Ukraine’s Just Coup

As Ukraine’s newly appointed government confronts a deepening separatist challenge in Crimea, Viktor Yanukovych continues to describe his removal from office as a “coup d’etat” (here). According to a recent poll by a reputable firm, roughly one-quarter of Russians agree. A month earlier, 84 percent of respondents in a similar poll saw the protests against Yanukovich as a coup attempt.

But that’s all spin and propaganda, right? Yanukovych is a friend of Moscow’s, which presumably views his ouster as part of a broader Western plot against it, and state-guided Russian media have been peddling this line from the start of the EuroMaidan protests a few months ago.

Well, pedantically, Yanukovych is correct. Academic definitions of coups d’etat generally include four criteria: 1) they replace the chief executive; 2) they do not follow constitutional procedure; 3) they are led or facilitated by political insiders; and 4) they involve the use or threat of force. Sometimes we attach modifiers to signify which political insiders strike the blow—military, palace, parliamentary, or judicial—and the criterion regarding the use or threat of force is often interpreted broadly to include arrest or even credibly menacing statements. When political outsiders topple a ruler, we call it a successful rebellion, not a coup. When political insiders remove a sitting leader by constitutional means, we call it politics.

Ukraine unambiguously satisfies at least a few of these criteria. The sitting chief executive was removed from office in a vote by parliamentarians, who qualify as political insiders. Those parliamentarians were encouraged by a popular uprising that represents a form of coercion. Even if we assume, as I do, that most participants in that uprising would not have physically harmed Yanukovich had they captured him, their forceful attempts to seize and occupy government buildings and their clashes with state security forces are clearly coercive acts.

And, crucially, the vote to remove Yanukovych doesn’t seem to have followed constitutional procedures. Under Articles 108-112 of Ukraine’s constitution (here), there are four ways a sitting president may leave office between elections: resignation, incapacitation, death, and impeachment. None of the first three happened—early rumors to the contrary, Yanukovych has vehemently denied that he resigned—so that leaves the fourth, impeachment. According to Article 111, impeachment must follow a specific set of procedures: Parliament must vote to impeach and then convene a committee to investigate. That committee must investigate and report back to parliament, which must then vote to bring charges. A final vote to convict may only come after receipt of a judgment from the Constitutional Court that “the acts, of which the President of Ukraine is accused, contain elements of treason or other crime.” Best I can tell, though, those procedures were not followed in this case. Instead, parliament simply voted—380 to 0, in a body with 450 seats—to dismiss Yanukovych and then to hand executive authority on an interim basis to its own speaker (here).

The apparent extra-constitutionality of this process gives us the last of the four criteria listed above. So, technically speaking, Yanukovych’s removal checks all of the boxes for what we would conventionally call a coup. We can quibble about how relevant the threat of force was to this outcome, and thus whether or not the label “parliamentary coup” might fit better than plain old coup, but the basic issue doesn’t seem especially ambiguous.

All of this should sound very familiar to Egyptians. Twice in the past three years, they’ve seen sitting presidents toppled by political insiders while protesters massed nearby. In both instances, the applicability of the “coup” label became a point of intense political debate. People cared, in part, because perceptions affect political outcomes, and what we call an event shapes how people perceive it. We shout over each other until one voice finally drowns out the rest, and what that voice says becomes the history we remember. In a world where “the will of the people” is seen by many as the only legitimate source of state authority, a whiff of illegitimacy hangs about “coup” that doesn’t adhere to “revolution.” In a peculiar twist of logic and semantics, many Egyptians insisted that President Morsi’s removal in July 2013 could not have been a coup because millions of people supported it. The end was right, so the means must have been, too. Coup doesn’t sound right, so it couldn’t have been one of those.

It’s easy to deride that thinking from a distance. It’s even easier with the benefit of a hindsight that can take in all the terrible things Egypt’s ruling junta has done since it seized power last July.

Before we sneer too hard at those gullible Egyptian liberals, though, we might pause to consider how we’re now describing events in Ukraine, and why. Most of the people I know personally or follow on social media believe that Yanukovych was a rotten menace whose removal from office was justified by his corruption and, more recently, his responsibility for the use of disproportionate force against activists massed on the Maidan. I agree, and I’m sure the documents his accomplices dumped in the Dnipro River on the way out of town will only clarify and strengthen that impression. Yanukovych’s election win in 2010 and his continuing popularity among a large (but dwindling) segment of the population weighed in his favor before 19-20 February, but the shooting to death of scores of unarmed or crudely armed protesters undoubtedly qualifies as the sort of crime that should trigger an impeachment and might even win a conviction. That is, those shootings qualify as an impeachable offense, but impeachment is not what happened.

As moral beings, we can recognize all of those things, and we can and should weigh them in our judgments about the justice of what’s transpired in Ukraine in the past week. Moral and analytical thinking aren’t the same thing, however, and they don’t always point in the same direction, or even occur on the same plane. I’d like to believe that, as analytical thinkers, we’re capable of acknowledging the parallels between Yanukovich’s removal from power and the things we usually call coups without presuming that this acknowledgement negates our moral judgment about the righteousness of that turn of events. Those two streams of thought can and should and inevitably will inform each other, but they don’t have to move deterministically together. Let there be such a thing as a just coup, and let this be an instance of it.

PS. For an excellent discussion of the philosophical issues I gloss over in that final declaration, see Zack Beauchamp’s “The Political Theory Behind Egypt’s Coup” (here).

Cold War Meddling and Coup Attempts

Last April, the American Economic Review published a fascinating paper by some prominent economists that’s only just come onto my radar screen, thanks to a recent Facebook post from Cullen Hendrix. Here’s the abstract:

We provide evidence that increased political influence, arising from CIA interventions during the Cold War, was used to create a larger foreign market for American products. Following CIA interventions, imports from the US increased dramatically, while total exports to the US were unaffected. The surge in imports was concentrated in industries in which the US had a comparative disadvantage, not a comparative advantage. Our analysis is able to rule out decreased trade costs, changing political ideology, and an increase in US loans and grants as alternative explanations. We provide evidence that the increased imports arose through direct purchases of American products by foreign governments.

That’s quite a result—not because we didn’t think stuff like this happened, but because that “stuff” is rarely discussed in academic work on international political economy and even more rarely makes it into our statistical models. We know that powerful states do lots of things to try to shape the world around them, but those things are often hard to observe and even harder to measure, so we usually just leave those “treatments” out of our statistical abstractions of international and domestic politics and hope for the best. (Hello, omitted variable bias!)

But I digress. Where Berger & co. were primarily interested in the economic effects of those interventions, their paper got me thinking about the political ones. Might the episodes of Cold War meddling the authors identify in their data help explain where and when coup attempts happened, too? Theory says “probably,” but not necessarily in a simple way. As Hein Goemans and Nikolay Marinov have argued (here), during the Cold War

The United States and the former colonial powers in Europe had an ambiguous attitude toward coup plots: sometimes helping, sometimes thwarting, and sometimes doing nothing… Because the world was thought to be a chessboard of West vs. East, attitudes toward both the seizure of power by the military and about whether to pressure for elections varied by which side of the ideological conflict the relevant actors took.

Well, was there a clear pattern? Berger & co. have posted extensive replication files (here), and I’ve already got data and scripts to estimate and compare statistical models of coup risk (here), so let’s have a look, starting with a few words on the measures of foreign meddling.

What Berger & co. have compiled are annual, binary indicators of CIA and KGB influence in domestic politics for all countries of the world except the USA and USSR during the period 1947-1990. By “influence,” they mean “periods in which a leader is installed or supported” by one or the other agency. For example, they tag Chile with a 1 (yes) in the column for U.S. influence from 1964, when the CIA supported Eduardo Frei’s successful election campaign and then went on to back various right-wing groups, until 1970, when the presidency passed to Salvador Allende, of whom the U.S. government was not so fond.

Instead of trying to assess the importance of these influence measures by estimating a model and looking at coefficients and p-values, I took some advice from Mike Ward & co. (here) and asked if these variables could help us predict coup attempts. If these episodes of CIA and KGB meddling had much effect on coup risk, then their addition to a base model of coup risk should noticeably improve that model’s predictive power.

To see if they do, I used a 10-fold cross-validation process to get out-of-sample estimates of coup risk from models without and with Berger & co.’s measures of CIA and KGB influence, then compared the accuracy of those two sets of estimates. The base model is the same one I used in my coup forecasts for 2014, and it covers a lot of ground, from national wealth and colonial legacies to political regime type and recent coup activity. Country-years are the units of observation, and the dependent variable in all cases is a binary indicator of whether or not any coup attempts (successful or failed) occurred during that calendar year. Except for the marker for election years, all covariates—including the measures of U.S. and Soviet influence—were lagged one year.

Here’s a plot of the ROC curves for out-of-sample estimates of coup risk from models without (black) and with (red) those intervention indicators. The numbers reported in the bottom right-hand corner of the plot are the areas under those curves (AUC), and bigger is better. As you can see, the addition of these lagged indicators of periods of CIA and KGB influence has no real effect on the model’s predictive power, suggesting that these variables don’t help explain the location and timing of coups, at least not in the context of this model.

A Comparison of the Predictive Power of Coup Risk Models Without and With Measures of CIA and KGB Influence

A Comparison of the Predictive Power of Logistic Regression Models of Coup Risk Without (black) and With (red) Lagged Indicators of CIA and KGB Influence

After seeing those results, I wondered if the effects of U.S. and Soviet influence on coup risk might depend on some of the other things in my model, like political regime type or the occurrence of elections. To allow for contingent effects without specifying exactly what those contingencies might be, I re-ran the analysis using Random Forests instead of logistic regression. The plot below shows the results. Again, nothing doing.

A Comparison

A Comparison of the Predictive Power of Random Forests of Coup Risk Without (black) and With (red) Lagged Indicators of CIA and KGB Influence

It would be easy to look at those plots and conclude that CIA and KGB meddling didn’t play much of a role in coups during the Cold War after all. It’d be easy, but it’d also be wrong.

One of the great things about the paper that set off this whole exercise is that the authors extensively document their data, to include short descriptions of the forms of U.S. and Soviet influence they observe and the sources from which that information came. When we take a closer look at those descriptions (“Summary_of_Interventions.pdf”), it becomes clearer that the source of my null result isn’t the fact that the CIA and KGB weren’t in the business of backing or thwarting coups. Instead, the issue is that the effects often flowed in the opposite direction. That is, coups and other forms of meddling in the selection of national leaders were often what started these episodes of influence in the first place. Influence doesn’t cause coups; coups cause influence.

Once we realize this, it’s less surprising to discover that coups were actually somewhat less likely during periods of CIA or KGB influence than not. When I estimate a logistic regression model using all of the available data (1960-1990), the coefficients on the lagged indicators of CIA and KGB influence are both negative and not tiny: -0.3 and -1.0, respectively, with standard errors of 0.2 and 0.5. If you think those agencies might have helped protect their clients from domestic rivals, that result makes sense.

Ultimately, there are two lessons here, one substantive and one methodological. On the substantive side, this exercise reaffirms the knowledge that the great powers of the Cold War era had significant sway over politics within many other states, including the selection and survival of their leaders. Of course, we didn’t need a statistical analysis to tell us that; we only needed to review the evidence Berger & co. have compiled on the way to their finding about how those efforts affected international trade.

On the methodological side, the counter-intuitive findings from the analysis described in this blog post are a useful reminder that we shouldn’t stop thinking when we hit Enter on our statistical estimations. When interpreting results like these, we have to think carefully about what is and isn’t being measured. The models we can specify with data and methods at hand don’t always match the ideas in our heads, and it’s on us to keep the two straight.

Coup Forecasts for 2014

This year, I’ll start with the forecasts, then describe the process. First, though, a couple of things to bear in mind as you look at the map and dot plot:

  1. Coup attempts rarely occur, so the predicted probabilities are all on the low side, and most are approximately zero. The fact that a country shows up in dark red on the map or ranks high on an ordered list does not mean that we should anticipate a coup occurring there. It just means that country is at relatively high risk compared to the rest of the world. Statistically speaking, the safest bet for any country almost any year is that a coup attempt won’t occur. The point of this exercise is to try to get a better handle on where the few coup attempts we can expect to see this year are most likely to happen.
  2. These forecasts are estimates based on noisy data, so they are highly imprecise, and small differences are not terribly meaningful. The fact that one country lands a few notches higher or lower than other on an ordered list does not imply a significant difference in risk.

Okay, now the the forecasts. First, the heat-map version, which sorts the world into fifths. From cross-validation in the historical data, we can expect nearly 80 percent of the countries with coup attempts this year to be somewhere in that top fifth. So, if there are four countries with coup attempts in 2014, three of them are probably in dark red on that map, and the other one is probably dark orange.

forecast.heatmap.2014

Now, a dot plot of the Top 40, which is a slightly larger set than the top fifth in the heat map. Here, the gray dots show the forecasts from the two component models (see below), while the red dots are the unweighted average of those two—what I consider the single-best forecast.

forecast.dotplot.2014

A lot of food for thought in there, but I’m going to leave interpretation of these results to future posts and to you.

Now, on the process: As statistical forecasters are wont to do, I have tinkered with the models again this year. As I said in a blogged research note a couple of weeks ago, this year’s tinkering was driven by a combination of data practicalities and the usual sense of, “Hey, let’s see if we can do a little better this time.”Predictably, though, I also ended up doing things a little different than I’d expected in December. Specifically:

  • I trained and validated the models on an amalgamation of two coup data sets—as described in a November post that showed an animated map of coup attempts worldwide since 1946—instead of just using the Powell and Thyne list. So that map and the bar plots with it should give you a clearer sense of what these forecasts are (and aren’t) trying to anticipate.
  • After waiting for Freedom House to update its Freedom in the World data, which it did a few days ago, I decided to go back to using Polity after all because the forecasts based on it were noticeably more accurate in cross-validation. The models include a categorical measure of regime type based on the Polity scale and a “clock” counting years since the last significant change in that score. I hard-coded updates to those measures, which are much coarser (and therefore easier to update) than the Polity scale or its component variables.
  • As with coup events, I used an amalgamation of GDP growth data from the World Bank and IMF instead of picking one. I also went back to summarizing this feature in the models with a binary indicator for slow growth of less than 2 percent (annual, per capita).
  • Finally, I did not include GDELT summaries in the models because they only slightly improved forecast accuracy, and they did not cover a country of great interest to me (South Sudan). The latter is surely a fixable glitch, but it’s not fixed now, and I really wanted to have a forecast for that particular country in this year’s list for reasons that should now be evident from the results. On the accuracy part, I should note that I’ve only done a little bit of checking, and there are still plenty of ways to try to squeeze more forecasting power out of those data, not the least of them being to build more dynamic models that use monthly instead of annual summaries.

The forecasts are an unweighted average of predicted probabilities from a logistic regression model and a Random Forest that use more or less the same inputs. Both models were trained on data covering the period 1960-2010; applied to data from 2011 to 2013 to assess their predictive performance; and then applied to the newest data to generate forecasts for 2014. Variable selection was based mostly on my prior experience working this problem. As noted above, I did a little bit of model checking—using stratified 10-fold cross-validation—to make sure the process worked reasonably well, and to help choose between some different measures for the same concept. In that cross-validation, the unweighted average got good but not great accuracy scores, with an area under the ROC curve in the low 0.80s. Here are the variables used in the models:

  • Geographic Region. Per the U.S. Department of State (and only in the Random Forest).
  • Last Colonizer. Indicators for former French, British, and Spanish colonies.
  • Country Age. Years since independence, logged.
  • Post-Cold War Period. Indicator marking country-years since 1991, when coup activity has generally slowed.
  • Infant Mortality Rate. Relative to the annual global median, logged, and courtesy of the U.S. Census Bureau. The latest version ends in 2012, so I’ve simply pulled those values forward a year here.
  • Political Regime Type. Four-way categorization based on the Polity scale into autocracies, “anocracies,” democracies, and transitional, collapsed, or occupied cases.
  • Political Stability. Count of years since a significant change in the Polity scale, logged.
  • Political Salience of Elite Ethnicity. Yes or no, per a data set on elite characteristics produced by the Center for Systemic Peace (CSP) for the Political Instability Task Force (PITF), with hard-coded updates for 2013 (no changes). This one is not posted on CSP’s data page and was obtained from PITF and shared with their permission.
  • Violent Civil Conflict. Yes or no, per CSP’s Major Episodes of Political Violence data set (here), with hard-coded updates for 2013 (a few changes).
  • Election Year. Yes-or-no indicator for any national elections—executive, legislative, or constituent assembly—courtesy of the NELDA project, with hard-coded updates for 2012 through 2014 (scheduled).
  • Slow Economic Growth. Yes-or-no indicator for less than 2 percent, as described above.
  • Domestic Coup Activity. Yes-or-no indicator for countries with any attempts in the past 5 years, successful or failed.
  • Regional Coup Activity. A count of other countries in the same region with any coup attempts the previous year, logged.
  • Global Coup Activity. Same as the previous tic, but for the whole world.

All of the predictors are lagged one year except for region, last colonizer, country age, post-Cold War period, and the election-year indicator. The fact that a variable appears on this list does not necessarily mean that it has a significant effect on the risk of any coup attempts. As I said earlier, I drew up a roster of variables to include based on a sense of what might matter (a.k.a., theory) and past experience and did not try to do much winnowing.

If you are interested in exploring the results in more detail or just trying to do this better, you can replicate my analysis using code I’ve put on GitHub (here). The posted script includes a Google Drive link with the requisite data. If you tinker and find something useful, I only ask that you return the favor and let me know. [N.B. As its name implies, the generation of a Random Forest is partially stochastic, so the results will vary slightly each time the process is repeated. If you run the posted script on the posted data, you can expect to see some small differences in the final estimates. I think these small differences are actually a nice representation of the forecasts' inherent uncertainty, so I have not attempted to eliminate it by, for example, setting the random number seed within the R script.]

For the 2013 version of this post, see here. For 2012, here.

UPDATE: In response to a comment, I tried to produce another version of the heat map that more clearly differentiates the quantiles and better reflects the fact that the predicted probabilities for cases outside the top two fifths are all pretty close to zero. The result is shown below. Here, the differences in the shades of gray represent differences in the average predicted probabilities across the five tiers. You can decide if it’s clearer or not.

forecast.grayscalemap.2014

A Coup Lexicon

Reasonable people disagree on the definition and proper usage of the word “coup.” Inspired by a tweet this morning from Erik Meyersson, I thought I’d put together a lexicon to try to clear things up.

  • Coup d’etat: A sudden usurpation of state power by illegal or extra-constitutional means involving the use or threat of force (or, apparently, a new restaurant in Minneapolis).
  • Military coup: Same as above, with elements of state security forces as the usurpers.
  • Executive coup (a.k.a. autogolpe)In democracies, an abrupt change in rules or procedures by the incumbent chief executive that effectively concentrates power in his or her hands and short-circuits electoral competition. See: India in 1975, Peru in 1992, and Russia in 1993.
  • Judicial coup: A decision by an apparently partisan judiciary that removes an elected government from power. See: Egypt in 2012Thailand in 2008, and, according to those bumper stickers you still see in my neighborhood every once in a while, Bush v. Gore.
  • Parliamentary coup: The removal from office of a directly elected president by a legislature using questionable procedures for apparently partisan purposes. See: Paraguay in 2012 and, for a dog that growled but didn’t quite bark, the United States in 1999.
  • Merci beaucoup: (Fr.) Thank you very much.
  • Mercy bro coup: The successful extrication of a persistently adolescent and self-absorbed young man from an embarrassing situation, probably involving bad beer consumed ironically.
  • Coup de grace: (Fr.) The final blow or stroke that finishes off a sufferer or weakened foe.
  • Coup de grass: What happened in Uruguay a few weeks ago.

What have I missed? Please use the Comments to suggest additions or corrections.

A Research Note on Updating Coup Forecasts

A new year is about to start, and that means it’s time for me to update my coup forecasts (see here and here for the 2013 and 2012 editions, respectively). The forecasts themselves aren’t quite ready yet—I need to wait until mid-January for updates from Freedom House to arrive—but I am making some changes to my forecasting process that I thought I would go ahead and describe now, because the thinking behind them illustrates some important dilemmas and new opportunities for predictions of many kinds of political events.

When it comes time to build a predictive statistical model of some rare political event, it’s usually not the model specification that gives me headaches. For many events of interest, I think we now have a pretty good understanding of which methods and variables are likely to produce more accurate forecasts.

Instead, it’s the data, or really the lack thereof, that sets me to pulling my hair out. As I discussed in a recent post, things we’d like to include in our models fall into a few general classes in this regard:

  • No data exist (fuggeddaboudit)
  • Data exist for some historical period, but they aren’t updated (“HA-ha!”)
  • Data exist and are updated, but they are patchy and not missing at random (so long, some countries)
  • Data exist and are updated, but not until many months or even years later (Spinning Pinwheel of Death)

In the past, I’ve set aside measures that fall into the first three of those sets but gone ahead and used some from the fourth, if I thought the feature was important enough. To generate forecasts before the original sources updated, I either a) pulled forward the last observed value for each case (if the measure was slow-changing, like a country’s infant mortality rate) or b) hand-coded my own updates (if the measure was liable to change from year to year, like a country’s political regime type).

Now, though, I’ve decided to get out of the “artisanal updating” business, too, for all but the most obvious and uncontroversial things, like which countries recently joined the WTO or held national elections. I’m quitting this business, in part, because it takes a lot of time and the results may be pretty noisy. More important, though, I’m also quitting because it’s not so necessary any more, thanks to  timelier updates from some data providers and the arrival of some valuable new data sets.

This commitment to more efficient updating has led me to adopt the following rules of thumb for my 2014 forecasting work:

  • For structural features that don’t change much from year to year (e.g., population size or infant mortality), include the feature and use the last observed value.
  • For variables that can change from year to year in hard-to-predict ways, only include them if the data source is updated in near-real time or, if it’s updated annually, if those updates are delivered within the first few weeks of the new year.
  • In all cases, only use data that are publicly available, to facilitate replication and to encourage more data sharing.

And here are some of the results of applying those rules of thumb to the list of features I’d like to include in my coup forecasting models for 2014.

  • Use Powell and Thyne’s list of coup events instead of Monty Marshall’s. Powell and Thyne’s list is updated throughout the year as events occur, whereas the publicly available version of Marshall’s list is only updated annually, several months after the start of the year. That wouldn’t matter so much if coups were only the dependent variable, but recent coup activity is also an important predictor, so I need the last year’s updates ASAP.
  • Use Freedom House’s Freedom in the World (FIW) data instead of Polity IV to measure countries’ political regime type. Polity IV offers more granular measures of political regime type than Freedom in the World, but Polity updates aren’t posted until spring or summer of the following year, usually more than a third of the way into my annual forecasting window.
  •  Use IMF data on economic growth instead of the World Bank’s. The Bank now updates its World Development Indicators a couple of times a year, and there’s a great R package that makes it easy to download the bits you need. That’s wonderful for slow-changing structural features, but it still doesn’t get me data on economic performance as fast as I’d like it. I work around that problem by using the IMF’s World Economic Outlook Database, which include projections for years for which observed data aren’t yet available and forecasts for several years into the future.
  • Last but not least, use GDELT instead of UCDP/PRIO or Major Episodes of Political Violence (MEPV) to measure civil conflict. Knowing which countries have had civil unrest or violence in the recent past can help us predict coup attempts, but the major publicly available measures of these things are only updated well into the year. GDELT now represents a nice alternative. It covers the whole world, measures lots of different forms of political cooperation and conflict, and is updated daily, so country-year updates are available on January 2. GDELT’s period of observation starts in 1979, so it’s still a stretch to use it models of super-rare events like mass-killing onsets, where the number of available examples since 1979 on which to train is still relatively small. For less-rare events like coup attempts, though, starting the analysis around 1980 is no problem. (Just don’t forget to normalize them!) With some help from John Beieler, I’m already experimenting with adding annual GDELT summaries to my coup forecasting process, and I’m finding that they do improve the model’s out-of-sample predictive power.

In all of the forecasting work I do, my long-term goals are 1) to make the forecasts more dynamic by updating them more frequently (e.g., monthly, weekly, or even daily instead of yearly) and 2) to automate that updating process as much as possible. The changes I’m making to my coup forecasting process for 2014 don’t directly accomplish either of these things, but they do take me a few steps in both directions. For example, once GDELT is in the mix, it’s possible to start thinking about how to switch to monthly or even daily updates that rely on a sliding window of recent GDELT tallies. And once I’ve got a coup data set that updates in near-real time, I can imagine pinging that source each day to update the counts of coup attempts in the past several years. I’m still not where I’d like to be, but I think I’m finally stepping onto a path that can carry me there.

One Outsider’s Take on Thailand

Justin Heifetz at the Bangkok Post asked me this morning for some comments on the current political situation in Thailand. Here is a slightly modified version of what I wrote in response to his questions.

I won’t speak to the specifics of Thai culture or social psychological theories of political behavior, because those things are outside my areas of expertise. What I can talk about are the strategic dilemmas that make some countries more susceptible to coups and other breakdowns of democracy than others. Instead of thinking in terms of a “coup culture”, I think it’s useful to ask why the military in the past and opposition parties now might prefer an unelected government to an elected one.

In the case of Thailand, it’s clear that some opposition factions recognize that they cannot win power through fair elections, and those factions are very unhappy with the policies enacted by the party that can. There are two paths out of that conundrum: either seize power directly through rebellion, or find a way to provoke or facilitate a seizure of power by another faction more sympathetic to your interests—in this and many other cases, the military. Rebellions are very hard to pull off, especially for minority factions, so that often leaves them with trying to provoke a coup as their only viable option. Apparently, Suthep Thaugsuban and his supporters recognize this logic and are now pursuing just such a strategy.

The big question now is whether or not the military leadership will respond as desired. They would be very likely to do so if they coveted power for themselves, but I think it’s pretty clear from their actions that many of them don’t. I suspect that’s partly because they saw after 2006 that seizing power didn’t really fix anything and carried all kinds of additional economic and reputational costs. If that’s right, then the military will only seize power again if the situation degenerates enough to make the costs of inaction even worse—say, into sustained fighting between rival factions, like we see in Bangladesh right now.

So far, Pheu Thai and its supporters seem to understand this risk and have mostly avoided direct confrontation in the streets. According to Reuters this morning, though, some “red shirt” activists are now threatening to mobilize anew if Suthep & co. do not back down soon. A peaceful demonstration of their numbers would remind the military and other fence-sitters of the electoral and physical power they hold, but it could also devolve into the kind of open conflict that might tempt the military to reassert itself as the guarantor of national order. Back on 1 December, red shirts cut short a rally in a Bangkok stadium after aggressive actions by their anti-government rivals led to two deaths and dozens of injuries, and there is some risk that fresh demonstrations could produce a similar situation.

On how or why this situation has escalated so quickly, I’d say that it didn’t really. This is just the latest flare-up of an underlying process of deep socio-economic and political transformation in Thailand that accelerated in the early 2000s and probably isn’t going to reach a new equilibrium of sorts for at least a few more years. Earlier in this process, the military clearly sided with conservative factions struggling to beat back the political consequences of this transformation for reasons that close observers of Thai politics surely understand much better than I. We’ll see soon if they’ve finally given up on that quixotic project.

Whatever happens this time around, though, the good news is that within a decade or so, Thai politics will probably stabilize into a new normal in which the military no longer acts directly in politics and parts of what’s now Pheu Thai and its coalition compete against each other and the remnants of today’s conservative forces for power through the ballot box.

Follow-Up on Bangladesh

Per BBC News this morning:

Bangladesh is entering a new phase of violence and uncertainty triggered by the opposition’s objections to elections due to be held on 5 January. In recent days the country has been paralysed by violent strikes and transport blockades. The BBC’s Bengali editor Sabir Mustafa in Dhaka says that there is now increasing speculation that a state of emergency may be declared to pull the country back from the brink…

Since 25 October they have held general strikes and road-rail blockades, leading to widespread violence and hitting the economy hard.

Dozens of vehicles have been burned or damaged by blockade supporters on the only highway linking the port city of Chittagong with the capital Dhaka. The all-important garments industry, which accounts for nearly 80% of Bangladesh’s exports, has been unable to make shipments for a week.

”If the current crisis continues for another month, then the whole economy will stumble to a halt and it will be very difficult to recover from it,” said Rubana Huq, managing director of the Mohammadi Group, a major garments exporting firm.

In a late-October post, I used Bangladesh as an example of “the political muddles that trap most countries for decades on a sine wave of democratization and de-democratization, and why durable exits from those oscillations are so hard to come by.” I concluded:

The histories of Europe and Latin America imply that Bangladesh will eventually find a way out of these oscillations onto a new equilibrium that includes durable democracy. Unfortunately, the history of countries born in the past half-century—never mind a cursory look at the politics on the streets of Dhaka right now—suggests this election cycle probably isn’t the moment that’s going to happen.

Things could still turn for the better, and this crisis could lead to a resolution that puts democracy in Bangladesh on firmer footing. That said, the latest news from BBC does not make me optimistic.

In fact, my statistical assessments of coup risk for 2013 lead me to believe that the prospects of another extra-constitutional seizure of power in Bangladesh in the near future are no longer small. That’s what happened when things last reached this kind of fever pitch in 2007, and Bangladesh’s presence among the 20 most coup-susceptible countries in the world this year suggests there’s a sizable chance we’ll see a similar turn of events again before election day on 5 January. If I treat the annual statistical forecast as my prior and use Bayes’ rule and some back-of-the-envelope estimates about the relationship between unrest this intense and coup risk to update it, I assess the probability of a coup attempt in Bangladesh in the run-up to elections at about 30 percent.

That may not sound like much, but it’s a lot higher than the estimate of about 10 percent I get when I do a similar exercise for Thailand right now, where a somewhat similar process is unfolding. In other words, if I had to pick between Bangladesh or Thailand as the country more likely to see a coup attempt in the next several weeks, I would bet on Bangladesh.

EVEN BETTER Animated Map of Coup Attempts Worldwide, 1946-2013

[Click here to go straight to the map]

A week ago, I posted an animated map of coup attempts worldwide since 1946 (here). Unfortunately, those maps were built from a country-year data set, so we couldn’t see multiple attempts within a single country over the course of a year. As it happens, though, the lists of coup attempts on which that animation was based does specify the dates of those events. So why toss out all that information?

To get a sharper picture of the distribution of coup attempts across space and time, I rebuilt my mashed-up list of coup attempts from the original sources (Powell & Thyne and Marshall), but now with the dates included. Where only a month was given, I pegged the event to the first day of that month. To avoid double-counting, I then deleted events that appeared to be duplicates (same outcome in the same country within a single week). Finally, to get the animation in CartoDB to give a proper sense of elapsed time, I embedded the results in a larger data frame of all dates over the 68-year period observed. You can find the daily data on my Google Drive (here).

WordPress won’t seem to let me embed the results of my mapping directly in this post, but you can see and interact with the results at CartoDB (here). I think this version shows more clearly how much the rate of coup attempts has slowed in the past couple of decades, and it still does a good job of showing change over time in the geographic distribution of these events.

The two things I can’t figure out how to do so far are 1) to use color to differentiate between successful and failed attempts and 2) to show the year or month and year in the visualization so we know where we are in time. For differentiating by outcome, there’s a variable in the data set that does this, but it looks like the current implementation of the Torque option in CartoDB won’t let me show multiple layers or differentiate between the events by type. On showing the date, I have no clue. If anyone knows how to do either of these things, please let me know.

Animated Map of Coup Attempts Worldwide, 1946-2013

I’m in the throes of updating my data files to prepare for 2014 forecasts of various forms of political change, including coups d’etat. For the past couple of years, I’ve used the coup event list Monty Marshall produces (here) as my primary source on this topic, and I’ve informally cross-referenced Monty’s accounting with the list produced by Jonathan Powell and Clayton Thyne (here).

This year, I decided to quit trying to pick a favorite or adjudicate between the two and just go ahead and mash them up. The two projects use slightly different definitions, but both are basically looking for the same thing: some faction of political insiders (including but not limited to military leaders) seizes executive power at the national level by unconstitutional means that include the use or threat of force.

After stretching the two data sets into country-year format and merging the results, I created separate indicators for successful and failed coups that are scored 1 if either source reports an event of that type and 0 otherwise. For example, Marshall’s data set doesn’t see the removal of Egyptian president Hosni Mubarak from office in 2011 as a coup, but Powell and Thyne’s does, so in my mashed-up version, Egypt gets a 1 for 2011 on the indicator for any successful coups.* The Marshall data set starts in 1946, but Powell and Thyne don’t start until 1950, so my observations for 1946-1949 are based solely on the former. Powell and Thyne update their file on the go, however, whereas Marshall only updates once a year. This means that Powell and Thyne already have most of 2013 covered, so my observations for this year so far are based solely on their reckoning.

The bar plot below shows what the data from the combined version look like over time. The trend is basically the same one we’d see from either of the constituent sources. The frequency of coup attempts grew noticeably in the 1960s and 1970s; continued apace through the 1980s and 1990s, but with fewer successes; and then fell sharply in the past two decades.

coups19462013

We can see those time trends and the geographic distribution of these events in the GIF below (you may need to click on it to get it to play). As the maps show, coup events were pretty well scattered across the world in the 1960s and 1970s, but in the past 20 years, they’ve mostly struck in Africa and Asia.

coups19462013

A .csv with the mashed-up data is on Google Drive (here), and you can find the R script I used to make these plots on Github (here).

Update: For a new-and-improved version that uses daily data and is interactive, see this follow-up post.

* This sentence corrects an error I made in the original version of this post. In that version, I stated that Marshall did not consider the 3 July 2013 events in Egypt to include a coup. That was incorrect, and I apologize to him for my error.

Bangladesh as Archetype of Contemporary Political Development

If you want to get a feel for the political muddles that trap most countries for decades on a sine wave of democratization and de-democratization, and why durable exits from those oscillations are so hard to come by, you might want to take a look at Bangladesh.

Bangladesh won its independence from Pakistan in 1971 after a genocidal struggle that left hundreds of thousands dead and displaced tens of millions. Since then, the country has roughly split its time between democratic and authoritarian rule. As happened in many newly independent states in the twentieth century, the champions of national independence came to power through elections and then refused to leave. Also typically, the one-party regime born of that refusal soon fell to a restive military. Seventeen years passed before another fairly-elected civilian government came to power, starting the longest spell of more or less democratic government in the country’s still-short history.

Over the ensuing two decades, the core feature of politics in Bangladesh has been acute polarization. Whenever elections approach, the rival Awami League (AL) and Bangladesh Nationalist Party (BNP) engage in bitter public showdowns that bring tens of thousands of supporters into the streets and often produce low-level violence on the margins. Unsurprisingly, the two parties carry that same animosity into government. “Once a party is in power in Bangladesh,” the Economist recently noted, “it is the unalterable tradition to declare nearly everything decreed by your opponents to be null and void.”

Meanwhile, the military has continued to play a more active role in politics than democratic theory would allow. In 2007, as elections approached and the cyclical clash between the AL and BNP cranked up, Bangladesh’s military leaders apparently saw intervention as the lesser of a few evils and tossed their civilian rulers. Two years passed under a caretaker government of the military’s choosing. Civilian supremacy returned at the end of 2008, when the AL won elections widely regarded as the fairest in Bangladesh’s history, but according to the International Crisis Group, Bangladesh’s military remains “visibly restive”:

On 19 January [2012, the military] announced it had foiled a coup by mid-level and retired officers who sought to install an Islamist government. This followed an assassination attempt on an AL member of parliament in October 2009 by mid-level officers seething over the deaths of 57 officers in a mutiny by their subordinate paramilitary border guards the previous February. Large-scale dismissals, forced retirements, deepening politicisation and a heavy-handed approach to curb dissent and root out militants have created an unstable and undisciplined force.

The systemic result of this struggle between two political rivals and the military is the familiar “truel,” or Mexican standoff, that characterizes politics in many countries stuck between stable dictatorship and durable democracy. The defining feature of this standoff is each player’s uncertainty about its rivals’ intentions; no one trusts that the others won’t make a grab for power and then shut out or destroy the others. That uncertainty, in turn, sharply increases the odds of undemocratic behavior, because even players fully committed to democracy in principle might feel pressed to cement or usurp power in order to block their distrusted rivals from doing the same to them first.

Now, in late 2013, elections are due again, and Bangladesh seems to be spiraling toward another local climax of this cyclical confrontation. As Reuters reports, the AL and BNP have called competing rallies in the capital this Friday, and at least one party leader has told followers to come “prepared with arms.” Already this year, state security forces have killed scores of protesters  in unrest spawned by the workings of a war-crimes tribunal that many BNP sympathizers see as a political bludgeon directed against them. According to my statistical forecasts, Bangladesh ranks among the 20 countries in the world most susceptible to coup attempts this year, a result that confirms many observers’ concerns that the military might respond to wider disorder as it did in 2007.

So how does a country get off of this roller coaster? Attempts to induce democratic consolidation often focus on institutional design, but Bangladesh shows how this prescription is more easily written than filled.

One of the focal points in the current confrontation is the AL government’s recent decision to dispense with an arrangement whereby a caretaker body would replace the elected government in the run-up to elections. The BNP has cast that decision as an attempt by the ruling AL to tilt the upcoming election in its own favor. Ironically, though, the caretaker arrangement has often been the focal point of mutual recriminations in past elections, as the two parties would fight over whether or not the caretakers were sufficiently unbiased.

In other words, the system that was meant to dampen that mutual distrust only seemed to end up stoking it, but when one party finally made a change, that act is seen through the same lens. The fundamental problem with expecting rule changes to induce democratic consolidation is that the process of institutional design and change is itself political, so it is subject to the same pathologies and touches off the same worries.

Outsiders can also exhort party leaders to negotiate in good faith, but parties aren’t unitary actors. Those leaders sit atop a massive pyramid of principal-agent problems, and internal rivals often respond opportunistically to attempts at compromise by stoking fears of capitulation and offering themselves as the bulwark against it. Aware of this risk, those leaders rarely take the first step.

The histories of Europe and Latin America imply that Bangladesh will eventually find a way out of these oscillations onto a new equilibrium that includes durable democracy. Unfortunately, the history of countries born in the past half-century—never mind a cursory look at the politics on the streets of Dhaka right now—suggests this election cycle probably isn’t the moment that’s going to happen.

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