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.  

Those were different times

 From the New York Times:  "[Danzy] Senna, 53, was born in Boston, the daughter of a white, patrician mother . . .  and an African American father. Her parents . . .  were in the first cohort of interracial couples who could legally marry in the United States."  Hold on a minute--in 1967, the Supreme Court ruled that state laws prohibiting interracial marriage violated the Constitution, but only a minority of states (all Southern or border states) had such laws.  Some states had laws against interracial marriage until the 1950s and 1960s, and in those it would be reasonable to speak of the "first cohort" of interracial couples, but Massachusetts had repealed its prohibition on interracial marriage in 1843.  It wasn't the first in that respect--five of the thirteen original states (New York, New Jersey, Pennsylvania, Connecticut, and New Hampshire) never had laws against interracial marriage.  So although interracial marriages were rare, they've been around since the beginning of the United States.  The Times wasn't the only one to get this wrong--Senna's Wikipedia biography says that her parents "married in 1968, the year after interracial marriage became legal," and cites a Canadian Broadcast Company article, which says her parents "wed a year after interracial marriage became legal."  Why would multiple sources make this mistake?  It's not hard to find the information on differences in state laws (the Wikipedia article on interracial marriage in the United States has it.  

I would guess that it involves a change in the way of seeing racial discrimination--in the 1950s and 1960s, the prevailing view was that it was mostly a regional issue--the problem was to get the South to catch up with the rest of America.  Since that time, there has been a reaction against this view, which has sometimes overshot the mark.  You could say that we've gone from a realization that racism is present even in Boston to an assumption that Boston was and is no different from anywhere else.  

Of course, at the time her parents were married there was a lot of opposition to interracial marriage, even where it was legal.  In 1968, a Gallup poll asked "do you approve or disapprove of interracial marriage?"--20% approved and 73% disapproved.  A NORC survey asked whites "Do you think there should be laws against marriages between negroes and whites?"  53% said yes and 43% said no.  There were some regional differences, but they weren't as large as I expected--there was 53% agreement in New England and 37% in the Middle Atlantic states. So on this issue, law generally ran ahead of public opinion.   Educational differences were much bigger--about 75% of people with a grade school education and only 12% of college graduates said yes.  

[Data from the Roper Center for Public Opinion Research]

Now and then

Opinion surveys began in the 1930s, when the state of the economy was obviously a major issue.  However, questions on "the economy" didn't appear until much later--the earliest ones I have found are from 1976.  Before then, questions focused on specific aspects of the economy--there were a few on "business conditions," but more on changes in your own situation.   The first of those was in June 1941:  "Financially, are you better off, or worse off than last year? "  31% said better off, 18% worse off, and 51% about the same.  The figure shows the net sentiment (better-worse) every time this question was asked (with some variation in form) from 1941 until the mid-1970s.  

Most of the questions asked about the previous year, but some asked about the "last few" or "last two or three" years.  It looks like assessments of the last few years were more positive than assessments of the last year.  After the mid-1970s, the questions get more numerous.   Here are results of the "last year" question from 1976-95.  

Here are results of the "last few years," which has been included in the GSS since 1972:

There is clearly a difference:  the balance on the last few years question is almost always positive--the only exceptions are in 2010 and 2012--while the balance on the last year question is often negative.  I'm not sure why this would be the case, but it means that you need to have different standards for evaluating the two questions.   Common sense suggest that they will rise and fall together to some extent, but how close is the connection?  I'll look at that in a future post.

[Data from the Roper Center for Public Opinion Research]

Too many people

 In 1947, the Gallup poll asked "Do you think this town [city] would be better off or worse off if more people lived here?"  31% said better off, 46% worse off, 9% the same, 5% that it depended on the type of people, and 10% weren't sure.  There was a parallel question about your state:  for this, it was 40% better off, 27% worse, 14% the same, and 20% no opinion.  So people were more positive about having more people in their state than in their town.  These questions were asked to a randomly selected half of the sample; the other half was asked "There are about 140 million people today in the United States. Do you think this country would be better off or worse off if there were more people living here?"  Only 16% said better off, with 56% saying worse off, 14% the same, 3% that it would depend, and 11% weren't sure.  That is, people were more negative about having more people in America than in their city or state.  Why?  One possibility is that 140 million sounds like a large number, so that mentioning it made people less inclined to say that we would benefit from having more.  But another possibility is that increases in the population of your town or state could involve people moving from other towns or states--an increase in the American population would have to involve immigration.*  

As far as group differences, people who lived in urban areas, more educated people, and people in New England and the Middle Atlantic states were more likely to say that a larger population would be good.  These qualities are all associated with "cosmopolitansim," supporting the idea that answers are related to attitudes towards immigration (the group differences for opinions about your city and state were generally smaller and had different patterns).**  However, negative opinions were more numerous than positive ones in every group.  There was little or no difference by party identification.  

This is one more piece of evidence for something I've mentioned before: Americans were not keen on allowing more immigration during the 1950s and 1960s--in 1964, when the restrictive 1924 law was still in force, more people favored reducing immigration than increasing it.  

*Over the long run, it could be natural increase, but people seem to think about the near future if the time frame isn't specified.  

**Most Gallup surveys asked about religion, but this one did not.

[Data from the Roper Center for Public Opinion Research]

Governors and presidents

 When people were talking about who Kamala Harris might choose as her running mate, Josh Shapiro's high approval rating was often mentioned.  I hadn't heard anything about how Tim Walz stood in that respect, so I looked and found that Morning Consult  tracks the approval rating for all governors.  As of July 24, Shapiro's net rating (favorable minus unfavorable) was +25, which is good but not exceptional (tied for 16th).  Walz was +13, which is below average but not exceptional either (tied for 36th).  There was no obvious pattern in the ratings, although there may be some tendency for governors in smaller states to have higher approval ratings:

The thing that I found most striking was simply that they were almost all positive--only two were "underwater" and those were at -1.  In contrast, Joe Biden has been underwater for most of his time in office, Donald Trump was for almost all of his, Barack Obama for about a third of his, and George W. Bush for about his last three years in office.  That led me to wonder if there was a general tendency for governors to get higher approval ratings than Presidents--often people feel more positive about things that are closer to them.  

There have been several questions about approval of the governor of your state, ranging from 1954 to 2023.*  The figure shows net approval ratings for governors, and presidential approval at the same times.  

Gubernatorial approval ratings have not been consistently higher than presidential--they were lower in six of the first seven times, and have been higher in the last three.  With only ten cases, it's hard to be confident about anything, but they suggest that the 21st century discontent is specifically about national politics, not about politics in general.   

*The Morning Consult data go back to 2017, but require a subscription which is beyond what my research budget can afford.  

Left behind?, part 3

The final issue I want to consider is whether people living in rural areas feel left behind in a material sense.  The GSS has a question on satisfaction with your financial situation which has been running since 1972.  There is no essentially no difference between the averages for people in MSAs, "other urban" counties (ie, with towns of over 10,000), and rural counties.*  The trend is -.01 in MSAs, -.02 in other urban counties, and -.04 in rural counties; the difference is probably statistically significant, but is too small to show up clearly in a figure.  Since 1994, there has been a question on how your standard of living compares to your parents' standard of living at the same age.  There's no clear difference on the average (if anything, people in rural areas are a bit more positive):  the correlations with time are -.07 in MSAs, -.10 in other urban, and -.06 in rural counties.  Finally, there's a question on how you see your family income relative to other American families--people in MSAs see it as higher, but there is little or no difference in the trends.  The GSS also asks about actual family income, which is indeed higher in MSAs.  The trend on family income is .15 in MSAs, .10 in other urban, and .16 in rural counties.  

So the growing relative dissatisfaction in rural areas doesn't seem to be a result of growing relative dissatisfaction with economic conditions.  That is, to the extent that people in rural areas feel "left behind," it's not by economic developments, but by something else.  

A couple of other notes:

1.  In the 21st century, over half of the white respondents live in MSAs, about a third in "other urban," and about 12% in rural counties.  

2.  In his speech at the Republican convention, J D Vance said he grew up in "Middletown, Ohio, a small town where people spoke their minds, built with their hands, and loved their God, their family, their community and their country with their whole hearts."  Middletown has a population of about 50,000 and is classified as part of the Cincinnati metropolitan area.  There's necessarily some fuzziness in the boundaries of metropolitan areas, and it's about 40 miles from Cincinnati, so you could argue about whether it should really be included.  But 50,000 isn't a small town--in Maine, where I live now, that would make it the second largest city.  So why did he say that it was?  I think it's an example of a tendency in political rhetoric and journalism to treat small town/"heartland"/working class/economically declining as more or less the same thing.  

*As before, I limit the analyses to whites.