A Brief Exchange on Coups in Africa

When I got up this morning, I had an email in my inbox from Patrick Mathangani, a writer for Kenya’s The Standard. He said he was researching a story on coups in Africa, had found my blog and piece for Foreign Policy on the subject, and wondered if I’d answer a few questions. I thought some of this blog’s readers might be interested in that exchange, too, so here are Mr. Mathangani’s questions and my replies.

In your 2013 forecast, 22 of these countries are in Africa. Checking through data over the years, the continent appears to have had more than its share of coups since the 1950s, perhaps explaining why coups have been seen as an African problem. Your analysis appears to confirm this. What’s your view on this?

I don’t think coups are an African problem so much as they’re a problem of poor countries with weak states, and Africa happens to have more than its fair share of those. We’ve seen the same pattern in every other part of the world, just at different times in history. Latin America, for example, suffered lots of coups in the 1960s and 1970s, but the incidence dropped off sharply in the past couple of decades as most countries in the region got less poor and more democratic—and, crucially, after the Cold War ended and the U.S. and USSR stopped sponsoring or supporting coups in the region as a way to scratch at each other.

I expect we’ll see the same decline in the frequency of coups in Africa as more and more countries get into positive spirals of development. We’ve already seen a decline in the post-Cold War period, probably due to the end of those superpower proxy struggles, and I’m guessing that current patterns of economic growth and democratization will solidify that shift just as they did in Latin America and Europe before.

What, in your view, makes Africa such fertile ground for coups?

I think my answer to number 1 goes about as far as I can on this question. I’m sure there are other aspects, too, but I’ll leave those to the regional pros to address.

This year, we’ve had two distinct political events in Africa that show a sharp contrast and mixed fortunes for the continent’s push for good governance. These are a seamless transition in Kenya, and a coup in CAR. What do these portend for Africa’s future and struggle for democracy?

As William Gibson supposedly said, “The future is already here. It’s just not evenly distributed.” To me, Kenya looks like a state that’s on the edge of that virtuous cycle of development I mentioned earlier, while CAR still isn’t even really a state in the conventional sense.

It’s interesting to see Tanzania, Kenya’s neighbour, at number 22 in your list. Tanzania has been relatively stable, why does it land on the model?

Tanzania ranks relatively high on the list because in spite of its reputation as a stable democracy, it’s got the basic features that have historically been associated with the occurrence of coups. Most notably, it’s got a high infant mortality rate relative to most of the world, political institutions that combine features of democracy and authoritarianism, and sharply polarized politics.

Now, it’s worth underscoring that the risk of a coup attempt in any one country in any given year is generally very low, even in the countries toward the top of those rankings. There are usually only a handful of coups and failed coup attempts worldwide each year, so the best prediction for even the highest-risk countries will almost always be that no coup will occur. If the forecasting models are working well, then all or nearly all of the coup attempts we do see will occur in the couple of dozen countries at the top of the annual rankings. Those rankings most definitely do not mean that we should expect to see coup attempts in all of those countries, and that certainly goes for Tanzania, too.

Coup Risk in 2013, Mapped My Way

This blog’s gotten a lot more traffic than usual since yesterday, when Max Fisher of the Washington Post called out my 2013 coup forecasts in a post on WorldViews.

I’m grateful for the attention Max has drawn to my work, but if it had been up to me, I would have done the mapping a little differently. As I said to Max in an email from which he later excerpted, the forecasts simply aren’t sharp enough to parse the world as finely as their map did. Our theories of what causes coup attempts are too fuzzy and our measures of the things in those theories are too spotty to estimate the probability of these rare events with that much precision.

But, hey, I’m a data guy. I don’t have to stick to grumbling about the Post‘s map; I can make my own! So…

The map below sorts the countries of the world into three groups based on their relative coup risk for 2013: highest (red), moderate (orange), and lowest (beige). I emphasize “relative” because coup attempts are very rare, so the estimated risk of coup attempts in any given country in any single year is pretty small. For example, Guinea-Bissau tops my list for 2013, and the estimated probability of at least one coup attempt occurring there this year is only 25%. Most countries worldwide are under 2%.

Consistent with an emphasis on relative risk, the categories I’ve mapped are based on rank order, not predicted probability. The riskiest fifth of the world (33 countries) makes up the “highest” group, the second fifth the “moderate” group, and the bottom three-fifths the “lowest” group.

This forecasting process doesn’t have enough of track record for me to say exactly how those categories relate to real-world risk, but based on my experience working with similar data and models, I would expect roughly four of every five coup attempts to occur in countries identified here as high risk, and the occasional “miss” to come from the moderate-risk set. Only very rarely should coup attempts come from the 100 or so countries in the low-risk group.

coup_risk_map_2013

FTR, this map was made in R using the ‘rworldmap‘ package.

If Only It Were That Simple

Hans Rosling has done a lot to popularize statistical thinking about human development, and that’s a very good thing, but yesterday he did something that drives me crazy. After word spread of an apparent coup attempt in Eritrea (more on that later), Rosling tweeted this:

If you create a Democracy x Income score Eritrea the lowest in the world! See the graphic predicting the coup.

And here’s a shot of that graphic:

rosling_eritrea

Brilliant, right? Just cross-tabulate a couple of commonly used measures of economic and political development and you get an index that accurately predicts this attempted coup in Eritrea that seemed to catch so many people by surprise.

Well, modelers have a name for this strategy, and it’s “overfitting.” ”Cherry picking” works, too. After the fact, it’s easy to construct a predictive index that does very well at spotlighting any single event. If you poke around enough in the data, you can usually find some combination of measures under which the case of the moment rises to the top. If yesterday’s coup attempt had happened in China, for example—the big red ball in the bottom middle of Rosling’s chart—Rosling could have treated population size as a third dimension in his index, and China would have occupied the bottom corner of the resulting cube. We saw a lot of this right after the uprisings in Tunisia and Egypt in early 2011, too, when for example New York Times columnist Charles Blow found a handful of factors that seemed to differentiate those two countries from many of their regional neighbors.

What those after-the-fact snapshots won’t tell you, however, is how reliable that forecasting strategy would be over time. Most of us don’t need an index that’s optimized to predict a specific event, and even if we did, we would still need to build it before the event happened in order for it to be useful. To build a good predictive model, we need to find things that consistently help separate the situations where events of interest will happen from the ones where they won’t. Going back to Rosling’s chart, we see that his index also puts North Korea, Myanmar, Togo, the Gambia, and Cameroon in the lower left-hand corner, yet none of those countries has suffered any coup attempts for many years. Meanwhile, the two countries that saw successful coups d’etat in 2012—Mali and Guinea-Bissau—are both in the upper left, poor but democratic. Dig a little deeper, and that scatterplot’s not looking quite as useful.

So how reliable is Rosling’s two-dimensional index as a device for forecasting coups? To get an empirical answer that question, I used the two variables Rosling picked—GDP per capita and degree of democracy—to estimate a simple logistic regression model in a training data set covering the period 1960-1994. I then applied that model to data from the period 1995-2010 to see how well it worked on cases it hadn’t already “seen.” The thing this model is trying to predict is the occurrence of any coup attempts (successful or failed) in a country during a particular calendar year, based on the value of the two predictors at the end of the previous year. Data on coup attempts come from the Center for Systemic Peace, and data for the two risk factors come from the World Bank’s World Development Indicators and the Polity project, respectively.

Before seeing how that model fared, it’s important to note that, just by modeling, we’ve already added some valuable information to the mix that isn’t in Rosling’s scatterplot. First, the logistic regression model includes an intercept that captures information about the historical base rate of coup attempts worldwide, and most forecasters can tell you that the base rate is a powerful predictor in its own right. Second, where Rosling’s scatterplot implicitly gives its two elements equal weight in its predictions, the statistical model estimates parameters for those two variables that incorporate historical evidence about the strength and direction of their association with coup risk. Ideally we would use Rosling’s two variables on their own, but we need a model to convert values of those variables into predicted probabilities, and the process of modeling itself already carries us a couple of steps beyond the two-dimensional plot.

Now, the results. Area under the ROC curve (AUC) is commonly used as a measure of predictive power for classification models like this one. AUC represents the probability that a randomly selected positive case (here, a country-year with any coup attempts) will have a higher predicted probability than a randomly selected negative case (a country-year with no coup attempts). It ranges from 0.5 to 1, with higher values indicating better discrimination. The bar chart below plots AUC for three models: 1) one with Rosling’s two variables as linear predictors of coup risk; 2) another with nonlinear versions of Rosling’s variables (logged GDP and a quadratic term for the degree of democracy); and 3) a more complex model that adds information about recent coup activity, the age of a country’s political institutions, participation in international human-rights treaty regimes, among other things.

rosling_auc

As the chart shows, a model with linear versions of Rosling’s axes does reasonably well at forecasting coup attempts, with an AUC of about 0.75. Transforming those variables to capture nonlinearities in those associations improves the predictive accuracy, but only a smidgen, to 0.76. Finally, the model that includes several other risk factors produces a bigger bump, pushing the AUC up to 0.80.

Based on those results, I think it’s fair to say that Rosling’s scatterplot is on the right track, but we can do a lot better by a) estimating a model instead of just using a scatterplot and b) including other useful predictors in that model. The fact that a modeled version of Rosling’s index did okay won’t surprise anyone who’s done quantitative analysis of political instability. If you want to assess the relative risk of various forms of domestic political crisis across many countries, you can get a pretty good handle on the problem just by seeing how poor and authoritarian it is. Still, a scatterplot alone doesn’t get us very far, and adding a few more things to the model that are specifically indicative of coup risk helps us do even better.

I’ll close this post on an ironic note: at this point, it’s not even clear at this point that yesterday’s tumult in Eritrea was really an attempted coup. According to an initial report from Reuters, the soldiers who occupied the Ministry of Information demanded the release of political prisoners but did not threaten to topple the government. Political scientists generally reserve the term “coup” for situations where challengers use or threaten violence to capture state power and call cases where disobedient soldiers demand policy changes “mutinies.” This might seem like hair splitting, but the latter is more common and usually less consequential than the former, and we wouldn’t necessarily expect a predictive model designed for the one to work well for the other.

Did Libya Cause Mali?

Did the fall of the Gaddafi regime in Libya cause the ongoing crisis in Mali?

A lot of people seem to think so. Number 4 on Max Fisher’s “Nine Questions about Mali You Were Too Embarrassed to Ask” is: “I heard that this whole crisis happened because of the war in Libya. Is that true?” Yesterday on the BBC’s This Week, former U.N. Secretary General Kofi Annan seemed to answer in the affirmative when he described Mali as “collateral damage” from Libya.

The accounts I’ve read from people who closely study the country generally attribute the crisis in Mali to two things: 1) the resumption of armed rebellion in northern Mali in January 2012; and 2) the mutiny and coup that ensued in March. As I understand those experts’ arguments, the scale of the current crisis is due to the intersection of these two. Neither the rebellion nor the coup alone was sufficient to produce the state collapse that is compelling the large-scale international response. If neither was sufficient alone, then both were necessary.

Did Libya’s collapse cause one or both of these events? It certainly seems to have played some role. As proponents of the “Libya caused Mali” line have pointed out, the resumption of rebellion in the north was driven, in part, by an inflow of fighters and arms fleeing Libya after the fall of their patron and purchaser, Moammar Qaddafi. The resumption of the Tuareg’s rebellion, in turn, appears to have helped trigger the military coup. After seizing power, the putschists sometimes identified the government’s weak support for their fight against the rebels as the motivation behind the mutiny that evolved into a coup when it encountered little resistance.

To make strong claims about the importance of Libya to Mali, though, we have to believe that one or both of these things—the rebellion and the coup—would not have happened if Libya hadn’t imploded. Here, I think the assertion that “Libya caused Mali” gets much weaker.

On the fight in the north, a recent Think Africa Press piece by Andy Morgan asserts that the resumption of rebellion had been planned for some time, suggesting that Libya’s collapse was not a necessary condition for its occurrence. “In truth, neither Gaddafi’s fall nor AQIM nor drugs and insecurity are the prime movers behind this latest revolt,” Morgan writes. “They are just fresh opportunities and circumstances in a very old struggle.” Morgan’s account isn’t gospel, of course, but it does imply that rebellion could have and probably would have recurred in the north regardless of Gaddafi’s fate. Libya’s collapse seems to have affected the timing and possibly the strength of that assault, but it doesn’t appear to have been necessary for its occurrence.

The connection between Libya and the March 2012 coup is even more tenuous. Statistical models I developed to forecast coups d’etat identified Mali as one of the countries at greatest risk in 2012 before the coup happened, and that assessment was not particularly sensitive to events in Libya. The chief drivers of that forecast were Mali’s extreme poverty (as captured by its infant mortality rate) and the character of its pre-coup political institutions. One of the models takes armed conflict in the region into account, but it’s not an especially influential risk factor, and the impact of Libya’s civil war on the final forecast is negligible.

This forecast suggests that a coup in Mali was entirely plausible absent the rebellion in the north, and that impression is bolstered by the reporting of Bruce Whitehouse from Bamako in a March 2012 blog post:

The way [coup leader Capt.] Sanogo went on to justify the coup was inconsistent and wide-ranging. His initial responses to questions about his troops’ demands indicated that their primary concerns centered around living conditions, pay, and education and job opportunities for their children. When prompted about insecurity in northern Mali, however, he claimed that this issue “occupied 70 percent of their preoccupations.” (During a later interview, Sanogo again had to be reminded about the rebellion after listing the factors that led to the coup.)

The statements of actors engaged in the politics in question aren’t always (often? ever?) honest or reliable, but in this case they align with the information we get from the statistical model. It really isn’t that hard to imagine a coup occurring in Mali in 2012 regardless of events in Libya.

In retrospect, it’s easy to construct narratives that connect Mali to Libya. What’s harder is to imagine the other ways things might have unfolded and assess how likely those counterfactual histories are. We’ll never know for sure, of course, but I think this quick accounting shows that we could have arrived at something very much like the current crisis in Mali even if the Gaddafi regime had never collapsed. That doesn’t mean events in Libya have had no effect on the crisis in Mali, but it does suggest that the one is not the cause of the other.

Coup Forecasts for 2013

Last January, I posted statistical estimates of coup risk for 2012 that drew some wider interest after they correctly identified Mali as a high-risk case. Now that the year’s almost over, I thought it would be a good time to assess more formally how those 2012 forecasts performed and then update them for 2013.

So, first things first: how did the 2012 forecasts fare on the whole? Pretty well, actually.

For purposes of these forecasts, a coup is defined as “as a forceful seizure of executive authority and office by a dissident/opposition faction within the country’s ruling or political elites that results in a substantial change in the executive leadership and the policies of the prior regime.” That language comes from Monty Marshall’s Center for Systemic Peace, whose data set on coup events serves as the basis for one of the two models used to generate the 2012 forecasts. Those forecasts were meant to assess the risk of any coup attempts at some point during the calendar year, whether those attempts succeed or fail. They were not meant to anticipate civil wars, non-violent uprisings, voluntary transfers of executive authority, autogolpes, or interventions by foreign forces, all of which are better thought of (and modeled) as different forms of political crisis.

Okay, so by that definition, I see two countries where coup attempts occurred in 2012: Mali (in March) and Guinea-Bissau (in April). As it happens, both of those countries ranked in the top 10 in January’s forecasts—Guinea-Bissau at no. 2 and Mali at no. 10—so the models seem to be homing in on the right things. We can get a more rigorous take on the forecasts’ accuracy with a couple of statistics commonly used to assess models that try to predict binary outcomes like these (either a coup attempt happens or it doesn’t):

  • AUC Score. The estimated area under the Receiver Operating Characteristic (ROC) curve, used as a measure of the ability of a binary classification model to discriminate between positive and negative cases. Specifically, AUC represents the probability that a randomly selected positive case (here, a country-year with coup activity) will have a higher predicted probability than a randomly selected negative case (e.g., country-year with no coup activity). Ranges from 0.5 to 1, with higher values indicating better discrimination.
  • Brier Score. A general measure of forecast performance, defined as the average squared difference between the predicted and observed values. Ranges from 0 to 1, with lower values indicating more accurate predictions.

Assuming that Mali and Guinea-Bissau were the only countries to see coup activity this year, my 2012 coup forecasts get an AUC score of 0.97 and a Brier score of 0.01. Those are really good numbers. Based on my experience trying to forecast other rare political events around the world, I’m pretty happy with any AUC above the low 0.80s and any Brier score that’s better than an across-the-board base-rate forecast. The 2012 coup forecasts surpass both of those benchmarks.

Of course, with just two events in more than 150 countries, these statistics could be very sensitive to changes in the list of coup attempts. Two possible modifications come from Sudan, where authorities claim to have thwarted coup plots in November and December, and Paraguay, where right-wing legislators pushed leftist President Lugo out of office in June. I didn’t count Sudan because country experts tell me those events were probably just a political ploy President Bashir is using to keep his rivals off balance and not actual coup attempts. I didn’t count Paraguay because President Lugo’s rivals used legal procedures, not force, to oust him in a rushed impeachment. I’m pretty confident that neither of those cases counts as a coup attempt as defined here, but for the sake of argument, it’s worth seeing how the addition of those cases would affect the accuracy assessments.

  • Sudan ranked 11th in the 2012 forecasts, just behind Mali, so the addition of an event there leaves the accuracy stats essentially unchanged at 0.96 and 0.02, respectively.
  • Paraguay would definitely count as a surprise. It ranked in the 80s in the 2012 forecasts, and counting its June events as a coup would drop the AUC to 0.80 and the Brier score to 0.02.
  • If we count both cases as yeses, we get an AUC of 0.84 and a Brier score of 0.02.

All of those are still pretty respectable numbers for true forecasts of rare political events, even if they’re not quite as good as the initial ones. Whatever the exact ground truth, these statistics give me some confidence that the two-model average I’m using here makes a useful forecasting tool.

So, without further ado, what about 2013? The chart below plots estimated coup risk for the coming year for the 30 countries at greatest risk using essentially the same models I used for 2012. (One of the two models differs slightly from last year’s; I cut out a couple of variables that had little effect on the estimates and are especially hard to update.) I picked the top 30 because it’s roughly equivalent to the top quintile, and my experience working with models like these tells me that the top quintile makes a pretty good break point for distinguishing between countries at high and low risk. If a country doesn’t appear in this chart, that means my models think it’s highly unlikely to suffer a coup attempt in the coming year.

2013 Coup Risk Estimates

2013 Coup Risk Estimates

The broad strokes are very similar to 2012, but I’m also seeing a few changes worth noting.

  • Consistent with 2012, countries from sub-Saharan Africa continue to dominate the high-risk group. Nine of the top 10 and 22 of the top 30 countries come from that part of the world. One of those 22 is South Sudan, which didn’t get a forecast in early 2012 because I didn’t have the requisite data but now makes an ignominious debut at no. 20. Another is Sudan, which, as Armin Rosen discusses, certainly isn’t getting any more stable. Mali and Guinea-Bissau also both stay near the top of the list, thanks in part to the “coup trap” I discussed in another recent post. Meanwhile, I suspect the models are overestimating the risk of a new coup attempt in Niger, which seems to have landed on firmer footing after its “democratizing” coup in February 2010, but that recent history will leave Niger in the statistical high-risk group until at least 2015.
  • More surprising to me, Timor-Leste now lands in the top 10. That’s a change from 2012, but only because the data used to generate the 2012 forecasts did not count the assassination attempts of 2008 as a coup try. The latest version of CSP’s coup list does consider those events to be failed coup attempt. Layered on top of Timor-Leste’s high poverty and hybrid political authority patterns, that recent coup activity greatly increases the country’s estimated risk. If Timor-Leste makes it through 2013 without another coup attempt, though, its estimated risk should drop sharply next year.
  • In Latin America, Haiti and Ecuador both make it into the Top 20. As with Timor-Leste, the changes from 2012 are artifacts of adjustments to the historical data—adding a coup attempt in Ecuador in 2010 and counting Haiti as a partial democracy instead of a state under foreign occupation. Those artifacts mean the change from 2012 isn’t informative, but the presence of those two countries in the top 20 most certainly is.
  • Syria also pops into the high-risk group at no. 25. That’s not an artifact of data revisions; it’s a reflection of the effects of that country’s devastating state collapse and civil war on several of the risk factors for coups.
  • Finally, notable for its absence is Egypt, which ranks 48th on the 2013 list and has been a source of coup rumors throughout its seemingly interminable transitional period. It’s worth noting though, that if you consider SCAF’s ouster of Mubarak in 2011 to be a successful coup (CSP doesn’t), Egypt would make its way into the top 30.

As always, if you’re interested in the details of the modeling, please drop me a line at ulfelder@gmail.com and I’ll try to answer your questions as soon as I can.

Update: After a Washington Post blog mapped my Top 30, I produced a map of my own.

The Coup Trap, Mali Edition

Mali had another coup d’etat yesterday, just 10 months after one that brought down the country’s elected civilian government and stunned a lot of observers in the process.

I’m not going to try to analyze the specifics of Mali’s latest coup or its repercussions, a task best left to area experts who actually know about those things, like Gregory Mann. Instead, I want to talk in broadly comparative terms about how and why this second coup is less surprising than the first.

The fact is, coups are often recursive. In a classic of the genre called “Poverty, the Coup Trap, and the Seizure of Executive Power” (alas, behind the dreaded JSTOR paywall), political scientists John Londregan and Keith Poole note that “the aftereffects of a coup include a heritage of political instability in the form of an increased likelihood of further coups.” This is the coup trap their title references; once you’ve had the first event, the risk of the next (and then the next, and then the next…) goes up.

This recursive pattern shows up loud and clear in statistical models I’ve used with some success to assess coup risk in countries worldwide. As noted in a previous post, my assessments are the average of estimated probabilities from two models, one of successful coups and the other of any coup attempts. In the former, the occurrence of a coup attempt at any time in the past five years roughly doubles the risk of a successful coup over the next year. In the latter, I use the natural log of the count of coup attempts in the previous five years minus one, but the strength of the association—and thus the general pattern—is essentially the same.

What’s more, it’s often not just the occurrence of the coup itself that affects the models’ estimates of the risk of a recurrence, but the ripple effects of that event on other risk factors. One of the models I use includes a nonlinear form of a scalar measure of democracy, the Polity scale. According to this model, countries in the mid-range between stark dictatorship and full democracy are at highest risk, and that’s often where countries wind up immediately after a successful coup. The other model uses Polity’s measure of the durability of a country’s political institutions. In this version, it’s the countries with recent institutional ruptures that are at higher risk, but again, the basic effect is the same.

We can see how this plays out in a case like Mali. If we adjust the inputs to our forecasting algorithm to catch up with the March coup and its ripples, Mali’s risk score jumps from about 4 percent, which was already high enough to land it in the Top 20 for 2012, all the way to 10 percent. Ten percent probably sounds small if you’re used to consuming probabilistic forecasts about routine things like the weather, but for rare events like coups, that’s a huge jump. Instead of being 11th on the global list, Mali would be 3rd, behind only Guinea-Bissau and Niger.

These models weren’t designed to test specific hypotheses about why coups recur, so I won’t make any bold assertions on the causal front. For what it’s worth, though, I will say that the patterns highlighted by these and many similar models strengthen my own belief that politics are, in no small part, a matter of confidence.

Whether they succeed or fail, coup attempts often disrupt established relationships among political elites. These disruptions increase elites’ uncertainty about the intentions of their potential rivals, and the proximity of the last attempt may lead them to overestimate the likelihood of the next one. In a kind of self-fulfilling prophecy, this intensification of uncertainty strengthens incentives to try to seize power. In game theoretic terms, the occurrence of a coup attempt pushes political elites out of a world resembling Stag Hunt, where coordination of action is their chief concern, to one more like Prisoner’s Dilemma, where uncertainty about the the other player’s intentions overwhelms incentives to cooperate. Once the original trust network has fallen apart, no one wants to be the sucker who keeps cooperating while the other guys are all planning to fink.

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