4.34

North Caroli

11

4.34

Feel free to disagree with my assessment of the South or of Appalachia. This was excluding a large number of districts that seem to have an inconclusive regional definition. For example, Florida-23 straddles the Miami metro region and wasn’t included on the list. I only included two districts in Texas (CD1 and CD5) because the rest had mixed definitions for culture; Oklahoma-2 was the only district included. The northern district of OH-17 was excluded because only part of it is included in the ARC regional map. Missouri’s regional definition produced conflicting results but the southern districts of MO-8, MO-7, and MO-4 appear to reliably count as “southern.” The rest are 100% Southern and/or Appalachian. I wouldn’t have included Northern Virginia but none of their districts had a low HDI score so it didn’t matter.

I would first like to note that this is not comparing the relative value of either indicator. What I mean is that instead of comparing the Human Development Index and military spending relative to other Middle Eastern countries, these values can be compared to any country for the year 2007. I found the data on spending from SIPRI and HDI values from several sources (for more on these look here and here). you can find both of these on really cool graphs on google’s public data explorer here and here. Secondly, I want to note that this map cannot conform to our previous definition of the Middle East due to a lack of complete data in several countries. The omitted countries are Iraq, Oman, and Lebanon.

This map is a little bit less intuitive as each index is measuring something different. The reason they matched up evenly was because excel used tenths for HDI (best value being 1.00) and hundredths for military spending (10% being the max)

This map blows away the notion HDI has anything to do with how much countries will spend on their military in the Middle East. It becomes clear that countries have different security strategies especially when you look at the GCC (Gulf Co-operation Council). Smaller countries like Qatar, Bahrain, and Kuwait all spend a relatively low amount on their military. While Qatar dedicates the lowest amount (% of GDP) in the Middle East, Saudi Arabia spends the most; both in terms of percent of GDP and as an overall amount. These GCC countries all have a relatively high HDI values and nothing about Saudi Arabia or the UAE distinguishes their scores. Saudi Arabia’s military spending is puzzling for many reasons but lets look at some other observations before exploring this further.

Yemen is one of the poorest countries in the Middle East (only the Gaza Strip has scored lower on recent HDI scores) yet it dedicates more of its GDP than Egypt, Syria, or Iran on its military. Why is this? One possible explanation could be that in order to maintain a competent military organization one needs to spend a certain base amount. Yemen’s population is about the same size as Syria, but its GDP is ~45-60% the size (depending on whether you use Nominal or Purchasing Power Parity). So 4% of GDP means as little as half as much money spent for Yemen compared to Syria. To examine this idea further I used the SIPRI’s numbers for 2007. Yemen spent $1.2 billion on its military while Syria spent $2.1 billion. Does this mean that for a country w/ a population of ~22 million one can expect no less than ~$1 billion to maintain a credible military? well it get’s more complicated than that unfortunately.

The Middle East as a region spends an enormous amount of its GDP on military spending. The countries we list here average 4.6% of GDP on military spending, nearly double the world average of 2.4%.

It would be foolish to assume that every country in the Middle East was compelled by one reason to spend so much on the military. Iraq and Iran were at war for 8 years in the 80s, with over a million lives lost, Israel has been in over 5 interstate wars, Saudi Arabia owns 25% of the world’s oil. Instead I think it would be wise look at every country’s spending and ask this question: What strategy would do the most to preserve this state’s future? I’m not advocating for militarism just trying to explain the mindset that might motivate actors in the region to spend so much on their militaries.

One way scholars try to measure power using the Realist paradigm is by focusing on relative power. in these graphs I will be using two metrics: military spending and gdp (ppp).

What becomes puzzling is that some countries have large shares of the GDP yet spend less on their military. Here is my hypothesis (which I will try to resolve later): countries with a high GDP per capita are able to tax their populations/dedicate oil revenues towards military expenditures more because individuals can withstand the cuts in income better. For example, Egypt’s per capita GDP is only $6,200 in 2010, while Israel and the UAE had an income of $29,500 and $40,200 respectively. For some countries, the numbers may not tell the whole story. In Iran there are at least 3 organizations who could be counted under military expenses: the IRCG, the Baseej, and finally their traditional military. SIPRI may have only counted the latter.

Note: I didn’t include Turkey because: a) they haven’t been involved in any interstate conflicts in the middle east in the past 65 years and b) their placement in the middle east isn’t entirely agreed on by middle east experts. (they would also fuck everything up by having an enormous economy/share of military spending).

key: yellow=non-china BRIC states, g8 members. blue=US allied states. light red/red= china and chinese allies.