Bonus

 About a month ago, I discovered that I am listed as the editor of the "EON International Journal of Arts, Humanities & Social Sciences."  In fact, I had never even heard of this journal, so I sent an e-mail to them telling me to remove my name.  I never heard back and am still listed as editor, so I decided to take further action.  They give their mailing address as:

 2055, Limestone Rd Ste 200C, Zip Code 19808 Wilmington,
Delaware, USA

I suspect that they are actually based outside the United States, since they clearly aren't familiar with American conventions for writing addresses, but Google Maps shows a building at 2055 Limestone Rd, so I wrote to them.  The concluding sentences in my letter are:

"Falsely claiming that I am the editor of a predatory journal is defamatory.  If you do not remove my name from the listing by October 15, I will consult with my attorney about the possibility of legal action."

Let's see if that has any effect.

Focused on the future, part 3

My last two posts were about answers to a question on confidence that "votes will be accurately cast and counted accurately" in elections, which has been asked a number of times since 2004.  As far as I know, there were no comparable questions before then.  However, a question on "dishonesty in the voting or counting of votes in your district" was asked in 1959 and 1964, and since 2004 there have been several "accurately cast and counted" questions that specified "at the facility where you vote."  I showed the overall results in a previous post,  and will look at party differences in this one.  There's a general tendency for people to be more positive about things that are closer to them, but my question is whether partisan differences in views on local elections might track partisan differences in views on national elections.    Here is average confidence in "the facility where you vote" by party:

It has declined for all groups, although the decline seems smaller for Democrats.  Independents are the least confident, which is probably because they tend to be more suspicious of politics in general.  Comparing confidence in national and local elections for each partisan group (red is local, blue is national):

They changes aren't parallel:  for Democrats and Independents, the gap between confidence in the national and local vote has become smaller; for Republicans, it's become bigger.  The results for Republicans aren't surprising, since their claims of fraud have focused on heavily Democratic places, like Philadelphia, Detroit, and Atlanta.  The general tendency seems to be for confidence in local voting to vary less than confidence in national voting.  

[Data from the Roper Center for Public Opinion Research]

Focused on the future, part 2

 In 2004, Gallup asked "How confident are you that, across the country, the votes for president will be accurately cast and counted in this year’s election – very confident, somewhat confident, not too confident or not at all confident?"  They have repeated the question a number of times, most recently just two weeks ago.  Their report says that the overall level of confidence has stayed about the same since 2008, but with a growing partisan division--Democrats becoming more confident and Republicans less confident.  The report merged "very confident" and "somewhat confident," which is a potentially important distinction, so I calculated the average, which is shown below:

The red dots indicate midterm elections (of course, those questions omitted the words "for president").  There was a substantial decline between 2004 and 2008--there were two surveys in 2004, with an average of about 3.0, two in 2006, with an average of about 2.85, one in 2007, also at 2.85, and two in October 2008, which averaged about 2.65 (about the same as the average in September 2024).   Why would this have happened?  I would have figured that confidence among Democrats would be low in 2004 because of  memories of 2000, and would rise as more time went by (especially after Democratic success in the 2006 midterms).  On the Republican side, it didn't seem like there was anything that should cause a dramatic change.  That would suggest an increase in overall confidence, not a decline.  

Breaking it down by party:

Relatively little change from 2004 to 2007, and then a large decline in Republican confidence between December 2007 and October 2008.  I could only get complete data for two surveys after 2008 but they showed further declines among Republicans.  The next figure shows the gap between Democrats and Republicans:

What might have caused the change in 2007-8?  Thinking back, I remembered that there were news stories about fraud in ACORN voter registration drives.  Also, in December 2007 Hillary Clinton was the frontrunner for the Democratic nomination, so it's possible that the decline among Republicans was a reaction to Obama--maybe his race, or his roots in Chicago politics.   The decline in confidence among Republicans meant that confidence was about the same in both parties.  Unfortunately, there don't seem to be any comparable questions before 2000, so we can't say if the lack of partisan difference was a return to normal.    

[Data from the Roper Center for Public Opinion Research]

Focused on the future

During the 2016 election campaign, Donald Trump refused to give a definite answer when asked whether he would accept the results if he lost:  as I recall, his usual response was something like "we'll see what happens."   A Fox News survey from late October of that year asked "If your candidate loses the presidential election in November, will you accept that his or her opponent won fair and square and will be the legitimate leader of the country?"  87% of the people who intended to vote for Hillary Clinton (or were leaning towards Clinton) said that they would; only 56% of those who intended to vote for Trump or were leaning towards Trump said that they would (34% said they would not and 10% weren't sure).  But it may be easier to say that you would be a good loser when you don't expect to lose.  The same survey asked "who do you think will win in November":  64% said Clinton, 26% Trump, and 10% weren't sure.  What if we adjust for expectations?  

Nearly all Clinton supporters expected her to win (93%), so it doesn't make much difference on that side:  for what it's worth, 88% of those who expected her to win and 79% of those who weren't sure or thought she would lose said they would accept Trump as the legitimate leader.  Among Trump supporters, 34% expected Clinton to win, 12% weren't sure, and 55% expected Trump to win.  64% of those who expected Clinton to win, 58% of those who weren't sure, and 51% of those who expected Trump to win said they would accept Clinton as the legitimate leader if she won.  That is, the gap in willingness to accept the other candidate as the legitimate leader is even larger when you adjust for expectations by comparing Clinton supporters who expected to win with Trump supporters who expected to win.  

Of course, the "fair and square . . . legitimate leader" question is open to interpretation:  someone might believe that a candidate had really gotten the votes, but had used unfair tactics.  Since 2004, Gallup has asked about confidence that votes "will be accurately cast and counted in this year’s election."  I'll look at that question in my next post.  

[Data from the Roper Center for Public Opinion Research]

Back to normal, part 2

 My last post suggested that the central result of a paper published in the American Economic Review was sensitive to the specification of the model:  specifically, that the evidence was weaker (and would just scrape in at "significant at the 10% level") with a negative binomial model rather than the models they fit:  a least-squares regression on the log of a ratio and a Poisson regression.  The negative binomial fits substantially better than the Poisson; although they can't be compared directly, there are several reasons to prefer the negative binomial over the least-squares regression (I won't go into them here).  The AER has a rigorous review process and the acknowledgments thank sixteen people by name, plus "other participants at numerous seminars for many constructive comments"--why didn't someone suggest (or insist) that they try a negative binomial regression?.  My ideas:

1.  A tendency to put too much faith in a combination of robust standard errors and "large" sample sizes at the expense of trying to find the right model, or something close to the right model.

2.   Taking the number of cases at face value.  The analysis includes about 35,000 municipalities, but many of them are very small:  80% are under 1,000.  On the average, there is about one collaborator per 1,000 people, so small villages (that is, most of them) generally don't provide much information.  Moreover, the analysis included a control for a larger geographical unit, department.  There were 95 of those, but in about half of them, every (or almost every) municipality had the same assignment in terms of service under  Pétain.  Those departments provide no information on the central question.  So you could regard the data as a (roughly) 50 by two table:  about 50 departments where troops from some municipalities served under  Pétain and others didn't.   You would lose something by analyzing it that way--the ability to adjust for other qualities of the municipalities.  But you would also gain something:  it would be easier to notice outliers or influential cases, and perhaps some unanticipated geographical patterns.

Back to normal

 This is a return to my usual kind of subject, although I may give an update on my adventures with predatory publishing in a future post.  

A few weeks ago, Andrew Gelman posted about a paper by Julia Cagé, Anna Dagorret, Pauline Grosjean, and Saumitra Jha that was published in the American Economic Review last year.  The paper argued that the experience of fighting in the battle of Verdun under Marshal Pétain created a sense of attachment, so that when Pétain turned to the extreme right and later headed the Vichy France regime, the municipalities that had supplied his troops (people from the same place generally served in the same unit) produced more collaborators.  Some critics had raised objections involving data quality, especially the list of collaborators, but I'll leave that aside and take the data as it is.

Elite leadership is important and frequently overlooked as an influence on public opinion, the authors seemed to have put a lot of effort into compiling and checking the data, the general method of analysis was appropriate, and there were a variety of robustness checks, so I was inclined to accept their conclusions.  But there were a few things that I wondered about.  They had two analyses, one a least squares regression with the log of collaborators per capita as the dependent variable, and the other a Poisson regression with the number of collaborators as the dependent variable (and including the log of the population as an independent variable).  In the first, the estimate for service with Pétain was .067 with a standard error of .018; in the second, the estimate was .190 with a standard error of .109.  They treated the first one as primary and described the second as showing that their "results were robust to Poisson estimation," but they didn't seem all that robust to me.  The Poisson estimate was almost three times as big, but the standard error was six times as big, so the 95% confidence interval went from -.024 to .404, or about -2.5% to +50%.   Also, the Poisson distribution applies when you count the number of events across a large number of independent cases, each with a small probability of experiencing the event.  But people in a town generally know and influence other people in the town, so one collaborator may recruit other collaborators, so the counts are likely to be "overdispersed" relative to what the Poisson distribution allows.  In this situation, the negative binomial distribution is appropriate, so I wanted to try it--maybe it would produce results more like those of the least squares regression.  I downloaded the replication data and reproduced their results and then fit a negative binomial regression.  The estimates for service with Pétain:

LS        Poisson        Negbin
.067        .190            .089
(.015)       (.014)        (.053)

The negative binomial regression fit much better than the Poisson regression.  The estimate was similar to that from the least squares regression, but the standard error was much bigger, and the 95% confidence interval is -.015 to .203.  Also, I show the ordinary standard errors--the robust, clustered standard errors that Cagé et al. used would be larger.  So there is only weak evidence, at best, that service under Pétain increased the number of collaborators.* 

In my next post, I'll discuss the more general implications of this analysis.  

*The also had results suggesting that service with Pétain affected electoral support for extreme right parties in the 1930s, and the points I've raised here don't apply to that analysis.  

It ain't me

 There is a journal called the EON International Journal of Arts, Humanities &Social Sciences.  I recently discovered that I am listed as the Editor .   I am not the editor--I had never even heard of this journal before, and would have declined if they asked me to be involved, since it looks pretty sketchy.  I have written to the publisher telling them to remove my name from their site but also wanted to announce it publicly just in case anyone has noticed.