From China, Another Strike Against Legitimacy

I’ve groused on this blog before (here and here) about the trouble with “legitimacy” as a causal mechanism in theories of political stability and change, and I’ve pointed to Xavier Marquez’s now-published paper as the most cogent expression of this contrarian view to date.

Well, here is a fresh piece of empirical evidence against the utility of this concept: according to a new Global Working Paper from Brookings, the citizens of China who have benefited the most from that country’s remarkable economic growth in recent decades are, on average, its least happy. As one of the paper’s authors describes in a blog post about their research,

We find that the standard determinants of well-being are the same for China as they are for most countries around the world. At the same time, China stands out in that unhappiness and reported mental health problems are highest among the cohorts who either have or are positioned to benefit from the transition and related growth—a clear progress paradox. These are urban residents, the more educated, those who work in the private sector, and those who report to have insufficient leisure time and rest.

These survey results contradict the “performance legitimacy” story that many observers use to explain how the Chinese Communist Party has managed to avoid significant revolutionary threats since 1989 (see here, for example). In that story, Chinese citizens choose not to demand political liberalization because they are satisfied with the government’s economic performance. In effect, they accept material gains in lieu of political voice.

Now, though, we learn that the cohort in which contentious collective action is most likely to emerge—educated urbanites—are also, on average, the country’s least happy people. The authors also report (p. 14) that, in China, “the effect of income increases on life satisfaction are limited.” A legitimacy-based theory predicts that the CCP is surviving because it is making and keeping its citizens happy; instead, we see that it is surviving in spite of deepening unhappiness among key cohorts.

To me, this case further bares the specious logic behind most legitimacy-based explanations for political continuity. We believe that rebellion is an expression of popular dissatisfaction, a kind of referendum in the streets; we observe stability; so, we reason backwards from the absence of rebellion to the absence of dissatisfaction, sprinkle a little normative dust on it, and arrive at a positive concept called legitimacy. Formally, this is a fallacy of affirmative conclusion from a negative premise: happy citizens don’t rebel, no rebellion is occurring, therefore citizens must be happy. Informally, I think it’s a qualitative version of the “story time” process in which statistical modelers often indulge: get a surprising result, then make up a richer explanation for it that feels right.

I don’t mean to suggest that popular attitudes are irrelevant to political stasis and change, or that the durability of specific political regimes has nothing to do with the affinity between their institutional forms and the cultural contexts in which they’re operating. Like Xavier, though, I do believe that the conventional concept of legitimacy is too big and fuzzy to have any real explanatory power, and I think this new evidence from China reminds us of that point. If we want to understand how political regimes persist and when they break down, we need to identify mechanisms that are more specific than this one, and to embed them in theories that allow for more complexity.

Polity Meets Joy Division

The Center for Systemic Peace posted its annual update of the Polity data set on Friday, here. The data set now covers the period 1800–2014.

For those of you who haven’t already fled the page to go download the data and who aren’t political scientists: Polity measures patterns of political authority in all countries with populations larger than 500,000. It is one of the mostly widely used data sets in the fields of comparative politics and international relations. Polity is also tremendously useful in forecasts of rare political crises—partly because it measures some very important things, but also because it is updated every year on a fairly predictable schedule. Thanks to PITF and CSP for that.

I thought I would mark the occasion by visualizing Polity in a new way (for me, at least). In the past, I’ve used heat maps (here and here) and line plots of summary statistics. This time, I wanted to try something other than a heat map that would show change over time in a distribution, instead of just a central tendency. Weakly inspired by the often-imitated cover of Joy Division’s 1979 album, here’s what I got. Each line in this chart is a kernel density plot of one year’s Polity scores, which range from -10 to 10 and are meant to indicate how democratic a country’s politics are. The small number of cases with special codes that don’t fit on this scale (-66, -77, and -88) have been set aside.

polity.meets.joy.division

The chart shows once again that the world has become much more democratic in the past half-century, with most of those gains occurring in the past 30 years. In the early 1960s, the distribution of national political regimes was bimodal, but authoritarian regimes outnumbering the more-democratic ones. As recently as the early 1970s, most regimes still fell toward the authoritarian end of the scale. Starting in the late 1980s, though, the authoritarian peak eroded quickly, and the balance of the distribution shifted toward the democratic end. Despite continuing talk of a democratic recession, the (political) world in 2014 is still mostly composed of relatively democratic regimes, and this data set doesn’t show much change in that basic pattern over the past decade.

 

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.

A Postscript on Measuring Change Over Time in Freedom in the World

After publishing yesterday’s post on Freedom House’s latest Freedom in the World report (here), I thought some more about better ways to measure what I think Freedom House implies it’s measuring with its annual counts of country-level gains and declines. The problem with those counts is that they don’t account for the magnitude of the changes they represent. That’s like keeping track of how a poker player is doing by counting bets won and bets lost without regard to their value. If we want to assess the current state of the system and compare it earlier states, the size of those gains and declines matters, too.

With that in mind, my first idea was to sum the raw annual changes in countries’ “freedom” scores by year, where the freedom score is just the sum of those 7-point political rights and civil liberties indices. Let’s imagine a year in which three countries saw a 1-point decline in their freedom scores; one country saw a 1-point gain; and one country saw a 3-point gain. Using Freedom House’s measure, that would look like a bad year, with declines outnumbering gains 3 to 2. Using the sum of the raw changes, however, it would look like a good year, with a net change in freedom scores of +1.

Okay, so here’s a plot of those sums of raw annual changes in freedom scores since 1982, when Freedom House rejiggered the timing of its survey.[1] I’ve marked the nine-year period that Freedom House calls out in its report as an unbroken run of bad news, with declines outnumbering gains every year since 2006. As the plot shows, when we account for the magnitude of those gains and losses, things don’t look so grim. In most of those nine years, losses did outweigh gains, but the net loss was rarely large, and two of the nine years actually saw net gains by this measure.

Annual global sums of raw yearly changes in Freedom House freedom scores (inverted), 1983-2014

Annual global sums of raw yearly changes in Freedom House freedom scores (inverted), 1983-2014

After I’d generated that plot, though, I worried that the sum of those raw annual changes still ignored another important dimension: population size. As I understand it, the big question Freedom House is trying to address with its annual report is: “How free is the world?” If we want to answer that question from a classical liberal perspective—and that’s where I think Freedom House is coming from—then individual people, not states, need to be our unit of observation.

Imagine a world with five countries where half the global population lives in one country and the other half is evenly divided between the other four. Now let’s imagine that the one really big country is maximally unfree while the other four countries are maximally free. If we compare scores (or changes in them) by country, things look great; 80 percent of the world is super-free! Meanwhile, though, half the world’s population lives under total dictatorship. An international relations theorist might care more about the distribution of states, but a liberal should care more about the distribution of people.

To take a look at things from this perspective, I decided to generate a scalar measure of freedom in the world system that sums country scores weighted by their share of the global population.[2] To make the result easier to interpret, I started by rescaling the country-level “freedom scores” from 14-2 to 0-10, with 10 indicating most free. A world in which all countries are fully free (according to Freedom House) would score a perfect 10 on this scale, and changes in large countries will move the index more than changes in small ones.

Okay, so here’s a plot of the results for the entire run of Freedom House’s data set, 1972–2014. (Again, 1981 is missing because that’s when Freedom House paused to align their reports with the calendar year.)  Things look pretty different than they do when we count gains and declines or even sum raw changes by country, don’t they?

A population-weighted annual scalar measure of freedom in the world, 1972-2014

A population-weighted annual scalar measure of freedom in the world, 1972-2014

The first thing that jumped out at me were those sharp declines in the mid-1970s and again in the late 1980s and early 1990s. At first I thought I must have messed up the math, because everyone knows things got a lot better when Communism crumbled in Eastern Europe and the Soviet Union, right? It turns out, though, that those swings are driven by changes in China and India, which together account for approximately one-third of the global population. In 1989, after Tienanmen Square, China’s score dropped from a 6/6 (or 1.67 on my 10-point scalar version) to 7/7 (or 0). At the time, China contained nearly one-quarter of the world’s population, so that slump more than offsets the (often-modest) gains made in the countries touched by the so-called fourth wave of democratic transitions. In 1998, China inched back up to 7/6 (0.83), and the global measure moved with it. Meanwhile, India dropped from 2/3 (7.5) to 3/4 (5.8) in 1991, and then again from 3/4 to 4/4 (5.0) in 1993, but it bumped back up to 2/4 (6.67) in 1996 and then 2/3 (7.5) in 1998. The global gains and losses produced by the shifts in those two countries don’t fully align with the conventional narrative about trends in democratization in the past few decades, but I think they do provide a more accurate measure of overall freedom in the world if we care about people instead of states, as liberalism encourages us to do.

Of course, the other thing that caught my eye in that second chart was the more-or-less flat line for the past decade. When we consider the distribution of the world’s population across all those countries where Freedom House tallies gains and declines, it’s hard to find evidence of the extended democratic recession they and others describe. In fact, the only notable downturn in that whole run comes in 2014, when the global score dropped from 5.2 to 5.1. To my mind, that recent downturn marks a worrying development, but it’s harder to notice it when we’ve been hearing cries of “Wolf!” for the eight years before.

NOTES

[1] For the #Rstats crowd: I used the slide function in the package DataCombine to get one-year lags of those indices by country; then I created a new variable representing the difference between the annual score for the current and previous year; then I used ddply from the plyr package to create a data frame with the annual global sums of those differences. Script on GitHub here.

[2] Here, I used the WDI package to get country-year data on population size; used ddply to calculate world population by year; merged those global sums back into the country-year data; used those sums as the denominator in a new variable indicating a country’s share of the global population; and then used ddply again to get a table with the sum of the products of those population weights and the freedom scores. Again, script on GitHub here (same one as before).

No, Democracy Has Not Been Discarded

Freedom House released the latest iteration of its annual Freedom in the World report yesterday (PDF) with the not-so-subtle subtitle “Discarding Democracy: Return to the Iron Fist.” The report starts like this (emphasis in original):

In a year marked by an explosion of terrorist violence, autocrats’ use of more brutal tactics, and Russia’s invasion and annexation of a neighboring country’s territory, the state of freedom in 2014 worsened significantly in nearly every part of the world.

For the ninth consecutive year, Freedom in the World, Freedom House’s annual report on the condition of global political rights and civil liberties, showed an overall decline. Indeed, acceptance of democracy as the world’s dominant form of government—and of an international system built on democratic ideals—is under greater threat than at any point in the last 25 years.

Even after such a long period of mounting pressure on democracy, developments in 2014 were exceptionally grim. The report’s findings show that nearly twice as many countries suffered declines as registered gains, 61 to 33, with the number of gains hitting its lowest point since the nine-year erosion began.

Once again, I’m going to respond to the report’s release by arguing that things aren’t nearly as bad as Freedom House describes them, even according to their own data. As I see it, the nine-year trend of “mounting pressure on democracy” isn’t really much of a thing, and it certainly hasn’t brought the return of the “iron fist.”

Freedom House measures freedom on two dimensions: political rights and civil liberties. Both are measured on a seven-point scale, with lower numbers indicating more freedom. (You can read more about the methodology here.) I like to use heat maps to visualize change over time in the global distribution of countries on these scales. In these heat maps, the quantities being visualized are the proportion of countries worldwide landing in each cell of the 7 x 7 grid defined by juxtaposing those two scales. The darker the color, the higher the proportion.

Let’s start with one for 1972, the first year Freedom House observes and just before the start of the so-called third wave of democratic transitions, to establish a baseline. At this point, the landscape has two distinct peaks, in the most– and least-free corners, and authoritarian regimes actually outnumber democracies.

fhm.1972

Okay, now let’s jump ahead to 1986, the eve of what some observers describe as the fourth wave of democratic transitions that swept the Warsaw Pact countries and sub-Saharan Africa. At this point, the world doesn’t look much different. The “least free” peak (lower left) has spread a bit, but there are still an awful lot of countries on that side of the midline, and the “most free” peak (upper right) is basically unchanged.

fhm.1986

A decade later, though, things look pretty different. By 1995, the “most free” peak has gotten taller and broader, the “least free” peak has eroded further, and there’s now a third peak of sorts in the middle, centered on 4/4.

fhm.1995

Jump ahead another 10 years, to 2005, and the landscape has tilted decisively toward democracy. The “least free” peak is no more, the bump in the middle has shifted up and right, and the “most free” peak now dominates the landscape.

fhm.2005

That last image comes not long before the run of nine years of consecutive declines in freedom described in the 2015 report. From the report’s subtitle and narrative, you might think the landscape had clearly shifted—maybe not all the way back to the one we saw in the 1970s or even the 1980s, but perhaps to something more like the mid-1990s, when we still had a clear authoritarian peak and a lot of countries seemed to be sliding between the two poles. Well, here’s the actual image:

fhm.2014

That landscape looks a lot more like 2005 than 1995, and it looks nothing like the 1970s or 1980s. The liberal democratic peak still dominates, and the authoritarian peak is still gone. The clumps at 3/3 and 6/5 seem to have slumped a little in the wrong direction, but there are not significant new accretions in any spot. In a better world, we would have seen continued migration toward the “most free” corner over the past decade, but the absence of further improvement is hardly the kind of rollback that phrases like “discarding democracy” and “return to the iron fist” seem to imply.

Freedom House’s topline message is also belied by the trend over time in its count of electoral democracies—that is, countries that hold mostly free and fair elections for the offices that actually make policy. By Freedom House’s own count, the number of electoral democracies around the world actually increased by three in 2014 to an all-time high of 125, or more than two-thirds of all countries. Here’s the online version of their chart of that trend (from this page):

fh.electoraldemocracies.2015

Again, I’m having a hard time seeing democracy being “discarded” in that plot.

So how can both of these things be true? How can the number of electoral democracies grow over a period when annual declines in freedom scores outnumber annual gains?

The answer is that those declines are often occurring in countries that are already governed by authoritarian regimes, and they are often small in size. Meanwhile, some countries are still making jumps from autocracy to democracy that are usually larger in scale than the incremental declines and thus mostly offset the losses in the global tally. So, while those declines are surely bad for the citizens suffering through them, they rarely move countries from one side of the ledger to the other, and they have only a modest effect on the overall level of “freedom” in the system.

This year’s update on the Middle East shows what I mean. In its report, Freedom House identifies only one country in that region that made significant gains in freedom in 2014—Tunisia—against seven that saw declines: Bahrain, Egypt, Iraq, Lebanon, Libya, Syria, and Yemen. All seven of those decliners were already on the authoritarian side of the ledger going into 2014, however, and only four of the declines were large enough to move a country’s rating on one or both of the relevant indices. Meanwhile, Tunisia jumped up two points on political rights in one year, and since 2011 its combined score (political rights + civil liberties) has gone up eight points, from 12 to 4. We see similar patterns in the declines in Eurasia, where nearly all countries already clustered around the “least free” pole, and sub-Saharan Africa, where only one country moved down into Freedom House’s Not Free category (Uganda) and two returned to the set of electoral democracies after holding reasonably fair and competitive elections (Guinea-Bissau and Madgascar).

In short, I continue to believe that Freedom House’s presentation of trends over time in political rights and civil liberties is much gloomier than the world its own data portray. Part of me feels like a jerk for saying so, because I recognize that Freedom House’s messaging is meant to be advocacy, not science, and I support the goals that advocacy is meant to achieve. As a social scientist, though, I also think it’s important that our analyses and decisions be informed by as accurate a sketch of the world as we can draw, so I will keep mumbling into this particular gale.

PS. If you want Freedom House’s data in .csv format, I’ve posted a version of them—including the 2014 updates, which I entered by hand this morning—on my Google Drive, here.

PPS. If you’re curious where I think these trends might be headed in the next 10 years, see this recent post.

PPPS. The day after I ran this post, I published another in which I tried to think of better ways to measure what Freedom House purports to describe in its annual reports. You can read it here.

A Forecast of Global Democratization Trends Through 2025

A couple of months ago, I was asked to write up my thoughts on global trends in democratization over the next five to 10 years. I said at the time that, in coarse terms, I see three plausible alternative futures: 1) big net gains, 2) big net losses, and 3) little net change.

  • By big net gains, I mean a rise in the prevalence of democratic regimes above 65 percent, or, or, because of its size and geopolitical importance, democratization in China absent a sharp decline in the global prevalence of democracy. For big net gains to happen, we would need to see a) one or more clusters of authoritarian breakdown and subsequent democratization in the regions where such clusters are still possible, i.e., Asia, the former Soviet Union, and the Middle East and North Africa (or the aforementioned transition in China); and b) no sharp losses in the regions where democracy is now prevalent, i.e., Europe, the Americas, and sub-Saharan Africa. I consider (a) unlikely but possible (see here) and (b) highly likely. The scenario requires both conditions, so it is unlikely.
  • By big net losses, I mean a drop in the global prevalence of democracy below 55 percent. For that to happen, we would need to see the opposite of big net gains—that is, a) no new clusters of democratization and no democratization in China and b) sharp net losses in one or more of the predominantly democratic regions. In my judgment, (a) is likely but (b) is very unlikely. This outcome depends on the conjunction of (a) and (b), so the low probability of (b) means this outcome is highly unlikely. A reversion to autocracy somewhere in Western Europe or North America would also push us into “big net loss” territory, but I consider that event extremely unlikely (see here and here for why).
  • In the absence of either of these larger shifts, we will probably see little net change in the pattern of the past decade or so: a regular trickle of transitions to and from democracy at rates that are largely offsetting, leaving the global prevalence hovering between 55 and 65 percent. Of course, we could also wind up with little net change in the global prevalence of democracy under a scenario in which some longstanding or otherwise significant authoritarian regimes—for example, China, Russia, Iran, or Saudi Arabia— break down, and those breakdowns spread to interdependent regimes, but most of those breakdowns lead to new authoritarian regimes or short-lived attempts at democracy. This is what we saw in the Arab Spring, and base rates from the past several decades suggest that it is the most likely outcome of any regional clusters of authoritarian breakdown in the next decade or so as well. I consider this version of the little-net-change outcome to be more likely than the other one (offsetting trickles of transitions to and from democracy with no new clusters of regime breakdown). Technically, we could also get to an outcome of little net change through a combination of big net losses in predominantly democratic regions and big gains in predominantly authoritarian regions, but I consider this scenario so unlikely in the next five to 10 years that it’s not worth considering in depth.

I believe the probabilities of big net gains and persistence of current levels are both much greater than the probability of big net losses. In other words, I am generally bullish. For the sake of clarity, I would quantify those guesses as follows:

  • Probability of big net gains: 20 percent
  • Probability of little net change: 75 percent
    • With regime breakdown in one or more critical autocracies: 60 percent
    • Without regime breakdown in any critical autocracies: 15 percent
  • Probability of big net losses: 5 percent

That outlook is informed by a few theoretical and empirical observations.

First, when I talk about democratization, I have in mind expansions of the breadth, depth, and protection of consultation between national political regimes and their citizens. As Charles Tilly argues on p. 24 of his 2007 book, Democracy, “A regime is democratic to the degree that political relations between the state and its citizens feature broad, equal, protected, and mutually binding consultation.” Fair and competitive elections are the most obvious and in some ways the most important form this consultation can take, but they are not the only one. Still, for purposes of observing broad trends and coarsely comparing cases, we can define a democracy as a regime in which officials who actually rule are chosen through fair and competitive elections in which nearly all adult citizens can vote. The fairness of elections depends on the existence of numerous civil liberties, including freedoms of speech, assembly, and association, and the presence of a reasonably free press, so this is not a low bar. Freedom House’s list of electoral democracies is a useful proxy for this set of conditions.

Second, we do not understand the causal processes driving democratization well, and we certainly don’t understand them well enough to know how to manipulate them in order to reliably produce desired outcomes. The global political economy, and the political economies of the states that comprise one layer of it, are parts of a complex adaptive system. This system is too complex for us to model and understand in ways that are more than superficial, partly because it continues to evolve as we try to understand and manipulate it. That said, we have seen some regularities in this system over the past half-century or so:

  • States are more likely to try and then to sustain democratic regimes as their economies grow, their economies become more complex, and their societies transform in ways associated with those trends (e.g., live longer, urbanize, and become more literate). These changes don’t produce transitions, but they do create structural conditions that are more conducive to them.
  • Oil-rich countries have been the exceptions to this pattern, but even they are not impervious (e.g., Mexico, Indonesia). Specifically, they are more susceptible to pressures to democratize when their oil income diminishes, and variation over time in that income depends, in part, on forces beyond their control (e.g., oil prices).
  • Consolidated single-party regimes are the most resilient form of authoritarian rule. Personalist dictatorships are also hard to topple as long as the leader survives but often crumble when that changes. Military-led regimes that don’t evolve into personalist or single-party autocracies rarely last more than a few years, especially since the end of the Cold War.
  • Most authoritarian breakdowns occur in the face of popular protests, and those protests are more likely to happen when the economy is slumping, when food or fuel prices are spiking, when protests are occurring in nearby or similar countries, and around elections. Signs that elites are fighting amongst themselves may also help to spur protests, but elite splits are common in autocracies and often emerge in reaction to protests, not ahead of them.
  • Most attempts at democracy end with a reversion to authoritarian rule, but the chances that countries will try again and then that democracy will stick improve as countries get richer and have tried more times before. The origins of the latter pattern are unclear, but they probably have something to do with the creation of new forms of social and political organization and the subsequent selection and adaptation of those organizations into “fitter” competitors under harsh pressures.

Third, whatever its causes, there is a strong empirical trend toward democratization around the world. Since the middle of the twentieth century, both the share of regimes worldwide that are democratic and the share of the global population living in democratic regimes have expanded dramatically. These expansions have not come steadily, and there is always some churn in the system, but the broader trend persists in spite of those dips and churn

The strength and, so far, persistence of this trend lead me to believe that the global system would have to experience a profound collapse or transformation for that trend to be disrupted. Under the conditions that have prevailed for the past century or so, selection pressures in the global system seem to be running strongly in favor of democratic political regimes with market-based economies.

Crucially, this long-term trend has also proved resilient to the global financial crisis that began in 2007-2008 and has persisted to some degree ever since. This crisis was as sharp a stress test of many national political regimes as we have seen in a while, perhaps since World War II. Democracy has survived this test in all of the world’s wealthy countries, and there was no stampede away from democracy in less wealthy countries with younger regimes. Freedom House and many other activists lament the occurrence of a “democratic recession” over the past several years, but global data just don’t support the claim that one is occurring. What we have seen instead is a slight decline in the prevalence of democratic regimes accompanied by a deepening of authoritarian rule in many of the autocracies that survived the last flurry of democratic transitions.

Meanwhile, some authoritarian regimes in the Middle East and North Africa broke down in the face of uprisings demanding greater popular accountability, and some of those breakdowns led to attempts at democratization—in Tunisia, Egypt, and Libya in particular. Most of those attempts at democratization have since failed, but not all did, Tunisia being the notable exception. What’s more, the popular pressure in favor of democratization has not dissipated in all of the cases where authoritarian breakdown didn’t happen. Bahrain, Kuwait, and, to a lesser extent, Saudi Arabia are notable in this regard.

Rising pressures on China and Russia suggest that similar clusters of regime instability are increasingly likely in their respective neighborhoods, even if they remain unlikely in any given year. China faces significant challenges on numerous fronts, including a slowing economy, a looming real-estate debt crisis, swelling popular frustration over industrial pollution, an uptick in labor activism, an anti-corruption campaign that could alienate some political and military insiders, and a separatist insurgency in Xinjiang. No one of those challenges is necessarily likely to topple the regime, but the presence of so many of them at once adds up to a significant risk (or opportunity, depending on one’s perspective). A regime crisis in China could ripple through its region with strongest effect on the most dependent regimes—on North Korea in particular, but also perhaps Vietnam, Laos, and Myanmar. Even if a crisis there didn’t reverberate, China’s population size and rising international influence imply that any movement toward democracy would have a significant impact on the global balance sheet.

The Russian regime is also under increased pressure, albeit for different reasons. Russia is already in recession, and falling oil prices and capital flight are making things much worse without much promise of near-term relief. U.S. and E.U. sanctions deserve significant credit (or blame) for the acceleration of capital flight, and prosecution of the war in Ukraine is also imposing additional direct costs on Russia’s power resources. The extant regime has survived domestic threats before, but 10 more years is a long time for a regime that stands on feet of socioeconomic clay.

Above all else, these last two points—about 1) the resilience of existing democracies to the stress of the past several years and 2) the persistence and even deepening of pressures on many surviving authoritarian regimes—are what make me bullish about the prospects for democracy in next five to 10 years. In light of current trends in China and Russia, I have a hard time imagining both of those regimes surviving to 2025. Democratization might not follow, and if it does, it won’t necessarily stick, at least not right away. Neither regime can really get a whole lot more authoritarian than it is now, however, so the possibilities for change on this dimension are nearly all on the upside. (The emergence of a new authoritarian regime that is more aggressive abroad is also possible in both cases, but that topic is beyond the scope of this memo.)

Talk about the possibility of a wave of democratic reversals usually centers on the role China or Russia might play as either an agent of de-democratization or example of an alternative future. As noted above, though, both of these systems are currently facing substantial stresses at home. These stresses both limit their ability to act as agents of de-democratization and take the shine off any example they might set.

In short, I think that talk of Russia and China’s negative influence on the global democratization trend is overblown. Apart from the (highly unlikely) invasion and successful occupation of other countries, I don’t think either of these governments has the ability to undo democratization elsewhere. Both can and do help some other authoritarian regimes survive, however, and this is why regime crisis or breakdown in either one of them has the potential to catalyze new clusters of regime instability in their respective neighborhoods.

What do you think? If you made it this far and have any (polite) reactions you’d like to share, please leave a comment.

Reactions to Reflections on the Arab Uprisings

Yesterday, Marc Lynch posted a thoughtful and candid set of reflections on how political scientists who specialize in the Middle East performed as analysts and forecasters during the Arab uprisings, not before them, the subject on which most of the retrospectives have focused thus far. The background to the post is a set of memos Marc commissioned from the contributors to a volume he edited on the origins of the uprisings. As Marc summarizes, their self-criticism is tough:

We paid too much attention to the activists and not enough to the authoritarians; we understated the importance of identity politics; we assumed too quickly that successful popular uprisings would lead to a democratic transition; we under-estimated the key role of international and regional factors in domestic outcomes; we took for granted a second wave of uprisings, which thus far has yet to materialize; we understated the risk of state failure and over-stated the possibility of democratic consensus.

Social scientists and other professional analysts of world affairs should read the whole thing—if not for the specifics, then as an example of how to assess and try to learn from your own mistakes. Here, I’d like to focus on three points that jumped out at me as I read it.

The first is the power of motivated reasoning—”the unconscious tendency of individuals to process information in a manner that suits some end or goal extrinsic to the formation of accurate beliefs.” When we try to forecast politics in real time, we tend to conflate our feelings about specific events or trends with their likelihood. After noting that he and his colleagues over-predicted democratization, Marc observes:

One point that emerged in the workshop discussions is the extent to which we became too emotionally attached to particular actors or policies. Caught up in the rush of events, and often deeply identifying with our networks of friends and colleagues involved in these politics, we may have allowed hope or passion to cloud our better comparative judgment.

That pattern sounds a lot like the one I saw in my own thinking when I realized that my initial forecasts about the duration and outcome of the Syrian civil war had missed badly.

This tendency is probably ubiquitous, but it’s also one about which we can actually do something, even if we can’t eliminate it. Whenever we’re formulating an analysis or prediction, we can start by ask ourselves what result we hope to see and why, and we can think about how that desire might relate to the conclusions we’re reaching. We can try to imagine how someone with different motivations might view the same situation, or just seek out examples of those alternative views. Finally, we can weight or adjust our own analysis accordingly. Basically, we can try to replicate in our own analysis what “wisdom of crowds” systems do to great effect on a larger scale. This exercise can’t fully escape the cognitive traps to which it responds, but I think it can at least mitigate their influence.

Second, Marc’s reflections also underscore our tendency to underestimate the prevalence of inertia in politics, especially during what seem like exceptional times. As I recently wrote, our analytical eyes are drawn to the spectacular and dynamic, but on short time scales at least, continuity is the norm. Observers hoping for change in the countries touched by the Arab uprisings would have done well to remember this fact—and surely some did—when they were trying to assess how much structural change those uprisings would actually produce.

My last point concerns the power of social scientists to shape these processes as they unfold. In reflecting on his own analysis, Marc notes that he correctly saw how the absence of agreement on the basic rules of politics would complicate transitions, but he “was less successful in figuring out how to overcome these problems.” Marc aptly dubs this uncertainty Calvinball, and he concludes:

I’m more convinced than ever that moving beyond Calvinball is essential for any successful transition, but what makes a transitional constitutional design process work—or fail—needs a lot more attention.

Actually, I don’t think the problem is a lack of attention. How to escape this uncertainty in a liberal direction has been a central concern for decades now of scholarship on democratization and the field of applied democracy promotion that’s grown up alongside it. Giuseppe di Palma’s 1990 book, To Craft Democracies, remains a leading example on the kind of advocacy-cum-scholarship this field has produced, but there are countless “lesson learned” white papers and “best practices” policy briefs to go with it.

No, the real problem is that transitional periods are irreducibly fraught with the uncertainties Marc rightly spotlighted, and there simply are no deus-ex-machina resolutions to them. When scholars and practitioners do get involved, we are absorbed into the politics we mean to “correct,” and most of us aren’t nearly as adept in that field as we are in our own. After a couple of decades of closely watching these transitions and the efforts of various parties to point them in particular directions, I have come to believe that this is one of those things social science can help us understand but not “fix.”

The era of democratization is not over

In the latest issue of the Journal of Democracy, (PDF), Marc Plattner makes the provocative claim that “the era of democratic transitions is over, and should now become the province of the historians.” By that, he seems to mean that we should not expect new waves of democratization similar in form and scale to the ones that have occurred before. I think Plattner is wrong, in part because he has defined “wave” too broadly. If we tighten up that concept a bit, I think we can see at least a few possibilities for new waves in the not-too-distant future, and thus an extension of the now–long-running era of democratization.

In his essay, Plattner implicitly adopts the definition of waves of democratization described by Samuel Huntington on p. 15 of his influential 1991 book:

A wave of democratization is a group of transitions from nondemocratic to democratic regimes that occur within a specified period of time and that significantly outnumber transitions in the opposite direction during that period of time.

Much of what’s been written and said about waves of democratization since that book was published accepts those terms and the three waves Huntington identifies when he applies them to the historical evidence: one in Europe from the 1820s to the 1920s; another and wider one in Europe, Latin America, and Asia from the 1940s to the early 1960s; and a third and so-far final one that began in Portugal in 1974, has been global in scope, and now appears to have stalled or ended.

I find Huntington’s definition and resulting periodization wanting because they focus on the what and don’t pay enough attention to the why. A large number of transitions might occur around the same time because they share common underlying causes; because they cause and reinforce each other; or as a matter of chance, when independent events just happen to cluster. The third possibility is not scientifically interesting (cf. the Texas sharpshooter fallacy). More relevant here, though, I think the first two become banal if we let the time lag or chain of causality stretch too far. We inhabit a global system; at some level, everything causes, and is caused by, everything else. For the wave idea to be scientifically useful, we have to restrict its use to clusters of transitions that share common, temporally proximate causes and/or directly cause and reinforce each other.

By that definition, I think we can make out at least five and maybe more such waves since the early 1900s, not the three or maybe four we usually hear about.

First, as Plattner  (p. 9) points out, what Huntington describes as the “first, long” wave really includes two distinct clusters: 1) the “dozen or so European and European-settler countries that already had succeeded in establishing a fair degree of freedom and rule of law, and then moved into the democratic column by gradually extending the suffrage”; and 2) “countries that became democratic after World War I, many of them new nations born from the midst of the European empires defeated and destroyed during the war.”

The second (or now third?) wave grew out of World War II. Even though this wave was relatively short, it also included a few distinct sub-clusters: countries defeated in that war, countries born of decolonization, and a number of Latin American cases. This wave is more coherent, in that all of these sub-clusters were at least partially nudged along by the war’s dynamics and outcomes. It wouldn’t be unreasonable to split the so-called second wave into two clusters (war losers and newly independent states) and a clump of coincidences (Latin America), but there are enough direct linkages across those sets to see meaning in a larger wave, too.

As for the so-called third wave, I’m with Mike McFaul (here) and others who see at least two separate clusters in there. The wave of democratization that swept southern Europe and Latin America in the 1970s and early 1980s is temporally and causally distinct from the spate of transitions associated with the USSR’s reform and disintegration, so it makes no sense to talk of a coherent era spanning the past 40 years. Less clear is where to put the many democratic transitions—some successful, many others aborted or short lived—that occurred in Africa as Communist rule collapsed. Based partly on Robert Bates’ analysis (here), I am comfortable grouping them with the post-Communist cases. Trends in the global economy and the disappearance of the USSR as a patron state directly affected many of these countries, and political and social linkages within and across these regional sets also helped to make democratization contagious once it started.

So, based on that definition and its application, I think it’s fair to say that we have seen at least five waves of democratization in the past two centuries, and perhaps as many as six or seven.

Given that definition, I think it’s also easier to see possibilities for new waves, or “clusters” if we want to make clearer the distinction from conventional usage. Of course, the probability of any new waves is partially diminished by the success of the earlier ones. Nearly two-thirds of the world’s countries now have regimes that most observers would call democratic, so the pool of potential democratizers is substantially diminished. As Plattner puts it (p. 14), “The ‘low-hanging fruit’ has been picked.” Still, if we look for groups of authoritarian regimes that share enough political, economic, social, and cultural connections to allow common causes and contagion to kick in, then I think we can find some sets in which this dynamic could clearly happen again. I see three in particular.

The first and most obvious is in the Middle East and North Africa, the region that has proved most resistant to democratization to date. In fact, I think we already saw—or, arguably, are still seeing—the next wave of democratization in the form of the Arab Spring and its aftermath. So far, that cluster of popular uprisings and state collapses has only produced one persistently democratic state (Tunisia), but it has also produced a democratic interlude in Egypt; a series of competitively elected (albeit ineffective) governments in Libya; a nonviolent transfer of power between elected governments in Iraq; ongoing (albeit not particularly liberal) revolutions in Syria and Yemen; and sustained, liberal challenges to authoritarian rule in Bahrain, Kuwait, and, perhaps, Saudi Arabia. In other words, a lot of countries are involved, and it ain’t over yet. Most of the Soviet successor states never really made it all the way to democracy, but we still think of them as an important cluster of attempts at democratization. I think the Arab Spring fits the same mold.

Beyond that, though, I also see the possibility of a wave of regime breakdowns and attempts at democracy in Asia brought on by economic or political instability in China. Many of the autocracies that remain in that region—and there are many—depend directly or indirectly on Chinese patronage and trade, so any significant disruption in China’s political economy would send shock waves through their systems as well. I happen to think that systemic instability will probably hit China in the next few years (see here, here, and here), but the timing is less relevant here than the possibility of this turbulence, and thus of the wider wave of democratization it could help to produce.

Last and probably least in its scope and impact, I think we can also imagine a similar cluster occurring in Eurasia in response to instability in Russia. The number of countries enmeshed in this network is smaller, but the average strength of their ties is probably similar.

I won’t hazard guesses now about the timing and outcome of the latter two possibilities beyond what I’ve already written about China’s increasing fragility. As the Arab Spring has shown, even when we can spot the stresses, it’s very hard to anticipate when they’ll overwhelm the sources of negative feedback and what form the new equilibrium will take. What I hope I have already done, though, is to demonstrate that, contra Plattner, there’s plenty of room left in the system for fresh waves of democratization. In fact, I think we even have a pretty good sense of where and how those waves are most likely to come.

Deriving a Fuzzy-Set Measure of Democracy from Several Dichotomous Data Sets

In a recent post, I described an ongoing project in which Shahryar Minhas, Mike Ward, and I are using text mining and machine learning to produce fuzzy-set measures of various political regime types for all countries of the world. As part of the NSF-funded MADCOW project,* our ultimate goal is to devise a process that routinely updates those data in near-real time at low cost. We’re not there yet, but our preliminary results are promising, and we plan to keep tinkering.

One of crucial choices we had to make in our initial analysis was how to measure each regime type for the machine-learning phase of the process. This choice is important because our models are only going to be as good as the data from which they’re derived. If the targets in that machine-learning process don’t reliably represent the concepts we have in mind, then the resulting models will be looking for the wrong things.

For our first cut, we decided to use dichotomous measures of several regime types, and to base those dichotomous measures on stringent criteria. So, for example, we identified as democracies only those cases with a score of 10, the maximum, on Polity’s scalar measure of democracy. For military rule, we only coded as 1 those cases where two major data sets agreed that a regime was authoritarian and only military-led, with no hybrids or modifiers. Even though the targets of our machine-learning process were crisply bivalent, we could get fuzzy-set measures from our classifiers by looking at the probabilities of class membership they produce.

In future iterations, though, I’m hoping we’ll get a chance to experiment with targets that are themselves fuzzy or that just take advantage of a larger information set. Bayesian measurement error models offer a great way to generate those targets.

Imagine that you have a set of cases that may or may not belong in some category of interest—say, democracy. Now imagine that you’ve got a set of experts who vote yes (1) or no (0) on the status of each of those cases and don’t always agree. We can get a simple estimate of the probability that a given case is a democracy by averaging the experts’ votes, and that’s not necessarily a bad idea. If, however, we suspect that some experts are more error prone than others, and that the nature of those errors follows certain patterns, then we can do better with a model that gleans those patterns from the data and adjusts the averaging accordingly. That’s exactly what a Bayesian measurement error model does. Instead of an unweighted average of the experts’ votes, we get an inverse-error-rate-weighted average, which should be more reliable than the unweighted version if the assumption about predictable patterns in those errors is largely correct.

I’m not trained in Bayesian data analysis and don’t know my way around the software used to estimate these models, so I sought and received generous help on this task from Sean J. Taylor. I compiled yes/no measures of democracy from five country-year data sets that ostensibly use similar definitions and coding criteria:

  • Cheibub, Gandhi, and Vreeland’s Democracy and Dictatorship (DD) data set, 1946–2008 (here);
  • Boix, Miller, and Rosato’s dichotomous coding of democracy, 1800–2007 (here);
  • A binary indicator of democracy derived from Polity IV using the Political Instability Task Force’s coding rules, 1800–2013;
  • The lists of electoral democracies in Freedom House’s annual Freedom in the World reports, 1989–2013; and
  • My own Democracy/Autocracy data set, 1955–2010 (here).

Sean took those five columns of zeroes and ones and used them to estimate a model with no prior assumptions about the five sources’ relative reliability. James Melton, Stephen Meserve, and Daniel Pemstein use the same technique to produce the terrific Unified Democracy Scores. What we’re doing is a little different, though. Where their approach treats democracy as a scalar concept and estimates a composite index from several measures, we’re accepting the binary conceptualization underlying our five sources and estimating the probability that a country qualifies as a democracy. In fuzzy-set terms, this probability represents a case’s degree of membership in the democracy set, not how democratic it is.

The distinction between a country’s degree of membership in that set and its degree of democracy is subtle but potentially meaningful, and the former will sometimes be a better fit for an analytic task than the latter. For example, if you’re looking to distinguish categorically between democracies and autocracies in order to estimate the difference in some other quantity across the two sets, it makes more sense to base that split on a probabilistic measure of set membership than an arbitrarily chosen cut point on a scalar measure of democracy-ness. You would still need to choose a threshold, but “greater than 0.5″ has a natural interpretation (“probably a democracy”) that suits the task in a way that an arbitrary cut point on an index doesn’t. And, of course, you could still perform a sensitivity analysis by moving the cut point around and seeing how much that choice affects your results.

So that’s the theory, anyway. What about the implementation?

I’m excited to report that the estimates from our initial measurement model of democracy look great to me. As someone who has spent a lot of hours wringing my hands over the need to make binary calls on many ambiguous regimes (Russia in the late 1990s? Venezuela under Hugo Chavez? Bangladesh between coups?), I think these estimates are accurately distinguishing the hazy cases from the rest and even doing a good job estimating the extent of that uncertainty.

As a first check, let’s take a look at the distribution of the estimated probabilities. The histogram below shows the estimates for the period 1989–2007, the only years for which we have inputs from all five of the source data sets. Voilà, the distribution has the expected shape. Most countries most of the time are readily identified as democracies or non-democracies, but the membership status of a sizable subset of country-years is more uncertain.

Estimated Probabilities of Democracy for All Countries Worldwide, 1989-2007

Estimated Probabilities of Democracy for All Countries Worldwide, 1989-2007

Of course, we can and should also look at the estimates for specific cases. I know a little more about countries that emerged from the collapse of the Soviet Union than I do about the rest of the world, so I like to start there when eyeballing regime data. The chart below compares scores for several of those countries that have exhibited more variation over the past 20+ years. Most of the rest of the post-Soviet states are slammed up against 1 (Estonia, Latvia, and Lithuania) or 0 (e.g., Uzbekistan, Turkmenistan, Tajikistan), so I left them off the chart. I also limited the range of years to the ones for which data are available from all five sources. By drawing strength from other years and countries, the model can produce estimates for cases with fewer or even no inputs. Still, the estimates will be less reliable for those cases, so I thought I would focus for now on the estimates based on a common set of “votes.”

Estimated Probability of Democracy for Selected Soviet Successor States, 1991-2007

Estimated Probability of Democracy for Selected Soviet Successor States, 1991-2007

Those estimates look about right to me. For example, Georgia’s status is ambiguous and trending less likely until the Rose Revolution of 2003, after which point it’s probably but not certainly a democracy, and the trend bends down again soon thereafter. Meanwhile, Russia is fairly confidently identified as a democracy after the constitutional crisis of 1993, but its status becomes uncertain around the passage of power from Yeltsin to Putin and then solidifies as most likely authoritarian by the mid-2000s. Finally, Armenia was one of the cases I found most difficult to code when building the Democracy/Autocracy data set for the Political Instability Task Force, so I’m gratified to see its probability of democracy oscillating around 0.5 throughout.

One nice feature of a Bayesian measurement error model is that, in addition to estimating the scores, we can also estimate confidence intervals to help quantify our uncertainty about those scores. The plot below shows Armenia’s trend line with the upper and lower bounds of a 90-percent confidence interval. Here, it’s even easier to see just how unclear this country’s democracy status has been since it regained independence. From 1991 until at least 2007, its 90-percent confidence interval straddled the toss-up line. How’s that for uncertain?

Armenia's Estimated Probability of Democracy with 90% Confidence Interval

Armenia’s Estimated Probability of Democracy with 90% Confidence Interval

Sean and I are still talking about ways to tweak this process, but I think the data it’s producing are already useful and interesting. I’m considering using these estimates in a predictive model of coup attempts and seeing if and how the results differ from ones based on the Polity index and the Unified Democracy Scores. Meanwhile, the rest of the MADCOW crew and I are now talking about applying the same process to dichotomous indicators of military rule, one-party rule, personal rule, and monarchy and then experimenting with machine-learning processes that use the results as their targets. There are lots of moving parts in our regime data-making process, and this one isn’t necessarily the highest priority, but it would be great to get to follow this path and see where it leads.

* NSF Award 1259190, Collaborative Research: Automated Real-time Production of Political Indicators

Indonesia’s Elections Offer Some Light in the Recent Gloom

The past couple of weeks have delivered plenty of terrible news, so I thought I would take a moment to call out a significant positive development: Indonesia held a presidential election early this month; there were no coup attempts and little violence associated with that balloting; and the contest was finally won by the guy who wasn’t threatening to dismantle democracy.

By my reckoning, this outcome should increase our confidence that Indonesia now deserves to be called a consolidated democracy, where “consolidated” just means that the risk of a reversion to authoritarian rule is low. Democracies are most susceptible to those reversions in their first 15–20 years (here and here), especially when they are poor and haven’t yet seen power passed from one party to another (here).

Indonesia now looks reasonably solid on all of those counts. The current democratic episode began nearly 15 years ago, in 1999, and the country has elected three presidents from as many parties since then—four if we count the president-elect. Indonesia certainly isn’t a rich country, but it’s not exactly poor any more, either. With a GDP per capita of approximately $3,500, it now lands near the high end of the World Bank’s “lower middle income” tier. Together, those features don’t describe a regime that we would expect to be immune from authoritarian reversal, but the elections that just occurred put that system through a major stress test, and it appears to have passed.

Some observers would argue that the country’s democratic regime already crossed the “consolidated” threshold years ago. When I described Indonesia as a newly consolidated democracy on Twitter, Indonesia specialist Jeremy Menchik noted that colleagues William Liddle and Saiful Mujani had identified Indonesia as being consolidated since 2004 and said that he agreed with them. Meanwhile, democratization experts often use the occurrence of one or two peaceful transfers of power as a rule of thumb for declaring democracies consolidated, and Indonesia had passed both of those tests before the latest election campaign even began.

Of course, it’s easy to say in hindsight that the risk of an authoritarian reversal in Indonesia around this election was low. We shouldn’t forget, though, that there was a lot of anxiety during the campaign about how the eventual loser, Prabowo Subianto, might dismantle democracy if he were elected, and in the end he only lost by a few percentage points. What’s more, the kind of “reforms” at which Prabowo hinted are just the sorts of things that have undone many other attempts at democracy in the past couple of decades. There were also rumors of coup plots, especially during the nerve-wracking last few weeks of the campaign until the official results were announced (see here, for example). Some seasoned observers of Indonesian politics with whom I spoke were confident at the time that those plots would not come to pass, but the fact that those rumors existed and were anxiously discussed in some quarters suggests that they were at least plausible, even if they weren’t probable. Last but not least, statistical modeling by Milan Svolik suggests that a middle-income presidential democracy like Indonesia’s won’t really be “cured” of its risk of authoritarian reversal until it gets much wealthier (see the actuarial tables on p. 43 in this excellent paper, which was later published in the American Political Science Review).

Even bearing those facts and Milan’s tables in mind, I think it’s fair to say that Indonesia now qualifies as a consolidated democracy, in the specific sense that the risk of an authoritarian reversal is now quite small and will remain so. If that’s right, then four of the world’s five most populous countries now fit under that label. The democratic regimes in India, the United States, Indonesia, and Brazil—roughly 2 billion citizens among them—all have lots of flaws, but the increased prevalence and persistence of democracy among the world’s largest countries is still a very big deal in the long course of human affairs. And, who knows, maybe China will finally join them in the not-too-distant future?

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