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  <journal-id journal-id-type="publisher-id">50</journal-id>
  <journal-id journal-id-type="short-title">grr</journal-id>
  <journal-id journal-id-type="doi">10.31703/grr</journal-id>
  <journal-title-group>
    <journal-title>Global Regional Review</journal-title>
    <abbrev-journal-title abbrev-type="publisher">grr</abbrev-journal-title>
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  <issn publication-format="print">2616-955X</issn>
  <issn publication-format="electronic">2663-7030</issn>
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    <publisher-name>Humanity Publications</publisher-name>
    <publisher-loc>Pakistan</publisher-loc>
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<article-meta>
  <article-id pub-id-type="publisher-id">391238</article-id>
  <article-id pub-id-type="doi">10.31703/grr.2016(I-I).16</article-id>
  <article-id pub-id-type="other" specific-use="submission-id">1707</article-id>
  <article-version article-version-type="publisher">1.0</article-version>
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    <subj-group subj-group-type="heading">
      <subject>article</subject>
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  <title-group>
    <article-title xml:lang="en">The Trump&apos;s Triumph Cards and the Future of GOP</article-title>
  </title-group>
<contrib-group>
  <contrib contrib-type="author" seq="1" corresp="yes">
    <name>
      <surname>Ahmad</surname>
      <given-names>Manzoor</given-names>
    </name>
    <email>manzoor@awkum.edu.pk</email>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Conceptualization" vocab-term-identifier="https://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – original draft" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-original-draft/">Writing – original draft</role>
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    <xref ref-type="corresp" rid="cor1"/>
  </contrib>
  <contrib contrib-type="author" seq="2">
    <name>
      <surname>Rizwan</surname>
      <given-names>Muhammad</given-names>
    </name>
    <email>manzoor@awkum.edu.pk</email>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
    <xref ref-type="aff" rid="aff2"/>
  </contrib>
  <contrib contrib-type="author" seq="3">
    <name>
      <surname>Shah</surname>
      <given-names>Zahir</given-names>
    </name>
    <email>manzoor@awkum.edu.pk</email>
    <role vocab="credit" vocab-identifier="https://credit.niso.org/" vocab-term="Writing – review &amp; editing" vocab-term-identifier="https://credit.niso.org/contributor-roles/writing-review-editing/">Writing – review &amp; editing</role>
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  <aff id="aff1">
    <label>1</label>
    <institution-wrap>
      <institution>Assistant ProfessorDepartment of Political Science, Abdul Wali Khan University Mardan</institution>
    </institution-wrap>
    <addr-line>KP</addr-line>
    <country>Pakistan</country>
  </aff>
  <aff id="aff2">
    <label>2</label>
    <institution-wrap>
      <institution>Department of Pakistan Studies, Abbottabad University of Science and Technology, Abbottabad</institution>
    </institution-wrap>
    <named-content content-type="author-role">Assistant Professor</named-content>
    <addr-line>KP</addr-line>
    <country>Pakistan</country>
  </aff>
  <aff id="aff3">
    <label>3</label>
    <institution-wrap>
      <institution>Department of Political Science, Abdul Wali Khan University Mardan, Mardan</institution>
    </institution-wrap>
    <named-content content-type="author-role">Assistant Professor</named-content>
    <addr-line>KP</addr-line>
    <country>Pakistan</country>
  </aff>
</contrib-group>
<author-notes>
  <corresp id="cor1">Corresponding Author: Manzoor Ahmad, Assistant ProfessorDepartment of Political Science,Abdul Wali Khan University Mardan, KP, Pakistan.. Email: <email>manzoor@awkum.edu.pk</email></corresp>
<fn fn-type="COI-statement" id="fn-coi">
  <p>The authors declare that they have no conflicts of interest.</p>
</fn>
<fn fn-type="ethics-statement" id="fn-ethics">
  <p>This study did not require formal ethics approval.</p>
</fn>
<fn fn-type="data-availability-statement" id="fn-data">
  <p>Data sharing is not applicable to this article.</p>
</fn>
</author-notes>
<pub-date pub-type="epub" date-type="pub" publication-format="electronic">
  <day>31</day>
  <month>12</month>
  <year>2016</year>
</pub-date>
<pub-date pub-type="collection">
  <month>12</month>
  <year>2016</year>
</pub-date>
<pub-date date-type="pub" publication-format="print">
  <day>16</day>
  <month>02</month>
  <year>2022</year>
</pub-date>
  <volume>1</volume>
  <issue>1</issue>
  <season>Fall</season>
  <fpage>205</fpage>
  <lpage>217</lpage>
  <history>
    <date date-type="accepted">
      <day>16</day>
      <month>02</month>
      <year>2022</year>
    </date>
  </history>
<funding-group>
  <funding-statement>
<p>The authors received no specific funding for this work.</p>
  </funding-statement>
</funding-group>
<permissions>
  <copyright-year>2016</copyright-year>
  <copyright-holder>Humanity Publications</copyright-holder>
  <license license-type="open-access" xml:lang="en" xlink:href="https://creativecommons.org/licenses/by/4.0/">
    <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p>
  </license>
</permissions>
<self-uri content-type="text/html" xlink:href="https://grrjournal.com/article/the-trumps-triumph-cards-and-the-future-of-gop-"/>
<self-uri content-type="pdf" xlink:href="https://grrjournal.com/pdf/grr/z2nzobPuYi.pdf"/>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://grrjournal.com/pdf/grr/z2nzobPuYi.pdf">
  <label>PDF</label>
  <caption>
    <title>Full Text PDF</title>
  </caption>
</supplementary-material>
  <abstract>
    <p>The US presidential elections are over and to the astonishment of media, surveys and polls; Donald Trump won a stunning victory over his democratic rival Hillary by 289 electoral votes. The election results were surprising and may not be digested by many Americans for long time and especially for the Democrats. The stunning victory of Trump does not seem so dramatic if the past elections are analyzed. This paper identifies few patterns through the analysis of past elections that support the Republican victory in the Elections 2016. This article highlights those patterns calling them the &quot;Trump&apos;s Triumph Cards&quot; and correlates them with the results of the current election. This paper identifies that the Role of the White Population, Population with 40+ years of age, The Rubio Factor in Florida, the vote of White Women, the increasing millennial Vote and the voting collapse for democrats were the contributing factors for Trump&apos;s dramatic win over Hillary Clinton. The data is collected from national polls&apos; surveys and statistical departments and tabulated.</p>
  </abstract>
<kwd-group kwd-group-type="author-keywords">
  <kwd>Presidential Elections</kwd>
  <kwd>Voting Patterns</kwd>
  <kwd>Primaries</kwd>
  <kwd>white Population</kwd>
  <kwd>Millennial Vote</kwd>
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</front>
<body>
<sec id="sec-1">
  <title>Introduction</title>
<p>The popularity index of Hillary was surprisingly higher than Trump as the Gallup claimed it to be 63 as compared to 30 for Trump, and the CNN reports that both candidates were viewed most unfavorable than their forerunners since 1984, as Trump scores a net negative of -33, with a favorable rating of 24% compared to 57% of voters who view him unfavorably and Clinton fares only slightly better with a net negative of -21, registering a 31% favorable rating and a 52% unfavorable rating (Wright, 2016), and that she had won the presidential debates on nearly all occasions, the Trump victory has been doubted and may not be digested easily by many Americans especially the democrat camps.</p><p>The election of 2016 has been analyzed by analysts in different perspectives with a different logic and argument. Drumnov (2016) assumes that Clinton did worst among the millennial, working class and white undergrads as compared to her predecessors. He also opines James Comey as major factor in Hillary loss costing her about 2% vote. Similarly Roberts (2016) identifies poor economic policies, trust deficit among the voters and message vacuum as the main contributing factors of Clinton’s ultimate loss in the election and Cooper (2016) believes Clinton’s personal weakness as candidate, the leaked emails perceived to be an outcome of Russian involvement and Trump’s victory in Rust Belt industrial states responsible for Trump’s victory over Clinton.</p><p>Hazen, Holloway, Pierson, Frel and Leopold (2016) give a more comprehensive analysis of the reasons for Trump’s win and Hillary’s loss in the shape of thirteen theories including the role of racism and white voters and high degree of party loyalty favoring Trump, the Comey factor causing a major blow to Clinton’s election campaign, the role of media and fake news tarnishing conspiracies against Clinton, perceived corruption, feeling of displacement and economic turmoil and the personal weakness of Hillary as a candidate. Similarly Domenico identifies that it was Clinton’s inability to fire up Obama’s coalition and secured 5 million less votes than Obama while Trump got nearly the same votes as Romney. The black voters showed less concern for Hillary than they did for Obama. Similarly among the non-degree holders, the support for Trump was higher than Clinton. Surprisingly, Wisconsin, Michigan and Pennsylvania which the democrats have won in the past six elections were also won by Trump. Hillary secured 4300 and 78000 less than Obama at Wisconsin and Michigan respectively.</p><p>Greenberg and Carville (2016) believe that Clinton would have won the election if she had secured higher number of votes in the democratic base voters and among the working class and especially if she would have won the Rust Belt states. Similarly Mehlman (2016) highlights five reasons for Trump win and five reasons for Hillary loss. He assumes that the Americans and wanted change and the change voters were dominated by Trump. Trump also did well on key issues scoring 4% more on economy and 2% more on security than Hillary. On the other hand, Clinton had weaker support than Obama, lacked the trust and her message was not as clear as Trump. Moreover the American dislike for third term and the absorption of critical vote by the third party resulted in Hillary loss. Norpoth (2016) forecasted through primary model that Trump would defeat Hillary by 87% certainty. The model is based on the assumptions that candidate with better primary vote will win the general election and the white party wins the second term but loses the third term.</p><p><break/></p><p><break/></p><p>The Argument</p><p><break/></p><p>Historical analysis of the data related to elections identifies some patterns, calling them triumph cards that support and contribute to Trump win in the election. These patterns are:</p><p>•	White Population more likely vote for Republican while the Non-Whites (Blacks, Latinos, Asian and Others) vote for Democrats,</p><p>•	Population with 40+ years of age more likely vote for Republicans,</p><p>•	The Rubio Factor in Florida,</p><p>•	White Women more likely vote for Republicans than for Democrats,</p><p>•	The voting collapse for democrats</p><p><break/></p><p>Card I: White Population more likely vote for Republican while the Non-Whites (Blacks, Latinos, Asian and Others) vote for Democrats</p><p><break/></p><p>The National Election study (1952-2012) identifies in the democratic partisan by race and show a gradual decrease in the white’s support for Democrats from 53% in 1960 to below 30% in 2008 and then an increase to 49% in 2012 while the democratic partisanship of blacks increased from 53% to 77% in 2012, and that of Hispanic remained 60% in 2012, with minor ups and downs in the past.</p>
</sec>
<sec id="sec-2">
  <title>Figure 1</title>
<p>Democratic Partisan Identification by Race, 1952-2012</p><p><italic>Source: </italic><italic>National Election Study</italic></p><p>In the election of
1992, 60% of the white population voted for Bush Sr. than 40% to Dukakis while
90% blacks and 46% Hispanic voted for Dukakis as compared to 11% blacks and 53%
Hispanic for Bush. Similarly in 2012 election 59% of white population voted for
Romney as compared to 39% for Obama, though Obama won but his victory was
fostered by the support of 93% blacks, 71% Hispanic, 73% Asians and 58% other
non-whites as compared to 6%, 27%, 26%, 38% to Romney respectively. The total
demography of white voters for Romney was 88% as compared to 56% for Obama,
however Obama had 24% black voters 20 % other non-whites which is much greater
than 12% non-whites for Romney including the 6% black voters.</p><p><bold>Table 1: Partisanship by Race in Election
1992 and 2012</bold></p> <table-wrap id="table1"><label>Table 1</label><caption><title>Table 1</title></caption><table><tbody><tr><td colspan="4" valign="top"> <p><bold>Presidential
  Election 1992</bold></p> </td><td colspan="3" valign="top"> <p><bold>Presidential
  Election 2012</bold></p> </td></tr><tr><td valign="top">  </td><td valign="top"> <p><bold>Dukakis</bold></p> </td><td valign="top"> <p><bold>Bush</bold></p> </td><td valign="top"> <p><bold>Total</bold></p> </td><td valign="top"> <p><bold>Obama</bold></p> </td><td valign="top"> <p><bold>Romney</bold></p> </td><td valign="top"> <p><bold>Total</bold></p> </td></tr><tr><td valign="top"> <p>White</p> </td><td valign="top"> <p>40%</p> </td><td valign="top"> <p>60%</p> </td><td valign="top"> <p>85%</p> </td><td valign="top"> <p>39%</p> </td><td valign="top"> <p>59%</p> </td><td valign="top"> <p>72%</p> </td></tr><tr><td valign="top"> <p>Black</p> </td><td valign="top"> <p>89%</p> </td><td valign="top"> <p>11%</p> </td><td valign="top"> <p>10%</p> </td><td valign="top"> <p>93%</p> </td><td valign="top"> <p>6%</p> </td><td valign="top"> <p>13%</p> </td></tr><tr><td valign="top"> <p>Hispanic</p> </td><td valign="top"> <p>46%</p> </td><td valign="top"> <p>53%</p> </td><td valign="top"> <p>3%</p> </td><td valign="top"> <p>71%</p> </td><td valign="top"> <p>27%</p> </td><td valign="top"> <p>10%</p> </td></tr><tr><td valign="top"> <p>Asian</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>73%</p> </td><td valign="top"> <p>26%</p> </td><td valign="top"> <p>3%</p> </td></tr><tr><td valign="top"> <p>Other</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>58%</p> </td><td valign="top"> <p>38%</p> </td><td valign="top"> <p>2%</p> </td></tr><tr><td valign="top"> <p>Total</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>-</p> </td><td valign="top"> <p>50%</p> </td><td valign="top"> <p>48%</p> </td><td valign="top"> <p>-</p> </td></tr></tbody></table></table-wrap> <p><italic>Source: National Exit Polls</italic></p><p><italic>The National Exit Polls 1982-2012 show a marked decrease in the voter turnout of whites for democrats from 77% in 1972 to 56% in 2012 while statistics of whites for Republican party remained about 90% during this period. </italic></p>
</sec>
<sec id="sec-3">
  <title>Figure 2</title>
<p>Percent of White and Non-White voter’s partisanship (1968-2012)</p><p>Source: National Exit Polls 1982-2012, The Washing Post</p><p><break/></p><p>Card II: Population with 40+ years of age more likely vote for Republicans</p><p><break/></p><p>Another pattern evident from the analysis of past elections is the popularity level</p><p>of Republican Party in aging population. Statistics reveal that the younger population between the age 18-40 years more likely vote for Democrats rather than Republican due to the non-conservative and more liberal polices of democratic party, however the aged population with more than 40 years of age tilt towards Republican party. In the Election 2012, Obama secured 60% in youth between 18-24 years, 60% between 25-29, 55% in between 30-39 years, 48% between 40-49, 47% between 50-64 and 44% in the population of 65+ age while Romney on the other hand secured 36%, 38%, 42%, 50%, 52% and 56% in those age categories respectively. This shows a gradual increase in the popularity level and voting turnout for republican with increasing age of population.</p>
</sec>
<sec id="sec-4">
  <title>Figure 3</title>
<p>Age and Candidate Support</p><p>Source: CNN/Fox/MSNBC Exit Polls (N=26565)</p><p><break/></p><p>Card III: The Rubio Factor in Florida</p><p>The Latinos, as the results of the past elections justify their support with the democrats, with a big chunk of population in Arizona, Colorado, Nevada and New Mexico will have its impact on favorable turnout for Democrats, however in Florida the Marco Rubio factor will come into play for the success of Republican candidate.</p>
</sec>
<sec id="sec-5">
  <title>Figure 4</title>
<p>The Latino Vote in Presidential Elections: 1980-2012</p><p>Source: Pew Hispanic Centre</p><p>Card IV: White Women more likely vote for Republicans than for Democrats</p><p><break/></p><p>It has been noted that like the white men, the white women also favor the republican candidates. Romney secured 56% votes among the white women population as compared to 42% by Obama. However the non-white women more likely voted in favor of Obama than Romney.</p>
</sec>
<sec id="sec-6">
  <title>Figure 5</title>
<p>Race-Ethnicity x Gender and Candidate Support</p><p>Source: CNN/Fox/MSNBC Exit Polls (N=26565)</p><p><break/></p><p>Card V: The Voting Collapse for Democrats</p><p><break/></p><p>It has been noted that voting turn out for the Democratic Party candidate has declined during the last few elections. It ranged high for Obama in the first presidential election and declined in 2012. The same trend followed in the number of votes for Hillary.</p>
</sec>
<sec id="sec-7">
  <title>Figure 6</title>
<p>Democratic Party Vote Collapse (2008-16)</p><p>Source: US Election Project, wwbs.org</p>
</sec>
<sec id="sec-8">
  <title>The Predictions and Results</title>
<p>The Republicans are enjoying the support of
159 Solid Republican Electoral votes, and Trump needed 111 more seats from the
Lean republic, lean democratic and tossup states to win the election.</p><p>Among the Lean
republican states including Arizona, Indiana, Missouri, and North Carolina, on
the basis of the patterns/ Triumph cards, Trump was expected to win all these
state as these states have high number of republican voters, greater population
of whites, reasonable number of aged population and greater probability of
winning election.</p><p>In Arizona and
Missouri, the percentage of republican voters decreased by 4% and 8%
respectively from the 2012 election however the republican candidate was able
to win election in these states by securing 50% and 57% votes.</p><table-wrap id="table2"><label>Table 2</label><caption><title>Triumph cards analysis in Lean Republican states</title></caption><table><tbody><tr><td> <p><bold>State</bold></p> </td><td> <p><bold>E.V</bold></p> </td><td> <p><bold>Pop.</bold></p> <p><bold>Millions</bold></p> </td><td> <p><bold>%  Rep.
  Voters 2012</bold></p> </td><td> <p><bold>% White Pop.</bold></p> </td><td> <p><bold>%65+ Years</bold></p> </td><td> <p><bold>% Latin</bold></p> </td><td> <p><bold>Prob.10</bold></p> </td><td> <p><bold>%  Rep.
  Voters 2016</bold></p> </td><td> <p><bold>Result 2016</bold></p> </td></tr><tr><td valign="top"> <p>Arizona</p> </td><td> <p>11</p> </td><td> <p>6.6</p> </td><td> <p>54</p> </td><td> <p>84</p> </td><td> <p>15.4</p> </td><td> <p>29.6</p> </td><td> <p>9</p> </td><td> <p>50</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Indiana</p> </td><td> <p>11</p> </td><td> <p>6.5</p> </td><td> <p>54</p> </td><td> <p>86</p> </td><td> <p>13.9</p> </td><td> <p>6</p> </td><td> <p>9</p> </td><td> <p>57</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Missouri</p> </td><td> <p>10</p> </td><td> <p>6.4</p> </td><td> <p>65</p> </td><td> <p>83.7</p> </td><td> <p>15</p> </td><td> <p>3.5</p> </td><td> <p>7</p> </td><td> <p>57</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>N.Carolina</p> </td><td> <p>15</p> </td><td> <p>9.8</p> </td><td> <p>51</p> </td><td> <p>76.7</p> </td><td> <p>14.3</p> </td><td> <p>8.4</p> </td><td> <p>8</p> </td><td> <p>51</p> </td><td> <p>R</p> </td></tr></tbody></table></table-wrap> <p>Among tossup states, Florida and Ohio are
very significant. Florida has 29 electoral votes with 83.2% white population
and 15.1% aged population and the number of republican voters is less than 50%.
However, the Rubio factor as forecasted played its role not only in his victory
as senator but also in capturing the 29 for Trump.</p><p>Iowa, Nevada
showed an increase in the voters turnout for republicans while Pennsylvania and
Wisconsin showed a decrease of 3, 4% percent for the republican nominee.
However, to the astonishment of Democrats, Trump won Michigan and Pennsylvania
which the Republicans have not won in the last six elections and also Wisconsin
which has not voted for a Republican candidate since 1984 (Drum, 2016).</p><p><bold>Table 3:
Triumph cards analysis in Toss up states</bold></p> <table-wrap id="table3"><label>Table 3</label><caption><title>Table 3</title></caption><table><tbody><tr><td valign="top"> <p><bold>State</bold></p> </td><td valign="top"> <p><bold>E.V</bold></p> </td><td valign="top"> <p><bold>Pop.</bold></p> <p><bold>Millions</bold></p> </td><td valign="top"> <p><bold>%  Rep.
  Voters 2012</bold></p> </td><td valign="top"> <p><bold>% White Pop.</bold></p> </td><td valign="top"> <p><bold>%65+ Years</bold></p> </td><td valign="top"> <p><bold>% Latin</bold></p> </td><td valign="top"> <p><bold>Prob.10</bold></p> </td><td valign="top"> <p><bold>%  Rep.
  Voters 2016</bold></p> </td><td valign="top"> <p><bold>Result 2016</bold></p> </td></tr><tr><td valign="top"> <p>Florida</p> </td><td> <p>29</p> </td><td> <p>19.5</p> </td><td> <p>49</p> </td><td> <p>83.2</p> </td><td> <p>15.1</p> </td><td> <p>22.5</p> </td><td> <p>7</p> </td><td> <p>49</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Iowa</p> </td><td> <p>6</p> </td><td> <p>3.1</p> </td><td> <p>47</p> </td><td> <p>92.5</p> </td><td> <p>15.6</p> </td><td> <p>5</p> </td><td> <p>5</p> </td><td> <p>52</p> </td><td> <p>R</p> </td></tr><tr><td> <p><bold>Nevada</bold></p> </td><td> <p>6</p> </td><td> <p>2.7</p> </td><td> <p>46</p> </td><td> <p>76.7</p> </td><td> <p>13.7</p> </td><td> <p>28.5</p> </td><td> <p>7</p> </td><td> <p>48</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Ohio</p> </td><td> <p>18</p> </td><td> <p>11.5</p> </td><td> <p>48</p> </td><td> <p>83.2</p> </td><td> <p>15.1</p> </td><td> <p>3.1</p> </td><td> <p>6</p> </td><td> <p>52</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Pennsylvania</p> </td><td> <p>20</p> </td><td> <p>12.8</p> </td><td> <p>52</p> </td><td> <p>83.2</p> </td><td> <p>16.4</p> </td><td> <p>5.7</p> </td><td> <p>-</p> </td><td> <p>49</p> </td><td> <p>R</p> </td></tr><tr><td valign="top"> <p>Wisconsin</p> </td><td> <p>10</p> </td><td> <p>5.71</p> </td><td> <p>53</p> </td><td> <p>88.1</p> </td><td> <p>14.8</p> </td><td> <p>5.9</p> </td><td> <p>-</p> </td><td> <p>48</p> </td><td> <p>R</p> </td></tr></tbody></table></table-wrap> <p>Table 4 shows an analysis of the fourth
triumph card that is the population with 40+ years of age more likely vote for
the republican candidate than for Democrat. It also shows the increase in
support for republicans with increasing age of both men and women.</p><p><bold>Table 4:
Presidential Support by Age in Election 2016</bold></p><table-wrap id="table4"><label>Table 4</label><caption><title>Table 4</title></caption><table><tbody><tr><td rowspan="2"> <p><bold>Age
  Years</bold></p> </td><td colspan="2"> <p><bold>%
  Among Men</bold></p> </td><td colspan="2"> <p><bold>%
  Among Women</bold></p> </td></tr><tr><td> <p><bold>Clinton</bold></p> </td><td> <p><bold>Trump</bold></p> </td><td> <p><bold>Clinton</bold></p> </td><td> <p><bold>Trump</bold></p> </td></tr><tr><td> <p>Total</p> </td><td> <p>43</p> </td><td> <p>49</p> </td><td> <p>59</p> </td><td> <p>35</p> </td></tr><tr><td> <p>18-34</p> </td><td> <p>51</p> </td><td> <p>40</p> </td><td> <p>69</p> </td><td> <p>25</p> </td></tr><tr><td> <p>35-49</p> </td><td> <p>47</p> </td><td> <p>45</p> </td><td> <p>55</p> </td><td> <p>34</p> </td></tr><tr><td> <p>50-64</p> </td><td> <p>37</p> </td><td> <p>57</p> </td><td> <p>60</p> </td><td> <p>35</p> </td></tr><tr><td> <p>65+</p> </td><td> <p>39</p> </td><td> <p>55</p> </td><td> <p>52</p> </td><td> <p>43</p> </td></tr></tbody></table></table-wrap><p>The Election of 2016 also justifies the
analysis that the white population more likely vote for republicans. It is
evident from the table that white population voted in favor of Donald Trump.</p><p><bold>Table 5:
Presidential Support by Gender and Marital Status in Election 2016</bold></p><table-wrap id="table5"><label>Table 5</label><caption><title>Table 5</title></caption><table><tbody><tr><td rowspan="2"> <p><bold>Race/
  Status</bold></p> </td><td colspan="2"> <p><bold>Among
  Men</bold></p> </td><td colspan="2"> <p><bold>Among
  Women</bold></p> </td></tr><tr><td> <p><bold>Clinton</bold></p> </td><td> <p><bold>Trump</bold></p> </td><td> <p><bold>Clinton</bold></p> </td><td> <p><bold>Trump</bold></p> </td></tr><tr><td> <p>White</p> </td><td> <p>35</p> </td><td> <p>59</p> </td><td> <p>52</p> </td><td> <p>42</p> </td></tr><tr><td> <p>Black</p> </td><td> <p>85</p> </td><td> <p>7</p> </td><td> <p>91</p> </td><td> <p>6</p> </td></tr><tr><td> <p>Hispanic</p> </td><td> <p>61</p> </td><td> <p>30</p> </td><td> <p>71</p> </td><td> <p>19</p> </td></tr><tr><td> <p>Married</p> </td><td> <p>37</p> </td><td> <p>56</p> </td><td> <p>52</p> </td><td> <p>41</p> </td></tr><tr><td> <p>Unmarried</p> </td><td> <p>51</p> </td><td> <p>41</p> </td><td> <p>65</p> </td><td> <p>28</p> </td></tr></tbody></table></table-wrap><p><bold>Adding to the
Misery</bold></p><p>The model based on the aforementioned
patterns was also augmented by decline in Hillary support among the Millennial,
women, average income group voters and working class. She secured less votes
than her predecessors. She also did not do well at the primaries.</p><p>1.	Millennial Vote</p><p><break/></p><p>The Millenials have always voted more for Republicans than democrates as the previous data reveal, however Obama and Clinton scored more votes as compared to the data of 1976-1988. Hillary could not sustain the support of millenial to the extent which Obama did.</p>
</sec>
<sec id="sec-9">
  <title>Figure 7</title>
<p>Favorable Response of Millennial towards Presidential Candidates.</p><p><italic>Source</italic>: <italic>Nelson, N. (2016, October 7). Making Sense Of The Race For The Millennial
Vote. Retrieved November 10, 2016, from Youth Radio: </italic><ext-link ext-link-type="uri" xlink:href="https://youthradio.org/journalism/making-sense-of-the-race-for-the-millennial-vote/"><italic>https://youthradio.org/journalism/making-sense-of-the-race-for-the-millennial-vote/</italic></ext-link></p><p><bold>1.      
</bold><bold>Performance
at the Primaries</bold></p><p>Unlike
the previous election trends, the candidate with good performance at primaries
won the election as it happened in the presidential election of 1912, 1964 and
2012 when the Democratic candidate with good performance won the election,
Hillary comparatively did not do well and had a narrow win from Sanders at the
primaries. This has a bad impact on her stature as the presidential candidate.</p><p><bold>Table 6: Performance at the Primaries (1914,
1964, 2012 &amp; 2016)</bold></p><table-wrap id="table6"><label>Table 6</label><caption><title>Table 6</title></caption><table><tbody><tr><td rowspan="2"> <p><bold>Year</bold></p> </td><td colspan="3" valign="top"> <p><bold>Primaries
  Republican</bold></p> </td><td colspan="4" valign="top"> <p><bold>Primaries
  Democratic</bold></p> </td></tr><tr><td> <p><bold>Candidate</bold></p> </td><td valign="top"> <p><bold>%</bold></p> </td><td> <p><bold>Rival</bold></p> </td><td> <p><bold>Candidate</bold></p> </td><td valign="top"> <p><bold>%</bold></p> </td><td> <p><bold>Rival</bold></p> </td><td valign="top"> <p><bold>Winner</bold></p> </td></tr><tr><td> <p>1912</p> </td><td> <p>Taft</p> </td><td valign="top"> <p>33.9</p> </td><td> <p>51.5</p> </td><td> <p>Wilson</p> </td><td valign="top"> <p>44.6</p> </td><td> <p>41.6</p> </td><td valign="top"> <p>Wilson</p> </td></tr><tr><td> <p>1964</p> </td><td> <p>Goldwater</p> </td><td valign="top"> <p>22.3</p> </td><td> <p>33.5</p> </td><td> <p>Johnson</p> </td><td valign="top"> <p>95.3</p> </td><td> <p>1.6</p> </td><td valign="top"> <p>Johnson</p> </td></tr><tr><td> <p>2012</p> </td><td> <p>Romney</p> </td><td valign="top"> <p>39.3</p> </td><td> <p>22.9</p> </td><td> <p>Obama</p> </td><td valign="top"> <p>82.0</p> </td><td> <p>1.0</p> </td><td valign="top"> <p>Obama</p> </td></tr><tr><td> <p>2016</p> </td><td> <p>Trump</p> </td><td valign="top"> <p>41.2</p> </td><td> <p>4</p> </td><td> <p>Hillary</p> </td><td valign="top"> <p>56.5</p> </td><td> <p>41.8</p> </td><td valign="top"> <p>Trump</p> </td></tr></tbody></table></table-wrap><p><italic>Source: Norpoth,
H. (Ed.). (2016, October). Primary
Model Predicts Trump Victory. Retrieved December 15, 2016, from
Primary.com: http://primarymodel.com/2016-forecast-full/</italic></p><p><bold>2.      
</bold><bold>Working
Class and Undergrads</bold></p><p>Clinton
also performed poor among the working class securing 14% less than Trump among
white working class, 8% less among the nonwhite class and 2% less among
nonwhite college graduates.</p>
</sec>
<sec id="sec-10">
  <title>Figure 8</title>
<p>Hillary Clinton and Education Divide</p><p>Source: Drum, K. (2016, November 21). The 3 Big Reasons Hilary Clinton Lost. Retrieved November 30, 2016, from Mother Jones: http://www.motherjones.com/kevin-drum/2016/11/why-clinton-lost-bitter-bernie-crooked-comey-and-wounded-working-class</p><p><break/></p><p>4.	Income Groups</p><p><break/></p><p>One of the contributing factors in the Hillary loss in the election was her poor performance in different income groups which was foreseen otherwise. Trump performed comparatively well among the income groups especially among the income groups with below $30000 income and those with income between $30000 to $49000.</p>
</sec>
<sec id="sec-11">
  <title>Figure 9</title>
<p>Net Vote Share change towards Republicans by income group (2012-2016)</p><p>Source: US Presidential Election Exit Polls</p>
</sec>
<sec id="sec-12">
  <title>Conclusion</title>
<p>The analysis of this paper reveals that Trump’s win in the election is not surprising and the decline of GOP as forecasted by some scholars based on their theories of assimilation seems very pessimistic. Trump’s antics, vulgar rhetoric, insulting speeches against the women and the Muslims, Latinos and Blacks may have some impacts on the voting turnout but the historical facts substantiate his success in the election 2016 and also give rays of hope for the future of GOP. The triumph cards will have their bearings in the future elections at the United States. The American whites being conservative in their beliefs and attitudes like the Republicans and will vote for their candidates. Similarly, the aging population is more in the favor of Republicans than the Democrats and the decline in the voting bank of Democrats during the past elections favor the future of GOP. Though the Non-Whites (Blacks, Latinos, Asian and Others) and the young population and women favor the Democrats for their liberal policies however the republicans can counter this trend through policies that can draw the attention of these strata of American population and will heal the splits in the American society as Trump pledged during the victory speech on Wednesday; &quot;I say it is time for us to come together as one united people&quot;. He added, &quot;I pledge to every citizen of our land that I will be president for all Americans&quot;.</p>
</sec>
</body>
<back>
<fn-group content-type="conflict-of-interest">
  <title>Conflict of Interest</title>
  <fn fn-type="conflict">
<p>The authors declare that they have no conflicts of interest.</p>
  </fn>
</fn-group>
<fn-group content-type="ethics-statement">
  <title>Ethics Statement</title>
  <fn fn-type="ethics">
<p>This study did not require formal ethics approval.</p>
  </fn>
</fn-group>
<fn-group content-type="data-availability">
  <title>Data Availability</title>
  <fn fn-type="data-availability-statement">
<p>Data sharing is not applicable to this article.</p>
  </fn>
</fn-group>
<app-group>
  <app id="app-suppl">
    <title>Supplementary Materials</title>
<supplementary-material id="suppl-pdf" content-type="pdf" xlink:href="https://grrjournal.com/pdf/grr/z2nzobPuYi.pdf">
  <label>PDF</label>
  <caption>
    <title>Full Text PDF</title>
  </caption>
</supplementary-material>
  </app>
</app-group>
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