There Are No “Best Practices” for Democratic Transitions

I’ve read two pieces in the past two days that have tried to draw lessons from one or more cases about how policy-makers and practitioners can improve the odds that ongoing or future democratic transitions will succeed by following certain rules or formulas. They’ve got my hackles up, so figured I’d use the blog to think through why.

The first of the two pieces was a post by Daniel Brumberg on Foreign Policy‘s Middle East Channel blog entitled “Will Egypt’s Agony Save the Arab Spring?” In that post, Brumberg looks to Egypt’s failure and “the ups and downs of political change in the wider Arab world” to derive six “lessons or rules” for leaders in other transitional cases. I won’t recapitulate Brumberg’s lessons here, but what caught my eye was the frequent use of prescriptive language, like “must be” and “should,” and the related emphasis on the “will and capacity of rival opposition leaders” as the crucial explanatory variable.

The second piece came in this morning’s New York Times, which included an op-ed by Jonathan Tepperman, managing editor of Foreign Affairs, entitled “Can Egypt Learn from Thailand?” As Tepperman notes, Thailand has a long history of military coups, and politics has been sharply polarized there for years, but it’s still managed to make it through a rough patch that began in the mid-2000s with just the one coup in 2006 and no civil war between rival national factions. How?

The formula turns out to be deceptively simple: provide decent, clean governance, compromise with your enemies and focus on the economy.

This approach is common in the field of comparative democratization, and I’ve even done a bit of it myself.  I think scholars who want to make their work on democratization useful to policy-makers and other practitioners often feel compelled to go beyond description and explanation into prescription, and these lists of “best practices” are a familiar and accessible form in which to deliver this kind of advice. In the business world, the archetype is the white paper based on case studies of a one or a few successful firms or entrepreneurs: look what Google or Facebook or Chipotle did and do it, too. In comparative democratization, we often get studies that find things that happened in successful cases but not in failed ones (or vice versa) and then advise practitioners to manufacture the good ones (e.g., pacts, fast economic growth) and avoid the bad (e.g., corruption, repression).

Unfortunately, I think these “best practices” pieces almost invariably succumb to what Nassim Taleb calls the narrative fallacy, as described here by Daniel Kahneman (p. 199):

Narrative fallacies arise inevitably from our continuous attempt to make sense of the world. The explanatory stories that people find compelling are simple; are concrete rather than abstract; assign a larger role to talent, stupidity, and intentions than to luck; and focus on a few striking events that happened rather than on the countless events that failed to happen.

The narrative fallacy is intertwined with outcome bias. Per Kahneman (p. 203),

We are prone to blame decision makers for good decisions that worked out badly and to give them too little credit for successful moves that appear obvious only after the fact… Actions that seem prudent in foresight can look irresponsibly negligent in hindsight [and vice versa].

When I read Tupperman’s “deceptively simple” formula for the survival of democracy and absence of civil war in Thailand, I wondered how confident he was seven or five or two years ago that Yingluck Shinawatra was doing the right things, and that they weren’t going to blow up in her and everyone else’s faces. I also wonder how realistic he thinks it would have been for Morsi and co. to have “provide[d] decent, clean governance” and “focus[ed] on the economy” in ways that would have worked and wouldn’t have sparked backlashes or fresh problems of their own.

Brumberg’s essay gets a little more distance from outcome bias than Tepperman’s does, but I think it still greatly overstates the power of agency and isn’t sufficiently sympathetic to the complexity of the politics within and between relevant organizations in transitional periods.

In Egypt, for example, it’s tempting to pin all the blame for the exclusion of political rivals from President Morsi’s cabinet, the failure to overhaul the country’s police and security forces, and the broader failure “to forge a common vision of political community” (Brumberg’s words) on the personal shortcomings of Morsi and Egypt’s civilian political leaders, but we have to wonder: given the context, who would have chosen differently, and how likely is it that those choices would have produced very different outcomes? Egypt’s economy is suffering from serious structural problems that will probably take many years to untangle, and anyone who thinks he or she knows how to quickly fix those problems is either delusional or works at the IMF. Presidents almost never include opposition leaders in their cabinets; would doing so in Egypt really have catalyzed consensus, or would it just have led to a wave of frustrated resignations a few months down the road? Attempting to overhaul state security forces might have helped avert a coup and prevent the mass killing we’re seeing now, but it might also have provoked a backlash that would have lured the military back out of the barracks even sooner. And in how many countries in the world do political rivals have a “common vision of political community”? We sure don’t in the United States, and I’m hard pressed to think of how any set of politicians here could manufacture one. So why should I expect politicians in Egypt or Tunisia or Libya to be able to pull this off?

Instead of advice, I’ll close with an observation: many of the supposed failures of leadership we often see in cases where coups or rebellions led new democracies back to authoritarian rule or even state collapse are, in fact, inherent to the politics of democratic transitions. The profound economic problems that often help create openings for democratization don’t disappear just because elected officials start trying harder. The distrust between political factions that haven’t yet been given any reason to believe their rivals won’t usurp power at the first chance they get isn’t something that good intentions can easily overcome. As much as I might want to glean a set of “best practices” from the many cases I’ve studied, the single generalization I feel most comfortable making is that the forces which finally tip some cases toward democratic consolidation remain a mystery, and until we understand them better, we can’t pretend to know how to control them.

N.B. For a lengthy exposition of the opposing view on this topic, read Giuseppe Di Palma’s To Craft Democracies. For Di Palma, “Democratization is ultimately a matter of political crafting,” and “democracies can be made (or unmade) in the act of making them.”


Big Data Won’t Kill the Theory Star

A few years ago, Wired editor Chris Anderson trolled the scientific world with an essay called “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” After talking about the fantastic growth in the scale and specificity of data that was occurring at the time—and that growth has only gotten a lot faster since—Anderson argued that

Petabytes allow us to say: “Correlation is enough.” We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

In other words, with data this rich, theory becomes superfluous.

Like many of my colleagues, I think Anderson is wrong about the increasing irrelevance of theory. Mark Graham explains why in a year-old post on the Guardian‘s Datablog:

We may one day get to the point where sufficient quantities of big data can be harvested to answer all of the social questions that most concern us. I doubt it though. There will always be digital divides; always be uneven data shadows; and always be biases in how information and technology are used and produced.

And so we shouldn’t forget the important role of specialists to contextualise and offer insights into what our data do, and maybe more importantly, don’t tell us.

At the same time, I also worry that we’re overreacting to Anderson and his ilk by dismissing Big Data as nothing but marketing hype.  From my low perch in one small corner of the social-science world, I get the sense that anyone who sounds excited about Big Data is widely seen as either a fool or a huckster. As Christopher Zorn wrote on Twitter this morning, “‘Big data is dead” is the geek-hipster equivalent of ‘I stopped liking that band before you even heard of them.'”

Of course, I say that as one of those people who’s really excited about the social-scientific potential these data represent. I think a lot of people who dismiss Big Data as marketing hype misunderstand the status quo in social science. If you don’t regularly try to use data to test and develop hypotheses about things like stasis and change in political institutions or the ebb and flow of political violence around the world, you might not realize how scarce and noisy the data we have now really are. On many things our mental models tell us to care about, we simply don’t have reliable measures.

Take, for example, the widely held belief that urban poverty and unemployment drive political unrest in poor countries. Is this true? Well, who knows? For most poor countries, the data we have on income are sparse and often unreliable, and we don’t have any data on unemployment, ever. And that’s at the national level. The micro-level data we’d need to link individuals’ income and employment status to their participation in political mobilization and violence? Apart from a few projects on specific cases (e.g., here and here), fuggeddaboudit.

Lacking the data we need to properly test our models, we fill the space with stories. As Daniel Kahneman describes on p. 201 of Thinking, Fast and Slow,

You cannot help dealing with the limited information you have as if it were all there is to know. You build the best possible story from the information available to you, and if it is a good story, you believe it. Paradoxically, it is easier to construct a coherent story when you know little, when there are fewer pieces to fit into the puzzle. Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance.

When that’s the state of the art, more data can only make things better. Sure, some researchers will poke around in these data sets until they find “statistically significant” associations and then pretend that’s what they expected to find the whole time. But, as Phil Schrodt points out, plenty of people are already doing that now.

Meanwhile, other researchers with important but unproven ideas about social phenomena will finally get a chance to test and refine those ideas in ways they’d never been able to do before. Barefoot empiricism will play a role, too, but science has always been an iterative process that way, bouncing around between induction and deduction until it hits on something that works. If the switch from data-poor to data-rich social science brings more of that, I feel lucky to be present for its arrival.

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