Racial Prejudice Measurement and Modeling Trump Vote Choice

What drove the voting for Donald Trump in the 2016 election most? While not always explicitly part of analyses of the 2016 political environment, the question was very often a central focus and approach from several angles. Much of the discussion has propagated the idea that economic anxiety and dissatisfaction pushed people–and the presidency–to Trump. Early political science analysis–largely by Michael Tesler at the Monkey Cage–however has shown greater evidence behind the idea that racial resentment was associated with voting for Trump to a degree that voting for Mitt Romney in 2012 and John McCain in 2008 was not. In a similar–but, importantly, not identical–vein of thought, other measures that get at old-fashioned racial prejudice have also been shown by Schaffner et al to predict Trump support more strongly that economic satisfaction does.

Recently released 2016 CCES data can help further check for signs of racial prejudice driving Trump support, as well as determining the impact of other factors. Before that, though, it’s worth touching on the nuanced literature of social science measurement of racial prejudice. This paper by Christopher DeSante and Candis Smith both introduces innovative new ideas in this realm and reviews the body of prior research very well. Several different types of questions have been used to approximate racial prejudice–what’s termed the “old-fashioned” kind, which now proves difficult to uncover in surveys due to social desirability bias among respondents. Perhaps the most commonly used has been the racial resentment scale, composed of agree/disagree answers to four questions. Here’s the format in which it appeared in the 2016 ANES Pilot survey:

It’s the prejudice metric constructed from these questions that what used by Tesler to show racism drove Trump support in 2016 in a way it did not for support of Romney and McCain. Schaffner et al.’s finding is based on a metric using different questions–an important bit of nuance in this debate. The problem with the above traditional racial resentment battery is that other research by Carney and Enos, for example, has argued that a wider conservative ideology (mainly one promoting rugged individualism) was driving responses to this racial resentment battery–more so than racial prejudice itself. Similar criticisms of the set of racial resentment questions motivated DeSante and Smith to find better measures to uncover prejudice. After testing a wide array of survey question attempting to do just that, the authors concluded that there are two new dimensions that are significant predictors of more old-fashioned racism: cognitive (awareness and acknowledgment of racism) and empathetic (empathy for and experiences with other racial groups) dimensions. Crucially, questions that make up these dimensions significantly affect opinion on conservative issues that involve race, but not conservative issues unrelated to it, distinguishing these measures from simply just conservative ideology.

Schaffner et al. use these questions–specifically those that DeSante and Smith recommend–for their own study. As one of the principal investigators for the CCES, Schaffner implemented many of those same questions in the survey. Here are the four questions that intend to proxy racial prejudice, which emerge from the two new dimensions of racism (cognitive and empathetic) that DeSante and Smith emphasize:

1. I am angry that racism exists.
2. White people in the U.S. have certain advantages because of the color of their skin.
3. I often find myself fearful of people of other races.
4. Racial problems in the U.S. are rare, isolated situations.

Respondents were asked to strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, or strongly disagree with these four statements. Disagreement on the first and second and agreement on the third and fourth connote greater racism.

To test the effects of these four questions–that I’ll call a “racism scale”–I averaged responses to them to create one measure of racial prejudice (ranging from a value of 1 to 5 where 5 is most prejudiced; mean = 2.20, standard deviation = 0.79). Here’s how the distribution of those values looks like–right-skewed, as most respondents don’t display too high levels of racism on these dimensions:

I tested the strength of this racism scale by including it in a regression along with several other variables typically included in vote choice/support models. There were nine other variables in addition to the racism scale:

1. Race (effects of black, Hispanic/Latino, and other races relative to that of whites)
2. Education (effects of some college, college, and postgraduate education relative to that of high school or less)
3. Age group (effects of 30-44, 45-54, 55-64, and 65+ age groups relative to that of 18-29)
4. Gender (effects of male relative to that of female)
5. Party identification (seven-point partisanship scale where the lowest value is most Democrat and highest is most Republican)
6. Ideology (five-point self-reported ideology scale where the lowest value is most liberal and the highest is most conservative)
7. Income bracket (effects of $30k-50k, $50k-70k, $70k-100k, $100k-200k, and $200k< income groups relative to that of $30>)
8. Religious importance in respondent’s life (1-4 point scale where 1 is “not all important” and 4 is “very important”)
9. Census-designated region (effect of urban region relative to that of rural)

In running this logistic regression (the table for which is at the bottom of this post), I regressed a binary vote choice variable–taking a value of 1 if the respondent reported voting for Trump and a value of 0 for Clinton vote–on these 10 different variables. Coefficients in a log model aren’t as easily interpretable as in a linear model, so I’ll just focus on the strength of the relationship (going by the size of the z-value in the regression output) of a certain variable–holding all others constant–with vote choice.

The strongest predictor of Trump vote in this log model is by far the partisanship score (where higher values indicate greater attachment to Republican identification). This should come as not much of a surprise, but does reaffirm the bearing of party identification on vote choice. The second strongest predictor, though, is the composite racism scale I explained before. (Both of these relationships are in the positive direction with vote choice for Trump.) Thus, racism–as expressed by this new dimension drawn from DeSante and Smith’s survey questions–is the variable most strongly associated with Trump vote outside of partisanship.

Other significant relationships with Trump vote in the positive direction include political ideology and religious importance, as well as older age group effects relative (to that of 18-29 year-olds) and male gender (relative to female gender). In the other direction, black race and Hispanic/Latino race (both relative to whites) and postgraduate education (relative to high school or less) are the strongest negative predictors of Trump. Urban region (relative to rural) as well as college degree (relative to high school or less) and “other” race (relative to whites) were also statistically significant.

	Dependent variable:
	Trump Vote Choice
Blacks (Whites Baseline)	-1.103***
	(0.088)
Hispanic/Latino (Whites Baseline)	-0.915***
	(0.090)
Other (Whites Baseline)	-0.357***
	(0.096)
Some College (HS or Less Baseline)	-0.108*
	(0.062)
College Degree (HS or Less Baseline)	-0.469***
	(0.060)
Postgraduate Degree (HS or Less Baseline)	-0.760***
	(0.089)
Age 30-44 (Age 18-29 Baseline)	0.449***
	(0.076)
Age 45-54 (Age 18-29 Baseline)	0.586***
	(0.084)
Age 55-64 (Age 18-29 Baseline)	0.567***
	(0.077)
Age 65+ (Age 18-29 Baseline)	0.509***
	(0.079)
Male (Female Baseline)	0.363***
	(0.047)
Party Identification	0.988***
	(0.014)
Self-Reported Ideology	0.584***
	(0.032)
Income $30k-50k ($30k> Baseline)	0.049
	(0.067)
Income $50k-70k ($30k> Baseline)	0.196***
	(0.071)
Income $70k-100k ($30k> Baseline)	-0.002
	(0.073)
Income $100k-200k ($30k> Baseline)	-0.200**
	(0.083)
Income $200k< ($30k> Baseline)	-0.444***
	(0.157)
Religious Importance	0.211***
	(0.022)
Urban Region (Rural Baseline)	-0.395***
	(0.068)
Racism Scale	1.573***
	(0.037)
Constant	-9.709***
	(0.171)
Observations	33,667
Log Likelihood	-6,559.886
Akaike Inf. Crit.	13,163.770
Note:	*p<0.1; **p<0.05; ***p<0.01

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Note: As with all current analyses using CCES data, self-reported vote is not validated. Read the second and third paragraphs of this article for an explanation of what this means and what it could imply.