Watch Experts’ Beliefs Evolve Over Time

On 15 December 2013, “something” happened in South Sudan that quickly began to spiral into a wider conflict. Prior research tells us that mass killings often occur on the heels of coup attempts and during civil wars, and at the time South Sudan ranked among the world’s countries at greatest risk of state-led mass killing.

Motivated by these two facts, I promptly added a question about South Sudan to the opinion pool we’re running as part of a new atrocities early-warning system for the U.S. Holocaust Memorial Museum’s Center for the Prevention of Genocide (see this recent post for more on that). As it happened, we already had one question running about the possibility of a state-led mass killing in South Sudan targeting the Murle, but the spiraling conflict clearly implied a host of other risks. Posted on 18 December 2013, the new question asked, “Before 1 January 2015, will an episode of mass killing occur in South Sudan?”

The criteria we gave our forecasters to understand what we mean by “mass killing” and how we would decide if one has happened appear under the Background Information header at the bottom of this post. Now, shown below is an animated sequence of kernel density plots of each day’s forecasts from all participants who’d chosen to answer this question. A kernel density plot is like a histogram, but with some nonparametric estimation thrown in to try to get at the distribution of a variable’s “true” values from the sample of observations we’ve got. If that sound like gibberish to you, just think of the peaks in the plots as clumps of experts who share similar beliefs about the likelihood of mass killing in South Sudan. The taller the peak, the bigger the clump. The farther right the peak, the more likely that clump thinks a mass killing is.

kplot.ssd.20140205

I see a couple of interesting patterns in those plots. The first is the rapid rightward shift in the distribution’s center of gravity. As the fighting escalated and reports of atrocities began to trickle in (see here for one much-discussed article from the time), many of our forecasters quickly became convinced that a mass killing would occur in South Sudan in the coming year, if one wasn’t occurring already. On 23 December—the date that aforementioned article appeared—the average forecast jumped to approximately 80 percent, and it hasn’t fallen below that level since.

The second pattern that catches my eye is the appearance in January of a long, thin tail in the distribution that reaches into the lower ranges. That shift in the shape of the distribution coincides with stepped-up efforts by U.N. peacekeepers to stem the fighting and the start of direct talks between the warring parties. I can’t say for sure what motivated that shift, but it looks like our forecasters split in their response to those developments. While most remained convinced that a mass killing would occur or had already, a few forecasters were apparently more optimistic about the ability of those peacekeepers or talks or both to avert a full-blown mass killing. A few weeks later, it’s still not clear which view is correct, although a forthcoming report from the U.N. Mission in South Sudan may soon shed more light on this question.

I think this set of plots is interesting on its face for what it tells us about the urgent risk of mass atrocities in South Sudan. At the same time, I also hope this exercise demonstrates the potential to extract useful information from an opinion pool beyond a point-estimate forecast. We know from prior and ongoing research that those point estimates can be quite informative in their own right. Still, by looking at the distribution of participant’s forecasts on a particular question, we can glean something about the degree of uncertainty around an event of interest or concern. By looking for changes in that distribution over time, we can also get a more complete picture of how the group’s beliefs evolve in response to new information than a simple line plot of the average forecast could ever tell us. Look for more of this work as our early-warning system comes online, hopefully in the next few months.

UPDATE (7 Feb): At the urging of Trey Causey, I tried making another version of this animation in which the area under the density plot is filled in. I also decided to add a vertical line to show each day’s average forecast, which is what we currently report as the single-best forecast at any given time. Here’s what that looks like, using data from a question on the risk of a mass killing occurring in the Central African Republic before 2015. We closed this question on 19 December 2013, when it became clear through reporting by Human Rights Watch and others that an episode of mass killing has occurred.

kplot2.car.20140207

Background Information

We will consider a mass killing to have occurred when the deliberate actions of state security forces or other armed groups result in the deaths of at least 1,000 noncombatant civilians over a period of one year or less.

  • A noncombatant civilian is any person who is not a current member of a formal or irregular military organization and who does not apparently pose an immediate threat to the life, physical safety, or property of other people.
  • The reference to deliberate actions distinguishes mass killing from deaths caused by natural disasters, infectious diseases, the accidental killing of civilians during war, or the unanticipated consequences of other government policies. Fatalities should be considered intentional if they result from actions designed to compel or coerce civilian populations to change their behavior against their will, as long as the perpetrators could have reasonably expected that these actions would result in widespread death among the affected populations. Note that this definition also covers deaths caused by other state actions, if, in our judgment, perpetrators enacted policies/actions designed to coerce civilian population and could have expected that these policies/actions would lead to large numbers of civilian fatalities. Examples of such actions include, but are not limited to: mass starvation or disease-related deaths resulting from the intentional confiscation, destruction, or medicines or other healthcare supplies; and deaths occurring during forced relocation or forced labor.
  • To distinguish mass killing from large numbers of unrelated civilian fatalities, the victims of mass killing must appear to be perceived by the perpetrators as belonging to a discrete group. That group may be defined communally (e.g., ethnic or religious), politically (e.g., partisan or ideological), socio-economically (e.g., class or professional), or geographically (e.g., residents of specific villages or regions). In this way, apparently unrelated executions by police or other state agents would not qualify as mass killing, but capital punishment directed against members of a specific political or communal group would.

The determination of whether or not a mass killing has occurred will be made by the administrators of this system using publicly available secondary sources and in consultation with subject-matter experts. Relevant evidence will be summarized in a blog post published when the determination is announced, and any dissenting views will be discussed as well.

Why More Mass Killings in 2013, and What It Portends for This Year

In a recent post, I noted that 2013 had distinguished itself in a dismal way, by producing more new episodes of mass killing than any other year since the early 1990s. Now let’s talk about why.

Each of these mass killings surely involves some unique and specific local processes, and people who study in depth the societies where mass killings are occurring can say much better than I what those are. As someone who believes local politics is always embedded in a global system, however, I don’t think we can fully understand these situations by considering only those idiosyncratic features, either. Sometimes we see “clusters” where they aren’t, but evidence that we live in a global system leads me to think that isn’t what’s happening here.

To fully understand why a spate of mass killings is happening now, I think it helps to recognize that this cluster is occurring alongside—or, in some cases, in concert with—a spate of state collapses and during a period of unusually high social unrest. Systemic thinking leads me to believe that these processes are interrelated in explicable ways.

Just as there are boom and bust cycles within economies, there seem to be cycles of political (dis)order in the global political economy, too. Economic crunches help spur popular unrest. Economic crunches are often regional or global in nature, and unrest can inspire imitation. These reverberating challenges can shove open doors to institutional change, but they also tend to inspire harsh responses from incumbents intent on preserving the status quo ante. The ensuing clashes present exactly the conditions that are ripest for mass killing. Foreign governments react to these clashes in various ways, sometimes to try to quell the conflict and sometimes to back a favored side. These reactions often beget further reactions, however, and efforts to manufacture a resolution can end up catalyzing wider disorder instead.

In hindsight, I don’t think it’s an accident that the last phase of comparable disorder—the early 1990s—produced two iconic yet seemingly contradictory pieces of writing on political order: Francis Fukuyama’s The End of History and the Last Man, and Robert Kaplan’s “The Coming Anarchy.” A similar dynamic seems to be happening now. Periods of heightened disorder bring heightened uncertainty, with many possibilities both good and bad. All good things do not necessarily arrive together, and the disruptions that are producing some encouraging changes in political institutions at the national and global levels also open the door to horrifying violence.

Of course, in political terms, calendar years are an entirely arbitrary delineation of time. The mass killings I called out in that earlier post weren’t all new in 2013, and the processes generating them don’t reset with the arrival of a new year. In light of the intensification and spread of the now-regional war in Syria; escalating civil wars in Pakistan, Iraq, and AfghanistanChina’s increasingly precarious condition; and the persistence of economic malaise in Europe, among other things, I think there’s a good chance that we still haven’t reached the peak of the current phase of global disorder. And, on mass killing in particular, I suspect that the persistence of this phase will probably continue to produce new episodes at a faster rate than we saw in the previous 20 years.

That’s the bad news. The slightly better news is that, while we (humanity) still aren’t nearly as effective at preventing mass killings as we’d like to be, there are signs that we’re getting better at it. In a recent post on United to End Genocide’s blog, Daniel Sullivan noted “five successes in genocide prevention in 2013,” and I think his list is a good one. Political scientist Bear Braumoeller encourages us to think of the structure of the international system as distributions of features deemed important by the major actors in it. Refracting Sullivan’s post through that lens, we can see how changes in the global distribution of political regime types, of formal and informal interdependencies among states, of ideas about atrocities prevention, and of organizations devoted to advocating for that cause seem to be enabling changes in responses to these episodes that are helping to stop or slow some of them sooner, making them somewhat less deadly on the whole.

The Central African Republic is a telling example. Attacks and clashes there have probably killed thousands over the past year, and even with deeper foreign intervention, the fighting hasn’t yet stopped. Still, in light of the reports we were receiving from people on the scene in early December (see here and here, for example), it’s easy to imagine this situation having spiraled much further downward already, had French forces and additional international assistance not arrived when they did. A similar process may be occurring now in South Sudan. Both cases already involve terrible violence on a large scale, but we should also acknowledge that both could have become much worse—and very likely will, if the braking efforts underway are not sustained or even intensified.

A Notable Year of the Wrong Kind

The year that’s about to end has distinguished itself in at least one way we’d prefer never to see again. By my reckoning, 2013 saw more new mass killings than any year since the early 1990s.

When I say “mass killing,” I mean any episode in which the deliberate actions of state agents or other organizations kill at least 1,000 noncombatant civilians from a discrete group. Mass killings are often but certainly not always perpetrated by states, and the groups they target may be identified in various ways, from their politics to their ethnicity, language, or religion. Thanks to my colleague Ben Valentino, we have a fairly reliable tally of episodes of state-led mass killing around the world since the mid-1940s. Unfortunately, there is no comparable reckoning of mass killings carried out by non-state actors—nearly always rebel groups of some kind—so we can’t make statements about counts and trends as confidently as I would like. Still, we do the best we can with the information we have.

With those definitions and caveats in mind, I would say that in 2013 mass killings began:

Of course, even as these new cases have developed, episodes of mass killings have continued in a number of other places:

In a follow-up post I hope to write soon, I’ll offer some ideas on why 2013 was such a bad year for deliberate mass violence against civilians. In the meantime, if you think I’ve misrepresented any of these cases here or overlooked any others, please use the Comments to set me straight.

The Fog of War Is Patchy

Over at Foreign Policy‘s Peace Channel, Sheldon Himmelfarb of USIP has a new post arguing that better communications technologies in the hands of motivated people now give us unprecedented access to information from ongoing armed conflicts.

The crowd, as we saw in the Syrian example, is helping us get data and information from conflict zones. Until recently these regions were dominated by “the fog war,” which blinded journalists and civilians alike; it took the most intrepid reporters to get any information on what was happening on the ground. But in the past few years, technology has turned conflict zones from data vacuums into data troves, making it possible to render parts the conflict in real time.

Sheldon is right, but only to a point. If crowdsourcing is the future of conflict monitoring, then the future is already here, as Sheldon notes; it’s just not very evenly distributed. Unfortunately, large swaths of the world remain effectively off the grid on which the production of crowdsourced conflict data depends. Worse, countries’ degree of disconnectedness is at least loosely correlated with their susceptibility to civil violence, so we still have the hardest time observing some of the world’s worst conflicts.

The fighting in the Central African Republic over the past year is a great and terrible case in point. The insurgency that flared there last December drove the president from the country in March, and state security forces disintegrated with his departure. Since then, CAR has descended into a state of lawlessness in which rival militias maraud throughout the country and much of the population has fled their homes in search of whatever security and sustenance they can find.

We know this process is exacting a terrible toll, but just how terrible is even harder to say than usual because very few people on hand have the motive and means to record and report out what they are seeing. At just 23 subscriptions per 100 people, CAR’s mobile-phone penetration rate remains among the lowest on the planet, not far ahead of Cuba’s and North Korea’s (data here). Some journalists and NGOs like Human Rights Watch and Amnesty International have been covering the situation as best they can, but they will be among the first to tell you that their information is woefully incomplete, in part because roads and other transport remain rudimentary. In a must-read recent dispatch on the conflict, anthropologist Louisa Lombard noted that “the French colonists invested very little in infrastructure, and even less has been invested subsequently.”

A week ago, I used Twitter to ask if anyone had managed yet to produce a reasonably reliable estimate of the number of civilian deaths in CAR since last December. The replies I received from some very reputable people and organizations makes clear what I mean about how hard it is to observe this conflict.

C.A.R. is an extreme case in this regard, but it’s certainly not the only one of its kind. The same could be said of ongoing episodes of civil violence in D.R.C., Sudan (not just Darfur, but also South Kordofan and Blue Nile), South Sudan, and in the Myanmar-China border region, to name a few. In all of these cases, we know fighting is happening, and we believe civilians are often targeted or otherwise suffering as a result, but our real-time information on the ebb and flow of these conflicts and the tolls they are exacting remains woefully incomplete. Mobile phones and the internet notwithstanding, I don’t expect that to change as quickly as we’d hope.

[N.B. I didn't even try to cover the crucial but distinct problem of verifying the information we do get from the kind of crowdsourcing Sheldon describes. For an entry point to that conversation, see this great blog post by Josh Stearns.]

Watch Locally, Think Globally

In the Central African Republic, an assemblage of rebel groups has toppled the government and installed a new one but now refuses to follow its writ. As those rebels loot and maraud, new armed groups have formed to resist them, and militias loyal to the old government have struck back, too. All of this has happened on the watch of a 2,000-person peacekeeping force from neighboring states. With U.N. backing, those neighbors are now sending more men with guns in hopes that another 1,500 soldiers will finally help restore some sense of order.

This is what full-blown state collapse looks like—as close to Thomas Hobbes’ “war of all against all” as you’re ever likely to see. As I wrote at the start of the year, though, CAR is hardly the only country in such shambles. By my reckoning, Libya, Syria, Yemen, Somalia still, and maybe DRC and South Sudan qualify as collapsed states, too, and if Mali doesn’t anymore, it only just squeaked back over the line.

As the very act of listing implies, we often think of these situations as discrete cases. In our social-scientific imaginations, countries are a bit like petri dishes lined up on a laboratory countertop. Each undergoes a similar set of experiments, and our job is to explain the diversity of their outcomes.

The longer I watch world affairs, though, the less apt that experimental metaphor seems. We can only really understand processes like state collapses—and the civil wars that usually produce them, and the regime transformations that  often precede and succeed them, and virtually everything else we study in international studies—by thinking of these “cases” as local manifestations of system-level dynamics, or at least the product of interactions between local and global processes that are inseparable and mutually causal.

If we think on a systemic scale, it’s easier to see that we are now living through a period of global disorder matched in recent history only by the years surrounding the disintegration of the Soviet Union, and possibly exceeding it. Importantly, it’s not just the spate of state collapses through which this disorder becomes evident, but also the wider wave of protest activity and institutional transformation to which some of those collapses are connected. These streams of change are distinct in some ways, but they also shape each other and share some common causes.

And what are those common causes? The 2007 financial crisis surely played a significant role. The resulting recessions in the U.S. and Europe rippled outward, shrinking trade flows and remittances to smaller and poorer countries and pulling down demand for commodities on which some of their economies heavily depend.

Those recessions also seem to have accelerated shifts in relative power among larger countries, or at least perceptions of them. Those perceptions—see here and here, for example—may matter even more than the underlying reality because they shape governments’ propensity to intervene abroad, the forms those interventions take, and, crucially, other governments’ beliefs about what kinds of intervention might occur in the future. In this instance, those perceptions have only been reinforced by popular concerns about the cost and wisdom of foreign intervention when so many are suffering through hard times at home. This amalgamation of forces seems to have found its sharpest expression yet in the muddled and then withdrawn American threat to punish the Syrian regime for its use of chemical weapons, but the trends that crystallized in that moment have been evident for a while.

The financial crisis also coincided with, and contributed to, a global run-up in food prices that still hasn’t abated by much (see the chart below, from the FAO). As I mentioned in another recent post, a growing body of evidence supports the claim that high food prices help produce waves of civil unrest. This link is evident at the level of the global system and in specific cases, from the countries involved in the Arab Spring to South Africa. Because food prices are so influential, I think it’s likely that climate change is contributing to the current disorder, too, as another force putting upward pressure on those prices and sometimes dislodging large numbers of people who have to pay them.

As Peter Turchin and others have argued, it’s possible that generic oscillations in human social order—perhaps the political analogue of the business cycle—are also part of the story. I’m not confident that these patterns are distinct from the forces I’ve already mentioned, but they could be, at least in part. In any case, those patterns seem sufficiently robust that they deserve more attention than most of us give them now.

Last but not least, the systemic character of these processes is also evident in the forms of negative and positive feedback that arise to try to reverse or accelerate the slide into entropy. Powerful players with a stake in extant structures—mostly states, but also private corporations and even transnational NGOs—work to restore local forms of order that reinforce rather than challenge those structures. At the same time, other actors try to leverage the entropy to their own advantage. Governments less invested in the prior order may see new opportunities to weaken rivals or husband allies. Transnational criminal enterprises often find ways to expand revenue streams and develop new ones by smuggling arms and other contraband to and through societies that have fallen apart. Since the late 2000s, for example, “there has been a significant increase in the number of attacks on vessels by pirates,” Interpol claims, and I don’t think this concurrence of this trend with the spikes in popular unrest and state collapse is purely coincidental.

This system-level view finds linkages between a host of recent trends that we usually only consider in isolation from each other. It also suggests that this, too, shall pass—and then occur again. If Turchin & co. are correct, the current wave of disorder won’t peak for another several years, and we can expect the next iteration to arrive in the latter half of the current century. I’m not convinced the cycles are as tidy as that, and I wonder if the nature of the system itself is now changing in ways that will produce new patterns in the future. Either way, though, I hope it’s now clear that the miseries besetting CAR aren’t as disconnected from the collapses of Libya, Syria, and Yemen or the eruptions of mass protest in a host of countries over the past several years as our compartmentalized reading and theorizing usually entices us to think.

Mass Atrocities in South Sudan

Since December 2012, state security forces in South Sudan’s Jonglei state have “repeatedly targeted civilians” in a “series of unlawful killings” that have killed scores and displaced tens of thousands, a new report from Human Rights Watch (HRW) says.

The report documents 24 incidents of unlawful killing that left 70 civilians and 24 ethnic Murle members of the security forces dead—and those are just the incidents HRW was able to document. In situations like this, the actual numbers of victims are almost always substantially higher than what groups like HRW can verify.

In academia’s grim typology of political violence against civilians, this episode doesn’t yet qualify as a mass killing, but it seems to be headed in that direction.

This episode also happens to fits the most common scenario for state-sponsored mass killing, in which security forces attempting to suppress an insurgency end up killing large numbers of civilians in areas where rebels are thought to operate or to enjoy popular support. As the HRW report discusses, the violence in Jonglei is part of a counterinsurgency campaign against a rebel group led by David Yau Yau, an ethnic Murle who took up arms against the government of South Sudan after failing to win a seat in 2010 elections, back when South Sudan was de facto but not yet de jure independent. Ironically but also typically, the army’s abuses are proving counterproductive. As HRW notes,

Murle civilians told Human Rights Watch that an abusive army disarmament of  civilians in 2012 in Pibor county fuelled the rebellion as Murle men, angered by abuses and unwilling to give up their guns, joined Yau Yau.

The fact that the atrocities are occurring in the context of a counterinsurgency campaign doesn’t mean that the insurgency is the only cause of the violence, however. As Caelin Briggs describes in a recent blog post for Refugees International (RI),

Other likely causes of violence have little to do with Yau Yau. NGOs told RI that SPLA soldiers frequently do not receive salaries, and that they are told by commanders that goods looted from civilians count as ‘payment’. As a result, looting of both civilian and NGO property is now one of the most visible abuses perpetrated by the SPLA in Jonglei. Impunity for these crimes is so extreme that soldiers are reportedly using stolen equipment inside their own barracks. The SPLA has also deliberately vandalized NGO property – perhaps, some NGOs say, with the express purpose of making it more difficult for international staff to return.

For better and for worse, this episode of atrocities was also foreseeable. Way back in the March 2012 issue of its bimonthly R2P Monitor (PDF), the Global Centre for the Responsibility to Protect (GCR2P) noted that efforts by the government of South Sudan to stop communal violence in Jonglei state by forcibly disarming local militias could have troubling side effects. “Several prominent NGOs have documented human rights abuses carried out by the SPLA during past disarmament campaigns,” the report noted. More recently, in a set of statistical forecasts I produced using data from the end of 2012, South Sudan showed up as one of the 10 countries worldwide at greatest risk of an onset of state-sponsored mass killing in 2013.

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.

“State Failure” Has Failed. How About Giving “State Collapse” a Whirl?

Foreign Policy magazine recently published the 2012 edition of the Fund for Peace‘s Failed States Index (FSI), and the response in the corner of the international-studies blogosphere I inhabit has been harsh. Scholars have been grumbling about the Failed States Index for years, but the chorus of academic and advocacy voices attacking it seems to have grown unusually large and loud this year. In an admirable gesture of of fair play, Foreign Policy ran one of the toughest critiques of the FSI on its own web site, where Elliot Ross of the blog Africa is a Country wrote,

We at Africa is a Country think Foreign Policy and the Fund for Peace should either radically rethink the Failed States Index, which they publish in collaboration each year, or abandon it altogether. We just can’t take it seriously: It’s a failed index.

As Ross and many others argue, the core problem with the FSI is that it defines state failure very broadly, and in a way that seems to privilege certain forms of political stability over other aspects of governance and quality of life that the citizens in those states may prize more highly. In a 2008 critique of the “state failure” concept [PDF] that nicely anticipated all of the recent sturm und drang around the FSI, Chuck Call wrote that

The ‘failed states’ concept—and related terms like ‘failing’, ‘fragile’, ‘stressed’ and ‘troubled’ states—has become more of a liability than an asset. Foundations and think tanks have rushed to fund work on ‘failing’ states, resulting in a proliferation of multiple, divergent and poorly defined uses of the term. Not only does the term ‘failing state’ reflect the schoolmarm’s scorecard according to linear index defined by a univocal Weberian endstate, but it has also grown to encompass states as diverse as Colombia, East Timor, Indonesia, North Korea, Cote d’Ivoire, Haiti, Iraq, and the Sudan.

In that essay, Call advocates abandoning the now-hopelessly-freighted concept of “state failure” in favor of a narrower focus on “state collapse”—that is, situations “where no authority is recognisable either internally to a country’s inhabitants or externally to the international community.” I agree.

In fact, in 2010, while still working as research director for the U.S. Government–funded Political Instability Task Force, I led a small research project that aimed to develop a workable definition of state collapse and coding guidelines that would allow researchers to know it when they see it. The project stopped short of producing a global, historical data set, but the coding guidelines were road-tested and refined, and I think the end results have some value. In light of the FSI brouhaha, I’ve posted the results of that project on the Social Science Research Network (SSRN) in hopes that they might be useful to a broader audience.

In those materials—a concept paper and a set of coding guidelines—I argue that we can get to a more workable concept by moving away from Max Weber’s aspirational vision of modern states as legitimate and orderly bureaucracies. Instead, I think we get further when we recognize that real-world states are a specific kind of political organization associated with a particular realization of global politics. That realization—the “Westphalian order,” or just “the international system”—constitutes states and delegates certain forms of political authority to them, but national governments in the real world vary widely in their ability to exercise that authority. When internationally recognized governments cease to exist, or their actual authority is badly circumscribed, we can say that the state has collapsed. That kind of collapse can happen in two different ways: fragmentation and disintegration.

When the failure to rule involves the national government’s territorial reach, we might call it collapse by fragmentation. The ideal of domestic sovereignty presumes final authority within a specific territory and international recognition of that authority, so situations in which large swaths of a state’s territory are effectively governed by organized political challengers whose authority is not internationally recognized represent a form of collapse. In practical terms, these situations usually arise in one of two ways: either 1) a rebel group violently pushes state agents out of a particular area, or 2) a regional government unilaterally proclaims its autonomy or independence and becomes the de facto sovereign authority in that region. In either situation, the rival group directly and publicly challenges the national government’s claim to sovereignty and effectively becomes the supreme political authority in that space. State military forces may still operate in these areas, but they do so in an attempt to reassert control that has already been lost, as indicated by the primacy of the rival organization in day-to-day governance…

State collapse also occurs when the national government fails to enforce its authority in the absence of a rival claimant to sovereignty. This type of failure might be called state collapse by disintegration. The ideal of domestic sovereignty presumes that a central government is capable not just of making rules but also of enforcing them. Dramatic failures of a state’s enforcement capabilities are indicated by widespread lawlessness and disorder, such as rioting, looting, civil violence, and vigilantism. In the extreme, central governments will sometimes disappear completely, but this rarely occurs. More often, a national government will continue to operate, but its rules will be ignored in some portions of its putative territory.

To distinguish state collapse from other forms of political instability and disorder, we have to establish some arbitrary thresholds beyond which the failure is considered catastrophic. Saying focused on the core dimensions of domestic sovereignty—territory and order—I do this as follows:

A state collapse occurs when a sovereign state fails to provide public order in at least one-half of its territory or in its capital city for at least 30 consecutive days. A sovereign state is regarded as failing to provide public order in a particular area when a) an organized challenger, usually a rebel group or regional government, effectively controls that area; b) lawlessness pervades in that area; or c) both. A state is considered sovereign when it is granted membership in the U.N. General Assembly.

If you’re interested, you can find more specific language on how to assess challenger control and lawlessness in the coding guidelines.

Applying this definition to the world today, I see only a handful of states that are clearly collapsed and just a few more that might be. In the “clearly collapsed” category, I would put Libya, Mali, Somalia, and Yemen. In the “probably collapsed” category, I would put Afghanistan and Democratic Republic of Congo. Those judgments are based on cursory knowledge of those cases, however, and I would be interested to hear what others think about where this label does (Chad? Haiti? Ivory Coast? Sudan? South Sudan?) or does not (Afghanistan? Mali?) fit. Either way, the list is much shorter and, I believe, more coherent than the 20-country sets the Failed States Index identifies as “critical” and “in danger.”

More important, this is a topic that still greatly interests me, so I would love to have this conceptual work critiqued, put to use, or both. Fire away!

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