Why My Coup Risk Models Don’t Include Any Measures of National Militaries

For the past several years (herehere, here, and here), I’ve used statistical models estimated from country-year data to produce assessments of coup risk in countries worldwide. I rejigger the models a bit each time, but none of the models I’ve used so far has included specific features of countries’ militaries.

That omission strikes a lot of people as a curious one. When I shared this year’s assessments with the Conflict Research Group on Facebook, one group member posted this comment:

Why do none of the covariates feature any data on militaries? Seeing as militaries are the ones who stage the coups, any sort of predictive model that doesn’t account for the militaries themselves would seem incomplete.

I agree in principle. It’s the practical side that gets in the way. I don’t include features of national militaries in the models because I don’t have reliable measures of them with the coverage I need for this task.

To train and then apply these predictive models, I need fairly complete time series for all or nearly all countries of the world that extend back to at least the 1980s and have been updated recently enough to give me a useful input for the current assessment (see here for more on why that’s true). I looked again early this month and still can’t find anything like that on even the big stuff, like military budgets, size, and force structures. There are some series on this topic in the World Bank’s World Development Indicators (WDI) data set, but those series have a lot of gaps, and the presence of those gaps is correlated with other features of the models (e.g., regime type). Ditto for SIPRI. And, of course, those aren’t even the most interesting features for coup risk, like whether or not military promotions favor certain groups over others, or if there is a capable and purportedly loyal presidential guard.

But don’t take my word for it. Here’s what the Correlates of War Project says in the documentation for Version 4.0 of its widely-used data set (PDF) about its measure of military expenditures, one of two features of national militaries it tries to cover (the other is total personnel):

It was often difficult to identify and exclude civil expenditures from reported budgets of less developed nations. For many countries, including some major powers, published military budgets are a catch-all category for a variety of developmental and administrative expenses—public works, colonial administration, development of the merchant marine, construction, and improvement of harbor and navigational facilities, transportation of civilian personnel, and the delivery of mail—of dubious military relevance. Except when we were able to obtain finance ministry reports, it is impossible to make detailed breakdowns. Even when such reports were available, it proved difficult to delineate “purely” military outlays. For example, consider the case in which the military builds a road that facilitates troops movements, but which is used primarily by civilians. A related problem concerns those instances in which the reported military budget does not reflect all of the resources devoted to that sector. This usually happens when a nation tries to hide such expenditures from scrutiny; for instance, most Western scholars and military experts agree that officially reported post-1945 Soviet-bloc totals are unrealistically low, although they disagree on the appropriate adjustments.

And that’s just the part of the “Problems and Possible Errors” section about observing the numerator in a calculation that also requires a complicated denominator. And that’s for what is—in principle, at least—one of the most observable features of a country’s civil-military relations.

Okay, now let’s assume that problem magically disappears, and COW’s has nearly-complete and reliable data on military expenditures. Now we want to use models trained on those data to estimate coup risk for 2015. Whoops: COW only runs through 2010! The World Bank and SIPRI get closer to the current year—observations through 2013 are available now—but there are missing values for lots of countries, and that missingness is caused by other predictors of coup risk, such as national wealth, armed conflict, and political regime type. For example, WDI has no data on military expenditures for Eritrea and North Korea ever, and the series for Central African Republic is patchy throughout and ends in 2010. If I wanted to include military expenditures in my predictive models, I could use multiple imputation to deal with these gaps in the training phase, but then how would I generate current forecasts for these important cases? I could make guesses, but how accurate could those guesses be for a case like Eritrea or North Korea, and then am I adding signal or noise to the resulting forecasts?

Of course, one of the luxuries of applied forecasting is that the models we use can lack important features and still “work.” I don’t need the model to be complete and its parameters to be true for the forecasts to be accurate enough to be useful. Still, I’ll admit that, as a social scientist by training, I find it frustrating to have to set aside so many intriguing ideas because we simply don’t have the data to try them.

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