cluster rents: strategic organisations or/and system resources?

Paper to be presented at the 25th Celebration Conference 2008

onENTREPRENEURSHIP AND INNOVATION - ORGANIZATIONS, INSTITUTIONS,

SYSTEMS AND REGIONSCopenhagen, CBS, Denmark, June 17 - 20, 2008

CLUSTER RENTS: STRATEGIC ORGANISATIONS OR/AND SYSTEM RESOURCES?

Brian WixtedCPROST at Simon Fraser University

[email protected]

Abstract:The long interest in economic rents has primarily focussed on whether it is industry or firm characteristics thatdominate the economic opportunity space. This paper explores the concept of a geographic basis to rent.Although we adopt the position that firms capture profits, on the basis of cluster theory, particular regionsshould do better than others. The data analysis is based on a time series of inter-country input-output modelsfor European economies covering the period 1965-1995. The result that some nations do better than othersleaves open the question as to whether it is firms or system resources behind the result.

JEL - codes: R12, M10, F15

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Brian Wixted: Cluster Rents - 30 May 2008

Abstract The long interest in economic rents has primarily focussed on whether it is industry or

firm characteristics that dominate the economic opportunity space. This paper explores

the concept of a geographic basis to rent. Although we adopt the position that firms

capture profits, on the basis of cluster theory, particular regions should do better than

others. The data analysis is based on a time series of inter-country input-output models

for European economies covering the period 1965-1995. The result that some nations do

better than others leaves open the question as to whether it is firms, industries or system

resources behind the result.

Introduction There can be little doubt that the issue of value creation and more typically value capture

remains at the centre of research on strategy issues (see e.g. Nickerson 2007). Within this

field there continues to be an ongoing debate regarding the degree to which firms and

their resources and capabilities (the Resource-Based View of the firm) or the industry-

structure within which the firms are situated (the so called Structure-Conduct-

Performance model), configure the opportunities for economic rents. There is also a

younger emerging concept that groups of firms in cooperative networks can extract

relational rent. This paper is interested in a different but related question; is there

geography to economic rent?

Rent, as it is has been studied by strategy scholars, has often been analysed as

profitability (see McGahan and Porter 1997) or return on assets (see Hawawini et al.

2003). This is a reasonable approximation of rent as it captures the ability of individual

firms to make outstanding profits over the long term. It also fits within the larger

paradigm of strategy research that firms have only a limited ability to successfully

position themselves within value chains, eking out ‘interstices’ or ‘impregnable positions’

(Penrose 1959). Attempting to compare profitability across borders, introduces a number

of conceptual difficulties. Instead, the goal here is to understand the degree to which

particular places gain from trade.

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Hawawini et al. (2003) suggest that within an industry there is a diversity of firm types,

some of which are industry leaders, for which firm factors are quite important. Such

findings in the strategy literature link to the literature on global business networks1 which

is based on the understanding that some organisations such as Dell (Fields 2006) and

Apple are able within an industry to dynamically re-configure the value architecture2

around them to maximise their opportunities.

Surprisingly, there has been little work to understand if there are geographic dimensions

to rent capture. The clusters literature to date has been largely pre-occupied with their

identity; why they exist and how they function (see a recent review by Santos Cruz and

Teixeira 2007). Cluster advantage is seen as ‘competitiveness’ which is often measured

by exporting success (Porter 1990) or like indicators. Although, not unimportant to the

issue of economic rents, competitiveness is not same.

The methodology employed here is based in inter-country input-output analysis. The

techniques developed are used to understand the extent to which rent (significantly high

levels of value) can be captured by a particular region (nation-state) by extracting it from

an international trading environment. It is used to show that particular regions capture

more value from international transactions than would be apparent from bilateral trade

patterns. As such it directly reveals that some regions (countries) in some industries are

able to capture a form of economic rent from trade. The data is a time series of inter-

country input-output (I-O) models for European economies for the years 1965, 1985 and

1995. The analysis reveals that ‘cluster rents’ can be measured. Further, it is apparent that

within the EU the number of rent situations grew dramatically between 1985 and 1995.

1 Global Commodity Chains – GCCs (Gereffi and Korzeniewicz, 1994), Global Production Networks – GPNs (see e.g Ernst and Linsu 2002 and Henderson, Dicken, Hess, Coe and Yeung 2002), International Production networks – IPNs (see e.g. Borrus, Ernst, and Haggard 2000) and Global Value Chains (see e.g. Gereffi, Humphrey and Sturgeon 2005). 2 The concept of ‘value architecture’ is enlarged from that of the value matrix concept presented by Froud et al. (1998). In their paper they insightfully suggest that too much attention is often paid to just production activities. They analyse the example of the auto industry pointing out that most analysis of the car industry focuses exclusively on new car sales and ignores analysis of the context, which includes used cars sales, car financing and leasing and the rental car industry (which many of the majors have participated in one time or another). Value architecture, here, includes not just the structuring of relations in production, and multi-industry business environment but also the interaction of the business model with key customers.

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The current study contributes to the literature on strategic management in a number of

important ways. By examining whether specific regions achieve higher rents than others

the study provides a first step in gaining more data on whether economic geography may

play a role in the value capture process. Further, does so with the context of an

international trading environment.

Value Creation in Firms, Industries and Clusters

Firms, Clusters and Trade

Thomas and Pollock suggest that to a large extent the strategy literature is concerned with

the question of ‘how, and with whom, firms compete’ (1999) and one might add, and how

successfully they manage it. There have been two traditional perspectives on this

question, as Mason (1949) notes. One perspective is focused on industries and the other is

focused on firms.

This current paper aims to add a geographic dimension to this ongoing debate. In so

doing we do not disassociate the concept of geographic industry clusters from the firms

within them, or their primary economic activity. Clusters are not independent entities;

they may in some ways be greater that their parts (firms, universities, human capital

resources etc) but they are not separate from them. Thus, if clusters are successful it is

because the organisations within them are successful.

However, the question behind this paper is constructed as not just whether a particular

industry in a particular geographic setting has achieved higher than average profits but

the relationship of a particular cluster to international value architecture. This question

may be understood better from the following illustration. A recent article suggested that

Apple with the introduction of its new IPhone is achieving significant economic returns

(Hesseldahl 2007). The article suggested that the bill of materials (excluding assembly

and Apple’s own development costs) for the IPhone is approximately US$200 for a

product retailing at US$500. Presumably, each of Apple’s suppliers are extracting profits

from their transactions but Apple is managing as the product’s designer, and with its

significant ability to position a product in a market, to extract higher profits (certainly in

absolute terms and probably in relative terms) than its suppliers. Apple, based in

California is managing supplier relationships with companies in Asia and Germany.

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Such an analysis of a new product highlights both the issues of the international

management of production and therefore value capture at the firm level but it also implies

the other dimension of our interest here – is there a geography to value and rent with the

international trading environment?

Here we are defining cluster rents as the ability of a region (nation-states in the available

data) to obtain statistically significant more value from international transactions that

would appear from trade relations data alone would suggest. Our primary interest in the

current paper is whether it is possible to measure the accumulation of value rather than

attributing it to organisations, industries or the use by firms of system resources (cluster

spill-ins – i.e. the benefits from being in a cluster).

Structure-Conduct-Performance

The SCP model of behaviour asserts that organisations have a limited ability to escape the

industry models of which they are a part and therefore their profitability is conditioned

primarily by industry factors. For example McGahan and Porter 1997 comment:

‘Our analyses provide strong support that industry really matters in three important ways. First, industry indirectly accounts for 19 per cent of aggregate variation in business-specific profits, and 36 per cent of explained variation. Second, industry influences the effect of corporate parent on business-specific profitability. Third, the absolute and relative influence of industry, corporate-parent, and business-specific effects differs substantially across broad economic sectors in ways that suggest characteristic differences industry structural context’ (1997: 29).

Spanos and Lioukas (2001) point out that Porter’s version of SCP is not the standard

industrial organization version. For Porter the environment is only partially stable, it isn’t

entirely exogenous and he is interested in firms within industries (2001: 908).

Nonetheless, as Spanos and Lioukos note in ‘this framework the firm is viewed as a

bundle of strategic activities aiming at adapting to industry environment by seeking an

attractive position in the market arena’ (2001: 907). The firm remains important within

this context, even being able to achieve market power.

In this framework, the firm has the ability to position itself within the larger industry

structure but, this might be an ‘on average’ situation. Hawawini et al. claim that

‘industry factors may have a large impact on the performance of the ‘also-ran’ firms, while for the industry leaders and losers it is firm factors that dominate. This

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result is robust across all the three measures of performance used in this study’ (2003: 14).

This latter research begins to bridge the SCP and RBV perspectives.

RBV

The resource based view of strategy turns SCP on its head, putting at the centre, a firm’s

bundle of resources. In general ‘current or future strategic decisions are constrained by

past resource deployments and result in further reinforcement of strategic profile’ (Spanos

and Lioukos 2001: 910). Teece et al. (1997) indicate that the individual strategic

capabilities of firms are based on their internal unique resources and the routines that

exist within the firm that are difficult to replicate outside of the unique circumstances of

that organisation. To emphasise this latter point, Barney (1991) makes it entirely clear

that the resources are ‘assets, capabilities, organizational processes, firm attributes,

information, knowledge etc. controlled by a firm that enable it to conceive of and

implement strategies’.

Thomas and Pollock comment that:

‘The rate and direction of a firm's growth is influenced by how management conceptualizes the firm's resource base. These conceptualizations in turn shape what management considers to be the firm's feasible expansion paths, and the growth strategies they choose to pursue’ (1999: 134).

For most firms, there is a need to seek out positions in the organisation of production to

obtain profit through particular strategic positions, or through creating an intermediation

position that adds value for other organisations in the market. As Penrose put it:

In the long run the profitability, survival, and growth of a firm does not depend so much on the efficiency with which it is able to organize the production of even a widely diversified range of products as it does on the ability of the firm to establish one or more wide and relatively impregnable ' bases ' from which it can adapt and extend its operations in an uncertain, changing, and competitive world. It is not the scale of production nor even, within limits, the size of the firm, that are the important considerations, but rather the nature of the basic position that it is able to establish for itself. (1959: 137).

But, there is increasing evidence that systems of related firms are able to extract

economic rent. Coriat suggests that the sharing of rent within the relational networks is

dependent on the power relations between firms but that ‘manufacturers essentially try to

reserve for themselves most of the monetary gain’ (1995: 223). Dyer and Singh (1998)

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suggest that such relational rents which can be created when there is investment in

relation-specific assets and when there is a co-evolution of capabilities. Within the Toyota

network for example, (Dyer and Nobeoka 2000) there is a system of tight knowledge

management between Toyota and its suppliers that facilitates this network achieving

relational rents over other players in the auto production system.

Curiously, there is yet to emerge a strong discourse on the role of external resources such

as that provided by innovation systems (national, regional or clusters) in the development

of internal resources of firms (see e.g. Hervás-Oliver and Albors-Garrigós 2007).

However, a number of authors have examined the topic.

Access to, and Absorption of System Resources

To assess the ability of firms to access what we are calling here, ‘system resources’ it is

necessary to examine the role of a firm’s network, not as a semi-closed group which is

operating like a larger entity (as is the case with the relational rent example), but as a

means of extracting important information from the business environment. McEvily and

Zaheer (1999) found that links to regional institutions (i.e. not other corporate

organisations such as suppliers or competitors) was an important source of information.

Networks can also aid the discovery of new opportunities (Gulati 1999), while direct and

indirect ties impact on innovation (Ahuja). More recently McEvily and Marcus report:

‘Previous network research on the acquisition of capabilities has identified information sharing and trust as key mechanisms promoting capability acquisition. The results reported here confirm that these mechanisms are in fact instrumental to the acquisition of capabilities through interfirm ties, but further indicate that they play a more subsidiary role than previously thought. In this study, joint problem-solving arrangements are the more prominent driver of capability acquisition and acts as a critical linking mechanism between embedded ties and the acquisition of capabilities. Joint problem solving provides a forum for managers to improve their comprehension of the tacit knowledge underlying capabilities and their understanding of how to customize a capability to the unique circumstances of their firm’ (2005: 1050).

As networks are typically more concentrated locally (although international

connectedness is typically underestimated (Wixted 2005), such findings begin to suggest

that clusters are an important source of resources for firms from a strategic management

perspective.

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Cluster theory and cluster advantage Reviews of the sprawling literature on industrial and innovative clusters emphasize that

researchers have adopted multiple techniques, multiple definitions and multiple

geographic scales (Martin and Sunley 2003). A recent review by Santos Cruz and

Teixeira (2007: 12) indicates that there are eight broad categories of research, namely:

1. Ideographic studies (growth and decline etc);

2. Knowledge based learning approaches;

3. Systemic analysis (clusters within broader structures);

4. Regional innovation policies;

5. Multinational corporations and clusters;

6. Social approaches to clusters;

7. Institutional approaches to clusters; and

8. Measuring clusters.

The cluster advantage has mostly been understood as the internal dynamics of clustering,

specifically knowledge flows and the benefits of a local labour pool.

The benefits for businesses residing in clusters can be divided into a number of

categories. The first is simply that clusters provide an environment that firms can benefit

from, or be hindered by, but it is their strategy that determines their overall success. This

is exemplified by Porter (1998):

‘Companies cannot employ advanced logistical techniques, for example, without a high quality transportation infrastructure. Nor can companies effectively compete on sophisticated service without well-educated employees. Businesses cannot operate efficiently under onerous regulatory red tape or under a court system that fails to resolve disputes quickly and fairly’ (1998: 80).

Many studies take a more positive view of the contribution of a cluster to the knowledge

creation process (see eg. OECD 1999 and 2001). However, there is yet to emerge a strong

research field that links the RBV of the firm with the bundle of external resources that

can be contributed by clusters.

Van der Linde reporting on a large study of clusters noted there is no agreed definition of

competitiveness but he adopted the standard definition based on a mix of exports, market

share and production share. In another meta study Brenner and Muhlig (2007) of the

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early success of clusters also highlights the lack of agreed marks of the external success

of clusters, although there has been considerable focus on internal dynamics.

The use of inter-regional data allows us to not only ask are they competitive but also

whether they capture more value than might be expected.

Measuring cluster rents To understand the rent possibilities of location it is necessary to acquire data for a number

of locations. Further, it is important to develop a methodology that can moderate the

influence of scale upon the results. We were also interested in the question of not just

comparing one region against another for their accounting ratios (see Hawawini et al.

2003), but in obtaining some information on the question of value leverage. In

comparison with the more standard approaches to rent, this conceptualisation comes

closest to that adopted in studies of relational rent. For these reasons, it was decided that

an inter-country input-output model could produce interesting results.

Input-output modelling

A single region input-output (I-O) table is a matrix for a given economy of the

movement of all goods and services. Each table of domestic transactions essentially has

three components. The first is the industry intermediate flows matrix. This square matrix

has in the rows a list of industries in an economy and as columns the same list of

industries. The space is filled with transactions between industries as producers and

industries as users of supplies for commercial use. To the right of this matrix is the

consumption sub-table. The columns in this section account for products not consumed

by local industry (i.e. final consumption, exports etc.). This ensures that the rows

(industries) tally to 100 per cent of output. Below the transaction matrix is the value

added section. Inputs from industries are not the only inputs into the production process,

other components such as wages need to be included as rows. This allows the columns to

tally to 100 per cent of output. Domestic I-O tables are also supplemented with an

imports table that provides details on the use of imported intermediate goods and

services.

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An input-output model does not just calculate the flows of value added for an industry

once, because it does not simply apply a rate of trade to a fixed rate of inputs (co-

efficients of use). The modelling must reconcile through successive rounds of

calculations all the inputs and the changes in the value of those inputs arising from a

particular increase in demand. Thus, if the transport industry requires a substantial

volume of inputs from the electronics industry to increase production by $1 then that

additional activity in electronics, and the flow on consequences to its suppliers, are

measured and can be uncovered. In an inter-country situation, this can lead to country Y

supplying inputs to country Z, but in turn requiring inputs from countries A-Y or even Z

to make those components. In this way, value added can move in an input-output model

in ways that a calculation based simply on trade data cannot consider.

To construct a multi-regional I-O model, in this case an inter-country input-output

model, harmonised3 domestic input-output tables are aligned with I-O import tables4 that

have been divided into a series of tables (one for each of the trading regions in the model

(plus a rest of the world category). To split a single imports transactions matrix into a

series of tables (15 for the 1995 analysis conducted here, – 14 tables for each country in

the model to square the matrix and then one more to capture imports from the rest of the

word), it is necessary to use bilateral trade ratios for each industry on row (supplier)

basis. For a discussion of this process see Wixted et al. (2006).

Data in the analysis

For this analysis, the datasets used are for European countries for the years 1965, 1985

and 1995 to provide both a time dimension and because of the evolving closer economic

ties between relevant countries. The data for European countries for 1965 and 1985 were

developed by van der Linden (1998) and Oosterhaven (1995) and van der Linden and

Oosterhaven (1995)5. The model for 1995 was developed from I-O data purchased from

Eurostat (2000).

3 Tables with the same industry classification and for the same time period. 4 National I-O tables come in two parts domestic transactions and import transactions. The latter table is a single table covering all imports (ie. it is not divided by imports sources). 5 These tables are publicly available for use by researchers http://www.regroningen.nl/irios/iriostables.htm#EUtablescp .

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Details of countries and the industry classification available are outline in Table 1.

Table 1. Countries and sectors incorporated in the modelling

1965 1985 1995 EU I-O Sectoral Classification Belgium Belgium Austria Agriculture, forestry and fishery

products France Denmark Belgium Fuel and power products Germany, France Denmark Ferrous and non-ferrous ores and metals Italy Germany Finland Non-metallic mineral products The Netherlands Italy France Chemical products The Netherlands Greece Metal products except machinery Ireland Agricultural and industrial machinery Italy Office and data processing machines Luxembourg Electrical goods The

Netherlands Transport equipment

Portugal Food, beverages, tobacco Spain Textiles and clothing, leather and

footwear Sweden Paper and printing products The UK Rubber and plastic products Other manufacturing products Building and construction Recovery, repair services, wholesale,

retail Lodging and catering services Inland transport services Maritime and air transport services Auxiliary transport services Communication services Services of credit and insurance

institutions Other market services Non-market services

Calculation of Rents

The first stage of the calculation of economic rents is to process the data through an I-O

measurement methodology, a number of which exist (see the Appendix 2 – technical

note).

For this study, Cooper’s (2000) block spatial path approach was adopted as it produces

net multipliers that aid with the next step in the analysis. Unlike other approaches, the

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advantage of a net multipliers approach is that in contrast to models of ‘production

effects’ it is much simpler to understand the value of imports to a given production

system. Using software based on Cooper’s methodology it is possible to calculate the

contribution of each industry as a supplier to every industry in the model, with the

percentage that each input contributes to an extra 1 unit of production.

Following, the calculation of net I-O multipliers, the second step, is to take this I-O data

and compare it with the original trade ratios that were used to create the import tables. By

comparing the two sets of data the goal is to look for significant differences due to the n

stage processing of the data in all I-O methodologies. The difference between the pattern

of value transfers and the pattern of intermediate input purchases is a zero sum game. To

the extent that there are “big winners” from the flow of value there must correspondingly

be either big losers or a sizeable number of small losers. Big winners in this sense

provide prima facie evidence for the existence of some economic force of agglomeration

that accumulate during rounds of added processing that leads to a substantially larger

degree of value going indirectly to certain regions than would be apparent from

examination of the direct intermediate transactions pattern in isolation (trade ratios).

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Fig 1. A graphical representation of the data comparison

A country could generate a lot of nominal trade, but the value added by its firms could be

quite low. Conversely, a country could participate very little in international systems of

production, but what it does contribute captures a (relatively) significant proportion of

value for a particular place.

To give this calculation process a name it is appropriate to think of it as an ‘above

coefficients’ or better as, supra-critical flows. We can summarise the calculation as:

1. The value of a linkage (to a particular country) [MINUS] the relevant trade

coefficient.

2. This ‘above coefficients’ value is then calculated as a percentage of the appropriate

coefficient. The ‘above coefficients’ value / the direct coefficient * 100.

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3. A statistical test of significance is then applied. If the ‘above coefficients’ flow (as

percentage of the direct coefficient) is greater than twice that of the nominal average6

then that flow has achieved supra-criticality.

The usefulness of this technique is that it is not reliant on the actual scale of the flows

between countries. Quite small value accumulations can in theory be supra-critical if they

are statistically significantly higher than what would be expected from the trade

coefficient. Thus this can be taken as a measure of the profits (rents) accruing from trade.

In the analysis presented here, the trade between countries has been agglomerated into

national clusters. Thus, as demand is increased in an individual industry (e.g. transport)

all of the industries necessary to supply that activity are grouped together as national

clusters of interdependencies.

Supra-critical results 1965-1995 Where does value accumulate? As input-output models calculate not just the first round

of requirements for imports but also the subsequent requirements they enable statistical

testing that cannot be conducted with trade data. The complex inter-play of relationships

offers the prospect of testing for a very interesting possibility. Some countries may

accumulate more (or less) value added through trade than would be apparent from just the

direct volume of trade because they can capture more round-about activity.

1965 and 1985

To maintain consistency with the 1995 analysis the earlier results can be subjected to the

same statistical test i.e. supra-criticality cut-off = 2*1/15 produces three supra-critical

flows.

If this is done for 1965 there were no supra-critical values.

If this is done for 1985, the analysis produces only three results. Belgium benefits from

trade with France in ‘agriculture, forestry and fishery products’ as well as in ‘lodging and

6 Although, average is the correct econometric approach, in this context is slightly problematic because the number of countries in the modelling differs between 1965, 1985 and 1995 it is necessary to make some arbitrary decisions. As most countries with many more than 15 countries it was thought that even using the twice the average benchmark of (i.e. 2 / 15 {no. of countries} = 13.3%) would still be a high test of supra-critical flows. In contrast, as the first model (1965) as only six economies included endogenously; the corresponding rate would be 33.33 percent.

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catering services’ and the Netherlands benefits from trade with Germany in ‘other market

services’. If the statistical test is further weakened, it nears the level of 1/15 before

numerous links appear. This evidence suggests that prior to 1985 supra-critical links were

not an important characteristic of the European economy.

1995

Interestingly, by 1995, a considerable number of supra-critical flows emerge. A précis of

the results is provided in Table 2.

The first point to make about Table 2 is that the methodology is successful in extracting a

number of linkages that achieve the supra-critical measure. Further, it emerges from the

data that Germany dominates all industries in which supra-critical flows are observed.

Table 2. Who gets the supra-critical flows – all supra-critical links & Germany

Meso-national cluster Total # of linkages Germany’s supra-critical links

Agriculture 6 4 Fuels 4 3 Ferrous & non-ferrous metals 4 4 Non-metallic mineral products 4 4 Chemicals 2 2 Metal products 7 6 Industrial machinery 8 8 Office and data processing machines 14 12 Electrical goods 7 6 Transport 7 7 Food 5 4 TCF 6 5 Wood & paper 2 2 Rubber and plastics 5 3 Total # of supra-critical links 81 70

As supra-critical value flows are achieved through being a supplier to increased demand

elsewhere in a multi-regional system, theoretically, a country could achieve a maximum

of 14 such flows within the EU 15 model for each industry / sector in the classification.

Although no country achieves this, Germany does achieve 12 links for its office and data

processing machines cluster. Of a possible 196 supra-critical links for one country,

Germany achieves more than one third (70 = 36%). Furthermore, Germany manages to

capture 86 per cent of all the measured supra-critical interdependencies. It can also be

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seen in Table 1 that Germany achieves many of its supra-critical flows in the mid to

higher R&D based technologies (transport, electrical, and office machines etc). Table 3

shows the pattern of ‘source countries’ for Germany’s supra-critical links.

Table 3. Germany’s supracritical links by country 1995. Country ScoreAustria 8Belgium 3Denmark 11Finland 3France 10Greece 0Ireland 7Italy 1Luxembourg 0The Netherlands 6Portugal 3Spain 3Sweden 14The UK 1

Both Italy and the UK only give up one supra-critical link each to Germany and that is in

office and data processing machines. Although many of the links are with small countries

such as Denmark, Austria and Ireland, France is an exception with 10 out of the 14 links

in non-service sector clusters, a finding that confirms other evidence on the economic

centrality of German production to the European economy not just in scale but also in

economic power.

The two examples of small and medium sized countries benefiting from supra-critical

connections is quite unexpected and some caution needs to be expressed. The two for the

Netherlands are achieved with Luxembourg (metal products and office and data

processing machines) whilst Belgium’s is also with Luxembourg in rubber and plastics.

Because of the problems with creating trade splits for Belgium and Luxembourg7, the

7 The OECD bilateral trade database which is used to generate the bilateral trade ratios groups Belgium and Luxembourg as a single unit. It was necessary therefore to use the same ratios for both economies. This may create difficulties if they economies have very different profiles.

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Belgium and Luxembourg links are considered highly doubtful, although such links

should not be dismissed completely.

The other countries that achieve supra-critical links are France (5) and the UK (3).

Importantly, only three supra-critical links are achieved by countries other than Europe’s

big three economies (Germany, France and the UK). Curiously, although Irish industry

requires a very high percentage of inputs (over 60%) in office equipment to be sourced

from imports, more than 30 per cent from the UK alone, the UK does not benefit with a

supercritical flow. This finding might indicate that in order to supply Ireland, the UK

needs to import from other countries. As it happens, the UK cluster in office and data

processing machines provides Germany with a supra-critical connection. In turn Ireland,

although heavily dependent on the UK for the direct supply of ICT components, gives up

supra-critical linkages to both France and Germany. This might indicate that other

industries are buying off these countries to make their components for supply to the

office and data machines cluster. However, choosing an arbitrary value such as twice the

average might hide many linkages which are close but not actually supra-critical.

Configuration of cluster values

As indicated earlier, in theory, we would expect if there are big winners (such as we have

shown to exist there should be offsetting big losers and small winners. Appendix 2

provides a series of charts that indicate the distribution of values for all 15 EU

economies. These charts do indeed show a number of interesting characteristics of value

flows. First, there are indeed negative flows – indicating that significant imports are

required to supply exports. Second, most flows are concentrated around zero, indicating

that for the most part I-O value structures are similar to bilateral trade patterns. Finally, it

is striking that the high end of the curve reveal that often those that benefit from supra-

critical flows are off by them selves. This implies that Germany (for the most part) isn’t

just at the leading edge but is uniquely positioned.

Limitations

Although the different model sizes between 1965 and 1995 is not ideal, it is not expected

that this would account for changes in the presence of supra-critical values, given that

core countries such as Germany and France are in all three data sets. The change would,

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however, account for the diversity of supra-critical, i.e. those that emerge from the

expanded data set.

The context: political and economic integration in Europe How can we understand the results presented above? Since the end of World War II there

has been a gradual process of greater political integration and the lowering of economic

barriers between countries in Western Europe and, since the beginning of the 1990s,

countries in Eastern Europe. The interplay and timing of the different economic and

political associations is somewhat complex and so Table 4 has been pieced together to

give some impression of the progression that has recently resulted in the European Union

expanding to encompass 27 countries.

In the 1950s and 1960s two competing economic associations emerged – the European

Free Trade Area (EFTA) and the European Coal and Steel Community (ECSC). EFTA

was originally an agreement between seven geographically peripheral countries while

ECSC and its successor the European Economic Community was an agreement between

countries more geographically and economically central to Europe, including France and

Germany. During the 1960s the European Economic Community became the European

Community which begun to expand in 1973. It has been expanding ever since,

incorporating a few new countries every decade, with the most ambitious step the

inclusion of 10 Eastern European countries on 1 May 2004.

Table 4. The Politico-economic integration of Europe Country EFTA , ECSC ,

EEC†, EC‡ EU‡ Euro € EU 25+ EEA◦

Austria 1960-1995 1995 (EU 15) 2002Belgium 1952 (ECSC 6) 2002Bulgaria 2007Cyprus 2004Czech Republic 2004Denmark 1960-1973 1973 (EC 9)Estonia 2004Finland 1961-1995 1995 (EU 15)) 2002France 1952 (ECSC 6) 2002Germany 1952 (ECSC 6) 2002Greece 1981 (EC 10) 2002Hungary 2004Iceland 1970Ireland 1973 (EC 9) 2002

18


Italy 1952 (ECSC 6) 2002Latvia 2004Liechtenstein 1991Lithuania 2004Luxembourg 1952 (ECSC 6) 2002Malta 2004The Netherlands 1952 (ECSC 6) 2002Norway 1960Poland 2004Portugal 1960-1986 1986 (EC 12) 2002Romania 2007Slovakia 2004Slovenia 2007 2004Spain 1986 (EC 12) 2002Sweden 1960-1995 1995 (EU 15))Switzerland 1960United Kingdom 1960-1973 1973 (EC 9)

EFTA – European Free Trade Zone (1960). ECSC – European Coal and Steel Community (1952-58).

†EEC – European Economic Community (1958-65). ‡EC – European Community (1965-91). ‡EU – European Union (1991). ◦EEA – European Economic Area (1994) incorporates EFTA and EU – except Switzerland. Euro € – Euro currency countries – introduced in 2002. Sourced from: http://secretariat.efta.int/Web/InfoKit/Info_Kit/History and http://ec.europa.eu/enlargement/enlargement_process/past_enlargements/index_en.htm

The degree to which political integration and resulting economic policy changes have

impacted on country performance is a question that has many dimensions, with many

issues concerning its impact on innovation systems trajectories remaining to be

addressed.

Both Armstrong (1995) and Cappelen et al. (2000) agree that there has been some

convergence in the GDP per capita of European Union countries from the 1950s to the

1990s, with most of the catch up during in the earlier years. Between 1965 and 1985,

there was both an increase in production specialisation and interdependence between the

members of the European Community according to van der Linden (1998), who

suggested that inter-country specialisation patterns ‘hardly changed’ (p267). Small

countries, he notes, became increasingly reliant on the larger economies, but the growing

interdependence of neighbouring economies was a strong feature. Germany had a ‘central

position’ (p. 267) in the industrial production systems of Europe as an important supplier

to many of the industries of Europe in most countries (and thus the use of the term

19


‘central’). van der Linden found that ‘as regards individual sectors, the strongest

interdependence is found for coal, oil, basic metals, cars, and chemicals’ (1998: 267).

Between 1985 and 1995, according to Davies et al. (2001), there appears to have been a

moderate growth in trade integration in the European Union. Their analysis points to an

increase of intra-EU imports moving from 61 per cent to 68 per cent of all imports

between 1987 and 1993. However, in the years between 1987 and 1993, Europe’s leading

firms (identified by the authors) increased their turnover generated outside their home

country but inside the EU from 30 to 37 per cent. Importantly for the analysis presented

here, Davis et al. make the observation that the geography of production has been

dispersed with German firms gaining market strength. Between 1987 and 1993:

‘There appears to have been no general increase in geographical concentrations. In terms of the nationality of the EU’s leading firms, German firms have increased their share, whilst firms from France, Italy, and (particularly) the UK have had a reduced presence. In terms of the location of production, the leading firms appear to have dispersed their operations across more, rather than fewer, member states’ (2001: 71).

Another piece of evidence comes from analysis by Andersson and Fredriksson (2000).

They investigated the operations of Swedish multinationals, differentiating between trade

in intermediate and finished goods in their analysis. Their research focused on vertical

and horizontal value chain integration. Multinationals that concentrated foreign affiliate:

‘production to a small number of countries favors internal supplies of intermediate goods but exerts no significant effect on the propensity to import finished goods. High export ratios in affiliates stimulate imports of intermediates, but diminish the propensity to import finished goods’ (2000: 787).

Thus, export oriented affiliates were purchasing greater levels of intermediate imports as

well. Later, the authors comment that during the 1980s there was ‘a shift in the

composition of intra-firm exports from Swedish parents in favour of intermediate

products (2000: 787). This suggests that intra-firm purchases could be one factor in

driving the emergence supra-critical links across time.

Taken together, this evidence suggests that there has been a growing concentration of

intra-European production, but which is not concentrating in Germany but is controlled

by German firms. Such evidence supports rather than contradicts the proposal here that

20


Germany has been increasingly successful in gaining from trade between European

countries.

Conclusions and Implications What then does this evidence tell us about cluster rents? Is it likely that the appearance of

supra-critical links mostly appearing after 1985 is due to an increase in trade and

technological specialisation driving higher than expected value added flows?

For supra-critical flows to appear in the format they do in the 1995 analysis, particular

clusters must be able to either command high prices for their output or are so placed in

the overall value architecture that they are supplying intermediate goods to countries

which are themselves suppliers. The proportion of production contributed by imports has

risen significantly in many cases since the 1960s.

It is not entirely possible to interpret or posit the implications of these results. Changes in

the European economy appear to have facilitated the emergence of supra-critical flows

over a comparatively rapid period (10 years). Taken on face value, the 1995 results

suggest that Germany is in a much more powerful position, economically, than trade data

alone would imply (see Davies et al. 2001). The time series approach developed here

makes it possible to suggest that there are important questions on the degree to which

whether firms are able to reconfigure international value architectures to their own

advantage that are yet to be fully explored. Alternatively, or simultaneously, do specific

places provide a strategic bundle of resources (an external source for RBV) that enable

them to not only grow but capture rents? What then are the connections between SCP,

RBV and cluster / innovation system perspectives? It is of some interest that all three

general propositions that industry, firms and systems resources find some support in the

analysis presented here.

‘Industry’ as a Structure for Opportunities

Clearly the results indicate that there is a greater opportunity to capture value from the

international trading environment in some economic activities as opposed to others. The

office equipment sector in the model stands out in contrast to many others activities.

Germany was able to capture cluster rents in this activity from most other countries

21


included in the modelling. There were 14 supra-critical flows in office and equipment (12

of which were captured by Germany) but, for example, only two in Chemical (both

captured by Germany). This lends some support to McGahan and Porter’s (1997) evidence

that some industries offer more opportunities than others.

Strategic organisations

However, the differential geographic spread of the supra-critical values suggests that

others forces, other than mere industry are significant. Schoemaker (1990: 1187), in

reviewing the concept of economic rent pointed out specialization and product

complexity matters and noted at that time that ‘asymmetry in resources and skills’, had

received less attention’ as a basis of rents. This is has changed only a little in the

intervening period.

It is worthwhile noting, however, that Sanchez and (1996), amongst others, has argued that

modularity is a management tool for managing complexity while Wixted (2005) has

shown that some of the leading economic sectors for production fragmentation are also

technologically complex (in breadth, depth and scale) particularly the auto, aerospace and

ICT production systems.

Two pieces of evidence presented here indicate that organisations based in Germany have

been able to strategically leverage their position. First, the relatively rapid emergence of

the supra-critical links, is in contrast to the slow timescale at which innovation systems

evolve (see Lundvall 1992), and secondly there is some empirical evidence of a change to

the operations of multinational firms in Europe towards intra-firm pan-European

operations (particularly those than are German owned).

System resources

Nevertheless, at first glance the results reported here by and large give strong support for

the national innovation systems theory that nations can provide a bundle of resources that

enable their firms to prosper. Certainly this seems to be true for Germany. Within the

evolving nature of the political and economic integration of Europe, Germany which is

near to the geographic centre of Europe has been able to develop organisations across a

wide spectrum of industries that are economically powerful.

22


However, although Germany certainly captures the majority of supra-critical flows, it

didn’t capture all of them. The fact that France and the United Kingdom in particular

managed, in specific activities, to achieve supra-critical flows suggests that clusters

(national clusters here) may provide some of the resources necessary for success.

Future research

The clear implication of this research is that the three dimensions of organisations,

industry and systems resources all play important roles in the success of particular places.

There are four lines of inquiry for future research to more clearly identify the roles of

each dimension. The first is to develop practical methodologies for mining the immense

amounts of data in these models to isolate the largest contributors to supra-critical flows.

The second is continue developing I-O models of the European Union to monitor the

development of supra-critical flows and cluster rents into the future. The third is to

develop a study designed to disentangle the organisational attributes from the system

resources bundle. Finally, the fourth area of research contributes to first but is also a

standalone urgent need. In the current study, it was only possible to investigate national

clusters, future research needs to focus on developing similar datasets for within nation

regions, and run the same approach under those conditions.

Acknowledgements Some of the analysis in this paper was previously written up in Wixted (2005). I would

like to thank Russel Cooper for the initial concept of the measures used here. I would also

like to thank Brian Gordon for his help in positioning the analysis within the strategy

literature and for comments on an earlier version of this paper. The usual disclaimers

apply.

23


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Appendix 1: Distribution of Values for Office Machines Value Architecture

Austria

02468

1012

Less than0

0-1/3 1/3-2/3 2/3-critical Supra-critical

Distribution of Critical Values

Belgium

02468

1012

Less than0



Finland

02468

1012

Less than0



Denmark

02468

1012

Less than0



France

02468

1012

Less than0



Germany

02468

1012

Less than0


Distribution of Critical Values Greece

02468

1012

Less than0



Ireland

02468

1012

Less than0



27


Italy

02468

1012

Less than0



Luxembourg

02468

1012

Less than0



Netherlands

02468

1012

Less than0



Portugal

02468

1012

Less than0



Spain

02468

1012

Less than0



Sweden

02468

1012

Less than0



UK

02468

1012

Less than0



28


Appendix 2: Technical Note Because I-O modelling is based on iterative rounds of data processing until all the second

and tertiary (etc) effects of an initial shock of economic demand is processed through an

economy, or in this case a series of economies it is possible to compare what a region

(country in this case) might have been expected to have gained with what it actually

gained.

Major analytical techniques in this regard include the ‘key sectors’ approach (see Sonis et

al. 1995), the ‘fields of influence’ technique (see van der Linden et al. 2000) and the

methodology employed by Nazara et al. (2001) to identify regional hierarchies (feedback

loop analysis). Key sector analysis is typically built on hypothetical extraction to

determine which sectors have the largest influence in the rest of the economy, a type of

modelling identified earlier. Fields of influence analyses the sensitivity of input-output

systems to particular changes. Van der Linden et al. (2000) looked at the dispersion of

effects induced by productivity improvements through changes to the technological

coefficients in the EC 1970 [five countries] and the EU 1980 [seven countries]. The

analysis revealed that German manufacturing gained most from productivity

improvements for both 1970 and 1980, according to van der Linden et al., who also

revealed that sector’s strong intra-sectoral backwards linkages. Not surprisingly, the

strongest influence on the dispersion of productivity changes, in general, is changes to

intra-sectoral technological coefficients (manufacturing-to-manufacturing, agriculture-to-

agriculture etc).

Lastly, feedback loop analysis (see Sonis and Hewings 2001 and Nazara, et al. 2001) has

been developed for multi-regional input-output systems. This methodology captures for

analysis the different flow on values that go back to the first region from subsequent

activity in second and third rounds of modelling (i.e. regions). While hierarchical network

diagrams are possible with feed back loop analysis, and thus provide a useful way of

understanding core-periphery structures it cannot be adopted here because the net value

added approach (tracing value added flows) differs from hypothetical extraction

(production effects) upon which feed back loop analysis is based. This provided both an

29


opportunity and need to devise a new tool for measuring the significance of

interdependencies based on the whole flow of trade – not just the initial trade.

cluster rents: strategic organisations or/and system resources?

Documents