5779 is upon us!

Last Thursday when I looked straight up as I ascended the gravelly knoll for shacharit it really hit me. That little bit of moon meant Rosh Hashanah must be really upon us. The yellow leaves on the fig tree offer further hints that we are well into the season of turning.

This year’s “first fruits” for Rosh HaShannah. Muscat of Alexandria and Zinfandel? (could be Syrah) grapes, Arkansas Black and Hudson’s Golden Gem apples, Warren pear, and the season’s icon, our first pomegranate (I used to think it was Desertnyi variety, but now I am thinking maybe Parfianka).

It was a fruitful end to 5778. Maybe 5779 be sweet and bountiful! See you on the other side.

 

 

 

 

 

 

Sweden, 2018

Sweden’s general election was today, and it seems it will be a close result. The anti-immigration Sweden Democrats were polling in second place, but the polls aggregated at the Wikipedia page on the election suggest their support was falling sharply in the last phase before the election.

I do not know Swedish politics well enough to tease out the likely coalition or support arrangements that might result. But I open this thread as a place for those who are following the results.

Besides, we learned in 2014 that Swedish inter-party bargaining can be a little unpredictable.

Spill time?

So, who is the PM of Australia at the moment? It’s getting interesting. Again.

Second and third questions: What is the origin of the term, spill, to refer to an intra-party leadership challenge? Is Australia the only country where this term is used?

And for some comparative data context, see this planting from 2010.

There also has been an ongoing conversation about the current case at a planting from 2015. This topic of spills really overflows down under.

Using ‘combomarginsplot’

In preparing graphs for a book I am working on, I have found a Stata package called ‘combomarginsplot’ very valuable. I wanted to share the experience in case it might help other reseachers. The package was written by Nicholas Winter, and I am indebted to him for this terrific tool.
The book is on how parties assign their MPs to legislative committees. The committees are divided into categories, including “high policy” (things like justice, foreign affairs, and defense), “public goods” (such as health and education), and “distributive” (principally agriculture, transport, and construction). The typology is based on one developed some years ago by Pekkanen, Nyblade, and Krauss (2006).
Covariates that we hypothesize are associated with assignment to a given committee category include gender, occupation, seniority, and various other personal and electoral variables.
One of the ways in which ‘combomarginsplot’ has come in handy is in setting covariates for purposes of simulating how various attributes of a politician are associated with increased or decreased probability that a member might be assigned to a given committee category.
Normally it would be fine to use standard ‘margins’ and ‘marginsplot’ for such purposes. For instance, if for a given party–let’s call it “Likud”–we want to know the odds that a candidate of a given gender is assigned to public policy (PG), we would run the logistic regression for this category, and then do the ‘margins’ command. It might look like this:
*
margins, at(female=(0 1) ) level(90)
  marginsplot, recast(scatter) plotopts(msiz(vsmall) mc(gs4)) ciopts(lw(medthick)) ///
xscale(range(0 1)) xsc(r(-.99 1.7)) ylabel(0(.2)1) ysc(r(0 1)) scheme(s1mono) ///
aspectratio(3) ytitle(“”) title(“Likud: PG”) name(Likud_pg_fem, replace)
*
A formatting note: Note the use of the “recast” option. The standard ‘marginsplot’ can produce some really dreadfully ugly graphs! The “recast(scatter)” option gives you these clean capped bars. (You can also use “recast(bar)”, but I find it less appealing.) The x-axis option, “xsc(r(-.99 1.7))”, is also useful because without doing something like this, the default has the bars right next to the box borders, and lots of white space in between. Obviously, Stata graph commands can be adjusted to user preference. This is mine; yours might vary.
The command above produces a graph like this:
Nice, right? No, not really. Please never accept a “result” like this! Look at the confidence interval for female MP. It goes above 1.00. But this is a logistic regression–the outcome can be, by definition, 1.00 or 0.00. It can’t be 1.2! Just because Stata says so, doesn’t make it so!
Sometimes setting ‘margins’ scenarios a certain way actually leads to the estimate being generated off a hypothetical case that is really unrealistic, given the data. That is, there may not be many real politicians who meeet the criteria. Then–especially if the overall sample is not very large–you can get utterly impossible “predictions”.
Go look at your data, and see what’s going on. In this case, it turns out that the problem is that a very small percentage of men in this party had what we term “high-policy occupations” (mostly lawyers), but a high percentage of the women did. When we run ‘margins’ without specifying values for any other covariates, we get an estimate at the sample means of the other variables on the right-hand side of the regression. So it is estimating men and women with the same likelihhod of also being of high-policy occupation, even though the male and female subsamples are rather different.
What we need is to estimate the men and women in separate ‘margins’ commands, each being more realistic on other covariates. However, we want the estimates for men and women to appear in the same box in our final graph. It won’t do to run separate ‘marginsplot’ commands and then do ‘graph combine’ because that will make two separate boxes in the space of one. So here is how you can make it look like the first graph, despite being based on two separate calls to ‘margins’:
*
margins, at(female=1 occu_hi=1) level(90) saving(“File1”, replace)
margins, at(female=0 occu_hi=0) level(90) saving(“File2”, replace)
  combomarginsplot “File2” “File1” , ///
    recast(scatter) plotopts(msiz(vsmall) mc(gs4)) ciopts(lw(medthick)) ///
ylabel(0(.2)1) ysc(r(0 1)) scheme(s1mono) ///
labels(0 1) xscale(r(.5 2.5)) xtitle(“Female MP”) ///
aspectratio(3) ytitle(“”) title(“Likud: PG”) name(Likud_pg_fem, replace)
*
When we do all the above, we get:
We see more plausible confidence intervals, because we are estimating on realistic politicians. There is essentially no difference in this party between the probabilities of men and women getting PG committees (or, more to the point, between women with high-policy occupations and men without them). We already knew from the first example plot above that there was not a significant difference. It was the confidence intervals that went haywire, due to the unrealistic scenario.
A challenging part of of this was getting the bars in the right place within the box. First, one needs to use the ‘labels’ option in order to have them marked “0” and “1” instead of the names of the saved file (e.g., “File1”, although you can name them just about anything you want). With a little–OK, a lot–of trial and error, it turned out that “xscale(r(.5 2.5))” worked about right.
I have found several other convenient uses for ‘combomarginsplot’ in this and other projects. A perhaps more common use than the one I demonstrated here would be when the plotted curves come from different regressions. You can save the results from each, then combine them into a single plot area. Another, which I have used, is combining multiple outcomes from one regression, such as a multinomial logit.
It is terrific that Stata has such a community of public goods providers to create tools like this!

Zimbabwe, 2018

The Zimbabwe election results were finally announced. Presumably to the surprise of few, the incumbent President Mnangagwa of ZANU-PF has “won” and the party will have a two-thirds majority of the assembly (elected by FPTP and with high malapportionment).

Amazingly, Mnangagwa won just enough to not require a runoff! Yes, I am being cynical.

The official results apparently show ZANU-PF getting more votes for assembly than for presidency. That would be unusual for a major party in a presidential system, but here’s assuming that the gap was even greater than officially reported. Either that, or the assembly election was even more rigged than the presidential.

Also unusual–and for me a strong indicator that things were being cooked–is that the assembly result was released days before the presidential. I do not have actual records on these things, but I believe such a sequence is highly unusual. Usually they either come out together, or the presidential result gets announced first.

Another indicator of fraud is that the reported turnout went down between an earlier announcement and the final one. It is not hard to imagine that sufficient opposition votes were discarded to ensure Mnangagwa had over 50%.

Not much more to say, really. But if you want to have your say, here’s the space.

I highly recommend this post at On Elections:

Zimbabwe: another doubtful and deadly election result

Pakistan, 2018

Pakistan elections were today.

Something to watch is how well new religious parties do. One of them, Tehreek-e-Labbaik Pakistan, has a campaign poster that features a woman candidate, although you will need a little imagination to see her.

One of the party’s male candidates explains, “The party has nominated a few women… because… it is mandatory under the election law.”

I understand (via experts on Twitter) that there could be many by-elections in the weeks to come. One even spoke of a “wave” of them. Some of these will be mandated by a provision that invalidates any constituency result in which at least 10% of women on the voter roll did not participate. Others will be necessary because candidates can run in multiple constituencies and, if they win more than one, they have to step aside in all but one. Still others will be needed because several candidates (at least 8) have died since nomination; it is not clear to the extent the deaths are natural or due to election violence.

The electoral system is mostly FPTP. The total size of the National Assembly is 342, and 272 of them are from single-seat districts via plurality. Others are reserved for women or ethnic minorities; some form of PR is supposedly used for these, but I do not have the details. Perhaps someone will enlighten us in the comments. This election is the first under a newly delimited constituency map.

Early results put Pakistan Tehreek-e-Insaf, led by Imran Khan, in the lead.


Unless I say so explicitly, mention of parties or candidates on this blog is not an endorsement. That is especially so when I have no clue what a party’s poster appearing with my entry even says!

Academic writing styles

I am working on two books this summer/fall. I hope both will be done by the end of December, although that may be over-optimistic. As a result of being engaged in these writing processes, questions of academic writing style have been on my mind.

I owe many debts of gratitude to my mentor and frequent coauthor, Rein Taagepera. But the most recent one was his suggestion that every empirical chapter in our new book (Votes from Seats, 2017) start with a presentation of the key result. Don’t drag the reader through prior literature and a bunch of “hypotheses” (a practice he hates, and I tend to agree) before getting to the point. Start with the point, and then explain how you got there, and only then why others did not get there. But the thing is, this almost never works with a journal article (and maybe doesn’t work with books for most scholars not named Shugart or Taagepera), because reviewers impose a standard format that just makes for plodding reading. And writing.

For probably the best demonstrations of our preferred presentation, if you have access to the book, see Chapter 7, which has an overview of “Duverger’s law” near its end, but starts with the Seat Product formula for effective number of seat-winning parties and a graph showing the payoff. Also Chapter 12, in which the previously proposed concept of “proximity” is discussed at the end of a chapter that opens with some data plots showing our preferred “elapsed time“. Other empirical chapters in the book mostly follow this format as well.