Category Insurgency & Counterinsurgency

Continuing with the next posting on the nineteenth lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

This lecture continues the discussion of terrorism, this time he is looking at a paper by Gassebner and Luechinger on terrorism (starts on page 13 of the lecture). This is similar to what was done for causes of war in a paper by Hegre and Sambanis that was presented in lecture 11:

Economics of Warfare 19 – 2 and 11 – 2

So, the two authors ended up running a large number of regressions trying out many different combinations of variables and looking for those that are consistently significant. They used three different terrorism databases, resulting in them testing 18 different variables to each of the three different databases and looking at locations, victims and perpetrators (see slides 14 and 15). This gets a little involved and you are probably not going to sort it all out unless you read their paper (which costs $40 to order a copy…I did not).

What is particularly interesting to me is that the same variable has different values depending on what database is used. The values are of Coef. (median coefficient estimate), CDF (cumulative distribution function) and % sig. (the percent the estimate was statistically signicant). For example “Ethnic tensions” has values of -0.0007, 0.544 and 18.4 in the Iterate database, has values of 0.040, 0.911 and 63.1 in the GTD databases, and has values of 0.011, 0.627 and 15.2 in the MIPT database. Not sure what this really means (see slide 14). I have never done any analysis using someone else’s data base. I have always collected by own data and analyzed it.

Anyhow, the results are (see slides 18 & 19).

1. Strong police states with religious tensions seem to be favored targets of terrorists.
2. Bigger, economically repressive and richer countries seem to attract terrorist attacks.
3. “Law and order” seems to discourage terrorism (this does conflict with point 1 above).
4. A more foreign portfolio investment seen positively associated with terrorism.
5. Higher natural resource exports as associated with fewer attacks on a county’s citizens.
6. Citizens from countries with a “youth bulge” are attacked relatively less and do not attack more often.
7. Fewer telephones are associated with more attacks.
8. Countries with centrist governments seem to export terrorists

Anyhow, not finished with this lecture yet. Will have one more posting to do. The link to his lecture is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2019.pdf

Continuing with a second posting on the nineteenth and second to last lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

This lecture continues the discussion of terrorism, this time he is looking at a paper by Gassebner and Luechinger on terrorism. This is similar to what was done for causes of war in a paper by Hegre and Sambanis that was presented in lecture 11:

Economics of Warfare 11

This discussion of Hegre and Sambanis covered only the last two pages of the lecture (slides 23 and 24 of lecture 11) and I did not mention it when I first blogged about it.. I guess I probably need to now, turning this posting into the follow-up post on lecture 11. Hegre and Sambanis looked at 88 variables related to causes of war and by running regressions tried to determine which ones are consistently correlated with the onset of war.

1. GDP per capita is negatively associate with civil war onset (meaning: rich countries are less likely to have civil wars).
2. Having had a previous war is positively associated with civil war onset…the more recent the war the stronger the association (meaning: war beget wars?).
3. Country size (population and territory) is positively associated with civil war onset (meaning: big countries tend to have more wars.).

This last point is interesting as country size and population also showed up in our insurgency studies related to the success of the insurgents. Big populated counties tended to have more successful insurgencies than small countries. In Chapter 3, page 47 of America’s Modern Wars I provided the following chart:

Insurgencies with Foreign Intervention

Circumstances                                                   Number of cases        Percent Blue Victory

Indigenous Population > 9 million                              10                                20

Intervening Force Commitment > 100,000                  8                                  0

Peak Insurgent Force Size > 30,000                         13                                23

“Blue Victory” = counterinsurgent victory

Anyhow, I have not gotten past the first sentence of slide 13 for this post, and we are already around 300 words in this post, so probably best to pick up the rest of lecture 19 in a subsequent post.

The link to lecture 19 is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2019.pdf

The link to lecture 11 is here: https://dupuyinstitute.org/2017/01/31/economics-of-warfare-11/

Continuing with the nineteenth and second to last lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

This lecture continues the discussion of terrorism, a subject we often deliberately avoid. We actually don’t even have a category for terrorism on the blog, in part because I consider it a tool of an insurgency, not a separate form of warfare.

On the first slide is a paper on the determinants of media attention for terrorist attacks. This is a significant subject as terrorism does rely on media attention to make their points. If there was no coverage……then the terrorist act would be relatively ineffective. The purpose of terrorism is not to kill people, it is to attract attention. Modern international terrorism started with the Palestinian Black September attack on the Munich Olympics in 1972, which turned the Palestinian issue from a Middle East concern into an issue that now garnered world wide attention.

Anyhow, the lecture starts with a paper by Michael Jetter, which is linked to on page 1 (one of the very nice things about this lecture series is that all the various papers he discusses are linked in the lecture…providing a extensive collection of interesting and useful papers to read). The question is “…why do some attacks generate more coverage than others do?” The answer is on slides 10 and 12, but the short answer is: attacks in wealthier countries, countries that trade with the U.S., that are closer to the U.S. get more coverage (in the New York Times).

Not sure how really meaningful this is except to note that obviously, terrorist attacks in Canada are going to get a lot more attention in the U.S. newspapers than terrorist attacks in Sri Lanka.

Anyhow, this is going to turn into a two-part posting, so will do the rest later this week. The link to his lecture is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2019.pdf

Linked here is the blog for a company that specializes in “data science & predictive analytics:” Elderresearch

It is run by Dr. John Elder, someone who I have known for more decades than I care to admit. We have not had much intersection in our respective businesses, although I did talk to him in 2007 about the use of classification trees when we were doing work on insurgencies. This work is summarized in my book America’s Modern Wars. In particular we were using them in our Task 12 report: Examining the Geographic Aspects of an Insurgency, dated 4 February 2008. We did both a logistic regression and several classification trees looking at terrain and its effects on insurgencies. I ended up getting a three page paper from John where he independently ran his own logistic regression from our data and ended up with results similar to ours. It was a useful (and free) confirmation of what we were looking at.

I generally was not happy with the results I was getting from these comparison and there was clearly some factors far more significant than terrain that was driving the results of these conflicts. This is what lead us to look at force ratios and insurgent cause. Somewhere between the first and final draft of the book, I did delete the classification trees from the book.

I gather some of Elder Research’s work is based upon classification trees. Most of his work is commercial. They do have a new blog, but with only one blog post so far in there “defense and intelligence” category. It addresses the third off-set strategy: defense-and-intelligence

Continuing the examination of the seventeenth lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

This lecture started with a paper by Hsiang, Burke and Miguel that was a survey of 60 different papers from 1994-2013 on interpersonal conflict and climate, intergroup conflict and climate, and “institutional breakdown and population collapse” and climate (see slides 3-8). This is discussed in more depth on my previous post. Starting on slide 26, he then reviews a group of authors who are critical of the findings of Hsiang, Burke and Miguel. The review makes three arguments

1. Many of 60 studies are quite similar to each other. For example, many contain African countries and some only contain African countries.
2. There is a lot of variation of what is modeled and how it is modeled.
3. They omit other studies that reach other conclusions.

They then provided their own meta-analysis for the effect of climate variability on civil conflict on slide 29. The argument here is not “….that climate has no effect on conflict, but, rather, that the effects of climate on conflict are less clear than claimed…” by the previous studies authors “…and that more research is needed to pin down what the real effects are.”

OK…..noted.

Now going back to America’s Modern War, which I seem to do a lot lately, I do have a chapter called “Other Similar Work” (Chapter 7, pages 70-77) that looks at other work similar to the work I present in that book. I provided in Chapter 6 a logistic regression model that keys off of force ratio and insurgent cause compared to outcome. At the time I wrote the book, there were only two similar studies I was aware of that addressed this. Done independently and at the same time as my study was Andrew Hossack’s study over at Ministry of Defence in the United Kingdom. Done after my study and using the database we developed was a study done by Center for Army Analysis (CAA). Certainly the first two criticisms provided above could be partially applied to this comparison, in that these three studies (The Dupuy Institute, Hossack and CAA) are 1) quite similar to each other, including two studies using the same database and, 2) there is some variation (but not much actually) in what is modeled (although all three studies effectively used the same dependent variable). On the other hand, as far as I know, there are no other quantitative studies out there that reach a contradictory conclusion. There are few studies done comparing force ratios to outcome. Shawn listed seven studies one of his posts, but several were related to troops per 1,000 population as opposed to force ratios:

https://dupuyinstitute.org/2016/01/08/force-ratios-and-counterinsurgency-ii/

and

https://dupuyinstitute.org/2016/01/13/force-ratios-and-counterinsurgency-iii/

Only five studies addressed force ratios and they found a positive correlation in all cases (although there was some debate over the significance).

Now, when I was out marketing my book to publishers, one British editor sent the manuscript to two expert reviewers to look at. One came back with the comment that contemporary studies clearly show that a force ratio model is not correct, and therefore they should not publish. I almost felt like trying to argue with this anonymous reviewer, but there were many other things on my plate (including finding another publisher for the book). I get the sense that because force ratio models of insurgency were discussed in the 1950s and 1960s, and were dismissed by some at the time, that people believe that they are passé. But, it does not appear that the original 10-to-1 or similar force ratio model that was quoted in the 50s and 60s was based upon any systematic quantitative analysis. It also does not appear that the dismissal of it in the 1960s was based upon any systematic quantitative analysis.

Anyhow, that was somewhat of a long and not clearly related aside. But sometimes, the Dr. Spagat lectures get me thinking back to my work and how it compares and contrasts these other attempts to quantify and model conflict phenomena. The link to his lecture is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2017.pdf

Examining the seventeenth lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

As is the case with the last two lectures, this one also focuses on climate and conflict. It starts with a paper by Hsiang, Burke and Miguel (a different paper than the one in the last lecture by the same authors). This paper is not a single test, but a survey of 60 different papers from 1994-2013. There are 15 that look at interpersonal conflict and climate (usually temperature compared to crime), 30 that look at intergroup conflict and climate (often rain or temperature compared to civil conflict), and 15 that look at “institutional breakdown and population collapse” and climate. All these are listed in slides 3-8.

The discussion after that goes into considerable depth (and is certainly worth perusing), but the conclusion on slide 25 is “HBM seem to present a pretty impressive accumulation of evidence associating higher temperatures with more conflict….” (my bolding)

And he notes: “The authors admit that there is not a lot of research spelling out plausible mechanism that might explain why higher temperatures are associated with more violence.”

I will stop here and pick this up tomorrow. Starting on slide 26, he then reviews a group of authors who are critical of the findings of Hsiang, Burke and Miguel.

The link to the lecture is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2017.pdf

Examining the sixteenth lecture from Professor Michael Spagat’s Economics of Warfare course that he gives at Royal Holloway University. It is posted on his blog Wars, Numbers and Human Losses at: https://mikespagat.wordpress.com/

This lecture also addresses climate change. In effect, it is looking at the impact of droughts on civil war incidence. The first part of the lecture focuses on a study done by Coutteneir and Soubeyran. It is also a cross-country analysis.

The thing that caught my attention was that they choose to exclude the anti-colonial wars and “internationalized wars:” (slide 2). These are a significant number of cases, although I can understand their reasons for doing so. Certainly the drivers behind these wars was much more the local climate conditions. So, they start by removing the cases that they know are not driven by climate change.

Now, when I originally did the Iraq Casualty Estimate (see Chapter One of America’s Modern Wars) I had a significant number of these “colonial wars of national liberation” in my data set, mostly dating from the 1950s and 1960s. As I was briefing the results of my estimates inside the Army and DOD community in early 2005, this suddenly became an issue. I think one person inside an organization glommed onto this issue as a way of obviating my results. I ended up going over to give the brief in DOD and was notified that they had been informed that my results were biased by having too many “colonial wars of national liberation” in my data set. Not exactly sure how having “colonial wars of national liberation” in my data set biased my estimate of casualties and duration in an Iraq insurgency, but this was the argument they were making. So for my briefing, I did my original briefing, and then added a little addendum that addressed my results if I took out the dozen or so cases of “colonial wars of national liberation.” It did not change my results. This was a fairly embarrassing exercise in that I think that person in question wanted to dismiss my results because they either did not believe them, it violated his cosmology, or the higher ups in DOD had already decided that we would not be facing a major guerilla war. Never really knew what the reasoning was. It is alluded to in my book on page 28.

Anyhow, there are valid reasons I believe for leaving out “colonial wars of national liberation” from this climate change test, but I do not believe there was for my Iraq Casualty Estimate.

With those cases left out, Couttenier and Soubeyran do come up with a fairly consistent and positive relationship between drought and internal armed conflict (slide 2 – 3)

The next part addresses a paper by Hsiang et al. (paper is linked on slide 4). It discussed El Nino. Having lived in California in the 1980s, El Nino is something very familiar to me (as are earthquakes), but Dr. Spagat provides a nice explanation of it on slides 4-5 for his British students. The advantage of looking at El Nino (hotter and dryer periods) and La Nina (colder and wetter periods) is that you again get sort of the side-by-side laboratory effect that social scientists have to struggle to find.

His next series of slides gets into the nuts and bolts of the study, but they ended up tracking “Annual Conflict Risk” (ACR) which is 2% for countries weakly affected by these weather patterns, 3% for countries affected by La Nina (cool and wet) and 6% for countries affected by El Nino (warm and dry). See slide 15.

The next slides examine the studies in more depth, with Dr. Spagat making the statement on slide 23 that “Ultimately, though, I feel that we need some convincing case studies linking ENSO with armed conflict in specific times and places for the Hsiao et al. work to be fully convincing.”

Slides 24-29 end up discussing “panel data”

The link to the lecture is here: http://personal.rhul.ac.uk/uhte/014/Economics%20of%20Warfare/Lecture%2016.pdf