Large-scale transition of economic systems--do CEECs converge toward western prototypes?

Ahlborn, Markus ; Ahrens, Joachim ; Schweickert, Rainer 等

INTRODUCTION

The economic system of a country and especially the coherence of its institutional characteristics are crucial determinants of economic performance. Theoretical models exploring capitalist diversity (such as the varieties-of-capitalism approach (VoC)) have been applied to explain the causes of the current persistent crisis in several EU and Eurozone member states (see, e.g., Hall, 2012; Hancke, 2013; and Johnston et al., 2014). These approaches and the related literature, however, have focused on the explanation of differences between traditional OECD countries. Central and Eastern European Countries (1) (CEECs) have not played a prominent role in the (empirical) analysis of economic systems, although their convergence or nonconvergence toward western prototypes is a highly interesting question; this is more so as all these countries are EU and (prospective) Eurozone members and thus are vulnerable to the same problems plaguing the current European crisis countries.

Moreover, contrary to other countries, after the collapse of socialism, CEECs had a chance to develop a new set of market institutions or could choose from the existing institutional prototypes. Clearly, the envisaged process of institutional and economic convergence of CEECs has been guided by the priority of EU accession and the corresponding Copenhagen Criteria. However, the actual variety of economic systems within EU-15 suggests that these criteria and especially the acquis communautaire have been less relevant when it comes to the core set of institutions characterizing economic systems (see, e.g., Foders et al., 2002). Hence, it is still unclear how the institutional convergence processes in CEECs had developed. Hypotheses range from national economic systems, which are similar to the existing prototypes (Bohle and Greskovits, 2012) to idiosyncratic systems with no role model (Nolke and Vliegenthart, 2009). Only a few empirical studies have investigated this topic. This paper seeks to close this gap. Our goal is to empirically identify the type(s) of economic system(s) that the CEECs have established in order to detect possible convergence or nonconvergence toward the existing prototypes.

In order to establish a benchmark of prototype economic systems, we deviate from previous studies, since we do not limit our analysis to one aspect of the economic system, as, e.g., done by authors focusing either on the production or on the welfare system. Instead, we relate our study to the literature on the joint VoC and Worlds of Welfare States (WWS) approaches (e.g., Amable, 2003; and Schroder, 2013). Based on this literature, we suggest four basic prototypes of economic systems. Three of which possess distinct but inherently consistent institutional configurations, reaping benefits from institutional complementarities: A Liberal prototype based on a Liberal Market Economy (LME, Anglo-Saxon countries,) at one end of the spectrum, as well as Nordic and Continental welfare state varieties based on a Coordinated Market Economy (CME) at the other end. As first described by Hall and Soskice (2001), these countries possess institutions in all spheres of their economy, which are complementary to each other, leading to superior economic performance due to matching incentive structures. In contrast, Mediterranean countries established mixed market economies (MME), characterized by a lack of institutional complementarity and the resulting performance problems.

Furthermore, we follow Iversen and Wren (1998) and Kitschelt (2006) and consider differences in performance patterns. Those authors argue that sustained employment growth and innovation can only be achieved via wage decreases (and subsequently rising inequality) or high public sector spending (with subsequent fiscal imbalances). According to underlying societal preferences, different prototype economic systems will react differently to this tradeoff: Liberal countries will focus on innovation/growth and budgetary restraint, while CMEs are expected to favor income equality combined with a focus on budgetary restraint (Continental) or growth/innovation [Nordics).

The Cluster and Principal Component Analysis in this paper addresses primarily the question of whether the economic systems of CEECs have converged toward (1) a consistent LME or CME model with the corresponding performance pattern; toward (2) an inconsistent, crisis-prone Mediterranean type of model; or if they have (3) established a distinct new prototype. To answer this question, the paper proceeds as follows. Section 2 introduces the prototype economic systems among traditional OECD countries and discusses the related cluster analyses on CEEC convergence available so far. This overview shows that a comprehensive analysis integrating CEECs into an OECD sample is still lacking in the literature. Section 3 describes our macro cluster approach, and Sect. 4 gives the main results. Our conclusions in Sect. 5 demonstrate that CEECs have still not completely converged toward OECD prototypes, but complement these by independent Liberal (Baltic States, Bulgaria, Romania, and Slovakia) and Coordinated (Visegrad countries, Croatia, and Slovenia) varieties, which show few signs of convergence toward the inconsistent and underperforming economic systems of the Mediterranean countries.

EMPIRICAL ANALYSES OF ECONOMIC SYSTEMS--A BRIEF OVERVIEW

Establishing a benchmark: four prototype economic systems

In order to establish a benchmark of prototype economic systems toward which CEECs may or may not have converged, we employ a joint VoC and WWS approach, along the lines of Schroder (2013). We acknowledge the need to go beyond an exclusive analysis of production systems (i.e., the traditional VoC approach) or the welfare state (i.e., the traditional WWS approach) when analyzing economic systems. Moreover, we emphasize the need to also include several dimensions of economic performance (going beyond an exclusive focus on income-related variables) because, following Iversen and Wren (1998) and Kitschelt (2006), the different prototype economic systems can be expected to possess an inherent focus on different combinations of the macroeconomic targets innovation capacity, macroeconomic stability, and income equality.

The VoC approach (see Hall and Soskice, 2001) focuses on the consistency of alternative types of institutional frameworks and identifies two polar cases of capitalist varieties. LMEs (mainly Anglo-Saxon countries) rely primarily on markets to achieve coordination among economic actors, while in CMEs (mainly Northern and Continental European countries), nonmarket institutions play critical roles and influence the processes of strategic interaction. With respect to economic performance, institutional consistency of the respective economic system matters. This implies that neither of the two polar cases performs better or worse by itself, but that it is crucial for the economic performance of a country to feature a consistent economic system where the institutions within all microeconomic spheres are complementary, i.e., they produce matching incentive structures. (2)

If such institutional complementarities are absent, i.e., if the institutional configurations in the different spheres of the economy are "mixed" and do not produce consistent incentives, a market economy will ceteris paribus perform worse. For the Mediterranean countries, these problems are often detected. Consequently, these countries have been labeled MMEs in the VoC literature (see, e.g., Molina and Rhodes, 2008). These noncomplementarities in their economic systems are conceived to have played a major role in the ongoing and persistent crisis of Mediterranean capitalism, amplified by their coexistence with pure CMEs in a monetary union (see, e.g., Hall, 2012; Hancke, 2013; and Johnston et al., 2014).

However, as pointed out recently (see, e.g., Schroder, 2013; Amable, 2003), a comprehensive analysis of economic systems is incomplete without considering welfare systems, i.e., government activity, as is done in the literature on worlds of welfare state (WWS) initiated by Esping-Anderson (1990). Doing thus widens the prototype varieties to be considered. While the LME model is rather homogeneous in complementing liberal production systems with a low level of government's intervention, CMEs are split up into a social-democratic (Northern Europe) and a conservative variant (Continental Europe). Each of these models possesses its own particular pattern of welfare state provision. Schroder (2013) suggests that an integration of the two strands of literature is possible. He argues that the similarity in country groupings, which arises through various comparative analyses of economic systems, cannot be due to coincidence but must be the result of underlying causal factors, i.e., institutional complementarities, which link a liberal welfare state to an LME and conservative or social democratic welfare states to a CME. Consistent with other typologies (e.g., Amable, 2003), this leads to the identification of four prototype economic systems among traditional OECD countries: Liberal, Continental, Nordic, and Mediterranean. (3)

Furthermore, the literature on economic systems has been controversial regarding the discussion of performance criteria. Acemoglu et al. (2012) claimed that performance in terms of innovative capacity is superior in LMEs. Iversen and Wren (1998), (4) however, argue that several performance aspects need to be taken into account in order to better understand the variety of economic systems. They, like Kitschelt (2006), (5) proposed xxa method to analyze policy effectiveness in terms of a (possible) trilemma where countries have to decide on potential tradeoffs between the three macroeconomic targets of innovative capacity, macroeconomic stability, and income equality.

In conclusion, these theoretical considerations lead to the characterization xxin the following manner of the four prototype economic systems, toward which the CEECs may possibly have converged:

1. Liberal LME, Liberal welfare state, restraint of government interference, focus on macroeconomic stability, and innovative capacity.

2. Continental CME, Conservative welfare state, active government, focus on income equality, and macroeconomic stability.

3. Nordic CME, Social Democratic welfare state, active government, focus on income equality, and innovative capacity.

4. Mediterranean MME, closest to Continental model, few institutional complementarities with resulting performance problems.

Cluster analyses on traditional OECD countries

In order to scrutinize the country classifications that have been proposed in traditional VoC and WWS approaches, Cluster Analysis has been repeatedly applied in comparative-capitalism research (e.g., Bambra, 2007; Danforth, 2014; Gough, 2001; Hassel, 2014; Kangas, 1994; Kautto, 2002; Powell and Barrientos, 2004; Saint-Arnaud and Bernard, 2003). However, only a few studies have taken a comprehensive and complex approach that includes both VoC and WWS variables in their empirical designs.6 A first notable exception is the analysis by Obinger and Wagschal (2001). They combine welfare state characteristics with public and economic policy as well as outcome variables. Their results resemble the four established prototypes, because they identify four "families of nations": Anglo-Saxon, Scandinavian, Continental, and Periphery (Mediterraneans plus Ireland). Amable (2003) and Pryor (2005) (7) confirm the benchmark economic systems as well. Amable (2003) analyzes five spheres of capitalism with data from the mid-1980s to the 1990s. His results resemble previous findings because he also distinguishes the four familiar models among Anglo-Saxon and European countries (adding yet another (Asian) model). Pryor (2005) also arrives at the four-cluster solution. As mentioned, this congruence of results in comparative-capitalism research among OECD countries is Schroder's (2013) main argument for establishing a joint typology of VoC and WWS. He conducts a cluster analysis as well, arriving at similar results, which serves as a foundation for his further theoretical work.

As a baseline, more complex approaches considering production- and welfare-regime characteristics confirm the hypothesis of four prototype economic systems, because they detect a homogeneous LME model with liberal welfare regimes, while the CME world is divided--according to varieties of welfare regimes--into a Nordic (social-democratic) and a Continental (conservative) model. Another common result is the ambiguity over the classification of the Mediterranean countries, which apparently differ from other OECD countries but appear to be relatively close to the Continental model.

Previous cluster analyses of economic systems in CEECs

So far, CEECs have not been included in the country sample of complex studies. In addition, only a few cluster analyses that include the whole sample of CEECs are available. Schneider and Paunescu (2012) provide an analysis of the VoC typology and identified clusters that resemble the LME/CME classification among OECD countries and a third variety of the so-called state-dominated economies (Mediterranean countries). Furthermore, several countries of their sample (e.g., Hungary, Poland, and the Czech Republic) constitute a group of "Hybrid" economies, which do not fit into any of the three varieties of capitalism. Farkas (2011) includes all CEECs in her analysis of capitalist diversity (following the approach of Amable 2003) and arrives at a similar result. Again, CEECs are sorted into one separate cluster, constituting a distinct Eastern European economic system (Table 1).

Other cluster studies including CEECs concentrate on the WWS typology and related aspects. Castles and Obinger (2008), Fenger (2007) and McMenamin (2004) also suggest a separate group of CEECs, which is not integrated into any of the traditional OECD clusters. However, contrary to VoC analyses, a separation between two groups of CEECs is revealed once welfare-regime characteristics are considered. These results reflect the consensus view that there is a clear distinction between a liberal group including the Baltic countries (clustering around Estonia) and a coordinated group including Visegrad countries (clustering around Slovenia).

Hence, there is some support for the two major hypotheses established in qualitative studies. On the one hand, Nolke and Vliegenthart (2009) claim that transition has come to an end, at least for some advanced CEECs, and that these countries have established another distinct variety of capitalism characterized as (externally) Dependent Market Economies. On the other hand, there is evidence for some variety within the group of CEECs. Bohle and Greskovits (2012) claim that there is a clear division mirroring the distinction in Western Europe: neo-liberals with a small fiscal and welfare state, atomized industrial relations, and a focus on macroeconomic stability (Baltics); neocorporatists with more generous welfare state regimes, stronger regulation, and less of a focus on macroeconomic stability (Visegrad countries), and a group of failing, hybrid cases (Southeast Europe). However, a variety of qualitative studies provides the confusing picture that some CEECs lean toward the CME type and others toward the LME type, with different conclusions for all countries except Estonia and Slovenia. (8)

All in all, in contrast to the empirical literature on OECD countries, there is no clear consensus over the empirical classification of the economic systems among CEECs. There appears to be evidence for both hypotheses. On the one hand, studies suggest that CEECs have established distinct economic systems that are different from the OECD prototypes (Farkas, 2011; and Schneider and Paunescu, 2012), confirming the hypothesis of Nolke and Vliegenthart (2009). On the other hand, studies focusing on welfare state aspects identify a distinction among the group of CEECs, mirroring the prototypes of western economic systems (Castles and Obinger, 2008; Fenger, 2007; and McMenamin, 2004), which reflects the confirmation for Bohle and Greskovits' (2012) hypothesis.

Overall, more empirical evidence is required in order to consolidate the evidence on production and welfare systems in CEECs. First, most studies do not integrate VoC and WWS approaches in order to provide a comprehensive analysis of production and welfare systems. Second, available studies end in the mid-2000s and do not consider post-EU-accession effects. Third, most studies do not consider transition effects. (9) Finally, no study has considered performance effects [except for Obinger and Wagschal, 2001), or a systematic integration of performance patterns as suggested by Kitschelt (2006) and Iversen and Wren (1998). Hence, while some studies have included some of these features, a comprehensive empirical analysis of the possible convergence of CEECs toward prototype economic systems is still lacking.

EMPIRICAL MODEL--A MACRO CLUSTER ANALYSIS

We provide a comprehensive view on economic systems based on a country sample consisting of 22 traditional OECD countries and 11 CEECs. (10) We employ cluster analysis using aggregated macroeconomic indicators for policy and performance. In addition, we acknowledge the transition aspect by looking at cluster history, i.e., we implement Cluster Analysis for different time periods. Finally, we use Principal Component (PC) Analysis, in order to identify the combinations of policy and performance variables that explain the largest part of the data variance. Based on the major combinations of variables (PCs), we are able to analyze the drivers for our cluster results.

The idea of clustering in comparative political economy arises from the different sets of institutions underlying alternative prototypes of economic systems. In this context, Cluster Analysis has proven to be a useful technique, because it is concerned with the discovery of patterns in the data and the creation of typologies. Concerning the cluster methodology, we choose the Ward Method as a clustering algorithm. This hierarchical clustering method merges two objects/clusters based on the within-cluster variance. An object is allocated to a cluster if this allocation causes the smallest increase of the within-cluster (and thereby the overall) variance. This method is widely used in applied Cluster Analysis, provides robust results, and has repeatedly performed well in simulation tests (Eckey et al., 2002). Most importantly, hierarchical methods allow us to identify subclusters within broader clusters, as suggested by the VoC and WWS theories.

However, a major critique put forward by Ahlquist and Breunig (2009) and Pryor (2008) with respect to Cluster Analysis is the fact that indicators for economic systems are often chosen on an arbitrary basis. Hence, results may be determined by the implicit weight given to certain arguments if a range of indicators measure similar institutions. As a possible solution, statistical methods such as PC analysis are applied in order to construct artificial cluster variables determined by the implicit weights of a set of microeconomic indicators. In line with our arguments developed above, we suggest an alternative approach and refer to aggregated macroeconomic indicators drawn from the Economic Freedom of the World (EFW) dataset, i.e., we import the weighting from a well-established source instead of applying PC analysis beforehand. Our basic assumption, following the line of reasoning in Schroder (2013) and Amable (2003), is that the overall design of government's intervention, i.e., government activity, is represented by general categories of government such as spending (not in the focus of VoC analyses) and regulation (not in the focus of WWS analyses). We assume that clusters of traditional OECD countries, i.e., the four prototype economic systems, will be confirmed by our approach. Therefore, we can use the aggregate macro variables on government activity to identify economic systems and do not have to implement PC analysis beforehand to construct weighted indicators based on microeconomic data. This allows us to save PC analysis for the analysis of the driving forces behind emerging clusters and to combine macroeconomic policy variables with macroeconomic performance variables along the line of the economic performance trilemma suggested by Kitschelt (2006) and Iversen and Wren (1998). (11)

The variables used in our analysis are described in detail in Table 2. Basically, we consider three macro indicators measuring government activity in order to distinguish different institutional configurations of economic systems:

--Overall size of government including transfers, government enterprises, taxes, etc.,

--transfer spending as a proxy for welfare-related involvement, and

--government regulation aggregated on the basis of sectorial regulation in trade, labor, and capital markets.

To capture the three dimensions of macroeconomic targets, we further included three variables measuring economic performance:

--Innovative capacity measured by an aggregation of indicators for determinants of innovation,

--macroeconomic stability measured by public debt levels (fiscal stability), and

--income equality measured by the (reversed) GIN1 index. (12)

An important issue concerning CEECs is transition and potential convergence over time. Hence, we define four different periods for which we average our variables and provide a cluster history by performing cluster analysis for all periods in order to reveal potential convergence. The time period analyzed is restricted by the availability of data. We use the Economic Freedom of the World dataset developed by the Fraser Institute in order to have internationally comparable data on government's policy, i.e., the variables on size of government, transfer spending, and regulation. Therefore, for initial transition positions we use data available for 1995 (after the initial transition recessions) and from the years following 2000, averaged over three periods (2000-2003, 2004-2006, 2007-2009). The latter periods cover pre- and post-EU-accession as well as the period before the global financial crisis. After 2009, the fiscal data are distorted by country-specific crisis reactions, and data on innovation capacity provided by the World Bank are not officially available. Hence, 2009 is the last year for which we could observe normal patterns based on internationally comparable innovation capacity information. (13)

As a second step, we use PC analysis to identify the drivers of cluster results. PC analysis transforms the original data orthogonally in order to obtain uncorrelated linear transformations of the variables that contain as much information as possible. These principal components (PCs) are correlated with the original cluster variables and explain a certain (quantifiable) amount of the variation among the data. Hence, knowing the clusters emerging from the cluster analysis, we are able to plot the clusters into a PCI/PC2 space. Together with the correlation of these first two PCs with the original variables, this allows us to detect the most important combinations of cluster variables responsible for the cluster results, as well as the most important differences between clusters. Furthermore, PC analysis allows us to further investigate the time dimension, since it will be carried out for our four different time periods separately. If the correlations of the PCs and the original variables remain stable, a comprehensive comparison of group characteristics over time becomes possible, and further insights into possible convergence paths of CEECs toward the prototype economic systems can be gained.

EMPIRICAL RESULTS--DO THE ECONOMIC SYSTEMS OF CEECS CONVERGE TOWARD WESTERN PROTOTYPES?

Based on the most recent period, Figure 1 provides first answer to the question whether or not there is a specific economic system among CEECs. If one allows for a level of heterogeneity where different varieties of CME can be distinguished, CEECs still form separate clusters and are not integrated into the clusters of prototype economic systems. It is interesting to note that the macrolevel analysis is able to reveal these four clusters highlighted in the empirical and theoretical literature on economic systems. Hence, there is a distinction between three "traditional" clusters of CMEs: Nordic, Continental, and Mediterranean. These clusters are distinct from the Liberal cluster of LME countries.

The macroanalysis also reveals that there are two distinct clusters of CEECs, which cluster with either the CME- (CEEC CME) or the LME-group (CEEC LME). At the same time, and this is the second answer to the question about a separate CEEC model, moving up the cluster tree shows the integration of CEEC clusters into traditional clusters. Hence, on a level of heterogeneity at which the traditional OECD world is divided into only two groups--CME and LME--CEECs become integrated.

Of course, as mentioned above, convergence is an important topic when talking about CEECs. The cluster history (Table 4 in the appendix) for comparable levels of heterogeneity reveals some rather stable clusters but also convergence as well as divergence since the mid-1990s. The most stable clusters are the Liberals, both traditional LMEs and the CEEC LMEs, mainly the Baltic countries. This also applies to a core group of Continental countries--Austria, France, Germany, and, to some extent, Belgium and the Netherlands, and, for most of the time, for the Mediterranean group consisting of Southern European countries.

[FIGURE 1 OMITTED]

Consistent with reform efforts in the region, the Nordic cluster diverged from a joint cluster with Continentals after 1995. For the cluster of CEECs, which tends toward a coordinated economic system (CEEC CME), some convergence toward the Continental group occurred in the context of accession. After the period 2004-06, however, these CEECs separated, forming their own cluster. Overall, it seems that the distribution into clusters seems to be stabilizing somewhat, but it is still too early to conclude that CEEC clusters will remain stable.

Figure 2 shows the standardized cluster averages for the six clusters, again for the most recent time period. Among the traditional OECD clusters, the polar cases are provided by the Liberal and Continental clusters. Liberal countries figure below average in terms of government size and regulation as well as in terms of debt and income equality, while the opposite is the case for the Continental countries. The fact that innovation capacity is somewhat lower in Continental countries seems to indicate a tradeoff for achieving higher income equality by means of higher spending, regulation, and debt. However, the performance of the Nordic group reveals that this needs not to be the case. This cluster combines best performance in terms of both innovation and income equality. This goes together with a below-average level of debt and regulation and a lower level of transfer spending. Hence, in contrast to the Continental group, regulation is rather a substitute for than a complement to spending. This quite different policy mix, together with the fact that the Nordic cluster outperforms both the Liberal and Continental clusters, suggests that there may be no inevitable tradeoff in being innovative and equal, but that there are some costs to the Liberal as well as to the Continental model.

[FIGURE 2 OMITTED]

Average performance of CEEC clusters reveals a rather clear distinction between these country groups. Among these, the CEEC LME cluster shows the most consistent picture with respect to the single variables. With the exception of the innovation variable, which seems to contain a development component, this cluster shows large similarities with the Liberal system. The CEEC CME clusters share with the CEEC LME cluster a rather low level of innovation and, in addition, a low level of indebtedness. Except for this, the pattern is similar to the Continental cluster.

Finally, the Mediterranean group reveals the highest degree of inconsistency with respect to policy variables. The cluster shows the highest degree of regulation going together with a rather small government size but still above average level of transfer spending. Hence, apart from redistribution and regulation, government is rather small. Furthermore, the Mediterranean cluster is characterized by the worst performance pattern with below-average innovation capacity, high public debt and, even compared to the Liberal group, low income equality.

Hence, the cluster results show two distinct CEEC clusters leaning toward traditional prototypes but not mixing up with the Mediterranean cluster, which, in line with the literature, is shown to provide an inconsistent institutional design and the worst performance. This result is confirmed by the PC analysis, which reveals the driving forces determining the observed clusters. As depicted in Table 3, there are three principal components, which explain 81 percent of the data variation. According to the correlation with our initial variables, they could be characterized as follows:

PC1--Government Restraint and Inequality revealed by negative correlations with government size (overall and transfer spending) and income equality.

PC2--Deregulation and Innovation revealed by a positive correlation with innovation but a negative correlation with regulation.

PC3--No Debt because it only shows a significant (negative) correlation with public debt.

[FIGURE 3 OMITTED]

The results for PC3 imply that debt is not highly correlated with other performance and policy variables and that the allocation of countries to clusters is not related to indebtedness in the first place. Therefore, we concentrated on the first two PCs and sorted the clusters revealed in Figure 1 into the PCI (Government Restraint and Inequality)/PC2 (Deregulation and Innovation)--space (Figure 3). This reveals some interesting insights:

--There are two "worlds of redistribution" expressed by PCI: the traditional LMEs joined by the more liberal CEECs, which spent less and have a higher degree of inequality compared to all the other groups. This confirms a positive interdependence between spending and distribution and that some groups of countries have a preference for equality and others do not.

--At the same time, there is also a distinction according to a regulation/ innovation mix (PC2) within these two "worlds of redistribution". Clearly, the Nordic countries are distinct from the other CMEs by revealing a rather low degree of regulation going together with a high degree of innovation. While this is not an analysis of causality, it fits the argument made by Kitschelt (2006) that it is especially the Continental group of CMEs facing a problem of inefficiency. On the contrary, Nordic countries are running large (redistributive) welfare states but rather liberal regulation regimes. If we assume some causality for lower regulation allowing for higher innovative capacity, comparing the Nordic and Liberal clusters in Figure 2 seems to reveal some kind of "unavoidable tradeoff" involved in having (efficient) redistribution by a welfare state.

--Again, the Mediterranean countries seem to represent the worst mix of high regulation/low innovation together with a rather undetermined spending/equality mix. As revealed here and by the cluster analysis in general, CEECs do not mix up in such a scenario (except Poland, and Croatia to some degree).

--The CEEC clusters perform better than the Mediterranean cluster and sort into the "two worlds of redistribution". While CEEC CMEs are quite close to the Continental group, the CEEC LMEs are still far from performance of the Liberal cluster possibly revealing their relatively low level of development in this sample.

As before, we look at the historical development in order to analyze convergence. Here we have to base our analysis on the three periods since 2000 because these periods reveal a rather stable pattern of PC correlations with initial variables. (14) For all six clusters, the average values of the first two PCs have been computed for all three periods and are depicted in Figure 4.

[FIGURE 4 OMITTED]

As predicted by theory, the Liberal and the Nordic clusters are fairly stable over the decade. However, the Continental cluster shows a rather clear movement in the direction of a less favorable mix of more regulation and/or less innovation between the periods: 2000-2003 and 2004-2006. The countries of the Mediterranean cluster show a certain institutional stability concerning the values of PCI. This suggests that these countries have indeed implemented a rather inconsistent pattern concerning the role of the state and redistribution, i.e., a fairly big government not leading to a comparable level of income equality (see Figure 2). However, the values for PC2 for the latest period reveal a large step toward an even more unfavorable mix of low innovation and high regulation, which could be seen as confirmation of the hypothesis that their inconsistent economic system is one root of the persistent crisis that these countries have faced in recent times.

The CEEC clusters clearly became more distinct from the Mediterranean cluster over the decade. CEEC CMEs performed rather stable with--if anything-a minor convergence toward a more Liberal model. This could be due to the conditionality of EU accession. The convergence of the CEEC LME cluster in this respect is much clearer. There seems to be a clear step in the direction of more restraint of government and less regulation/more innovation, i.e., a more liberal model after the pre-EU period 2000-2003. Hence, our data indicate that the CEECs established a more liberal type of capitalism after they joined the European Union, which remained fairly stable in the subsequent periods. Overall, however, CEECs have established still rather distinct varieties of capitalism, which show a certain degree of convergence toward the prototype economic systems, but not toward the Mediterranean cluster, which even diverged from other clusters.

SUMMARY AND CONCLUSION

In this paper, we suggested a data-based macro cluster approach in order to investigate the question of what kind of economic systems have been established by the CEECs and whether or not these have converged toward western prototypes. This analysis offered a comprehensive investigation of a combined OECD and CEEC country sample based on a joint VoC/WWS approach, augmented by variables on economic performance tradeoffs. Furthermore, we accounted for the development/transition sphere by not restricting our analysis to one time period but by investigating several points in time via Cluster and Principal Component Analysis.

Indeed, we were able to reproduce earlier results about prototype economic systems among traditional OECD countries and, at the same time, added new insights into the transition experience of CEECs:

--Our comprehensive approach reveals that, among (traditional) OECD countries, stable clusters of countries exist, which constitute the prototypes LME (Liberal) and CME (Continental and Nordic), identified by the respective theories. These prototypes offer the expected institutional mix with the corresponding tradeoff in performance for the Liberals (small government, low regulation, more innovative capacity, and inequality) and Continentals (big government and high regulation, less inequality, and innovative capacity). The Nordics, however, seem to have escaped this tradeoff, because their model (big government, low regulation) allows the best performance in terms of both high innovative capacity and income equality. Fiscal and macroeconomic stability do not seem to be a characteristic that clearly distinguishes these clusters.

--Italy, Greece, and Portugal form a distinct group usually labeled MME or Mediterranean in the literature, while Spain at times clusters with CEECs. These countries were shown to offer an incomprehensive mix of institutions. They run rather large governments combined with high levels of public debt but nevertheless possess the most unequal income distribution of the sample. There is a clear trend that this inconclusive institutional composition is increasingly accompanied by a very unfavorable mix of high regulation and low innovative capacity. This allows the conclusion that these countries indeed offer a disadvantageous pattern of institutions, which lacks complementarity and thereby leads to underperformance.

--For the CEECs, two distinct models were identified, which share characteristics with the respective prototypes LME and CME. Our Cluster analysis reveals that one group of CEECs (the CEEC CME cluster) leans toward a Continental/Nordic or CME type of capitalism, while other CEECs (the CEEC LME cluster) apparently have converged toward a Liberal type of capitalism. There is little evidence that the CEECs constitute a hybrid model similar to the Mediterranean economic system. The PC analysis revealed a large step toward convergence to the Liberal model for the CEEC LME cluster. Evidence for liberalization of the CEEC CME cluster was present but much more limited.

In the introduction, we proposed three possible scenarios for the convergence of the CEECs' economic systems: (1) convergence toward the consistent western prototypes LME and/or CME; (2) convergence toward an inconsistent MME; or (3) the emergence of an idiosyncratic, distinct Eastern European economic system. Overall, our results show that in Central and Eastern Europe two different clusters of economic systems have emerged, which are not fully integrated in any other cluster but oriented at polar LME/ CME prototypes. This confirms Bohle and Greskovits' (2012) thesis of institutional divergence within the group of CEECs. We identified a CEEC LME cluster (Baltics, Bulgaria, Romania, and, since the latest period, Slovakia) leaning toward a Liberal model and a CEEC CME cluster (Croatia, Czech Republic, Hungary, Poland, Slovenia) sharing a great deal of institutional characteristics with the coordinated prototypes of the Nordic and especially the Continental group. Hence, our empirical results point toward the first scenario of convergence toward consistent western prototypes, while still indicating particular, idiosyncratic features of the CEECs' economic systems. Concerning the second scenario, little evidence of convergence toward a hybrid Mediterranean model was found. This group exhibits the most unfavorable mix of institutions, leading to economic inefficiency due to the lack of institutional complementarity, which is one root of the current crisis in these countries. The CEECs do not share many similarities with this Mediterranean economic system and thus appear less prone to the systemic crises those countries are undergoing.

Acknowledgments

Rainer Schweickert received funding for this research from the European Union's Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 290647.

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APPENDIX

Appendix Table 4

Table 4: Cluster history, 1995-2009

                     07-09           04-06

Australia        Liberal         Liberal
Canada           Liberal         Liberal
Ireland          Liberal         Liberal
New Zealand      Liberal         Liberal
Switzerland      Liberal         Liberal
UK               Liberal         Liberal
USA              Liberal         Liberal
Japan            Outlier 1       Outlier 1
Austria          Continental     Conti/CEEC
Belgium          Continental     Conti/CEEC
France           Continental     Conti/CEEC
Germany          Continental     Conti/CEEC
Netherlands      Continental     Nordic
Denmark          Nordic          Nordic
Finland          Nordic          Nordic
Norway           Nordic          Conti/CEEC
Sweden           Nordic          Nordic
Czech Republic   CEEC CME        Conti/CEEC
Slovenia         CEEC CME        Conti/CEEC
Croatia          CEEC CME        Conti/CEEC
Hungary         CEEC CME        Conti/CEEC
Poland           CEEC CME        Mediterranean
Slovakia         CEEC LME        CEEC LME
Estonia          CEEC LME        CEEC LME
Latvia           CEEC LME        CEEC LME
Lithuania        CEEC LME        CEEC LME
Romania          CEEC LME        CEEC LME
Bulgaria         CEEC LME        CEEC LME
Greece           Mediterranean   Mediterranean
Italy            Mediterranean   Mediterranean
Portugal         Mediterranean   Mediterranean
Spain            CEEC CME        Mediterranean
Turkey           Outlier 2       Outlier 2

                     00-03              95

Australia        Liberal          Liberal
Canada           Liberal          Liberal
Ireland          Continental      Liberal
New Zealand      Liberal          Liberal
Switzerland      Liberal          Liberal
UK               Liberal          Liberal
USA              Liberal          Liberal
Japan            Outlier 1        Outlier 1
Austria          Continental      Conti/Nordic
Belgium          Continental      Mediterranean 2
France           Continental      Conti/Nordic
Germany          Continental      Conti/Nordic
Netherlands      Continental      Conti/Nordic
Denmark          Nordic           Conti/Nordic
Finland          Nordic           Conti/Nordic
Norway           Nordic           Conti/Nordic
Sweden           Nordic           Conti/Nordic
Czech Republic   CEEC CME         CEEC CME
Slovenia         CEEC CME         CEEC CME
Croatia          CEEC LME         No data
Hungary         Nordic           Conti/Nordic
Poland           CEEC CME         CEEC CME
Slovakia         CEEC CME         CEEC CME
Estonia          Baltics/ESP/PT   CEEC LME
Latvia           Baltics/ESP/PT   CEEC LME
Lithuania        Baltics/ESP/PT   CEEC LME
Romania          CEEC LME         CEEC LME
Bulgaria         CEEC LME         Mediterranean
Greece           Mediterranean    Mediterranean
Italy            Mediterranean    Mediterranean 2
Portugal         Baltics/ESP/PT   Mediterranean
Spain            Baltics/ESP/PT   Mediterranean
Turkey           Outlier 2        No data

(1) We define CEECs as all former socialist countries that had joined the European Union as of 2016.

(2) A CME, for example, combines industrial relations shaped by employer-employee cooperation and higher job security with a focus on firm- and industry-specific skills in their education system. The institutional configuration in one sphere of the economy (job security on labor market) enhances the efficiency of the institutions in another sphere (job security reduces sunk costs of firm/industry-specific education). In a consistent variety of capitalism, i.e., LME or CME, all institutions of the different spheres of the production system are complementary to each other, and these institutional complementarities allow for superior performance (Hall and Soskice, 2001).

(3) The nomenclature for clusters varies in the literature. Therefore, we use our own nomenclature for the clusters in our analysis.

(4) Iversen and Wren (1998) analyzed what they called the "trilemma" problem of postindustrial economies, in which employment growth is concentrated in service sectors with low productivity growth. They argue that tradeoffs involved in achieving employment, equality, and stability lead to different solutions depending on economic systems. The Liberal response allows service sector wages to decrease, which will lower relative prices and lead to growing demand and employment while maintaining budgetary restraint at the cost of increasing income inequality. The Christian democratic (Continental] response prevents service sector wages from decreasing, subsequently sustaining income equality and budgetary restraint at the cost of lower employment and demand. Finally, the social democratic (Nordic) response maintains high wages for service sector workers, which are increasingly employed in the public sector and thereby increases employment and demand while maintaining income equality at the cost of declining fiscal stability (higher taxes and/or debt).

(5) Kitschelt (2006), closer to our comprehensive analysis of economic systems, explains a different variant of the "trilemma" of macroeconomic targets. He focuses on innovation capacity, which is closely linked to employment creation/economic growth. He concludes that the Nordic is superior to the Continental model because there, redistribution is much more efficient on the macrolevel allowing for both fiscal stability and a low level of regulation supporting innovation and growth, while still upholding a comparatively equal income distribution. In achieving this, the Nordic countries have managed to escape the "trilemma" of macroeconomic targets to a certain degree, whereas the Continental countries find themselves in a state requiring reform steps either in a more Liberal or Nordic direction.

(6) Traditionally, cluster studies have figured prominently in analyses on WWS typology. Saint-Arnaud and Bernard (2003), for example, used a dataset of 36 variables on the political and social situations in 20 OECD countries and identified the three groups proposed by the WWS approach plus one cluster of Mediterranean countries. Bambra (2007), Danforth (2014), Gough (2001), Kangas (1994), Kautto (2002), Powell and Barrientos (2004) also conducted cluster analyses based on welfare state characteristics and arrived at comparable results: Esping-Andersen's hypothesis of three distinct models of welfare state provision is supported. Only a few cluster studies on VoC typology exist, including Schneider and Paunescu (2012) and Hassel (2014). Most studies confirm the LME-CME pattern predicted by Hall and Soskice, but also identify a cluster of Mediterranean countries, whose model of production and welfare state provision apparently differ from those of other European countries.

(7) An exceptional study is provided by Pryor (2006) because only developing countries are considered in the analysis. Doing this, Pryor identifies somewhat different models: Business, Labor, Statist, and Traditional.

(8) Studies investigating the CEECs' economic systems (not employing cluster analysis] are offered by Babos (2010), Bartlett (2007), Cernat (2006), Crowley (2008), Feldmann (2006), King (2008), Knell and Srholec (2007), Myant (2007) and Mykhnenko (2007). These studies in part arrive at differing results for several countries. Exceptions are Slovenia and Estonia, since for these two, fairly advanced, CEECs, clearer results emerged from an application of the VoC approach. Following the results of Feldmann (2008) and Buchen (2007), Slovenia (CME) and Estonia (LME) quite closely resemble VoC prototypes.

(9) Exceptions are Castles and Obinger (2008) and Schneider and Paunescu (2012), who examined more than one period for traditional OECD countries. Schneider and Paunescu (2012) conducted separate cluster analyses for five periods between 1990 and 2005. Considering the time dimension reveals some interesting and unconventional insights. Spain, Denmark, Finland, the Netherlands, and Sweden are assigned to the LME cluster in the most recent period. They interpret this as a result of liberalization efforts in these countries. Furthermore, Danforth (2014), not considering CEECs, tests the validity of the WWS grouping across time, conducting cluster analysis for each 5-year interval between 1950 and 2000. His analysis confirms the hypothesis of three distinct welfare state models among the fully developed OECD countries, which emerged in the mid-1970s and have remained stable since.

(10) Strictly speaking, we consider the 10 CEECs plus Croatia. For reasons of simplicity, we refer to this subsample as CEECs.

(11) An additional advantage of our empirical strategy is that it would allow us to extend the country sample well beyond OECD countries in future studies.

(12) While income and distribution of income are established target variables, we also tested inflation and fiscal deficit as alternative stability indicators. However, analyzing rather stable institutional designs using variables influenced by business cycles leads to distorted results.

(13) We measure innovative capacity by an aggregated indicator of the World Bank's Knowledge Assessment Methodology based on several innovation parameters. This variable is supposed to capture a country's "capacity" to innovate, i.e., the degree to which it is capable of producing new technologies/products/etc., which enables it to achieve economic growth in the future.

(14) The results for the PC analyses of the previous periods are available upon request. As mentioned, the correlations between PCs and initial variables remain remarkably stable for 2000-2003 and 2004-2006. The results of the principal component analysis for the first period (1995), however, have been strikingly different. In this period, CEECs still constituted extreme cases of redistribution positively correlated with both spending and regulation. At the same time, innovation has been positively correlated with transfers. Arguably, early transition of the CEECs in the mid-1990s after the disintegration of the Eastern Bloc distorted the result to a great extent because economic systems were apparently not comparable at that point in time.

MARKUS AHLBORN [1], JOACHIM AHRENS [1] & RAINER SCHWEICKERT [2]

[1] PFH Gottingen, Weender Landstr. 3-7, 37073 Gottingen, Germany.

E-mail: ahlborn@pfh.de E-mail: ahrens@pfh.de

[2] Kiel Institute for the World Economy, Kiellinie 66, 24105 Kiel, Germany. E-mail: rainer.schweickert@ifw-kiel.de

Table 1: Cluster analyses of economic systems including CEECs

Article                  Variables                       Sample

Castles and   16 variables on Size of            20 OECD countries in
Obinger       Government, Spending               60-75 and 00-04, 25
(2008)        Priorities, Expenditure            EU countries 00-05
              Financing, Labor Market            (including CEECs)
              Performance, Gender-Related
              Outcomes

Farkas        5 spheres: Variables on Product    25 EU countries
(2011)        Markets, R&D Innovation,           (including EU-CEECs).
              Financial System, Labor Market     Early 2000s
              and Industrial Relations, Social
              Protection, Education

Fenger        19 variables on: Governmental      30 countries (15 OECD,
(2007)        Programmes, Social Situation,      15 CEEC). 1998-2003
              Political Participation

McMenamin     40 variables on: Polity, Social    22 countries
(2004)        Welfare and Economy                (OECD + Czech Rep.,
                                                 Hungary, and Poland).
                                                 Early 2000s

Schneider     8 variables on 4 spheres:          26 OECD countries
and           Labour Market Regulation,          (including HUN, PL,CZ)
Paunescu      Training and Skill Formation,
(2012)        Inter-firm Relations and
              Financial System

Article                   Results

Castles and   CEECs with distinct economic
Obinger       system separated from
(2008)        western prototypes
              Division into Baltics and
              Eastern Europe (Hungary,
              Slovenia, Poland, Slovakia)
              Characterized by low state
              interference
              Czech Republic part of
              Continental cluster

Farkas        CEECs with distinct economic
(2011)        system separated from
              western prototypes
              All CEECs included, Slovenia
              as borderline Continental case
              Characterized by lack of
              domestic capital, weak civil
              society, strong foreign (and
              EU) influence

Fenger        CEECs with distinct economic
(2007)        system separated from
              western prototypes
              Characterized by low level of
              welfare provision
              Divided into 2 groups: Former
              USSR (Baltics with Belarus,
              Russia, Ukraine), Post-
              Communist European (rest of
              CEECs)

McMenamin     CEECs with distinct economic
(2004)        system separated from
              western prototypes
              Close to Mediterranean model
              and characterized as
              consensus democracies with
              relatively generous welfare
              regimes
              CEEC countries (and
              Mediterraneans) separated
              from traditional OECD groups

Schneider     CEECs with 'hybrid' economic
and           system, not classified
Paunescu      4 classifications: LME, CME,
(2012)        Mediterranean countries
              (State-Dominated), Hybrid
              economies

Table 2: Variables, sources, and definitions

Variables          Source                     Description

Variables on macroeconomic policy

Size of       EFW: 'Size of       Indicator 1 'Size of Government',
government    Government'         aggregate of subindicators on
                                  government size, adjusted: 0 (small)
                                  to 10 (large)

Transfer      EFW: 'Transfers     Gives a 'rating' for 'Transfers and
spending      and Subsidies'      Subsidies as a percentage of GDP':
                                  From 0 (many) to 10 (few)

                                  Transformation: Variable 'turned':
                                  Higher value = higher share of
                                  Transfers and Subsidies

Government    EFW: 'Regulation'   Indicator for the EFW Report's Fifth
regulation                        Section: 'Regulation', gives a
                                  rating from 0 (many) to 10 (few
                                  regulations)

                                  Transformation: Variable 'turned':
                                  Higher value = more regulation

Variables on macroeconomic performance

Income        WDI: GINI Index     GINI index, adjusted: higher values
equality                          reflect more equal income
                                  distribution

Innovation    KAM                 Aggregate measure for efficient
                                  innovation system of firms, research
                                  centers, universities, consultants,
                                  and other organizations with respect
                                  to acquiring and implementing new
                                  technology (based on variables
                                  measuring: no. of technical journal
                                  articles, patents granted, and
                                  royalty payments)

Public debt   WEO                 General government gross debt as a
                                  percentage of GDP

Note: All variables were standardized, using the z-transformation.

The following sources were used: EFW: Economic Freedom of the World
Report by the Fraser Institute; WDI: World Development Indicators
by the World Bank; KAM: Knowledge Assessment Methodology by the
World Bank; WEO: World Economic Outlook by the International
Monetary Fund.

Table 3: PC analysis: correlations and shares
of explained variance (2007-2009)
                                                      Explained
Correlations             PCI     PC2      PC3    PC   variance

Transfer spending       -0.80   -0.43     0.07   1      0.38
Size of government      -0.82   -0.17     0.28   2      0.63
Government regulation   -0.03   -0.93    -0.21   3      0.81
Public debt             -0.29   -0.004   -0.93   4      0.91
Income equality         -0.71    0.19     0.18   5      0.96
Innovation              -0.59    0.65    -0.24   6      1.00

Bold figures signify an absolute value above 0.6, i.e. a strong
correlation between PC and original variable. Our further
interpretation of the PCs is based on these correlations

Note: This table summarizes the correlation of the principal
components with the six original variables (averages 2007-2009)
and the cumulated shares of variance, explained by the principal
components.

Note: Figures signify an absolute value above 0.6 indicated with *.