Coups Slow Economic Growth

In the week or so since Egypt’s military removed President Morsi from office, political scientists have shown how military coups—and yes, that was a coup in Egypt—and the reactions to them can have enduring political consequences. For the Monkey Cage, Clayton Thyne showed how international responses to coups have historically affected the speed with which elected civilian government is restored. For Political Violence at a Glance, Brent Sasley responded to dueling pieces by Shadi Hamid and Barbara Slavin to consider how the ouster of the Muslim Brotherhood might affect the strategies of other Islamist parties and groups in the region.

To that roster of possible political repercussions, let me add an economic one: coups often hamper growth.

That’s one of the findings from a statistical analysis I did a couple of years ago for a private-sector client who was concerned about how various forms of political instability might affect investments in poorer countries. I had already generated probabilistic forecasts of coups for the coming year, but those forecasts couldn’t tell us how much he should worry about coup risk. To help answer that, we needed to look at the effects coups might have on economic processes that more directly influence the value of his investments, including growth in gross domestic product (GDP).

This isn’t a simple thing to do. It’s tempting to take historical data on as many countries as possible and compare growth rates in and after coup years with growth rates in coup-free periods, but the results would probably be misleading. The problem is that coups are much more likely to occur in a subset of cases that don’t look like the hypothetical “average” country, so the differences we’d see in a simple comparison could just as well stem from the things that cause coups in the first place as they could from the coups themselves.

To try to get a sharper sense of how coups affect economic growth in the face of these potentially confounding factors, I used a technique called coarsened exact matching (CEM) to sift and prune the data first. As with other matching techniques, the process starts by identifying the “treatment” whose effects we want to estimate—in this case, the occurrence of a coup. In contrast to laboratory experiments, we can’t randomly assign countries to treatment and control groups that do and don’t experience coups. Instead, we have to use what we know about the things that cause coups to approximate that experimental design by sifting countries into sets that faced similar risks of coups but didn’t all have them. By carefully comparing growth rates across coup and non-coup cases within these clusters of similarly coup-prone countries, we can get a more reliable estimate of the specific effects of the coup “shocks” on economic performance than we’d get from a simple comparison of all available cases.

The results of my analysis are shown in the series of charts that follow (with technical details at the end of the post). The charts summarize the distribution of estimates of the difference in economic growth rates between coup and non-coup cases. Of particular interest here are the estimated first differences, shown in purple in the middle of each set of plots. The peaks of those distributions identify the mean difference, while their spread tells us about the variance of those estimates.

As the first set of charts shows, in the year a coup occurs, the economies of coup-struck countries grow about 2 percentage points slower on average than the economies of similar countries that don’t suffer coups. The second set of charts shows that this drag seems to persist into the following year, when growth rates for coup-struck countries lag 1 to 2 percentage points behind their coupless peers. According to the third set of charts, this difference finally disappears in the second year after a coup, but by then the accumulated difference between the growth that was and the growth that might have been is already substantial. (They aren’t shown here, but results for the couple of years after that continue to show no more differences.)

Effect of Coup on GDP Growth in Year of Coup

Effect of Coup on GDP Growth in Year of Coup

Effect of Coup on GDP Growth in Next Year

Effect of Coup on GDP Growth in Next Year

Effect of Coup on GDP Growth Two Years Later

Effect of Coup on GDP Growth Two Years Later

Of course, it’s impossible to say exactly how the coup in Egypt will affect that country’s economy, which had already stagnated badly before the army led the president away under armed guard. Reports that Saudi Arabia and U.A.E. are rushing to lend money to the post-coup government, and the rally that occurred in the Egyptian stock market immediately after Morsi was toppled, might be grounds for optimism that Egypt will avoid or at least mitigate the typical damage. Still, I think this analysis should temper any such optimism by reminding us—as if we should need it!—that coups aren’t surgical strikes which neatly cure political cancers without producing myriad consequences of their own.

Now, for the technically inclined: This analysis was done in R using the MatchIt, Coarsened Exact Matching (cem), and Zelig packages. I used the Center for Systemic Peace’s list to identify when and where coups had occurred and Angus Maddison’s estimates to measure GDP growth. Coarsened exact matching was based on GDP per capita (log), Polity score (quadratic), post-Cold War period (binary), and any coup attempts in the previous five years (binary). Post-matching estimates of the effects of coups on growth were derived from a linear regression model that included all of those covariates as well as previous year’s GDP growth rate. I’m traveling at the moment and haven’t had time to post the data and R script for replication but will do so soon.

UPDATE: The R script I used for this analysis is now on Github, here. The data used in that script is on my Google Drive, here. If you find any errors of have any suggestions on how to do this better, please let me know.

Do Elections Trigger Mass Atrocities?

Kenya plans to hold general elections in early March this year, and many observers fear those contests will spur a reprisal of the mass violence that swept parts of that country after balloting in December 2007.  The Sentinel Project for Genocide Prevention says Kenya is at “high risk” of genocide in 2013, and a recent contingency-planning memo from Joel Barkan the Council on Foreign Relations asserts that “there will almost certainly be further incidents of violence in the run-up to the 2013 elections.” As a recent Africa Initiative backgrounder points out, this violence has roots that stretch much deeper than the 2007 elections, but the fear that mass violence will flare again around this year’s balloting seems well founded.

All of which got me wondering: is this a generic problem? We know that election-related violence is a real and multifaceted thing. We also have works by Jack Snyder and Amy Chua, among others, arguing that democratization actually makes some countries more susceptible to ethnic and nationalist conflict rather than less, as democracy promoters often claim. What I’m wondering, though—as someone who has long studied democratization and is currently working on tools to forecast genocide and other forms of mass killing—is whether or not elections substantially increase the risk of mass atrocities in particular, where “mass atrocities” means the deliberate killing of large numbers of unarmed civilians for apparently political ends.

Best I can tell, the short answer is no. After applying a few different statistical-modeling strategies to a few measures of atrocities, I see little evidence that elections commonly trigger the onset or intensification of this type of political violence. The absence of evidence isn’t the same thing as evidence of absence, but these results convince me that national elections aren’t a major risk factor for mass killing.

If you’re interested in the technical details, here’s what I did and what I found:

My first cut at the problem looked for a connection between national elections and the onset of state-sponsored mass killings, defined as “a period of sustained violence” in which ” the actions of state agents result in the intentional death of at least 1,000 noncombatants from a discrete group.” That latter definition comes from work Ben Valentino and I did for my old research program, the Political Instability Task Force, and it restricts the analysis to episodes of large-scale killing by states or other groups acting at their behest. Defined as such, mass killings are akin to genocide in their scale, and there have only been about 110 of them since 1945.

So, do national elections help trigger this type of mass killing? To try to answer this question, I thought of elections as a kind of experimental “treatment” that some country-years get and others don’t. I used the National Elections Across Democracy and Autocracy (NELDA) data set to identify country-years since 1945 with national elections for chief executive or legislature or both, regardless of how competitive those elections were. I then used the MatchIt package in R to set up a comparison of country-years with and without elections within 107 groups that matched exactly on several other variables identified by prior research as risk factors for mass-killing onset: autocracy vs. democracy, exclusionary elite ideology (yes/no), salient elite ethnicity (yes/no), ongoing armed conflict (yes/no), any mass killing since 1945 (yes/no), and Cold War vs. post-Cold War period. Finally, I used conditional logistic regression to estimate the difference in risk between election and non-election years within those groups.

The results? In my data, mass-killing episodes were 80% as likely to begin in election years as non-election years, other things being equal. The 95% confidence interval for this association was wide (45% to 145%), but the result suggests that, if anything, countries are actually somewhat less prone to suffer onsets of mass killing in election years as non-election years.

I wondered if the risk might differ by regime type, so I reran the analysis on the subset of cases that were plausibly democratic. The estimate was effectively unchanged (80%, CI of 35% to 185%). Then I thought it might be a post-Cold War thing and reran the analysis using only country-years from 1991 forward. The estimate moved, but in the opposite of the anticipated direction. Now it was down to 60%, with a CI of 17% to 215%.

These estimates got me worried that something had gone wacky in my data, so I reran the matching and conditional logistic regression using coup attempts (successful or failed) instead of elections as the “treatment” of interest. Several theorists have identified threats to incumbents’ power as a cause of mass atrocities, and coups are a visible and discrete manifestation of such threats. My analysis strongly confirmed this view, indicating that mass-killing episodes were nearly five times as likely to start in years with coup attempts as years without, other things being equal. More important for present purposes, this result increased my confidence in the reliability of my earlier finding on elections, as did the similar estimates I got from models with country fixed effects, country-specific intercepts (a.k.a. random effects), and interaction terms that allowed the effects of elections to vary across regime types and historical eras.

Then I wondered if this negative finding wasn’t an artifact of the measure I was using for mass atrocities. The 1,000-death threshold for “mass killing” is quite high, and the restriction to killings by states or their agents ignores situations of grave concern in which rebel groups or other non-state actors are the ones doing the murdering. Maybe the danger of election years would be clearer if I looked at atrocities on a smaller scale and ones perpetrated by non-state actors.

To do this, I took the UCDP One-Sided Violence Dataset v1.4 and wrote an R script that aggregated its values for specific conflicts into annual death counts by country and perpetrator (government or non-government). Then I used R’s ‘pscl’ package to estimate zero-inflated negative binomial regression (ZINB) models that treat the death counts as the observable results of a two-stage process: one that determines whether or not a country has any one-sided killing in a particular year, and then another that determines how many deaths occur, conditional on there being any. In addition to my indicator for election years, these models included all the risk factors used in the earlier matching exercise, plus population size and the logged counts of deaths from one-sided violence by government and non-government actors (separately) in the previous year. All of these variables were included in the logistic regression “hurdle” model; only elections, population size, and the lagged death counts were included in the conditional count models.

To my surprise once again, the results suggested that, if anything, atrocities the risk of mass atrocities is actually lower in years with national elections. In the model of government-perpetrated violence, the coefficient for the election indicator in the hurdle model was barely distinguishable from zero (0.04), and the association in the count portion was modestly negative (-0.20, s.e. of 0.20). In the model of violence perpetrated by other groups, the effect in the hurdle portion was modestly negative (-0.25, s.e. of 0.20), and the effect in the count portion was decidedly negative (-0.82, s.e. of 0.19). When I reran the models with separate indicators for executive and legislative elections, the results bounced around a little bit, but the basic patterns remained unchanged. None of the models showed a substantial, positive association between either type of election and the occurrence or scale of one-sided violence against civilians.

In light of the weakness of the observed effects, the noisiness of the measures employed, and my prior beliefs about the effects of elections on risks of mass killing—shaped in part by the Kenyan case I discussed at the start of this post—I’m not quite ready to assert that election years actually reduce the risk of mass atrocities. What I am more comfortable doing, however, is ignoring elections in statistical models meant to forecast mass atrocities across large numbers of countries.

If you’re interested in replicating or tweaking this analysis, please email me at, and I’ll be happy to send you the data and R scripts (one to get country-year summaries of the UCDP data, another to run the matching and modeling) I used to do it. [UPDATE: I’ve put the scripts and data in a publicly accessible folder on Google Drive. If you try that link and it doesn’t work, please let me know.] Ideally, I would cut out the middleman by putting them in a Github repository, but I haven’t quite figured out how to do that yet. If you’re in the DC area and interested in getting paid to walk me me through that process, please let me know.

How Risky and Costly Are Coups, Really?

Coups d’etat don’t happen as often as they used to, but they do still happen. In the past few year, coups have toppled leaders in Honduras, Niger, and arguably Egypt, while coup bids have fizzled in Guinea-Bissau (twice), Democratic Republic of CongoMadagascar, and, less than a year after the aforementioned success, Niger. Meanwhile, just this morning, we’re hearing fresh rumblings of a possible coup in Pakistan, which last saw the military openly seize power in 1999. (Whether it’s ever really taken its hands off the levers of power at any point in Pakistan’s history is another matter.)

The persistence of coups is a bit of a puzzle, because coup attempts are typically costly to their perpetrators in at least two ways.

1. Most coup attempts fail. From 1955 to 2008, half of all coup bids worldwide failed (158 of 316). As the chart below shows, the failure rate has been much higher in the past two decades than it was in earlier years. And these are just the coup bids that make it all the way to an overt attempt. If our tally also included all of the plots that were uncovered and foiled before they could be put in motion, the failure rate would be much higher. If coup attempts usually fail, and the punishment for a failed coup is often imprisonment or death, then coup bids would seem to be a pretty risky gamble for their plotters.

 2. Coups take a toll on the economy. The figure below plots the estimated impact of a successful coup on a country’s GDP growth rate over the several years following the coup event (data geeks: see the technical notes at the end of this post for details on the method used to derive these estimates). The impact is substantial: on average, a couple of percentage points knocked off the growth rate in the year of the coup and another point or so the following year. Expected growth rates resume a few years after the coup, and there even seems to be something of a rebound effect several years down the road, but three or four years is a long wait in political time. This knock on the GDP growth rate matters for coup plotters because if they succeed, they’re liable be blamed for the damage done (Pakistani generals, take note!).

In light of these facts, it’s hard to understand why coup plotters keep trying, even if they are trying less often. To attempt a coup, you’ve either got to be ignorant of these facts or to consider them irrelevant to your particular situation. You might consider them irrelevant because you’re exceedingly optimistic about your own coup’s chances for success, as behavioral psychology suggests many military and political leaders will be. Alternatively, you might expect your attempt to fail but still think it’s worth a try because you believe that success will produce large private benefits (like the opportunity to loot the state treasury) or non-economic public benefits that will reflect well on you and your co-conspirators (like liberation from an awful tyrant, or defense against public disorder).

My hunch is that all of these forces–ignorance, optimism, greed, and benevolence–factor into the decision-making behind many coup attempts. We’ll never really know for sure, because even first-hand accounts of plotters’ motivations are highly unreliable. What we can say with some confidence, I think, is that people are going to keep trying anyway.

Technical Notes

The figures and charts in this post are based on coup event data compiled by Monty Marshall at the Center for Systemic Peace. You can find them here, under the Polity IV heading.

To estimate the impact of successful coups on GDP growth, I first used “coarsened exact matching” to pre-process a time-series cross-sectional data set of all countries worldwide for the period 1955-2008. Country-years were matched on several risk factors for successful coups, including infant mortality rates, degree of democracy (Polity scores, quadratic), recent coup activity, and a marker for the post-Cold War period. After matching, I used Zelig to estimate the average “treatment effect” for successful coups. The values used in the plot above are the medians from six iterations of this drill, one for each year away from the coup event. The GDP growth data are from the World Bank’s World Development Indicators.

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