GeoCurrent Atlases

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

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

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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 Religion in Montana (and the Rest of the US)

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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).

Cannabis Legalization and the Electoral Geography of Montana

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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.

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

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

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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.

Voting Patterns of Native Americans in Montana

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In racial/ethnic terms, Montana is not a diverse state. It has the lowest percentage of Black Americans in the country, the fifth lowest percentage of Hispanic and Latino Americans, and the third lowest percentage of Asian Americans (tied with Wyoming). It does, however, have the fifth largest percentage of Native Americans. Its indigenous population, moreover, is expanding. As can be seen in the paired maps, the Total Fertility Rate is significantly higher on Native American Reservations than it is elsewhere in the state.

 

 

 

Native Americans in Montana, as in most other parts of the country, vote heavily for candidates in the Democratic Party, influencing statewide elections. As the Missoula Current reported just after the 2020 election, “The Native vote in Montana has made the difference before, when Indigenous voters helped Sen. Jon Tester, a Democrat who has advocated for Indian Country, … get elected the last three terms in often-close races.” Although the indigenous vote has not had such an impact on presidential contests in Montana, it is still visible on electoral maps. In 2020, sparsely settled Glacier County, the main home of the Blackfeet people, stood out as the state’s bluest county, defying the norm of Republican-voting in areas of low population density. The same pattern is found in other Montana counties with Native American Reservations, although not to the same extent. On the 2020 electoral map, Blaine and Bighorn counties, both with large reservations, appear in light blue. In contrast, Roosevelt and Lake counties, which also have large reservations, are mapped in pink. Donald Trump won both these counties, but did so by narrow margins, especially in Roosevelt.

These seeming discrepancies can be partly explained by a combination of local political geography and demography. As can be seen on the modified Wikipedia map to the left, county boundaries and reservation boundaries do not coincide. The Flathead Reservation, for example, is centered in Lake but extends over portions of three other counties. More important, not all residents of the reservations are Native Americans. The Flathead lands in particular were opened to white settlement in the early 1900s; as a result, non-indigenous people significantly outnumber indigenous people on the reservation. Lake County’s Republican-voting behavior is thus easily explained.

 

 

 

The question remains as to why Glacier County votes more heavily Democratic than other Montana counties with large Native populations. The “American Indian Population by County” map, excerpted from a Vivid map covering the United States, seems to offer a partial explanation: Glacier is depicted here as more than 95 percent indigenous, a much higher figure than either Blaine or Roosevelt counties. But Bighorn County is also depicted as more than 95 percent indigenous, yet it gave 46 percent of its vote to Trump (as opposed to Glacier’s 33 percent). Could it be that the Crow people are somewhat less supportive of the Democratic Party than the Blackfeet? Or is there a problem with the data? As it turns out, Wikipedia articles on Montana counties give very different figures from those found on the Vivid Map, with Bighorn County being reported as only 60 percent Native America.* But then again, Glacier County is listed at only 62 percent Native. Precinct-level maps for both counties show sharply differentiated red and blue zones, presumably reflecting racial differences in different areas. The blue zones in Glacier are, however, distinctly bluer than those of Bighorn.

 

Elsewhere in Montana as well, the correlation between American Indian ethnicity and voting behavior is close enough that one can apparently discern which areas of the reservations have the main concentrations of Native Americans. Fort Peck Reservation, for example, spans several counties, but its only blue precincts are located in southwestern Roosevelt County.

 

*Both sources rely on U.S. census data. The Wikipedia articles give figures from 2000 or 2010, whereas the Vivid map is based on 2017 data. It is highly unlikely, however, that there have been enough demographic change in the intervening years to account for these different numbers,

Montana Population Change in Comparative Context

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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

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(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