data analysis regression paper

DID BIGOTRY AFFECT THE 2016 PRIMARY election?

Chris Dombrowski [Email address]

AbstractThis paper explores whether Donald Trump’s provocative remarks during the 2016 primary campaign contributed to his success. We use a series of OLS regressions to

estimate the share of votes he received in each state, controlling for different measures of hatred. We found that on average, Donald Trump performed better during the 2016 primary elections in states with higher hate crimes and more hate groups per 100000

capita.

1. INTRODUCTION

Donald Trump won the Republican nomination for president as a rather unconventional

candidate. At his rallies and during interviews, he consistently made inflammatory remarks about

various groups, including women, Hispanics, and Muslims. Despite making these bold,

provocative claims throughout his campaign, Donald Trump still won 1543 delegates out of a

possible 2472. He beat Republican Party mainstays by wide margins along his path to the general

election. We will explore if racial prejudice affected the results of 2016 Republican primary

elections.

Whether or not racial animosity played a role in election outcomes has been examined before

(Stephens-Davidowitz 2013). However, much of the literature discusses how a candidate has

been hurt by racial animosity. We take a different approach to this issue and attempt to determine

if Donald Trump harnessed voters’ racial animosity and used it to his benefit. There are two

main reasons this research is important. First, it would reveal the extent of contemporary

prejudice in the United States. It would also help us better understand the determinants of voting.

Does racial prejudice affect the results of an election? The most recent review of the literature is

inconclusive. "Despite considerable effort by numerous researchers over several decades, there is

still no widely accepted answer as to whether or not prejudice against blacks remains a potent

factor within American politics" (Huddy and Feldman, 2009).

2. LITERATURE REVIEW

A study conducted by Huddy and Feldman (2009) focused on determining whether or not

white prejudice hurt black candidates. To determine whether prejudice influenced white voters in

the 2008 election, Huddy and Fledman (2009) used data from the May 2009 release of the

American National Election Studies (ANES) 2008 series survey. These surveys were conducted

face-to-face. For questions about stereotypes and other sensitive subjects, the respondents

entered their responses into a computer, out of view from the interviewer to reduce social

desirability bias. The authors conducted a series of multivariate logistic regression analyses of

presidential elections from 2008 dating back to 1992. The dependent variable was vote choice of

the interviewees and the independent variables was an index which included the difference

measure for “lazy” and “unintelligent” questions that were given to the interviewees. This study

found that, among white Americans, prejudice continues to present a bigger split for Democrats

than Republicans. Among Independents, the effects of racial prejudice may be the greatest. It

was a challenge for the authors to conclude that there was a racial bias affecting voting in the

Republican party because the Republicans who were interviewed that exhibited no racial bias

were still not going to vote for Obama due to differences of opinion.

Stephens-Davidowitz (2013) sought to find if racial animus can cost a black candidate

votes in contemporary America. This paper used non-survey based methods and instead

measured an area’s racial animus based on the percent of Google search queries that included

racially charged language. This method had not previously been used to study prejudice. The

author used the data from Barack Obama’s presidential votes, controlling for the previous

Democratic candidate, John Kerry’s presidential votes. In conclusion, this paper suggests that

prejudice cost Obama 4.2 percentage points of the national popular vote in 2008 and 4.0

percentage points in 2012. These numbers imply that, among white voters who would have

supported a Democratic presidential candidate in 2012, 9.1 percent did not support a black

Democratic presidential candidate (Stephens-Davidowitz 2014). This paper was effective in

demonstrating that racial bias does impact votes for a black presidential candidate.

In a similar study, Mas and Moretti (2009) demonstrated that racial prejudice was

strongly related to the state-level nonblack vote in the 2008 presidential election. The hypothesis

assumed that Barack Obama lost nonblack Democratic votes in the 2008 election. To analyze the

uncertainty of the outcome, Mas and Moretti (2009) used regression analysis with independent

variables of nonblack partisanship and ideology, which were measured using exit poll questions

and past presidential election votes for Democratic candidates. Mas and Moretti (2009) relied on

the Voter News Service (2000) and National Election Pool, which are state exit polls. The

authors conclude that there was a strong relationship between the state-level nonblack vote for

Obama in 2008 and racial attitudes. Obama received fewer votes in states with higher levels of

prejudice.

Redlawsk (2012) examined how emotions play an important role in American politics

and claims that racial resentment towards African American candidates has an effect on the

outcomes of elections. To test their argument, they used a national pre- and post-election

telephone survey carried out at the Eagleton Center for Public Interest Polling at Rutgers

University. This survey had a total of 863 respondents. In the survey, they asked the respondents

to evaluate the 2012 candidates before and after the election on a four-point scale, where 1 is

very well and 4 is not well at all. The two primary independent variables were symbolic racism,

and the second primary explanatory variable is the measure responses about the candidates. The

questions in the survey helped them measure these variables. Questions such as agreeing or

disagreeing with statements like “many other minority groups have overcome prejudice and

worked their way up, African Americans should do the same without special favors.”

Respondents also received the following question, “I’d like you to rate each question using

something called the feeling thermometer”, for which they could choose any number between 0-

100. The higher the number, the more favorable you feel towards the candidate. Using the OLS

model, they were able to predict the 2012 pre- and post-election feeling thermometer ratings of

President Obama. The author’s findings showed that racism did in fact have a strong negative

effect on Obama’s evaluation. Results also revealed that high levels of racial resentment do lead

to lower evaluations of Obama, all else equal, as they expected from the voluminous literature on

attitudes toward minority candidates.

Lee and Roemer (2006) argue that racism among American voters has reduced the

income tax rate by 11-18%. They attribute this phenomenon to two mechanisms. The first is the

“anti-solidarity effect” in which some voters oppose government transfer payments to minorities.

The second mechanism is “that some voters who desire redistribution nevertheless vote for the

anti-redistributive (Republican) party because its position on the race issue is more consonant

with their own” (2006). This effect was dubbed the “policy bundle effect.” The authors retrieved

survey data from the National Election Studies and analyzed 1,905 responses from 1976 to 1992.

The dependent variable was Republican vote share, and the independent variables were the

participants’ responses to the survey. From the data, they were able to identify participants’

attitudes towards a variety of racial and political issues. Lee and Roemer (2006) found that when

comparing racism, libertarianism, compassion for the poor, and feminism, racism was the best

predictor of whether or not a participant voted for a Republican president.

Geys (2006) explored the effect that socio-economic, political, and institutional variables

have on voter turnout. The paper is a meta-analysis of the empirical work that has been done to

study the factors that motivate people to vote. The data used were not raw data, but the results

from 83 aggregate-level analyses. Geys (2006) points out how different techniques used for the

same measure may change results, and which explanatory variables were consistently significant

across the studies. The study suggests a standard model be used when determining voter-turnout.

3. DATA

This study analyzes cross-sectional state-level data to determine whether measures of

racism can be correlated with the percentage of votes Donald Trump received in the 2016

Republican primary elections and caucuses. After compiling political, economic, education, and

demographic variables, as well as, measures of racism, we were left with 46 states to analyze.

Neither Colorado nor North Dakota held Republican primaries or caucuses. Washington D.C.,

Alaska, and Hawaii were omitted because they were major outliers across numerous independent

variables. (See Table 1. and Table 2. for independent variable and dependent variable descriptive

statistics.)

3.1. Variables and Hypothesis

For this study, the dependent variable is the percentage of votes Donald Trump received

in each state’s primary election (TRUMPVOTES). Stephens-Davidowitz (2014) used the

difference between the percentage of votes John Kerry received in 2004 and the percentage of

votes Barack Obama received in 2008. This technique currently does not work for our purposes.

However, future studies, after the 2016 General Election, will be able to compare Mitt Romney’s

share of votes in 2012 to Donald Trump’s share in 2016. We also used (CLINTONVOTES) as a

dependent variable in our other model. Like (TRUMPVOTES), (CLINTONVOTES) is the

percentage of votes Hillary Clinton received in each state’s primary election.

We chose not to use survey data for our study. We agree with Stephens-Davidowitz’s

(2014) concerns that surveys may not always provide accurate data due to participants’

adherence to social norms. To measure racism by state, we use three different independent

variables, including Stephens-Davidowitz’s (2014) state-level Racially Charged Search Rate data

(STEPHENS_RCSR). Our second measure is the hate crime rate in each state (HCPER100000).

While only the most extreme racists commit hate crimes, we believe that the hate crime rate in

each state is a good barometer for the level of intolerance in a state. For our final measure of

racism, we used the Southern Poverty Law Center’s Hate Map to determine the number of active

hate groups in each state. We then divided the number of active hate groups by the state’s

population, and multiplied by 100,000. This gave us the number of hate groups per 100,000

people (HGPER100000).

Our education variables include each state’s high school dropout rate

(DROPOUT_RATE) and the percentage of the population with an advanced degree. For our

purposes, we defined an advanced degree as a master’s degree and above (PERCENT_W_AD).

Our final education variable is the state’s spending per pupil in 2014 (SPP_2014).

Lee and Roemer (2006) focused on which party voters with racist motivations vote for.

Our intent, however, is to determine whether a single candidate harnessed voters’ attitudes

toward race. In some states, as many as six Republicans received at least 5% of the vote during

the primaries. When more candidates are drawing votes, it skews the percentage of votes

received by each candidate. To control for this, we counted the number of Republicans in each

state that received at least 5% of the vote (NUMBER_REPUBS_W5). For other political

variables, we used a dummy variable for states with closed primaries (CLOSED_DUMMY) and

a dummy variable to distinguish between a primary and a caucus (PRIMARY_DUMMY).

The economic variables we use include the change in average unemployment rate from

2014 to 2015. We calculated this by finding the average unemployment rate in 2014 and

subtracted it from the average unemployment rate in 2015 (CHANGE_IN_UR). We also used

the 2013 Gini Coefficient from each state to measure income inequality (GINI_2013).

For demographic variables, we calculated a diversity index based on the United States

Census estimates for the 2015 population. Our index works like a Herfindahl-Hirschman Index.

We squared the percentage of the population of each race and summed the results

(DIVERSITY_INDEX). Another demographic variable is the percentage of the population in

each state that lives in rural areas (POPPCT_RURAL). (For a list of variables and expected

signs, see Table 3.)

4. MODELS

We tested our regressions for heteroskedasticity with the White test. The results from the

White test showed that none of our models suffered from heteroskedasticity.

4.1. Inclusive Models

Our inclusive models use all data gathered for each state for independent variables, and

use (TRUMPVOTES) and (CLINTONVOTES) as the dependent variables. (See Regressions 1

and 2.)

TRUMPVOTES= β0+β1 [POLITICAL ]+β2[DEMOGRAPHIC ]+β3 [HATEMETRICS ]+β4[EDUCATIONAL ]+ β5 [ECONOMIC ]+∈

CLINTONVOTES=β0+β1 [POLITICAL ]+β2 [DEMOGRAPHIC ]+β3 [HATEMETRICS ]+β4 [EDUCATIONAL ]+β5 [ECONOMIC ]+∈

4.2. Semi-log Models

The estimated models in this section use the semi-log form. This is based on the

assumption that the effect of each independent variable on the dependent variable

(TRUMPVOTES) increases at a decreasing rate. (See regressions 3 and 4.)

TRUMPVOTES=β0+β1 [ log (NUMBERREPUBSW 5 ) ]+ β2 [ log (HCPER100000 ) ]+β3 [ log (HGPER100000 ) ]+β4 [DIVERSITYINDEX ]+β5 [ log (PERCENTWAD ) ]+β6 [ log (GINI 2013 ) ]+∈

Our Clinton model used the same variables as our Trump model, excluding

(NUMBERREPUBSW5).

CLINTONVOTES=β0+β2 [ log (HCPER 100000 ) ]+β3 [ log (HGPER 100000 ) ]+β4 [DIVERSITYINDEX ]+ β5 [ log (PERCENTWAD ) ]+β6 [ log (GINI 2013 ) ]+∈

4.3. Simple Regressions

Since [log(HCPER100000)] proved to be significant in each of our semi-log models, we

ran simple regressions with each candidate’s share of votes from each state’s primary election as

the dependent variable, and the hate crime rate for each state as the sole independent variable.

(See regressions 5 and 6.) TRUMPVOTES=β0+β2 [ (HCPER 100000 ) ]+∈

CLINTONVOTES=β0+β2 [ (HCPER 100000 ) ]+∈

5. RESULTS

5.1. Inclusive Models

For the inclusive Trump model, the adjusted R2 was .28. Only three of the independent

variables proved to be significant. The number of Republicans with at least 5% of the vote, as

expected, was negative and significant at the 1% level. The hate crime rate and the number of

hate groups per 100000 population were both significant at the 10% level and positive. No other

independent variables proved to be significant.

The inclusive Clinton model had an adjusted R2 of .59. The only significant independent

variable was the diversity index. The diversity index was positive and significant at the 1% level.

5.2. Semi-log Models

We used the semi-log models above to analyze 46 states, excluding Alaska, Colorado,

Hawaii, North Dakota, and Washington D.C. The adjusted R2 for the Trump model is .35. As

expected, the number of Republicans that received at least 5% of the votes was the biggest

driving factor of the percentage of votes Donald Trump received.

Interestingly, the [log(DIVERSITY_INDEX)] was negative and significant at the 10%

level. This means that the more diverse a state is, the more votes Donald Trump received from

that state on average.

Both metrics used to quantify hate in a state have positive and significant coefficients.

[log(HCPER100000)] is significant at the 5% level, and [log(HGPER100000)] is significant at

the 10% level. A simple regression, using TRUMPVOTES as the dependent variable and

HCPER100000 as the independent variable, also produces a positive coefficient significant at the

10% level. In our semi-log model, [log(PERCENT_W_AD)] and [log(GINI_2013)] were not

statistically significant.

For our Clinton model, our adjusted R2 is .66. The [log(DIVERSITY_INDEX)] is

significant and positive at the 1% level, meaning the more diverse a state is, the higher

percentage of votes Clinton received, on average. Only one measure of hate in the Clinton model

proved to be significant at the 5% level. [log(HCPER100000)] was significant at the 5% level

and negative. The higher the hate crime rate in each state, the less votes Clinton received, on

average.

5.3. Simple Regressions

The results of our simple regressions jibed with the results from our semi-log and

inclusive models. On average, Donald Trump received a greater share of primary election votes

in states with a higher hate crime rate. These results are significant at a 10% level.

For the simple regression comparing hate crimes and the share of votes Hillary Clinton

received, the sign for (HCPER100000) remained negative and significant at a 5% level.

6. CONCLUSION

Our findings are similar to Lee and Roemer’s (2006) in that we believe some voters are

indeed motivated by racist ideologies. Provocative remarks by Donald Trump regarding groups

of people different than himself (and, admittedly, similar to himself) may have motivated voters

with negative racial attitudes to vote for him. Stephens-Davidowitz (2014) found that a black

candidate, Barack Obama, was hurt by racial animosity among voters. It follows that a candidate

could be helped by that same racial animosity by tailoring his rhetoric toward it.

Whether Donald Trump intentionally manipulated voters should be left to other studies.

However, it is likely that his rhetoric—building a wall, banning Muslims, that the black

population in the United States is living in Hell—motivated some voters to vote for him during

the 2016 primary election.

Further research could compare the 2016 General Election Results with the 2012 General

Election Results, following Stephens-Davidowitz (2014). Mitt Romney was a more traditional

Republican candidate for President, and could serve as a baseline, much like John Kerry did in

Stephens-Davidowitz (2014). When comparing our Trump models with our Clinton models, it

seems as though Donald Trump benefited from voters’ bigoted attitudes, while Hillary Clinton

did not, and was possibly even hurt by them.

Stephens-Davidowitz (2014), Lee and Roemer (2006), and Mas and Moretti (2009) have

all shown that racial animosity can affect elections. Stephens-Davidowitz (2014) and Mas and

Moretti (2009) focused on how candidates can be hurt by racial animosity. It should follow, then,

that racial animosity can also help a candidate.

REFERENCES

Huddy, Leonie and Stanley Feldman, “On Assessing the Political Effects of Racial Prejudice,”

Annual Review of Political Science, June 2009, 12 (1), 423–447.

Geys, Benny. "Explaining voter turnout: A review of aggregate-level research." Electoral Studies

25.4 (2006): 637-663.

Lee, Woojin, and John E. Roemer. "Racism and redistribution in the United States: A solution to

the problem of American exceptionalism." Journal of Public Economics 90.6 (2006):

1027-1052.

Redlawsk, David P., Tolbert Caroline J., and McNeely Natasha Altema. "Symbolic Racism and

Emotional Responses to the 2012 Presidential Candidates." Political Research Quarterly

67.3 (2014): 680-94. Web.

Stephens-Davidowitz, Seth Isaac. 2013. Essays Using Google Data. Doctoral dissertation,

Harvard University.

Mas, Alexandre, and Enrico Moretti. 2009. “Racial Bias in the 2008 Presidential Election.”

American Economic Review 99 (2): 323–29.

Table 1.

Variable Mean Median Standard Deviation Minimum MaximumDROPOUT_RATE 0.173 0.151 0.056 0.095 0.315GINI_COEFFICIENT_2013 0.606 0.596 0.037 0.541 0.699HCPER100000 2.190 1.950 1.531 0.130 6.000HGPER100000 0.316 0.287 0.164 0.056 0.739MEANIMM_RATE 2.364 1.370 3.446 -3.820 12.540NUMBERREPUBS 3.717 4.000 0.807 3.000 6.000ONEMINUSDI 0.315 0.310 0.124 0.098 0.547PERCENT_W_AD 0.098 0.092 0.025 0.061 0.164POPPCT_RURAL 26.628 26.265 14.646 5.050 61.340SPP_2014 11232.957 10435.000 3144.091 6500.000 20610.000STEPHENS_RCSR 63.391 64.500 13.924 30.000 100.000

Table 2.

Candidate Mean Median Standard Deviation Minimum MaximumTrump Votes 0.4609 0.4440 0.1753 0.0720 0.8060

Clinton Votes 0.5142 0.5129 0.1489 0.1362 0.8263

Table 3.

Variable Expected SignDropout Rate PositivePop Pct in Rural Communities PositiveStephens RCSR PositiveHC Per 100000 PositiveHG Per 100000 PositiveDiversity Index NegativeNumber of Republicans NegativePct With Advanced Degree NegativeSpending Per Pupil NegativeGini Coefficient ?Mean Immigration Rate ?

Regression 1.

Dependent Variable: TRUMPVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:14Sample: 1 46Included observations: 46

Variable Coefficient Std. Error t-Statistic Prob.

C 1.093971 0.556165 1.966990 0.0574ONEMINUSDI 0.097001 0.303290 0.319831 0.7511

DROPOUT_RATE 0.798192 0.477145 1.672848 0.1035GINI_COEFFICIENT__2013... -1.344724 0.865225 -1.554190 0.1294

MEANIMM_RATE 0.003571 0.008200 0.435512 0.6659NUMBERREPUBS -0.111947 0.031447 -3.559861 0.0011PERCENT_W_AD 1.552671 1.874362 0.828374 0.4132POPPCT_RURAL -0.001850 0.002791 -0.662842 0.5119

SPP_2014 5.10E-06 1.39E-05 0.367137 0.7158STEPHENS_RCSR 0.001354 0.002308 0.586734 0.5613

HCPER100000 0.030141 0.016933 1.779975 0.0840HGPER100000 0.343565 0.198442 1.731306 0.0925

R-squared 0.454657 Mean dependent var 0.460935Adjusted R-squared 0.278223 S.D. dependent var 0.175324S.E. of regression 0.148951 Akaike info criterion -0.750945Sum squared resid 0.754334 Schwarz criterion -0.273908Log likelihood 29.27174 Hannan-Quinn criter. -0.572244F-statistic 2.576918 Durbin-Watson stat 1.316855Prob(F-statistic) 0.016945

Regression 2.Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:18Sample: 1 46Included observations: 46


C 0.282970 0.356112 0.794610 0.4322ONEMINUSDI 0.930379 0.194804 4.775980 0.0000

DROPOUT_RATE -0.190737 0.306915 -0.621466 0.5383GINI_COEFFICIENT__2013... -0.012104 0.556498 -0.021751 0.9828

MEANIMM_RATE 0.002666 0.005258 0.506994 0.6153PERCENT_W_AD -1.626025 1.166216 -1.394274 0.1720POPPCT_RURAL 4.02E-05 0.001715 0.023451 0.9814

SPP_2014 7.61E-06 8.68E-06 0.876982 0.3865STEPHENS_RCSR 0.000844 0.001483 0.569470 0.5727

HCPER100000 -0.009977 0.010882 -0.916832 0.3655HGPER100000 0.041663 0.127355 0.327145 0.7455


Regression 3.

Regression 4.



C 1.476150 0.346776 4.256782 0.0001LOGREPUBS -0.491933 0.109880 -4.476991 0.0001

LOGHC 0.053828 0.024775 2.172670 0.0359LOGHGPER100000 0.089563 0.044711 2.003134 0.0521LOG(ONEMINUSDI) 0.089838 0.050532 1.777819 0.0832

LOGAD 0.147760 0.104381 1.415578 0.1648LOGGINI -0.340982 0.395402 -0.862366 0.3938


Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:11Sample: 1 46Included observations: 46


C 0.722439 0.192912 3.744919 0.0006LOGHC -0.036507 0.015195 -2.402620 0.0210

LOGHGPER100000 0.033212 0.026545 1.251140 0.2182LOG(ONEMINUSDI) 0.235681 0.030974 7.608948 0.0000

LOGAD -0.048793 0.063866 -0.763978 0.4494LOGGINI -0.061235 0.239815 -0.255342 0.7998


Regression 5.

Regression 6.



C 0.393219 0.044261 8.884129 0.0000HCPER100000 0.030924 0.016626 1.860013 0.0696


Dependent Variable: CLINTONVOTESMethod: Least SquaresDate: 11/14/16 Time: 16:06Sample: 1 46Included observations: 46


C 0.576507 0.037322 15.44666 0.0000HCPER100000 -0.028453 0.014019 -2.029534 0.0485


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