The American Public’s Views about Legal Immigration: The Case of the Diversity Visa Lottery Program

Abstract

This study assesses contemporary attitudes toward the Diversity Visa Lottery program. Specifically, we examine the public’s views about the Diversity Visa Lottery, an immigrant visa program that was criticized by former President Donald Trump. Using a data set that approximates a nationally representative sample of U.S. residents, we found evidence that those who voted for Donald Trump in 2016, those who did not vote for president in 2016, those who identified as conservative/very conservative, and older citizens favor eliminating the Diversity Visa Lottery program. On the contrary, Blacks, the more highly educated, and those who identified as very liberal/liberal oppose eliminating the Diversity Visa Lottery program. The implications of our findings for group relations, policy, and future research are discussed.

Keywords

immigrants Donald Trump political affiliation ideology documented migration immigration Diversity Visa Lottery

Introduction

In this paper, we examine the Diversity Visa Lottery (commonly referred to as the Green Card program), an immigrant visa program that allows citizens of other countries to immigrate to the United States and receive lawful permanent resident status.¹ Immigrants and immigration have always generated strong debates because of the perceived societal changes associated with the arrival of newcomers into any geographical region (Blau and Mackie 2017; Chouhy and Madero-Hernandez 2019; Kaufmann 2019; Kulig et al. 2020; Ousey and Kubrin 2018). However, these immigration debates grew fiercer and more polarizing when Donald Trump served as president of the United States, especially because of the former president’s strident opposition to all types of immigration—documented and undocumented. In spite of the best intentions of government officials to welcome new immigrants, native-born citizens may all not readily endorse the government’s decision to admit the new arrivals because of concerns about greater competition for jobs, increased crime, changes to the dominant culture, and/or modifications to public policy to accommodate the new arrivals (Pryce 2018). Even for the migrants themselves, immigration to any country generates a potpourri of emotions because of the uncertainties that they are likely to face on arrival; these uncertainties may be heightened as a result of moving into a new environment with a cultural identity unlike the one they were accustomed to in their home countries. Thus, immigration generates forces that drive public views and decisions, and significantly affects how community members view the world around them (Boateng, Pryce, and Chenane 2021a; Boateng et al. 2021b; Chenane, Morabito, and Gonzales 2022a; Chouhy and Madero-Hernandez 2019).

We employ René Flores’s theoretical framework examining the impact of political elites’ speeches on the public’s views about vulnerable social groups (e.g., immigrants) to explain Americans’ attitudes toward immigrants and immigration, specifically the Diversity Visa Lottery (DVL) program. Flores’s theoretical framework, discussed further in the background section, thus helps to explain attitudes toward immigrants and immigration. This paper makes salient contributions to the extant literature on Americans’ attitudes toward immigrants and immigration (Blau and Mackie 2017; Goldstein and Peters 2014; Hainmueller and Hopkins 2015; Ousey and Kubrin 2018; Perez 2010; Peters 2015; Pryce 2018). First, it adds to prior research that explored more contemporary views about immigrants and immigration, as the immigration debate has not waned in contemporary America (Chenane, Pryce, and Seungeun Lee 2022b; Goldstein and Peters 2014). Second, the paper examines how political factors, including the 2016 presidential election vote choice, political affiliation, ideology, and religion, shape views about the DVL program.

As far as we know, this paper is the first to examine empirically the impact of these and other pertinent variables on views about the DVL program. We note that the bulk of research on public perceptions about immigrants focuses on undocumented immigrants and refugees, with fewer studies focusing on documented migration programs such as the DVL program. Thus, this paper makes a unique contribution to the literature on documented migration.

The YouGov data employed for the present study, which approximates a nationally representative sample, was conducted in 2020 by the research arm of a leading U.S. university, and the aforementioned research question is explored using variables known to predict attitudes toward immigrants and immigration. We note here that YouGov surveys have been used extensively in empirical research studies (Graham, Pickett, and Cullen 2020; Harris and Socia 2016; Socia, Rydberg, and Dum 2019).

Background

The History of Immigration to the United States

Between 1819 and 1882, the first large-scale immigration to the United States took place, with about 10 million immigrants from Northern and Western European countries landing in the United States (Diaz 2011). Around the same time, large numbers of Africans were forcibly brought from the African continent to provide cheap, slave labor (Diaz 2011). The next large-scale immigration occurred between 1882 and 1921, when many Southern and Eastern Europeans, numbering about 20 million, arrived in the United States (Diaz 2011; Lee and Bean 2004).

Opposition to immigrants and immigration is not just a contemporary phenomenon; Southern and Eastern Europeans of Italian and Jewish descent were subjected to overt opposition in the 1920s. This is because native-born Anglo-Saxons viewed the Southern and Eastern Europeans as “non-White,” and therefore inferior (Lee and Bean 2004). As a result, the latter were discriminated against and subjected to occasional violence by the dominant Anglo-Saxon population (Cordasco 1973; Diaz 2011; Yans-Mclaughlin 1990). Indeed, it took many years after this second wave of immigration for Southern and Eastern Europeans to be accepted as equals by the Anglo-Saxons (Alba 1999; Foner 2000; Lee and Bean 2004), but this change of attitude occurred primarily after World War II (Foner 2000). Once Southern and Eastern Europeans assimilated, native-born Anglo-Saxons began to turn their attention toward Asians, Hispanics, and Blacks, leading to the coining of the terms “illegal” and “undocumented” to describe the newer immigrants (Schmid 2013; also see Chenane et al. 2022b).

A third wave of immigration occurred after 1965, when the United States admitted persons from otherwise underrepresented areas of the world (Crosnoe and Lopez Turley 2011; Ngai 2004). By this time, non-Whites were immigrating in far larger numbers to the United States than their White counterparts (Massey, Durand, and Malone 2002). The DVL program can be classified as part of the third wave of immigration to the United States. The following statement by C. B. Goodman (2016) supports this argument: “The Immigration Act of 1965, the launching point for the diversity visa legislative debate, stands as the turning point in recent immigration history” (p. 11). Passed into law by the Immigration Act of 1990, the DVL program is a chance for people from geographical regions with traditionally low immigration rates to the United States to obtain permanent residence via a lottery under the auspices of the U.S. Department of State (Goodman 2016).

Diversity Visa Lottery

The DVL program is a derivative of the Immigration Act of 1990 (United States Congress 1990). It allows for persons from areas across the globe, historically underrepresented in the United States due to once-restrictive immigration laws, to travel to the United States and obtain a Green Card (this is the informal term used to describe lawful permanent resident status), as part of their journey to becoming American citizens (U.S. Department of State 2021). Indeed, the DVL has become a means to immigrate to the United States outside of the traditional channels of family reunification, refugee designation, and specialized employment (Goodman 2016).

Each year, millions of people participate in the DVL (Batalova, Blizzard, and Bolter 2020). Although 100,000 people are initially selected each year, this number is whittled down to 55,000, as some applicants abandon the process, do not meet eligibility requirements, or are denied visas at a U.S. consulate (Goodman 2016). Transitioning from a paper application to an online one has also made the application process simpler, hence the increasing number of entries each year in countries that are eligible (Goodman 2016). Another reason why the DVL program holds so much appeal is because it circumvents the routinely long wait times for family-sponsored visas, which can take more than a decade to materialize (Gelatt 2019). For example, as of April 2019, U.S. citizens may have to wait at least 7 years to sponsor their adult, unmarried children living in another country, but the wait time is 21 years for those wishing to immigrate from Mexico (Gelatt 2019). These long wait times have made the DVL program very attractive to many across the globe, as the wait time to receive a visa once a candidate is chosen from the annual lottery is generally less than 1 year (Gelatt 2019; Goodman 2016).

Although DVL program recipients make up a small percentage of individuals who receive permanent residence in the United States every year—around 5 percent—the percentages vary from one country to another. For example, in 2017, the DVL accounted for 46 percent of all Green Card holders from Benin, 44 percent from Togo, and 34 percent each from Ivory Coast, Democratic Republic of Congo, and Liberia (Echeverria-Estrada and Batalova 2019). While some African nations had a high percentage of immigrant visas in the DVL category, overall, the foreign born from Africa gained legal permanent residence through varying routes: 48.3 percent obtained Green Cards through family relationships, 23.6 percent through the DVL program, 22.3 percent as refugees or asylees, 5.2 percent through employment, and 0.6 through other routes (Migration Policy Institute 2011).

The DVL numbers were quite high for some nations outside of Africa as well. For example, in 2018, the DVL accounted for 65 percent of Green Card holders from Uzbekistan, 64 percent from Tajikistan, and between 40 and 48 percent for those who immigrated from Azerbaijan, Albania, Armenia, and Turkmenistan (Batalova et al. 2020). Overall, the DVL program has given several million immigrants the opportunity to become permanent legal residents of the United States (Batalova et al. 2020). Some of these immigrants have since gone on to become U.S. citizens. This point is important because these newly “minted” citizens are now able to bring over relatives² through the family reunification visa category, which is the largest category of permanent visas.

It has not been all good news about the DVL program, however. On October 31, 2017, an Uzbek national named Sayfullo Saipov drove a rented Home Depot truck down a New York City bicycle path, killing eight people and injuring a dozen (Mueller et al. 2017). Shortly after the incident, Saipov admitted to authorities that he was an agent of the Islamic State (ISIS) (Mueller et al. 2017). Former President Trump afterward called on the U.S. Congress to eliminate the DVL program after the attack by Saipov, himself a beneficiary of the DVL program in 2010 (Levy 2017). In June 2020, Trump gave the DVL program even more negative publicity when he signed an executive order preventing the 2019 DVL winners from traveling to the United States to obtain their Green Cards (Penton 2020). Trump cited competition for jobs, due to Covid-19, as a primary reason for the executive order. It appears many of the 2019 lottery winners missed out on a Green Card as a result, as the law requires that Green Cards be obtained by DVL winners no later than September 30 of the year following their winning the lottery (U.S. Department of State 2020).

Regarding legal immigration generally, Trump had stated his displeasure at the large numbers of people who immigrate to the United States each year—about one million—and wanted to see that number cut in half by 2027 (Soergel 2017). Thus, our study is important because some of the people who voted for Trump in the 2016 election expressed support for his immigration policies (Gimpel 2017; Wright and Esses 2019). Furthermore, political affiliation, ideology, and religion should influence research participants’ views about the DVL program.

Political Elites and Public Perceptions of Social Groups

Researchers have shown that society’s most influential people can shape community members’ attitudes toward politicians, policies, and general beliefs about the society (Flores 2018; Mendelberg 2001). As R. D. Flores (2018) has argued, “politicians’ use of symbolic language blaming vulnerable groups, such racial minorities, immigrants, and poor families, for society’s problems may not only encourage popular support for exclusionary policies but also influence public views of these groups themselves” (p. 1649; also see Citrin et al. 1990; Santa Ana 2002). Specifically, scholars have observed political elites’ influence over community members’ attitudes toward laws (Druckman 2001; Nicholson 2012), values and beliefs (Goren et al. 2009; Kuklinski and Hurley 1994), and candidates for political office (Arceneaux 2008). Although political elites’ statements that are overtly racist may be rejected by voters and the public (Mendelberg 2001), Flores’s research shows that there might be an exception when the issue is about immigration. Flores argues that the “contested legality” of immigrants may provide the conduit for political elites to attack the former without severe consequences to themselves or their political careers.

Research on the behaviors of political elites, using the symbolic politics perspective (Kincaid 2017; Sears, Huddy, and Schaffer 1986) notes that statements by political elites have the tendency to influence how the larger community sees smaller social groups. The symbolic politics perspective is believed to influence the socialization of community members, leading to the “acquisition” of negative affect, such as ethnocentrism and racism, which then affects the community members’ attitudes about politics and social life (Easton and Dennis 1969; Sears 1993). K. Beckett (1997) observed that political elites take advantage of symbols and metaphorical statements to tap into community members’ negative affect, in order to promote their own self-serving agenda. Once an emotional connection is made between the two groups, elites are able to rally community members to their “causes.”

René Flores was one of the first scholars to empirically examine the hypothesis that political elites’ statements may directly influence the public’s views about immigrants. Specifically, Flores tested this hypothesis using former President Donald Trump’s statements about immigrant groups. Employing both a Gallup survey and a survey experiment to gauge community members’ attitudes toward immigrants in the aftermath of Trump’s political speeches targeting immigrants, Flores found that the public’s views about immigration worsened several days after Trump’s speech on the same topic. For the Gallup results specifically, the plurality of respondents indicated that (1) immigration should be decreased, and (2) immigration was a bad thing for the United States, days after Trump’s speech. Flores also observed an enduring, hardening opinion among respondents about immigration after Trump’s speech. Examining subpopulations in the Gallup data, Flores observed that Republicans and respondents without a college degree were more likely than others to indicate that they would prefer a decrease in immigration levels to the United States. Even after controlling for gender, race, age, ethnicity, educational attainment, employment status, partisanship, and community type, Flores noted that there was a statistically significant positive association between being interviewed after Trump’s speech and a preference for lower levels of immigration to the United States.

Flores also found notable results from the survey experiment. Using a brief newspaper article but manipulating the message’s sentiment (neutral, pro-immigration, or anti-immigration) and author (whether politician or non-politician), the respondents were interviewed twice: when the survey was deployed and also a few weeks later. Flores found that negative messaging was correlated with negative views about immigration. Also, negative messages about immigrants were consequential, whereas positive messages had no effect on respondents’ attitudes toward immigration, irrespective of the source of the message: politician or non-politician.

Divergent Views on Immigration

Although some members of the American public frown upon immigration, even documented immigration, others see it as integral to the prosperity and long-term well-being of the United States (Kulig et al. 2020; Ousey and Kubrin 2018; Pryce 2018). N. Theodore (2012) argued that, in the five-year span between 2007 and 2012, more than 6,000 “pro-immigrant” bills were deliberated upon in state legislatures, with the goal of extending benefits such as health care and education to documented immigrants and their dependents to help them assimilate into mainstream society. Conversely, these bills were paralleled by many “anti-immigrant” bills, as a segment of the U.S. population is opposed to all forms of immigration, including documented immigration (Jardina 2019b). As a result, it is important that community members’ views about immigration are regularly gauged, as these divergent sentiments inform public policy.

Demographics of Donald Trump’s Supporters

Donald Trump’s supporters during the 2016 presidential campaign can be grouped into the following categories: working-class White citizens (Hochschild 2016), Republicans, evangelical (born-again) Christians who believe that America’s status and values were being eroded, and middle-aged White men and women (Setzler and Yanus 2018). We added born-again status to our analysis because research has shown that born-again Christians and conservatives tend to hold similar views on immigration enforcement (Chenane 2022). Complementing the work of other scholars, A. Cherlin (2016) observed that Trump’s supporters included non-college educated voters, immigration opponents, and people who distrusted mainstream institutions. The common thread in this narrative is that Trump’s supporters were predominantly White, working class, and non-college educated citizens.

Method

Participants and Procedures

The data for the present study come from a larger research project based on an online panel-based survey, commissioned by the Center for Public Opinion and Research at the University of Massachusetts Lowell (Dyck, Talty, and Cluverius 2018). Conducted in February 2020, more than 1,300 U.S.-based adults took part in the survey, administered by YouGov. YouGov samples use a two-stage sampling process, whereby surveys are first administered to a nonprobability “over-sample” drawn from an opt-in Internet panel, after which the initial sample is reduced to a representative final sample by algorithmically matching respondent characteristics to an established sampling frame (Rivers 2006). Several studies in the extant literature have used data from YouGov surveys (e.g., Chenane et al. 2022b; Cohn 2014; Harris and Socia 2016; Rydberg, Dum, and Socia 2018; Socia et al. 2019; Vavreck and Rivers 2008).

YouGov initially interviewed 1,330 respondents, who were then matched down to a sample of 1,200 to produce the final data set. Survey participants were matched to a sampling frame on gender, age, race, and education. The frame was constructed by stratified sampling from the full 2016 American Community Survey (ACS) 1-year sample with selection within strata by weighted sampling with replacements (using the person weights on the public use file). Using propensity scores, the matched cases were weighted to the sampling frame. The matched cases and the frame were combined, and a logistic regression was estimated for inclusion in the frame. The propensity score function included age, gender, race/ethnicity, years of education, and region. The propensity scores were grouped into deciles of the estimated propensity score in the frame and post-stratified according to these deciles. The weights³ were then post-stratified on 2016 presidential vote choice, and a four-way stratification of gender, age (four categories), race (four categories), and education (four categories), to produce the final weight.

Sample

The sample included 55 percent (n = 656) women and 45 percent (n = 544) men. Participants’ ages (recoded from birth year to age, in years) ranged from 19 to 95 years (M = 50.21, SD = 16.67). For respondents’ race, 68 percent (n = 820) were White, 13 percent (n = 159) were Hispanic,⁴ 11 percent (n = 126) were Black, and 8 percent (n = 95) were categorized as “Other” (this group included those who identified as Asian, Native American, mixed, or Middle Eastern). Fifty-one percent (n = 615) of respondents were either employed full-time or part-time, whereas 49 percent (n = 585) were not employed (this category included those who were temporarily laid off, unemployed, retired, permanently disabled, or a homemaker). As for educational attainment, 35 percent (n = 414) had a high school diploma or less, and 65 percent (n = 786) had some college education or better. In terms of family income, 45 percent (n = 475) earned $49,999 or less per annum, and 55 percent (n = 587) earned $50,000 or more per annum.

Dependent variable

The Diversity Visa Lottery Program should be eliminated

Respondents were asked to respond to the statement, “The Diversity Visa Lottery should be eliminated.” A four-point Likert-type scale—(1) strongly disagree, (2) disagree, (3) agree, and (4) strongly agree—was employed to measure this dependent variable (M = 2.47; SD = 1.036). Of the 604 respondents, 127 strongly agreed, 153 agreed, 203 disagreed, and 121 strongly disagreed that the DVL program should be eliminated. Overall, 280 (46 percent) respondents support the elimination of the program, whereas 324 (54 percent) do not support the elimination of the program. Thus, a slight majority of the respondents opined that the DVL program should not be scrapped. For the DVL program generally, the descriptive results suggest that respondents who oppose the DVL program are in the minority.

Independent variables

Candidate voted for in 2016 presidential election

The respondents were asked who they voted for in the 2016 presidential election. Two dummy variables were constructed to represent those who voted for Donald Trump and those who did not vote for president at all. Hillary Clinton was the reference category.

Political affiliation

The respondents’ political affiliations were captured as Democrat, Republican, and Independent. Two dummy variables were created to represent Republican and Independent. Democrat was the reference category.

Ideology

The study participants’ ideological leanings were initially captured as very liberal, liberal, moderate, conservative, and very conservative. This variable was then collapsed into three categories: very liberal/liberal, moderate, and conservative/very conservative. Two dummy variables were created to represent very liberal/liberal and conservative/very conservative. Moderate was the reference category.

Religion (whether or not respondent is born again)

This independent variable was coded as: born again = 1; not born again = 0.

Control variables

The following demographic variables were included in the analyses to control for bias in the estimates in the regression equations:

Gender: This variable is coded as: Female = 0, Male = 1.

Age: This is measured as a continuous variable.

Race: Two dummy variables were created for race: Black and Other. White is the reference category.

Educational Level: This was initially an ordinal measure: 1 = no high school diploma; 2 = high school graduate; 3 = some college; 4 = two-year college, 5 = four-year college, 6 = post-graduate education. However, educational level was recoded: high school or less = 0; some college or higher = 1. This recoding would capture Trump’s supporters’ educational background more accurately.

Employment: This is an ordinal measure: 1 = full-time; 2 = part-time; 3 = temporarily laid off; 4 = unemployed; 5 = retired; 6 = permanently disabled; 7 = homemaker. This variable was then collapsed into two categories: employed full-time or part-time = 1; not employed (all the other categories) = 0.

Family income: This is an ordinal measure: 1 = less than $10,000; 2 = $10,000–$19,999; 3 = $20,000–$29,999; 4 = $30,000–$39,999; 5 = $40,000–$49,999; 6 = $50,000–$59,999; 7 = $60,000–$69,999; 8 = $70,000–$79,999; 9 = $80,000–$99,999; 10 = $100,000–$119,999; 11 = $120,000–$149,999; 12 = $150,000–$199,999; 13 = $200,000–$249,999; 14 = $250,000–$349,999; 15 = $350,000–$499,999; 15 = more than $500,000. This variable was then recoded as 1 = $49,999 or less; 0 = $50,000 or more.

Ethnicity: This was originally part of the race variable. To tease out the views of Hispanics, we recoded the ethnicity variable as: Hispanic = 1; non-Hispanic = 0.

Appropriate Tests and Analytic Strategy

We conducted appropriate tests to ensure that there was no violation of the assumptions of normality, linearity, homoscedasticity, and independence of residuals. An inspection of the Q-Q plots (not shown) shows that scores appear to be normally distributed for the dependent variable. We also checked for outliers by inspecting the Mahalanobis distances (Tabachnick and Fidell 2007). Tolerance and variance inflation factor values were in the normal ranges, and we detected no strong evidence of multicollinearity (Pallant 2010). Finally, the skewness and kurtosis values for the variables, especially for the dependent variable, were near normal (less than an absolute value of 2). According to B. G. Tabachnick and L. S. Fidell (2007), skewness and kurtosis will not substantively affect the analysis of the data if the sample size is larger than 200, which is the case in the present study. Thus, multivariate regression analysis was an appropriate methodology for analyzing the data.

All the variables in the present study were subjected to ordinary least squares (OLS) regression analyses. The use of regression analyses to test the models (shown below) accomplished two things: (1) to help determine the relative influence that each independent variable had on the dependent variable, and (2) to help reach the conclusion that the influence of any one independent variable was independent of the influence of the other independent variables in each of the regression equations (Pryce 2019; Sunshine and Tyler 2003). None of the correlations (see Table 2) between the dependent variable and the independent variables, and between any two independent variables, exceeded .70 (Pallant 2010), so all of the independent variables and the dependent variable were retained for analysis. In fact, the highest correlation of .393 was between ideology and political affiliation.

Table 1.

Descriptive Statistics of the Variables.

Items	Responses	M	SD	Minimum	Maximum
The DVL should be eliminated
The Diversity Visa Lottery (Green Card) program should be eliminated.	604^a	2.47	1.036	1	4
Candidate voted for in 2016 presidential election
Hillary Clinton	398	—	—	0	1
Donald Trump	377	—	—	0	1
Did not vote for president	345	—	—	0	1
Political affiliation
Democrat	411	—	—	0	1
Republican	317	—	—	0	1
Independent	334	—	—	0	1
Ideology
Very liberal/liberal	361	—	—	0	1
Moderate	340	—	—	0	1
Conservative/very conservative	398	—	—	0	1
Religion
Born again = 1; not born again = 0	1,200 (343/857)	.29	.452	0	1
Employment
Full-time/part-time = 1, not employed = 0	1,200 (615/585)	.51	.500	0	1
Gender
Female = 0; male = 1	1,200 (656/544)	.45	.498	0	1
Family income
Less than $10,000 to $500,000 or more. Recoded as: $49,999 or less = 1; $50,000 or more = 0	1,200 (475/587)	.45	.497	0	1
Age (years)
Recoded from birthyear; continuous variable	1,200	50.21	16.67	19	95
Race
White	820	—	—	0	1
Black	126	—	—	0	1
Hispanic	159	—	—	0	1
Other (Asian, Native American, mixed, Middle Eastern, other)	95	—	—	0	1
Educational Level
1 = no high school to 6 = post-graduate education; recoded as high school or less =0; some college or higher = 1	1200 (414/786)	.65	.475	0	1

Note. DVL = Diversity Visa Lottery.

The dependent variable has only half of the responses of the other variables because only half of the respondents were asked the question about the DVL program.

Table 2.

Bivariate Correlation Results for the Variables.

Scale items	1	2	3	4	5	6	7	8	9	10
1. DVL should be eliminated	1	.035	.375**	.144**	−.048	.054	−.034	.182**	−.090*	−.012
2. 2016 Pres. vote		1	.197**	.324**	.112**	−.096**	.117**	.038	−.216**	.030
3. Ideology			1	.393**	.071*	−.070*	.019	.214**	−.193**	.091*
4. Party affiliation				1	.089**	−.005	.154**	.029	−.112**	.091**
5. Employment status					1	−.125**	.042	.042	−.241**	.119**
6. Gender						1	−.036	−.072*	.017	−.029
7. Race							1	−.002	.000	.071*
8. Born again status								1	−.069*	−.034
9. Educational level									1	.007
10. Family income										1

Note. DVL = Diversity Visa Lottery.

p < .05 (two-tailed test). **p < .01 (two-tailed test).

Results

Table 3 presents results from two OLS regression models. In Model 1, we analyzed the effects of the control variables (gender, age, race, employment status, educational level, family income, and ethnicity) on whether the DVL program should be eliminated. Age (beta = .150, p < .001), being Black (beta = −.098, p = .023), and educational level (beta = −.126, p = .005) are statistically significantly related to whether or not the DVL program should be eliminated. Thus, older respondents were more likely to agree that the DVL program should be eliminated. Conversely, compared with White respondents, Blacks and more highly educated respondents were less likely to agree that the DVL program should be eliminated. Gender, race_other, employment status, family income, and ethnicity were not statistically significantly related to the dependent variable. This model explained about 6 percent of the variation in whether or not respondents agree with the elimination of the DVL program (F = 4.575, p < .001).

Table 3.

DVL Should Be Eliminated Is the Dependent Variable.

Variables	Model 1			Model 2
Variables	B	SE	β	B	SE	β
Controls
Gender	.062	(.089)	.030	.053	(.082)	.025
Age	.009	(.003)	.150***	.003	(.003)	.041
Race^a
Black	−.331	(.145)	−.098*	−.144	(.135)	−.043
Other	.086	(.164)	.022	.037	(.148)	.010
Employment status	.121	(.096)	.058	.109	(.087)	.053
Educational level	−.275	(.098)	−.126**	−.104	(.091)	−.048
Family income	−.151	(.097)	−.073	−.084	(.089)	−.040
Primary predictors
2016 presidential vote
Donald Trump				.639	(.131)	.286***
Did not vote for president				.250	(.112)	.109*
Political affiliation
Republican				−.037	(.131)	−.016
Independent				.047	(.104)	.020
Ideology
Very liberal/liberal				−.353	(.108)	−.156**
Conservative/very conservative				.282	(.117)	.128*
Religion (born again or not)				.124	(.094)	.054
R square		.057			.248
Adjusted R square		.045			.228

Note. N = 604. β entries are standardized coefficients, and standard errors are in parentheses. DVL = Diversity Visa Lottery.

p < .05 (two-tailed test). **p < .01 (two-tailed test). ***p < .001 (two-tailed test).

White is the reference category for race, Hillary Clinton is the reference category for 2016 presidential vote, Democrat is the reference category for political affiliation, and Moderate is the reference category for ideology.

In Model 2 of Table 3, the dependent variable was regressed on all the control variables (gender, age, race, employment status, educational level, family income, and ethnicity) and the predictor variables (2016 presidential vote, political affiliation, ideology, and whether or not the respondent is born again). Here, none of the control variables are significantly related to the dependent variable due to the presence of the predictor variables in the regression equation. Compared with those who voted for Hillary Clinton, those who voted for Donald Trump (beta = .286, p < .001) and those who did not vote for president (beta = .109, p = .026) were more likely to agree that the DVL program should be eliminated. In terms of ideological leanings, compared with moderates, respondents who noted that they were very liberal/liberal (beta = −.156, p = .001) were less likely to support the elimination of the DVL program. On the contrary, compared with moderates, those respondents who identified as conservative/very conservative (beta = .128, p = .016) were more likely to support the elimination of the DVL program. Political affiliation was not significantly related to the dependent variable. In other words, there were no significant differences in the opinions of those who identified as Democrats, Republicans, or Independents as far as eliminating the DVL program was concerned. Being born again was also not significantly related to the dependent variable. This model explained about 25 percent of the variation in whether or not respondents support the elimination of the DVL program (F = 18.798, p < .001).

Discussion and Conclusion

Using a data set that approximates a nationally representative sample, the current study examined the American public’s opinions about the DVL program. Employing René Flores’s theoretical framework that explains the connection between political elites’ speeches and the public’s views about vulnerable social groups, such as immigrants, we argue that Donald Trump’s rhetoric about the DVL program may have affected how certain segments of the American population have come to view the DVL program. Consistent with the argument that Donald Trump’s supporters were mostly working-class White citizens, Republicans, evangelical (born-again) Christians, middle-aged White men and women, non-college-educated voters, and immigration opponents, we show from our results that those without a college education, those who voted for Trump, and those who noted that they were conservative or very conservative were likely to oppose the DVL program (Cherlin 2016; Hochschild 2016; Setzler and Yanus 2018). But the demographic variables were no longer statistically significant once we introduced the predictor variables into the regression equation. Overall, Flores’s theoretical perspective is suitable for explaining why some individuals may have negative attitudes toward immigrants and programs such as the DVL program aimed at increasing the number of documented immigrants in the United States. Not only are our findings intriguing, but they are in line with what we expected to find in terms of attitudes toward immigrants and immigration, and thus warrant further discussion.

Our findings revealed several significant differences between groups—such as liberals versus moderates versus conservatives, and younger versus older individuals. It is important to note that the findings were robust even after we controlled for important correlates—age, race, gender, educational level, employment status, family income, and ethnicity—of public attitudes toward immigrants and immigrants. We found that liberals and conservatives were on opposite ends of the spectrum when it came to their views on eliminating the DVL program. On the one hand, respondents who identified as very liberal/liberal were less likely to support the elimination of the DVL program. And on the other hand, those who identified as conservative/very conservative were more likely to support the elimination of the DVL program. These findings are in line with extant research alluding to ways in which conservatives and liberals differ from each other in their responses to topics that require moral judgment. For example, some have argued that conservatives and liberals tend to employ different considerations when making moral judgments on issues such as illegal immigration (Hibbing, Smith, and Alford 2014).

Liberals rely primarily on concerns for equality and harm avoidance, whereas conservatives are more likely to take into account considerations such as purity, authority, and in-group/out-group status (Hibbing et al. 2014). Moreover, public opinion polls have noted that the divide in attitudes toward immigration between conservatives and liberals is growing. The opinions (about immigration and immigrants) of conservatives are trending more negative while the opinions of liberals and moderates are more positive (National Immigration Forum 2019). Thus, it is not surprising to find that individuals who identify as conservative were more likely to favor the elimination of the DVL program. We note that it is possible that Trump’s stentorian opposition to the DVL program may explain why conservatives were opposed to it.

We also found that those who voted for Donald Trump as well as those who did not vote in the 2016 presidential election favored eliminating the DVL program. During his presidential campaign, Trump, a Republican, was vocal about policies such as building a wall to end undocumented immigration to the United States (Davis and Shear 2019; Kulig et al. 2020). Subsequent to his election, Trump had called on the U.S. Congress to eliminate the DVL program, and in June 2020 signed an executive order preventing the 2019 DVL winners from traveling to the United States to obtain their Green Cards (Levy 2017).

We surmise that those who did not vote, yet are opposed to the DVL program, may be primarily those who do not endorse immigration. Research on the link between political ideology and the likelihood of voting is mixed: While some studies show that non-voters tend to be poorer, less educated, more liberal, and more Democratic (Shaffer 1982; Sorauf 1980), other studies point to voters not being significantly different from non-voters when it comes to public policy (Ladd and Hadley 1973; Shaffer 1982). S. Rosenstone and R. Wolfinger (1978) argued that, absent the difficulties people experience while trying to vote (e.g., early registration closing dates, registration requirements, absentee registration, etc.), voter turnout would be several percentages higher each election cycle. Thus, the decision to not vote in an election is tied not only to ideological leanings, but also to the ease and convenience of casting the actual ballot. Based on the large percentage of non-voters in our sample, we recommend that future studies examine why non-voters might be supportive of restrictive immigration policies.

Unsurprisingly, we found that the opinions of those who identified themselves as conservative as well as those who voted for Donald Trump in 2016 were more averse to the DVL program. These findings are in congruence with Flores’s (2018) theory, which argues that “politicians’ use of symbolic language blaming vulnerable groups, such racial minorities, immigrants, and poor families, for society’s problems may not only encourage popular support for exclusionary policies but also influence public views of these groups themselves” (p. 1649). In fact, Donald Trump cited competition for jobs as a reason for preventing the 2019 DVL winners from moving to the United States (Penton 2020). Prior research has revealed that those generally opposed to migration do so for a variety of reasons, including fear that immigrants would take their jobs or increase crime (Esses et al. 1998; Pryce 2018). Within the context of immigration, it could be that those who support the elimination of the DVL program are in fact embracing their own (i.e., native-born Americans) and ostracizing outsiders (i.e., immigrants). In other words, Trump’s stance on eliminating the DVL program may have resonated well with those who are opposed to immigrants and immigration in general for self-preservation.

Indeed, our first regression model also revealed interesting trends for control variables such as age, race, and educational level. Older respondents were more likely to support the banning of the DVL program. Conversely, Black respondents were less likely to support the elimination of the DVL program. In addition, individuals with higher levels of education were less likely to support the elimination of the DVL program. Public opinion polls have consistently shown that the more educated favor immigration than the less educated (Chandler and Tsai 2001). The association between educational level and support for immigrants and immigration may be explained by the fact that individuals with higher levels of education are less likely than less-educated individuals to face competition for jobs (Pantoja 2009; Pryce 2018). It also may be that those with less education may be more likely to embrace the negative rhetoric about immigrants without probing the validity and/or reliability of this information. Moreover, education exposes people to diverse viewpoints (Sinatra et al. 2003), which may help the more educated individual to counter stereotypes about immigrants and immigration. We also found that older individuals were more inclined to support the elimination of the DVL program. The finding about age comports with prior research on attitudes toward immigrants and immigration where older individuals were found to be more prone to opposing immigrants and immigration (Goldstein and Peters 2014). As D. K. Pryce (2018) has noted, the difference between older and younger individuals can be explained by a tolerance gap between the two groups. We re-emphasize that the demographic variables were no longer statistically significant once we introduced the predictor variables into the regression equation.

Similar to any research project, the current study is not without limitations. First, Internet polling such as YouGov may suffer from several limitations, including sampling bias, lack of a sampling frame, lower response rates, and mode effects (Sparrow and Curtice 2004). Not everyone has unlimited access to the Internet, and those who do have it tend to be younger and relatively more affluent. Second, probability sampling requires a sampling frame, which makes it possible to randomly select participants; however, YouGov utilizes nonprobability methods to recruit a panel of volunteers. Third, the question asked about the DVL program (the dependent variable) was not asked for specific regions or countries, so we are unable to make comparisons to assess whether people from certain regions are perceived more negatively or positively. Fourth, the DVL program is not a well-publicized form of documented immigration, although Donald Trump’s attacks on the program may have shed greater light on it for many U.S. citizens. Still, we do not know how familiarity with the program, or a lack thereof, affected participant responses. Fifth, because the questions did not include a “Don’t know” response category, we are unable to know if any of the participants had not previously heard about the DVL program. Sixth, the data set did not contain a citizenship category to delineate the views of citizens from non-citizens. Future research should include these variables to capture more nuances in respondent views. Seventh, participants’ responses to the survey questions may depend on the sensitivity of the questions that they were being asked. Less sensitive questions may yield more responses compared with items that require participants to provide more sensitive information. Nonetheless, YouGov surveys have been previously validated in several studies (e.g., Cohn 2014; Harris and Socia 2016) and offer a great tool for examining the public’s attitudes on various topics.

In conclusion, our findings reveal certain proclivities that the American public has toward the DVL program. While public attitudes toward immigrants and immigration have been examined extensively by other researchers (Blau and Mackie 2017; Chouhy and Madero-Hernandez 2019; Kaufmann 2019), it is important to continue to study public opinions on immigration as there are constant changes taking place within the populace regarding immigration sentiments. Indeed, Trump’s call for the U.S. Congress to eliminate the DVL program necessitates an examination of the public’s views because there is the possibility that Trump could be elected as president again, should he run in the 2024 U.S. presidential election.

Our findings revealed that certain segments of the population are opposed to legal immigration (in this case, the DVL program). Given that discussions about immigration will likely continue to be on the center stage of political and daily discourse, understanding contemporary attitudes toward immigration is pertinent (Jardina 2019a; Kulig et al. 2020). As A. Jardina (2019a) has observed, Trump’s use of condescending language to describe Mexicans partly led to his election, because the verbiage resonated with opponents of immigration and also drew White nationalists, who have felt that their influence was being eroded by large-scale immigration, toward Trump (Jardina 2019b).

Ultimately, research on public opinions on immigrants and immigration is of paramount importance as these views may shape and alter public policy (Chenane and Wright 2021). As an illustration, members of the public who support eliminating the DVL program may vote to elect like-minded individuals who would then change the course of policy on immigration. Indeed, President Joseph Biden reversed some of former President Trump’s policies that banned DVL winners and other immigrant visa recipients from traveling to and taking permanent residence in the United States (Hudak and Stenglein 2021). We noted earlier in the current paper that a segment of Trump’s supporters does not support immigration; as such, President Biden’s Democratic Party faced an uphill task retaining their majority in the U.S. House of Representatives in the 2022 midterm elections, perhaps because President Biden failed to halt the surge in undocumented immigration, especially at the U.S.-Mexico border (Hesson and Holland 2021). Thus, U.S. citizens’ attitudes toward immigrants and immigration may impact votes cast during an election cycle, which is why the topic of immigration would remain an important policy issue for the foreseeable future.

Footnotes

Acknowledgements

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.

ORCID iDs

Daniel K. Pryce

Joselyne L. Chenane

Notes

Author Biographies

Daniel K. Pryce, Ph.D., is an Assistant Professor in the Department of Sociology and Criminal Justice at Old Dominion University. He was the recipient of the 2017 Dean’s Excellence in Research Award and the 2018 Chancellor’s Award for Research at North Carolina Central University, his previous employer. The author/co-author of several peer-reviewed articles, his recent scholarly work has appeared in Child Abuse & Neglect, Crime & Delinquency, Criminal Justice and Behavior, Police Quarterly, Policing and Society, and Du Bois Review. His co-authored paper that carried out an experimental assessment of the American public’s views on immigration when the terms illegal and undocumented were juxtaposed was recently published in Crime & Delinquency.

Joselyne C. Nkogo, Ph.D., is an Assistant Professor in the School of Criminology and Justice Studies at the University of Massachusetts Lowell and an alumnus of the Carnegie African Diaspora Fellowship program. She earned her doctorate in Criminology and Criminal Justice from the University of Nebraska, Omaha. She holds a Master of Science in Criminal Justice from the State University of New York, college at Buffalo and a Bachelor of Education Arts from Egerton University (Kenya). Her research interests include police/citizen relations, police legitimacy, race and justice, neighborhood contexts and crime, and immigration. Her work has been published in Criminal Justice and Behavior, Race & Justice, Crime & Delinquency, Feminist Criminology, British Journal of Criminology, and Policing and Society among others.

References

Alba

R. D.

1999. “Immigration and the American Realities of Assimilation and Multiculturalism.” Sociological Forum 14(1):3–25.

Arceneaux

2008. “Can Partisan Cues Diminish Democratic Accountability?” Political Behavior 30:139–60.

Batalova

Blizzard

Bolter

2020. Frequently Requested Statistics on Immigrants and Immigration in the United States. Washington, DC: Migration Policy Institute. Retrieved April 1, 2023 (https://www.migrationpolicy.org/article/frequently-requested-statistics-immigrants-and-immigration-united-states).

Beckett

1997. Making Crime Pay: Law and Order in Contemporary American Politics. Oxford, England: Oxford University Press.

Blau

F. D.

Mackie

, eds. 2017. The Economic and Fiscal Consequences of Immigration. Washington, DC: National Academic Press.

Boateng

F. D.

Pryce

D. K.

Chenane

J. L.

2021a. I may be an immigrant, but I am not a criminal: Exploring the association between the presence of immigrants and crime rates in Europe. Journal of International Migration and Integration 22(3):1105–1124.

Boateng

F. D.

McCann

Chenane

J. L.

Pryce

D. K.

2021b. Perception of immigrants in Europe: A multilevel assessment of macro-level conditions. Social Science Quarterly 102(1):209–227.

Chandler

C. R.

Tsai

Y. M.

2001. “Social Factors Influencing Immigration Attitudes: An Analysis of Data from the General Social Survey.” The Social Science Journal 38(2):177–88.

Chenane

J. L.

Wright

E. M.

(2021). The role of police officer race/ethnicity on crime rates in immigrant communities. Race and Justice 11(1):3–27.

10.

Chenane

J. L.

2022. Public opinion on immigration law enforcement, support for the police, and obligation to report undocumented immigrants to the police. Race and Justice. Online first. DOI: https://doi.org/10.1177/21533687221120950.

11.

Chenane

J. L.

Morabito

M. S.

Gonzales

T. I.

2022a. Perceptions of police among Kenyan female immigrants in the United States. Feminist Criminology 17(5):587–618.

12.

Chenane

J. L.

Pryce

D. K.

Seungeun Lee

2022b. An experimental assessment of the public’s views on immigration when the terms ‘illegal’ and ‘undocumented’ are juxtaposed. Crime & Delinquency. DOI: https://doi.org/10.1177/00111287221125391.

13.

Cherlin

2016. “The Downwardly Mobile for Trump.” The New York Times August 25. Retrieved April 1, 2023 (https://www.nytimes.com/2016/08/25/opinion/campaign-stops/the-downwardly-mobile-for-trump.html?searchResultPosition=1).

14.

Chouhy

Madero-Hernandez

2019. “‘Murderers, Rapists, and Bad Hombres’: Deconstructing the Immigration–Crime Myths.” Victims & Offenders 14(8):1010–39.

15.

Citrin

Reingold

Green

D. P.

1990. “American Identity and the Politics of Ethnic Change.” Journal of Politics 57:1124–54.

16.

Cohn

2014. “Explaining Online Panels and the 2014 Midterms.” Retrieved April 1, 2023 (http://www.nytimes.com/2014/07/28/upshot/explaining-online-panels-and-the-2014-midterms.html?smid=pl-share).

17.

Cordasco

1973. “The Children of Immigration in the Schools: Historical Analogues of Education Deprivation.” The Journal of Negro Education 42(1):44–53.

18.

Crosnoe

Lopez Turley

2011. “K-12 Educational Outcomes of Immigrant Youth.” The Future of Children 21(1):129–52.

19.

Davis

J. H.

Shear

M. D.

2019. Border Wars: Inside Trump’s Assault on Immigration. New York: Simon & Schuster.

20.

Diaz

2011. “Immigration Policy, Criminalization and the Growth of the Immigration Industrial Complex: Restriction, Expulsion, and Eradication of the Undocumented in the U.S.” Western Criminology Review 12(2):35–54.

21.

Druckman

J. N.

2001. “On the Limits of Framing Effects: Who Can Frame?” Journal of Politics 63:1041–66.

22.

Dyck

J. J.

Talty

Cluverius

2018. “Umass Lowell Center for Public Opinion 2018 Faculty Research Poll.” Retrieved April 1, 2023 (http://www.uml.edu/polls).

23.

Easton

Dennis

1969. Children in the Political System. New York: McGraw Hill.

24.

Echeverria-Estrada

Batalova

2019. The Diversity Visa Program Holds Lessons for Future Legal Immigration Reform. Washington, DC: Migration Policy Institute. Retrieved April 1, 2023 (https://www.migrationpolicy.org/article/sub-saharan-african-immigrants-united-states-2018#Pathways).

25.

Esses

V. M.

Jackson

L. M.

Armstrong

T. L.

1998. “Intergroup Competition and Attitudes toward Immigrants and Immigration: An Instrumental Model of Group Conflict.” Journal of Social Issues 54(4):699–724.

26.

Flores

R. D.

2018. “Can Elites Shape Public Attitudes toward Immigrants? Evidence from the 2016 US Presidential Election.” Social Forces 96:1649–90.

27.

Foner

2000. From Ellis Island to JFK: New York’s Two Great Waves of Immigration. New Haven, CT: Yale University Press.

28.

Gelatt

2019. Explainer: How the U.S. Legal Immigration System Works. Washington, DC: Migration Policy Institute. Retrieved April 1, 2023 (https://www.migrationpolicy.org/content/explainer-how-us-legal-immigration-system-works).

29.

Gimpel

J. G.

2017. Immigration Opinion and the 2016 Presidential Vote: Issue Relevance in the Trump-Clinton Election. Washington, DC: Center for Immigration Studies. Retrieved April 1, 2023 (https://cis.org/sites/default/files/2017-12/gimpel-2016-vote.pdf).

30.

Goldstein

J. L.

Peters

M. E.

2014. “Nativism or Economic Threat: Attitudes Toward Immigrants During the Great Recession.” International Interactions 40(3):376–401.

31.

Goodman

C. B.

2016. “Global Game of Chance: The U.S. Diversity Visa Lottery, Transnational Migration, and Cultural Diplomacy in Africa, 1990–2016.” Doctoral Dissertation, Temple University. Retrieved April 1, 2023 (https://digital.library.temple.edu/digital/collection/p245801coll10/id/370310).

32.

Goren

Federico

C. M.

Kittilson

M. C.

2009. “Source Cues, Partisan Identities, and Political Value Expression.” American Journal of Political Science 53:805–20.

33.

Graham

Pickett

J. T.

Cullen

F. T.

2020. “Advantages of Matched Over Unmatched in Samples for Studying Criminal Justice Attitudes: A Research Note.” Crime & Delinquency 67:1962–81. doi:10.1177/0011128720977439.

34.

Hainmueller

Hopkins

D. J.

2015. “The Hidden American Immigration Consensus: A Conjoint Analysis of Attitudes toward Immigrants.” American Journal of Political Science 59(3):529–48.

35.

Harris

A. J.

Socia

K. M.

2016. “What’s in a Name? Evaluating the Effects of the ‘Sex Offender’ Label on Public Opinions and Beliefs.” Sexual Abuse: A Journal of Research and Treatment 28(7):660–78.

36.

Hesson

Holland

2021. “Biden Moves to Reverse Trump Immigration Policies, Too Slowly for Some.” Reuters, February 2. Retrieved April 1, 2023 (https://www.reuters.com/article/us-usa-biden-immigration-actions/biden-moves-to-reverse-trump-immigration-policies-too-slowly-for-some-idUSKBN2A212D).

37.

Hibbing

J. R.

Smith

K. B.

Alford

J. R.

2014. “Differences in Negativity Bias Underlie Variations in Political Ideology.” Behavioral and Brain Sciences 37(3):297–307.

38.

Hochschild

2016. Strangers in Their Own Land. New York: The New Press.

39.

Hudak

Stenglein

2021. “Biden’s Immigration Reset.” The Brookings Institution, February 19. Retrieved April 1, 2023 (https://www.brookings.edu/blog/fixgov/2021/02/19/bidens-immigration-reset/).

40.

Jardina

2019a. “White Consciousness and White Prejudice: Two Compounding Forces in Contemporary American Politics.” The Forum 17:447–66.

41.

Jardina

2019b. White Identity Politics. Cambridge, England: Cambridge University Press.

42.

Kaufmann

2019. Whiteshift: Populism, Immigration and the Future of White Majorities. New York: Abrams Press.

43.

Kincaid

2017. “The Symbolic Politics behind Why Sane People Vorte for (Seemingly) Insane Things.” Retrieved April 1, 2023 (https://blogs.lse.ac.uk/usappblog/2017/02/03/the-symbolic-politics-behind-why-sane-people-vote-for-seemingly-insane-things/).

44.

Kuklinski

J. H.

Hurley

N. L.

1994. “On Hearing and Interpreting Political Messages: A Cautionary Tale of Citizen Cue-taking.” Journal of Politics 56:729–51.

45.

Kulig

T. C.

Graham

Cullen

F. T.

Piquero

A. R.

Haner

2020. “‘Bad Hombres’ at the Southern U.S. Border? White Nationalism and the Perceived Dangerousness of Immigrants.” Australian & New Zealand Journal of Criminology 54:283–304. doi:10.1177/0004865820969760.

46.

Ladd

Hadley

1973. Political Parties and Political Issues: Patterns in Differentiation since the New Deal. Beverly Hills, CA: Sage Publications.

47.

Lee

Bean

F. D.

2004. “America’s Changing Color Lines: Immigration, Race/Ethnicity, and Multicultural Identification.” Annual Review of Sociology 30:221–42.

48.

Levy

2017. “Trump Calls for Cancellation of Visa Lottery.” U.S. News, November 1. Retrieved April 1, 2023 (https://www.usnews.com/news/politics/articles/2017-11-01/trump-calls-on-congress-to-eliminate-visa-program-after-nyc-attack).

49.

Massey

D. S.

Durand

Malone

N. J.

2002. Beyond Smoke and Mirrors. New York: Russell Sage Foundation.

50.

Mendelberg

2001. The Race Card: Campaign Strategy, Implicit Messages, and the Norm of Equality. Princeton, NJ: Princeton University Press.

51.

Migration Policy Institute. 2011. “US in Focus: African Immigrants in the United States.” Retrieved April 1, 2023 (http://www.migrationinformation.org/feature/display.cfm?ID=847#6).

52.

Mueller

Rashbaum

W. K.

Baker

2017. “Terror Attack Kills 8 and Injures 11 in Manhattan.” The New York Times, October 31. Retrieved April 1, 2023 (https://www.nytimes.com/2017/10/31/nyregion/police-shooting-lower-manhattan.html).

53.

National Immigration Forum. 2019. “Polling Update: American Attitudes on Immigration Steady, but Showing More Partisan Divides.” Retrieved April 1, 2023 (https://immigrationforum.org/article/american-attitudes-on-immigration-steady-but-showing-more-partisan-divides/).

54.

Ngai

M. M.

2004. Impossible Subjects: Illegal Aliens and the Making of Modern America. Princeton, NJ: Princeton University Press.

55.

Nicholson

S. P.

2012. “Polarizing Cues.” American Journal of Political Science 56:52–66.

56.

Ousey

G. C.

Kubrin

C. E.

2018. “Immigration and Crime: Assessing a Contentious Issue.” Annual Review of Criminology 1:63–84.

57.

Pallant

2010. SPSS Survival Manual: A Step by Step Guide to Data Analysis Using SPSS. 4th ed. New York: McGraw Hill.

58.

Pantoja

2009. “Against the Tide? Core American Values and Attitudes Toward U.S. Immigration Policy in the Mid-1990s.” Journal of Ethnic and Migration Studies 32:515–31.

59.

Penton

M. J.

2020. “They Won the Visa Lottery to Emigrate Legally to the U.S. Now They Are Suing Trump.” Miami Herald, July 14. Retrieved April 1, 2023 (https://www.miamiherald.com/news/local/immigration/article244226867.html).

60.

Perez

E. O.

2010. “Explicit Evidence on the Import of Implicit Attitudes: The IAT and Immigration Policy Judgments.” Political Behavior 32:517–45.

61.

Peters

2015. “Open Trade, Closed Borders: Immigration in the Era of Globalization.” World Politics 67(1):114–54.

62.

Pryce

D. K.

2018. “U.S. Citizens’ Current Attitudes toward Immigrants and Immigration: A Study from the General Social Survey.” Social Science Quarterly 99:1467–83.

63.

Pryce

D. K.

2019. The relative effects of normative and instrumental factors of policing on police empowerment: Evidence from a sub-Saharan African immigrant community. Criminal Justice Policy Review 30(3):428–450.

64.

Rissing

B. A.

Castilla

E. J.

2014. “House of Green Cards: Statistical or Preference-based Inequality in the Employment of Foreign Nationals.” American Sociological Review 79(6):1226–55.

65.

Rivers

2006. “Sample Matching: Representative Sampling from Internet Panels.” Polimetrix White Paper Series. Palo Alto, CA: YouGovPolimetrix.

66.

Rosenstone

Wolfinger

1978. “The Effects of Registration Laws on Voter Turnout.” American Political Science Review 72:22–45.

67.

Rydberg

Dum

C. P.

Socia

K. M.

2018. “Nobody Gives a #%&!: A Factorial Survey Examining the Effect of Criminological Evidence on Opposition to Sex Offender Residence Restrictions.” Journal of Experimental Criminology 14(4):541–50.

68.

Santa Ana

. 2002. Brown Tide Rising: Metaphoric Representations of Latinos in Contemporary American Public Discourse. Austin, TX: University of Texas Press.

69.

Schmid

C. L.

2013. “Undocumented Childhood Immigrants, the Dream Act and Deferred Action for Childhood Arrivals in the U.S.A.” International Journal of Sociology and Social Policy 33(11/12):693–707.

70.

Sears

D. O.

1993. “Symbolic Politics: A Socio-psychological Theory.” Pp. 113–49 in Explorations in Political Psychology, edited by Iyengar

McGuire

W. J.

Durham, NC: Duke University Press.

71.

Sears

D. O.

Huddy

Schaffer

L. G.

1986. “A Schematic Variant of Symbolic Politics Theory, as Applied to Racial and Gender Equality.” Pp. 159–202 in Political Cognition, edited by Lau

R. R.

Sears

D. O.

Mahwah, NJ: Lawrence Erlbaum.

72.

Setzler

Yanus

A. B.

2018. “Why Did Women Vote for Donald Trump?” Political Science & Politics 51:523–27.

73.

Shaffer

S. D.

1982. “Policy Differences between Voters and Non-voters in American Elections.” Western Political Quarterly 35(4):496–510.

74.

Sinatra

G. M.

Southerland

S. A.

McConaughy

Demastes

J. W.

2003. “Intentions and Beliefs in Students’ Understanding and Acceptance of Biological Evolution.” Journal of Research in Science Teaching 40:510–28.

75.

Socia

K. M.

Rydberg

Dum

C. P.

2019. “Punitive Attitudes toward Individuals Convicted of Sex Offenses: A Vignette Study.” Justice Quarterly 38:1262–89.

76.

Soergel

2017. “Report: Trump Pushing to Cut Annual Immigration by Half.” U.S. News, July 13. Retrieved April 1, 2023 (https://www.usnews.com/news/national-news/articles/2017-07-13/report-trump-pushing-to-cut-annual-immigration-by-half).

77.

Sorauf

1980. Party Politics in America. 4th ed. Boston, MA: Little, Brown.

78.

Sparrow

Curtice

2004. “Measuring the Attitudes of the General Public Via Internet Polls: An Evaluation.” International Journal of Market Research 46(1):23–44.

79.

Sunshine

Tyler

T. R.

2003. “The Role of Procedural Justice and Legitimacy in Shaping Public Support for Policing.” Law and Society Review 37:513–48.

80.

Tabachnick

B. G.

Fidell

L. S.

2007. Using Multivariate Statistics. 5th ed. Boston, MA: Pearson Education.

81.

Theodore

2012. “Policing Borders: Unauthorized Immigration and the Pernicious Politics of Attrition.” Social Justice 38(1/2):90–106.

82.

United States Congress. 1990. “S.358–Immigration Act of 1990.” Retrieved April 1, 2023 (https://www.congress.gov/bill/101st-congress/senate-bill/358).

83.

U.S. Department of State. 2020. “Diversity Visa Instructions.” Retrieved April 1, 2023 (https://travel.state.gov/content/travel/en/us-visas/immigrate/diversity-visa-program-entry/diversity-visa-instructions.html).

84.

U.S. Department of State. 2021. “Instructions for the 2022 Diversity Immigrant Visa Program (DV-2022).” Retrieved April 1, 2023 (https://travel.state.gov/content/dam/visas/Diversity-Visa/DV-Instructions-Translations/DV-2022-Instructions-Translations/DV-2022-Instructions-and-FAQs_English.pdf).

85.

Vavreck

Rivers

2008. “The 2006 Cooperative Congressional Election Study.” Journal of Elections, Public Opinion and Parties 18(4):355–66.

86.

Wright

J. D.

Esses

V. M.

2019. “It’s Security, Stupid! Voters’ Perceptions of Immigrants as a Security Risk Predicted Support for Donald Trump in 2016 US Presidential Election.” Journal of Applied Social Psychology 49:36–49.

87.

Yans-Mclaughlin

1990. Immigration Reconsidered: History, Sociology, and Politics. New York: Oxford University Press.