Biomechanical and physiological aspects of legged locomotion in humans

Changing varieties of capitalism and revealedcomparative advantages from 1990 to 2005: atest of the Hall and Soskice claims

Martin R. Schneider1,* and Mihai Paunescu2

1Faculty of Business Administration, Economics and Business Computing, University of Paderborn, Paderborn,

Germany;2SAS, Vienna, Austria

*Correspondence: [email protected]

In this paper, the varieties of capitalism (VoC) approach by Hall and Soskice (2001)

is tested with longitudinal data and with measures covering all four institutional

spheres differentiated in this approach. Among 26 OECD countries, we find

various institutional configurations including a group of liberal market economies

(LMEs) and of coordinated market economies (CMEs). However, these configura-

tions are not stable. From 1990 to 2005, Denmark, Finland, the Netherlands and

Sweden all moved from the CME model closer to the LME model. Capitalist

variety is strongly linked to sector-specific comparative advantages, as predicted

by Hall and Soskice—CMEs export more heavily in medium high-tech industries,

and LMEs in high-tech industries. In addition, the economies that have moved

towards the LME model have also specialized more strongly in high-tech over

time. In sum, we find mixed evidence for the claims made by Hall and Soskice.

Comparative advantages develop as predicted in the VoC approach, but the

types of capitalism are more varied and more dynamic than the VoC approach

suggests.

Keywords: capitalism, varieties of, institutional change, technological change,

trade

JEL classification: P52 comparative studies of particular economies, F10 trade,

O57 comparative studies of countries

1. Introduction

The varieties of capitalism (VoC) approach (Soskice, 1997; Hall and Soskice,

2001) has become an influential version of the idea that sector-specific competi-

tive advantages of companies and countries heavily depend on country-specific

institutional conditions (Porter, 1990; Amable, 2003; Lundvall, 2007; Whitley,

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Socio-Economic Review (2011) 1–23 doi:10.1093/ser/mwr038

Socio-Economic Review Advance Access published February 6, 2012 by guest on N

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2007). Hall and Soskice posit that among affluent economies two main types of

capitalism exist: liberal market economies (LMEs), exemplified by the USA, and

coordinated market economies (CMEs), exemplified by Germany. They further

claim that the institutional setup of LMEs supports radical innovation, whereas

that of CMEs supports incremental innovation. As a result, each type of capital-

ism should have its own, industry-specific technological and comparative advan-

tages. This strong proposition has served as a starting point for country case

studies (Haake, 2002; Schmidt, 2003; Allen, 2006; Campbell and Pedersen,

2007) and has inspired cross-country comparisons of innovation patterns and

success at the firm level (Lehrer, 2000; Casper and Whitley, 2004; Love and

Roper, 2004).

The strong influence of the VoC approach is surprising because the empirical

validity of the approach is still unclear. There are two main reasons for this. First,

a successful test of the link between capitalist variety and sector-specific compara-

tive advantages involves a two-step procedure—first the CME–LME typology

and then the link to industry-specific comparative advantages must be estab-

lished. Previous studies have performed either the first or the second step but

not both. Some studies have performed the first step by clustering economies

according to various institutional dimensions or producing a continuous

measure of the degree of coordination (Hall and Gingerich, 2004, 2009; Ken-

worthy, 2006). Other studies have performed the second step by studying the

link between the LME–CME dichotomy and industry-specific comparative

advantages (Taylor, 2004; Allen et al., 2006; Akkermans et al., 2009). But the

studies performing only the second step have adopted the dichotomy suggested

by Hall and Soskice ‘at face value’ (Geffen and Kenyon, 2006). Hall and

Soskice (2001, pp. 19–20) assign six economies to the LME camp (the USA,

the UK, Australia, Canada, New Zealand and Ireland) and 10 countries to the

CME camp (Germany, Japan, Switzerland, the Netherlands, Belgium, Sweden,

Norway, Denmark, Finland and Austria). Examining differences between the

two groups of countries in terms of sector-specific comparative advantages

may be based on a false assumption if the assignment of economies to capitalist

variety is incorrect.

A second reason why the available evidence is still insecure lies in the choice of

measures for the institutional arrangements. The VoC approach focuses on how

companies—and, therefore, economies—produce innovations. The precondition

for innovations is learning, which in turn is influenced by institutional arrange-

ments related to the supply of knowledge and skilled labour (Furman et al., 2002).

But in tests of the VoC approach, the measures used to portray differing institu-

tional setups were mostly limited to the labour market regulation and the finan-

cial system. The two other institutional ‘spheres’ that Hall and Soskice mention

and that strongly influence how firms in different capitalist varieties learn have

of 23 M. R. Schneider and M. Paunescu

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largely been ignored—the education/occupational training system and inter-firm

relations.

There is only one study that has sought to overcome both problems by per-

forming two steps in testing the VoC approach and by including measures of

the education/occupational training system and inter-firm relations. Schneider

et al. (2010) conducted a fuzzy-set qualitative comparative analysis (QCA) relat-

ing institutional configurations to revealed comparative advantage in the high-

tech sector. They showed that, overall, LME-like institutions foster comparative

advantage in high tech. But they also found that some countries typically consid-

ered hybrid economies also acquire comparative advantages in high tech, and this

can mostly be attributed to the institutions of the higher education/occupational

training system as well as inter-firm relations. Hence, the design of a two-step test

sheds light on the plausibility of the VoC approach and on its limits.

Overall, the findings by Schneider et al. (2010) provide mixed support for the

VoC approach. They resonate with the results presented by Allen et al. (2006) and

Akkermans et al. (2009) who find some—but not complete—support for the

sector-specific comparative advantages posited by Hall and Soskice (2001). Fur-

thermore, the findings show once more that a dichotomy is not sufficient to

describe the heterogeneity of institutions (Crouch, 2005); more than two types

of capitalism exist, as has been acknowledged in studies within the VoC ‘para-

digm’ (Hall and Gingerich, 2004, 2009; Geffen and Kenyon, 2006; Kenworthy,

2006; Hancke et al., 2007) and in related work (Esping-Anderson, 1990;

Amable, 2003; Boyer, 2004).

In this paper, we adopt important features of the study by Schneider et al.

(2010). In particular, we include the same variables (and additional ones) to

portray institutional arrangements, and we also test the VoC approach in two

steps. But we overcome some limitations of the study by Schneider et al.

(2010) in various ways. Our data extend the observation period to the year

2005 and include 26 rather than 19 OECD countries. This allows us to

examine whether affluent economies that were not studied by Hall and Soskice

(2001) fit into the LME–CME dichotomy or whether the economies call for a

revised typology of capitalist variety. In addition to comparative advantage in

high tech, we also include as dependent variable revealed comparative advantage

in medium high tech. This allows us to test whether the comparative advantages

of LMEs and CMEs are mirror images of each other in the two most

R&D-intensive sectors. Furthermore, we use two additional institutional

measures—average tenure and the number of strategic alliances, both of which

are related to the way in which companies acquire new knowledge.

Most importantly, we examine how institutional features and comparative

advantages change over time. Though a number of tests of the VoC approach

have used time-series data before (Geffen and Kenyon, 2006; Hall and Gingerich,

Test of the Hall and Soskice claims of 23

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2009; Schneider et al., 2010), the nature of data analysis has been cross-sectional

rather than longitudinal. This is problematic. It implies that institutional config-

urations and their link to comparative advantages are constant, mirroring the

idea of path dependence in the VoC approach (Jackson and Deeg, 2008b,

pp. 694–695). This assumption is clearly at odds with the bulk of evidence

from other studies. Some of the institutional measures discussed in the VoC

approach have changed considerably since 1980 (Geffen and Kenyon, 2006;

Hall and Gingerich, 2009). In particular, a ‘liberalization’ has occurred in many

market economies during the 1990s, a liberalization that has reduced employ-

ment protection and union strength, which are features of the CME model

(Howell, 2003; Glyn, 2006; Hall and Thelen, 2009).

Overall, we address two questions. First, how do economies, in terms of insti-

tutional configurations, cluster into different types of capitalism, and how do

these clusters evolve? And second, are the types of capitalism related to industry-

specific comparative advantages?

2. Hypotheses

According to Hall and Soskice (2001), the positive interactions or complementar-

ities between CME-like institutions favour incremental innovation. Conversely,

the positive interactions between LME-like institutions favour radical innovation.

This claim of the VoC approach contains two propositions. First, because of insti-

tutional complementarities, LMEs and CMEs differ systematically in their institu-

tional configurations, that is, in their systematic combination of institutions, and

these differences will be stable over time. This is because investments are channelled

into economic activities—radical or incremental innovation—in which the insti-

tutions grant comparative advantages, and these investments will in turn re-

inforce existing institutions. As a result, institutional configurations tend to

gravitate towards either the LME or the CME model and will tend to remain

stable because of path-dependent institutional change (Deeg, 2007; Hall and

Thelen, 2009).

In their original formulation, Hall and Soskice did not actually test that part of

their claim. Rather, they assigned countries to either the LME or the CME model

in an informal way. A test of the proposition with time-variant data on a whole

range of complementary institutions promises to shed light on a number of im-

portant issues. It uncovers whether institutions actually cluster, that is, whether

institutional characteristics fall into only a few types of capitalism (Boyer,

2006, p. 21; Deeg, 2007, p. 613). The test may uncover additional types of capit-

alism or may detect that the membership of single countries differs from the ori-

ginal assignment. Furthermore, the test discovers if, and how, the type of

capitalism in a particular country evolves over time.


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The second step in testing the VoC claim refers to the existence of ‘comparative

institutional advantages’. These refer to the idea that ‘the institutional structure of

a particular political economy provides firms with advantages for engaging in

specific types of activities there’ (Hall and Soskice, 2001, p. 37). More particularly,

Hall and Soskice predict that LMEs will hold comparative institutional advantages

in radical innovation, that is, the implementation of completely new processes and

the development of new product lines. Conversely, CMEs will hold comparative in-

stitutional advantages in incremental innovation, that is, the continuous improve-

ment of existing product lines or processes.

Rather than focusing on revealed technological advantages (Taylor, 2004;

Akkermans et al., 2009), we examine revealed comparative advantages in

various sectors (Allen et al., 2006; Schneider et al., 2010). We also follow the

studies by Allen et al. (2006) and Schneider et al. (2010) in equating radical

innovation with the high-tech sector, and incremental innovation with the

medium high-tech sector. The empirical distinction between high tech and

medium high tech is based on the OECD’s empirical classification of industries

following three indicators: R&D expenditures divided by value added, R&D

expenditures divided by production and the sum of R&D expenditures and tech-

nology embodied in intermediate and investment goods divided by production

(OECD, 2007).

3. Data and empirical methods

3.1 Institutional measures

In measuring the institutional setup, we largely followed earlier studies in

describing the labour market and the financial system but included four mea-

sures to describe training and skill formation as well as inter-firm relations (see

Table A1 in the Appendix). The sphere of labour market regulation was cap-

tured using three variables. Employment protection refers to the costs or bar-

riers for employers when they seek to terminate an employment contract. It

is defined as the OECD’s well-known index of the strictness of employment

protection, with a higher index implying higher costs or stronger barriers to

terminating employment contracts. Average tenure is the average duration of

private–sector employment relations in years. Finally, collective bargaining

coverage reflects the importance of union-led wage negotiations by giving

the share of workers whose wage and working conditions are regulated in a

collective agreement. These three indicators obtain higher values for CMEs

than for LMEs. The strictness of employment protection and tenure have

been used as indicators of labour market regulation before (Hall and Ginger-

ich, 2004, 2009).


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The sphere of training and skill formation was captured using two measures.

Occupational training is defined as the percentage of young people who hold a

degree from some scheme of occupational, non-university training. Likewise, uni-

versity training is defined as the percentage of young people who hold a degree from

a university or a similar institution. A high share of university graduates is a char-

acteristic of LMEs, whereas a high share of graduates from occupational training is

a characteristic of CMEs. These measures follow Schneider et al. (2010).

We found inter-firm relations to be the most difficult sphere to characterize.

This is because inter-firm relations refer to partly informal cooperation that

evades easy measurement. Geffen and Kenyon (2006) provide the only attempt

to measure inter-firm relations; they include OECD indicators of product

market competition. We looked for measures that are closer to the process of

knowledge acquisition by firms and finally used two proxy measures. Strategic alli-

ances are measured as the absolute number of national and international alliances

divided by GDP. Mergers and acquisitions are measured as the market value of

cross-border mergers and acquisitions as a share of GDP. These variables proxy

the extent to which firms rely on contracts to source new knowledge. The two mea-

sures include cross-country activity because multinational enterprises’ attempts to

acquire knowledge resources cut across national boundaries (Oliveira et al., 2003;

Singh, 2007). Hall and Soskice (2001, p. 57) term this type of resource acquisition

‘institutional arbitrage’. As it is predominantly companies in LMEs that acquire

new knowledge from external sources, a high degree of strategic alliances and of

cross-border mergers and acquisitions are likely to be properties of LMEs.

The financial system was characterized by stock market capitalization. It is

defined as the capitalization of indigenous firms as a share of GDP. We calculated

a 3-year average to even out large fluctuations. In LMEs, firms tend to draw finan-

cial capital from the stock market, whereas in CMEs firms often rely on more

long-term relations with banks and are confronted with rather thin stock

markets. Hence, LMEs are characterized by a higher stock market capitalization

than CMEs. Stock market capitalization was also used in previous studies as a key

indicator to portray the financial system (Hall and Gingerich, 2004, 2009; Geffen

and Kenyon, 2006; Schneider et al., 2010).

3.2 Measures of comparative advantages

To capture an economy’s success in the two most R&D-intensive sectors, we

applied two different concepts of measurement. We first compared export

shares in high tech and medium high tech according to the OECD (2005)

results. We then compared that with Balassa’s revealed comparative advantage

(Balassa, 1965; Allen et al., 2006). It captures the extent to which a national

economy exports certain goods, for instance those in the high-tech sector, in


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comparison to the other OECD countries. Formally, Balassa’s index is defined as

follows. Given X exports, the revealed comparative advantage of sector or

industry i in a country j is given by:

Balassa’s index = 100 × Xij/Xj

XiOEXD/XOECD.

We retrieved the raw data for the calculation of the Balassa index from the OECD

STAN database (OECD, multiple years) and used the export shares reported in

various issues of the OECD Technology Scoreboard (2005, 2007).

3.3 Methods and data issues

Our data refer to the years 1990, 1995, 1999, 2003 and 2005, and thereby capture

changes in institutions and in comparative advantages. Any OECD country for

which sufficient data were available in any of those years was included in the

study. This gave an unbalanced panel comprising up to 26 countries. In addition

to the 16 countries originally discussed by Hall and Soskice as either CMEs or

LMEs, appropriate data could be assembled for South Korea, some central Euro-

pean countries (the Czech Republic, Hungary and Poland) and some Mediterra-

nean countries (France, Greece, Italy, Portugal, Spain and Turkey).

To shed light on the first proposition of the LME–CME dichotomy, a cluster

analysis was conducted on the pooled data with our eight institutional measures.

In a next step, to shed light on the second proposition, several pooled regression

analyses were computed. The main explanatory variables were dummy variables

capturing the membership to a certain cluster.

We used the two-step procedure rather than including single institutional

measures directly in the regressions because of institutional complementarities.

They imply that high-order interaction terms would have to be included in

order to capture the effect of configurations rather than single institutions.

This is unfeasible with eight institutional indicators. In the cluster analysis, com-

plementarities become visible in two main ways. First, because of a clustering of

institutions, complementarities produce a small number of homogeneous clus-

ters. Second, two important clusters show institutional configurations reflecting

the CME and the LME model.

An interesting alternative to cluster and regression analysis exists. QCA (Ragin,

2008) conflates the two steps into one and has been applied to test the VoC ap-

proach before (Kogut and Ragin, 2006; Schneider et al., 2010). We opted not for

QCA, but for the two-step procedure due to a number of limitations of QCA.

Most importantly, it is problematic in QCA to analyse longitudinal data (Kogut

and Ragin, 2006), which is an essential objective of our study.


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As is common in cross-country research, several observations were missing in

our institutional measures. We dealt with missing observations before conducting

the cluster analysis in the following ways. A country-year of observation was

dropped from our analysis if three or more institutional variables were

missing. If only one or two institutional variables were missing, we imputed

the data in two alternative ways. For all country panels that included at least

three out of the five observation years, the missing data for the institutional vari-

able were intra- or extrapolated. However, if the panel consisted of only two

observations, we imputed a normalized value constructed in a way that neutra-

lized the institutional variable for the cluster analysis (but retained the remaining

institutional variables). The data that we used in our cluster analysis are reported

in the Supplementary Table S1.

In the cluster analysis, all variables were z-normalized to avoid any artificial

effects of the size of the variables. As a distance concept, Pearson’s R was

applied. This measure identifies two observations as similar insofar as they cor-

relate in the variables considered. Hence, this measure neatly fits the idea of

the VoC approach that institutional complementarities lead to institutional clus-

tering. But additional analyses with alternative distance measures provided very

similar results, as was examined by projecting the clusters on two dimensions

with multi-dimensional scaling. In the cluster analysis, we first applied the single-

linkage methods (nearest to neighbours) to identify outliers. We then continued

with the remaining observations, applying the average linkage method (centroid

method). Based on a qualitative analysis of the dendogram, we arrived at our

clusters, each of which supposedly representing one type of capitalism.

In the pooled regression analyses, export shares and the Balassa indexes

were analysed at the level of the sector: high tech versus medium high tech.

At the level of single industries, data were only available for export shares

(not the Balassa indexes) and for the years until 2003. Finally, we also exam-

ined whether the countries that went through pronounced institutional change

according to our cluster analysis also experienced considerable changes in their

comparative advantages.

4. Findings

4.1 Five clusters with three distinct institutional configurations

When applying the single-linkage method in the cluster analysis, two observa-

tions turned out to be outliers: Sweden in 2003 and Ireland in 1995. After exclud-

ing these observations, we arrived at the five clusters depicted in Table 1. Three

groups of countries that emerged as clusters lend support to the VoC proposition.

The ‘CMEs’ cluster consists of 32 country-year observations. It includes Germany


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and Austria across all 5 years and a number of other countries that Hall and

Soskice theoretically assigned to the CME camp, such as Belgium (1995, 1999,

2003, 2005), Finland (1990, 1995), the Netherlands (1990, 1995) and Sweden

(1990, 1995). The ‘LMEs’ and ‘LME-like economies’ clusters consist of 46 obser-

vations altogether and include many of the economies that Hall and Soskice

assigned to the LME model. The most constant exemplars in the LME cluster

are the USA and the UK (all years) and Canada (1990, 1995, 1999, 2005). The

LME-like cluster contains, among others, Australia (1990, 1995), Ireland (1990,

2003, 2005) and New Zealand (1990, 2003, 2005). In sum, the dichotomy of

the VoC is not mirrored perfectly by the data—which is highly unlikely—but

still receives some support. A large cluster of countries overlaps strongly with

what Hall and Soskice considered the CME model, and two groups overlap

strongly with what Hall and Soskice considered the LME model.

Moreover, the institutional configurations across all eight variables differ in

the way suggested by the theory (see Figure 1). The institutional configuration

of pure LMEs is a perfect mirror image of that of the CMEs; it is characterized

by laxer employment protection, shorter average tenure, lower collective bargain-

ing coverage, less occupational training but more university training, a higher

number of cross-border mergers and acquisitions and of strategic alliances and

a higher degree of stock market capitalization. The countries with an LME-like

configuration are not as extreme in their values but show the same profile as

pure LMEs; they are not fully coherent configurations. The differences we find

between CMEs, on the one hand, and LMEs and LME-like countries, on the

other, are in line with the VoC approach.

Another distinct type of capitalism emerged from the analysis. The cluster

‘state-dominated economies’ consists of 19 observations. They include Mediter-

ranean economies, which Hall and Soskice (2001) noted in passing as a distinct

group of countries, along with Belgium in 1990 (see Table 1). As Figure 1 shows,

the state-dominated economies have an even stricter employment protection

index and longer average tenure than CMEs, a reflection of the strong state inter-

vention in these economies, most of which have had a long tradition of heavy

labour market regulation. Since its institutional pattern differs markedly from

that of the CME and the LME models, the state-dominated economies should

be considered a third type of capitalism.

4.2 Where our findings contradict the VoC approach

Our results are at odds with the VoC approach in three important ways. First, a

number of observations do not fit any of the three types of capitalism—LME,

CME or state-dominated economies—that Hall and Soskice discussed. A

cluster of ‘hybrid economies’ consists of 23 observations and includes


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Table 1 Capitalist varieties according to our cluster analysis, 1990–2005

Cluster 1990 1995 1999 2003 2005

State-dominated economies TURITAESPBelGRE

PORGREESP

TURPORGREESP

TURPORGREESP

PORGRETUR

CMEs AutGerDenFinSweFRANed

AutBelGerCzeNorFinFRASweNedIta

AutCzeItaFRAGerBel

AutBelGerITAFRA

AutBelGerFRA

Hybrid economies NorJpn

KorHunPolJpn

KorPolHunNorJpn

HunPolCzeKorJpn

PolITANorCzeHunKorJpn

LME-like economies AusNzlIrlCh

Ch DenSwe

FinNedDenIrlNzl

ESPFinNedSweAusIrlNzl

LMEs CanUSAUK

DenAusCanNzlUSAUK

ChFinIrlNzlCanAusNedUKUSA

ChAusUKUSA

ChDenUKCanUSA

Notes: Bold: economies discussed as CMEs by Hall and Soskice; Italics: economies discussed as LMEs by Halland Soskice.Capitalized: economies discussed as Mediterranean by Hall and Soskice.Country abbreviations: Aus, Australia; Aut, Austria; Bel, Belgium; Can, Canada; Ch, Switzerland; Cze, CzechRepublic; Den, Denmark; Esp, Spain; Fin, Finland; Fra, France; Ger, Germany; Gre, Greece; Hun, Hungary; Ita,Italy; Irl, Republic of Ireland; Jpn, Japan; Kor, South Korea; Ned, Netherlands; Nor, Norway; Nzl, New Zealand;Pol, Poland; Por, Portugal; Swe, Sweden; Tur, Turkey.Sources: For sources of data see Table A1; own calculations.


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observations for Japan, Norway, South Korea and a number of Central European

economies that were still in a process of transformation in their employment re-

lation systems (Aguilera and Dabu, 2005). Given this rather heterogeneous com-

position, this cluster should not be considered a distinct type of capitalism.

Nonetheless, the fact that yet another empirical cluster emerged is not strictly

in line with the VoC propositions.

Second, the cluster membership of a number of countries contradicts the VoC

analysis. Notable examples are Japan and Switzerland. Hall and Soskice (2001)

discussed these as typical CMEs, but in our cluster analysis Japan consistently

belongs in the cluster of hybrid economies, and Switzerland belongs in the

LME-like cluster in 1990 and 1995, and to the pure LME cluster in 1999, 2003

and 2005. Hence, according to our data, neither of these countries should be con-

sidered a CME. Another example is France. As a Mediterranean economy with

strong state intervention, France was not considered to be a CME by Hall and

Soskice (2001). In our analysis, however, France (along with only Germany and

Austria) was found among the group of pure CMEs across all 5 years of

observation.

Third, our findings contradict the idea that countries usually remain in one

variety of capitalism on account of path dependency. Rather, our data point to

an astonishing degree of change between clusters across the observation period

for a number of countries (see Table 1). In particular, Spain started off as a state-

Figure 1 Institutional configurations of the clusters (means of z-normalized values).


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dominated economy (1990, 1995, 1999. 2003), as predicted by Hall and Soskice,

but by 2005, had moved to the LME-like cluster. Similarly, Denmark, Finland, the

Netherlands and Sweden were all pure CMEs in 1990, as suggested by Hall and

Soskice, but by 2005, had shifted all the way to the LME-like cluster or even to

the pure LME cluster. The direction and the extent of change do not appear to

be consistent with the idea of stable types of capitalism. Rather, it lends some

support to the liberalization thesis, according to which a number of national gov-

ernments have adapted the institutional framework during the 1990s to the

so-called shareholder value model, leading to institutional change towards the

LME type of capitalism. If we compare the different institutional measures in

1990 and 2005 (see Supplementary Table S2), we see that change in the five

‘mover’ countries was most pronounced in employment protection and univer-

sity training, but each country followed its own path towards the pure LME or

LME-like cluster. A move to the LME model is not universal, however. Austria,

France and Germany remained in the CME cluster across the whole period,

and there are examples of movements towards—rather than away from—the

CME model; Italy and Belgium are cases in point.

In sum, the changes in cluster compositions tell us a ‘nuanced story’ (Hall and

Gingerich, 2009, p. 479). It is neither fully compatible with the liberalization

thesis nor with the stable dichotomy suggested by Hall and Soskice (2001).

It is remarkable that except for Spain the mover countries are relatively small

economies. Is our finding an artefact of smallness? We think that the change in

indicators reflects true institutional change. Our finding that Sweden and

Denmark, in particular, have changed considerably in their institutional setup

has been endorsed by more qualitative accounts (Blyth, 2001; Lindvall and

Sebring, 2005; Campbell and Pedersen, 2007). Furthermore, the smallness of

these economies may be the reason why they manage to implement change

more rapidly than larger economies (Campbell and Hall, 2009). That is particu-

larly true if smaller countries are homogenous, for example, in terms of ethnicity:

all the mover countries except for Spain show low ethnic fractionalization scores

by cross-country comparison (Alesina et al., 2003). But while smallness facilitates

change, it does not guarantee it—other smaller economies, even if they are homo-

geneous in terms of ethnicity, have not changed much in their institutional setup,

e.g. Austria and Australia.

4.3 Comparative institutional advantages: the key result

To test our second hypothesis, we first computed pooled cross-section regressions

at the sector level (see Tables 2 and 3). The dependent variables are the export

shares in high tech and medium high tech, respectively, and the respective

Balassa indexes. The explanatory variables of interest are four dummy variables


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that depict an economy’s capitalist variety following our cluster analysis (see

Table 1 above). The reference category is the pure LME cluster. In addition, a

set of controls were included in the regressions: year dummy variables and—

since export success in R&D-intensive sectors may depend on an economy’s

overall wealth—gross national income per capita.

The key findings from the regression estimates confirm the pattern of com-

parative advantages predicted by Hall and Soskice. In the estimates for the high-

tech sector (Table 2), the coefficients for the CME cluster are negative, which

indicates a comparative disadvantage compared with the LME cluster. Converse-

ly, in the estimates for the medium high-tech sector (Table 3), the coefficients for

the CME cluster are positive, which indicates a comparative advantage compared

with the LME cluster. These findings are all statistically significant at the 1% level.

The effect sizes for export shares show that the capitalist variety matters con-

siderably for comparative advantages. The coefficients are expressed in per cent of

export shares. Starting from the average export share in high tech (17.88%) and

medium high tech (34.10%), the estimates can be interpreted in relative terms.

The institutional configuration in CMEs, then, yields an estimated discount in

high tech of around 40% and an estimated premium in medium high tech of

around 30%, compared with the institutional configuration in LMEs. As this

Table 2 Impact of capitalist varieties on revealed comparative advantage and export share in thehigh-tech sector (results of regression analyses)

Independent variables

Dependent variables

Export share: high-techsector

Balassa index: high-techsector

CME cluster 27.36*** (2.65) 236.99*** (12.71)State-dominated economies cluster 212.53*** (3.48) 264.87*** (16.68)Hybrid economies cluster 22.06 (3.22) 214.66 (15.43)LME-like cluster 21.65 (3.06) 215.80 (14.69)Gross national income per capita 0.00* (0.00) 0.00 (0.00)Year 1995 23.68 (3.05) 228.90* (14.60)Year 1999 2.18 (3.05) 225.18* (14.62)Year 2003 2.48 (3.32) 224.03 (15.92)Year 2005 20.92 (3.52) 229.33* (16.86)Constant 15.46*** (4.82) 106.74*** (23.09)R2 0.303 0.243F-test 5.269 3.878

Notes: High-tech sector: aircraft and spacecraft; pharmaceuticals; office, accounting and computingmachinery; radio, TV and communications equipment; medical, precision and optical instruments industry(from 1999 onwards).n ¼ 119; standard errors in parentheses.Significance levels: ***P , 0.01, **P , 0.05, *P , 0.1


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implies, differing institutional configurations offer firms sector-specific oppor-

tunities, and these translate into starkly different patterns of sector-specific

specialization at the macro level between LMEs and CMEs.

The findings indicate a strong comparative disadvantage in the high-tech

sector for the state-dominated cluster and a certain comparative disadvantage

in the high-tech and the medium high-tech sector for the LME-like cluster

(the coefficient was not statistically significant, however). As this implies, it

pays off for economies to follow either of the two ideal types of capitalism,

whereas economies that fail to do so suffer a substantial loss in comparative

advantage (Hall and Gingerich, 2004, 2009).

4.4 Robustness checks

To test the robustness of our findings, we ran additional regressions (see Supple-

mentary Tables S3 and S4). We only included in the regressions those countries

that Hall and Soskice originally assigned to either of the two types of capitalism.

The findings of these regressions confirm our previous findings. We also used the

typology suggested by Hall and Soskice as an explanatory variable. This allowed

us to compare the strength of our taxonomy stemming from the cluster analysis

Table 3 Impact of capitalist variety on revealed comparative advantage and export share in themedium high-tech sector (results of regression analyses)

Independent variables

Dependent variables

Export share: mediumhigh-tech sector

Balassa index: mediumhigh-tech sector

CME cluster 8.34*** (2.89) 20.12*** (6.78)State-dominated economies cluster 21.10 (3.80) 22.93 (8.90)Hybrid economies cluster 5.55 (3.51) 13.23 (8.24)LME-like cluster 24.49 (3.34) 210.45 (7.84)Gross national income per capita 0.00 (0.00) 0.00 (0.00)Year 1995 2.79 (3.32) 5.53 (7.79)Year 1999 0.12 (3.33) 8.78 (7.80)Year 2003 0.51 (3.62) 8.47 (8.50)Year 2005 22.27 (3.84) 7.53 (9.00)Constant 26.42*** (5.25) 57.25*** (12.33)R2 0.196 0.195F-test 2.946*** 2.928***

Notes: Medium high-tech sector: electrical machinery and apparatus; motor vehicles, trailers and semi-trailers;chemicals (excluding pharmaceuticals); railroad and transport equipment; machinery and respective equip-ment; medical, precision and optical instruments industry (before 1999).n¼119; standard errors in parentheses.Significance levels: ***P , 0.01, **P , 0.05, *P , 0.1.


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relative to the original typology. The regression results show that our taxonomy

better explains comparative advantages than the original typology suggested by

Hall and Soskice. Our taxonomy is more fine-grained than the typology sug-

gested by Hall and Soskice because it separates the pure LMEs and the pure

CMEs from the remaining economies. It therefore performs better empirically

in explaining sector-specific comparative advantages.

As a further robustness check, we examined how the pattern of comparative

advantages differs between industries. For four high-tech industries and five

medium high-tech industries, the regression estimates were repeated with the

export shares at the industry level as dependent variables (see Supplementary

Tables S5 and S6). The findings from that analysis mostly support our key find-

ings. In three out of four high-tech industries, the CME cluster yields lower

export shares than the LME cluster. The only exception is radio, television and

communication equipment. In all five medium high-tech industries, the CME

cluster yields higher export shares than the LME cluster. Except for the chemical

industry—where the effect is relatively low—the coefficients indicate substantial

effect sizes. Hence, despite some industry-specific patterns, the overall conclusion

we have drawn for the sector level is retained at the industry level.

4.5 Changes in institutions and comparative advantages

In a final step of the analysis, we examined how institutional changes were linked

to changes in comparative advantages. Five economies underwent the strongest

institutional change across the observation period: Denmark, Finland, the Neth-

erlands, Spain and Sweden. We compared how these economies (‘mover coun-

tries’) fared as a group compared with the other economies for which data

from 1990 to 2005 was available (‘non-mover countries’) in terms of comparative

advantages at sector and industry level (see Supplementary Table S7).

The change in export shares at the sector level indicates that the mover coun-

tries specialized more strongly in high tech than the non-mover countries. For the

mover countries, the export share in the high-tech sector increased by 8.56 per-

centage points on average, compared with only 4.0 percentage points on average

for the non-mover countries. Conversely, for the non-mover countries the export

shares in medium high tech increased slightly by 0.48 percentage points on

average, but for the mover countries it decreased by 1.24 percentage points

on average.

The industry-level data help to identify the industries that have contributed

most to this specialization pattern. The increase of the export share in high

tech was particularly strong among the movers in two industries: radio, television

and communication equipment (5.10 percentage points on average) and the

pharmaceuticals (2.92 percentage points on average). Conversely, the decrease


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in export shares in medium high tech was particularly strong among the movers

in machinery and equipment (21.86 percentage points on average).

Overall, economies with strong institutional change towards the LME model

increased their comparative advantages in high tech. Hence, a longitudinal ana-

lysis uncovers a pattern that is consistent with the sector-specific comparative

advantages that Hall and Soskice suggested.

5. Discussion

5.1 Main findings and implications

In this paper, we examined for the first time how the claims made in the VoC

approach stand up to a longitudinal analysis. We also described institutional con-

figurations more comprehensively than previous studies, by a set of measures that

extend to the four institutional spheres distinguished by Hall and Soskice, which

include measures for the education/occupational training system and inter-firm

relations.

We found some but not complete support for the VoC approach. Echoing

earlier studies, we detected more than two empirical types of capitalism

(Esping-Anderson, 1990; Amable, 2003; Boyer, 2004). In particular, in addition

to the CME and the LME clusters, we could delineate a cluster of state-dominated

economies, a cluster of economies that resemble the pure LMEs and a cluster con-

sisting of heterogeneous hybrid economies such as Japan and the central Euro-

pean economies. But in line with the VoC approach, we found a core of

economies that conform to the LME and CME ideal types. The institutional con-

figurations of these economies show the pattern predicted by Hall and Soskice.

Finding such a strong, consistent pattern is surprising because we used no less

than eight institutional variables across five observation points extending to 15

years. The clustering of institutions indicates that the LME–CME distinction is

meaningful, albeit only for a limited number of economies and not as an exclusive

dichotomy.

The findings differ from Geffen and Kenyon (2006), who also used time-series

data to cluster economies but failed to uncover the LME or the CME model. The

difference may to some extent be accounted for by the different time period we

studied. More importantly, we also used different institutional measures, includ-

ing the proportion of graduates from university versus those from occupational

training. Given the importance of learning, and hence education and training

within the VoC approach (Busemeyer, 2009), it is surprising that previous

studies omitted such institutional variables. Our findings imply that this omis-

sion is severe, and they cast doubt on any study that fails to include the educa-

tion/occupational training system to portray institutional configurations.


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Also consistent with the VoC approach, we found that the institutional config-

uration of CMEs affords a substantial comparative advantage compared with

LMEs in medium high-tech, and a substantial comparative disadvantage com-

pared with LMEs in high-tech. Economies that do not conform to either the

LME or the CME model do not show comparative advantages in those two

sectors. This lends support to the implication that the LME and the CME

models are consistent configurations, whereas other models may get stuck in

the middle (as do the LME-like cluster and the hybrid economies cluster) or

show a configuration that is altogether inconsistent (as do the state-dominated

economies) (Kenworthy, 2006). Furthermore, we also found that economies

which have shifted their institutional setup towards the LME model have also

increased their high-tech exports disproportionately. Hence, despite considerable

institutional change, an important link between institutions and comparative

advantages claimed by the VoC perspective is discernible. This is in line with

Allen et al. (2006) and Akkermans et al. (2009), who find some support for the

claim by the VoC literature that LMEs will excel in radical innovation.

In sum, the findings imply that the VoC approach presents an appropriate

starting point for the portrayal of institutional frameworks in international com-

parisons of innovative activity at the firm, industry and national levels (Jackson

and Deeg, 2008a). But the static dichotomy and the informal assignment of coun-

tries to types of capitalism should be overcome. Our findings provide evidence as

to how this may be achieved.

5.2 Future research

Important avenues for future research of capitalist variety derive from three key

assumptions made in our study. First, the link between institutions and compara-

tive advantage was examined at an aggregate level. This aggregate analysis neces-

sarily glossed over important differences that catch the eye at more disaggregate

analyses. For example, comparative advantages were explained theoretically at the

sector level by distinguishing radical and incremental innovation. As our own

empirical analysis showed already, comparative advantages of LMEs versus

CMEs differ essentially between industries. Future studies may seek to explain

such differences, as has been done for a limited number of industries and coun-

tries already (for example, Herrmann, 2008; Bluhm and Martens, 2009; Casper,

2009; Lange, 2009). These more fine-grained analyses may involve historical

factors and produce revised typologies of industries, as Lehrer’s (2007) analysis

exemplifies.

Another interesting route for future research is the analysis of within-country

variation of institutional configurations. The VoC approach has been criticized

for being overly simplistic by assuming that national institutions determine


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firm behaviour (Allen, 2004). In principle, our aggregate test is also subject to that

critique. For example, contrary to our implicit assumption, firms may be able to

draw on certain institutional arrangements that are not typical for other firms in

the same country. As a result, various types of capitalism may be relevant inside a

single economy, as Jackson and Miyajima (2007) have argued with reference to

Japan. The inclusion in our study of cross-border mergers and acquisitions

may be interpreted as a first attempt at overcoming the deterministic nature of

the VoC approach. The variable captures the extent to which economies may

tap the advantages offered by other institutional configurations (Schneider

et al., 2010). But similar arguments may be made with reference to the financial

system (firms may be listed on the stock market in other countries) and with

regard to training and skill formation (firms may hire scientists coming from

other countries). Hence, more systematic industry-level analyses that also cut

across national boundaries may be helpful in overcoming the determinism of

the VoC approach.

Second, we assumed that institutions can be measured through quantitative

indicators. This also raises a number of issues for future research. Countries

were assigned to a type of capitalism based on measurement rather than qualita-

tive analysis. Though certain countries showed findings that are at odds with con-

ventional wisdom, our findings resonate well with some recent analyses. For

example, Kenworthy (2006, p. 75) notes that the coordination measure developed

by Hall and Gingerich found some values incompatible with the VoC approach:

they were conspicuously low for Japan and conspicuously high for France and

Italy. Whereas this is at odds with conventional wisdom, it is compatible with

our results, which categorized Japan as a unique economy, and France and

Italy as countries with strong similarities to the CME model. Similarly, our

data reflect Denmark’s adoption of LME-type policies that, according to Camp-

bell and Pedersen (2007, p. 308), have been overlooked in the VoC literature.

Future studies could examine, for example, whether Denmark’s move to

become an economy that resembles the LME model will be permanent. Similarly,

France has been considered a third model termed ‘state capitalism’ (Schmidt,

2003). But Albert and Gonenc (1996) have proposed the term ‘Rhenish capital-

ism’ to summarize France and Germany. The existence of a Rhenish type of cap-

italism is reflected in our data, and future studies may examine the process that

led to the similarity between France and Germany in the institutional indicators

we study, despite differences in other areas such as state intervention.

Our second assumption raises another issue for future research. The indicators

we used are proxies for institutions. Their change may not always track perfectly

how the underlying institutions function and change. More qualitative future

studies could focus on the five mover countries we identified in order to


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examine the extent to which quantitative indicators are valid measures of institu-

tional change.

As a third assumption, we treated institutions as exogenous and studied a

causal link flowing from institutions to comparative advantages. In fact, the

links may be reciprocal. Institutional change may trigger a change in specializa-

tion, but as comparative advantages shift, institutional measures may change as a

result (Nelson, 2008). Recent work has focused on the political reasons for change

(Hall, 2005; Thelen and Kume, 2006; Amable and Palombarini, 2009; Hall and

Thelen, 2009; Liebmann, 2009). Future research could focus more on the techno-

logical or economic reasons for institutional change implied by our analysis.

Supplementary material

Supplementary material is available at Socio-Economic Review online.

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Appendix

Table A1 Definition and source of data

Measure Definition and source

Employment protection Strictness of overall employment protection, Version2. Source: OECD Employment Outlook (variousissues)

Average tenure Average length of employment contracts. Source:OECD Employment Outlook (various issues)

Collective bargaining coverage Adjusted collective bargaining coverage, i.e. numberof employees covered by collective bargainingagreements as a percentage of all wage and salaryearners. Source: ICTWSS database (http://www.uva-aias.net/208)

Occupational training Upper secondary graduation rates. Source: OECDEducation at a Glance (various issues)

University training Graduation rates in tertiary education (all pro-grammes). Source: OECD Education at a Glance(various issues)

Strategic alliances Absolute number of completed cross-border anddomestic alliances divided by GDP (USD, currentprices, current purchasing power parities). Source:Thomson SDC Platinum

Mergers and acquisitions Market value of cross-border mergers and acquisi-tions (sales and purchases) as a share of GDP(3-year average, USD, current prices, current pur-chasing power parities). Source: UNCTAD (http:://stats.unctad.org/FDI/), OECD in Figures (variousissues)

Stock market capitalization Stock market capitalization of indigenous firmsexcluding mutual funds as a share of GDP (3-yearaverage, USD, current prices, current purchasingpower parities). Source: OECD, World Federation ofExchanges (http://www.world-exchanges.org/

WFE/home.asp), for recent data: additional infor-mation form Nordic Exchange, Euronext, CzechExchange


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Biomechanical and physiological aspects of legged locomotion in humans

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