Suchergebnisse

 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    2.51Large fringe     3.54Medium           2.76Small               2.73Micropolitan   2.81Rural               2.53However, 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.  

 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.  

 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    SD1996     44    29    13     6      22004     31    37    16   11      32014     24    33    16   18      4The 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.

 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.

 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         NetFeb  1952    45   33     12   10           +12July 1962    55   23     12   10           +32Jan  1979    46   46      3    6             0Sept 1989*   57   28     12    4           +29Jan  2009    61   31      3    5           +30Aug  2018    53   40      2    5           +13At 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      Ind1979         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]

 Back in 1971, an Opinion Research Corporation survey asked "Do you feel that things in this country are generally going in the right direction today, or do you feel that things have pretty seriously gotten off on the wrong track?"  The question has been asked many times since then, by a number of survey organizations.  I wrote about it in February 2017, and then forgot about it.  But I recently rediscovered the question and in this post I'll will update the figures and look at several periods in more detail.As I observed in my previous post, people were not especially discontented at the time of Trump's election--only 31% said we were going in the right direction and 62% said we were on the wrong track, but those figures are close to the sample averages of 34% and 58%.  Moreover, there was no downward trend (actually a small positive correlation with time in 1971-2016), which goes against the popular idea that Trump's election was a response to a long period of growing  popular frustration.  The question hasn't been asked as frequently in the Trump and Obama administrations as it was at times in the past, but it seems that opinion generally became more positive, despite political turmoil and the Covid pandemic, up to the last time it was asked (January 2022).  Opinions hit a low in December 2008, but since that time the most striking thing is that they've varied in a pretty narrow range--the figure shows the first and third quartiles, and 33 out of the 40 observations since March 2009 have been in between those.  I checked a few of the surveys, and opinions seem to have become more closely related to partisanship than they were in the past--supporters of the president's party are more likely to say we are on the right track--which may help to explain the decline in variation.  More generally, opinions are clearly related to economic conditions, but can't be fully explained by them.  Opinions also seem to be related to presidential approval--see the highs during the first Gulf War and early in Obama's presidency--but have held up under two unpopular presidents.  I'll look at the relationships in a future post, and see if there is any pattern in the residuals.  [Data from the Roper Center for Public Opinion Research]

 A couple of years ago, I wrote about a question asked in 1974 and 1976 and revived in 2001--"how much trust and confidence do you have in the American people as a whole when it comes to making judgments under our democratic system about the issues facing our country--a great deal, a fair amount, not very much, or none at all?" I recently discovered that this was part of a series of questions about "trust and confidence" in various things related to politics.  I'll consider some of them today.  First, the updated figure for the American people:There is a decline, which seems to have been especially sharp between about 2010 and 2015, but has continued since then.  (The one relatively high figure in recent years was in a survey taken just days after Joe Biden's inauguration).  Next, one on trust and confidence in "men and women in political life in this country who either hold or are running for political office."It's a similar downward trend, but there is more year-to-year variation.  It was unusually high in 2008--maybe that was because of Obama.  It also rose during the Trump administration, which seems surprising, but might have been because of Democrats uniting in opposition to Trump.  But overall, they track each other closely--the correlation is 0.84.  Next, one on trust and confidence in "the government of the state where you live when it comes to handling state problems."There is a downward trend, but it's weaker than with the two previous questions.  There are some unusual cases--very high in 1998, then low in 2003 and 2009-11.  The 2009-11 figures are probably because of the recession, which squeezed state government finances.  Economic circumstances may also have contributed to the high in 1998, but I can't think of any reason for the 2003 lowFinally, trust and confidence in "local governments in the area where you live when it comes to handling local problems."  There's no discernible trend, and not much year-to-year variation (by my rough calculation, not much more than you'd expect from sampling error alone).  The figures for 2021 and 2023 are the lowest since the 1970s, so maybe a downward trend is starting to emerge, but 2022 was at a normal level.  People are often more favorable towards things that are close to them (for example, the local schools vs. schools in general), but the differences here involve the trends.  You might expect confidence in "the American people" to be highest, and that was true in the earlier surveys, but is no longer true today.  My interpretation is that the declines for the first two questions are a reaction to conflict and polarization in national politics--ironically, confidence in state and local governments has held up because people pay less attention to them.[Data from the Roper Center for Public Opinion Research]

 On March 30, Nicholas Kristof had wrote   "Survey data indicates that married couples on average report more happiness, build more wealth, live longer and raise more successful children than single parents or cohabiting couples, though there are plenty of exceptions."  The most popular reader comment, with over 2000 likes, said "I notice that you didn't talk about the research that shows differences in gender in marriage happiness levels.    Marriage is generally GREAT for men, who report being far happier in marriage than being single.  Much research indicates the reverse is true for women.  Single women report being happier, in general, than married women are."  Most of the other leading comments were along the same lines.  Since the 1970s, the General Social Survey has asked whether "taken all together, how would you say things are these days."  41% of married women say that they are very happy, and 8% say they are "not too happy" (the rest chose "pretty happy"); among single (never married) women, 23% say they are very happy and 17% not too happy.  This is just one survey, but it's one that puts a lot of effort into selecting a representative sample and gets a high response rate, so it can be considered to be pretty definitive.  Of course, the difference in happiness between married and never-married women is not necessarily caused by marriage, but it's there.  But maybe the difference is smaller for women--married women are somewhat happier than single women, but married men are a lot happier?  The figure shows the averages for married and never-married men and women over the years (higher numbers mean happier).  There's a lot of sampling variation, so I also show the smoothed averages.  The pattern seems similar for men and women.The next figure shows the difference between married and never-married people among men and women:  Up until about 2000, the difference was a little bigger for men, but since then it's been about equal.  I looked to see if the gap varied by other characteristics--for example, education, race, political views.  To make a short story even shorter, I found nothing worth mentioning.  In the course of doing this analysis, I noticed that the GSS had a question on whether you thought that married people were generally happier than unmarried people.  45% agreed (or strongly agreed), 24% disagreed (or strongly disagreed), and 32% chose "neither agree nor disagree."  There were some group differences in average views on this question:  men were more likely to agree than women; whites more likely to agree than blacks or people of other races; conservatives more likely to agree than liberals; married and widowed people more likely to agree than never-married, with divorced people least likely to agree.  Despite what is sometimes said about "elites," there was no discernible difference by education, and people in higher status occupations were more likely to agree.  The question was asked only four times, most recently in 2012, but it seems like the gender difference was growing:  the means for men were 3.6 in 1988, 3.43 in 1994, 3.47 in 2002, and 3.32 in 2012; the means for women were 3.50, 3.26, 3.17, and 2.97 (higher means more agreement)--that is, the gender difference went from .10 to .35.  There were also signs that the gender difference varied among groups--for example, agreement was particularly low among black women (20% agreed and 48% disagreed).

 I won't try to give a table of regression coefficients in this post, just summarize the differences between the analysis of the geographical origins of January 6 insurrectionists by Pape, Larson, and Ruby and my re-analysis of their data.  1.   Control variables:  my main change was to use the logarithm of population rather than population as a predictor variable, for reasons discussed in my previous post.  I also created a variable for people living within driving distance, which I defined as 700 kilometers (which includes Boston, Cincinnati, and Detroit) and an interaction between distance and that variable.  My idea was that (a) if you were in driving distance you could make the trip without spending much money and (b) with driving, the cost in time and money is strongly related to the distance; if you have to fly the relation is weaker (a lot depends on distance to the nearest airport, whether you can get a nonstop flight, and the mysterious pricing decisions of airlines).2.  Points in common:  the number of insurrectionists increased with the percent of the county that was non-Hispanic white; decline in manufacturing employment didn't make any clear difference; number of insurrectionists was higher in urban areas (although the estimated effect was much smaller in my analysis).3.  Points of divergence:  a decline in the white population led to more insurrectionists in their analysis but had no effect in mine; the percent who voted for Trump led to fewer insurrectionists in their analysis but more in mine.  They also considered the difference between percent for Trump in 2020 and percent for Romney in 2012--that is, voters who were specifically attracted by Trump.  That also didn't have an effect in their analysis.  I ran a model including both Romney support in 2012 and the difference, and found that they both had similar positive estimates.  I think this is important--it suggests that the insurrectionists were drawn both from new Trump followers and traditional Republicans.  However, if you think of the population at risk of being insurrectionists as Trump voters in a county, it would be log(np)=log(n)+log(p), where n is the number of people and p is the percent for Trump (of course, there are people who weren't eligible to vote and people who were eligible but didn't vote, but suppose they are roughly constant across counties).  That suggests that both log(n) and log(p) should have coefficients of about 1.0.  Log(n) did, but log(p) was about 0.55.  The fact that it's not zero is interesting, but so is the fact that it's less than 1.  My thought is that a lot of people, especially those who aren't very interested in politics, just go along with the local climate.  That is, if you're a Trump voter in Manhattan, you're probably highly committed; if you're a Trump voter in Wyoming, you may just be following the crowd.  So the proportion of Trump voters increases, the fraction who are highly committed may decline.  Overall, they conclude that participation in the insurrection was largely a response to perceived ethnic threat, and that the sources of "violent populism" are very different from those of "electoral populism."  My conclusion is that the sources are similar--after you control for population and distance, the places where Trump got votes were also the placed where he got supporters on January 6.

 The Atlantic recently published a critical review of the new book by Tom Schaller and Paul Waldman, White Rural Rage: the Threat to American Democracy. The review, by Tyler Austin Harper, concluded by saying that they were not just wrong, but had it backwards--the threat is from the cities and suburbs:  "Schaller and Waldman are right: There are real threats to
American democracy, and we should be worried about political violence. But by
erroneously pinning the blame on white rural Americans, they've distracted the
public from the real danger. The threat we must contend with today is not white
rural rage, but white urban and suburban rage.Instead of reckoning with the ugly fact that a threat to our
democracy is emerging from right-wing extremists in suburban and urban areas,
the authors of White Rural Rage contorted studies and called unambiguously
metro areas 'rural' so that they could tell an all-too-familiar story about
scary hillbillies. Perhaps this was easier than confronting the truth: that the
call is coming from inside the house. It is not primarily the rural poor, but
often successful, white metropolitan men who imperil our republic."The report that Harper links to says:  "the more rural a county, the lower its rate of sending insurrectionists, a finding which is significant with a p-value <.01%."  A  just-published paper by Robert A. Pape, Kyle D. Larson, Keven G. Ruby in PS: Political Science and Politics gives a more detailed analysis.  The results are from a negative binomial regression in which the  dependent variable is the number of people from a county who were charged with crimes related to the January 6 attack on the Capitol.  The number is estimated to be 2.88 times as large in urban than in rural counties, controlling for other factors.  Of course, the population of the county is one of the other factors.  But a negative binomial regression predicts the logarithm of the dependent variable and their control is population (in 100,000s).   The estimated coefficient for population is .148, meaning that the natural log of the predicted number of insurrectionists goes up by .148 for every 100,000 increase in county population.  If the natural log of the predicted number goes up by .148, the predicted number goes up by about 15%.*  If you're starting from a population of 1,000, an increase of 100,000 means that population goes up by a factor of of about 100; if you're starting from a population of 1,000,000, it's 10%; if you're starting from a population of 5,000,000, it's only 2%.  So the model controlling for population builds in a relationship between county population and the chance that a person will be an insurrectionist:  declining and then increasing.  The figure shows the nature and size of the relationship using their estimate:The number 1 on the y-axis represents the rate in a county of average size (about 100,000).  In a county with population of 10,000, the rate is about 8.5; in a county with 500,000, it's about .4, and in one of 5,000,000, it's about 80.  The biggest county in the United States (Los Angeles) has a population of about 10,000,000, but I don't extend the x-axis that far because it would make the figure too hard to read.   Of course, there is no reason to expect that there really is a relationship of this form.A straightforward alternative would be to model the rate--number of insurrectionists (x) divided by county population (n).  But log(x/n)=log(x)-log(n), so you could express that by a regression with log(x) as the dependent variable and log(n) as one of the predictors.  Then a coefficient of 1.0 on log(n) would mean that the rate was the same across different county populations; a coefficient of less than one would mean it was higher in counties with smaller populations and a coefficient of greater than 1.0 would mean it was higher in counties with larger populations.  What happens if you use log(population) rather than population as a control variable?                                                                        Population                    Log% white population decline                            .111***                    .035                                                                        (.019)                        (.020)manufacturing employment decline                .011                            -.006                                                                        (.0054)                        (.006)extra Trump %                                                    -.039***                .003                                                                            (.0081)                    (.0082)% non-Hispanic white                                           .009***                .014***                                                                            (.0033)                    (.003)Metro county                                                        1.095***             .326*                                                                           (.1335)                    (.135)Distance to DC                                                    -.304***            -.210***                                                                            (.0623)                (.051)(log) population                                                    .148***            .999***                                                                             (.0210)                (.056)  The fit of the model with the logarithm as control is better.  Several of the estimates for the other variables change substantially.  The estimate for metro counties is still statistically significant, but not overwhelmingly so (p=.019), and is much smaller than when using population.  So I don't think that the evidence justifies sweeping condemnation of urban and suburban men.I have experimented with other specifications of the model, but this is enough for one post.  *My figures are from my analyses using their replication data file, which are slightly different from the numbers implied by their tables.    

 After O. J. Simpson's death last week, I looked for surveys that asked for views about whether he was guilty of the murders of Nicole Brown Simpson and Ronald Goldman.  The figure is based on surveys which offered options of definitely guilty, probably guilty, probably not guilty, and definitely not guilty (counted as +2,+1, -1, and -2).*  All of the figures are positive, meaning that more people thought him guilty than not guilty.  Over the whole period, there was a move towards thinking that he was guilty--the latest surveys found that about 40% thought he was definitely guilty and another 40% thought he was probably guilty.  I thought that the civil case that found he was responsible for the murders might have helped to move opinion in that direction, but if anything it seems to have been followed by a short-term shift away from thinking he was guilty.  One survey taken at the time of the controversy over the release and withdrawal of his book, "If I did it," showed a move towards thinking him guilty.  But basically, it seems to have been a gradual movement over a long period.  The next figure is the same data, but limited to the period up to the end of the criminal trial.  I don't recall most of the details of the trial, but the way the story is often told now, there were a number of dramatic moments in which the defense succeeded in raising doubts about his guilt.  You don't see much sign of this in the survey results--there seems to have been a small move towards thinking him guilty during the trial, followed by a drop after the verdict.  The absence of ups and downs during the trial surprised me--a lot of people followed it closely, and although many had strong opinions, it wasn't a partisan issue, so I would expect them to be more open to changing their minds.  During and after the criminal trial, many people noted that there was a large division by race--black people were a lot less likely to think that Simpson was guilty.  This raises the question of whether that was there from the beginning or emerged during the trial, and whether it remained in the later surveys.  I'll look at this issue in a future post.  *Some asked if the charges were definitely true, probably true, probably not true, or definitely not true.  Immediately after he was found not guilty, some prefaced the question with "whether or not you agree with the jury's decision."  There was no evidence that these variations made a difference.  

 Conservatives typically report being happier than liberals do, but the size of the gap appears to change over time.    In 2015, I looked at data from the GSS  and found that the "happiness gap" became larger during the GW Bush presidency, but fell in the Obama presidency.  A few days ago, Ross Douthat had a column called "Can the Left be Happy," which said that "the left-right happiness gap is wider than before"--that is, the relative happiness of the left has declined in the last decade or so.   He made a plausible case but didn't offer any systematic data, so I'll take another look.The GSS asks people to rate their political ideology on a seven point scale, from very liberal to very conservative, which I collapsed into three groups:  extremely liberal or liberal; slightly liberal, moderate, or slightly conservative; conservative or extremely conservative.  The liberal and conservative groups are both about 15-20% of the sample.  The figure is hard to interpret, partly because of sampling error in individual years and partly because of the big drop among all groups in 2021 and 2022, so here's a figure showing the difference between the averages for liberals and conservatives (positive numbers mean conservatives report being happier than liberals do):The higher reference line is the average difference.  The gap was larger than average in 2000, 2002, 2004, 2006, and 2008, and then fell in 2010 (liberals were actually happier than conservatives).  It's generally remained smaller than average since then.  Here's the corresponding figure for the moderate/conservative gap:A similar story:  the gap became a lot smaller in 2010, and has generally remained below average since then.  So it's not liberals who have become relatively unhappier in recent years, but conservatives.  Going back to the original figure, there was little or no happiness gap in the 1970s.  Conservatives pulled ahead in the 1980s, and the size of the gap seemed to be gradually increasing until 2008.  Then conservatives became less happy in 2010, and the gap has been smaller since then.  You could say that the shifts in the 21st century are just another example of increasing political polarization:  liberals are relatively happy when a Democrat is in office, and conservatives are relatively happy when a Republican is.  But I don't think that fits the pattern very well.  Although there were signs of growing polarization under Bush, they were pretty small--you didn't get a big increase until the Obama years.  And although conservatives became relatively happier in 2018, the change was not that big, and the gap remained smaller than it had been under Bush. But considering the whole period suggests that there may be a connection to the general political and social climate.  In the 1970s, the tide seemed to be running to the left, but with the tax revolt of the late 1970s and the election of Ronald Reagan in 1980, things seemed to stabilize and maybe even reverse.  Before the 2008 election, conservatives could feel pretty good--Republicans had won five of the last seven presidential elections, and no Democrat had received a majority of the popular vote since Jimmy Carter got 50.1% in 1976.  Republicans had also gained parity in Congress, and the courts had moved to the right.  Then Obama was elected with a lot of popular enthusiasm, a solid Congressional majority, and an economic crisis that provided a rationale for vigorous government action.  Prominent conservatives reacted to the threat by a strategy of scorched-earth opposition--e. g. they denounced Obamacare not just as ineffective and expensive, but as the end of the American way of life.  Since then, conservatives have felt like they are on the defensive, even when Trump was president--fighting against the deep state, the media, Big Tech, etc.  So my idea is that although ratings of happiness are primarily affected by individual factors, there is some spillover from feelings about the general direction of society.

 In 1951, the Gallup Poll asked "Comparing your present family circumstances with those when you were a child, would you say you are better off, or worse off, than your parents were then?"  They asked it again in 1991, and other organizations asked it in 1994 (twice) and 2016.  The results:                    better      worse    same (vol.)Feb 1951       60%         24%      14%Dec 1991      78%         12%        9%Aug 1994      65%        22%      11%Sep  1994      72%        21%        6%Dec  2016      72%        20%        7%The distributions are all about the same except for 1991, when people were more positive.  I can't think of a plausible reason that opinions would change that much between December 1991 and 1994 (economic conditions were similar, but somewhat better in 1994), so I think that difference is at least partly sampling error.  The important point is that opinions weren't more pessimistic in 2016 than in 1950 or 1994.  It's often suggested that Americans always felt like they were making economic progress from generation to generation until recently, and that the loss of that sense has led to a variety of social and political problems, like "deaths of despair," the decline in rates of marriage and childbearing, and the rise of Trump.  But I don't think that's the case--people are discontented with some aspects of society, but most still believe that there's an upward trend in their standard of living.[Data from the Roper Center for Public Opinion Research] 

 There's been a lot of talk recently about Blacks and Latinos shifting to the Republican party (an example).   Most of the accounts I've seen say that it results from a weakening of party loyalty, so that Black and Latino conservatives start moving to their "natural" home.  However, you would expect that to be a gradual process, much of it the result of generational replacement--why would it be happening now more than it was 10, 20, or 30 years ago?  I looked at the Echelon Insights surveys, which ask people about their views of Joe Biden and Donald Trump, comparing the latest one available (Feb 2024) to October 2021, which I chose as the baseline so that we would be past Biden's "honeymoon" and the reaction to January 6 would have had time to fade.  The figure shows change in mean favorability (4=very favorable, 3=somewhat favorable, 2=somewhat unfavorable, 1=very unfavorable), for groups that were included in  reports (party, race/ethnicity, sex, income, education).Biden's ratings have declined among most groups, and Trumps have risen, but three stand out:  blacks, Hispanics, and people without a college degree.  These groups have had the largest anti-Biden and pro-Trump shifts.  People without a college degree are different from Blacks and Hispanics because they already gave strong support to Trump--that is, the decline of party loyalty explanation can't account for their change.  Is there any common factor that might explain the recent shift of these three groups?  I think that it might be the increase in foreign conflicts and illegal immigration.  As I've mentioned before, many people think that other countries take advantage of the United States--they we try to be fair but they don't.  The political importance of this feeling is likely to increase when times are tough--people will think that under the circumstances we can't afford to worry about other countries and their problems and need to focus on our own national interests.  This point is relevant to the group shifts because blacks, Hispanics, and less educated people tend to be more suspicious of others--less likely to think that other people can be trusted or will generally treat you fairly--and the differences are big by the standard of individual-level survey data.  That suggests that they will respond more strongly to the kind of appeals that Trump makes.  But it also suggests that they may shift strongly in the other direction if Biden seems to be getting things under control.  

 I want to follow up on my previous post, which proposed that the recent rightward move of Black, Hispanic, and less educated voters might have a common cause--concern about foreign affairs.  My idea is that these groups tend to have a more suspicious view of the world--to feel that you can't count on other people and have to look out for yourself.  When applied to foreign affairs, this means an affinity for an "America first" approach--don't get involved unless our interests are at stake, and when we do get involved, rely on military or economic threats rather than diplomacy and alliances.  That's exactly in line with Trump's view of foreign policy.  So Trump has an advantage with these groups in this sphere (I don't call it an issue, since it's more of a general orientation than a policy position).  Most voters aren't that concerned with foreign affairs most of the time, so if things were going well, it wouldn't have much influence.  But now, with the wars in Ukraine and Gaza and the rise in illegal immigration, people are more concerned, and Trump's approach makes sense to a large number of voters.So that's my idea--what about evidence?  I alluded to some last time, but didn't give details.  On the "suspicious view of the world," there are three questions that have been regularly included in the General Social Survey, whether "most people can be trusted, or that you can't be too careful in life," "most of the time people try to be helpful, or that they are mostly just looking out for themselves," and "most people would try to take advantage of you if they had a chance, or would they try to be fair."  If you make an index of positive views of human nature by counting "can be trusted," "try to be helpful," and "try to be fair" answers, the means are:Black            0.9Hispanic       1.0NH White     1.6Non-grad       1.4Coll Grad       2.0The GSS doesn't have many questions on foreign affairs, but one was included regularly until 2006:  "Do you think it will be best for the future of this country if we take an active part in world affairs, or if we stay out of world affairs?"  More recently, a question asking if they agree or disagree that international organizations are taking too much power from the American government has been asked several times.  The table shows opinions by scores on the index:       Active part           Taking power (yes minus no)0     54%                          171     60%                          132     69%                          -33     82%                          -30So general views of people are strongly related to these foreign policy opinions.  The racial/ethnic and educational differences in general views of people are longstanding and haven't changed much, which suggests that an "America first" policy has always had the potential to appeal to less educated people and racial minorities (and repel educated people).  However, from the 1940s until the rise of Trump, that kind of policy was almost universally rejected by elites of both parties, so it wasn't offered as a choice.