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“Kevin Lewis and Paul Alper send me so much material, I think they need their own blogs.”

In my previous post, I wrote:

Kevin Lewis and Paul Alper send me so much material, I think they need their own blogs.

It turns out that Lewis does have his own blog. His latest entry contains a bunch of links, starting with this one:

Populism and the Return of the “Paranoid Style”: Some Evidence and a Simple Model of Demand for Incompetence as Insurance against Elite Betrayal

Rafael Di Tella & Julio Rotemberg

NBER Working Paper, December 2016

Abstract:
We present a simple model of populism as the rejection of “disloyal” leaders. We show that adding the assumption that people are worse off when they experience low income as a result of leader betrayal (than when it is the result of bad luck) to a simple voter choice model yields a preference for incompetent leaders. These deliver worse material outcomes in general, but they reduce the feelings of betrayal during bad times. We find some evidence consistent with our model in a survey carried out on the eve of the recent U.S. presidential election. Priming survey participants with questions about the importance of competence in policymaking usually reduced their support for the candidate who was perceived as less competent; this effect was reversed for rural, and less educated white, survey participants.

I clicked through, and, ugh! What a forking-paths disaster! It already looks iffy from the abstract, but when you get into the details . . . ummm, let’s just say that these guys could teach Daryl Bem a thing or two.

Not Kevin Lewis’s fault; he’s just linking . . .

On the plus side, he also links to this:

Turnout and weather disruptions: Survey evidence from the 2012 presidential elections in the aftermath of Hurricane Sandy

Narayani Lasala-Blanco, Robert Shapiro & Viviana Rivera-Burgos

Electoral Studies, forthcoming

Abstract:
This paper examines the rational choice reasoning that is used to explain the correlation between low voter turnout and the disruptions caused by weather related phenomena in the United States. Using in-person as well as phone survey data collected in New York City where the damage and disruption caused by Hurricane Sandy varied by district and even by city blocks, we explore, more directly than one can with aggregate data, whether individuals who were more affected by the disruptions caused by Hurricane Sandy were more or less likely to vote in the 2012 Presidential Election that took place while voters still struggled with the devastation of the hurricane and unusually low temperatures. Contrary to the findings of other scholars who use aggregate data to examine similar questions, we find that there is no difference in the likelihood to vote between citizens who experienced greater discomfort and those who experienced no discomfort even in non-competitive districts. We theorize that this is in part due to the resilience to costs and higher levels of political engagement that vulnerable groups develop under certain institutional conditions.

I like this paper, but then again I know Narayani and Bob personally, so you can make of this what you will.

P.S. Although I think the “Populism and the Return of the Paranoid Style” paper is really bad, I recognize the importance of the topic, and I assume the researchers on this project were doing their best. It is worth another post or article explaining how better to address such questions and analyze this sort of data. My quick suggestion is that each causal question deserves its own study, and I don’t think it’s going to work so well to sift through a pile of data pulling out statistically significant comparisons, dismissing results that don’t fit your story, and labeling results that you like as “significant at the 7% level.” It’s not that there’s anything magic about a 5% significance level, it’s that you want to look at all of your comparisons, and you’re asking for trouble if you keep coming up with reasons to count or discard patterns.

P.P.S. Paul Alper writes:

I don’t know about Kevin Lewis, but in general the world is not in desperate need of more bloggers. And the increasingly intemperate language. For example, this came from 19 December 2016

Anyway, three people emailed me today about a much-publicized science news item that pissed them off.

and from 28 December 2016

Uh oh, significance tests. It’s almost like they’re trying to piss me off!

and from 17 October 2009

It’s possible to write things that piss off conservatives while still retaining an edgy, transgressive feeling–take a look at Nate Silver (or, to take a less analytical example, Michael Moore)–but I think it’s a little harder to do.

To see how endemic this is, I googled *Gelman pissed off* and brought this, which got me you and other Gelman’s using potty language—Maxsim, Brett, Natalie, Larry, Suzie, Vlada, Libby—and many more web pages including something about a Gelman dioxin plume.

It is one thing for you to use poor terminology but I fear this will promote unthinking responses by your contributors who will reply in kind in order to be quick and cute. Try these synonyms to raise the level of discourse:

annoyed
bothered
unsettled
irritated
put off
miffed
irked
upset
vexed
peeved
offended

OK. No guarantees but I’ll try my best.

2 Comments

  1. Anti Paul Alper says:

    I’m just going to say that I always found it the most entertaining when you went to the “potty” (how dweeb do you have to be to use that word) language. Feck discretion! Don’t lose your attitude, Andrew! Sláinte!

  2. Jack PQ says:

    I think you’re a bit too harsh with the Di Tella and Rotemberg paper. In economics, one way we deal with forking-paths issues is to begin by developing theory, and deriving a specific, and formal, mathematical model (something that is not commonly done in other fields). Thus, the empirical analysis is not one of many possible analyses, but rather the analysis that is implied by the derived model. Now, deriving a nice model is a lot of work, so it’s not as though they derived N different models and only presented the one that worked with the data. On the other hand, ad hoc models, sure you’re just talking about putting a bunch of variables together and saying “I think this idea is worth testing”. And then, lots of forking-paths trouble.

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