The Politics of Stay-At-Home Orders

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On her blog, journalist Marcy Wheeler helpfully tallied the twenty-seven states whose governors have imposed stay-at-home orders during the COVID-19 pandemic. Virginia joined this group late Monday. I have used her data, and figures from Johns Hopkins University on the number of identified cases, to do a quick analysis of the political forces driving the decision to impose such orders.

I used simple binary logit models for these tests.  The predictors include whether each state’s governor and legislature is controlled by Democrats, the February net job approval rating (approve – disapprove) for Donald Trump in each state from Morning Consult, and the number of reported cases in each state as of March 15th and March 30th.  Model (1) below includes all these factors; model (2) includes just the two that proved significant.

As you can see, only two factors proved nominally “significant,” whether the governor is a Democrat, and Trump’s approval rating in the state.  States with Democratic governors, and those where Trump’s net job approval is negative (“underwater”), are more likely to have instituted a stay-at-home policy. The number of COVID-19 cases surprisingly did not seem to matter.  (Using the logarithms of the number of cases did not improve things nor did looking at rates of growth.)

Using these results, I have generated the predicted probability that each state will have instituted a stay-at-home order and compared those predictions to the actual policies.

There are ten states where the predicted policy does not match the actual decision.  Thirty-three states are predicted to have imposed stay-at-home orders, but only twenty-eight have done so. Democratic strongholds like California and New York all have predicted probabilities above 0.9. However Nevada, Maine, Pennsylvania, Massachusetts, and Kentucky should all have instituted stay-at-home policies but have yet to do so.  In contrast, the governors of West Virginia, Idaho, Indiana, Alaska, and Ohio have all instituted such policies despite the political context of their states.

We can use the same set of predictors to estimate the duration of a state quarantine. Here I use a “Tobit” model, which handles dependent variables with zero lower bounds. States without a quarantine are coded zero on the duration variable.

The general pattern here is the same as for whether a quarantine was imposed.  However, the growth in cases between 3/15 and 3/30 has a weak statistical relationship with duration. Because the case figures are expressed as base-10 logarithms, the coefficient of 26.8 implies that a state whose caseload grew by a factor of ten during the latter half of March would impose a quarantine of 26.8 days, other things equal.

 

Money in Senate Elections

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Senate campaigns that outspent their opponents by two-to-one in 2016 and 2018 typically gained a bit over one percent at the polls. Spending by outside groups, and the “quality” of challengers, had no measurable effects.

My earlier model of recent contest for the U.S. Senate relied entirely on two measures of popularity, the favorability score for the incumbent Senators in their states, and support for President Trump in those same states.  While those two measures alone explain 81 percent of the variance in the vote for Senatorial candidates, the model obviously lacks a few important items, most notably data on campaign spending and on challenger “quality.”  In this post I add measures of campaign spending and of challenger quality.

For campaign spending I have used the figures reported to the Federal Election Commission and reported by OpenSecrets.  I chose to use spending rather than funds raised because most cases campaigns spent nearly all of what they raise, and sometimes more. For instance, here is the record for campaign spending in the 2018 Missouri Senate race where incumbent Claire McCaskell lost to Republican Josh Hawley, then Attorney General.

The other major source of campaign financing is, of course, spending by outside groups.  Here, OpenSecrets separately reports funding in support of and opposed to each candidate.  My measure of outside spending adds together monies spent supporting a candidate and those spent criticizing her opponent.1 I use the base-10 logarithm of spending which has a better fit to the data and incorporates the basic economic intuition of decreasing returns to scale.

Spending by the Campaigns

I first added the campaign spending figures for Republicans and Democrats separately with results as shown in column (2). Democratic spending appears to have had a larger effect than Republican spending, but a statistical hypothesis test showed the two coefficients were not significantly different in magnitude. So in (3) I use the difference between the two spending measures, which is equivalent to the base-10 logarithm of the ratio of Democratic to Republican spending.*

An increase of one unit in these logarithms is equivalent to multiplication by ten. So the coefficient of 4.39 tells us that a ten-fold increase in the Democrats’ spending advantage would improve their share of the two-party vote by somewhat over four percent.  While a ten-fold advantage might seem implausibly high, some races have seen such lopsided spending totals. In Alabama’s 2016 Senate election Republican incumbent Richard Shelby spent over twelve million dollars on his race for re-election; his Democratic opponent spent less than $30,000. In that same year, Hawaii Democrat Brian Schatz spent nearly eight million dollars while his opponent spent $54,000.  These sorts of drastic imbalances typically appear in non-competitive races where the incumbents are seen as shoo-ins to retain their seats.

To see more intuitively how spending affects results I have plotted the predicted change in the Democratic vote for various ratios of Democratic to Republican spending.  The state codes represent the seven most competitive races as identified by my model. (I will examine the implications for 2020 in a separate post.)

In states where the Democrats outspent the Republicans by a ratio of two-to-one, the Democrats were rewarded on average with an increase of about 1.3 percent in their vote shares.

Spending by Outside Groups

In sharp contrast to the results for spending by the campaigns themselves, I find no systematic influence for spending by outside groups. Neither including separate terms for pro-Democratic and pro-Republican outside spending as in model (4) above, nor including the difference between those figures in model (5), displays significant effects.

While I’m not ready to make strong claims for this rather surprising finding without an expansive review of the literature on spending in Senate campaigns, I don’t find the result all that surprising. Since outside groups may not, by law, “coordinate” with the campaigns they support, these groups must focus their attention on television advertising, direct mail, and other messaging strategies.  Perhaps these strategies simply are not as effective as they once were, as demonstrated by the Presidential primary candidacies of Michael Bloomberg and Tom Steyer. They both spent hundreds of millions of dollars on television advertising but garnered few votes on election day.

Effects of Challenger “Quality”

Another common factor used to explain legislative elections is the “quality” of the challengers that choose to take on an incumbent. While some people launch vanity Senatorial campaign to make themselves better known to the public at-large, most Senatorial bids are undertaken by people who already hold elective office at either the state or the Federal level.  I have coded the backgrounds for the challenger facing each incumbent in my dataset of 2016 and 2018 elections.  They fell into four categories — current or former Members of Congress, current or former members of the state legislature, governors and others who have held state-wide office, and a miscellaneous category that includes local-level politicians like mayors and non-politicians like activists.  I find no statistical effects for any of these categories either separately or in combination.


We are thus left with a model of Senate elections that includes three factors — the incumbent’s net favorability, the state’s level of support for Donald Trump, and the ratio of spending by incumbent and opposition campaigns.

 

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*Remember from high-school math that log(A/B) = log(A) – log(B).

 

Senate Update, March, 2020

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The Democrats have a decent chance to take control of the Senate.

I have updated my Senate predictions using the fourth-quarter, 2019, favorability data for Senators and February, 2020, job approval ratings for Donald Trump. Both come from Morning Consult.  I have also cleaned up a few errors in the earlier data used to estimate the model’s coefficients. Here are the updated results:

Maine’s Susan Collins now joins Alabama’s Doug Jones as the most-vulnerable Senators up for re-election.  Both Senators face adverse political environments in the states they represent.  Mainers don’t care for Collins very much, and they’re slightly negative when it comes to Donald Trump. Unlike Collins, Jones is liked by a plurality of Alabamians, but Trump is liked so much more that it overwhelms Jones’s personal popularity.

Steve Bullock’s musings about running against incumbent Montana Senator Steve Daines find little support in the data here.  Both Daines and Donald Trump have positive ratings in Big Sky Country, with the Senator predicted to win re-election with 57 percent of the vote. Jaime Harrison also faces a pretty uphill quest in his bid to oust Lindsey Graham in South Carolina.

If these estimates were to hold, the Democrats stand a good chance of flipping the Senate in November. If Jones, Collins, Gardner, and Ernst all lose, the Democrats would net three seats. That would create a 50-50 tie and require the Vice President to be decisive.  Also defeating one of McConnell, McSally, or Tillis would give the Democrats a 51-seat majority.

 

The 2020 Senate Elections

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In a prior series of posts, I constructed a “simple model of Senate elections” using national data across elections.  This helped identify some key factors that influence the overall vote for Senators but provided no insight on the results in specific states.  In this post I develop another “simple” model that is designed to predict the voting outcome based on two factors, a state’s partisanship as measured by support for President Trump, and the net favorability of the incumbent Senator.  I estimated the model using data from the 2016 and 2018 elections. The results appear here and are best summarized in this chart:

The lines portray how the vote for an incumbent Senate Democrat improves as her net favorability grows. The top line represents the result for a Senator from a strongly pro-Democratic state, one where only 40% of the state’s voters approve of the President.  Even a Democratic incumbent with a net favorability of zero is predicted to win nearly 55% of the vote in this state and hold the seat.  In contrast, a Democratic incumbent in a pro-Trump state like Doug Jones in Alabama fails to win 50% of the vote even if he is unusually popular despite the party mismatch.  Overall the Republicans hold a slight advantage. The model predicts that in a state where support for Trump is 50-50, the purple line, only a Democratic incumbent with at least a +8 favorability has a chance of holding the seat.

We can apply the results of this model to the 2020 Senate elections.  We only have available the current measures for Trump support and candidate favorability, so we obviously cannot predict how things will stand a year from now.  For the estimates below, I have used the most recent Trump approval rating and Senate incumbent favorability ratings as reported by Morning Consult.  The President’s score is from the month ending September 1st; the Senators’ ratings are averages over the third quarter, July-September, 2019.

The highlighted rows at the top of the table correspond to incumbent Senators whose predicted vote is below fifty percent.The top and bottom spots on the list are held by Democrats. The most vulnerable incumbent is Doug Jones’s whose slight positive favorability rating of +5 is nowhere near large enough to overcome Alabama’s warm feelings for Donald Trump.

Jones is followed by the four most commonly discussed vulnerable Republicans — Susan Collins of Maine, Cory Gardner of Colorado, Joni Ernst of Iowa, and Thom Tillis of North Carolina. Martha McSally would hold her Arizona seat by the slimmest of margins. Majority Leader Mitch McConnell is lucky to represent solidly pro-Trump Kentucky or else his dismal favorability score might lead to his defeat.

It’s anyone’s guess what Donald Trump’s approval rating might be come the election next November, though his score has remained remarkably persistent in the face of events.  Using the averages at FiveThirtyEight, we see his low point came in the summer of 2017 when he fell to 37%. Over that winter and into the spring of 2018 his approval rating improved to about 42% where it has largely remained. There was a dip in his popularity during the government shutdown, and another now as the impeachment inquiry expands.  Given the observed variation in his popularity since the Inauguration, Trump’s approval rating might move up or down by three or four points over the course of the next year.  A four-point movement would represent a ten-percent change from his current rating of 41%.  The chart below shows how each Senator’s predicted vote would change given a ten percent increase or decrease in Trump’s approval rating in each state.


The four Senators at the top of the list in the darker grey area are predicted to lose their seats even if Trump’s approval rating were to improve by ten percent.  The next three Senators survive their re-election bids if Trump’s approval runs about where it is today or improves by November, 2020.  However a ten-percent decline in Trump’s approval threatens the seats of Thom Tillis, Martha McSally, and even Mitch McConnell.

Right now the Republicans control the Senate by a 53-47 margin, plus the tie-breaking vote of the Vice President. Assuming a Democratic victory in the Presidential election next fall, the Democrats need to flip at least three seats, while losing Alabama back to the Republicans. Maine, Colorado, and Iowa look promising for the Democrats and North Carolina and Arizona are both tightly contested.

Four Republican seats have vacancies. In Georgia a special election will be conducted in 2020 alongside the regular election to fill the seat that Johnny Isakson will leave at the end of this year.  Three other Republican-held seats will also be vacant in 2020.  My model predicts the Republicans will hold all these seats with Georgia the most competitive.  (To construct these estimates I impute a favorability score for a “normal” Democrat by regressing net favorability on Trump support to account for the partisanship component of favorability.)

In strong Republican states like Wyoming and Tennessee, we see support for Trump running in the mid-fifties.  In states like these, a Democratic challenger would do well to card a favorability score better than -15.  The states where the Democrat might have some chance are Georgia and Kansas, where support for Trump splits evenly, but still the Democrats are predicted to lose those elections by three or four points.

 

It’s Not All Name Recognition

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The polling organizations at both Quinnipiac and Monmouth Colleges this week released “favorability” scores for all the Democratic candidates. Respondents are asked “is your opinion of [person] favorable, unfavorable, or haven’t you heard enough about him/her?” These provide a richer view of the state of the race than the simple who would you vote for today type of question.

In general, the better-known candidates are also the better-liked.  In the chart above the percentage of likely Democratic voters able to rate a candidate appears on the horizontal axis.  The vertical axis measures “net favorability,” the difference between the percent of the voters rating a candidate favorably and those rating the candidate unfavorably.  The figures in the chart represent the averages of the two polls.  The regression equation in the upper-left-hand corner of the chart shows that a ten percent increase in exposure brings the average candidate a net +7 increase in favorability.

At the top of the rankings is, no surprise, Joe Biden. 92 percent of the Democrats polled could give an assessment of Biden, and he scored at the top of the list in favorability with 76 percent favorable versus just 15 unfavorable.  Bernie Sanders is nearly as well known (89 percent) as Biden but not as well liked, with a net favorability score of 47. Two other candidates join Sanders at just under fifty percent favorability, Elizabeth Warren and Kamala Harris.  Harris’s favorability, however, substantially exceeds the value we would predict given her familiarity score. At the other end of the spectrum is New York mayor Bill deBlasio.  About half the respondents said they knew him well enough to give him a rating; unfortunately for him only an average of 16 percent of the Democrats in the two polls viewed him favorably versus 32 percent who viewed him unfavorably. (Removing him from the regression increases R2 from 0.76 to 0.92, and reduces the standard error from 9.9 points to 5.4. The slope is largely unchanged, but the intercept naturally moves slightly upward since it no longer needs to incorporate deBlasio’s negative score.)

Here are the actual and predicted net favorability scores for every candidate from the model where deBlasio is omitted. Harris is 10 points ahead in terms of favorability than her exposure predicts.  She’s followed by Pete Buttigieg and Eric Swalwell at around six percent.  (Swalwell’s frequent appearances on MSNBC might have something to do with this.) At the other end of the spectrum is the remarkably poor showing for Beto O’Rourke. Fifty-five percent of Democratic voters say they can score Beto, but his net 21 percent favorability is nearly nine points what we would expect to see given his familiarity.  Sanders’s unfavorable numbers also put him near the bottom of this list.  89 percent of Democrats know enough about Sanders to give him a favorability score, but his 47 percent net favorability lags about eight points behind what we would expect given how well known he is.

Gerrymandering: Finding the Deviant Elections

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During oral argument in Rucho v. Common Cause, the North Carolina gerrymandering case, the Supreme Court and the attorneys for the parties considered a variety of criteria to identify what Justice Stephen Breyer called “real outliers” in terms of election results. In the first article of this series I considered his half-the-vote/one-third-the seats criterion. In the last posting I considered the notion of measuring deviations from some predicted baseline result. In that article I proposed a formula for estimating the baseline based on historical voting data:

Expected % Democratic Seats = 2 X (% Democratic Two-Party Votes) – 50

This formula provides a simple, yet historically accurate baseline for estimating the share of seats we should expect the Democrats to win given their share of the Congressional vote statewide. (I should note that this formula is entirely symmetric. We could use the Republican vote and seat shares and get the identical result.) Armed with a method for determining the baseline prediction, I turn now to a method for identifying deviant electoral results.

Measuring the Deviation

How big a deviation from that baseline should be considered “significant” depends on both statistical and legal/constitutional criteria. I will only be talking about “significance” in the statistical sense. As we’ll see, the size of the deviation you are willing to tolerate depends on the proportion of outcomes you consider to be possibly unconstitutional.  In that sense, Justice Potter Stewart’s famous comment about identifying pornography, “I know it when I see it,” applies to gerrymandering just as well.

In the discussion about proportionality, plaintiff’s attorney Paul Stewart suggested and dismissed a “one standard deviation” away from some baseline criterion for gerrymandering. I have dealt with his objection concerning estimating a baseline result, but just one standard deviation is much too low a bar. As this graph shows, about 32% of elections should fall outside the one-standard-deviation criterion, many too many to qualify for judicial review. Statisticians often use two standard deviations as a minimal criterion for “statistical significance.” That would subject about five percent of the elections to additional scrutiny. Justice Breyer’s criterion works out to about one election in a thousand, which corresponds to a standard deviation difference of about 2.5.

Now it turns out the regression method also generates an estimate of the “standard deviation” of the predicted values. This quantity is called the “standard error,” and for the regression using state-years as the unit of analysis, the estimated standard error for the percent of seats won is 10.2.  So, using two standard errors as a minimum criterion, we should look for results where the difference between the actual number of seats won, and the prediction from the formula above, is at least 20 percent.  Here are the elections held since 2010 where the actual outcome differs from the predicted value by a least 20 percent. The “standardized deviation” column measures the absolute value of the quantity (Actual – Predicted)/(Standard Error).  The larger the value the further the election deviated from the prediction. Using the absolute value treats both parties symmetrically.

All three elections identified by the “Breyer criterion” also appear in this list. However there are a number of elections that fail his criterion, but where the actual seat outcome differs from the predicted value by at least two standard errors.  Connecticut persistently sent five Democrats to Congress since 2010 when the vote suggests there should have been at least one Republican in the delegation. Connecticut would not be identified by Breyer’s criterion, but a reasonable observer would conclude that state’s Congressional district lines appear to have been gerrymandered in the Democrats’ favor. Democrats also got “too many” seats in Maryland in 2014, but that pattern did not recur in other elections since the 2010 Census. Similar “one-offs” like VA12, NJ18, and MI12 might also be attributed to chance rather than systematic discrimination via gerrymandering.

Most of the other elections in the list show an excess number of Republicans winning House seats given the statewide vote for Democrats. Both North Carolina and Pennsylvania appear twice as does Ohio, whose map was just thrown out by a Federal court.

By either Breyer’s criterion or by measuring deviations from a predicted baseline, the map created by the North Carolina legislature qualifies as gerrymandered. Ohio and Connecticut also deserve judicial scrutiny.

 

Gerrymandering and “Proportionality:” Setting the Baseline

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In my last post I considered what I called the “Breyer criterion” for identifying partisan gerrymandering — a party winning half the vote in a state receives only a third of the seats.  That criterion identified just seven races out of the nearly eight hundred I examined, or just 0.9 percent of the state-level elections to Congress where candidates of both major parties stood. Breyer proposed his criterion to identify “real outliers,” elections that are “really extraordinary.” A one-in-a-thousand criterion probably fits that definition.

However the Court also discussed the general concept of how to measure “proportionality” between seats and votes. The attorney for the plaintiffs, Paul Clement, brought up the notion of a “one standard deviation from proportional representation” criterion mostly as a straw horse. Leaving aside his use of “proportional representation,” which as the oral argument shows is fraught with constitutional issues, Clement then claimed that it is impossible to know what the correct baseline should be from which to measure seat outcomes.

So I think the fundamental problem is there is no one standard deviation from proportional representation clause in the Constitution. And, indeed, you can’t talk even generally about outliers or extremity unless you know what it is you’re deviating from.

Clement’s argument ignores decades of political science research into the relationship between votes won and seats awarded.  Studies dating back to at least 1948 have theorized about and examined empirically the relationship between seats and votes.

Measuring the Baseline

I’ve written a number of times about the relationship between votes won and seats awarded in “first-past-the-post” or “plurality” electoral systems like ours.  These types of electoral systems routinely award the majority winner of the vote a disproportionately greater share of seats. Here is a simple example, using national electoral results for Congress.

The dark blue line represents the “best-fit” relationship between the percent of votes won by the Democrats in each election year and the percent of House seats the party won using simple “ordinary least squares” regression. The historical relationship is substantially steeper than the thin line in the chart representing parity, or when a party’s share of seats equals its share of votes.1

Using simple regression the equation that best describes this relationship is, in round numbers,2

% Seats Democrat = 2 X (% Votes Democrat) – 50

So, for instance, in a year when the Democrats win 55 percent of the vote, they should receive on average (2 X 55) – 50 = 60 percent of the seats.

Since gerrymanders take place at the state level, data from national elections do not provide the correct basis for determining whether a particular state’s election deviated “too far” from some predicted baseline. To develop such a baseline for Congressional elections I turn again to the MIT database of Congressional races I used in the preceding blog post.  Here is the relationship between votes and seats for state-year combinations. Each point represents a general election in a given state in a particular year, like Alabama in 1976.

A number of races resulted in one party or the other winning all the seats. These unanimous outcomes pose mathematical problems for our method, so I excluded those 84 races in the calculation of the slope and intercept for the regression line in the chart.

(The horizontal lines come from states with small numbers of districts where the number of outcomes is mathematically restricted. For instance, a state with four districts will often return a 3-1 result for one party. That leads to clustering at values of 25 or 75 percent.)

Using state-level election results gives us a model that is numerically quite similar to the simple method based on election years above:

% Seats Democrat = 2.3 X (% Votes Democrat) – 66

Here the slope of the line is slightly steeper than two and the intercept slightly more negative. In practice, though, the difference between these results and predictions using the simpler model from national-level data are negligible. The lines are so close that I could not represent them both on the chart.

Given the convergence between these two sets of estimates, I propose that

The best “baseline” estimate for the division of seats given the division of the vote in state-level Congressional elections is

% Seats Democrat = 2 X (% Votes Democrat) – 50

That formula uses simple numbers like two and fifty and produces results nearly identical to those using the estimated regression coefficients of 2.3 and -66.

The regression method also produces a measure of the “standard deviation” of actual outcomes around the predicted values. I use that quantity in the next post to identify potential gerrymanders using the deviation from proportionality method.

 

 

 

1The results for the last two Democratic off-year House victories, retaking the chamber in 2006 and 2018, both fall on this parity line. Given the historical relationship, the Democrats did not receive the usual reward in the House for their victories in the popular vote. The elections in 2012 and 2018 also show significant negative effects for Democrats.

2Complete results for both models here.

Gerrymandering and the “Breyer Criterion”

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On March 26th the Supreme Court heard oral argument in Rucho v. Common Cause. The case concerns whether North Carolina’s post-2010 electoral map so disadvantages Democratic candidates that it should be ruled unconstitutional. This case raises many Constitutional and legal issues that fall outside the purview of this blog; for instance, whether the Republican-controlled North Carolina legislature showed the intent to discriminate against Democrats in their choice of map. However some of the issues raised during oral argument lend themselves to empirical examination.

A persistent concern during oral argument was whether “proportionality” should be used as a Constitutional standard to determine if a particular electoral outcome might be ruled unconstitutional.  In one of these discussions Justice Stephen Breyer proposed that “when a party wins a majority of the votes in a state, … but the other party gets more than two-thirds of the seats” the result could be declared unconstitutional.

How frequently might Justice Breyer’s criterion apply to actual state-level results comparing votes cast for Congress and the proportion of seats awarded? The Court has an incentive to establish a highly-restrictive criterion to deter future filings by state parties hoping to overturn an unfortunate result. How restrictive is the Breyer criterion? How often might we see electoral results flagged as potentially unconstitutional by the workings of this rule?

What Elections to Analyze

To address these questions, I begin with an invaluable dataset compiled by the MIT Election Lab. It comprises election returns for all candidates who ran for Congress between 1976 and 2018. Using these candidate records as a basis, I created a new aggregated dataset containing results by party for each combination of state and election year.

In the process I eliminated a number of records from consideration. First, because it is impossible to gerrymander a state with just one Congressional district, I excluded any state-year combinations when the state was apportioned into a single district. Examples include Alaska and Wyoming throughout the 1976-2018 period, and states like Montana and Nevada in the years when they had but one district.

I further eliminated states with just two Congressional districts. In those cases an election would fit the criterion if one party won over half the vote and lost both seats. However that outcome would occur by random chance a quarter of the time if both seats had even odds of going to either party.  Courts would likely not be willing to rule a particular seat distribution was unconstitutional when the result could have happened by chance a quarter of the time. As a result I also removed state-years when the state was apportioned only two seats.

Even this set of races needs further refinement to use as a basis to examine Breyer’s criterion. The canonical notion of a two-party race between a Democrat and a Republican dissolves once we look at the data.  Most races include minor candidates and not every seat has both a Democratic and a Republican contender.  Many seats were left uncontested over this period by one or the other major party, especially in the South.  And with the introduction of “top-two” voting in California and Washington, general elections can pit two Democrats or two Republicans against one another.

So I further limited the sample by selecting only Congressional elections with both a Democratic and a Republican contender. That left a total of 7,701 eligible races which I then aggregated to the level of state-years, e.g., Alabama in 1976. Some state-year combinations then had fewer than three contested races; those observations were also excluded. That left me with a total of 799 state-years for the analysis to follow.

Justice Breyer’s Criterion

So in this sample of nearly eight hundred Congressional outcomes, how often do we find the particularly egregious combination where a party won at least half the Congressional vote in a state but was awarded fewer than a third of the seats.

In practice Breyer’s criterion turns out to be highly restrictive.  Of the 799 Congressional elections that qualified for my sample, only seven (0.9 percent) would have fit his rule.  Moreover, only four seats were contested in the three Alabama races and the one in South Carolina. Assuming even odds of each seat electing a Democrat, but a Democratic majority overall, the chance of getting an outcome with at least three Republican seats is 1/8.1 Intuitively that seems too low a bar for declaring a particular result unconstitutional.

Of more interest is that three of the seven Alabama seats, and two seats in the South Carolina race, were uncontested. The totals for these states represent the votes cast and seats awarded in the contested districts. Leaving seats uncontested may itself be an indicator of gerrymandering, If maps are too distorted, it may make little sense for a party to invest resources in races where their opponents are certain to be victorious.

Pennsylvania and North Carolina are another story entirely though.

Breyer’s criterion flags three elections in those states. all of which took place after the 2010 Census. Since then both states have become poster children for gerrymandering. The Pennsylvania map that took effect in 2012 awarded Republicans fully thirteen of the state’s eighteen seats while the Democrats won the popular vote statewide by a small margin. The Pennsylvania State Supreme Court ruled in January, 2018, that the Congressional map was so unfair that it violated the state’s own Constitution. The Court threw out the map and later that month commissioned Stanford Law School professor Nate Persily to draw a new one.  The 2018 election using the redrawn district lines resulted in a 9-9 tie, compared to the 13-5 advantage Republicans had maintained since 2010.

North Carolina is, of course, the state at issue in Rucho v Common Cause, so it is appropriate that it should be flagged here as well. Twice since the 2010 Census have the Democrats won a small majority of the popular vote, but were awarded only three or four of the state’s thirteen Congressional seats. So if Justice Breyer wanted to establish a criterion that would pick out the most egregious partisan gerrymanders, his one-half the vote/one-third the seats rule seems to fit the requirement.

Justice Breyer’s rule was not the only criterion discussed in oral arguments that day. Both plaintiff’s attorney Paul Clement and Justice Neal Gorsuch discussed a measure based on the difference between a state’s actual seat distribution and some measure of what its “proportionate” share might be. I turn to that subject in my next posting.

 

1Imagine a state with four districts. In three of them the Democrats and Republicans tie. In the fourth seat the Democrats win by one. That gives them a one-vote majority in the popular vote and one seat. If we flip a coin for each of the three tied districts, a result with three Republicans occurs one time in eight. I  thank my friend Jim Stodder for making me rethink the calculation of this probablity.

Guns Still Hold Little Allure for Younger Americans and Women

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Thirty-six percent of 18-49 households with a man present own guns as compared to just sixteen percent in those households with no man in residence. Gun ownership has persistently fallen across the generations, but the proportion of people who own a gun in any specific cohort remains fairly stable over the cohort’s lifetime.

With the release this week of the results from the 2018 NORC General Social Survey it seemed a good time to revisit my analyses of gun ownership patterns across the generations. This table replicates the ones found in those earlier reports with the sample expanded to include all interviews from 1972 through 2018.

Here are the ownership rates for households based on the respondent’s age cohort, or “generation,” and the respondent’s actual age at the time of the interview.  This method lets us see differences among generational cohorts and also measure how gun ownership changes as people age.  There are few representatives of “Generation Z” in the newest data, those who born in 1997 or later and came of age beginning in 2006.


Once again the major findings are clear. Gun ownership has persistently fallen across the generations, but the proportion of people who own a gun in any specific cohort remains fairly stable over the cohort’s lifetime. Ownership falls off among the elderly, in part because men, who are more likely to own guns, have a shorter life expectancy than women.

The oldest generations have shown little change in gun ownership rates as their members aged. That may not be true for Millennials who may be buying guns as they get older.  Even with that uptick among the oldest Millennials, they still remain below their Generation-X forbears.

Pretty much all this growth in ownership among Millennials took place in households with a man present. Left to their own devices, women are much less likely to own a gun. This table presents the rate of gun ownership by type of household.  I have limited it to adults 18-49 interviewed in 2000 or later.  This pool of younger, more recent respondents represents the future of gun ownership.

Thirty-six percent of younger households with a man present own guns as compared to just sixteen percent of households where no man resides. Married households show the highest rates of gun ownership, but unmarried men own guns at nearly the same rate. At the other end of the spectrum, single, unmarried women, and all women with children, have especially low ownership rates of just thirteen and eleven percent. In stark contrast, twenty-three percent of households with two or more women living alone together own guns. That is not a statistical anomaly; it is based on a sample of 833 households and is significantly greater than the 11-13% figures for the other two groups.

While the National Rifle Association and other gun-advocacy groups have targeted women over the past few years, these results suggest their efforts have not borne much fruit among the younger women in this country.

 

 

Win Early or Go Home

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Only twice in nine elections has the eventual Democratic nominee not won one of Iowa or New Hampshire. Iowa remains fairly wide open, but Bernie Sanders may have the inside route in New Hampshire.

The pace of attrition is swift in Presidential primaries. Usually only a few candidates remain standing after March 1st, so a victory in Iowa or New Hampshire can put a candidate far ahead of his or her rivals.  In fact the eventual Democratic nominee won one or both of these states in seven of the past nine contested Presidential nominations.


We can see a few different patterns over these campaigns. In 2000 and 2004 the “establishment” candidates, Al Gore and John Kerry, began strong and finished strong. Hillary Clinton’s campaigns resemble Walter Mondale’s effort in 1984.  Both Clinton and Mondale were establishment choices but faced strong opposition from outsiders Gary Hart, Barack Obama, and Bernie Sanders.

2020 has no establishment Democratic candidate, so might it follow one of the other patterns?

Regional Appeals in Iowa and New Hampshire

In the two outlier elections, 1972 and 1992, the strong regional appeal of a few candidates masked the underlying strength of the eventual nominee.

Maine Senator Edmund Muskie came second in Iowa in 1972 by just 0.3% of the vote behind “uncommitted.” When he arrived in neighboring New Hampshire, Muskie was expected to build on his Iowa win with a strong victory among his fellow New Englanders. Sadly for Muskie, he soon found himself the target of an infamous smear campaign involving confederates of Richard Nixon and the then virulently right-wing newspaper, the Manchester Union Leader. Confronted with a barrage of falsehoods, Muskie delivered an emotional speech, dubbed the “crying speech” in some quarters, outside the offices of the Union Leader.  Muskie did win New Hampshire and the Illinois primary a few weeks later, but it was the second-place finisher in the Granite State, George McGovern, who went on to win the 1972 Presidential Nomination.

In 1992 Iowa’s senior Senator, Tom Harkin, tossed his hat into the ring and promptly collected 77% of the Iowa caucus vote. After Super Tuesday he never contended again. New Hampshire that year was won by the junior Senator from neighboring Massachusetts, Paul Tsongas, but it was the strong second-place performance of Arkansas governor Bill Clinton that caught the eye of political observers. Tsongas beat Clinton by only 33% to 25%. The “Comeback Kid,” as Clinton billed himself, went on to win a number of primaries on Super Tuesday and eventually consolidated his hold on the nomination.

Regional Appeals in 2020?

In an earlier article I discussed Iowa‘s apparent preference for Midwestern candidates.  Sherrod Brown’s decision not to run for President leaves only Amy Klobuchar, the senior Senator from Minnesota, and South Bend, Indiana, mayor Pete Buttigieg as candidates who can lay claim to Midwestern roots.  So far, though, neither candidate has caught on in the early Iowa polling.  Klobuchar polls in the single digits, and Buttigieg has yet to break onto the list.

Averaging the current Iowa polls gives former Vice President Joe Biden the lead at 27.3%, followed by Sanders at 15.4%, and California Senator Kamala Harris, former Texas Congressman “Beto” O’Rourke, and Massachusetts Senator Elizabeth Warren all with about ten percent. Name recognition obviously has a substantial influence on these figures, and the race could change substantially should Biden decide not to run.

At the moment, though, Iowa seems fairly open.  That is less true for New Hampshire.

Bernie Sanders won a dramatic upset victory over Hillary Clinton in the 2016 New Hampshire primary by a margin of 60-38%.  There is no reason to expect him not to do well again in 2020.  His current polling average in the Granite State is about 21%, not far behind Joe Biden’s 25%.  Harris and Warren come in around 9-10%.

New Hampshire has certainly preferred fellow New Englanders over the years. Candidates from neighboring states have won five of the eight competitive New Hampshire primaries held starting in 1972. Despite his travails, Muskie did win the 1972 primary, as did Massachusetts Governor Michael Dukakis in 1988 and Massachusetts Senator Paul Tsongas in 1992. New Hampshire voters in 2004 spread their votes across three candidates from New England including eventual nominee Massachusetts Senator John Kerry.


Yet with the exception of Sanders impressive victory in 2016, candidates from neighboring states have not won New Hampshire by overwhelming margins. And both Muskie’s and Sanders’s victories came in largely two-person races, which will certainly not be true next year.

Sanders’s strength in New Hampshire comes at the expense of Elizabeth Warren.  Together Sanders and Warren poll in the low to mid thirties, or about the total vote won by Dukakis and Tsongas. However Sanders outpolls Warren 21-9 on average. If Warren hopes to follow in the steps of Howard Dean in 2004 she’ll need to up her game.

All told, though, the chances look good that Bernie Sanders could win the New Hampshire primary again and secure one of those two valuable slots in the early states.