Demography and Democracy Revisited

Last spring on this blog, I used Richard Cincotta’s work on age structure to take another look at the relationship between democracy and “development” (here). In his predictive models of democratization, Rich uses variation in median age as a proxy for a syndrome of socioeconomic changes we sometimes call “modernization” and argues that “a country’s chances for meaningful democracy increase as its population ages.” Rich’s models have produced some unconventional predictions that have turned out well, and if you buy the scientific method, this apparent predictive power implies that the underlying theory holds some water.

Over the weekend, Rich sent me a spreadsheet with his annual estimates of median age for all countries from 1972 to 2015, so I decided to take my own look at the relationship between those estimates and the occurrence of democratic transitions. For the latter, I used a data set I constructed for PITF (here) that covers 1955–2010, giving me a period of observation running from 1972 to 2010. In this initial exploration, I focused specifically on switches from authoritarian rule to democracy, which are observed with a binary variable that covers all country-years where an autocracy was in place on January 1. That variable (rgjtdem) is coded 1 if a democratic regime came into being at some point during that calendar year and 0 otherwise. Between 1972 and 2010, 94 of those switches occurred worldwide. The data set also includes, among other things, a “clock” counting consecutive years of authoritarian rule and an indicator for whether or not the country has ever had a democratic regime before.

To assess the predictive power of median age and compare it to other measures of socioeconomic development, I used the base and caret packages in R to run 10 iterations of five-fold cross-validation on the following series of discrete-time hazard (logistic regression) models:

  • Base model. Any prior democracy (0/1), duration of autocracy (logged), and the product of the two.
  • GDP per capita. Base model plus the Maddison Project’s estimates of GDP per capita in 1990 Geary-Khamis dollars (here), logged.
  • Infant mortality. Base model plus the U.S. Census Bureau’s estimates of deaths under age 1 per 1,000 live births (here), logged.
  • Median age. Base model plus Cincotta’s estimates of median age, untransformed.

The chart below shows density plots and averages of the AUC scores (computed with ‘roc.area’ from the verification package) for each of those models across the 10 iterations of five-fold CV. Contrary to the conventional assumption that GDP per capita is a useful predictor of democratic transitions—How many papers have you read that tossed this measure into the model as a matter of course?—I find that the model with the Maddison Project measure actually makes slightly less accurate predictions than the one with duration and prior democracy alone. More relevant to this post, though, the two demographic measures clearly improve the predictions of democratic transitions relative to the base model, and median age adds a smidgen more predictive signal than infant mortality.

transit.auc.by.fold

Of course, all of these things—national wealth, infant mortality rates, and age structures—have also been changing pretty steadily in a single direction for decades, so it’s hard to untangle the effects of the covariates from other features of the world system that are also trending over time. To try to address that issue and to check for nonlinearity in the relationship, I used Simon Wood’s mgcv package in R to estimate a semiparametric logistic regression model with smoothing splines for year and median age alongside the indicator of prior democracy and regime duration. Plots of the marginal effects of year and median age estimated from that model are shown below. As the left-hand plot shows, the time effect is really a hump in risk that started in the late 1980s and peaked sharply in the early 1990s; it is not the across-the-board post–Cold War increase that we often see covered in models with a dummy variable for years after 1991. More germane to this post, though, we still see a marginal effect from median age, even when accounting for those generic effects of time. Consistent with Cincotta’s argument and other things being equal, countries with higher median age are more likely to transition to democracy than countries with younger populations.

transit.ageraw.effect.spline.with.year

I read these results as a partial affirmation of modernization theory—not the whole teleological and normative package, but the narrower empirical conjecture about a bundle of socioeconomic transformations that often co-occur and are associated with a higher likelihood of attempting and sustaining democratic government. Statistical studies of this idea (including my own) have produced varied results, but the analysis I’m describing here suggests that some of the null results may stem from the authors’ choice of measures. GDP per capita is actually a poor proxy for modernization; there are a number of ways countries can get richer, and not all of them foster (or are fostered by) the socioeconomic transformations that form the kernel of modernization theory (cf. Equatorial Guinea). By contrast, demographic measures like infant mortality rates and median age are more tightly coupled to those broader changes about which Seymour Martin Lipset originally wrote. And, according to my analysis, those demographic measures are also associated with a country’s propensity for democratic transition.

Shifting to the applied forecasting side, I think these results confirm that median age is a useful addition to models of regime transitions, and it seems capture more information about those propensities than GDP (by a lot) and infant mortality (by a little). Like all slow-changing structural indicators, though, median age is a blunt instrument. Annual forecasts based on it alone would be pretty clunky, and longer-term forecasts would do well to consider other domestic and international forces that also shape (and are shaped by) these changes.

PS. If you aren’t already familiar with modernization theory and want more background, this ungated piece by Sheri Berman for Foreign Affairs is pretty good: “What to Read on Modernization Theory.”

PPS. The code I used for this analysis is now on GitHub, here. It includes a link to the folder on my Google Drive with all of the required data sets.

Leave a comment

4 Comments

  1. Helpful code, insightful analysis. Great stuff!

    P.S. It would be great to be able to link the scripts in your GitHub repo back to the original posts. I clicked a few and it was not always that obvious.

    Reply
  2. bruce kay

     /  February 5, 2015

    could the finding also be read as the youth bulge effect in reverse, the flipside of the old hypothesis from Huntington, Goldstone, Collier and others that linked the relative size of the youth cohort to the risk of instability/conflict?

    Reply
    • That’s how Cincotta explains it, and I think that interpretation is also consistent with this evidence. I’m more inclined to emphasize the wider systemic process, however, in light of related evidence on other dimensions of “modernization,” such as economic change, education, and changes in values. In other words, I think the larger body of evidence supports the original idea of “modernization” as a social syndrome to which demographic change is endogenous.

      Reply

Leave a Comment

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: