The same logic holds in international affairs. Pretty much every theory of domestic political instability starts from the assumption that, other things being equal, poorer countries are more susceptible to crisis than wealthier ones. Simple, right? Just toss per capita GDP in your algorithm and move on to the next predictor.
Not so fast. As it happens, GDP estimates are produced by government agencies whose data-making capacity is directly related to the thing they're trying to measure. Some countries, including Cuba and North Korea, don't even report national economic statistics to the international bodies that collect them. And that's close to the best-case scenario. Reliable measures of many other oft-mentioned risk factors, like unemployment and income inequality, were simply unavailable for almost all countries until very recently, and coverage is still largely confined to richer parts of the world.
These gaping holes in the historical record don't make it impossible to generate useful statistical forecasts of international affairs. They do mean, however, that the forecasts we can make are much less accurate than the ones the poll-averaging modelers can produce for U.S. elections.
For rare events like coups or outbreaks of civil war -- in most years, only a few of these events will occur worldwide -- it's easy to be right almost all the time by saying nothing will happen anywhere, but that's also not particularly useful. The harder task is identifying where and when the occasional exceptions will occur without crying wolf too often.
This problem bears some resemblance to forecasting U.S. presidential elections, in which most of the 50 states dependably vote Democrat or Republican; the hard part is predicting the dozen or so swing states. In international politics, there are many cases that seem reliably "immune" to certain crises, and there's often also a small but self-evident set of usual suspects. It's the small but critical set of cases in between those two extremes that make us work to earn our paychecks.
Again, though, difficult does not mean impossible. As Pennsylvania State University political scientist Philip Schrodt has pointed out, well-designed models have achieved a respectable level of accuracy on a range of forecasting problems, including outbreaks of civil war and mass atrocities and the occurrence of coups d'état. Still, these models usually aren't as precise as we'd like. For every high-risk case that suffers a crisis, there is usually at least a handful of them that don't, and occasionally a supposedly low-risk case just plain surprises us.
Data gleaned from the deluge of information now pouring over the Internet may soon help fill some of these gaps, but we're not there yet. In the meantime, we must create forecasts with the data we have, not the data we want. It's great that statistical forecasters won wider respect for their methods by nailing the outcome of this year's U.S. presidential election. But it's important for people to appreciate that not every forecasting problem can be solved by sprinkling it with math and silicon.