Elites, Beliefs, and Learning

We consider the decision problem of the decisive group in power in country $$ i $$ at time $$ t $$.Footnote ⁷ This decision-maker faces a choice between autocracy, $$ {D}_{i,t}=0 $$, and democracy, $$ {D}_{i,t}=1 $$. The incumbent’s objective is to retain power in period $$ t+1 $$. Let $$ {Y}_{i,t} $$ denote country $$ i $$’s per capita GDP in period $$ t $$, and let $$ {y}_{i,t}\equiv \log \left({Y}_{i,t}\right)-\log \left({Y}_{i,t-1}\right) $$ denote its growth rate. At the beginning of period $$ t $$, the incumbent solves

(1)$$ \underset{D_{i,t}\in \left\{0,1\right\}}{\max }{E}_{i,t-1}\left[\frac{\exp \left({\alpha}_i+{\theta}^{D={D}_{i,t}}{y}_{i,t}-{K}_{i,t}{D}_{i,t}\right)}{1+\exp \left({\alpha}_i+{\theta}^{D={D}_{i,t}}{y}_{i,t}-{K}_{i,t}{D}_{i,t}\right)}|{D}_{i,t}\right],$$

where the integrand represents the probability of remaining in power in period $$ t+1 $$, and the expectation is taken with respect to $$ {y}_{i,t} $$, as explained below, conditional on the information available in country $$ i $$ at the conclusion of period $$ t-1 $$. The integrand is increasing in the index $$ {\alpha}_i+{\theta}^{D={D}_{i,t}}{y}_{i,t}-{K}_{i,t}{D}_{i,t} $$, where coefficient $$ {\alpha}_i $$ establishes a baseline for country $$ i $$, coefficients $$ {\theta}^{D=0} $$ and $$ {\theta}^{D=1} $$ respectively measure the (de)stabilizing effect of GDP growth on elite turnover under autocracy and democracy, and $$ {K}_{i,t} $$ captures the political cost of democracy to the incumbent—that is, its direct effect on the likelihood of retaining power.

The incumbent chooses $$ {D}_{i,t} $$ at the start of period $$ t $$, forming a subjective forecast of its effect on GDP growth, $$ {y}_{i,t} $$, to solve (1).Footnote ⁸ Incumbents believe that the relationship between GDP growth and democracy takes the form

(2)$$ {\displaystyle \begin{array}{ccc}{y}_{i,t}=\left(1-{D}_{i,t}\right){\beta}_i^{D=0}+{D}_{i,t}{\beta}_i^{D=1}+{\epsilon}_{i,t},\end{array}} $$

where $$ {\beta}_i^{D=0} $$ and $$ {\beta}_i^{D=1} $$ denote country $$ i $$’s long-run GDP growth rates under autocracy and democracy, respectively, and $$ {\epsilon}_{i,t} $$ is an exogenous shock to growth that is uncorrelated over time but potentially correlated across countries.Footnote ⁹ Specifically, the vector $$ {\epsilon}_t\equiv {\left[{\epsilon}_{1,t},\dots, {\epsilon}_{n,t}\right]}^{\prime } $$ of GDP growth shocks across the $$ n $$ countries of the world is independently and identically distributed (i.i.d.) over time according to a mean-zero Normal distribution with covariance matrix $$ \Sigma $$, i.e.,

$$ {\epsilon}_t\overset{\mathrm{i}.\mathrm{i}.\mathrm{d}.}{\sim }N\left(0,\Sigma \right). $$

Incumbents do not know $$ {\beta}_i\equiv {\left[{\beta}_i^{D=0},,,{\beta}_i^{D=1}\right]}^{\prime } $$, but they have perfect knowledge of all other features of the model, including $$ {K}_{i,t} $$, at the time of their choice.Footnote ¹⁰

The timing of events is as follows. At the end of period $$ t-1 $$, incumbents collect data on worldwide GDP growth rates and systems of government, and they update their beliefs about long-run economic growth under autocracy and democracy accordingly. At the beginning of period $$ t $$, incumbents observe $$ {K}_{i,t} $$ and decide what system of government, $$ {D}_{i,t} $$, to adopt that period. Growth rates conditional on incumbents’ choices are then realized, which together determine elite turnover.

Learning

In period $$ t=0 $$, incumbents start out with a Normal prior over the vector of unknown long-run GDP growth rates $$ \beta \equiv {\left[{\beta}_1^{D=0},\dots, {\beta}_n^{D=0},{\beta}_1^{D=1},\dots, {\beta}_n^{D=1}\right]}^{\prime } $$,

(3)$$ {\displaystyle \begin{array}{ccc}\beta \sim N\left({\bar {\;\beta}}_0,,,{P}_0^{-1}\right),\end{array}} $$

where $$ {\bar {\;\beta}}_0 $$ and P ₀ denote, respectively, the prior mean and precision matrix. We assume that incumbents’ initial beliefs assign no correlation and the same degree of uncertainty to growth under autocracy and democracy:

$$ {P}_0^{-1}={I}_2\otimes (VRV), $$

where $$ V=\operatorname{diag}\left(\left[{v}_1{\sigma}_1,\dots, {v}_n{\sigma}_n\right]\right) $$ is a diagonal matrix whose $$ i $$th diagonal entry measures prior uncertainty (standard deviation) about country $$ i $$’s long-run growth rate under autocracy/democracy, and $$ R $$ is the cross-country prior correlation matrix. Prior uncertainty is parameterized by $$ {\left\{{v}_i\right\}}_{i=1}^n $$, normalized by the standard deviation of growth shocks in each country $$ {\left\{{\sigma}_i\right\}}_{i=1}^n $$ (the square roots of the diagonal elements of $$ \Sigma $$).

Our assumptions yield simple, recursive, Bayesian updating formulas for beliefs in each period: letting $$ {D}_t\equiv {\left[{D}_{1,t},\dots, {D}_{n,t}\right]}^{\prime } $$ and $$ {y}_t\equiv {\left[{y}_{1,t},\dots, {y}_{n,t}\right]}^{\prime } $$,

$$ {\displaystyle \begin{array}{cccc}& {P}_t={P}_{t-1}+{\mathbf{D}}_t^{\prime }{\Sigma}^{-1}{\mathbf{D}}_t,& & \\ {}& {\bar {\;\beta}}_t={\bar {\;\beta}}_{t-1}+{P}_t^{-1}{\mathbf{D}}_t^{\prime }{\Sigma}^{-1}\left({y}_t-{\mathbf{D}}_t{\bar {\;\beta}}_{t-1}\right),& & \end{array}} $$

where $$ {\mathbf{D}}_t\equiv \left[\operatorname{diag}\left(1-{D}_t\right),\operatorname{diag}\left({D}_t\right)\right] $$ is an $$ n\times (2n) $$ matrix such that the $$ i $$th element of the vector $$ {\mathbf{D}}_t{\bar {\;\beta}}_{t-1} $$ equals $$ \left(1-{D}_{i,t}\right){\bar {\;\beta}}_{i,t-1}^{D=0}+{D}_{i,t}{\bar {\;\beta}}_{i,t-1}^{D=1} $$. The impact of new data on the posterior mean $$ {\bar{\beta}}_t $$ is determined by $$ {P}_t^{-1}{\mathbf{D}}_t^{\prime }{\Sigma}^{-1} $$, which depends on three key factors. First, higher initial uncertainty in beliefs (higher $$ {\left\{{v}_i\right\}}_{i=1}^n $$) raises the relative precision of new information, increasing its impact. Second, higher correlation in growth shocks across countries (off-diagonal elements of $$ \Sigma $$) reduces the informational content of observed growth rates and slows down learning. Lastly, higher cross-country correlation in initial beliefs (off-diagonal elements of $$ R $$) increases belief responsiveness to data from other countries.

We allow incumbents to potentially learn more from neighboring (or more similar) countries. Letting $$ {Z}_{i,j} $$ denote a vector that may include various measures of distance (geographic or otherwise) between countries $$ i $$ and $$ j $$, we write

$$ {R}_{i,j}=\exp \left(-{Z}_{i,j}^{\prime}\gamma \right), $$

where $$ \gamma $$ is constrained to be nonnegative to ensure correlations between 0 and 1.Footnote ¹¹

Incumbents’ Optimal Choice

While incumbents observe the political cost of democracy, $$ {K}_{i,t} $$, prior to choosing $$ {D}_{i,t} $$, this cost is unobservable to the researcher. We assume that $$ {K}_{i,t} $$ has the following structure:

(4)$$ {\displaystyle \begin{array}{ccc}{K}_{i,t}={f}_i+{X}_{i,t}^{\prime}\xi +{\kappa}_{i,t}.\end{array}} $$

Coefficient f_i establishes a country-specific baseline, and the control vector X_i,t may include various observable economic and political characteristics of country i (e.g., lagged per capita GDP or incumbents’ time in power). Every period, country i also experiences an exogenous idiosyncratic shock, $$ {\kappa}_{i,t} $$, to the political cost of democracy, where

$$ {\kappa}_{i,t}\sim N\left(0,{\varsigma}_i^2\right). $$

The volatility of shocks to the political cost of democracy, $$ {\varsigma}_i $$, is allowed to be country-specific, but $$ {\kappa}_{i,t} $$ is assumed to be independently distributed over time and across countries.

As discussed, when choosing $$ {D}_{i,t} $$ incumbents have perfect knowledge of $$ {K}_{i,t} $$ and all features of the model except for the effect of their choice on GDP growth, $$ {y}_{i,t} $$. Together, (1), (2), and (4) imply that the optimal choice for country $$ i $$’s incumbent at time $$ t $$ is

(5)$$ {\displaystyle \begin{array}{cc}\begin{array}{cc}{D}_{i,t}=1\left\{{E}_{i,t-1}\left[\frac{\exp \left({\alpha}_i+{\theta}^{D=1}\left({\beta}_i^{D=1}+{\epsilon}_{i,t}\right)-{f}_i-{X}_{i,t}^{\prime}\xi -{\kappa}_{i,t}\right)}{1+\exp \left({\alpha}_i+{\theta}^{D=1}\left({\beta}_i^{D=1}+{\epsilon}_{i,t}\right)-{f}_i-{X}_{i,t}^{\prime}\xi -{\kappa}_{i,t}\right)}\right]\right.& \\ {}\left.>{E}_{i,t-1}\left[\frac{\exp \left({\alpha}_i+{\theta}^{D=0}\left({\beta}_i^{D=0}+{\epsilon}_{i,t}\right)\right)}{1+\exp \left({\alpha}_i+{\theta}^{D=0}\left({\beta}_i^{D=0}+{\epsilon}_{i,t}\right)\right)}\right]\right\}, \end{array}& \end{array}} $$

where the expectations are taken only with respect to $$ {\beta}_i $$ and $$ {\epsilon}_{i,t} $$ in accordance with the incumbent’s beliefs at the conclusion of period $$ t-1 $$.

Empirical Strategy

Like incumbents in our model, we adopt a Bayesian inference approach to recover the unknown structural parameters of our model, listed in Table 1.Footnote ¹⁵ Let the vector $$ \varphi $$ collect all the parameters in Table 1, and let $$ {I}_{i,t} $$ be an indicator of whether the incumbent in country $$ i $$ retained power ($$ {I}_{i,t}=1 $$ ) or not ($$ {I}_{i,t}=0 $$ ) at the conclusion of period $$ t $$. Denote by $$ {W}^T\equiv {\left\{{I}_t,{y}_t,{D}_t,{X}_t\right\}}_{t=1}^T $$ the set of all data available up to period $$ T $$, where $$ {I}_t\equiv {\left[{I}_{1,t}\dots, {I}_{n,t}\right]}^{\prime } $$ and $$ {X}_t\equiv {\left[{X}_{1,t}\dots, {X}_{n,t}\right]}^{\prime } $$. Our goal is to estimate the true value of $$ \varphi $$ by computing the mode of the posterior distribution of the model parameters,

$$ p\left(\varphi |{W}^T\right)\kern0.5em \propto \kern0.5em \mathbf{\mathcal{L}}\left({W}^T|\varphi \right)\pi \left(\varphi \right), $$

given the likelihood of the data, $$ \mathbf{\mathcal{L}} $$, and our prior, $$ \pi $$. We describe $$ \mathbf{\mathcal{L}} $$ and $$ \pi $$ in turn.

TABLE 1.

Model Parameters

Likelihood of the Data

While the structure of our model described thus far specifies incumbents’ beliefs about how the data are generated as well as their optimal choices given those beliefs, we have refrained from specifying the “true” data generating process (DGP). Below, to perform counterfactual experiments, we discuss and specify the true DGP. Next, we only make one key assumption about the true DGP that simplifies inference about the model parameters.

We assume that observed outcomes are only affected by actual choices and not by the beliefs that led to those choices. That is, transitions of power ($$ {I}_t $$), GDP growth ($$ {y}_t $$), and other economic and political characteristics of countries ($$ {X}_t $$) are shaped by realized institutions ($$ {D}_t $$), but they are not directly affected by beliefs about the potential effects of transitioning into or out of democracy. Formally, this assumption implies that the parameters in Table 1 are only involved in the component of the likelihood that describes the conditional probabilities of countries’ observed systems of government. While we relegate a full derivation of the likelihood to Online Appendix A3, with a slight abuse of notation—using $$ \mathbf{\mathcal{L}} $$ to denote arbitrary densities of the data—it can be written as

$$ \mathbf{\mathcal{L}}\left({W}^T|\varphi \right)\kern.5em \propto {\Pi}_{t=1}^T{\Pi}_{i=1}^n\mathbf{\mathcal{L}}\left({D}_{i,t}|{X}_{i,t},{W}^{t-1},\varphi \right), $$

where

(6)$$ {\displaystyle \begin{array}{ccc}\mathbf{\mathcal{L}}\left({D}_{i,t}|{X}_{i,t},{W}^{t-1},\varphi \right)=\Phi {\left(\frac{{\overline{\kappa}}_{i,t}\left({X}_{i,t},{W}^{t-1},\varphi \right)}{\varsigma_i}\right)}^{D_{i,t}}{\left[1-\Phi \left(\frac{{\overline{\kappa}}_{i,t}\left({X}_{i,t},{W}^{t-1},\varphi \right)}{\varsigma_i}\right)\right]}^{1-{D}_{i,t}},\end{array}} $$

$$ \Phi $$ denotes the standard Normal cumulative distribution function, and $$ {\overline{\kappa}}_{i,t}\left({X}_{i,t},{W}^{t-1},\varphi \right) $$ is the threshold value of $$ {\kappa}_{i,t} $$—the realized shock in period $$ t $$ to the political cost of democracy in country $$ i $$—that leaves country $$ i $$’s incumbent indifferent between autocracy and democracy. Note that (6) resembles the likelihood of a standard binary-choice Probit model. However, $$ {\overline{\kappa}}_{i,t}\left({X}_{i,t},{W}^{t-1},\varphi \right) $$ is a nonlinear function (with no closed-from expression) of the model parameters and the data up to period $$ t $$ that encodes how each country’s propensity for democracy evolves with incumbents’ beliefs.

Structural Parameter Estimates

To understand how our model is fitting the data, we summarize our main parameter estimates and discuss their substantive implications.Footnote ²⁶ We begin with our estimates of the (de)stabilizing effect of GDP growth on elite turnover under autocracy and democracy. This question, itself, has been a separate subject of academic inquiry for decades.Footnote ²⁷ We find that the impact of growth on the likelihood that the incumbent group retains power indeed differs across autocracies and democracies.Footnote ²⁸ The quantitative implications of our estimates are summarized in Figure 2, which plots the estimated probability (averaged across countries) that the incumbent remains in power at different rates of GDP growth. In blue, we plot our estimates under democracy and, in red, our estimates under autocracy.

FIGURE 2.

(De)Stabilizing Effect of GDP Growth on Elite Turnover

Note: This figure plots the average (across countries) estimated probability of retaining power at different rates of GDP growth under autocracy (red) and democracy (blue). Observed growth rates between 1951 and 2000 are shown over the horizontal axis.

In line with a substantial empirical literature in political economy, we find that, in democracies, economic growth is stabilizing for elites.Footnote ²⁹ In other words, the party in government is more likely to win reelection when growth is high. In contrast, we find that growth is destabilizing in autocracies. This result comports with the view that rapid economic expansion in non-democracies creates inequalities of expectation or inequalities of outcomes that, in turn, engender attempts to subvert the political system (Huntington Reference Huntington1968; Hirschman and Rothschild Reference Hirschman and Rothschild1973; Olson Reference Olson1963). That is, autocrats are less likely to remain in power when economic growth produces actors—a middle class, for example—able to place demands upon and challenge the authority of the group in power.

Coupling these results with learning helps explain the observed cross-sectional correlation between democracy and per capita GDP. Democracy becomes more appealing to incumbents as they come to believe that it is conducive to high rates of GDP growth. Conversely, autocracy entails an incentive to suppress economic growth.

Of course, there are notable exceptions. For example, over the past two decades China has experienced high rates of growth and nevertheless remained autocratic. Similarly, India for the first four decades of its independence experienced low rates of growth and yet remained democratic. Underlying these prominent cases is the country-specific political cost of democracy. We recover estimates of the structural parameters describing the baseline political cost of democracy for each country, $$ {f}_i $$, and plot them in Figure 3. Notably, the Chinese Communist party faces, all else equal, the lowest probability of remaining in power under democracy. In contrast, Congress at India’s independence had the fourth-highest ex-ante probability of retaining power under democracy.

FIGURE 3.

Political Cost of Democracy

Note: This figure plots our estimates—along with 90% confidence intervals—of the baseline political cost of democracy, fi, across countries. Positive (negative) values imply higher (lower) elite turnover, all else equal, under democracy than autocracy.

Next, in Figure 4, we present estimates of the spatial decay of learning—that is, the extent to which the cross-country correlation in prior beliefs depends on the geographic distance between capitals.Footnote ³⁰ Consistent with the observed spatial clustering of democratization events noted in the literature, we find that learning is highly circumscribed geographically. As shown in Figure 4, at 5,000 km (the approximate distance between the United States and Ecuador), the prior correlation of beliefs is only 0.12. At 10,000 km (the distance between the United States and the Central African Republic), the potential for learning between countries is virtually absent. This estimated feature of our model reveals that elites learn from the experiences of relatively proximate countries, and it underpins the ability of our model to capture the spatial clustering of transitions to and from democracy.

FIGURE 4.

Cross-Country Correlation in Prior Beliefs

Note: This figure shows the spatial decay of learning, plotting our estimate of the cross-country correlation in prior beliefs about the economic effects of democracy as a function of geographic distance between capitals. For reference, Ecuador and the Central African Republic are approximately five and ten thousand kilometers, respectively, from the U.S.

Finally, to understand the dynamics of democracy adoption, Figure 5 presents our estimates of the evolution of beliefs about the economic effects of democracy. The top panel plots the evolution of the worldwide distribution of the mean of beliefs about $$ {\beta}_i^{D=1}-{\beta}_i^{D=0} $$, the difference in long-run GDP growth rates under democracy versus autocracy. The bottom panel shows the evolution of worldwide uncertainty (standard deviation) about these beliefs. The initial state of beliefs in 1951 simply reflects our prior-calibration exercise. In the presample period (1875–1950), democracies grew, on average, about 0.4 percentage points faster than autocracies. The median of mean initial beliefs in the top panel of the figure is consistent with this statistic.

FIGURE 5.

Worldwide Beliefs about Economic Effects of Democracy

Note: The top panel of this figure plots estimates of the 20th–80th worldwide percentiles of the mean of beliefs about the percentage-point difference in long-run GDP growth rates under democracy versus autocracy. The lower panel shows estimates of the 20th–80th worldwide percentiles of uncertainty (standard deviation) in these beliefs.

While estimated beliefs remain relatively flat for the first three decades of the in-sample period, in the 1980s and, even more dramatically, in the 1990s there is a sharp expansion of beliefs in favor of democracy’s superior potential to foster economic growth. This divergence in beliefs reflects an increasingly large gap between autocracies and democracies in observed economic performance. Between 1951 and 1979, little new information was revealed about the relative ability of democracy to generate growth: on average over this period, autocracies grew just 0.37 percentage points slower than democracies, roughly identical to the observed difference in the presample period. By contrast, between 1980 and 2000, democracies produced, on average, 1.54 percentage points higher annual growth than autocracies.

This widening gap in economic performance—accelerating through the 1980s—reached a peak in 1987, when democracies outgrew autocracies by 3.3 percentage points. The resulting discrepancy in observed growth rates from countries’ prior expectations led them to progressively update their beliefs. Together with our estimates of the (de)stabilizing effects of GDP growth on elite turnover, this change in worldwide beliefs helps explain our model’s ability to correctly predict the striking rise in the percentage of world democracies observed in the same period (Figure 1).Footnote ³¹

This process operated through changes in both the mean and precision of beliefs. After the oil crisis, as democracies came to outperform autocracies, this induced countries to revise their estimates of democracy’s superior economic potential, which encouraged transitions to democracy. As the first set of countries transitioned, democracy became less rare worldwide, reducing belief uncertainty about its relative economic merits. This helped solidify the growing consensus, leading to further democratization. The result was the cascade in beliefs between the 1980s and mid-1990s shown in Figure 5 and, ultimately, the wave of democratization that followed over the same period.

A Second Great Depression

In the year before the market crash of 1929, 51% of the world’s independent states were democracies. A year later this proportion dropped to just over 43%. By 1935, the fraction of countries that remained democratic decreased by another 5%, reaching a low of 36% by 1938. In Europe, democratic backsliding was even starker. At its theretofore high in 1920, twenty-six out of twenty-eight European states were democratic, but by 1938 thirteen of these countries had transitioned away from democracy.

A substantial body of both historical and quantitative research has linked the global decline of democracy in the inter-war period directly to the economic downturn of the Great Depression (de Bromhead, Eichengreen, and O’Rourke Reference de Bromhead, Eichengreen and O’Rourke2013; Frey and Weck Reference Frey and Weck1983). Likewise, both in academic and popular discourse, the global economic recession of 2008 has been put forward as a contributing factor in the observed wave of recent democratic breakdowns (Armingeon and Guthmann Reference Armingeon and Guthmann2014; Bartels Reference Bartels2013). Next, we provide evidence that systemic economic crises indeed engender reversals to autocracy and, moreover, highlight how this is driven by changes in beliefs about the economic effects of democracy.

To that end, we simulate two sorts of crises. First, we generate a “short-deep” counterfactual crisis where, for our last in-sample year (2000), we simulate a 5.9% average worldwide contraction of per capita GDP, comparable to the worst year of the Great Depression (1931). In our second crisis, we construct a “long-shallow” counterfactual condition where we perturb growth by a smaller amount—1.7% annually (the average contraction between 1929 and 1933)—but extend this contraction over a five-year period (2000–2004). We present results where we concentrate these counterfactual conditions in autocratic and democratic countries, respectively. Under the “autocratic-bias” condition, recessions are twice as deep in autocracies as in democracies, while keeping the worldwide average contraction consistent with our intervention. Conversely, under the “democratic-bias” condition, recessions are twice as deep in democracies.

Model estimates of the worldwide percentage of democracies are shown in Figure 6. In the left-hand panel, we present estimates from the autocratic-bias condition and, in the right-hand panel, estimates from the democratic-bias condition. In both plots, the short-deep counterfactual is shown in green, and the long-shallow counterfactual is shown in purple. Note that both the short-deep and long-shallow counterfactual crises negatively affect the worldwide prevalence of democracy. While the large single-period decline in growth has a larger effect in the first year, thereafter the proportion of democracies begins to recover. In comparison, the smaller but longer crisis has a larger overall impact, with the proportion of democracies continuing to decline through the duration of the economic contraction and then recovering more slowly.

FIGURE 6.

Second Great Depression Counterfactuals

Note: This figure reports results from two counterfactual exercises. First, we present predictions following a short-deep crisis of a single year (2000) of 6% average worldwide economic contraction (green). Second, we present predictions based on a long-shallow crisis of five years (2000–2004) of 2% average annual contraction (purple). We plot the true percentage of world democracies (gray) as well as baseline model predictions (blue). We concentrate the contraction in autocracies in the left-hand panel and in democracies in the right-hand panel.

Both patterns are consistent with our actors learning about the economic merits of democracy. In both counterfactuals, the initial shock forces agents to revise their beliefs. However, in the long-shallow counterfactual, as would be expected from a continued process of learning, our agents update their beliefs following each additional negative perturbation of worldwide growth and continue to select out of democracy accordingly. In contrast, in the short-deep scenario, we observe a single large drop in the percentage of democracies. Since after the initial shock to growth there is no “new” information revealed to our agents, the worldwide percentage of democracies starts to recover. Importantly, comparing effects across the two bias conditions, it is clear that the reduction in world democracies is larger when the economic contraction is concentrated among democracies. This is consistent with the evolution of beliefs prior to our intervention, as discussed above. When democracies perform poorly, counter to the prevailing consensus, elites sharply revise their beliefs and select out of democracy.

The Third Wave

A number of studies highlight the influence of external actors on the prospects for democracy (Pevehouse Reference Pevehouse2005). Especially for the early “third-wave” democratization events in Greece, Portugal, and Spain, the potential for accession to the European Community (EC) has been put forward as a crucial determinant of their respective transitions (Whitehead Reference Whitehead1996). Brussels’ requirement that community members maintain a form of government consistent with liberal democracy, coupled with the economic benefits of access to the common market, generated an incentive to democratize. In this section, we show that, rather than serving as an institutional target, much of the impact the EC had upon third-wave democratization operated through its constituent states’ economic performance, which affected beliefs about the economic benefits of democracy in potential member states.

To show this, we construct a counterfactual wherein we generate a recession in the EC’s three largest economies, Britain, France, and Germany, of 2% average annual contraction for the two years preceding the first transition of Greece in 1974.Footnote ³³ Then, to obtain an estimate of this counterfactual recession’s impact on the transitions of Greece, Portugal, and Spain, we compare our predicted transitions in this counterfactual world with the truth. Our results are given in Figure 7.

FIGURE 7.

Counterfactual Third Wave

Note: The top panel shows the true timeline of democratization of Greece, Portugal, and Spain. In the bottom panel, we present the corresponding timeline under our counterfactual scenario of a two-year recession (1972 and 1973) in Britain, France, and Germany of 2% average annual contraction.

For Portugal and Spain, the effect of lower growth in Britain, France, and Germany is considerable, delaying their transitions to democracy by almost 20 years. In contrast, we find no effect of this recession on Greece’s transition. The reason for this becomes apparent once we compare the evolution of beliefs in the three countries. We plot in Figure 8, for each of these cases, our estimates of beliefs under the observed economic conditions (solid) and under our counterfactual timeline (dashed). Note that for Portugal and Spain there is a substantial divergence in beliefs between the observed and counterfactual timelines. Following our counterfactual recession, Portuguese and Spanish beliefs become markedly less favorable towards the potential for growth under democracy. In contrast, for Greece this is not the case: there is no substantial difference in beliefs. The reason, according to our model, is that Greek elites pay little attention to the large, relatively distant Western economies used to construct our counterfactual scenario. Britain, France, and Germany are simply too different from Greece to be used as a reference for learning.

FIGURE 8.

Counterfactual Third-Wave Beliefs

Note: We plot estimates of Spain, Portugal, and Greece’s mean beliefs under the true growth rates (solid) and under a counterfactual two-year recession (1972 and 1973) in Britain, France, and Germany of 2% average annual contraction (dashed).

Ultimately, our counterfactual experiment suggests that the distinguishing characteristic of Portugal and Spain, in contrast to, for example, Brazil or Mexico, is not their underlying propensity for democracy. In terms of their estimated baseline political cost of democracy, Portugal and Brazil and Spain and Mexico are fairly close.Footnote ³⁴ Rather, Portugal and Spain democratized early because they learned from Western Europe, benefiting from proximity to successful liberal democracies.

Chinese (and North Korean) Democracy

With an eye to contemporary politics, we evaluate the stability of a pair of geopolitically important autocracies. We explore conditions under which our model predicts China and North Korea would democratize. We focus first on the Chinese case. Here, we look for the minimal average growth rate among China’s democratic neighbors, in a five-year economic expansion, that would result in transitions of two types.Footnote ³⁵ First, we find the rate of growth that would result in at least a single year of democracy. Second, we establish the growth rate that would deliver a permanent transition to democracy.

To obtain a predicted single-year Chinese transition to democracy, we estimate that it would take five years (2000–2004) of 6.5% average annual growth in China’s democratic neighbors. After this single year of democracy (2005), our model predicts an immediate reversal to autocracy. To obtain a “permanent” transition—that is, a prediction of democracy until the end of our sample—we estimate that China’s democratic neighbors would have to grow at an average annual rate of 11% between 2000 and 2004. In contrast, under the same set of counterfactual conditions, North Korea would not democratize for any period. North Korea would democratize for a single year following five years of 16.5% average growth in its democratic neighbors and permanently following five years of 20.5% average growth.

To highlight the differences in learning, Figure 9 plots Chinese and North Korean beliefs under the “Chinese democracy” scenarios of 6.5% and 11% average growth in their democratic neighbors. While our intervention increases the perception that democracy outperforms autocracy in both countries, the change in beliefs in North Korea is too small to induce a transition.Footnote ³⁶ These results suggest that the prospects for Chinese and North Korean democracy are limited. It would take a remarkably large economic boom in Asian democracies for China to democratize and an even larger, implausible expansion to generate the same outcome in North Korea.Footnote ³⁷

FIGURE 9.

Chinese and North Korean Beliefs

Note: We plot estimates of Chinese and North Korean mean beliefs about the economic impact of democracy under observed growth (solid) and under two counterfactual five-year expansions (2000–2004) in their democratic neighbors of 6.5% (dashed) and 11% (dots) average annual growth.