What Causes Social Unrest? Apparently, Everything

What causes people in large numbers to step outside their daily routines and gather in public to voice demands on issues that affect a group much larger than themselves?

If we’re to believe a recent article in Time on the protests bursting across Brazil, then the answer is, well, a lot of things. Here’s a list of all the “causes” of the recent Brazilian protests identified in the space of just a few hundred words:

  • Social inequality
  • World Cup spending
  • Police violence
  • A nine-cent rise in bus fares
  • Corruption
  • A lack of return on high taxes
  • Inadequate government spending on infrastructure, education, and health care
  • “The country’s dramatic rise on the world stage”
  • “The incapacity of traditional political representation to deal with the new and unheard of demands of a changing society”
  • Youth
  • Inflation

I don’t mean to pick on Time, which does a lot of solid international reporting, or on the authors of that particular piece. As Christian Davenport observes in a recent post on the blog Political Violence @ a Glance, press coverage of social unrest often gives us “a blow-by-blow account of the street battles taking place” without carefully connecting those events or their alleged causes to prior theory or empirical research. When comparisons are made, it’s usually by analogy, with strong bias toward cases that are recent, geographically proximate, and emotionally salient. As a result, “It all just seems to be new and eventful without much rhyme or reason.”


I realize that journalism isn’t rigorous social science, and it’s not supposed to be. Still, one of the intellectual dangers of laundry-list explanations is that they make it even easier to succumb to confirmation bias—that is, to pick through the list and pull out the items that support our prior beliefs. In the Time piece on Brazil alone, for example, there’s “evidence” to support many different and potentially competing hypotheses about causes of mass protest, from neo-Marxist claims about the centrality of economic inequality to socio-demographic theories emphasizing youth bulges and an expanding middle class to “homo economicus” models focused on price hikes and public spending.

We can’t learn a whole lot about the causes of mass protest by simply cataloging the conditions and things participants tell us about their motivations in cases where they occur. That information is useful, but not so much on its own.

To make real headway on causal analysis, we have to engage in contrasts. To learn about the origins of mass protest, for example, we need to compare cases where uprisings occur with ones where they don’t. Yes, income inequality is high in Brazil, but the same can be said for many of its regional neighbors. If inequality foments uprisings, why aren’t we seeing waves of mass protest in Honduras or Bolivia or Colombia or Paraguay? Meanwhile, inequality was comparatively low in many countries touched by the “Arab awakening.” According to World Bank data, income inequality is lower in Tunisia, Egypt, and Syria than in virtually every country in Latin America. Together, these contrasts imply that high inequality is neither necessary nor sufficient for mass protest, but that’s probably not what you’d expect if you saw the Time headline proclaiming that “Social Inequality and World Cup Spending Fuel Mass Unrest.”

In general, laundry lists of concerns and plausible causes like the one proffered in that Time article can be useful as fodder for what Alex George and Andrew Bennett call “heuristic case studies,” which “inductively identify new variables, hypotheses, causal mechanisms, and causal paths.” What they most certainly don’t do is test theory. These analyses are the social-science equivalent of ambulance chasing. When we hear the noise of the crowd, we rush toward it, ask the participants why they’re angry, read their signs and banners, and try to spin a coherent story from what we see and hear. That’s fine as far as it goes—which, it turns out, isn’t very far.

Enough about Inequality and Unrest Already!

Can we please, PLEASE stop it with the assertions that a country’s income inequality tells us a lot about its propensity for social unrest?

This claim pops up all the time. Exhibit A from an article I read this morning, on China’s official Gini coefficient for 2012:

China’s reality of inequality – and the challenge to narrowing the gap – remains unchanged: the current coefficient of 0.474 poses a high risk for social unrest.

I understand, and am even sympathetic to, the claim that gross disparities in wealth are unjust, particularly in societies where the poorest want for basic needs like food and shelter. I also recognize that organizers of, and participants in, contemporary social unrest often call out economic inequality as one of their chief grievances.

What I’m just not seeing, though, is empirical evidence that countries with higher economic inequality are more susceptible to social unrest.

For starters, there’s the general observation that economic inequality is common and persistent, but large-scale social unrest is uncommon and usually fleeting. What Jim Fearon and David Laitin wrote in 1996 about inter-group tensions and ethnic violence applies just as well here:

Among existing theories of ethnic conflict, accounts focusing on past tensions between groups that are memorialized in narratives of blame and threat tend to dramatic overprediction of violence. Such narratives are almost always present, but large-scale interethnic violence is extremely rare.

The same goes for inequality and popular rebellion. The former is ubiquitous while the latter is scarce, so it’s hard to see how the presence of the one can be said to predict the risk of the other.

Okay, so maybe inequality doesn’t help explain the timing of social unrest, but it does predispose certain societies to erupt when other forces come together. It’s not the spark that starts the fire; it’s the dry tinder that helps the spark catch and spread.

Well, I’m just not seeing this, either.

To look at the association between inequality and unrest, I started by downloading the World Bank’s data on income inequality from Hans Rosling’s Gapminder site. These data summarize occasional national surveys on income or consumption in a Gini coefficient. The higher the Gini coefficient, the more unequal the distribution of incomes in that society. Because the data are only updated occasionally—many countries have just one or two reported values since 1979, the start of the World Bank’s observation period for this measure—I reduced the time series into a single value by taking the maximum (or, in some cases, lone) value for each country. Then I used Erica Chenoweth and Maria Stephan’s data on nonviolent uprisings to identify which countries had seen at least one civil-resistance campaign emerge between 1980 and 2006. Finally, I used the ‘sm‘ package in R to produce kernel density plots that visually compare the distribution of Gini coefficients across those two sets of countries.

The results are shown below. As you can see, there seems to be virtually no difference in the level of income inequality among countries that have and have not produced popular uprisings since 1980. In a bivariate logistic regression model estimated from these same data, the coefficient for the Gini index is <0.01. Not exactly the powerful discriminator we keep hearing about, eh?


That chart only looks at nonviolent uprisings, but published research on violent conflict suggests that the association isn’t especially strong there, either. In a 2008 paper (h/t Cyrus Samii), political scientist Gudrun Østby finds only a weak link between income inequality among individuals and the risk of civil-war onset in 36 developing counties. Interestingly, she does find evidence that higher levels of inequality between ethnic groups increase the risk of violent rebellion, suggesting that inter-group comparisons play a role in fomenting conflict. Still, this isn’t the rich vs. poor narrative on which the conventional wisdom about inequality and rebellion depends, and on that score, Østby’s analysis only strengthens my prior belief.

In light of that empirical evidence, it’s hard to put much stock in the oft-heard claim that highly unequal countries are especially prone to social unrest. Given how noisy the data on income inequality are, it seems particularly absurd to treat small fluctuations in a single country’s Gini coefficient as a useful indicator of rising or falling prospects for a popular uprising or civil war. I don’t think this blog post is going to do much damage to the conventional wisdom, but if there are any takers out there, I would be happy to bet against anyone who wants to use Gini coefficients to predict where the next rebellion will occur.

Update: In the Comments, Rex Brynen suggested I also compare the distributions of Gini coefficients in a couple of subsets where inequality would arguably have a stronger effect: poorer countries, and poorer countries with no history of democracy before 1980 (the start of my period of observation). The plots below do that, where “poorer” is defined broadly as countries that weren’t OECD members as of 1980. As you can see, there’s still virtually no separation in the broader non-OECD subset (the plot on the left). When we limit our view to non-OECD countries with no democratic experience before 1980 (the plot on the right), we get a little bit of separation in the expected direction, but the difference is still rather marginal. (In a bivariate logistic regression estimated from this subset, the coefficient is 0.03 with an s.e. of 0.04.)


Economic Inequality, Democracy, and Inferential Sand Castles

One of the most important and influential research programs in comparative politics in my professional lifetime depends on data that are, in my view, far too flimsy to support the inferential edifices we keep trying to build with them.

I’m talking about research on the relationship between economic inequality and democracy. This topic is hardly new–Karl Marx had some important things to say about it in the mid-1800s–but interest in the subject was renewed in the early 2000s with the publication of books by comparativist Carles Boix (2003) and economists Daron Acemoglu and James Robinson (2006).  Drawing intellectual inspiration from Marxist political sociology, both books casts politics as, at its roots, a struggle between rich and poor over the distribution (or redistribution) of wealth. The poor want more of it, but they have a hard time getting and staying organized enough to take it from the rich, who can usually use their wealth and power to dispel or repel any challenges. When the poor finally do manage to organize a credible and formidable threat, however, wealthy elites may offer democratic government as a form of compromise, allowing them to concede the redistribution of some wealth without having their assets seized or suffering the costs of a long fight.

Boix and Acemoglu & Robinson identify several factors that contribute to the relevant actors’ strategies, but the one around which a major research program has emerged is economic inequality. According to Boix, democratic transitions are most likely to occur when inequality is low. In Acemoglu & Robinson’s model, democratic transitions are most likely when inequality is either very low or very high. Whichever model we use, though, the implication is that democracy emerges as a strategic concession to pressures on the haves from the have-nots under conditions that are specific enough to test, provided we have the requisite data.

These authors’ theoretical models are explicitly intended to explain hundreds of years’ worth of institutional stability and change in all parts of the world, and their work has inspired many new and interesting research projects in comparative politics. When I started attending academic conferences in the mid-2000s, this topic seemed to be gulping down most of the intellectual oxygen in the field of comparative democratization. Whole panels were devoted to the topic, usually more than one per conference, and I was often told that my statistical analyses which excluded inequality (see here and here for examples) were incomplete. Some of the projects spawned by this burst of activity have produced articles that have appeared in the discipline’s most influential journals, including one in the most recent issue of the American Political Science Review.

Here’s the problem, though: Democratic transitions are rare events. So, to test the broad historical claims these authors make, we need reliable measures of economic inequality from a large number of countries for long periods of time. Coarse measures would suffice if the relevant theories were only concerned with gross and static variations in inequality, but they’re not. These theories are meant to be dynamic, and they posit that modest differences or changes in the degree of inequality can have significant effects.

The measures of economic inequality we actually have, however, are nowhere near that good. To accurately measure economic inequality, we need to observe variation in assets, income, or consumption at the individual or household level. (See this paper for a careful discussion of different ways to measure inequality.) That kind of observation can only happen through well-designed surveys or carefully kept tax records. Everything else is guesstimation, often with very wide confidence intervals. Of course, household-level surveys rarely happen in poor countries, and they hardly happened anywhere until fairly recently in human history. Poor countries also tend to have poor tax records, and even the records in wealthy countries are sometimes suspect.  We also know that some dictatorships simply don’t share this kind of data with the outside world–Cuba and North Korea are still black holes in major cross-national economic data sets–and when they do, the validity of the reported values is often suspect.

These problems are all clearly reflected in the gaps and confidence measures in the leading source of data on this topic, the World Bank’s Measuring Income Inequality Database (a.k.a. Deininger & Squire). Browsing the data in country-year format, it’s easy to see that many countries (e.g., Afghanistan) have few or no observations; countries generally come online as they get richer (e.g., Latin America in the latter half of the 20th century); and where poor countries are included, the data are often marked as unreliable. In one paper on the topic, Christian Houle notes that the Deininger & Squire dataset includes observations for just 10% of all country-years during the period 1950-2001. Ten percent! And that’s just for the most recent half-century. Other scholars have attempted to improve on those data–see here for one prominent effort–but no alchemy can spin reliable measures from thin air.

In short, there’s a systematic relationship between the existence and quality of our observations of inequality and the very outcomes we’re trying to explain. For statistical analysis that’s meant to generate causal inferences, this is the worst kind of problem to have.

Given that problem, it’s hard for me to understand how the field of comparative politics has come to take the results of these studies so seriously. If we want to stick to cases where we have reliable measures of inequality, we have to limit our analysis to recent decades in richer countries, where there’s little or no variation on the dependent variable. What we can’t and never will be able to do with confidence–because no one can go back in time or reconstruct surveys or records that never existed–is a global analysis of the relationship between income inequality and political instability in the 19th and 20th centuries. Maybe the requisite data will become available to study this relationship in poorer societies of the future, but the past is mostly lost to us.

This hasn’t stopped many from trying, but the flimsy data on which those studies are usually based makes me wonder how we’ve come to consider the results to be much more than intriguing curiosities. I understand and agree that this is a really interesting and important question. One of the frustrating things about being a social scientist, though, is that there are often important questions to which we simply can’t provide clear answers. I believe this is one of those questions, and I hope this post has convinced a few of you of that, too.


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