2

Defining and Characterizing Rural America

Discussion of science, technology, engineering, and mathematics (STEM) education in rural America requires an understanding of how rural America is defined in federal, state, local, and societal terms. This chapter first reviews some definitions of rurality, including the committee’s approach to defining it, and then considers consequences of those definitions on funding and other outcomes for rural schools and communities in the context of rural STEM education. The chapter also discusses diversity in rural areas and the changing rural landscape. Subsequent chapters will discuss in more detail the effects of these definitions and characteristics on effective STEM education and workforce development in rural settings.

Throughout this chapter and report, the terms United States and America are inclusive of U.S. territories (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands), the Commonwealth of Puerto Rico, and the Federated States of Micronesia, Republic of the Marshall Islands, and Republic of Palau.

THE CHALLENGE OF DEFINING RURALITY

What is rural? The word encompasses a multitude of definitions, making it difficult to determine what types of communities are classified as rural. In many ways, the definition is dependent on the audience. Most definitions focus on “where is rural” and assume that “who is rural” is contained in those definitions.

Rurality varies by geography unique to each region of the United States. It includes, for example, the following types of communities, which exist all over the country:

• a remote village located off the road system, accessible only by air, and dependent on traditional subsistence activities for livelihood;
• a remote town inhabited by families with very low or high income levels, with growing school and job opportunities nearby;
• an island teeming with vibrant life and diverse culture but isolated geographically, resulting in a high cost of living;
• a picturesque mountain community with seasonal influx of population yet lacking affordable housing for year-round residents; and
• a two-building school district serving a farming community 80 miles from the nearest services.

Rurality is distinctly linked to local culture, which frequently involves a profound connection to the land. Connections are further defined by economic drivers that attract or retain certain types of people who thrive in these environments. Perhaps most importantly, the combination of these factors creates a deep-rooted sense of community with strong relational ties that are sometimes difficult to find outside of rural settings.

It is also important to recognize that rural people have their own self-definitions based on their lived experiences. People tend to create their understanding of the term rural based on where they have lived in comparison to places they have visited or heard about. Ardoin and Koon (2024) describe three main categories of rural individuals’ self-definitions: (a) Prevalence of nature/land may be related to the vast openness or unique geographic features of the land as well as the wildlife and people’s synergistic relationship with the local fauna. Most rural residents also self-report a high level of appreciation for and reliance on the land. (b) Proximity to stores is a factor of rural life, as residents refer to the distance and modality of travel involved in getting to the nearest grocery store, gas station, or shopping mall, or they may use proximity to a large city or metropolitan area to place their hometown. (c) Close-knit community is often mentioned by rural residents, who note the level of interconnectedness in their community—statements like “everyone knows everyone” or “the people make the place” are common. There is also an emphasis on familial relationships and the value of being a good neighbor.

The next section surveys various definitions of rurality across both policy and research, and the following section discusses various implications of several approaches to defining rurality.

Formal Definitions of Rural Populations and Areas

The federal government relies on two main geographic/demographic definitions of what is considered rural. The U.S. Census Bureau defines rural as any population, housing, or territory not in an urban area, which is defined as an area with either 5,000 or more people or 2,000 units of housing.¹ The Office of Management and Budget (OMB) defines rural in the context of county classifications as metropolitan (if it includes an urban area with a population of 50,000 or more), micropolitan (if it includes an urban area with a population between 10,000 and 50,000), or neither.² These definitions present measurement challenges, with the Census definition often reporting larger numbers and OMB consistently reporting lower numbers of rural areas, and both running the risk of missing communities that may self-identify as rural (Castle & Tak, 2021).

The U.S. Department of Education’s National Center for Education Statistics (NCES) created urban-centric locale codes that divide public schools into four categories: city, suburb, town, or rural (Geverdt, 2019). Each group is then further delineated by population size or proximity to urban areas or clusters. The NCES locale criteria rely on three primary geographic concepts to define and classify territory—urban areas, core based statistical areas (CBSAs), and places. “To qualify as an urban area, the territory must encompass at least 2,500 people, at least 1,500 of which reside outside institutional group quarters. Urban areas that contain 50,000 or more people are designated as Urbanized Areas (UAs); urban areas that contain at least 2,500 and less than 50,000 people are designated as Urban Clusters (UCs)” (Geverdt, 2019, pp. 2–3). The following NCES definitions influence many of the datasets used in education research:

City: territory located within principal cities (incorporated places with a large population of residents) of CBSA;

Suburban: territory in a UA that is located outside the boundary of a principal city of a CBSA;

Town: all UCs; and

Rural: territory outside of urban areas.

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¹ https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural.html. It should be noted that this definition of urban increased from a threshold of 2,500 people, which increased the numbers of places classified as rural that were previously classified as town. In addition, most studies and reports published prior to 2023, including NCES data, were based on the 2,500 cutoff, which may have implications for interpreting the evidence base.

² https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/

Each of these areas can be further subdivided to create 12 different categories, or consolidated into a rural-town vs. suburban-city comparison. For most purposes in this chapter, the consolidated (rural-town vs. suburban-city) view of rural areas is used when discussing NCES data.

The U.S. Department of Agriculture’s Economic Research Service (USDA ERS) maintains the Rural-Urban Continuum Code (RUCC),³ which delineates the OMB definition into nine categories (codes 1–3 representing metropolitan counties and codes 4–9 representing nonmetropolitan counties) based on a county’s population and whether it is adjacent to a metropolitan area. Because the RUCC uses threshold values for the population criteria, it may be deceptive because “similar counties may be classified as different, whereas counties that are very dissimilar may be grouped together in the same category” (Waldorf, 2006, p. 6).

The Rural-Urban Density Typology (Isserman, 2005), an alternative to the RUCC, defines thresholds for the following variables: percentage of urban residents, total number of urban residents, population density, and population size of the county’s largest urban area, yielding four types of counties: rural, mixed rural, mixed urban, and urban. The committee notes that (a) although the typology improves the definitions for rural and urban counties, it does not adequately define the two “mixed” categories and is applicable only to counties; and (b) although definitions based on thresholds are simple and result in a finite number of categories, thresholds can be controversial because they rely on arbitrary figures that do not adequately adjust for small changes (Waldorf, 2006).

One approach to defining rural using multiple measures without the dichotomous classification of rural versus nonrural was proposed in 2022 by the Alliance for Research on Regional Colleges to define regional institutions of higher education as “rural-serving institutions” (Koricich, 2022). This definition incorporates five variables from three data sources and creates an index that can be used to compare institutions. Two variables use Census data for the percentages classified as rural in both the county where the institution is located and the counties adjacent to it. The index also incorporates USDA ERS classifications for the home county, with six threshold-based definitions for metro, urban, or rural populations as well as a dichotomous variable of whether the county where the institution is located is adjacent to a metro area. Finally, the index uses Integrated Postsecondary Education Data System data for the percentage of parks and recreation, natural resources, and agriculture degrees conferred at the institution (Koricich, 2022). This work thus uses multiple data points to conceptualize a framework of rurality on a spectrum and affords a more nuanced view of postsecondary institutions that serve rural areas.

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³ https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx

Perhaps the most comprehensive definition of rurality is the Index of Relative Rurality (IRR) proposed by Waldorf (2006). This all-encompassing measure produces a spectrum of rurality on a 0–1 scale, with 0 being most urban and 1 most rural. The scale includes four dimensions: population size, population density, extent of urban (built-up) area, and remoteness. These dimensions are components of existing definitions of rurality, and the IRR uses the unweighted average of the dimensions rescaled to the 0–1 scale to create a comparative index. This approach allows the measure to be applicable to a wider array of groupings, from individual counties to groups of counties, regions, townships, and census tracts. It also allows rurality to become a relative measure, useful for research on trajectories of rurality over periods of time. This IRR also reflects the multidimensional nature of rurality that varies across locations. The committee discusses the affordances and limitations of this approach to defining rurality later in this chapter.

Visualizing Discrepancies

The discrepancies among these definitions are clearly illustrated in Figure 2-1, a visual representation of three definitions used in the state of Indiana and the resulting differences in which counties are considered “rural.” For example, Newton, Benton, and Warren counties in northwest Indiana are labeled metro in the OMB map designating metropolitan areas (left), yet in the RUCC map (center), Newton and Benton counties are classified with one of the three metro designations, while Warren is one of the most nonmetropolitan counties in the state and. Further, in the IRR (right) map all three are designated nonmetropolitan and some of the counties with the highest degree of rurality in the state.⁴

In addition to divergent meanings, definitions are used in different ways by federal agencies, national organizations, individual states, localities, private organizations, and philanthropic foundations. The resulting datasets are therefore not always comparable. Table 2-1 outlines further variability in definitions of rural for selected federal agencies and national organizations most relevant to the charge of this consensus study, and Figure 2-2 shows these discrepancies graphically.

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⁴ The preceding paragraph and Figure 2-1 were changed after release of the report to accurately reflect the classification of “rural” on the maps.

TABLE 2-1 Federal Agencies’ and National Organizations’ Definitions of Rural

SOURCE: Committee generated.

IMPLICATIONS OF THE VARIOUS DEFINITIONS OF RURAL

Given the broad variability of definitions, both formal and informal, the committee acknowledges both the difficulties of delineating what is and is not rural and the implications of the lack of a standard definition of rurality.

Consistency and Challenges in Research

The various definitions of what is rural create inconsistency and fragmentation across research on rural STEM education, making it difficult to draw clear conclusions. The literature referenced in this report includes a variety of definitions of the word rural, so although related findings are helpful, they do not provide comprehensive reliable results. Consensus definitions in rural-related education research are needed to better inform the development of effective programming and funding opportunities.

Rural populations also present challenges to research on STEM education methods and outcomes. Small population sizes in some communities make it difficult to determine statistical significance, and differing characteristics among rural communities hamper efforts to compare or combine datasets or research results. Research based on governmental data carries with it the specific definition of rurality under which the data were collected; the resulting research papers may appear to be contradictory simply because their underlying definitions and datasets were inconsistent. Many governmental datasets do not identify rural as a keyword or a concept in the data, further limiting the practicality of conducting research in this space. These factors comport with the observation by the U.S. National Science Foundation (NSF) Committee on Equal Opportunities in Science and Engineering (NSF, 2024, p. 6) that “demographic statistics for rural students in NSF-supported activities over time were not readily available regarding students reached and served,” and reports often rely on examples. The lack of agreed-upon definitions used across federal agencies and researchers, as well as limited data collection on rural students, results in an incomplete and inconsistent picture of rural K–12 STEM education.

Implications for Schools and Districts

The NCES designations, when translated into definitions of school districts, can have a profound effect on the ability of a rural school district to obtain federal funding. For example, the Rural Education Achievement Program (REAP), funded through Title V-B of the Every Student Succeeds Act, requires that every school in a district be classified as NCES 41, 42, or 43 for the district to qualify for REAP funds meant to support schools with small student population sizes. The Eagle Point High School on the Rogue River in southern Oregon is coded 22 (suburb midsize) because of the proximity of a micropolitan area. But the district extends far north along the river, with elementary and middle schools that are small and isolated: The Lake Creek Learning Center (K–5) is about 35 miles from the high school, has 40 percent of its students in poverty, and is coded appropriately as 43 (remote). Yet the Lake Creek school cannot receive REAP support because of the NCES coding of the high school at the other end of

the district (Jesse Longhurst, Southern Oregon University, presentation to the committee, January 2024).

These definitions also have implications for community colleges. For example, Iowa is the 15th least populous state in the country, with a population density of 57 people per square mile and more than 85 percent of the land used for farming. Iowa has the third highest number of farms in the country and over 50 percent of its micropolitan statistical areas are considered rural by Census definitions. By these metrics it seems the state is overwhelmingly rural. But application of a different metric suggests otherwise. Iowa is also home to 15 community colleges. Even considering these community colleges in the context of urban-centric definitions and population density, we would anticipate many of them to be classified as rural, as 10 of the 15 are in communities with fewer than 25,000 residents, and six of those 10 communities have fewer than 10,000 residents. Yet, according to the NCES definition of rurality, only 3 of Iowa’s 15 community colleges are classified as rural (Rush-Marlowe, 2024): those 3 would be eligible for rural-specific funding programs, but others with very similar characteristics and infrastructure would not. In addition, the community colleges that are not classified as rural by NCES could end up competing for grants with institutions that have much higher levels of staffing and administrative capacity.

Funding Inequity

Inconsistent definitions of what is considered rural, as well as threshold numbers that differently classify similar communities, schools, or districts, can result in inequitable and inconsistent funding for rural communities as well as less access to opportunities targeted to rural populations. In addition to the two examples above, consider two communities applying for the same grant that is specific to rural communities as based on the Census definition (rural is a community of fewer than 5,000). One has a population of 1,000 and is 45 miles from the nearest UA and 30 miles from a UC; it is classified as remote by NCES. The other has 4,900 inhabitants and is only 5 miles from a UA and 2.5 miles from a UC; it is classified as fringe by NCES. Both communities would qualify for a grant aimed at rural communities, but the second, by virtue of its size and proximity to more populous areas as well as educational institutions and resources, would more likely be able to access the administrative capacity and expertise needed to write a successful proposal for that funding. Thus although the communities are viewed as equivalent by the funding agency, it is likely that the proposals written by those with more resources and capacity will have an advantage over requests from those with very limited resources, staffing, or expertise in federal grant applications and management. This inequity results in a lack

of awards to the most rural and remote schools and communities simply because they do not have the human capital and expertise to put together a competitive application.

THE INDEX OF RURALITY: A COMMON DEFINITION FOR RURALITY

Given the differing definitions used by federal agencies, state agencies, policymakers, and researchers, and the implications of that inconsistency on rural communities and schools, the committee argues that future research and policy relevant to rural K–12 STEM education and workforce development should reframe the conversation around a more nuanced and flexible definition of rurality. We find that the spectrum definition of rurality provided by the IRR acknowledges that the variability, challenges, and strengths of each community are unique. As Waldorf (2006, p. 2) explains, “It does not answer the question ‘Is a county rural or urban?’ but instead addresses the question ‘What is a county’s degree of rurality?’” In short, the tool recognizes that a series of factors delineate the scale to which a community is rural. For these reasons we are confident that the IRR is well aligned with the context of this report. Although all of the definitions discussed can be relevant for improving K–12 STEM education and workforce development, the IRR better enables users to determine the level of rurality for each community.

Another benefit of the IRR is that it avoids what Waldorf calls the “threshold trap,” the likes of which can lead to funding inequities faced by rural schools in urban counties like Lake Creek Learning Center, by pigeonholing “counties, thereby potentially separating similar counties and joining dissimilar counties” (Waldorf, 2006, p. 2). One example considers three communities with populations of 20, 497, and 502, with a definition threshold of under 500 people to be considered in one category: thus the first two will be grouped together although they are quite different, and the second pair will be grouped separately although they are quite similar (Waldorf & Kim, 2015).

The IRR also allows for small changes in variables to impact classification. Other definitions that are more aligned with threshold tend to require large changes to shift categorization. The reality is that, in rural settings, small changes to variables have significant implications for small communities. The IRR can be applied to a variety of spatial scales and offer a regional perspective across communities that may not otherwise be working together. These regional snapshots could identify opportunities for partnerships and coordinated regional development efforts that maximize human capital for smaller communities. The map in Figure 2-3 shows the IRR as applied to counties throughout the United States, clearly highlighting both the difference between urban centers and other counties in a state and the presence of rural counties in every state.

CHARACTERISTICS OF RURAL AMERICA

The common narrative of the rural-urban divide is not only inaccurate, it is also harmful to both rural and urban communities. Just as no two cities are alike, there is no singular version of rurality and, contrary to common narratives, rural communities are not all defined by whiteness, agriculture, or political conservatism. The reality is that rural America is diverse in every way—geographically, economically, and racially (Showalter et al., 2023). Its diverse ideas, people, and economies are also inherently linked to urban communities, which provide support functions and access to hospitals and other public services that are often unavailable in rural communities, while the latter are critical contributors of food, energy, and economic growth. Rural and nonrural areas are thus deeply interconnected (Cattaneo et al., 2022).

Furthermore, the composition of U.S. rural communities continues to evolve. Common misconceptions of rural America as either a nostalgic remnant of a simpler and more wholesome time or a provincial undereducated backwater influence attitudes and undermine support for rural communities. The outdated idea that rural areas have been “left behind” began with a 1967 Presidential Advisory Report that described the economic depression in rural America at the time (Carnevale et al., 2024). The resulting overemphasis on rural challenges (e.g., lower educational attainment, higher poverty rates, and declining population sizes) without recognizing the assets of rural communities has created a problematic dichotomy, where the “struggling rural” is unfavorably compared to the

“thriving urban” (Carnevale et al., 2024). Rural areas are often viewed from deficit perspectives, characterized as underdeveloped and needing to be saved (Brenner, 2022, 2023; Crain & Newlin, 2021; Fulkerson & Lowe, 2016; Tieken, 2014). They are also often presented as a monolith (Brenner, 2022; Burrola et al., 2023).

This outlook contributes to stereotypical views and misconceptions about the people who live in rural areas, their cultures, and their values. For example, there is an assumption that rural America is largely White, agriculturally based, and uneducated. In reality, rural communities are increasingly diverse (Center for Public Education, 2023) and have industries in the social sectors (e.g., education, health care) as their largest employers (Kusmin, 2016), and chronic problems such as poverty and workforce development are experienced by both urban and rural areas alike (Liu & Peng, 2023).

The diversity of people, economics, and geography across rural areas makes it understandably difficult to pin down a precise definition of what it means to be rural. However, as discussed, existing definitions are both deficient and deficit based, and confusing or misaligned definitions of rurality can be a barrier for rural communities attempting to access state, federal, or philanthropic funding. Improved definitions are critical to better understand rural communities and help ensure that they have access to the resources they need. In essence, there is no one-size-fits-all definition of rural and it should be acknowledged that this fact makes understanding the rural context complicated.

Demographics of Rurality

According to a USDA report, 46 million U.S. residents—approximately 14 percent of the population—live in nonmetropolitan or rural counties (Davis et al., 2023). The Census Bureau classified 66.3 million U.S. residents (20%) as nonurban in 2020. The percentage of the population classified by the OMB as nonmetropolitan has declined from 33 percent in 1974 to 14 percent in 2023, largely from an increase in the effective size of urban centers (Kenneth Johnson presentation to the committee, January 2024). While some rural areas have seen an increase in population since 2022, rural areas overall have experienced a decrease (Carnevale et al., 2024; Davis et al., 2022, 2023), attributed in part to a declining birth rate and an increasing death rate driven by an aging population.

Some nonmetropolitan counties that saw a slight population increase in 2020–2022 after a decade of population loss (Davis et al., 2023) benefited from the state and local economy, proximity to larger metropolitan areas, association as a destination for retirement or recreational activities, or the ability to work remotely from a rural setting, including the availability of broadband (Kenneth Johnson presentation; Davis et al., 2023;

Dobis et al., 2021). Slight population growth in some nonmetropolitan counties is due to the increase in remote work and out-migration from metropolitan areas during the COVID-19 pandemic, but out-migration rates in 42 percent of such counties decreased their population, especially in the Great Plains (Davis et al., 2023).

Rural America has also seen demographic changes related to racial and ethnic diversity, age, and poverty. While White residents constitute the largest share of rural populations, communities of color and immigrant populations are growing (Davis et al., 2022; Parker et al., 2018). For example, while not true for all rural counties, in aggregate roughly 20 percent of people living in rural counties are Indigenous, Black, Latine, Asian, Pacific Islander, or multiracial (Parker et al., 2018). The rural Latine population grew by almost 20 percent and the rural multiracial population increased by 148 percent between 2010 and 2020 (HAC, 2021). The increase in diversity is particularly large among children: 32 percent of people under the age of 18 in rural America are from historically minoritized groups (Kenneth Johnson presentation). At the same time, the average age of adults is higher than in nonrural places (Carnevale et al., 2024; Davis et al., 2022; Parker et al., 2018): in 2021 rural counties had a larger share of adults 65 and older (more than 20%) than nonrural counties (Davis et al., 2022).

The 2020 Census marked the first time that rural America lost population (Johnson & Lichter, 2022): it showed declines in about two thirds of nonmetropolitan counties from 2010 to 2020 and chronic population loss in nearly a third. These losses were most extreme in the most remote places (Kenneth Johnson presentation). With this population loss, these places face additional challenges maintaining critical services and programs, including education.

Rural America has also experienced persistent inequities due to poverty (Davis et al., 2023), affecting housing, health care, and education. A 2006 study noted that high school students in rural areas in the Appalachian region were more likely than their urban counterparts to have grown up in poverty (Ali & McWhirter, 2006). These students were more likely than urban youth in the region at the time to say that “postsecondary education is unaffordable and not necessary” (Carnevale et al., 2024). Today, approximately 14 percent of children ages 5 to 17 in rural areas live in poverty. This is higher than in suburban areas (12%) but lower than town and city locales (21%; NCES, 2023). In addition, Black/African American, Hispanic/Latine, and Indigenous/Native communities in rural areas experience higher rates of poverty than White communities (Carnevale et al., 2024). Many studies have shown that poverty is associated with lower academic achievement. For example, rural students begin school with lower reading achievement than their suburban peers, and this gap continues through elementary and middle school in both mathematics and reading. But it is important to note

that when controlling for poverty the reading achievement scores are no longer significantly different (Clarke, 2014).

Spatial Inequities

Notwithstanding the strengths, assets, and resources of rural communities, they face inequities and challenges related to infrastructure. Opportunity is unevenly distributed across rural U.S. counties (Brown & Schafft, 2011; Galster & Killen, 1995; Lobao & Saenz, 2002; powell, 2008; Soja, 2010; Tieken & Auldridge-Reveles, 2019), as some enjoy more resources, like grocery stores or good medical care or quality housing, than others. Factors that create this “spatial injustice” (Soja, 2010), or “the fair and equitable distribution in space of socially valued resources and opportunities to use them” (Soja, 2009, p. 2), include discriminatory policies and practices, the political and social organization of space, and uneven economic development.

Both rural and urban communities are subject to spatial injustice, though the isolation and distance of the latter tend to exacerbate disparities. For example, many rural communities are food deserts, lacking access to healthy and affordable food (Beaulac et al., 2009), and more than 85 percent of U.S. counties facing persistent poverty—that is, where more than 20 percent of the population has lived in poverty for the past 40 years—are nonmetropolitan counties (Davis et al., 2023). These challenges often intersect in ways that create unique and overlapping challenges for rural communities and schools.

Spatial injustice tends to intersect with racial and economic inequities (powell, 2008; Tieken, 2014, 2017). For Black, Indigenous, and Latine communities, spatial injustice is both a manifestation and continuation of historic practices of exploitation. Enslavement, Jim Crow laws, gerrymandering, and racial violence, for example, have prevented many Black residents in rural communities from accumulating wealth, land, or power, resulting in generational poverty, entrenched isolation, and neglect from policymakers. Indigenous communities also face spatial injustices tied to colonization, including dispossession of land, erasure of cultural practices, and a continuing fight for sovereignty and recognition. Rural Latine communities, too, have histories of territorial disputes, linguistic oppression, and cultural erasure as well as nativist immigration policies and exploitative labor practices. The legacy of these histories, coupled with the impact of current policies and practices, is that rural communities of color experience the most severe spatial injustices, such as higher rates of persistent poverty and child poverty, greater levels of segregation, and more limited employment options (Duncan, 1999; Lichter & Parisi, 2008; Lichter et al., 2012; Schaefer et al., 2016; Tieken, 2022).

One of the most critical spatial injustices facing rural communities, especially related to STEM education and workforce development, is the digital divide, the inequitable and uneven distribution of broadband connectivity that prevents many rural communities from accessing the internet (Center for Public Education, 2023; Federal Communications Commission, 2024; Morehart et al., 2009). At the Federal Communications Commission’s new benchmark speed of 100/20 Mbps, 72 percent of rural areas and 76 percent of tribal areas have access to fixed broadband (which includes technologies such as T1, cable, DSL, and FiOS and excludes cellular data), compared to 98 percent of urban areas (Federal Communications Commission, 2024, pp. 32–33). In addition, whereas about 63 percent of urban households have at least two provider options at this speed, the same is true of less than 24 percent of rural households and 31 percent of households on tribal land (Federal Communications Commission, 2024, pp. 37–38). Rural Black and Latine households are particularly likely to lack access to affordable fixed broadband (Center for Public Education, 2023). The committee discusses the issue of broadband connectivity in more detail in Chapter 7.

Many policies and practices may also intensify spatial inequalities. Policymakers and practitioners often take a “place-neutral” approach when crafting policies and developing practices, failing to consider the unique funding opportunities and challenges or the strengths and needs of local communities that shape implementation, or they assume an urban context, with large schools and big population sizes (Brenner, 2022; Eppley, 2009; Johnson & Howley, 2015). Such approaches ignore the particular challenges presented by distance and sparsity, such as teacher shortages, multiage classrooms, long bus rides, or small staffs (and they also overlook the unique strengths of rural schools, such as nimbleness and close relationships). Related, much of the “evidence” cited in “evidence-based practice” comes from urban settings, especially given the difficulty in producing adequate n-sizes in many small, rural contexts. Thus, many policies may not work as intended in rural settings, and recommended practices—even so-called best practices—may be ill suited for rural communities and schools.

Rural Workforce

Although rural communities are often portrayed as primarily relying on agriculture, the workforce in rural America has become much more diverse (Davis et al., 2022; Laughlin, 2016). The six largest employment industries in rural U.S. areas are health, social assistance; accommodation, food services; government; retail; agriculture; and manufacturing. In addition, real estate, education, administrative and professional services, health, and finance are the highest growth industries in rural America (Davis et al.,

2022), and the number of clean energy jobs has also increased as a result of federal legislation (Davis et al., 2023).

On the other hand, agriculture, mining, logging, and other resource-dependent industries have all shrunk in recent years, because of resource exhaustion, mechanization, outsourcing, and industry consolidation (Green, 2020; Lyson & Falk, 1993). And the number of farms in the United States has dropped from a high of 7 million in 1935 to only 2 million in 2022 (Keller & Kassel, 2024), and jobs in extractive industries have been declining since 1920 (Freudenburg, 1992). Today, only about 10 percent of rural workers are employed in traditional rural industries like agriculture and mining (Laughlin, 2016), and another 12 percent work in manufacturing.

As explained in Chapter 1, the committee defines rural STEM occupations both as those that require focused STEM skills and expertise (e.g., jobs in the life sciences, physical sciences, engineering, mathematics and computer sciences, social sciences, and health care) and as those that require STEM skills but not an advanced STEM degree, such as construction; manufacturing installation, maintenance, and repair of infrastructure; and production.

Restructuring continues to impact rural economies (Smith & Tickamyer, 2011; Ulrich-Schad & Duncan, 2018), with automation, globalization, and resource depletion reducing rural jobs and wages (Freudenburg, 1992; Johnson & Lichter, 2019; Pender et al., 2019; Smith & Tickamyer, 2011). Employment in each industry varies greatly across regions. For example, in rural counties in the West a larger percentage of the workforce is employed in agriculture, forestry, fishing, hunting, and mining compared to the manufacturing industry (Laughlin, 2016).

Two factors have recently affected industries in rural America: technological advances and the COVID-19 pandemic (Davis et al., 2022). Technological advances in agriculture led to higher labor productivity, and the manufacturing and mining industries also saw an increase in labor productivity. And although the COVID-19 pandemic had a devastating effect on job and career opportunities in rural (and other parts of) America, as of 2023 they had almost fully rebounded (Davis et al., 2023). However, a lack of economic diversity in many rural places means that, as industries shrink and leave, rural workers have substantially fewer options than their urban or suburban counterparts: they must find employment in a new sector—underscoring the critical need for good workforce development in rural places—or leave to find work elsewhere, often in cities, where jobs are more available and better paid (Marré, 2017).

The demographics of the workforce in rural America have also changed. The working-age population in nonmetropolitan areas declined between 2010 and 2020 (Carnevale et al., 2024; Davis et al., 2022). Carnevale et al. (2024) note that some of the most common occupations

in rural America require physical labor and are more likely than urban jobs to result in workplace injuries. Given both the average age of the rural population and workplace injuries from physical labor demands, 15 percent of rural individuals have disabilities compared with 10 percent in urban areas.

While the largest share of workers in rural America are White, a recent study reports that “the rural Black, Asian, and Hispanic workforces each increased, with rural growth in the Hispanic workforce outpacing metro area growth. In addition, employment growth rates were higher for all other races and/or Hispanic workers than for White workers in every rural industry except agriculture” (Davis et al., 2022, p. 3). However, Carnevale et al. (2024) find gender, racial/ethnic, educational, and regional disparities in access to good rural job⁵ opportunities. Women in rural areas are less likely to have a good job than both rural men and urban women, and Asian, Black, and Latine individuals are less likely than both their urban counterparts and White workers (both urban and rural) to have a good job. Although fewer rural than urban individuals hold bachelor’s degrees, rural individuals with high school or associate’s degrees are more likely to have a good job than urban individuals with similar educational backgrounds. But rural workers with bachelor’s or advanced degrees are less likely than their urban counterparts to have good jobs, and across educational levels gender differences persist, a notable statistic in light of the average compensation for licensed rural public school teachers with a bachelor’s degree meeting the minimum pay required to be considered a “good job” ($53,750).⁶ Although over half of rural workers in the Northeast and Midwest areas of the United States have good jobs, across all regions the likelihood of having a good job is higher in urban areas compared to rural (Carnevale et al., 2024).

Rural Education

In Making the Invisible Visible: STEM Talent of Rural America (NSF, 2024), the NSF Committee on Equal Opportunities in Science and Engineering (CEOSE) describes five characteristics associated with rural education: It (a) takes place at a distance from large urban areas and (b) in small schools; (c) has access to fewer resources, such as high-quality professional development and curricula; (d) cooperates with and tries to meet the needs of the community and local economy; and (e) is placed or rooted in the lives of the

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⁵ A good job is defined as “one that pays a minimum of approximately $43,000 for workers ages 25–44 and a minimum of approximately $55,000 for workers ages 45–64 (in 2022 dollars)” (Carnevale et al., 2024, p. 8).

⁶ https://nces.ed.gov/programs/digest/d22/tables/dt22_211.10.asp

community. The committee notes that while these characteristics can reflect the experience of many rural schools, no one rural school must exhibit all or even most of these characteristics to be classified as rural, and many rural schools exhibit many more assets than are described here. For example, there are many large rural schools in Florida, Oklahoma, California, and elsewhere (Showalter et al., 2023).

Rural Districts and Schools

State definitions of district boundaries determine whether districts are classified as rural or not. In addition, NCES defines three subcategories of rural districts: fringe refers to a community less than 5 miles from a UC, distant districts are between 5 and 10 miles from a UC, and remote rural districts are more than 25 miles from a UC (Geverdt, 2019). However, the definitions do not discriminate between 10 miles of highway, dirt road, mountain switchbacks, or access by float plane. In western states, rural districts cover greater areas of land than in other states and are less likely to be designated rural because they include one or more urban areas and so are classified as urban, whereas districts in eastern and midwestern states are smaller and unlikely to include urban areas. Rural districts are more likely to be classified as remote in the northern and western U.S. regions (Gutierrez & Terrones, 2023).

In addition to the term rural, states use the words small, sparse, and isolated to describe certain school districts that may need additional support. The term small is generally based on an enrollment threshold (generally around 200 students), sparse is based on a student density threshold metric, and isolated is based on a school’s distance to other state districts with schools that teach the same grade level. However, each state sets its own metrics; in the 25 states that use the term small, enrollment thresholds vary from 244 in Wyoming to 24,000 in Florida, while in the 15 states that use sparse, student density thresholds vary from 1.2 students per square mile in Arkansas to 35 in Massachusetts. Some states’ definition of small categorizes most of their districts as small; for example, more than 70 percent of the school districts in Alaska, Idaho, Kansas, Louisiana, Nebraska, New Mexico, and Utah are considered small by their state definition. In contrast, just 20 percent or less of the school districts in Arkansas, North Dakota, and West Virginia are considered small by their state definition (Gutierrez & Terrones, 2023).

Roughly 7.3 million public school students are enrolled in rural school districts—more than one in every seven students across the United States. Nearly one in seven of those rural students experiences poverty, one in 15 lacks health insurance, and one in ten has changed residence in the

previous 12 months. Significantly, the number of children attending rural schools is greater than the number of students in rural school districts because many children attend rural schools in districts that are not designated “rural” by the National Center for Education Statistics (Showalter et al., 2023). Over half of U.S. school districts are rural, and in 28 states they may account for at least one third of public schools. In fact, “more students in the United States attend rural schools than attend the 100 largest U.S. school districts combined,” making rural students a critical population to pay attention to in policy, practice, and research (Showalter et al., 2023, p. 2). One in five U.S. students attends a rural school, half of which are considered small, enrolling fewer than 493 students (Showalter et al., 2023). However, because every state has its own methodology for defining school districts and determining whether they are rural, the percentages of rural students in some states may be underestimated; and this inconsistent methodology can compromise the availability of government support for rural school districts. Table 2-2 shows the number and percentage of rural students in each state.

Rural School Staff

Over a million staff—instructional, support services, and school- and district-level administrators—were employed in rural school districts in fall 2019 (Irwin et. al., 2022). The majority (69%) are school staff: teachers and instructional aides, counselors, assistant principals, principals, support staff, and librarians. All kinds of U.S. public schools and districts have had difficulties filling teacher vacancies and addressing teacher turnover (Ingersoll & Tran, 2023). Turnover rates in rural areas broadly (15.4%) mirror those in urban (17.7%) and suburban areas (15.1%), but on closer inspection, high-poverty rural public schools experience significantly more teacher turnover each year (28%) than high-poverty urban schools (19.1%) (Ingersoll & Tran, 2023). Addressing this turnover is critical, as there is a strong correlation between high teacher turnover and low student achievement, especially for students from low-income backgrounds and students of color (Djonko-Moore, 2016; Ronfeldt et al., 2013).

Rural students are often viewed as a monolith, but they are diverse in race and ethnicity, language, social class, and learning needs (Greenough & Nelson, 2015). There is a significant difference in the percentage distribution by race/ethnicity of public elementary and secondary students between rural communities and urban or suburban ones (NCES, 2023). Though the largest percentage of students enrolled in public schools is White (68%), approximately one in three students is Indigenous/Native, Asian, Pacific Islander, Black/African American, Latine/Hispanic, and/or multiracial (NCES, 2023); and 3.5 percent of rural students are multilingual learners

(Showalter et al., 2017). Across rural school districts, 13.6 percent of children aged 5–17 are experiencing poverty, and 15 percent of students have a documented disability and an Individualized Education Plan (Showalter et al., 2023). Among the parents/guardians of children in rural schools in 2019, 71 percent had education beyond high school: an associate’s degree (13%), bachelor’s degree (26%), graduate/professional degree (16%), or a vocational/technical certificate or some college (16%; NCES, 2023).

RURAL STEM EDUCATION AND WORKFORCE DEVELOPMENT LANDSCAPE

While it is not in the scope of this report to present an exhaustive discussion of future workforce trends in rural America, it is important to note that across all employment sectors, technical and digital skills are increasing in importance. There has been a growing demand for individuals to enter STEM fields (Harris & Hodges, 2018), across all employment areas and geographic zones. Digital literacy, facility with computerized equipment and software, and awareness of the benefits and pitfalls of data analysis and statistics pervade every discipline. These skills are grounded in STEM education subjects throughout the K–12 experience, and play a vital role in the transition from school to workforce. These trends are as true for rural schools, rural students, and rural employment as they are for urban or suburban settings.

However, there is a conflict in attitudes about STEM education in rural communities, between encouraging youth to explore STEM careers, which may result in out-migration, and pressuring them to remain close to home. Many jobs in STEM fields, especially those that require a bachelor’s degree and pay well, are located in major cities (Rothwell, 2013). Although STEM and related careers are available in rural communities, there is a notion that STEM careers exist only in urban and suburban areas. Rural families may be conflicted about their children pursuing STEM education if it may lead to a college education and then an adult life far from home—a cost that suburban and urban families, living in places rich with both colleges and STEM jobs, do not have to pay. Indeed, much research has documented the feelings of conflict that rural youth and parents alike experience around college going and its implications for their community (Corbett, 2007; Grimes et al., 2019; Hektner, 1995; Petrin et al., 2014; Tieken, 2016); for this reason, some parents may discourage college attendance (Grimes et al., 2019; Tieken, 2016). In addition, new STEM economies may be perceived as at odds with traditional industries, such as fishing or millwork or agriculture, such that pursuing a career in a new STEM-related industry feels like a rejection of local lifeways (Rush-Marlowe, 2024). For all these reasons, STEM education and workforce

development may be seen as perpetuating rural out-migration and furthering the decline of traditional rural industries—and, understandably, viewed with skepticism or misgivings.

The recent CEOSE report (NSF, 2024) highlights this tension as an important opportunity for NSF programming to improve awareness of local STEM employment opportunities. The out-migration concern of rural communities may also have funding implications. If a community believes that STEM careers are not available or accessible, they will not prioritize applying for those funding opportunities. Effective recruitment activities to combat out-migration should be considered in this context. STEM professionals who are trained in urban settings may not consider the opportunities that exist in rural areas when there is a lack of intentional exposure activities such as internships, place-based learning, and community-based experiential learning.

As of 2019 (NSB, 2021) STEM workers represented 23 percent of the total U.S. workforce, and this portion has been growing more rapidly than other workforce segments—three times faster than non-STEM over the past 20 years—and healthcare-related employment is expected to grow the most rapidly. In 2021 the National Science Board expanded its definition of STEM workers to include those that do not have bachelor’s degrees but do highly technical work. These skilled technical workers are in all employment categories but particularly in health care, construction (including electricians, carpenters, and plumbers), equipment installation and repair, production (including mining), and agriculture (including forestry, fishing, and hunting). With this expanded definition, the number of STEM workers in the United States more than doubled.

Another recent change in rural workforce trends is the shifting community demographics that result in older residents choosing to age in place (Davis et al., 2022). A larger aging population correlates to an increased need for healthcare workers in areas already experiencing workforce shortages. Hospitals are often one of the largest employers in rural communities. Because of staffing shortages, and given adaptations in the post-COVID-19 world, rural healthcare facilities, like those in other areas, are leaning into telehealth practices, which provide opportunities to expand both medical services and educational supervision of professionals. These new methods of healthcare practice will redefine the rural STEM workforce, requiring both digital and healthcare literacy.

Health care is not the only STEM-related rural workforce experiencing this transformation. A variety of other industries now rely on remote workers, who can live and work from wherever they choose so long as it has reliable internet access, a criterion that may compel residence in a nonrural area (Hylton et al., 2022). In some regions, for example in the Pacific Northwest and Mountain West states, the number of STEM job postings involving remote work in certain rural and small-town areas has risen

significantly and even outpaced urban areas since the COVID-19 pandemic (Western Governors University Labs, 2023).

Schools are often a major employer in rural areas, and capable STEM educators are essential to create learning opportunities that can inspire youth to pursue a STEM-related career, whether by leaving their home community or staying in a local STEM occupation, thus fighting out-migration trends and improving retention and community vitality. Effective STEM teachers can help ensure that every student, regardless of background and home community, has the confidence and, ideally, the opportunity to explore their potential and choose the path of their interest.

In terms of opportunities, place-based learning and industry engagement in rural communities are not always straightforward by nature because priorities for STEM education are not necessarily aligned with local industry needs. Partnerships between schools and industry can help bridge the gaps so that STEM education provides students with transferable skills that will be beneficial to professional growth and development regardless of their field of study and occupation.

Given increasing reliance on web-based services and technologies across all employment sectors, affordable and reliable internet access is central to the development of relevant skills, in addition to its direct impacts on the delivery of educational content and the support of individual curiosity and creativity. Pandemic-era programs made significant headway in bringing broadband connectivity to underserved schools and communities (predominantly rural and tribal), yet the digital divide persists. Studies in Michigan (Hampton et al., 2023) reported that while home broadband access improved in 2020–2021, much of it was through school-based hotspots, and access declined from 2021 to 2022 as school resources declined. Furthermore, any expansion of broadband access in rural America must be paired with training to enhance technical literacy. Rural residents who suddenly gain access to unfamiliar technology must learn how to use, understand, and manage it. Failure to provide adequate technical training could result in access without use or vulnerability to scams or unsafe digital environments.

SUMMARY AND CONCLUSIONS

This chapter provides a high-level overview of some definitions of rurality, potential consequences of different definitions on rural schools and communities, and the committee’s approach to the development of a more nuanced and useful definition of rurality. It discusses misconceptions about rural areas, demographic diversity in them, and the changing rural landscape. Finally, it provides an overview of rural K–12 STEM education and workforce development.

Conclusion 2-1: Multiple definitions of rural are used across and within federal agencies, making it difficult to both accurately identify the number of districts and schools served by federal programs and ensure that resources are equitably distributed.

Conclusion 2-2: Multiple definitions of rural are used by researchers, making it difficult to aggregate findings across studies in order to build a rigorous knowledge base about what works to improve rural STEM education and workforce development.

Conclusion 2-3: Most federal agencies base their definitions of rural on one of two sources, one developed by the U.S. Census Bureau and the other by the Office of Management and Budget. Both define rural mainly as nonurban. This approach fails to adequately capture important characteristics that vary across rural areas, such as population density and remoteness.

Conclusion 2-4: Many rural areas are undergoing substantial demographic shifts and will continue to do so. While approximately 20 percent of those living in rural communities are people of color, almost a third of children under 18 in rural communities are people of color. K–12 STEM education and workforce development need to be responsive to these changes.

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