Population Geography Archives

Maps and Graphs to Help Explain Italy’s Turn to Rightwing Populism

Article-Grid, Elections, Europe, Migrations / By Martin W. Lewis

Rightwing populist parties have gained support over much of Europe over the past decade. Italy, however, is the first western European country to see a rightwing coalition led by a populist party come to power. The success of Giorgia Meloni’s Brother of Italy is partly explicable on the basis of Italy’s extremely low fertility rate in combination with its highly negative attitudes toward immigration, as can be seen in the map and charts posted below. With few children being born and immigrants generally unwelcome and no longer staying in large numbers, Italy faces an impending financial/demographic crisis. Unless something changes, future retirees will no longer be easily supported. Meloni’s pro-natalist plans, which call for substantial subsidies for child-bearing couples, thus proved attractive to many voters. Widespread antipathy to immigrants also helps explain the appeal of Meloni’s majoritarian identity politics, focused on nationalistic sentiments.

Why the Italian population is so averse to immigrants is an open question. The country’s foreign-born population is not high by western European standards. It is significant, however, that Italy does not have a long history of receiving immigrants; for most of its time as a nation-state, it has been noted instead for sending out emigrants.

Italy’s economic malaise is another important factor in its swing to the right. In the late twentieth century, the Italian economy was in good shape. In the Il Sorpasso phenomenon of 1987, Italy’s GDP overcame that of the United Kingdom, making it the sixth largest economy in the world. Today Italy’s GDP stands at 2,058,330 (US$ million) whereas the UK stands at 3,376,000 (US$ million). Italy has experienced pronounced economic decline over the past dozen years, and most of its regions suffer from high unemployment. Considering as well Italy’s chaotic political system, it is perhaps not surprising that its voters have turned against their country’s political establishment. Such dissatisfaction also helps explain the recent rise of its left-populist Five Star Movement. But Five Star saw a massive decline in support in the 2022 election. Perhaps its suspicions about economic growth were a factor here.

Urbanization, Economic Productivity, and the Industrial Revolution

Article-Grid, Economic Geography, Energy, Population Geography / By Martin W. Lewis

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…

Hispanic Vs. Non-Hispanic White Life Expectancy in Texas

Article-Grid, Health, North America, Population Geography / By Martin W. Lewis

Life expectancy generally correlates with income, but other factors also play an important role. In the United States, non-Hispanic white households earn significantly more money than Hispanic households: $74,912 vs. $55,321 in 2020 (median household income). But Hispanics outlive non-Hispanic whites. The Wikipedia article on “Race and Health in the United States” notes that “as of 2020, Hispanics Life Expectancy was 78.8 years, followed by Non Hispanic Whites at 77.6 years and Non Hispanic blacks at 71.8 Years.” The table in the same article, however, puts the figures at 78.6 for non-Hispanic whites and 82.0 for Hispanics. It also lists Hispanics as outliving non-Hispanic whites in every state except New Mexico, where the gap was only one tenth of a year.

In Texas, Hispanics can be expected to outlive non-Hispanic whites by 2.8 years. The gap between the two groups, however, varies widely by county, as can be seen in the map posted here (derived from this data source). The patterns are clear and intriguing. In the most heavily Hispanic – and quite poor – counties of south Texas, non-Hispanic whites have the advantage. In east Texas counties with proportionally fewer Hispanics, Hispanics have a decided advantage.

Some of the data in this tabulation, however, must be questioned. In Lamar County, for example, Hispanics are listed as have a “100+” life expectancy, as opposed to a non-Hispanic-white figure of only 73.7 years. Lamar, a county of roughly 50,000 residents, is 8.8 percent Hispanic (4,412 persons in 2020). I have a difficult time believing that a population this large could really have a life expectancy of over 100 years. The same table also lists non-Hispanic whites in Starr County in far south Texas as having a life expectancy of over 100 years. Non-Hispanic whites only make up 1.78 percent of this county’s population. Intriguingly, Starr County’s non-Hispanic white population plummeted from 2,449 in 2010 to 1,171 in 2020. These patterns are difficult to explain and deserve further investigation.

Demographic Patterns in Montana (and the Rest of the United States)

Article-Grid, Montana Atlas, North America, Population Geography / By Martin W. Lewis

This penultimate post on county-level maps of Montana and the rest of the United States examines some basic demographic patterns. We begin with sex ratio, as measured by males per females in the population. The national map shows some clear patterns, but they are not always easy to interpret. Sex ratios are high (more males than females) in the interior West and the northern and western Midwest, and are low (more females than males) across much of the lower south, in most of New England, in most major metropolitan areas, and in many counties with large Native American communities. Some of these patterns can be explained by employment opportunities. It is no surprise, for example, to see male-biased populations in the Bakken oil lands of western North Dakota or in the Permian Basin of west Texas and southeastern New Mexico. If anything, I would have expected higher figures in the latter place. Most outdoor-amenity counties in the West also have high sex ratios.

The map of sex ratios in Montana is especially difficult to interpret. The Blackfeet nation in Glacier County has a very low ratio, but not so the Native American communities of Roosevelt County in northeastern Montana. Gallatin County has a high sex ratio, as might be expected in a booming community with large number of construction jobs, but equally booming Flathead County has a low sex ratio. By the same token, some languishing Great Plains countries have high sex ratios, others low.

On the national map of the population over the age of 65, high levels are seen in counties with large numbers of retirees (parts of Arizona and much of Florida) and in those with declining populations marked by the out-migration of the young. Low levels or elderly people are found in counties with high birth rates and low longevity figures, and in those that attract large numbers of workers. Western counties with many farm workers, such as those in California’s San Joaquin Valley, have low proportions of residents over the retirement age. In Montana, the richest county (Gallatin) has a relatively low number of elderly residents, as do the state’s poor Native American counties. Why Prairie County in the east would have such a large percentage of elderly residents is a mystery. In 2010, its median age was 53.6. If more than 60 percent of its population was really more than 65 years-of-age in 2017, as the map indicates, there must have been some major changes in the intervening period.

The national map of the population under age 18 is in large part a reflection of birth rates. Here the LDS (Mormon) region of Utah and eastern Idaho stand out, as do many areas with large Hispanic populations. In Montana, counties with Native American reservations have high percentages of residents below 18 years-of-age. Western counties that attract retirees or young adult job- and amenity-seekers have relatively few children.

The Geography of Health and Longevity in Montana (and the Rest of the U.S.)

Article-Grid, County Atlas of Montana, Health, Montana Atlas, Northern California / By Martin W. Lewis

Maps of health and longevity show many of the same patterns seen on earlier maps posted in this GeoCurrents sequence. In the United States as a whole, several county-clusters of relatively low life expectancy stand out. The most prominent is in eastern Kentucky, southern West Virginia, and southern Ohio, an area mostly inhabited by Euro-Americans. Several areas demographically dominated by African Americans also post low longevity figures, including the southern Mississippi Valley and a few of the large cities that are visible on this county-level map, such as Baltimore and Saint Louis. Almost all counties with large Native American populations also have relatively low figures. Counties with Hispanic majorities, in contrast, generally have average or high levels of longevity, as is clearly visible in southern Texas. Although life expectancy tends to correlate with income, the correlation collapses in many of these areas. Hidalgo County, Texas is over 91 percent Hispanic and has a per capita income of only $12,130, making it “one of the poorest counties in the United States,” but it ranks in the highest longevity category on this map. In Camp County, in northeast Texas, the average life expectancy of white residents is only 74 years, whereas that of Hispanics is 92 years. In most Texas countries, Hispanics outlive whites. Presumably, diet and activity levels are major factors.

Life expectancy varies significantly across Montana, with some counties falling in the highest category and others in the lowest. In Montana, low figures are found in counties with large Native American populations and in the former mining and smelting counties of Silver Bow and Deer Lodge. The relatively wealthier counties of south-central Montana post high longevity figures.

The map of heart-disease deaths in the United States shows some stark geographically patterns. Rates are highest in the south-central part of the country but are also elevated across most of the eastern Midwest. Heart disease death rates tend to be lower in major metropolitan counties as well as in rural countries across much of the West and western Midwest. Many of the patterns seen on this map are difficult to interpret. Why, for example, would heart-disease death rates be much lower in western North Carolina than in eastern Tennessee? (Perhaps the obesity map, posted below, offers a partial explanation, although only by begging the question.) And why would some counties that are demographically dominated by Native Americans have very high rates whereas others, especially those in New Mexico and Arizona, have very low rates? In Montana heart disease tends to be elevated in Native American counties – and in Silver Bow (Butte). South-central Montana has low rates.

On the U.S. cancer death-rate map of 2014, high rates are clearly evident in areas demographically dominated by poor white people (central Appalachia) and poor Black people (the inland delta of the Mississippi in western Mississippi and eastern Arkansas). Low rates tend to be found in the Rocky Mountains, south Florida, and parts of the northern Great Plains. No clear patterns are evident in Montana. Gallatin and Liberty counties fall in the lowest category found in the state, yet have almost nothing in common. The small population of Liberty County, however, makes comparison difficult.

Finally, the patterns seen on the U.S. obesity map are similar to those seen on the preceding maps. The low rates found in the Rocky Mountains, extending from northern New Mexico to northwest Montana, are notable. The northern Great Plains have a higher obesity rate than might be expected based on other health indicators. Some odd juxtapositions are found on this map, with several neighboring counties of similar demographic characteristics posting very different figures. Why, for example, would Pecos County in West Texas have such higher rates than its neighbors? In Montana, it is not surprising that Gallatin County, with its youthful, outdoor focused population, has the lowest obesity rate.

The Cost of Housing and Essential-Worker Relocation in Booming Southwestern Montana

Article-Grid, County Atlas of Montana, Migrations, Montana Atlas, North America, Population Geography / By Martin W. Lewis

Casual conversations in Bozeman, Montana often turn to housing. Some verge on the tragic. I have spoken with several young couples who spent years saving for a down payment and were finally on the verge of making an offer – and then Covid hit, remote work intensified, and prices soared. As can be seen on the map, housing prices are high in Gallatin County as a whole, but in central Bozeman they approach the level found in California’s Bay Area. As a result, the city faces a mounting labor crisis, particularly for essential workers such as nurses. According to a confidential source, in a single unit of approximately 30 nurses in Bozeman’s Deaconess Hospital, 12 nurses quit in the past 12 months, with 9 citing housing costs as their reason.

Nurses and other financially stressed residents are abandoning Bozeman for more affordable places. Several nurses have relocated to Spokane, Washington, where wages are higher and housing is cheaper. Most have opted for another Montana city. As can be seen in the paired maps, nurses make more money in Yellowstone County (Billings) than in Gallatin County, and the cost of living is much lower.* Trade-offs, of course, must be factored in. Yellowstone County has a much higher crime rate than Gallatin County, and both its natural and cultural amenities are less appealing.

For those who value natural amenities above all and want to remain in Montana, options are more limited. Most of the scenic valleys of western Montana also have high housing costs. As can be seen on the ArcGIS “housing affordability” map, made just before the Covid price surge, Madison County, with fewer than 10,000 residents, was even less affordable than Gallatin. In such a low-population county, it does not take many affluent amenity seekers to skew the market upwards. Housing is less costly, however, in a few western counties, such as Silver Bow (Butte), Deer Lodge (Anaconda), and Lewis & Clark (Helena).

Anecdotal evidence from casual conversations suggests that the favored destination of priced-out Bozeman residents is Helena. Surrounded by mountains, Helena is nestled in a scenic location, and as the state capital it has a descent array of cultural resources. More important, it is relatively affordable and its crime rate is relatively low.

Another popular relocation place is Anaconda in Deer Lodge County, a former copper-smelting city of some 9,000 people. After a long period of decline, Anaconda is again growing. As an extended headline in a recent article in the Montana Free Press (MTFP) reads, “Building on its past, Anaconda draws new residents seeking best of Montana: The long-struggling southwestern Montana town is gaining popularity with recreationists and homebuyers. Can it retain its historic character?” As the article notes:

Anaconda is the last best place of the last best places,” said Vanessa Romero, 39, who moved to Anaconda in 2017 from Boise, Idaho, and is opening a wine shop downtown.

This isn’t the typical Montana discovery tale. Movie stars aren’t buying sprawling ranches here. Tourists aren’t pouring into town, though they are coming at a steady trickle. The rich and famous aren’t flocking to the local ski area. Rather, Anaconda’s newcomers are often young couples and extended families who want a low-pressure lifestyle in a Montana community but have been priced out of other areas. The town is an example of a historically industrial community that is adapting to a recreation economy. …

We’re seeing the refugees of Missoula and Bozeman” and other rapidly growing towns in the West, said Adam Vauthier, executive director of Discover Anaconda, an economic development organization. “The other recreation towns in the vicinity just got so big.”

But will such cities as Anaconda and Helena retain their affordability? Footloose and often well-compensated Zoom workers also find them attractive. And prices are increasing. As the Montana Free Press(MTFP) article notes, “the Multiple Listing Service shows a median home price of $294,000 in Anaconda, up 36.7% over the same time in 2021.” Some local residents are also concerned about the city’s changing demographic characteristics. As one resident told MTFP reporter Erin Everett, “’We want to get rid of the buffalo touchers’ … referring to visitors who get too close to bison in Yellowstone National Park.”

*As the paired maps also show, Golden Valley County is an even more advantageous location for nurses, but with only 823 residents, employment opportunities are limited.

What Is a Zoom Town?

Article-Grid, Migrations, North America, Population Geography / By Martin W. Lewis

Bozeman, Montana is often described as a quintessential “Zoom town,” a city or small town that has experienced explosive growth owing to the relocation of remote workers since the beginning of the Covid pandemic. Bozeman is certainly booming, and many of its new residents do work remotely, usually through Zoom. But how widespread is this phenomenon, and where might other “Zoom Towns” be located?

Although many article have been written recently on Zoom towns, the term remains poorly defined, with most designations based on informal impressions. Maps showing Zoom town locations are all but non-existent. The only map that I was able to find comes from the Ownerly website, specializing in data for homeowners. This map was made to illustrate an article called “Zoom Towns USA: America’s Best Cities for Remote Workers.” Ownerly devised a metric to measure Zoom-town suitability by analyzing:

445 cities across the nation, looking at rent and housing prices, cost of living, safety data, level of broadband and free Wi-Fi coverage. Besides expanding our list of cities by nearly 50% from our 2021 Best Zoom Towns list, we also added new metrics that include cost and availability of childcare, restaurants and coworking spaces.

The resulting map is reproduced here. Ownerly’s “best” Zoomtowns are concentrted in the northeastern quadrant of the country, with Wisconsin and Pennsylvania leading the list. But ideal though they may be, few of the towns and cities on this map are commonly deemed “Zoom towns.” Consider, for example, a map showing Wikipedia’s “examples of Zoom towns and regions.” Intriguingly, none of these places makes the Ownerly list. Wikipedia’s Zoom towns are concentrated in the West, with California in the lead position.

Other sources have their own lists, often focused on a particular part of the country. Rate.com emphasizes towns in the Rocky Mountain region, calling special attention to Lewistown, Idaho; Walla Walla, Washington,; and Caspar Wyoming. Northwest News, focusing on the Northwest, focuses on Bend, Oregon while also mentioning Washington’s Methow Valley and San Juan Islands; Kelowna in British Columbia; Sandpoint, Idaho; and (yet again) Bozeman Montana. A BBC article on Zoom towns begins with a discussion of Fayetteville, Arkansas while also mentioning Sandpoint, Idaho; Moab, Utah; and Durango, Colorado. The article stresses the location of Zoom towns in “rural enclaves.”

Rural enclaves certainly do not dominate Wikipedia’s list of exemplary Zoom towns. Roughly half of the places on the Wikipedia list are suburbs with populations over 100,000. Such cities were growing quickly before Covid and are within the normal commuting range of their metropolitan cores. Placing such cities in the same category as Aspen, Colorado; Truckee, California; and Bethel, Maine might be a bit misleading.

A rigorous definition of Zoom town would emphasize population growth since 2020 and the percentage of workers working remotely. The latter piece of information is not easily obtainable. Using LinkedIn data, however, a 2021 Make It article claims that:

These small cities have the highest proportion of remote work applications:

1. Bend, Oregon: 41.8%
2. Asheville, North Carolina: 38.7%
3. Wilmington, Delaware: 35.9%
4. Johnson City, Tennessee: 35.2%
5. Eugene, Oregon: 34.9%

…

These larger cities have the highest proportion of remote work applications:

1. Cape Coral, Florida: 33.1%
2. Charleston, South Carolina: 31.6%
3. Tampa Bay, Florida: 29.6%
4. Jacksonville, Florida: 29.4%
5. Orlando, Florida: 29.2%

Based on the information contained in this GeoCurrents post, Bend, Oregon is a good candidate for the title of America’s quintessential Zoom town. Bend was booming, however, well before it was Zooming; its population surged from 20,000 in 1990 to 99,000 in 2020.

Montana Population Change in Comparative Context

Article-Grid, County Atlas of Montana, North America, Population Geography / By Martin W. Lewis

Recent posts have emphasized population decline in the Great Plains of eastern Montana. Comparative analysis shows, however, that eastern Montana has fared better than many other parts of the region. Almost every county in western Nebraska, for example, experienced population decline from 2010 to 2020. Yet Nebraska as a whole saw moderate population growth in the same period. Most of this growth was focused in the Omaha metropolitan area in the far eastern part of the state.

Although outside of the Great Plains region, Illinois also makes an interesting contrast. In that state, almost every rural county declined, many sharply. Illinois as a whole, along with West Virginia and Mississippi, lost population during this decade.

To the east of eastern Montana, the western Dakotas experienced pronounced population growth from 2010 to 2020. Growth in western North Dakota was almost entirely a result of the oil boom in the Bakken Formation. The Bakken also extends into far eastern Montana, but most of the oil extraction there took place before 2010. In western South Dakota, growth has been focused on the forested Black Hills region.

Montana’s Changing Population Geography

Article-Grid, County Atlas of Montana, Historical Geography, North America, Population Geography / By Martin W. Lewis

(Note: The next several posts will focus on the geography of Montana.)

As noted in previous posts, Montana is experiencing a population boom. Three decades ago, however, there was widespread worry about population stagnation and possible decline. Since becoming a state in 1889, Montana has experienced several demographic cycles, each marked by different geographical patterns. Geographer William Wyckoff has extensively documented these changes. In particular, I recommend his essay “Peopling the Last Best Place, 1870-1990,”* published by the Burton K. Wheeler Center at Montana State University at the time of lagging growth and concern about the state’s future. I will briefly summarize some of Wyckoff’s arguments before considering the more recent era of population growth.

The early cycles of boom and bust, Wyckoff argues, were linked to economies of natural resource development, governmental investments, and climatic fluctuations. Prior to 1890, the non-native population was heavily focused in the southwestern part of the state, particularly in a handful of mining districts. As Wyckoff’s map of settlement in 1870 shows, southwest Montana at the time was linked most closely to Utah. A decade before the turn of the century, however, the settlement of the Great Plains in the east accelerated, propelled by railroad development, a series of relatively wet years, and high wheat prices.

But as the map shows, Montana’s population in 1900 was still concentrated in the west, particularly in the four labeled counties. Far above the others was Silver Bow, containing the extraordinarily productive and profitable copper mines of Butte. Neighboring Deer Lodge, a copper-smelting hub, also stands out (its shuttered Anaconda Smelter Stack, at 585 feet, is the tallest surviving masonry structure in the world). Relatively large population clusters were also found in Lewis & Clark County, another mining center, and Cascade County, an emerging energy (hydropower) and transportation hub. Helena, in the former county, boasted of having the world’s highest concentration of millionaires (50 in a population of 12,000 in 1888). Cascade’s county seat of Great Falls exploded from 3,979 residents in 1890 to 14,930 in 1900. Gallatin County in southwest, with 9,533 residents, was the main agricultural region supporting the mining economy. As can be seen in Wyckoff’s second map, the Butte mining district had become the state’s demographic core by 1890.

In the first two decades of the twentieth century, agricultural expansion transformed the Great Plains, at least in areas accessible to the rapidly growing rail network. World War I was an especially promising time. As a result, many eastern Montana counties were subdivided. Ironically, the demographic cycle went into reverse just as new counties were proliferating. Drought struck in the late teens and returned periodically in the 1920s, a decade that did not roar in Montana; the state’s population dropped by 2.1 percent during this period. As Wyckoff notes, over half of Montana’s banks failed between 1920 and 1926.

The agricultural boom of the period from 1890 to 1920 resulted in a far more geographically balanced population structure for the state as a whole. As can be seen on the 1930 population map, the mining district of the southwest were no longer dominant. We also see the rise of Yellowstone County and its key city of Billings, an emerging agricultural, energy, and transportation hub. Billings would surpass Great Falls as Montana’s largest city around 1970, and today has almost twice Great Fall’s population.

Moderate population growth returned to Montana in the post WWII period, with the 1950s and 1970s posting strong gains. With the exceptions of Cascade and Yellowstone countries, however, the Great Plains continued to stagnate. Agricultural mechanization reduced the need for labor, and the region’s harsh climate and remote location discouraged settlement. As Wyckoff notes, population growth in the post-war period was propelled by the forestry industries in the northwest, energy in the greater Billings and Great Falls area, and the early emergence of natural-amenities development in some of the more scenic valleys in the mountainous west.

In the 1980s, Montana’s population growth rate fell to 1.6 percent. As Wyckoff writes, “More than two-thirds of Montana’s 56 counties lost population, and the state’s struggling economy since 1980 prompted over 50,000 residents to leave.” Stagnation in a time of national economic growth generated concern, leading Montana State University to commission the study in which Wyckoff’s essay appears. But the situation would soon turn around. Montana’s population grew by almost 13 percent in the 1990s and by almost 10 percent in each of the first two decades of the new century. Much of this growth was focused on amenity-rich areas in the west. Of particular note are the two college towns of Missoula and Bozeman, home, respectively, of the University of Montana and Montana State University. Missoula County grew by almost 22 percent in the 1990s, while Gallatin County, where Bozeman is located, posted a gain of more than 34 percent. As Montana State University increasingly emphasized science and engineering, Bozeman emerged as a regional tech hub, contributing to population gains of more than 30 percent in Gallatin County during both the 2000-2010 and 2010-2020 periods.

The 2020 map of population by county again reveals a stark demographic divide, especially when contrasted with the map of 1930. With the exceptions of Yellowstone (Billings) and Cascade (Great Falls) counties, the Great Plains remains very sparsely settled. Most of its counties had larger populations in 1920 than they do today, and several have fallen below 1,000 residents. Population is now concentrated in the west, but even in western Montana most countries have small populations and have experienced little growth. That situation is perhaps changing, however, as exorbitant real estate costs lead many people to seek housing outside of the region’s booming cities, especially Bozeman. Many other parts of western and central Montana have excellent natural amenities, and thus appeal to those who value outdoor experiences.

*In “Population Decline in Montana,” by Patrick C. Jobes, Craig Wilson, and William Wyckoff. Published by the Burton K,. Wheeler Center, Montana State University, Bozeman, Montana. April 30 and May 1, 1991

Recent Population Growth — and Decline — in Montana

Article-Grid, Elections, North America, Population Geography, Uncategorized / By Martin W. Lewis

(Note: As I am spending the summer in Montana to be with my granddaughters and their parents, a number of forthcoming post will focus on the state.)

As can be seen in the map on the left, Montana is a booming state, posting the third largest rate of population growth (in percentage terms) in the United States from 2020 to 2021. In the 2022 election, Montana will gain a second seat in the U.S. House of Representatives, which it had lost in 1993 due to lagging population growth at the time. Montana is still a sparsely settled state, with the third lowest population density in the country (after Alaska and Wyoming). Its population surge, moreover, is relatively recent. As can be seen in the second map, from 2010 to 2020 it experienced a moderate rate of growth (9.6%), higher than the national average (7.4%), but well below those of neighboring Idaho (17.3%) and North Dakota (15.8%), as well as nearby Utah (18.4%).

As might be expected, Montana’s recent population expansion is very unevenly distributed. Whereas some counties are experiencing sizzling grown, others continue to decline. Growth is concentrated in the mountainous west, whereas decline is pronounced across much of the Great Plains in the east. But as can be seen in the map on the left, two far-eastern counties (Richland and Carter) experienced rapid growth from 2010 to 2020, defying the regional norm. This map, however, is somewhat misleading; as Richland and Carter are sparsely settled countries, small increases in absolute numbers translate into rapid proportional growth. A gain of a mere 255 residents in Carter resulted in a 22% growth rate over the decade. Previously, the tendency had been one of steady decline. In 1920 the county had 3,972 residents, dropping to 1,415 by 2020.

The growth in Richland County from 2010 to 2020 is easily explained; adjacent to the booming oilfields of eastern North Dakota, it became something of a bedroom community for housing-short Williston, ND. But as the oil boom has receded, so too has Richland County. As can be seen in some of the maps posted below, the county lost population from July 2020 to July 2021. Explaining the growth in Carter, one of the most remote counties in the lower 48 states, is more challenging. A recent article in the excellent Montana Free Press, however, is quite helpful. A newly paved road improved access to eastern South Dakota, another area of recent population expansion (due in part to the natural amenities of the Back Hills region). The article also cites a healthy county budget, owing in part to transit fees from energy pipelines, and, unlikely as it might seem, dinosaur tourism. Ekalaka, Carter’s county seat, is evidently a high point on Montana’s so-called Dinosaur Trail.

To help readers make sense of changing population patterns in Montana, I have made several versions of my population-change-by-county maps (posted below). As can be seen in one of these iterations, growth has been concentrated in and around Montana’s largest cities, although Great Falls has lagged behind. Later posts will explore some of these patterns in more detail.

The most recent census data, covering the period from July 2020 to July 2021, shows a continuation of most of the trends seen in the 2010-2020 period. Although most Montana countries grew sharply during this time of COVID, the northern Great Plains continued in its seemingly inexorable decline. All of Montana’s larger cities, except Great Fall and Butte, saw rapid growth. So did Ravalli County in the scenic Bitterroot Valley, a zone of high rural population density (by Montana standards). Also of note is the growth rate of Flathead County in the northwest surpassing that of Gallatin County (which includes Bozeman) in the southwest. This somewhat surprising; as an expanded headline in the Montana Free Press notes, “Montana’s fastest-growing city last year? It wasn’t Bozeman. New Census Bureau estimates chart Montana’s population shifts during the first full year of the COVID-19 pandemic. Kalispell led the pack.” This article, by Eric Dietrich, is well worth reading, especially for Dietrich’s superb cartography. (Note that there are a few discrepancies between his map and mine; although I have re-checked the data, his figures may be more accurate.)

The competition between Bozeman and Kalispell is interesting. Intriguingly, Kalispell is Montana’s only significant city with no “blue” (Democratic voting) precincts, although the nearby tourism-oriented town of Whitefish is a different matter. Bozeman, in contrast, is markedly blue, as is Gallatin County as a whole. Gallatin’s smaller towns and most of its rural areas, however, are decidedly red. I have posted details from one of Eric Dietrich’s excellent Montana electoral maps, also published in the Montana Free Press. Later posts will explore Montana’s changing electoral geography in more detail.

Are Small Towns Really Urban Places? Eastern Montana According to the U.S. Census Bureau

Article-Grid, North America, Population Geography / By Martin W. Lewis

When teaching population geography, I always discuss urbanization rates. Perceptive students sometime ask about the official statistics that I use, wanting to know how “urban” is defined. When I tell them that that U.S. Census Bureau classifies all settlements with a population of 2,500 or more as urban, they look surprised and sometimes question the designation. That threshold, selected in 1910, seems far too low, not fitting the current concept of urban settlement.

Eastern Montana is a good place to examine the mismatch between the official designation and the popular view of what constitutes an urban place. As can be seen on the map posted here, the eastern quarter of the state is one of the most sparsely populated parts of the contiguous United States, with most census tracks having fewer than one person per square mile. A Purdue University study gave most of the counties in the region a high “relative rurality score.” A more recent U.S. Census report classifies most counties in eastern Montana as “100 per cent rural.” Yet the same report also placed four of the region’s counties in the “mostly urban” category. It did so because most of their residents live in the county seats, small towns that exceed the 2,500 urban threshold.

One of these counties is Dawson, a sparsely settled place with only 3.4 people per square mile (1.4/km²) and fewer than ten thousand total residents. But more half of them live in Glendive (population 4,873) and adjacent West Glendive (population 1,998). This distributional pattern makes Dawson a “mostly urban” county, at least according to the Census Bureau.

It is probably safe to say that most observers would reject the idea that the small and declining town of Glendive constitutes an “urban cluster,” as it is designated by the Census Bureau. But that does not mean that it is rural place. Intermediate terms are needed. Fortunately, several exist, including “suburb,” “exurb,” and “small town.” Glendive clearly fits the latter category. As a typical small American town, it shows far greater cultural affinity with rural areas than it does with urban ones. It does not, for example, exhibit anything like an urban voting pattern. In 2020, Glendive gave roughly 70 percent of its votes to Donald Trump. But as can be seen in the map posted here, a minor urban effect is visible: rural areas of Dawson County gave Trump more than 80 percent of their votes.

In 1910, when the urbanization rate in the United States stood at only 45.6 percent (with Montana recording 35.5 percent), it made sense to classify small towns as urban places. It no longer does. Depicting Dawson County now as “mostly urban” is misleading, based on an antiquated classification system.

Total Fertility Rates by Country, 1950 and 2015

Article-Grid, Population Geography, Sub-Saharan Africa / By Martin W. Lewis

(Note to readers: The distribution of free customizable base-maps will recommence later this week)

I was recently asked to make a world map of the Total Fertility Rate (TFR) using World Bank data for the period 2010-2015. The same data sheet includes TFR figures for each five-year period starting in 1950. The contrast between fertility rates in 1950 and 2015 is so striking that I could not resist the temptation to make contrasting maps.

As can be seen, the overall drop in the human fertility rate has been pronounced. In countries ranging from China, to Iran, to Libya, to Brazil it has been nothing short of extraordinary.

Tropical Africa, and especially the western portion of the region (“Middle Africa,” according to the World Bank), forms the major exception to this pattern. Here, several counties had higher TFR figures in 2010-2015 than they did in 1950-1955. In DR Congo, for example, the respective numbers are 6.15 and 5.98. Gabon also ended up in a higher category in the 2010-2015 map than on the 1950-1955 map, although the actual difference between its TFR in these two periods is not statistically significant: 4.00 and 3.99. As the graph shows, Gabon’s TFR did not stay the same during this period, but rather rose gradually and then began to decline gradually. Similar graphs can be found for other countries in “Middle Africa.”

It is quite significant that extremely high fertility figures are now mostly confined to tropical Africa, with only a few exceptions (such as Afghanistan and East Timor). It also seem to me that this phenomenon has been under-reported. Certainly most of my own students come into class with the impression that high fertility rates characterize most of the word’s less-developed countries.

Population History, Population Density, and Cultural Values in the Philippines

Article-Grid, Cultural Geography, Population Geography, Southeast Asia / By Martin W. Lewis

With 103 million inhabitants, the Philippines now ranks 12^th on the list of countries by population. Owing to its moderately elevated birthrate, its population is still expanding at a brisk rate, outpacing those of its Southeast Asian neighbors. Steady population growth has been a feature of the country for some time. In 1903, the archipelago held fewer than eight million people, by 1940 its population had doubled, and by 1990 it had reached 60 million.

The distribution of the inhabitants of the Philippine has historically been decidedly uneven. Some areas have been thickly settled for centuries, but vast expanses long remained open. As can be seen in the map posted to the left, in 1939 relatively few people lived in Mindoro, Palawan, northern Luzon (with the exception of the narrow Ilocos Coast of the northwest), and Mindanao (with the exception of the north-central coast). Central Luzon and the central Visayas islands (Cebu, especially) were densely populated. These same patterns are reflected in the map of religion from the late Spanish period. As can be seen, in the late 1800s most of northern Luzon, Mindoro, Palawan, and even Mindanao were inhabited primarily by animist tribal populations, most of which were small in numbers (although not all, see below).

Some of the patterns seen on the 1939 Philippine population map were of long-standing, but others had emerged more recently. The island of Mindoro, for example, had been a lightly settled land since the early Spanish period, although prior to that it had been an important trading hub; evidently, struggles between the Spaniards and the Muslims of the south resulted in partial depopulation followed by the spread of malaria. The Central Plain of Luzon, now the agricultural heartland of the country, tells a different tale. Up to the mid and late 1800s, its central region was still heavily forested. As Marshal McLennan’s maps show, only its southern and northern margins were settled in 1837 by farming communities, those of the Kapampangan and Pangasinan peoples respectively. Starting at about that time, however, a major stream of migrants from the densely populated Ilocos Coast began to flow southward into the Central Plain. Somewhat later, Tagalog-speaking people began to move north into the same belt of fertile lowlands. By the mid-20^th century, the formerly peripheral central zone of the Central Plain had been added to the core region of the Philippines.

The Central Plain of Luzon was by no means the only region of the Philippines to see large-scale migrations in the early 1900s. Ilocanos from northwestern Luzon were also moving into the Cagayan Valley of northeastern Luzon and other places as well, including Hawaii and California (the vast majority of Filipino migrants to the United States in the first half of the 20^th century were from the Ilocos region). Other densely settled areas, such Cebu, Bohol, and southeastern Panay in the Visayas Island, also sent out large numbers of migrants, most of whom settled in Mindanao. A seemingly odd feature on Spencer and Wernstedt’s map of internal Philippine migration from 1948 to 1960 is the high level of emigration from Samar in the eastern Visayas, as Samar has never been a densely populated island. But it was—and is—a very poor place, another factor that often encourages people to seek better opportunities elsewhere.

Owing to this long history of internal migration, the population distribution of the Philippines has become somewhat more even. Mindanao is no longer a frontier zone, and is now moderately populated by Philippine standards. To be sure, Palawan, Mindoro, and much of the Cordillera of northern Luzon still have relatively few inhabitants, but they also lack extensive lowlands that attract agricultural settlers. Central Luzon, on the other hand, has intensified its position as the demographic core of the country, a process closely associated with the explosive growth metro Manila, which now counts a population of almost 12 million. Today, the focus of Filipino migration is foreign rather than internal. An estimated 10.2 million persons born in the Philippines currently live abroad, representing around 10 percent of the country’s population.

On the population density map of 2010 (above), the Ilocos Coast of northwestern Luzon appears as a zone of low to moderate population density. This standing seems surprising, as this area was noted for its dense settlement dating back to the time of the Spanish conquest in the late 1500s. The mapping here is a little bit deceiving, however, as the three provinces of the region all include rugged highlands as well as the narrow coastal plain. But it is true that the Ilocos Coast is no longer very demographically distinctive. Many Ilocanos, as we have seen, settled elsewhere, and in their homeland their fertility rate dropped, doing so sooner than almost all other areas of the country. As a result, the Ilocos region has seen relatively little population growth in recent decades.

In the Philippines, Ilocanos have a reputation for being hard working, thrifty, and somewhat stingy. As a result of this stereotype, they have been occasionally deemed the “Yankees of the Philippines.” To the extent that this common view is is accurate, the industriousness and frugality of the Ilocanos can be partially explained by the powerful theory of the Danish economist Ester Bosurup (1910-1999). In her classic anti-Malthusian work, The Conditions of Agricultural Growth: The Economics of Agrarian Change under Population Pressure, Boserup argued that population pressure, which the Ilocanos long faced, forces agricultural communities to engage in ever more frequent and intensive cropping cycles. As this process generates diminishing returns over time, people living under such conditions are forced to develop a strong work ethic. Although Boserup framed her theory in cultural terms, a number of geneticists now claim that there is a substantial genetic component to this process as well.* This notion is, of course, extraordinarily controversial, and I remain unconvinced although open-minded. But I am convinced that Boserup figured out one of the most important dynamics of human history, and I also think that she should occupy a much more prominent position in intellectual history than she does.

In 2012, The Inquirer, a major Philippine newspaper, reported on a modest test of the supposed frugality of the Ilocanos based on an examination of savings rates by region in the Philippines. As the report concluded:

Finally, Ilocanos have statistical proof to show that their detractors are wrong in calling them the most tightfisted Filipinos. A study by the National Statistical Coordination Board (NSCB) shows that Ilocanos now rank only sixth among the prolific savers in the country’s 17 regions.

Intriguingly, the same study found that the Cagayan region of northeastern Luzon—populated almost entirely by Ilocanos—ranks as the second thriftiest region of the Philippines. The most savings-oriented Filipinos by a wide margin, however, are the highlanders of the Cordillera of Northern Luzon. The Inquirer article notes that most people of this region also speak Ilocano, but in actuality they do so as their second (or third, or fourth) language, and they certainly do not belong to the Ilocano ethnic group. But we can again turn to Boserup to gain insight on this finding. The ancestors of most Cordilleran groups migrated out of the lowlands into the highlands in order to escape Spanish taxes and to continue to worship their ancestors and practice their indigenous religion. There they had to undertake herculean labors to wrest a living from the rugged landscape, constructing some of the world’s most impressive agricultural terraces in the process. The Cordilleran people, in general, had to develop a strong work ethic to survive under such circumstances.

Owing in part to the highlanders’ predilection for intensive labor, and in part to their own prejudice against Hispanicized cultures, American administrators in the Philippines in the early 1900s often favored the mountaineers over the more civilized Filipino lowlanders. This attitude is explored by the historian Frank Jenista in his 1987 book White Apos: American Governors on the Cordillera Central. As Jenista frames the issue in his preface:

The purpose of this study is to examine the Philippine experience, the fascinating but hitherto untold story of the interaction between the American colonial authorities and the independent headhunting, terrace-building people of Luzon’s Gran Cordillera Central…. It focuses primarily on the province of Ifugao, for the American presence there was longest and best remembered. Despite the great cultural differences between the turn-of-the-century Ifugaos and Americans, the oral accounts which make up this story portray for us an intriguing relationship—a mutually satisfactory symbiosis due in large measure to an unexpected congruence of important cultural values…

*For an overview, see Nicholas Wade, A Troublesome Inheritance: Genes, Race, and Human History.

Mapping Crime and Substance Abuse in Russia

Article-Grid, Asya Pereltsvaig, Geography of Crime and Punishment, Guest Posts, Health, Russia, Ukraine, and Caucasus / By Asya Pereltsvaig

In the previous post, I examined regional differences in demographic issues across Russia. As many sources note, alcoholism is one of the biggest factors contributing to low life expectancy and high rate of death from non-natural causes. In fact, Russia ranks at the top in terms of both alcohol consumption (especially by men), as discussed in detail in my earlier post. Russia and the neighboring FSU countries also top the charts in percentage of deaths attributable to alcohol. According to the World Health Organization report, such a high level of alcohol-related deaths results not only from what and how much people drink (e.g. drinking spirits causes more alcoholism and alcohol-related deaths than drinking wine or beer), but also how they drink. The WHO report includes data on what the authors refer to as “Patterns of Drinking Score” (PDS), which is “based on an array of drinking attributes, which are weighted differentially in order to provide the PDS on a scale from 1 to 5”; the attributes include the usual quantity of alcohol consumed per occasion; festive drinking; proportion of drinking events, when drinkers get drunk; proportion of drinkers who drink daily or nearly daily; and drinking with meals and drinking in public places. Some countries with high per capita alcohol consumption have low PDS scores (particularly, countries in southern and western Europe), while Russia has one of the highest PDS scores worldwide.

A combination of what, how much, and how people drink—as well as who is doing the drinking—creates the observed patterns of alcoholism within Russia as well. (The data on alcoholism and drug abuse, discussed below, comes from the Wikipedia.) The prevalence of alcoholism differs from region to region even more than other social indicators considered in previous posts. The range between the highest and lowest alcoholism levels is one of three orders of magnitude, from less than one case reported per 100,000 population in Ingushetia to nearly 590 cases per 100,000 in Chukotka. Overall geographical patterns are quite clear: the highest levels of alcoholism are registered mostly across the Far North (and some of the regions of the Far East, which despite their relatively low latitude qualify legally as the “Far North”), while the lowest levels of alcoholism are found in the Caucasus. According to the alcoholizm.com website, alcohol consumption is also the lowest in the north Caucasus, especially in Chechnya, Ingushetia, and Dagestan, and the highest in the harshest areas of the Far North/Far East (particularly in the Jewish Autonomous oblast, Nenets Autonomous Okrug, Magadan, and Kamchatka). However, they do not list the specific figures of alcohol consumption in various regions and I could not find them elsewhere, but I suspect that the quantity of alcohol consumed per capita in the northeast Caucasus and in the Far North does not differ by three orders of magnitude. Much more significant is the type of alcoholic drink being consumed: vodka is the most common choice in the Far North, whereas the Caucasus region has a long history of viniculture going back 8,000 years. Besides viniculture, the Caucasus is well-known for its venerable tradition of wine-drinking with meals, accompanied by elaborate toasts, which slows down the consumption and metabolism of alcohol. Another important factor is genetically based difficulty that people from many indigenous groups have in breaking down alcohol. This genetic propensity causes particularly high levels of alcoholism in such areas of northern Russia as Nenets Autonomous Okrug, Sakha Republic, and Chukotka.

Unlike alcoholism, drug abuse is considerably less common than alcoholism across the Russian Federation: all but four federal subjects register over 50 cases of alcoholism per 100,000, whereas none reports more than 50 cases of drug abuse per 100,000, and fewer than a dozen register over 30 cases. Geographical patterns of drug abuse are less obvious and harder to explain than those of alcoholism. Particularly high levels of drug abuse (over 30 cases per 100,000) are found in Murmansk, Chelyabinsk, Sverdlovsk, Kurgan, Novosibirsk, Kemero, Amur, and Sakhalin oblasts, and in Primorsky Krai. One common denominator is that all of these regions had a high level of industrial urban development during the Soviet period but have experienced significant economic stagnation since the fall of the USSR. One area that does not fit this generalization is Sakhalin, which is still a major area of natural resource extraction and processing. It would be interesting to know if any of our readers can think of another explanation for these patterns of drug abuse.

Interestingly, neither alcoholism nor drug abuse correlate very closely with crime rates, although there is a connection. Of the areas where drug abuse is most prevalent, only Sakhalin and Kemerovo oblasts also exhibit high crime rates (over 2500 cases per 100,000), while several others—Murmansk, Sverdlovsk, and Novosibirsk—have an average level of crime. Alcoholism correlates with crime even less, as only Magadan and Sakhalin oblasts exhibits high levels of both. A number of regions with a high level of alcoholism, particularly Nenets Autonomous Okrug and Sakha Republic, which have a large percentage of indigenous population, exhibit little crime. Other areas with high proportions of indigenous peoples, such as the Caucasus and the Middle Volga, also have particularly low levels of crime. The low level of reported crime in Tula, Ryazan, and Belgorod oblasts is perplexing, however.

When it comes to murder, eastern and especially southern Siberia is clearly more dangerous than European Russia or western Siberia. The “murder capital” of Russia is Tuva, which registered over 35 homicides per 100,000 population. Neighboring Altai Republic has over 25 murders per 100,000 population, as does Zabaikalsky Krai. Curiously, high rates of alcoholism may contribute to the elevated murder rate in such areas as Sakha Republic, Chukotka, and Jewish Autonomous oblast, but other areas where alcoholism is prevalent, such as Karelia and Sakhalin, register only slightly above-average murder rates. Similarly, the prevalence of drug abuse does not correlate with murder rates. Nor do crime rates or murder rates correlate with the level of urbanization, unemployment level, or regional GDP.

Mapping Russia’s Demographic Problems

Article-Grid, Asya Pereltsvaig, Guest Posts, Population Geography, Russia, Ukraine, and Caucasus / By Asya Pereltsvaig

[Note to readers: customizable maps of Russia are now available in Russian here.]

Much has been written about Russia’s demographic problems, particularly in the 1990s and 2000s. The country as a whole is characterized by low birth rates and high abortion rates; high death rates, especially from non-natural causes; rather low life expectancy, especially for men; and skewed sex ratios. This post examines some of these issues, focusing on regional differences across Russia. The GeoCurrents maps presented below are based primarily on data from the Federal State Statistics Service; some of the indicators, such as the percentage of working age adults and of pensioners as well as sex ratios, have been calculated directly from the FSSS data. Additional data comes from the “Children in Russia” publication by the FSSS, available (in Russian) here.

As maps of Russia’s birth rates, death rates, TFR, and natural population growth by federal subject can be found in Wikipedia, we begin by mapping life expectancy (at birth). According to data from the World Bank, the life expectancy of an average Russian male is a whopping 10 years shorter than that of an average Russian female: the figure for men is 66 years (the same as in Kazakhstan, Iraq, and North Korea), while that for women is 76 years (the same as in Iran, Honduras, and Tonga). But as the FSSS data mapped on the left reveals, there are significant differences in life expectancy among Russia’s federal subjects. For example, life expectancy for an average Ingush woman is almost 15 years longer than that of her Chukotkan counterpart (81.32 years vs. 66.42 years). The contrast is even more striking with respect to men: life expectancy for an average Ingush male it is almost 20 years longer than for his Tuvan counterpart (75.97 years and 56.37 years, respectively). Overall, the highest life expectancy, for both genders, is found in the two federal cities (Moscow and Saint Petersburg) and in the northeastern and north-central Caucasus. For both genders, Ingushetia has the highest life expectancy figures, while Dagestan is in the top four (72.31 years for men, 78.82 years for women). North Ossetia ranks 3rd in life expectancy for women (79.06 years), with above-average life expectancy for men (68.46 years). Curiously, Chechnya ranks 4th in male life expectancy (70.23), the ongoing insurgency notwithstanding, while life expectancy for Chechen women is close to average. Neighboring regions of southern Russia also post fairly high life expectancy figures. Moscow City ranks 2nd and Saint Petersburg 5th in life expectancy, for both men and women. As with many other standard-of-living indicators, some of which are discussed in the previous post, the oblasts surrounding the two federal cities present a sharp contrast to the cities themselves: both Moscow and Leningrad oblast post average figures for female life expectancy and below-average figures for male life expectancy.

Outside the Caucasus and the federal cities, life expectancy is shorter, with most regions in Siberia posting lower figures than those of European Russia. There are a few exceptions, however, including higher-than-average figures for both genders in Belgorod oblast in south-central Russia, Tatarstan in the Middle-Volga region, and Khanty-Mansiysk, and higher-than-average figure for men in Yamalo-Nenets Autonomous Okrug. For women, life expectancy is higher than average in a number of regions in south-central Russia (Belgorod, Voronezh, and Tambov oblasts) and Middle-Volga region (Chuvashia and Penza oblast). At the bottom of the ranking, one finds Tuva and Chukotka (and for men, the Jewish Autonomous Oblast): life expectancy in these regions is almost a decade shorter than the country’s average.

A large gap in life expectancy for men and women helps explain the skewed sex ratios. As can be seen from the map of the 2013 FSSS data, most Russian regions have more women than men, with many regions having fewer than 85 males per 100 females (shown in the two darkest shades of red). There are only three regions where there are more men than women (shown in blue): Yamalo-Nenets Autonomous Okrug, Chukotka, and Kamchatka.

The map above can be instructively compared with an earlier map, based on the 2002 census data. The two maps are constructed so that the data is binned in the same way, although the lowest (most female-dominated) category of the older map has been broken down into two categories in the newer map. A comparison of the two maps reveals that the situation did not improve in the decade or so separating the two maps; on the contrary, many regions became even more skewed in the female direction in this period. A good example comes from what is today Krasnoyarsk Krai. In 2002, Krasnoyarsk region consisted of three administrative units: Evenk autonomous district with a ratio of 1.007 (i.e. more men than women), Taimyr (Dolgano-Nenets) autonomous district with a ratio of 0.948, and Krasnoyarsk territory with a ratio of 0.889. In 2013, the entire Krasnoyarsk Krai, amalgamated from these three regions, had a ratio of 0.875, which was more female-biased than the ratio in any of its constituent parts in 2002. Similarly, in most regions in Siberia and the Urals (i.e. Zabaikalsky, Primorsky, Khabarovsk, and Altai Krais; Amur, Irkutsk, Kemerovo, Tyumen, Sverdlovsk, Kurgan, and Chelyabinsk oblasts, Jewish Autonomous Oblast, Khakassia and Sakhalin), sex ratios also became more skewed towards women. The sex ratio of Murmansk oblast followed the Siberian trend in becoming more skewed towards women. Movement towards a more balanced ratio have been registered only in a small number of regions: Novosibirsk (from 0.866 to 0.872), Ulyanovsk (from 0.856 to 0.878), Belgorod (from 0.848 to 0.854), Tambov (from 0.844 to 0.859), Kaluga (from 0.841 to 0.859), and Moscow oblasts (from 0.850 to 0.858), Chuvashia (from 0.863 to 0.872), and Mari El (from 0.869 to 0.872). In the north Caucasus and southern Russia, the overall tendency has also been towards more skewed sex ratios, the most striking case being that of Ingushetia, where the sex ratio dropped from 0.876 to 0.819, the fourth lowest in Russia. The lowest sex ratio is found in Yaroslavl oblast, whereas Ivanovo oblast—whose capital has been known unofficially as “the city of brides”—ranks second from the bottom.

Regional differences in the age structure of the population are also significant—and help explain the demographic and economic situations in the various regions. Let’s begin by examining the youngest segment of the population, children under age 17 (the data comes from the “Children in Russia” publication by the FSSS). Clear geographical patterns can be seen on the map on the left: children constitute a larger proportion of the population (25% or more) in eastern and southern Siberia (but generally not the Russian Far East), northeastern Caucasus, and Nenets Autonomous Okrug. A comparison with the second map posted on the left shows that areas with a higher proportion of children are generally those also with a higher proportion of indigenous peoples (i.e. a lower proportion of ethnic Russians). This correlation is confirmed by the figures listed in the Wikipedia article on the demographics of Russia: ethnic Russians have the country’s second-lowest fertility rate. (Russia’s Jews have the lowest fertility figure of all ethnic groups.) As can be expected from the high birth rates in these three regions, Chechnya, Ingushetia, and Tuva rank at the top in the percentage of children, with about a third or more of their populations under the age of 17. In contrast, in most of European Russia (and a few regions in Siberia) children constitute less than 20% of the population, and in most oblasts in central Russia the figure is below 17%. Unsurprisingly, the lowest percentage of children is found in the two federal cities: 14.3% in Moscow and 14.4% in Saint Petersburg.

At the other end of the age spectrum are the pensioners, a group that is now raising a lot of concerns. Despite Russia’s relatively low life expectancy (especially for men, as discussed above), the percentage of pensioners has been growing for some time. According to a paper by Julie DaVanzo and David Adamson “Russia’s Demographic “Crisis”: How Real Is It?” (published in 1997), “between 1959 and 1990, the number of persons aged 60 and over doubled… [by] the beginning of the 1990s, reach[ing] 16 percent”. Since then, the trend has continued, as noted by the Ministry of Labor and Social Protection. Russia currently has one of the lowest retirement age thresholds: according to the Pension Fund website, “men older than 60 and women older than 55 qualify for [the old age] pension” (people living in the Far North and other harsh regions have an even lower retirement age threshold). However, recently there has been much discussion about the possibility of raising the retirement age to 65, possibly as early as in 2016. One of the main aims of this proposal is to alleviate the shortages of pension funds that resulted from Russia’s current economic woes. These problems may also lead to a potential decrease in pensions paid to current retirees, as reported by Gazeta.ru.

As with other demographic indicators, the percentage of pensioners differs widely from region to region. Only three regions—all of them in the north Caucasus—had fewer than 22% retirees in 2013, and half a dozen others (including Moscow City, the two autonomous okrugs in Western Siberia and three additional regions in the north Caucasus) posted figures below 25%. Conversely, the highest percentages of pensioners are found in northern European Russia (Republic of Karelia, Arkhangelsk oblast, and Komi Republic), four oblasts south of Moscow (Bryansk, Oryol, Tula, and Ryazan), as well as in Kurgan oblast and on Sakhalin. (Curiously, a lower percentage of pensioners does not correlate closely with high GDP: for example, Yamalo-Nenets and Khanty-Mansiysk Autonomous Okrugs have relatively few pensioners, whereas Sakhalin ranks 7th highest in the percentage of pensioners.) The two federal cities, especially Moscow, have low percentages of pensioners.

To conclude our discussion of the age structure, let’s consider the map of the percentage of working age adults. As with the other indicators, regional differences are quite pronounced and are due to different factors. For example, Kurgan oblast has the lowest percentage of working age adults (55.3%), and Chechnya ranks 3rd lowest (with 56.3%), but the population structures in those two regions are quite different: Kurgan oblast has a high percentage of pensioners, whereas Chechnya ranks highest in the percentage of children. At the other end of the spectrum, one finds such regions of high GDP as the Yamalo-Nenets and Khanty-Mansiysk Autonomous Okrugs and Chukotka, as well as adjacent Kamchatka and Magadan oblast. However, despite similar percentages of working age adults, the overall population structure of these regions is distinctive: Chukotka, for instance has substantially more children and fewer pensioners than Magadan oblast. The latter region has experienced a significant depopulation trend, which affects the younger adult population more than the older people, resulting in a disproportionately aging population. Moreover, this trend feeds itself: even without taking into account the out-migration, an aging population results over time in lower birth rates.

The shortages of working age adults in regions such as Chechnya and Tuva are further exacerbated by high levels of unemployment, as can be seen from the map on the left: 26.9% and 19.3%, respectively. The highest unemployment figure, 43.7%, comes from Ingushetia. Elevated unemployment rates (over 10%) are found also in several other regions in the North Caucasus (Dagestan, Kalmykia, Karachay-Cherkessia) and southern Siberia (Altai Republic, Zabaikalsk Krai). Unsurprisingly, these regions also have the country’s lowest GDP figures. In the following (and last) post on the regional differences across Russia, we will examine unemployment patterns in the context of substance abuse and crime rates.

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