








































The 2016 Pennsylvania Presidential and U.S. Senate Elections


COMMONWEALTH, Volume 19, Issue 2 (2017). © 2017 The Pennsylvania Political Science Association.  
ISSN 2469-7672 (online). http://dx.doi.org/10.15367/com.v19i2.152. All rights reserved.

The 2016 Pennsylvania Presidential  
and U.S. Senate Elections

Breaking Pennsylvania’s Electoral Habits

BERWOOD YOST
Franklin & Marshall College

JACKIE REDMAN
Franklin & Marshall College

SCOTTIE THOMPSON
Franklin & Marshall College

This article uses pre-election survey data, post-election survey data, and voter 
registration and election data to interpret the outcomes of the 2016 presidential 
and U.S. Senate races in Pennsylvania. This analysis shows how changes in 
voter registration and voter turnout in specific areas of the Commonwealth, 
driven in large part by less-educated voters, those dissatisfied with the cur-
rent direction of the country, and the performance of the incumbent president, 
explain the 2016 election results.

That a Republican won the White House in 2016 was not, according to 
political science forecasters, a breathtaking surprise. The surprise was 
that Donald Trump was the Republican nominee and that he won not 

only the White House but also Pennsylvania, a state that had supported the 
Democratic presidential candidate in six straight elections. This article uses 
pre-election survey data, post-election survey data, and voter registration and 
election data to interpret the outcomes of the 2016 presidential and U.S. Senate 
races in Pennsylvania. This analysis shows that significant changes in voter 
registration and voter turnout in specific areas of the Commonwealth, driven 
in large part by less-educated voters, those dissatisfied with the current direc-
tion of the country, and the performance of the incumbent president, explain 
the 2016 election results.



4 Berwood Yost, Jackie redman, and scottie thompson

Data Sources

This article includes data from three primary sources. The pre-election poll-
ing data on candidate favorability, candidate preferences, and candidate char-
acteristics come primarily from Franklin & Marshall College Polls conducted 
during July, August, September, and October 2016. Interviewing dates and 
sample sizes for each survey are as follows: July 29–August 1 (n = 661), August 
25–29 (n = 736), September 28–October 2 (n = 813), and October 26–October 
30 (n = 863).1 The post-election polling data come from re-interviews with 
respondents from these four pre-election polls.2 The registered voter samples 
for these surveys were obtained from L2 and all sampled respondents were 
notified by letter about the survey. Interviews were completed over the phone 
and online depending on each respondent’s preference. Survey results were 
weighted (by age, gender, region, education, and party registration) using an 
iterative weighting algorithm to reflect the known distribution of those char-
acteristics as reported by the Pennsylvania Department of State.

The data on polling averages leading up to Election Day come from the 
Huffington Post Pollster website. County-level election and registration data 
come from the Pennsylvania Department of State. The election results for 
2016 were not official at the time the manuscript was being prepared, so final 
vote totals by county could differ slightly.

The Presidential Race

Political scientists expected the 2016 race for president to be closely contested 
because the election fundamentals predicted a tight contest. Still, the Trump 
candidacy caused some to wonder if such models would be valid because of 
his nontraditional candidacy—nontraditional both in the way he campaigned 
(relying on television advertising less and social media more than traditional 
candidates) and in the issues he emphasized. Some of Trump’s signature issues 
were at odds with traditional Republican positions, such as his opposition to 
free trade agreements, his concerns about NATO and foreign intervention, 
and his desire to undertake massive infrastructure spending. By the end of 
September, models that relied on the election fundamentals gave Clinton a 
modest advantage, but it was also clear that economic conditions and voter 
fatigue with Democrats were liabilities that had the potential to harm her 
candidacy (Campbell 2016). A great many contemporary newspaper accounts 
and some scholarship also documented a sizable disaffection among rural, 
white, non-college-educated voters that had the potential to carry Trump to 
victory despite his personal liabilities.3 The following section documents the 
candidate ratings, pre-election polling, and election outcomes for the 2016 
presidential race in Pennsylvania.



The 2016 Pennsylvania Presidential and U.S. Senate Elections  5

Candidate Ratings

Favorability
Both presidential candidates began the general election campaign as well-
known among voters and with more voters feeling negatively than positively 
about their candidacies. In July, more than nine in 10 voters were able to offer 
an opinion about how favorably they viewed both Trump (95%) and Clinton 
(96%). Donald Trump was perceived far more negatively than positively by 
Pennsylvania voters with a minus 29 favorability rating. Hillary Clinton was 
also perceived more negatively than positively, but her rating was only minus 
two. The ratings of both candidates, although fluctuating a bit during the 
campaigns with the most notable movement taking place around the time of 
the party conventions, eventually ended at about the same place they started, 
as Table 1 illustrates. One of the unusual features of this election was that a 
large proportion of voters (16%) had an unfavorable opinion of both candi-
dates.4 In 2012, only 3% of voters in Pennsylvania had an unfavorable opinion 
of both presidential candidates.5

Issues and Perceived Capabilities
Registered voters, when asked about the reasons they supported their candi-
date, most often said their candidate was the better option; Clinton voters also 
believed she was more qualified, while Trump’s voters preferred him because 
he was not a typical politician. Specifically, in response to the question “What 
is the main reason you plan to vote for [the candidate]?” Clinton’s supporters 
believed she was the better option (35%), was the more qualified (30%), was 
more aligned with their political views (17%), or had some desirable personal 

table 1. Favorability ratings of presidential candidates, pennsylvania 2016

candidate month Favorable Unfavorable
net Favorable 
rating

aware of 
candidate

Donald 
Trump

July 0.33 0.62 –0.29 0.95

Aug. 0.37 0.58 –0.21 0.95

Sept. 0.32 0.60 –0.28 0.92

Oct. 0.35 0.62 –0.27 0.97

Hillary 
Clinton

July 0.47 0.49 –0.02 0.96

Aug. 0.38 0.54 –0.16 0.92

Sept. 0.47 0.50 –0.03 0.97

Oct. 0.46 0.52 –0.06 0.98

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



6 Berwood Yost, Jackie redman, and scottie thompson

characteristic (11%). Trump’s supporters most often preferred him because 
they thought he was the better option (30%), because he was not a politician 
(17%), because his political views better aligned with theirs (16%), and because 
they believed he could fix the country’s problems (13%). Registered voters 
provided consistent responses throughout the campaign about their reasons 
for supporting each candidate.

Registered voters were also relatively consistent in which candidate they 
believed was better described by a series of statements about their perceived 
strengths. Secretary Clinton had a clear advantage in having sufficient expe-
rience to be president and in her ability to handle foreign policy, as Figure 1 
shows. Clinton and Trump were rated similarly, however, in the ability to 
encourage change and to fix the economy.

Figure 1. Percentage of Respondents Who Believe Trump or Clinton Is Best Described by 
Each Statement, by Month of Survey. Poll question: “Regardless of how you plan to vote, 
which of these Presidential candidates do you think is best described by each of the following 
statements?” (Franklin & Marshall College Polls.)



The 2016 Pennsylvania Presidential and U.S. Senate Elections  7

Pre-election Polling

Poll Averages
The average of polls conducted in Pennsylvania suggested that Hillary Clinton 
led Donald Trump throughout the entire fall campaign. Her monthly average 
lead was four points in July, eight points in August, five points in September, 
seven points in October, and three points in November.6 Only rarely did indi-
vidual polls show either candidate with support from a majority of voters; the 
averages in the final week showed Clinton’s expected vote share at 47% and 
Trump’s expected vote share at 44%. The monthly polling averages showed a 
sizable number of undecided and other voters that, given the tightening of the 
race over the final weeks, suggested movement toward Trump. The undecided 
and third-party candidates totaled nearly 20 points in July, 19 in August and 
September, 14 in October, and 11 in November. The rate of undecided and 
third party voters was much higher in 2016 than in 2012. Polls released in the 
final month of the 2012 campaign found only 5% of voters were undecided 
or planning to vote for a third-party candidate.7 Figure 2 shows the results of 

Figure 2. Pennsylvania 2016 Presidential Polling Results, July–November 2016. (Compiled and 
created by the authors using data downloaded from Huffpost Pollster, December 6, 2016.)



8 Berwood Yost, Jackie redman, and scottie thompson

pre-election polls conducted during the fall campaign. Clinton’s share of the 
vote appeared to be increasing during the course of the fall campaign at the 
same time the margin between the two candidates converged in November.

Support within Demographic Subgroups
Much of the media narrative during the 2016 election had to do with sizable 
differences in the votes of white and nonwhite voters and the votes of college-
educated and non-college-educated voters. These differences were appar-
ent in Pennsylvania. Besides his expected support among conservatives and 
Republicans, Donald Trump’s strongest support throughout the campaign 
came from white, non-college-graduates. In addition, there were geographic 
differences likely reflecting some cultural or social features amplified by this 
demographic support within specific areas of the state. Notably, pre-election 
polls showed Trump performing strongly in western Pennsylvania outside 
of Pittsburgh. Figure 3 presents the demographic support for the candidates 
during the fall campaign.

Figure 3. Clinton Advantage or Disadvantage in Percentage Points among Demographic 
Subgroups, by Month of Survey. (Franklin & Marshall College Polls.)



The 2016 Pennsylvania Presidential and U.S. Senate Elections  9

Election Results

Donald Trump won a narrow and to many a surprising victory over Hillary 
Clinton in Pennsylvania, 48.6% to 47.9%. Trump’s triumph came from having 
a significant advantage among late-deciding voters. Nearly all (97%) of the 
respondents who planned to vote for Trump in Franklin & Marshall’s pre-
election polls and who made their final decision in the last week of the cam-
paign did vote for him, while only three in four (74%) who planned to vote for 
Clinton and made their final decision in the last week of the campaign voted 
for her.8 The other sizable advantage for Trump came from voters who were 
undecided in our pre-election polls: Trump had a sizable advantage whether 
these undecided voters decided in the final week or earlier.9 Table 2 shows 
the composition of presidential voters pre- and post-election in Pennsylvania. 
Most voters had consistent preferences pre- and post-election. Trump had two 
major advantages: more voters who supported Clinton pre-election moved 
away from her than moved away from him, and more voters who preferred 
neither candidate prior to the election voted for Trump than for Clinton.

Post-election Survey Results: Regression Analysis
A logistic regression was performed to determine the effects of demographic 
characteristics (gender, age, education, race, marital status, employment, 
union membership, and veteran status), political characteristics (partisan 
affiliation and political ideology), and attitudinal characteristics (Obama 
job performance, expected future financial condition, and direction of the 
country) on the likelihood that respondents voted for Donald Trump for 
president. The logistic regression model was statistically significant, χ2(23) = 
1456.02, p < .0001. The model explained 82.3% (Nagelkerke R2) of the vari-
ance in presidential vote choice and correctly classified 92% of cases. Model 
sensitivity was 89.1% and model specificity was 92.6%. Table 3 provides the 
logistic regression coefficients and confidence intervals for the variables in 
the model.

table 2. change in Voter preferences pre- and post-election as proportion of all 
Voters, pennsylvania 2016

pre-election preference

post-election preference

clinton trump neither

Clinton 43.5% 0.6% 0.2%

Trump — 41.5% 0.2%

Neither 4.2% 6.7% 3.3%

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



10 Berwood Yost, Jackie redman, and scottie thompson

Republicans were more likely to vote for Trump than were Democrats, 
and conservatives were more likely to vote for Trump than were liberals. Mod-
erates were also less likely than conservatives to vote for Trump. Educational 
attainment also predicted vote choice: those with a high school degree or less 
and those who attended some college were more likely to vote for Trump than 
were college graduates. Those who believed the United States is on the wrong 
track were more likely to vote for Trump, and those who believed Obama was 
doing a poor job as president were more likely to vote for Trump.

table 3. Logistic regression model for trump preference, pennsylvania 2016

dependent Variable: probability Voting for trump

Party independent 0.134 (–0.566, 0.834)

Party Republican 1.616*** (1.116, 2.116)

Male 0.300 (–0.162, 0.761)

Over 55 –0.239 (–0.830, 0.351)

Under 35 –0.624 (–1.490, 0.243)

White 0.479 (–0.507, 1.466)

High school or less 1.238*** (0.654, 1.822)

Some college 0.877*** (0.363, 1.391)

Liberal –1.758*** (–2.460, –1.057)

Moderate –0.760*** (–1.218, –0.301)

Urban county –0.193 (–0.702, 0.317)

Not married 0.299 (–0.291, 0.890)

Single, never married 0.336 (–0.361, 1.032)

Not retired or employed –0.375 (–0.968, 0.218)

Retired 0.300 (–0.262, 0.863)

United States on wrong track 1.120*** (0.515, 1.726)

Obama poor job 3.597*** (2.951, 4.244)

Obama excellent job –1.520** (–3.009, –0.031)

Obama fair job 1.804*** (1.220, 2.388)

Better finances next year 0.402 (–0.150, 0.955)

Worse finances next year 0.074 (–0.568, 0.716)

Labor union Household –0.169 (–0.715, 0.377)

Veteran –0.278 (–0.877, 0.321)

Constant –3.797*** (–5.203, –2.392)

Observations 1,556
Log Likelihood –314.399
Akaike Inf. Crit. 676.798

Note: *p < 0.1; **p < 0.05; ***p < 0.01

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



The 2016 Pennsylvania Presidential and U.S. Senate Elections  11

Comparison with 2012 by County
There were strong and consequential regional differences in the presiden-
tial election results in Pennsylvania. Hillary Clinton outperformed Barack 
Obama’s vote totals in nonrural counties by 18,789 votes, but underperformed 
his vote totals by 82,606 votes in the state’s rural counties. These stark dif-
ferences in voter preference are evident regionally. Clinton outperformed 
Obama by 52,258 votes in the Southeast and by 14,930 votes in Allegheny 
County, but underperformed his vote in all other parts of the state: Central 
(34,419), Northwest (34,072), Northeast (29,351), Southwest (28,382), and Phil-
adelphia (4,781). Only 11 of the state’s 67 counties yielded more votes for the 
Democratic presidential candidate in 2016 than 2012. Figure 4 presents the 
county-level changes in support for the Democratic presidential candidate 
in Pennsylvania between 2012 and 2016. The counties in Figure 4 are color-
coded by geographic region.10

Figure 4. Change in County Presidential Votes, Pennsylvania, 2012 and 2016. Counties are 
color-coded by geographic region. (Compiled and created by the authors using data downloaded 
from the Pennsylvania Department of State website, January 24, 2017.)



12 Berwood Yost, Jackie redman, and scottie thompson

The U.S. Senate Race

Incumbent Senator Pat Toomey was considered one of the most vulnerable 
Senate Republican incumbents in the 2016 election cycle. Although Toomey 
had significant crossover appeal, there was a belief that a strong Clinton vic-
tory in the state would be too much for him to overcome. During the cam-
paign, Toomey emphasized his ability to work with Democrats and cited 
his efforts to close loopholes in gun background checks as proof. Toomey’s 
challenger, Katie McGinty, touted her working-class roots and ran as a tra-
ditional Democrat while wholeheartedly embracing Hillary Clinton’s candi-
dacy. Toomey, on the other hand, seemed deeply concerned that his party’s 
candidate might harm his chances of re-election. Toomey never endorsed 
Trump and he would not say whether he intended to vote for Trump until 
he admitted voting for the candidate one hour prior to the polls closing on 
election night (Stolberg 2016). Because of Toomey’s perceived vulnerabilities, 
money from both sides poured into Pennsylvania and the Pennsylvania Sen-
ate race became the most expensive in the 2016 cycle; spending in the race 
was nearly $125 million.11 The following section documents the candidate 
ratings, pre-election polling, and election outcomes for the 2016 U.S. Senate 
race in Pennsylvania.

Candidate Ratings

Favorability
Neither U.S. Senate candidate began the general election with high name 
recognition and incumbent Senator Pat Toomey had clear disadvantages in 
his personal popularity and job performance ratings as the fall campaign 
began. In July, fewer than two in three voters were able to offer an opinion 
about how favorably they viewed Toomey (63%) and only one in two (50%) 
were able to offer an opinion about McGinty. By the end of the campaign 
more than three in four (77%) voters were able to offer an opinion about 
both candidates.

Senator Toomey was perceived far more negatively than his challenger 
throughout the campaign; he had a net favorability rating of minus 17 by the 
end of October compared to McGinty’s net favorability of plus one.12 Mir-
roring the course of the presidential campaign, the ratings of both Senate 
candidates eventually ended at about the same place they started, as Table 4 
illustrates. More of a concern for Toomey than his lack of personal popularity 
was that only one in three (29%) voters believed he was doing an “excellent” 
or “good” job as U.S. senator by late October.



The 2016 Pennsylvania Presidential and U.S. Senate Elections  13

Pre-election Polling

Poll Averages
The polling averages in the U.S. Senate race in Pennsylvania showed a rela-
tively tight race throughout the fall until there was some late movement toward 
McGinty as Election Day approached. The polling averages suggested the race 
was tied in July, but that McGinty’s lead increased to about three points in 
August, slipped to one point in September and October, and then rose to three 
points in November.13 Only rarely did individual polls show either candidate 
with support from a majority of voters; the averages in the final week showed 
McGinty’s expected vote share at 47% and Toomey’s expected vote share at 
44%. The estimated vote share in the Senate race mirrored the presidential 
vote shares precisely, suggesting there would be a strong relationship between 
the presidential and Senate votes. Another key feature of this race was the large 
proportion of undecided and third-party voters reported throughout the cam-
paign. Undecided and third-party voters totaled 21 points in August, 24 points 
in September, 15 points in October, and 13 points in November. Figure 5 shows 
the results of pre-election polls conducted during the fall campaign. As with 
Clinton, McGinty’s share of the vote appeared to be increasing during the 
course of the fall campaign although, unlike the presidential race, the margin 
between the two candidates appeared to be expanding in November.

Support within Demographic Subgroups
Pat Toomey’s performance in pre-election polling among demographic sub-
groups was similar to Trump’s. He did well as expected among conservatives 

table 4. Favorability ratings of U.s. senate candidates, pennsylvania 2016

candidate month Favorable Unfavorable
net Favorable 
rating

aware of 
candidate

Katie McGinty July 0.25 0.25 0.00 0.50

Aug. 0.28 0.29 –0.01 0.57

Sept. 0.33 0.33 0.00 0.66

Oct. 0.39 0.38 0.01 0.77

Pat Toomey July 0.23 0.40 –0.17 0.63

Aug. 0.29 0.37 –0.08 0.66

Sept. 0.30 0.39 –0.09 0.69

Oct. 0.30 0.47 –0.17 0.77

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



14 Berwood Yost, Jackie redman, and scottie thompson

and Republicans and his pattern of support geographically was also similar. 
Figure 6 presents the demographic support for the candidates during the fall 
campaign.

Election Results

Pat Toomey won a narrow victory over Katie McGinty in Pennsylvania, 48.8% 
to 47.3%. Table 5 shows the composition of U.S. Senate voters pre- and post-
election in Pennsylvania. Most voters had consistent preferences pre- and 
post-election. More voters who preferred neither candidate prior to the elec-
tion voted for Toomey than McGinty.

Post-election Survey Results: Regression Analysis
A logistic regression was performed to determine the effects of demographic 
characteristics (gender, age, education, race, marital status, employment, 
union membership, and veteran status), political characteristics (partisan 

Figure 5. Pennsylvania 2016 U.S. Senate Polling Results, July–November 2016. (Compiled and 
created by the authors using data downloaded from Huffpost Pollster, December 6, 2016.)



The 2016 Pennsylvania Presidential and U.S. Senate Elections  15

Figure 6. McGinty Advantage or Disadvantage in Percentage Points among Demographic 
Subgroups, by Month of Survey. (Franklin & Marshall College Polls.)

table 5. change in Voter preferences pre- and post-election as proportion of all 
Voters, pennsylvania 2016

pre-election preference

post-election preference

mcGinty toomey neither

McGinty 38.4%   1.4% 0.2%

Toomey   1.1% 36.3% 0.8%

Neither   8.5% 10.3% 3.0%

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.

affiliation and political ideology), and attitudinal characteristics (Obama job 
performance, expected future financial condition, and direction of the coun-
try) on the likelihood that respondents voted for Pat Toomey for U.S. Senate. 
The logistic regression model was statistically significant, χ2(23) = 1458.47, 
p < .0001. The model explained 81.7% (Nagelkerke R2) of the variance in U.S. 



16 Berwood Yost, Jackie redman, and scottie thompson

Senate vote choice and correctly classified 91% of cases. Model sensitivity was 
89.3% and model specificity was 93%. Table 6 provides the logistic regression 
coefficients and confidence intervals for the variables in the model.

Republicans and independents were more likely to vote for Toomey than 
Democrats. Conservatives were more likely to vote for Toomey than were 
liberals or moderates. Those who believe the United States is on the wrong 
track were more likely to vote for Toomey, and those who believed Obama was 

table 6. Logistic regression model for toomey Vote share, pennsylvania 2016

dependent Variable: probability Voting for toomey

Party independent 0.658** (0.046,  1.269)

Party Republican 1.799*** (1.343,  2.255)

Male –0.077 (–0.516,  0.362)

Over 55 –0.385 (–0.968,  0.198)

Under 35 –0.185 (–1.033,  0.663)

White 0.492 (–0.410,  1.394)

High school or less –0.212 (–0.755,  0.332)

Some college 0.091 (–0.410,  0.593)

Liberal –3.069*** (–3.751, –2.387)

Moderate –1.228*** (–1.696, –0.760)

Urban county 0.109 (–0.398,  0.616)

Not married –0.171 (–0.739,  0.397)

Single, never married –0.405 (–1.078,  0.268)

Not retired or employed –0.930*** (–1.507, –0.354)

Retired –0.207 (–0.762,  0.347)

United States on wrong track 1.076*** (0.546,  1.606)

Obama poor job 2.314*** (1.720,  2.908)

Obama excellent job –1.655*** (–2.743, –0.568)

Obama fair job 1.495*** (0.958,  2.032)

Better finances next year 0.347 (–0.174,  0.868)

Worse finances next year 0.121 (–0.544,  0.787)

Labor union household –0.531** (–1.052, –0.010)

Veteran –0.414 (–1.001,  0.173)

Constant –1.310** (–2.580, –0.040)

Observations 1,556
Log Likelihood –333.978
Akaike Inf. Crit. 715.956

Note: *p < 0.1; **p < 0.05; ***p < 0.01

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



The 2016 Pennsylvania Presidential and U.S. Senate Elections  17

doing a poor job as president were more likely to vote for Toomey. Labor union 
households were less likely than nonunion households to vote for Toomey.

Comparison with 2012 by County
As in the presidential race, the change in support for the Democratic can-
didate at the county level was substantial. McGinty received fewer votes 
than Senator Bob Casey received in 2012 in all but five Pennsylvania coun-
ties. McGinty received significantly fewer votes than Casey in Lackawanna 
and Luzerne counties in the northeastern part of the state and from western 
counties, including Beaver, Cambria, Erie, Washington, and Westmoreland. 
Regionally, McGinty underperformed Bob Casey by 5,009 votes in Allegheny 
County, by 43,107 votes in central Pennsylvania, by 62,708 votes in western 
Pennsylvania, and by 12,077 votes in Philadelphia and the Southeast. Figure 
7 displays the differences in the Democratic U.S. Senate voting by county 
between 2012 and 2016.

Figure 7. Change in County U.S. Senate Votes, Pennsylvania, 2012 and 2016. Counties are 
color-coded by geographic region. (Compiled and created by the authors using data downloaded 
from the Pennsylvania Department of State website, January 24, 2017.)



18 Berwood Yost, Jackie redman, and scottie thompson

Split-Ticket Voting

Increased partisanship has produced less split-ticket voting in recent elec-
tions (Beck et al.1992), but Donald Trump’s nontraditional positions on key 
issues raised the possibility that split-ticket voting might be more common 
in 2016. Post-election survey data show that among those who cast a vote 
for both president and Senate, one in 10 (9%) split their tickets.14 A split-
ticket voter is defined as a voter who voted for Trump for president but not 
Toomey for senator, or as a voter who voted for Toomey for Senate but not 
Trump for president. Figure 8 displays the geography of split-ticket voting 
in Pennsylvania for the 2016 election. Voters in southeastern Pennsylva-
nia cast more votes for Senator Toomey than President Trump, while many 
more voters in western and northeastern Pennsylvania voted for President 
Trump.15

Figure 8. Difference in County Presidential and U.S. Senate Votes, Pennsylvania, 2016. 
Counties are color-coded by geographic region. (Compiled and created by the authors using data 
downloaded from the Pennsylvania Department of State website, January 24, 2017.)



The 2016 Pennsylvania Presidential and U.S. Senate Elections  19

Table 7 presents the regional variation evident in the vote differences 
between the Republican presidential and Senate candidates in 2012 and 2016 
and demonstrates a likely increase in Republican ticket splitting. In 2012, Mitt 
Romney’s vote totals exceeded Senate candidate Tom Smith’s in every region 
of the state, with a difference between the two candidates of about 146,000 
votes, which was about 5% of the votes cast for Romney. In 2016, Toomey 
actually outperformed Trump in Allegheny County, Philadelphia, and in the 
Southeast. The absolute difference in votes cast for the two candidates was 
about 182,000 votes or about 6% of the presidential vote total. In raw votes, 
Romney outperformed Trump in Allegheny County and the Southeast, while 
Toomey outperformed Smith in all parts of the state. These differences hint 
at the slightly different coalitions assembled by the two candidates; these dif-
ferences are discussed in the next section.

table 7. regional Vote totals for republican presidential candidates in 
pennsylvania, 2012 and 2016

region

total 
Vote for 
president 
2016

registered 
Voters 
2016

trump 
Votes

toomey 
Votes turnout

trump 
minus 
toomey 
Votes

Allegheny 650,114 920,346 259,480 261,316 70.6%  (1,836)

Central 1,600,415 2,267,666 979,503 957,106 70.6% 22,397

Northeast 723,607 1,066,205 378,653 352,135 67.9% 26,518

Northwest 515,001 743,183 324,435 307,329 69.3% 17,106

Philadelphia 707,631 1,082,240 108,748 116,714 65.4%  (7,966)

Southeast 1,345,460 1,788,298 553,906 625,499 75.2% (71,593)

Southwest 573,218 791,562 366,039 331,672 72.4% 34,367

region

total 
Vote for 
president 
2012

registered 
Voters 
2012

romney 
Votes

smith 
Votes turnout

romney 
minus 
smith 
Votes

Allegheny 634,608 916,470 262,039 236,546 69.2% 25,493

Central 1,491,431 2,167,498 860,579 820,589 68.8% 39,990

Northeast 665,181 1,038,882 305,170 282,261 64.0% 22,909

Northwest 496,141 722,563 275,170 266,370 68.7%   8,800

Philadelphia 690,724 1,055,319 96,467 84,461 65.5% 12,006

Southeast 1,274,517 1,712,173 566,653 534,492 74.4% 32,161

Southwest 543,385 801,859 314,356 284,413 67.8% 29,943

Source: Pennsylvania Department of State, available at http://www.dos.pa.gov/Pages/default.aspx. 
Calculations by the authors.



20 Berwood Yost, Jackie redman, and scottie thompson

Regression Analysis: Split-Ticket Voting

A logistic regression was performed to determine the effects of demographic 
characteristics (gender, age, education, race, marital status, employment, 
union membership, and veteran status), political characteristics (partisan 
affiliation and political ideology), and attitudinal characteristics (Obama 
job performance, expected future financial condition, and direction of the 
country) on the likelihood that respondents split their tickets between the 
Republican presidential and Senate candidates. The logistic regression model 
was statistically significant, χ2(23) = 139.2, p < .0001. The model explained 
18.5% (Nagelkerke R2) of the variance in split-ticket voting and correctly clas-
sified 90.6% of cases. Model specificity was 90.6%. Table 8 provides the logistic 
regression coefficients for the model.

Independent voters were more likely than Republicans or Democrats to 
split their tickets. Moderates were more likely and liberals less likely than 
conservatives to split their tickets. Those who rated Obama’s job performance 
as excellent were less likely to split their tickets, and those who rated Obama’s 
job performance as fair were more likely to do so.

Voter Turnout

High voter turnout was a determinative factor in the presidential and Sen-
ate races. More than seven in 10 (70.6%) registered voters cast a ballot in the 
presidential election in 2016, which was higher as a proportion of registered 
voters than either 2008 or 2012. Table 7 earlier showed the changes in regional 
voter turnout between 2012 and 2016. That table showed increased turnout in 
every region of the state except Philadelphia and that turnout in the Northeast 
(3.8%) and Southwest (4.7%) showed the largest comparative change. Table 
9 shows the regional changes in voter registration from 2012 to 2016. Since 
2012, Republican registration increased markedly in western and central 
Pennsylvania.

The patterns of voter turnout were remarkable and are apparent in Figure 
9. Figure 9 plots the turnout among registered voters in relation to Republican 
performance relative to Republican registration in each county. The upper-
right-hand quadrant of Figure 9 (turnout above the state average of 71% on 
the x-axis and Republican performance that exceeded Republican registra-
tion, a ratio of 1.0 on the y-axis) shows that the Republican victory was made 
possible primarily by a surge in Republican performance in western Penn-
sylvania. The central Pennsylvania counties, the traditional Republican “T,” 
underperformed relative to registration. Given the relatively high turnout in 



The 2016 Pennsylvania Presidential and U.S. Senate Elections  21

southeastern Pennsylvania, these overperforming counties coupled with low 
turnout in Philadelphia and Centre counties, made the difference for both 
Trump and Toomey.

The changes in turnout and in the Democratic share of the presidential 
vote between 2012 and 2016 display the county-level dynamics underlying the 
election. Table 10 shows the changes in turnout and Democratic vote share for 

table 8. Logistic regression model for ticket splitting, pennsylvania 2016

dependent Variable: probability of splitting ticket for republican candidates

Party independent 0.839*** (0.258, 1.419)

Party Republican 0.290 (–0.215, 0.795)

Male –0.253 (–0.653, 0.146)

Over 55 –0.207 (–0.717, 0.303)

Under 35 0.265 (–0.443, 0.973)

White 0.644 (–0.438, 1.725)

High school or less –0.417 (–0.942, 0.107)

Some college –0.306 (–0.761, 0.150)

Liberal –1.097*** (–1.838, –0.355)

Moderate 0.394* (–0.058, 0.846)

Urban county 0.101 (–0.359, 0.561)

Not married 0.017 (–0.518, 0.551)

Single, never married 0.046 (–0.535, 0.628)

Not retired or employed –0.061 (–0.574, 0.453)

Retired 0.101 (–0.394, 0.597)

United States on wrong track 0.064 (–0.470, 0.598)

Obama poor job –0.450 (–1.090, 0.190)

Obama excellent job –1.946*** (–3.006, –0.887)

Obama fair job 0.683** (0.118, 1.249)

Better finances next year –0.018 (–0.486, 0.449)

Worse finances next year 0.131 (–0.447, 0.708)

Labor union household –0.371 (–0.892, 0.149)

Veteran 0.425 (–0.095, 0.946)

Constant –2.642*** (–4.011, –1.273)

Observations 1,556
Log Likelihood –414.800
Akaike Inf. Crit. 877.600

Note: *p < 0.1; **p < 0.05; ***p < 0.01

Source: July, August, September, and October 2016 Franklin & Marshall College Polls.



table 9. changes in Voter registration between 2012 and 2016 by region, pennsylvania

region
dem 
2016 rep 2016

dem 
2012 rep 2012

dem 
change

rep 
change

% dem 
change

% rep 
change

Allegheny 538,103 258,946 553,099 249,086 (14,996) 9,860 –2.7% 4.0%

Central 808,739 1,128,228 803,384 1,063,054 5,355 65,174 0.7% 6.1%

Northeast 520,439 384,266 531,068 357,499 (10,629) 26,767 –2.0% 7.5%

Northwest 308,086 341,818 325,078 314,900 (16,992) 26,918 –5.2% 8.5%

Philadelphia 839,165 123,307 824,130 129,720 15,035 (6,413) 1.8% –4.9%

Southeast 788,803 726,612 740,803 717,744 48,000 8,868 6.5% 1.2%

Southwest 385,344 319,258 436,961 282,865 (51,617) 36,393 –11.8% 12.9%

totals 4,188,679 3,282,435 4,214,523 3,114,868 (25,844) 167,567 –0.6% 5.4%

Source: Pennsylvania Department of State, available at http://www.dos.pa.gov/Pages/default.aspx. Calculations by the 
authors.

Figure 9. Registered Voter Turnout and Republican Presidential Performance by County, 
Pennsylvania, 2016. Counties are color-coded by geographic region. (Compiled and created by 
the authors using data downloaded from the Pennsylvania Department of State website, January 
24, 2017.)



The 2016 Pennsylvania Presidential and U.S. Senate Elections  23

five counties, three with above-average turnout and above-average Republican 
performance and two with below-average turnout and below-average Republi-
can performance. Turnout was markedly higher in the three western Pennsyl-
vania counties included in the table, while the share of votes won by Hillary 
Clinton declined substantially in these counties compared to Barack Obama. 
In the two counties expected to favor Clinton, turnout actually declined in 
both and Clinton’s share of the vote compared to Obama’s decreased in one 
of them. This dynamic perfectly captures what happened in Pennsylvania in 
2016: counties with more working-class voters turned out in greater numbers 
and gave less support to Democratic candidates than in previous elections, 
while areas that should have been supportive of Democrats had lower turnout 
and offered little change in support.

Discussion

This analysis shows that significant changes in voter registration and voter 
turnout in specific areas of the Commonwealth, driven in large part by less-
educated voters, those dissatisfied with the current direction of the country, 
and the performance of the incumbent president, best explain the 2016 elec-
tion results in Pennsylvania.

Trump overcame his pre-election polling deficit for two major reasons: 
more voters who supported Clinton pre-election moved away from her than 
moved away from him, and more voters who preferred neither candidate prior 
to the election voted for Trump rather than for Clinton. The high number of 
undecided and third-party voters throughout the campaign should have been 
a warning that such movement was possible, particularly in light of common 
electoral models that highlighted Clinton’s many potential vulnerabilities.

table 10. turnout and democratic Vote share for selected pennsylvania counties, 
2012 and 2016

county region clinton %
turnout 
2016 obama %

turnout 
2012

Beaver Southwest 38.9% 73.8% 45.3% 71.3%

Cambria Southwest 29.9% 73.0% 40.1% 69.9%

Centre Central 48.7% 62.8% 48.5% 65.3%

Philadelphia Philadelphia 82.5% 65.4% 85.2% 65.5%

Westmoreland Southwest 32.8% 74.1% 37.4% 71.9%

Source: Pennsylvania Department of State, available at http://www.dos.pa.gov/Pages/default.aspx. 
Calculations by the authors.



24 Berwood Yost, Jackie redman, and scottie thompson

The foundational importance of Trump’s working-class supporters mani-
fested itself by shifting traditional voting patterns in the state. Educational 
attainment predicted vote choice: those with a high school degree or less and 
those who attended some college were more likely to vote for Trump than 
were college graduates. The desire for change was also clear in the election 
results: those who believed the United States is on the wrong track were more 
likely to vote for Trump, and those who believed Obama was doing a poor job 
as president were more likely to vote for Trump. Taken together, these factors 
led to strong and consequential regional differences in the presidential elec-
tion results in Pennsylvania compared to prior elections. Only 11 of the state’s 
67 counties yielded more votes for the Democratic presidential candidate in 
2016 than 2012.

As in the presidential race, the change in support for the Democratic 
Senate candidate between 2012 and 2016 at the county level was substantial. 
McGinty received fewer votes than Senator Bob Casey received in 2012 in all 
but five Pennsylvania counties. Toomey voters were similar to Trump vot-
ers in their desire for change: those who believed the United States is on the 
wrong track were more likely to vote for Toomey as were those who believed 
Obama was doing a poor job as president.

But Toomey had a slightly different coalition than Trump. There were no 
educational differences among Toomey supporters, he attracted fewer inde-
pendents than Trump, and union members were also less likely to support 
him. His support was more conservative and traditionally Republican than 
was Trump’s, although there can be little doubt that the surge in Republican 
turnout driven by Trump’s candidacy made the difference for Toomey.

High voter turnout was a determinative factor in the presidential and Sen-
ate races. More than seven in 10 (70.6%) registered voters cast a ballot in the 
presidential election in 2016, which was higher as a proportion of registered 
voters than either 2008 or 2012. Since 2012, Republican registration increased 
markedly in western and central Pennsylvania. The Republican victory was 
made possible primarily by that surge in Republican performance in western 
Pennsylvania.

This dynamic perfectly captures what happened in Pennsylvania in 2016: 
counties with more working-class voters turned out in greater numbers and 
gave less support to Democratic candidates than in previous elections, while 
areas that should have been supportive of Democrats had lower turnout and 
offered little change in their support for Democratic candidates.

Trump’s victory in Pennsylvania scrambles what has been the state’s tra-
ditional electoral habits and raises significant and important political ques-
tions about future state elections. The main questions include the durability 



The 2016 Pennsylvania Presidential and U.S. Senate Elections  25

of Trump’s electoral coalition, his ability to transfer his voters’ energy to other 
Republican candidates in future elections, and the calculus that other elected 
and aspiring Republicans make in embracing his nontraditional positions. 
The answers to these questions will make clear the battle lines for future state 
races, beginning with the 2018 gubernatorial race.

NOTES

1. Most of the interviewing for the October survey (625 of the 863 interviews) was 
conducted prior to the FBI’s statement about Secretary Clinton’s emails on October 28.

2. The Center for Opinion Research at Franklin & Marshall College completed post-
election interviews with 2,287 of the 3,077 individuals (74%) who had participated in our 
July, August, September, and October 2016 pre-election polls. The post-election interviews 
were completed over the telephone (n = 1,202) or using a self-administered online (n = 
974) or paper and pencil (n = 111) format. Post-election interviews were completed from 
November 16, 2016 to January 13, 2017. The response rates for the post-election survey 
by month of pre-election interview were as follows: July (n = 487/661) = 74%, August (n 
= 541/736) = 73%, September (n = 602/813) = 74%, and October (n = 657/863) = 76%. The 
post-election survey did not verify whether respondents actually voted, so it is possible 
that some of those interviewed did not vote.

3. An excellent scholarly description of these voters is Cramer 2016. Examples of 
contemporary news accounts of these voters included Seib and O’Connor, “Republicans 
Rode Waves of Populism until They Crashed the Party.” Wall Street Journal, October 26, 
2016; Bob Davis and Gary Fields, “The Great Unraveling,” Wall Street Journal, September 
15, 2016; and Keith O’Brien, “Uprising in the Rust Belt,” Politico Magazine, June 24, 2016.

4. This estimate is confirmed by the 2016 Pennsylvania exit poll estimate: Edison 
Research exit polls showed 17% of Pennsylvania voters had an unfavorable opinion of 
both candidates. The exit polling showed that these voters broke for Trump 56% to 31%.

5 These ratings are from the October 2012 Franklin & Marshall College Poll. The poll 
showed that Mitt Romney’s net favorability rating was minus four and Barack Obama’s 
net favorability was plus four.

6. Huffpost Pollster 2016a. 
7. Authors calculation from data compiled: Yost and Borick 2013.
8. Although the survey asked voters in our post-election interviews when they 

decided they would vote for their candidate, it did not specifically ask about the effect on 
their choice of the FBI director’s October 28 statement about reopening the investigation 
into Secretary Clinton’s email server.

9. During the campaign, many speculated that there was a “hidden” Trump vote. It 
is possible that those who made their decision in October or before while claiming to be 
undecided were, in fact, hiding their support for Trump.

10. The regional breakdowns used in this article are those used by the Franklin & 
Marshall College Poll. For the counties included in each region, see https://www.fandm 
.edu/fandmpoll/survey-releases.

11. Data were provided by opensecrets.org, accessed January 29, 2017.
12. In the 2012 Senate race, Republican Tom Smith’s net favorability was even, 

although only about half (54%) of voters recognized him, and Bob Casey’s net favorabil-
ity was plus five, according to the October 2012 Franklin & Marshall College Poll (https://
www.fandm.edu/fandmpoll/survey-releases).



26 Berwood Yost, Jackie redman, and scottie thompson

13. Huffpost Pollster 2016b. 
14. The proportion of voters who cast a vote for both parties in this election cycle 

would likely be much higher had I included all the different races on the ballot. For 
instance, Democratic candidates won all of the state’s row offices in November, with more 
than 50% of ballots cast.

15. Although there were sizable differences in votes cast, there was a significant cor-
relation in vote share. The correlation in county-level vote share for Toomey and Trump 
is r(65) = 0.95, p < .001.

REFERENCES

Beck, Paul Allen, Lawrence Baum, Aage R. Clausen, and Charles E. Smith, Jr. 1992. “Pat-
terns and Sources of Ticket Splitting in Subpresidential Voting.” American Political 
Science Review 86 (December): 916–928.

Campbell, James E. 2016. “Seeing the Forest for the Trees: Presidential Election Fore-
casts and the Fundamentals.” Sabato’s Crystal Ball 14 (September 22). Available at 
http://www.centerforpolitics.org/crystalball/articles/seeing-the-forest-for-the-trees 
-presidential-election-forecasts-and-the-fundamentals/. Accessed September 23, 
2016. 

Center for Opinion Research. 2012. “October 2012 Franklin & Marshall College Poll—
Statewide.” Available at https://www.fandm.edu/uploads/files/427891958633141665 
-keyoct12-1.pdf. Accessed January 27, 2017.

Cramer, Katherine. 2016. The Politics of Resentment: Rural Consciousness in Wisconsin and 
the Rise of Scott Walker. Chicago: University of Chicago Press.

Huffpost Pollster. 2016a. “2016 Pennsylvania President: Trump vs. Clinton.” Available at 
http://elections.huffingtonpost.com/pollster/2016-pennsylvania-president-trump-vs 
-clinton. Accessed December 6, 2016.

———. 2016b. “2016 Pennsylvania Senate: Toomey vs. McGinty.” Available at http://elec 
tions.huffingtonpost.com/pollster/2016-pennsylvania-senate-toomey-vs-mcginty. 
Accessed December 6, 2016. 

O’Brien, Keith. 2016. “Uprising in the Rust Belt.” Politico Magazine, June 24.
Stolberg, Sheryl Gay. 2016. “Patrick Toomey Wins Re-election in Pennsylvania Senate 

Race.” New York Times, November 9.
Yost, Berwood, and Christopher Borick. 2013. “Polling Accuracy in Pennsylvania: The 

2012 Presidential Election.” Presented at the Annual Meeting of the Pennsylvania 
Political Science Association, Harrisburg.

Berwood Yost is the director of the Center for Opinion Research and the Floyd Institute 
for Public Policy at Franklin & Marshall College. He is also the head methodologist for 
the Franklin & Marshall College Poll.

Jackie redman is a project manager at the Center for Opinion Research at Franklin & 
Marshall College.

scottie thompson is a project and data specialist at the Center for Opinion Research at 
Franklin & Marshall College.


	Structure Bookmarks
	Data Sources
	The Presidential Race
	The U.S. Senate Race
	Split-Ticket Voting
	Voter Turnout
	Discussion




