Refugee Flows and Disorder in the Global System

This

The number of people displaced by violent conflict hit the highest level since World War II at the end of 2013, the head of the United Nations refugee agency, António Guterres, said in a report released on Friday…

Moreover, the impact of conflicts raging this year in Central African Republic, South Sudan, Ukraine and now Iraq threatens to push levels of displacement even higher by the end of 2014, he said.

…is, I think, another manifestation of the trends I discussed in a blog post here last September:

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.

If that’s true, then Mr. Guterres is probably right when he predicts that this will get even worse this year, because things still seem to be trending toward disorder. A lot of the transnational activity in response to local manifestations is still deliberately inflammatory (e.g., materiel and cash to rebels in Syria and Iraq, Russian support for separatists in Ukraine), and international efforts to quell some of those manifestations (e.g., UN PKOs in CAR and South Sudan) are struggling. Meanwhile, in what’s probably both a cause and an effect of these processes, global economic growth still has not rebounded as far or as fast as many had expected a year or two ago and remains uncertain and uneven.

In other words, the positive feedback still seems to be outrunning the negative feedback. Until that turns, the systemic processes driving (and being driven by) increased refugee flows will likely continue.

Addendum: The quote at the start of this post contains what I think is an error. A lot of the news stories on this report’s release used phrases like “displaced persons highest since World War II,” so I assumed that the U.N. report included the data on which that statement would be based. It turns out, though, that the report only makes a vague (and arguably misleading) reference to “the post-World War II era.” In fact, the U.N. does not have data to make comparisons on numbers of displaced persons prior to 1989. With the data it does have, the most the UNHCR can say is this, from p. 5: “The 2013 levels of forcible displacement were the highest since at least 1989, the first year that comprehensive statistics on global forced displacement existed.”

The picture also looks a little different from the press release if we adjust for increases in global population. Doing some rough math with the number of displaced persons in this UNHCR chart as the numerator and the U.S. Census Bureau’s mid-year estimates of world population as the denominator, here are some annual statistics on displaced persons as a share of the global population:

1989: 0.65%
1992: 0.84%
2010: 0.63%
2014: 0.72%

In no way do I mean to make light of what’s obviously a massive global problem, but as a share of the global population, the latest numbers are not (yet) even the worst since 1989, the first year for which UNHCR has comparable data.

There Is No Such Thing as Civil War

In a 2008 conference paper, Jim Fearon and David Laitin used statistics and case narratives to examine how civil wars around the world since 1955 have ended. They found that deadly fights between central governments and domestic challengers usually only end after an abrupt change in the relative fighting power of one side or the other, and that these abrupt changes are usually brought on by the beginning or end of foreign support. This pattern led them to ruminate thus (emphasis in original):

We were struck by the high frequency of militarily significant foreign support for government and rebels. The evidence suggests that more often than not, civil wars either become – or may even begin as –the object of other states’ foreign policies…Civil wars are normally studied as matters of domestic politics. Future research might make progress by shifting the perspective, and thinking about civil war as international politics by other means.

Their study recently came to mind when I was watching various people on Twitter object to the idea that what’s happening in Ukraine right now could be described as civil war, or at least the possible beginnings of one. Even if some of the separatists mobilizing in eastern Ukraine really were Ukrainian nationals, they argued, the agent provocateur was Russia, so this fight is properly understood as a foreign incursion.

As Jim and David’s paper shows, though, strong foreign hands are a common and often decisive feature of the fights we call civil wars.

In Syria, for example, numerous foreign governments and other external agents are funding, training, equipping, and arming various factions in the armed conflict that’s raged for nearly three years now. Some of that support is overt, but the support we see when we read about the war in the press is surely just a fraction of what’s actually happening. Yet we continue to see the conflict described as a civil war.

In the Central African Republic, it’s Chad that’s played “an ambiguous and powerful role” in the conflict that has precipitated state collapse and ethnic cleansing there. As the New York Times described in April,

[Chad] was accused of supporting the overthrow of the nation’s president, and then later helped remove the rebel who ousted him, making way for a new transitional government. In a statement on Thursday, the Chadian government said that its 850 soldiers had been accused of siding with Muslim militias in sectarian clashes with Christian fighters that have swept the Central African Republic for months.

At least a couple of bordering states are apparently involved in the civil war that’s stricken South Sudan since December. In a May 2014 report, the UN Mission to South Sudan asserted that government forces were receiving support from “armed groups from the Republic of Sudan,” and that “the Government has received support from the Uganda People’s Defence Force (UPDF), notably in Juba and Jonglei State.” The report also claimed that “some Darfuri militias have allied with opposition forces in the northern part of Unity State,” which borders Sudan. And, of course, there is a nearly 8,000-strong UN peacekeeping operation that is arguably shaping the scale of the violence there, even if it isn’t stopping it.

Pick a civil war—any civil war—and you’ll find similar evidence of external involvement. This is what led Jim and David to muse about civil wars as “international politics by other means,” and what led me to the deliberately provocative title of this post. As a researcher, I see analytic value in sometimes distinguishing between interstate and intrastate wars, which may have distinct causes and follow different patterns and may therefore be amenable to different forms of prevention or mitigation. At the same time, I think it’s clear that this distinction is nowhere near as crisp in reality as our labels imply, so we should be mindful to avoid confusing the typology with the reality it crudely describes.

A Useful Data Set on Political Violence that Almost No One Is Using

For the past 10 years, the CIA has overtly funded the production of a publicly available data set on certain atrocities around the world that now covers the period from January 1995 to early 2014 and is still updated on a regular basis. If you work in a relevant field but didn’t know that, you’re not alone.

The data set in question is the Political Instability Task Force’s Worldwide Atrocities Dataset, which records information from several international press sources about situations in which five or more civilians are deliberately killed in the context of some wider political conflict. Each record includes information about who did what to whom, where, and when, along with a brief text description of the event, a citation for the source article(s), and, where relevant, comments from the coder. The data are updated monthly, although those updates are posted on a four-month lag (e.g., data from January become available in May).

The decision to limit collection to events involving at least five fatalities was a pragmatic one. As the data set’s codebook notes,

We attempted at one point to lower this threshold to one and the data collection demands proved completely overwhelming, as this involved assessing every murder and ambiguous accidental death reported anywhere in the world in the international media. “Five” has no underlying theoretical justification; it merely provides a threshold above which we can confidently code all of the reported events given our available resources.

For the past three years, the data set has also fudged this rule to include targeted killings that appear to have a political motive, even when only a single victim is killed. So, for example, killings of lawyers, teachers, religious leaders, election workers, and medical personnel are nearly always recorded, and these events are distinguished from ones involving five or more victims by a “Yes” in a field identifying “Targeted Assassinations” under a “Related Tactics” header.

The data set is compiled from stories appearing in a handful of international press sources that are accessed through Factiva. It is a computer-assisted process. A Boolean keyword search is used to locate potentially relevant articles, and then human coders read those stories and make data from the ones that turn out actually to be relevant. From the beginning, the PITF data set has pulled from Reuters, Agence France Press, Associated Press, and the New York Times. Early in the process, BBC World Monitor and CNN were added to the roster, and All Africa was also added a few years ago to improve coverage of that region.

The decision to restrict collection to a relatively small number of sources was also a pragmatic one. Unlike GDELT, for example—the routine production of which is fully automated—the Atrocities Data Set is hand-coded by people reading news stories identified through a keyword search. With people doing the coding, the cost of broadening the search to local and web-based sources is prohibitive. The hope is eventually to automate the process, either as a standalone project or as part of a wider automated event data collection effort. As GDELT shows, though, that’s hard to do well, and that day hasn’t arrived yet.

Computer-assisted coding is far more labor intensive than fully automated coding, but it also carries some advantages. Human coders can still discern better than the best automated coding programs when numerous reports are all referring to the same event, so the PITF data set does a very good job eliminating duplicate records. Also, the “where” part of each record in the PITF data set includes geocoordinates, and its human coders can accurately resolve the location of nearly every event to at least the local administrative area, a task over which fully automated processes sometimes still stumble.

Of course, press reports only capture a fraction of all the atrocities that occur in most conflicts, and journalists writing about hard-to-cover conflicts often describe these situations with stories that summarize episodes of violence (e.g., “Since January, dozens of villagers have been killed…”). The PITF data set tries to accommodate this pattern by recording two distinct kinds of events: 1) incidents, which occur in a single place in short period of time, usually a single day; and 2) campaigns, which involve the same perpetrator and target group but may occur in multiple places over a longer period of time—usually days but sometimes weeks or months.

The inclusion of these campaigns alongside discrete events allows the data set to capture more information, but it also requires careful attention when using the results. Most statistical applications of data sets like this one involve cross-tabulations of events or deaths at a particular level during some period of time—say, countries and months. That’s relatively easy to do with data on discrete events located in specific places and days. Here, though, researchers have to decide ahead of time if and how they are going to blend information about the two event types. There are two basic options: 1) ignore the campaigns and focus exclusively on the incidents, treating that subset of the data set like a more traditional one and ignoring the additional information; or 2) make a convenient assumption about the distribution of the incidents of which campaigns are implicitly composed and apportion them accordingly.

For example, if we are trying to count monthly deaths from atrocities at the country level, we could assume that deaths from campaigns are distributed evenly over time and assign equal fractions of those deaths to all months over which they extend. So, a campaign in which 30 people were reportedly killed in Somalia between January and March would add 10 deaths to the monthly totals for that country in each of those three months. Alternatively, we could include all of the deaths from a campaign in the month or year in which it began. Either approach takes advantage of the additional information contained in those campaign records, but there is also a risk of double counting, as some of the events recorded as incidents might be part of the violence summarized in the campaign report.

It is also important to note that this data set does not record information about atrocities in which the United States is either the alleged perpetrator or the target (e.g., 9/11) of an atrocity because of legal restrictions on the activities of the CIA, which funds the data set’s production. This constraint presumably has a bigger impact on some cases, such as Iraq and Afghanistan, than others.

To provide a sense of what the data set contains and to make it easier for other researchers to use it, I wrote an R script that ingests and cross-tabulates the latest iteration of the data in country-month and country-year bins and then plots some of the results. That script is now posted on Github (here).

One way to see how well the data set is capturing the trends we hope it will capture is to compare the figures it produces with ones from data sets in which we already have some confidence. While I was writing this post, Colombian “data enthusiast” Miguel Olaya tweeted a pair of graphs summarizing data on massacres in that country’s long-running civil war. The data behind his graphs come from the Rutas de Conflicto project, an intensive and well-reputed effort to document as many as possible of the massacres that have occurred in Colombia since 1980. Here is a screenshot of Olaya’s graph of the annual death counts from massacres in the Rutas data set since 1995, when the PITF data pick up the story:

Annual Deaths from Massacres in Colombia by Perpetrator (Source: Rutas de Conflicta)

Annual Deaths from Massacres in Colombia by Perpetrator (Source: Rutas de Conflicta)

Now here is a graph of deaths from the incidents in the PITF data set:

deaths.yearly.colombia

Just eyeballing the two charts, the correlation looks pretty good. Both show a sharp increase in the tempo of killing in the mid-1990s; a sustained peak around 2000; a steady decline over the next several years; and a relatively low level of lethality since the mid-2000s. The annual counts from the Rutas data are two or three times larger than the ones from the PITF data during the high-intensity years, but that makes sense when we consider how much deeper of a search that project has conducted. There’s also a dip in the PITF totals in 1999 and 2000 that doesn’t appear in the Rutas data, but the comparisons over the larger span hold up. All things considered, this comparison makes the PITF data look quite good, I think.

Of course, the insurgency in Colombia has garnered better coverage from the international press than conflicts in parts of the world that are even harder to reach or less safe for correspondents than the Colombian highlands. On a couple of recent crises in exceptionally under-covered areas, the PITF data also seems to do a decent job capturing surges in violence, but only when we include campaigns as well as incidents in the counting.

The plots below show monthly death totals from a) incidents only and b) incidents and campaigns combined in the Central African Republic since 1995 and South Sudan since its independence in mid-2011. Here, deaths from campaigns have been assigned to the month in which the campaign reportedly began. In CAR, the data set identifies the upward trend in atrocities through 2013 and into 2014, but the real surge in violence that apparently began in late 2013 is only captured when we include campaigns in the cross-tabulation (the dotted line).

deaths.monthly.car

The same holds in South Sudan. There, the incident-level data available so far miss the explosion of civilian killings that began in December 2013 and reportedly continue, but the combination of campaign and incident data appears to capture a larger fraction of it, along with a notable spike in July 2013 related to clashes in Jonglei State.

deaths.monthly.southsudan

These examples suggest that the PITF Worldwide Atrocities Dataset is doing a good job at capturing trends over time in lethal violence against civilians, even in some of the hardest-to-cover cases. To my knowledge, though, this data set has not been widely used by researchers interested in atrocities or political violence more broadly. Probably its most prominent use to date was in the Model component of the Tech Challenge for Atrocities Prevention, a 2013 crowdsourced competition funded by USAID and Humanity United. That challenge produced some promising results, but it remains one of the few applications of this data set on a subject for which reliable data are scarce. Here’s hoping this post helps to rectify that.

Disclosure: I was employed by SAIC as research director of PITF from 2001 until 2011. During that time, I helped to develop the initial version of this data set and was involved in decisions to fund its continued production. Since 2011, however, I have not been involved in either the production of the data or decisions about its continued funding. I am part of a group that is trying to secure funding for a follow-on project to the Model part of the Tech Challenge for Atrocities Prevention, but that effort would not necessarily depend on this data set.

Early Results from a New Atrocities Early Warning System

For the past couple of years, I have been working as a consultant to the U.S. Holocaust Memorial Museum’s Center for the Prevention of Genocide to help build a new early-warning system for mass atrocities around the world. Six months ago, we started running the second of our two major forecasting streams, a “wisdom of (expert) crowds” platform that aggregates probabilistic forecasts from a pool of topical and area experts on potential events of concern. (See this conference paper for more detail.)

The chart below summarizes the output from that platform on most of the questions we’ve asked so far about potential new episodes of mass killing before 2015. For our early-warning system, we define a mass killing as an episode of sustained violence in which at least 1,000 noncombatant civilians from a discrete group are intentionally killed, usually in a period of a year or less. Each line in the chart shows change over time in the daily average of the inputs from all of the participants who choose to make a forecast on that question. In other words, the line is a mathematical summary of the wisdom of our assembled crowd—now numbering nearly 100—on the risk of a mass killing beginning in each case before the end of 2014. Also:

  • Some of the lines (e.g., South Sudan, Iraq, Pakistan) start further to the right than others because we did not ask about those cases when the system launched but instead added them later, as we continue to do.
  • Two lines—Central African Republic and South Sudan—end early because we saw onsets of mass-killing episodes in those countries. The asterisks indicate the dates on which we made those declarations and therefore closed the relevant questions.
  • Most but not all of these questions ask specifically about state-led mass killings, and some focus on specific target groups (e.g., the Rohingya in Burma) or geographic regions (the North Caucasus in Russia) as indicated.
Crowd-Estimated Probabilities of Mass-Killing Onset Before 1 January 2015

Crowd-Estimated Probabilities of Mass-Killing Onset Before 1 January 2015

I look at that chart and conclude that this process is working reasonably well so far. In the six months since we started running this system, the two countries that have seen onsets of mass killing are both ones that our forecasters promptly and consistently put on the high side of 50 percent. Nearly all of the other cases, where mass killings haven’t yet occurred this year, have stuck on the low end of the scale.

I’m also gratified to see that the system is already generating the kind of dynamic output we’d hoped it would, even with fewer than 100 forecasters in the pool. In the past several weeks, the forecasts for both Burma and Iraq have risen sharply, apparently in response to shifts in relevant policies in the former and the escalation of the civil war in the latter. Meanwhile, the forecast for Uighurs in China has risen steadily over the year as a separatist rebellion in Xinjiang Province has escalated and, with it, concerns about a harsh government response. These inflection points and trends can help identify changes in risk that warrant attention from organizations and individuals concerned about preventing or mitigating these potential atrocities.

Finally, I’m also intrigued to see that our opinion pool seems to be sorting cases into a few clusters that could be construed as distinct tiers of concern. Here’s what I have in mind:

  • Above the 50-percent threshold are the high risk cases, where forecasters assess that mass killing is likely to occur during the specified time frame.  These cases won’t necessarily be surprising. Some observers had been warning on the risk of mass atrocities in CAR and South Sudan for months before those episodes began, and the plight of the Rohingya in Burma has been a focal point for many advocacy groups in the past year. Even in supposedly “obvious” cases, however, this system can help by providing a sharper estimate of that risk and giving a sense of how it is trending over time. In the case of Burma, for example, it is the separation that has happened in the last several weeks that tells the story of a switch from possible to likely and thus adds a degree of urgency to that warning.
  • A little farther down the y-axis are the moderate risk cases—ones that probably won’t suffer mass killing during the period in question but could more readily tip in that direction. In the chart above, Iraq, Sudan, Pakistan, Bangladesh, and Burundi all land in this tier, although Iraq now appears to be sliding into the high risk group.
  • Clustered toward the bottom are the low risk cases where the forecasters seem fairly confident that mass killing will not occur in the near future. In the chart above, Russia, Afghanistan, and Ethiopia are the cases that land firmly in this set. China (Uighurs) remains closer to them than the moderate risk tier, but it appears to be creeping toward the moderate-risk group. We are also running a question about the risk of state-led mass killing in Rwanda before 2015, and it currently lands in this tier, with a forecast of 14 percent.

The system that generates the data behind this chart is password protected, but the point of our project is to make these kinds of forecasts freely available to the global public. We are currently building the web site that will display the forecasts from this opinion pool in real time to all comers and hope to have it ready this fall.

In the meantime, if you think you have relevant knowledge or expertise—maybe you study or work on this topic, or maybe you live or work in parts of the world where risks tend to be higher—and are interested in volunteering as a forecaster, please send an email to us at ewp@ushmm.org.

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.

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