From China, Another Strike Against Legitimacy

I’ve groused on this blog before (here and here) about the trouble with “legitimacy” as a causal mechanism in theories of political stability and change, and I’ve pointed to Xavier Marquez’s now-published paper as the most cogent expression of this contrarian view to date.

Well, here is a fresh piece of empirical evidence against the utility of this concept: according to a new Global Working Paper from Brookings, the citizens of China who have benefited the most from that country’s remarkable economic growth in recent decades are, on average, its least happy. As one of the paper’s authors describes in a blog post about their research,

We find that the standard determinants of well-being are the same for China as they are for most countries around the world. At the same time, China stands out in that unhappiness and reported mental health problems are highest among the cohorts who either have or are positioned to benefit from the transition and related growth—a clear progress paradox. These are urban residents, the more educated, those who work in the private sector, and those who report to have insufficient leisure time and rest.

These survey results contradict the “performance legitimacy” story that many observers use to explain how the Chinese Communist Party has managed to avoid significant revolutionary threats since 1989 (see here, for example). In that story, Chinese citizens choose not to demand political liberalization because they are satisfied with the government’s economic performance. In effect, they accept material gains in lieu of political voice.

Now, though, we learn that the cohort in which contentious collective action is most likely to emerge—educated urbanites—are also, on average, the country’s least happy people. The authors also report (p. 14) that, in China, “the effect of income increases on life satisfaction are limited.” A legitimacy-based theory predicts that the CCP is surviving because it is making and keeping its citizens happy; instead, we see that it is surviving in spite of deepening unhappiness among key cohorts.

To me, this case further bares the specious logic behind most legitimacy-based explanations for political continuity. We believe that rebellion is an expression of popular dissatisfaction, a kind of referendum in the streets; we observe stability; so, we reason backwards from the absence of rebellion to the absence of dissatisfaction, sprinkle a little normative dust on it, and arrive at a positive concept called legitimacy. Formally, this is a fallacy of affirmative conclusion from a negative premise: happy citizens don’t rebel, no rebellion is occurring, therefore citizens must be happy. Informally, I think it’s a qualitative version of the “story time” process in which statistical modelers often indulge: get a surprising result, then make up a richer explanation for it that feels right.

I don’t mean to suggest that popular attitudes are irrelevant to political stasis and change, or that the durability of specific political regimes has nothing to do with the affinity between their institutional forms and the cultural contexts in which they’re operating. Like Xavier, though, I do believe that the conventional concept of legitimacy is too big and fuzzy to have any real explanatory power, and I think this new evidence from China reminds us of that point. If we want to understand how political regimes persist and when they break down, we need to identify mechanisms that are more specific than this one, and to embed them in theories that allow for more complexity.

Visualizing Strike Activity in China

In my last post, I suggested that the likelihood of social unrest in China is probably higher than a glance at national economic statistics would suggest, because those statistics conceal the fact that economic malaise is hitting some areas much harder than others and local pockets of unrest can have national effects (ask Mikhail Gorbachev about that one). Near the end of the post, I effectively repeated this mistake by showing a chart that summarized strike activity over the past few years…at the national level.

So, what does the picture look like if we disaggregate that national summary?

The best current data on strike activity in China come from China Labour Bulletin (CLB), a Hong Kong–based NGO that collects incident reports from various Chinese-language sources, compiles them in a public data set, and visualizes them in an online map. Those data include a few fields that allow us to disaggregate our analysis, including the province in which an incident occurred (Location), the industry involved (Industry), and the claims strikers made (Demands). On May 28, I downloaded a spreadsheet with data for all available dates (January 2011 to the present) for all types of incidents and wrote an R script that uses small multiples to compare strike activity across groups within each of those categories.

First, here’s the picture by province. This chart shows that Guangdong has been China’s most strike-prone province over the past several years, but several other provinces have seen large increases in labor unrest in the past two years, including Henan, Hebei, Hubei, Shandong, Sichuan, and Jiangsu. Right now, I don’t have monthly or quarterly province-level data on population size and economic growth to model the relationship among these things, but a quick eyeballing of the chart from the FT in my last post indicates that these more strike-prone provinces skew toward the lower end of the range of recent GDP growth rates, as we would expect.


Now here’s the picture by industry. This chart makes clear that almost all of the surge in strike activity in the past year has come from two sectors: manufacturing and construction. Strikes in the manufacturing sector have been trending upward for a while, but the construction sector really got hit by a wave in just the past year that crested around the time of the Lunar New Year in early 2015. Other sectors also show signs of increased activity in recent months, though, including services, mining, and education, and the transportation sector routinely contributes a non-negligible slice of the national total.


And, finally, we can compare trends over time in strikers’ demands. This analysis took a little more work, because the CLB data on Demands do not follow best coding practices in which a set of categories is established a priori and each demand is assigned to one of those categories. In the CLB data, the Demands field is a set of comma-delimited phrases that are mostly but not entirely standardized (e.g., “wage arrears” and “social security” but also “reduction of their operating territory” and “gas-filing problem and too many un-licensed cars”). So, to aggregate the data on this dimension, I created a few categories of my own and used searches for regular expressions to find records that belonged in them. For example, all events for which the Demands field included “wage arrear”, “pay”, “compensation”, “bonus” or “ot” got lumped together in a Pay category, while events involving claims marked as “social security” or “pension” got combined in a Social Security category (see the R script for details).

The results appear below. As CLB has reported, almost all of the strike activity in China is over pay, usually wage arrears. There’s been an uptick in strikes over layoffs in early 2015, but getting paid better, sooner, or at all for work performed is by far the chief concern of strikers in China, according to these data.


In closing, a couple of caveats.

First, we know these data are incomplete, and we know that we don’t know exactly how they are incomplete, because there is no “true” record to which they can be compared. It’s possible that the apparent increase in strike activity in the past year or two is really the result of more frequent reporting or more aggressive data collection on a constant or declining stock of events.

I doubt that’s what’s happening here, though, for two reasons. One, other sources have reported the Chinese government has actually gotten more aggressive about censoring reports of social unrest in the past two years, so if anything we should expect the selection bias from that process to bend the trend in the opposite direction. Two, theory derived from historical observation suggests that strike activity should increase as the economy slows and the labor market tightens, and the observed data are consistent with those expectations. So, while the CLB data are surely incomplete, we have reason to believe that the trends they show are real.

Second, the problem I originally identified at the national level also applies at these levels. China’s provinces are larger than many countries in the world, and industry segments like construction and manufacturing contain a tremendous variety of activities. To really escape the ecological fallacy, we would need to drill down much further to the level of specific towns, factories, or even individuals. As academics would say, though, that task lies beyond the scope of the current blog post.

In China, Don’t Mistake the Trees for the Forest

Anyone who pays much attention to news of the world knows that China’s economy is cooling a bit. Official statistics—which probably aren’t true but may still be useful—show annual growth slowing from over 7.5 to around 7 percent or lower and staying there for a while.

For economists, the big question seems to be whether or not policy-makers can control the descent and avoid a hard landing or crash. Meanwhile, political scientists and sociologists wonder whether or not that economic slowdown will spur social unrest that could produce a national political crisis or reform. Most of what I remember reading on the topic has suggested that the risk of large-scale social unrest will remain low as long as China avoids the worst-case economic scenarios. GDP growth in the 6–7 percent range would be a letdown, but it’s still pretty solid compared to most places and is hardly a crisis.

I don’t know enough about economics to wade into that field’s debate, but I do wonder if an ecological fallacy might be leading many political scientists to underestimate the likelihood of significant social unrest in China in response to this economic slowdown. We commit an ecological fallacy when we assume that the characteristics of individuals in a group match the central tendencies of that group—for example, assuming that a kid you meet from a wealthy, high-performing high school is rich and will score well on the SAT. Put another way, an ecological fallacy involves mistakenly assuming that each tree shares the characteristic features of the forest they comprise.

Now consider the chart below, from a recent article in the Financial Times about the uneven distribution of economic malaise across China’s provinces. As the story notes, “The slowdown has affected some areas far worse than others. Perhaps predictably, the worst-hit places are those that can least afford it.”

The chart reminds us that China is a large and heterogeneous country—and, as it happens, social unrest isn’t a national referendum. You don’t need a majority vote from a whole country to get popular protest that can threaten to reorder national politics; you just need to reach a critical point, and that point can often be reached with a very small fraction of the total population. So, instead of looking at national tendencies to infer national risk, we should look at the tails of the relevant distributions to see if they’re getting thicker or longer. The people and places at the wrong ends of those distributions represent pockets of potential unrest; other things being equal, the more of them there are, the greater the cumulative probability of relevant action.

So how do things look in that thickening tail? Here again is that recent story in the FT:

Last month more than 30 provincial taxi drivers drank poison and collapsed together on the busiest shopping street in Beijing in a dramatic protest against economic and working conditions in their home town.

The drivers, who the police say all survived, were from Suifenhe, a city on the Russian border in the northeastern province of Heilongjiang…

Heilongjiang is among the poorest performers. While national nominal growth slipped to 5.8 per cent in the first quarter compared with a year earlier — its lowest level since the global financial crisis — the province’s nominal GDP actually contracted, by 3.2 per cent.

In the provincial capital of Harbin, signs of economic malaise are everywhere.

The relatively small, ritual protest described at the start of that block quote wouldn’t seem to pose much threat to Communist Party rule, but then neither did Mohamed Bouazizi’s self-immolation in Tunisia in December 2010.

Meanwhile, as the chart below shows, data collected by China Labor Bulletin show that the incidence of strikes and other forms of labor unrest has increased in China in the past year. Each such incident is arguably another roll of the dice that could blow up into a larger and longer episode. Any one event is extremely unlikely to catalyze a larger campaign that might reshape national politics in a significant way, but the more trials run, the higher the cumulative probability.

Monthly counts of labor incidents in China, January 2012-May 2015 (data source: China Labor Bulletin)

Monthly counts of labor incidents in China, January 2012-May 2015 (data source: China Labor Bulletin)

The point of this post is to remind myself and anyone bothering to read it that statistics describing the national economy in the aggregate aren’t a reliable guide to the likelihood of those individual events, and thus of a larger and more disruptive episode, because they conceal important variation in the distribution they summarize. I suspect that most China experts already think in these terms, but I think most generalists (like me) do not. I also suspect that this sub-national variation is one reason why statistical models using country-year data generally find weak association between things like economic growth and inflation on the one hand and demonstrations and strikes on the other. Maybe with better data in the future, we’ll find stronger affirmation of the belief many of us hold that economic distress has a strong effect on the likelihood of social unrest, because we won’t be forced into an ecological fallacy by the limits of available information.

Oh, and by the way: the same goes for Russia.

Egypt as a Case Study in the Causes of Political Inertia

For LARB, Max Strasser has just reviewed (here) Thanassis Cambanis’ new book on the arc of Egyptian revolution (here). I haven’t read the book, but from Strasser’s review, it sounds like Cambanis’ account makes for a useful case study on the causal mechanisms of political inertia.

Here, for example, is how we are to understand how the military managed to retain and even strengthen its hold on political power in Egypt over the course of the past four years:

After the initial protests forced President Hosni Mubarak from power, a military junta known as the Supreme Council of Armed Forces (SCAF) took control of Egypt.

I think this sentence gets the sequence wrong—that the officers who formed SCAF played a direct role in forcing Mubarak’s departure to clear the way for their junta (source). That is no minor detail when we’re talking about how those officers managed to avert transformational change. Anyway, back to Strasser:

The generals vociferously claimed they were the defenders of the revolution, but they did everything in their power to stymy [sic] radical change. They fast-tracked constitutions and dissolved parliaments, they cut backroom deals and initiated prosecutions. Most of all, they sowed fear and chaos that ultimately served them perfectly.

The “men with guns” sowed that fear through violence—at Maspero, at Port Said, and in many other situations that challenged their claim to power. In this behavior, we see how entrenched hierarchical organizations deploy familiar routines that simultaneously protect and reproduce their established positions. The marginal costs of deploying these routines are relatively low, precisely because they are routinized. In their parts if not in their whole, they have been rehearsed and repeated, and their propriety is etched in the extant culture. Metaphorically speaking, no new software is required; instead, organizational leaders only have to hit ‘run’ on the scripts in place. When circumstances demand innovation, preexisting modules—parts of organizations and behavioral routines—can be reassembled or lightly tweaked and then employed in short order.

And what about the revolutionaries? They possess none of those advantages, and it shows. Back to Strasser:

The revolutionaries — the leftists and liberals who formed the core of the uprising and tried to keep its goals alive amid military massacres and Brotherhood backroom dealing — do not emerge blameless from the tumultuous 2011–2013 period. Cambanis is unabashedly sympathetic to them. (I was, and am, too.) But he can’t help but point out their foibles. The revolutionaries failed to take advantage of electoral politics; they neglected political organizing in the countryside and the small cities in favor of Cairo and Alexandria (and Tahrir Square in particular); they made demands on the government that were at times unreasonable; they squandered opportunities to have their voices heard by those who held power; far too often they fought among themselves. (Something that some — such as the Revolutionary Youth Coalition, of which Moaz, one of Cambanis’s central characters, was a member — came to admit only too late.)

Nothing exemplifies the revolutionaries’ pitfalls and failures as well as the ill-fated Tahrir Square sit-in of July 2011. Amid feelings that the revolution had stalled under military rule, the revolutionary groups repaired to their favorite tactic: a tent camp in the center of Cairo. But unlike the initial uprising demanding Mubarak leave the presidency, this time the goals were diffuse and hazy. Protesters called for prosecution of members of the former regime, including hanging Mubarak, but other arguments were presented poorly. The protesters gathered under the conveniently ambiguous slogan “The Revolution First.” Once they were stuck in the square — in the sweltering weather of Cairo in July — they couldn’t back down. Each group was concerned about looking somehow less revolutionary than the others. The sit-in lacked public support and petered out. The memory of the July sit-in, like so much from that decisive year, will likely wither into oblivion. It was one of many missteps. But by focusing a chapter around it (“Stuck in the Square”), by describing the way the revolutionaries argued among themselves and aimlessly checked social media on their iPhones from the center of Tahrir, Cambanis makes clear what exactly went wrong, giving a microcosmic preview of the ways the revolution would falter. Every political organizing meeting in Cairo that devolved into pointless bickering under a cloud of cigarette smoke feels like a tragic missed connection — what if that one had only worked out?

To gain power, the forces seeking deep change must act collectively and purposefully. Unfortunately for them, the organizations and routines through which they would do those things do not exist, and they are difficult and costly to create. Even when participants agree on the broad objectives, inevitable and frequent disputes over the details—and, crucially, the procedures by which those disputes will be resolved—hamper efforts to convert shared intentions into effective action. Absent prior routines for taxing and policing members, free-rider problems abound. Organizations that have already solved some of these problems—in Egypt in 2011, the Muslim Brotherhood—enjoy significant advantages over their aspiring civic collaborators and rivals, but they rarely match the capacity of their bureaucratized rivals within state.

So, in most cases most of the time, even when incumbents are unloved and frustrations abound, the revolutionary moment never emerges. And in the rare instances that it does, incumbent power-holders usually manage to repress it or ride it out. These outcomes have less to do with the attraction of the underlying ideas and individuals than the power of prior organization. Routines are hard to create, and then hard to dislodge once created.

On Revolution, Theory or Ideology?

Humans understand and explain through stories, and the stories we in the US tell about why people rebel against their governments usually revolve around deprivation and injustice. In the prevailing narratives, rebellion occurs when states either actively make people suffer or passively fail to alleviate their suffering. Rebels in the American colonies made this connection explicit in the Declaration of Independence. This is also how we remember and understand lots of other rebellions we “like” and the figures who led them, from Moses to Robin Hood to Nelson Mandela.

As predictors of revolution, though, deprivation and injustice don’t fare so well. A chart in a recent Bloomberg Business piece on “the 15 most miserable economies in the world” got me thinking about this again. The chart shows the countries that score highest on a crude metric that sums a country’s unemployment rate and annual change in its consumer price index. Here are the results for 2015:

Of the 15 countries on that list, only two—Ukraine and Colombia—have ongoing civil wars, and it’s pretty hard to construe current unemployment or inflation as relevant causes in either case. Colombia’s civil war has run for decades. Ukraine’s war isn’t so civil (<cough> Russia <cough>), and this year’s spike in unemployment and inflation are probably more consequences than causes of that fighting. Frankly, I’m surprised that Venezuela hasn’t seen a sustained, large-scale challenge to its government since Hugo Chavez’s death and wonder if this year will prove different. But, so far, it hasn’t. Ditto for South Africa, where labor actions have at least hinted the potential for wider rebellion.

That chart, in turn, reminded me of a 2011 New York Times column by Charles Blow called “The Kindling of Change,” on the causes of revolutions in the Middle East and North Africa.  Blow wrote, “It is impossible to know exactly which embers spark a revolution, but it’s not so hard to measure the conditions that make a country prime for one.” As evidence, he offered the following table comparing countries in the region on several “conditions”:

The chart, and the language that precede it, seem to imply that these factors are ones that obviously “prime” countries for revolution. If that’s true, though, then why didn’t we see revolutions in the past few years in Algeria, Morocco,  Sudan, Jordan, and Iran? Morocco and Sudan saw smaller protest waves that failed to produce revolutions, but so did Kuwait and Bahrain. And why did Syria unravel while those others didn’t? It’s true that poorer countries are more susceptible to rebellions than richer ones, but it’s also true that poor countries are historically common and rebellions are not.

All of which makes me wonder how much our theories of rebellion are really theories at all, and not more awkward blends of selective observation and ideology. Maybe we believe that injustice explains rebellion because we want to live in a universe in which justice triumphs and injustice gets punished. When violent or nonviolent rebellions erupt, we often watch and listen to the participants enumerate grievances about poverty and indignity and take those claims as evidence of underlying causes. We do this even though we know that humans are unreliable archivists and interpreters of their own behavior and motivations, and that we could elicit similar tales of poverty and indignity from many, many more people who are not rebelling in those societies and others. If a recent study generalizes, then we in the US and other rich democracies are also consuming news that systematically casts rebels in a more favorable light than governments during episodes of protest and civil conflict abroad.

Meanwhile, when rebel groups don’t fit our profile as agents of justice, we rarely expand our theories of revolution to account for these deviant cases. Instead, we classify the organizations as “terrorists”, “radicals”, or “criminals” and explain their behavior in some other way, usually one that emphasizes flaws in the character or beliefs of the participants or manipulations of them by other nefarious agents. Boko Haram and the Islamic State are rebel groups in any basic sense of that term, but our explanations of their emergence often emphasize indoctrination instead of injustice. Why?

I don’t mean to suggest that misery, dignity, and rebellion are entirely uncoupled. Socioeconomic and emotional misery may and probably do contribute in some ways to the emergence of rebellion, even if they aren’t even close to sufficient causes of it. (For some deeper thinking on the causal significance of social structure, see this recent post by Daniel Little.)

Instead, I think I mean this post to serve as plea to avoid the simple versions of those stories, at least when we’re trying to function as explainers and not activists or rebels ourselves. In light of what we think we know about confirmation bias and cognitive dissonance, the fact that a particular explanation harmonizes with our values and makes us feel good should not be mistaken for evidence of its truth. If anything, it should motivate us to try harder to break it.

Observing and Understanding Social Unrest in Real Time

Game theoretic models of social unrest often represent governments and oppositions as unitary actors engaged in a sequence of moves involving binary choices. At any given time, an opposition can keep playing by the rules or choose to protest. If the opposition chooses to protest, the government can respond by conceding to protesters’ demands or repressing them. If the government represses, protesters can respond by dissipating or escalating. Ditto for the government on its next turn, and so on until either one side wins decisively or a bargain is struck that lets everyone get back to “normal” politics.

That class of models can and has produced important insights into the absence, occurrence, and dynamics of social unrest. At the same time, those models deliberately bracket some of the most interesting and arguably important aspects of social unrest—that is, the politics occurring within those camps. “Government” and “opposition” are shorthand for large assemblages of diverse individuals, each making his or her own choices under different circumstances and with different information. The interactions summarized in those formal models depend on—are constituted by—the actions and interactions occurring at this lower, or “micro,” level.

That micro level is harder to understand, but it’s what we actually see when we observe these eventful periods up close in real time. The ongoing occupation of parts of central Hong Kong—which, yes, is still happening, even if it has mostly fallen out of the international news stream—offers a case in point. As Chris Buckley and Alan Wong describe in today’s New York Times, protesters in Hong Kong right now are openly and self-consciously struggling to make one of those strategic choices. Here’s how Buckey and Wong describe the efforts to escalate:

Most mornings for weeks, in one of the pro-democracy protest camps here, Wong Yeung-tat has berated, mocked and goaded the government and, increasingly, the student protest leaders and democratic politicians he deems too timid.

“The occupy campaign needs to be taken to a new level,” he said in an interview. “There needs to be escalation, occupation of more areas or maybe government buildings. The campaign at this stage has become too stable”…

Mr. Wong’s organization, Civic Passion, and a tangle of like-minded groups, Internet collectives and free-floating agitators have grown impatient with the milder path supported by most protesters. They argue that only stronger action, such as new occupations, can force concessions from the Hong Kong government and the Chinese Communist Party.


Mainstream protesters fear confrontational tactics could tear the movement apart and anger ordinary residents, many already tiring of the protest camps.

“It will be difficult to narrow the differences,” said Lee Cheuk-yan, the chairman of the pro-democracy Labor Party, who has been castigated by the movement’s more zealous wing. “We have already escalated to a high point. If it would further alienate public opinion, then that’s something we don’t want to see.”

Through Buckley and Wong’s eyes, we see the participants standing at the figurative fork in the road—or, if you like, the node in the decision tree. And, as protesters argue and experiment their way toward a phase shift of one form or another, the government does the same. We usually don’t get to witness much of the government’s internal debating, but their tactical experiments are easy to spot, and Hong Kong is no exception on that front, either.

We still aren’t very good at understanding exactly how those decisions get made or predicting how the larger process will unfold. We are, however, pretty good at recognizing some of the patterns that comprise these episodes (which are themselves figments of our theoretical imaginations, but still). In fact, the dynamic unfolding in Hong Kong right now is very much like what Sidney Tarrow described in Power in Movement (p. 24):

The power to trigger sequences of collective action is not the same as the power to control or sustain them. This dilemma has both an internal and an external dimension. Internally, a good part of the power of movements comes from the fact that they activate people over whom they have no control. This power is a virtue because it allows movements to mount collective actions without possessing the resources that would be necessary to internalize a support base. But the autonomy of their supporters also disperses the movement’s power, encourages factionalism and leaves it open to defection, competition and repression.

The similarity between that description and the evolution of the unrest in Hong Kong implies that we can sketch the causal terrain with some confidence, even if we can’t reliably predict exactly how social forces will flow through it each time.

Naturally, though, we still wonder: how will it turn out? Historical base rates imply that the factions advocating more aggressive tactics probably won’t tip the larger crowd toward escalation, and even if they do, that crowd will probably fail to achieve its objectives, at least in the short term. If I had to make a prediction, I would bet that this particular episode of unrest will conclude without having achieved any of its major demands. Still, base rates aren’t destiny, and if we already knew how this was going to turn out, it probably wouldn’t be happening in the first place.

Reactions to Reflections on the Arab Uprisings

Yesterday, Marc Lynch posted a thoughtful and candid set of reflections on how political scientists who specialize in the Middle East performed as analysts and forecasters during the Arab uprisings, not before them, the subject on which most of the retrospectives have focused thus far. The background to the post is a set of memos Marc commissioned from the contributors to a volume he edited on the origins of the uprisings. As Marc summarizes, their self-criticism is tough:

We paid too much attention to the activists and not enough to the authoritarians; we understated the importance of identity politics; we assumed too quickly that successful popular uprisings would lead to a democratic transition; we under-estimated the key role of international and regional factors in domestic outcomes; we took for granted a second wave of uprisings, which thus far has yet to materialize; we understated the risk of state failure and over-stated the possibility of democratic consensus.

Social scientists and other professional analysts of world affairs should read the whole thing—if not for the specifics, then as an example of how to assess and try to learn from your own mistakes. Here, I’d like to focus on three points that jumped out at me as I read it.

The first is the power of motivated reasoning—”the unconscious tendency of individuals to process information in a manner that suits some end or goal extrinsic to the formation of accurate beliefs.” When we try to forecast politics in real time, we tend to conflate our feelings about specific events or trends with their likelihood. After noting that he and his colleagues over-predicted democratization, Marc observes:

One point that emerged in the workshop discussions is the extent to which we became too emotionally attached to particular actors or policies. Caught up in the rush of events, and often deeply identifying with our networks of friends and colleagues involved in these politics, we may have allowed hope or passion to cloud our better comparative judgment.

That pattern sounds a lot like the one I saw in my own thinking when I realized that my initial forecasts about the duration and outcome of the Syrian civil war had missed badly.

This tendency is probably ubiquitous, but it’s also one about which we can actually do something, even if we can’t eliminate it. Whenever we’re formulating an analysis or prediction, we can start by ask ourselves what result we hope to see and why, and we can think about how that desire might relate to the conclusions we’re reaching. We can try to imagine how someone with different motivations might view the same situation, or just seek out examples of those alternative views. Finally, we can weight or adjust our own analysis accordingly. Basically, we can try to replicate in our own analysis what “wisdom of crowds” systems do to great effect on a larger scale. This exercise can’t fully escape the cognitive traps to which it responds, but I think it can at least mitigate their influence.

Second, Marc’s reflections also underscore our tendency to underestimate the prevalence of inertia in politics, especially during what seem like exceptional times. As I recently wrote, our analytical eyes are drawn to the spectacular and dynamic, but on short time scales at least, continuity is the norm. Observers hoping for change in the countries touched by the Arab uprisings would have done well to remember this fact—and surely some did—when they were trying to assess how much structural change those uprisings would actually produce.

My last point concerns the power of social scientists to shape these processes as they unfold. In reflecting on his own analysis, Marc notes that he correctly saw how the absence of agreement on the basic rules of politics would complicate transitions, but he “was less successful in figuring out how to overcome these problems.” Marc aptly dubs this uncertainty Calvinball, and he concludes:

I’m more convinced than ever that moving beyond Calvinball is essential for any successful transition, but what makes a transitional constitutional design process work—or fail—needs a lot more attention.

Actually, I don’t think the problem is a lack of attention. How to escape this uncertainty in a liberal direction has been a central concern for decades now of scholarship on democratization and the field of applied democracy promotion that’s grown up alongside it. Giuseppe di Palma’s 1990 book, To Craft Democracies, remains a leading example on the kind of advocacy-cum-scholarship this field has produced, but there are countless “lesson learned” white papers and “best practices” policy briefs to go with it.

No, the real problem is that transitional periods are irreducibly fraught with the uncertainties Marc rightly spotlighted, and there simply are no deus-ex-machina resolutions to them. When scholars and practitioners do get involved, we are absorbed into the politics we mean to “correct,” and most of us aren’t nearly as adept in that field as we are in our own. After a couple of decades of closely watching these transitions and the efforts of various parties to point them in particular directions, I have come to believe that this is one of those things social science can help us understand but not “fix.”

On the Consumption of Protest Art in Real Time

Today’s New York Times carries a story describing efforts by “preservationists, historians and art lovers” to capture and share art produced by the ongoing occupations in Hong Kong:

Because most of the art is still on the streets, the archiving is largely digital. Some digital renditions and objects are already running alongside the “Disobedient Objects” exhibition at the Victoria and Albert Museum in London.

The Umbrella Movement Visual Archives and Research Collective, led partly by academics, is creating open-data platforms and Google maps to mark the locations of art pieces.

A new group—Umbrella Movement Art Preservation, or UMAP—has “rescue team members” on the ground, armed with cellphones and ready to mobilize volunteers to evacuate art on short notice. They have received offers of help from sympathetic truck drivers and about a dozen private galleries…

“It is all installation art,” said Mr. Wong of UMAP.

This process strikes me as unavoidably exploitative. The objects of this preservation campaign are art, but it is art that is meant to serve a specific and immediate political purpose. Removed from its original context and displayed online or in galleries, protest art becomes a form of found-object art. The “discovery” and display of these objects produces aesthetic and, in some cases, commercial value for its conveyors and consumers, but those returns are not shared with the original producers. Preservers, gallerists, and viewers inevitably engage in appropriation as well as appreciation.

More important, these preservation efforts give onlookers a way to enjoy the art without getting enmeshed in the politics. They treat the demonstrations as a creative performance, a kind of entertainment—”It is all installation art”—for the benefit of the viewer. In so doing, they implicitly ignore the strong political claims that this “performance” and the objects it generates are meant to produce.

The location of the original production is an essential part of its political meaning. The fact that it is confrontational and therefore dangerous to produce and display that art in those places is precisely what imbues it with any political power. By removing the art from that location, preservationists give distant onlookers a chance to enjoy the show without directly engaging in those politics. Politics is suffused with symbolic expression, but in situations like this one, the symbols are meant to serve a political purpose. When you try to separate the former from the latter, you implicitly ignore—and thus, in a fashion, reject—that purpose.

This rejection becomes less problematic, or at least less consequential, with the passage of time. When done in the moment, though, the decision to consume the aesthetic without engaging in the politics can have political consequences. “Wait, let me just move this sculpture out of the way before you smash everything to bits…” could imply that you care more about the sculpture than the people who produced it. More likely, it implies that you feel powerless to help defend those producers. I imagine that neither of those messages is particularly encouraging to the protesters or discouraging to those who would do the smashing.

I arguably engage in a related form of exploitation in my own work. My trade is explaining and forecasting political calamities that often involve substantial human suffering. To make my work more credible, I avoid public advocacy or activism on the topics and cases I study. So, I am finding and exploiting commercial value in the actions and suffering of others while adopting a public posture of indifference to that suffering. I’m not sure what to do with that fact right now, but I thought it only fair to acknowledge it in a post that scolds others for the same.

The Inescapable Uncertainty of Popular Uprisings

On Tuesday, hundreds of thousands of people turned out in the streets of Ouagadougou to protest a plan to remove terms limits ahead of next year’s presidential election in Burkina Faso. Blaise Compaore has held that country’s top office for 27 years by way of a 1987 coup and four subsequent elections that have not been fair, and his party dominates the legislature for the same reason. Tuesday’s protests are part of a wider and ongoing wave of actions that includes a general strike and stay-aways from schools and universities. A similar wave of protests occurred over several months in 2011. The state’s efforts to repress those challenges killed several people on at least two occasions, and virtually nothing changed in their wake.

Protesters in Ouagadougou on 28 October 2014 (Photo credit: Issouf Sanogo/AFP)

So, will the latest protests in Burkina Faso coalesce into a sustained campaign, or will they soon peter out? If they do coalesce, will that campaign spur significant reform or even revolution, or will it dissipate against repression, redirection, and resistance from incumbent power-holders?

The truth is, no one really knows, and this uncertainty is not specific to Burkina Faso. After decades of thoughtful research, social scientists still can’t reliably predict which bouts of unrest will blow up into revolutions and which won’t.

We can say some useful things about which structural conditions are more conducive, and thus which cases are more susceptible, to sustained popular challenges. A study I co-piloted with Erica Chenoweth (details forthcoming) found several features that can help assess where nonviolent campaigns are more likely to emerge, but the forecasting power of models based on those features is not stellar. Efforts to develop predictive models of civil-war onset have achieved similar results.

Once unrest starts to burble, though, we still don’t understand and can’t model the ensuing process well enough to reliably predict which way it will tip. Across many cases, a simple base-rate forecast will produce very accurate results. Keep betting on the persistence of the status quo, and you’ll almost always be right. If you’re trying to predict what will happen in a specific case at a specific juncture, however, it’s still hard to improve much on that crude baseline.

This persistent uncertainty can be maddening. Lots of smart people have spent a lot of time studying and thinking about these processes, and it feels like all that effort should have yielded bigger gains in predictive power by now.

That failure is also enlightening. If we believe that our efforts to date have been thoughtful and thorough, then the lack of progress on predicting the dynamics of these situations is telling something important about the nature of the underlying process. Uncertainty isn’t just a consequence of these political behaviors; it’s a prerequisite for them. As Phil Arena said on Twitter:

And it’s not just uncertainty about the potential for harsh state repression, which is what I took Phil to mean by “violence.” Uncertainty about who else will turn out under what conditions, what forms that violence will take and exactly whom it will directly affect, how challengers will organize and adapt in response to those events, what changes in policy or institutions those actions will produce, and who will benefit or suffer how much from those changes are all relevant, too.

In short, the rare political “events” we wish to predict are shorthand for myriad interactions over time among large numbers of heterogeneous individuals who plan and learn and screw up in a changing environment in which information is inevitably incomplete and imperfect. The results are not random, but they are complex, in both the conventional and scientific sense of that term. If we could reliably foresee how things were going to go, then we would adapt our behavior accordingly, and the whole thing would unravel before it even started.

Under these conditions, central tendencies can and do still emerge. A small but growing body of work in political science shows that we can use structural patterns and observations of leading-edge activities to smudge base-rate forecasts a bit in either direction and achieve marginal gains in accuracy. Systems that properly elicit and combine forecasts from thoughtful crowds also turn out to have real predictive power, especially on short time horizons.

Still, the future trajectories of individual cases of incipient revolution will remain hard to foresee with accuracy much beyond the banal prediction that tomorrow will most likely resemble today. That persistent fuzziness is not always what politicians, activists, investors, and other interested or just curious observers want to hear, but on this class of events, it’s probably as clairvoyant as we’re going to get.

Two Tidbits on Social Unrest

1. We like to tell tidy stories about why social unrest happens, and those stories usually involve themes of grievance or social injustice—things like hardship, inequality, corruption, discrimination, and political repression. One or more of those forces probably plays a role in many bouts of unrest, especially the ones that emerge from or evolve into sustained action like we’re seeing right now in Hong Kong and Ferguson.

Still, a riot over the weekend at a pumpkin festival in semi-rural Keene, New Hampshire, reminds us that you don’t need those big issues or themes to get to social unrest. According to the L.A. Times, in Keene,

Young people chucked beer cans and cups at each otherjumped off roofstore down, kicked and smashed road signsset a large fire and chanted profanitycelebrated on top of a flipped cartook selfies in front of lines of riot policegot the attention of a police helicopterchanted “U-S-A!”pushed barricades and threw a street sign at policethrew bottles at the police after the police threw tear gas, and left behind a huge mess.

Why? Who knows, but the main ingredients in this instance seem to have been youth, alcohol, numbers, and the pleasure of transgression:

The description of the scene in Keene reminded me of the riots that sometimes erupt in college towns and sports-mad cities after big games, some of which have proven extremely destructive. These riots differ qualitatively from the rallies, marches, sit-ins, and the like that social scientists generally study. For two things, they usually aren’t planned in advance, and the participants aren’t making political claims. Still, I think our understanding of those ostensibly more political forms of collective action suffers when we make our causal narratives too tidy and ignore the forces that also produce these other kinds of outbursts.

2. Contagion is one of those forces that seems to operate across many forms of unrest. We’re sure that’s true, but we still don’t understand very well how that process works. Observers often use dominoes as a metaphor for contagion, implying that a given unit must fall in order for the cascade to pass through it.

A new paper on arXiv proposes another mechanism that allows the impulse to “hop” some units—in other words, to pass through them without producing the same type of event or effect. Instead of dominoes, contagion might work more like a virus that some people can catch and transmit without ever becoming symptomatic themselves. The authors think this mechanism could help to explain the timing and sequencing of protests in the Arab Spring:

In models of protests and revolutions, populations can have two stable equilibria—the size of the protest is either large or negligibly small—because of strategic complementarities (protest becomes more attractive as more people protest). During the Arab Spring, each country had unique grievances and agendas, and we hypothesize that each country had a unique proximity to a tipping point beyond which people would protest. Once protests began in one country (Tunisia), inspiration to protest spread to other countries via traditional media (such as newspapers) and via social media (such as Twitter and Facebook). This cross-border communication spread strategies for successful uprisings, and it increased expectations for success. Consequently, the uprisings began within a short window of time, seemingly cascading among countries more quickly than earlier revolutions did.

In coarse-grained data on the number of Facebook friendships between countries, we find evidence of the “cascade hopping” phenomenon described above. In particular, Saudi Arabia and Egypt appear to play the role of an intermediate country Y that propagated influence to protest from protesting countries to non-protesting countries, thereby helping to trigger protest in the latter countries, without themselves protesting until much later. Attributes of these intermediate countries and of the countries that they may have influenced to protest suggest that protests first spread to countries close to their tipping points (high unemployment and economic inequality) and strongly coupled to other countries via social media (measured as high Internet penetration). By contrast, we find that traditional measures of susceptibility to protest, such as political freedoms and food price indices, could not predict the order in which protests began.

As with the structural and dynamic stuff discussed around this weekend’s riot in Keene, this hopping mechanism will never be the only force at work in any instance of social unrest. Even so, it’s a useful addition to the set of processes we ought to consider whenever we try to explain or predict where and when other instances might happen.


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