North America

The Importance of the County in U.S. Geography

Recent GeoCurrents posts have examined county-level socioeconomic data, both for the state of Montana and the United States as a whole. Whenever I look at U.S. counties I am reminded of my father’s response when I told him that I wanted to pursue a Ph.D. in geography: “What does that mean? You’re going to memorize all the county seats?” Meant as a good-natured joke, his quip was telling, as it reflected the widespread skepticism of academia in the United States, the low reputation of geography as a scholarly discipline, and my own childhood proclivity for memorizing geographical information. It was also a comment on the perceived triviality of the county as a geopolitical unit. But U.S. counties are, at least in most parts of the country, anything but trivial. As recent posts have shown, counties are essential units for tabulating and recording information. When one wants a fine-grained depiction of any social or economic indicator in the United States, county-level mapping is usually the place to go.


“County,” however, is not exactly the right word to denote the 3,243 basic subdivisions of the country’s 50 constituent states. Sticklers for precision insist on “county equivalent,” as Louisiana is divided into parishes and Alaska into boroughs and “census areas.”  Not all parts of the United States, moreover, belong to a county. Virginia has 38 “independent cities” that are primary administrative divisions of the state. St. Louis (Missouri), Baltimore (Maryland), and Carson City (Nevada) are similarly unattached to any county. More common is the “consolidated city county” in which governmental functions have been merged. As the Wikipedia explains, “A consolidated city-county differs from an independent city in that the city and county both nominally exist, although they have a consolidated government, whereas in an independent city, the county does not even nominally exist.”


New York City is uniquely composed of several counties, as each of its five boroughs is also constituted as a county. Confusingly, for three of them the name of the borough differs from that of the county: Brooklyn is Kings County; Staten Island is Richmond County; and Manhattan is New York County. New York County is thus merely one part of New York City, inverting the normal city-county relationship. Many U.S. cities, however, are divided between two counties. Alabama alone counts 45 of these municipal anomalies.

The role and significance of counties varies significantly across the country. Wikipedia categorizes U.S. counties into three types: minimal scope, moderate scope, and broad scope. In most of New England, county scope is minimal indeed. In Connecticut, Rhode Island, and parts of Massachusetts, countries are no more than geographical designations used to tabulate information. Moderate scope counties, which have several essential functions, are found over most of the Mid-Atlantic and Midwestern regions. Broad-scope counties are typical of the West and South. As the Wikipedia notes:

In western and southern states, more populated counties provide many facilities, such as airports, convention centers, museums, recreation centers, beaches, harbors, zoos, clinics, law libraries, and public housing. They provide services such as child and family services, elder services, mental health services, welfare services, veterans assistance services, animal control, probation supervision, historic preservation, food safety regulation, and environmental health services. They have many additional officials like public defenders, arts commissioners, human rights commissioners, and planning commissioners.


The “less populated” counties of the West and South may not provide all these services, but they are nonetheless very important. In rural areas with few or no incorporated cities, it is the county that provides local administration, with the county seat serving as the crucial center of governance. I mostly grew up in such a non-municipal administrative town (San Andreas in California’s Calaveras County) and can attest to the significance of its county-seat status.



U.S. counties vary greatly in population. Los Angeles County, with 10,014,009 residents counted in the 2020 census, would be the country’s 11th most populous state if it were to secede from California. Contrastingly, Loving County in Texas had only 64 residents in 2020. I have some difficulty understanding how such a lightly populated area manages to function as a county. Evidently, it does not do so easily. As Texas Monthly reported in 1997:




Loving County residents are always thinking about the next election. Thirty-seven percent of the work force is employed by the county, mainly in elected positions. Elections are often knock-down-drag-out fights that have less to do with the issues at hand than the proclivity to “get caught in echoes of the past,” as one longtime resident puts it: the tangled web of family rivalries, personal vendettas, and enduring grudges among locals. Wide-open spaces don’t necessarily breed open minds; Loving’s spiteful, tribal politics have occasionally threatened to destroy the détente between the county’s families. “Years ago, when we found boot prints under Mother’s window, we knew it wasn’t prurience—it was politics,” says justice of the peace McKinley Hopper. “Although nothing’s more prurient than politics.

“Voter turnout is always a hundred percent, sometimes more,” says Hopper, who, as the 75-year-old patriarch of the Hopper clan, has witnessed plenty of election shenanigans. “Oh, there have been some red-hot lawsuits over voting! Some outlandish things have happened that made people madder than hornets. Texas Rangers have been brought in to view the whole thing, votes have been counted in an El Paso courtroom. People would ‘move’ to Loving County the night before Election Day and set up their bedrolls in different precincts. When my brother was sheriff, he showed up to vote once without his poll tax receipt. When he went across the street to get it, they closed the polls an hour early so he couldn’t vote.”

U.S. counties also vary greatly in terms of area. San Bernardino County, California, covers 20,105 square miles (52,072 km2), which means that it is significantly larger than the Netherlands. Contrastingly, Kalawao County, Hawaii, covers a mere 11.88 square miles. Problems can be generated by large county territories. San Bernardino’s seat, the city of San Bernardino, is located in the southwestern corner of the sprawling county, presenting a major inconvenience for people living in the town of Needles, 212 miles to the east. San Bernardino County itself has major issues with the state of California, its leaders complaining that they receive inadequate state funding. A secession measure will be placed before the county’s voters this November. As The Guardian  reports:

We need our state legislators to look at the return they are supposed to deliver to the people they serve,” she said. “We are one of the fastest-growing regions and it’s time to pay attention to that … We don’t have beaches, we don’t have all the skyscrapers but what we have is a family. We are a family-oriented county.”

For those interested in the “trivialities” of county geography, Visual Capitalist has a remarkable animated map of U.S. county development going back to the early 1600s.

The Importance of the County in U.S. Geography Read More »

Voting Patterns and Population Density – and “State of Jefferson” Exception

As has often been noted, electoral patterns in the United States increasingly correlate with population density; in general, densely populated areas vote for candidates in the Democratic Party while sparsely settled areas vote for candidates in the Republican Party. As a result, major metropolitan areas generally exhibit a pattern of concentric rings, which turn from blue to red as one travels outward from the urban center.

Different cities, however, show major variations on this theme. Minneapolis-Saint Paul exemplifies the annular pattern, with a dark-blue core surrounded by rings of lightening blue that eventually turn pink. Dallas-Fort Worth is more complicated. Here one finds urban cores that are just as blue as those in Minneapolis-Saint Paul, but the urban peripheries and suburbs are more variegated, while the exurbs and enveloping rural areas are a darker shade of red. The greater Milwaukee area is notable for its steep gradient; as one leaves the city, blue yields to pink. Seattle, in contrast, has a gentle gradient, with light blue extending across and beyond the suburban fringe. (The dark red precinct west of Everett is misleading: it recorded all of three Trump votes – but no Biden votes.)


It is not just major cities where this density-dependent electoral dynamic plays out. As can be seen on the map of the 2020 presidential election in central Illinois and southeastern Iowa, all cities with more than 50,000 residents went blue, or at least had blue cores, whereas no town with fewer than 5,000 residents had any blue precincts. Pekin and Quincy were unusual in having more than 30,000 residents but no blue precincts in 2020. Yet even here, the town centers were light pink rather than red.

Several parts of the United States do not follow this pattern. Rural areas dominated by Native Americans and Blacks tend to be heavily Democratic voting. The same has historically been true of Hispanic-majority rural areas, although many are now trending Republican. And as noted in previous posts, rural areas dominated by affluent amenity-seekers also tend to be blue.

In a few parts of the country, this linkage between population density and voting behavior disappears. Consider, for example, far-northern-interior California, a white-dominated region with strong anti-California sentiments associated with the “State of Jefferson” movement. Although the cities and towns here are more pink than red, they lack blue precincts. The one exception is in Susanville, a small city economically based on prisons. But Susanville’s exceptional blue precinct is small, with just 54 votes cast. In contrast, many of the more remote parts of this region are Democratic voting. This is particularly true in the west, an area that has been heavily involved in cannabis cultivation.

The city of Redding, the “metropolis” of far northern California, has grown rapidly in recent decades, surging from under 17,000 people in 1970 to almost 100,000 today. Many of its newcomers have relocated from California’s major metropolitan areas, seeking lower prices and a more conservative social and political milieu. Although the Redding areas has many attractive natural features, its average July high temperature of 99.9 degrees dissuades the affluent, as does its political climate. Left-wing outdoor enthusiasts find places like Mt Shasta City, Weed, and Dunsmuir, all located in the mountainous blue-zone between Redding and Yreka, far more attractive. In 60%-Biden-voting Dunsmuir, population 1,700, class-three whitewater rapids are uniquely located in the middle of town.

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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.

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

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.

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

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.

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

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 Geography of Health and Longevity in Montana (and the Rest of the U.S.) Read More »

The Geography of Education in Montana (and the Rest of the United States)

Education levels vary widely across the United States, as can be seen on the map to the left. (Unfortunately, this map of high school graduates has an extremely broad lowest category, reducing its utility.) The patterns seen here are relatively simple. Low rates of high-school education are found across the rural southeast, and particularly in Kentucky. Agricultural counties in the West that have large populations of farm workers also have low rates of educational attainment. In contrast, rural counties in the Midwest and Rocky Mountains characterized by ranching and mechanized farming generally have high levels of secondary-school graduates.  Basic educational attainment rates are also high across most of the north, as well as in suburban counties across the county.

Overall, Montana is shown as having a high rate of secondary school completion, but county-level variation is pronounced. Few clear patterns are apparent on the map, and some of the information is curious, such as the lower educational levels found in two small counties (Liberty and Wheatland). Intriguingly, Montana counties with large Native American reservations all fall in middle category.


As is apparent on the third map, the United States is much more geographically differentiated when it comes to college education. Here the north/south divide is less glaring, although still visible. What stands out is the high percentages of bachelor’s degrees in three different geographical categories: affluent urban and suburban countries, especially those associated with tech hubs; non-metropolitan countries with major universities (typified by adjoining Whitman County in eastern Washington and Latah County in northwestern Idaho); and affluent high-amenity rural counties in the West (typified by Pitkin County, Colorado).

Montana’s rates of college completion are relatively high. Even many of the state’s sparsely populated agricultural counties have at least mid-level rates. Gallatin County in the southwest stands out for its elevated figure. Not only is Gallatin the home of Montana State University, but it is also becoming a small tech hub. More recently, it has attracted many well-educated remote workers, as discussed in previous posts.

Patterns similar to those found on the college-completion map are apparent on the map of graduate and professional degrees. A few seeming anomalies, however, stand out. I was perplexed, for example, by the dark green polygon in a remote corner of west Texas: Jeff Davis County (population 1,996). As it turns out, Jeff Davis is the site of the McDonald Observatory, a major scientific institution. Presidio County, just to the southeast, also ranks surprisingly high on this map. The answer to this puzzle is found in the county seat of Marfa, which, despite its small size (1,600), is a “cultural center for contemporary artists and artisans.” In 2012, National Public Radio described Marfa as “An Unlikely Art Oasis in A Desert Town.” (But although it is indeed an “unlikely art oasis,” Marfa is not a “desert town”; receiving over 15 inches of precipitation annually, its climate is distinctly semi-arid.)

Three Montana counties stand out on the map of advanced and professional degrees: Missoula, Gallatin, and Lewis and Clark. The first two are the homes of the state’s two major universities, and the third contains the state capital, Helena.

The Geography of Education in Montana (and the Rest of the United States) Read More »

The Geography of Religion in Montana (and the Rest of the US)

The map of religious adherence in the United States defies some common perceptions. Membership in a religious organization, for example, is shown as higher rate in southern New England than in the eastern part of the so-called Bible Belt. The data used to make these maps, however, are not necessarily accurate, and they do not measure the intensity of religious belief. Religious adherence, moreover, has been declining almost everywhere over the past several decades. But the basic patterns depicted in these maps are still worth examining. As they show, membership in an organized faith is highest in the central part of the country, especially the northern and southern Great Plains, and in the LDS (Mormon) region of Utah and eastern Idaho. It is lowest in central Appalachia and the greater Pacific Northwest, including western Montana.  Colorado, Maine, and the lower peninsula of Michigan also have low rates of membership. In the southeast, religious adherence is low in counties with large Black populations.

Montana is revealed as a religiously divided state. Many counties in the northeastern and north-central parts of the state have very high adherence rates, while many in the west-central and south-central regions have very low rates. Demographic history plays a role here. Northeastern Montana was heavily settled by Norwegian farmers, a group that historically had high rates of (Lutheran) religiosity. In several northeastern counties, Lutheranism is still the dominant faith. Most of the first Euro-American settlers in the rest of Montana were ranchers and miners, groups that generally had low rates of adherence. In the copper counties of Silver Bow (Butte) and Deer Lodge (Anaconda), however, relatively devout Irish Catholic workers later gained demographic domination. These are now the most religious counties in the western part of the state.

Roman Catholicism has been historically mapped as the leading faith over almost all Montana except the northeastern Lutheran belt. More recent maps, however, show Mormonism as the top religion of several western counties. These areas have not historically been mapped as part of the LDS cultural zone. More recently, geographer Paul F. Starrs has remapped the Mormon cultural region to account for its expansion. He now includes southwestern Montana’s Beaverhead Country within its outer sphere. More than 11 percent of Beaverhead’s residents belong to the LDS church. Statewide, the figure is roughly five percent, making it Montana’s second largest faith (after Roman Catholicism). Montana currently has the country’s seventh highest percentage of LDS member – or eighth, if one includes territories (American Samoa).

The Geography of Religion in Montana (and the Rest of the US) Read More »

Cannabis Legalization and the Electoral Geography of Montana

As one of the maps in the previous post shows, cannabis (marijuana) use is higher than average across most of the Western states – with the signal exceptions of Utah, Idaho, and Wyoming. Not surprisingly, these are the only Western states in which non-medical cannabis use remains illegal. Cannabis legalization began in the West (Colorado and Washington) but is now more firmly instituted in the Northeast. In general, this pattern reflects political inclinations. The Northeast is a generally Democratic-voting region, and Democrats are much more likely to support legalization than Republicans. According to a 2021 Gallup poll, 83 percent of Democrats support legalization, as do 71 percent of independents. Republicans, by contrast, are almost evenly split, with 49 percent opposing legal status.

Only two reliably Republican-voting states have fully legalized cannabis: Montana and Alaska.  It is not coincidental that both are in the West. Western conservatism leans in a more libertarian direction than Southern or Midwestern conservatism, with the important exception of the deeply religious LDS (Mormon) region centered on Utah and eastern Idaho. Although cannabis is now allowed across Montana, sales are prohibited in counties that opposed legalization. These counties can hold their own referendums on retail sale.


Geographical patterns of support for cannabis legalization in Montana are similar to those of the West as a whole. As the paired maps show, Democratic-voting counties all supported the 2020 I-190 Montana Marijuana Legalization and Tax Initiative, whereas the state’s overwhelmingly Republican counties (more than 80 percent Trump vote) opposed it. But several strongly Republican counties (70-80 percent Trump vote) did vote in favor of the initiative, albeit by relatively narrow margins. These counties are concentrated in northwestern Montana. In the east, all strongly Republican counties except Valley voted against legalization.





Differences in religiosity might help explain these patterns. As the Gallup poll also shows, people who regularly attend religious services are less likely to support cannabis legalization than those who do not – although a bare majority of regular attendees (52 percent) still favor legality. As it turns out, western Montana is less religiously inclined than eastern, and especially northeastern, Montana. In the northeast, heavily Republican but cannabis-supporting Valley County is distinctly less religious than its neighbors. But other countries defy this pattern, including deeply religious but legalization-supporting Sheridan County and heavily non-religious but legalization-opposing Carter and Petroleum counties.

Religious affiliation across Montana will be considered in more detail in the next post.

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The Geography of Drug Use in Montana – and in the Rest of the United States

The GeoCurrents series on Montana will conclude this week by examining a series of maps of social and economic indicators, both for Montana and the United States as a whole. Many such maps showing county-level data can easily be found for the United States, but far fewer are available for individual states. It is fairly easy, however, to excerpt and magnify a portion of these national maps, and then block out a specific state. If the resolution of the national map is high enough, the derivative state map will be reasonably crisp.

We will begin in today’s post with a brief examination of drug use (including alcohol and tobacco). I will generally assume that the data used to make these maps is reasonably accurate, but that is not always necessarily the case.

The national map of the drug overdose rate (in 2014) shows some clear geographical patterns. The rate is lowest in the agricultural regions of the Midwest and is also relatively low across the inland south and in most major metropolitan areas. The overdose rate is also mapped as low across New York; why this state would have such a lower rate than New England seems odd. Overdose rates are mapped as high in the Appalachian region (and in the upper South more generally), along the Gulf Coast, and across much of the West, including even Utah. Montana has highly variable county rates, which are not easy to explain. The patterns seen here do not correlate well with Montana’s other cultural, economic, or demographic indicators.



The patterns seen in the national map of alcohol use are markedly different from those found on the drug overdose maps. Here Appalachia and the upper South more generally show low rates of use, whereas the upper Midwest – particularly Wisconsin – show high rates. Low reported alcohol use in Appalachia might, however, correlate with high unreported consumption of “moonshine.” Very low drinking rates are unsurprisingly reported for the LDS (“Mormon”) region of Utah and eastern Idaho. Montana, like other northern states, shows generally high levels of alcohol consumption. The Montana-specific map indicates somewhat higher rates of use in the western part of the state. Surprisingly, it indicates low drinking rates in counties dominated by Native American reservations (as does the map of South Dakota). Alcohol sales are often restricted on reservations, but actual use may be higher than the map indicates.




The Mapporn map of “binge and heavy drinking” shows patterns similar to those found on the alcohol-use map. The fact that certain states stand out clearly on this map (West Virginia, Wisconsin) does make me question the underlying data. Montana, like neighboring North Dakota, appears as a binge-heavy state.  On the low-resolution Montana excerpt map, two counties stand out: Missoula and Gallatin, home of the state’s two major universities. I doubt that this is coincidental.






The national tobacco-smoking prevalence map shows high rates in the South and eastern Midwest, and low rates across much of the Northeast and West. Metropolitan countries, particularly suburban ones, have low smoking rates. In Montana, tobacco-use tend to be more prevalent in counties with Native American reservations.






The remaining maps, taken from the website of the National Survey on Drug Use and Health, are based on idiosyncratic “substate regions” rather than counties. As a result, I have not excerpted Montana maps, although I have outlined the state for comparative purposes.

The map of cannabis (marijuana) use shows some sharp and intriguing patterns, particularly in the West. Use is evidently highest the Pacific Northwest (extending through central California), and in Colorado, but low in Utah, Idaho, and Wyoming. Western Montana falls in the same high-use category as the Pacific Northwest. (The issue will be explored in more detail in a separate post on cannabis legalization in Montana.)



The last three maps show some roughly similar patterns. Western Montana has a higher rate of cocaine use than eastern Montana, although not nearly as high as western Colorado. Heroin use is above the national average in the northwestern corner of the state. (The high level of heroin use in Maine and the low levels in Georgia and Texas are curious). Methamphetamine use is fairly high across Montana and is especially elevated in the north-central part of the state. Rural Oregon seems to be the core area of this particularly damaging substance.



If there is a take-home message from these maps, it is that drug use tends to be higher in rural areas than in major metropolitan zones, particularly their suburban counties. This pattern is mostly clearly evident on the methamphetamine map. Many rural areas of the United States are experiencing economic and social distress, which is often associated with heightened drug use.

The Geography of Drug Use in Montana – and in the Rest of the United States Read More »

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

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.

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

What Is a Zoom Town?

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. 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.


What Is a Zoom Town? Read More »

Montana’s Changing Electoral Geography

Although Montana has usually opted for Republican candidates in U.S. presidential elections, it was until recently something of a “purple” state, often dividing its votes relatively evenly between the two main political parties. As can be seen in the map series on the left, it has been trending in a decidedly red direction. In 2008, Barack Obama received 47 percent of Montana’s votes; in 2016, Hillary Clinton got only 35.7 percent.

As can be seen on these maps, Montana’s patterns of electoral geography have changed as well. The first two maps (1948 and 1960) show a north/south divide, with the south favoring Republicans and the north favoring Democrats. Many counties, however, were almost evenly split, with few experiencing landslide elections. These patterns disappear in the later maps. The north/south divide is now only vaguely evident, and landslide elections are common, at least in the Republican-voting east. Several counties have switched their party alignment. Cascade (Great Falls) formerly trended blue, but is is now reliably red. As the second map show, Cascade County even saw a minor red-shift from 2016 to 2020 (moving from a  57.1 % to a 58.46 % Trump vote). Gallatin County (Bozeman) has moved in the opposite direction. As recently as 2004, Gallatin voted Republican. It is now reliable blue – and getting bluer. It remains, however, Montana’s most libertarian county.





County-level maps of the Trump and Biden vote in 2020 reveal some interesting but subtle patterns. At the crudest level, the state’s main geographical divide now separates the east from the west. Although most western counties are still solidly red, several of the more populous ones are blue. Equally notable, no western county gave more than three-quarters of its votes to Trump. The statistical website 538 thus maps Montana’s western congressional district as leaning Republican, in contrast its solidly Republican eastern district. Twelve eastern counties gave more than 80 percent of their votes to Trump. But Biden did win two eastern counties and came very close in a third. As we shall see in tomorrow’s post, all three of these counties have Native American majorities.



The main electoral geographical divide in the United States now pits metropolitan areas against small towns and rural areas. This pattern, however, is only vaguely apparent in Montana’s county-level data. As can be seen in the paired maps, the most sparsely settled counties gave the highest percentage of their votes to Trump, and several relatively densely populated western counties supported Biden. But Montana’s population leader, Yellowstone County (Billings), solidly backed Trump, and several rural counties that are demographically dominated by Native Americans voted for Biden.



Montana’s rural/urban divide is more clearly evident at the precinct level. Consider Silver Bow County, which is politically consolidated with the city of Butte. Historically, Silver Bow was Montana’s bluest county, its many miners consistently supporting Democratic candidates. Today, the mines are largely shuttered, and the city now specializes in reclaiming toxic sites. It is still blue, although not to the extent that it formerly was. As can be seen, central Butte remains dark blue, whereas most of the outlying areas of Silver Bow County are red. The electoral maps of Billings, Helena, and Livingston all show blue urban cores surrounded by red rural hinterlands. Even the small town of Havre on the Great Plains, population 9,362, had one light blue precinct in 2020. On the other side of the ledger, two of Montana’s largest cities, Great Falls and Kalispell, had no blue precincts in 2020. But they are not as red as their surrounding areas.




A few rural areas and small towns in Montana that are not on native American reservations now habitually vote for Democratic candidates. The college towns of Bozeman and Missoula are both surrounded by rural blue precincts, although they are not as blue as those in the urban cores. Several remote towns and rural areas situated in areas with abundant natural amenities are distinctly blue. Big Sky, noted for its luxury ski resort, falls into this category, as do the small towns of Gardiner and Cooke City, adjacent to Yellowstone National Park. Red Lodge, also near Yellowstone and adjacent to the spectacular Beartooth Highway, falls into the same category. Near Glacier National Park one finds the small blue towns of West Glacier and Whitefish. Nearby Columbia Falls, however, is decidedly red. This difference reflects demographic sorting tendencies: Whitefish became an early center of outdoor recreation and environmentalism, which in turn attracted newcomers with similar interests and values. As Bill Bishop argued in The Big Sort more than a decade ago, Americans are increasingly moving to places that that match their political orientations.

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