30 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Too Much Development or Not Enough Democracy? Exploring an Anomaly in the Democratization of Post-Communist Countries Alo Raun, Tallinn University Abstract This study of 28 post-communist regimes distinguishes a group of countries significantly less democratic than predicted by its very high Human Development Index score: Russia, Kazakhstan, and Belarus. It also appears that contrary to theoretical assumptions, such ‘developed dictatorships’ convert their economic growth into human development remarkably well. To measure such conversion, a new tool, the Growth Conversion Index, is introduced. Considering these results, the explanatory power of several theories is briefly examined. While some theories imply possible explanations (e.g., the concept of patronal politics and the conditional approach to resource dependence), none of them sufficiently disclose the actual workings of such conversion mechanism, implying the need for more in-depth studies.1 1 Some sections of the article draw upon or are republished in revised form from a previous publication in East-West Studies (Raun, 2022). 31 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Introduction Comparing 40 years of data, Adam Przeworski et al. (2000) found that the average economic growth rates of democracies and dictatorships are similar. This downplayed one argument supporting the positive agenda of democracy and democratization; thus, they proposed to observe social development (or the quality of life of ordinary citizens) instead: “Although democracies are far from perfect, lives under dictatorships are grim and short” (Przeworski et al. 2000, 271). A similar approach has prevailed in several studies (examples include Gerring, Thacker, and Alfaro 2012; Gerring et al. 2021; Kudamatsu 2012; Wang, Mechkova, and Andersson 2019) and seems intuitively plausible, but is, on the other hand, empirically challenging. Could it be argued that while democracy does not bring faster economic growth, it still provides better human welfare when compared to autocracies?2 As one of the most prominent indices measuring basic well-being, the Human Development Index (HDI) by the United Nations Development Programme (UNDP) indicates, the answer to the question concerning development is not that simple. Several autocracies display substantial human development: when comparing UNDP and Freedom House data, it appears that as many as 19 out of 66 countries with a very high human development score are autocracies, outperforming dozens of democracies. This is not just a question about regimes that have been autocratic and affluent for decades (such as Singapore, Saudi Arabia, or Bahrain) since several post-communist countries have followed a similar path. Seven autocracies (“Partly Free” or “Not Free” according to Freedom House) from Central and Eastern Europe and the former Soviet Union belong to the very high human development category (Hungary, Montenegro, Kazakhstan, Russia, Belarus, Georgia, and Serbia) and most of them outperform democratic countries from the same region, e.g., Bulgaria (Freedom House 2023; UNDP 2022). Thus, at least when the UNDP approach is concerned, it is evident that a very high level of human development is possible under autocracies. Since such a model has proved its resilience over the decades with new cases emerging, it is reasonable to analyze this phenomenon in more detail instead of ignoring such countries as anomalies. The aforementioned trend challenges not only modernization theory,3 the alternative approach proposed by Przeworski et al. (2000), and related scholarly research but also the prospect of further democratization in the world and in the post- communist realm in particular. The debate about the relationship between development and regime type has gained prominence due to the democratic backsliding of the last decade, e.g., Hungary, Poland (Gora and Wilde 2022) and the stabilization of autocratic regimes delivering a very high level of human development (e.g., Russia and Kazakhstan), challenging several approaches and highlighting the need for further investigation. 2 Following Alan Siaroff (2005), this article uses “autocracy” as an umbrella term referring to all non-democratic regimes, encompassing both hybrid cases and full autocracies, both “Partly Free” and “Not Free” according to Freedom House. 3 According to which socioeconomic development is the dominant explanatory variable for democracy (see Diamond 1992; Landman 2003). 32 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Correspondingly (and following its exploratory approach), the first purpose of this article is to map and briefly examine the relationship between democracy and human development in post- communist countries. Being a part of a more sophisticated research project focusing on high social/human development in post-communist autocracies, this article aims to gain familiarity with the phenomenon and relevant literature to help formulate a relevant theoretical framework for a more definite investigation. Ensuing from the controversy emphasized by UNDP development data and drawing on previous research by the author (Raun 2022), a special interest is taken in the cases that display, on the one hand, very high human development scores but are, on the other hand, not democracies. It will be analyzed whether the modernization logic ‘the higher the level of human development, the higher the score of democracy’ applies to the region, but also whether any anomalous cases emerge that contradict that overall trend. Hence, the first research question asks: is there a group of developed post-communist countries that are significantly less democratic than predicted by their human development score? Besides the snapshot of such an anomaly, the dynamics behind it will be briefly measured—with the aim of capturing not only the level but also the pace of development (the growth behind it). As one of the results of the analysis conducted in this article demonstrates, a group of countries emerges with a very high human development score but remarkably low democracy records (hereafter referred to as ‘the developed dictatorships’). Following the exploratory approach of the article, this group will be briefly statistically analyzed. It will be evaluated whether the developed dictatorships appeared initially (in the 1990s) more developed than other autocracies in the region. Alternatively, their notable level must be in large part achieved later, and thus, one is witnessing the demonstration effects of the current regimes. If the latter is the case, another explanation deserves attention: it could be argued that their very high human development score is the result of their absolute economic growth since the faster it is, the more resources there are at the disposal for such things as investments in health and education sectors. Therefore, as a next step, it will be measured whether these countries convert their economic growth into human development (expressed by HDI) better than the average autocracies in the region and how well do they fare compared to democracies. For this purpose, a novel indicator (Growth Conversion Index) will be introduced. The aim of this part of the article is to specify the observed anomaly by exploring the relationship between HDI and the economic growth in developed dictatorships.4 The results of the statistical analysis in this article, especially the emergence and the demonstration effects of the aforementioned anomalous group and the remarkably high growth conversion rate of some of its members, challenge, to a degree, several theories that are employed to explain post- communist authoritarianism (e.g., modernization theory and the proposition by Przeworski et al. 2000, rentier state theory, and the neopatrimonialism approach). Such comparatively positive developmental outcomes could facilitate the resilience of any regime, be it democratic or autocratic, and are often not expected to emerge under autocracies. Thus, following the explorative 4 A more detailed analysis of possible explanations of the anomaly is out of the scope of the current paper but could be the focus of another article. 33 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 design of this research, the second purpose of the article is to examine the main theoretical approaches based on their ability to explicate the results of the empirical analysis in this paper. The second research question asks: which theoretical approaches appear more promising in explaining the remarkably high human development score combined with a high rate of conversion of economic growth into human development in the post-communist developed dictatorships? Several main theoretical approaches employed to explain the resilience and operating mechanisms of (post-communist) autocracies are briefly mapped based on, first and foremost, their ability to explain the inner logic that stimulates some regimes to convert their economic growth relatively successfully into human development. For example, how successfully do they disclose the actual workings of the hidden conversion mechanism that differentiates between such countries as Kazakhstan and Azerbaijan—cases that, on one hand, display comparable structural features (e.g., oil wealth) but on the other manifest opposite levels of growth conversion? Due to the limitations of a scientific article, this part is designed to offer a very preliminary explanation. The results of the analysis are designed to be used in future more in-depth articles, and thus, the theories will be analyzed from the perspective of synthesizing them in the future. To sum up, the answer to the first research question briefly explores the anomaly of developed dictatorships (both the level and growth of HDI), and the second research question tests the ability of theories to explain that phenomenon. In this comparative case study, the relationship between development and democracy will be explored based on 28 post-communist countries in the regions of the former Soviet Union (FSU) and Central and Eastern Europe (CEE).5 First, a large-N framework is applied to test all post- communist countries, followed by small-N comparative analyses of the discovered cluster and the explanatory power of relevant theories. Within this framework, the article broadly adheres to the deviant case study approach, both by disconfirming a deterministic argument and by probing new explanations (Seawright and Gerring 2008). Following the problem setting at the beginning of the article and the approach suggested by Diamond (1992), this article employs the widely used HDI by UNDP as the main measure of development. It is ontologically based on the capabilities approach to human welfare (advocated by Amartya Sen and Martha Nussbaum) and measures capabilities in health, education, and income that are the basic building blocks of well-being and opportunity, universally valued around the world. In addition to having examples such as Diamond (1992) and a solid philosophical background, this approach is preferred because measurable, intuitively sensible, and reliable indicators exist to represent them (Gandjour 2008; Measure of America, n.d.; UNDP 2023). In contrast to the gross national income (GNI), gross national product (GNP), and gross domestic product (GDP), HDI is more sensitive to the social dimension of development. Compared to another popular approach, employing infant mortality rate and similar indices, HDI is more nuanced. Combining three subindices, it is a proxy that measures the ‘grimness and shortness’ of 5 The total number of countries in these regions is 29, but Kosovo is excluded due to a lack of data. For simplification purposes, CEE and FSU are referred to as one region in the article. 34 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 life that Przeworski et al. (2000) stressed.6 To measure democracy, in order to retain comparability with Diamond (1992), one of the most prominent and oldest annual surveys of democracy, Freedom in the World Index (FIW) by Freedom House (2022) is used. The article focuses on autocracies, i.e., countries classified as “Partly Free” (most hybrid regimes) and “Not Free” (more closed cases) by Freedom House.7 Capturing the demonstration effects of the new post-communist regimes, the study uses data mostly from the years 1995 and 2019. The starting point is a year by when the turbulence of the initial post-Soviet transition was mostly over, and 2019 is a symbolic end of an era or at least a partial retreat from the political phenomenon focused on in this article; it was the year of Nursultan Nazarbayev's resignation as the President of Kazakhstan. The year 2020 was marked by Belarus' presidential elections and major protests, and in 2022, Russia attacked Ukraine, followed by unprecedented sanctions imposed upon Vladimir Putin’s regime. The article adheres to the approach that emphasizes the importance of descriptive arguments in political science but also favours studies that combine description with initial explanation (Gerring 2012). Thus, this is an exploratory article that focuses on mapping the phenomenon of the developed dictatorships in the former Soviet Union region. The results of the article also refer to the need for in-depth case studies of the different regimes singled out in the initial analysis, which is out of the scope of this paper. Considering the space limitations of the article and following its exploratory approach, however, it is possible to map potential explanations to the anomaly, determining the three most promising theoretical approaches that form the basis of further analysis and synthesis of theories. The article is structured as follows: first, a brief overview of the literature on the relationship between (human) development, democracy and autocratic resilience is provided. Next, the methodology employed in this article is analyzed, focusing on developing the Growth Conversion Index. This is followed by two empirical and analytical sections addressing the research questions. The former concentrates on mapping relationships between development and democracy and between economic and human development, with the latter section focusing on charting relevant theories. From Democratization to Development and Autocratic Resilience This section briefly reviews literature focusing on the role of institutions in fostering human development and aiming to explain autocratic resilience (in post-communist countries) in the 21st century. It draws on material previously published in an exhaustive literature review on the subject by the author.8 Comparative democratization studies have been dominated by four main traditions: 6 See also Anand and Sen 2000. 7 For statistical analysis, the more informative 13-point scale of numerical ratings by Freedom House is used in this article. 8 See Raun 2022. 35 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 modernization theory (examples include Boix and Stokes 2003; Diamond 1992; Lipset 1959), historical approaches (Moore 1966; Rueschemeyer, Huber Stephens, and Stephens 1992), the transitionalist school (OʹDonnell and Schmitter 1986; Rustow 1970) and more recently also by a ‘new structuralist’ (and often game-theoretical economic) approach (Acemoglu and Robinson 2006; Boix 2003; Levitsky and Way 2010; Pengl 2013; Teorell 2010). For over half a century, the modernization school has considered economic development the dominant explanatory variable for democracy. The majority of such (predominantly quantitative) studies claim that socioeconomic development progressively accumulates the kind of social changes that make a society ready for democratization (Landman 2003). Yet, comparing 40 years of data, Przeworski et al. (2000) found that although there is a correlation between development and democracy, there may be no causation. In other words, political regimes do not transition to democracy as per capita incomes rise; rather, such a movement is random. There have also been attempts to advance the modernization approach. As a fruitful example, Larry Diamond proposed HDI to be a better development variable to associate with democracy (compared to national income). Diamond upheld his idea statistically: he compared HDI with the Freedom in the World Index (FIW) by Freedom House and found strong statistical correlations. HDI showed a substantially stronger correlation with the index of democracy (0.71 significant at the 0.0001 level) than per capita Gross National Income—0.51 at 0.0001 (Diamond 1992, 459-460). This finding advanced the modernization approach by introducing HDI as a possible predictor of democracy. Thus, a partly similar approach is employed in this article. The role of a political regime and its institutions in fostering human development has been the focus of several studies, with most of them addressing the comparison between democracies and autocracies. An extensive literature finds that democracy improves the quality of life (examples include Gerring, Thacker, and Alfaro 2012; Gerring et al. 2021; Kudamatsu 2012; Wang, Mechkova, and Anderson 2019); meanwhile, others dispute this approach (Halleröd et al. 2013; Miller 2015; Ross 2006; Truex 2017). On the other hand, relatively limited attention has been devoted to studying differences in the human development performance of different forms of autocracy. Based on the data on child mortality and school enrolment, Cassani (2021) finds that competitive authoritarian regimes, which hold elections and allow for some degree of contestation, outperform other non-democracies, except for hereditary autocracies. According to him, the competitive authoritarian situation motivates incumbents to improve citizen living conditions (mostly performance-based legitimation9). The idea of competitive authoritarianism as a regime in between democracy and other types of autocracy also finds support in Cassani and Garbone (2016) on the example of sub-Saharan Africa. Miller (2015) argues that autocracies that hold elections achieve better results in healthcare and education than other non-democracies. These conclusions are, however, challenged by other authors. Kim and Kroeger (2018) assert that autocratic multiparty elections do not affect infant mortality. In addition, Wang, Mechkova, and Andersson (2019) claim that democratization has a threshold effect on health outcomes. Thus, the question is 9 See von Soest and Grauvogel 2017. 36 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 not settled, and further research is necessary. Although these studies underscore the need to be more specific concerning the human development implications of institutional variations of autocracy and establish several significant statistical relationships, they fail to analyze in sufficient detail the explanatory mechanisms concerning the reasons why some (hegemonic party) autocracies can sustain and advance a very high human development index score, which is the focus of this article. Post-Soviet Autocratic Resilience Several authors have also focused on analyzing the phenomenon of hybridization and autocratic resilience, especially after the dissolution of the Soviet Union. In this section, such theories are discussed, focusing on their explanatory power concerning the post-communist developed dictatorships, which also display remarkably high conversion of economic growth into human development. Most prominent explanations for autocratic resilience as well as approaches potentially more sensitive to aspects focused on in this article are briefly mapped. They will be additionally assessed in the section that focuses on answering the second research question.10 Some of the most notable researchers in this area, Steven Levitsky and Lucan A. Way (2010), have developed the concept of competitive authoritarianism—a hybrid regime where a power struggle is real but unfair. Accordingly, three main variables explain the trajectories of competitive authoritarian regimes: linkage to the West, organizational power of the regime, and Western leverage. Russia and Belarus—the countries this article is focusing on—started out as competitive authoritarian countries after the dissolution of the Soviet Union, but in the course of time, they transformed into full authoritarianism, being examples of how low linkage to the West and high organizational power contribute to authoritarian stability (Levitsky and Way 2010). Martin K. Dimitrov (2009), however, partly challenges and partly advances Levitsky and Way's approach by adding an even more prominent component—the popularity of the authoritarian ruler. According to Dimitrov (2009), popular autocrats (like in Russia and Kazakhstan) possess the support of the populace, and they seldom need to resort to using brute force. They use three strategies to ensure their popularity—economic populism, anti-Western nationalism, and muzzling the media—producing a high level of legitimacy and stability. Economic populism is in accordance with the market social contract, a concept Dimitrov introduced together with Linda J. Cook (Cook and Dimitrov 2017). Considering the high cost of open repression in today’s world, it is useful to leverage other mechanisms to make the regime more resilient, such as catering to the consumption needs of the population (Cook and Dimitrov, 2017). Recently, Sergei Guriev and Daniel Treisman (2022) proposed a similar but more elaborate concept of spin dictators. They are incumbents that employ a ruling strategy that has as its key elements manipulating the media, engineering popularity, faking democracy, limiting public violence, and opening up to the world. According to the authors, the first rule of spin dictators is to be popular, and the most common tool to achieving 10 For a more in-depth account on relevant literature see Raun (2022). 37 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 it is fostering economic performance. As their modus operandi they are focused more on shaping public opinion (spin) than on violent repression (fear). When looking at the region that is the focus of this article, neopatrimonialism, rentier states, and similar schools appear to be prominent. The proponents of the neopatrimonialism theory (Isaacs 2011; Paiziev 2014; Peyrouse 2012) have argued that most or many former Soviet countries are not governed by legal-rational bureaucratic systems and that this also explains their failure to become democratic. According to Erali Paiziev (2014), the secret of the longevity of the authoritarian regimes of Kazakhstan and Uzbekistan is the fact that they manage to profit from formal, non-formal, traditional, and non-traditional institutions and practices; the mixture of all these can be described as neopatrimonialism. One of the key elements of this theory is the patron- client relationship where, in order to secure his regime’s resilience, the leader uses public resources to buy the loyalty of the elite (his cronies) (Bratton and van de Walle 1994; Erdmann and Engel 2007; Guliyev 2011). A similar approach is the selectorate theory, where rulers, to remain in power, pay limited attention to the welfare of the electorate but to that of ‘the selectorate,’ which is— under authoritarian conditions—typically the elite (Bueno de Mesquita et al. 2003). The neopatrimonalism approach is similar to the concept of patronal politics by Henry E. Hale (2015). Concerning autocracies with contested elections, the author acknowledges the importance of the personalized exchange of rewards and punishments, but he also takes the power of public opinion seriously. The leader needs mass support, and to achieve that, he or she implements several public policies. Mass support is vital since it shapes the expectations of both the people and the elite (the latter being more important)—it either facilitates or hinders leadership change. According to Hale (2015), post-Soviet patronal presidential systems feature a significant and powerful accountability mechanism forcing their leaders to cater to and cultivate public opinion. While neopatrimonialism was initially used to explain underdevelopment, some countries with a comparable political system became rich but did not democratize; many of them are rentier states, which depend on profits earned from oil and other natural resource exports to maintain their state budgets (Luciani 1987). Such income damages the motivation to collect and raise taxes, and consequently, the regime does not need to provide political representation to the people in exchange for raising taxes (Ross 2001). However, since several resource-abundant countries (including post-communist cases) do not fit easily into the above-mentioned criteria, a conditional approach to the resource curse and rentierism has emerged, claiming other aspects mediate (or enforce) the relationship between mineral wealth and the efficiency of political institutions (Gel’man 2010; Jones Luong and Weinthal 2010; Raun 2007). As a prominent example, Pauline Jones Luong and Erika Weinthal (2010) assert that mineral-rich states are cursed not by their wealth per se but rather by the ownership structure they chose to manage their mineral wealth (e.g., state ownership with control) and that weak institutions (particularly fiscal regimes) are not inevitable in mineral-rich states. Overall, despite this not being an exhaustive review of theories of democratization and authoritarian resilience in the region, this section illustrates the variety of approaches and the difficulties of selecting one over another. This article helps to advance this debate by exploring 38 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 statistical evidence from post-communist countries and testing the potential of these theories in explaining the results. Measuring Democracy, Development and Growth Conversion To map the phenomena that the first research question is focusing on, the relationship between democracy and development is statistically analyzed, employing correlation and cluster analysis and constructing a proxy indicator of growth conversion. In the final section of the article, theoretical approaches with the inclination to explain the mechanisms behind such an anomaly (cf. second research question) are charted. Research synthesis as a method is used to evaluate relevant theories (Cooper 2010). The list of the main variable codes is shown in Table 1. Table 1: Variables of Democracy and Development Code Variable FIWa Freedom in The World average numerical rating (7 to 1)b HDIa Human Development Index score (0 to 1)c INCa Score of HDIs subindex of income (0 to 1)c DIF DIF index (imbalance between levels of democracy and human development)d GCI Growth Conversion Index (economic growth into human development)d Notes: a The three-letter variable code (e.g., INC) describes the indicator in general. In case it is followed by two digits, it marks the indicator for a specific year (e.g., INC19 is the income index for year 2019). b Freedom House (2023); author’s calculations. c UNDP (n.d.); author’s calculations. d author’s calculations. Following Diamond (1992), this article employs the widely used HDI by UNDP as a measure for development and the FIW index by Freedom House for democracy. HDI combines the sub-indices of income, education, and health. As a result, countries are listed based on their HDI score—a number on a continuous scale between 0 and 1. According to the FIW, countries are classified as “Free,” “Partly Free,” or “Not Free.” This is based on numerical ratings. Although since 2020, Freedom House has used a 100-point scale as their main approach, in this article, their traditional scale from 1 to 7 (Free = 1–2.5; Partly Free = 3–5; Not Free = 5.5–7 points) is used, because even Freedom House (2022; 2023) uses the traditional scale for historical comparisons. Based on these indices, clusters of countries were computed, and indicators were created. This study is based on data from the years 1995 and 2019. Data from 1995 is used only for the Growth Conversion Index; this is the first year HDI data is available for at least three-quarters of the countries in the study. It is also assumed that by 1995, the turbulence of the initial transition had 39 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 concluded in most of the CEE and FSU countries, providing the possibility to focus on measuring the demonstration effects of the regimes that followed. In most cases, the decline of HDI that followed the collapse of the Soviet system was over by 1995 (UNDP, n.d.). Finally, to measure economic development, the income index that was used to create HDI is employed. Even though the correlation between democracy and development is strong in the region that is focused on in this article, it is not linear, making it meaningful to search for deviating cases. For this purpose, a proxy measure is calculated, employing the residuals of the regression analysis with the HDI score for 2019 as the independent variable and the FIW score for the same year as the dependent variable (Z-standardized, FIW results inverted). This measure, DIF, is calculated using the following formula: 𝐷𝐼𝐹 = 𝐹𝐼𝐼19 − 𝜇FII19 𝜎FII19 − 𝐻𝐷𝐼19 − 𝜇HDI19 𝜎HDI19 Where: DIF – indicator DIF (residual) FII19 – FIW score for 2019, inverted HDI19 – HDI score for 2019 μFII19 – mean FII19 score μHDI19 – mean HDI19 score σFII19 – the standard deviation of FII19 σHDI19 – the standard deviation of HDI19 Hence, DIF is expected to show the difference between the actual level of democracy according to the FIW and that projected by HDI, especially helping scholars to detect the strongest anomalies. Data used to calculate DIF can be found in Appendix 1. However, it is only the first part of the process of mapping the phenomenon described in the introduction to the article since it describes the amount of deviation, not whether the country is also comparatively highly developed but at the same time delivering a low level of democracy. To map such countries, DIF will be used next as one input in a cluster analysis in conjunction with indicators of development (HDI19) and democracy (FIW19). While exploring the developed dictatorships cluster, it will be asked whether the growth of the HDI score of the countries is simply the result of high economic growth (fuelled by oil exports) or is the national income of these countries also rather successfully converted into human development. Following the line of reasoning in the human development approach (Anand and Sen 2000), it is expected that when national income grows, a responsible government directs a significant share of it to develop such things as the health and education sectors. This, in turn, results in the enhancement of the quality of life of its citizens, that is the growth of human development (and the relevant index score). On the other hand, based on the theory of neopatrimonialism (e.g., Bratton and van de Walle 1994), one could assume low conversion to human development in autocratic countries since a large share of resources is expected to be directed to benefit ‘the clients’ of the leader at the expense of the populace. To estimate such a 40 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 conversion rate, the Growth Conversion Index (GCI) will be developed. This measure is a proxy since it does not imply the effort of the regime directly but of the society as a whole and does not consider all the aspects of human development directly. However, it helps to shed light on the anomaly observed in this article and introduces a more in-depth analysis of the phenomenon. GCI measures the efficiency of converting economic growth into human development (HDI). Since the human development level is affected over a longer time span, a wider temporal distance is expected to better describe such a conversion; therefore, data from 1995 and 2019 are compared. The HDI for 1995 is available only for 22 countries out of the 28 that are analyzed in this article, but the possibility of keeping the time span as long as possible outweighs the alternative of replacing 1995 with later data (cf. discussion in the introduction).11 The GCI is calculated employing the residuals of the regression analysis where the HDI score for 2019 is the independent variable and the HDI score predicted for 2019 based on the growth of the income index between 1995 and 2019 is the dependent variable. The high correlations between the HDI and income index for both 1995 and 2019 (0.957 at 0.000 and 0.939 at 0.000, respectively) confirm the mutual convertibility of the results. Data used to calculate GCI can be found in Appendix 1. Its formula is as follows: 𝐺𝐶𝐼 = 𝐻𝐷𝐼19 − 𝐻𝐷𝐼95 × 𝐼𝑁𝐶19 − 𝐼𝑁𝐶95 𝐼𝑁𝐶95 Where: GCI – Growth Conversion Index (residual) HDI19 – Human Development Index for 2019 HDI95 – Human Development Index for 1995 INC19 – Income index for 2019 INC95 – Income index for 1995 One could argue that it is problematic to compare HDI and the income index since there is a smaller empirical overlap between the two. Considering this, it is important to understand what the income component represents in HDI. According to Sudhir Anand and Amartya Sen (2000), it does not reflect GNI but is a proxy that reflects basic capabilities not already incorporated in the two other measures (longevity and education). It describes basic concerns that must be captured in any accounting of elementary capabilities. As Anand and Sen (2000, 86) put it: “For example, going hungry is a deprivation that is serious not just for its tendency to reduce longevity, but also for the suffering it directly causes. Similarly, resources needed for the shelter and for being able to travel may be quite important in generating the corresponding capabilities.” Thus, it is an indirect indicator of some capabilities not well reflected in the measures of education and longevity. In 11 For 1995, data is missing for Bosnia and Herzegovina, Georgia, North Macedonia, Turkmenistan, Uzbekistan, and Montenegro (UNDP, n.d.). 41 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 conclusion, following this logic of the creators of the HDI, the income index can be employed in this article. Anomalous Autocracies, Human Development, and Growth Conversion This section briefly maps and analyzes the phenomenon of very high human development without democracy in the region, employing correlation and cluster analysis as well as the Growth Conversion Index. The correlation between economic development and democracy is strong in the region (-0.641 at 0.001) and even stronger when HDI is applied as the indicator for development (-0.729 at 0.001). Therefore, in general, modernization theory finds support in as much as correlation is concerned and not causation. However, as the comparison of UNDP and Freedom House data showed, there are stable autocracies with a very high human development score that counter that mainstream trend. While statistical analysis often regards such countries as outliers, in this article, such an approach is regarded as problematic. In this way, not only stable regimes but also major players in global politics and the economy are neglected (e.g., Russia). Considering the region in focus, in addition to Russia, Kazakhstan, Belarus, Montenegro, Hungary, Georgia, and Serbia appear to be autocratic and, at the same time, have a very high human development score (Freedom House 2023; UNDP 2022). Therefore, it is credible to assume that the relationship between (human) development and democracy is non-linear, and it is reasonable to analyze it further. It will be considered whether these seven countries constitute a deviant group of post- communist autocracies. Table 2: Difference between Predicted and Actual Levels of Democracy Country DIFa Country DIFa Russia -1.710 Latvia 0.103 Belarus -1.695 Slovakia 0.198 Kazakhstan -1.479 Georgia 0.213 Azerbaijan -0.636 Bosnia and Herzegovina 0.224 Slovenia -0.456 Armenia 0.287 Hungary -0.451 Croatia 0.340 Poland -0.366 Romania 0.456 Montenegro -0.303 Albania 0.482 Turkmenistan -0.235 Bulgaria 0.645 Czechia -0.187 Ukraine 0.735 Uzbekistan -0.066 Tajikistan 0.756 Estonia -0.060 North Macedonia 0.814 42 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Country DIFa Country DIFa Serbia 0.060 Moldova 0.946 Lithuania 0.098 Kyrgyzstan 1.288 Average 0.000 Notes: a Difference between the actual level of democracy and the one estimated by human development for year 2019. Source: author’s calculations Next, countries that are more developed than their level of human development presumes are mapped using the new measure DIF (cf. previous section). As shown in Table 2, four autocracies emerge at the top of the list: Belarus, Russia, Kazakhstan, and Azerbaijan. The first three were expected to emerge based on the initial mapping of outliers. On the other hand, the other four cases mentioned above (Montenegro, Hungary, Georgia, and Serbia) fail to be examples of that anomaly, representing the general trend (score close to zero). As an exception, Hungary shows a score comparable to Azerbaijan, but since it is a case of de-democratization of a once consolidated democracy, it is not reasonable to analyze it in more detail in this article. Next, to map a group of countries delivering a very high level of human development in conjunction with a low level of democracy, a cluster analysis is conducted using DIF and measures of democracy and development as indicators.12 As depicted in Table 3, a group of countries— labelled here as ‘developed democracies’—appears unquestionably more democratic and developed than others.13 However, a contrasting group, ‘underdeveloped autocracies,’ emerges as clearly the least developed and almost the most autocratic group. The two other groups are situated mostly between these two. Among them, ‘the grey zone’ countries score closest to the regional average democracy score, and their development score is also not far from the average. The final cluster, the ‘developed dictatorships,’ is, on the other hand, an extreme case with its DIF score dramatically deviating from the average and all the other countries. They appear ‘too autocratic’ considering their level of development. Regarding the article’s first research question, the latter group is the most valuable finding from the cluster analysis: it is a group of countries where a very high human development score coexists with full autocracy. The countries belonging to this group are Russia, Kazakhstan, and Belarus. The fourth candidate, Azerbaijan, with its lower level of human development, did not appear anomalous enough and is categorized under ‘underdeveloped autocracies’ instead. The other four countries with a very high human development score (Hungary, 12 The four-cluster model is preferred over the three-cluster alternative since it helps to differentiate between hybrid regimes and more traditional authoritarian cases. In the process, z-standardization is applied. 13 The titles of the clusters are descriptive. While characterizing most cases, they are not intended to describe the regime type of each country in the group. As an example, autocratic Hungary and Montenegro are included in the group ‘developed democracies.’ 43 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Montenegro, Georgia, and Serbia) did not demonstrate comparably low levels of autocracy. This forms the answer to the first research question. Table 3: Patterns of Democracy and Development within Clusters Cluster Members DIFa HDI19b FIW19c Cluster 1: the grey zone Albania, Armenia, Bosnia and Herzegovina, Georgia, Moldova, North Macedonia, Serbia, Ukraine .470 .784 3.38 Cluster 2: underdeveloped autocracies Azerbaijan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan .221 .711 6.20 Cluster 3: developed dictatorships Belarus, Kazakhstan, Russia -1.63 .824 6.33 Cluster 4: developed democracies Bulgaria, Croatia, Czechia, Estonia, Hungary, Latvia, Lithuania, Montenegro, Poland, Romania, Slovakia, Slovenia .001 .865 1.75 Average .000 .810 3.50 Notes: Source: author’s calculations a Difference between the actual and estimated levels of democracy for 2019 (mean). b Level of human development (HDI for 2019; mean). c Level of democracy (FIW index for 2019; mean). Although Ward’s method employing squared Euclidean distance based on z-standardized transform is used for clustering, for comprehensibility original indicators are shown in the table. According to the ANOVA, the values of the F-statistics are 41.397 (HDI19*Cluster) and 61.844 (FIW19*Cluster), and according to the Kruskal-Wallis H test, the value of the Chi-square statistic for DIF is 11.961 (DIF*Cluster), each is significant at 0.01. As expected, results of Bonferroni’s post-hoc test show that the group most important regarding the research question, ‘the developed dictatorships’, differs significantly from ‘the grey zone’ and from ‘the developed democracies’, when level of democracy is considered, and from ‘underdeveloped autocracies’, when level of development is considered. In addition, Tamhane’s post-hoc test shows, as expected, that the DIF-score of ‘the developed dictatorships’ differs significantly from all other clusters, proving the 4-cluster solution useful. Next, the cluster—developed dictatorships is described in more detail in order to better understand the anomaly. Previous tests can be considered a snapshot depicting years of development. Thus, it does not tell scholars how these very high development scores were achieved. Are these countries also anomalous when it comes to the pace (growth) and efficiency of human development? Was this group of countries already initially more developed comparatively speaking (cf. Soviet legacy), or has their level been achieved during the current regime? Are these countries converting their economic growth into human development better than the average autocracies in the region? While a more in-depth analysis is out of the scope of this article, the question of conversion will be given more attention next. The aim is to better describe the phenomenon in relation to several 44 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 theories examined in this article. For example, the high absolute growth of HDI does not describe the share of national income (GNI) directed to human development. Since both Kazakhstan and Russia are major oil exporters (cf. rentier state approach), the export of natural resources may fuel their economic growth so intensively that human development follows almost incidentally. Therefore, even if the growth of the national income of these countries is remarkable, their efficiency in converting it into human development could remain poor, implying a more ordinary case of ‘bad-governance-cum-oil-wealth’ (e.g., as the neopatrimonialism approach presumes). Table 4. Growth Conversion, Income, and Development Country GCIa, i Regimeb δHDIc δINCd Cluster Czechia 0.066 F 0.139 0.079 4 Croatia 0.058 F 0.148 0.097 4 Slovenia 0.043 F 0.127 0.086 4 Russia 0.024 NF 0.122 0.103 3 Kazakhstan 0.019 NF 0.161 0.145 3 Latvia 0.014 F 0.186 0.174 4 Hungary 0.011 PF 0.108 0.100 4 Estonia 0.009 F 0.163 0.155 4 Romania 0.005 F 0.134 0.135 4 Ukraine 0.004 PF 0.093 0.085 1 Lithuania -0.011 F 0.172 0.182 4 Poland -0.012 F 0.135 0.143 4 Bulgaria -0.013 F 0.106 0.118 4 Serbia -0.016 PF 0.107 0.116 1 Albania -0.019 PF 0.158 0.162 1 Kyrgyzstan -0.019 PF 0.108 0.104 2 Slovakia -0.023 F 0.108 0.129 4 Belarus -0.026 NF 0.163 0.176 3 Moldova -0.028 PF 0.112 0.134 1 Tajikistan -0.029 NF 0.119 0.118 2 Azerbaijan -0.120 NF 0.152 0.231 2 45 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Country GCIa, i Regimeb δHDIc δINCd Cluster Armenia -0.124 PF 0.149 0.226 1 Democracies f, h 0.140e 0.142e 0.130e Autocracies g, h -0.027e 0.129e 0.142e ‘Dev. dictatorships’ 0.006e 0.149e 0.141e Total average h -0.009e 0.135e 0.136e Notes: a Growth Conversion Index, author’s calculations. b Ranking of democracy in 2019 (FIR19). Free = F; Partly Free = PF; Not Free = NF (Freedom House, 2023). c Human Development Index, difference between scores for 1995 and 2019. d Index of Income, difference between scores for 1995 and 2019. e Significant at 0.05 level. f Ranked Free in 2019 (Freedom House, 2023). g Ranked Not Free or Partly Free in 2019 (Freedom House, 2023). h Mean of only the countries with GCI score. i Standard deviation of GCI is 0.045 with only 4 cases exceeding it. Considering the question of the initial level and regime demonstration effects, the results are mixed. While analyzing levels of HDI for 1995 and the growth of HDI between 1995 and 2019 (δHDI, Table 4), it appears that, on average, developed dictatorships (HDI95 = 0.675) used to be slightly more developed than the average of countries that are today autocratic (HDI95 = 0.650), and they definitely outpace other clusters when absolute growth is concerned (δHDI = 0.149). However, it appears that on a country level, the differences are remarkable. Russia had a better starting position (HDI95 = 0.702), but it did not grow comparatively fast (δHDI = 0.122). Kazakhstan and Belarus, on the other hand, started from a medium level of human development (HDI95 = 0.664 and 0.660, respectively), but grew even faster than the average among democracies (δHDI = 0.161 and 0.163, respectively). However, the results must be considered with some caution due to a lack of data from 1995, as almost one quarter of the countries are left out of the analysis. As a theoretically more challenging aspect, next, it will be measured whether developed dictatorships convert their national income into human development better than other autocracies. For this purpose, the Growth Conversion Index (GCI) developed in the previous section is used. As seen in Table 4, a general trend emerges: democratic countries (FIW = 1 to 2.5) show better growth conversion than autocracies. Within the latter, three subgroups emerge, out of which one functions contrary to the mainstream logic. It appears that, opposite to expectations, some autocracies (Russia, Kazakhstan, Hungary, and Ukraine) have a positive score of GCI and appear to convert their economic growth into human development better than several democracies, with Russia and Kazakhstan located amongst the top five most successful converters. Eight autocracies show negative GCI, as expected, with six of them constituting the second logical group with a conversion rate close to the average of autocracies (-0.027). Finally, two extreme cases, Azerbaijan 46 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 and Armenia, constitute the third group, with their negative GCI score exceeding even two standard deviations. Despite being the most autocratic, members of the anomalous cluster developed dictatorships (GCI — 0.006) deliver higher GCI scores than other autocracies, and two of its members, Russia and Kazakhstan, appear as success stories of conversion. This finding emphasizes the anomaly of that group of countries. They (full autocracies ranked “Not Free” by Freedom House) are able to sustain the pace of human development similar to several consolidated democracies and therefore challenge the demonstration effects of democracy. However, when looking at single cases, Belarus appears to differ from other members of the developed dictatorships group, delivering merely the average growth conversion of autocracies (—0.026) and being situated in the lower half of the chart (as one would expect from a full autocracy). This finding does not change the answer to the first research question. However, it helps to evaluate theoretical concepts (in the next section) and highlights aspects to be analyzed in future more in-depth research. Additionally, oil-exporting Azerbaijan, the country that was the fourth case with the anomalous level of indicator DIF, appears to be the contrasting case (compared to Russia and Kazakhstan), showing an extremely low rate of conversion (GCI = -0.120), and an example of a ‘traditional’ authoritarian case where (oil) wealth is poorly converted into human development (cf. rentier state theory, neopatrimonialism school). In conclusion, yes, on average, post-communist developed dictatorships convert their economic growth into human development score remarkably well—better than other autocracies in the region. They are even able to sustain a conversion rate common in several post-communist democracies. This is due to the irregular cases of Kazakhstan and Russia. Thus, it is suggested that further research should focus more on understanding the phenomena of these two countries. The results in this section also imply that modernization theory is unable to explain these three cases; thus, in the next section, better alternatives will be mapped. This article focuses on the capability approach to well-being according to which health, education, and command over resources (like food, travel, and shelter) are universal basic components of well-being (measured by HDI). The aspects examined in this article are, of course, not the only components of human development, and other features and measures could be incorporated in further studies (e.g., level of corruption). The Potential of Theories in Explaining the Anomaly Arising from the second research question, this section briefly analyzes the potential of theoretical approaches to explain the remarkably high HDI score and, first and foremost, the conversion of economic growth into human development in the post-communist developed dictatorships. Drawing on an analysis from the author's previous publication in East-West Studies (Raun 2022), these aspects were mapped in the article, and the results of the analysis in the previous section (growth conversion performance in particular) appear to challenge several theories. Next, the main theoretical approaches employed to explain the resilience and operating mechanisms of (post- communist) autocracies are outlined as possible avenues for a more in-depth study of the phenomenon. 47 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 First, the authors of the concept of competitive authoritarianism, Levitsky and Way (2010), explain autocratic consolidation in Russia and Belarus via low to medium Western influence (linkage and leverage) and the relatively strong organizational power of the regime. It is probable that the situation is also similar in Kazakhstan. Although it seems plausible that a strong state apparatus helps incumbents to remain in office and to pursue their policies more efficiently, the authors unfortunately fall short of explaining why a leader of a strong authoritarian state should favour redistribution and rather efficient growth conversion ‘at the expense’ of the enrichment of his cronies (Raun 2013). Second, the concept of neopatrimonialism, prominent in explaining the autocratic resilience in Central Asia and Russia, appears remarkably problematic in the case of the developed dictatorships. A prominent feature of this theory is the patron-client relationship, where the ruler remains in office by abusing state resources to ensure the loyalty of the elite at the expense of the populace (Guliyev 2011). Under such circumstances, however, one would expect that national income is poorly converted to benefit social development. Yet, instead of a low score on the Growth Conversion Index, the developed dictatorships stand in contrast to other autocracies by delivering both very high levels of human development and a high rate of conversion, comparable even to several democracies. Therefore, although elements of neopatrimonialism may be present in these countries, the neopatrimonialism approach appears deficient in explaining the anomaly observed in this study. Based on the selectorate theory (Bueno de Mesquita et al. 2003), as a more promising avenue, one could argue that multi-party elections (even as a façade) combined with a relatively modern society create a situation that supports better growth conversion. As a successful survival strategy, incumbents could incorporate large segments of a wider population (voters) into the selectorate and the winning coalition. In order to win their support, the leaders may be more eager to invest in health, education, and other public goods that benefit the wider populace. Next, ‘traditional’ rentier state theory (Luciani 1987) appears deficient in explaining the observations made in this article. The fiscal situation in post-communist developed dictatorships is too different. One can compare, for instance, Kuwait—a typical Gulf (rentier) state—and Kazakhstan: the tax burden constitutes 1.4% of GDP in Kuwait versus 15.1% in Kazakhstan; 56.6% share of government expenditure (in 2022) compared to 21.2% in Kazakhstan. Even the share of oil exports in government revenues is below 40% in Kazakhstan (Heritage Foundation 2022; IMF 2022). However, there is a conditional approach to resource dependence, claiming other aspects mediate (or enforce) the relationship between mineral wealth and the efficiency of political institutions. As Jones Luong and Weinthal (2010) assert, the decisive question is who owns and controls the mineral reserves—is it the state or private companies—and whether the latter are of domestic or foreign origin. According to the authors, strong fiscal regimes are most likely to emerge in the case of private domestic ownership of the mineral sector, as was the situation in Russia until 2005. The second-best scenario, private foreign ownership, existed in Kazakhstan until the same year, with other main mineral-rich post-communist countries as examples of more problematic structures. At the same time, Kazakhstan and Russia appeared as the most anomalous cases of (positive) growth 48 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 conversion. Thus, more predictable, and responsible fiscal regimes could support positive structural outcomes and encourage translation of economic growth into human development. The concepts of popular autocrats and the market social contract also appear promising. According to Dimitrov (2009), all three strategies of popular autocrats (economic populism, muzzling the media, and anti-Western nationalism) are present in Russia, while Belarus and Kazakhstan are examples of economic populism. The latter comes close to the market social contract approach (Cook and Dimitrov 2017). All these countries employ social spending that could be seen as investments in popular support—at least more than the neopatrimonial logic expects. For example, the Gini Index for Belarus and Kazakhstan is as low as 24.4 and 27.8 (World Bank, n.d.); in Russia, approximately 20% of the GDP is spent on the social system (McCullaugh 2013), and the rate of poverty decreased tenfold in 12 years in Kazakhstan—from 50% to 5% by 2012 (UNDP 2016). It is possible that such a focus on the poorer segments supports human development. This could also mean better growth conversion since there is less money to be directed via patron-client networks. On the other hand, the quality and sustainability of social spending may have an effect and need to be addressed in future assessments of these concepts. On the other hand, the empirical observations made in this article can be rather successfully explained using the concept of patronal politics (Hale 2015). It combines, in a way, the neopatrimonialism approach with the centrality of public support in explaining authoritarian resilience. Hale describes Russia, Belarus, and Kazakhstan as if they were the success stories of patronal politics—cases of ‘nonrevolution’ where presidents had never become ‘lame ducks’ or where handpicked successors were popular enough to win the competition (Russia in 2000, 2008, and 2012). According to Hale’s (2015) approach, elections with real candidates make patronal presidents interested in securing mass support. It seems logical to assume that one main way to achieve that is to invest in economic and social performance and that one of its by-products is the positive development of the HDI score as well as a better-than-expected growth conversion rate. Thus, patronal politics is one of the more promising explanations for the phenomenon where a remarkably high HDI score is combined with a high rate of conversion of economic growth into human development. The concept of spin dictators by Guriev and Treisman (2022) comes close to the approach by Hale, especially in emphasizing the importance of popular support. According to them, all the main components of a spin dictatorship are present in both Russia and Kazakhstan: manipulating the media, engineering popularity, faking democracy, limiting public violence, and opening up to the world. The main aspect that differs in Putin’s Russia is its militancy. Nazarbayev’s Kazakhstan, on the other hand, deviates by displaying stronger traits of a cult of personality. According to the authors, in case an incumbent follows the spin dictator strategy, his first rule is to become and remain popular, and the most common way to achieve this is to foster economic performance. This strategy could, in this way, translate to a higher HDI score and explain the remarkable rate of growth conversion in these countries. However, one must consider that there are over 30 spin dictatorships in the world, thus, this theory can mostly provide a broader framework. In addition, some fear dictatorships—according to Guriev and Treisman (2022), the other kind of autocracies— have a very high HDI score (e.g., Saudi Arabia). Another relevant aspect is that Belarus, the third country examined in this article, is classified as a fear dictatorship instead (already before 2020). 49 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 In sum, the concept of spin dictatorship appears as one of the more promising explanations that could be included in a future theoretical synthesis. In conclusion, the analysis in this chapter indicates that the neopatrimonialism approach, the traditional rentier state theory, Western linkage and leverage in conjunction with the organizational power of the regime, at best, only partly help to explain the high levels of development and conversion described in the previous chapter. The concept of popular autocrats (together with the market social contract) and the selectorate theory emerge as more promising avenues for further research of the anomaly. The concepts of patronal politics and spin dictatorship, however, rather successfully incorporate public politics into the legitimation and survival strategy of modern autocrats. Corresponding performance-based politics may also translate into human development. In addition, the conditional approach to resource dependence by Jones Luong and Weinthal (2010) highlights Kazakhstan and Russia—the two countries that appeared the most anomalous in this study—as cases of (previously) more responsible ownership structures of mineral reserves and more sustainable fiscal regimes. These three approaches display a high possibility of being combined in future analysis of the anomaly mapped in this article. However, existing theories provide only initial or general explanations of how the growth conversion process could take place. None of them sufficiently disclose the actual workings of the conversion mechanism and related legitimacy-building process, serving mostly as guidelines for more in-depth studies. Thus, to get a better understanding of its causal mechanisms, the phenomenon discussed in this article deserves further investigation. As implied, several aspects may have cumulative explanatory effects and could, therefore, be combined in a further study of the anomaly. This is the answer to the second research question. Conclusion This article explored the relationship between development and democracy in post-communist countries with a focus on anomalous autocracies delivering higher levels of human development scores than their low level of democracy presupposes. The human development of 28 countries was studied employing both descriptive and inferential statistics as well as research synthesis. In the process, cluster analysis and a new measure, the Growth Conversion Index (GCI), were employed. Acknowledging that the aspects in focus in this article are not the only components of human development, it focused on the capability approach to well-being according to which health, education, and command over resources (like food, travel, and shelter) are universal basic components of well-being (measured by the Human Development Index). An outlying group, the developed dictatorships, was observed, consisting of Russia, Kazakhstan, and Belarus. This group appeared to be better at converting national income into human development than other autocracies in the region, despite at the same time being full autocracies. Their average GCI score was close to the average of democracies, with Russia and Kazakhstan appearing among the top five converters. The results of the analysis imply that a high Human Development Index score can coexist with a low level of democracy as a stable success strategy, thus challenging democratization as the path to economic as well as social well-being. At least in 50 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 the post-Soviet space (except for the democratic Baltic States), Russia and Kazakhstan appear as the flagships of human development (according to the UNDP approach), in spite of being clearly undemocratic and, over time, becoming increasingly autocratic. In addition, major theoretical approaches used to explain post-communist autocratic resilience were briefly mapped according to their ability to explain the high HDI score and high growth conversion. While modernization theory, the neopatrimonialism school, and the traditional rentier state approach appeared significantly deficient, several theories emerged as more promising avenues for further research of the anomaly. Amongst them are the concept of patronal politics by Henry E. Hale (2015) and spin dictatorships by Sergei Guriev and Daniel Treisman (2022) as well as the conditional approach to resource dependence by Pauline Jones Luong and Erika Weinthal (2010), displaying a high possibility to be combined in the future analysis. Although these theories indicated possible explanations, none of them sufficiently disclosed the actual workings of the conversion mechanism, thus serving mostly as guidelines for more in-depth studies and implying the necessity to combine aspects from different concepts. The need for further study also seems evident since, over decades, developed dictatorships have proven to be sustainable and self-reproducing and thus, one should not rule out the possibility that this phenomenon may even be expanding. 51 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 REFERENCES Acemoglu, Daron, and James A. Robinson. 2006. Economic Origins of Dictatorship and Democracy. Cambridge, UK: Cambridge University Press. 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Data Used to Calculate DIF and GCI COUNTRY FIW19a FII19b HDI95c HDI19d DIFe INC95f INC19g EHDI19h GCIi Albania 3 5 0.637 0.795 0.482 0.584 0.746 0.814 -0.019 Armenia 4 4 0.627 0.776 0.287 0.519 0.745 0.900 -0.124 Azerbaijan 6.5 1.5 0.604 0.756 -0.636 0.513 0.744 0.876 -0.120 Belarus 6.5 1.5 0.66 0.823 -1.695 0.613 0.789 0.849 -0.026 Bosnia and Herzegovina 4 4 0.78 0.224 0.474 0.756 Bulgaria 2 6 0.71 0.816 0.645 0.706 0.824 0.829 -0.013 Croatia 1.5 6.5 0.703 0.851 0.340 0.755 0.852 0.793 0.058 Czechia 1 7 0.761 0.9 -0.187 0.819 0.898 0.834 0.066 Estonia 1 7 0.729 0.892 -0.060 0.734 0.889 0.883 0.009 Georgia 3 5 0.812 0.213 0.525 0.751 Hungary 3 5 0.746 0.854 -0.451 0.768 0.868 0.843 0.011 Kazakhstan 6 2 0.664 0.825 -1.479 0.676 0.821 0.806 0.019 Kyrgyzstan 4.5 3.5 0.589 0.697 1.288 0.483 0.587 0.716 -0.019 Latvia 1.5 6.5 0.68 0.866 0.103 0.689 0.863 0.852 0.014 Lithuania 1 7 0.71 0.882 0.098 0.706 0.888 0.893 -0.011 Moldova 3.5 4.5 0.638 0.75 0.946 0.609 0.743 0.778 -0.028 Montenegro 3.5 4.5 0.829 -0.303 0.811 North Macedonia 3 5 0.774 0.814 0.678 0.765 Poland 2 6 0.745 0.88 -0.366 0.727 0.87 0.892 -0.012 Romania 2 6 0.694 0.828 0.456 0.724 0.859 0.823 0.005 Russia 6.5 1.5 0.702 0.824 -1.710 0.738 0.841 0.800 0.024 Serbia 3.5 4.5 0.699 0.806 0.060 0.661 0.777 0.822 -0.016 57 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-58 ISSN 2562-8429 Appendix 1. Data Used to Calculate DIF and GCI COUNTRY FIW19a FII19b HDI95c HDI19d DIFe INC95f INC19g EHDI19h GCIi Slovakia 1.5 6.5 0.752 0.86 0.198 0.743 0.872 0.883 -0.023 Slovenia 1 7 0.79 0.917 -0.456 0.812 0.898 0.874 0.043 Tajikistan 6.5 1.5 0.549 0.668 0.756 0.437 0.555 0.697 -0.029 Turkmenistan 7 1 0.715 -0.235 0.558 0.756 Ukraine 3 5 0.686 0.779 0.735 0.653 0.738 0.775 0.004 Uzbekistan 6.5 1.5 0.72 -0.066 0.481 0.645 Notes: a Freedom in the World numerical rating for 2019 (Freedom House, 2023). b FIW19 inverted. Average (mean) is 4.5 and its standard deviation 2.018 (author’s calculations). c Human Development Index for 1995 (UNDP, n.d.). d Human Development Index for 2019 (UNDP, n.d.), average (mean) of HDI19 is 0.810 and its standard deviation 0.063. e Indicator DIF (author’s calculations). f Income index for 1995 (UNDP, n.d.). g Income index for 2019 (UNDP, n.d.). h Predicted value of HDI (only countries included in calculating GCI; author’s calculations). i Growth Conversion Index score (difference between HDI19 and EHDI19; author’s calculations). 58 Canadian Journal of European and Russian Studies, 16 (3)2023: 30-56 ISSN 2562-8429 58 Published by the Centre for European Studies at Carleton University, Ottawa, Canada Available online at: https://ojs.library.carleton.ca/index.php/CJERS/index The Canadian Journal of European and Russian Studies (CJERS – formerly Review of European and Russian Affairs) is an open-access electronic academic peer-reviewed journal: articles are subject to double-blind peer-review. 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