Presidential Voting in the 2016 US Presidential Election

This is a prepublication version. The Version of Record has been accepted for publication by the Journal of Borderlands Studies. When processing is completed it will be available at https://doi.org/10.1080/08865655.2018.1483736.

Presidential Voting in the 2016 U.S. Presidential Election: Impacts of the U.S.-Mexico Border and Border Integration ABSTRACT The 2016 presidential election brought many proposals to the fore, several with potentially significant impacts in the U.S.-Mexico border region. Republican candidate, Donald Trump, promised to build a border wall, return manufacturing jobs to the U.S., impose import tariffs, and scrap or renegotiate existing trade agreements, including the North American Free Trade Agreement (NAFTA). This paper examines county-level presidential voting in the four U.S. states bordering Mexico. Two hypotheses are tested. One, in general, that voters in Mexicoadjacent counties voted differently that did voters in non-border-adjacent counties. Two, that voters in border-adjacent counties voted differently based on the degree of interdependence between their county and residents on the Mexican side of the border. The evidence suggests that votes for candidate Trump were negatively related to the degree county of interdependence with Mexico. Word Count: 5870 words plus abstract, references, and tables Key Words voting for president, 2016 presidential election, U.S.-Mexico border, voting models Contact Information: Richard V. Adkisson, Ph.D. (corresponding author) Department of Economics MSC 3CQ, Box 30001 New Mexico State University Las Cruces, NM 88003-8001 E-mail: [email protected] Phone: 575-646-4988 Francisco J. Pallares, DED Economist City of Las Cruces Las Cruces, NM E-mail: [email protected]

Presidential Voting in the 2016 U.S. Presidential Election: Impacts of the U.S.-Mexico Border and Border Integration ABSTRACT The 2016 presidential election brought many proposals to the fore, several with potentially significant impacts in the U.S.-Mexico border region. Republican candidate, Donald Trump, promised to build a border wall, return manufacturing jobs to the U.S., impose import tariffs, and scrap or renegotiate existing trade agreements, including the North American Free Trade Agreement (NAFTA). This paper examines county-level presidential voting in the four U.S. states bordering Mexico. Two hypotheses are tested. One, in general, that voters in Mexicoadjacent counties voted differently that did voters in non-border-adjacent counties. Two, that voters in border-adjacent counties voted differently based on the degree of interdependence between their county and residents on the Mexican side of the border. The evidence suggests that votes for candidate Trump were negatively related to the degree of county interdependence with Mexico. Word Count: 5870 words plus abstract, references, and tables Key Words voting for president, 2016 presidential election, U.S.-Mexico border, voting models

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Presidential Voting in the 2016 U.S. Presidential Election: Impacts of the U.S.-Mexico Border and Border Integration

Introduction This paper has two main purposes. The first is to explore the extent to which residents in the U.S.-Mexico border region voted differently in the 2016 U.S. presidential election as compared to non-border residents in the Border States. The second is to ask whether variation in the degree of U.S.-Mexico interdependency along the border led to variation in the presidential vote. National borders exist in a variety of contexts and there can be substantial heterogeneity across a particular border region. Borders can be ‘natural’ in the sense that they separate unique geographic, environmental, or cultural areas. They can also be quite unnatural in that they are established by power and political struggles beyond other, for example cultural, geographic, or linguistic considerations. Consider the U.S.-Mexico border. [T]he lengthy US-Mexico border is a region of multiple contrasts. It can be characterized as an asymmetrical, interdependent border, with the United States as the more powerful and Mexico as the less powerful in relational perspective. […] Enormous demographic, socioeconomic, political, and cultural differences can be found across the nearly 2,000 mile border. (Correa-Cabrera and Staudt 2014, 385) It is common for policymakers and others to overlook this uniqueness and heterogeneity when public policies are made, especially since border controlling policies are typically made at the national, not local or regional, level (Correa-Cabrera and Staudt 2014). To the extent that borders are political, they are institutions tied to past actions, decisions, and agreements (Brunet-Jailly 2005). Historically political boundaries have been rather fluid but once defined they can take on great symbolic significance and be fiercely defended by claimants on either side. “Everywhere, the legislation generated by the state and its instruments of socialization aim at constructing the limits of nationality, citizenship and identity by defining the borders of inclusion and exclusion” (Paasi 2016, 14-15). Scholars across the globe have studied the unique characteristics of borderlands for years and interest in border studies has grown, especially with the end of the Cold War and the creation of the European Union (Paasi 2016). By its nature, border studies is an interdisciplinary project making it difficult to develop a single general theory of borders (Paasi 2016). In an effort to move toward a general theory, BrunetJailly (2005, 645) posits a theory of borderland studies that incorporates four complementary analytical lenses, local cross border culture, local cross border political clout, market forces and trade flows, and the policy activities of multiple levels of government, all topics shown to be important in previous work. Viewed through Brunet-Jailly’s analytical lenses one sees the following characteristics of the U.S. Mexico border. The entire region was once part of Mexico and that history continues to shape border culture. Local culture changes as one moves north or south from the border but 2

there is no obvious cultural break at the political boundary (Tam Cho & Nicely 2008). Adjacent local governments from both sides of the political border often cooperate to address regional problems. Local governance is complicated by the need to engage the interests of multiple levels of government -- counties, municipalities, and states -- on both sides of the border (Anderson and Gerber 2008). Important to this paper, in the more bi-nationally interdependent regions of the border, goods, services, and people flow relatively freely across the political boundary while in other regions, for example the more remote or agricultural portions of the border region, residents have a somewhat different border experience than do residents of the urbanized, border city-pair regions (Correa-Cabrera and Staudt 2014). Whether defending the geographic integrity of the border or trying to control flows of humans, goods, and services across the border, national policies play out in a local context (Herzog 1996; Lenderking 1996; Martinez 2006). The unique characteristics of the U.S.-Mexico border region led Lenderking to refer the region as a “’living laboratory’ to observe what many describe as the most complex bilateral relationship in the world” (1996, 194). This paper will tap into the “living laboratory” aspect of the U.S. Mexico border region to ask whether voters in the border region voted differently than voters further from the border during the 2016 American presidential election. This interest is inspired largely by the observation that candidate Donald Trump made several campaign proposals regarding the border, illegal immigrants and their character, and North American trade that, if implemented, would be likely to disproportionally impact the border region, for better or worse. Voting is observed at the county level for the four states that border Mexico: Arizona, California, New Mexico and Texas. Two hypotheses are posited and tested. Hypothesis One (H1) states that general county voting outcomes in border adjacent counties differed from voting outcomes in non-border-adjacent counties in the four Border States. Hypothesis Two (H2) states that border counties with greater degrees of interdependence with Mexico were less likely to vote for Mr. Trump than were border residents in less interdependent counties. Degrees of interdependence will be explained below. Background The 2016 U.S. presidential primary election was hard fought and brought many border-related issues to the fore. Republican candidate, Donald Trump, made many anti-Mexican, antiimmigrant, and anti-NAFTA statements. Mr. Trump’s battle for the nomination began with the accusation that illegal Mexican immigrants are “Bringing drugs. They are bringing crime. They’re rapists… but some, I assume, are good people” (Reilly 2016; Martin 2017). The attitudes implied by this statement were evident in Mr. Trump’s campaign rhetoric through the entire presidential campaign. Reflecting on Mr. Trump’s eventual victory, McGann (2017), credited a combination of nationalism, nativism, and protectionism for the Trump victory. Beyond candidate Trump’s overall campaign theme to “make American great again,” Qiu (2016) identified Trump’s top 10 campaign promises, four of which, if implemented, will have direct border-region impacts. 

Build a wall – and make Mexico pay for it.



Bring manufacturing jobs back.



Impose tariffs on goods made in China and Mexico. 3



Renegotiate or withdraw from the North American Free Trade Agreement and Trans-Pacific Partnership.

Because of their potential impacts on the border region, these promises could presumably affect border voting for Trump, positively or negatively, depending on the extent of agreement with Mr. Trump’s assessments. Anecdotally, the border election results were mixed. Mr. Trump won the popular vote in two of the four Border States (Arizona and Texas) and voting outcomes in border adjacent counties were split with Trump winning more votes in some border counties and Hillary Clinton winning more votes in other border counties. The first of the two hypotheses outlined above, that border voters tend to vote differently than non-border voters, is based on past empirical work reviewed in the literature review below. The second hypothesis, that variation in the degree of interdependence between border counties and Mexico also influenced the 2016 vote, has not been previously tested but seems appropriate given the uniqueness of the 2016 presidential election. Four things motivate the second hypothesis. First, the binational city pairs are where the economic benefits of binational manufacturing and the North American Free Trade Agreement have tended to accrue (Hanson 1997). Second, according to Martinez (2006), in the 1990s, U.S. efforts to crack down on illegal immigration tended to focus on the urban regions of the border. Fences were built, the Border Patrol presence strengthened, and overall, apprehensions declined into the early 2000s. However, while urban region apprehensions declined, rural region apprehensions increased. [A] by-product of the major shift in border-crossing patterns has been the drastic decline in apprehensions in the San Diego and El Paso Border Patrol sectors, made up mostly of urbanized spaces, and the dramatic increases in the Tucson and El Centro sectors, comprised largely of sparsely populated, remote, and dangerous desert areas. (Martinez 2006, 136) Third, beginning in 1969 and continuing through the present, the U.S. War on Drugs led to militarization of the U.S.-Mexico border and drug-related violence became a major focus along the border, in part because stronger enforcement at other (non-Mexico) U.S. entry points pushed more drug traffic toward the border region (Martinez 2006). “In many ways, border society has been destabilized because of rising lawlessness associated with the drug trade” (Martinez 2006, p. 141). Thus, at the same time illegal immigration flows were being pushed toward the more remote border regions, illegal immigration became to be associated, rightly or wrongly, with illegal drug trafficking. With illegal border crossing activity being pushed into the rural areas, rural/remote border residents are disproportionately exposed the darker aspects of border traffic while being less likely to enjoy the benefits coming from extensive border economic activities (as compared to urban border residents). In this circumstance it is possible that voters in the more remote/rural could be more sympathetic than their urban counterparts with Mr. Trump’s assessment of the border situation. Additionally, a nationwide survey conducted in 2004 indicated that, in general, rural residents tended to me more in favor of restrictive immigration policies than their urban counterparts (Fennely and Federico 2008). Fourth, a recent poll of 1,427 residents of the U.S.-Mexico border’s seven sister cities indicates that “the border is increasingly moving toward one giant economically integrated, bicultural society” and that respondents to the poll considered themselves to be dependent on their neighbors (both sides) for economic survival, that they were strongly opposed to the proposed 4

border wall, and that they were largely supportive of a road to U.S. citizenship for unauthorized Mexican immigrants (Corchado 2016). This poll result is in line with Popescu’s (2012) assessment. In numerous cases, the physical presence of a state linear border has not resulted in a thorough separation of the two sides. Rather, social relations at the local scale often have continued to stretch internationally recognized border lines, creating distinctive zonal patterns of interaction that are in many ways similar to earlier frontiers. It is more appropriate to think of borderlands rather than border lines when attempting to critically understand modern interstate borders. (Popescu 2012, 38) Together this evidence suggests that a resident’s daily experience with border life could vary between the more economically integrated and less economically integrated border regions. If this is true, support for the type of policies proposed espoused by Mr. Trump could vary along the border depending on one’s border experience, location, and the extent of local ties to Mexico. Review of Related Literature Social scientists have studied the impacts of both geographic adjacency and borders on political preferences and tendencies. As an example, Tam Cho and Nicley (2008) study U.S. county-level political preferences (normal vote) and find, as expected, that geographically adjacent counties tend to be politically similar. They also find evidence that this spatial impact decreases or disappears when the adjacent counties are in different states. Importantly, they find that county political preferences tend to diverge where a river or other separating feature defines the boundary, but that preferences tend to converge in areas with higher levels of local population (centers of influence). Social integration at borders adds a leveling dynamic to regional political preferences. In the end, they conclude that: Geographic location plays a vital role in the everyday formation of beliefs and identities – people are partly products of their spatial context. [… ] [I]ndividuals are influenced not only by their sociodemographic characteristics but also by the sociospatial setting in which they are located. (Tam Cho and Nicley 2008, 17) Tam Cho and Nicley’s (2008) observation is important in the U.S.-Mexico border context studied here. From a distance, one might think of the border as a homogeneous region, yet it is not. The border extends some 2,000 miles. The Rio Grande delineates the Texas portion of the border (about 1,200 miles). Then the border continues westward to the Pacific coast through the Chihuahua and Sonora deserts. Along the way are several city pairs with substantial, socially and economically interdependent populations on both sides of the border. In other areas there are ports of entry that invite interactions among cross-border neighbors. Elsewhere in the border region are vast, sparsely populated areas where one would likely be unable to identify the border’s location if not for the occasional boundary marker in the desert. In these remote counties, interactions between residents on the U.S. side and their Mexican neighbors may be limited to encounters with illegal immigrants, pushed into remote regions by aggressive border enforcement (Cornelius 2001; Gathmann 2008). Thus, as with U.S. state boundaries, the degree of social and economic integration across the U.S.-Mexico border varies with local conditions and, hypothetically, the degree of local interdependence colors local political perspectives and 5

attitudes toward particular candidates and/or policy proposals. “Integration is therefore seen as an inevitable consequence of the opening of state borders to flows of commodities, services, knowledge and people and has been conceptualized according to an evolutionary process based on increasing border region interactions and a progressive erosion of barrier effects” (Sohn 2014, 590). Holbrook (1991) also includes a border theme in his study of state-level presidential voting over the 1960-1984 period. Among other things he hypothesizes both a home-state and home-region advantage for presidential candidates. To test for regional advantage Holbrook includes a dummy variable for states bordering a candidate’s home state. He finds statistical evidence of a regional (border) effect although it is not as strong as the home-state effect. In a related study, using 1972-2000 data, Mixon, Jr. and Tyrone (2004) find some evidence that the home state impact extends to a second tier, to states that border the state bordering the candidate’s home state. Economists who study voting often begin by assuming voters to be economically rational (Downs 1957; Fair 1996); voters assess their self-interest and vote accordingly. However, rational choice models must be supplemented with other information to capture influences beyond pure economic rationality (Walker 2006). Two papers in this tradition specifically study the impact of border adjacency on presidential voting. Using 1992 and 1996 voting data, Adkisson and Peach (1999) find that border counties tend to vote more Democratic than their respective states but that preferences in border counties still reflect broader state preferences to some extent. For example, border-county voting might be more Democratic than in other regions of a state but still hand victories to Republican candidates. Based on a pool of five elections, 1992-2008, Adkisson and Saucedo (2011) show that these relationships hold up through time, across a variety of candidate combinations, and extend to congressional and senatorial elections as well. These findings invite the paper’s first hypothesis (H1), that general county voting outcomes in border adjacent counties differ from voting outcomes in non-border-adjacent counties in the four border states. The work of Tam Cho and Nicley (2008) and others invites the paper’s second hypothesis (H2), that varying degrees of local interdependence with Mexico will also impact the presidential vote. Related to hypothesis H2, Branton, et al (2007) study the impact of distance from the U.S.Mexico border on Anglo voting for nativist ballot propositions in California. They find that distance to the border has no impact on Republican Anglos but that support for the nativist propositions among Anglo Democrats grows as the distance to the border shrinks. This hints that ethnicity might be an important factor to consider in border voting models. They also conclude that “[a]lthough the nation as a whole is becoming increasingly more ethnically diverse, […] proximity to the border retains a unique political importance” (Branton, et al 2007, 894). Primary guidance for the model specification comes from the work reviewed above. Other voting models similarly specified are offered by Lewis-Beck and Nadeau (2011), Galbraith and Hale (2008), Kramer (1971), Hersh and Nall (2016), Collingwood, Barreto, and Garcia-Rios (2014), Manza and Brooks (1998). Data and Method

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The two hypotheses stated above are tested using linear regression analysis. The dependent variable is the number of votes cast for Donald Trump as a percentage of the total votes cast in the 2016 general presidential election. Data are observed for 360 counties in four Border States: Arizona (15 counties), California (58 counties), New Mexico (33 counties), and Texas (254 counties). Five categories of explanatory variables are included in the model. The first category includes variables to operationalize the border region (BORDER) and degree of border integration (NOPORT, SMALLPORT, and LARGEPORT). The model is estimated using two alternative specifications. The first includes only the BORDER variable. This model provides initial evidence for hypothesis H1 and allows the outcome of this model to be compared to previous voting studies where the border was treated as a homogenous region (Adkisson and Peach 1999; Adkisson and Saucedo 2011). The second model replaces the BORDER variable with three variables, NOPORT, SMALLPORT, and LARGEPORT to distinguish border counties by their degree of integration with Mexico. Estimates from this second model provide evidence on both hypotheses H1 and H2. Beyond the border variables, both models incorporate an identical set of independent variables to control for economic, demographic, social, and political influences. The variables used and their sources are summarized in Table 1. Table 2 provides descriptive statistics for the continuous variables. A broader discussion of each category of variables follows. Place Table 1 about here Place Table 2 about here Operationalizing the Border and Border Integration Adkisson and Peach (1999) and Adkisson and Saucedo (2011) operationalized the border in two tiers, border adjacent counties and counties adjacent to border counties. Otherwise the border was treated as a politically homogeneous region. This paper focuses only on the border adjacent counties (BORDER, see Table 3) but allows for heterogeneity across border counties. Given the findings of Tam Cho and Nicley’s (2008) and other work reviewed above, it seems expedient to allow for border region heterogeneity. If imposed, Mr. Trump’s proposals are likely to have differential impacts across the border counties. Adopting Tam Cho and Nicley’s (2008) finding that regional integration impacts political patterns, this paper treats border counties as heterogeneous in the extent to which they are interdependent with Mexico and Mexicans. The presence of land ports of entry (LPOE) and the relative volume of truck traffic are used to differentiate among border counties. The model identifies three types of border counties. The first are border adjacent counties with no (or little used) border crossings (NOPORT). These are presumed to be the counties least interdependent with Mexico and the counties with the least to lose (and perhaps most to gain) if cross-border trade is restricted or immigration controls are tightened. Second are counties with ports of entry that have significant levels of truck traffic but that are not among the busiest ports (SMALLPORT). These are presumed to be more interdependent with Mexico than are counties in the first group but less interdependent than counties in the third group, the counties with the highest volumes of truck traffic flowing through their ports of entry (LARGEPORT). Of 25 border counties, nine are in the NOPORT group, six are in the SMALLPORT, and 10 are in the LARGEPORT, most interdependent, group. Table 3 provides details. 7

The ten LPOEs in Table 3 marked with a double asterisk (**) are the 2016 top 10 ports in terms of truck entry volume. Together these ports processed nearly 96 percent of trucks entering the U.S. from Mexico in 2016. Nearly 82 percent of people entering the U.S. by bus, private vehicle, or on foot also entered through these ports in 2016. None of the LARGEPORT counties voted majority for Mr. Trump in the 2016 election. In the SMALLPORT group, Trump won three of six counties. Of the nine NOPORT border counties, six voted majority for Mr. Trump in 2016 versus three that did not. This cursory evidence suggests the pattern posited as hypothesis H2 but judgement must be withheld until other possible influences have been accounted for. Place Table 3 about here Operationalizing Economic Influences Because economic conditions are widely expected to impact voting decisions, the models estimated include two variables to control for economic variations across counties, per-capita personal income (INCOME) and unemployment (UNEMPLOYMENT). A Ramsey RESET test indicated non-linearity in the initial estimates and further investigation pointed to personal income (INCOME) as the culprit variable (Grennes, Guerron-Quintana, and Leblebicioglu 2011; Malizia & Ke 1993). To allow for the non-linearity, the square of per-capita income (INCOMESQR) is included in the model. Given that neither candidate in the race is an incumbent, economic expectations will depend on voter perceptions about which candidate will likely protect or increase job and income prospects. Given that Mr. Trump presents himself as an agent of change and has promised more American jobs, it seems likely that the UNEMPLOYMENT/TRUMP relationship will be positive (hope of better job prospects) and the INCOME/TRUMP relationship will be negative (we are doing OK now). Operationalizing Demographic Influences The models include two variables to account for demographic differences across counties, AGE65PLUS and POPULATION. Mr. Trump promised to make America great again. As different age groups will have different experiential bases, they might also vary in their perception of America’s past, current, and potential greatness. For example, older voters, perhaps comparing current America to the growth and prosperity associated with the post WWII era may tend to agree that America has lost some of its greatness and therefore support Mr. Trump’s promise of a return to greatness. If this is so, AGE65PLUS will be positively related to TRUMP. Similarly, candidate Trump promised to bring back jobs to declining regions, presumably regions with shrinking populations driven by economic decline (Roberts and Stoll 2016). If high population is indicative of economic success rather than economic decline it seems reasonable to expect POPULATION to be inversely related to TRUMP. Operationalizing Social Influences HISPANIC, BLACK, EVANGELICAL, and HIGH SCHOOL ONLY are included to control social differences across counties. HISPANIC and BLACK are included to control for ethic and racial variation across counties but predictions as to their impacts are difficult. Traditionally these two groups have tended to favor Democrats but candidate Trump also challenged voters in underrepresented groups by asking what they had to show for their loyalty to Democrats (Glanton 2016). In addition, the data being analyzed are aggregate data. Thus, the aggregate vote 8

combines votes cast by Hispanic and Black voters (who are unlikely to vote as a bloc anyway) with votes of their neighbors who may or may not enjoy racial and ethnic diversity. Thus, no predictions are made regarding relationships between HISPANIC or BLACK and TRUMP. Because conservative evangelicals (Worthen 2017) and the less educated (Thompson 2016) are well represented in Mr. Trump’s base of support, the EVANGELICAL and HIGH SCHOOL ONLY relationship with TRUMP are both expected to be positive. Operationalizing Political Influences Finally, although candidate Trump did make several statements of possible concern to border residents, border residents, like all voters, have political/ideological preferences and concerns beyond their immediate region. To control for non-border political influences the model includes REPUBVOTE, OTHERVOTE, and state fixed-effect variables ARIZONA, CALIFORNIA, and NEW MEXICO (Texas is the base state). Because Mr. Trump was the Republican candidate, party loyalty is expected to have influence. REPUBVOTE is expected to have a positive relationship with TRUMP. OTHERVOTE is included to control for third-party or “none of the above” votes. Historically, third-party candidates have provided a means of expressing displeasure with main-party candidates and can potentially swing an election by taking more votes away from one candidate than another (Adkisson and Peach 1999). Whether votes for other candidates helped, hurt, or had no impact on TRUMP is an empirical question so no prediction is provided. The state fixed-effects variables are included to allow for state-specific differences in the border vote that can be attributed to statewide concerns rather than border-specific concerns. The Models Two models are estimated. The first, the homogeneous border model, treats all border counties as single group (BORDER). The hypothesis tests on the estimated coefficient for BORDER will provide some evidence regarding hypothesis, H1, that general county voting outcomes in border adjacent counties differ from voting outcomes in non-border-adjacent counties in the four Border States. The homogeneous model also makes it possible to compare the current findings to previous voting research. The second model, the heterogeneous border model, breaks border counties into three groups according to their degree of integration with Mexico, as described above. Hypothesis tests on the estimated coefficients for the variables NOPORT, SMALLPORT, and LARGEPORT will provide additional evidence on hypothesis H1 and will provide a test of hypothesis H2, that border counties with greater degrees of integration with Mexico are less likely to vote for Mr. Trump and his proposed polices than are border residents in less interdependent counties. Beyond this, the models are identical, incorporating control variables to operationalize economic, demographic, social, and political influences as described above. The models were first estimated using ordinary least squares followed by standard regression diagnostics. As previously mentioned, a Ramsey RESET test indicated non-linearity in the model that was corrected by including the square of INCOME. A Breusch-Pagan test indicated heteroscedastic error terms so White-Huber robust standard errors were used for hypothesis testing. Variance inflation factors (VIFs) were used to check for multicollinearity. Most VIFs are well below the common rule of thumb value of five and all are below the more generous rule of thumb value of 10 with two exceptions. The VIFs on INCOME and its square, INCOMESQR are high as is expected when quadratic terms are included. Ultimately multicollinearity is deemed

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non-severe. The high values of adjusted R-squared suggest a strong model. Table 4 reports the estimated results. Results Place Table 4 about here In interpreting the results it is important to remember that the estimated results relate the aggregate characteristics and conditions in the county to the aggregate vote in the county. So, for example, if a variable X is shown to be positively related to TRUMP and a hypothesis test leads to rejection of the null hypothesis of no relationship, one can only say that voters in counties with higher X values tended to vote more strongly in favor of Mr. Trump. One cannot conclude that specific voters with characteristic X did or did not favor Mr. Trump. The relationships explored are between county-level variables and county-level votes for Mr. Trump. Results on the control variables will be discussed first. A discussion of the border region variables and their implications for hypotheses H1 and H2 will follow. The estimates are very similar between the two models. In the homogenous border model, the estimated coefficient on BORDER is negative but statistically insignificant. The negative sign agrees with past research although previous work found this variable to be statistically significant indicating that the border counties have, on average, tended to lean Democrat in the past (Adkisson and Peach 1999, Adkisson and Saucedo 2011). Beyond this finding, only the coefficients and hypothesis tests on control variables from the heterogeneous border model are interpreted here. Economic Influence Results Per-capita income (INCOME) and unemployment (UNEMPLOYMENT) account for variation in economic conditions across counties. The estimated model suggests a negative relationship between UNEMPLOYMENT and TRUMP but the evidence is weak. The estimate predicts a 0.123 percentage point decrease in the Trump vote for each one percentage point increase in the unemployment rate, however, the hypotheses that there is no relationship between the two variables cannot be rejected at the 90 percent or above confidence level. Alternatively, both INCOME and INCOMESQR show strong statistical relationships with TRUMP. The estimates indicate a parabolic relationship between personal income and the Trump vote with a minimum at $81,707. INCOME values range from $22,008 to $132,989. Thus, the relationship between INCOME and TRUMP is estimated to be negative and decreasing for incomes below $81,707 and positive and increasing for higher incomes. With weak evidence, the estimated result on UNEMPLOYMENT is counter to expectation; higher unemployment tends to reduce the vote for Trump. The results on INCOME both match and defy expectations. In line with expectations, voters in lower income (currently less prosperous) counties seem to give Mr. Trump more support than voters in higher income (currently more prosperous) counties, but only until INCOME reaches $81,707. Beyond this level the Trump vote tends to increase with the level of per-capita income. Voters in poorer but relatively prosperous counties seem to give credit for their relative prosperity to the previous Democratic administration (associated more with candidate Hillary Clinton) while voters in higher income counties seem to put their hope in Mr. Trump’s promises of even better economic conditions or perhaps tax changes which he also addressed in his campaign. Demographic Influence Results 10

The models include two demographic controls, AGE65PLUS and POPULATION. Both variables show statistically detectable relationships with the vote for Mr. Trump. The model estimates a 0.177 percentage point increase in the Trump vote for each one percentage point increase in a county’s population of age 65 or older. The relationship between population and the Trump vote is predicted to be negative. The very small coefficient indicates a small decrease in the Trump vote for every one person increase in population. For a 100,000 person increase in population the model implies a 0.837 percentage point decrease in the Trump vote. Given the range of POPULATION values, from 117 to over 10 million, population would seem to have been an important factor in the election. These estimates are in accord with prior expectations. Social Influence Results Four variables control for social variation across counties, HISPANIC, BLACK, EVANGELICAL, and HIGH SCHOOL ONLY. Neither HISPANIC nor BLACK show a statistically detectable relationship with TRUMP. It seems that variations in the relative size of the Hispanic or Black populations across counties did not influence the vote for Trump. Statistically detectable relationships are found for EVANGELICAL and HIGH SCHOOL ONLY. The model estimates that for every one extra evangelical protestant adherent per thousand of population in a county, TRUMP increased by 0.0033 percentage points. Over the range of EVANGELICAL values (281.11-898.6), this suggests a two percentage point difference in TRUMP based on adherence to evangelical Protestantism alone. The estimated coefficient on HIGH SCHOOL ONLY estimates that for every one percentage point increase in the share of the 25 years and older population in a county that is educated only to the high school level, the county’s vote for Mr. Trump increased by 0.239 percentage points. These findings match expectations. Political Influence Results Specific political variables included are REPUBVOTE to capture past Republican tendencies in each county and OTHERVOTE to capture the influence of voters who most likely didn’t care for either major party candidate. As expected, the stronger the county vote for the Republican candidate in 2012, the stronger the vote for Mr. Trump in 2016. The estimates indicate a 0.932 percentage point increase in TRUMP for every one percentage point increase in REPUBVOTE. Across the four Border States, county level votes for other candidates ranged from 0.60 percent of the vote total to 15.37 percent of the vote total. The question here is how this vote influenced the vote for major party candidates. The estimated coefficient on OTHERVOTE indicates that for each one percentage point increase in OTHERVOTE, TRUMP decreased by 0.459 percentage points. The OTHERVOTE coefficient for Trump being less than 0.50 suggests that the other vote cost candidate Clinton slightly more votes than it cost Mr. Trump. The three state dummy variables, ARIZONA, CALIFORNIA, and NEW MEXICO control for nonspecific statewide influences on county voters. The significant negative coefficient on ARIZONA suggests that, after controlling for all of the other influences accounted for in the model, Arizona voters were somewhat less supportive of Mr. Trump than were Texans (by about 1.5 percentage points). The positive coefficients on CALIFORNIA and NEW MEXICO suggest the opposite although neither estimate meets typical standards of statistical detectability. Border and Border Integration Results 11

Hypothesis H1 posits that general county voting outcomes in border adjacent counties differ from voting outcomes in non-border-adjacent counties in the four border states. If the border is treated as a politically homogeneous region as it is in the Homogeneous Border model, the evidence indicates that hypothesis H1 should be rejected. The estimated coefficient on BORDER is negative suggesting that, in general, border county voters are less supportive of Mr. Trump than are voters in non-border counties, but the relationship is not statistically significant. The story changes if the border is treated as a region that is heterogeneous in its degree of integration with Mexico and Mexicans. The estimated coefficient on NOPORT is 1.254 and the relationship between NOPORT and TRUMP is statistically detectable, at least at the 90 percent confidence level. Thus, the model suggests that, other things equal, voters in counties with no significant land port of entry with Mexico gave Mr. Trump a 1.254 percentage point higher share of the total vote than did voters in non-border counties. Counties with small ports of entry (SMALLPORT) voted (statistically) the same as voters in non-border counties. Alternatively, voters in the most interdependent counties were clearly more opposed to Mr. Trump as compared to voters in nonborder counties. The model suggests that, other things equal, voters in LARGEPORT counties voted less for Mr. Trump by 2.824 percentage points. Jointly, the results on NOPORT, SMALLPORT, and LARGEPORT match the expectations put forth as hypothesis H2, that border counties with greater degrees of integration with Mexico are less likely to vote for Mr. Trump and his proposed polices than are border residents in less interdependent counties. They also suggest that the rejection of hypothesis H1 was driven by the assumption that the border counties are homogeneous in their political interests and preferences. Voters in border counties do appear to vote differently than voters in non-border counties but the difference, at least for the 2016 election, changes with the degree of the county’s integration with Mexico.

Conclusions Previously published research (Adkisson and Peach 1999, Adkisson and Saucedo 2011) found evidence of a border effect on presidential voting. They found that, other things equal, border counties tended to vote more Democratic than did non-border voters in the same state. This conclusion was based on 1992-2008 presidential elections. In their research the border was operationalized as a politically homogeneous region. The homogeneous model estimates presented indicate a similar, Democratic leaning border, but the relationship is not statistically significant in the 2016 case. The 2016 presidential election was unique in many ways, to include an unprecedented focus on issues of import in the border region. Candidate Donald Trump proposed to build a border wall, return manufacturing jobs to the U.S., place tariffs on Mexican goods, and withdraw from or renegotiate existing trade agreements, including NAFTA. Given that pursuit of such policies would likely impact some border areas more than others this paper adopted a different approach by operationalizing the border in a way that accounts for varied levels of U.S.-Mexico integration along the border. Two hypotheses were posited. One, that there is a general border effect on presidential voting and another that the border effect would vary with the degree of a counties integration with Mexico. The results indicate that less interdependent counties tended to support candidate Trump more strongly than did more interdependent counties. 12

Given the uniqueness of the 2016 election it may be that this paper’s results capture an anomaly. However, the results do indicate that researchers should take care in how they operationalize border regions. An assumption of regional homogeneity may not be appropriate in every circumstance.

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References Adkisson, R. V. and Peach, J. 1999. Voting for president: Elections along the US‐Mexican border. Journal of Borderlands Studies 14, no.2: 67-79. Adkisson, R.V., and Saucedo, E. 2011. Voting for president in the US–Mexico border region. The Social Science Journal 48, no 2: 273-282. Anderson, J. B., and Gerber J. 2008. Fifty years of change on the US-Mexico border: Growth, development, and quality of life: University of Texas Press. Branton, Regina, Gavin Dillingham, Johanna Dunaway, and Beth Miller. 2007. Anglo voting on nativist ballot initiatives: The partisan impact of spatial proximity to the U.S.-Mexico border. Social Science Quarterly 88, no. 4: 882-897. Brunet-Jailly, E. 2005. Theorizing borders: An interdisciplinary perspective. Geopolitics 10, no. 4: 633-649. Collingwood, L, Barreto, M.A. & Garcia-Rios, S.I. (2014). Revisiting Latino voting: Cross-racial mobilization in the 2012 election. Political Research Quarterly 67, no. 3: 632-645. Corchado, Alfredo. 2016 Common ground: Pool finds U.S.-Mexico border residents overwhelmingly value mobility, oppose wall. Dallas: The Dallas Morning News http://interactives.dallasnews.com/2016/border-poll/. Accessed February 15, 2018. Corerea-Cabrera, Guadalupe, and Kathleen Staudt. 2014. An introduction to the multiple USMexico borders. Journal of Borderlands Studies 29, no. 4: 385-390. Cornelius, W. A. 2001. Death at the border: Efficacy and unintended consequences of US immigration control policy. Population and Development Review 27, no. 4: 661-685. Downs, Anthony. 1957. An economic theory of political action in a democracy. Journal of Political Economy 65, no. 2: 135-150. Fair, R. C. 1996. Econometrics and presidential elections. The Journal of Economic Perspectives 10, no. 3: 89-102. Fennelly, Katherine and Christopher Federico. 2008. Rural residence as a determinant of attitudes toward US immigration policy. International Migration 46, no. 1: 151-190. Galbraith, J. K., and Hale, J. T. 2008. State income inequality and presidential election turnout and outcomes. Social Science Quarterly 89, no. 4: 887-901. Gathmann, C. 2008. Effects of enforcement on illegal markets: evidence from migrant smuggling along the southwestern border. Journal of Public Economics 92, no. 10: 1926-1941. Glanton, D. (2016, August 22). Trump’s message to African-Americans – Just trust me.” Chicago Tribune. Retrieved from http://www.chicagotribune.com/news/columnists/ct-trumpblack-vote-glanton-20160822-column.html

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Grennes, T., Guerron-Quintana, P., and Leblebicioglu, A. 2011. Economic development and heterogeneity in the great moderation among the states. The B.E. Journal of Macroeconomics, 11, no. 1: 1-21. Hanson, Gordon H. 1997. The effects of offshore assembly on industry location: Evidence from U.S. Border Cities. Chapter 11 in The Effects of U.S. Trade Protection and Promotion Policies, Robert C. Feenstra, Ed. Hersh, E. D., and Nall, C. 2016. The primacy of race in the geography of income‐based voting: New evidence from public voting records. American Journal of Political Science 60, no. 2: 289303. Herzog, L. A. 1996. Border commuter workers and transfrontier metropolitan structure along the U.S.-Mexico border. In O. J. Martínez (Ed.) US-Mexico Borderlands: Historical and Contemporary Perspectives (176-189). Lanham, MD: Rowman & Littlefield Publishers. Holbrook, T. M. 1991. Presidential elections in space and time. American Journal of Political Science 5, no. 2: 91-109. Kramer, G. H. 1971. Short-term fluctuations in US voting behavior, 1896–1964. American Political Science Review 65, no. 1: 131-143. Lenderkin, B. 1996. The U.S.-Mexico border and NAFTA: Problem or paradigm? In U.S. Mexico Borderlands: Historical and Contemporary Perspectives, edited by Oscar J. Martinez, Oxford: Rowman & Littlefield. Lewis-Beck, M. S., and Nadeau R. 2011. Economic voting theory: Testing new dimensions. Electoral Studies 30, no. 2: 288-294. Manza, J., and Brooks, C. 1998. The gender gap in US presidential elections: When? Why? Implications? American Journal of Sociology 103, no. 5: 1235-1266. Malizia, E. E., and Ke, S. 1993. The influence of economic diversity on unemployment and stability. Journal of Regional Science 33, no. 2: 221-235. Martin, P. L. 2017. Election of Donald Trump and migration. Migration Letters 14, no. 1: 161171. Martínez, Oscar J. 2006. Troublesome Border, Tucson: University of Arizona Press. McGann, J. G. 2016. Why Donald Trump won the election and does it mean the end to think tanks and policy advice as we know it? Retrieved from http://repository.upenn.edu/ttcsp_papers/1. Mixon, F. G., and Tyrone, J. M. 2004. The ‘home grown’ presidency: Empirical evidence on localism in presidential voting, 1972–2000. Applied Economics 36, no. 16: 1745-1749. Paasi, Anssi. 2016, A Border theory: An unattainable dream or a realistic aim for border scholars? In The Ashgate Research Companion to Border Studies, edited by Doris Wastl-Walter. New York: Routledge11-31. 15

Qiu, L. 2016, July 15. Donald Trump’s top 10 campaign promises. Politifact. Retrieved from http://www.politifact.com/truth-o-meter/article/2016/jul/15/donald-trumps-top-10-campaignpromises/. Popescu, Gabriel. 2012. Bordering and Ordering in the Twenty-first Century: Understanding Borders. Lanham: Rowan and Littlefield Publishers. Reilly, C. 2017, February 27. Here are all the times Donald Trump insulted Mexico. Time. Retrieved from http://time.com/4473972/donald-trump-mexico-meeting-insult/. Roberts, A. and Stoll, J. D. 2016, November 9. Donald Trump’s promise of bringing back jobs worked with many Michigan voters. The Wall Street Journal. Retrieved from https://www.wsj.com/articles/donald-trumps-promise-of-bringing-back-jobs-worked-with-manymichigan-voters-1478728229. Sohn, C. 2014. Modelling cross-border integration: The role of borders as a resource. Geopolitics 19, no. 3: 587-608. Tam Cho, W. K., and Nicley, E. P. 2008. Geographic proximity versus institutions: Evaluating borders as real political boundaries. American Politics Research 36, no. 6: 803-823. Thompson, D. 2016, March 1. Who are Donald Trump’s supporters, really? Four theories to explain the front-runner’s rise to the top of the polls. The Atlantic. Retrieved from https://www.theatlantic.com/politics/archive/2016/03/who-are-donald-trumps-supportersreally/471714/. Walker, D. A. 2006. Predicting presidential election results. Applied Economics 38, no. 5: 483490. Worthen, M. 2017, May. A match made in heaven: Why conservative evangelicals have lined up behind Trump. The Atlantic. Retrieved from https://www.theatlantic.com/magazine/archive/2017/05/a-match-made-in-heaven/521409/.

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Variable TRUMP

BORDER NOPORT SMALLPORT LARGEPORT INCOME INCOMESQR

Table 1 Descriptions and Sources of Variables Description

Source

Dependent Variable Percentage of the voters in the county that voted for Trump in the presidential election of 2016. Independent Variables Border County in AZ, CA, NM, and TX. (1 if border county, 0 if otherwise. Border county in AZ, CA, NM, and TX without a port of entry in 2016. (1 if border county with NOPORT, 0 if otherwise) Border county in AZ, CA, NM, and TX with a small land port of entry in 2016. (1 if border county with SMALLPORT, 0 if otherwise) Border county in AZ, CA, NM, and TX with a large land port of entry in 2016. (1 if border county with LARGEPORT, 0 if otherwise) Per capita personal income in the county in 2015 (Dollars).

1

2 2 2 2 3

Per capita personal income in the county in 2015 (Dollars), squared.

3

Average unemployment rate per county in 2015.

4

Percentage of the county population age 65 and above in 2015.

5

County population in 2015 (persons).

5

HISPANIIC

Percentage of the county population reported a Hispanic, any race, in 2015.

5

BLACK

Percentage of the county population reported as Black, one race, in 2015.

5

UNEMPLOYMENT AGE65PLUS POPULATION

EVANGELICAL HIGH SCHOOL ONLY REPUBVOTE OTHERVOTE ARIZONA

Rate of adherence per 1000 population considered evangelical protestant in 2010. Percentage of the population with only High School degree (includes equivalency), 2015. Percentage voters in the county that voted for the Republican candidate in the presidential election of 2012. Percentage of the voters in the county that voted for a third-party candidate (neither Clinton nor Trump) in the 2016 presidential election Dummy variable = 1 if state is Arizona, otherwise=0, Texas omitted

CALIFORNIA

Dummy variable = 1 if state is California, otherwise=0, Texas omitted

NEW MEXICO

Dummy variable = 1 if state is New Mexico, otherwise=0, Texas omitted Sources

1. 2. 3. 4. 5. 6.

Dave Leip’s Atlas of U.S. Presidential Elections (http://www.uselectionatlas.org/) Bureau of Transportation Statistics (2017), and author calculations. Bureau of Economic Analysis. (2015). Local Area Personal Income 2015. www.bea.gov Bureau of Labor Statistics, Local Area Unemployment Statistics, States and Selected Areas: Employment Status of the Civilian Noninstitutional Population, 1976-2015. U.S. Census Bureau, 2015-2015 American Community Survey 5-Year Estimates Association of Religion Data Archives, U.S. Religion Census: Religious Congregations and Membership Study (2010), http://www.thearda.com/Archive/Files/Descriptions/RCMSCY10.asp

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6 5 1 1

Table 2 Descriptive Statistics - Continuous Variables N=360 Mean Maximum Minimum TRUMP 64.00 94.58 9.23 INCOME 43351 132989 22008 UNEMPLOYMENT 5.6 24.0 2.1 AGE65PLUS 16.7 35.2 8.0 POPULATION 204,684 10,038,388 117 HISPANIC 34.12 98.70 2.80 BLACK 5.26 33.60 0.00 EVANGELICAL 281.11 898.60 0.00 HIGH SCHOOL ONLY 30.21 51.90 10.80 REPUBVOTE 64.19 95.90 13.00 OTHER 4.51 15.37 0.60

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Table 3 Border Counties, Ports of Entry, and Popular Vote Outcome in the County State

Border County Cochise

Arizona

Pima Santa Cruz Yuma Imperial

California *San Diego

New Mexico

Dona Ana Hidalgo Luna Brewster Cameron Culberson Dimmit El Paso Hidalgo

Texas

Hudspeth Jeff Davis Kinney Maverick Presidio Starr Terrell Val Verde Webb Zapata

LPOE Douglas, AZ Naco, AZ Lukeville, AZ Sasabe, AZ **Nogales, AZ San Luis, AZ Andrade, CA Calexico, CA **Calexico East, CA **Otay Mesa, CA San Ysidro, CA Tecate, CA **Santa Teresa, NM None* Columbus, NM None **Brownsville, TX None None **El Paso, TX Fabens, TX **Hidalgo, TX Progreso, TX None* None None **Eagle Pass, TX Presidio, TX Rio Grande City, TX Roma, TX None **Del Rio, TX **Laredo, TX None

Voted Majority Trump? Yes No No Yes No

No No Yes Yes Yes No No No No No Yes Yes Yes No No No Yes No No No

Sources: Dave Leip’s Atlas of U.S. Presidential Elections (http://www.uselectionatlas.org/), Bureau of Transportation Statistics (2017), and author calculations. *Notes: County does have a LPOE, however BTS does not report it due to the small number of crossings. These two counties are categorized as having no port of entry. ** Indicates that LPOE is one of the top 10 land ports of entry in terms of truck crossings in 2016 according to the Bureau of Transportation Statistics, https://transborder.bts.gov/programs/international/transborder/TBDR_BC/TBDR_BC_Index.html

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Table 4 Results, Dependent Variable= TRUMP, n=360 Ordinary Least Squares Regression with Heteroscedastic Corrected Standard Errors Independent Homogeneous Border Heterogeneous Border Homogeneous Heterogeneous Variables Coefficient Coefficient Variance Variance (t-ratio) (t-ratio) Inflation Factors Inflation Factors CONSTANT -0.033 0.379 NA NA (-0.151) (0.166) BORDER -0.818 1.395 (-1.274) NOPORT 1.254 1.503 (1.946)*** SMALLPORT -0.375 1.184 (-0.282) LARGEPORT -2.824 1.623 (-3.735)* INCOME -0.000192 -0.000201 17.714 17.746 (-5.102)* (-5.271)* INCOMESQR 1.17E-09 1.23E-09 15.257 14.934 (4.381)* (4.531)* UNEMPLOYMENT -0.146 -0.123 2.318 2.396 (-1.757)*** (-1.592) AGE65PLUS 0.188 0.177 2.207 2.191 (5.710)* (5.436)* POPULATION -8.87E-07 -8.37E-07 1.895 1.859 (-2.289)** (-2.358)** HISPANIC 0.005 0.004 3.112 3.675 (0.507) (0.435) BLACK 0.005 -0.006 2.460 2.583 (-0.172) (-0.246) EVANGELICAL 0.003 0.0032 2.618 2.795 (2.814)* (3.505)* HIGH SCHOOL 0.295 0.293 2.727 2.752 ONLY (10.373)* (10.466)* REPUBVOTE 0.933 0.932 4.786 5.418 (65.866)* (66.037)* OTHER -0.435 -0.459 9.570 9.319 (-3.3779)* (-4.085)* ARIZONA -1.598 -1.504 2.204 2.214 (-1.895)*** (-1.883)** CALIFORNIA 0.203 0.359 5.101 4.908 (0.279) (0.505) NEW MEXICO 1.161 1.338 6.164 5.970 (1.366) (1.631) Adjusted R-Squared 0.9883 0.9887 Notes; t-statistics reported in parentheses and Variance inflation factors reported in italicized * **indicates statistical significance with at least 90% confidence **indicates statistical significance with at least 95% confidence *indicates statistical significance with at least 99% confidence

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