A Quick Comparative Assessment of Georgia and Venezuela

Two countries with competitive authoritarian regimes held elections this past week, with very different results. In Georgia, President Mikheil Saakashvili’s United National Movement (UNM) lost its parliamentary majority for the first time since it took power in the Rose Revolution nearly a decade ago. In Venezuela, however, President Hugo Chávez won a fourth term by his slimmest margin yet, defeating challenger Henrique Capriles by “only” a 9-percent margin.

As a forecaster, I went 1 for 2 in predicting the outcome of these elections. Because they are—or, in the case of Georgia, were—electoral authoritarian regimes, I expected the ruling parties to win in both cases. There seemed to be a lot of uncertainty about the outcome in both, but as I said on Twitter, the illusion of uncertainty is a design feature of this type of regime. Regarding Venezuela, I gave Chavez 4:1 odds of beating Capriles. I recognized that the election machinery introduced some uncertainty into the process, but I believed Chavez had tilted the playing field steeply enough in his own favor to return himself to office, regardless of Capriles’ appeal. I didn’t make a specific prediction about Georgia, but if I had, it would’ve been about the same, and for the same reasons. The challenging Georgian Dream coalition clearly had some momentum heading into the election, but I thought Georgia’s machine politics and byzantine electoral system would allow Saakashvili’s UNM to retain a parliamentary majority anyway.

So, how did two apparently similar cases produce two different outcomes? On the fly, I can think of three explanations, all of which could be true at the same time.

First, it’s quite possible that I read the two cases wrong in advance of the election. Maybe Georgia really was less authoritarian than I thought. Electoral authoritarian regimes are inherently ambiguous, and this ambiguity makes it especially hard to observe small changes, or to be confident that the small changes we do see will be meaningful ones. For cases in this boundary area, however, small differences can have a big impact on the results.

Second, Georgia had a national scandal erupt over prison abuse in the campaign’s final weeks, and it’s possible that this “October surprise” was severe enough to knock the system off its old equilibrium. Video clips showing male prisoners being tortured and sexually assaulted by guards sparked mass demonstrations in cities across the country, and many Georgians seemed to see the abuse as metaphor for deeper systemic problems that the Rose Revolution had failed to correct.

Third, I think the two countries’ different positions in the international system played a role. Hugo Chavez has explicitly positioned his country as a counterweight to “Western hegemony,” and that adversarial posture has encouraged him to thumb his nose at critics and election observers from countries and organizations he sees as hostile to his “Bolivarian revolution.” Mikheil Saakashvili, by contrast, has hugged the United States and Europe, aggressively—almost desperately—pursuing entree into NATO and the European Union as a way to catalyze Georgia’s “modernization” and to protect it from the angry Russian bear next door.

This Westernization strategy led Saakashvili to subject his electoral process to much closer scrutiny and made him far more sensitive to criticisms from Europe and the U.S. than Chavez could ever be. Criticisms from previous elections about bias in state-owned media and partisan abuse of state resources led to specific reforms that certainly were not revolutionary but probably helped regrade the electoral landscape into more level terrain.

In retrospect, then, I think I can see why Georgia was riper for change than Venezuela was, and how the ambiguity inherent in electoral authoritarian regimes made that contrast hard to spot in advance. Whatever the specific causes, though, I think I need to tweak my mental model of electoral authoritarianism to allow for more uncertainty about the outcome of their elections. My old model emphasized the authoritarian part and saw the elections as pure theater. My new version will be less confident in its judgment of the character of these ambiguous cases, and it will leave more room for those theatrics to have real consequences.

Electoral Systems Are Like Ecosystems

Evidence is mounting that efforts to quash election fraud often displace it instead, and this pattern should change the way we think about the problem of promoting democracy and encouraging clean elections.

Earlier this month, I blogged about a new journal article showing a statistical link between the presence of international election observation missions and the occurrence of declines in the quality of governance. According to that paper’s authors,

As election monitoring has increased, governments intent on cheating have learned to strategically adapt, relying less on election-day fraud, and instead increasing their use of pre-election manipulation that is less likely to be criticized and punished…When election monitoring missions encourage an increase in pre-election manipulation, they can unwittingly have negative effects on institutional quality and governance.

This morning, the Monkey Cage blog ran a guest post from NYU post-doc Fredrik Sjoberg, whose analysis of election data from Azerbaijan suggests that the installation of web cameras in polling stations doesn’t reduce electoral fraud so much as it changes how fraud is conducted. In the election Sjoberg studied, authorities seem to have responded to the new technology by tinkering with the count after the ballots were cast, and the net impact of the webcam rollout on the integrity of the vote was nil. That pattern led Sjoberg to the following depressing conclusion:

By replacing one form of fraud with another, incumbents are able to prevent vote share losses while contributing a veneer of legitimacy by self-initiating anti-fraud measures.  It therefore seems like a win-win for the autocrat.

As Joshua Tucker said in a follow-up post at the Monkey Cage, Sjoberg’s study…

…raises a very tricky question for anyone advocating for free and fair elections in countries with less than stellar records in this regard. Should webcams in polling stations be embraced as a technology that at the very least decreases one form of electoral fraud? Or perhaps should they be a cause for concern as a technology that is likely to replace a more easily observable (and easier to publicize) form of fraud—ballot stuffing—with one that is more subtle and less observable: the manipulation of precinct level results…If we want to take this one step further, then we could argue…that by making local agents engage in a type of fraud that is less likely to be publicly discovered, webcams could perhaps make leaders more likely to engage in fraud than otherwise.

These studies do not mean that people interested in cleaning up elections should stop trying to fight electoral fraud and abuse. Even if current efforts are not always producing the intended effects, it’s hard to imagine that they are not at least marginally reducing opportunities for cheating and making it costlier.

Instead, these studies underscore the importance of thinking about electoral interventions and their likely impacts in more holistic terms. Consistent with modernist thinking about politics more generally, efforts to study and manipulate the conduct of elections in recent decades have often treated electoral systems like machinery. The whole can be described as the sum of its parts, each of which addresses a distinct technical problem that can be considered and solved in isolation.

What these studies suggest, though, is that electoral systems are more like ecosystems. In ecosystems, a disruption in one element or region can ripple through the whole in ways that are often difficult to predict. As Nigel Greening blogged, that’s because…

…ecosystems are non-linear systems. A system is usually non-linear when more than one factor mutually affects other factors. The mutual bit is the important part as it results in a feedback loop. For example: wolves eat deer. The more wolves, the more deer get eaten, so the less deer there are to breed, so the fewer deer there are to eat, so the less wolves have to eat, so the fewer wolves, so less deer get eaten. You get the idea: any change to one side changes the other side, which in turn changes the first side, which again changes the second and so on for ever. It looks like a cycle, but it isn’t. Ever.

As Greening goes on to say, non-linearity means that change in the system is sometimes radical; the timing of those radical changes is often unpredictable; and those radical changes are always, in some sense, irreversible. For example, apparently incremental changes in the size of one population can sometimes push that population over a threshold that leads to mass death, as famously happened with reindeer on St. Matthew Island, Alaska, in the early 1960s. In retrospect, we can understand this causes of crash, but in real time it must have been freakish and stunning.

If electoral systems function more like ecosystems than engines, then our attempts to manipulate them will always be confounded by unpredictable shifts and unintended consequences. Again, though, that shouldn’t stop us from trying. Instead, I think it just means we will usually be more successful when we treat the system as a coherent whole instead of fixating on the parts we think we can most readily manipulate.

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