Economic Growth and the Survival of New Democracies

Last week, a senior official in Egypt’s Muslim Brotherhood warned the U.S. against cutting aid to his country at a time when Egypt is, he suggested, on the brink of economic collapse. In an interview with the Washington Post, Khairat Al-Shater said that reductions in Western aid would exacerbate an economic crisis that could “transform a peaceful revolution into a hunger revolution.”

Al-Shater’s warning reflects a widely held view that new democracies can be made or broken by their economic performance. “It is a cardinal principle of empirical democratic theory,” democratization scholar Larry Diamond writes, “that hard economic times are supposed to mean hard times for democracy.” This principle has been confirmed by a few statistical studies on the survival of democratic regimes: other things being equal, the risk of democratic breakdown does seem to be higher when GDP growth rates are slower. (See here, here, here, and here for affirmative findings and here for a negative one.)

After reading Al-Shater’s warning, I decided to revisit this question with an emphasis on the real-world concerns of the moment. Instead of looking at the entire life course of all democracies, as previous studies generally do, I wondered what economic performance around the time of a democratic transition–like we saw in Tunisia in 2011 and like we might be seeing right now in Egypt and Libya–would tell us about the prospects that a democratic regime will survive well beyond its founding elections. Perhaps these earliest years create impressions and encourage strategies that enable or afflict the ensuing regime during this formative period in ways we can’t see when we lump entire episodes of democracy together.

To test this conjecture, I used a global data set to identify all transitions to democracy that occurred during the period 1955-2008. With that case list in hand, I built a logistic regression model of the relationships between the conditions under which those transitions occurred and the odds that the ensuing regimes would survive for at least five years. (As it happens, surviving for just five years is actually a pretty big deal. Of the 103 transitions to democracy that occurred during that period, only 62 produced regimes that lasted longer.)

From prior research, we know that higher levels of economic development, the absence of political polarization, prior democracy, and the end of the Cold War are all associated with improved prospects for democratic consolidation, so all of those factors were included in the model. To capture the marginal effects of economic performance on prospects for democratic survival–the original point of this exercise–I added measures of annual percent change in GDP per capita for the three years bracketing the transition: the one before, the year of the transition, and the year after. (See the end of the post for more details on the modeling.)

As expected, I found that a new democracy’s survival prospects are indeed better when its economy grows faster around the time of its birth. In contrast to al-Shater’s gloomy prognostication, however, the effects I observed were not large. The marginal effects from GDP growth in the year of the transition are illustrated in the line plot below. As the chart shows, a difference of several percentage points in GDP growth–a large swing in most real-world situations–would produce only a very modest difference in the estimated likelihood of surviving past five years, other things being equal. (I don’t think p-values are as informative as estimates of marginal effects, but for those of you wondering, the p-value in this instance is 0.28; the coefficient is 0.046.) The association with growth on either side of the transition are not captured in that chart, in part because they were even weaker (coefficients of 0.027 and 0.018 and p-values of 0.54 and 0.70, respectively).

We can also see the weakness of this effect in the modest contribution of those growth rates to the statistical model’s ability to accurately assess risk in the historical cases. The figure below plots Receiver Operating Characteristic (ROC) curves for versions of my model with and without the measures of initial GDP growth. ROC curves summarize a model’s ability to discriminate between cases with and without some feature of interest–in this instance, surviving past five years. The better the model does, the farther the line pushes toward the upper left-hand corner, and the larger the area under the curve (AUC). As you can see, adding measures of GDP growth to the model doesn’t improve the accuracy by a whole lot, producing just about a 2% bump in AUC.

On the whole, I’d say these results run counter to the spirit, if not the letter, of prevailing expectations. The marginal effects of economic performance flow in the anticipated direction, but they don’t have anything close to the kind of “make or break” impact that Al-Shater and Diamond’s statements imply. A new democracy’s level of economic development and the occurrence of acute political polarization tell us the most about its survival prospects, and variations in economic performance around the transition don’t seem to move the needle a whole lot beyond that.

I wonder if the prevailing wisdom about the dire consequences of poor economic performance for democratic consolidation isn’t at least in part a case of the availability heuristic at work. Historical cases of economic crisis followed by democratic collapse easily spring to mind (Weimar Germany, anyone?), and it’s not hard to generate a plausible story linking those two events. What those plausible stories seem to overlook, though, is that many of those attempts at democracy probably would have failed anyway, even in the absence of economic crisis, because that’s the fate of most democracies across a wide range of conditions. Meanwhile, there are plenty of countervailing examples of young democracies that survived sharp economic contractions (say, Greece after military rule, or much of post-Communist Europe), but these null cases seem to be more forgettable.

Even if economic growth had a stronger impact on prospects for democratic survival than my analysis indicates it does, I’m skeptical that this information would be as useful to policy-makers seeking to promote democracy as I suspect they think it is. Assume for a moment that a bump of a few percentage points in GDP growth in the transition year would double the odds in favor of democratic survival. Can anyone tell me what policy interventions will reliably pump growth rates that far, that fast? If foreign aid or economic policy could work that kind of magic, wouldn’t the “developing” world already be a lot richer?

I’ll wrap this post up by going back to where we started, namely, the Middle East after the “Arab awakening.” Even though GDP growth doesn’t contribute much to it, the model’s overall performance isn’t bad. After looking at those ROC curves, I wondered what the model would say about the prospects for the survival of new democracies in three Arab countries on the cusp of new tries at democracy: Tunisia, Egypt, and Libya. Of the three, only Tunisia would already qualify as democratic by my definition, but Egypt and Libya are both in the midst of transitions from authoritarian rule that could put them over the threshold soon. So I took the IMF’s latest projections of their growth rates and plugged them into the model, along with recent data on their levels of economic development and my best guess as to whether or not they would qualify as acutely polarized according to the data set I used for that indicator. Here’s what came back as estimates of the  probability that each of those new democracies would make it to their sixth birthday, assuming that, of the three, only Tunisia would not qualify as acutely polarized:

  • Tunisia: 82%
  • Egypt: 48%
  • Libya:  89%

The contrast between Tunisia and Egypt’s survival prospects did not surprise me, but the high estimate for Libya did. Interestingly, expected economic growth seems to be contributing to this result. According to the IMF, Libya’s economy contracted by more than 60% in 2011, but it’s expected to recoup some of those losses in 2012 with an astonishing annual growth rate of nearly 70%. That value is so unusually large that it packs a lot of wallop, even though the weight for GDP growth in the equation is small. Whether that anomalous leap translates into a tremendous boost for democratic consolidation in the real world is another matter. Color me dubious.

Details of the Modeling

The sample for the statistical analysis described here comprises 103 democratic transitions that occurred in countries worldwide during the period 1955-2008.  These transitions were identified using the same data set on episodes of democracy that was summarized in my book. To focus on transitions most like the ones occurring in the Middle East today, cases where new countries were “born” with democratic regimes were excluded from the analysis. I did not use the well-known and widely-used Democracy and Dictatorship Data Set because, as elaborated in this working paper, I have serious concerns about its utility for survival analysis. (That said, I would be very interested to see how sensitive the results reported here are to the choice of measures of democratic transitions and breakdowns. I’d do it myself if this were an academic paper, but, hey, it’s just a blog post.)

Once I’d assembled a roster of relevant cases, I used the ‘glm’ command in R to estimate a logistic regression model that included the covariates listed below (with sources in parentheses). The analysis file includes one record per transition. The dependent variable in this model was a binary one indicating whether or not a democratic episode lasted more than five years beyond its transition year. As noted above, 62 of the 103 cases did.

  • Annual percent change in GDP per capita in years t-1, t, and t+1, where t is the year in which the democratic transition occurred (World Development Indicators)
  • Infant mortality rate, relative to annual global median and logged (U.S. Bureau of the Census)
  • Political polarization (a.k.a. “factionalism,” indicated by a score of 3 on Polity’s PARCOMP variable)
  • Any prior episodes of democracy
  • Post-Cold War period

The ROC curves were created using ROCR.

If you’d like to replicate and tinker with this analysis, please email me to ask for the data set and R script. My address is ulfelder <at> gmail <dot> com.

Update: The code I used for this analysis is now on Github, here. The data set in .csv form can be downloaded from my Google Drive, here.

Leave a comment


  1. I like the fact that this article challenges the collective wisdom. In Tunisia, economic growth wasn’t the cause of the revolution. In fact Tunisia prior to the revolution had been growing at a reasonable rate. And unless things fall off a cliff, it will probably resume positive, if not stellar, growth in the near future.

    The problem, rather, was that growth accumulated to a kleptocratic, corrupt regime that left Tunisians feeling excluded from economic gains. In this sense, one might guess that a sharp reduction in perceptions of corruption might be just as important as positive economic growth to the stability of the nascent democracy. I would guess, for example, that in Eastern Europe the fact that corrupt regimes were replaced by less corrupt regimes helped their transitions toward democracy.

    Does your sample set account for corruption or perceptions thereof?

    • Thanks, Erik. Interesting idea. Unfortunately, I don’t know of any reliable measures of corruption that cover enough countries for enough time to include in this kind of analysis. Transparency International’s corruption perceptions index is the closest thing, but it starts in the 1990s and doesn’t cover most countries worldwide until fairly recently.

  2. Felix

     /  February 6, 2012

    There is the International Country Risk Guide data on corruption, which has better coverage than Transparency International. Sadly, it is not available for free.

    Anyway, I really like your analysis and I learned a lot regarding the proper use of statistical analysis techniques, like ROC curves and found your research design interessting in general. However, I got two questions:

    - How did you include the GDP-growth variable in the analysis? You mention that you use the values for t-1, t and t+1. Do you average these 3 values for each case in your dataset?

    - Related to this coding of gdp growth, there might be problems of endogeneity or reverse causality. Maybe it is not growth which fosters democratic stability but vice versa those regimes which a priory have better odds of democratic consolidation are also better in terms of economic performance.

    • Good point about ICRG’s data, although I know there are some issues with gaps and depth there, too. As to your questions:

      1. I included the three years’s observations as three separate covariates. There’s sure to be multicollinearity there, so it’s not an optimal approach, but I wanted to see if the association varied across those years. I was intrigued to see the strongest association by far for the transition year. As noted in the post, the coefficients for the other two were close to 0. (I also ran a version with just the transition year, and the coefficient for that year was essentially equivalent to the one in the model with all three.)

      2. As for endogeneity, that’s certainly a possibility. I hope it’s clear that I don’t see this design as a strong test of causality. Whatever the mix of cause and effect, though, the conventional story is that growth rates around the transition have a big impact, and I do think this design is a reasonable way to check that belief.

      Thanks for reading and engaging!

  3. neil wilson

     /  February 6, 2012

    Am I missing something???

    “‘According to the IMF, Libya’s economy contracted by more than 60% in 2011, but it’s expected to more than recoup those losses in 2012 with an annual growth rate of nearly 70%.”

    When you lose 60% then you are down to 40%. The next year you gain 70% that only gets you up to 68%. In other words, down 60 and up 70 combine to a loss of 32.

    Then again, maybe I am missing something

  4. Hassan

     /  February 9, 2012

    Great analysis. Perhaps a more salient conclusion would be that GDP/Capita is a poor indicator of economic well being of the critical mass that drives political shifts

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