Economic Geography

Mapping zones of wealth and poverty; illustrating the global geography of economic development

Turkey’s Dependence on Russian Energy, and Its Recent Natural Gas Discoveries

Turkey (officially, Türkiye) is an energy-poor country. Roughly 85% of its energy supply comes from fossil fuels, roughly equally divided among coal, oil, and natural gas. Coal is mined domestically and imported, but almost all of Turkey’s natural gas and oil comes from other countries. Russia supplies roughly half of its natural gas. Unlike most NATO countries, Turkey has not placed sanctions on Russia, and as a result its natural gas imports continue unabated.

Turkey also figures prominently in Russia’s global energy strategy. Two major pipelines transport Russian natural gas to Turkey, one of which (TurkStream) was recently completed (2020). Moscow and Ankara have hoped to turn Turkey into a major natural gas hub, allowing Russia to export gas to southern Europe without having to move it across Ukraine. The Ukraine War has complicated but not undermined such plans. As recently reported by Natural Gas Intelligence, “Russia is turning to Turkey as a potential natural gas hub partner with a new sense of urgency to find new export outlets for volumes left stranded by damages to the Nord Stream pipelines in September.” The Russian-Turkish energy partnership extends beyond natural gas. In 2021, Russia was Turkey’s second largest supplier of coal, following Colombia. Turkey’s first nuclear power plant (Akkuyu), has been jointly built by the Russian firm Atomstroexport and the Turkish construction company Özdoğu; it is expected to come online in 2023.  Financing has been almost entirely provided by Russian investors.

Political tensions between Russia and Turkey have periodically intruded on their energy collaboration. Most recently, Ankara irritated Moscow by asking for a 25 percent discount on natural gas payments and requesting that all payments be delayed until 2024 due to domestic economic problems. Building a Turkish hub for Russian gas exports also faces external economic and geopolitical obstacles, particularly from other NATO countries. As recently noted by Natural Gas Intelligence:

A Russia/Turkey gas hub would have to secure financing for the billions of dollars needed to construct new subsea pipelines under the Black Sea, which is currently in a war zone. Without access to Western technology and financing, a new pipeline project could take years to build.

Turkey’s energy prospects, however, have recently been transformed by substantial natural gas discoveries in its Black Sea Exclusive Economic Zone. In the final week of 2022, an estimated 170 billion-cubic-meter gas reserve was found at the Çaycuma-1 field. Combined with previous recent discoveries in the Sakarya field, Turkey’s natural gas reserves have been elevated to 710 billion cubic meters. Gas from these fields should become available sometime in 2023, alleviating Turkey’s energy crunch. Discoveries to date, however, are not adequate to meet long-term needs, as the country consumes 50 to 60 billion cubic meters of gas every year. To put recent Turkish discoveries in global context, Russia has proven natural gas reserves of some 50,000 cubic kilometers.

Coal remains one of Turkey’s major energy sources, thwarting its ability to decrease its carbon emissions. Turkey’s domestic coal industry is highly subsidized in order to reduce natural gas imports. Lignite, the least energy-dense and most polluting form of coal, predominates. Coal production surged from 2015 to 2019 but has declined since then. To meet the country’s growing demand, imports have surged in recent years. The war in Ukraine, however, has disrupted supplies and increased costs, generating economic hardship. Particularly hard hit is Turkey’s large and energy-intensive cement industry. CemNet reported in March 2022 that the Turkish cement industry was facing an “imminent crisis.”  In 2014, Turkey was the world’s fifth largest cement producer, following only China, India, the United States, and Iran.

Turkey has made relatively modest investments in renewable energy. Hydropower has long provided roughly 20 percent of its electricity, but recent droughts have reduced the supply. Turkey’s has good climatological potential for solar and wind, and wind power is now growing, providing about 10 percent of the country’s electricity. In 2021 the energy think tank Ember reported that “it is now cheaper to build new wind or solar for power generation in Turkey than running even the most efficient hard coal power plant that relies on coal imports.” Ember’s analysts thus foresee an accelerating transition to renewable power, with a “win-win-win situation” resulting in “lower import bills, lower emissions, [and a] lower carbon levy by the EU.” Such hopes may be overstated. Energy intermittency and the lack of economically feasible storage still poses a major obstacle to solar and wind energy development in Turkey – as in the rest of the world.

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Famine in Ethiopia and the Enset Solution in the Southern Highlands

Ethiopia is a notoriously drought and famine plagued country. Although the western highlands receive abundant precipitation, the densely populated eastern highlands are much drier. Almost all the precipitation that this area receives falls in a brief window during the summer. When summer rains are inadequate, as they frequently are, famine typically results. Ethiopia’s most densely populated areas are in the southern highlands, an ethnolinguistically diverse area that has long been both politically and economically marginalized. Despite its rapid economic growth since 2003, Ethiopia is still a very poor country, and the southern highlands is one of its poorer areas. One would therefore expect it to be particularly vulnerable to famine.

Yet despite all of these disadvantages, the southern highlands are much less prone to famine than Ethiopia’s core zone in the northern and central highlands. The answer to the seeming paradox is simple, found in the area’s staple crop. The people of the northern and central highlands subsist largely on grain, which is highly vulnerable to dry weather during the growing season. Those of the southern highlands, in contrast, subsist largely on enset, which is far more resilient. This crop, unique to Ethiopia, is a close relative of bananas and plantains. It is not cultivated for its fruit, however, but rather for its carbohydrate-rich leaf sheaths and corms.

            The advantages of enste are nicely explained in a recent article entitled “Enset in Ethiopia: A Poorly Characterized But Resilient Starch Staple.” As the authors write:

Enset has historically been ascribed as a ‘tree against hunger’ (Brandt et al., 1997), due to the domesticated plant having important attributes that support the food security of communities that cultivate it. These attributes were evident during the devastating famines of the 1980s, where enset-growing communities reported little-to-no food insecurity (Dessalegn, 1995). Most significant is the apparent ability of enset to withstand environmental stress, including periods of drought (Quinlan et al., 2015). Enset can also be harvested at any time of the year and at any stage over several years (including when it is immature), and enset-derived starch can also be stored for long periods (Birmeta, 2004). Enset also provides fibres, medicines, animal fodder and packaging material (Brandt et al., 1997). It stabilizes soils and microclimates (Abate et al., 1996) and is culturally significant (Kanshie, 2002Negash and Niehof, 2004Tewodros and Tesfaye, 2014). Enset has a complex management system supported by extensive ethnobotanical knowledge (Borrell et al., unpubl. res.). In a comparison of starch crops, enset has been reported to produce the highest yield per hectare in Ethiopia (Tsegaye and Struik, 2001Kanshie, 2002) with relatively low inputs and management requirements. Enset therefore has the ability to support a larger population per unit area than regions relying on growing cereals (Yirgu, 2016). As a result of these qualities, enset farming provides a long-term, sustainable food supply capable of buffering not only seasonal and periodic food deficits, with minimum off-farm input, but also demonstrates potential that exceeds its current utilization in South-West Ethiopia.

Enset could be cultivated in many tropical areas. If it were to spread, food security could be significantly enhanced in some very poor and marginalized areas. Unfortunately, most international agricultural research has focused on a handful of crops, particularly wheat, rice, and corn (maize). If more attention could be given to highly productive minor crops such as enset, major benefits could probably be gained.

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William: Not Just Prince of Wales But Also Duke of Cornwall

Now that Charles has become king, Prince William has become Prince of Wales. That title is customarily given to the heir apparent by the reigning monarch. The day after he became King, Charles bestowed the title on his eldest son. The position is not without controversy. Thousands of Welsh people have signed a petition calling for the abolition of the title, which they see as an insult to Welsh national and historical identity. Many want much more than that: the independence of Wales. In late September, an estimated 10,000 people marched for Welsh sovereignty in Cardiff.

Public opinion polls, however, show that only around a quarter of Welsh people want independence, a much lower figure than that found in Scotland. Another poll found that 55 percent of the Welsh people also approve of the seemingly antiquated title of the royal heir, “Prince of Wales.” But Wales is not doing well economically and is now one of the poorest parts of the United Kingdom. As a result, the desire for Welsh independence does seem to be growing. As Welsh journalist Will Hayward recently argued:

Most [independence] supporters have simply looked at the state of the United Kingdom, seen that it isn’t working for Wales, and view independence as the most effective vehicle for fixing Wales’s problems. That doesn’t mean independence necessarily is the answer, just that the status quo is leaving the country both impoverished and unable to fix [its] problems…

“Prince of Wales” is not the only title held by William. When Charles became king, William automatically became Duke of Cornwall. Although the former is the more illustrious title, the latter is in some ways more consequential. Being Duke of Cornwall does not give any power over Cornwall, but it does bring financial rewards. This Dutchy controls landholdings of some 135,000 acres (55,000 hectares) as well as a portfolio of financial assets. All told, it is worth about $1.3 billion. In 2021, it provided Prince (and Duke) Charles with an income of some $25 million.

The land holdings of the Dutchy of Cornwall are not actually concentrated in Cornwall, the historical county located in far southwestern England. As is typical of such premodern and essentially feudal holdovers, they are widely scattered. Roughly half of the estate is located in Dartmoor, a scenic low plateau located in Devon, the county just to the east of Cornwall. Most of Dartmoor is administered as a national park. Unlike national parks in the United States, those of the UK include considerable private properties. But, also unlike in the United States, private land holders in the UK are not always allowed to exclude the public from enjoying their lands.


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Economic and Class Factors in the 2022 Italian Election

Historically, leftwing political parties and movements have championed the working class and, in turn, have received its support. But as cultural and social issues have increased in importance, this connection has weakened and now seems to be disappearing. In Europe, concerns about immigration and European integration have also pushed working-class voters from the political left to the right.

Such dynamics were clearly evident in the 2022 Italian election. As the graph posted above shows, the most left-leaning of the major Italian parties, the Greens and Left Alliance, found the bulk of its support in the higher income quintiles. The Democratic Party, the heart of the left coalition, did poorly with lower-income voters. Higher-income voters were much more inclined than low-income voters to support the pro-EU, centrist “Action/Viva Italia” alliance. The one left-leaning party to gain most of its support from the working class was the neo-populist Five Star Movement. But while the Five Star Movement supports economic redistribution and many other leftist policies, it is also hostile to immigration and suspicious of the European Union. As a result, it has sometimes been shunned by the other left-leaning parties.

Parties belonging to the victorious rightwing coalition received a significant amount of support from the working class. Giorgia Meloni’s right-populist (or national conservative, sometimes deemed post-fascist) Brothers of Italy did well across the income spectrum but appealed most strongly to those in the lower-middle income quintile. Surprisingly, Silvio Berlusconi’s Forza, an establishment oriented, pro-business party, did best among those in the lowest quintile. Matteo Salvini’s populist and regionalist/federalist Lega party also had slightly higher support among lower-income voters.

Patterns of economic geography are less visible in the Italian election returns of 2022. As can be seen on the map of multi-member electoral constituencies posted above, the left-populist Five Star Movement received most of its support in the south, which is by far the poorest part of Italy. In northern Italy, however, no economic correlations are apparent. The three richest provinces of Italy, as assessed by per capita GDP in 2019 (see the map posted below), supported different parties. Bologna gave most its votes to the leftwing coalition, as it always does. Monza and Brianza, just north of Milan, supported the rightwing coalition, as it generally does. In the far north, the Autonomous Province of Bolzano (or South Tyrol) supported its own regionalist party, as it almost always does. South Tyrol is very distinctive from the rest of Italy, mostly because more than half of its people speak German as their first language.

  

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Urbanization, Economic Productivity, and the Industrial Revolution

Levels of urbanization and levels of economic development roughly correlate. As can be seen on the paired maps, countries with very low levels of urbanization tend to have low levels of economic productivity (as measured by per capita GDP in Purchasing Power Parity). Burundi, for example, has the world’s second lowest urbanization rate (13.7 in 2020) and the lowest level of per capita GDP ($856 in 2022). Conversely, Singapore is completely urbanized and has the world’s second highest level of per capita GDP ($98,526 in 2022). The linkage is strong enough that urbanization is sometimes used as a proxy for economic development, especially for earlier time periods. Consider, for example, this passage from a recent study published by the Hoover Institution at Stanford University:

We find that a vector of exogenous factors that were binding constraints on food production, transport, and storage within the densely populated nuclei from which nation states later emerged account for 63 percent of the cross-country variance in per capita GDP today. Importantly, this vector accounts for progressively less of the variance in economic development (as measured by urbanization ratios) going back in time. [emphasis added]

 

This maneuver is understandable, but its validity is questionable. Historical urbanization rates are difficult to determine, and the figures produced are often controversial. Even today, measuring urbanization is often tricky, due mainly to variations in the population-size and population-density thresholds for urban standing. More important, the correlation between urbanization and economic development is not particularly strong. Some primarily rural countries have moderately high levels of developmental, while some primarily urban countries have low levels. One finds such deviations at both the top and bottom of the urbanization spectrum. Germany, for example, is more than twice as economically productive as Argentina, but is significantly less urbanized. Sri Lanka is (or was, in 2020) is almost six times more economically productive than The Gambia, but is far less urbanized.

Non-urban areas can be very economically productive, especially if they have relatively high population density, good transportation networks, and proximity to larger markets. Britain’s industrial revolution itself began in rural landscapes. Although maps of the industrial revolution usually emphasize coal and iron ore deposits, industrialization was originally dependent on hydropower, which requires abundant precipitation and significant drops in elevation. Areas around the Pennine Chain, the “backbone of England,” were thus selected for the first mechanized mills, despite their lack of urban infrastructure. The first modern factory, a water-powered cotton spinning mill, was built in the village of Cromford in Derbyshire, England in 1771; others quickly followed elsewhere in the Derwent Valley. Factory owners had to build housing for their workers due to the region’s rural nature. Despite its early economic productivity, Cromford never urbanized, and today has fewer than 2,000 residents

 

 

As industrialization proceeded and coal supplanted hydropower, small and mid-sized towns in northern and central England transformed into major cities. Proximity to markets and ports allowed the factories of Lancashire to supplant those of Derbyshire, and by the second half of the nineteenth century the water-powered mills of the Derwent Valley were mostly abandoned. Today the area is a world heritage site, commemorating the industrial revolution. Currently, hydropower is being restored to make the site more economically sustainable. On August 1 of this year, the BBC reported that :

 

 

Hydroelectric power is due to return to a textile mill which helped spark the industrial revolution.

Cromford Mill in Derbyshire – built in 1771 by Sir Richard Arkwright – was the world’s first successful water-powered cotton spinning mill.

The Arkwright Society has secured a total of £330,000 from Severn Trent Water and Derbyshire County Council.

Work is due start in September with the aim of being fully operational by June 2023.

The project will involve reinstating a waterwheel and installing a 20kW hydro-turbine to power the buildings…

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Cross-Class Connectedness in the Pacific Northwest and the Proposed State of Jefferson

On the map of “Economic Connectedness of Low-Socioeconomic-Status Individuals by County” (see the post of August 11), the Pacific Northwest appears as a highly variegated zone. Counties in Washington, Oregon, and Northern California range from the highest to the lowest category, with few obvious spatial patterns.

 

 

 

But as is true in the northern Rockies and northern Great Plains, ethnic differences underlay many of these county-level disparities. Counties with large numbers of relatively poor Hispanic farmworkers generally rank low, indicating a lack of connections across ethnic and economic lines. In agricultural southeastern Washington, counties dominated by highly mechanized dryland grain farming, such as Whitman, rank high, whereas as those dominated by more labor-intensive irrigated crops, such as neighboring Adams, rank low. Whitman’s elevated standing also reflects the presence of Washington State University. Across the country, counties with major universities tend to have relatively large numbers of cross-class friendships.

Across a sizable area of southwestern Oregon and far northern California, however, both ethnic/racial diversity and the level of economic connectedness is low. On both scores, this region looks more like southern and central Appalachia than other parts of the Pacific Northwest. As the first set of triplicate maps shows, this region has a moderate level of economic differentiation (GINI Coefficient), a relatively low level of per capita GDP, and a relatively low level of income in its top economic tier. Here as well, county-rankings in southwestern Oregon and far northern California are not dissimilar to those of central Appalachia. As the next map series shows, the region is heavily Republican-voting – although it is not as Trump-inclined as West Virginia. It also has a large percentage of people over age 65. It deviates most sharply from central Appalachia in its relatively high percentage of people with high-school diplomas.

 

 

 

Although Oregon is conventionally divided east-by-west, in the western part of the state the north/south divide is equally important. Although the fertile Willamette Valley in the northwest was settled heavily by New Englanders, most of the rest of western Oregon was substantially settled by people from the upper south. Many rural areas still have an Appalachian feel.

Not coincidentally, the low-connectedness region of southwestern Oregon and far northern California forms the core of the controversial proposed state of Jefferson, which has received local support for decades. Proponents argue that their largely rural and relatively remote region has been ignored – and bullied – by the state governments of Oregon and California. Intriguingly, Wikipedia’s article on the State of Jefferson pushes its would-be borders farther to the south than has been the norm, including even Mendocino County. Although Mendocino may have a local Jefferson movement, it receives little support. As the precinct-level electoral map of the county shows, Mendocino is distinctly blue (Democratic Party voting). As a result, most of its residents want nothing to do with the populist right-wing Jefferson movement.

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Mapping Cross-Class Social Connectedness

A new map by Harvard economist Raj Chetty and his associates has been getting a lot of attention. The map, based on a massive array of data, shows the share of friends among people below median in socioeconomic status who are above median in socioeconomic status. (See this short article for a more complete explanation; also here and here.) The conclusion of the study is simple: “Children who grow up in communities with more cross-class interaction are much more likely to rise out of poverty.” The counties in blue on the map thus have more favorable conditions for the upward mobility of the poor; those in red, more unfavorable.

Some of the spatial patterns seen on the map are stark. The socioeconomic connectedness of lower-level individuals is low across most of the southern third of the country, with the exception of much of central Texas and Oklahoma. Anomalously high-connectedness counties within this low-connectedness zone are generally suburban to some extent (for example, Williamson County in Tennessee, just south of Nashville’s Davidson county). But even many affluent suburban counties in this “greater south” post below average levels, including Ventura and Santa Barbara in southern California.

A corridor of high socio-economic connectedness extends from central Virginia to southern Maine (excluding the Delmarva Peninsula and southernmost New Jersey). This region is highly urbanized, with many major cities. Anomalously low-connectedness counties within this high-connectedness zone generally contain cities with relatively poor urban cores, such as Springfield, Massachusetts in Hampden County. The several counties of New York City all rank relatively low; only Richmond County (Staten Island) appears in a shade of blue. Intriguingly, the suburban counties around Washington DC and Philadelphia rank higher than those near New York.

A much larger although less populous area of high socio-economic connectedness is found the north-center-west portion of the country, centered on the western Great Lakes, northern Great Plains, and northern Rockies regions. This is, contrastingly, a largely rural and mostly agricultural area, although it does contain a few major cities, including Denver and Minneapolis-St. Paul. Anomalously low-connectedness countries within this high-connectedness macro-region generally contain large Native American or Hispanic populations.

To a certain extent, the patterns found on this map replicate those found on a map of per capita income. The main reason is simple: poorer people living in rich countries meet relatively high socioeconomic status people far more often than do poor people living in poor countries. But there are many exceptions to this crude generalization. Several wealthy counties in southern Florida and southern California, for example, have low rates of cross-class connectivity. In contrast, much of Kentucky and southern Indiana have higher rates of connectivity than might be expected based on their socioeconomic characteristics.

In many parts of the county, the closest connection with class connectivity seems to be educational levels. This point is illustrated by blocking off roughly the same “north-center-west” macro-region on Chetty’s map and on a map of the percentage of people with high school diplomas. But again, the correlation does not hold everywhere. The paired Kentucky maps, for example, do show a general correlation between education and connectivity, but several counties, such as Grayson, have low numbers of high-school-educated residents but healthy levels of cross-class friendship.

 

 

 

 

 

 

 

 

 

 

 

 

In the United States as a whole, the map of cross-class friendships correlates poorly with race. The “whitest” American countries have some of the highest and some of the lowest levels of connectivity. Overall, however, Black, Hispanic, and Native American areas rank low on Chetty’s map. And in “north-center-west” zone of high connectivity, race/ethnicity seems to be the crucial variable. As the four juxtaposed maps showing the region where Minnesota, Iowa, South Dakota, and Nebraska converge illustrate, counties with large Hispanic and Native American populations have much lower rates of cross-class friendship than those dominated by Euro-Americans. Many of the counties in this part of the country that have large Hispanic populations are the sites of major meat-packing facilities. Workers in these plants tend to be socially isolated from the surrounding community. In early 2020, COVID-19 outbreaks were often particularly severe in these areas. As the Minnesota Department of Health noted:

In April 2020, early on in the COVID-19 pandemic, a COVID-19 outbreak temporarily shut down the JBS pork plant in Worthington, Minnesota [in Nobles County]. Employees at this pork plant identify as Hispanic or Latinx, African, or Asian immigrants, communities that have been hardest hit by COVID-19 due to many systemic barriers and challenges. Over 600 employees tested positive, leaving the small community in Nobles County with a ripple effect that was incalculable at the time.

   In tomorrow’s post, we will look at socioeconomic connectivity in the Pacific Northwest.

Mapping Cross-Class Social Connectedness Read More »

The Income of the One Percent Across the United States (and in Montana)

I was anticipating that yesterday’s post would conclude the current series on county-level maps of the United States, with a focus on Montana, but I came across another fascinating map that cannot be ignored. Posted on the graphics-rich Visual Capitalist webpage, this map shows the average annual income of the top one percent of residents in each U.S. county. The map, however, does have problems; note how much of California’s Bay Area – a place where the elite tend be very wealthy indeed – has been oddly erased.

Most of the patterns evident on this map are not all that surprising. But it is interesting to see the huge variations in the income level of the “one percent” at the county scale. The callouts, which point to the counties with the highest income levels, are useful. Note how the figure found in Teton County, Wyoming – the site of Jackson Hole – dwarfs all others. Many other Wyoming counties also rank quite high, yet Wyoming still has the third lowest level of income inequality in the country (as reflected in its  GINI Coefficient). I have a difficult time understanding why Wyoming, a wealthy state with an abundance of natural beauty, has avoided the recent population surge that has occurred in neighboring Idaho, Utah, Colorado and Montana. In 1980, the state had a mere 469,557 residents; in 2020, the number had increased only to 576,851.

Several other natural-amenity counties in the West are also near the top of the chart, including Summit County, Utah (Park City); Blaine County, Idaho (Sun Valley); and Pitkin County, Colorado (Aspen). (As its Wikipedia article notes, when “measured by mean income of the top 5% of earners, [Pitkin] is the wealthiest U.S. county.) A more surprising listing is Douglas County, Nevada, located just east of the Sierra Nevada crest. Scenic Douglas has seen rapid growth over recent decades, surging from fewer than 7,000 residents in 1970 to almost 50,000 in 2020. Many of its newcomers are wealthy former California residents seeking a low-tax haven.

Quite a few sparsely populated counties fall into the top tiers on this map despite having few natural or culturally amenities. Many are in energy-producing areas, but others are largely agricultural. Across the Great Plains, figures very widely. South Dakota, for example, has several counties in the second-highest tier and several in the lowest. Union County SD, with fewer than 17,000 residents, posts an elevated figure of 4.1 million. Also in the top tier is Minnehaha, two counties to the north. Minnehaha contains booming Sioux Falls, South Dakota’s largest city (population 197,000). Sioux Falls has emerged as an important financial hub, particularly for credit-card companies, owing largely to South Dakota’s relaxed usury laws. South Dakota’s extraordinarily relaxed residency and taxation laws help explain its other centers of wealth. As the South Dakota Residency Center webpage notes:

Unlike many states in the Union, which don’t make being a full-time traveler easy, South Dakota welcomes full-time travelers to its ranks. Here are some of our favorite reasons why South Dakota has become one of the most popular places for travelers to establish residency.

We don’t have state income tax, pension tax, personal property tax, or inheritance tax. 

That’s right, South Dakota is one of the lowest taxed states in the country! When you consider all the different types of taxes, South Dakota comes in well below the average. Plus, you can’t beat the 0% personal income tax.

With only 24 hours of actually being in the state, you can become a resident for at least five years before you’ll need to renew your driver’s license again.

It’s easy to license and register your vehicle.

Not only are our fees for licensing and registration low, but you can also complete your license and registration remotely. That means you don’t have to make a special trip to the state whenever you need to update your registration or register a new vehicle. Plus, you don’t have to undergo yearly vehicle inspections.

 

Most counties in the upper-interior south post relatively low income levels for their top earners. Although levels are somewhat higher in Kentucky’s storied Bluegrass region, noted for its horse breeding operations, none of its counties make the top tiers. In the Ozarks, one county does rank very high: Benton County, Arkansas, in the extreme northwestern corner of the state. Not coincidentally, Benton is the home of several major corporations: Walmart, JB Hunt Transport Services, Tyson Foods, and Simmons Foods. Unlike the rest of the state, northwestern Arkansas has been booming over the past few decades.

I find the Montana map of high earners quite surprising. The dark blue counties along the state’s eastern border are energy producers, and are thus expected to fall into a high category. But Powder River County is in a very low category even though its GINI Coefficient (see yesterday’s post) is very high and its poverty rate is moderately low. I am not sure how these figures could add up. I also find it mystifying that Gallatin County (Bozeman) would not make the top tier, and would be exceeded by Missoula County. The level of wealth among the rich people of Bozeman – and especially of Big Sky – is staggering. If the map is indeed accurate, I can only conclude that most of Gallatin’s wealthiest property owners maintain their official residences in other states. (South Dakota, perhaps?)

In conclusion to this series, I would like to thank William and Linda Wyckoff, who have been my mentors in the geography of Montana. Invaluable lessons have been given in fieldtrips and brew-pub conversations. Bill’s knowledge of the American West is unparalleled. I strongly recommend all of his books, but would like to draw special attention to How to Read the American West: A Field Guide. As noted on the book’s Amazon page:

From deserts to ghost towns, from national forests to California bungalows, many of the features of the western American landscape are well known to residents and travelers alike. But in How to Read the American West, William Wyckoff introduces readers anew to these familiar landscapes. A geographer and an accomplished photographer, Wyckoff offers a fresh perspective on the natural and human history of the American West and encourages readers to discover that history has shaped the places where people live, work, and visit.

The Income of the One Percent Across the United States (and in Montana) Read More »

Economic Disparities in Montana (and the Rest of the United States)

Although out-of-date and without a numerical scale, Wikipedia’s map of per capita income in the United States reveals some interesting patterns. As would be expected, income levels are high in major metropolitical areas (particularly in their suburban countries) and across the northeastern corridor extending from southern Maine to central Virginia. Western amenity counties also stand out, particularly Blaine County, Idaho, home of the Sun Valley ski resort. Many energy-producing counties also rank high, as can be seen most clearly in western North Dakota. North Dakota as a whole also ranks high, as does Wyoming. Agricultural counties in the northwestern Midwest tend to have higher per capita income levels than those further to the east; compare, for example, Iowa and Indiana. Low income levels are concentrated in rural counties in the South and in agricultural counties in the West that are home to large numbers of farm workers. Most counties with large Native American reservations also rank low.

The Montana map of per capita income yields some surprises. In 2014, Gallatin County, now the state’s center of wealth, ranked only in the second tier, with several lightly populated rural countries in the southwest and northeast falling in a higher slot. Relatively large payrolls from mining operations help explain some of these seeming discrepancies. The high figure posted for Jefferson County, located to the northwest of Gallatin, may be connected to its Golden Sunlight gold mine. High income levels in northeast Montana might be linked to the oil economy of western North Dakota. Still, to find Daniels County, ranked as the most rural county in the U.S. in 2000, in the top tier is surprising. Low income levels in Montana are concentrated in counties with large American Indian populations. Many rural counties in the Great Plains also rank relatively low. Overall, Montana is characterized by large gaps among its 56 counties.

The maps of median household income are similar to those of per capita income, but with some interesting differences. Much of Utah, for example, ranks higher on the former map than on the latter, reflecting the state’s relatively large families. The oil-rich Permian Basin of west Texas stands out clearly on the national map. In Montana, Stillwater County in the south-center surprisingly falls in the top tier. The Stillwater and East Boulder platinum and palladium mines, with a workforce of almost 3,000, probably plays a major role here. These mining operations are also carried out in neighboring Sweetgrass County, which, as we shall see below, has the state’s lowest poverty rate.

County-level per capita GDP maps are often misleading, as a single facility can make a lightly populated county look wealthier than it actually it. Several high per-capita-GDP rural oddities on the national map reflect productive mining and energy-drilling operations. Eureka Country Nevada, prominent on this map, is the site of the Mt. Hope Molybdenum Project, which contains “one of the world’s biggest and highest-grade molybdenum deposits.” In Montana, Rosebud County, the site of major coal-mining and power-generation operations, stands out. Rosebud does not, however, rank high on per capita income map, mostly because income levels are low on the Northern Cheyenne Reservation, mostly located in the county. Surprisingly, many western Montana countries rank quite low on this map, which is partly a reflection of their large number of retirees.

 

The patterns seen on poverty-rate maps are familiar, requiring little explanation. Montana counties range from the top to the bottom tier. Those with high levels of poverty all have major Native American communities. I was surprised that the poverty rate in Yellowstone County is as low as it is, as Billings has a reputation elsewhere in Montana as a rough town with relatively high crime and poverty rates. Note that in nearby Sweetgrass County the poverty rate falls below five percent

 

 

Finally, maps of the GINI Coefficient give a sense of income disparities. On the maps posted here, red coloration indicates relatively small income gaps while purple indicates relatively large ones. (If the GINI coefficient is “0,” all residents have the same income; if it is “1,” one person earns everything.) In the United States as a whole, the highest GINI figures are found in some of the richest states (New York, Connecticut, California) and in some of the poorest (Louisiana, Mississippi), while the lowest figures are found in the Western states of Utah, Idaho, and Wyoming. The LDS (Mormon) Church runs extensive economic programs for its members, which help account low GINI figures in Utah and Idaho.  County-level maps do not convey these GINI patterns very well, however, mostly because the largest gaps tend to be found in major cities that disappear in the county matrix.

In overall-terms, Montana  falls near the middle on the GINI rankings.It is not surprising that Madison County in southwest Montana has a strikingly high GINI figure. This particularly scenic rural county has been attracting high-income earners for several decades, driving up housing prices and forcing many local people out of the market. Why Powder River County in the southeast would post such a high figure is a bit of a mystery. But with only 1,694 residents, it would not take many high earners to skew the number upwards. On the Wikipedia’s list of notable residents of Powder River, three of the four persons noted gained their fame in rodeo competitions, but it seems unlikely that this would be a major factor in its GINI ranking. Owners of oil and gas leases are probably more important. It is interesting that the sparsely settled counties of eastern Montana post both high and low GINI figures.

Economic Disparities in Montana (and the Rest of the United States) Read More »

Areas of Relatively High Human Development in Greater South Asia

Today’s post continues the GeoCurrents series on the Human Development Index (HDI), focusing initially on greater South Asia. Here we look at areas with relatively high HDI figures.

For decades, the region’s highest human development levels have been found in the far south and southwest, specifically in the Indian states of Kerala and Goa and in Sri Lanka. All invested heavily in health and education, reaping substantial rewards. For decades, Kerala was well ahead of the rest of India, especially in female literacy. This patterns partly reflects the region’s social structure, which has long been less male dominated than most of the rest of India.

Sri Lanka has also long outpaced other parts of South Asia, but its edge has been steadily slipping. In 1990, Sri Lanka posted an HDI figure of .629, substantially ahead of Goa’s .552 and Kerala’s .544, and well ahead of Tamil Nadu’s .471. By 2019, Sri Lanka has advanced to .782, but it was now behind Kerala’s .790 and just ahead of Goa’s .761. At the time, Tamil Nadu’s HDI figure had surged to .708. Give Sri Lanka’s current political crisis and economic meltdown, it would not be surprising to see Tamil Nadu and several other Indian states overtake it in the next few years.

 

 

In northern India, the adjacent states of Punjab and Haryana, along with the National Capital Territory of New Delhi, exhibit relatively high HDI numbers, and have done so for decades. These two states are at the center of India’s agricultural “green revolution,” and are the site of substantial agro-industrial economic growth. Although neither Punjab nor Haryana top of the list agricultural production by Indian state, their relative productivity becomes apparent when population is factored in (see the chart posted here). These two states were also the hub of the massive 2020-2021 farmer protest movement that roiled India and caused its government to backtrack on planned prom-market agricultural reforms.

 

 

The neighboring state of Himachal Pradesh also exhibits a relatively high HDI figure, but the factors behind its development are distinctive. Whereas Punjab and Haryana are lowland states with fertile soils, Himachal Pradesh is a land of rugged topography, located in the Himalayan mountains and foothills. Its developmental ascent, moreover, has been much more recent. Through the 1960s, its indicators remained relatively low. A World Bank report credits its transformation on effective and non-corrupt political leadership, female empowerment, and mass electrification based on hydropower. Intriguingly, the population of Himachal Pradesh is overwhelmingly rural, with the state posting one of India’s lowest urbanization rates. In general, both in India and the world at large, low urbanization correlates with low social and economic development. This seeming paradox has received relatively little attention, and as a result it will be the subject of a future GeoCurrents post.

 

 

Higher than average HDI figures are posted across greater southwestern India. This large region has been the site of most of India’s recent industrial and financial expansion. As the next map shows, it is also contains almost all of India’s major tech hubs.

 

 

 

 

 

A far different situation is found in India’s far northeastern periphery. This is another rugged area that was long noted for its relative isolation, poorly developed infrastructure, and numerous ethnic insurgencies. Yet its human development indicators are now well above the average for the country. Many of the so-called tribal peoples of this region converted from animism to Christianity under the influence of missionaries during the colonial period, and three of its states (Nagaland, Mizoram, and Meghalaya) now have solid Christian (mostly Protestant) majorities. Missionaries stressed education, resulting in mass literacy. Women also have a relatively high social position in these societies, which are more culturally related to those of Southeast Asia than they are to those of South Asia. Recent infrastructural initiatives by the Indian and local governments, especially in electrification and road construction, have significantly improved economic conditions.

 

In neighboring Burma, several rugged and so-called tribal regions with Christian majorities or large minorities also post higher than expected HDI figures.

Areas of Relatively High Human Development in Greater South Asia Read More »

GeoCurrents Summer Schedule

GeoCurrents returns to publication this week. New posts are planned for each weekday going forward. Two themes will command our attention for the remainder of this summer. One is a GeoCurrents atlas of global human development, which will entail original maps based on the UN’s Human Development Index. Today’s post gives an indication of what this atlas will look like. Posts on this topic will alternate irregularly with ones derived from a much larger project that I have been working on since GeoCurrents went into suspension in 2016. This project, called Seduced by the Map: How the Nation-State Model Prevents Us from Thinking Clearly About the World, will be explained and outlined in tomorrow’s post. Starting in late September, GeoCurrents will turn its attention to current global events. These posts will be done in conjunction with a Stanford Continuing Studies (adult education) class that I will be teaching remotely in the Fall Quarter called “The History and Geography of Current Global Events.”

The Human Development Index (HDI), created by the United Nations, is described by the Wikipedia as “a statistic composite index of life expectancy, education (mean years of schooling completed and expected years of schooling upon entering the education system), and per capita income indicator, which are used to rank countries into four tiers of human development.” The information used on most of the HDI maps that I will be posting is from 2019, most recent year in which comprehensive global data is available. The HDI, like other development indicators, is far from perfect and the data used to construct it are not always reliable. But it is the most referenced measurement of global human development, and it can be used to make maps at the subnational level across the world.

The maps that will posted ignore the UN’s four-tier scheme of “very high, high, medium, and low” social development, instead arraying countries into a larger number of categories. These maps also break down large countries into their first-order division (provinces, states, etc.) to convey regional variation more finely. Many of these maps are based on unconventional world regions, such as the South China Sea region and Greater Central Asia.

The two maps posted today, showing human development levels in the core part of North America, give an indication of how the atlas will look. The first map shows HDI levels in independent countries. The pattern seen here is simple: The United States and Canada are at the top, slotted into the same high-level category. In 2019, these two countries had almost identical HDI figures, with Canada coming in at .929 (15th highest in the world) and the United States coming in at .926 (16th highest). Mexico was rated significantly lower, at .779, but that figure still puts it in the UN’s “high social development” category. Northern Central America is shown to be significantly lower, and Haiti much lower still. Honduras posts the lowest figure in Central America, coming in at .634. This puts it in the U.N.’s “medium human development” category.

On this map, only Haiti, with a figure .510, is slotted in the “low human development” category. Elsewhere in the world, however, much lower figures are found. According to official statistics, three African countries, Niger, Central African Republic, and Chad, come in at below .40. Somalia probably has a significantly lower figure, but it is excluded from most tabulations for having unreliable or unavailable data. One Wikipedia article, however, places Somalia at only .361, giving an appalling low number of .232 for its Middle Juba region.

When the larger countries in this part of the world are broken down into their main political subdivisions, a somewhat different picture emerges. The United States is shown to be slightly more regionally differentiated than Canada, with three states (Minnesota, Massachusetts, and Connecticut) posting figures above .95. Only three independent countries, Norway, Ireland, and Switzerland, fall into this exceptionally high category. Several southern U.S. states post figures below .90, as do two Canadian provinces in the Atlantic maritime region (Newfoundland and New Brunswick). Neighboring Nova Scotia just misses this category, with a figure of .903.

This map also shows relatively wide levels of differentiation across Mexico, with much higher HDI figures found in the north and much lower one in the south. Although the U.S.-Mexico border is easily visible in this map, the Mexico-Central America border disappears. The heavily indigenous southern Mexican state of Chiapas, with an HDI figure of .698, falls into the same category as neighboring Guatemala (at .663).

GeoCurrents Summer Schedule Read More »

Customizable Maps of Brazil and Colombia, and Brazilian Social Development

Tocantins in Brazil mapGeoCurrents is continuing its initiative of providing free customizable maps. Today’s offerings are of Brazil and Colombia, based on their first-order administrative divisions (states in the case of Brazil; departments in that of Colombia). The Brazil maps also includes portions of neighboring countries, and one of its versions includes as well the map on which it was constructed, which allows one to “reveal” individual states, as demonstrated here for Tocantins. As in previous offerings, these maps are constructed with simple presentation software, and are available at the links at the bottom of this post in both PowerPoint and Keynote formats.

I have used the Brazil base-map to make several thematic maps of the country. The purpose here is mainly to demonstrate the significant socio-economic progress that Brazil has made since 1990. The current economic and political situation of the country is, of course, grim. As noted in a recent Forbes article, “According to the weekly Focus survey by Brazil’s Central Bank, 2016 will herald a depression-era contraction rate of 3.8% this year instead of the previous estimate of 3.5% made by dozens of Brazilian economists at the big banks.” The detention of former president Luiz Inácio Lula da Silva (“Lula”)—seen by many Brazilians as the main architect of their country’ recent social progress—underscores the current political crisis, although some observers think that his arrest indicates the strength of Brazilian democracy.

Brazil 1991 HDI MapBut regardless of what one thinks of either Lula or of Brazil’s current situation, the progress that it made in the last decade of the 20th century and the first decade of the 21st is difficult to deny. Consider, for the example, the composite Human Development Index (HDI), which takes into account education, longevity, and income. Here we see that the highest Brazil 2010 HDI Mapranking unit in 1991, the Federal District (Brasília), scored lower than the lowest ranking state (Alagoas) did in 2010 (0.616 vs. 0.631). Other countries have also seen major improvements in their HDI figures over this period, but few have made gains as large as those of Brazil. As the maps posted here show, the geographical patterns of Brazilian HDI did not change much during this period. The large interior state of Mato Grosso, noted for its soy boom, did register a particularly large jump, however.

Brazil Life Expectancy MapI also mapped more recent data for life expectancy and infant mortality, found on the Wikipedia page of the states of Brazil. In terms of life expectancy, the northeast no longer appears as the worst-off part of Brazil. Instead, the north as a whole occupies most of the lower categories. It is interesting to see that Mato Grosso does not show particularly well on this Brazil Infant Mortality Mapmap, which may indicate that its recent gains have been more in the economic than in the social sphere. Somewhat similar patterns are found on the map of infant mortality. Here I am surprised by the low showing of the state of Rio de Janeiro and by the very low showing of Bahia.

Brazil Customizable Maps  (Keynote)

Brazil Customizable Maps (powerpoint)

 

Customizable Maps of Brazil and Colombia, and Brazilian Social Development Read More »

Customizable Maps of Switzerland and Poland, and Swiss Per Capita GDP by Canton

Switzerland Per Capita GDP MapToday’s post continues the GeoCurrents initiative of distributing free customizable base maps made with easy-to-use presentation software (PowerPoint and Keynote [preferred]). The files found at the bottom of this post contain customizable base maps of Switzerland (by canton) and Poland (by voivodeships, or województwo). The customizable map of Switzerland also includes a few of the country’s largest cities. As the base map of Poland was used to make maps that illustrated a previous GeoCurrents post, it is not used for today’s thematic maps. Instead, the maps featured in the current post are based on the customizable map of Switzerland.

 

In making the per capita GDP map of Switzerland, I was somewhat surprised by disparities found among the country’s cantons. The high levels of economic production found in Geneva, the city of Basel, and Zurich were not unexpected—but that of Zug was. But as it turns out, the answer here is simple. As noted in the Wikipedia article on the canton:

The capital Zug is home to a large number of companies which only have their headquarters in the city. This is the case because Zug has one of the lowest taxes in Switzerland. Trade in particular is of great significance. There are a large number of small and middle sized businesses in all areas of the economy. There are over 24,300 registered companies and over 70,000 jobs in the canton, with 12,900 of the registered companies in the city of Zug.

 

Switzerland Foreign Nationals MapI was curious to see of there is a relationship between per capita GDP and the presence of foreign nationals in Switzerland, as migrants often head for the most economically productive areas. I was not, however, able to find high-quality data on the percentage of foreign nationals by Swiss cantons. But as all but one Wikipedia article on the cantons contain this information, albeit in outdated form, I was able to make a map. As one can see, Zug does have a high percentage of foreign nationals, although the figures for Geneva and the city of Basel are, not surprisingly, higher. Italian-speaking Ticino also has a relatively high percentage of foreign nationals.

 

Switzerland Population Density MapI was also a little surprised by the high level of per capita GDP in remote and lightly populated Grisons/Graubünden. High-end tourism seems to be the main reason. As noted in the Wikipedia article on the canton:

24 per cent of the workforce are employed in industry whereas 68 per cent work in the service industry where tourism reaches a remarkable 14 per cent of the GDP. Tourism is concentrated around the towns of Davos/Arosa, Flims and St. Moritz/Pontresina. There are, however, a great number of other tourist resorts in the canton, divided by the official tourist board for winter sports for example into categories “Top – Large – Small and beautiful” -yet still not including all of them.

Customizable Maps Switzerland, Poland (PowerPoint)

Customizable Maps Switzerland, Poland (Keynote)

 

Customizable Maps of Switzerland and Poland, and Swiss Per Capita GDP by Canton Read More »

Customizable Base Maps of Italy

 

Italy Regions MapThis post continues the current GeoCurrents initiative of distributing free customizable base maps, made in easy-to-use presentation software (Keynote and Powerpoint). These files can be downloaded by clicking at the links at the bottom of the post.

As can be see seen, today’s offering is of Italy, with the maps based on the regions of the country. In the links below, these customizable regional maps are offered with both English and Italian versions.

Italy Population Density Map

 

 

 

 

 

 

 

I have used this base-map to construct two thematic maps, one of population density and the other of per capita GDP. As can be seen, at the regional level there is no clear spatial pattern of population density in Italy, as both densely and relatively sparsely populated regions are found across the country. When it come to per capita GDP, on the other hand, Italy Per Capita GDP MapItaly extortion rate mapthe regional patterning is clear, as northern Italy is much more economically productive than southern Italy. I have also included a Wikipedia province-level map of extortion in Italy. As can be seen, the poorest regions of Italy are all plagued by high extortion rates.

 

 

 

 

 

Italy Autonomous Regions MapFinally, I have included as well a simple map showing Italy’s autonomous regions. The issue of regional autonomy in Italy, however, is rather complicated. As noted in the Wikipedia, “all the regions except Toscana [Tuscany] define themselves in various ways as an ‘autonomous Region’ in the first article of their Statutes,” yet “fifteen regions have ordinary statutes and five have special statutes, granting them extended autonomy.” As the same Wikipedia article goes on to note:

Article 116 of the Italian Constitution grants to five regions (namely Sardinia, Sicily, Trentino-Alto Adige/Südtirol, Aosta Valley and Friuli-Venezia Giulia) home rule, acknowledging their powers in relation to legislation, administration and finance. In return they have to finance the health-care system, the school system and most public infrastructures by themselves.

These regions became autonomous in order to take into account cultural differences and protect linguistic minorities. Moreover, the government wanted to prevent their secession from Italy after the Second World War.

Trentino-Alto Adige/Südtirol constitutes a special case. The region is nearly powerless, and the powers granted by the region’s statute are mostly exercised by the two autonomous provinces within the region, Trentino and South Tyrol. In this case, the regional institution plays a coordinating role.

Italy Base Maps (Keynote)

Italy Base Maps (PowerPoint)

Customizable Base Maps of Italy Read More »