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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). 



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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). 



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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. 



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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. 



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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. 



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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. 



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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). 



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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 



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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 



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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 



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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.). 



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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 



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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.’ 



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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 



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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 



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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 



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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. 



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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 



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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). 



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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 



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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. 

 

  



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Appendix 1. 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 



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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). 

   
 

  



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