I don't need no doctor, part 2

 In August, I had a post about confidence in Anthony Fauci to provide reliable information on the coronavirus.  In March 2020, it was high among Republicans, Democrats, and Independents.  After that, it dropped among Republicans and (less strongly) independents, but stayed the same or maybe increased a little among Democrats.  This one looks at confidence in Donald Trump:  

I show two lines for each party:  one is for surveys by the Kaiser Family Foundation, and the other is surveys by other organizations.   There is a downward trend among everyone, although the difference between the KFF and other surveys makes it hard to be sure about the exact size of the trend.  But it's clear that Republican and independent loss of confidence in Fauci was not accompanied by an increase of confidence in Trump.  In the last survey (March 2022), 18% of Republicans said they trusted him a great deal, 31% said a good deal, 32% not much, and 19% none at all.

Many accounts treat Trump's support as unshakeable--that as he once said, he could shoot someone on Fifth Avenue and wouldn't lose any voters.  But in terms of public support, he's not that different from other politicians:   he has a core of enthusiastic supporters, but also people who support him on some things but not others, and even some who don't think much of him but regard him as better than the alternatives.  In fact, although Trump has changed the Republican party, the Republican party (specifically, the right wing of the party) has changed Trump:  on issues where he was once seen as moderate or at least flexible, he's adopted right-wing positions.  On this one, he could have pivoted to talking about the success of Operation Warp Speed, but instead tried to appeal to anti-vaccine sentiment and continued to downplay the risk of Covid.  I think this helps to explain the decline of support in his second term--when things go wrong, he doesn't think of moving towards the center, but tries to generate more enthusiasm from his base.

[Data from the Roper Center for Public Opinion Research]

How many more years?

 On September 11, Matthew Yglesias wrote that after the terrorist attacks 25 years before "trust in the federal government soared, temporarily reversing the secular increase in cynicism that’s been happening since Watergate and the decline of the three-network news oligopoly."  I've had several posts about long-term changes in trust in the government which suggested that the decline started earlier.  Those were based on the American National Election Studies, which only happen every two years, so they don't tell much about the influence of specific events.  But one of the ANES questions "How much of the time do you think you can trust the government in Washington to do what is right--just about always, most of the time, or only some of the time"--was picked up by other organizations in the 1970s and asked pretty frequently.  It hasn't been asked since 2017--it's been replaced by questions including one or two additional categories for less than some of the time--but it still covers 60 years.   

Here is the percent who choose most or almost all of the time

It peaked in 1962, then declined steadily throughout the 1960s and 1970s.  After that, there were ups and downs, but more downs:  all/most responses didn't fall below 20% until 1993, but were pretty consistently below 20% in 2013-17.  9/11 had a big impact, but it didn't last:  in July 2003, after initial success in the Iraq war, 36% chose all or most, which put it about equal with November 1974, when the economy was in a recession and Gerald Ford had recently issued an unpopular pardon of Richard Nixon.

So the major story is of a general downward trend that started well before Watergate and the decline of network news.  I think that the underlying cause is the growth of education and the mass media, which led to a decline of deference:  people became less likely to assume that government officials knew better than they did.  Of course, these were long-term developments, but in the 1960s there was a cascade effect--the media became more critical, so people became more confident in expressing any negative views they might have, so people were surrounded by a more negative climate of opinion.  

Taking a closer look, here is the percent who choose "almost always".   Most of the surveys also recorded some volunteered "never" or "almost never" responses.   There is probably some additional variation because of differences among survey organizations, but the general pattern is clear:  the decline in confidence continued after the 1970s

I've mentioned before that the public mood seemed to become negative in the first half of the 1990s.  It's not clear why--there was a recession in 1991-2, but it wasn't that severe and the decline in confidence continued after it was over.  It's also not clear why confidence grew in the late 1990s:  the economy did well, but the main political events were the attempt to remove Bill Clinton from office and the the appearance of standoffs over debt limits, which seem like they should have had a negative impact.  But in any case, the experience of the 1990s may have some lessons for today.

[Data from the Roper Center for Public Opinion Research]

  

Too little information

     I've seen a few things on social media saying that the Republicans stole the 2024 presidential election, but didn't pay much attention to them until a recent post by Andrew Gelman pointed to an article in Votebeat by Carter Walker and Jessica Huesman.  The basic story:  Walter Mebane, a political scientist at the University of Michigan, has developed a model that is supposed to detect election fraud.  When he applied it to data from Pennsylvania, he got estimates ranging from about 25,000 to 200,000 "fraudulent" votes.  Trump's margin in the state was about 120,000, so the analysis suggests that voter fraud might have made the difference.  But other political scientists criticized his analysis and said that there was no evidence of significant voter fraud. Although Walker and Huesman found the critics more credible, that seemed to be based on personal impressions rather than evaluation of Mebane's model: they speak of "complex assumptions and unfamiliar methods."  So here is my attempt to state the assumptions in a reasonably simple fashion.   

The model uses only two pieces of information:  turnout rates and party support at the precinct level.  How can you get evidence of fraud from such limited information?  Mebane's model implements the ideas offered a paper by Peter Klimek et al.  They propose that very high turnout and lopsided support for one party may be a sign of fraud.  This seems reasonable--if one party has complete control over the voting or counting of votes, you would expect them to run up the score.  But you don't need complex methods to identify those cases of potential fraud--you can just look at the figures for turnout and votes.  Their second kind of fraud, which they call "incremental fraud," involves adding (or switching) a moderate number of votes in a large number of precincts--e. g., changing 60% turnout and 50% Republican vote to 70% turnout and 60% Republican vote.  But in this case, you can't just look at the numbers for individual districts, since they aren't unusual.  Instead, you compare the distribution of the total numbers--are there more 70%/60% districts in the voting records than there are in reality?  But then you need a standard for "reality"--that is, the true distribution of turnout and votes in different precincts.  Klimek et al. assume that it follows a normal distribution, and I think Mebane does too.**  But there's no compelling theoretical reason to think that the actual distribution of turnout and voting choices follows a normal distribution, or any other specific distribution.  Another justification for using the normal distribution as the standard would be if data from elections generally agreed to be fair almost always followed a normal distribution, but there doesn't seem to be any evidence of this kind.  So we can't be confident about the true distribution, and therefore can't say whether the observed distribution differs from it.  Even if there is "incremental fraud," there's no way to identify it from the data on turnout and voting choices alone.  Cases of extremely high turnout and lopsided support may result from fraud (although they may occur naturally), but Mebane identifies only nine precincts like that out of about 9,000, involving a total of about 2,000 votes, which is much smaller than the margin in Pennsylvania.     

  * Another part of the story was the use (and maybe misuse) that the Election Truth Alliance had made of Mebane's analysis, and how much responsibility he had for that, but I'll leave it aside and just consider the model.

**I say "I think" because I found his paper hard to follow and I wasn't really motivated to figure it out--the important point is that there's no justification for any assumptions about the true distribution.

The plague years

 I'd heard about a decline of self-reported happiness in recent years, but I hadn't looked at the numbers until I saw a post by Noah Smith a few days ago.  He gave a figure showing that the percent who said they were "not too happy" was almost twice as large in 2021-2024 as it had been in 2015-9.  My first thought was that the numbers should be broken down by year--it's understandable that people were unhappy during the Covid pandemic, but things probably bounced back in 2024.  However, it turns out that average happiness (3=very happy, 2=pretty happy, 1=not too happy) in 2024 was lower than in 2022 and not much higher than in 2021 (data are from the GSS, which is conducting their 2026 survey now but hasn't released the data).  

My next thought was that it was because of political polarization.  Traditionally, partisanship affected views about the state of the country--people were more favorable when their party had the presidency--but had little or no effect on individual happiness.  But as partisanship gets stronger, it might affect a wider range of things--that is, if you think the economy is in bad shape and we're at risk of war, it's reasonable to be unhappy.  However, once again my thought was wrong:

Republicans were consistently a bit happier, but the gap didn't change depending on the party in power, even in recent years.  

I looked at differences by various demographic variables--age, gender, race, education, and more.  Although changes weren't exactly parallel, they were similar in all groups--a big decline between 2019 and 2021 and only modest recovery (see this paper for a more detailed analysis).  Then for some reason I compared people born in the United States to people who were born outside.

The 2019-2021 drop was smaller and the 2021-2024 recovery was larger among people born outside the United States.  Their 2024 average was just about the same as the average over the whole period (2.13 vs. 2.16).  Why the difference?  My hypothesis is that it reflects a difference in expectations:  because of a combination of the nation's wealth and power, a government that functions pretty well, and good luck, Americans have become used to thinking that things will work out.  So when Covid turned out to be as bad or worse than people had expected and recovery was slower, it was a shock.  Most of the people who were born outside were from places with more history of misfortune and misgovernance, so their expectations were lower.  

Of course, I developed this hypothesis after looking at the data, but Gallup collects annual data on happiness (using a somewhat different question) in a large number of nations.  My hypothesis has implications for the size of post-Covid changes in happiness in different nations.  I may look at that in a future post.  

Can't complain?

 In a recent op-ed in the New York Times, Gregory Conti and Aaron Sibarium wrote "as race and gender preferences became more overt and widespread, so did the grievances of young white men, who saw their professional opportunities diminish."  The widely-discussed article by Jacob Savage that they link to says the growth of racial preferences began around 2014 and continued until very recently.  Coincidentally, in 2013 I wrote about a General Social Survey question--"What do you think the chances are these days that a white person won't get a job or promotion while an equally or less qualified black person gets one instead? Is this very likely, somewhat likely, or not very likely to happen these days?"--and found that since 1990, white opinions had moved in the direction of "not very likely" and black opinion had moved in the direction of "very likely."  As a result, black/white differences in opinion had become smaller.  Has there been a change in the direction of opinion since then?

White opinion has continued to move in the "not very likely" direction.  However, there seems to be a change in the direction of black opinion--since around 2010, it's also moved towards seeing it as less likely, and the difference in opinions by race has increased slightly.

It's generally agreed that if there was a growth in racial preferences, it happened in professional and managerial jobs, not in blue collar or routine service jobs.  If we break down white opinions by race:

Whites who didn't graduate from college have continued to move towards seeing it as less likely but white college graduates seem to have moved towards seeing it as more likely, or at least stopped moving towards seeing it as less likely.   

So the change in the opinions of white college graduates is consistent with Conti and Sibarium's account.  I don't think that's had much effect on voting patterns--growth in Republican support among whites has been stronger among less educated people.  But regardless of whether it does, it's an interesting development.  At the same time, the fact that less educated whites have continued to move towards seeing it as less likely that an equally or less qualified black person would get a job or promotion is also important.  That is, this question doesn't show a general white backlash against "wokeness."  

  

For better or worse

 A few years ago, I said that beliefs about changes in crime rates were affected both by actual conditions and partisanship, but that the effect of partisanship seemed to be bigger for Republicans.  This post will look at the issue in more depth.  

The Gallup Poll has two questions about change in crime rates; "is there more crime in the U.S. than there was a year ago, or less?" and "is there more crime in your area than there was a year ago, or less?"  Both of them have been asked almost annually since 1989, usually in October.  The percent who think that there is more crime in the nation, by party ID:

The effect of partisanship is clear:  for example, between 2024 and 2025, Republicans became less likely to think that crime was increasing while Democrats became more likely.  Judging the effect of actual crime rates is more difficult, because "crime" can include a lot of things, most of which aren't well measured, but here are homicide rates:

Here's the percent who think there's more crime in their area, by partisanship:

If you regress beliefs on party of the president and homicide rates:

                             Constant          Rate       D
Reps, nation:           32                 4.7          16
Dems, nation:          38                 5.0         -9.2

D is one if a Democrat is president, 0 if a Republican is.  So if a Democrat is president, Republicans are more likely to say that the crime rate is increasing and Democrats are less likely.  The estimated size of the partisan effect is larger for Republicans, although the difference is not statistically significant.

                             Constant       Rate          D
Reps,  area:             25                 1.9          18
Dems, area:             28                 2.4        -3.5

It's the same general pattern, but the difference in partisan effects is larger (and statistically significant).   It might seem odd that the effect of partisanship on beliefs about local conditions would be as strong as the effect on beliefs about national conditions, but even getting a clear view of "your area" goes beyond personal experience.  

The effect of partisanship seems to be growing.  The average gaps (Republican minus Democratic percnet) by administration:

                         Area       Nation
GHWB              -7.3         -1
Clinton                3.5          3.8
GWB                  -9.5        -14.8
Obama                10.3         15.4
Trump-1              -3           -2.5
Biden                  34.8         39
Trump-2                9            8

There's also a more subtle point:  in the two Bush administrations, Republicans were less likely to think that crime was increasing than Democrats were, but if you average the two Trump administrations, there's essentially no difference.  That doesn't mean the effect of partisanship has disappeared--look at the Biden administration--but that Republicans have become more negative in general.  Although the 2025 homicide rate was the lowest of the whole period, Republicans were more likely to see crime as increasing than they were in 1998-2004.  This fits with something Paul Krugman mentioned the other day:  that Trump seems reluctant to talk about the decline in crime:  although he talks about the decline in Washington DC, he suggests that things are terrible in the "blue cities."  But it's noteworthy that the increasingly negative mood among Republicans seems to apply to perceptions of your area as well.  I wonder if it's a general rule that it's easier to influence your followers in a negative than in a positive direction.  That seems plausible, although I don't know of any research on the subject.  

Note:  the regression estimates are  OLS, but regressions allowing for serial correlation in the residuals produced similar results.  

[Data from Gallup and the Roper Center for Public Opinion Research]

I don't need no doctor

 Between March 2020 and December 2022, several polls asked people how much trust they had in Dr Anthony Fauci:  a great deal, a fair amount, not much, or not at all.  Most of the questions mentioned Covid--"trust him to provide reliable information on coronavirus." The means by party identification, counting "great deal" as 4 .... "not at all" as 1.

In the first survey (3/21-34/2020) the average level of trust was almost identical among Democrats, Republicans, and independents.  Democratic trust seemed to increase a little in 2020 and then declined, but the changes were small.  Republican trust dropped sharply in the second survey (late August/early September 2020), and then continued to declined at a slower rate.  Independents were just about halfway in between.  The gap between average Democratic and Republican trust:

There's very little change between June 2021 and December 2022.  That is, the partisan polarization of trust took place between March 2020 and June 2021--nothing that happened in the next 18 months made much difference.  It's also a high degree of polarization:  in June 2021, 61% of Democrats said they had a great deal of trust in Fauci and only 5% said they had none; among Republicans, 11% said they had a great deal and 50% said not at all.  A few days ago, Bret Stephens wrote "I was prepared to cut Fauci and others in government a lot of slack in the first months of the pandemic, when the take-no-chances approach seemed like the safest course. But at some point many of the measures that he insisted were necessary .... felt both onerous and performative, and guaranteed to create a backlash."  Whether or not that was true for him, it doesn't seem to have been true for most Republicans--they shifted against him right away rather than waiting.  

There were also several questions about trust in national public health officials or the CDC.  They followed the same basic pattern:  no partisan differences at the beginning and then a split, but the eventual partisan gap was smaller than with Fauci.  Finally, there were some questions about trust in Donald Trump to provide reliable information about Covid.  Of course, there was a partisan split on Trump from the beginning.  But did it also get bigger, or had it already reached its ceiling?  I'll look at that in a future post.

[Data from the Roper Center for Public Opinion Research]