sources of variation in firm profitability and growth

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Sources of variation in firm profitability and growth

John Goddard a,, Manouche Tavakoli b,1, John O.S. Wilson b,2

aBangor Business School, Bangor University, Gwynedd LL57 2DG, United Kingdomb School of Management, University of St Andrews, Gateway Building, North Haugh, St Andrews, Fife, KY16 9AS, United Kingdom

Received 1 August 2006; received in revised form 1 August 2007; accepted 1 October 2007

Abstract

This article reports an analysis of the sources of variation in profitability and growth for manufacturing firms located in eleven European

countries. A variance decomposition analysis determines the importance of the country, industry, corporate group and firm effects on profitability

and growth. The analysis reveals evidence of differences between industries in the comparative advantage offered by different countries, reflecting

a tendency for specialization and geographic concentration. However, as in several previous studies, the firm-level effects are the most important

class of effect in explaining the variation in performance.

2008 Elsevier Inc. All rights reserved.

Keywords: Firm, industry and country effects; Growth; Profitability; Variance decomposition

1. Introduction

Identification of the sources of variation in firm performance is

a recurrent theme in applied business research. The traditional

approach based on the Structure-Conduct-Performance (SCP)

paradigm focuses on the influence of the industry-level

determinants of competitive conditions such as concentration,

economies of scale and entry and exit barriers (Bain, 1956; Porter,

1980). Later literature emphasizes the influence of the internal

resources that are specific to the firm (Barney, 1991, 2001;

Levinthal, 1995; Peteraf,1993; Teece, 1981, 2007). The resource-

based view is that organizational structures and management

practices represent the main source of heterogeneity in perfor-

mance between firms (Barney and Arikan, 2001; Barney andHesterly, 2006; Newbert, 2007).

In a seminal empirical contribution to the debate concerning the

relative importance of the industry, corporate and firm-levelinfluences on firm performance, Schmalensee (1985) reports a

variance decomposition analysis of profitability data at US Federal

Trade Commission business unit level, identifying the industry and

corporate group effects. Schmalensee's analysis, based on data for

a single year, is purely cross-sectional, but several later contribu-

tions draw on panel data comprising several annual profitability

observations on each firm. With data including both a cross-

sectional and a time-series dimension, the firm and year effects can

be identified alongsidethe industry and corporate effects (Bowman

and Helfat, 2001; Chang and Singh, 2000; McGahan, 1999;

McGahan and Porter, 1997, 1999, 2003; Misangyi et al., 2006;

Roquebert et al., 1996; Ruefli and Wiggins, 2003, 2005; Rumelt,1991). However, variance decomposition only describes and does

not explain the influences on firm performance: the technique

offers no informationabout the drivers of business performance or

about the mechanisms by which performance is generated

(McGahan and Porter, 2005, p873).

With a few recent exceptions, the previous empirical literature

on the sources of variation in firm performance draws on US data.

Partly as a consequence, this literature tends to underemphasize

the role of country effects on performance (Hawawini et al., 2004;

McNamara et al., 2005). Country effects may arise due to

differences between countries in their resource endowments,

Available online at www.sciencedirect.com

Journal of Business Research 62 (2009) 495 508

The authors are grateful to two anonymous referees for many helpful

comments and suggestions. The usual disclaimer applies. John Goddard

gratefully acknowledges Ente Luigi Einaudi for Monetary, Banking and

Financial Studies, Rome, for hospitality and financial support during a visiting

appointment as a Targeted Research Fellow during Autumn 2007. John O.S.

Wilson gratefully acknowledges financial support from the Leverhulme Trust. Corresponding author. Tel.: +44 1248 383221; fax: +44 1248 383228.

E-mail addresses: [email protected] (J. Goddard), [email protected]

(M. Tavakoli), [email protected](J.O.S. Wilson).1 Tel.: +44 1334 462810; fax: +44 1334 462812.2 Tel.: +44 1334 462803; fax: +44 1334 462812.

0148-2963/$ - see front matter 2008 Elsevier Inc. All rights reserved.doi:10.1016/j.jbusres.2007.10.007
mailto:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.jbusres.2007.10.007http://dx.doi.org/10.1016/j.jbusres.2007.10.007mailto:[email protected]:[email protected]:[email protected]


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financial and technological infrastructures, institutional and

regulatory frameworks, openness to international trade and access

to markets. This study investigates the sources of heterogeneity in

firm-level profitability and growth between manufacturing firms

located in 11 European countries. This study uses a nested

analysis of variance technique to assess the relative importance of

the industry, firm and corporate group effects in driving firmperformance, incorporating a full set of permanent and transient

country effects, and allowing for industry-level variation in the

magnitudes of the country effects. The analysis of the country

effects, and the analysis of a growth performance indicator,

represent the paper's two main original contributions to the

literature on the sources of variation in firm performance.

The rest of this paper is structured as follows. Section 2

reviews the literature on the application of variance decomposi-

tion analysis to firm-level performance data. Section 3 describes

the data and sample selection criteria, and discusses the definition

of firm performance measures using company accounts data.

Section 4 describes the specification of the empirical model andthe estimation method. Section 5 reports the empirical results.

Finally, Section 6 concludes with a summary of the main findings

and suggestions for further research.

2. Antecedents of firm performance

This section examines a number of theoretical determinants

of firm performance. The first subsection reviews the sources of

industry, firm and corporate group effects. The second

subsection examines the arguments for a home country effect.

The third subsection compares the use of profitability and

growth performance indicators in a variance decomposition

analysis of firm-level performance data. The final subsection provides a brief review of previous empirical studies of the

sources of variation in firm-level profitability data.

2.1. Industry, firm and corporate group effects

Explanationsfor variation in firm performance in the industrial

organization and strategic management literatures focus on the

factors attributable to industry membership, and the resources and

strategies that are specific to the firm or corporation. Industry

effects derive from the number and size distribution of producers,

product characteristics, the extent to which producers exercise

control over prices, the ease of entry and exit, and the ease withwhich information flows between producers and consumers.

According to the SCP paradigm, the structure of an industry

influences the conduct of its constituent firms, which in turn

influences the firms' performance (Bain, 1951, 1956; Mason,

1949). The early industrial organization literature investigates the

effects of industry structure on performance. Porter's (1980) five

forces model of the firm's competitive environment was central to

the development of the early strategic management literature. The

five forces are theextent and intensity of competition; the threat of

entrants; the threat of substitute products and services; the power

of buyers; and the power of suppliers.

Criticism of the SCP paradigm led to a shift away from the

presumption that the industry structure is the main determinant

of performance, with an increased emphasis placed on the

strategies (conduct) of individual firms (Bass et al., 1978;

Brozen, 1971; Demsetz, 1973; Galbreath and Galvin, in press;

Scherer and Ross, 1990) or strategic groups defined as naturally

occurring subsets of firms that tend to operate in an

homogeneous manner (Dranove et al., 1997; Leask and Parker,

2007; Short et al., 2007). Individual firm effects derive from afirm's competitive positioning with respect to price and non-

price product characteristics, and the internal resources used to

achieve and maintain its competitive position. According to Kay

(1993), the sources of distinctive capability are architecture,

innovation and reputation. Architecture refers to the firm's

internal organization, its relationships with its suppliers, dis-

tributors and retailers, and its specialized knowledge that allow it

to maintain a competitive edge over its rivals or entrants. Inno-

vation, if combined with mechanisms to protect intellectual

property, can produce either temporary monopoly power and

profit; or longer-lasting advantages through learning economies

of scale (Cefis and Ciccarelli, 2005; Cefis et al., 2006).Reputation effects may also confer a decisive long-term ad-

vantage over competitors, enabling the firm to sustain a large or

dominant market share.

The resource-based view defines the firm in terms of its

property-based and knowledge-based resources (Barney, 1991;

Miller and Shamsie, 1996; Penrose, 1959; Rumelt, 1984;

Wernerfelt, 1984). Property-based resources are legally defined

property rights held by the firm, such as the right to use labor,

finance or raw material inputs. Knowledge-based resources, such

as technical expertise or good relationships with trade unions, are

not legally protected, but may still be difficult for others to access.

To the original owner, the value of a resource is its value in its next

best use (opportunity cost). To the firm, value is added bycombining and coordinating the resource with other firm-specific

resources. The ability of the firm to use the resource in ways the

owner cannot envisage creates a rent, deriving from the difference

between the original owner's and the firm's valuations of the

resource. According to the resource-based view, this rent is the

ultimate source of a firm-specific effect on performance indicators

such as profitability or growth.

For firms that are members of a larger corporate group, such

factors might operate either at firm level or at the level of the

corporation, depending on the degree of centralization of the

decision-making authority (Prahalad and Hamel, 1990). There-

fore the corporate group effects reflect the advantages or dis-advantages accruing to the individual firm from its membership

of a larger corporation, deriving from the management strategies

(e.g., diversification or vertical integration) operative at the

corporate level, the quality of resources or core competence

(managerial talent) available at the corporate level, or the ability

of the ultimate owner, while legally separate, to provide ad-

ditional resources to member firms in times of difficulty, thereby

reducing the risk of failure.

2.2. Country effects

Recently, Hawawini et al. (2004) argue that while globaliza-

tion has been a major strategic concern since the 1990s (at least),

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the impact of country effects on firm performance is neglected in

much of the previous literature. Country effects might be

important for several reasons. First, even after controlling for

distance and market size, trade within national borders tends to

exceed cross-border trade by a large factor (Chen, 2000).

Second, aggregate savings and aggregate investment at national

level are highly correlated, suggesting that domestic savings isthe main source of finance for domestic investment, and most

capital does not cross national borders in pursuit of the highest

return. Third, the equity portfolios of investors tend to be biased

in favor of home country stocks, and investors appear to be

reluctant to diversify their portfolios geographically (Phylaktis

and Xia, 1998). More generally, the international business

literature stresses the importance of a range of country-level

variables (resource endowments, financial and technological

infrastructures, institutional and regulatory frameworks, open-

ness to international trade, and access to international markets)

in driving foreign direct investment and the creation and dif-

fusion of knowledge (Caves, 1996; Rugman and Verbeke, 2004;Wan and Hoskisson, 2003).

It might be argued that within the EU single market for

goods and services, the impact of country-specific factors on

performance should be small. The removal of physical barriers

to the free movement of goods and people within the EU

(customs and immigration controls) was a prime motive for the

creation of the single market in 1992. Other objectives were

the elimination of barriers to free trade, including differing

national standards for product testing and certification,

national variations in company law and patenting arrange-

ments, national preferences in public procurement, and bar-

riers to the diffusion of new technologies including restrictions

on access in broadcasting and telecommunications. Post-1992experience suggests the creation and maintenance of the single

market has been a continuing process, requiring both the

enforcement of existing rules and the creation of new rules as

products, technologies and markets evolve (Cockerill and

Johnson, 2003). For the period 1994-98 there is limited

evidence that the post-1992 integration of formerly fragmented

economies in Europe has induced any strong tendency for

industrial structures in Europe to converge (Geroski and

Gugler, 2004, p.617).

A single market should permit the realization of external

economies of scale through specialization and large volume

production. Industry clusters, within which firms relate by closehorizontal or vertical relationships, are characterized by access to

traditional home-grown or specialized factors of production, the

presence of complementary or support activities, and demand

conditions such that consumers associate quality or innovation

with specific locations (Porter, 1990). The evidence suggests that

specialization has increased since the formation of the single

market (Aiginger and Davies, 2000; Brulhart, 1998; Coombes

and Overman, 2003; European Commission, 2002; Fujita et al.,

1999; Morosini, 2004; Porter, 1998a,b). An implication of

specialization for the analysis of the sources of variation in

firm-level performance is that the interactions between the

country and industry effects should make a significant contribu-

tion to the model's explanatory power.

Business cycle patterns also have implications for firm-level

performance. Accordingly, a set of year effects, and interactions

between the country and year effects, should also contribute to

the model's explanatory power. The importance of these

interactions is inversely related to the degree of synchronicity

of business cycles across countries. Business cycle convergence

is an important condition for EU countries that have adopted orintend to adopt the European single currency, the euro.

Furthermore, when trade barriers are eliminated, advances in

technology and knowledge are more easily transferred across

borders; consequently demand shocks have similar effects in

each country (Frankel and Rose, 1998). Artis et al. (1997, 2004)

identify business cycle turning points for several countries using

industrial production data. The expansion and contraction

phases of a core European group (Germany, France, Italy,

Belgium, Netherlands and Ireland) correspond closely. The

UK's business cycle is idiosyncratic, but is loosely related to

those of the US and Spain. Drueker and Wesche (2001) report a

close correspondence between the business cycles of singlecurrency member countries, but less correspondence between

members and non-members. Imperfectly synchronized business

cycles are associated with a transitory component in the country

effects on firm performance.

Country effects may also arise from differences in legal

tradition. The degree of protection available to shareholders and

creditors has implications for the development and efficiency of

banks and stock markets, for capital availability and allocation,

for corporate valuation, and for financial stability. Beck et al.

(2003a,b) suggest economic performance should be more dy-

namic under a legal system that protects the rights of individual

investors, and where institutions and laws are strong but able to

adapt quickly to changingeconomic conditions and opportunities.Under the common law tradition (characteristic of the UK, US,

Canada, Australia and New Zealand) the law develops incremen-

tally through precedent, while under the civil law tradition

(characteristic of many continental European countries) the law is

constructed through the passing of statutes and codes. Legal

scholars identify three families within the civil law tradition: the

French, the German and the Scandinavian. Legal systems in the

common law tradition provide greater legal protection to share-

holders and creditors than those in the civil law tradition, and

within the latter category the German and Scandinavian systems

afford greater protection than the French system (Fagernas et al.,

2007; La Porta et al., 1998, 1999).Country effects on firm performance may reflect variations

between countries in accounting practice and disclosure. For

example, under a common law system profits and losses

(especially) are often reported faster than they are under a

civil law system, and managers may be under pressure to

attend to the sources of losses more rapidly ( Ali and Hwang,

2000; Alford et al., 1993; de Fond et al., 2007; Hung, 2001).

Ultimately, in a variance decomposition analysis of the

sources of variation in measured firm performance, variations

due to differences in accounting standards and practice are

not separable from variations due to differences in legal

tradition or other features of the institutional structure. Ac-

cordingly, it is appropriate to interpret the country effects

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reported in the empirical analysis as upper-bound estimates of

the true effects.

2.3. Comparison between profitability and growth performance

indicators

This paper reports the application of variance decompositiontechniques to both profitability and growth data for the same

samples of firms, permitting direct comparisons between the

sources of variation for these two performance indicators. This

subsection examines the application of variance decomposition

to firm-level growth data.

Gibrat's (1931) Law, or the Law of Proportionate Effect

(LPE), is a reference point for empirical studies of firm growth

(Hart, 2000; Trau, 1996). According to the LPE, growth rates are

independent of firm sizes, logarithmic sizes follow random walks,

and the variance of firm sizes tends to increase over time. Several

early empirical studies find either no relationship or a positive

relationship between firm sizes and growth rates (Mansfield,1962; Singh and Whittington, 1975). Some later studies identify a

weak inverse size-growth relationship (Hart and Oulton, 1996).

Overall, however, the consensus seems to be that LPE provides a

reasonably close and serviceable approximation to reality (Coad,

2007; Geroski et al., 1997; Goddard et al., 2002).

The persistence of profit (POP) literature provides stronger

evidence of systematic firm- and industry-level variation in

firm-level profitability. According to the POP hypothesis, there

are differences between firms in their long-run equilibrium

profit rates, and in the degree of inter-temporal (year-on-year)

persistence or the speed of convergence towards long-run

equilibrium (Bou and Satorra, 2007; Cubbin and Geroski, 1990;

Geroski and Jacquemin, 1988; Glen et al., 2001, 2003; Goddardand Wilson, 1996, 1999; Gschwandtner, 2005; Wiggins and

Ruefli, 2002). These comparisons between the growth and

profit strands in the empirical IO literature suggest that the inter-

temporal (year-on-year) persistence of performance above or

below the norm should be stronger when measured using a

profitability performance indicator than when measured using a

growth indicator; and that the variance decomposition analysis

should be capable of accounting for a larger proportion of the

variation in firm-level profitability than the variation in growth.

2.4. Variance decomposition analysis of firm-level profitability

Table 1 summarizes some of the principal contributions to

empirical literature on the application of variance decomposi-

tion techniques to US business unit profitability data. The

profitability variance decomposition literature includes rela-

tively limited evidence from outside the US. Using 1991-94

Spanish manufacturing data, Gonzalez-Fidalgo and Ventura-

Victoria (2002) report a firm effect of 31%, and industry and

strategic group effects of 13% and 15%, respectively. Using

1994-98 data on non-diversified Spanish manufacturing firms,

Claver et al. (2002) report a firm effect of around 40%, together

with very small industry and year effects. Using survey data for

a sample of 280 Greek manufacturing firms relating to the

period 1994-6, Caloghirou et al. (2004) identify industry effects

and firm effects of 48.2% and 16.3% for large firms, and 14.6%

and 6.0% for SMEs. For a larger sample of 1921 Greek firms

observed over the same period, Spanos et al. (2004) report

industry and firm effects of 6.5% and 15%, respectively. Finally,

for a sample of 5000 Spanish firms, Bou and Satorra (2007)

report industry and firm effects of around 5% and 23%,

respectively. An estimated persistence coefficient of 0.64suggests that short-run profits converge a relatively slow rate.

The speed of convergence is similar at both the industry level

and the firm level.

Furman (2000) reports comparisons between Australia,

Canada, the UK and US using 1992-98 data. In contrast to

the present study based on pooled data from several EU

countries and with country effects included among the list of

factors, Furman analyses the profitability data for each country

separately. Hawawini et al. (2004) apply variance decomposi-

tion analysis to value added and market value performance

indicators for a sample of 1314 non-financial corporations from

the US, UK, Germany, Belgium, Netherlands and Luxembourg.The firm (corporate) effects account for a larger proportion of

the variation in performance than the industry or country effects.

3. Data and variable definitions

Amadeus, a pan-European company accounts database

compiled by Bureau van Dijk, is the data source for this study.

Amadeus reports unconsolidated company accounts data

extending back over several years for a large number of firms

located throughout Europe. In this paper the term firm refers to

an operation that is either independently owned, or forms part of

a larger multi-firm corporate group. The official national public

bodies responsible for maintaining records of company accountssupply the source data forAmadeus. Some European countries

(including France and Belgium) operate a compulsory format for

the preparation of company accounts, while others (Germany,

Netherlands, UK) operate a recommended or suggested format.

BvD's pro-forma for the return of company accounts data is

standardized, and attempts to strike a balance between the

accounting standards operated in different countries.

For France, Germany, Italy, Spain and the UK, Amadeus

claims to provide coverage of all firms with operating revenue

above 15 m, total assets above 30 m and at least 200

employees. For other countries the criteria are operating revenue

above 10 m, total assets above 20 m and at least 150employees. The database has records for more than 200,000 firms

satisfying these criteria, although the number of firms for which

the records are complete over any specific period is smaller. The

duration of the coverage varies between countries. In order to

incorporate as much of the data as possible into the analysis, this

reports variance decomposition analyses of firm-level profit and

growth data for two overlapping samples: one with a wide cross-

sectional dimension and a short time duration, and one with a

narrower cross-sectional dimension but a longer duration. Sample

A comprises 13,827 firms providing five years' complete

profitability data (and four years' growth data), located in 11

EU member countries. The countries selected are limited to those

for which there are at least 50 firms with complete data for the



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Table 1

Firm and industry effects in determining profitability: previous evidence from US studies

Study (chronological order) Sample Performance

Measure

Industry effects

(%)

Schmalensee (1985) 456 firms with 1775 lines of business in 242 manufacturing industries, 1975 ROA 19.5

Wernerfelt and

Montgomery (1988)

247 firms drawn from the FTC line of business database, 1976 Tobin's q 20.1

Kessides (1990) 456 firms across 242 industries and 1775 line of business for 1975. Return on sales 20.1

Rumelt (1991) 457 firms across 242 industries and 1774 line of business, 1974-77 ROA 17.9

Powell (1996) Survey of top executives of a sample of 54 manufacturing firms. ROA 20.0

Roquebert et al. (1996) 10 samples comprising between 94-114 firms, drawn from between 223-266

industries, covering between 387-451 lines of business, 1985-91.

ROA 10.2.

McGahan and Porter

(1997)

7003 firms with 12296 lines of business in 628 industries, 1981-1994. ROA 9.4

Mauri and Michaels

(1998)

264 firms from 69 manufacturing industries, 1978-92. ROA 6.2

McGahan (1999) 4947 firms with 9904 lines of business in 668 industries ROA 29.4

Chang and Singh

(2000)

305 firms with 1519 lines of business in 142 industries, 1983, 1985, 1987, 1989 Market share 4.8

McGahan and Porter

(2002)

7793 firms, 1981-1994 ROA 9.6

Hawawini et al. (2003) 562 firms drawn from top 1000 listed firms across 55 3-digit industries, 1987-1996 (i) Economic Value

Added

(i) 6.5

(ii) Total market

value

(ii) 11.4

(iii) ROA (iii) 8.1

McGahan and Porter

(2003)

7005 firms from 638 industries and 58340 business segments, 1985-1992. ROA (a) above average

performers: 29.6

(b) below average

performers: 22.5

Ruefli and Wiggins

(2003)

2496 firms, 1980-1996. ROA 0.1

Adner and Helfat

(2003)

30 energy companies, 1977-1997. ROA 2.1

Misangyi et al. (2006) 1512 firms, with 2055 lines of business in 76 industries, 1984-1999. ROA 7.6

Short et al. (2007) 1165 firms from 12 industries, 4 strategic groups per industry, firm average performance

data, 1993-1997.

(i) ROA (i) 16.9

(ii) Tobin's q (ii) 5.8

(iii) Altman's z ( iii) 1.8


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years 1996 to 2000 (inclusive). This criterion is satisfied by

Austria, Belgium, Finland, France, Germany, Greece, Italy, theNetherlands, Spain, Portugal and the UK. Sample B comprises

7211 firms providing nine years' complete profitability data (and

eight years' growth data), located in five of these countries:

Belgium, France, Italy, Spain and the UK. Using the typology of

La Porta et al. (1998) to classify the 11 Sample A countries by

legal tradition, only the UK falls under the common law tradition.

Among the 10 civil law countries, Belgium, France, Greece, Italy,

Netherlands, Portugal and Spain fall under the French tradition,

Austria and Germany fall under the German, and Finland fall

under the Scandinavian.

All firms with principal NACE (Nomenclature generale des

activites economiques dans les commumautes Europeeanes) two-

digit activity codes between 15 and 36 inclusive are eligible forinclusion in the sample. The required data are total assets, current

liabilities, profit before tax and interest paid. The accounting profit

rate is ROA (return on assets) defined as (profit before tax plus

interest paid)/total assets. The addition of interest payments back

into the numerator makes the profitability measure independent of

the firm's capital structure: the numerator should not depend upon

whether the assets that appear in the denominator are financed by

means of debt or equity. However, the ROA measure is dependent

on the accounting methods used to value assets, and is therefore

sensitive to accounting practices concerning the treatment of

depreciation, current and capital expenditures, and inflation.

Previous studies in this field, including McGahan and Porter(1997), Rumelt (1991) and Schmalensee (1985) acknowledge

similar difficulties. Fisher and McGowan (1983) and Stark (2004)

discuss the issues that arise in the measurement of profitability.

The annual growth rate is (net assets in year tminus net assets

in year t1)/net assets in year t1, where net assets is total assets

minus current liabilities. An assets-based firm size measure tends

to reward capital-intensive and penalize labor-intensive firms.

However, in Amadeus the assets size measure is available for

many more firms than any alternative equity, turnover or em-

ployment-based firm size measures. The use of an assets-based

firm size measure is the standard practice in the empirical

industrial organization literature (see e.g., Dunne and Hughes,

1994; Geroski et al., 2003; Hart and Oulton, 1996; Kumar, 1985).

The variance decomposition analysis omits the observations

that fall in the top and bottom percentiles of the distributions of

the profit and growth rates, in order to prevent the analysis from

being unduly influenced by outliers. The omission of any single

profit rate or growth rate observation implies that the data for

the firm concerned is incomplete, resulting in the elimination of

the firm from the sample.If a firm has a single majority shareholder, Amadeus identifies

this shareholder as the ultimate owner. This study adopts the

following definitional conventions. Sample firms with the same

ultimate owner are members of a single corporate group. Sample

firms with either a unique ultimate owner or no ultimate owner are

independent. An ultimate owner is unique if it is a majority share-

holder in one sample firm only. A firm has no ultimate owner if

there is no single majority shareholder. This study reports separate

estimations of the profit and growth equations for independent

firms and for firms that are corporate group members. Of the

13,827 firms in Sample A, 10,987 are independent and 2840 are

members of corporategroups. Of the7211 firms in SampleB, 5805are independent and 1406 are members of corporate groups. For

the two samples, Table 2 shows the distribution of the corporate

groups by the number of constituent sample firms.

Tables 3 and 4 report summary statistics for both the

independent firms and the corporate group member firms

combined, based on subdivisions of Samples A and B by year

(Table 3) and by country and industry group (Table 4). Table 4

lists the industry groups at the 2-digit level, but in the variance

decomposition analysis, the definitions of the industry group

dummy variables are at the 4-digit level. In Tables 3 and 4, the

mean profit and growth rates by year reflect greater buoyancy in

the EU economies during the mid-1990s than at the start and the

very end of the decade.

4. Model specification and estimation method

For the purposes of the variance decomposition analysis,

each firm has a country index c; an industry index i; and, in the

Table 2

Distribution of corporate groups by number of constituent firms

N=No.

of firms

No. of corporate

groups comprising

N firms

N=No.

of firms

No. of corporate

groups comprising

N firms

Sample A Sample B Sample A Sample B

1 (independents) 10987 5805 11 5 22 343 211 12 5 2

3 132 75 13 4 5

4 64 32 14 1 1

5 32 18 15 3 0

6 27 16 16 4 1

7 16 9 17 2 0

8 17 1 18 2 0

9 10 3 19 3 0

10 8 7 20 or more 10 4

Table 3

Descriptive statistics, profit rate and growth rate, by year

PROFIT RATE GROWTH RATE

Mean (%) S.D. (%) Median (%) Mean (%) S.D.(%) Median (%)

SAMPLE A: FIVE YEARS, ELEVEN COUNTRIES

1996 8.7 7.7 7.6 - - -1997 8.7 7.5 7.4 14.6 35.6 6.9

1998 8.8 7.6 7.5 13.5 33.0 6.7

1999 8.3 7.7 7.0 12.7 33.1 6.2

2000 7.8 7.8 6.5 12.3 34.2 6.2

SAMPLE B: NINE YEARS, FIVE COUNTRIES

1992 8.3 7.2 7.8 - - -

1993 7.7 7.2 7.2 9.3 32.2 3.2

1994 8.4 6.8 7.5 12.0 29.3 6.1

1995 9.1 7.1 8.1 11.8 28.4 6.5

1996 8.6 7.0 7.6 12.1 27.2 6.8

1997 8.5 6.9 7.3 10.4 26.0 5.8

1998 8.6 7.2 7.3 10.5 26.0 5.9

1999 8.2 7.3 6.9 9.7 26.4 5.6

2000 7.7 7.3 6.5 10.1 26.7 5.6



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case of firms that are corporate group members, a corporate

group index k. The model for firm f's performance measure

(profit rate or growth rate) in year t, denoted yf,t, is as follows:

yf;t A gt kc uc;t ai di;t gi;c bk /f ef;t 1

In (1), is the overall mean profit or growth rate across all

firms and years. The terms in are the year effects, which

represent the difference between the mean profit or growth rate

in year t and the overall mean. The terms in and are the

permanent country and industry effects. The country year and

industryyear interaction terms in and are the transient

country and industry effects. The industrycountry interaction

terms in allow for inter-country differences in the industry

effects. The terms in are the corporate group effects, omitted

for the independent firms. The terms in are the firm effects.

Finally, the term in is the residual variation in the profit or

growth rate, unaccounted for by any of the other effects.

Several researchers, including Chang and Singh (2000) andMcGahan and Porter (1997), allow for first-order autocorrela-

tion in the residuals of their counterparts of (1):

ef;t

qef;t1

xf;t

2

In (2), allows for inter-temporal persistence in f,t. The term

in is a random disturbance term. To derive a single equation

incorporating both (1) and (2), substitute into (2) expressions

forf,t and f,t-1 obtained by rearranging (1):

yf;t 1 q A gt 1 q kc u

c;t 1 q ai

di;t 1 q gi;c bk /f

xf;t 3

Table 4

Average profit and growth rates, by country and industry group, all years

Number

of firms

Mean profit

rate (%)

Mean growth

rate (%)

Number

of firms

Mean profit

rate (%)

Mean growth

rate (%)

SAMPLE A: FIVE YEARS, ELEVEN COUNTRIES

Country Industry group, NACE 2-digit code

Austria 60 7.4 9.9 20 308 8.0 10.4Belgium 1107 6.4 9.3 21 477 7.7 10.9

Finland 253 12.0 11.1 22 604 8.4 15.8

France 3044 8.2 9.3 23 91 7.6 12.6

Germany 118 6.7 7.4 24 1414 9.9 15.0

Greece 556 9.7 2.7 25 848 9.2 10.1

Italy 3884 7.8 14.1 26 748 8.5 15.3

Neth. 94 10.8 7.3 27 625 7.2 9.3

Portugal 59 5.0 12.1 28 1341 8.7 12.4

Spain 2818 9.5 16.9 29 1280 9.1 14.6

UK 1834 9.2 12.1 30 65 8.7 13.5

Industry group, NACE 2-digit code 31 539 9.3 11.9

15 2247 7.3 14.6 32 241 8.8 14.6

16 19 11.0 13.5 33 274 8.4 12.8

17 849 7.4 12.7 34 422 8.7 24.6

18 397 9.2 12.3 35 194 7.6 13.819 264 8.4 12.7 36 580 9.0 15.7

SAMPLE B: NINE YEARS, FIVE COUNTRIES

Country Industry group, NACE 2-digit code

Belgium 788 8.6 6.4 23 45 7.5 6.9

France 2056 8.5 7.5 24 742 9.4 10.7

Italy 2007 8.6 12.4 25 468 8.8 11.6

Spain 1645 8.2 14.7 26 398 8.6 11.2

UK 715 7.7 11.2 27 312 7.0 9.0

Industry group, NACE 2-digit code 28 718 8.3 11.7

15 1139 7.8 10.6 29 682 8.7 10.9

16 9 12.2 9.2 30 24 9.3 7.5

17 497 7.7 9.1 31 271 9.1 10.9

18 190 9.9 11.2 32 178 8.1 12.8

19 158 8.7 10.8 33 143 8.4 9.820 151 7.4 11.4 34 206 7.9 11.6

21 255 8.1 10.6 35 95 7.3 11.2

22 287 7.8 9.3 36 293 8.8 11.3

Note

NACE 2-digit manufacturing industry definitions (abridged) are as follows: 15=food products and beverages; 16=tobacco products; 17=textiles; 18=clothing;

19= leather, luggage and footwear; 20= wood products; 21 = pulp and paper; 22 = publishing and printing; 23 = coke, refined petroleum products and nuclear fuel;

24= chemicals; 25= rubber and plastics; 26 = non-metallic mineral products; 27 = basic metals; 28= fabricated metal products; 29= machinery and equipment;

30=office machinery and computers; 31 electrical machinery; 32=radio, TVand communication equipment; 33=medical, precision and optical instruments, watches

and clocks; 34= motor vehicles and trailers; 35 = other transport equipment; 36= furniture.



8/14

In (3), the transformed variables are yf,t= yf,tyf,t 1,

t=tt1, and c,t and i,t defined in the same way as

t. The estimation of (3) takes place in two stages. First, apply

Arellano and Bond's (1991) generalized method of moments

(GMM) dynamic panel estimator to the following equation:

yf;t qyf;t1 A /f xf;t 4

Second, estimate (3) using the estimated from (4) to define

the transformed variables. The variance decomposition analysis

examines the contribution of each set of effects to the

explanation of the variation in yf,t. GMM allows for unbiased

estimation of using panel data with a large cross-sectional and

a short time dimension. The OLS estimation of (4) would result

in a downward biased estimate of, due to the presence of both

the lagged dependent variable yf,t1 and the firm effectf. The

OLS estimation of (4) with f omitted would result in an

upward biased estimate of, because the latter would tend to

proxy for the firm effect.The previous literature reports the following types of variance

decomposition analysis: nested analysis of variance (ANOVA),

hierarchical linear modelling (HLM) and variance components

analysis (VCA). The present study uses the method that has been

most widely employed in the previous literature, nested

ANOVA. Each stage of the nested ANOVA is equivalent to an

ordinary least squares (OLS) regression, in which the dependent

variable is the profit or growth rate and the covariates are 0-1

dummy variables corresponding to each set of effects. Nested

ANOVA adds each set of effects to the model sequentially, in

order to identify the contribution to the model's explanatory

power, measured by the increment to R2 (proportionate

reduction in the residual sum of squares). An F-test assessesthe joint significance of each set of effects as it is entered, in the

following order: year effects, country effects, year country

interactions, industry effects, yearindustry and countryin-

dustry interactions, corporate group effects (where applicable),

and firm effects. If there is non-zero covariance between the

effects, the order in which they are entered affects the attribution

of the increment to R2. Those effects entered earlier may pick up

some of the increment actually associated with the (correlated)

effects entered later and it is important to examine the sensitivity

of the estimated effects to changes in the order of entry.

The present data set contains extreme cases of a non-zero

covariance between effects, involving the firm effects. Thedummy variables for the latter are perfectly collinear with those

for the country effects, because each firm belongs uniquely to

one country. Similarly, the firm dummies are perfectly collinear

with the industry dummies, and with the corporate group

dummies (when the latter are present). Therefore the OLS

regressions that compute the firm effects exclude the country,

industry and corporate group effects. An implication is that the

reported estimates of the country, industry and corporate effects

from the OLS regressions that exclude the firm effects may

actually contain some element of firm-level variation, and

should therefore be interpreted as upper-bound estimates.

The other estimation techniques used elsewhere in the

literature are HLM and VCA. In common with nested ANOVA,

HLM is based on fixed effects regression. The sequential

modelling of each set of effects is carried out in stages, at each

stage using the fitted value of the dependent variable

(performance measure) obtained from the previous stage as

the dependent variable. The contribution of each set of effects to

the variation in performance is calculated using the estimated

variances of the residuals from these regressions. HLM allowsfor the cross-nesting of business segments within both

corporations and industries that is a feature of the Compustat

business segments data on which most US studies are based, but

not of the Amadeus company accounts data used in the present

study (in which there is no cross-nesting of effects). In a recent

application, Misangyi et al. (2006) find little difference between

their results obtained using HLM and those of several earlier

studies that were obtained using nested ANOVA.

VCA, also known as random effects ANOVA, assumes the

effects are drawn randomly from a hypothetical population with

specific distributional properties. In contrast, nested ANOVA

assumes the (fixed) effects to be distribution-free. In VCA theordering of the random effects is unimportant, because the

underlying assumption is that each set of effects is independent.

In practice, however, this assumption is unlikely to be valid.

While nested ANOVA estimates a coefficient for each

individual dummy or other covariate, VCA reports only an

estimated variance component for each set of effects, and so

does not permit any statistical test for the significance of the

effects. The estimation method for VCA does not constrain the

estimated variance components to be positive. A preliminary

analysis of the present data using VCA shows several negative

estimated variance components. Although the choice between

the two estimation methods remains a matter for debate, the

present study uses nested ANOVA because it allows thefollowing: distribution-free effects; non-independent effects;

non-negative effects; and a statistical test for the significance of

each set of effects. McGahan and Porter (1997) provide an

insightful discussion of these and other methodological issues.

5. Empirical result

5.1. Persistence of profitability and growth

For the profit rate data, the four estimates of in (2), (3) and

(4) for the independent and corporate group members in Samples

A and B respectively are between 0.46 and 0.49. All fourestimates are significant. For the growth rate data, the four

estimates of are between0.03 and 0.04. One of these estimates

is borderline significant and the other three are insignificant. This

finding represents strong evidence of inter-temporal persistence in

profitability. However, the growth rate data appear to be free of

any such persistence effect. Accordingly, the profit rate variance

decomposition analysis is based on (3) and (4) incorporating

the adjustment for first-order autocorrelation, while the growth

rate analysis is based on (1) with no adjustment.

The persistence of profitability estimates, which are

quantitatively similar to those reported in most of the earlier

POP studies, suggest it is possible for a competitive profitability

advantage achieved in one year to be sustained in subsequent



9/14

years (Bou and Satorra, 2007; Villalonga, 2004; Wiggins and

Ruefli, 2002). With an estimated of just under 0.5, however,

the rate at which any advantage tends to dissipate is quite fast.

For example, a 10% increase in the profit rate in any given year

would correspond to only a 1% increase in the profit rate three

years later. Meanwhile the persistence of growth estimates

suggest little or no tendency for superior performance measuredin terms of growth to be sustained, even from one year to the

next.

5.2. Profit rate analysis

Table 5 reports the profit rate variance decomposition

analyses, comprising separate tabulations for Samples A (five

years, eleven countries) and B (nine years, five countries), and

within each sample, separate tabulations for the independent

firms and the corporate group members. For the Sample A

independent firms, the year effects explain only a very small

proportion (less than 0.5%) of the variation in the profit rate.The increments to R2 for the country effects and yearcountry

interactions are also small in absolute terms (less than 1.5%).

Nevertheless these effects are all highly significant according to

the F-tests, due in part to the large sample size. The industry

effects and the yearindustry and countryindustry interac-

tions add around 10%, and the firm effects add a further 32%, to

R2. In total, the analysis explains around 44% of the variation in

the profit rate.

With nested ANOVA the increments to R2 are sensitive to

the order in which the country and industry effects and the

associated interaction terms are entered. However, further

investigation of this issue suggests that the effect of varying

the ordering on the attribution of the increment to R2 is small. In

the results reported in the top-left panel of Table 5, the

increments to R2 attributed to the country, year country,

industry and year industry effects are 1.00%, 0.43%, 2.9% and

2.4%, respectively. Entering the industry and year industryeffects before the country and yearcountry effects produces

the following increments (listed in the same order): 0.90%,

0.33%, 3.0% and 2.4%. The effects of changing the ordering are

of similar magnitude in the other analyses reported in Tables 5

and 6.

For the Sample B independent firms, the proportion of the

total variation in profitability attributed to the year effects is

slightly larger than for Sample A, and the proportion attributed

to the country effects is smaller. Naturally, these patterns reflect

the differing cross-sectional and time dimensions of the two

samples. However, the total increment to R2 attributed to the

year and country effects and the yearcountry interactions issimilar. The industry effects and associated interaction terms

contribute around 11% to R2. The present study's industry

effects are similar in magnitude to those of Roquebert et al.

(1996), but smaller than those of Hawawini et al. (2003) and

Rumelt (1991). The increment to R2 attributed to the firm

effects is around 22%. This figure is considerably smaller than

the corresponding figure of 32% for the Sample A independent

firms. Again, the discrepancy reflects the differing cross-

sectional and time dimensions of the two samples: Sample A

has more cross-sectional (between firms) variation than Sample

Table 5Nested ANOVA variance decomposition analysis: Profit rate

Independent firms Corporate group members

Deg. of

freedom

Increment

to R2R2 F-stat. p-value Deg. of

freedom

Increment

to R2R2 F-stat. p-value

SAMPLE A. FIVE YEARS, ELEVEN COUNTRIES

Year 3 0.34 0.34 50.01 0.000 3 0.34 0.34 12.94 0.000

Country 10 1.00 1.34 44.72 0.000 10 0.92 1.26 10.60 0.000

Year Country 30 0.43 1.77 6.36 0.000 30 0.64 1.90 2.44 0.000

Industry 298 2.92 4.69 4.49 0.000 238 4.65 6.55 2.32 0.000

Year Industry 894 2.35 7.05 1.22 0.000 714 5.04 11.59 0.83 1.000

Country Industry 1081 4.66 11.71 2.03 0.000 570 8.60 20.19 1.85 0.000

Corporate group - - - - - 655 11.00 31.20 2.23 0.000

Firm 9597 32.15 43.85 1.91 0.000 1366 17.03 48.23 1.87 0.000

Error 32034 7773

SAMPLE B. NINE YEARS, FIVE COUNTRIES

Year 7 0.70 0.70 46.47 0.000 7 0.66 0.66 10.72 0.000

Country 4 0.77 1.47 91.19 0.000 4 0.80 1.47 22.89 0.000

Year Country 28 0.59 2.06 9.99 0.000 28 1.32 2.78 5.43 0.000

Industry 279 2.43 4.49 4.21 0.000 208 5.67 8.45 3.27 0.000

Year Industry 1953 5.23 9.72 1.31 0.000 1456 11.88 20.33 0.98 0.710


Corporate group - - - - - 359 8.23 34.11 3.10 0.000

Firm 4916 22.35 35.66 2.73 0.000 566 9.25 43.36 2.41 0.000

Error 38647 8351

Note

Estimated model is: yf,t=(1-)+t+(1-)c+c,t+(1-)i +i,t+(1-)(i,c+k+f) +f,t.

Estimated values of for independent firms are 0.4871 (Sample A), and 0.4670 (Sample B).Estimated values of for corporate group members are 0.4775 (Sample A), and 0.4830 (Sample B).



10/14

B. In total, the analysis explains around 36% of the variation in

the profit rate for the Sample B independent firms.

For the corporate group member firms, the contributions of

the year, country and year country interactions are qualita-

tively similar to those of the corresponding effects for the

independent firms, but the increment to R2 attributed to theindustry effects and the associated interaction terms is much

larger for the corporate group members than for the indepen-

dents. However, while the contribution of the yearindustry

interactions is relatively large in absolute magnitude, these

terms consume many degrees of freedom and are not

statistically significant.

5.3. Growth rate analysis

Table 6 reports the growth rate analyses for the independents

and corporate group members. For profitability R2 varies

between 35% and 53%, but for growth the equivalent range is21% to 34%. Therefore not only is the persistence of

profitability greater than the persistence of growth, but even

after adjusting for persistence, the proportion of the remaining

variation that the model explains is greater for profitability than

for growth. In other words, the growth data exhibits greater

randomness than the profitability data.

The contributions of the individual sets of factors to the

explanatory power of the growth models are as follows. In all

cases, the increments to R2 attributed to the year effects, country

effects and yearcountry interactions are small. The industry

effects and associated interaction terms account for a smaller

increment to R2 for Sample A than for Sample B. For the

independents, the increment to R2 attributed to the firm effects

is 26% in Sample A and 13% in Sample B. For the corporate

group members, the increment to R2 attributed to the corporate

and firm effects combined is 19% in Sample A, and 8% in

Sample B. For the corporate group members, several sets of

effects are insignificant, but for independents all effects are

significant except the year industry interactions in Sample A.

5.4. Analysis for subgroups of countries

Finally, this subsection presents the results of the variance

decomposition analysis for Sample A with the countries divided

into three geographical subgroups: Northern Europe, compris-

ing Belgium, Finland, Netherlands and UK; Central Europe,

comprising Austria, France and Germany; and Southern Europe,

comprising Greece, Italy, Portugal and Spain. This subsection

also presents results for a French civil law subgroup,

comprising countries whose legal system derives from the

French civil law family according to La Porta et al. (1998). Theseven Sample A countries that belong to this group are Belgium,

France, Greece, Italy, Netherlands, Portugal and Spain.

Table 7 reports the results of the variance decomposition

analysis for the profit rate (upper two panels) and the growth

rate (lower two panels). In each case, the first column

reproduces the increments to R2 from the corresponding

analyses for the entire sample (as reported in full in Tables 5

and 6). The other four columns report the increments to R2 for

the four subgroups. To economize on space, Table 7 reports

only the increments to R2 for each set of effects, and R2 for the

model with all effects included.

Several patterns emerge from the analysis for subgroups of

countries. For the independent firms' sample, the results seem

Table 6

Nested ANOVA variance decomposition analysis: Growth rate


Deg. of freedom Increment to R2 R2 F-stat. p-value Deg. of freedom Increment to R2 R2 F-stat. p-value

SAMPLE A. FIVE YEARS, ELEVEN COUNTRIES

Year 3 0.06 0.06 9.07 0.000 3 0.15 0.15 5.57 0.001

Country 10 1.37 1.43 60.86 0.000 10 0.69 0.83 7.85 0.000Year Country 30 0.28 1.71 4.17 0.000 30 0.33 1.17 1.27 0.147

Industry 298 1.33 3.04 2.01 0.000 238 2.27 3.43 1.09 0.161

Year Industry 894 2.05 5.08 1.03 0.260 714 6.39 9.82 1.03 0.297


Corporate group - - - - - 655 7.04 22.11 1.26 0.000

Firm 9597 24.30 32.52 1.20 0.000 1366 11.78 33.89 1.01 0.363

Error 32034 7773

SAMPLE B. NINE YEARS, FIVE COUNTRIES

Year 7 0.22 0.22 14.43 0.000 7 0.10 0.10 1.55 0.145

Country 4 1.16 1.37 136.21 0.000 4 0.71 0.80 20.06 0.000

Year Country 28 0.55 1.92 9.24 0.000 28 0.51 1.32 2.09 0.001

Industry 279 0.96 2.88 1.64 0.000 208 2.10 3.42 1.15 0.072

Year Industry 1953 4.53 7.42 1.11 0.001 1456 12.52 15.93 0.98 0.727


Corporate group - - - - - 359 3.91 22.59 1.25 0.001

Firm 4916 12.02 21.11 1.20 0.000 566 4.35 26.94 0.88 0.981

Error 38647 8351

Note

Estimated model is: yf,t=+t+c+c,t+i +i,t+i,c +k+f+f,t.



11/14

reasonably stable, with the explained proportions of the

variation in profitability and growth similar in the aggregatedand disaggregated analyses. For the corporate group member

firms' sample, the results are more variable, with the explained

proportions mostly higher in the disaggregated analyses. In part,

however, this finding reflects the smaller size of the corporate

group members' sample, and the depletion of degrees of free-

dom due to the disaggregation of this sample.

In general the contributions to the model's explanatory

power of the industry effects and the year industry interactions

are increased by breaking Sample A down into more

homogeneous groups of countries. Conversely, the contribution

to the model of the countryindustry interactions tends to be

smaller. These patterns highlight the importance in theaggregate model of the country industry interactions, asso-

ciated with external economies of scale through specialization

and geographical industry clusters (see Section 2).

Finally, the contribution to the model's explanatory power of

the country effects and associated interactions (yearcountry

and country industry) is always smaller in the analyses for the

French civil law subgroup than it is in the analyses across all

Sample A countries. This finding is circumstantial evidence in

favor of the hypothesis that differences in legal tradition have

observable implications for the profitability and growth

performance indicators. However, the extent to which the

measured differences in performance derive from real differ-

ences in institutional or legal structures, or from differences

between countries in their accounting standards and practice,

remains an unresolved question.

6. Conclusions

Identification of the sources of variation in firm performance,

and quantification of the relative importance of the industry-

level factors (concentration, economies of scale, and entry and

exit barriers) and the corporate group and firm-level factors

(organizational resources and management practices) motivates

a burgeoning empirical literature based on the application of

variance decomposition techniques to firm-level profitability or

other performance data. The previous empirical literature draws

predominantly on US evidence. Consequently, most of theliterature neglects the impact of country effects on firm

performance. The present study reports new results for

manufacturing firms located in 11 European Union (EU)

member countries, allowing for a full set of year, country,

industry, corporate group and firm-level effects, as well as

interactions between the year, country and industry effects.

Profitability and growth indicators measure performance. The

analysis of country effects, and the analysis of a growth

performance indicator, are the paper's two main original

contributions to the literature on the sources of variation in

firm performance.

The firm performance measures adopted in this study are

standard in the literature, but are subject to several limitations.

Table 7

Nested ANOVA variance decomposition analysis: Profit and growth rates, Sample A, Subgroups of countries


All

countries

Northern

Europe

Central

Europe

Southern

Europe

French

civil law

All

countries

Northern

Europe

Central

Europe

Southern

Europe

French

civil law

PROFIT RATE: INCREMENTS TO R2

Year 0.34 1.10 0.12 0.50 0.31 0.34 1.06 0.13 0.49 0.16Country 1.00 1.49 0.02 1.10 0.92 0.92 1.56 0.12 0.99 1.01

Year Country 0.43 0.22 0.10 0.18 0.21 0.64 0.53 0.20 0.41 0.27

Industry 2.92 3.71 7.02 4.75 3.56 4.65 7.10 9.45 12.72 6.59

Year Industry 2.35 6.63 7.58 3.44 2.64 5.04 10.38 9.59 13.69 6.50

Country Industry 4.66 3.97 1.32 2.47 4.25 8.60 4.70 1.34 7.27 8.42

Corporate group - - - - - 11.00 13.10 17.56 19.80 13.00

Firm 32.15 26.08 33.27 35.09 34.02 17.03 8.56 17.68 8.21 17.28

R2 43.85 43.20 49.44 47.54 45.90 48.23 46.98 56.07 63.58 53.62

GROWTH RATE: INCREMENTS TO R2

Year 0.06 0.39 0.04 0.03 0.03 0.15 0.26 0.11 0.16 0.09

Country 1.37 0.14 0.02 0.98 1.49 0.69 0.43 0.03 0.73 0.87

Year Country 0.28 0.15 0.05 0.31 0.24 0.33 0.31 0.21 0.40 0.25

Industry 1.33 2.74 3.87 1.98 1.53 2.27 6.02 3.73 7.60 3.08

Year Industry 2.05 5.69 7.27 3.16 2.37 6.39 12.71 12.79 17.10 7.99

Country Industry 3.14 3.48 0.75 1.94 2.64 5.25 2.31 0.55 3.33 4.21

Corporate group - - - - - 7.04 11.53 8.60 10.09 7.57

Firm 24.30 22.04 22.52 25.72 24.61 11.78 7.95 11.47 6.43 10.86

R2 32.52 34.63 34.53 34.13 32.92 33.89 41.53 37.50 45.84 34.93

Note

See notes to Tables 5 and 6 for model specifications.

Northern European countries are Belgium, Finland, Netherlands, UK. Central European countries are Austria, France, Germany. Southern European countries are

Greece, Italy, Spain, Portugal.

French civil law countries are Belgium, France, Greece, Italy, Netherlands, Portugal, Spain.



12/14

Although the profitability variable is independent of the firm's

capital structure, this variable is subject to the usual difficulties

associated with any company accounts-based profit measure.

Furthermore, the assets-based firm size variable that measures

growth tends to be upwardly biased for firms that use capital

intensive production technologies, and downwardly biased for

firms that are labor intensive.A finding of significant inter-temporal persistence of profit-

ability echoes the results of several recent US studies on the

sustainability of competitive advantage. In common with a

number of other variance decomposition studies, the firm effects

and (where applicable) corporate group effects make the largest

contributions to the explanation of the variation in profitability.

The contribution of firm effects in the case of independent firms is

always larger than the combined contribution of corporate group

and firm effects in the case of corporate group member firms. The

industry effects make a larger contribution than the country

effects, although qualitatively both of these sets of effects are

smaller relative to the firm and the corporate group effects.Therefore the present results provide some (indirect) support for

previous empirical findings that organizational structures and

management practices at the firm or corporate level represent the

main source of diversity in performance between firms.

In the profitability analysis, the yearindustry and coun-

tryindustry interactions both make significant contributions

to the explanation of the variation in performance. The

year industry interactions suggest the industry effect contains

a significant transient component. This suggests that the

structural characteristics of industries should not be treated as

fixed or exogenously determined. The tendency for the country

effects to differ significantly between industries suggests that

while a country effect on performance is likely at industry level,a country effect that is uniform across all sectors is less

plausible. In other words, industries differ systematically in

terms of the comparative advantages that are offered by

different countries. The significance of the countryindustry

interaction terms reflects the resulting tendency for specializa-

tion and geographic concentration.

Any inter-temporal persistence of growth is weak or

negligible. As in the case of profitability, the firm and corporate

group effects make larger contributions to the explanation of the

variation in growth than the industry or country effects. In

general, however, the magnitudes of all of these effects are

smaller in the growth analysis than in the profitability analysis.Therefore the results are in accordance with the hypothesis that

there is less systematic variation and more randomness in

growth data than in profitability data.

Further research would benefit from the use of accounts-

based performance measures that are more closely standardized

across countries. Greater standardization would permit clearer

differentiation between the variations in measured performance

between countries attributable to accounting standards and

practice, and the variations attributable to differences in

institutional structures and legal traditions. Another interesting

avenue for future research would be an investigation of the

impact of EU single market program and other forms of

European integration on the sources of variation in perfor-

mance. Such an investigation could take the form of

comparisons of firm performance data inside and outside the

single market, or comparisons over time by tracking changes in

performance as the program for economic integration proceeds.

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