Immigrant Opposition in a Changing National Demographic

Abstract

This article advances the argument that the effects of demographic change on individual-level immigration policy preferences is dependent on the level of segregation in the individuals’ local context. Increases in the immigrant population in highly segregated counties should increase opposition to immigration because opportunities for contact and exposure are missing and group differences are emphasized. Meanwhile, population increases in more integrated counties should lead to an alleviation of interethnic tensions due to more frequent opportunities for contact. Furthermore, whites may react differently to changes in racial/ethnic composition of a local context depending on the particular group moving into the area because some groups are closer to fulfilling Allport’s equal status contact condition than others. The empirical analysis finds strong support for the first assertion that population growth of Latina/os and Asian Americans in highly segregated areas results in support for restrictive immigration policy, while population growth in more integrated areas results in support for permissive immigration policy. The results are inconclusive for the second assertion as the effects of Asian American and Latina/o population growth are so highly dependent on segregation levels.

Keywords

segregation demographic change immigration policy interaction effects public opinion

Introduction

The last decade has seen debate over immigration policy take center stage in American politics from the 2006 immigration reform protests to the “Gang of Eight” immigration bill to more recent battles over the Deferred Action on Childhood Arrivals (DACA) program. Over the same period, immigrant demographics have changed dramatically, with a significant increase in immigration from Latin America and Asia. According to the 2010 Census, both the Latina/o and Asian American populations in the United States have more than tripled since 1980, with Latina/os comprising more than 16 percent of the total U.S. population, and Asian Americans 5 percent (Iceland, Weinberg and Hughes 2014). Sharp increases in outgroup populations, combined with declines in the non-Hispanic white population, have increased interethnic tensions, often directed toward immigrants entering the United States. Throughout U.S. history, increased immigration has been met with hostility by citizens (Abrajano and Hajnal 2017; Higham 2002), with immigrants being framed as economic threats (Borjas 1999), cultural threats (Huntington 2004), and security threats (Martinez and Valenzuela 2006).

Extant scholarship shows that racial context can have a strong effect on immigration attitudes of white Americans (e.g., Hopkins 2010; Massey and Denton 1988; Newman 2013; Rocha and Espino 2009). The focus of this paper is on the effects of demographic change and residential segregation on white attitudes on immigration policy, measured using five questions from the Cooperative Congressional Election Study (CCES). This paper attempts to answer two questions. First, to what extent does the level of residential segregation mediate the effect of demographic change on immigration policy views? Second, do whites react differently to increases in the Asian American population in their county than to Latina/os, or are they simply reacting to an increase in the outgroup population regardless of ethnicity?

This study contributes to the literature by showing that our understanding of the effects of racial context on policy preferences is incomplete without analyzing demographic change and segregation interactively. Opportunities for contact are rare or nonexistent in highly segregated areas. In segregated areas, it is difficult to dispel stereotypes, and misconceptions of an outgroup can be intensified. In more integrated areas, opportunities for contact are more frequent which, according to contact theory, can lead to improved interracial relationships. Thus, population growth of an outgroup is more likely to intensify interethnic tensions in a segregated county, while population growth in integrated counties is likely to alleviate tensions.

In addition to segregation, it is likely that policy preferences are affected differently depending on the group moving into the area. Through negative media framing, Latina/os have come to be implicitly associated with illegality (Pérez, 2016). Consequently, threatening media framing of Latina/os has pushed native-born whites toward supporting restrictive immigration policies (Massey 2014). Asian Americans have faced a long history of discrimination and social exclusion (Kim 1999) and, in many cases, continue to deal with intense discrimination and barriers to inclusion (Chang 1993; Phoenix and Arora 2018; Yogeeswaran and Dasgupta 2010). However, the dominant political and media frame of Asian Americans is often of the “model minority” (Junn 2007). Contact between whites and Asian Americans likely fulfills Allport’s (1954) equal status condition to a greater extent than contact between whites and Latina/os.

The article proceeds as follows: the next section reviews the relevant scholarship and introduces the theory and hypotheses. Subsequent sections will outline the multilevel approach used in this study and present the results. The findings demonstrate that segregation mediates the effect of Asian American and Latina/o population growth on immigration policy views. Demographic change predicts increased opposition to permissive immigration policy among whites living in highly segregated counties but predicts decreases in opposition in more integrated counties. Essentially, there is an interactive relationship between the rate of change of the Latina/o or Asian American population in a county and the degree of segregation in that county. The findings are inconclusive for group variance in the effects of demographic change on policy attitudes as the effects for both groups are so dependent on segregation rates. The article concludes with a discussion of the limitations, possibilities for further research, and broader implications of the results.

Theory

More than half a century ago, Key (1949) observed that conservative candidates performed well in areas with high levels of African American residents. This observation, along with subsequent analysis, gave rise to what is referred to as the group threat hypothesis, which posits that an increase in the outgroup population should reinforce and intensify interethnic antagonism (Blalock 1967). A number of studies, mostly focused on black–white relations, found that white hostility was higher in areas with greater proportions of African Americans (e.g., Quillian 1996).

An alternative to this theory is contact theory which states that contact with a minority group should bring about greater interracial affability (Allport 1954; Ellison and Powers 1994; Meer and Freedman 1966; Tsukashima and Montero 1976). Allport (1954) formulated that contact will reduce prejudice under a set of optimal conditions: there is equal status between the groups in question; common goals; intergroup cooperation; and support from authorities, law, or custom. A meta-analytic study of 515 studies of intergroup contact find that greater contact is typically associated with a reduction in prejudice (Pettigrew and Tropp 2006). Furthermore, Allport’s four conditions help facilitate the reduction of intergroup prejudice that results from contact, but these conditions being present are not essential for alleviating prejudice.

Until recently, contact and threat theories had not been applied to attitudes toward immigrant groups. While most studies examining the effect of black populations on attitudes have been consistent, studies focusing on immigrant populations have sometimes found no relationship (e.g., Citrin et al. 1997; Scheve and Slaughter 2001). Among studies that have found relationships, the results have been somewhat mixed. Hood and Morris (1997) find that living in proximity to Asian Americans or Latina/os can lead to a stronger belief that immigrants make contributions to society and more liberal stances on immigration policy. Other studies have shown that proximity to, or increased demographic change of, Latina/os can lead to increased hostility (Ha 2010; Newman 2013). However, Ellison, Shin, and Leal (2011) find that the effects on immigration policy attitudes depend on the level of contact with Latina/os. For example, having close friendships with Latina/os leads to less restrictive immigration policy preferences among whites, but having Latina/o acquaintances or living in an area with a large Latina/o population has a small or inconsistent effect.

There are several theories to explain inconsistent results. Animosity toward Latina/os and Asian Americans is different in nature than that toward blacks because it is not rooted in the system of slavery (Dixon 2006). Some of the inconsistency results from methodological challenges such as using the “right” geographic unit of analysis and relying on observational data to draw causal conclusions (Enos 2017). Selection bias can also affect results. People’s attitudes may not be affected by contact or racial context, but rather because in-group members with particular attitudes and behaviors chose to live in close proximity with members of outgroup(s) (Enos 2017). Finally, inconsistencies are due to not accounting for aspects of racial context such as segregation (Enos, 2016, 2017; Oliver and Wong 2003; Rocha and Espino 2009) and the particular outgroup in the area (Ha 2010). I extend this argument to contend that not accounting for interactive effects between aspects of racial context can produce inconsistent findings. Therefore, the focus of this study is on the interactive effects of demographic change of Latina/o and Asian American populations, and segregation.

One important way that contact takes place is when there is an increase in the immigrant population in an individual’s geographic area. Segregation can dull these effects by making immigrant groups less visible, making opportunities for contact limited or nonexistent. Individuals living in areas that are racially mixed are much more likely to report higher levels of interracial contact (Welch et al. 2001). Ha (2010, 29) finds that racial integration is important in “alleviating interethnic tension” in his study of how racial context affects immigration attitudes, and a long history of scholarship posits segregation as central to outgroup hostility (Massey and Denton 1993; Myrdal 1944). Enos (2017) argues that geography can distort people’s perceptions of a group. An individual will come to see the segregated group as more different than they actually are. Thus, not only can segregation block opportunities for whites to dispel the stereotypes they hold of an outgroup, but segregation also emphasizes differences between whites and the segregated group.

This means there may be an important, but overlooked, interactive effect between demographic change and segregation. Changes in the ethnic composition of a given area are likely to be noticed by residents (Kahneman and Tversky 1979) and can work to destabilize the identity of the community (Hopkins 2009, 2010). Recent ethnic and racial changes to the composition of a community are likely to be noticed in ways that existing levels of diversity may not be (Hopkins 2010). Demographic changes in segregated areas may reinforce suspicion and hostility toward outgroups by emphasizing differences. However, demographic changes in areas that are more integrated provide greater opportunities for exposure and contact, which, according to contact theory, can dispel negative stereotypes and improve interracial and interethnic relationships. The effects of demographic change, then, depend on the level of segregation in a given area.

Increases in the immigrant population in counties with higher levels of residential segregation should increase opposition because the opportunity for contact and exposure is missing and group differences will be emphasized, while population increases in more integrated counties should lead to an alleviation of interethnic tensions. Or, more formally stated,

Hypothesis 1: Increases in the Asian American and Latina/o population in segregated counties predict increased opposition to permissive immigration policy among white residents.

Hypothesis 2: Increases in the Asian American and Latina/o population in integrated counties predict increased support for permissive immigration policy among white residents.

In addition to the interactive effects with segregation, it is likely that groups have an independent effect on immigration policy attitudes. In their review of twenty-seven intergroup contact studies, Paluck, Green, and Green (2018, 20) find that “the extent to which contact diminishes prejudice seems to vary according to the target of prejudice.” If, in fact, the effects of contact on white attitudes vary by group, we would expect contact with Asian immigrants to have a more positive influence on white Americans’ attitudes about immigration. Extant scholarship finds that increases in the Latina/o population tend to increase oppositional attitudes (Ha 2010; Hopkins 2010; Newman 2013), while increases in the Asian population potentially alleviate hostility (Ha 2010; Hood and Morris 1997). After all, Asian Americans have long been depicted in a more positive light than Latina/os. In large part due to the 1965 Immigration and Nationality Act, a considerable percentage of Asian Americans are entering the United States with high levels of education and strong economic skills. Indeed, Masuoka and Junn (2013) argue that Asian Americans are higher on the racial hierarchy than Latina/os.

Asian Americans are often depicted as “model minorities” who are successful in the economic and educational realms (Hurh and Kim 1989; Kim 1999; Wong et al. 1998; Masuoka and Junn 2013). Junn (2007) directly traces the effect of U.S. immigration policy on the construction of racial tropes that have constructed the model minority image. Asian Americans also tend to be depicted in media and viewed by the general public in a relatively favorable light. Hurh and Kim (1989, 515) show that by the mid-1970s, the verbal stereotypes used by white Americans to describe the most populous Asian American groups (Korean, Filipino, Indian, and Chinese Americans) are largely positive: “successful, intelligent, hard-working model minority.” While the authors are quick to point out that these stereotypes mask hidden issues, such as underemployment and higher than average rates of poverty, in the Asian American community, stereotypes nonetheless have a powerful impact on how people perceive minority communities. This is also not to suggest that Asian Americans have not experienced discrimination or that the discrimination has been any less severe than other minority groups. Rather, positive perceptions of a minority group can affect interethnic relations.

Latina/os, by contrast, have been framed as “illegal” immigrants who are taking away jobs from, and driving down the wages of, hard-working white Americans (Massey and Pren 2012). Chavez (2013) finds that Latina/os are viewed as more culturally threatening than Asian Americans. Furthermore, Latina/os tend to be depicted more negatively in the media (Children Now 2004; Mastro and Behm-Morawitz 2005). Latina/os are rarely depicted in favorable roles (only about 11% are shown having “high-status jobs”), while they are most often characterized by “limited intelligence, inarticulate speech, laziness, and verbal aggression” (Mastro, Behm-Morawitz and Kopacz 2008, 2). This is particularly problematic considering that the main source of information about Latina/os for most white Americans is mass media (Navarrete and Kamasaki 1994). While both groups are not viewed as “American,” the image of Asian Americans is often positive while the dominant perception of Latina/os tends to be more negative. So much so, in fact, that media coverage has led white Americans to implicitly associate Latina/os with illegality (Pérez 2016). Consequently, Massey (2014) demonstrates that threatening framing of Latina/os by political and media elite has moved native whites to support restrictive immigration policies. Abrajano and Hajnal (2017) persuasively demonstrate that increases in the Latina/o population, along with negative portrayals of the group, have stoked racial fears of white Americans.

Of course, these independent effects are most likely to appear in counties with low levels of segregation in which opportunities for contact are more prevalent (Welch et al. 2001). In segregated counties, equal status, or lack thereof, of the outgroup is less important because opportunities for contact are limited or nonexistent.

In sum, contact between whites and Asian Americans may fulfill one of Allport’s conditions for alleviating prejudice (i.e. equal status) to a great extent than contact between whites and Latina/os. Indeed, the above-cited research on contact and threat theories, combined with the differing perceptions of Asian and Latina/o immigrants, leads to the final formal hypothesis in this paper:

Hypothesis 3: Asian American population growth will decrease opposition to permissive immigration policy among whites more than Latina/o population growth.

The three hypotheses presented in this paper combine to suggest that the effects of demographic change on white attitudes on immigration policy are contingent on (1) segregation rates and (2) the particular group that is moving into the area.

Data and Method

To test the hypotheses, I use multiple data sources. The focal explanatory variables (demographic change of Asian and Latina/o immigrants) come from the 2000 Census and 2012 American Community Survey (ACS), while segregation rates are calculated using the 2000 and 2010 Census. The dependent variable (immigration policy attitudes) comes from the 2012 CCES. The individual level (level 1) control variables also come from the CCES. County (level 2) level control variables come from the 2012 ACS. The dataset includes all 3,144 counties and “county-like” geographies in all fifty U.S. states plus Washington, D.C.¹

The Common Content of the CCES (questions that were asked to all respondents) includes a sample of 54,535 cases, which consists of samples in all fifty U.S. states plus the District of Columbia. The analysis conducted below focuses on the respondents in the CCES who identify as “white.” The survey was conducted over the Internet by YouGov/Polimetrix using matched random sampling. The main benefit of using CCES data is having a large-N sample of nationally representative data that allows researchers to make inferences at the state and county level.

The analysis in this study includes Asian Americans and Latina/os, but not other immigrant populations for theoretical and statistical reasons. The analysis includes all Asian Americans and Latina/os residing in each county, not just immigrants, because it is unlikely that Americans are consistently able to distinguish immigrants from their U.S.-born children. Extant scholarship indicates that to analyze the effect of racial context on immigration policy views, it is better to include all Asian Americans and Latina/os in the analysis (Ha 2010; Hood and Morris 1997). In addition, it is unlikely that attitudes toward immigration policy hinge upon European immigrants, who are often identified by others simply as white, or African immigrants, who are generally identified as black (Ha 2010).

Local Analysis

Several studies have been conducted to test the effect of racial demographics in a given geographical unit on attitudes. Samples have used respondents in regions of the United States (Glaser 1994), U.S. states (Tolbert and Hero 1996), and metropolitan areas (Welch et al. 2001). The use of county as the geographic area in this study adheres to, and has been defended by, previous research on immigration attitudes (Campbell, Wong and Citrin 2006; Citrin et al. 1997; Hood and Morris 1997; Hopkins 2010; Newman 2013). Conducting county-level analysis also allows for more efficient calculation of segregation rates, with census blocks fitting neatly into counties.

To conduct a localized analysis of change in racial composition on attitudes, the focus of this paper is on the county level. At this level, county residents are likely to be cognizant of the effects of demographic change through contact and exposure, as well as through economic and cultural changes.

Variables

The dependent variable in this model is immigration policy preferences. Five survey questions from the 2012 CCES were used to create an index that ranges from lower values equating more support for permissive immigration policy to higher values equating opposition to permissive immigration policy (or support for more restrictive policies). Each question has two answer choices: “Yes” or “No” regarding whether the respondent supports or opposes the policy. I fit a two-parameter (2PL) item response theory (IRT) model allowing difficulty and discrimination parameters to be freely estimated for each item. Finally, I obtained IRT scores from the model, which represent the underlying latent construct of attitudes toward immigration policy. These five questions load into one factor indicating that views on these policies are consistent. Finally, the scale is coded to lie on a 0 to 1 scale.²

Asian American and Latina/o population changes at the county level are calculated using the 2000 and 2012 ACS, which are standardized to lie on a 0 to 1 scale and then grand mean centered. The 2000 population levels are subtracted from 2012 population levels, and then divided by the baseline (2000) population.³

The individual-level variables included in the model are as follows; a five-point ideology score, seven-point party identification score, a measure of racial attitudes, age, gender, a measure of political knowledge, and a dummy variable for having a bachelor’s degree.⁴ Each of these control variables have been shown to have strong effects on attitudes on immigration. All the level 1 variables are group mean centered.⁵

The main independent variables (demographic change in Asian and Latina/o immigration, as well as segregation rates for both groups) are included in the model as county-level factors. The acculturating-contexts hypothesis (Newman 2013, 375) states that increases in immigrant populations will be most threatening in contexts with “minimal preexisting immigrant populations and least culturally threatening for those residing in contexts with larger extant immigrant populations.” As such, the model includes population levels of Asian Americans and Latina/os at the initial time point (2000). All the level 2 variables are grand mean centered.⁶

Finally, a set of interaction terms are included with segregation rates for both groups (Asian Americans and Latina/os) interacting with demographic change.

Calculating Segregation Scores

To measure residential segregation of Latina/o and Asian immigrants, an index of dissimilarity was calculated for each county in the United States. This index has long been used by demographers and is one of the most common measures of segregation (see, for example, Enos 2017; Massey and Denton 1988; Rocha and Espino 2009). The index runs from 0 (completely integrated) to 100 (completely segregated). The analysis in this study uses the index of dissimilarity to calculate segregation scores for Latina/os in each U.S. county and Asian Americans in each U.S. county.

The coefficients show whether segregation influences immigration policy preferences. In addition, interaction terms are included in the model with Latina/o segregation interacting with demographic change of Latina/os and Asian segregation interacting with demographic change of Asian Americans. The idea being that whites living in a county with 20 percent Latina/o population and high segregation will be affected differently by demographic change than whites living in a county also with 20 percent Latina/o population but low levels of segregation. Put another way, the demographic changes of Latina/os or Asian Americans living in a county should interact with the level of segregation in a county to effect attitudes (Enos 2017; Rocha and Espino 2009).⁷ Essentially, the marginal effect of demographic change should decrease opposition to permissive immigration policy and then reverse sign as county-level segregation increases.

Analysis

The method employed in this paper is mixed-effects multilevel modeling with individuals nested within counties. A series of models estimated using ordinary least squares (OLS) regression are used to examine the effect of demographic change at the county level on individual attitudes toward immigrants in the United States. Models 1 and 2 include the key independent variables (demographic change, segregation, and an interaction of the two) with no control variables. Model 3 is the full model which includes a series of control variables. All three models include county-level random effects. Finally, a series of interaction plots demonstrate the effect of demographic change in counties with low, medium, and high levels of segregation.

Results

Figure 1 displays the effects of Asian American population growth at three different segregation levels: the upper, middle, and lower terciles.⁸ The results strongly suggest that demographic change functions differently depending on the level of segregation in a given county. In counties with low levels of segregation (more integrated counties), Asian American population growth predicts a decrease in opposition to permissive immigration views. Population growth in counties with medium levels of segregation has very little effect on policy preferences, while growth in counties with high levels of segregation predicts an increase in opposition.

Figure 1.

Effects of Asian population change across levels of segregation.

The results for Latina/o population growth by levels of segregation are almost identical. Figure 2 uses the same three levels of segregation to demonstrate that the effects of demographic change are largely dependent on county-level residential segregation. In low-segregation counties, demographic change is predictive of a decrease in opposition to permissive immigration policy. In counties with medium levels of segregation, the effects of demographic change largely disappear, while growth in counties that are highly segregated is predictive of an increase in opposition to permissive immigration policies.

Figure 2.

Effects of Latina/o population change across levels of segregation.

The results of Figures 1 and 2 provide evidence for hypotheses 1 and 2. Population increases of Asian Americans and Latina/os predict increases in oppositional views in highly segregated counties. The results are reversed in more integrated counties, with population increases of both groups predicting decreases in oppositional views.

The regression models provide further evidence for the mediation effect of segregation on demographic change but provide inconclusive results for the assertion that Asian American population growth has a different effect on immigration policy preferences than Latina/o population growth.

Table 1 displays the results of models 1 and 2. Model 1 includes only the key variables testing the effect of Asian American population growth on the immigration policy views of whites, while model 2 includes the key variables for testing the effect of Latina/o population growth. The interaction terms provide evidence for the earlier assertion that two counties with the same proportion of Latina/os or Asian Americans will have differing effects on immigration policy views depending on the level of segregation. Both interaction terms have positive coefficients, indicating that higher levels of segregation interact with minority population increases to predict increases in opposition to permissive immigration policy. Thus, segregation can act as a mediating variable between demographic change and individual-level policy views.

Table 1.

Key Independent Variables with No Controls Included.

	Dependent variable
	Opposition to restrictive immigration policy
	(1)	(2)
Asian Pop. Change 2000–2012	−0.113^† (0.062)
Asian Segregation	−0.176*** (0.025)
Asian Change × Asian Segregation	0.974* (0.449)
Latina/o Pop. Change 2000–2012		−0.014 (0.052)
Latina/o Segregation		−0.155*** (0.022)
Latina/o Change × Latina/o Segregation		0.776* (0.322)
Constant	0.473*** (0.003)	0.472*** (0.003)
Observations	54,487	54,487
Log likelihood	−18,375.690	−18,383.100
Akaike information criterion	36,763.380	36,778.190
Bayesian information criterion	36,816.810	36,831.620

†

p < .1. *p < .05. **p < .01. ***p < .001.

Results for the population change variable (Asian Pop. Change 2000–2012) show that growth in the Asian American population is predictive of a decrease in opposition to permissive immigration policy. The coefficient predicts a roughly eleven percentage-point reduction in opposition to permissive immigration policy (p < .1) moving from the lowest rates of population growth to the highest rates. The coefficient for Latina/o population change (Latina/o Pop. Change 2000–2012) is small and outside of statistical significance. This finding is suggestive of differing effects of Asian American and Latina/o population growth on policy views.

Table 2 displays the full model including all the key independent variables and control variables, using observed values for each of the covariates. The interaction terms with segregation (Latina/o Change × Latina/o Segregation and Asian Change × Asian Segregation) have positive coefficients, again showing that demographic change of either ethnic group in high segregation counties is predictive of an increase in opposition. The results for the demographic change variables remain roughly unchanged. Asian American population growth predicts a decrease in opposition, while Latina/o growth appears to have no effect.

Table 2.

Full Model.

	Dependent variable
	Opposition to restrictive immigration policy
County-level variables
Asian Pop. Change 2000–2012	−0.104^† (0.059)
Latina/o Pop. Change 2000–2012	0.002 (0.046)
Latina/o 2000 Pop.	−0.079*** (0.020)
Asian 2000 Pop.	−0.345*** (0.037)
Asian Segregation	−0.043** (0.015)
Latina/o Segregation	0.010 (0.013)
Individual-level variables
Education	−0.013** (0.004)
Party ID	0.239*** (0.005)
Racial resentment	0.314*** (0.005)
Ideology	0.224*** (0.006)
Age	0.002*** (0.0001)
Gender	−0.034*** (0.002)
Political knowledge	−0.004 (0.004)
Interaction terms
Latina/o Change × Latina/o Segregation	0.051 (0.254)
Asian Change × Asian Segregation	0.147 (0.308)
Constant	0.397*** (0.007)
Observations	48,622
Log likelihood	−4,683.036
Akaike information criterion	9,402.072
Bayesian information criterion	9,560.319

†

p < .1. *p < .05. **p < .01. ***p < .001.

The effects of the control variables are generally in line with what one would expect. Having a bachelor’s degree, identifying as a Democrat, low levels of racial resentment, liberal ideology, youth, and identifying as female are all predictive of support for permissive immigration policies. In addition, higher baseline (2000) population levels of Latina/o and Asian immigrants predict support for permissive policies.⁹

Discussion

The analysis demonstrates the importance of residential segregation in mediating the effects of demographic change. In support of hypotheses 1 and 2, the results suggest that demographic change functions much differently in highly segregated counties than in more integrated counties. Population increases of Asian Americans and Latina/os predict greater support for permissive immigration policy in highly integrated counties. In contrast, population increases of Asian Americans and Latina/os predict greater opposition in highly segregated counties. Essentially, the expectations of contact theory are much more likely to be realized in integrated counties than in more segregated counties. Scholars must contend with the effects of segregation when analyzing how diversity, whether in the form of group size or demographic change, impacts attitudes.

Evidence for hypothesis 3 is mixed. Regression results indicate that Asian American population growth decreases opposition to permissive immigration policy among white Americans, while Latina/o population growth has no effect. This finding would suggest that immigration policy preferences are influenced differently depending on the outgroup moving into the area. However, the interaction plots show that population growth of both groups have almost identical effects on immigration policy preferences in high, medium, and low segregation contexts. This is a particularly telling result in low segregation contexts. In segregated counties, equal status, or lack thereof, of the outgroup is less important because opportunities for contact are limited or nonexistent. But if contact functions differently by outgroup, we would expect Asian American population growth to decrease opposition to permissive immigration policy more than Latina/o population growth in counties with low levels of segregation in which opportunities for contact are more prevalent (Welch et al. 2001). Thus, it is difficult to conclude from these results that the effects of demographic change vary by immigrant population.

But why is it that the results from the regression models do not correspond with the results from the interaction plots? In other words, why does regression analysis predict different effects of Asian and Latina/o immigrant population growth on immigration policy preferences while the interaction plots show that the effects of both groups are almost identical? One likely explanation is that Asian Americans tend to live and work in less segregated environments than Latina/os, meaning that whites are more likely to come into contact with Asian Americans. For example, Iceland, Weinberg, and Hughes (2014) find that Latina/os, on average, are more residentially segregated than Asian Americans, and are significantly less likely to interact with non-Hispanic whites.¹⁰ Furthermore, Latina/os face higher levels of segregation in schools and work places (Alsono-Villar, Del Rio, and Gradin 2012; Gradín, 2013; Orfield et al. 1994). Indeed, Junn (2007) finds that Asian Americans are more likely than other minorities, including Latina/os, to marry, live close to, work with, or attend school with whites. Thus, because whites are more likely to come into contact with Asian Americans, population growth is likely to lead to support for permissive immigration policy. In contrast, whites and Latina/os have fewer opportunities for contact, so population growth is less likely to lead to support for permissive immigration policy. It is likely, then, that the regression results are caused by differences in segregation levels. Future research can further elucidate this discrepancy.

The results are robust to different specifications of demographic change (different measurement, only including immigrants, measurement using amount instead of percent change) and segregation (using an isolation index).¹¹ However, there are several limitations that need to be acknowledged. First, the analysis is not conducted using experimental or quasi-experimental data, which can lead to biased results (Enos 2017). Second, support for permissive immigration policy in counties may be due to residential selection being driven by views on immigration policy rather than racial context. Although the analysis relies upon observational data, the myriad robustness checks provide confidence that the results are tapping into something real. Previous scholarship that controls for self-selection find that contact remains a significant predictor of racial attitudes (e.g., Oliver and Wong 2003; Powers and Ellison 1995) and that racial preferences are a weaker determinant of residential selection than economic factors (e.g., Bobo and Zubrinsky 1996). These studies provide evidence that racial context does have an independent effect on individual policy preferences.

Conclusion

In the current political climate, a great deal of attention is being given to white resentment and hostility toward outgroups. As the racial and ethnic composition of the United States continues to change, immigration policy is likely to remain a hotly contested political subject. The findings from this study contribute to the long line of evidence that social geography plays a critical role in influencing interethnic relations and policy preferences.

An important scholarly and policy implication arises from this study. Residential segregation interacts with population increases of outgroups to predict varying effects on immigration policy preferences. These two important aspects of racial context cannot be separated. These factors should be studied interactively for a more comprehensive understanding of the effect of racial context on individual-level policy views. Policy makers and those who are interested in policy reform should consider the influence of residential segregation in how the public views immigration policy.

Future studies can look back at the impressive work done regarding immigrants and public opinion and look for opportunities to disaggregate. It is important to study public opinion of and toward immigrants in general, but it is just as important to investigate the commonalities and differences among immigrant populations. Examining subpopulations within the Latina/o and Asian American panethnic groupings can help illuminate how whites react differently to national origin groups. There is also a great deal of opportunity for future work regarding demographic change. For example, Ha (2010) demonstrates that racial contexts affect smaller geographic areas such as neighborhoods differently than larger areas such as metropolitan areas. Looking at racial context in more localized areas can help parse out differing effects of demographic change at various geographic levels.

Supplemental Material

Online_Appendix – Supplemental material for Immigrant Opposition in a Changing National Demographic

Supplemental material, Online_Appendix for Immigrant Opposition in a Changing National Demographic by Maneesh Arora in Political Research Quarterly

Footnotes

Acknowledgements

I am extremely grateful to Michael Tesler, Louis DeSipio, and Sara Wallace Goodman for their thoughtful comments and feedback in the creation of this article. I would also like to thank Hannah June Kim, Ines Levin, Michael Giordano, John Poe, Mark Manning, Bryan Wilcox-Archuleta, Ben Newman, and the anonymous Political Research Quarterly (PRQ) reviewers for their helpful suggestions.

Author’s Note

Previous versions of this article were presented at the 2016 annual meeting of the American Political Science Association and the 2017 meeting of the Southern Political Science Association.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Supplemental Material

Supplemental materials and replication materials for this article are available with the manuscript on the Political Research Quarterly (PRQ) website.

ORCID iD

Maneesh Arora

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