Abstract
Scholarship and popular opinion regard cities as more racially and ethnically diverse than rural communities. However, recent trends hint at the possibility of less distinctive diversity profiles on either side of the metro-nonmetro divide. To explore this, we compare the magnitude and structure of ethnoracial diversity in more than 27,000 census-defined places arrayed across ten different types of county contexts that spanned the rural-urban continuum in 2010. Even as average residents’ exposure to diversity steadily declines as contexts become more rural and remote, place-based (unweighted) results show an uneven pattern of diversity across most of the continuum. Multivariate analysis supports the unevenness scenario: when place characteristics are taken into account, many of the associations between type of context and diversity weaken to the point of nonsignificance. Taken together, these findings suggest a blurring of rural-urban boundaries with respect to community ethnoracial composition.
Population diversity has long been considered a defining feature of the urban environment. As Chicago School sociologist Louis Wirth (1938) put the matter in his treatise on urbanism, cities are not only large and densely settled but heterogeneous in composition. Age, household type, socioeconomic status, and race-ethnicity rank among the major demographic dimensions of diversity. The last dimension is arguably the most consequential, given its correlations with the others. During Wirth’s career, the racial diversity of northern U.S. cities increased in response to the Great Migration of blacks from the rural South. Metropolitan ethnoracial diversity has risen even more impressively since the 1970s thanks to the arrival of Latinos and Asians, two immigrant groups with youthful age structures and high rates of natural increase (Frey 2015; Lee, Iceland, and Farrell 2014). Whatever the effects of the recent diversification trend—on the economy, politics, education, and social relations (see, e.g., Hopkins 2009; Lichter 2013; Portes and Vickstrom 2011)—these effects are assumed to be strongest in the nation’s gateway cities. A large number of these cities now have majority-minority compositions.
The multiethnic character of large cities contrasts sharply with accepted wisdom about rural communities. These communities are often perceived as homogeneous in many respects, including their racial-ethnic mix (Lichter and Brown 2011). Certainly the “Lake Wobegon” image of places in the nonmetropolitan Midwest inhabited by descendants of Northern European immigrants has a factual historical basis (Lieberson and Waters 1988). Rural white homogeneity can also be traced to blatant forms of discrimination that kept people of color from settling in “sundown towns” (Loewen 2005) and, more recently, to the redrawing of municipal boundaries for exclusionary purposes (Lichter et al. 2007). Even when nonwhites are present, they may live in equally homogeneous settings—think African Americans in small “Black Belt” places, Mexicans throughout South Texas, or American Indian residents of reservation communities. Such examples, which reinforce popular stereotypes, stress the wide gulf between low-diversity nonmetro communities and their high-diversity metropolitan counterparts.
Our research proceeds from the premise that the destinations of international and domestic migration flows have changed sufficiently over the past half century to challenge this conventional view. Since the Immigration and Nationality Act of 1965, shifts in immigration-related policies at the federal, state, and local levels have increased foreign-born persons’ freedom of movement within the United States while deflecting them from established gateways (Light 2006; Massey 2008; Tienda and Sanchez 2013). Many members of immigrant-rich groups are leaving behind saturated urban markets—where they traditionally have concentrated in ethnic enclaves—for the economic opportunities, affordable housing, and quality of life available in suburban and nonmetropolitan places. Their ruralward shift is especially striking, motivated in part by low-skill labor demand in agricultural processing, oil and natural gas production, and other sectors (Kandel and Parrado 2005). Similarly, rural retirement and amenity destinations are drawing immigrants to fill construction and service jobs (Johnson and Lichter 2013; Nelson, Lee, and Nelson 2009). Recruitment efforts and ethnic social networks continue to increase awareness of rural employment among Hispanics in particular, who show signs of further geographic dispersion outside the West and Southwest (Kandel and Cromartie 2004; Lichter and Johnson 2006).
As a result, nonmetro places have become more ethnoracially diverse, sometimes in dramatic fashion. A robust literature documents the nonmetro diversification trend and weighs its potential consequences for communities unaccustomed to incorporating minorities (Crowley and Ebert 2014; Lichter 2012; G. Sharp and Lee 2016). Consistent with the “blurring” theme of this volume, some of these communities qualify as “urban” in Census Bureau terms, exceeding a population threshold of 2,500 by themselves or as part of a cluster of adjacent places. Others are close to metro areas, a fact that increases their chances of being absorbed through centrifugal expansion or, at minimum, of being subject to metropolitan influences. This urbanization of rural America has been fueled by transportation advances, innovations in information technology, globalization, industrial restructuring, growing corporate dominance, and the pull—for recreation or extraction—of natural resources (Brown 2014; Lichter and Brown 2011). At the same time, many metro areas contain swaths of sparsely settled territory and nontrivial rural populations. The persistence of rurality in metropolitan settings is most evident in exurban or fringe zones, which tend to attract affluent white inhabitants (J. Sharp and Clark 2008). More generally, the share of racial residential segregation due to place-level sorting has increased over time, yielding homogeneous communities in the midst of metro-wide diversity (Lichter, Parisi, and Taquino 2015).
We evaluate a key implication of these intersecting trends: that the diversity profiles of rural and urban communities may no longer be as distinct as commonly thought. Our investigation addresses several limitations of existing research, including the inconsistent conceptualization and measurement of diversity and the rarity with which both rural and urban diversity are considered in the same study. Moreover, when rural-urban comparisons on any characteristic (not just diversity) are made, they tend to be crude, for example, between places inside and outside of metro areas or of different population sizes. Such approaches, which treat rural and urban in dichotomous or unidimensional fashion, may not adequately reflect the complexity of community types emerging at the new rural-urban interface. Neither do they recognize the potential for variation among communities within different kinds of metropolitan or nonmetropolitan settings. A preferable strategy, adopted here, emphasizes gradations along a rural-urban continuum, with a community’s position on the continuum determined by aspects of the surrounding context in which it is embedded.
Our analysis compares 2010 patterns of ethnoracial diversity for more than 27,000 places found in the largest metro areas, the most remote rural counties, and a variety of contexts in between. The majority of the places are incorporated as cities, suburbs, towns, or villages, while others (termed census-designated places) lack municipal status; both types of places constitute meaningful social, symbolic, and institutional entities. We assign each place to one of ten rural-urban continuum categories based on the metropolitan status or proximity of its host county and, in nonmetro instances, the size of that county’s urban population. Comparisons across categories allow us to address three central questions. First, how does the magnitude and racial-ethnic structure of diversity for places vary by position on the continuum? Second, are the average residents of places along the continuum exposed to similar or different diversity magnitudes and structures? And third, does county context capture features of places, such as housing and labor market characteristics, that are associated with ethnoracial diversity? Our answers to these questions shed new light on the ways in which contemporary forms of spatial interdependence can complicate traditional binary thinking about rural homogeneity and urban heterogeneity.
Background
Dimensions of diversity
The term diversity is regularly used in loose fashion to refer to the presence of African Americans, Latinos, or some other minority group in a community. Here we stick to a more precise demographic definition that emphasizes two aspects of the ethnoracial composition of the local population. The magnitude or level of diversity is determined by the number of racial-ethnic groups that make up the population and their relative sizes (White 1986). A place comprising many groups of equal size would be judged highly diverse. Obversely, homogeneity prevails when all residents belong to the same group, that is, when the population exhibits an absence of diversity. Several statistics are available with which to capture the magnitude dimension of diversity. We favor the entropy index (described in the methodology section) for both its conceptual congruence and its desirable statistical properties.
Diversity also varies in terms of racial-ethnic structure, or the specific groups constituting a population. The importance of the structural dimension lies in the fact that it may differ among places with identical diversity magnitudes. As an illustration, the entropy index values for a pair of “50-50” communities—one half white and half Asian, the other half black and half Hispanic—would be exactly the same despite the likelihood of divergent socioeconomic mixes and interethnic relations in the two communities. To understand ethnoracial diversity, we must know which groups are present, not just their number and sizes. Compositional bar graphs and a “majority rule” typology allow us to distinguish among places on the structural dimension.
Another valuable distinction can be made between place-based (or unweighted) and person-based (or weighted) diversity. In the former, all communities—from the principal cities of major metropolitan areas to the smallest rural villages—are considered conceptually equivalent and thus count the same from an analytic standpoint. However, more people in total may reside in the largest cities despite the greater number of villages. This uneven distribution of the population across types of communities suggests that larger places should count more (i.e., be weighted by size). It also means that the level or structure of place diversity experienced by the average American could differ from the average level or structure of place diversity if, as Wirth (1938) predicted, a positive relationship exists between community size and diversity. Given the validity of both approaches, our research compares person-based diversity exposure to place-based diversity across and within rural-urban continuum categories. Previous studies of which we are aware have chosen one or the other.
Urban versus rural?
A glaring lacuna in the diversity literature is the failure to examine communities over the full range of the rural-urban continuum in a single analysis. Due to minority overrepresentation in cities and the perceived homogeneity of rural settlements, diversity research focuses primarily on metropolitan areas, places, and neighborhoods (Frey 2015; Lee, Iceland, and Sharp 2012; Logan and Zhang 2010). Across metro units of varying spatial scale, rapid Hispanic and Asian growth has combined with absolute or relative white declines to erode the demographic primacy of whites, especially in the South and West. Most large cities now feature multigroup racial-ethnic structures, and fewer suburbs conform to the all-white image embedded in popular culture (Berube 2003; Hall and Lee 2010). Still, many metro places are dominated by one ethnoracial group, and some have even become more homogeneous over time (Lee and Hughes 2015).
A few recent studies do include both metropolitan and nonmetropolitan units. Parisi, Lichter, and Taquino (2015) document a substantial increase between 1990 and 2010 in the number of metro and nonmetro places with four-group racial-ethnic structures (white-black-Hispanic-Asian), although the metro-nonmetro gap in mean diversity magnitude widened during that period. A 1980 to 2010 comparison of diversity in metropolitan and micropolitan areas rather than places yields similar findings (Lee, Iceland, and Farrell 2014). Hall, Tach, and Lee (2016) show that the steepest upward-sloping trajectories of diversity change have disproportionately involved metro places instead of micropolitan or rural ones. Population size, an alternative to the metro-nonmetro dichotomy, exhibits a consistent positive correlation with ethnoracial diversity (Allen and Turner 1989; Hall and Lee 2010; Lee, Iceland, and Sharp 2012). However, it only taps a single dimension of the rural-urban continuum.
What is missing—and what we aim to provide—are finer-grained distinctions among the larger settings in which places are located. Metropolitan areas, for example, vary dramatically in population size, as the contrast between New York (18.9 million residents in 2010) and Carson City, Nevada (55,274) attest. Nonmetro counties vary as well, not only in size but in degree of urbanization and distance to the nearest metropolis. Intuitively, such features of areal contexts would appear to have implications for the diversity of their constituent places. Imagine three towns of a few thousand inhabitants each, one situated in a top-ten metro area, one in a nonmetro county adjacent to a medium-size metro area, and one far removed from any type of metropolitan environment. Because influences of the surrounding context are presumably strongest in the first town, the traditional urbanism paradigm espoused by Wirth (1938) would lead us to anticipate high diversity there as immigrants and minority groups—and the forces attracting them—disperse throughout the metropolis. These dynamics may operate to some degree in the second hypothetical town but be weakest in the third.
Fortunately, classification systems are available that capture basic differences across metropolitan and nonmetropolitan contexts. The U.S. Department of Agriculture’s (USDA) nine-category rural-urban continuum (or RUC) scheme is particularly useful (USDA 2013). Its first three categories, which we expand to four, differentiate metropolitan counties by the total population size of the metro area to which they belong. The remaining six distinguish among nonmetropolitan counties based on the size of their urban population and proximity (adjacency) to a metro area. The RUC scheme facilitates a shift from dichotomous, urban versus rural thinking to a perspective that identifies gradations along a continuum of county contexts that are multidimensionally defined. The scheme’s conceptual and operational advantages have made it popular in research on other topics. To date, though, it has not been used to study place ethnoracial diversity (but see Winkler and Johnson 2016).
Research questions
By sorting places into their appropriate RUC categories, we can shed empirical light on three descriptive research questions. The first question asks how the magnitude and racial-ethnic structure of place-based diversity varies over the rural-urban continuum. From a classic urbanism point of view, the most likely scenario would be a linear decline in diversity as one moves away from the largest metropolitan settings and toward the least urbanized, most isolated rural contexts. A threshold or stairstep pattern could occur if the metro-nonmetro distinction is meaningful, that is, if the drivers of ethnoracial diversity are present in all metropolises regardless of size but are absent from nonmetropolitan America. The blurring of rural and urban domains noted earlier, however, suggests more unevenness across continuum categories. For instance, places in highly urbanized nonmetro counties that are adjacent to large metropolitan areas might be as diverse as—if not more diverse than—their counterparts located in small metro contexts. Carried to an extreme, blurring logic yields the null hypothesis: that diversity levels and racial-ethnic structures for places do not vary by context, given the limited relevance of traditional urban-rural or metro-nonmetro distinctions in the contemporary United States.
Our second question reframes the first in person- rather than place-based terms. Namely, do the average residents of places at various points along the rural-urban continuum experience different or similar forms of ethnoracial diversity? The same hypothetical patterns just mentioned—linear decline, threshold, uneven, null—also apply to this question, yet the pattern receiving the most support need not be the same as for the first question. If people are disproportionately concentrated in larger places across and within the RUC categories, the exposure of individuals to diversity (which is what weighting places by population size tells us) will diverge from the place-based results (when places are weighted equally regardless of size).
The final research question inquires about correlates of the magnitude and structure of diversity among places. We are interested in whether the RUC categories independently predict place diversity or whether each category proxies more detailed characteristics of places found within a particular type of context. Three salient sets of characteristics have been identified in prior investigations (Allen and Turner 1989; Lee, Iceland, and Sharp 2012; Hall and Lee 2010; G. Sharp and Lee 2016). With respect to the context of reception provided by a place, location in the West or South (closer to Hispanic and Asian countries of origin) and a critical mass of foreign-born residents, which implies access to co-ethnic resources (social capital, employment niches, etc.), are related to higher diversity and more balanced racial-ethnic structures. A larger retirement-age population, however, tends to undermine ethnoracial diversity, perhaps because it signals a stagnant economy unattractive to immigrants and minorities or because its members are uncomfortable living near these groups. The disproportionate whiteness of persons 65 and older probably contributes to the association between retirees and diversity as well (West et al. 2014).
A place’s housing and labor market characteristics, reflective of economic opportunity, must also be considered. Access to housing—indexed by features such as affordability, an abundance of rental units, and new construction activity—is a draw for most ethnoracial groups and hence should promote diversity. Higher incomes and lower unemployment rates have similarly widespread appeal. So does a local economy with types of jobs that suit a wide range of educational and skill levels.
Last, places serving as institutional hubs for government, the military, and higher education are more likely to have diverse, multigroup compositions. Committed to affirmative action, these institutions provide avenues of upward mobility for people of color. The presence of correctional facilities, a less voluntary kind of institution, is also positively associated with diversity in a community.
Methodology
Places as units
To address our three questions about variation in ethnoracial diversity across the rural-urban continuum, we extract data from the 2010 decennial census and the 2008–2012 American Community Survey (ACS) five-year summary file for 27,163 places with at least 100 residents. These places comprise cities, suburbs, towns, and villages that contain 74 percent of the total U.S. population, or 228.4 million people. Two-thirds of the places are incorporated, with legally vested powers and obligations, and the largest—principal cities of major metropolises—often approximate housing and labor markets. Among other duties, incorporated places are responsible for developing fiscal or policy responses to diversity-related issues that occur inside their boundaries. The remaining third of the sample consists of unincorporated or census-designated places. Residents recognize both incorporated and census-designated places by name and may feel some degree of attachment to them. More concretely, the ethnoracial diversity of a place influences the composition of local neighborhoods, schools, work settings, and voluntary organizations, not to mention the social relationships that form in these venues. On a number of criteria, then, places qualify as “real” communities in addition to being convenient statistical aggregations.
While most Americans live in our sample places, roughly one-fourth do not. The 80.4 million people excluded from the sample either reside in places with fewer than 100 inhabitants or are part of the nonplace population. They are also disproportionately white (80.2 percent versus 58 percent of those in places of 100 or more) and overrepresented in the six nonmetropolitan county contexts identified by the RUC classification scheme. These differences limit the applicability of our results to communities that exceed the minimal (100-person) size threshold imposed here.
Measuring diversity
We employ 2010 census tabulations to delineate six panethnic groups that form the building blocks for our diversity measures: Hispanics of any race, non-Hispanic whites, blacks, Asians (including Pacific Islanders), Native Americans (American Indians and Alaska Natives), and all other non-Hispanics (multirace and other-race individuals). Counts of these panethnic groups have been assembled for each place and, taken together, provide exhaustive, mutually exclusive coverage of the local population.
The panethnic counts are used to capture the two diversity dimensions of interest. The first dimension, magnitude, reflects how evenly residents of a place are divided among the six panethnic groups. We operationalize diversity magnitude with the entropy index, symbolized by E (White 1986). E takes a maximum value equal to the natural log of the total number of groups, or 1.792 in our six-group case. For ease of interpretation, we divide each diversity score by this theoretical maximum and multiply by 100, resulting in a 0 to 100 range of possible values. A diversity score of zero indicates complete homogeneity: that only one group inhabits a place. Of the 221 such places in our sample, 176 have all-white populations, 43 are entirely Hispanic, and 2 are entirely Native American. At the opposite extreme, a community that contains identical shares (16.7 percent) of each of the six groups would receive a score of 100. Although none of our places reach that level, the village of Hillburn, New York (near New York City), comes closest with an E score of 87.1.
Racial-ethnic structure, the second dimension of diversity, refers to the specific groups that live in a community. Our investigation captures this dimension in a couple of ways. At various points E scores are accompanied by bar charts that convey the group proportions underlying the magnitude of diversity. We also utilize a majority rule typology (see Farrell and Lee 2011) that classifies communities as group-majority (white-majority, Hispanic-majority, etc.) if one ethnoracial group constitutes more than 50 percent of the total population. White-majority places are further subdivided into white-dominant, in which whites are at least 90 percent of the local population, and white-shared, in which the white percentage is more than one-half but less than nine-tenths. We name white-shared subtypes based on any minority groups that constitute 10 percent or more of all residents (e.g., white-Hispanic, white-black-Hispanic). No-majority places are defined as those where none of the ethnoracial groups exceeds the 50-percent threshold. Applying this typology, we are able to gauge how places with particular kinds of racial-ethnic structures are distributed along the rural-urban continuum.
Rural-urban classification
The nine-category classification scheme developed by USDA’s Economic Research Service allows us to assign places to different types of metropolitan and nonmetropolitan county contexts. We have revised the original scheme slightly, dividing the most urban category (RUC1) into counties in super metro and large metro areas (RUC1a and 1b, respectively). Operational definitions for the ten county contexts are as follows:
• Super metro (RUC1a)—county in metro area of 3+ million population; place N = 3,585;
• Large metro (RUC1b)—county in metro area of 1 million to 2,999,999 population; place N = 3,393;
• Medium metro (RUC2)—county in metro area of 250,000 to 999,999 population; place N = 4,639;
• Small metro (RUC3)—county in metro area of less than 250,000 population; place N = 3,372;
• High-urban proximate (RUC4)—nonmetro county with urban population of 20,000+, adjacent to metro area; place N = 2,472;
• High-urban distant (RUC5)—nonmetro county with urban population of 20,000+, not adjacent to metro area; place N = 808;
• Low-urban proximate (RUC6)—nonmetro county with urban population of 2,500 to 19,999, adjacent to metro area; place N = 4,008;
• Low-urban distant (RUC7)—nonmetro county with urban population of 2,500 to 19,999, not adjacent to metro area; place N = 2,474;
• Rural proximate (RUC8)—nonmetro county with urban population less than 2,500, adjacent to metro area; place N = 939; and
• Rural distant (RUC9)—nonmetro county with urban population less than 2,500, not adjacent to metro area; place N = 1,473.
Each continuum category contains a substantial number of places (from a low of 808 in RUC5 to a high of 4,639 in RUC2) and thus permits more nuanced comparisons of diversity magnitude and structure across a wider range of community contexts than usual.
Other variables
In the multivariate portion of the analysis, we use three sets of place characteristics to assess the robustness of any zero-order associations detected between diversity and position on the rural-urban continuum. The local context of reception is captured with indicators of region, immigrant presence, and the retirement-age population. Four conventional census-defined regions are recognized: Northeast, Midwest, South, and West. We operationalize immigrant presence as the percentage of foreign-born residents in a place. Despite potential collinearity concerns, this variable is only moderately correlated with the entropy index (r = .45), and some of the most immigrant-heavy communities prove to be among the least diverse (e.g., all-Hispanic towns in Texas and California). The percentage of residents 65 years of age or older constitutes our measure of the retirement-age population.
The second set of place variables covers aspects of the housing and labor market that may make a place more or less attractive to multiple ethnoracial groups. Housing characteristics include the stock of new homes (measured as the percentage of units built since 2000), the percentage of renter-occupied units, and rent burden (median rent as a percentage of household income). We represent labor market opportunities with median household income in the past 12 months, the percentage of civilians 16+ years old who report being unemployed, and an occupational diversity variable that reflects the range of job types available in a community. The diversity variable, constructed using the entropy index, quantifies how evenly workers are distributed across five general occupational categories extending from professional (management, business, science, and arts occupations) to blue collar (production, transportation, and material moving occupations).
Finally, we develop four measures of institutional hub status that denote whether a place specializes in government, military, higher education, or correctional functions. Places qualify as government hubs if the percentage of their employed residents holding federal, state, or local government jobs is at least double the percentage in the total (summed) place population nationally (coded 1 if yes, 0 otherwise). The same threshold has been incorporated into the remaining measures. For military specialization, the share of a place’s labor force participants employed in the armed forces must be two times (or more) greater than the national percentage. In the case of educational and correctional specialization, we apply the doubling rule to the percentage of local residents who are enrolled in college or who are incarcerated in adult or juvenile facilities, respectively.
Results
Place-based diversity
Our initial research question concerns how ethnoracial diversity varies across places in different contexts along the rural-urban continuum. Average E scores, which tap diversity magnitude, are reported for the ten RUC categories at the right edge of Figure 1. At first glance, the diversity of places located in super metro counties stands out: their mean E (41.9) is twice that for the nonmetro places located in rural distant counties. The largest principal cities (populations greater than 300,000) of the super metro counties epitomize the high diversity found in this context, with an average E of 67.7. But changes in diversity are hardly monotonic as one moves from the top to the bottom of the figure. Nonmetro places in the high-urban distant and low-urban proximate contexts, for example, reach the same diversity levels as their medium and small metro counterparts. In fact, the mean E for the high-urban distant places—which often contain one or two sizable minority groups—equals the mean for the large metro places. Taken together, these mean patterns best conform to the unevenness scenario.
Mean Place Diversity by RUC Category
The overlap in diversity across the rural-urban continuum becomes clear when we examine the boxplots of E scores in Figure 2. What initially catches the eye is the impressive amount of variation that exists in place diversity within each RUC context. A comparison of the boxplots also reveals the elevated diversity levels of super metro places. Observe, however, that the value of E at the 75th percentile for these places falls between the 25th and 75th percentiles of the distributions for the nine other continuum categories. Places at all points on the continuum have long top “whiskers” as well, with the most diverse places in five of the six nonmetro contexts exhibiting Es between 76 and 85. Thus, there seems to be little evidence of linear or threshold diversity declines with decreasing urban character, aside from the pronounced downward shift between the super metro and large metro contexts.
Distribution of Place Diversity (E) by RUC Category
The racial-ethnic structures of communities in different continuum categories also appear broadly similar. Returning to Figure 1, the segments that make up each compositional bar reflect the representation of panethnic groups in the average place within that category. Whites constitute seven-tenths or more of place residents across the board, and Native Americans are overrepresented in nonmetro places, especially the low-urban distant and rural distant types. In the case of other groups, generalizations are less obvious. Blacks constitute a larger share of place populations in rural proximate settings than anywhere else. Hispanics reach double-digit percentages in super metro, medium metro, and high-urban distant places. Asian percentages, though relatively small, are highest in super metro and high-urban distant places, similar to the Hispanic pattern.
Our majority-rule typology helps to unpack the means summarized by the compositional bars. In online appendix Table A1, we show the distribution of places across types within each continuum category (see the online version of the article for the appendix). Once again, the distinctive ethnoracial structures of places in super metro counties can be seen. Almost three-fifths of these places are white-shared (whites in the numerical majority but less than 90 percent of the total population), with white-multigroup and white-Hispanic communities the most common subtypes. Super metro places are also the least likely to be white-dominant (whites making up 90 percent or more of all residents) and the most likely to qualify as no-majority. In contrast, the frequency of white dominance exceeds that of a white-shared composition in every continuum category other than the super metro context, sometimes by wide margins, and no-majority places are rare. With respect to majority-minority structures, rural proximate counties boast the greatest share of black-majority places, medium-size metro areas the greatest share of Hispanic-majority places and rural distant counties the greatest share of Native American–majority places.
Two lessons emerge from the place-focused portion of the analysis. First, places in super metro areas have higher diversity levels and more complex racial-ethnic structures than places elsewhere, on average. Oakland and Jersey City exemplify this point: their E scores fall in the mid-80s and they contain roughly equal percentages of white, black, Hispanic, and Asian residents. The second lesson is that sharp deviations in diversity patterns are not apparent over the rest of the rural-urban continuum; instead, a degree of unevenness prevails. Put another way, nonmetro places can be quite diverse, resembling their metro siblings. As an illustration, Unalaska, Alaska—in the rural distant category—displays a diversity magnitude (E = 84) that rivals those of Oakland and Jersey City and is driven by nontrivial proportions of Asians (34.1 percent), whites (33.7 percent), and Hispanics (15.2 percent). Other no-majority places in nonmetropolitan America include Winslow, Arizona (in the high-urban proximate category), Nanawale Estates, Hawaii (high-urban distant), Andarko, Oklahoma (low-urban proximate), and Crescent City, California (low-urban distant). All have entropy scores above 75 but varying combinations of panethnic groups.
Diversity exposure
Unlike the first question, which treats every place as equal, the second question guiding our research recognizes the varied distribution of populations among communities. Specifically, it asks how person-based (or weighted) estimates of ethnoracial diversity compare with place-based (unweighted) estimates not only for the total sample but across and within the ten contextual categories that make up the rural-urban continuum. For the sample as a whole, the weighted E reaches 50.5. This signifies the diversity level to which the average inhabitant of our 27,163 places was exposed in 2010. The racial-ethnic structure experienced by that hypothetical inhabitant remains primarily white (58 percent) but with nontrivial shares of Hispanic (19.5 percent), black (13.7 percent), and Asian (5.8 percent) dwellers. By contrast, unweighted or place-based means for the total sample indicate a much lower magnitude of diversity (E = 28.1), greater white representation (77.6 percent), and mean minority-group shares below 10 percent.
Such differences are consistent with the notion that exposure to diversity falls in a roughly linear manner as county contexts become less urban. The weighted E scores and compositional bars in Figure 3 support this inference. With the exception of the high-urban distant category, diversity magnitude declines rather steadily from the super metro to the rural distant end of the continuum. While the typical denizens of rural distant and rural proximate places encounter white-dominated homogeneity, ethnoracial heterogeneity is the norm for people living in super, large, and medium metro areas. These people experience high levels of diversity—Es ranging from the mid-40s to nearly 60—and racial-ethnic mixes in which 40 to 50 percent of their fellow residents are people of color. Hispanics constitute the largest minority in the super, large, and medium metro categories (roughly one-fifth of the population), followed by blacks (10–15 percent) and Asians (4–8 percent).
Exposure to Place Diversity by RUC Category
A category-by-category comparison of the Figure 3 results with those in Figure 2 shows person-based diversity levels to be higher than place-based ones across the board. That is, within each type of RUC context, more individuals live in bigger places that tend to be more diverse. We illustrate the principle in online appendix Table A2 (see the online version of the article for the appendix), using the ten largest places in the United States. Eight of the ten are located in super metro areas; the two that are not, San Antonio and San Jose, anchor large metro contexts. Diversity levels in most of the places substantially exceed that of the average super metro resident (E = 59.0), with New York leading the way (79.7). Aside from Hispanic-majority San Antonio, the cities are all no-majority in nature: the fact that three of them have white pluralities, two have black pluralities, and four have Hispanic pluralities attests to their varied multigroup racial-ethnic structures.
Accounting for diversity
As documented earlier, large cities do not enjoy a monopoly on diversity. Flows of migrants among RUC settings, immigrant moves to new destinations, and other redistribution processes have produced ethnoracially heterogeneous places in nonmetropolitan as well as metropolitan areas. Our final research question asks whether such variation in diversity across the rural-urban continuum reflects differences in the characteristics of places. To address this question, we first employ ordinary least square (OLS) estimation procedures to regress diversity magnitude (E) on nine dummy variables tapping the ten RUC categories (with the rural distant category, RUC9, the omitted reference). We then add the context of reception, housing and labor market, and institutional hub measures identified earlier. The same two-step approach is used to estimate logistic regression models in which dichotomous measures of the no-majority and white-shared types of racial-ethnic structure serve as dependent variables. Table 1 summarizes the results from the regression.
Regression Models of Diversity Magnitude and Majority-Type Structures on RUC Categories and Other Place Characteristics
NOTE: N = 27,163. See text for operationalization of place characteristics.
p < 05. **p < .01. ***p < .001.
In the partial model for diversity magnitude, all RUC variables have significant positive associations with E relative to the rural distant category, and the largest effect is for location in a super metro setting. After the other place attributes are entered, however, the size of the super metro coefficient shrinks by roughly 50 percent, and the remaining RUC coefficients are diminished as well, some becoming nonsignificant or negative. A similar pattern can be seen in the pair of logistic regression models for no-majority status. This similarity is not surprising since, by definition, E values will rise as community racial-ethnic structures incorporate more groups. Although positive and significant RUC coefficients are common in the partial model, most of them—including the super metro coefficient—fail to attain statistical significance in the full model or they reverse direction. And in the regressions for the white-shared type of structure, the super metro coefficient is the only positive and significant RUC indicator in the full model. These findings are robust to various model specifications (e.g., omission of percentage foreign born). More importantly, they demonstrate that position along the rural-urban continuum hardly seems decisive in predicting the diversity levels and structures of places.
On the other hand, a number of place characteristics, proxied in part by the RUC categories, do play a central role. As anticipated, the coefficients for the full models show high E values and diverse racial-ethnic structures to be more likely in places with receptive contexts (i.e., located in the South or West, having more immigrants, and having few older residents). Indeed, the context of reception measures make the largest contribution to overall model fit of any set of independent variables. Abundant rental housing, higher median income, and functional specialization as a military or corrections hub are also conducive to place diversity in at least two of the three models. Yet some discrepancies can be seen across models in how the predictors operate. The local unemployment rate, for example, exhibits the hypothesized negative association with diversity magnitude and no-majority status but is positively related to the white-shared type of structure, contradicting our reasoning about the appeal of economic opportunity. And occupational diversity, which reveals the expected positive relationship with our first two diversity measures (magnitude and no-majority status), also switches sign in the white-shared model.
Conclusion
This is the first study to examine racial and ethnic diversity across the rural-urban continuum for a full range of communities, encompassing small hamlets in the countryside as well as cities teeming with millions of inhabitants. The main substantive lesson from our study—consistent with the spatial interdependencies highlighted in this volume—is that diversity should no longer be considered an exclusive property of metropolitan America. When places are treated as equivalent (unweighted) units, diversity variation among RUC contexts is modest and conforms most closely to the unevenness scenario; places in super metro settings represent the lone outlier. At the same time, the person-based (weighted) approach offers some support for conventional wisdom about differences in diversity experienced by average metropolitan and nonmetropolitan residents. Thus, another lesson emerges from our first two research questions: the distinction between person- and place-based diversity matters.
Multivariate analysis pertinent to our third question provides additional evidence favoring the unevenness perspective. When we include relevant characteristics of places as predictors, many of the associations between the RUC categories and ethnoracial diversity become nonsignificant or take signs contrary to the linear decline hypothesis. Put another way, the RUC measures partially reflect whether places have contexts of reception, housing and labor market features, and institutional hubs that are more or less compatible with diversity. 1 Future research should explore how best to capture the larger settings that surround places. The USDA’s RUC codes, for example, might be modified in fruitful ways (Winkler and Johnson 2016), or other classification schemes might be tried. Ideally, multilevel modeling strategies will move us beyond the dummy-variable representation of rural-urban context, illuminating how the detailed social and economic aspects of counties or areas shape the diversity of places nested within them.
Multilevel models could also prompt closer examination of aspects of place as well as context. One intriguing attribute is place population size, a simple unidimensional measure of rurality-urbanism that we criticized previously. A supplemental analysis (available upon request) reveals that the population of a place is strongly related to diversity, as Louis Wirth (1938) would have expected: the larger the place, the higher its diversity level and more complex its racial-ethnic structure. The positive and significant effect of size persists when separate multivariate models are estimated for places within each of the RUC contexts. This last finding suggests that something about the demographic scale of a place influences ethnoracial diversity irrespective of the context in which the place is embedded. Perhaps the “critical mass” principle proposed by Fischer (1976) operates well below typically urban thresholds. That is, even for smaller communities an incremental increase in population size may be sufficient to boost the likelihood that groups will coalesce around a shared attribute or interest such as race-ethnicity, form supportive networks and organizations, and ultimately attract additional members from elsewhere.
Its limitations notwithstanding, the current study upends long-held assumptions about urban heterogeneity and rural homogeneity. Our results suggest that if one measures urbanism by ethnoracial diversity, then a new urbanism has spread across rural and suburban spaces in a manner unforeseen by Wirth (1938). We regard the similar diversity patterns among places in most types of contexts as another manifestation of the blurring of traditional metro-nonmetro boundaries. Of course, similar diversity levels and structures may still have quite different consequences. Because of their extensive histories as destinations for immigrants and minorities, many metropolitan communities are accustomed to dealing with the educational, healthcare, housing, and other needs of a diverse population. Yet the recency and pace of ethnoracial diversification experienced by some nonmetro places poses challenges in virtually every institutional domain. To give but one illustration, the heavy rural vote for Donald Trump during the 2016 presidential election may have partly channeled a sense of anxiety among white inhabitants of small towns that was aroused by the growing presence of Latinos. Will these whites attempt to maintain political control at the local level, or will they move to a less threatening (i.e., less diverse) residential environment? In general terms, the issue is whether the potential benefits of diversity for rural places—especially the demographic, economic, and cultural revitalization that ethnic newcomers can bring—exceed the perceived costs to cohesion and quality of life. If not, increasing diversity could lead to greater intergroup avoidance and conflict rather than integration.
Footnotes
NOTE:
Support for this research has been provided by a grant from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (R01HD074605). Additional support comes from the Penn State Population Research Institute, which receives infrastructure funding from NICHHD (2P2CHD041025). The content of the article is solely the responsibility of the authors and does not reflect the official views of the National Institutes of Health.
Notes
Barrett A. Lee is a professor of sociology and demography at The Pennsylvania State University. He studies community diversity, racial segregation, neighborhood change, residential mobility, and urban homelessness. An interest in spatial manifestations of inequality runs throughout his work.
Gregory Sharp is an assistant professor of sociology at the University at Buffalo, SUNY. His current research examines ethnoracial stratification in housing in America, contextual effects on health, and community social organization in Los Angeles.
References
Supplementary Material
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