The Election in Pictures

Some data updated through September 2.

Sunday, July 29th, marked the point when there are just 100 days left until the November midterm.  In this post I will try to pull together my various writings and predictions for both the 2018 House and Senate elections.  I begin with the most important factor that influences both types of races, the President’s job-approval figures.

Presidential Approval

The President’s job-approval rating is an important predictor of midterm election results in both my model for Senate elections and the one for House elections.  The Democratic advantage on the “generic-ballot” question about voting in House elections has waxed and waned as Donald Trump’s approval rating first fell after he was inaugurated then rose again over the past few months.

Donald Trump enters the 2018 election with about the same level of public support Barack Obama had in 2010. Though Obama was much more popular when he was inaugurated, that goodwill faded over the following eighteen months. In the 2010 midterm that followed, the Democrats were “shellacked,” losing sixty-three seats in the House of Representatives. Support for Trump also ebbed away during 2017, but he has rebounded slightly from his nadir last December.


Though Trump’s overall approval rating heading into the first midterm is largely identical to Obama’s, Trump is more intensely disliked.  Pew reported that the proportion of people saying they “strongly” disapproved of Obama’s performance in office grew from 18 percent in April, 2009, to 32 percent by September, 2010.  For Trump, CNN found that he took office with over forty percent of Americans already strongly disapproving, a figure that has remained relatively unchanged.  In its June, 2018, poll CNN reports a “strongly disapprove” figure of 45 percent.

The House of Representatives

As the first chart shows, the president’s job-approval rating bears directly on the “generic-ballot” question.  Based on polls through September 2nd, the Democrats’ lead on the generic ballot has grown slowly since Inauguration Day and inversely with Presidential job approval.  The grey area in the chart below represents the likely range of outcomes.  Since April, Donald Trump’s net approval rating has ranged from about -8 to -14.  Those values define the left and right sides of the shaded region.  When I include estimates of any methodological or unique “house effects,” I find that polls conducted over the Internet show a pro-Republican tilt of about 2.6 percent.  Gallup’s polling shows an ever greater Republican edge of nearly five percentage points.  I use the values for live polling, which are most favorable to Democrats, and those from the much-less favorable Gallup, to define the height of the grey area.

Notice that, according to these results, even if Trump were to achieve a net approval rating of zero, Democrats are still predicted in live polling to lead on the generic ballot by about six points on Election Day. That reflects the slow growth in support for Democratic House candidates over Trump’s presidency from about four points on Inauguration Day to a predicted six points this November. In past elections the margin of victory in generic-ballot polls has proven to be a pretty accurate predictor of the actual division of the vote.

An earlier version of this model showed a small, marginally significant positive boost for Democrats in polls of likely voters.  That difference has disappeared as the number of polls has increased.  Since Democrats are generally disfavored in likely-voter polls, especially ones conducted in midterm years, a finding of no difference between registered and likely voters is actually positive news for Democrats.

One other major problem for House Republicans has been the historically large number of their Members who are leaving, or have left, the House.  Forty Republicans will not be returning to the House next January creating an excess number of more vulnerable open seats on that side of the aisle.  Only 18 Democrats are leaving the chamber.  With 22 more retirements than the opposition, the largest midterm gap since the New Deal, the Republicans face a loss of 39 seats based on the historical relationship between the two measures.

The Senate

There is no national generic-ballot question for Senate elections because only two-thirds of the states have a Senate race in any given year.  Looking back historically over Senate elections, the fate of the President’s party depends directly on his job-approval rating and, unlike for House elections, the state of the economy as measured by the growth in real disposable personal income per capita.  Any plausible combination of approval for Donald Trump and income growth predicts that the Republicans will fail to win a majority of the popular vote for Senate in November.  One reason is that the popular vote for Senate candidates of the President’s party runs four points lower when the President is not on the ballot.


Much has been made of the 4.1 percent increase in Gross Domestic Product reported for the second quarter of the year.  That figure represents the growth in nominal GDP; after adjusting for inflation the figure is 2.8 percent. Unfortunately for the Republicans little of that growth appears to be “trickling down” to ordinary Americans.  Here are the recent trajectories for both real GDP and real per-capita disposable personal income.

Personal income has hovered around a two-percent growth rate for the last three quarters, while real GDP grew more quickly.  If voters respond to changes in the amount of money in their pockets, then the economy will not be sufficient to power the Republicans to victory in the fall.  From the chart above, a two-percent growth rate in per-capita income and even a 45 percent approval rating for Donald Trump still leaves Republican candidates short of a majority in the national popular vote for Senate.

From Votes to Seats: The House

Americans became much more cognizant of the word “gerrymander” after the redistricting that followed the publication of the 2010 Census.  Drawing lines for partisan advantage has become easier as voters have segregated themselves geographically by party.  Together the two forces have combined to create a Republican “bulwark” in the House. Democrats need to win the popular vote by more than fifty percent to take half the seats in the body.  How much more is subject to debate, but most estimates put the needed margin of victory in the range of six-to-eight percentage points, or an election where the Democrats win somewhere between 53 and 54 percent of the popular vote.

For instance, The Economist currently projects the Democrats to win 54.3 percent of the popular vote, or a margin of 8.6 percent, but take just 51.3 percent of the seats in the House.  Both Dave Wasserman at the Cook Political Report and Nate Cohn at the New York Times cite a seven-point margin as the minimum required for a slim Democratic win. My model agrees.  It uses historical voting data back to 1940 to estimate the relationship between seats and votes. I include adjustments for redistricting after each Census and for the decline in political competition since the 1994 “Contract with America” midterm. Using those data I estimate the Democrats need at least 53 percent of the national popular two-party vote to win a majority in the House of Representatives.

From Vote to Seats: The Senate

Unlike House districts, Senate seats cannot be gerrymandered because they constitute entire states.  That makes the Senate more competitive than the House.  There is no equivalent “bulwark” in the Senate; winning half the popular vote generally translates into about half the seats.  Since the model predicts that the Democrats will win a majority of the 2018 popular vote for Senate, we should expect the Democrats to win a majority of the 35 Senate seats at risk.  Winning 18 of those seats would not be enough to flip control of the Senate because of the Vice President’s tie-breaking vote.  Nineteen seats would give the Democrats control.


From Here to November

Can the Republicans rebound between now and election day?  Unfortunately, recent history suggests they will face even larger obstacles in November than they do todayPresidential approval generally declines as the election nears, and the opposition party’s advantage on the generic ballot grows.

Job approval for first-term presidents fell on average about five points between May/June and October of midterm election years.  Trump might see a smaller decay because his current popularity is historically low, around 42 percent.  Presidents whose approval was above fifty percent in May/June saw a 3.3 percent drop in approval; those who started below fifty percent in May/June saw their ratings fall an average of just 1.9 percent.


In off-year elections, generic ballot polls for both of Obama’s midterms fell as the election neared.  For George W. Bush in 2006, Republicans recovered slightly from their early summer deficit, then watched support for their candidates crater in October.  Most observers credit that sharp decline to the Mark Foley scandal that fall.

 

Governors Hold the Cards in Congressional Redistricting

In “split-control” states, Republicans won 6.2 percent more seats than expected when they held the governorship; when Democrats held that office, Republicans won 6.5 percent fewer seats than expected.

Americans will elect thirty-four governors to four-year terms this fall.  They will still be in office after the 2020 Census and will have a say in how states redraw their Congressional and legislative district plans. All states where legislatures draw district lines except North Carolina grant the governor the power to veto any plan.  In states where control over the branches is split between the parties, this process should lead to compromises acceptable to both parties.  As well see, however, the evidence from the redistricting after the 2010 Census suggests governors hold all the cards.

In nine states, politicians play no direct role as redistricting is left up to nonpartisan commissions.  Courts, too, can override the lines drawn by legislatures.  This year’s dramatic redrawing of the lines in Pennsylvania follows similar judicial interventions in Florida and New York.  The New York decision affects my analysis since it applied to elections beginning with 2012.  (The other decisions have yet to come into force.)  I have added New York to the commission list, but have analyzed it, and California, separately as well.

I have also excluded the seven states which have only one Congressional district like Wyoming and Alaska since gerrymandering is not possible with no lines to draw.  That leaves 43 states which can be categorized as follows:

  • maps drawn by nonpartisan commissions or courts (8 states);
  • maps drawn by Republican legislatures facing Republican governors (16 states)
  • maps drawn by Democratic legislatures facing Democratic governors (6 states); and,
  • maps drawn when either the houses of the legislature were held by opposing parties, or where the legislature had unified control but faced a governor of the opposite party (13 states).

To avoid relying too heavily on a single year, I have added together the votes cast for Republican and Democratic House candidates in each group of states for the 2012-2016 elections.  I have applied the same method to seats won, again summing up the number of Republican and Democratic seats won across all three elections.  That method produces these results:

House Votes and Seats Won 2012-2016 by Redistricting Method

 

 

 

 

 

 

 

 

 

 

The first column reports the Republican percent of the total two-party popular vote summed across the three elections, 2012, 2014, and 2016.  In the sixteen states where Republicans held both houses of the state legislature and the governorship, they won 56.5 percent of the two-party House vote and 71.7 percent of the seats.  In solidly Democratic states the Republicans won both a minority of the popular vote and of the seats awarded.  The results for commissions and courts is complex; I will deal with it in a later article.

In various articles here I have described the natural inflation of the proportion of seats won due to the operation of our first-past-the-post electoral system.  As parties win larger and larger proportions of the vote, they gain an ever-increasing share of seats.  I have estimated this inflation factor using both biennial election results back to 1946, and across states in 2012.  Both methods produce equivalent results, for instance.

To estimate the share of seats awarded you need only square1 the value of the ratio (Republican Votes)/(Democratic Votes) to get the ratio (Predicted Republican Seats)/(Predicted Democratic Seats).  This approach gives rise to the third column in the table, the proportion of seats that are predicted to be won by the Republicans after applying this “square law” rule.2 In the entry for Republican control, that party’s 56 percent share of the House vote should produce a share of about 63 percent of seats.  In practice, the Republicans won nearly 72 percent of the seats.  The final column measures the over- or under-representation of the Republicans in the House as a percentage gain or loss compared to the predicted share.  In this case, the Republican’s 72 percent is about 14 percent higher than the expected 63 percent. This figure provides a criterion for evaluating how over- or under-advantaged a party was compared to expectations.

The normal expectations for states with unified control are confirmed:  Republicans win a disproportionate share of seats in states where they controlled the redistricting process, and won disproportionately fewer seats than expected in states where the Democrats were in control.  Notice that the size of Republican advantage in states that party controlled is larger than the disadvantage the Republicans faced in states controlled by Democrats, +14 percent versus -4 percent.

By this measure states with some form of split party control show hardly any partisan advantage at all.  Republicans won a share of the seats awarded in these states nearly equal to their expected share.  However, it turns out this overall result hides a lot of significant variation.

We can identify two different forms of split control:

  • ones where both chambers of the state legislature are held by one party but the governor is of the opposite party; and,
  • ones where the chambers of the state legislature are held by different parties.

As it turns out there are nearly equal number of each type of split control; in seven states unified legislatures faced an opposition governor, while in six states the chambers themselves were split.  Once we break out these various patterns, the power of governors becomes clear.  In both types of split control, the governor’s party is disproportionately advantaged during redistricting.  In fact, if we group these split-control states together simply by the partisanship of the governor, Republicans were over-represented in seats awarded by 6.2 percent where they held the governorship; when Democrats held that office, Republicans were under-represented by 6.5 percent.

These results are rather striking.  They suggest that opposite-party governors can force a redistricting map that is actually more favorable to the governor’s party than to the legislature’s.  Similarly when the two legislative chambers are held by opposite parties, it is again the governor who appears to determine which map wins approval.  It appears the governor’s veto is a more powerful weapon in the fight over Congressional district lines than the legislature’s control over drawing the lines themselves.  This fact could weigh heavily over redistricting fights in states like Colorado, Michigan, Florida and Georgia where Democratic governors may win election and end up facing Republican legislatures.  In Massachusetts and Maryland the reverse will likely hold true.

 


1The longitudinal estimate was 2.04; the cross-sectional estimate was 2.08. For simplicity I have rounded down to two, which is well within the confidence intervals for each estimate of beta.

2The tendency for first-past-the-post systems to disproportionately advantage the winning party was first observed in elections in the United Kingdom. There the coefficient reached three, giving rise to the name “cube law” rule, since cubing the ratio (Labour Votes)/(Conservative Votes) does a good job of predicting the ratio (Labour Seats/Conservative Seats). Following this tradition, I have named the US version of this relationship the “square law” rule.

How Well Do Generic-Ballot Polls Predict House Elections?

I have compiled the results of generic-ballot polls taken near to an election and compared them to the actual division of the Congressional vote.  The table below presents the margin between support for the President’s party and support for the opposition.  For each election I have used about half-a-dozen polls from different agencies taken just before voting day.  Averaging the differences between these two quantities shows that these polls have fared rather well since 2002.  The average deviation in the four midterm elections is 0.6 percent; in Presidential years that falls to 0.1 percent.

Still these averages hide some fairly wide fluctuations.  In four of the eight elections the difference between the polls and the election results exceeds two percent.  The error was especially egregious in 2006 when the polls predicted nearly a fourteen-point Democratic margin compared to 8.6 percent in the election itself.

In the most recent election, 2016, the polls predicted a slight positive swing in favor of the Democrats, but the outcome went slightly in the opposite direction.  All the cases where the polls erred in picking the winner occurred in Presidential years and usually when the polling margin was close.  The four polls taken during midterm years all predicted the correct winner, though the size of the victory was off by more than three points in two of those elections.

 

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.

 

Update on Family Separations

Some 1,500 children remain separated from their parents and in government custody.  Only 63 percent of children whose cases have been officially been reviewed have been reunited with their parents.

The Federal Government has provided new figures concerning the migrant children separated from their parents at the border as part of the proceeding before Judge Dana Sabraw.  I have used these figures, along with my previous accounting, to develop estimates of the number of children reunited with their parents, and those not yet reunited for a variety of reasons. I start with the total of 4,954 separated children I estimated earlier including both children separated during the “zero-tolerance” period and those separated during the “pilot program.”  There are three official sources of information on reunifications.  The first is a “fact sheet” from DHS which reported that 538 children had been reunited with their parents “as part of the Zero Tolerance initiative.” Some number of these children may have been separated before zero tolerance, but we have no way of knowing how many.  The “Fact Sheet” also fails to mention the number of children whose cases were processed but who remain in government custody.  I will estimate that number below.

The other two sources of data on reunifications comes from the filings made by the government in the case of Ms. L.v. ICE before Judge Dana Sabraw.  On June 26th Judge Sabraw ordered that the government must reunite all separated children.  The government has since submitted two filings to the court reporting on its progress.  The first, filed July 12th, concerned the disposition of the supposed 103 children in custody who were under five years of age.  (This figure of 103 remains suspiciously low.)  Late this past Thursday, July 19th, the government reported on its progress with the older children in its custody.

The government could only identify and clear the parents of 57 of the youngest children.  The other 46 remain in government custody because it determined the child might be subject to abuse, the parents were in custody or had criminal records, the associated adult was not the child’s parent, or, in twelve cases, the parent(s) had already been deported.  The clearance rate was higher for the older children.  364 of them were reported to be reunited with parents, and in another 848 cases the parent(s) were identified and cleared, but the reunification had yet to take place.  However the government also identified another 908 children whose parent(s) were either declared ineligible or whose eligibility was still unresolved.

The one remaining piece of the puzzle concerns the number of children separated before zero tolerance began who were subsequently reunited with their parent(s).  If we assume that their cases were disposed of at the same rate as cases during zero-tolerance, we can estimate the number of reunited children from that earlier period.  Combining together the 57 reunited young children with their 364 older reunited peers, and counting the 848 children awaiting reunification, gives us a total of 1,269 children either reunited or awaiting reunification.  In comparison 46 young children and 229 older ones remain separated because their parents were not cleared.  If we add to that the 908 children who may also be ineligible, we get a total of 1,183 children whose cases have been reviewed without reunification,  Applying the same “clearance rate” to the period before zero tolerance gives us an estimate of 502 children from that period who remain ineligible for reunification.


The rate at which parents are declared ineligible should be a major source of concern.  Of the 3,492 separated children whose cases I estimate were resolved by July 19th, only 1,807, or just 52 percent, were reunited or awaiting reunification with their parents. If we rely solely on official reports, 1,045 children were ineligible or uncleared.  Even that more conservative figure results in a reunification rate of just 63 percent.

 

Tracking Family Separations

How the number of separated children grew to nearly 4,300, and why many more than 103 of them must be under five years of age.

In the last post I offered a simple bookkeeping model using reported figures from DHS and plausible extrapolations to estimate the number of children separated from their parents at the border since October, 2016.  Here is the time track of the number of children in custody. The solid lines connect points based on DHS reports; the dotted lines are estimates.


The black lines represent the period of “zero tolerance,” during which nearly 3,000 children were taken from their parents.  Since then DHS has reported 538 reunifications that span zero-tolerance and maybe earlier.  I estimate another 126 children were reunited with parents between the end of family separations on June 20th and July 5th when DHHS Secretary Azar told reporters the number of children in custody numbered “fewer than 3,000.”

Much attention has been paid to the 103 children under five who were supposed to be reunited with their parents last week.  A rate of 103 children under five from a population of  “fewer than 3,000” separated children is entirely preposterous.  About one in three children living in the countries from which most families migrate is under five years of age.  There must be hundreds more “tender-aged” children in custody than the government has accounted for so far.

How Many Separated Children are in Custody?

The Department of Homeland Security’s own reports indicate that nearly 5,000 children have been separated at the border. Some 4,300 remain in custody, many more than the current official figure of “fewer than 3,000.”  Also there must be many more than 102 children under five years of age.

Since the introduction of the policy of “zero-tolerance” on our southern border, the Department of Homeland Security (DHS) has released a set of inconsistent reports about the number of children taken into custody and the number reunited with family members.  Now that a Federal judge has ordered that all the separated children be reunited expeditiously, the Department of Health and Human Services, in whose custody parent-less child immigrants are placed, now states that the number in their care is “fewer than 3,000.”  That figure is not consistent with the existing reports from DHS.

In a telephone call with reporters DHHS Secretary Alex Azar said on July 5th that an extensive review led him to believe his agency had custody of “fewer than 3,000” separated children.  Yet a week earlier DHS had reported just over 2,000.  Azar attributed this discrepancy to the court order requiring “officials to go back further in time and comb through thousands of cases to find any separated children.”

We have one good estimate of the number of children taken prior to zero-tolerance.  On June 29th DHS told NBC News that a “pilot program” had already separated 1,768 children through February, 2018,  a figure which had probably grown to 2,000 by the time zero-tolerance took effect on May 7th.

Also the last DHS report on the number of separated children ends on June 9th, eleven days before the President’s ordered an official end to separating families.  Extending the DHS figures through June 20th adds nearly 700 more children to the total.  In the sections below I will review the DHS reports then use them to estimate the total number of separated children.

The Evidence

Here is a compilation of the varying reports on the number of separated children from the Department of Homeland Security:


According to the NBC News report, nearly 1,800 children were already in custody by the end of February, 2018, weeks before the Attorney General ordered “zero-tolerance” for all migrants.  Separations were fairly rare in this period though, averaging fewer than five per day between last October and February of this year.

DHS issued two overlapping reports after the imposition of zero-tolerance.  One covered the period from April 19th until May 31st; the other covered May 5th through June 9th.  Even this second report did not include the entire period of zero-tolerance which lasted until President Trump’s order on June 20th.

Reports since then have been inconsistent.  On June 20th DHS reported 2,053 children in Health and Human Services custody; a week later that figure fell by just six to 2,047.  Yet in between those two reports, on June 23rd, DHS reported that over 500 children had been reunited with their parents, which should have brought those totals down considerably.  Finally we have Secretary Azar’s announcement of fewer than 3,000 children in custody as of July 5th.  (Update:  The official figure has been set at 2,551, still much lower than my estimates here.)


The Estimates

I have tried to reconcile these figures as best I can.  I use the daily separation rate for periods when it can be calculated and use it to estimate the number of separated children for periods when data are not available.   Including the “pilot” program, and unreported periods during zero-tolerance, I estimate that nearly 4,300 separated children remained in government custody on July 5th, more than forty percent higher than Secretary Azar’s figure:


As an example of the calculations involved, take the number of children before zero tolerance began.  Official DHS figures cover the period from October, 2016, through February, 2018.  That leaves some 66 days between the end of that report and the onset of zero tolerance on May 7th.   I use the estimate of 4.7 separations per day from the second DHS report to estimate that another 309 children were separated from their parents during those 66 days.  The same method is used to generate the other figures marked “Estimate” in the table above.  Note that using the 4.7 per-day rate is probably a conservative estimate of the rate of separations between March 1st and May 5th. These estimated figures appear as dotted lines in the chart above.

On June 23, DHS reported that 538 children had been reunited with their parents “as part of the Zero Tolerance initiative.”  I have attributed all those children to the zero-tolerance period.  (There is no data on the number of reunited children taken before zero-tolerance began. I return to that issue below.)  If we assume that reunifications continued at the same pace through July 5th, another 126 children would have been returned to their parents between June 23rd and July 5th, raising the estimated total to 664.

Adding together the estimates for separations before and during zero tolerance gives us the total number of children reportedly detained at the border since late 2016. Subtracting the estimated number of family reunions provides the estimate for how many separated children likely were still in government custody as of July 5th.  That figure of 4,290 is some forty-three percent higher than Secretary Azar’s estimate of  “fewer than 3,000.”

I have not found figures for reunifications prior to zero-tolerance.  Some of the 2,077 children taken before May 7th may have been reunited with their parents.  But even if we assume as many as a quarter of them were placed back in their parents’ arms, that would still leave the total number of children separated from their parents closer to 4,000 than to Azar’s 3,000.


How many children are under five years of age?

The court order mandated that children under the age of five had to be reunited with their parents fourteen days after the order went into effect, or July 10, 2018; for older children the deadline was thirty days (July 26).  Azar told reporters in the telephone call-in that only about 100 of the children in DHHS custody, or about three percent, were under five.  This past weekend, under order from Judge Sabraw, the government turned over to the ACLU a list of 102 children that purportedly included all those under five in the government’s custody.  This figure is implausibly low.

First, even if the proportion of young children was only three percent, my estimate of 4,290 total separations implies that the total number of young children should be closer to 143.  However the three percent figure itself is highly suspect given the age distribution of children living in the three countries from which most asylum-seekers emigrate — El Salvador, Guatemala, and Honduras.

The CIA’s World Factbook reports the “population pyramids” for every country.  Here are the charts for those nations:


The bottom four bars of each graph depict the numbers of boys and girls in four age groups, 0-4, 5-9, 10-14, and 15-19.  In both Guatemala and Honduras about a quarter of all children and teens are under the age of five.  For El Salvador, whose birth rate has slowed in comparison to the other two countries, young children constitute a somewhat smaller share.

Now we can certainly imagine that worried parents might be reluctant to carry a two-year-old child across hundreds of dangerous miles to seek refuge in the United States.  That would imply that the age distribution of the separated children skews somewhat older than the source populations from which they are drawn.  Even with such an older skew though, Secretary Azar’s figure of just three percent remains highly dubious.  If the proportion of separated young children were just ten percent, or half or less of the actual proportion of very young children living in these countries, there should be over four hundred children subject to the July 10th deadline, not 102.

One other feature of the DHS reports also bears mentioning.  Notice in the first chart that the numbers of separated children and parents are nearly equal. That ratio is consistent with either single parents carrying a single child, or couples with two children. It is unlikely that most of these are single-parent families given the high proportion of Catholics in the source countries and the consequent low divorce rates like those reported for Guatemala. Some of these parents might have left their spouses behind to look after other children or have come to be reunited with a spouse already in the United States.

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.

Will there be a “Singapore Surge?”

Republicans are hoping the Singapore summit will boost the President’s approval ratings, but summit meetings have historically had only small effects. Nor were those effects uniformly positive.

Pundits have speculated that Donald Trump’s meeting with North Korea’s Kim Jong-un might improve Republican chances in the November elections.  “[Trump’s] new willingness to sit down with Kim to work on a peace deal, RNC members believed, would boost his approval rating and make him far less of a drag on congressional Republicans trying to keep control of the House and Senate this November,” wrote S. V. Date in the Huffington Post at the end of last month.  Some of that same optimism was apparent again yesterday. “Ron Kaufman, an RNC member from Massachusetts and once a top adviser to 2012 GOP nominee Mitt Romney, said Trump’s ability to get a meeting with Kim would play well with voters. ‘In real America? Absolutely,’ he said. ‘It will help the president’s numbers, and it will help to some degree in the midterms.'”

The problem with this theory is that summits have had limited effects on presidential approval ratings.  Using the job-approval data from Gallup I compiled the average approval score for roughly the two months prior to a summit and the two months following and calculated the change as shown below.

Most summit meetings have had only marginal effects on the President’s popularity on the order of a few percent up or down.  Nor can we attribute all the changes we see to the summits themselves.  For instance, the fall in Ronald Reagan’s approval ratings after his Washington summit with Mikhail Gorbachev in 1987 resulted largely from the revelations of the Iran-Contra scandal which appeared soon after the summit.  Nor do all summit meetings improve the President’s image. The 1974 meeting between Gerald Ford and Leonid Brezhnev led to the SALT II accords, but they were denounced by Republican opponents like Ronald Reagan and criticized by major newspapers like the New York Times.

A number of summits considered important by historians had little influence on public opinion.  Even an event as tumultuous as Nixon’s trip to China in 1972 increased his approval rating by less than four points.  Kennedy’s supposed failure to stand up to Khrushchev in Vienna cost him a bit over five points. A year before Khrushchev had stormed out of the Paris meeting with Eisenhower over the U-2 crisis.  Eisenhower’s popularity suffered a slightly larger hit.

As I wrote earlier about the effect of the Syrian bombings, opinions about Donald Trump are much too crystallized to react to something like a summit.  And if the intention was to influence the fall election, why didn’t Trump hold out for a meeting in September or October when it might have mattered?  Few voters will be taking the Singapore summit into account when they go to the polls in November.

Update: I added Jimmy Carter’s involvement with the Camp David accords in September, 1978, to the list.  His approval rating rose substantially in the short term, from an average of 40.8 in the two months before the summit to 49.5 in the two months thereafter.  His approval scores were already moving upward after hitting a nadir of 38 the previous July, but they did jump five points between the poll earlier in September and the next sounding in October.  This bump in Carter’s approval persisted through the November, 1978, midterm election.  Perhaps Trump should have waited until the fall to meet with Kim.