From Job Approval to the Ballot Box

“Generic-ballot” polls predict a nine or ten point Democratic victory in House elections this fall, enough to flip the chamber.

As most observers know, support for the Democrats on the so-called “generic-ballot” question has moved inversely to the public’s opinion about Donald Trump’s performance in office.


Can we use this link between support for the parties on the generic-ballot question and Trump’s job approval figures to forecast possible election results in November?

There are 228 generic-ballot polls in my current dataset covering the period from Inauguration Day, January 20, 2017, through June 9, 2018.  Of those 228 polls, 209 also collected data on presidential approval.  Those 209 polls constitute the sample for this analysis.

A weighted-least-squares model using my standard predictors — days in office, polling method, and polling sample — plus the net Trump job approval score “explains” about half the variance in the size of the Democrats’ lead on the generic-ballot question.  Overall, the Democrats’ lead has increased at a slow, but discernible pace since the Inauguration.  Even after taking Trump’s job approval into account, I find support for the Democrats grows about 0.3 percent every hundred days.  At that pace, the Democrats will have picked up about 2.2 percentage points by the time the election takes place on November 6th, some 655 days after Trump took office.

Two other factors influence the size of a poll’s Democratic lead.  The Democrats do about 2.2 percent worse in online polls than in ones conducted by telephone. On the other hand, polls restricted to “likely” voters show a marginally significant (p < 0.11) boost for the Democrats of 1.3 percent.  That might indicate a slight Democratic advantage in voter mobilization going into the fall, but the effect is still too small, and too variable, to be taken seriously at the moment.

These factors remain constant in the face of changes in Trump’s job approval and the passage of time.  We can thus set them at their values on Election Day and see how the margin in the generic House ballot varies with changes in Trump’s job-approval rating.  This chart presents the likely Democratic lead on the generic-ballot question in polls conducted with live interviewers on Election Day, November 6th. The dotted line represents the effect of adding the small, marginally-significant boost for polls of likely voters:


As of July 20, Trump’s net job approval (approve-disapprove) is averaging about -11 (42-53). My estimates indicate that difference translates into a Democratic margin of 8.8 percent in generic-ballot polls.  Notice that the remaining factors in the model predict a Democratic margin of about six to seven percent even if Trump’s net approval score is zero. That might be a decent estimate of the so-called “blue-wave.”

Models of the relationship between House votes won and House seats won find that, to take back the House, the Democrats will likely need to win by a margin of +7 or better to overcome gerrymandering and partisan geographic self-selection.  For instance, The Economist’s simulation model for the House midterms predicts that the Democrats will win 54.3 percent in November, or a margin of 8.6 percent.  In their model that gap translates into a Democratic seat margin of 224-211, enough to retake control of the House.

History suggests that both the President’s approval rating and support for the President’s party in the House are lower on election day than they are in the spring.

 

Are Some Republicans Leaving Donald Trump?

Recent polling suggests Trump has been losing support among Republican voters since the spring.  Likely Republican voters show less support than other Republicans.

It has become a commonplace among journalists and pundits to observe that Republican voters have remained largely behind President Trump.  Recent polling still shows job-approval ratings for the President among Republicans remaining in the 85 percent range.  But that focus on individual polls obscures a more complex trend, one that does not bode well for President Trump and his Republican Party.

This graph presents the “net approval” score (percent approving minus percent disapproving) for Republican voters in polls that disaggregate their results by partisanship.  Like in the country at large, support for Trump declined during 2017 but has rebounded this year.  (These data end before the decision to separate children and parents at the southern border became a national news event.)  I estimated the trajectory of support using a fourth-order polynomial based on time in office, with the usual array of dummy variables to adjust for polling methods and “house effects.”

The bold line representing the President’s approval rating among likely Republican voters should be especially troubling.  The Republicans most likely to turn out in November average about five points lower on net job approval than do other Republicans.

Even casual examination of the data points displayed here show that Republicans’ opinions about Trump’s performance in office have displayed wide variability since he entered the Oval Office.  However the most recent polling shows a decline in approval since the spring of 2018.

How Popular will the President be in November?

Most presidents are less popular when mid-terms are held than they are in late spring.

In an earlier piece about the “generic” Congressional ballot question, I presented the trends in support for the parties over the course of the summer for a few mid-term elections.  However that question has been asked on a more limited basis than the standard Presidential job-approval question, which Gallup began asking back when FDR served.  As I showed earlier, trends in the job-approval question track inversely with changes in the generic-ballot item. Given the tie between presidential popularity and mid-term outcomes, we might ask how the job-approval question has tracked before mid-term elections.  Given that President Trump’s approval rating stands at a bit under 42 percent today, where might it be come November?

I have compiled Gallup’s job-approval scores for all mid-term elections back to Harry Truman’s second term in 1950.  I have calculated separate averages for first-term and second-term presidents since the additional experience voters have with second-term presidents should limit the effects of most events before the election.

As expected, first-term presidents show a larger change in their job-approval scores than second-termers.  Indeed, excluding the anomalous 1974 election after Nixon resigned, second-term presidents saw their popularity grow on average by about a percentage point.  Nearly all first-term presidents saw their popularity decline as the election grew near, falling an average of 3.8 percent between May and October.

Among first-term presidents, only Jimmy Carter had an approval rating as low as Donald Trump’s at the end of the spring.  Carter saw his rating improve over the course of the summer, but much of that rise came after the Camp David summit with the then-presidents of Egypt and Israel in September, 1978.  Otherwise first-term presidents generally see a decline of about four points on average in their job-approval scores as the summer wears on.  (Leaving out Carter in 1978 raises that figure to five.) On that basis we might expect to see Trump’s job-approval score in the high thirties come Election Day.

One mitigating factor might be that Presidents who start off with ratings under fifty percent show smaller declines than those with a majority of citizens approving of their performance in the spring.  Presidents whose approval score was above fifty percent in May/June watched their ratings fall an average of 4.5 percent by October.  For those Presidents, like Trump, starting with a rating below fifty the average decline was just 1.6 percent.

Don’t Count Those Chickens Just Yet

Support for the President’s party on the generic House ballot is historically lower on election day than in the spring.

Most “generic ballot” polls attempting to forecast the 2018 general election have shown a narrowing of the gap between Democrats and Republicans in the past month or two.  The gap was widest at the end of 2017 when Trump’s job-approval numbers reached their lowest point.  While Republicans have regained a bit of ground since then, Democrats still have about a 5-7 point lead in recent polls.

Before Republicans get too excited by this rebound, we should examine the trends for the Trump Administration in the context of previous elections.  Here are two charts that depict the trends in the difference between support for the President’s party and support for the opposition in generic ballot polls.  The data for 2006 and 2008 comes from RealClearPolitics; the remainder comes from the archives at HuffPost Pollster that I have used in earlier postings.  In each case I have averaged polls by month, combining together May and June, and July and August, when polling is less frequent than in the fall.

For both Obama years, 2010 and 2014, we see a fairly linear decline in the Democrats’ margin over the Republicans on the generic ballot.  Unsurprisingly the fall in Democrats’ fortunes was much more substantial in 2010, when Obama observed that his party had been “shellacked” in the midterm.

In 2006 the Republicans faced a double-digit deficit in the spring.  Though they shaved a couple of points off the Democrats’ lead by September, the Mark Foley scandal ended any hopes of a Republican come-back.

The Republicans begin the summer of 2018 facing a five-point deficit which might not be enough to swing the House of Representatives to the Democrats.  If the history of past presidents is to be believed, though, the Republicans’ prospects may worsen as we head into November.

 

 

The “Generic” Congressional Ballot Question

Democrats would lead by nearly fourteen points in “generic” Congressional ballot polls next November if the trends seen since Trump took office continue.

I have written earlier about how methodological differences among pollsters can lead to significantly different results.  In my analyses of Presidential approval I showed how Donald Trump’s approval ratings varied depending on the choice of sample to interview and the interviewing method chosen.  In this piece I apply the same approach to the so-called “generic” ballot question, typically “If the elections for Congress were being held today, which party’s candidate would you vote for in your Congressional district?”  Some pollsters mention the Democrats and Republicans in this question, others leave it more open-ended like the example I just gave.

I have focused on the net difference in support for generic Democratic and Republican candidates.  This ranges from a value of -4 (Republican support being four points greater than Democratic support) to a high of +18 in the Democrats’ direction.  Here is a simple time plot showing how support for the Democrats on this question has grown while Trump has held office.

The Democrats held a small lead of just under four points on the day Trump took office.  Since then the Democrats’ lead has slowly increased to an average of eight points.

What’s surprising about these data is that they do not show the usual methodological differences we see in the presidential series.  Here are a few regression experiments using my standard array of predictors.

Choice of polling method has no systematic relationship with Democratic support on the generic ballot question. In contrast, Trump’s job-approval ratings run one to two points higher in polls taken over the Internet.  Another striking difference is the greater level of support found for Democrats in polls of registered or “likely” voters.  Again, the job-approval polls show an opposite effect, with polls of voters displaying greater levels of support for Trump than polls that include all adults.  I have also included separate effect measures for the two most-common pollsters in this sample, Politico/Morning Consult and YouGov/Economist.  Job-approval polls taken by the former organization show a pro-Trump “bias” of about three percent; on the generic ballot their polls place Republican support about five points higher than other polls.  YouGov/Economist polls also have Republican tilt on this question, though they show a slight anti-Trump bias in job-approval polls.

If we extrapolate these results to the fall election on November 6th (655 days after the Inauguration), and include the effect for registered voters, the model predicts the Democrats’ lead in generic ballot polls would reach nearly fourteen percent (=4.07+2.62+0.011*655).  A margin that large would easily overwhelm the built-in advantage Republicans hold based on partisan self-selection and gerrymandering.  Even if the Politico figure is correct, adding in that pro-Republican factor brings Democratic support down to nine points on election day.  That result would still reach nine percent, or a Democratic/Republican split of about 54-45.  That 54 percent figure still exceeds the 53 percent minimum I estimated earlier would result in Democratic control of the House of Representatives.

Using the model for the relationship between seat and vote divisions presented earlier, a 57 percent margin in the national Congressional vote would translate into the Democrats’ winning 55 percent of the House seats for a margin of 239-196.

Republicans Continue to Leave the House

Three more Republican House Members announced they would not seek re-election this week, bringing the total number of retiring Republican Members to 34 according to the New York Times. That figure compares to 16 Democrats, for a net Republican difference of +18.  We have to look back to the Democratic landslide in 1958 to see a mid-term with double-digit net Republican retirements.  For Democrats, only in 1938 and 1978 did the number of their retirements exceed Republican retirements by ten or more.

This increase of three net Republican retirements raises the predicted Democratic seat swing to 41 using the relationship depicted in the previous article.

I have shown in earlier postings that the relationship between seats and votes that advantaged Democrats in the years after World War II moved steadily in the Republicans’ direction beginning in 1980 and, with the help of gerrymandering, became even more favorable for the Republicans after 2010.  That might temper our belief in a prediction for an election being held in 2018.  First, the 2018 retirement margin of -18 is close to the observed maximum of -21 in 1958.  Perhaps the 1958 election is an “outlier” and without it the relationship is less steep than we observe.  However slope and intercept coefficients estimated with 1958 excluded are numerically nearly identical to those estimated with that year included.  So it’s unlikely that the historical model is radically overestimating the likely result next fall.

Another test is to let the relationship differ before and after 1992 to see whether the structural changes that we observe in the seats/votes relationship in the current era appear in the relationship for retirements and seat swings.  Once again, allowing the coefficients to differ before and after 1992 showed no measurable statistical difference. While the effects of gerrymandering and partisan self-segregation may make the House less vulnerable to “waves” of Democratic support, there is no evidence for that thesis looking at retirements as a predictor of seat outcomes.

These estimates have a lot of uncertainty attached.  The standard error of estimate is about 28 seats.  That means there is about a two-thirds chance that the actual swing will be somewhere between 13 and 67 seats.  Since the Democrats need a swing of at least 24 seats to win control of the House, even a retirement margin of 18 is not enough to ensure a change in party control.

The regression model taking President’s partisanship into account is a bit more conservative; it predicts a swing of 37 seats.

Update (2/26/18) – One more Republican has announced he is leaving the House, along with one more Democrat.  The net difference remains at +18.

What Do House Retirements Tell Us About the Future?

The pace of Republican retirements predicts that Democrats should take back control of the House of Representatives this fall by a margin of eight or nine seats.

This week Edward Royce (R-CA), Chair of the House Foreign Affairs Committee, and Darrell Issa (R-CA), former Chair of House Oversight and Reform, joined 27 of their fellow House Republicans by announcing that they are retiring from the chamber.  About half are leaving public office entirely, while the remainder are seeking another office like governor or senator.  (Issa has threatened to run for the House again in an adjacent district.)

On the other side of the aisle, fourteen Democrats have announced that they will be leaving the House of Representatives.  Only five of them are leaving public office, though Ruben Kihuen (D-NV) may be joining them subject to an investigation into allegations of sexual harassment.

Many pundits have interpreted the much higher retirement rate for Republicans to be a bellwether for this fall’s Congressional election.  If no one else announces a retirement, the Republicans will face a net loss of fifteen seats going into the midterms.  Just how large a threat do such retirements pose to Republican control of the House?

These two charts show the relationship between the net Republican margin of victory in terms of seats in the House and the net partisan difference in retirements.  It turns out that retirements tell us essentially nothing about Congressional outcomes in Presidential years, but they are quite informative in mid-term elections like 2018.  Here is the chart for Presidential years:

In presidential years we see little relationship between net Republican retirements and how well the party fares in the upcoming general election. What matters more are Presidential “coattails” with Republican swings in years when Eisenhower (1952), Nixon (1960, 1972), and Reagan (1980) ran.  Democrats were favored when they ran along side Johnson in 1964, Obama in 2008, and Franklin Roosevelt in 1944.

A much different picture appears if we look at the same relationship for mid-term years.

Now the number of Republican seats won or lost depends much more directly on the number of retirements.  The line is anchored by the Republicans’ success in the 1938 midterm and their dramatic losses in the 1958 election. If the figure for net retirements remains at fifteen, the Republicans are predicted to lose about thirty-two seats next November.  That would give the Democrats control of the House with a margin of eight seats.

However, because the President’s party historically loses seats in midterms, we should expect to see more retirements from the President’s party in midterm years.  When Democrats presided over a midterm, an average of four more Democrats retired from office than did Republicans.  When the Republicans held the White House in a midterm year, retirements from their ranks outnumbered Democratic retirements by an average of six.

So some of the strong relationship we see between retirements and midterm losses arises simply because the President’s co-partisans are jumping ship knowing that their party will do more poorly in the upcoming midterm.  That leaves us with the question of whether retirements have any additional predictive power once we take the President’s partisanship into account.  Retirements still matter in this better specification, though that effect just achieves statistical significance.


Based on these estimates, in a year where the Democrats hold the White House, and the number of retirements on both sides of the aisle is equal, the model predicts that the Republicans should gain about thirty seats.  When the Republicans hold the White House, and retirements are equal, the Democrats should gain about twelve seats (= 30.2 – 42.1 = -11.9).  Regardless of which party controls the Presidency,* Republicans are predicted to lose 1.4 seats for every retirement.  Applied to the current circumstances, this formula predicts a Democratic victory by thirty-three seats, or one more than predicted by the simpler model.

 

 

*Allowing the relationship between retirements and seat outcomes to vary separately depending on which party controlled the White House added no explanatory power.

 

Trump’s Job Approval Rating Key to Democratic Victory in 2018

In the previous article I showed that Democrats must win at least 53 percent of the national two-party vote for Congress in order to retake control of the House of Representatives.  That higher hurdle to success reflects the combined effects of more extensive partisan gerrymandering by Republican state governments and the tendency of Democrats to live in densely-populated urban districts.  These factors make Democratic votes for the House less “efficient” than Republican votes when it comes to determining which party controls the chamber.

So what combination of political and economic factors might result in a Democratic vote of 53 percent?  Political scientists have presented a number of models for mid-term elections over the years.  In an early paper, Edward Tufte showed that presidential approval and short-term changes in personal economic conditions both influenced support for the incumbent using the small sample of mid-term elections he had available at the time.  I find little support for an economic effect, but presidential job approval does play an important role.

I have analyzed both all Congressional elections and off-year elections separately.  The overall results are quite similar.  I am basing the conclusions below on the data for the seventeen off-year elections in my sample from 1950 to 2014.  Rather than treat the parties symmetrically and examine support for the President’s party as I did for the Senate, I am focused this time specifically on factors influencing support for the Democrats in off-year elections since their vote is what matters to this analysis.  It turns out just three variables account for over 90 percent of the variance in the Democratic vote for the House:

As always, the dependent variable is measured as a logit. Values above zero are associated with probabilities above 0.5; negative values represent probabilities below 0.5.  So the positive constant term indicates that the Democrats had an advantage over the period, but the coefficient for the dummy variable representing elections after 1992 is about equal in size and opposite in value.  That pattern corresponds to what we saw in the last article where Democrats had a seat advantage in the House until 1994 that vanished for two decades and has now turned significantly negative.

The other two variables capture the “referendum” aspect of off-year elections.  The Democrats do worse on average when one of their partisans occupies the White House.  However rising job approval ratings do translate into more support at the polls in the off year.  (The approval variable is coded positively for Democrats and negatively for Republicans.  If separate terms are included for Democratic and Republican presidents, the estimated coefficients are nearly identical in size but opposite in sign.  The coding I used imposes the constraint that changes in Presidential approval ratings have the same sized effect for both parties. The job approval data comes from Gallup and is based on averages of their polls near the election.)

I tried a variety of measures of economic conditions, specifically changes in real per capita disposable personal income, and none of them showed any additional effect.  I included a test of the “myopic” voter theory using only the change in income comparing the third and second quarters of the election year.  That fared no better than an approach with a longer time horizon, the growth rate over the past twelve months.  Thus there is no term in my model for economic conditions.

Since we have a Republican president, my estimates are based on the sum of the constant term and the term for elections after 1992.  If I plot the model’s predictions against President Trump’s potential approval ratings, I get this relationship:

If the President’s job approval rating falls below 32 percent, the model predicts the Democrats would win the 53.2 percent of the national House vote that we saw in the last article is required to obtain a majority of the seats in the chamber.  The last three Gallup polls reported Trump’s job approval at 38 or 39 percent.

An approval rating below thirty is historically very unlikely.  Richard Nixon in 1974 and George W. Bush in 2008 had ratings in the mid-twenties.  Jimmy Carter in 1978, George H. W. Bush in 1992, and his son in 2006 received job approval scores in the mid-thirties.  Of course, all of these incumbents had much higher ratings when they took office than did Donald Trump.

The average decline in Presidential job approval between Inauguration Day and the first subsequent off-year election has been a bit under nine points.  That would take Trump’s score down toward the mid-thirties.  However because he started at just 45 percent approval when inaugurated, he may not experience the same decline as did presidents who started from a higher rating.  For instance, it seems unlikely that Trump will experience a decline on the order of 23 points like Barack Obama did going into the 2010 midterm.   In fact, the table suggests the public treats Republican and Democratic presidents quite differently.  The Democrats all posted double-digit declines in job approval by the first mid-term election; none of the Republicans lost more than nine points over the same period, and approval for both Bush presidencies actually increased.

 

 

Can the Democrats Retake the House in 2018?

Now that all the gnashing of teeth has ended after the Republicans managed to hold on to the Georgia Sixth, perhaps we can step back and take a more systematic look at the Democrats’ prospects in 2018. Democrats will likely not make any gains in the Senate since the Republicans have only eight seats at-risk compared to twenty-three Democrats and both independents, Maine’s Angus King and Vermont’s Bernie Sanders.  That leaves the House as the only target.

There are two steps involved in answering this question.  The first is to use our historical experience with House elections to examine how votes are translated into seats.  With that information we can estimate the proportion of the two-party House vote that the Democrats need to win to take back the House in 2018.

As I wrote back in 2012, a combination of geographic clustering by party and good old partisan gerrymandering has created a “Republican bulwark” in the House since the last redistricting after the 2010 Census.  That means that the Democrats will need to win more than a majority of the popular vote for Congress if they intend to win a majority of House seats.

I have refined this simple seats and votes model in two ways.  First, I let the “swing ratio” vary between two historical periods, 1940-1992 and 1994-2016. Empirically the effects of voting “swings” on seat “swings” is significantly smaller in the more recent period.  As Tufte argues in his classic paper on the seats/votes relationship, a decline in the swing ratio indicates an increase in the proportion of “safe” seats.  As fewer and fewer seats have vote shares around fifty percent, there are consequently fewer that can be “flipped” by an equivalent shift in voters’ preferences.

I also use the results for the 2014 and 2016 elections to more sharply estimate the effect since 2010.  If we calculate the popular vote share required for the Democrats to win half the seats in the House, they would need to secure a bit over 53 percent of the (two-party) votes cast.

That brings us to the second question, what are the chances that the Democrats could win 53 percent of the Congressional vote in 2018?  Answering that question deserves an article unto itself.

 

Technical Appendix: Seats and Votes in the 2018 Election

I am extending the simple model I presented in 2012 relating the proportion of House seats won by Democrats against that party’s share of the (two-party) national popular vote for Congressional candidates.  It uses dummy variables to represent each redistricting period (e.g., the 2000 Census was used to redistrict elections from 2002-2010), and a slope change that starts with the Republican House victory of 1994.

To review, the earlier model showed this pattern of partisan advantage for elections conducted since 1940:

The results for the 2010 redistricting were based solely on the 2012 election.  As we’ll see in a moment, adding in 2014 and 2016 only made that result more robust.

As I argued earlier, not all of this trend results from partisan gerrymandering.  Americans have sorted themselves geographically over the past half-century with Democrats representing seats from urban areas and Republicans holding seats from suburban and rural areas.  As partisans self-segregate, the number of “safe” seats rises, and electoral competitiveness declines.

Partisan self-segregation also makes gerrymandering easier.  Opponents can be “packed” into districts where they make up a super-majority.  House Minority Leader Nancy Pelosi routinely wins 80 percent or more of the voters in her tiny, but densely populated San Francisco district.  Many of these seats are held by minority Members of Congress because of our national policy of encouraging “majority-minority” districts.   These efforts were well-motivated as a response to racist gerrymandering that would “crack” minority areas and distribute pieces of them in a number of majority-white districts.  Unfortunately for the Democrats these policies have meant that too many of the party’s voters live in heavily-Democratic districts.

Here is the result of an ordinary least squares regression for the share of House seats won by the Democrats in elections since 1940:

If I plot the predicted and actual values for Democratic seats won, the model unsurprisingly follows the historical pattern quite closely:

The Democrats routinely won around sixty percent of House seats between 1940 and 1992.  Since then they have only held a majority in the House twice, in 2006 and 2008.  Notice, too, that both the actual and predicted values for 1994 to the present show much less variance than the earlier decades.  The results above show that the “swing ratio” relating seats and votes has become much smaller falling from 1.92 before 1994 to 1.33 (=-0.59+1.92) since.  A smaller swing ratio indicates that House elections have become less competitive since Bill Clinton was elected President in 1992.  Changes in vote shares are still amplified in seat outcomes, as they are in all first-past-the-post electoral systems like ours, but the effect has been diminished because of the increase in the number of safe seats on both sides of the aisle.

We can use this model to estimate the share of votes required in order for the Democrats to win a majority in the House.  This chart shows the predicted relationships between seats and votes for two historical periods, one through the election of Bill Clinton in 1992, and the other beginning with the Republican victory in the House election of 1994 under Newt Gingrich and his “Contract with America.”

The slope in the latter period is substantially flatter than in the earlier period, meaning that Congressional elections have become somewhat less competitive since 1992.  Changes in vote shares have a smaller effect on changes in seat shares than they did before 1994.

Finally, the third line represents an estimate for the relationship in 2018, using the 1994-2016 slope and only the post-2010 intercept shift.  The chart shows that for the Democrats to win half the seats in 2018 they will need to garner a bit over 53 percent of the two-party popular vote for the House.

 

 

*The intercepts in these charts represent weighted averages of the adjustments for the various Census years. For instance, the 1994-2016 line includes the coefficients for the 1990, 2000, and 2010 Census weighted by the number of elections in each decade. So in this case the 1990 and 2000 adjustments would have weights of five, and the 2010 adjustment a weight of three. The 2018 line applies only the 2010 redistricting adjustment.