Each new point in the GOP primary calendar seems more important than the previous one, but after the latest slew of state contests winnowed the field down to three candidates, as well as eliminated the presumptive establishment favorite, that feeling has never been stronger.
In John Kasich, Ted Cruz, and Donald Trump we have, respectively, a staunchly conservative governor that for some reason moderated his tone during an appeal to the most ideologically extreme of his party (during primary season, as opposed to the general election, when candidates typically change their approach in front of a more moderate audience), one of the most extreme conservatives in the U.S. Senate but the type of candidate the establishment usually successfully screens out from the nomination, and almost assuredly a living anomaly of a presidential candidacy that has defied well-established principles of political science.
A contested convention has never occurred in the post-1972 McGovern-Fraser reforms era that democratized the nomination process, so if Trump does not meet the 1,237 delegate threshold for the nomination, there’s some guidance as to how it would play out–namely that it favors the frontrunner–but there’s also a chance we experience the brute institutional force of one segment of the two-party system. In other words, the outcome is still up in the air, making it very difficult to attach some kind of probabilistic estimate to the GOP wresting the nomination away from essentially the guaranteed delegate frontrunner–Trump.
Regardless, through the 29 states that have held elections, several patterns have already emerged to explain the electoral outcomes we’re seeing–especially with the surprising success of Trump.
Prominence of white racial identity that becomes more pronounced in states with higher non-white populations represents just one quantifiable racial component to Trump’s support. Another one cannot be so easily accessed with conventional methods.
Due to the effect of social desirability bias, survey research often fails to properly tap into a sensitive issue like one pertaining to race relations, unable to reveal indication of racial hostility in a respondent. The advent of Google search data can help solve this problem. Drawing from a paper that measures racial animus with this Google data and uses it to show that racial attitudes cost Barack Obama more of the national vote share than studies using surveys previously concluded, I use the same proxy for racial animus–racially charged language, such as the word “nigger,” in searches (see page 3 for more on how it’s defined)–to see whether it explains Trump’s vote share across different states. I restrict the frequency of the search data from the start of the Obama presidency to the present.
Here’s a link showing how the hostility indicator maps out across the U.S. Following the hyperlink, you’ll find that the search frequency concentrates in the southern part of the U.S., and reaches its peak in West Virginia. Interestingly, the percentage of black population in a state and the state’s racial animus measure correlates very highly at 0.70 (i.e. black resentment exists in larger quantities where more blacks live).
Across all states, the correlation between racial animus and Trump vote share is only moderately strong at 0.31, though much stronger than the same relationships for the other two candidates in the race: Cruz (-0.05) and Kasich (0.06). Examining this same association by different regions in the country–using the ones defined by the U.S. Census Bureau–brings more interesting results.
As seen in the above plot, the correlation between racial animus and Trump vote does not become much stronger when looking only at states in the South, resulting in a 0.34 correlation. For the West and Northwest, the relationship turns negative, but that’s far from the same case for the Midwest:
Bearing in mind it’s only a sample of seven states, the animus indicator correlates very strongly with Trump support in the Midwest, producing an unexpectedly, very high correlation at 0.91.
States with low racial animus as expressed by Google search data and low support for Trump–Minnesota, Kansas, and Iowa–and those with higher animus indicators and concurrently high vote shares for Trump–Illinois, Ohio, Michigan, and Missouri–fuel this trend. At the same time, the racial animus scores remain far and away highest in the South, in comparing either states that have already voted or across all of them (see below table). The Midwest follows the South in this animus score, though it’s closer to the region with the third highest animus measure–the Northeast–than the first one. On average, the South and Northeast tie for the regions voting for Trump at the highest rates.
As an aside, the bottom row on this table may suggest that Trump might not get as much support from the remaining states in the primary calendar. Given the moderate correlation between his vote share and racial animus in a state, and the higher animus indicator in the states that have already voted relative to the national average, the remaining slate of states have a lesser racial animus score on average (58 in fact) and thus could prove less favorable to him. At the same, the dynamics in the race–namely the number of candidates running–have changed considerably, which could obscure a correlative relationship moving forward.
The economic aspect
Economic difficulties among Americans mark another key way to explain Trump’s strength in the GOP primaries. Using panel data with observations from several months before Trump’s candidacy and several months into it, some have discovered a very strong positive relationship between dissatisfaction with life/economic status in January 2015 and Trump support in December 2015. Similar relationships using vote share and economic indicators at the state level appear as well.
Using exit poll data for percentages that the five main income groups (seen above) made up in each state that had this data (18 of 29 did), the table above shows the correlation between each income level and Trump’s share of the vote in that state. While the relationships are not very strong across the board, a class dynamic in Trump’s support becomes very evident. The higher representation of the lowest income bracket you get in a state, the more that state will vote for Trump. This association progressively weakens as one move up in income levels and then gradually becomes more negatively correlated, to the point where when a state has a higher percentage of top earners (>200k), it becomes least the favorable to Trump relative to the effect that the other groups’ presence has.
Looking beyond characteristics of only the Republican electorate and instead at state-wide economic conditions, the idea of economic struggles and poor economic condition potentially driving Trump support gets thrown into sharper relief. Three variables across states relating to income paint this picture: 1) median household income figures, 2) trajectory of median household income over the last 15 years (the percent change from 1999 to 2014), and 3) the the net change of a state’s national rank in median household income over this same time span. (All data for these three measures from here.)
1) In the most straightforward and frequently used of these measurements, median household income in 2014 ranged from 35,521 to 76,165 among different states, with an average of 54, 963. Out of the 29 states that have held elections this year, Trump vote correlates only weakly with income at the state level with a -0.23 coefficient, though in the correct direction given the hypothesis of economic struggles fueling Trump support: states with lower median income levels tend to vote for Trump more, but not by much. The plot below displays this relationship.
2) Over the last decade and a half or so since 2014, all states experienced a percentage decline in median household income. Importantly, however, these declines vary considerably, from no decline to -24.7% over the time span considered. Once again, a weak but present negative correlation emerges between decline in income and Trump share of the vote in a state at -0.30. But one observation has an outsize effect here on attenuating the correlative strength: the state of Wyoming, which only gave Trump 6.7 percent of its vote–his lowest total thus far–and one possibly affected by a caucus system. Excluding this outlier case, the correlation increases greatly (in absolute value sense) to -0.55, a moderate to strong relationship between the two variables.
The greater decline in median household income a state has experienced over the last 15 years, the greater share of votes it affords to Trump, thus the negative relationship. Such an association becomes much more pronounced when excluding one of the 29 initial state observations, as seen in the above plot (excluding Wyoming) and with the correlation coefficient.
3) Finally, another way of assessing the effect of rising economic struggles on votes going to Trump focuses on the change in national median income rank from 2000 to 2014 by state. For example, North Dakota rose from having the 42nd highest income level to 13th over this time frame, and so it gets a value of 29 for this measure, while Nevada dropped from 15th to 35th, thus receiving a value of -20 (these two state values represent the range for the variable). A larger (positive) rank change indicates more of an economic/income improvement, while a smaller (more negative) rank change signifies a greater decline in this area.
The above graph places the rank change and Trump vote in each state side by side (note: the units of measurement for the two variables are obviously different, but the graph should still be interpretable). For reference, the average vote share for Trump is 34.97, and the average income rank change is -1.86 among states that have voted. Even a preliminary look at how these two variables relate reveals that the states with the lowest rank values–those seeing their income levels relative to the nation decline the most as of late–vote for Trump in higher numbers (e.g. Nevada, Georgia, Illinois, Florida). On the other hand, those with better economic fortunes–positive rank changes–vote for Trump relatively less (e.g. Iowa, Oklahoma, Texas).
Accordingly, there’s a moderately strong -0.46 correlation between Trump share of the vote and the net change in national median income rank over 15 years (as the above plot also shows). In other words, as economic struggles–in terms of household income–increase by state, so too does Trump support to some degree. (Remove the somewhat outlier cases from the Northeast in Vermont and Massachusetts and an even stronger correlation–at -0.55– results). In sum, deteriorating economic conditions, even when measured at a fairly broad level such as the state one, certainly can explain some of the support for Trump during this election cycle.
(Another interesting note/aside: relative to the national average for this rank measure–at -0.42–the states that have already voted contain a lower rank measure–at -1.86–and therefore slightly poorer economic conditions than states on average and worse than those remaining to vote. That would mean the states that have already voted disproportionately favored Trump by this measure to at least some degree. This presents a similar scenario to the distribution of racial animus by states discussed above. Though the same limitations apply here, it seems as though Trump may soon compete in states not as favorable to him as the first 29 were.)