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.