IMMIGRATION AND THE QUALITY OF LIFE IN U.S. METROPOLITAN AREAS by
Qiong (Miranda) Wu and
Michael Wallace
Department of Sociology
University of Connecticut
Storrs, CT 06269-2068

ABSTRACT
As the size of the immigrant population in the U.S. has increased in recent decades, the effects of immigration on the quality of life have become hotly contested. Existing research has provided evidence of both positive and negative effects of immigration, a result that we contend may reflect differing aspects of immigration. In this paper, we conceptualize immigration as having two faces that tap different dimensions of immigrant presence in urban areas: immigrant concentration (the presence of large , concentrated populations of immigrants) and immigrant diversity (the presence of large , diverse populations of immigrants). For 366 U.S. metropolitan statistical areas, we examine how these two measures have influenced four dimensions of quality of life: economic well-being, social well-being, healthy living and urban mobility. Controlling for appropriate covariates, we find that immigrant concentration tends to have negative effects on urban quality of life, but these effects disappear when immigrant diversity is included in the models. Additionally, immigrant diversity has positive and more robust effects on all four dimensions of urban quality of life. We conclude that both faces of immigration provide are useful in understanding urban quality of life, but immigrant diversity—or lack thereof—provides more leverage in conceptualizing the effects of immigration.
INTRODUCTION
As a primary destination for global immigrants, the United States has experienced tremendous growth in its foreign-born population over the last three decades. According to the
U.S. Census, by 2010 the foreign-born population reached 39.9 million or about 12.9% of the population, the largest percentage since the 1920s. Immigrants made up an even larger share of the labor force, 15.8% in 2010. Moreover, despite the surge of U.S. immigration to “new destinations” (Massey 2008), the influence of immigration is still largely concentrated in large, metropolitan areas (Mackun and Wilson 2011; Wilson and Singer 2011). This surge in the immigrant population has revitalized the debate among scholars, policy makers, and ordinary citizens about the impact of immigrants on U.S. society. What are the implications of this surge in immigration on the quality of life in the United States? Specifically, does this influx of immigration enhance or diminish the quality of life in the major urban areas of the U.S.?
While immigrants have become an increasingly prominent share of population, the effects of immigration on U.S. metropolitan areas are contested. Immigration scholars have identified
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both positive and negative impacts of immigrants on American life. But this debate has been heavily skewed by the impassioned debate over undocumented immigrants.
On the negative side, many believe that immigrants hurt the quality of the labor market by taking jobs away from native-born workers, reducing wages at the lower end of the market, and undercutting wages in the middle of the occupational structure (Card 2005; Ruark and Graham
2011). For example, Borjas (2003) found that immigrant workers negatively affected wages of lower-skilled native workers, especially those without high school degrees (see also Borjas and
Katz 2007). In addition, some scholars contend that immigrants, particularly undocumented immigrants, place undue strain on public services such as housing, education, and health care, which increases government expenditures and leads to budget deficits. As Borjas (1999:125) states, “Immigration would generate huge fiscal losses for natives, as they would have to share the fiscal savings generated by economic growth with more people.” Finally, some have argued that inflows of ethnically heterogeneous immigrants and cultural influences have undermined social cohesion in the U.S. For instance, Putnam (2007:149) suggests that persons living in ethnically heterogeneous communities are less likely to trust their neighbors and participate in social life, or as he says, to “hunker down, to pull in like a turtle.” One needs to look no further than the early stages of the 2016 presidential campaign to see evidence for the anti-immigrant fervor among the American electorate, particularly in wide swaths of the Republican base.
However, many of these negative effects of immigration are associated with the largest authorized and unauthorized immigrant group—immigrants from Mexico. As Camarota (2001) reported, the vast majority of Mexican immigrants are unskilled or poorly educated, living in or near poverty (65.5%), and having no health insurance (52.6%), which significantly increases the size of the vulnerable and dependent population in the United States. Thus, the issue of
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immigration is often confounded with the issue of immigrant concentration , that is, high concentrations of immigrants from one or a few native countries in a local area.
On the positive side, some have argued that immigrants bring valuable resources to
American society. First, Peri and Sparber (2009) find that there is no significant negative effect of immigration on net job growth for U.S. native workers. In other words, immigrants do not displace native workers, but rather they seem to increase the overall quantity of jobs. Low-skilled immigrants tend to take jobs that native workers do not want and high-skilled immigrants bring needed skills and ingenuity to the labor force. Second, a large body of empirical research finds that immigration actually increases average wages of all U.S. native workers and lowers prices
(Cortes 2008; Ottaviano and Peri 2012). Native workers in high-immigrant cities tend to have relatively higher wages than those who work in low-immigrant cities (Card 2009). Combining the first two points, widespread anti-immigrant job prejudice in many parts of the United States is largely misplaced and can better be explained by a variety of contextual factors in highimmigrant areas such as economic competition, labor market deregulation, and globalization
(Wallace and Figueroa 2012.) Third, immigrants, even those who are undocumented, do pay sales tax, property tax, and income tax as other residents do, and on average, higher-educated immigrants pay more taxes than they use in services (Orrenius and Zavodny 2011). Finally, the growing population of immigrants from various origin countries contributes to cultural diversity and provides new talents and innovative ideas to improve society. As Ottaviano and Peri (2006:
39) claim, “A more multicultural urban environment makes US-born citizens more productive.”
IMMIGRANT CONCENTRATION AND IMMIGRANT DIVERSITY
Overall, the literature provides mixed messages about the impact of immigration on the quality of life: It sometimes emphasizes the negative effects which tend to be linked with the
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concentration of immigrants (Browning and Cagney 2002; Xue, Leventhal, Brooks-Gunn, and
Earls 2005), and sometimes focuses on the positive impacts of the diversity of immigrants
(Ottaviano and Peri 2006; Alesina Harnoss and Rapoport 2013; Trax, Brunow and Suedekum
2013). Moreover, empirical research on these issues is unsettled, in part, because of limited conceptualizations of immigration. Specifically, most research uses a simple measure of percent foreign-born population as the best approximation of the effect of immigration. This measure capture the size of immigration but fails to address its composition , that is, the degree to which the immigrant population is concentrated in one or a few origin groups or dispersed among many origin groups. For instance, the U.S. Census Bureau identifies 136 possible places of origin for immigrants to the U.S., but the actual distribution of immigrants is highly skewed: 29.3% are from Mexico, 5.4% from China (including Taiwan), 4.5% from India, 3.1% from Vietnam, and
3% from El Salvador (Grieco, Acosta, Cruz, Gambino, Gryn, Larsen, Travelyan and Walters
2012). These immigrant groups are also unevenly distributed across regions of the U.S. For example, immigrants from Mexico are the largest immigrant groups in the Midwest (30%), the
South (31%), and the West (42%), but are not among the five largest groups in the Northeast
(6%).
We contend that taking both the size and composition of the immigrant population is important for understanding the consequences of immigration. Thus, in this paper we develop two measures to capture variation in the composition of immigration—immigrant concentration and immigrant diversity—and examine their implications for urban quality of life. Immigrant concentration refers to large, concentrated populations of immigrants in which immigrants come from one or a few countries of origin. Immigrant diversity refers to large, diverse populations of immigrants in which immigrants come from many different countries of origin.
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Past research on the effects of immigration has placed disproportionate emphasis on the economic outcomes in labor markets and, aside from a few studies on the effect of immigration on crime (Hagan and Palloni 1998; Reid, Weiss, Adelman, and Jaret, 2005), relatively few scholars have rigorously examined the social and cultural consequences of immigration. Yet, given the prominence of these topics in the public discourse, a broader analysis of the impacts of immigration on urban quality of life seems warranted. Of particular concern, to what extent does the level and composition of immigration in metropolitan areas differentially affect quality of life? That is, is there a distinction to be made between urban areas such as border towns that have high concentrations of immigrants from one or a few Latin American countries and large, metropolitan areas that have a diverse cultural milieu due to immigrants from a broad range of national origins? This is the central question that we address in this paper, but before we do, we discuss what we mean by “quality of life.”
QUALITY OF LIFE
“Quality of life” has been a popular research topic among scholars from different disciplines for over half a century. The term has been variously used to describe the well-being of nations, cities and individuals. Quality of life is a multi-faceted concept that, under different formulations, might include social, economic, political, health-related, environmental, and psychological dimensions. It has been conceptualized both as the objective conditions of social life experienced by individuals or society as a whole (Diener and Suh 1997; Lee 2003; Shapiro
2006), and as the subjective indictors of individuals’ cognitive satisfaction with their lives
(Argyle 1996; Cummins 1997; Diener 1995, 2000; Ferriss 2004).
At one end of the spectrum, the Organization for Economic Co-operation and
Development devised the Better Life Index, a comprehensive, cross-national measure which
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captured nine essential aspects: housing, income, jobs, community, education, environment, civic engagement, health, life satisfaction, safety and work-life balance (OECD 2014). At the other extreme, Schuessler (1982) developed a comprehensive survey based on a battery of subjective indicators to measure the “social life feelings” of individual respondents in a national survey.
Needless to say, there is no consensual or universal definition of quality of life (Schuessler and
Fisher 1985). As Liu (1976:10) proclaimed almost 40 years ago with little exaggeration, “There are as many quality of life definitions as there are people.”
In this paper, we seek to develop a sociologically grounded, place-based conceptualization of quality of life that focuses on objective features (rather than subjective perceptions) of large urban areas. Among sociologists, Lauer (1978:13) provides a serviceable general definition of quality of life that encompasses “economic opportunity, health facilities, an environment conducive to good health, access to recreational and cultural activities, and minimal crime.” In urban studies, conceptualizations focus on objective physical, social, and cultural features of urban environments that enhance the quality of life of urban residents. We seek to draw upon the best of these traditions in developing a sociological conceptualization that encompasses the major dimensions of urban quality of life that most directly impact urban dwellers, and then use this conceptualization to examine the question of how immigrants have affected the quality of life in U.S. metropolitan areas.
There have been several efforts to develop quality of life measures for the city or metropolitan level. Liu (1976) developed a set of quality of life indices for U.S. metropolitan areas by standardizing and weighting hundreds of objective indicators, eventually reducing them to five components: economic, political, environmental, social, and health and education.
Blomquist, Berger, and Hoehn (1988) examined the attractiveness of 253 urban counties within
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185 U.S. metropolitan areas based on quality of life factors such as weather, location, violent crime, education and environment. Johnston’s (1988) Quality of Life Index is also based on 21 indictors that were subdivided into nine “areas of social concern”: health, public safety, education, employment, earnings and income, poverty, housing, family stability, and equality.
Moreover, based on time series analysis using 1969 values as a baseline, his Comprehensive
Quality of Life Index provided comparative values of quality of life for 1969-1985. Suffian
(1993) constructed an index of urban quality of life based on nine indicators: public safety, food cost, living space, housing standards, communication, education, public health, noise levels, and traffic flows. Cummins’ (1997, 2000) Comprehensive Quality of Life Scale covers both objective and subjective measures for seven domains: material well-being, health, productivity, intimacy, safety, community and emotional well-being. More recently, Serag El Den, Shalaby,
Farouh, and Elariane (2013) summarized seven principles of urban quality of life: environmental, physical, mobility, social, economic, political, and psychological.
As Florida (2008:6) suggests, investigating the urban quality of life is important because it “affects every aspect of our being.” These indices capture the livability of metropolitan areas for residents (Florida 2008), inform individual decisions to relocate to new areas (Campbell,
Converse, and Rodgers 1976; Zehner 1977), and allow policy makers and urban planners to assess the effectiveness of their efforts (Marans 2002). Ultimately, the various ways of operationalizing the quality of life depend on “data availability, the aims of each study, the methodology used and the spatial disaggregation level examined” (Lambiri, Biagi, and Royuela
2007:9).
Blomquist (2006:500) suggests that “quality of life indexes should be tailored to the purpose of the study.” To that end, we seek to develop a comprehensive, although by no means
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exhaustive, set of urban quality of life indicators in order to address our central question of how urban quality of life has been affected by immigration. Using factor analysis, we identify four broad indices that capture the attractiveness of a metropolitan area as a place to live and work: economic well-being, social well-being, healthy living, and urban mobility. Further details about the construction of these indices are provided below.
Hypotheses
In this paper, we investigate the impact of immigrant concentration and immigrant diversity on four dimensions of urban quality of life in 366 U.S. metropolitan areas. Past research suggests divergent effects of immigration, depending on whether one emphasizes immigrant concentration or diversity. The two main questions we address are: 1) How do immigrant concentration and immigrant diversity affect urban quality of life? 2) Does one dimension or the other take precedence in influencing urban quality of life?
The iconic case of immigrant concentration in the U.S. is that of immigrants from
Mexico, the largest single immigrant group in the U.S. In 2010, about 64% of all Mexican immigrants were concentrated in three states (California, Texas, and Illinois) and 52% were concentrated in just 10 U.S. metropolitan areas (Migration Policy Institute 2016). A disproportionate number of Mexican immigrants—particularly those who are undocumented— live in poverty, have little education, work in low-paid, labor-intensive jobs and have no health insurance. These populations are most heavily concentrated in border towns like El, Paso, TX which are caught on a “disheartening treadmill of … growth without prosperity” (Simcox 1993).
These negative conditions of work and everyday life experienced by large, concentrated immigrant populations are argued to spill over into the general quality of life of the urban areas in which these populations live. Thus, our first hypothesis:
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Hypothesis1: Immigrant concentration is negatively associated with urban quality of life.
On the other hand, many urban areas are characterized by large immigrant populations that originate from a diverse range of national origins. This immigrant diversity is often regarded as a sign of economic vitality, cultural diversity, and technological innovation (Florida 2002).
Kemeny (2012) found that cultural diversity, as indicated by large, diverse immigrant populations, enhance economic productivity and workers’ wages throughout the metropolitan area. Thus, from this perspective diverse immigrant populations brings valued assets and resources that invigorate the metropolitan scene and enhance the urban quality of life for everyone. Thus,
Hypothesis 2: Immigrant diversity is positively associated with urban quality of life.
DATA AND METHODS
To address the hypotheses, we use data from the METRO_MICRO_2010 dataset collected by a team of researchers from XXX University. This dataset includes variables derived from a variety of public sources including the American Community Survey (ACS), Bureau of
Economic Analysis (BEA), and U.S. Census for the year 2010 from 374 Metropolitan Statistical
Areas and 589 Micropolitan Statistical Areas in the U.S. including Puerto Rico. In this analysis, we drop the micropolitan areas and Puerto Rican metropolitan areas and restrict our analysis to
366 U.S. metropolitan areas.
Metropolitan Statistical Areas (MSAs) are geographic units comprised of counties that have a central city of over 50,000 population, and any contiguous counties that are economically connected by commuting patterns. MSAs are largely representative of the U.S. native and immigrant population. According to the 2010 Census, about 83.7% of the U.S. population and
95.2% of immigrants live in the metro areas (Mackun and Wilson 2011; Wilson and Singer
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2011). Because MSAs are the place where millions of residents live and work, they are an ideal unit of analysis for this study.
We use OLS regression to estimate determinants of four dimensions of urban quality of life, utilizing a strategy of alternating models in which we enter immigrant concentration and immigrant diversity individually, then jointly, with other covariates. We utilize two-tailed tests of significance. In addition to using conventional levels of significance, we also display significance levels up to .10 because of the relatively small number of cases in the analysis.
Dependent Variables
We use factor analysis to construct four scales measuring different dimensions of urban quality of life—economic well-being, social well-being, healthy living and urban mobility.
Higher values on the scales denote better quality of life and individual items are re-keyed as needed to meet this requirement. Items on the scales are transformed to z-scores and averaged so that each scale has the properties of a z-score.
Descriptive statistics for the scale and the items comprising them are shown in Table 1.
First, the economic well-being scale (Cronbach’s alpha=.85) consists of six items: real GDP per capita, median household income, the poverty rate, the unemployment rate, the percentage of households that receive supplemental security income (SSI), and the percentage of households that receive public assistance or food stamps. The first two are adjusted by the Regional Price
Parity (RPP) index to standardize for the metropolitan cost of living. The remaining four are rekeyed. Second, the social well-being scale (Cronbach’s alpha=.77) is made up of six items: the percent of residents who are college graduates, the percent of population with health insurance, the labor force participation rate, property crime rate, the percent of population with low income
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and low access to stores, and the percent of households with no cars and low access to stores.
The last three items are re-keyed.
—Table 1 here—
Third, the healthy living scale (Cronbach’s alpha=.90) consists of four items, all of which are re-keyed so that high values convey high levels of health: the adult obesity rate, the diabetes rate, the percent of population who are physically inactive, and the rate of preventable hospital stays. Fourth, the urban mobility scale (Cronbach’s alpha=.85) contains three items: logged weighted population density, the percent of population who commute to work by public transportation and the sprawl index, which is re-keyed. The correlations among the four scales range from .36 to.74, indicating that they capture distinctive, but interrelated, dimensions of urban quality of life.
Key Independent Variables
The key independent variables in our analysis are immigrant concentration and immigrant diversity. Measures of both concepts are derived from the Herfindahl-Hirschman
Index (HHI) which has been widely used in social science research. For example, in industrial economics the HHI has been used as a measure of trade or industrial concentration (Scherer,
1970), but it can be applied to any categorical variable to measure the degree of concentration.
Adapted to the study of immigration, the HHI is calculated by taking the sum of the squared population shares from each place of origin as follows:
𝐻𝐻𝐼 = ∑ 𝑘 𝑘=1 𝑝 2 𝑘
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where k indicates the number of different places of origin
1
from which immigrants come ( k
=1…
136), 𝑝 𝑘 is the share of immigrant population from the k th place of origin. The HHI can be interpreted as the probability that two randomly chosen immigrant residents in a MSA come from different places of origin. The HHI ranges from 0 to 1, where 0 indicates that immigrants in a MSA are evenly dispersed among each of 136 places of origins, and 1 indicates that all immigrants are concentrated within a single place of origin group.
Though useful, the HHI neglects the size of immigration in metropolitan areas. For example, the immigrant populations of two metropolitan areas might be equally concentrated, but if one constitutes 20% of the total population and the other constitutes 1%, the influence of the larger immigrant population on the quality of urban life will be more pronounced. Our measure of immigrant concentration IC thus weights the HHI by PFB , the proportion foreignborn in the MSA and is computed as follows:
IC = 𝐻𝐻𝐼 ∗ 𝑃𝐹𝐵 = ∑ 𝑘 𝑘=1 𝑝 2 𝑘
∗ 𝑝 𝑓𝑏 where p fb
is the proportion foreign-born population in the MSA. High IC values identify MSAs with large, concentrated populations of immigrants from relatively few places of origins. For example, the IC of .2549 for Laredo, TX is the product of a HHI of .8969 which indicates that its foreign-born population comes mostly from a single place (i.e., Mexico) and a PFB of .2842 suggesting a relatively large foreign-born population. This gives Laredo the third highest IC value among MSAs in our analysis. Panel A in Figure 1 shows the distribution of immigrant concentration scores among the 366 MSAs in our dataset. It also identifies the five MSAs with highest values on immigrant concentration as El Centro, CA (.2817), McAllen-Edinburg-
1 We use the term “places of origin” because some of the places designated by the Census are countries (e.g., Mexico), but others are regions that encompass multiple countries (e.g., “other
Central America”).
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Mission, TX (.2576), Laredo, TX (.2549), El Paso, TX (.2192), and Brownville-Harlingen, TX
(.2134), all of which are clearly separated from MSAs with lower values.
—Figure 1 here—
Our measure of immigrant diversity reverses the logic of the HHI to create the “reversed
HHI
” or
RHHI which we calculate as follows:
𝑅𝐻𝐻𝐼 = 1 − ∑ 𝑘 𝑘=1 𝑝 2 𝑘
The RHHI can be interpreted as the probability that two randomly chosen immigrant residents in a MSA come from the same place of origin. The RHHI ranges from 0 to 1, where 0 indicates that all immigrants in a MSA are concentrated within a single place of origin group, and 1 indicates that all immigrants are evenly dispersed among each of 136 places of origin.
The RHHI is identical to Taylor and Hudson’s (1972) fractionalization index which has been widely used to measure immigrant diversity, but it has the same limitation of the HHI noted above that it does not take into account the size of the immigrant population. Thus, two MSAs may be equally diverse as measured by the RHHI but the lived experience of diversity will be more apparent in the MSA where the immigrant population is larger. To remedy this shortcoming, our measure of immigrant diversity ID weights the RHHI by PFB , the proportion foreign-born in the population and is computed as follows:
ID= 𝑅𝐻𝐻𝐼 ∗ 𝑃𝐹𝐵 = (1 − ∑ 𝑘 𝑘=1 𝑝 2 𝑘
) ∗ 𝑝 𝑓𝑏
The ID varies between 0 and 1, where high ID values identify MSAs with large, diverse populations of immigrants from many different places of origin. For example, the ID for New
York-Northern New Jersey-Long Island, NY-NJ-PA is .2763 which is the product of a RHHI of .9661 indicating an extremely diverse foreign-born population and a PFB of .2860 suggesting a relatively large foreign-born population. This gives the New York City metro the fourth highest
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ID score among MSAs in our analysis. Panel B in Figure 1 shows the distribution of immigrant diversity scores among the 366 MSAs in our dataset. It identifies the five MSAs with highest values on immigrant diversity as Miami-Fort Lauderdale-Pompano Beach, FL (.3335), San Jose-
Sunnyvale-Santa Clara, CA (.3243), Los Angeles-Long Beach-Santa Ana, CA (.2809), New
York-Northern New Jersey-Long Island, NY-NJ-PA (.2763), and San Francisco-Oakland-
Fremont, CA (.2723).
It is worth noting that the correlation between IC and ID is .19, which confirms that these two indices capture quite distinctive dimensions of immigration. Reflecting that distinction, the two MSAs highlighted in this discussion, Laredo, TX and New York-Northern New Jersey-Long
Island, NY-NJ-PA, both have foreign-born populations of about 28%, but the character of immigration in these two MSAs is quite different. Laredo is relatively high on immigrant concentration (.2549) and low on diversity (.0300), while New York is relatively low on immigrant concentration (.0097) and high on diversity (.2763). Thus, a similar share of foreignborn population can yield vastly different degrees of immigrant concentration or diversity.
Control Variables
We include several covariates as control variables. Logged population is used to control for the size of the MSA which is likely associated with many dimensions of urban quality of life.
College town is a dummy variable coded 1 if the student enrollments of all local colleges and universities with enrollment of at least 5,000 students exceeds 10% of total MSA population.
Percent non-Hispanic black and percent Hispanic control for the racial composition of MSAs.
Since immigration is considered a prominent dimension of globalization, we include variables tapping three other dimensions of economic globalization (Wallace, Gauchat and
Fullerton 2011): global capital, foreign direct investment, and exports. Global capital is the
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average of seven z-transformed indicators for Fortune 1000 firms headquartered in the MSA: number of firms, sales, profits, assets, stockholders equity, market value and number of employees. Foreign direct investment is the percent of employment in companies owned by foreign firms.
2
Exports/GMP is measured as the value of exports originating in the MSA as a percentage of the MSA’s Gross Metropolitan Product. Reflecting a cultural dimension of globalization, logged intl. air passengers is the natural logarithm of international air passengers per 100,000 arriving in the MSA (Mahutga, Ma, Smith and Timberlake 2010).
We also include deindustrialization measured as the ratio of percent employed in manufacturing in 1970 to percent employed in manufacturing in 2010. This measure taps the extent of deindustrialization over the past 40 years and is a key measure of labor market transformation. Ratios above 1 reflect deindustrialization and ratios below 1 reflect growth in manufacturing employment. Also, the Gini coefficient is a measure of income inequality that ranges between 0 (perfectly equal) and 1 (perfectly unequal). Income inequality has been shown to be related to inequality at the international level (Hanssen 2011). Finally, we include dummy variables for eight geographic divisions as defined by the U.S. Census Bureau: Middle Atlantic,
East North Central, West North Central, South Atlantic, East South Central, Mountain, and West with the New England division as the reference category. Descriptive statistics for immigrant concentration, immigrant diversity and the other covariates are shown in Table 2.
—Table 2 here—
RESULTS
Table 3 displays the determinants of economic well-being (models 1-3) and social wellbeing (models 4-6). In each set, the first model includes immigrant concentration, the second
2 This variable is measured at the state level and state-level values are assigned to all metros in a state.
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includes immigrant diversity and the third includes both concentration and diversity with a set of covariates. This analytic strategy permits us to assess the contribution of concentration and diversity individually and jointly to explaining urban quality of life.
—Table 3 here—
Turning first to the results for economic well-being, we see in model 1 that immigrant concentration has a negative effect that is significant at non-conventional levels (p<.10). In model 2, on the other hand, immigrant diversity has a strong positive association with economic well-being. Moreover, there is a sizable improvement in adjusted R squared from .435 to .502 from model 1 to model 2, suggesting that immigrant diversity has greater explanatory power.
Model 3 reinforces this impression as it shows that when both immigrant measures are included in the model, immigrant diversity remains positive and strongly significant while the magnitude of immigrant concentration is drastically reduced and becomes non-significant.
Several of the covariates significantly affect economic well-being in a manner consistent with expectations. College towns and global capital positively and significantly affect economic well-being in all three models. Logged population and logged international air passengers increase economic well-being in model 1, but not in the two models in which immigrant diversity is included. This suggests that size of the metro and international travel strongly contribute to immigrant diversity and its effect on economic well-being. On the other hand, earnings inequality as reflected in the Gini coefficient and deindustrialization significantly decrease economic well-being. Also, economic well-being is significantly reduced in MSAs with higher percentages of black and Hispanic residents. The geographic division variables have little effect possibly because the use of the regional price parity index for the economic items in the economic well-being scale controls for cost of living at the metropolitan level.
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Next, we observe the results for social well-being in models 4-6. Model 4 shows that the effect of immigrant concentration is modestly negative but far from statistically significant. In model 5, however, immigrant diversity has a strong negative and significant effect on social well-being and the adjusted R square jumps substantially from .593 to .630 between models 4 and 5. In model 6, immigrant diversity retains its strong positive effect while immigrant concentration turns positive and approaches statistical significance (p<.14).
The covariates also affect social well-being in interesting ways. Logged population and college towns have significant positive effects, and percent blacks, percent Hispanics, deindustrialization and the Gini coefficient are negatively associated with social well-being. All the geographic divisions are significantly lower on social well-being than the New England division reference category with the three southern divisions (South Atlantic, East South Central, and West South Central) showing the largest deficits.
Table 4 follows the same format as Table 3 and shows the determinants of the healthy living (models 7-9) and urban mobility (models 10-12) scales. In model 7, we see that immigrant concentration has a strong, negative and significant effect on healthy living. But in model 8, immigrant diversity has a statistically significant positive relationship and the adjusted R squared improves modestly from .558 to .591. When the two variables are included jointly in model 9, the effect of immigrant diversity remains strongly significant and positive and the effect of immigrant concentration is reduced to non-significance.
—Table 4 here—
Among the covariates, logged population and college towns have consistently positive and significant effects on healthy living. Oddly, the Gini coefficient is positively related to healthy living in model 7, but this effect becomes nonsignificant when immigrant diversity is
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included in models 8 and 9. Similarly, deindustrialization has a negative effect in model 7, but becomes nonsignificant in models 8 and 9. Percent blacks is consistently negatively related to healthy living, but percent Hispanics is significant only in model 8. Interestingly, two of the southern divisions fare significantly worse than the New England division reference category and the two western divisions fare significantly better.
Finally, we turn to the models predicting urban mobility and once again find conflicting results for the two immigrant measures. In model 10, immigrant concentration is negatively associated with urban mobility, net of other covariates, but in model 11, immigrant diversity has a positive effect. Moreover, the model with immigrant diversity has greater explanatory power as the adjusted R square increases from .663 to .731 from model 10 to model 11. When both immigrant variables are included in model 12, immigrant diversity retains its strong positive and significant effect and immigrant concentration once again is reduced to nonsignificance.
The covariates show some patterns consistent with previous results. Logged population and college towns have significant positive effects on urban mobility across all three models, as does logged international passengers—although the latter is only marginally significant (p<.10) in models 11 and 12. Again the Gini index is positive and significant in the first model, but nonsignificant in the latter two models. Percent Hispanics is positively related to urban mobility in the first two models, but not the last, and percent blacks is consistently unrelated to urban mobility. There are modest effects of geographic location as the Middle Atlantic division ranks significantly higher on urban mobility than the New England division reference category while the southern divisions are lower.
DISCUSSION AND CONCLUSIONS
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In this paper, we have argued that research examining the effects of immigration on quality of life should consider both the size and composition of the immigrant population. Based on this premise, we derived measures of the two faces of immigration. Immigrant concentration measures the extent to which metropolitan areas have large immigrant populations that are concentrated among one or a few places of origin. Immigrant diversity measures the extent to which metropolitan areas have large immigrant populations that are diversified among many places of origin. We then investigate the extent to which these two faces of immigration affect four dimensions of urban quality of life: economic well-being, social well-being, healthy living and urban mobility.
Overall, the results lend qualified support for Hypothesis 1 that immigrant concentration leads to negative outcomes for urban quality of life. That is, the first face of immigration— immigrant concentration—is negatively related to three of four dimensions of urban quality of life, net of a variety of covariates. These results provide prima facie support for the argument that high concentrations of immigrants from relatively few places of origin experience reduced quality of life on several dimensions. This is most consistent with the lived experience of residents in U.S. border towns like El Paso, Laredo, and McAllen, TX with heavy Mexican immigrant populations as well as many small-town destinations like Hazleton, PA that have experienced rapid influxes of Hispanic immigration.
However, these results pale in importance when we consider the second face of immigration—immigrant diversity. Here, we find strong, consistent support for Hypothesis 2 that immigrant diversity is significantly and positively related to all four dimensions of urban quality of life. These results are most consistent with the lived experiences in diverse, multicultural milieus typified by large metropolitan centers like New York, Miami, Los Angeles, and San
19

Francisco. Moreover, when both immigrant measures are considered jointly, the effects of immigrant concentration become nonsignificant while the effects of immigrant diversity remain strong. This suggests that both faces of immigration provide some leverage in understanding urban quality of life. However, immigrant diversity—or lack thereof—is more indispensable in conceptualizing the effects of immigration.
Our results suggest that future social science research on immigration should take into account the qualitative differences between immigrant concentration and diversity. These two aspects of immigration represent fundamental differences in the lived experiences of residents in areas with a high immigrant presence. Future research should extend this approach to the study of other important outcomes such as educational achievement, housing discrimination, and earnings inequality. Also, researchers should also seek to learn whether these processes operate similarly in America’s “new small-town destinations” (Hyde, Pais, and Wallace. 2015). Some research suggests that these processes may be more nuanced in smaller towns and contingent upon a variety of cultural and public policy factors. For instance, Carr, Lichter, and Kefalas
(2012) document widely disparate outcomes in two small towns, Hazleton, PA and St. James,
MN, both of which experienced large influxes of Hispanic immigration. They argue that if immigration is “done right,” it can provide a remedy to the economic distress of depopulation and out-migration of young people. We hope that our research helps advance these new avenues of investigation.
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23

Figure 1. Distribution of Immigrant Concentration and Immigrant Diversity Measures
Panel A: Immigrant Concentration (IC)
Panel B: Immigrant Diversity (ID)
Note: PFB —Percent of foreign-born population
HHI
—Herfindahl-Hirschman Index of immigrant origins
RHHI —reversed Herfindahl-Hirschman Index of immigrant origins
IC
—Immigrant Concentration=
PFB*HHI
ID —Immigrant Diversity= PFB*RHHI
24

Table 1. Scale Construction and Descriptive Statistics for Urban Quality of Life Scales, N=366
Variable
Economic well-being (α=. 85)
Median household income RPP
Real GDP per capita RPP
Poverty rate R
Unemployment rate R
% Households receiving SSI R
% Households receiving public assistance R
Social well-being (α=. 77)
% College graduates
% Health insurance
Mean S. D. Min. Max.
.00 .76
-2.73 2.02
519.71 80.06
345.26 868.99
376.53 113.36
132.66 884.54
16.00 4.13
7.6 35.5
10.14 2.74
3.16 20.08
.05 .018
.02 .11
.13
.00
.04
.68
.05
-2.46
.35
1.65
25.65
82.75
63.70
7.90
5.24
4.85
11.80
61.91
45.69
58.30
95.18
75.82 Labor force participation rate
Property crime per 100,000 persons R
3160.69 901.37 1122.40
% Low income & low access to stores R
% No car & low access to stores R
7.33 3.35 1.31
2.09 .97 .24
Healthy living (α=. 90)
.00 .51 -1.27
% Adult diabetes R
9.76 1.94 4.50
% Adult obesity R
28.85 4.08 13.80
% Physically inactive R
24.49 4.86 10.80
Preventable hospital stays rate R
63.19 16.63 24.55
Urban mobility (α=. 85)
.00 .88 -1.34
Logged weighted population density
7.56 .61 6.26
% Commuting by public transportation
1.66 2.48 .04
Sprawl index R
71.93 20.04 9.94
RPP—Item is adjusted by the Regional Price Parity (RPP) index for all consumer goods to standardize for metropolitan cost of living
R—Item is re-keyed
5824.60
26.55
6.97
1.60
15.73
39.70
35.90
121.53
6.26
10.35
30.74
100.00
25

Table 2. Descriptive Statistics for Independent Variables in the Analysis, N=366
Variable Mean S. D. Min
Immigrant concentration a
Immigrant diversity b
Logged population
.02
.06
12.68
.04
.05
1.06
.00
.01
10.92
% Non-Hispanic Black
% Hispanic
College town
11.33
12.36
.14
10.83
15.49
.35
.22
.71
0
Globalization
Global capital
Foreign direct investment
Exports/GMP
Logged intl. air passengers
.01
5.18
8.19
1.82
.99
1.49
9.67
2.75
-.69
.89
.23
.00
Deindustrialization
Gini coefficient
Region
New England (reference)
Middle Atlantic
East North Central
West North Central
South Atlantic
2.69
.45
.04
.08
.17
.09
.21
1.17
.03
.20
.28
.37
.28
.41
.42 9.12
.38 .54
0
0
0
0
0
East South Central
West South Central
Mountain
.08
.11
.10
.28
.32
.29
0
0
0
West .12 .33 0 a
Immigrant concentration (IC): HHI*PFB=HHI*proportion of foreign-born population b
Immigrant diversity (ID): RHHI*PFB=RHHI*proportion of foreign-born population
1
1
1
1
1
1
1
1
1
Max
.28
.33
16.76
52.56
95.74
1
2.56
8.81
89.05
10.56
26

Table 3. Determinants of Economic and Social Well-being Scales in U.S. Metropolitan Areas, N=366
Variable
Immigrant concentration a
)Immigrant diversity
Logged population
% Non-Hispanic Black
% Hispanic
College town
Globalization
Global capital
Exports/GMP
Middle Atlantic
East North Central
South Atlantic
East South Central
Mountain
West
Constant
Adjusted R Squared b
West North Central
West South Central
Foreign direct investment
Model 1
-3.925† (2.059)
--- ---
.158** (.057)
-.019*** (.004)
-.010† (.006)
.268** (.095)
.143** (.048)
.007
-.005
Logged intl. air passengers .043*
Deindustrialization
Gini coefficient
(.024)
(.004)
-.128*** (.031)
-3.795** (1.438)
Region
New England (reference)
-.129*
-.386
.141
-.197*
-.468
-.123
-.184
-.290
.555
.435
(.017)
(.183)
(.176)
(.196)
(.177)
(.195)
(.196)
(.208)
(.194)
(.943)
Economic Well-Being
---
Model 2
---
6.619*** (.925)
.071 (.055)
-.018*** (.004)
-.023*** (.003)
.187* (.090)
-.299
Model 3
(2.006)
6.581*** (.960)
.070 (.055)
-.018*** (.004)
-.023*** (.005)
.187* (.090)
-.148 (1.568)
---
Model 4
---
Social Well-being
Model 5
--- ---
4.195*** (.716)
.119** (.045) .119** (.046)
.284*** (.043) .221*** (.043)
-.011*** (.003) -.010*** (.003)
-.012** (.004) -.015*** (.002)
.463*** (.072) .411*** (.069)
.066† (.037) .049 (.035)
.010
-.002
(.022) .010
(.003) -.002
(.023)
(.003)
.024 (.016) .024 (.016)
-.106*** (.030) -.106*** (.030)
-5.491*** (1.371) -5.495*** (1.373)
-.004
-.001
-.009
(.018)
(.003)
(.013)
-.081*** (.024)
-.003
.002
-.022†
-.065**
(.017)
(.003)
(.013)
(.023)
-2.394* (1.095) -3.582*** (1.062)
Model 6
2.325 (1.548)
4.488*** (.741)
.224*** (.043)
-.010*** (.003)
-.020*** (.004)
.408*** (.069)
.050 (.035)
-.002 (.017)
.001 (.003)
-.022† (.013)
-.066** (.023)
-3.553*** (1.060)
-.050 (.172) -.051 (.173) -.498*** (.140) -.453*** (.133)
-.181
.355†
-.112
-.213
.218
.096 (.198) .093 (.200)
-.359*
1.886 (.905) 1.892 (.907)
.502
(.168) -.181
(.186) .355†
(.166) -.113
(.187) -.213
(.189) .215
(.181) -.355†
.501
(.168)
(.187)
(.167)
(.187)
(.191)
(.183)
-.677** (.134) -.541*** (.130)
-.395*** (.149) -.249† (.144)
-1.002*** (.135)
-.968*** (.149)
-1.232*** (.150)
-.646*** (.158)
-.562*** (.148)
-1.310
.593
(.718)
-.948*** (.129)
-.794*** (.145)
-1.027*** (.147)
-.484** (.153)
-.580*** (.140)
-.348
.630
(.701)
-.444*** (.133)
-.537*** (.130)
-.249† (.144)
-.944*** (.129)
-.794*** (.145)
-1.002*** (.147)
-.457** (.154)
-.606*** (.141)
-.398 (.700)
.631
* p < .05, ** p < .01, *** p<. 001, † p < .10 (two-tailed tests) a
Immigrant concentration is derived from the Herfindahl-Hirschman Index (HHI*PFB) b Immigrant diversity is derived from the reversed Herfindahl-Hirschman Index (RHHI*PFB)

Table 4. Determinants of Healthy Living and Urban Mobility Scales in U.S. Metropolitan Areas, N=366
Variable
Immigrant concentration
Immigrant diversity
Logged population b
% Non-Hispanic Black
% Hispanic
College town a
Model 7
-2.475*
---
(1.226)
---
.121*** (.034)
-.009*** (.002)
-.004 (.003)
.172** (.056)
Healthy Living
Model 8
--- ---
3.196*** (.565)
.081* (.034)
-.008*** (.002)
-.004* (.002)
.133* (.058)
Globalization
Global capital
Foreign direct investment
Exports/GMP
Logged intl. air passengers -.001
Deindustrialization
Gini coefficient
West
.037
-.020
-.002
-.037*
(.029)
(.014)
(.002)
(.010)
(.019)
1.707* (.856)
Region
Middle Atlantic
East North Central
West North Central
South Atlantic
East South Central
West South Central
Mountain
New England (reference)
-.103 (.109)
-.183 (.105)
-.025 (.117)
-.159 (.105)
-.591*** (.116)
-.408*** (.117)
.338** (.124)
.393*** (.116)
.026 (.028)
-.018 (.014)
.000 (.002)
-.007 (.010)
-.027 (.018)
.916 (.882)
-.063 (.105)
-.085 (.103)
.076 (.114)
-.119 (.102)
-.471*** (.114)
-.241* (.116)
.477*** (.121)
.353** (.111)
Constant
Adjusted R Squared
-1.924** (.561)
.558
-1.311*
.591
(.553)
Model 9
-.768
3.099*** (.586)
.080* (.034) .381*** (.051)
-.008*** (.002) -.001 (.003)
.002 (.003) .019*** (.005)
.134* (.055) .419*** (.085)
.025 (.028) -.007 (.043)
-.018
.000 (.002) .001 (.003)
-.007 (.010) .045** (.015)
.027 (.018) -.016 (.028)
.906 (.838) 3.188* (1.288)
-.066
(1.224) - 4.248* (1.844)
(.014) -.004
(.105)
---
Model 10
---
(.021)
.325* (.164)
-.087 (.103) -.158 (.158)
.076 (.114) -.158 (.175)
-.120 (.102) -.735*** (.158)
-.471*** (.114) -.858*** (.175)
-.249* (.117) -.601*** (.176)
.468*** (.122) -.146 (.186)
.362*** (.112) .201 (.174)
-1.294* (.554) -6.200 (.845)
.590 .663
Urban Mobility
Model 11
--- ---
7.665*** (.788)
.279*** (.047)
.001 (.003)
.005* (.002)
.325*** (.076)
-.035 (.039)
.000 (.019)
.004 (.003)
.024† (.014)
.010 (.025)
1.210 (1.169)
.416** (.147)
.080 (.143)
.091 (.159)
-.637*** (.142)
-.561*** (.159)
-.208 (.162)
.177 (.169)
.125 (.154)
-4.644
.731
(.771)
* p < .05, ** p < .01, *** p<. 001, † p < .10 (two-tailed tests) a Immigrant concentration is derived from the Herfindahl-Hirschman Index (HHI*PFB) b
Immigrant diversity is derived from the reversed Herfindahl-Hirschman Index (RHHI*PFB)
28
Model 12
-.027 (1.710)
7.662*** (.819)
.279*** (.047)
.001 (.003)
.005 (.005)
.325*** (.077)
-.035 (.039)
.000 (.019)
.004 (.003)
.024† (.014)
.010 (.025)
1.210 (1.171)
.416** (.147)
.080 (.143)
.091 (.159)
-.637*** (.142)
-.561*** (.160)
-.208 (.163)
.176 (.170)
.125 (.156)
-4.644 (.773)
.731