More on confidence in higher education

 As I observed in my last post, public confidence in higher education is fairly high compared to other institutions.  However, it's lower than it was a decade ago.  The figure shows the percent who say they have a great deal or quite a lot of confidence.  There's a definite decline, and it's arguably the largest decline for any of the institutions in the Gallup data over the 2016-2026 period.  

Although the Gallup data on confidence in institutions goes back to the 1970s, they didn't start asking about higher education until 2016.  The General Social Survey also asks about confidence in institutions, but they don't include higher education.  But the Opinion Research Corporation asked about confidence in "colleges and universities" in 1977, 1979, 1981, and 1983, so we can use that to get a sense of long-term change.

There are some differences in terminology--for example, Gallup asked about "the military" and the ORC asked about "the armed forces."  There's also a difference in the scale of the responses--Gallup gives the percent in the top two categories out of four, and the ORC gives low, medium, and high.*  So you might wonder if they can be treated as measuring the same thing.  The next figure compares Gallup and ORC measures of confidence for institutions that they both asked about in 1977.  The correlation is about 0.92, suggesting that they can.  

Now for the comparison of 1977 and 2026:

Colleges and universities ranked third out of twelve institutions in 1977, behind churches and banks.  They were also third in 2026, behind small business and the military. Small business and the military stand out for large relative increases.  For small business, some of that may be question wording--the ORC asked about "small companies," which I would interpret as including only businesses that have employees, not people who are working on their own or with family members.  But the Gallup question on small business shows a clear upward trend, although it wasn't asked regularly until the 21st century:

One final bit of information:  in 1994, Gallup asked about confidence in public colleges and universities and private colleges and universities.  49% said they had a great deal or quite a lot of confidence in private colleges and universities, and 46 in public ones.  So that suggests that confidence increased between 1994 and 2016.   

The general point is that over the last 50 years, Americans haven't lost confidence in higher education specifically--the decline is typical of other institutions.  

*The original surveys used a seven point scale, but I am working from published reports that group them into three categories. 

[Data from the Roper Center for Public Opinion Research]

Unite the right?

 A few days ago, Michael Brendan Dougherty had an op-ed in the New York Times which said that the post-Trump GOP would include a wider range of views:  "What will unify this fractious coalition after Mr. Trump is a common enemy in the liberals of the credentialed class. That class staffs the very institutions — such as universities — that Americans have come to broadly distrust."  This seems like a poor choice of "common enemy":  about 40% of American adults have a bachelor's degree, and they vote at higher rates than people without a degree.  When you start by calling half of the voters the enemy, it's going to be hard to get a majority.  Moreover, even people who didn't attend college generally have friends or family members who did.  

Gallup regularly asks questions about how much confidence you have in various institutions:  a great deal, quite a lot, some, or very little.  In June 2026, the responses for higher education were 18%, 20%, 37%, 24%, with 1% volunteering "no confidence."  Of the 17 institutions that Gallup asked about, higher education ranked fourth in combined great deal and quite a lot responses, in between the police (45%) and "the church or organized religion" (33%).  That provides a good standard for assessing Dougherty's vision for the Republicans:  a proposal that the Democrats could succeed by uniting against the police or organized religion as a "common enemy" would universally be regarded as foolish.  

OK, who should the Republicans unite against?  Traditionally the left has used rich people and big business as the enemy:  in the Gallup survey, only 17% say they have a great deal or quite a lot of confidence in big business.  In contrast, the right has generally maintained that everyone shares a common interest ("what's good for the country is good for General Motors and vice versa").  Rather than uniting against an enemy, they have focused on attaching themselves to widely popular things like the nation, the dominant religion, families, small business.  To the extent that there's an enemy, it's the misguided people who try to divide the country.  When the right has had an enemy defined by social position, it's been ethnic or religious minorities, and most of those cases are not ones that conservatives are proud of today.

Dougherty's dislike of the "credentialed class" is not unique to him--it seems to be widespread on the contemporary right.  It didn't come from Trump, who frequently mentions his graduation from an elite university as a point of pride and accuses his betes noires like Obama and Bill Maher of having been poor students.   The actions that the second Trump administration has taken against universities seem to have been driven by people who have attached themselves to Trump (e. g., Harmeet Dhillon, Chris Rufo, Linda McMahon) rather than Trump himself.   A few years ago, I had a post on the trend of conservative hostility to educated people, and I'll return to the issue in the future.  

Pretend he's on the ballot

 It's been a long time between posts, but I'll be back on a fairly regular schedule now.  The New York Times had a story by Nate Cohn on the upcoming midterm elections which noted Trump's low approval ratings.  It said "the president’s party almost always fares poorly in midterm elections, but especially when the president’s approval rating is low."  That sounds reasonable, but they didn't actually look at the relationship, so I will.  If you regress the number of seats held by the president's party in the House of Representatives after the elections on the number held before and the president's approval rating in October (1946-2022 midterms), you get:

Constant     -59.4
                   (45.0)

Before           0.91
                     (0.13)

Approval      107.5
                      (46.7)

The predicted value for the 2026 elections is 177 Republican seats, which is below most other forecasts (the consensus seems to be a little over 200).  Looking more closely:

The most encouraging example for the Republicans is 2022, when the Democrats lost only a few seats even though Biden  had low approval ratings.  Probably that was partly due to negative polarization--there were people who had negative views of Biden but just wouldn't vote for a Republican.  That undoubtedly still applies today, although in the other direction.  The residual for that election is +28, so if that applies in 2026 it brings us near the consensus forecast.  But of course, there were idiosyncratic features of the 2022 elections that don't apply today.  Also, I suspect that the linkage between presidential approval and midterm results is becoming stronger (ie, people make less distinction between the president and his party).  So I'll guess a residual of +10 or +15, giving the Republicans about 190 seats.  

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.