Abstract
We examine the spatial distribution of Hispanic children and analyze its relationship to the geography of opportunity. We describe the spatial distribution of Hispanic children across all U.S. counties, document their exposure to salutary and deleterious conditions, and compare exposure to these conditions among children living in metropolitan and nonmetropolitan counties that represent traditional and new destinations for immigrants. We find clear evidence of racial and geographic differences in opportunity, at least as defined by spatially uneven patterns of intergenerational mobility. We show that the typical Hispanic child is highly isolated, living in a county with a majority-minority population, high rates of poverty, low levels of education, and poor public health. Opportunities are limited in metropolitan core counties, where the large majority of Hispanic children live, and the movement of immigrant families from traditional gateways to new destinations provides little to children in terms of exposure to more opportunity.
Keywords
Latino children 1 make up an increasing number and share of America’s population under age 18—18.7 million or roughly 26 percent of all children in 2019 (Kids Count 2020). They are distributed unevenly across the United States, overrepresented both in poor urban neighborhoods (Tienda and Fuentes 2014) and, more recently, in “new destinations” throughout America’s rural heartland, the Carolinas, and many other parts of the South and Northwest (e.g., where their parents often work in aquaculture; meat processing; and the dairy, timber, hospitality, and construction industries) (Johnson and Lichter 2016; Saenz 2012). Many live in Spanish-speaking barrios in the Southwest, in underdeveloped rural colonias along the Mexico-Texas border (e.g., Hidalgo county), or with their families on corporate vegetable and fruit farms in Southern California (e.g., Santa Cruz county), which have historically provided few opportunities for upward mobility into mainstream American society. In parts of the lower Rio Grande Valley—Laredo, McAllen, Brownville and El Paso—Hispanics live in neighborhoods that on average are more than 85 percent Hispanic. 2 For Hispanic children, place and opportunity are inextricably linked.
Whether the spatial redistribution of America’s Latino children and youth signals a new geography of opportunity is unclear. Our article addresses this question at a politically contentious period in U.S. social and demographic history. Hispanic children are in the vanguard of the nation’s changing racial and demographic composition—especially as the mostly White baby boom generation is succeeded by more diverse birth cohorts. Diversity occurs from the “bottom up”—with children and youth at the demographic forefront (Johnson and Lichter 2010; Lichter 2013).
Hispanic population growth, fueled by immigration and natural increase (i.e., excess of births over deaths), has occurred unevenly across regions, cities, suburbs, and rural areas in ways that could impede cultural and economic integration of Latino children and youth. Most studies have focused on residential segregation of racial and ethnic populations across neighborhoods (i.e., census tracks or blocks) in metropolitan areas (Ludwig et al. 2012; Sharkey and Faber 2014), emphasizing so-called neighborhood effects—how children’s developmental trajectories are shaped by where they live. Indeed, studies show that Hispanic children remain isolated from mainstream American society, exposed instead to underresourced and highly segregated schools, crowded and dilapidated housing, and poor and dangerous neighborhoods that restrict economic opportunities for parents and caregivers. Sharkey and Faber (2014, 560) suggest that this preoccupation with urban neighborhoods “has distracted attention from the larger question of how different dimensions of the residential context, which operate at multiple geographic and social scales, become salient in the lives of individuals and families” (emphasis added).
This is especially the case among Latino children, who historically have been concentrated in the Southwest but have dispersed rapidly over the past three decades from traditional gateways to new destinations throughout the United States (Lichter 2012; Massey 2008; Tienda and Fuentes 2014). Our article has three objectives. First, we provide an overview of population growth and the changing spatial distribution of Hispanic children across all 3,141 U.S. counties. Our spatially inclusive empirical approach considers the residential context of all children—not just those living in big city or metropolitan neighborhoods. Second, we use conventional demographic measures (i.e., exposure indices) to document Latino children’s exposure both to salutary and deleterious county conditions, which we then compare with non-Hispanic White and other minority children. Here we focus on the local-area exposure of children to non-Hispanic Whites, to poverty and inequality, to social and human capital, to poor health conditions, and to local opportunities for upward mobility. Third, we examine differences between children living in metropolitan and nonmetropolitan counties, including those in traditional gateways and new Hispanic destinations. This is especially appropriate now, when the Hispanic population is dispersing widely throughout America.
The Geography of Opportunity
The changing geography of opportunity is reflected in the recent work of Chetty and colleagues (Chetty and Hendren 2018; Chetty et al. 2014, 2020), which highlights substantial geographical variation in intergenerational mobility across the United States. Chetty et al. (2020) show, for example, that Hispanic children have recently experienced substantial upward mobility—comparable to White children. However, it is unclear whether the social conditions experienced by Hispanic children are different from those of other historically disadvantaged populations or whether Hispanic children will continue to experience upward mobility, at a time when class boundaries have seemingly crystallized, income inequality has increased, and upward intergenerational mobility has slowed (Beller and Hout 2006; Torche 2015).
The canonical view is that spatial assimilation is a necessary precondition for the social and economic integration of immigrants and other disadvantaged minorities into American society (Alba and Nee 2003; Waters and Pineau 2016). The spatial assimilation model posits that Hispanics will follow the classical assimilation process, one where differences between Hispanics and Whites in socioeconomic status (e.g., educational and occupational attainment) decline and distinctive cultural expressions (e.g., mother tongue) fade over successive generations or with length of residence in the United States (Iceland and Nelson 2008). Moving to opportunity—to new destinations—presumably liberates disadvantaged Latino children from the ascriptive constraints of place and promotes upward intergenerational mobility.
Recent patterns of Hispanic population redistribution also “leaves behind” the most disadvantaged in traditional settlement areas or other economically disadvantaged places (e.g., poverty traps). Indeed, the place stratification model emphasizes the continuing regional and community residential isolation and deleterious social conditions that many Latino face. The widespread spatial dispersion of Hispanics after passage of the 1986 Immigration Reform and Control Act (IRCA) can be viewed either positively or negatively, as either reducing or heightening exposure to social and economic conditions that either promote or constrain opportunity among Latino children and youth. Chetty et al. (2014) estimate intergenerational income mobility at multiple spatial scales, including the county level, 3 providing clear evidence of spatial variation in upward intergenerational mobility between parental and filial generations. Subsequent work by Chetty and Hendrin (2018) documented widely divergent patterns of upward mobility and highlighted the new “geography of opportunity” for children and youth. Interestingly, they found that urban areas exhibited lower levels of intergenerational mobility than did rural areas.
Reexamining these data, Weber et al. (2017) explored upward intergenerational mobility across metropolitan and nonmetropolitan counties, including both micropolitan and noncore counties. Micropolitan counties, which include places of 10,000 to 49,999 population, seemingly provide a spatial scale and population size and density that is positively associated with upward intergenerational mobility among poor children. They argued that micropolitan counties represented a “unique geography” at the rural-urban interface that function “much like metropolitan areas in their support of upward mobility, revealing a blurred border between metro and micro areas and a bright boundary between the noncore and the metro and micro counties” (Weber et al. 2017, 120). This study did not examine spatial patterns of intergenerational mobility among Hispanic children.
Children’s Exposure to Opportunity
Few studies have examined the racial residential segregation or spatial isolation of historically disadvantaged children. In previous segregation studies, the Index of Dissimilarity (D) has been the statistical workhorse, used to uncover declining but still high levels in Black-White neighborhood segregation since 1960 but lower levels of Hispanic-White and Asian-White segregation (Logan and Stults 2011; Rugh and Massey 2014). 4 For America’s rapidly growing Hispanic population, the metropolitan segregation rate (D) in 2010 was largely unchanged over the same period. The paradox is that rapid Hispanic growth and redistribution have nevertheless led to more geographic isolation from Whites, resulting in “more intense ethnic enclaves” in many parts of the country (Logan and Stults 2011, 1). 5 In fact, Rugh and Massey’s (2014) comprehensive analysis of segregation trends in 287 consistently defined metropolitan areas shows that Hispanic isolation increased from 27 to 46 over the 1970 to 2010 period. This means that Hispanics, on average, live in neighborhoods that are 46 percent Hispanic. Big city neighborhood isolation of Blacks and Hispanics are virtually identical today. In 1970—50 years ago—the average Black person lived in a neighborhood that was 70 percent Black, while Hispanics lived in neighborhoods that were less than 30 percent Hispanic (Rugh and Massey 2014).
How these overall patterns of residential segregation affect the living conditions of Latino children is unclear. If more racial residential integration or, alternatively, greater exposure to Whites provides an indirect measure of access to opportunity—to economic resources and cultural capital—then Hispanics today live on average in neighborhoods that are only 35 percent White (Logan and Stults 2011). This exposure measure can also be applied to other salient social, economic, environmental, or health-related conditions at multiple geographic scales (Massey, Rothwell, and Domina 2009; Massey 2004). Previous studies have focused largely on children living under widely varying neighborhood social and economic conditions (e.g., racial composition, crime rates, school quality, among other neighborhood or ecological characteristics) and on the consequences for various outcomes, including children’s developmental trajectories (Crouch 2021; Sharkey and Faber 2014). Other studies now show that racial segregation and other social conditions operate along a continuum from the micro- to macro-scale (e.g., from blocks to counties and regions) (Lichter, Parisi, and Taquino 2015; Massey, Rothwell, and Domina 2009). If correct, this requires greater sensitivity to the geographic scale at which the effects of different traits or social conditions are likely to be revealed in the uneven life chances of America’s children.
Our Analytic Approach
We focus here on the county level. Counties—more than 3,100 of them—are inclusive of all U.S. children, including those residing in rural and small-town America (Johnson and Lichter 2010). Our focus on exposure to social or environmental conditions at the county level also explicitly acknowledges distal rather than proximate effects on opportunity, on life chances, and on children’s emotional and cognitive development. Most research to date has focused on neighborhood traits that exhibit substantial heterogeneity within metropolitan cities. These include violence and crime (Theall et al. 2017), housing conditions (Solari and Mare 2012), school quality and outcomes (Sattin-Bajaj and Roda 2020; Wodtke, Harding, and Elwert 2011), or even neighborhood social structure or dynamics (e.g., collective efficacy; see Browning and Cagney 2002; Duncan et al. 2003; Sampson 2006). We argue that heterogeneity also exists between cities, counties, regions, or other ecological-based units. For example, rural children’s exposure to poor drinking water or other pollutants in Appalachia are qualitatively different from environmental toxins to which urban children are exposed in inner-city neighborhoods in Detroit or in affluent suburban communities in northern Virginia.
Children’s exposure to deleterious conditions often extends well beyond arbitrary or ambiguous neighborhood boundaries. For example, counties are sometimes used to demarcate local labor market areas, especially in rural areas, or broader or extralocal arenas of social activity (e.g., civic engagement or commerce). Unlike neighborhoods, counties represent administrative or political units that regulate commercial activities, impose land use or zoning regulations, provide transportation planning, fire and police protection, and administer social services (e.g., Supplemental Nutritional Assistance Program [SNAP]), and fund local schools. The political economy of counties matter in the lives of its residents—including its children—especially in rural America.
We consider several indicators of exposure that are expected to link county of residence to opportunity among children (Chetty and Hendren 2018; Weber et al. 2018):
1. Isolation and exposure. Exposure to non-Hispanic Whites is associated with social and economic residential integration (Massey, Rothwell, and Domina 2009). Hispanic children exposed to higher percentages of Whites in their county of residence benefit in myriad ways compared to children who are isolated in predominately Hispanic counties or majority-minority counties. Physical proximity or geographic access to Whites is arguably associated with more cultural and economic resources that promote opportunity among disadvantaged minorities.
2. Human and social capital. Human capital in the form of higher education is distributed unequally over geographic space. The pool of highly educated workers, the local mix of high- and low-skill occupations, the quality of schools, and college enrollment levels among young adults, for example, vary significantly across the rural-urban county continuum, including between suburban counties and metropolitan counties with large and economically diverse populations (Saenz and Morales 2019; Tienda and Fuentes 2014). Highly educated populations also provide cultural and economic resources and social capital that benefit children (Putnam 2016).
3. Poverty and inequality. Whether children live in poor or prosperous counties or regions (e.g., Appalachia, the Texas Borderland, or the Delta) matters in their day-to-day lives and shapes their futures (Curtis et al. 2019; Poston et al. 2010). Poverty and inequality also matter independently of whether parents or neighborhoods are rich or poor. Poor counties, especially in rural areas, are typically slow-growing counties—both in terms of population and jobs. They generate less tax revenue to support physical infrastructure, elementary and secondary schools, and cultural amenities and resources (e.g., civic centers, theatres, or community-owned hospitals or clinics).
4. Intergenerational mobility. Chetty et al. (2014) and Weber et al. (2017) have documented enormous spatial heterogeneity in patterns of intergenerational mobility and the local-area characteristics that give rise to these differences. Today, Hispanic children benefit from living in counties with higher rates of intergenerational mobility. Our empirical approach asks a straightforward question: For the average Hispanic child, what are the typical county percentile ranks in the income distributions of parents (i.e., when children were teenagers) and adult children (a rank-rank income measure)? High intergenerational mobility rates are proxies for “opportunity,” which reflects both easily observed traits (e.g., levels of education) but also less obvious unobserved indicators that are more difficult to measure (e.g., collective efficacy or culture).
5. Health conditions. The COVID-19 pandemic has further revealed large racial and economic fault lines in American society (Alsan et al. 2020; Johnson and Lichter 2020). Racial minorities—Blacks and Hispanics—have been especially hard hit by COVID-19, in part because of the kinds of work they do (e.g., essential work that increases exposure to the virus), crowded housing and neighborhood conditions, more underlying chronic health conditions (e.g., cardiovascular disease, obesity, or diabetes), and the relative lack of health insurance or access to health care providers (Alsan et al. 2020). Although children have a lower risk of death from COVID-19, their exposure at the county level nevertheless provides a clear indicator of vulnerability to poor overall health conditions, and perhaps to the lack of access to medical care, including health insurance (Perreira and Allen 2021). COVID-19 does not recognize neighborhood or other geographic boundaries. Here we develop and present a new risk measure for COVID-19 at the county level (see section on measuring exposure to opportunity).
For each of the aforementioned domains, we provide child-centered estimates of exposure. Specifically, we document the county-level conditions of the average child, comparing exposure rates of Hispanic children with those of children of other racial and ethnic backgrounds.
Our primary goal is to build on previous studies by examining racial and ethnic variation in children’s exposure to disparate social and economic conditions at the county level. Our approach supplements previous studies of neighborhood-based social conditions (e.g., Chetty and Hendren 2018; Minh et al. 2017; Sharkey and Faber 2014), explicitly recognizing that Hispanic children, by virtue of highly uneven county residence patterns, are subject to highly uneven opportunities and barriers. These are expressed both at the county level in differences between Hispanics and children from different racial backgrounds, and in differences along the rural-urban continuum and in traditional settlement areas and new Hispanic destinations.
Data and Measurement
Data
Counties are the unit of analysis and are spatially inclusive of all territory in the United States. They also provide historically stable boundaries that are well suited for our analytical purposes. Counties—all 3,141 of them—provide a basic unit for reporting population size and composition. 6 We benchmark all of our analyses to the population of children and youth, aged 0 to 19 in 2019, regardless of whether the various county indicators (described in the section on measuring exposure to opportunity) are similarly measured in the same year. 7 These county population estimates come from the U.S. Census Bureau’s Population Estimates Series (U.S. Census Bureau 2020), and are disaggregated by racial and ethnic group. We focus on Hispanic children, as well as children who are identified as non-Hispanic White, non-Hispanic Black, and non-Hispanic Asian, to identify whether and in which ways the place-based opportunities of Latino youth differ from those of other youth.
Geography
We classify counties by metropolitan status as defined by the U.S. Office of Management and Budget (OMB). We use a constant 2013 definition of metropolitan and nonmetropolitan counties throughout our analyses (Johnson and Lichter 2020). 8 We further disaggregate metropolitan counties by whether they are core counties—those that contain the principal cities in the urbanized area—and those metropolitan counties that are contiguous to core counties, hereafter, suburban counties. In this study, we identify nonmetropolitan counties by whether they are micropolitan counties or noncore (Other) counties. 9 Micropolitan counties are nonmetropolitan counties that contain an urban cluster with at least ten thousand but fewer than fifty thousand people. Noncore counties may contain smaller urban areas of fewer than ten thousand people or may be completely rural with no urban places.
Some of America’s poorest counties are rural and remote from large metropolitan employment centers. Some also contain rapidly increasing shares of migrants, including first- and second-generation Hispanics (Johnson and Lichter 2016; Lichter 2012). The Hispanic population has been characterized by rapid dispersal across the United States since the Immigration Reform and Control Act (IRCA). To evaluate the emergence and growth of new Hispanic destinations we further disaggregate counties into three groups: (1) traditional gateway counties, (2) new destinations, and (3) other counties. Traditional gateway counties are characterized by having sizable and long-established Hispanic populations (e.g., nonmetro counties in the Texas Borderlands) before IRCA. New destinations are counties that have experienced substantial recent growth of their Hispanic population; they had relatively few Hispanics in 1990 but that have grown rapidly in Hispanic population. Other counties represent a residual category that does not meet either of these criteria (see Johnson and Lichter 2016; Lichter and Johnson 2020).
Measuring exposure to opportunity
In this study, we measure children’s exposure to county conditions with P*, the so-called exposure or interaction index. In the neighborhood segregation literature, P* tells us that the average minority person lives in a neighborhood that is x percent White. This measure is interpreted, for the typical child, as the average percentage of some characteristic (e.g., percent White) in the neighborhoods or, in our case, counties. We calculate P* as
where P* is the average exposure of Hispanic children to Whites across all U.S. counties; Σi is the sum of products (i.e., [hi/H][wi/pi]) across all i counties in the United States; hi is the number of Hispanic children in county i, H is the total U.S. Hispanic population, wi is the number of Whites in county i, and pi is the population size of the county. Values vary from 0 to 100, with higher values indicating more exposure. In the case of Hispanic-to-White exposure, a value of 0 would indicate that the average Hispanic child lives in a county with no Whites. A value of 85 indicates that the average Latino child lives in a county that is 85 percent White.
P* can be measured using county percentages of other county demographic and economic characteristics (e.g., percentage poor) as well as county averages on some trait (e.g., average social capital, as described below). Exposure to higher county social capital, for example, implies greater well-being among children in the county and more access to resources—financial and otherwise—that presumably benefit the average Latino child living in the county. In this case, it measures the minority-weighted county averages across all U.S. counties or categories of counties (e.g., metropolitan fringe counties). Significantly, this measure considers the residential circumstances of all children, not just those living in big city neighborhoods. Our study, in this respect, builds on previous studies highlighting the emergence of macro-segregation or multiscale residential segregation (Lichter, Parisi, and Taquino 2015; Massey, Rothwell, and Domina 2009).
We consider several indicators of county exposure. For example, we measure the exposure of children to racial and ethnic diversity by children’s average exposure to non-Hispanic Whites using the aforementioned exposure index (Iceland, Weinberg, and Steinmetz 2002), but applied nationally and subnationally at the county level. County estimates of the number and percentage of non-Hispanic Whites in 2019 come from the U.S. Census Bureau (2020).
This study measures exposure to human capital by the percentage of all adults, age 25 and older, in the county with a college degree or more. These county data are compiled from the American Community Survey’s five-year cumulative files (2013–2017) by the U.S. Department of Agriculture (USDA; 2019). We also use data from Chetty et al. (2014) on the county indicator that identifies children who, as young adults in their early 20s, were enrolled in college (regardless of the county of enrollment). These data come from the Opportunity Insights data archive. 10 This measure is an indicator of whether children growing up in specific counties are adequately prepared—academically and financially—for enrollment in college or universities. 11 This is a relative measure that compares counties with national figures: negative percentage scores indicate college attendance rates that are below the national average while positive scores indicate attendance rates above the national mean.
County social capital is based on an index first developed by Rupasingha, Goetz, and Freshwater (2006), which involved a principal components analysis of total associations per ten thousand people in the county; number of not-for-profit organizations per ten thousand people; mail response rate in the 1990 census; and votes cast for president in 1988. The first principal component defined the social capital index, varying from –4.4165 (low social capital) to 2.0 or more (high social capital) (Rupasingha, Goetz, and Freshwater 2006). This is a measure of the civic culture in local areas, that is, whether residents contribute to community life and engage in local organizations. Children growing up in community contexts with high social capital presumably have more positive developmental trajectories (“it takes a community”) (Crouch et al. 2021; Zhou and Gonzales 2019). Following Chetty et al. (2014), we also measure, at the county level, the percentage of all family households with children that are headed by single women. Parents and families presumably invest time and money in their children and socialize them in ways that shape aspirations for marriage and work.
Inclusion or exclusion can occur at many different levels of geography. We consider inclusion, both in terms of economic inequality and racial segregation. We calculate, for the average child, the overall county poverty rate using data from the American Community Survey’s five-year 2013–2017 file. 12 We also measure children’s exposure to county income inequality using Gini coefficients drawn from Chetty’s (2014) data archive. County Ginis range between 0 (no inequality) and 1 (extreme inequality). These estimates are drawn from the Opportunity Insights data archive, which also provides a county-based measure of racial and ethnic segregation using census tracts. Children’s exposure in counties to racial segregation is based on the multigroup Theil index, which acknowledges segregation across all racial groups rather than making pair-wise comparisons with Whites or between other racial groups (Lichter, Parisi, and Taquino 2015).
We measure local opportunity using Chetty et al.’s measure of intergenerational mobility, which compares the rank in the income percentile of households as teens growing up with household income rank of these children when they were young adults (in the early 2010s). We measure the predicted family income rank of adult children, conditioned at the 25th percentile of the income rank of parents. To illustrate, a score of 40 indicates that children, as adults, experienced a 15 percentile increase in family income over their family incomes when growing up. These scores are assigned to the counties of childhood, regardless of where they lived as young adults in their 30s. Variation in scores indicates uneven spatial opportunities for Hispanic children and different prospects for intergenerational mobility. The assumption is that more upward intergenerational mobility is a proxy for place-based opportunities.
Finally, we evaluate children’s exposure to health risks, which is measured indirectly by exposure to COVID-19. We define “risk” at the county level in terms of the likelihood of hospitalization or death from COVID-19 infection (Johnson and Lichter 2020). High-risk counties have a greater estimated likelihood of hospitalization or death as a result of exposure to COVID-19. 13 The estimates of risk are not influenced by the proportion of a county population that is infected but measure the response to infections (and an indicator of underlying population health and age structure). Data on cases and deaths by county through December 31, 2020, come from the USAFacts data archive. 14 By the end of 2020, every county in the United States had been exposed to COVID-19, including isolated rural counties in America’s heartland (e.g., the Dakotas).
Analytical approach
For ease of explanation, we begin by providing exposure indices at the county level for all U.S. counties, disaggregated by type of metropolitan and nonmetropolitan counties. We then shift to measures drawn from the Chetty data archive on intergenerational mobility. County estimates are missing in some thinly populated counties where data are sparse or unreliable (see Chetty et al. 2014). In the case of intergenerational mobility, for example, the county sample is limited to roughly 2,350 counties, while excluding nearly 800 counties. In this case, we assigned scores to counties with missing data, using estimates from commuting zones, which are available in Chetty’s data archive (see Chetty et al. 2014). 15 Last, we disaggregate our analyses by traditional and new destinations in nonmetropolitan counties, where population and economic characteristics are most likely to reflect economic impacts of growing Hispanic native and immigrant populations.
Findings
The geographic distribution of Latino children
How children fare today provides a preview of America’s future: its racial composition, health, and social and economic well-being. But an exclusive focus on the national picture also can be misleading. This is clearly revealed in Figure 1, which provides estimates of the population distribution of Hispanic children and youth (aged 0–19) across U.S. counties in 2019. These data reveal large concentrations of Hispanic children in the Southwest, including many counties with majority Hispanic populations. There also are significant rural “enclaves” in the agricultural heartland, especially in pork- and poultry-producing counties in Kansas, Nebraska, Iowa, and southern Minnesota. Hispanic children also are concentrated in the Pacific Northwest, North Carolina and Florida, and in the northeastern urban corridor from Washington, D.C., to Boston and in metropolitan Chicago. Georgia also contains many counties with significant percentages of Hispanic children, especially in the Atlanta metropolitan area (e.g., DeKalb or Gwinnet counties) and in rural counties (e.g., Echols or Atkinson counties).
Percent Hispanic, Ages 0–19, 2019
These data highlight the extraordinary spatial dispersal of Latino children, even though many remain in established Hispanic counties in the Southwest. Regional differences exist in the share of Hispanic children from national origin groups (e.g., Cubans in Florida, Puerto Ricans in metropolitan New York, or Mexicans in Texas). Many counties with large Hispanic populations also vary in their generational mix or legal status. However, such finely grained data are not available in the Census Bureau’s estimates program.
Hispanic children’s uneven spatial distribution across counties is associated with uneven opportunity. Figure 2 clearly demonstrates this by overlaying the distribution of children from different racial and ethnic backgrounds with the distribution of counties identified as having “persistent child poverty” by the USDA’s Economic Research Service (ERS). The ERS has defined counties as having persistent child poverty if 20 percent or more of their populations under age 18 were living in poverty over each of the three decades from 1980 to 2010. 16 Figure 2 reveals the concentration of Hispanic children in the Southwest in persistently poor counties (outlined in bold). However, Latino children are not the only ones exposed to high rates of child poverty in the counties where they reside. These data also show concentrations of persistently poor counties in the so-called Black belt of the South, in Indian country in the upper Midwest, and in the Ozarks and Appalachia, which is mostly White. Links between opportunity and place have a large racial and ethnic dimension.
Persistent Child Poverty, 1980–2011, and Distribution of Minority Population, Ages 0–19, 2019
Macro-segregation and exposure: Full universe of counties
We begin with county-based measures of children’s exposure to Whites—a kind of macro-segregation, where low scores indicate little exposure to Whites and more cultural and economic isolation. Data in Table 1 show that the average Hispanic child lives in a county that is 45.8 percent White, which compares with 59.9 percent for all children, regardless of race. This means that the average Hispanic child today lives in a majority-minority county—one that is 54.2 percent non-White. This pattern is most pronounced among Hispanic children living in metropolitan core counties with principal cities (40.4 percent White, on average). 17 Hispanic children’s exposure to Whites is greatest in nonmetro counties, especially in noncore counties (P* = 70.0). To put these figures in perspective, White children’s exposure to other Whites (i.e., the isolation index) is much higher—69.4 percent overall—and it is especially high in suburban (P* = 72.4) and nonmetro counties (where P* exceeds 80 percent White).
Exposure of Children to County Conditions, by Race and Geography
Data in Table 1 also highlight racial differences in children’s exposure, at the county level, to adults aged 25 and older who hold a bachelor’s degree or more. Perhaps surprisingly, differences across racial and ethnic groups are small—roughly 30 percent for all groups, except Asian children (37.5 percent). In other words, if children’s interaction with adults in their counties of residences were entirely random, the typical child would have about a one in three chance of interacting with a college graduate. For Hispanics, the similarity in exposure is mostly due to their concentration in metropolitan counties, which tend to have more highly educated adults. Regardless of metro/nonmetro status, Black and Hispanic children have the lowest probability of interaction with college-educated adults. Hispanic children’s low rates of interaction with college-educated Whites are especially high in noncore rural counties, where the exposure rates are less than one-half those of Latino children living in suburban counties (16.7 vs. 35.4 percent). Racial disparities are lowest in nonmetropolitan noncore counties, ranging from a high of 18.2 percent among Asian children to a low of 14.4 percent among Black children. That the percentage share of Black children residing in noncore counties is about five times greater than the share of Asian children heightens the potential developmental consequences of this disparity among rural minorities. Exposure rates among Hispanic children are intermediate but are closer to those of Blacks than to either Whites or Asians.
County poverty estimates from the 5-year American Community Survey (ACS) and reported by the U.S. Bureau of Labor Statistics show, on average, that Hispanic children live in a county where the poverty rate is 14.4 percent, compared to an overall average of 13.5 percent. Again, the exposure of Black children to poverty is highest, while exposure at the county level is lowest among Asians and Whites (11.8 and 12.7 percent). These data also reveal that Hispanic children living in nonmetropolitan counties are exposed to especially high rates of poverty—17.3 percent. This is roughly 28 percent higher than the poverty exposure rate (i.e., 13.5 percent) for all children, regardless of residence.
Finally, we examine exposure of children to high rates of COVID-19 mortality and to underlying vulnerabilities from age and preexisting population health conditions, a measure of underlying health risks. The data on COVID-19 mortality rate (as of January 1, 2021) indicate that children, on average, live in a county where the mortality rate is 102.6 per 100,000 people. For Black and Hispanic children, the rates are higher—115.0 and 108.2, respectively. Rates of COVID-19 mortality are higher in nonmetropolitan than metropolitan areas, especially among African American children. For Black children living in noncore counties, exposure to COVID-19 mortality is 165.3 per 100,000. This rate compares with 129.9 among their Hispanic counterparts. Spatial differences reflect many factors, including the timing of the spread of the virus, the age and underlying conditions of the population, access to health care, and the success of treatment. These high numbers suggest many secondary effects of the pandemic on Hispanic and other disadvantaged minority children, such as lockdowns on schools, higher rates of unemployment among their parents, and the emotional tolls from losing a parent and relative to COVID-19.
Children also are exposed to widely disparate population health conditions, which is measured here by considering these underlying mortality risks (based on age and preexisting health conditions) rather than mortality rates alone (which depend on the incidence of infection). Here we benchmark COVID-19 risk to the U.S. population, with 100 set to the U.S. average. These data generally reveal lower than average exposure among Hispanic children to high-risk counties (95.3). However, like the mortality data, children’s vulnerability to poor population health is much higher in rural than in urban areas. Hispanic children’s exposure to vulnerable county populations is very high by national standards—121.6, for example, in noncore counties. If infections become widespread in these counties, the risk of death or hospitalization is much higher here than in metro-core counties, which are below the risk level for the United States as a whole (95.6).
Exposure to opportunity: The Chetty universe of counties
We next turn to measures of children’s opportunities for upward intergenerational mobility using Chetty and colleague’s county level measures. In Chetty et al. (2014), analyses showed that each 10-percentile increase in parents’ income was linearly associated, on average, with a 3.4 percentile increase in children’s income when they were in their early 30s. 18 Intergenerational income mobility, however, was highly variable across the nation. Moreover, areas with high intergenerational mobility were associated with less residential segregation, less income inequality, better primary schools, greater social capital, and greater family stability. In this section, we examine Hispanic and other children’s exposure to these indicators at the county level.
We start with an analysis of children’s exposure to counties with different levels of “absolute upward mobility” (Chetty et al. 2014, 1556), which measures the mean income rank of children with parents whose income is at the 25th percentile of all parents. 19 Our interest here is in understanding county social and economic conditions that provide a platform for adult economic mobility in the income distribution. Data in Table 2 indicate that Hispanic children, on average, are exposed to counties with rates of intergenerational mobility that are similar to those observed for all children (income ranks of 42.2 vs. 41.9) and White children. Our results also show that, on average, rural and suburban children live in counties with the highest likelihood of upward intergenerational mobility. These higher exposure rates fail to support Weber et al.’s (2017) claim that micropolitan areas provide greater opportunities for upward mobility. 20 Our exposure measures show that the highest levels of upward mobility are among children who originated from noncore nonmetropolitan counties. If anything, African American children are the statistical outlier, revealing the least exposure to high-opportunity counties among the ethno-racial groups considered here.
Exposure of Children to Chetty County Conditions, by Race and Geography
Table 2 also includes five county indicators that represent possible mechanisms underlying spatial variation in county patterns of upward intergenerational mobility. 21 A key empirical question is whether Hispanic children typically grow up in counties that fail to provide adequate preparation or a springboard for attending college. Hispanic children grew up disproportionately in counties that fell below the national average on this measure. For example, compared with White children (0.5), Hispanic children grew up, on average, in counties where college attendance fell 1.5 percent below the national average for young adults. In metropolitan core counties, where most Hispanic children live, the average college attendance gap was much greater (–5.3 percentage points), a gap that was observed regardless of racial background. Consistent with patterns among noncore Hispanic children living in counties with high intergenerational mobility, these children also grew up in counties where college enrollment rates were well above the national average, exceeding even their White counterparts in noncore counties.
The data in Table 2 also show that Hispanic children are no more or less likely than other children to have grown up in counties with high percentages of female-headed families. On average, Hispanic children lived in counties where 21.9 percent of all households with children were headed by single mothers. The comparable figure for all children was 21.6 percent. Across all residential categories, Hispanic children’s exposure to female-headed families with children fell within a relatively narrow range, from 17.9 in the suburbs to 23.4 in the cities.
Estimates of the social capital index are negative across the racial and geographic distribution of children, which suggests that children, unlike the entire or adult populations, are situated differently across U.S. counties. These exposure estimates highlight the social disadvantages facing Hispanic children and other disadvantaged minority children. On average, Hispanic children live in a county with a social capital index of –0.954, compared with an overall index of –0.507. Average exposure, as measured by the social capital index, is –0.242 for White children. Consistent with Weber et al. (2017), the overall exposure to social capital is much less negative or even positive in nonmetropolitan counties—both in micropolitan and noncore counties. The exception is among nonmetropolitan Black children, whose average exposure to social capital is comparatively low. This result is consistent with evidence that nonmetropolitan Blacks are concentrated in the South, often in high-poverty rural counties of the slave and planation South (see Figure 2 for reference).
Last, we examine income inequality and residential segregation, which we consider in terms of children’s exposure to local-area economic and racial exclusion. Children’s exposure to county income inequality varies more across residence categories (from 0.373 to 0.503) than across demographic groups (from 0.422 to 0.494). For example, regardless of racial and ethnic background, metropolitan children are exposed to more income inequality than nonmetropolitan children. The typical Hispanic child lives in a county with a Gini of 0.384 in noncore counties, but a Gini of 0.491 in the core counties of metropolitan areas. Much of the difference reflects the comparative lack of affluence and wealth in most rural counties (Thiede, Sanders, and Lichter 2018). Exposure to neighborhood racial segregation, measured using the multigroup Theil index (see Chetty et al. 2020; Lichter, Parisi, and Taquino 2015), also is much higher in metropolitan counties than in nonmetropolitan counties. In each type of county, the average Hispanic child is exposed to higher levels of segregation than are their White counterparts. Chetty et al. (2014, 1609) reported that more residentially segregated areas had lower levels of intergenerational mobility.
A coda on rural children in new destinations
Geographic mobility is typically linked to upward economic mobility. For Hispanic children, the movement from traditional gateways in the Southwest to new destinations throughout the country suggests a new kind of spatial assimilation at the macro-scale level. In Table 3, we provide exposure measures for nonmetropolitan Hispanic children living in traditional gateways and new destinations. Unlike metropolitan counties, less densely settled rural counties are where local-area impacts on population composition and labor markets are likely to be especially marked (Lichter 2012; Lichter, Sanders, and Johnson 2015). Our results point to a singular conclusion: new rural destinations provide a rather poor geographic platform for success, on many different indicators of opportunity.
Exposure of Children to County Conditions in Traditional and New Destinations, by Race and Geography
Nonmetropolitan Hispanic children in traditional gateway counties live, on average, in counties that are only 39.0 percent White (Table 3). This compares with an exposure rate of 71.2 and 78.3 in new destinations and other destinations, respectively. The implication is that post-1990 geographic redistribution is associated with greater Hispanic access to Whites and, presumably, to greater economic, political, and economic resources in nonmetropolitan areas. Closer inspection of the data, however, suggests that this salutary interpretation is not always warranted. For example, Table 3 reveals that the average Hispanic child lives in a new destination county where only 19.0 percent of adults have a college degree, an exposure rate that is only slightly higher than in traditional gateway counties (16.6) and far lower than the exposure rate for all Hispanic children (30.0 % see Table 1).
It is also the case that Hispanic children living in new destinations are exposed to higher rates of poverty (15.9 percent, as shown in Table 3), on average, than are Hispanic children overall (14.4 percent, as shown in Table 1). Little indication exists that Hispanic children are living in relatively affluent new destinations; indeed, the poverty rate of White children in the same new destinations is 15.2 percent—nearly as high as their Hispanic counterparts. Hispanics are exposed to more Whites but not more nonpoor Whites in the county.
Finally, these data reveal comparatively high exposure to the risk of COVID-19 in traditional gateways and new destinations, regardless of ethno-racial background. Rural children, including Hispanic children, are exposed to poor population health, as indicated by the health indicator that includes COVID-19 mortality and underlying health conditions. Comparing Hispanic children with White children is instructive. Because we focused on the same set of counties, any Hispanic-White differences in mortality risk are entirely the result of differences in the distribution of Hispanics and Whites across these counties. And these results show that Hispanic children are disproportionately concentrated in those counties with the highest health risk associated with the spread of COVID-19.
Finally, the results based on Chetty’s archival county data reveal much the same exposure. In nonmetropolitan counties, neither traditional Hispanic gateways (P* = 45.0) nor new destinations (43.1) provide local living conditions that promote rates of intergeneration mobility that compare favorably with rates of children living in metropolitan counties (cf. Tables 2 and 4). For Hispanic children, traditional gateway counties are more likely than new destinations to provide a positive geographic platform for attending college (4.9 vs. 1.5). Hispanic children in traditional gateways in nonmetropolitan areas, however, are less likely to be exposed to high levels of social capital, at least as measured by the social capital index. Hispanic children in new destinations fare better, but still fall below national averages. Finally, neither children in traditional gateways nor new destinations are exposed to high rates of racial segregation or income inequality, at least as measured by national standards (cf. Table 2). In traditional gateways, however, Hispanic children are much more likely to live, on average, in majority-minority counties that disproportionately comprise low-income families while the affluent are underrepresented in these counties.
Exposure of Children to Chetty County Conditions in Traditional and New Destinations, by Race and Geography
Discussion and Conclusion
America’s future—the strength of its democracy, the well-being of its citizens, and its competitiveness in an increasingly competitive global economy—depends on whether our children become civically engaged, productive workers who eventually form their own healthy families. Unfortunately, increasing shares of U.S. children and youth are “at risk,” if only because racial and ethnic diversity is so tightly coupled to variation in opportunities for SES mobility (Lichter and Qian 2018; Putnam 2016). America’s future depends on the political will to acknowledge and address existing social, economic, and geographic barriers to an inclusive society.
Our goal has been to focus attention on opportunity and place, shining a spotlight on America’s children, especially on America’s fastest-growing population under age 20—Hispanic children. In this article, we provided new estimates of uneven exposure of Hispanic and other children to deleterious social and economic local conditions. By locating children “in context” at the county level, we build directly on previous studies that have centered on metropolitan neighborhood segregation and isolation. Ours is a spatially inclusive approach that includes children living in all 3,100 U.S. counties spread unevenly across the rural-urban gradient—from remote rural areas to the nation’s largest and most densely settled metropolitan areas. Recent work by Chetty and his colleagues (2014, 2020) has highlighted racial and ethnic disparities in opportunity and how they are expressed in spatially uneven patterns of intergenerational mobility (and their “causes”). Our exposure measures shift attention from county averages to the averages of children in the counties where they actually live.
Our study provides clear evidence that the uneven spatial distribution of Hispanic children underlies the inequality of opportunity. Hispanic children are spatially isolated, on average living in counties that have majority-minority populations, high poverty rates, low levels of education, and poor public health, at least as indicated indirectly by the risk of COVID-19 (Perreira and Allen 2021). We found that Hispanic children’s exposure to deleterious county conditions were often, but not always, expressed in the very counties where most Hispanic children live. Indeed, using Chetty et al.’s (2014) county data archive, we found that intergenerational mobility—and many of the presumed causal conditions that arguably produce spatial inequality in opportunity (e.g., high income inequality or segregation)—were often worse in metropolitan core counties than in rural counties (i.e., noncore). Yet the conditions facing Hispanic children in new destinations give little indication that the on-going movement from traditional gateways to new destinations will enhance opportunity. Their greater exposure to Whites (to their social and human capital) in these new rural destinations has not raised the likelihood of more intergenerational mobility for the average Hispanic child.
Our results present a mixed picture of linkages between place and opportunity among Latino children and their economic and social integration into American society. On one hand, upward intergenerational mobility is generally high and comparable to patterns observed among White children, at least according to estimates provided by Chetty et al. (2014). On the other hand, fitting regression models (with varying specifications) may inappropriately place the emphasis on counties themselves (as units of analysis) rather than on the disparate number of children actually living in these counties. As an alternative, our estimates of exposure provide a child-centric approach that focuses directly on county-level opportunities and barriers faced by the average child living in the county. Of course, our emphasis on the experiences of the average child may mask different rates of exposure for children located differently in the income distribution. For example, we estimated all children’s exposure to intergenerational mobility but conditioned the analyses for counties at the 25th income percentile of family income, when these children were teenagers. With our approach, however, it is possible to calculate exposure rates for different vulnerable populations: poor children, immigrant children, children of single parents, and so on.
Our study, therefore, provides a starting point rather than the final answers about putative linkages between place and opportunities. It aims to rebalance the past emphasis on big city neighborhoods, shifting attention to counties. Our analyses of county conditions highlight existing opportunities and barriers to Latino children’s achievement and success, as well as suggest the need for place-based policies that target children where they live. This shift of emphasis arguably is more important than ever. The majority of babies born now have parents who self-identify as racial or ethnic minorities (including mixed race). Hispanic children are in the vanguard, a demographic fact that reflects new immigration, comparatively high rates of fertility, and substantial interracial marriage. How families, government, and the economy respond—or fail to respond locally—to diversity will ultimately shape America’s future.
Footnotes
NOTE:
This research was supported by an Andrew Carnegie Fellowship from the Carnegie Corporation of New York and by the New Hampshire Agricultural Experiment Station in support of Hatch Multi-State Regional Project W-4001 through joint funding of the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 1013434, and the state of New Hampshire. Barbara Cook of the Carsey School of Public Policy provided GIS support. The content is solely the responsibility of the authors and does not necessarily represent the official views of the agencies supporting their research. Finally, the coauthors acknowledge the helpful comments of the coeditors and external reviewers.
Notes
Daniel T. Lichter is the Ferris Family Professor, Emeritus, in the Department of Policy Analysis and Management at Cornell University and a research associate of the Cornell Population Center. His recent work has focused on changing ethno-racial boundaries, as measured by changing patterns of interracial marriage and residential segregation in the United States. He is especially interested in America’s racial and ethnic transformation, growing diversity, and the implications for the future.
Kenneth M. Johnson is a senior demographer at the Carsey School of Public Policy, Class of 1940 Professor of Sociology at the University of New Hampshire, and an Andrew Carnegie Fellow. His recent research examines county patterns of migration and population redistribution, chronic rural depopulation and natural decrease, and the relationship between demographic and environmental change.