Is Russia really a normal country? A numerical taxonomy of Russia in comparative perspective

What sort of country has Russia become since the collapse of the Soviet Union? Scholars have tracked Russia’s political economy closely for almost three decades but have yet to agree on a method for classifying it. Using cluster analysis and eleven different measures of political, economic, and social development, the article constructs a numerical taxonomy of Russia and nineteen other countries. The results show that, for the most part, Russia no longer resembles other post-communist countries, nor does it resemble the political economy of the developed West or the poorer countries of the developing world. Instead, the results indicate it increasingly approximates other rent extractive political economies, such as China, Brazil, Mexico, and Indonesia.


Introduction
What sort of country has Russia become since the collapse of the Soviet Union? Is it a "normal" country? This question has dominated scholarly discourse on Russia for over almost three decades. One way of approaching it is to inquire whether Russia's political economy increasingly resembles that of other developing countries, that of the West, or that of its post-communist neighbors. Plausible accounts can be offered for each; and in turn each of these models carry profound implications for Russia's future place in the global order. Using data on a dozen political, economic, 1 3 and social indicators, this paper deploys cluster analysis to construct a "numerical taxonomy" of political economies and it situates Russia within it. Approaching the question of Russia's "normality" this way highlights the impersonal features of Russia's post-communist trajectory and may give us insight on whether it is truly being absorbed into the global order. It also tells us what kind of political economy Russia has become. To anticipate the conclusion: an analysis of the data suggests that more than a quarter century after communism, after showing the potential for other paths of development, Russia now displays characteristics typical of other rent extractive political economies.
Why should we care about what kind of country is or the mode by which it integrates (or fails to integrate) into the global order? The hope immediately after communism's collapse was Russia would somehow and at some point "return" to the equilibrium path of development, away from dreams of global ideological domination and toward some sort of acceptance of, and integration within, the global political and economic division of labor. A decade into the "transition," however, things were not going as smoothly as planned. By the late 1990s (and certainly by 2007 with Putin's speech to the Munich Security Conference), Russia seemed increasingly authoritarian and intent on pursuing its "national interests" abroad in ways not always in harmony with the West (Stent, 2008). How long Russia's foray into authoritarian great power particularism will last remains uncertain, but one way to evaluate the country's long-term prospects is to assess its underlying fundamentals.
The question of Russia's "normality" goes back more than two centuries. In the hundred years before the Russian revolution, rulers and intellectuals debated whether the country's future belonged to the West or in some special "Russian" path. This debate between "Westernizers" and "Slavophiles" was never resolved but it provided the raw material for heated disputes in the pre-revolutionary period (Rabow-Edling, 2012;Walicki, 1975). In the West, the question of Russia's normality continued after the revolution and extended throughout the cold war. Western historians and social scientists spilled an inordinate amount of ink asking whether communist-led industrialization and socio-cultural modernization would ultimately render Russia increasingly similar to the "normal" West no matter what the intention of the country's rulers (Fitzpatrick, 2017;Gerschenkron, 1962;Hough, 2014). Pervasive corruption and sluggish growth in the late communist period, however, led some scholars to maintain that the Soviet Union (and it's Russian core) increasingly resembled a "developing" country with all the attendant pathologies, Nigeria or Mexico with nuclear weapons, as it were (Jowitt, 1992): if Russia was "normal" it was a normal developing country. After communism, the question quickly became would Russia continue to resemble other post-communist countries in its neighborhood (that is, would the concept of post-communism itself continue to be meaningful?), would it quickly "rejoin" the West, or would it come to resemble other large, middle income countries (Kopstein & Reilly, 2000)?
In the post-communist era, Andrei Shleifer and Daniel Treisman first put the question of Russia's "normality" on the agenda in a series of books and articles. In contrast to those who expressed disappointment in the first years of the current century that "between Russia and most other developed, capitalist societies there was a qualitative difference" (Hoff & Stiglitz, 2002), Shleifer and Treisman (by 2005) maintained that since Russia in its history "never was a developed, capitalist society" the proper comparison group needs to be changed. They argue that whatever Russia's political, social, economic failings it was essential a normal, middle-income country. "That Russia is only a normal middle-income democracy is, of course, a disappointment to those who had hoped for or expected more. But that Russia today has largely broken free of its past, that it is no longer the evil empire, threatening both its own people and the rest of the world, is an amazing and admirable achievement." (Shleifer & Treisman, 2005: 175).
Shleifer and Treisman's logic is basically modernization theoretic: convergence is to be expected but is dependent primarily on level of economic development, a logic they extend to other post-communist countries (Shleifer and Treisman, 2014). The purpose of this paper is to take Shleifer and Treisman's point seriously and to ask, what kind of country is Russia? (Shleifer, 2005;Treisman, 2011). Even if, as Kathryn Stoner notes (2021), Shleifer and Treisman were overly optimistic in their assessment of Russia's trajectory, what are the alternatives for understanding what Russia has become more than three decades after communism's disappearance?
One answer to this question is "Russia is Putin's Russia," that is, we should focus on the country's leader. For some problems it is of course perfectly appropriate to focus on the leader's intentions or personality, but we share Timothy Frye's (2021) argument that Russia should be analyzed in an appropriate comparative context and that we should be skeptical of arguments about the sui generis nature of Russian politics (in particular, those that hinge on Putin or Russia's unique geography). One way of considering the question is to ask: what sort of political economy has Russia become over the past 25 years and how does it stack up again other countries? Answering the question this way allows us to focus on more impersonal factors that determine long-range trajectories. To say that Russia is a middle-income country is undoubtedly true, but what sort of middle-income country? As Shleifer and Treisman note, there are different kinds. Some countries focus on investment, others on consumption; some possess "developmental states," and others fall into the clutches of "predatory" elites; some turn militaristic and others prefer to adapt to their broader international environment rather than trying to shape it; some are democratically governed, others less so (Janos, 1986).

Expectations, evidence, and method
In what follows, we offer an analysis of Russia's political economy profile in the context of other large countries that are neither system-makers nor system-takers. There are grounds to hypothesize Russia's political economy may have evolved in one of several directions. Its geographical location and former position at the core of the communist world may incline it to resemble other post-communist states. Russia's GDP/capita, on the other hand, may lead us to expect a convergence with other middle-income countries. Alternatively, its well-educated population possessing high human capital may lead us to expect an increasing convergence with the developed West. On the other hand, Russia's reputation for corruption and lack of transparency might indicate a convergence with rent-seeking countries of the developing world. Finally, its transition from flawed democracy to outright authoritarianism under Vladimir Putin could have pushed it in the direction of other authoritarian cases.
Analytical models of authoritarian regimes agree that all rulers and ruling groups want to remain in power and take steps to assure that they do which, as Wintrobe (1990Wintrobe ( , 2019 suggests, can involve efforts to increase citizen loyalty to the ruler(s) and to repress resistance to their rule. What such rulers do beyond such activities varies with their aims. Some simply aim to maximize their "extractions" from the population subject to the constraint of retaining authority (tin pots), some want to impose particular ideas of the good society and good life on their societies (totalitarians), others might attempt to advance the interests of the persons ruled as the citizens or subjects themselves perceive them (benevolent or enlightened rulers). In the roughly triangular space defined by these extremes are a variety of possible combinations of interests and policies that particular leaders or juntas may attempt that vary with personalities, ideologies, and circumstances.
The nature of authoritarian regimes thus can vary significantly both in terms of the breadth of their support in the territories ruled and in the sorts of policies and practices adopted. No rulers will be entirely indifferent to the welfare of their residents, because even extractive regimes have an interest in economic development (Olson, 1993(Olson, , 2000Bueno de Mesquita et al., 2005). Yet very few-if any-will focus all of their energies on citizen welfare. Different aims imply different mixes of government policies and different kinds and degrees of extractive and repressive practices. Insofar as we cannot read the minds of a ruling group-who may publicly express all manner of sentiments-to classify autocratic regimes requires discerning categories of policies and policy consequences into which regimes can be divided. Rather than intuitively hypothesize such categories, we apply cluster analysis to statistically identify groups of countries that have commonalities. These clusters or categories can then in turn be used to characterize individual regimes as more or less extractive, enlightened, or totalitarian. Our main interest is taxonomical rather than causal, that is, our goal is to classify the post-communist regime of Russia, although other points of interest are also developed. Once a range of political and economic indicators are taken together, does Russia more resemble the West, the post-communist world, other countries of the developing world, or simply author authoritarian cases?
We take as our point of departure Steve Chan's (2001) comparative study of Asian, Latin American, and North American economic models. Chan's work is a quantitative test of Fajnzylber's (1990) qualitatively derived claim that the United States and Japan serve as exemplars for their respective neighbors. Chan develops what he terms a "numerical taxonomy" of the larger political economies of the Asia-Pacific region and the Americas using cluster analysis. The purpose of cluster analysis is "to group similar items into a common category while separating dissimilar items in different categories." (Chan, 2001(Chan, : 1142. It has been used extensively in the biological and social sciences to classify and categorize, especially when these tasks are central to testing and confirming hypotheses (Kopstein & Reilly, 2006). Chan uses a range of social, economic, and educational indicators in a cluster analysis to examine whether there is, in fact, an emulative effect at work-that is, whether it makes sense to speak of a US or Japanese model in the two regions. He rejects the assertion. The political economies of the Americas and Asia do not cluster in patterns that Fajnzylber's qualitative analysis predicts.
What works for Asia and the Americas, may help us understand Russia's comparative trajectory. With which countries should we compare Russia? The question does not lend itself to a straightforward answer. If the purpose of the exercise is to gauge which country or sets of countries Russia resembles, then we should have a sampling of the different modal outcomes over time. As a first cut, we included: Bangladesh, Brazil, China, France, Germany, India, Indonesia, Italy, Japan, Mexico, Pakistan, Philippines, Russia, South Korea, Thailand, Turkey, Ukraine, United Kingdom, United States, and Vietnam. The decision rule here was relatively simple: countries with a population of at least 50 million inhabitants with some regional dispersion and not part of World Bank defined macro-regions of the Middle East and North Africa. Upon further consideration, however, to make the sample more salient for the research question at hand, we excluded Philippines and Thailand and added Kazakhstan and Poland as two further post-communist countries on the not unreasonable expectation that Russia could conceivably still fall into a post-communist cluster.
Case selection is important both on methodological and theoretical grounds. Methodologically it stands to reason that the countries Russia resembles will be closely related to the countries with which it is being compared. If, hypothetically, one were to say "Russia's model of political economy most closely resembles Brazil's," a logical response could be "that may be true unless you consider its resemblance to Kazakhstan." Put most simply, comparisons are sensitive to the composition of the group being compared. With that in mind, the countries we have chosen reflect a range of reasonable possibilities and correspond to the basic models of political economy discussed in the broader literature.
An equally important question are the indicators chosen. Theoretically, asking which indicators should be chosen for inclusion in the analysis is basically the same as asking what kinds of variables determine the basic nature of political economy. The problem is that students of political economy do not agree on the answer to this question. Some economists argue that what matters is the extent of government control and government expenditures as a percentage of GDP. Others maintain that we should focus on GDP/capita or percentage of GDP generated from agriculture or industry. Some focus on institutions such as central bank independence, trade unions and employer associations, and planning agencies. Others distinguish between import and export-oriented economies. Yet another group of scholars concentrates on governance, the rule of law, corruption, and violence. This list goes on. 1 Furthermore, it is entirely possible for a political economy to combine different admixtures of markets, taxes, corruption, political stability, and violence. We remain agnostic on these important but unresolved debates and suggest that even though for purposes of parsimony it may make sense to choose one variable, for purposes of empirical accuracy, when comparing political economies and attempting to devise taxonomies, we believe it important not to place too much weight on any one indicator. In other words, this is exactly the kind of exercise where one wants a "kitchen sink" model in which includes in analysis a reasonably large range of economic and political variables. In this way, no one factor can either make or break the results, and countries will cluster according to their overall socio-economic and political profiles.
We have chosen eleven indicators for inclusion that, taken together, can reasonably characterize part of a country's political economy. Several of them are modernization theoretic, some gauge state capacity and bureaucratic rectitude, others measure degree of integration into the global economy, while a final group taps into political freedom and the rule of law. Sources and further description of these measures can be found in our appendix.
The measures adopted are: from the World Bank-World Development Indicators: (1) the log of GDP per capita (in constant 2010 USD); (2) the Infant Mortality Rate (per 1000 live births); (3) the Tax Revenue expressed as % of GDP. We then add (4) the Freedom Status according to the classification proposed by the Freedom of Press dataset. A series of indicators from the World Governance Indicators (WGI) released by the World Bank as well, these are: (5) the level of Control of Corruption Index; (6) the index of Government Effectiveness; (7) the index of Political Stability and Absence of Violence; (8) the Rule of Law index. We finally add (9) the polity2 democracy score from the PolityIV database; the indexes of (10) Economic and (11) Political globalization, these last two from the KOF Swiss Economic Institute.
Our method is cluster analysis. Various cluster analytic techniques share the objective of classifying units. Although the objective may be the same, the techniques-for example, whether the starting point is distinguishing the most different cases or identifying the most similar ones-have consequences for what the clusters ultimately look like. Accordingly, if the emphasis is on identifying outliers first, the clusters will reflect the most extreme behaviors of the cases examined. If the approach seeks to identify the central tendency of the data, the clusters represent a spectrum of units from those that appear "most normal" to those with "abnormal tendencies." Our approach, by contrast, is to identify the groups where the units within each cluster display commonalities and where there is little divergence in behavior. Clusters are "built" in stages. In the first stage, each state is its own cluster. In the second, the two states that display the least variation across all variables are grouped together. In each subsequent round, a state or cluster is added to another cluster until all states are in a single cluster. This technique, termed Ward's method of hierarchical clustering, minimizes the variance within clusters. This approach is also known as the within-groups sum of squares or the error sum of squares and has been a popular method in the social sciences (Aldenderfer & Blashfield, 1984). For our purposes, this technique makes sense as a means of identifying where groups of states exhibit common developmental and behavioral patterns.
By tracking the stages, it is possible to identify where the logical "breaking points" are between groups of states. At each stage in the analysis, where a cluster is calculated and represented on an agglomeration schedule as a coefficient. The R 2 term is also calculated for each stage. In the initial stage, all of the variance across units is explained by the fact that the units are independent (or unclustered), so the R 2 score is a perfect 1.0. As clustering occurs in each stage, part of the variance within clusters is unexplained and the R 2 decreases. The amount that the R 2 decreases from stage to stage indicates how well the joining clusters fit together. By tracking the changes by stage, it is possible to identify similar and dissimilar clusters and what the logical number of clusters is. Rather than reporting the agglomeration schedules, in Figs. 1, 2, 3 and 4, we simply report the L2 dissimilarity measure in terms of Euclidean distances.
In order to track Russia's evolution over time, we construct four different "waves" of analysis : 1995-1999, 2000-2004, 2005-2009, 2010-2015. The logic for choosing these waves is intuitive. The first wave represents Russia at the outset of its postcommunist journey in the 1990s, the second after Putin assumes the country's presidency, the third as Russia moves away from democracy and accommodation with the West, and the final stage as Russia becomes an autocracy (Gurev & Treisman, 2019). For the sake of transparency and clarity of presentation we present the cluster analysis in several formats. In the first we show the dendograms (Figs. 1, 2, 3 and 4) that demonstrate how the clusters are formed. This provides a graphic display of what cluster analysis is. We then present a simple table of six clusters in each of the four waves (Table 1). For reporting the clusters, we adopt a k-means criterion which Fig. 1 The Y-axis measures the continuous dissimilarity measure from the hierarchical clustering method using Ward's linkage and adopting a continuous dissimilarity measure L2. Inspection and clustering results suggest a clustering in 5 or 6 groups by k-means criterion. L2 dissimilarity is the Euclidean distance applied to the countries observed and ruling their clustering. This starts at the bottom (= 0) as each country is maximally similar to itself and then clusters by increasing heterogenous countries with increasing dissimilarity shows that the bet tradeoff between meaningful clustering and accepting increasing dissimilarity should be five or six groups maximum. We then present an analysis of which variables are most characteristic of each cluster, concentrating on those countries that cluster with Russia. In the appendix we report further evidence showing which countries most closely resemble Russia using proximity matrices.

Results
Although the results are slightly noisy and a few countries group non-intuitively and jump around between waves, several features of these figures and the table are worth noting. First, as shown in the figures and Table 1, although Russia may have begun its post-communist journey with a profile that somewhat resembled other developmental states and Poland, in the three subsequent periods this ceased to be the case. It also falls, unsurprisingly, well outside of the developed Western cluster in all periods but still does not join up with the South Asia cluster in any sustained way.
Second, although in the first wave Russia and Poland grouped together, in subsequent waves this ceased to be the case. Poland makes "progress" and joins a group of more advanced states. In fact (as shown in the index), the proximity matrices in periods two, three, and four, all indicate the post-communist country with the smallest overall difference in the indicators from Russia was Ukraine. This is prima facie evidence for the continued importance of the post-communist space of political Fig. 2 The Y-axis measures the continuous dissimilarity measure from the hierarchical clustering method using Ward's linkage and adopting a continuous dissimilarity measure L2. Inspection and clustering results suggest a clustering in 5 or 6 groups by k-means criterion. L2 dissimilarity is the Euclidean distance applied to the countries observed and ruling their clustering. This starts at the bottom (= 0) as each country is maximally similar to itself and then clusters by increasing heterogenous countries with increasing dissimilarity economy. But what exactly "post-communism" means depends upon the factors driving the clusters. We turn to this question in the following section.
Third, by the end of the period under consideration (represented by the fourth wave), Russia takes its place in a group of countries that include China, Mexico, Brazil, Indonesia, and Turkey. Post-communism may have come to an end, but Russia had not "re-joined" the West (the scare quotes indicating some doubt that it was ever part of the West).
Interpreting these results requires that we understand what is actually driving them. Which indicators are doing the work? This is a crucial question for a multivariate cluster analysis. If, hypothetically, despite differences in GDP per capita or infant mortality, these differences were not that so large as to overwhelm the differences in other factors, differences in economic development would not be shaping the clusters. What makes the clusters group the way they do? To answer these questions, we computed the ratios between group and wave averages values to show the relative magnitudes of the indicators for each group. These are shown in Table 2.
This table shows that what appears to be driving Russia's grouping by the time we arrive at wave four (2010)(2011)(2012)(2013)(2014)(2015) are a few indicators: high levels of corruption and high levels of political stability rather than differences in levels of income. With the proximity matrices (reported in the index) showing Russia closest to Ukraine, with a shift from Turkey and Mexico over the period of interest, a picture starts to take Fig. 3 The Y-axis measures the continuous dissimilarity measure from the hierarchical clustering method using Ward's linkage and adopting a continuous dissimilarity measure L2. Inspection and clustering results suggest a clustering in 5 or 6 groups by k-means criterion. L2 dissimilarity is the Euclidean distance applied to the countries observed and ruling their clustering. This starts at the bottom (= 0) as each country is maximally similar to itself, and then clusters by increasing heterogenous countries with increasing dissimilarity shape of Russia as a fairly well run state with high levels of corruption. It resembles China, Mexico, Brazil, and Turkey in these respects more than it resembles any other set of countries in any other respect (most notably in levels of freedom and economic development). This is a surprising finding, suggesting an underlying reality behind the "BRICS" category, which is frequently seen more as a rhetorical than empirical construct.

Discussion
What kind of political economy does Russia's group possess? The combination of Russia's distinctive characteristics that emerge from our cluster analysis-high levels of corruption, reasonable economic performance, and political stability through most of the Putin era-approximates what public choice scholars term a "rent extraction" model of political economy. We say "approximates" because to our knowledge there is no agreed upon empirical standard to measure the type. Scholars have yet to devise reliable indicators for the presence or absence of this model-that is, to provide relatively straightforward measures of its presence. Our analysis provides a plausible way forward. Fig. 4 The Y-axis measures the continuous dissimilarity measure from the hierarchical clustering method using Ward's linkage and adopting a continuous dissimilarity measure L2. Inspection and clustering results suggest a clustering in 5 or 6 groups by k-means criterion. L2 dissimilarity is the Euclidean distance applied to the countries observed and ruling their clustering. This starts at the bottom (= 0) as each country is maximally similar to itself and then clusters by increasing heterogenous countries with increasing dissimilarity 1 3 Is Russia really a normal country? A numerical taxonomy of Russia… The empirical measures we propose may perhaps be best understood in the rent extractive model by contrasting it with its cousin, the "rent seeking" model, in which private individuals benefit by controlling the political process; rent extraction, on the other hand, points us to politicians as the main actors and highly important (if not the sole) distributional beneficiaries of the order. Whereas the rent seeking model emphasizes control over the "gold," the rent extractive model points us to the primacy of the "sword." According to one account, politicians "gain not only when compensated by successful rent seekers, but also by threatening private individuals or groups with losses and then allowing themselves to be bought off rather than make good on the threats. Private wealth is extracted in the process." 2 Table 1 Groups by wave This table shows the evolution of Russia's grouping throughout the 1995-2015 period divided in four waves, as also indicated from 1, 2, 3 and 4. Six groups (clusters) are obtained by k-means clustering First wave (1995)(1996)(1997)(1998)(1999) Second wave (2000)(2001)(2002)(2003)(2004) Third wave (2005)(2006)(2007)(2008)(2009)  This model seems especially appropriate for contemporary Russia. But how did it evolve? Gaddy and Ickes (2013) and Wintrobe (2019) both offer a sensible origins story for the resource extraction model in the Russian case. In the Yeltsin era (that is, during the 1990s), oligarchs concentrated wealth in their own hands and kept the state weak. Tax collection deteriorated, private rent seeking took over, and the policy process had been mostly captured by the "winners" of partial reform (Hellman, 1998). But the winners themselves, although able to protect their rents from the state, confronted an angry and resentful public in addition to predatory fellow oligarchs who were ready to confiscate each other's ill-gotten gains. "The oligarchs," maintains Wintrobe, "also feared that the state would be captured by one of their rivals. And they feared empowering the state, since a powerful state could recapture their wealth through 'renationalization'" (Wintrobe 2019, 300).
Putin reigned in the violent and wasteful rent-seeking competition among the oligarchs by centralizing compromising information about them in his own hands and disempowering the rival agencies. At the same time, he protected the oligarchs against attacks from the others, but could more than credibly threaten all if they broke the rules of the game. In return for this set of arrangements, Putin and his team have been able to extract a share of the rents but have not destroyed the wealth creation machine, becoming very rich in the process. Had Russia more closely approximated the rent-seeking model, we would have expected the lower rates of growth and far higher levels of political instability characteristic of different clusters in our analysis.
The story of the past two decades in Russia has been a transition from a rentseeking economy to a rent-extractive model. By this we mean that in the transition period, there was initially a scramble for authority and control over resources among rival individuals and groups that eventually reached a stable pattern, after which rents could be harvested by political elites in a more or less sustainable way. This was not always good for the average Russian but it was for Russia's political and economic elites. The key feature is political coercion and surely this characterization captures part of Putin's Russia (Belton, 2020). Politicians in this model are only interested in wealth being distributed to others on the condition that they can transfer it to themselves.
This short sketch of the transition from rent-seeking to rent-extraction is suggestive but leaves an important question unanswered: why was Russia able to make this transition? Although our answer is speculative, the institutional legacy of communism may have assisted Putin and his team, drawn from the remnants of the security services, in reasserting the primacy of the "public" over the "private." Russia's rulers possessed an apparatus with residual administrative and coercive capacity on which to draw, a source of power separate from the oligarchs. Seen this way, postcommunist countries may have an evolutionary advantage in arriving at the rentextractive model over their counterparts elsewhere in the world at similar levels of development.
Is Russia really a normal country? A numerical taxonomy of Russia…

Conclusion: Russia as a normal rent-extractive political economy
This article has used cluster analysis to classify Russia's political economy in the post-communist period. Based upon a comparison of multiple indicators across a large group of countries, we have shown that Russia increasingly resembles other countries with its reasonable economic performance, high levels of corruption, and political stability. We have hypothesized that this represents a movement over time from a rent-seeking to a rent-extractive model. One important contribution of this study is in offering a method for measuring these two models of political economy in a meaningful way.
Our analysis has focused on the past three decades. What about the future? Despite the obvious social costs entailed in such a model, there is no reason why it cannot reach a state of equilibrium by creating disincentives to group organization. When considered this way, post-communism may continue to be a useful concept but mostly because it describes and predicts, under certain conditions, a central developmental tendency toward a rent extractive political economy. Other post-communist countries, such as Poland, may not choose this path but some, such, such as contemporary Hungary may be able to do so.
If Russia is now a normal country, its normality resides in its resemblance to other rent extractive countries. On the one hand, these results are not that surprising. It stands to reason that Russia is not completely unique (Frye, 2021). But whereas Russia may be a middle-income country, its "normality" consists not primarily in its level of economic development, its public health system, its level of democracy or autocracy, but rather in its stability and corruption over the past decade. Whether the rent extractive model is sustainable over the long run is not something our data allows us to predict. Modernization theory anticipates that over time economic development will destabilize this model, as citizens demand a measure of accountability, predictability, and a share in power (Przeworski and Limongi, 1997). Judging by the other countries that Russia increasingly resembles, however, the underlying rent extractive model may remain in place for a long time to come no matter what other cosmetic changes occur. Whether the current government's self-image of Russia as a great power projecting particularism to its neighbors and beyond will assist in further solidifying the current order must remain a matter of speculation.
An important limitation to our analysis is its failure to address the geopolitical dimension of "normality." When scholars speculate on whether Russia will become a normal country, they usually refer not only to its model of political economy but also its ambitions on the global stage (Stoner, 2021). Will Russia remain satisfied to join the global economic order or will it in some sense attempt, as it did during the communist era, to change it? Over the past century, some have argued, Russia has repeatedly chosen conquest over development. Our analysis restricts itself to the domestic dimension of political economy. The relationship between rent-extraction models of political economy and foreign policy remains a question for further research.