Could it have been worse?

Among people who viewed Thursday's debate, 60% thought that Trump won and only 21% thought that Biden won.  In this post, I look at popular judgements after some other debates.   The questions all have the same basic form "who would you say did the best job--or won."  Except when indicated, they were asked only of people who watched the debates (or listened on the radio).  I list the figures for the Democrat first, the Republican second, and both equal as third.  They don't add to 100%, because there were some who said that they weren't sure or couldn't say.  The fourth column is the size of the gap between the "winner" and "loser."  

10/17-24 1960:            36%    32%      24%             4%

This was the last of four debates.  The first debate is widely remembered as a big win for Kennedy, but I couldn't find any surveys that asked about it.  

  

10/28            1980:      36%    44%       14%             8%
10/29-30       1980:     26%    34%       31%              8%

10/30-11/01 1980:      25%    38%        25%           13%

This was the only Carter-Reagan debate (there was a Reagan-Anderson one in September).  It's remembered for Reagan's "there you go again" line, which some people implausibly say changed the outcome of the election.   More people saw Reagan as the winner, but it wasn't a big gap.  

10/7/1984              :      38%     35%        13%               3%
10/7/1984              :      43%     34%        16%               9%
10/10-12/1984              64%     12%        21%             52%

The first debate in 1984 is remembered as a disaster for Reagan.  But the two surveys taken right after the debate showed only a small edge for Mondale--one taken several days later showed a big gap.  That point suggests that media coverage made a difference--that some people who initially thought Reagan did a decent job changed their minds after hearing discussion (a survey on October 9 which included people who hadn't watched it but had heard or read about it also found a big margin in favor of Mondale).  

10/7/1996                    50%      29%      19%                  21%
10/10-13 1996             62%      17%      14%                  45%

Some people have said that incumbent presidents always do badly in the first debate, but Clinton easily prevailed over Dole in 1996.  

10/3/2012                    22%      46%       32%                 24%

10/7-9/2012                 14%      75%         6%                 61%

Obama's first debate against Romney in 2012 is remembered as a bad performance, and that's how people saw it at the time.  

The 39% gap in perceptions of the 2024 debate is not the biggest ever, but it's bigger than the gap in surveys taken immediately after any of the debates I've looked at.  Although there are only a few cases, it seems like there's a tendency for the gap to grow as people discuss and see media coverage of the debate.  That suggests that things will get worse for Biden in the next few days (or maybe have already gotten worse since I started this post yesterday).

[Data from the Roper Center for Public Opinion Research]

Who are they talking about?

 A recent survey by RMG Research which is billed as "a first-of-its-kind look at the views of the American Elite" defines "elites" as people who have graduate degrees, annual incomes of more than $150,000, and live in places with more than 10,000 people per square mile.  Education and income are reasonable criteria for defining elite status, although you could argue about where to draw the lines, but population density?  I guess you could argue that being in a large metropolitan area means that you're closer to top decision makers in a social as well as a physical sense.  But the definition uses population density by zip code, and in the contemporary United States, there's a strong association between neighborhood preference and political views:  people who prefer to live in dense areas tend to be more liberal.  Moreover, in much of the country, even downtown urban areas don't reach 10,000 per square mile.  There are 580 zip codes that meet the standard, and 496 of them are in just six states:  New York, California, New Jersey, Illinois, Pennsylvania, and Massachusetts (data can be found here).  Only 5% (28) are in states won by Donald Trump in 2020.  So rather than a sample of the "American Elite," it would be more accurate to call it a sample of upper-middle class urbanites in blue states.    But that's still an important group, so maybe we can still learn something from the survey?

The RMG survey reports that 67% of "elites" had a favorable opinion of members of Congress, compared to only 28% of the general public.  The 2021 and 2022 General Social Surveys have a question on confidence in Congress:  6% say they have "a great deal," 41% "only some," and 52% "hardly any."  Although it's not possible to reproduce the RMG "elite" exactly with the GSS data, it is possible to come close:  among people with graduate degrees, it's 5%, 48%, and 48%; among people with incomes of more than about 150,000 it's 3%, 43%, and 54%, and among people living in the central cities of the twelve largest metropolitan areas, it's 11%, 43%, and 46%.  So people with more education and income do not have more confidence in Congress; people living in big cities have a bit more, but that's because they are more likely to be non-white--among whites living in the central cities of the twelve largest metro areas, 7% have a great deal of confidence, 41% only some, and 52% hardly any.   There are only 39 people in the GSS who meet all three criteria, but 60% of those have only some confidence and 40% hardly any:  in other words, the opinions of "elites" are about the same as those of the general public.  The questions aren't exactly the same, but the patterns are so different that it's safe to say that there's a conflict between the surveys.  Which one should we believe?  The GSS is transparent about its sampling methods; the RMG survey is not--it doesn't say anything.  I don't know whether the problem is an unrepresentative sample or a mistake in reporting the results, but the RMG survey can't be taken seriously as a measure of any group's opinion.  

The problem is you?, part 3

 This is a return to the analysis of the geographical origins of people involved in the Jan 6, 2021 assault on the Capitol.  My original point was that if you're predicting the logarithm of the expected number of insurrectionists from a county, you should control for the logarithm of the population of the county, and you would expect the estimate to be near 1.0--that is, if the population of county B is X times as large as the population of county A, then the number of insurrectionists from county B will be X times as large as the number from county A.  But on further reflection, it seems likely that the number will depend not just on the size of the population, but the mix of Trump voters, Biden voters, and everyone else.  You'd expect that most of the people involved were Trump supporters, but there could also have been Trump sympathizers who were ineligible to vote, people who were generally against "the system" and voted for minor parties like the Libertarians, and people who were just looking for trouble or had come along with friends.  If we had data on how the insurrectionists voted in 2020, we could do separate analyses for each group--e. g., Trump-voting insurrectionists -- but we don't.  However, we have data on the votes in each county, so you can estimate a model for the total number of insurrectionists with the logs of Trump voters, Biden voters, and others as predictors.  '

The estimates and standard errors from a negative binomial regression:

White decline       .016         (.020)
Mfg decline         -.007        (.005)
% NH white         .011*       (.004)
NCHS                 -.161***   (.048)
Distance              -.052         (.065)
Drive                   1.001***  (.220)
Drive*Dist         -1.436*** (.431)
log(Biden)          -0.042       (.105)
log(Trump)            .632*** (.165)
log(Other)             .409*      (.186)

The first three variables, white population decline, manufacturing employment decline, and percent non-Hispanic white, were considered in the original analysis by Pape, Larson, and Ruby.  NCHS is a 6-category classification scheme developed by the National Center for Health Statistics:  large central metro, large fringe metro, medium metro, small metro, micropolitan, and non-core.  Pape, Larson, and Ruby divided that into two groups:  the first three categories vs. the last, but I treated it as a numerical variable (more or less urban) since that generally produced a better fit.  The next three variables are all related:  "Drive" is a 0/1 variable for being in driving distance which I defined as 700 kilometers of Washington, DC.  Distance is measured in hundreds of kilometers, so the estimates imply that distance reduces the number of insurrectionists until you get to about 700 kilometers from Washington, and makes no difference beyond 700 miles--that is, the rate is about the same if you're 700 kilometers or 3700 kilometers away.  Finally, the number of Biden voters doesn't matter, the number of Trump voters does, and there's some evidence that the number of other people does as well.  The fact that the number of Trump voters is an important predictor of the number of insurrectionists might seem like a matter of common sense, but it's contrary to the conclusions of Pape, Larson, and Ruby.  

Although the change in the method of controlling for population changes some conclusions, it leaves one point unchanged:  insurrectionists tended to come from more urban places (controlling for the other variables).  There's no clear difference in the overall rates--the average rate per million is:

Large central    2.51
Large fringe     3.54
Medium           2.76
Small               2.73
Micropolitan   2.81
Rural               2.53

However, the less urban areas tend to have more Trump voters, so when you adjust for that you would expect them to have a higher rate of insurrectionists.  I can think of a few ideas about why people in urban areas might be more likely to have participated, but don't have a way to test them, so I'll leave it at that.  

PS:  The estimates given above are from a negative binomial regression.  Results from a Poisson regression are almost the same.  I also tried ordinal probit, ordinal logit, and Cox (proportional hazards) regression.  With those, the standard errors were generally larger, but the relative values of the estimates were about the same:  the only notable difference was that in the Cox regression the estimate of log(Other) was near zero and non-significant.  

The great divide?

 A couple of weeks ago, Thomas Edsall had a column called "The gender gap is now a gender gulf," which said that there was growing divergence between the political views of young men and young women.    He discussed some people who offered explanations, but didn't give much evidence that it was actually happening, so I went to the GSS.  I divided people into three age groups (18-34, 35-59, and 60+) and used self-rating on a liberal/conservative scale as a summary of political views.

There's a lot of sampling error in the estimates for individual years, so I also show smoothed curves.  Young men have been more conservative than young women since the early 1980s--there may have been some increase in the gap through 2021, but nothing dramatic.  But in 2022, something happened--young men became much more conservative.  In 2021, 24% of young men said they were liberal or extremely liberal and 12% that they were conservative or extremely conservative; in 2022, the numbers flipped (14% liberal and 25% conservative).  The difference is too large to be plausibly explained by sampling error.  

  Here is the corresponding figure for people aged 35-59 (the y-scales are the same in order to make it easier to compare them).  Men are consistently a little more conservative (for some reason I switched the colors), and the gap hasn't changed much.  Men were more conservative in 2022 than in 2021, but the difference was small enough to be just sampling error.  

For people aged 60 and up, there is a small gap, which may be increasing, but 2022 does not stand out.  

So there's some support for Edsall's claim (which surprised me--large shifts involving subgroups are unusual).  But it just appeared in 2022, so it can't reasonably be explained by long-term social changes like the loss of factory jobs.  And nothing unusual was visible in 2016, 2018, or 2021, so it's not a general reaction to Trump.  The most obvious novel thing in 2022 was increased attention to abortion because of the Dobbs decision, but that would suggest a leftward movement among young women rather than a rightward movement among young men.  I looked at a number of other political views, and found one other case of a large shift among young men between 2021 and 2022.  Support for capital punishment increased from 48% to 65% among young men, against only 46% to 51% among young women.  You'd expect opinions among younger people to be more flexible, and it seems plausible that men might be more inclined to turn to "get tough" policies when crime is increasing.  (Serious crime fell from 2021 to 2022, but perceptions tend to lag behind reality, and opinions are also affected by a general sense of disorder).  Of course, it's possible that I'm just picking out a large chance variation, but it seems like it's worth further investigation.  

Equity and Exclusion

 I don't have time for a longer post now, so here is a quick one.  In 1996, 2004, and 2014, the General Social Survey asked for reactions to the statement:   "America should take stronger measures to exclude illegal immigrants."  Options were strongly agree, agree, neither agree nor disagree, disagree, strongly disagree.  The results:

             SA    A      N     D    SD
1996     44    29    13     6      2
2004     31    37    16   11      3
2014     24    33    16   18      4

The balance of opinions was always on the "agree" side, but there was a substantial change:  the total agree minus disagree went from 65 to 54 to 35.  Unfortunately, the question hasn't been asked since then.  The change could be because people became less concerned about illegal immigration or because they came to think that the government was doing a better job in controlling it.  I would guess that the first was more important, since I don't recall any changes in policy that got much attention in the media.

Too soon to tell?

 Donald Trump currently has a narrow lead in the polls (about 1% in the 538 average), so all you can say now is that it could go either way.  But what if one of the candidates opened opened up a substantial lead--how much would that tell us about what is likely to happen in November?  I looked up surveys from late May/early June in presidential elections from 1948-2020 and compared the margin in the survey to the actual popular vote margin in November.*

The regression is y=.44x, where y is the election margin and x is the May survey margin.**  

There was a large residual in 1948, which was a notorious failure of the polls, followed by pretty good predictions in 1952-1968.  Then there were large errors in 1972, 1980, 1984, 1988, and 1992--in the first four of those, the Republicans did substantially better in November than the May surveys suggested, and in 1992 the Democrats did substantially better.  Since then, the predictions have been pretty good, except for 2008, when there was an obvious reason for a late shift towards the Democrats.  A graph of the absolute value of the residuals:

The sample is small, so you could plausibly claim the apparent pattern is just a matter of chance.  However, I think that there probably was a real change.  In the earlier years, there was fairly strong party identification based on ethnicity, region, religion, and family tradition.  Although candidate qualities and current conditions made a difference, the main thing that happened during the campaign is that some of the people who had been thinking of voting against their normal party would drift back "home."   Then there was a stretch when party identification was weaker, so that events during the campaign could make more difference.  That was followed by a period in which party identification became stronger again, but now based more on ideology.  

Note:  Through the 1960s, only one organization (Gallup) regularly did election polling.  The number has grown since then, and in recent years you can calculate an average based on large numbers of polls.  In order to make things comparable, I just selected one survey for each election, based partly on the date (whatever was closest to May 24, when I compiled the data) and partly on my judgement about the general reputation of different organizations.  

*Of course, the candidate who leads in the popular vote may not win the Electoral College, but that's a different issue.  

**If you include an intercept, the estimate is -1.7 with a standard error of about 1.7; the estimated coefficient for x is still .44.  A non-zero intercept could mean either a consistent bias in the polls or a tendency for the vote to shift in favor of a particular party during the campaign, neither of which seemed likely in principle.

The future ain't what it used to be

 Back in 2011, I wrote about the question "As you look to the future, do you think life for people generally will get better, or will it get worse."  It's been asked once since then (in 2018), so here is an update.  

         Better  Worse  Same   DK         Net

Feb  1952    45   33     12   10           +12
July 1962    55   23     12   10           +32
Jan  1979    46   46      3    6             0
Sept 1989*   57   28     12    4           +29
Jan  2009    61   31      3    5           +30
Aug  2018    53   40      2    5           +13

At the time, my main point was simply that opinion had been a lot more pessimistic in the 1970s than it was in the most recent survey.  This point continues to be relevant, but the difference between 2009 and 2018 is also interesting.  In recent years, Republican assessments of things seem to have been more affected by the party of the president than Democrats:  Republicans become substantially more positive in Republican administrations and more pessimistic in Democratic administrations, while Democrats are less variable.  Consequently, other things equal, average opinion is more positive under Republican administrations (an example).  This has been suggested as a reason that opinions about the economy today are more negative than you would expect from the basic economic conditions.  This question could be an exception--opinion was less optimistic under Trump than under Obama.  However, the 2009 survey was taken at the very beginning of the Obama administration, when there seemed to be a general feeling of goodwill.**  It's unfortunate that it wasn't asked again during the Obama administration.

Breaking it down by party identification, and adding 1979 for comparison, here is the percent saying that life will get better:

            Rep       Dem      Ind

1979         50%       44%     49%
2009         53%       70%     59%
2018         61%       48%     51%

In 1979, party differences were small (and Democrats were least optimistic); by 2009, they were substantial, and they didn't grow between 2009 and 2018.  

*This question asked about people in the United States over the next ten years.  The same survey also asked about people in "developing nations" and "other industrialized nations" over the next ten years:  answers were slightly more optimistic (60%-20% and 64%-16%).

**It was actually taken a few days before Obama's inauguration.  

[Data from the Roper Center for Public Opinion Research]