Introduction
Although immigration evokes strong sentiments among citizens in the contemporary United States, discussions about new migrants, especially the undocumented, become even more heated in a presidential election year. In the current political climate, especially with only a few days left before U.S. citizens go to the polls on November 5, 2024, to elect a new president, both former President Trump, who has railed against immigration and has accused his successor of dereliction of duty in his handling of migrants at the Southern border, and President Biden, who has argued that he needs an immigration bill from the U.S. Congress to enact meaningful immigration reform (Miller et al., Reference Miller, Long and Kinnard2024), understand that immigration is a key topic for voters this election year. In a 2024 AP-NORC poll, 55% of Republicans and 22% of Democrats noted that immigration was a priority for them. These immigration debates are not lost on citizens, who hold divergent views about how the United States should handle immigrants and immigration. These debates have received more traction in recent years because some have argued that unchecked immigration would bring about unwanted social and cultural changes to U.S. society. Indeed, some opponents of unrestricted immigration see immigration as a “threat to group homogeneity, community cohesion, and group values” (Boateng et al., Reference Boateng, Pryce and Chenane2021a, p. 1106; also see Adamson Reference Adamson2020; Berardi and Bucerius, Reference Berardi, Bucerius, Bucerius and Tonry2013).
According to Victoria M. Esses and colleagues (Reference Esses, Jackson and Armstrong1998), immigration debates taking place inside developed nations have become increasingly strident and divisive because of increased migration to these richer nations as well as native-born citizens’ belief that the influx of immigrants is unmanageable. The authors blamed increased migration on economic instability and political trouble in many regions of the world, two powerful forces that coalesce to push people from poorer nations to the developed world. Several immigration-related studies conducted over the last few decades point to varying attitudes toward immigrants and immigration (Gorodzeisky and Semyonov, Reference Gorodzeisky and Semyonov2015; Ousey and Kubrin, Reference Ousey and Kubrin2018). These studies have addressed individual- and contextual-level influences on immigration. Individual-level factors known to influence immigration include race, education, age, social class, political ideology, and gender (Boateng et al., Reference Boateng, McCann, Chenane and Pryce2021b; Gorodzeisky and Semyonov, Reference Gorodzeisky and Semyonov2015; McCann and Boateng, Reference McCann and Boateng2020; Quillian Reference Quillian1995; Pryce Reference Pryce2018; Savelkoul et al., Reference Savelkoul, Scheepers, Tolsma and Hagendoorn2011). Prior research that has examined contextual-level factors have found that the lack of resources (Esses et al., Reference Esses, Jackson and Armstrong1998), gross domestic product (Boateng et al., Reference Boateng, McCann, Chenane and Pryce2021b), crime rates (Chenane and Wright, Reference Chenane and Wright2021; Chouhy and Madero-Hernandez, Reference Chouhy and Madero-Hernandez2019; Ousey and Kubrin, Reference Ousey and Kubrin2018; Sampson Reference Sampson2008), and the size of the out-group (Savelkoul et al., Reference Savelkoul, Scheepers, Tolsma and Hagendoorn2011; Scheepers et al., Reference Scheepers, Gijsberts and Coenders2002), among other factors, significantly influenced attitudes toward immigrants and immigration.
The Current Study
The aim of the present study is to evaluate Hampton Roads residents’ attitudes toward immigrants and immigration. The following seven cities, with a population of approximately 1.5 million, comprise the Hampton Roads region: Newport News, Hampton, Portsmouth, Suffolk, Chesapeake, Norfolk, and Virginia Beach. Of the aforementioned cities, Virginia Beach is the largest and is also the largest city in the Commonwealth of Virginia. Specifically, this study: (a) assesses Hampton Roads residents’ views about immigration, (b) increases scholars’ and local residents’ understanding of the antecedents of support for immigration, and (c) adds to the literature on the importance of citizens’ support (or otherwise) for immigration policy, which is linked to the welfare of community members. Moreover, the presence of multiple racial groups, with diverse social, political, and economic backgrounds, and the fact that the region houses the world’s largest naval base, drawing in people from many countries around the world, make the Hampton Roads region of Southeast Virginia a good choice for evaluating community member attitudes toward immigrants and immigration. Lastly, I chose the Hampton Roads region for the current study so as to increase scholars’ and community members’ understanding of attitudes toward immigrants and immigration in different geographical and sociopolitical contexts. I address the following specific research questions:
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1) What are the effects of demographic variables (gender, income, marital status, age, race, ethnicity, and educational level) on support for immigration in the Hampton Roads region?
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2) Does quality of life in the neighborhood, city, and the larger Hampton Roads region influence community members’ support for immigration?
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3) Do political affiliation and ideology impact attitudes toward immigration in the Hampton Roads region?
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4) What are the effects and relative impacts of crime rates and availability of jobs on Hampton Roads community members’ support for immigration?
Addressing these questions empirically would provide important answers that can guide elected officials’ decision-making as far as immigration policy is concerned. The study’s findings will also be a crucial addition to the literature on immigration, as this is the first study to comprehensively examine immigration sentiments in the Hampton Roads region of Southeast Virginia using a probability sample derived from the Life In Hampton Roads probability data.
Overall, the current study examines the effects of important demographic variables on support for immigration in the Hampton Roads region. The study also assesses the effects of several substantive independent variables on support for immigration. The current study makes important contributions to the immigration literature by: (a) increasing scholars’ and community members’ understanding about the effects of gender, income, marital status, age, race, ethnicity, and educational level on support for immigration; (b) pointing out substantive predictors of community members’ support for immigration; (c) building on the immigration literature by being the first to examine residents’ attitudes toward immigrants and immigration using the Life In Hampton Roads probability data, and (d) being the first study to examine the effect of quality of life on attitudes toward immigrants and immigration in the Hampton Roads region.
Literature Review
The Stratification Beliefs Model
Stratification beliefs research has generally pointed to three types of belief about stratification: individualistic, structuralist, and fatalistic (Feagin Reference Feagin1975; Hunt Reference Hunt2016). Individualistic beliefs point to the lack of effort as the cause of poverty, while structuralist beliefs blame the larger system (e.g., poor wages and lack of adequate jobs) for the plight of the poor (Hunt Reference Hunt2016). The final category—fatalistic beliefs—blames supraindividual forces (e.g., illness and bad luck) for the woes of society’s poor (Huber and Form, Reference Huber and Form1973; Hunt Reference Hunt2016). Of the three types of belief, individualism remains the dominant strand for explaining social inequality (Hunt Reference Hunt2016).
One’s propensity to see others in society as competitive threats may influence one’s understanding of inequality (Bobo and Hutchings, Reference Bobo and Hutchings1996). Lawrence D. Bobo and Vincent L. Hutchings (1996), echoing individualistic beliefs about inequality, elucidate the dominant stratification model’s primary argument: that there are enough opportunities for everyone to succeed in the United States, and that success or failure is thus a direct result of and application of one’s talent and effort in life. As such, inequality exists because people possess varying degrees of talents, which are applied differentially with varying outcomes (Feldman Reference Feldman1988; Kluegel and Smith, Reference Klueger and Smith1986). According to Bobo and Hutchings (Reference Bobo and Hutchings1996), individualism, the core concept underpinning the stratification beliefs model, theorizes that “individuals obtain valued social outcomes by dint of their individual abilities” (p. 954). Thus, community members who see individualism as key to success frown upon certain social programs (e.g., affirmative action) designed to give certain groups a “leg up” (Sowell Reference Sowell1984).
Bobo and Hutchings’ Modified Group Position Model
According to Herbert Blumer (Reference Blumer1958), intergroup hostility goes beyond material resources and negative feelings and beliefs, to include judgments about group positions formed over a long period. For example, Whites, according to Blumer, hold favorable opinions about their superordinate position, vis-à-vis the subordinate positions of out-groups, as a result of Whites’ long history of enjoying individual and structural privileges denied to minority members, or out-groups, in U.S. society. Thus, there is an element of entitlement to the supremacy claims made by Whites within the U.S. populace.
Blumer’s theory of group position identified four types of feelings necessary for race prejudice to occur. These feelings include: (a) feeling superior to other groups, (b) seeing subordinate groups as different and alien, (c) feeling necessarily privileged, and (d) being suspicious of the intentions of subordinate groups with respect to resources. Unquestioningly, the first feeling noted above has its origins in the superordinate group’s belief that out-groups are inferior. According to Blumer (Reference Blumer1958), this feeling is expressed in the language used by the superordinate group to describe out-groups, which includes words such as stupid, immoral, lazy, dishonest, greedy, and unreliable. These terms are used to clearly create a dichotomy between the in-group and out-groups. The second feeling also helps to justify Whites’ superordinate position, vis-à-vis the positions of out-groups. In other words, subordinate groups are seen as distinct from the superordinate group, and hence not deserving of the privileges enjoyed by the latter.
The third feeling might be the most consequential, according to Blumer, as it undergirds the superordinate group’s argument that it has earned the right to the best real estate and lands in society; it deserves society’s most important professions; it must rule society by occupying the most important positions in government; and it deserves, collectively, society’s most esteemed positions. Finally, the fourth feeling is meant to keep an eye on subordinate groups’ advancement in society, so they are not allowed to “plunder” the privileges of the superordinate group. Any attempts by out-groups to rise above their “predetermined” state might trigger a backlash from the superordinate group, leading to greater race prejudice. According to Blumer (Reference Blumer1958), “these four basic feelings of race prejudice definitely refer to a positional arrangement of the racial groups” (p. 4). Blumer clarified his group position theory by noting that the dominant group’s position of superiority is a shared one, which solidifies the superordinate group’s position over other groups. Superordinate group members’ interactions with weaker groups might occur on an individual basis, yet the feeling of superiority by the former is collective, hence the group’s ability to successfully stereotype the out-groups. In effect, the superordinate group deploys these four feelings to keep subordinate groups “in their place.” These feelings also develop as “leaders or significant segments of social groups contend with one another through public discourse and political struggle” (Bobo and Hutchings, Reference Bobo and Hutchings1996, p. 955).
Bobo and Hutchings extended Blumer’s (Reference Blumer1958) group-position theoretical framework by “focus[ing] on the individual-level dynamics of perceived threat and theor[izing] about attitudes of both dominant and minority racial group members” (1996, p. 955). Blumer’s theory combines material resources with negative affect toward out-groups. This combination of material things and affect clearly situates Whites atop a social hierarchy in which others are automatically considered inferior and less deserving. As Lawrence D. Bobo and Mia Tuan (Reference Bobo and Tuan2006) argued, the sense of group position held by the superordinate group draws from a trifecta of ideas: individual psychology, cultural values and orientation, and self-interest. Bobo and Hutchings (Reference Bobo and Hutchings1996) argue, therefore, that Blumer’s theoretical model has two implications: (a) the focus is on perceived group competition, and (b) the focus on perceived group competition is “rooted in race-specific beliefs about the society’s opportunity structure” (p. 956).
Expatiating on the first focus or implication, Bobo and Hutchings (Reference Bobo and Hutchings1996) argue that the degree to which the in-group feels threatened by the loss of resources to less powerful groups is relevant to any discussion about the group position model. In other words, the level of racial prejudice experienced by out-groups may be a by-product of the “perceptions of zero-sum competition” for scarce resources in society. Expatiating on the second focus or implication, Bobo and Hutchings employ the term racial alienation in their application of Blumer’s theory to racial minority group members. By racial alienation, the two authors were pointing to feelings of “enfranchisement and entitlement” experienced by the superordinate group as well as feelings of “disenfranchisement and grievance” experienced by minority group members.
When minority group members feel alienated, they tend to view other out-groups as competition and threat to their own place on the social totem pole. Furthermore, feelings of alienation are shared by group members, and may be based on a collective history of oppression and discrimination. As Bobo and Hutchings (Reference Bobo and Hutchings1996) put it, “[t]o understand why members of one group feel threatened by members of another group, individuals’ feelings about the treatment, conditions, and opportunities that have historically faced members of their own group must be measured” (p. 956). Indeed, the degree of alienation is largely correlated with a group’s historical position in the social hierarchy. Because Whites have enjoyed institutional and structural privileges for as long as the United States of America has existed, Whites experience the least alienation in the society. Conversely, not only will subordinate groups experience greater alienation than their superordinate counterparts, but subordinate group members will experience varying degrees of alienation based on their efforts at ameliorating their social, economic, and political disadvantage.
Immigration and Crime Rates
For decades, immigrants have been the focus of anti-immigration proponents, who have blamed the former for an increase in crime rates in the United States (Davies and Fagan, Reference Davies and Fagan2012; Diaz Reference Diaz2011; McDonald Reference McDonald and McDonald2009), even if the evidence points to the contrary (Butcher and Piehl, Reference Butcher and Piehl1998; Wang Reference Wang2012). For example, Xia Wang (Reference Wang2012) found that “perceptions of criminal threat are not prompted by the actual presence of immigrants or the unemployment rate of communities” (p. 759). Thus, perceptions of criminal threat may result from some community members’ exaggerated or stereotypical views about the size of the immigrant population (Wang Reference Wang2012). Moreover, Wang (Reference Wang2012) found that, compared to Whites, Blacks were less likely to see undocumented immigrants as a source of criminal activities.
Several important research studies have found that an increase in the population of immigrants does not lead to a corresponding increase in crime (Chouhy and Madero-Hernandez, Reference Chouhy and Madero-Hernandez2019; Davies and Fagan, Reference Davies and Fagan2012; Sampson Reference Sampson2008; Shaw and McKay, Reference Shaw and McKay1943). In fact, several researchers found an inverse relationship between immigration and crime in their studies (for example, see Butcher and Piehl, Reference Butcher and Piehl1998). Some researchers have even argued that when immigrants move into a community, it creates a buffer against crime, as the influx of immigrants “may increase the concentration of motivated, goal-directed, law-conforming individuals in a community” (Ousey Reference Ousey2017, p. 31; also see Ousey and Kubrin, Reference Ousey and Kubrin2009; Reference Ousey and Kubrin2018). Moreover, immigrants replace native-born citizens who have moved out of their local community in droves, which may reverse economic decline in the community (Ousey Reference Ousey2017). In his study that examined the connection between immigrants and crime, Robert J. Sampson (Reference Sampson2008) found a negative correlation between the immigration rate and homicide rate from 1990 to 2004. Although the evidence does not support their claim of a direct relationship between immigration and crime, politicians tend to claim otherwise in order to whip up negative attitudes toward immigrants and immigration (Stumpf Reference Stumpf2006).
Immigration and Jobs
There is a long history of U.S. workers complaining about immigrants taking away their jobs, but studies have not always supported this claim (Esses et al., Reference Esses, Jackson and Armstrong1998; Stephan et al., Reference Stephan, Ybarra and Bachman1999). Whenever the U.S. economy experiences a downturn, native-born citizens are likely to see immigrants as competitors and thus be opposed to increased immigration (Chouhy and Madero-Hernandez, Reference Chouhy and Madero-Hernandez2019; Espenshade and Hempstead, Reference Espenshade and Hempstead1996). This perception that immigrants take jobs away from natives may be tied to the fact that immigrants tend to concentrate in poor areas with low-paying jobs, hence would-be employers have easy access to this pool of relatively cheap labor (Portes and Rumbaut, Reference Portes and Rumbaut2014). Drawing a distinction between high-skilled and low-skilled workers, researchers have argued that low-skilled workers are the ones more likely to compete for jobs with immigrants, or are at an increased risk for job loss, compared to their high-skilled counterparts (Coleman and Pencavel, Reference Coleman and Pencavel1993a; Simon Reference Simon1987). These trends are more likely to hold during a downturn in the economy, as was evident in 2008, for instance. However, the job loss suffered by low-skilled workers, for which immigrants are sometimes blamed, is more the result of the skill gap that exists between companies’ needs and would-be employees’ skillsets (Espenshade and Hempstead, Reference Espenshade and Hempstead1996).
An additional argument posited by some who oppose immigration is that immigrants are quick to go on welfare, so the more immigrants there are in the community, the more likely it is that local taxes will go up to sustain immigrants’ dependence on the welfare system (Goldstein and Peters, Reference Goldstein and Peters2014). This welfare-exploiting accusation leveled against immigrants thus becomes an important talking point for those who blame immigrants for job loss by natives. Generally, the perceived fear of out-group members, such as immigrants (documented or otherwise), culminates in the desire to exclude foreigners and immigrants from mainstream U.S. society (Raijman et al., Reference Raijman, Davidov, Schmidt and Hochman2008; Stephan and Stephan, Reference Stephan and Stephan1985). This desire sometimes manifests itself as economic threat to native-born citizens (Bobo and Hutchings, Reference Bobo and Hutchings1996).
Quality of Life
According to Joseph E. Stiglitz and colleagues (Reference Stiglitz, Sen and Fitoussi2009), quality of life refers to “those aspects of life that shape human well-being beyond the command of economic resources” (p. 143). This definition encompasses the concept of happiness, which addresses one’s overall appreciation of life (Hajiran Reference Hajiran2006; Touburg and Veenhoven, Reference Touburg and Veenhoven2015). Quality-of-life studies fall into three conceptual strands. The first strand addresses subjective well-being, which notes that community members know what is best for them (Stiglitz et al., Reference Stiglitz, Sen and Fitoussi2009). The second strand reflects on human endeavors that elevate the human condition, such as not living in a crime-prone neighborhood, being able to provide for oneself, being able to avoid dangerous situations that can lead to premature death, and being able to attain adequate formal education for proper functioning in society. The final strand focuses on non-economic indicators that address community members’ preferences with respect to goods and services, which is tied to the earlier argument: people, not the government, are the best judges of what is best for them. Measuring quality of life would help policymakers address the needs of community members at a macro-level. In fact, understanding community members’ views about quality of life enhances the delivery of goods and services to provide the greatest good for the greatest number, which is an important utilitarian principle (Mill Reference Mill1843).
Demographics and Immigration Sentiments
Research on the educational level-immigration nexus shows that the more highly educated generally hold more favorable views about immigrants and immigration (Chandler and Tsai, Reference Chandler and Tsai2001; Chenane et al., Reference Chenane, Morabito and Gonzales2022; McCann and Boateng, Reference McCann and Boateng2020; Pantoja Reference Pantoja2009; Pryce Reference Pryce2018; Simon and Alexander, Reference Simon and Alexander1993). In terms of age, some researchers have noted that older U.S. citizens hold more negative views about immigrants and immigration (Goldstein and Peters, Reference Goldstein and Peters2014), a finding that I call the “tolerance gap” between older and younger people (Pryce Reference Pryce2018). Conversely, other scholars have noted that younger people, in fact, hold more negative views about immigrants and immigration (McCann and Boateng, Reference McCann and Boateng2020). The correlation between gender and support for immigration is mixed: some studies found that females held more favorable attitudes toward immigrants and immigration (Buckler et al., Reference Buckler, Swatt and Salinas2009; Chandler and Tsai, Reference Chandler and Tsai2001; Pryce Reference Pryce2018), others found that males held stronger pro-immigration sentiments (Buckler Reference Buckler2008; Burns and Gimpel, Reference Burns and Gimpel2000), and still others found no statistically significant gender differences in attitudes toward immigrants and immigration (Berg Reference Berg2009; Espenshade and Hempstead, Reference Espenshade and Hempstead1996).
In terms of race, compared to minority groups, White Americans held more negative views about immigrants and immigration (Valentino et al., Reference Valentino, Brader and Jardina2013). Concerning ethnicity, Hispanic Americans were more likely than any other group to support increased immigration to the United States (Smeltz et al., Reference Smeltz, Kafura, Rondeaux, Rasool-Ayub and Avant2023). Income tends to be negatively correlated with pro-immigration attitudes (Facchini and Mayda, Reference Facchini and Mayda2012). Compared to Democrats and Independents, those who identify as Republican were more likely to hold stronger anti-immigrant attitudes (Oliphant and Cerda, Reference Oliphant and Cerda2022). Finally, liberals were more likely than their conservative counterparts to hold pro-immigration attitudes (Pryce and Chenane, Reference Pryce and Chenane2023; Silver and Silver, Reference Silver and Silver2017).
Method
Participants and Procedures
I employed the 2023 Life In Hampton Roads (LIHR) surveyFootnote 1, which is undertaken yearly by Old Dominion University’s Social Science Research Center (SSRC). The 2023 survey, which was funded by the SSRC, focused on local residents’ views about immigration, politics, personal finances, quality of medical and health care, policing, housing, governance, and other important socioeconomic indicators that affect residents of the seven cities that make up Hampton Roads. As of 2021, the combined population of the seven cities was about 1.5 million people, as noted earlier. Importantly, Hampton Roads residents come from a variety of demographic, social, and economic backgrounds (U.S. Census Bureau 2022), making the region an ideal location for evaluating residents’ attitudes toward immigrants and immigration.
A total of 610 questionnaires were completed between June 6 and August 25, 2023, by residents of the seven cities in Hampton Roads. The final data set was weighted, within cities, to make it more representative of the region’s demographics and populations (i.e., race, gender, age, etc.). My findings from the 2023 LIHR data hold important implications for what we know about immigrants and immigration, and should be relevant to scholars, practitioners, local and state officials, and the general public. Table 1 provides descriptive statistics of the variables employed in the current study.
Table 1. Descriptive Statistics of the Variables
Sample
The sample included 48.6% (n = 297) males and 50.2% (n = 306) females. Respondents ranged in age from eighteen to ninety-four years (mean = 45.76, standard deviation [SD] = 18.04). In terms of educational attainment, 16.3% (n = 100) had a high school diploma/equivalency or less; 38.4% (n = 234) completed trade school/some college or earned an associate’s degree; and 44.6% (n = 272) completed a bachelor’s degree or higher. Regarding income, 16.1% (n = 98) earned $50,000 or less per year, 28.4% (n = 187) earned between $50,000 and $100,000 per year, and 31.4% (n = 191) earned more than $100,000 per year. In terms of race, Whites accounted for 49.8% (n = 304), Blacks accounted for 32.3% (n = 197), and other racial groupsFootnote 2 accounted for 14.8% (n = 90) of the sample. In terms of marital status, 45.1% of the respondents were married (n = 275), whereas 53.1% of the respondents were not married (n = 324). Regarding political ideology, 26.4% (n = 161) indicated that they were slightly liberal, liberal, or extremely liberal; 33% (n = 202) noted that they were moderate; and 27.4% (n = 167) stated that they were slightly conservative, conservative, or extremely conservative. Finally, 16.3% (n = 99) of the respondents noted that they leaned toward the Republican Party, 31.5% (n = 192) noted that they were Democratic Party-leaning, and 32.2 % (n = 196) considered themselves Independents.
Variables
Immigration is good
The dependent variable, immigration is good, was measured using the following item: “Immigration is generally good for the Hampton Roads economy.” A four-point Likert-type scale—(1) strongly agree, (2) agree, (3) disagree, and (4) strongly disagree—was employed to measure this item in the survey. The item was recoded so that higher scores reflected higher levels of immigration is good.
Crime rates
The first substantive independent variable, crime rates, was measured with a single item that stated, “Immigrants increase crime rates in Hampton Roads.” A four-point Likert-type scale—(1) strongly agree, (2) agree, (3) disagree, and (4) strongly disagree—was employed to measure this item in the survey. The item was recoded so that higher scores reflected higher levels of crime rates.
Jobs
The second substantive independent variable, jobs, was measured with a single item, “Recent immigrants (legal or otherwise) will take jobs away from people in Hampton Roads.” A four-point Likert-type scale—(1) strongly agree, (2) agree, (3) disagree, and (4) strongly disagree—was employed to measure this item in the survey. The item was recoded so that higher scores reflected higher levels of jobs.
Political affiliation
The third substantive independent variable, political affiliation, was measured with a single item that asked, “Do you generally feel closer to the Republican Party, the Democratic Party, or do you consider yourself to be an Independent?” The response categories were: Republican Party = 1, Democratic Party = 2, Independent = 3. Two dummy variables were created to represent Democratic Party and Independent, with Republican Party as the reference category.
Ideology
The fourth independent variable, ideology, was measured with a single item that asked whether the respondents were: extremely liberal = 1, liberal = 2, slightly liberal = 3, moderate = 4, slightly conservative = 5, conservative = 6, or extremely conservative = 7. I subsequently collapsed all the liberal designations into one category and the conservative designations into another category. Conservative was the reference category.
Quality of life
The fifth, sixth, and seventh independent variables, under the umbrella term of quality of life, were measured with the following items: (1) “How would you rate the quality of life in your neighborhood?” (2) “How would you rate the overall quality of life in your city?” (3) How would you rate the overall quality of life in Hampton Roads? A four-point Likert-type scale—(1) excellent, (2) good, (3) fair, and (4) poor—was employed to measure each of these items. The items were recoded so that higher scores reflected higher quality of life.
Control Variables
Several control variables, noted below, were included in the current study to provide unbiased estimates of the effects of the substantive independent variables on the dependent variable (Chenane et al., Reference Chenane, Morabito and Gonzales2022).
Age: This was a continuous variable, and participants ranged in age from eighteen to ninety-four years.
Gender: This variable was measured as male = 1, female = 0.
Race: This variable was measured as White, Black, and Other, with White as the reference category.
Ethnicity: This variable was measured as Hispanic/Latino = 1; Non-Hispanic/Latino = 0.
Educational level: This variable was originally measured as: Some grade school = 1; Some high school = 2; High school diploma/GED = 3; Completed trade/professional school = 4; Some college = 5; Associate’s degree = 6; Bachelor’s degree = 7; Graduate degree (master’s, PhD, MD, JD) = 8. I recoded education as: Some college or less = 1; Associate’s degree or higher = 0.
Income: This variable was measured as: Less than $15K = 1; More than $15K to $30K = 2; More than $30K to $50K = 3; More than $50K – $75K = 4; More than $75K to $100K = 5; More than $100K - $150K = 6; More than $150K to $200K = 7; More than $200K = 8. I recoded income as: $75,000 or less = 1; more than $75,000 = 0. Note that the median household income for the Hampton Roads region was $79,540 in 2023.
Marital status: This variable was measured as married = 1, not married = 0. The “not married” reclassification consists of the following categories: single, not living with a partner; single, living with a partner; divorced/separated; widowed.
Findings
Correlations
Table 2 presents the Pearson’s r coefficients for the substantive variables employed in the present study. The correlation value of .534 between crime rates (“immigrants increase crime rates in Hampton Roads”) and jobs (“recent immigrants [legal or otherwise] will take jobs away from people in Hampton Roads”) is the highest in the correlations table, which points to the likely absence of multicollinearity in the data.
Table 2. Bivariate Correlations for the Substantive Variables
The skewness and kurtosis values for the dependent variable – immigration is good – are near normal (less than an absolute value of 1). In addition, the skewness and kurtosis values are less than an absolute value of 2 for the substantive independent variables, which are in the normal range.
** p < 0.01 (2-tailed)
* p < 0.05 (2-tailed).
Statistical Tests and Analytic Strategy
I conducted a number of statistical tests to test for the assumptions of normality, linearity, homoscedasticity, and independence of residuals. Inspections of the Q-Q plot (see Fig. 1) and histogram (not shown) show that scores appear to be approximately normally distributed for the dependent variable. I also checked for outliers by inspecting the Mahalanobis distances (Tabachnick and Fidell, Reference Tabachnick and Fidell2007). Tolerance values were all greater than 0.1 and VIF (1/Tolerance) values were all well below 10 (see Table 3 for additional informationFootnote 3), so multicollinearity is likely not an issue in the data (Pallant Reference Pallant2010), as noted earlier. The skewness and kurtosis values for the variables, shown in Table 2, were near normal (less than an absolute value of 1 for the dependent variable and less than an absolute value of 2 for the independent variables). Barbara G. Tabachnick and Linda S. Fidell (2007) have argued that skewness and kurtosis will not substantively affect the findings if the sample size is larger than 200, which is the case in the current study. Thus, the use of multivariate regression analysis was an appropriate methodology for analyzing the data. Specifically, I employed two OLS regression models to isolate the independent effect of each independent variable on the dependent variable.
Figure 1. (Q-Q Plot of the Dependent Variable).
Table 3. Predictors of support for immigration Footnote 4 in Hampton RoadsFootnote 5, Footnote 6
Regression Analyses
In model 1, I analyzed the effects of the control variables (gender, income, marital status, age, race, ethnicity, and educational level) on support for immigration in the seven cities of Hampton Roads. The results show, compared to older residents, younger residents indicated that immigration is generally good for the Hampton Roads economy (β = -.262, p < .001). In addition, compared to their more highly educated counterparts, the less highly educated do not believe that immigration is generally good for the Hampton Roads economy (β = -.162 p < .001). Gender, income, marital status, race, and ethnicity were not statistically significant in the model.
This model explained 10% of the variation in support for immigration (F = 5.530, p < .001).
In model 2, I analyzed the effects of the control variables (gender, income, marital status, age, race, ethnicity, and educational level) and the substantive independent variables (crime rates, jobs, political affiliation, ideology, quality of life in one’s neighborhood, quality of life in one’s city, and overall quality of life in Hampton Roads) on support for immigration in the seven cities of Hampton Roads. The findings show that males were more supportive of immigration than were females (β = .106, p = .007). Also, compared to older respondents, younger respondents indicated that immigration is generally good for the Hampton Roads economy (β = ‒.094, p = .031). Respondents who did not believe that immigrants increased crimes rates (β = –.352, p < .001) and respondents who did not believe that recent immigrants (legal or otherwise) will take jobs away from Hampton Roads residents (β = ‒.291, p < .001) also indicated that immigration is generally good for the Hampton Roads economy. In addition, respondents who indicated that the quality of life in their neighborhood was good (β = .119, p = .012) and respondents who indicated that the quality of life in their city was good (β = .123, p = .017) noted that immigration is generally good for the Hampton Roads economy. On the contrary, respondents who did not believe that the quality of life in Hampton Roads was good (β = ‒.101, p = .030) agreed that immigration is generally good for the Hampton Roads economy. Income, marital status, race, educational level, political affiliation, and ideology were not statistically significantly related to support for immigration in the model. Independent of the other predictor variables in the model, crime rates and jobs were the strongest predictors of support for immigration among this sample of survey respondents. The implications of these findings are addressed in the discussion section of the paper. This model explained 40% of the variation in immigration (F = 23.067, p < .001).
Discussion
The current study attempts to evaluate the antecedents of support for immigration in the seven cities that constitute Virginia’s Hampton Roads region. A study about immigration in this region is opportune because Hampton Roads residents come from an array of racial, economic, and social backgrounds; as such, their views are important for understanding immigration in the community. These findings are also a reflection, somewhat, of national attitudes toward immigrants and immigration. For example, past research has shown that older and less educated respondents were less supportive of immigration (Pryce Reference Pryce2018). In addition, respondents who did not believe that immigrants increased crimes rates or took jobs away from them were more supportive of immigration (Goldstein and Peters, Reference Goldstein and Peters2014; Ousey and Kubrin, Reference Ousey and Kubrin2018). Specifically, I addressed the effects of important control variables and substantive independent variables on the respondents’ agreement that immigration is generally good for the Hampton Roads economy.
Although the present study elevates scholars’ and Hampton Roads residents’ understanding of the antecedents of support for immigration, like all empirical studies, the current study has limitations. First, the 2023 annual LIHR survey was the fourteenth collected by Old Dominion University’s SSRC. The first ten LIHR surveys were random surveys carried out via the telephone; the 2020 survey was web-based as a result of Covid-19; the 2021 survey employed a mixed-methods approach consisting of telephone calls and web-based surveys; the 2022 survey returned to the pre-Covid format—telephone interviews; and the 2023 survey employed a telephone survey, similar to the 2022 approach. These varying data collection approaches over time thus limit to a degree: (a) the ability to compare the pre-2020, 2020, 2021, 2022, and 2023 LIHR results, and (b) the generalizability of the 2023 data. Second, these findings are largely a reflection of participants’ views about support for immigration in the region. Although individuals’ views may be similar to their actual experiences, it is possible that findings based on actual experiences may differ from those based on perceptions. Third, the use of single-item variables, an artifact of the data collection process, may have decreased the robustness of the study’s findings, although the use of a probability sampling approach means that one is able to glean a great deal about community members’ evaluations of support for immigration in the region, as well as generalize, to a degree, the findings of the present study beyond Hampton Roads. Fourth, the current study excludes some variables known to measure support for immigration. This is also due to the limited number of questions found in the survey, rather than a deliberate attempt by the author to exclude such variables. Thus, the present study does not claim to capture the full spectrum of the antecedents of support for immigration in the Hampton Roads region.
In answering the first research question, I note that age, educational level, and gender were statistically significantly related to support for immigration in Hampton Roads. Specifically, younger, more highly educated, and male respondents were of the opinion that immigration is generally good for the Hampton Roads economy. These findings are largely in line with previous research, an indication that attitudes toward immigrants and immigration tend to be fairly predictable. The discrepant attitudes between younger and older participants toward immigrants and immigration—the “tolerance gap” (Pryce Reference Pryce2018)—has been observed in many immigration-related studies. Perhaps, younger people are just more open to welcoming new people into their communities and may just be more accommodating as well. While it is likely that younger people in Hampton Roads may face greater competition from new arrivals for jobs, this possibility does not seem to affect their attitudes toward immigrants. Also, it is possible that younger people are simply more “malleable” when it comes to their feelings about migrants moving into their communities. On the contrary, older participants may be opposed to the idea that immigration is generally good for the Hampton Roads economy because of the false narrative that the influx of immigrants leads to higher crime rates (Ousey Reference Ousey2017; Ousey and Kubrin, Reference Ousey and Kubrin2018). Ultimately, more research needs to be done to understand the persistent, yet important, differences in attitudes toward immigrants and immigration based on age.
In answering my second research question, I show that quality of life in the neighborhood and quality of life in the city predicted support for immigration. Quality of life encompasses many things—a low-crime neighborhood, economic well-being, physical well-being, being employed, being happy, etc.—that influence our views of others around us. It appears, then, that the majority of Hampton Roads residents do not believe that the presence of immigrants negatively affects their quality of life in their immediate neighborhood and city. These are important findings that counter some prior research that shows that people do not want immigrants in their neighborhoods and cities. Further research to understand why Hampton Roads residents appear to be welcoming of immigrants to their neighborhoods and cities would contribute immensely to the literature on attitudes toward immigrants and immigration. Conversely, there was an inverse statistical relationship between quality of life in the larger Hampton Roads region and the notion that immigration is generally good for the Hampton Roads economy. Perhaps this finding is due to people being more aware of what transpires in their immediate neighborhood and city than what transpires in the larger Hampton Roads region. In other words, it is reasonable to argue that people are more conscious of what is happening in their immediate environment or city than in the larger region to which they belong.
In answering the third research question, political affiliation and ideology were not significantly related to support for immigration. These are interesting findings, as I had expected both political affiliation and ideology to be significantly related to the notion that immigration is generally good for the Hampton Roads economy. Taken in isolation, political affiliation was significantly related to support for immigration, with Republicans holding the strongest anti-immigrant views, a finding that mirrors the results of prior research. However, political affiliation was not significant in the regression model as its effect was diminished by other predictor variables, such as immigrants’ impact on crime rates and jobs.
In answering the fourth—and final—research question, I note that concerns about immigrants increasing crime rates and immigrants taking jobs away from local residents are significantly related to the notion that immigration is generally good for the Hampton Roads economy. In fact, more than 71% of the sample disagreed/strongly disagreed with the statement, “Recent immigrants (legal or otherwise) will take jobs away from people in Hampton Roads.” In addition, over 73% of the sample disagreed/strongly disagreed with the statement, “Immigrants increase crime rates in Hampton Roads.” These are overwhelming statistics that repudiate: (a) fearmongering about immigrants’ alleged higher propensity to commit crimes (Ousey and Kubrin, Reference Ousey and Kubrin2018), and (b) unfounded claims about immigrants taking jobs away from locals and natives (Portes and Rumbaut, Reference Portes and Rumbaut2014). Also, it is important to point out that crimes rates was the strongest predictor of the dependent variable: immigration is generally good for the Hampton Roads economy. In other words, Hampton Roads residents mostly reject the idea that the presence of immigrants increases crime rates in their community.
According to Robert M. McNab (Reference McNab2023), the net international migration into Virginia was over 265,000 persons between 2010 and 2019. Also, the net international migration between April 2020 and June 2022 was about 53,000, which helped to compensate for the negative net internal migration—people moving out of Virginia to other states than people moving from other locations to Virginia—of about 30,000 for the same period (McNab Reference McNab2023). Also, after 2020, average monthly job openings were higher than 320,000, while average monthly quits were about a third of the openings, meaning that employers practically had to compete for workers in Virginia (McNab Reference McNab2023). These dynamics probably meant that international migration was a welcome development for the Hampton Roads region. Moreover, Hampton Roads has experienced a negative internal migration of people between twenty and thirty-four years of age, an important working age group. This net migration to other states, such as North Carolina and Georgia, means that several job openings have not been filled (McNab Reference McNab2023). Thus, it is not surprising that Hampton Roads residents do not believe that immigrants will take jobs away from them.
It appears that my findings support the stratification beliefs model. Here, I argue that Hampton Roads residents are not opposed to the presence of immigrants, and do not blame immigrants for their economic woes. Indeed, eight of ten immigrants in Hampton Roads migrated from Asia or Latin America, with Philippines, Mexico, Vietnam, El Salvador, and Jamaica the top five countries of birth of the migrants (Mendes and Goren, Reference Mendes and Goren2020). Because the individualistic concept derived from the stratification beliefs model argues that people’s efforts account for their lot in life, I argue that, rather than blame immigrants for taking jobs and opportunities away from them, Hampton Roads residents appear to welcome immigrants, whose presence they likely see as necessary for revitalizing the Hampton Roads workforce and economy and to make life better for all in the region. Moreover, according to Bobo and Hutchings (Reference Bobo and Hutchings1996), “the more one adheres to existential and normative individualistic beliefs, the less inclined one is to view members of other racial groups as competitive threats” (p. 954). Thus, it appears that Hampton Roads residents do not see immigrants as a competitive threat—at least, not on a scale that threatens them. Furthermore, it appears that the survey participants see personal effort as necessary for success in life, and do not blame others (e.g., immigrants) for their circumstances.
I do not find support for Bobo and Hutching’s modified group-position theory, as it does not appear that the “superordinate” group in the current study—locals and natives—rejects the presence of immigrants due to the zero-sum concept of resource distribution. Indeed, native-born citizens’ welcoming of immigrants does show that the former are not fearful of sharing resources with the latter. In fact, it appears that the presence of immigrants is seen as beneficial to the economic growth of Hampton Roads. I am not able to test the racial alienation concept using the current cross-sectional data, however, as the idea persists on a continuum: feelings of enfranchisement and entitlement for the superordinate group and feelings of disenfranchisement and grievance for minority groups (Bobo and Hutchings, Reference Bobo and Hutchings1996).
In conclusion, the current study has identified important antecedents of the notion that immigration is generally good for the Hampton Roads economy. In terms of policy, and in response to the employment opportunities found in Hampton Roads, I suggest that elected officials provide incentives to attract migrants to the region, as it appears that migrants’ contribution to the growth of Hampton Roads cannot be discounted. The results of the current study could also be widely disseminated to assuage the fears of those Hampton Roads residents who believe that immigrants increase crime rates and/or take jobs away from locals and natives.
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