View of The Whos and the Whys Behind Donald Trump’s Victory in the 2016 U.S. Presidential Election

DOI: https://doi.org/10.47405/mjssh.v7i6.1532

The Whos and the Whys Behind Donald Trump’s Victory in the 2016 U.S. Presidential Election

Yap Yi Sheng^1*

1School of Economics, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor Darul Ehsan, Malaysia.

Email: scyyy3@nottingham.edu.my

CORRESPONDING AUTHOR (*):

Yap Yi Sheng

(scyyy3@nottingham.edu.my) KEYWORDS:

Donald Trump Hilary Clinton

2016 United States Presidential Election

CITATION:

Yap, Y. S. (2022). The Whos and the Whys Behind Donald Trump’s Victory in the 2016 U.S. Presidential Election. Malaysian Journal of Social Sciences and Humanities (MJSSH), 7(6), e001532.

https://doi.org/10.47405/mjssh.v7i6.1532

ABSTRACT

Given the unexpected victory of Donald Trump in the 2016 United States (U.S.) Presidential Election, this study aims to examine the 2016 electorate, unfolding the factors behind Trump’s victory, especially how different groups of people voted in the election. Hence, this paper addresses two questions, the “who” and “why” in the 2016 U.S.

Presidential Election - who voted for The Apprentice’s talk show host, Donald Trump, to be the most influential individual globally, and why did they do so? A logistic regression model is deployed to model survey data from the 2016 American National Election Studies, a series of election studies conducted since 1948 on public opinion in the U.S. presidential elections. This empirical methodology determined the socio-economic and political factors underlying these voters’ preferences. The findings showed that racial identity, education level, and income level on a demographic basis were crucial in determining voting choice while controlling for the respondents’ party affiliation. On an issue basis, voters were primarily dissatisfied with Obama’s performance and were attracted to Trump’s conservative tones and his exuberant personality, which resembles leadership qualities to his supporters.

Contribution/Originality: This study examines how different groups of people voted in the 2016 United States Presidential Election and the reason behind their voting intention. It provides a clear understanding of the reasons leading up to Trump’s victory and how different socio-economic and political factors could sway voters’

preferences.

1. Introduction

Imagine today was the 2016 United States (U.S.) presidential election, and there were two political rallies in Washington with banners flying and placards held high. One side of the crowd was shouting, "Vote for Donald Trump because he will Make America Great Again!" and another side of the crowd was chanting, "Stick it to the man by voting for Hilary Clinton!". Which side would you choose, and why? The answer was simple for the

Americans. They voted in Donald Trump to be the 45th President of the U.S. This unexpected verdict shocked the world as no one expected The Apprentice's talk show host to be elected as the most influential individual.

Many political commentators were quick to credit this victory to white "working-class"

voters who believed in Trump's campaign slogan - "Make America Great Again" (Nate, 2016; Vance, 2016). Given the massive turnout of white people during Trump's presidential campaign in 2016, these claims were no surprise. However, empirically, it is difficult to conclude that because both the terms "working-class" and "Make America Great Again" was not clearly defined.

Firstly, political journalists and media overly used the term "working-class", resulting in a broad interpretation that conflated the term's meaning (Walley, 2017). Some people labelled the working-class as the bottom 30% of income earners, and some labelled the working-class as non-college graduates. Thus, it is difficult to gauge the identity of Trump's voters based on just the term. Secondly, the slogan "Make America Great Again"

itself is not sufficient to explain Trump's voters' motivation. What precisely that Trump's voters wish Trump to do so to make America great again?

I address two questions in this paper: 1) Who exactly voted for Trump in the 2016 U.S presidential election? 2) Why did they do so? Using available survey data from The American National Election Studies (ANES) (2016), I develop two logistic regression models - Model (1) and Model (2) to answer the questions above.

Model (1) identifies Trump's supporters and Model (2) identifies the reasons behind their support for Trump. Model (1) findings suggest that white, non-college-educated, below-median income earners are more likely to vote for Trump while holding other factors constant. White respondents are 78% more likely to vote for Trump than non- white respondents. Non-college-educated respondents are 32% more likely to vote for Trump than college-educated respondents. Respondents earning below-median income are 33% more likely to vote for Trump than those earning above-median income. A closer look also suggests a polarisation trend among white respondents, where non- college-educated white respondents are 44% more likely to vote for Trump than their counterparts. Also, among college-educated respondents, college-educated respondents earning above-median income are 52% less likely to vote for Trump than their counterparts.

Model (2) findings suggest several motivations behind their support for Trump. Firstly, respondents find Trump is a competent, empathetic, and trustworthy leader compared to Clinton. This perception increases the likelihood of them voting for Trump by 42%.

Secondly, respondents are dissatisfied with President Barack Obama administration's handling of social policy and foreign affairs. This dissatisfaction translates to a 210% and 104% increase in the likelihood of voting for Trump. Thirdly, respondents find building a Mexico-United States wall necessary, and they are 23% more likely to vote for Trump.

Lastly, respondents who want the black assistance programs removed are 20% more likely to vote for Trump.

The remaining paper is structured as follows: The following chapter reviews relevant literature on theories related to voting behaviour and discusses these theories. Chapter 3 shows the methodology and model development to analyse the election data. Chapter 4

shows the overview of the Model's variables. Chapter 5 explains the result of the analysis. Chapter 6 concludes the study with a future research avenue.

2. Literature Review 2.1. Economic Voting

Much of the variability in voting choice can be explained by different theories discussed in the earlier literature by various scholars. Perhaps the most convenient way of explaining voting behaviour is economic voting. An extensive literature has contributed to studying the effects of national economic performance on changes in the incumbent’s popularity within many democratic countries.

Kramer (1971) believed that short-term economic fluctuations significantly affect elections, where an economic boom benefits the incumbent, and an economic recession helps the opposition. Besides, real income seemed to be an essential indicator. For example, a 20% decrease in real income would reduce the incumbent party’s vote share by 9% to 10% when all else is equal. Holding real income constant, a change in inflation or unemployment rate will not significantly affect vote choice. Kramer (1971) then concluded that short term economic fluctuations, especially per capita real income have an important influence on vote choice.

Contrary to Kramer, Stigler (1973), while partially agreeing that economic fluctuations were important overall to the elections, he believed that voters are rational enough to understand that economic recession may be due to external occurrences beyond the control of the incumbent. Hence, it is premature to think that voters would abandon the incumbent party just because per capita income falls in the short term. He emphasised that the incumbent party’s policies’ effectiveness amid economic fluctuations changes the voter’s perspective on the incumbent party, not the economic fluctuation.

Incorporating theories from both the scholars above, Fair (1978) presented a model to explain voting behaviour based on voters’ expected utility derived from the national economic performance under different parties. Assuming voters are self-interested, well informed and that they use the same performance measurement, Fair’s model can be spelt out by equations (2.1), (2.2) and (2.3) (Fair, 1978).

𝑈_𝑖𝑡^𝐷 = 𝜉_𝑖^𝐷 + 𝛽₁ ^{𝑀𝑡𝐷1}

(1+𝑝)^{𝑡−𝑡𝐷1}+ 𝛽₂ ^{𝑀𝑡𝐷2}

(1+𝑝)^{𝑡−𝑡𝐷2} (2.1) 𝑈_𝑖𝑡^𝑅 = 𝜉_𝑖^𝑅 + 𝛽₃ ^{𝑀𝑡𝑅1}

(1+𝑝)^{𝑡−𝑡𝑅1}+ 𝛽₄ ^{𝑀𝑡𝑅2}

(1+𝑝)^{𝑡−𝑡𝑅2} (2.2)

where 𝑈_𝑖𝑡^𝐷 and 𝑈_𝑖𝑡^𝑅 denote the voter i’s expected future utility if the Democratic(Republican) party is elected in election year t; 𝜉_𝑖^𝐷 and 𝜉_𝑖^𝑅denote voter i’s fixed expected utility for Democratic(Republican) party; 𝑀_𝑡𝐷1, 𝑀_𝑡𝐷2, 𝑀_𝑡𝑅1, 𝑀_𝑡𝑅2 denote measure of Democratic(Republican) party last(second-to-last) election’s performance from election year t; 𝛽₁ to 𝛽₄ are unknown parameters. p denotes discount rate;

𝑉_𝑖𝑡 = {1 𝑖𝑓 𝑈_𝑖𝑡^𝐷 > 𝑈_𝑖𝑡^𝑅 0 𝑖𝑓 𝑈_𝑖𝑡^𝐷 < 𝑈_𝑖𝑡^𝑅 (2.3)

where 𝑉_𝑖𝑡 denotes a dummy variable which takes value 1, indicating voter i prefers to vote for the Democrats party in election year t, and 0 otherwise.

This model shows that given fixed party affiliation 𝜉_𝑖^𝐷 and 𝜉_𝑖^𝑅, voters tend to evaluate the past and current economic performances of the competing parties using the measure of 𝑀_𝑡𝐷1, 𝑀_𝑡𝐷2, 𝑀_𝑡𝑅1, 𝑀_𝑡𝑅2. These measurements then form the voters’ expected future utilities under each party, 𝑈_𝑖𝑡^𝐷 and 𝑈_𝑖𝑡^𝑅. Voters then choose the party that maximise their expected future utility. For example, if voters perceive that the expected future utility is greater under the Democratic party, where 𝑈_𝑖𝑡^𝐷 > 𝑈_𝑖𝑡^𝑅 , voters will vote for Democratic party, where 𝑉_𝑖𝑡 = 1.

2.2. Candidate Traits Voting

Besides, Rapoport, Metcalf and Hartman (1989) believed that the political candidate traits, independent of their parties, could be a critical addition to evaluating voting choice. Using experimental and survey data from the 1984 National Election Study, they found clear support for the hypothesis that individuals can make inferences about candidate traits. Some of these traits can increase a candidate’s vote share. However, it is unclear whether there is a need to differentiate traits substantively and whether the different traits impact candidate evaluations.

Understanding this, Pierce (1993) centered the discussion of candidate traits on four critical dimensions: leadership, integrity, empathy, and competence. He believed that these four candidate characteristics significantly impact voting choice and are important in evaluating political candidates. He further examined the differences in candidate evaluation between individuals, focusing on candidates’ characteristics in determining their candidate preference.

Pierce (1993) included candidate traits, especially the four critical traits alongside partisanship, ideology, and issue salience. This allowed one to evaluate the impact of candidate traits on candidate preference while controlling other factors. As candidate traits are included in this model, the results showed that candidate traits make significant independent contributions to candidate preference among all voters. Pierce (1993) also acknowledged that one should consider the context more fully when evaluating whom to vote for, such as issue salience.

2.3. Issue Voting

Voters tend to cast their ballots for a series of issues that are deemed important to them.

Kernell (1978) showed that issue salience contributes to the candidate's vote share fluctuations. That is, voters will evaluate information about the candidate's policy stance on an issue and use the issue as a basis for making voting decisions. For example, President Harry S. Truman's public support decreased by about 15 points with every tenfold increase in American casualties incurred in the Korean War, as he was the one who ordered the U.S. military intervention (Warren, 1977). However, not all issues have the same degree of salience, and because of the variability of the salience, The Bush Paradox occurs.

The Bush Paradox says that despite President George W. Bush's high overall presidential approval rating of 70% in 1989, his performance was not rated highly on some policies.

Hence, how could President Bush have a high approval rating if the public equally

accorded the same weight to all policies? This shows that the "most important problem"

is not equivalent to salience. Understanding this, Brody (1991) showed that issues vary over time by measuring the press coverage of these issues and then concluded that the presidents' evaluations change over time.

Brody (1991) also confirmed a direct relationship between media coverage and the importance of issues. As press coverage on specific issues increases, the importance of these issues increases as well. Using Brody (1991) findings, III, Mitchell and Welch (1995) further tested this hypothesis empirically by first identifying the important issues based on the amount of media coverage, then analysing the individual data to conclude individuals' opinions. They also examined time-series data to evaluate the changes in these opinions over time. They showed that salience issues vary over time in their impact on vote choice.

Incorporating the theories above, Ansolabehere and Puy (2018) provided a new approach to measuring political issues' effect on voting choice using utility maximisation.

They analysed how voters' and political parties' issue-position-divisiveness translates to the utility differential in equation (2.4). They then showed that voters form their voting choice based on these utility differences in equation (2.5).

∆(𝑈_𝑖) = −𝛼(𝑋_𝐷− 𝑋_𝑖)²+ 𝛼(𝑋_𝑅− 𝑋_𝑖)²− 𝛽(𝑌_𝐷− 𝑌_𝑖)²+ 𝛽(𝑌_𝑅 − 𝑌_𝑖)² (2.4)

where ∆(𝑈_𝑖) denotes the voter i’s differential utility between Democrats and Republican party; 𝑋_𝐷, 𝑋_𝑅, 𝑌_𝐷, 𝑌_𝑅 denotes the Democrats (Republican) party stance on issue X and Y;

𝑋_𝑖 , 𝑌_𝑖denotes the voter i’s stance on issue X and Y; 𝛼, 𝛽 denotes the salience parameter.

𝑉_𝑖 = {

2 𝑖𝑓 𝑈_𝑖 > 0 1 𝑖𝑓 𝑈_𝑖 = 0 0 𝑖𝑓 𝑈_𝑖 < 0

(2.5)

where 𝑉_𝑖 denotes the ordered categorical variable which takes value 2, indicating voter i prefers to vote for the Democratic party, 1 indicating indifferent and 0 for Republican party;

In this model, political parties first convey their policies stance to voters, and the voters evaluate whether those policies are aligned with their beliefs to decide their vote choice.

For example, if (𝑋_𝐷− 𝑋_𝑖) is small, this means that the Democrats party and the voters share similar stance on issue X. Hence, they are more likely to support the Democrats party because the future utilities derived from sharing the same policy view is maximized. 𝛼, 𝛽 are salience parameters that account for salience variability.

2.4. Racial Voting

Besides that, racial identity has always been the central feature of American politics, given Americans' diverse backgrounds, and its importance in determining election outcomes should not be overlooked. Washington (2006) believed that the running candidate's racial identity increases voter turnout, translating to a higher chance of winning the election because it motivates certain distinct groups of voters to vote. This motivation is called racial attitude. For example, when black candidates competed against non-black candidates for election, black and white voters' turnout increased by 2% to 3% from 1988 to 2000. Washington (2006) also pointed out that regardless of

voters' belief on the impact of elected candidates on resource allocation, a Black candidate may increase or decrease turnout simply because the voters prefer a black or white candidate. Also, black candidates are sometimes perceived as more liberal than white candidates, thus attracting voters with the same ideology to vote.

Using the 2008 U.S. presidential election as a standard, racial prejudice lowered the vote share for President Barack Obama. As shown in Figure 1, the increase in the Democratic vote share between the 2004 and 2008 presidential elections was smaller in some southern states as the south is the part of the country that would historically be associated with racial dissonance. However, Mas and Moretti (2009) denied Washington (2006) claim. They believed that it is the role of maturity that affects voting choice.

Although racial prejudice might lower the vote share for President Barack Obama, a complete examination of voter demographic characteristics by Mas and Moretti (2009) cast doubt on this result.

Figure 1: 2004-2008 Change in Democratic Vote Share in U.S Presidential Elections

Source: Mas and Moretti (2009)

To prove this, a racial bias index is built to capture the geographic variation of race attitudes towards non-white people. States with less racial tolerance will have a high and low index if otherwise. Figure 2 shows the relationship between the shift in vote share in the 2004-2008 presidential elections relative to race attitudes index by state. If there is racial prejudice, then Obama should underperform in relatively more racially biased states according to the racial attitudes index. However, Figure 2 proves that Obama, on average, did not perform worse in less tolerant areas.

Then, breaking down the 2008 U.S. election state data by age and education, Mas and Moretti (2009) found that the racial tolerance index and white turnout rate are negatively significant. This is because older people tend to be less tolerant of minorities.

Likewise, people with lower education levels feel the same way too. Due to Obama's factor, this section of the population was not likely to vote for Obama in the 2008 U.S.

presidential election. Hence, Obama's vote share was lower in 2008 than in 2004.

Figure 2: 2004-2008 Change in Democratic vote share in U.S. Presidential Elections relative to race attitude index

Source: Mas and Moretti (2009)

3. Methodology

3.1. Theoretical Rationale

To empirically identify the whos - who voted for Trump and the whys - why they voted for Trump in the 2016 U.S. presidential election, I construct two logistic regression models to model these relationships. Model (1) develops a demographic analysis on voters’ backgrounds to differentiate Trump’s voters from Clinton’s. Model (2) determines the socioeconomic and political factors underlying these voters’ preferences. These variables in these models are derived from several scholars’ theoretical frameworks mentioned in the literature review.

Given the dependent variable’s binary nature: 0 = Clinton and 1 = Trump, using a logistic regression model is a suitable approach for modelling the association of multiple independent variables with a discrete dependent variable with only two outputs. This dependent variable would then be a calculated odd ratio to compare the two outputs’

occurrence relative odds. This odd ratio is defined as the likelihood that output will occur as a proportion of the likelihood that the other output will not occur, which in our case, is the possibility of voting for Clinton against the possibility of voting for Trump. For example, if there is a 60% possibility that Clinton will win the presidential race, the odd ratio is 0.60 / (1 - 0.60) = 1.5, or 1.5:1. The odd ratio is defined in equation (3.1).

𝑝

1−𝑝= exp (𝛽₀+ 𝛽₁𝑋₁+ ⋯ + 𝛽_𝑛𝑋_𝑛) (3.1)

Given the non-linear nature of the dependent variable, a logistic regression model uses Maximum Likelihood Estimation (MLE) instead of Ordinary Least Square 11 (OLS) for parameter estimation, which allows us to account for the non-linearity in the dependent variable. Using logistic regression, I could apply a logarithmic transformation on the odd

ratio to model the non-linear association linearly by mapping the probability ranging between two outputs on a linear scale. The log-odd ratio is defined in equation (3.2)

𝑙𝑜𝑔 ^𝑝

1−𝑝= 𝛽₀+ 𝛽₁𝑋₁+ ⋯ + 𝛽_𝑛𝑋_𝑛 (3.2)

The coefficients of interest in this regression are 𝛽₁ to 𝛽_𝑛. These coefficients mean the change in the log-odd ratio of the dependent variable due to a 1 unit change in the independent variable. For example, a 1 unit increase in the dummy variable 𝑋₁ (from 0 to 1) increases the dependent variable’s log odds by a certain ratio holding other independent variables constant. 𝛽₀ is a constant which represents the intercept point when all the independent variables are 0.

𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 = (𝑒𝑥𝑝^𝛽𝑛− 1) × 100% (3.3)

To properly address the variables’ impact, the coefficients of interest, 𝛽_𝑛 is converted into percentage using equation (3.3). Firstly, the coefficient 𝛽_𝑛 is transformed back to odd-ratio using 𝑒𝑥𝑝^𝛽𝑛. Secondly, the odd-ratio is subtract by 1 to obtain the percentage increase. If the odd-ratio is less than 1, it would be a percentage decrease.

Besides, logistic regressions tend to have "small sample bias", which means that the odds ratios produced are sometimes too large for small samples (Nemes et al., 2009). Thus, logistic regression typically requires a larger sample size because a smaller sample size can be expected to have a more considerable bias. Long suggests that logistic regression with a sample size of fewer than 100 cases is considered risky, 500 cases are generally sufficient, and there should be at least 10 cases per independent variable (Long, 1997).

This is not an issue as the model's sample size for this paper is close to three thousand.

Logistic regression also requires minor or negligible multicollinearity among the independent variables. This means that there should not be correlations among the 12 independent variables. Hence, the logistic regression model in this paper is tested for multicollinearity. The logistic regression model's specificity and sensitivity are also tested to ensure no specification problem in the model. The results of the two tests are reported in the following section. Moreover, the robust standard error is used to correct for heteroskedasticity.

3.2. Empirical Strategy 3.2.1. Model (1)

The Model (1)’s logistic equation is specified as:

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝜀_𝑖𝑠 (3.4)

where i is the respondent’s id, s is the respondent’s state of registration; 𝜀 is an error term; 𝛽₀ is the constant; 𝛽₁... 𝛽₆ are parameters to be estimated. Detailed description of the variables used in the two models are given in the following section.

Understanding that there might be potential interaction effects between the three variables of interest: race, education, income and gender, interaction terms are added

separately into the baseline model to check for the specific changes among the variables, as shown in equation (3.5), (3.6), (3.7), (3.8), (3.9), (3.10).

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠) + 𝜀_𝑖𝑠 (3.5)

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝑅𝑎𝑐𝑒_𝑖𝑠× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠) + 𝜀_𝑖𝑠 (3.6)

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝑅𝑎𝑐𝑒_𝑖𝑠× 𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠) + 𝜀_𝑖𝑠 (3.7)

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠× 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠) + 𝜀_𝑖𝑠 (3.8)

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠× 𝑅𝑎𝑐𝑒_𝑖𝑠) + 𝜀_𝑖𝑠 (3.9)

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐴𝑔𝑒_𝑖𝑠+ 𝛽₂𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠+ 𝛽₃𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠+ 𝛽₄𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠+ 𝛽₅𝑅𝑎𝑐𝑒_𝑖𝑠+ 𝛽₆𝑃𝑎𝑟𝑡𝑖𝑠𝑎𝑛𝑠ℎ𝑖𝑝_𝑖𝑠+ 𝛽₇(𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠× 𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠) + 𝜀_𝑖𝑠 (3.10)

3.2.2 Model (2)

Understanding that the basis for voting changes over time, data selected for this model must be of high importance to the 2016 U.S. presidential election. Hence, to ensure relevancy, the data selected revolved around the top ten important voting issues identified by the Pew Research Center, an American think tank in 2016 (PewResearchCenter, 2016). According to Figure 3, the ten issues were important to vote choice in the 2016 U.S. presidential election. Of the ten issues, economy and terrorism top the chart, with more than 80% of voters believing that these two issues are important in their vote choice.

Figure 3: Survey conducted by Pew Research Center

The Model (2)’s logistic equation is specified as:

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 = 𝛽₀+ 𝛽₁𝐸𝑐𝑜𝑛𝑜𝑚𝑦_𝑖𝑠+ 𝛽₂𝐵𝑙𝑎𝑐𝑘𝐴𝑠𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒_𝑖𝑠+ 𝛽₃𝐹𝑜𝑟𝑒𝑖𝑔𝑛𝑃𝑜𝑙𝑖𝑐𝑦_𝑖𝑠+ 𝛽₄𝐺𝑢𝑛𝐶𝑜𝑛𝑡𝑟𝑜𝑙_𝑖𝑠+ 𝛽₅𝑀𝑒𝑥𝑖𝑐𝑜𝑊𝑎𝑙𝑙_𝑖𝑠+ 𝛽₆𝑆𝑜𝑐𝑖𝑎𝑙𝑃𝑜𝑙𝑖𝑐𝑦_𝑖𝑠+ 𝛽₇𝐶𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑇𝑟𝑎𝑖𝑡𝑠_𝑖𝑠+

𝛽₈𝐹𝑖𝑔ℎ𝑡𝐼𝑆𝐼𝑆_𝑖𝑠+ 𝜀_𝑖𝑠 (3.11) 3.3. Source of Data

This study uses survey data from the 2016 American National Election Studies (hereafter referred to as ANES 2016), a collaboration of the University of Michigan and Stanford University, funded by the National Science Foundation (The American National Election Studies [ANES], 2016). It is a series of election studies conducted since 1948 on public opinion, voting behaviour and choices in the U.S. presidential elections. The American National Election Studies (ANES) (2016) is designed to cover a wide range of topics such as social backgrounds, opinions on public policy, evaluations of candidates and others to map the political landscape in the United States correctly.

The survey featured both dual-design with traditional face-to-face and online interviews with a total sample size of 2,927 (n = 2,927). Weighted samples within all 50 states are taken in the homes of U.S. citizens who are eligible voters (age>18), the same standard of the procedure followed by the U.S. Census Bureau and federal agencies for many of the quality and influential surveys conducted today (Malhotra & Krosnick, 2007). The timeframe for The American National Election Studies (2016) lasted four months, from early September 2016 and continued into January 2017. Respondents typically spent more than an hour answering lengthy questionnaires regarding the 2016 U.S.

presidential election.

This study extracted two types of data - voter demographic data and voter political opinions from the The American National Election Studies (2016).

4. Overview of Variables 4.1. Model (1)

4.1.1. Hypothesis

Table 1 displays the Model (1) hypothesis. It shows the summary of the hypothesis for every variable included in Model (1).

Table 1: Hypothesis of variables in Model (1) Variable Sign Hypothesis

Race - Respondents are less likely to vote for Trump if they are non-white people.

Education - Respondents are less likely to vote for Trump if they are college- educated.

Income - Respondents are less likely to vote for Trump if they earn more than

$25,000 annually.

Age + Respondents are more likely to vote for Trump if they are aged 40 and above.

Gender - Respondents are less likely to vote for Trump if they are female.

Partisanship + Respondents are more likely to vote for Trump if they are Republican- leaning

4.1.2. Dependent Variable

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 denotes the vote choice. It takes 0 if the respondent voted Hilary Clinton, 1 if the respondent voted Donald Trump in the 2016 U.S. presidential election.

4.1.3. Independent Variable a. Variable of interest

𝑅𝑎𝑐𝑒_𝑖𝑠 denotes the respondent i’s racial identity. It takes 0 if the respondent is a white person, 1 if the respondent is a non-white person. Race is expected to be negatively correlated with vote choice. Given Trump’s unfriendly racial attitudes toward a person of colour, non-white respondents are less likely to vote for him (Reny, Collingwood &

Valenzuela, 2019).

𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛_𝑖𝑠 denotes the respondent i’s education level. It takes 0 if the respondent is not a college graduate, 1 if the respondent is a college graduate. Education is expected to be negatively correlated with voting choices as non-college-educated voters were a key reason for Trump’s victory (Sides, Tesler & Vavreck, 2017).

𝐺𝑒𝑛𝑑𝑒𝑟_𝑖𝑠 denotes the respondent i’s gender identity. It takes 0 if the respondent is a male, 1 if the respondent is a female. Gender is expected to be negatively correlated with vote choice. Given Trump’s history of controversial comments about females, they are less likely to vote for him (Bracic, Israel-Trummel & Shortle, 2018).

𝐼𝑛𝑐𝑜𝑚𝑒_𝑖𝑠 denotes the respondent i’s income level. It takes 0 if the respondent earns less than the median income of $59,000 annually, 1 if the respondent earns more than the median income of $59,000 annually. This income classification followed closely with the standard guideline set by the 2016 U.S. Census Bureau data. Income is expected to be negatively correlated with vote choice as voters earning below-median income are strongly associated with enthusiasm for Trump (Smith & Hanley, 2018).

b. Controlled variable

𝐴𝑔𝑒_𝑖𝑠 denotes the respondent i’s age. It takes three values: 0 if the respondent is between the age group of 18-39; 1 if the respondent is between 40-59; 2 if the respondent is beyond 60. Age is expected to be positively correlated with vote choice as older people aged 40 and above made up a larger share of Trump’s vote (Wang, Li & Luo, 2016).

𝑃𝑎𝑟𝑡𝑛𝑒𝑟𝑠ℎ𝑖𝑝_𝑖𝑠 denotes the respondent i’s party affiliation. It takes three values: 0 if the respondent self-identified as Democratic supporters, 1 if the respondent self-identified as independent and 2 if the respondent self-identified as Republican supporters.

Partisanship is expected to be positively correlated with vote choice as republican

leaning voters are more likely to vote for the Republican party’s presidential candidate (Bartels, 2000).

4.1.4 Summary Statistics

Table 2 displays the summary statistics of Model (1). It represents the raw data results of Model (1). Most variables have average means of close to 0.5 or 1, depending on the minimum and maximum value. For example, Gender takes a minimum value of 0 and maximum value of 1; hence the average mean is 0.5, indicating that the data is evenly distributed. However, the average mean for Race is slightly skewed to the left (0 = White), indicating that most respondents are white people. This is acceptable as it accurately depicts the racial demographics in the United States, where white people made up 77% of the total population in 2016.

Table 2: Summary statistics of variables in Model (1)

Variable Num of

obs Mean Variance Standard

Deviation Min Max

Choice 2,927 0.46 0.25 0.50 0 1

Race 2,927 0.27 0.20 0.45 0 1

Gender 2,927 0.54 0.25 0.50 0 1

Education 2,927 0.56 0.25 0.50 0 1

Partisanship 2,927 0.92 0.92 0.96 0 2

Age 2,927 1.11 0.57 0.75 0 2

Income 2,927 0.53 0.25 0.499 0 1

4.1.5 Multicollinearity

Table 3 shows the multicollinearity test results for the model. The consensus is that a tolerance level less than 0.1 or a VIF larger than 10 indicates multicollinearity.

Table 3: Multicollinearity test in Model (1)

Variable VIF Tolerance

Race 1.14 0.88

Partisanship 1.12 0.90

Income 1.10 0.91

Education 1.08 0.92

Gender 1.03 0.97

Age 1.02 0.98

Note: The ‘rule of thumb’ in econometric literature is that a tolerance level less than 0.1 or a VIF larger than 10 suggests serious multicollinearity problems.

There is no multicollinearity problem among the independent variables in the model, as none of the independent variables has a VIF level of more than 10 or a tolerance level less than 0.1.

4.1.6 Sensitivity and Specificity

Table 4 displays the sensitivity and specificity test results of the model. The consensus is that a model with a sensitivity and specificity around 90% is considered to have good diagnostic performance. The model’s sensitivity and specificity are more than 90%. Thus, the model is correctly classified at 90.84%

Table 4: Sensitivity and Specificity test in Model (1)

Test Percentage

Sensitivity (Pr +~D) 91.60%

Specificity (Pr -~D) 90.19%

Correctly Classified 90.84%

4.2. Model (2) 4.2.1. Hypothesis

Table 5 displays the Model (2) hypothesis. It shows the summary of the hypothesis for every variable included in Model (2).

Table 5: Hypothesis of variables in Model (2)

Variable Sign Hypothesis

Economy + Respondents are more likely to vote for Trump if they disapprove of the incumbent’s economic performance.

BlackAssistance + Respondents are more likely to vote for Trump if they are against affirmative action for black people.

ForeignPolicy + Respondents are more likely to vote for Trump if they disapprove of the incumbent’s handling of foreign affairs.

GunControl + Respondents are more likely to vote for Trump if they wish for stricter gun control.

MexicoWall - Respondents are less likely to vote for Trump if they do not want the government to build a wall between the U.S. and Mexico border.

Social + Respondents are more likely to vote for Trump if they disapprove of the incumbent’s handling of social policy.

CandidateTraits + Respondents are more likely to vote for Trump if they believe Trump is a good leader overall than Clinton.

FightISIS N/A Both candidates hold strong stances in tackling terrorism. Hence the effect is ambiguous.

4.2.2. Dependent Variable

𝐶ℎ𝑜𝑖𝑐𝑒_𝑖𝑠 denotes the vote choice. It takes 0 if the respondent voted Hilary Clinton, 1 if the respondent voted Donald Trump in the 2016 U.S. presidential election.

4.2.3. Independent Variable a. Variable of interest

𝐸𝑐𝑜𝑛𝑜𝑚𝑦_𝑖𝑠 denotes the respondent i’s approval of the incumbent’s economic performance. It takes 0 if the respondent approves the incumbent’s economic performance, 1 if otherwise. This variable is expected to have a positive relationship with vote choice. Respondents are more likely to vote for Trump if they disapprove of the incumbent’s economic performance, as depicted by Fair (1978).

𝑆𝑜𝑐𝑖𝑎𝑙_𝑖𝑠 denotes the respondent i’s approval of the incumbent’s social policy. It takes 0 if the respondent approves the incumbent’s social policy, 1 if otherwise. denotes the respondent i’s approval of the incumbent handling of foreign affairs. It takes 0 if the respondent approves the incumbent’s performance, 1 if otherwise. Similarly, both variables are expected to be positively correlated with vote choice. Respondents are more likely to vote for Trump if they disapprove of the incumbent’s social policy and foreign affairs handling.

𝐺𝑢𝑛𝐶𝑜𝑛𝑡𝑟𝑜𝑙_𝑖𝑠 denotes the respondent i’s support for gun control. It takes 0 if the respondent agrees that the United States government should regulate gun ownership, 1 if otherwise. denotes the respondent i’s 6-point support scale of the need for government assisting the black people. It takes a value of 0 to 6. The greater the scale value, the lower the support for the said assistance. Both variables are expected to be positively correlated with vote choice. This is because respondents who share similar stances would vote for the candidate, as depicted by Ansolabehere and Puy (2018). Thus, respondents who share Trump’s view on both issues - low support for gun control and black assistance are more likely to vote for him.

𝑀𝑒𝑥𝑖𝑐𝑜𝑊𝑎𝑙𝑙_𝑖𝑠 denotes the respondent i’s 6-point support scale towards building a wall with Mexico. It takes a value of 0 to 6 - the greater the scale value, the lower the support for the said wall. In contrast, this variable is expected to be negatively correlated with vote choice. Respondents who do not believe in Trump’s advocacy for building a wall with Mexico will not vote for him.

𝐶𝑎𝑛𝑑𝑖𝑑𝑎𝑡𝑒𝑇𝑟𝑎𝑖𝑡_𝑖𝑠 denotes the comparative perception index of respondent i’s towards the running candidates. A comparative index is explicitly used for this variable because voters evaluate candidates comparatively, not separately (Sullivan et al., 1990). It derives from four critical candidate traits: leadership, integrity, empathy, and competence, which are reported to be important in candidate evaluation, according to Pierce (1993). This variable is created by summing a respondent’s rating of both candidates on four critical candidate traits, ranging from 1 (Extremely Well) to 6 (Not Well At All). Then, Hilary’s total score is subtracted from Trump’s total traits score to create this index. The index ranges from -12 (Pro-Clinton) to +12 (Pro-Republican). It is expected to be positively correlated with voting choice as respondents who believe that Trump is overall a better person in terms of the above four traits will vote for Trump, as suggested by Pierce (1993).

𝐹𝑖𝑔ℎ𝑡𝐼𝑆𝐼𝑆 denotes the respondent i’s 6-point support scale towards sending U.S troops to fight ISIS. It takes a value of 0 to 6—the greater the scale value, the lower the support for the said action. The expected relationship of this variable is ambiguous as both candidates share a similar stance on this issue, which is to send troops to fight ISIS.

4.2.4. Summary Statistics

Table 6 displays the summary statistics of Model (2). It represents the raw data results of Model (2). Most variables have evenly distribution of means, indicating that the data is evenly distributed.

Table 6: Summary statistics of variables in Model (2)

Variable Num of

obs Mean Variance Standard

Deviation Min Max

Choice 2,927 0.46 0.25 0.50 0 1

CandidateTraits 2,927 -1.55 91.24 9.55 -16 16

Economy 2,927 0.47 0.25 0.50 0 1

Social 2,927 0.55 0.25 0.50 0 1

BlackAssistance 2,927 3.38 3,51 1.87 0 6

GunControl 2,927 0.45 0.25 0.50 0 1

ForeignPolicy 2,927 0.50 0.25 0.50 0 1

MexicoWall 2,927 3.31 6.08 2.47 0 6

FightISIS 2,927 2.90 4.29 2.07 0 6

4.2.5. Multicollinearity

Table 7 shows the multicollinearity test results of Model (2). The consensus is that a tolerance level less than 0.1 or a VIF larger than 10 indicates multicollinearity. There is no multicollinearity problem among the independent variables in the model, as none of the independent variables has a VIF level of more than 10 or a tolerance level less than 0.1.

Table 7: Multicollinearity test in Model (2)

Variable VIF Tolerance

CandidateTraits 4.20 0.24

Economy 3.53 0.28

ForeignPolicy 2.89 0.35

Social 2.58 0.39

MexicoWall 2.20 0.46

GunControl 1.57 0.64

BlackAssistance 1.53 0.65

FightISIS 1.14 0.87

Note: The ‘rule of thumb’ in econometric literature is that a tolerance level less than 0.1 or a VIF larger than 10 suggests serious multicollinearity problems.

4.2.6 Sensitivity and Specificity

Table 8 displays the sensitivity and specificity test results of Model (2). The consensus is that a model with a sensitivity and specificity of around 90% is considered good diagnostic performance. The model’s sensitivity and specificity are more than 96%.

Hence, the model is correctly classified at 97.10%.

Table 8: Sensitivity and Specificity test in Model (2)

Test Percentage

Sensitivity (Pr +~D) 91.60%

Specificity (Pr -~D) 90.19%

Correctly Classified 90.84%

5. Result and Discussion 5.1. Model (1)

Table 9 displays Model (1)’s logistic regression result without interaction terms. Overall, the model performs well and can explain more than 60% of the vote choice variation.

Race, education, income and partisanship are statistically significant at a 99% level while holding other variables constant. The remaining variables are statistically insignificant.

The effects of race, education, income and partisanship are just as hypothesized. For race, a change in the respondent’s racial identity from white to non-white reduces the log-odd ratio of voting for Trump by 1.513, indicating that the voters are 78% less likely to vote for Trump. Given Trump’s racial views toward non-white, as depicted by Reny, Collingwood and Valenzuela (2019), this finding proves that racial attitude motivates respondents’ voting choice.

For education, a change in the respondent’s education level from non-college to college graduate reduces the log-odd ratio of voting for Trump by 0.380. This indicates that college-educated respondents are 32% less likely to vote for Trump. This finding confirms the previous studies by Mas and Moretti (2009) that the education level of respondents plays a deciding role in the voting choice, as college-educated respondents tend to be liberal, which is the opposite of Trump’s political position.

Table 9: Model (1) Result without interaction terms

Independent Variable Coefficient

Race -1.513***

Gender -0.205

Education -0.380***

Partisanship

Moderate 2.891***

Republican 5.041***

Age

Middle Age 0.111

Old Age 0.182

Income -0.401**

_Cons -1.842***

Pseudo R2 0.604

Number of observations 2.927

Note: Entries are logistic regression coefficients with standard errors in paratheses.

*p=.05; **p=.01; ***p=0.001.

For income, a change in the respondent’s income from below the median income to above the median income reduces the log-odd ratio of voting for Trump by 0.401, indicating that they are 33% less likely to vote for Trump. This resembles the finding by Smith and Hanley (2018) that the odds of voting enthusiastically for Trump decline significantly as income increases.

For partisanship, a change in the respondents’ party affiliation from Democrat to moderate and subsequently to Republican increases the log-odd ratio of voting for Trump increases by 2.891 and 5.041, respectively, indicating that the respondents are 801% and 1546% more likely to vote for Trump. Although the finding reflects a substantial percentage, it is in line with the previous studies by Bartels (2000) that Republican-leaning respondents are more likely to vote for the representative from the party.

Table 10 displays Model (1)’s logistic regression result with interaction terms. Overall, the model performs well and can explain more than 60% of the vote choice variation.

Race, income and partisanship remain statistically significant at a 99% level across all regressions while holding other variables constant.

When the interaction terms are added, 𝑅𝑎𝑐𝑒 × 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 and 𝐼𝑛𝑐𝑜𝑚𝑒 × 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 interaction terms are statistically significant at 99% level, whereas the rest of the interaction terms are statistically insignificant. For 𝑅𝑎𝑐𝑒 × 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, this means that among white respondents, a change in education level from non-college-educated to

college-educated reduces the log-odd ratio of voting Trump by an additional value of 0.578, indicating that the white college-educated respondents are 44% less likely to vote for Trump, in addition to the baseline percentage.

Table 10: Model (1) Result with interaction terms

Independent

Variable Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient

Race -1.514*** -1.950*** -1.541*** -1.507*** -1.748 -1.514***

Gender -0.168 -0.201 -0.222 -0.217 -0.204 -0.179

Education -0.341 -0.578*** -0.380** 0.001 -0.384** -0.381**

Partisanship

Moderate 2.889*** 2.889*** 2.892*** 2.906*** 2.894*** 2.890 Republican 5.040*** 5.037*** 5.042*** 5.062*** 5.041*** 5.040***

Age

Middle Age 0.113 0.097 0.111 0.138 0.114 0.112

Old Age 0.183 0.142 0.184 0.208 0.171 0.183

Income -0.402** -0.405** -0.401** 0.008 -0.513*** -0.371*

Interaction Term

Gender x Education -0.074 - - - - -

Race x Education - 0.820** - - - -

Race x Gender - - 0.056 - - -

Income x

Education - - - -0.752*** - -

Income x Race - - - - 0.467 -

Income x Gender - - - - - -0.056

_Cons -1.842*** 1.707*** -1.837*** -2.026*** -1.777*** -1.860***

Pseudo R2 0.604 0.604 0.606 0.606 0.604 0.604

Number of

observations 2,927 2,927 2,927 2,927 2,927 2,927

Note: Entries are logistic regression coefficients with standard errors in paratheses.

*p=.05; **p=.01; ***p=0.001.

For 𝐼𝑛𝑐𝑜𝑚𝑒 × 𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛, this means that among college-educated respondents, 26 a change in income level from below-median income to above-median income reduces the log-odd ratio of voting Trump by an additional value of 0.744, indicating that the college- educated respondents earning above-median income are 52% less likely to vote for Trump, in addition to the baseline percentage.

These results reveal interesting information about the effect of demographic characteristics on voting choice. When considering the effect individually, non-white, college-educated, above median-income earners, and less-republican leaning respondents are less likely to vote for Trump. One unanticipated finding is that polarization occurs among white and college-educated voters when considering the interaction effect. White respondents without college degrees are more likely to vote for Trump, whereas white respondents with college degrees are more likely to vote for Clinton. Similarly, college-educated respondents earning above-median income are more likely to vote for Trump, whereas college-educated respondents earning below-median income are more likely to vote for Clinton.

5.2. Model (2)

Table 11 displays Model (2)’s logistic regression result. Overall, the model performs exceptionally well and can explain more than 85% of the vote choice variation.

CandidateTraits, Social, ForeignPolicy and MexicoWall are statistically significant at 99%

level while holding other variables constant. BlackAssistance is statistically significant at 95% level while holding other variables constant. The remaining variables are statistically insignificant.

Table 11: Model (2) Result

Independent Variable Coefficient

CandidateTraits 0.521***

Economy 0.298

Social 1.130***

BlackAssistance 0.183*

GunControl 0.392

ForeignPolicy 0.712**

MexicoWall -0.267***

FightISIS -0.027

_Cons -0.661

Pseudo R2 0.878

Number of observations 2.927

Note: Entries are logistic regression coefficients with standard errors in paratheses.

*p=.05; **p=.01; ***p=0.001.

The effects of all five statistically significant variables on voting choice are just as hypothesised. Looking at the CandidateTraits coefficient, with every unit increase in the perception that Trump is overall better than Clinton in terms - leadership, integrity, competence, empathy, it increases the log-odd ratio of voting Trump by 0.521, indicating that the voters are 42% more likely to vote for Trump. This result is as hypothesised. It proves that candidate traits are a critical addition to evaluating vote choice and that

voters judge both candidates comparatively based on the four traits and subsequently form their vote choice, as stated by Pierce (1993).

Turning to the Social coefficient, if the respondent disapproves of the incumbent’s handling of social policy, such as healthcare policy, the log-odd ratio of voting for Trump increases by 1.130, which means that the voters are 210% more likely to vote for Trump than Clinton. Besides, ForeignPolicy coefficient also shows that if the respondent disapproves of the incumbent’s handling of foreign affairs, the log-odd ratio of voting for Trump increases by 0.712, indicating that the voters are 104% more likely to vote for Trump than Clinton. This is expected as respondents are more likely to vote for the opposition - Trump, if they disapprove of the incumbent’s handling of social policy and foreign affairs.

Looking at the MexicoWall coefficient, if there is one unit increase in the belief that government should not build a wall with Mexico, the log-odd ratio of voting for Trump reduces by 0.267, indicating that the voters are 23% less likely to vote for Trump.

Moreover, the BlackAssistance coefficient shows that if there is one unit increase in the belief that government should not assist the blacks, the log-odd ratio of voting for Trump increases by 0.183, indicating that the voters are 20% more likely to vote for Trump than Clinton. These results reflect that voters who share similar stances with the candidate would vote for the particular candidate, and otherwise, for respondents who do not share similar stances as depicted by Ansolabehere and Puy (2018).

One unexpected finding is that the economic factor played an insignificant role, suggesting that Trump’s victory was shaped more by other concerns than economics.

One possible explanation is that the economic factor is a historical issue that affects Americans’ everyday lives. However, recent issues such as building a wall with Mexico and foreign affairs with countries such as China are prioritised over the historical issue.

6. Conclusion

Firstly, the empirical findings in the first model indicate that racial identity, education level and income level were crucial in determining voting choice while controlling for the respondents' party affiliation. These findings are somewhat in line with the literature provided by Washington (2006), Mas and Moretti (2009) and the rest. Surprisingly, the findings also show a polarisation among white respondents and college-educated respondents. Indeed, the increasing polarisation of national politics in the 2016 U.S.

presidential election was more profound than in any other U.S. election (Abramowitz, 2016).

Secondly, the empirical findings in the second model suggest that the voters were primarily dissatisfied with Obama's performance on social and foreign affairs. This dissatisfaction drives voters away from his successor, Clinton. Also, Trump's restrictive immigration policy attracted voters to him. He communicated his policy position very well to the voters. These policies are well-aligned with specific groups of voters who share a similar political view, which motivates these voters to vote for him, just as Ansolabehere and Puy (2018) suggested. Moreover, as Pierce (1993) stated, Trump's outgoing, exuberant personality also resembles leadership qualities to his voters, and these traits highly influence his voters.

While this paper presented a comprehensive review of Trump's victory in the 2016 U.S.

presidential election and the associated factors behind his victory, the empirical results reported herein should be considered in light of some limitations. This paper did not consider the role of media exposure in shaping voters' vote choices. Given the digitalisation of political discourse in this election, candidates utilise social media for effective campaigning. One example was Trump's frequent usage of Twitter to express himself, which might affect voters' voting decisions. Hence, it is difficult to determine whether voters genuinely think and behave the way they do or are just "told" by the media.

Moreover, this paper did not consider the effect of intergenerational transmission on voting behaviour. Parents have a strong influence on their children's political preferences, given that parents are the main socialisation agents for their young children.

Children develop values when they are young, and parents' political preferences might directly shape the children's political values and their vote choice. Additional future research addressing these concerns would be very timely.

Acknowledgement

The authors would like to thank the lecturers whose support made this study possible.

Funding

This study received no funding.

Conflict of Interests

The authors declare no conflict of interest in this study.

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