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.)

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.