On March 14th of 2019, The United States Senate voted against President Trump who had earlier declared a national emergency on the U.S./Mexico border in an attempt to redirect federal appropriations toward the building of a southern border wall. The vote was 59 to 41 and included every Democratic member of the body along with 12 Republicans. (Cochrane & Thrush 2019). The previous day, the Senate voted against another one of the President’s stated positions, this time in an effort to stop U.S. military assistance to Saudi Arabia in their ongoing conflict with Yemen. Here the vote was 54 to 46 with 7 Republicans joining all of their Democratic colleagues. (Sanders 2019). This Yemen bill had previously passed the Senate in December of 2018, but it never came before the House due to opposition by then-Speaker Paul Ryan (Detrow 2018). When comparing the lists of individual Senators who opposed the president’s wishes in the recent votes, one finds complete overlap of Democratic Senators, but also some commonalities in the list of Republicans. They are Senators Lisa Murkowski of Alaska, Jerry Moran of Kansas, Rand Paul of Kentucky, Susan Collins of Maine, and Mike Lee of Utah (Edmondson 2019 & Pramuk 2019).
Although these two high-profile cases may not be representative of Congressional support for the president in the aggregate, it does raise an important question. How can one go about creating a model which accurately predicts Congressional vote support for the president?
Trump Vote Percentages in the 2016 Elections
Earlier in 2019, I found an ongoing project on Nate Silver’s FiveThirtyEight website which tracks how often individual members of Congress support President Donald Trump’s position on legislation. The website uses only a few variables. They are: how often a senator or representative votes the same way as the president would prefer, Trump’s two-party vote share in the 2016 elections in a given state or Congressional district, how often a member of Congress is predicted to vote with the president using this 2016 electoral data, and lastly the margin between the actual vote percentage and the one anticipated using this one variable. Why would a researcher consider this model? “Legislators who face a choice between supporting the government (and their parties) or the specific interests of their constituencies will tend to prefer the latter because, in so doing, they maximize their chances of re-election without imposing any costs on the government” (Cheibub 2009, 120). Furthermore, “if voters connect their votes in executive and legislative elections, the legislators will have incentives to support the executive on some key votes” (Cheibub 2009, 122-123). Thus, as the argument goes, a legislator should support or oppose the president through his or her votes in Congress in roughly equivalent amounts as their constituents rewarded the president with their vote in the previous election.
On the surface, it appears that this model has little predictive power. For example, as of March 15th, 2019, only 16 of the 100 Senators presently serving have actual Trump vote scores which are within 2.5% of their predicted scores. Expanding to 5 points either above or below still encompasses only 36 senators, with a majority still lying outside of this range. The largest differences are Senator Rick Scott (R-FL) who has a Trump support score which is 55.1 points higher than predicted and Senator John Tester (D-MT) whose Trump support score is 49.6 points lower than predicted. In the House of Representatives, the margins are even greater. While 136 of the 432 current members are within this 2.5-point range, at the extremes one can find Rep. David Valadao (R-CA-21) at 59.1 points higher than expected and Rep. Anthony Brindisi (D-NY-22) at 74.9 points lower than predicted. (Bycoffe 2019). Given the significant variation and the fact that over 66% of Congressional legislators fall more than 2.5 points outside of their expected values, one might make the claim that, by itself, the 2016 vote margin for President Trump is a poor predictor for levels of Congressional support.
However, if we consider our earlier list of Republican Senators, we find that four of the five of them, Murkowski, Lee, Collins, and Paul, are clustered toward the bottom of Republicans when it comes to how often their votes lineup with President Trump’s positions.
The Importance of Partisanship and Polarization
It seems obvious that partisanship is a key defining factor in all aspects of American political behavior in the present day. It would be easy to say that Congressional support for the president is driven first and foremost by partisan considerations and if this were the sole consideration of this paper, it would add nothing to the existing literature.  After all, just a cursory glance of the support score data provides ample evidence. In the U.S. Senate, even the Republican who has the lowest support score for President Trump, newly elected Mitt Romney of Utah, has a higher support score of 70% than any Democratic senator currently serving in that body. His closest cross-party competitor is, not surprisingly, Joe Manchin III of West Virginia at 58.5%. Perhaps coming as a shock though, Senator Romney supported the president on both of these high-profile rebukes mentioned in the previous section of this paper. Likewise, in the House of Representatives of the current serving members, Justin Amash from the 3rd district of Michigan has the lowest current support score for his party’s president among Republicans at 60%. Nevertheless, Amash’s support score is still higher than every single Democratic member of the House (Bycoffe 2019).
However, this clean party break is a fairly recent phenomenon. For example, looking back at support scores for President Obama during the 2009 and 2010 sessions reveals at least some level of party crossover. Among Republican Senators the Democratic president’s top three supporters in both years were Senators Collins of Maine, Snowe of Maine, and Voinovich of Ohio. Only Senator Collins remains in office as the last of the New England Republicans; Voinovich retired in 2011 and Snowe retired in 2013. Considering Democrats, in 2009 Senators Bayh of Indiana, McCaskill of Missouri, Feingold of Wisconsin, and Nelson of Nebraska expressed the greatest levels of opposition. It should be noted Republican Collins supported the president at higher levels than the Democrat Bayh. For 2010 Democrats with the highest levels of opposition, we find Senators Nelson again, followed by Feingold, and the Lincoln of Arkansas. As to their fates, Bayh retired in 2011, McCaskill lost to a Republican in 2018, Feingold lost to a Republican in 2010, Nelson retired in 2013, and Lincoln was defeated in the 2010 elections (CQ Almanac. 2009 & 2010).
Moving over to the House in 2009, a multitude of Republicans supported the president to a greater extent than Democratic Representatives Taylor of Mississippi and Bright of Alabama. Republican Representatives Cao of Louisiana and LoBiondo of New Jersey had support scores of over 66%. In 2010, both Taylor and Bright had the lowest support scores for Obama among Democrats while Republicans Cao and Representative Castle of Delaware has support scores of over 60%. Much like the case with the Senators, Representatives Taylor, Bright, and Cao lost their reelection bids to the nominee of the opposite party. Representative Castle ran for the Senate in 2010 and lost his party’s nomination. Of the four partisan contrarians, only LoBiondo continued to serve in elected office after 2010 (CQ Almanac. 2009 & 2010).
When considering the average support scores for the president in the U.S. Senate, according to the 2010 CQ Almanac, here’s what we find.
Although support from the opposition party in the U.S. Senate has remained relatively stable over time, we observe a widening gulf in presidential support by party as the average level of support among the president’s fellow partisans has been increasing.
By comparison, again using data from the 2010 CQ Almanac, the disparity of support for the president in the House between his own party and the opposition has become even more pronounced. Not only is support among the president’s own party increasing, as is the case in the U.S. Senate, but since the Carter administration, the president has been less successful at persuading opposition party members to vote for his proposals. As Therianault finds, “since the early 1970’s, the Senate has polarized about 80 percent as much as the House” (Theriault 2008, 197).
Not only has partisanship played a role in predicting presidential support scores in the past, but partisanship is also becoming increasingly an even more important indicator as polarization in both the House and Senate expands.
While in an earlier era, it may have been possible for scholars accurately to assert that political parties were of little theoretical importance in explaining political behavior and legislative results in the House, it is certainly not true now. Parties are consequential in shaping members’ preferences, the character of the issues on the agenda, the nature of legislative alternatives, and ultimate political outcomes, and they will remain important as long as the underlying forces that created this partisan resurgence persist (Rohde 1991, 192)
The 2016 presidential elections continued the longstanding gender gap trend in American politics. According to the Pew Research Center, women preferred Clinton to Trump by a 12-point margin. In addition, that election featured the largest gender gap since at least the 1972 election (Tyson et al. 2016). That news isn’t particularly shocking, especially given the vulgar and objectifying comments Donald Trump expressed regarding women as part of the Access Hollywood tape (Transcript 2017). The difference of attitudes between women and men regarding the president hasn’t been limited to just his election. In mid-2018, the Cook Political Report stated that “the most recent NBC/Wall Street Journal poll finds that just 39 percent of women give Trump a favorable approval rating, compared to 58 percent who disapprove of the job he’s doing. And, among white, college-educated women…the gap is staggering-just 26 percent approve to 71 percent disapprove.” Furthermore, during that time period, time white college women voters expressed their preference for a Democratically-controlled Congress by a 25-point margin (Walter 2018). According to exit polls from the 2018 midterms, 59% of women cast a ballot for Democratic Congressional candidates while only 40% picked Republicans, arguably one important reason why the Democratic Party won control of the House of Representatives in November (Velencia 2018).
Recent research has found that “eight attitudes predict Trump support: conservative identification; support for domineering leaders; fundamentalism; prejudice against immigrant, African Americans, Muslims, and women; and pessimism about the economy” (Smith & Hanley 2018, 11-12).
Considering the theory of descriptive representation advanced by Mansbridge and others (Mansbridge 1999), which advocates that in democratic systems representative legislators should not only advance the preferences of their constituents but also share other traits such as ethnicity and gender. Presumably then, given the extreme negativity women express toward the current president as compared to men, it is reasonable to expect that female members of Congress, (along with those from immigrant families, African Americans, and Muslims) irrespective of party, ought to be less inclined to support President Trump as compared to their male counterparts. 
Career Politician Support for Trump
Throughout the 2016 election cycle, the Trump campaign focused its rhetoric on three issues or slogans, “Build the wall”, “Lock her up”, and “Drain the swamp” (Overby 2017). While being sworn in in January of 2017, now President Trump continued to rail against beltway politicians. “For too long, a small group in our nation’s capital has reaped the rewards of government while the people have bore [sic] the cost” (Hemmingway 2017). Therefore, one might expect that the longer a politician is in Congress, the less likely he or she would be to support the current president. However, I would argue that the opposite is more likely the case.
As one example, consider the rather remarkable turnaround in attitude of Senator Lindsey Graham, the three-term Senator from South Carolina who also served almost a decade in the House of Representatives. A recent article from CNN explores this transformation. Before receiving the Republican nomination for president:
Graham said this of Trump: “You know how you make America great again? Tell Donald Trump to go to hell.” And, oh yeah, Graham also called Trump a “race-baiting, xenophobic, religious bigot”.
Fast-forward to the here and now. “To every Republican, if you don’t stand behind this President, we’re not going to stand behind you,” Graham said in South Carolina recently (Cillizza 2019).
So why has Graham reversed his tactics? “While Graham’s number used to lag those of other Republicans among GOP identifiers, since he has taken up the President’s banner on most every issue, his approval among Republicans in South Carolina has steadily risen” (Cillizza 2019). Senator Lindsey Graham is not burdened either by ideology or consistency and thus serves as a perfect illustration of David Mayhew’s theory that many politicians are “single-minded reelection seekers” (Mayhew 2004, 17).
Although simply looking at rhetoric and tweets might lead one to believe that Democratic leaders such as Speaker Nancy Pelosi, who has been in the House for the last 32 years, and Senate Minority Leader Chuck Schumer, who has been in the Senate for 20 years, are bitter enemies of the president and thus would have little desire to work together, much of their mutual animosity is kabuki theater. For example, as taken from an article from late March 2019, “President Donald Trump says he wants to work with Democrats to pass legislation to rebuild U.S. infrastructure…‘They want it, I want it,’ Trump said, adding that he spoke to House Speaker Nancy Pelosi ‘the other day’ about the issue” (Breuinger 2019).
While senior members of both parties have seen presidents come and go, staying in power by working to pass legislation for the benefit of their constituents and thus bolster their reelection chances, the current crop of freshman Democrats have been a largely vocal group, several of them making headlines for bucking their own party leadership openly calling for the impeachment of the president despite opposition from Speaker Pelosi. (Perticone 2019). In addition, others have found that once in office, legislators are typically relatively stable in their voting behavior (Asher & Weisber 1978) and thus new members, who are more polarized than the generation who came before, would be less likely to support the president.
Strength of the Opposition
Based upon the assumption of Mayhew, one would expect that congressional legislators are keenly sensitive to the power of presidential opposition in his or her district. In the case of a Republican President such as Donald Trump, the greater Democratic candidates perform electorally, the less likely it would be for the member of Congress, regardless of their partisan affiliation, to support the president’s agenda. “It may also be true that legislators who are truly insecure about their political standing, or that of the president, might be more willing to base their decisions on whatever local information they do have than to make risky inferences from national trends” (Borrelli & Simmons 1993, 107).
Hypothesis 1 – Representatives from states and districts which reported lower vote totals for Donald Trump in the 2016 elections ought to have correspondingly lower levels of support for him while in Congress.
Hypothesis 2 – Female legislators as a whole ought to have lower levels of support for President Trump as compared to male legislators, regardless of their party affiliation.
Hypothesis 3 – Support for the president in Congress ought to be positively correlated with legislators’ tenure in office, thus more senior members are more likely to support the president as compared to incoming freshman.
Hypothesis 4 –Higher vote totals for Democratic Congressional candidates in the 2018 midterm elections, regardless of victory, ought to correlate with lower support scores for a Republican president for the legislator of that district.
Data Collection and Analysis
I gathered the data for my regressions from several sources. My dependent variable, the Trump support score, and my independent variable of the 2016 Trump vote margin both come from Aaron Bycoffe on the website fivethirtyeight.com which he reports was compiled using data from ProPublica, Daily Kos, the Cook Political Report, and the U.S. Senate (Bycoffe 2019). Although his data includes every senator and representative who have served in any portion of the Trump presidency, I’ve restricted my analysis to current members of Congress and thus have 100 observations for the U.S. Senate and 432 for the U.S. House of Representatives. Although earlier political scientists have wrestled with the question of what Congressional votes one should consider, such as overall support, non-unanimous support, single-vote support, or the use of key votes, no matter what method one uses, so long as it is done uniformly, the differences between the measurements are usually minor. (Edwards 1985).
My remaining independent variables, Republican, Years in the U.S. Senate/U.S. House, Female, and Percentage of the Democratic vote in the last relevant general election all come from Politico as listed on four different sections on their website (2014 Election Results Senate, 2016 Election Results: Senate, House Election Results 2018, Senate Election Results 2018).
The U.S. Senate results provide highly statistically significant evidence for the first hypothesis only, which tested the theory put forth by Aaron Bycoffe, that legislators are influenced by presidential election outcomes as illustrated by the 2016 election results. A greater percentage of the vote that President Trump captured in a state in the 2016 election is positively correlated with an increased likelihood of a U.S. Senator from that state voting with the president’s wishes. Given that the Trump margin had a range of -32.2 to positive 46.3, means that two senators who have the highest and lowest Trump margins respectively are predicted to differ in support for President Trump’s legislative proposals by about 26.4% points. As expected, the partisan variable is remarkably strong, predicting a Trump support score difference of 56.46 points and it is significant at the 99.9% level.
In addition, the percentage of the Democratic vote in the last general election had a P value of .9, thus only statistically significant at the 90% CI level, but surprisingly it had a positive coefficient thus indicating that a greater level of Democratic support in a district is related to stronger support for the president. Running the model again, with the percentage of the vote for the last Democratic candidate for Senate alongside the partisan control while excluding the other previously used variables, yields a negative coefficient for the Democratic vote, as predicted, but it is still not statistically significant.
Looking at the results for the U.S. House paints a markedly different picture. Here, we find statistically significant evidence for the first three hypotheses. Although part of the explanation could revolve around the sample size, which is more than four times as large as the previous model, research from other political scientists leads me to believe that there is more to this phenomenon than such a simple explanation. As with the Senate, the partisanship plays the largest role in predicted support scores for President Trump though it is even larger than the value predicted for the U.S. Senate. This finding coincides with the research of Sean Theriault who found that party polarization in the U.S. House of Representatives is greater than what is found in the U.S. Senate. “Since the early 1970s, the Senate has polarized about 80 percent as much as the House” (Theriault 2008, 197). In addition, almost all of Theriault’s “Gingrich Senators”, members of the Senate who previously served in the House with Newt Gingrich and are believed to be more polarized than those who have not, are no longer members of that chamber.
The House’s coefficient on the 2016 Trump vote margin is only about a third as strong as it in the Senate model, though I would suspect that part of this difference stems from the increased partisan polarization as well as state legislative efforts at gerrymandering to draw as many safe, noncompetitive districts as possible within their borders. As potential evidence of gerrymandering, we observe an even greater disparity in the 2016 Trump vote margin ranging from a staggering -88.9 to a positive 63. Thus, when considering legislators from two different House districts, one with the highest observed Trump vote margin and another from the lowest, this model would predict a support score difference of 16.1 points, holding everything else equal.
As predicted, the coefficient on the female variable in the House is negative and also statistically significant. Again, this difference could stem from the fact that there are more women in the House, so the sample size is larger. By proportion, they are roughly equal at the present time. While 25 of the 100 U.S. Senators are female or 25%, 102 of the 432 or 24% of Representatives are female. But there is a considerable disparity in partisanship between the two groups. While 32% of female Senators are Republican, less than half of that number, 13%, of women in the House are members of the GOP (Women in the U.S. House of Representatives 2019, Women in the U.S. Senate 2019).
Lastly, as was the case with the Senate, the coefficient of the 2018 Democratic vote percentage in the district is positive and this time statistically significant. Running the regression again with just the last Democratic vote tempered by partisanship still produces a positive coefficient, therefore I have to conclude that my hypothesis that greater support for Democrats in a district should produce lower Trump support scores does not hold up, at least with this data set. I would be interested to see if other researchers have found similar outcomes, and, if so, what can account for this result.
As for the remaining hypotheses, if the reader will recall, the Senate data only provides evidence only for the first hypothesis, that lower Trump margins in the 2016 election coincide with lower support scores for the president. By comparison, the House data indicates backing for the first hypothesis along with the second, that female legislators ought to be less likely to support Trump’s proposals as compared to their male counterparts, and the third, the longer a representative has been in office, the greater likelihood it is that he or she will back the current president.
When David Mayhew wrote Congress, the Electoral Connection back in 1974, he observed that when it comes to the United States Congress, “its parties are exceptionally diffuse. It is widely thought to be especially ‘strong’ among legislatures as a checker of executive power” (Mayhew 2004, 7). Although presumably true at the time that they were written, his words sound out of place in the present American political climate where many activists expect their elected officials to steadfastly stand with their party’s president or in opposition to the other party’s president regardless of supposed party principles or previously held positions. But Mayhew wrote during a period when the parties were less cohesive and before the rise of polarization in the mid-1990s. It would be interesting to hear how he would update the theories in his book if it were written today. As he admits in the preface to the second edition, published in 2004, “I have not tried to revise or update this 1974 work. That would be a nightmarish task” (Mayhew 2004, xiii).
The once common conservative Democrat, liberal Republican, or ideologically moderate Congressman has become a relic of a bygone area. Although non-conformists thrived in the mid 20th century, due to the pressures of partisan polarization, by the 1990s they had become all but extinct. Legislators such as Jim Jeffords of Vermont or Richard Shelby of Alabama who often voted against the interests of the majority of their party or their party’s president either ideologically sorted themselves into a different party or found themselves replaced by partisans who did a better job at toeing the party line (Fleisher and Bond 2004).
Although the parties have split, in part over support for the current president, given historic trends one does have to wonder about the fate of Congressional Republicans who oppose President Trump more than their fellow partisans or Democrats who unduly support him. As two examples, there is talk that Representative Amash may end up leaving the Republican Party and seeking the Libertarian nomination to challenge Trump in 2020 (Kopp 2019). Presumably, if he were to do so, he would be expelled from the party and likely lose his seat in the House should he decide to run again. On the other side of the aisle, there are rumors that Senator Manchin might run for West Virginia Governor in 2020 (Everett 2019). If successful, the Senate would lose one of the few Democrats left in an increasingly Republican state.
As mentioned in an earlier footnote, as an avenue of future exploration along the lines of descriptive representation, it would be interesting to explore additional personal attributes of members of Congress. For example, are they are immigrants to this country or the children of immigrants? For those recently arrived individuals, do those with a European background support President Trump to a greater degree than those who come from, as the president calls them, “shithole countries” (Watkins & Phillip 2018)? What about race and religion? How much of a role do these personal factors play in levels of Congressional support?
At the end
of the day, it seems obvious that political party affiliation is the most
important factor in determining the level of a legislator’s support for
President Trump, although it isn’t the only issue at play. About a decade ago, Cheibub wrote that “separation
of power leads to independent legislators who act on the basis of their
individual electoral needs; in response to these needs, they build personal
ties with their constituencies.
Consequently, parties will play smaller roles and legislative behavior
will be more individualistic.” (Cheibub 2009, 127). But, after observing trends, especially now, during
the years of The Donald, the reverse may be the case in the United States.
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 Noting the exception of political scientists in the mindset of Keith Krehbiel who have argued that political parties have no influence on legislative behavior.
 As I’m conducting final revisions on this paper, I realize that this thought may help explain why Representative Justin Amash (R-MI-3) has the lowest support score for President Trump among Republicans in the House of Representatives given that his father is a Palestinian immigrant and his mother is a Syrian immigrant. It would be a good variable to explore in future research.