Productivity efficiency and firm size: An empirical analysis of foreign owned companies

ARTICLE IN PRESS international business review International Business Review 16 (2007) 713–731 www.elsevier.com/locate/ibusrev

Productivity efficiency and firm size: An empirical analysis of foreign owned companies George Emmanuel Halkos, Nickolaos G. Tzeremes Department of Economics, University of Thessaly, Korai 43, Volos 38221, Greece Received 3 March 2006; received in revised form 17 April 2007, 29 May 2007, 7 June 2007; accepted 8 June 2007

Abstract There are various arguments about the impact of firm size on productivity growth. On one hand, it is claimed that large firms could be more efficient in production because they could use more specialized inputs, better coordinate their resources, etc. On the other hand, it is emphasized that small firms could be more efficient because they have flexible, non-hierarchical structures, and do not usually suffer from the so-called agency problem. This paper argues that size exerts an indirect effect on firms’ productivity, as it conditions the impact of internal factors on productivity. By using different methodological approaches to assess the impact of different characteristics of foreign owned firms on productivity, this paper analyzes to what extend the heterogeneous pattern of productivity can be accounted for by the levels of those factors. r 2007 Elsevier Ltd. All rights reserved. Keywords: Foreign owned firms; Malmquist productivity index; Performance measurement; Productivity levels; Resource based view

1. Introduction The literature on the productivity of multinationals suggests that firms with foreign equity tend to be more productive. This could be due to the firm-specific tangible assets such as exclusive technology and product designs, or to the intangible know-how embodied in foreign equity such as marketing, networking and sourcing. Such assets may Corresponding author. Alexandroupoleos 31, Ano Melissia, Athens 15127 Greece. Tel.: 0 030 24210 74920;

fax: 0 030 24210 74772. E-mail addresses: [email protected], [email protected] (G.E. Halkos), [email protected] (N.G. Tzeremes). URL: http://www.halkos.gr/ (G.E. Halkos). 0969-5931/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ibusrev.2007.06.002

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be more readily available in large multinational corporations (hereafter MNC). As such, being part of MNCs allows the local subsidiaries with foreign equity to gain access to these assets, which in turn enables them to produce more output given the same level of inputs, and thus a higher level of total factor productivity (TFP) than the solely domestic owned firms. Such a hypothesis has some empirical support based on samples of manufacturing firms in Venezuela studied in Aitken and Harrison (1999). Bartelsman and Doms (2000) discuss the factors that are important in explaining firm productivity of firms. These factors are regulatory environment, ownership, technology, quality of the workforce and international exposure. Thus, foreign ownership has been found to be an important contributory factor. There is evidence that firms under foreign ownership are more productive than domestic firms (Aitken & Harrison, 1999; Djankov & Peter, 2002). This view is consistent with the hypothesis that firms which establish overseas operations have an advantage in efficiency. The question why some firms perform better than others is central to analysis of many business disciplines and the subject of never-ending debate. In particular, the field of strategic management has traditionally focused on business concepts that affect the performance of firms providing answers to the question why some firms perform better than others. Since the late 1980s the dominant paradigm with regard to those issues is the resource-based view of the firm (Amit and Schoemaker, 1993; Barney, 1991; Collis, 1994; Grant, 1991; Peteraf, 1993; Wernerfelt, 1984), which focuses on internal, firm-specific factors and their effect on performance. A resource-based view explains why some firms in the same industry perform better than others. On the other hand, the literature on efficiency and productivity has been developed substantially over the last decades yielding a number of studies which use a number of sophisticated quantitative techniques applied in different empirical settings. In this way, the productivity research could benefit substantially from strategic management theory. At the same time, in strategic management not enough attention has been paid to performance measurement issues (Banker et al., 1996; Majumdar, 1998). Hence, this stream of research could benefit from productivity research and its advance in performance measurement. Based on these considerations, the main research question of this paper concentrates on: ‘‘how size affects foreign owned companies’ productivity growth’’. Geroski (1998) argues that size exerts an indirect effect on the productivity of firms, as it conditions the impact of other factors on productivity. Bearing this in mind, one of the main contributions of the present analysis is to use different methodological approaches to assess the impact of different characteristics of firms on productivity, and then to analyze how far the heterogeneous pattern of productivity can be accounted for, either by the levels of these variables or by their returns, (supposing that the latter follow different patterns in small and large firms as suggested by Geroski, 1998). By using a sample of 395 firms with different levels of foreign ownership operating in the Greek manufacturing sector, the paper determines the effect of different factors on firms’ productivity growth. This is obtained by using the Malmquist productivity index and its decomposition. The paper is structured as follows. In the next section, the main factors from a resourcebased view and productivity studies are analyzed with respect to firms’ performance and productivity growth. In Section 2, we analyze the measurement of productivity change, the data used and the proposed methodology for the construction of productivity indexes.

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Section 3 focuses on the empirical results derived, while the last section concludes the paper and discusses the policy implications of our analysis. 2. Literature review Different empirical studies present a positive correlation between foreign ownership and firm or plant productivity growth across a number of countries (Boateng and Glaister, 2002; Doms and Jensen, 1998; Girma, Greenaway, & Wakelin, 2001; Globerman et al., 1994). The productivity advantage of foreign owned firms is usually seen as reflecting multinationals’ technological advantage over the domestic firms (Buckley, Whang, & Clegg, 2007). Multinationals are assumed to have a firm-specific asset, such as know-how, technology, etc., which may be transferred easily across borders from the parent to subsidiaries abroad, which allows them to be more productive than domestic firms (Markusen, 2002). However, high productivity growth is not exclusively derived from technical progress, at least in theory where the direct link between technology and productivity is only valid in a neoclassical production framework with perfect competition, long run equilibrium, and constant returns to scale (CRS). Specifically, the literature on productivity analysis highlights the role of changes in economies of scale for productivity growth (Balk, 2001). This is consistent with the notion of learning-by-doing effects as described by Lucas (1988). Intuitively, as output expands, workers and firms gain an advantage at producing particular products. Thus changes in scale efficiency can also provide an explanation for the observed productivity advantage of foreign firms. Seth (1990) has proposed that an increase in output will allow fixed costs to be spread over a larger amount, thereby reducing the average total cost. In the event that the production factor can be used to produce other product varieties, joint utilization of that factor will result in scope economies. However, economies of scope are not only restricted to production factors. The joint use of a distribution channel or a sales organization and the common use of an R&D department may all lead to scope economies. Most of the studies measuring firms’ productivity were focused on a resource-based view using mainly accounting performance ratios such as return on total assets (ROA), return on investment (ROI) and return on sales (ROS). Banker et al. (1996) have criticized the use of ratios in the analysis of firm productivity and performance by emphasizing the need to decompose those accounting ratios in order to assess the changes in the productivity of a firm compared to itself over time and to another firm in the industry. This argument has been followed by Majumdar’s view which proposes data envelopment methodology (DEA) for resource-based view studies (Majumdar, 1998). Moreover, Miller and Ross (2003) support the fact that the productive use of resources revealed by the efficiency of utilization is a basic element of resource-based view theory. RBV claims to have its roots in the work of Schumpeter (1934), Penrose (1959) and Nelson and Winter (1982) and is closely associated with the concepts of core competencies (Prahalad and Hamel 1990), knowledge-based view of the firm (Grant, 1991) and dynamic capabilities (Teece, Pisano, & Shuan, 1997). Resources can be both of a tangible and intangible nature and resource-based theory has explicitly addressed the question what conditions are required to generate competitive advantage. As such, resources include tangible entities such as production systems, technologies, and machinery, as well as intangibles like brands, or property rights such as landing rights for an airline or bandwidth for a telecoms company (Teece et al., 1997). As pointed out by Wernerfelt

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(1984) products (activities) and resources are two sides of the same coin. Resources are utilized in the firm’s activities to convert inputs into outputs. In the conventional RBV, it is the firm itself which is seen to be in control of its own resources. A common classification of resources is described by Hoskisson and Hitt (1990) and Chatterjee and Wernerfelt (1991). A distinction is made between several types of resources: physical or tangible resources usually include plant and equipment, sales forces and distribution channels. They are less flexible than the other resources. Moreover, intangible resources include brand names or innovative capabilities and know-how. These resources were identified by Rumelt (1982) as core factors. Finally, financial resources are more mobile and less rare and are thus more likely to create less value than the other resources (Hoskisson and Hitt, 1990). Porter (1987) stressed that resource sharing and transfer of skills are the most important concepts in corporate strategy because they allow business units to increase their productivity and build a competitive advantage in their respective industries. Carmeli and Tishler (2004) and Michalisin et al. (2000) found significant positive relationship between intangible assets and the performance of organization. Furthermore, Bontis, Chua, and Richardson (2000) in their study found a positive relationship between intangible assets and firm performance. Mujumdar (1998) using data envelopment analysis (DEA) investigated the different efficiency levels produced by resource utilization. Miller and Ross (2003) analyzing resource utilization using DEA, found different efficiency levels within the firm. Lucas (1978) proposed a theory of the size distribution of business firms. The empirical literature on productivity at firm level agrees in considering size to be a main source of heterogeneity in firms’ performance. Large firms are systematically found to be more productive than small ones, and, as Geroski (1998) argues, controlling for firm size in regressions can be considered as routine. He claims that size may have a direct effect on productivity that is as a variable that Ceteris paribus improves efficiency. Additionally, it may have an indirect effect on productivity through other variables as they will create different patterns of behavior for small and large firms. Griliches and Mairesse (1983) found further that in the United States and France the variance of firm-level growth rates decreases with size and age. In contrast, Griliches and Regev (1995) found higher labor productivity growth rates for larger firms in Israel. Zheng, Liu, and Bigsten (2003), analyzing a sample of state owned enterprises (SOEs), found that both large and small SOEs registered positive productivity growth during 1980–1994, with the former having a much better performance. This paper based on RBV theories tries to contribute to the existing productivity literature by using Malmquist productivity indexes among 395 companies with foreign ownership operating in the Greek manufacturing sector. The basic elements of resource-based theories are used in order to capture the determinants of foreign owned firms’ productivity growth relative to their size. 3. Methodology 3.1. Extracting the Malmquist productivity indexes This section deals with assessment of the performance of firms with foreign ownership using measures of productivity growth and decomposition into technical progress and efficiency change (or catch-up) components. Standard measures of productivity growth

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(Solow, 1957) are based on the concept of TFP which is defined as growth in output net of growth in inputs used. These measures are used as measures of technical change on the assumption that the observed output and input quantities are derived within a framework of optimizing behavior by producers, based also on the assumption of fully efficient use of the technology available and allocative efficiency in observed combinations of outputs and inputs. The Malmquist TFP index is defined using distance functions. Distance functions allow one to describe a multi-input, multi-output production technology without the need to specify a behavioral objective (such as cost minimization or profit maximization). One may define input and output distance functions. An input distance function characterizes the production technology by looking at a minimal proportional contraction of the input vector, given an output vector. An output distance function considers a maximal proportional expansion of the output vector, given an input vector. For the purposes of this paper, we need to consider only output distance functions. A production technology, satisfying standard axioms, may be defined using the output (possibility) set, P(x), which represents the set of all output vectors, y, which can be produced using the input vector, x. That is, PðxÞ ¼ fy : x can produce yg.

(1)

The output distance function is defined on the output set, P(x), as: d o ðx; yÞ ¼ minfd : ðy=dÞ 2 PðxÞg.

(2)

The distance function, do(x,y), will take a value which is less than or equal to one if the output vector, y, is an element of the feasible production set, P(x). Furthermore, the distance function will take a value of unity if y is located on the outer boundary of the feasible production set, and will take a value greater than unity if y is located outside the feasible production set. The Malmquist TFP index came to prominence through the work of Caves, Christensen, and Diewert (1982). The Malmquist index can be defined using an output-oriented approach or an input-oriented approach. In the present study, an output-oriented measure of TFP change is the most appropriate. The Malmquist (output-oriented) TFP index measures the TFP change between two time periods by calculating the ratio of the distances of each data point relative to a common technology. Following Fa¨re, Grosskopf, and Lovell (1994), the Malmquist (output-orientated) TFP change index between period s (the base period) and period t is given by "     #1=2   d so yt ; xt d to yt ; xt  t  mo ys ; xs ; yt ; xt ¼ s  , (3) d o ys ; xs d o ys ; xs where the notation dos(xt, yt) represents the distance from the period t observation to the period s technology. A value of mo greater than one will indicate positive TFP growth from period s to period t, while a value less than one indicates a TFP decline. Note that Eq. (3) is, in fact, the geometric mean of two TFP indices. The first is evaluated with respect to period s technology and the second with respect to period t technology. An equivalent way of writing this productivity index is  "    #1=2   d to yt ; xt d so yt ; xt d so ys ; xs    t  mo ys ; xs ; yt ; xt ¼ s  , (4) d o ys ; xs d to yt ; xt d o ys ; x s

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where the ratio outside the square brackets measures the catching-up effect, or change in the output-oriented measure of Farrell technical efficiency between periods s and t. That is, the efficiency change (EFF) is equivalent to the ratio of the Farrell technical efficiency in period t to the Farrell technical efficiency in period s, which will be higher than unity if there has been an increase in efficiency. The remaining part of the index in Eq. (4) is a measure of technical change (TEC). It is the geometric mean of the shift in technology between the two periods, evaluated at xt and also at xs. It is an indicator of the distance covered by the efficient frontier from one period to another and thus a measure of technological improvement between the periods. The square root term (TEC) is greater than, equal to or less than one when the technological best practice is improving, unchanged, or deteriorating, respectively. Thus the two terms in Eq. (4) are:   d to yt ; xt   Efficiency change ¼ EFF ¼ s d o ys ; xs

(5)

and "

   #1=2 d so yt ; xt d so ys ; xs  t  . Technical change ¼ TEC ¼ t  d o yt ; xt d o ys ; xs

(6)

Moreover, this efficiency change component can be separated into scale efficiency and pure technical efficiency change. The latter is obtained by re-computing efficiency change under variable returns to scale (EFFVRS). The former is therefore the ratio of efficiency under constant and variable return to scale (EFFCRS/EFFVRS) Fa¨re, Grosskopf, Norris, and Zhang (1994b). Ray and Desli (1997) criticized this decomposition by regarding it as not an economic meaningful decomposition of productivity. In their decomposition, Ray and Desli use the same term of efficiency change but their technical change is defined on the best practice technologies where scale change factor is the geometric mean of a pair of scale efficiency ratios, one measured on period s technology and the other measured on period t technology. Furthermore, Wheelock and Wilson (1999) criticized both decompositions and introduced a four-way decomposition. Technical efficiency change and technical change were defined by the method introduced by Ray and Desli and scale efficiency change component were measured according to best practice technology changes, as introduced in the decomposition by Fa¨re et al. (1994b). However, the fourth component added on Wheelock and Wilson’s decomposition has been described by Lovell (2003) as the scale bias of technical change.1 Nevertheless, as pointed out by Grifell-Tatje´ and Lovell (1995), a Malmquist index may not correctly measure TFP changes when variable returns to scale (VRS) are assumed for the technology. Ray and Desli (1997) also contend that there may be confusion in the simultaneous use of CRS and VRS technologies within the same decomposition of the Malmquist index. For the purpose of our analysis this paper uses the initial decomposition of Malmquist (Eq. 4) as illustrated above and as introduced by Fa¨re, Grosskopf, Lindgren, and Roos (1994a). 1

Analytically for the decomposition issue on Malmquist productivity indexes see Lovell (2003).

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Since the empirical analysis in this paper involves more than two years, it uses a moving period formulation of the Malmquist index instead of the base-period formulation. Moorsteen (1961) argues that when we wish to analyze the productivity change of a long time series, the use of a fixed technology may cause problems the further we get from the base year. If one has data on N firms in a particular time period, the linear programming (LP) problem that is solved for the ith firm in an output-orientated DEA model is as follows: Max

j

j;l

st

 jyi þ Y lX0, xi  X lX0, lX0,

ð7:1Þ

where yi is a M  1 vector of output quantities for the ith firm; xi is a K  1 vector of input quantities for the ith firm; Y is a N  M matrix of output quantities for all N firms; X is an N  K matrix of input quantities for all N firms; l is a N  1 vector of weights; and j is a scalar. Observe that j will take a value greater than or equal to one. The j-parameter provides information on the technical efficiency score for the ith firm and the l-vector provides information on the peers of the (inefficient) ith firm. The peers of the ith firm are those efficient firms that define the facet of the frontier against which the (inefficient) ith firm is projected. Following Fa¨re et al. (1994), we can calculate the required distance measures for the Malmquist TFP index using DEA-like linear programs. For the ith firm, we must calculate four distance functions to measure the TFP change between two periods, s and t. This requires the solving of four LP problems. Fa¨re et al. (1994) assume a CRS technology in their analysis. Following the suggestion by Coelli (1996), the distance functions can be estimated by solving the following DEA-like linear programs:  t 1 d o ðyt ; xt Þ ¼ max j, f;l

st

 jyit þ Y t lX0, xit  X t lX0, lX0,

ð7:2Þ

 s 1 d o ðys ; xs Þ ¼ max j, f;l

st

 jyis þ Y s lX0, xis  X s lX0, lX0,

ð7:3Þ

 t 1 d o ðys ; xs Þ ¼ max j, f;l

st

 jyis þ Y t lX0, xis  X t lX0, lX0,

ð7:4Þ

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and



d so ðyt ; xt Þ

1

¼ max j, f;l

st

 jyit þ Y s lX0, xit  X s lX0, lX0.

ð7:5Þ

Note that in LPs (7.4) and (7.5), where production points are compared to technologies from different time periods, the f parameter needs not be greater than or equal to one, as it must be when calculating standard output-orientated technical efficiencies. The data point could lie above the production frontier. This will most likely occur in LP (7.5) where a production point from period t is compared to technology in an earlier period, s. If technical progress has occurred, then a value of fo1 is possible. Note that it could also possibly occur in LP (7.4) if technical regress has occurred, although this is less likely. The above programs must be solved for each firm in the sample in each period, and an extra three programs for each firm solved to construct the chained index. Overall for N firms and T periods, with the decomposition of the technical efficiency N, (4T2) LPs are solved (7900 LP in our study). 3.2. The data Estimating an efficient frontier requires specification of the input and output factors of the production process. Dyson et al. (2001) suggest four key criteria when selecting the inputs and outputs for a DEA frontier estimation: (i) the factors cover the full range of resources used; (ii) the factors capture all activity levels and performance measures; (iii) the factors are common to all units; and (iv) environmental variation has been assessed and captured if necessary. One benefit of using DEA to estimate the frontier is that it is unit invariant and does not require specification of a functional form between inputs and outputs. Taking into consideration Dyson’s criteria for input/output selection this paper analyzes the impact of the key factors mentioned in the resource-based theories and other studies on productivity and firm performance. Therefore, a data set of 395 firms with foreign ownership operating in the Greek manufacturing sector has been used. The data set is provided by ICAP directory2 and covers the time period from 1995 to 2001. In order to extract the Malmquist productivity indexes we need to clarify the sets of inputs and outputs to be considered. The inputs used are:

    

liquidity ratio (LR) ¼ current assets/current liabilities (creditors due within one year); working capital (WC) ¼ current assets-current liabilities; number of employees (NE)3; intangible fixed assets (000 s $); tangible fixed assets (000 s $) and

the percentage of total foreign ownership. 2

The ICAP directory provides financial data (based on published accounts for all the years covered in our study) for all Plc. and Ltd. firms operating in Greece. http://www.icap.gr/isologismoi/intro/login/index.asp. 3 According to EU definition of firm size large firms are considered those with more than 250 employees, medium firms those with 50–249 employees and small firms those with fewer than 50 employees.

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Moreover, the two outputs considered for our model are:

 

sales (000 s $) (SA) and profit margin (PM) ¼ (profit before interest and taxation/sales)  100. The empirical results of our analysis are presented next.

4. Empirical results Following the above methodology we coded each firm operating in the Greek manufacturing sector from 1995 to 2001 and we produced TFP, TEC and EFF indexes for 1995–1996, 1996–1997, 1997–1998, 1998–1999, 1999–2000, 2000–2001.4 Moreover, we averaged the results of the years under consideration and we formed three sub-tables according to firms’ size5 (Table 1). Table 1 provides information regarding firms’ (DMUs’decision making units) average score value of TFP, TEC, and EFF indexes over the years under consideration. Furthermore it provides descriptive statistics of 42 large firms, 101 medium sized firms and 252 small firms.6 As discussed previously when total TFP is more than one, this indicates that the firm is productive either through efficiency change (EFFX1) or technological change (TECX1). Table 1 illustrates chronically the TFP, EFF and TEC changes of the foreign owned firms according to their sizes. The results reveal that the highest TFP change has been observed for medium sized firms for the years 1998–1999 (8.424). Whereas the second highest change for TFP has been recorded for small firms for the years 1999–2000 (7.054). Furthermore, in terms of efficiency changes (EFF) the highest change was observed for small firms for the years 1997–1998 (9.228). The second highest value for efficiency change has been observed for the years 1998–1999 (7.7). Finally in the case of technological change (TEC), the highest value has been observed in small firms for the period 1996–1997 (9.93). The second highest TEC was observed for medium sized firms for the period 1999–2000 (4.289). In general, we observe that there are differences among productivity changes of the foreign firms according to their size. The fact that there can be a loss of productivity in larger, more hierarchical firms was first proposed by Williamson (1967) in a model of hierarchical control that determines the optimum firm size. According to Williamson economies of scale and other related factors may cause the size of the firm to grow unboundedly but the decreasing returns to managerial efficiency limit the optimal firm size. Utterback (1994) has argued that small firms utilizing their greater organizational responsiveness are better at adapting to environmental changes than large firms. Scherer 4

Due to the enormous quantity of results we do not present the names or the sub-sectors, in which the firms are operating, nor do we show the results of TFP, TEC and EFF separately for the years 1995–1996, 1996–1997, 1997–1998, 1998–1999, 1999–2000 and 2000–2001. However, this information is available upon request. 5 Firm size as indicated previously is measured by the number of firms’ employees. 6 Some of the well-known firms belonging to Table 1 (large firms with more than 249 employees) are Coca-Cola Hellening Bottling Company S.A. (DMU 1), Heracles General Cement Co. S.A. (DMU 2), Nestle´ Hellas S.A. (DMU 7), Aluminium de Gre`ce S.A. (DMU 3), Club Mediterrane´e Hellas S.A. (DMU 90) and so on. Some medium sized firms (50–249 employees) used in our study are Minerva S.A. Edible oils enterprises (DMU 49), Eltrak S.A. (DMU 59), Beiersdorf Hellas A.G. (DMU 54), Bristol-Myers Squibb Ltd. (DMU 21), Faiax S.A. (DMU 116) and so on. Lastly, some small firms (o50 employees) used in our study are Hilti Hellas S.A. (DMU 201), Palco S.A. De tricotage (DMU 159), Lloyds Register S.A. (DMU 192), FIAT Credit Hellas S.A. (DMU 13), IDEAl Group S.A. (DMU 295) and others.

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Table 1 Descriptive statistics of total factor productivity (TFP), Efficiency chance (EFF) and technical change (TEC) according to firm size Total count Mean St Deviation Variance Minimum Median Maximum Big size firms Total factor productivity 95-96 Total factor productivity 96-97 Total factor productivity 97-98 Total factor productivity 98-99 Total factor productivity 99-00 Total factor productivity 00-01 Efficiency change 95-96 Efficiency change 96-97 Efficiency change 97-98 Efficiency change 98-99 Efficiency change 99-00 Efficiency change 00-01 Technological change 95-96 Technological change 96-97 Technological change 97-98 Technological change 98-99 Technological change 99-00 Technological change 00-01

42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42

1.114 1.158 1.023 0.987 1.437 0.803 1.082 1.102 0.899 0.934 0.966 1.021 0.113 0.424 0.132 0.079 0.485 0.251

0.646 0.424 0.225 0.223 0.593 0.343 0.858 0.910 0.203 0.214 0.280 0.491 0.165 0.674 0.121 0.078 0.557 0.287

0.418 0.179 0.051 0.050 0.351 0.118 0.737 0.828 0.041 0.046 0.079 0.241 0.027 0.454 0.015 0.006 0.310 0.083

0.447 0.573 0.288 0.447 0.602 0.209 0.150 0.471 0.227 0.429 0.501 0.427 0.000 0.000 0.000 0.000 0.000 0.000

1.006 1.064 1.020 1.001 1.282 0.810 0.957 0.990 0.923 0.972 1.000 1.000 0.065 0.272 0.101 0.049 0.221 0.174

4.539 2.499 1.549 1.461 3.400 2.183 5.026 6.650 1.359 1.336 1.644 3.345 1.025 4.208 0.488 0.317 2.351 1.162

Medium size firms Total factor productivity 95-96 Total factor productivity 96-97 Total factor productivity 97-98 Total factor productivity 98-99 Total factor productivity 99-00 Total factor productivity 00-01 Efficiency change 95-96 Efficiency change 96-97 Efficiency change 97-98 Efficiency change 98-99 Efficiency change 99-00 Efficiency change 00-01 Technological change 95-96 Technological change 96-97 Technological change 97-98 Technological change 98-99 Technological change 99-00 Technological change 00-01

101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101 101

1.025 1.325 1.077 1.265 1.548 0.802 0.998 1.004 1.004 1.296 0.782 1.576 0.119 0.506 0.142 0.122 0.797 0.846

0.274 0.871 0.382 1.018 0.912 0.522 0.330 0.278 0.427 1.166 0.606 1.239 0.099 0.687 0.175 0.133 0.621 0.940

0.075 0.759 0.146 1.036 0.832 0.272 0.109 0.077 0.182 1.359 0.367 1.534 0.010 0.471 0.031 0.018 0.385 0.884

0.289 0.406 0.297 0.308 0.388 0.212 0.258 0.381 0.251 0.262 0.140 0.110 0.000 0.000 0.000 0.000 0.000 0.000

1.000 1.136 1.070 1.005 1.373 0.714 0.990 0.982 0.985 0.997 0.679 1.094 0.094 0.260 0.096 0.084 0.726 0.445

1.877 7.009 3.008 8.424 5.876 4.436 2.074 2.155 3.118 7.700 4.232 4.946 0.416 3.602 1.365 0.724 4.289 3.633

Small size firms Total Factor productivity 95-96 Total factor productivity 96-97 Total factor productivity 97-98 Total factor productivity 98-99 Total factor productivity 99-00 Total factor productivity 00-01 Efficiency change 95-96 Efficiency change 96-97 Efficiency change 97-98 Efficiency change 98-99

252 252 252 252 252 252 252 252 252 252

1.070 1.204 1.278 1.189 1.550 0.904 1.057 0.910 1.415 1.345

0.509 0.828 0.880 0.835 1.104 0.810 0.605 0.521 1.131 1.000

0.259 0.686 0.774 0.697 1.218 0.656 0.366 0.271 1.279 1.001

0.005 0.088 0.069 0.021 0.143 0.077 0.003 0.068 0.063 0.017

1.000 0.989 1.000 1.000 1.219 0.739 1.000 0.817 1.112 1.074

5.583 4.618 6.700 6.617 7.054 5.293 6.429 4.059 9.228 6.812

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Table 1 (continued ) Total count Mean St Deviation Variance Minimum Median Maximum Efficiency change 99-00 Efficiency change 00-01 Technological change 95-96 Technological change 96-97 Technological change 97-98 Technological change 98-99 Technological change 99-00 Technological change 00-01

252 252 252 252 252 252 252 252

0.909 1.419 0.173 0.651 0.310 0.280 0.771 0.923

0.945 1.244 0.193 0.969 0.969 0.461 0.815 1.214

0.894 1.548 0.037 0.939 0.939 0.213 0.664 1.473

0.068 0.012 0.000 0.000 0.000 0.000 0.000 0.000

0.717 0.990 0.112 0.408 0.115 0.141 0.560 0.547

8.086 4.914 1.212 9.933 8.416 4.351 5.497 9.035

(1991) has also noted that managers of small firms are more likely to be risk takers making them more open to adoption of innovations. Specifically, looking at the results of TEC, efficiency change and TFP (Table 1), the highest values for the majority of the years were reported for small foreign owned firms. As Acs and Audretsch (1990) have shown, small firms out-perform large firms when it comes to their innovation rate even though the bulk of research and development expenditures in the economy are undertaken by large firms. Finally, Carlsson (1989) argues that smaller firms given their flexibility are also better organized to respond to the changing market structure and consumer tastes which have shifted production away from standardized mass-produced goods and towards stylized and personalized products. Fig. 1 illustrates graphically the differences of TFP, EFF, and TEC average values for the time period 1995–2001. The results indicate that in the Greek manufacturing sector the productivity growth of small foreign owned firms is higher compared to the medium and large firms. Many studies of firms in developed countries have shown that the distribution of firm size at any point in time corresponds remarkably well to predictions made by Gibrat’s law of proportional effect. This states that all firms, irrespective of size, grow each year by some random draw from the distribution of growth rates. However, many studies using panel data sets that allow the calculation of firm-level growth rates have found that there is a negative correlation between growth rate and size or age. Caves (1998) discusses these and some other determinants and regularities of firm-level growth. Sleuwaegen and Goedhuys (2002) cite a large number of sources from both developed and developing countries confirming that larger and older firms grow at significantly slower rates. Griliches and Mairesse (1983) find further that in the United States and France the variance of firm-level growth rates decreases with size and age. In contrast, Griliches and Regev (1995) find higher labor productivity growth rates for larger firms in Israel. Even among firms of similar size and age productivity dispersion is typically very large, but it falls as cohorts age. Relocation of resources is a crucial determinant of aggregate TFP growth (Bartelsman and Dhrymes, 1998 for US results and Levinsohn and Petrin, 1999 for Chile). Furthermore, Table 2 illustrates the results derived from the Mann–Whitney test in order to observe the differences of productivity growth among firms relative to their size. Table 2 illustrates the results of the performance of firms derived from the decomposition of the Malmquist productivity index. For the purpose of constructing the Mann–Whitney test, the paper uses the average values of all the years of TFP, TEC and EFF. In Table 2, the values in brackets are the p values and the significance at the 1%, 5% and 10% is

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1.6

Medium size firms

1.4

Small size firms

1.2 1 0.8 0.6 0.4 0.2 0 TFP95-96

TFP 96-97

TFP 97-98 TFP 98-99 TFP 99-00

TFP00-01

Average values of EFF per year, per firm size 1.8 1.6 1.4

Big size firms Medium size firms Small size firms

1.2 1 0.8 0.6 0.4 0.2 0 EFF95-96

EFF96-97

EFF 97-98

EFF 98-99

EFF 99-00

EFF00-01

Average values of TEC per years, per firm size 1 0.9 0.8

Big size firms Medium size firms Small size firms

0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 TEC 95-96 TEC 96-97 TEC 97-98 TEC 98-99 TEC 99-00 TEC 00-01

Fig. 1. Trends of total factor productivity (TFP), efficiency chance (EFF) and Technical change (TEC) according to firm size.

signaled. According to the results, TFP score values are different between large and small firms. Differences in TFP values have been observed also for large and medium sized firms. However, differences for TFP score values have not been observed for medium sized and

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Table 2 Mann-Whitney test statistic results according to firm size (p-values in brackets) Big size firms

Small size firms

Total factor productivity Medium size firms Small size firms

2640 (0.0892) 5142.5 (0.0392)

17480 (0.6473)

Technological chance Medium size firms Small size firms

3231.5 (0.3589) 7506 (0.0102)

19504 (0.0605)

Efficiency change Medium size firms Small size firms

2342 (0.0025) 3849.5 (0.0000)

16047 (0.0347)

 Significant at 1% level. Significant at 5% level. Significant at 10% level.

small firms. Moreover, in the case of TEC, differences in the median score values have been observed between small and medium sized firms and between large and small firms. However, in the case of large and medium sized firms, the results revealed no significant differences. Finally, according to the Mann-Whitney results, comparing the different EFF score levels between firms, it appears that firm size is a major determinant of firms’ performance. In general, our results support productivity studies which emphasize that the firm size affects firms’ productivity growth. However, the main question in our study is how the different factors in the resourcebased theories and other productivity studies appear to influence firms’ TFP, EFF and TEC performance levels. Moreover, the research question focuses on how the influence of the factors used in our analysis of firms’ TFP, EFF and TEC performance differentiates them according to the size of the firms. Table 3 illustrates chi square results between the variables used in our study and the firms’ productivity (performance levels) relative to their size. For this paper, we grouped the different scores of TFP, EFF, TEC and the variables in our study according to the levels of their values (if their values are above or below the group’s average values). The results in Table 3 indicate that for large firms, liquidity levels influence the firm’s TFP performance. Moreover, working capital and sales appear to influence large firms’ EFF and TEC performance. Finally, the results indicate that tangible assets also influence large firms’ EFF performance. Looking at the results for medium sized firms, their levels of liquidity and working capital are influencing their levels of EFF performance. Moreover, their levels of performance for EFF and TEC are influenced by the number of employees and the levels of tangible assets. Finally, TFP levels for medium sized firms are influenced by the levels of intangible assets, total ownership and sales. Lastly, analyzing the results for small firms, we observe that the levels of TFP performance are influenced by firms’ intangible and tangible assets and by firms’ sales and profit margins. Moreover the levels of EFF performance are influenced by levels of liquidity, sales and profit margin. Finally, the levels of TEC performance are affected by levels of tangible assets and sales.

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Table 3 Chi-square test statistic results between variables and firm size (p-values in brackets) Variables

Total Factor Productivity

Efficiency Change

Technological Chance

Big size firms Liquidity Working capital Number of employees Intangible fixed assets Tangible fixed assets Percentage of total ownership Sales Profit margin

6.067 0.000 0.518 0.546 0.737 0.006 0.000 1.591

(0.02) (1) (0.510) (0.719) (0.466) (1) (1) (0.178)

0.467 (0.734) 1.393 (0.298) 6.108 (0.02) 0.223 (0.729) 0.066 (1) 1201 (0.335) 4.388 (0.047) 2.355 (0.191)

3.249 3.732 4.273 0.349 3.375 0.766 3.732 2.613

Medium size firms Liquidity Working capital Number of employees Intangible fixed assets Tangible fixed assets Percentage of total ownership Sales Profit margin

0.092 2.457 0.193 3.610 0.012 3.940 7.959 0.316

(0.808) (0.135) (0.670) (0.093) (1) (0.061) (0.005) (0.535)

6.823 (0.013) 11.164 (0.001) 4.918 (0.034) 0.433 (0.608) 3.561(0.079) 2.721 (0.139) 0.120 (0.826) 0.212 (1)

0.982 (0.357) 2.233 (0.161) 5.342 (0.026) 0.000 (1) 6.131 (0.016) 0.268 (0.689) 1.422 (0.294) 2 90.495

Small size firms Liquidity Working capital Number of employees Intangible fixed assets Tangible fixed assets Percentage of total ownership Sales Profit margin

0.02 (1) 1.992 (0.189) 0.07 (1) 3.193 (0.089) 6.287 (0.022) 0.105 (0.769) 19.317 (0.000) 5.916 (0.015)

6.143 (0.016) 1.603 (0.244) 0.921 (0.389) 0.052 (1) 1.014 (0.297) 0.653 (0.478) 15.589 (0.000) 9.952 (0.002)

0.033 0.378 0.167 0.041 3.317 0.000 4.509 1.909

(0.108) (0.093) (0.057) (0.739) (0.083) (0.535) (0.093) (0.130)

(0.845) (0.564) (0.701) (0.867) (0.091) (1) (0.045) (0.200)

 Significant at 1% level.  Significant at 5% level.  Significant at 10% level.

In conclusion, the size of the firm determines the factors influencing firms’ productivity levels. Different strategies according to firm size affect their productivity through transfer of knowledge, skills, technology, etc., taking advantage of economies of scope; while other firms increase their productivity through strategies of resource seeking, taking advantage of economies of scale (Dunning 1979, 1980, 1988a, 1988b, 1995, 2001; Dunning and Bansal, 1997; Rumelt 1982). 5. Conclusions and policy implications According to theory, the productivity leader is at the forefront of technology and its rate of productivity growth relies on new technological breakthroughs. Follower firms can achieve faster rates of productivity growth and catch-up the leader’s productivity level as they engage in ‘technological catch-up’ by adopting the leader’s technologies and knowhow, without undergoing the same investment, learning and adjustment costs; and by

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‘leapfrogging’ some technologies rendered obsolete by the leader’s later discoveries. Productivity levels among a group of firms can converge, as a result of catch-up. During the life span of a firm, changes may occur in the ownership and/or management. These changes may be related to changes in the organizational structure, such as mergers, take-overs and divisions (or scissions). However, changes in ownership and/or management can also occur without changes in organizational structure. This occurs, for example, in the case of a management buy-out, the arrival of a new owner/manager in a small firm or the appointment of a new CEO of a large firm. Changes such as these are likely to affect the way the firm operates, and therefore influence the productivity of the firm. In the short run the changes will often result in a temporary slowdown (or even decrease) in productivity growth, due to organizational changes that occur when a new owner/manager is installed. The effects in the long run are largely dependent on how successfully the changes are implemented (Boone, Brahander, & Witteloostuijn, 1996). Huergo and Jaumandreu (2004) include dummy variables in their analyses to account for some sources of discrete changes in firms’ efficiency levels (mergers, acquisitions, scissions). Mergers or acquisitions and scissions turn out to have a significant impact on productivity growth (with a one-year time lag). On average, the impact they report is positive for mergers or acquisitions and negative for scissions. Productivity levels are likely to be correlated with the size of the firm, as measured by the number of employees. In general, smaller firms will organize the production process differently than larger firms. An increase in firm size is, initially, expected to have a positive effect on productivity levels, due to economies of scale (and scope). However, when a firm grows beyond a certain size, diseconomies of scale may have a dominating effect, thereby negatively influencing productivity levels. This study contributes to the field of productivity research using strategic management theory. As has been emphasized by Banker et al. (1996) and Majumdar (1998) studies using performance measurements can benefit by strategic management theories. Therefore, relying on a sample of 395 firms with foreign ownership operating in the Greek manufacturing sector, this study analyses the effect of firms’ size on productivity growth. A number of variables are used, derived from resource based, productivity and performance measurement studies. These are: the number of employees, foreign ownership, profit margin, sales, intangible and tangible assets, working capital and the liquidity ratio. Firms’ productivity growth is evaluated for a time span of seven years (1995–2001). The results indicate that firm-specific factors derived from resource-based theories (which are been conditioned from firms’ size) have different impacts on firms’ productivity. These results support the studies, which relate the size of firms to levels of productivity and firm performance (Geroski, 1998; Griliches and Mairesse, 1983; Griliches and Regev, 1995; Lucas, 1978; Zheng et al., 2003). Specifically, the results reveal that large firms’ productivity growth is affected by the levels of liquidity, working capital, number of employees, tangible assets and sales. Additionally, productivity growth for medium sized firms is influenced by levels of liquidity, working capital, number of employees, intangible and tangible assets, percentage of total ownership and sales. In the case of foreign ownership the results indicate that foreign ownership influences only medium sized firms’ TFP and fully support the studies which relate foreign ownership and firm productivity (Boardman, Shapiro, & Vining, 1997; Doms and Jensen, 1998; Girma et al., 2001; Globerman et al., 1994; Markusen, 2002). Finally, the productivity of small firms is influenced by the levels of liquidity, intangibles and tangible assets, sales and profit margin.

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In general, firms’ productivity growth in all cases is affected by the levels of liquidity, tangible assets and sales. However, the factors influencing the TFP of firms, efficiency and TECs differ relatively to firms’ size. According to several scholars these factors, analyzed in this study, explain why some firms in the same industry perform better than others (Wernerfelt, 1984; Barney, 1991; Grant, 1991; Peteraf, 1993; Amit and Schoemaker, 1993; Collis, 1994). Moreover, these factors are determined by firms’ needs and corporate strategy. The results of our research support those resource based, productivity and performance measurement studies, which suggest that firms use different strategies relative to their size in order to obtain vital resources which in turn will increase their productivity. Additionally, the productivity and performance of individual firms will be strongly related to the characteristics of the sector in which they are active. This effect can amongst others be explained by product life cycle theories. Young sectors bring new products to the market. Firms tend to focus on product innovations (Klepper, 1996) and low competition results in relatively high margins. Under these market conditions, firms are likely to experience high productivity growth rates. As sectors become more mature, competition becomes stronger and innovation activities are likely to shift towards process innovations (Klepper, 1996). Mature sectors may therefore show a slowdown or even negative productivity growth. Some sectors may innovate and reinvent their product, or come up with entirely new products. By increasing their attention to product innovation, these sectors enter a new phase of the product life cycle and exhibit increases in productivity growth rates again. Sectors failing to enter this new phase will eventually vanish, or continue only on a marginal level. However, this paper does not examine this relationship on an industry-by-industry basis. As a result, the main limitation of this work lies in the fact that it is unable to control for industry specific factors that may affect the relationship between size and productivity growth. Therefore future research can be directed to those industry specific factors which influence foreign owned firms’ productivity growth. Finally, the limitations of the technique and the selection of inputs and outputs used in this study must be taken into consideration. Nevertheless, this study provides an in depth analysis of factors influencing foreign owned firms’ productivity growth. Acknowledgements We would like to thank two anonymous referees and Richard Dawson for their useful and constructive comments on earlier versions of this article. Any remaining errors are solely the authors’ responsibility. References Acs, Z. J., & Audretsch, D. B. (1990). Innovation and small firms. Cambridge, MA: MIT Press. Aitken, B. J., & Harrison, A. E. (1999). Do domestic firms benefit from direct foreign investment? Evidence from Venezuela. American Economic Review, 89(3), 605–618. Amit, R., & Schoemaker, P. (1993). Strategic Assets and Organizational Rent. Strategic Management Journal, 14, 33–46. Balk, B. M. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 159–183. Banker, R. D., Chang, H., & Majumdar, S. K. (1996). A framework for analysing changes in strategic performance. Strategic Management Journal, 17(9), 693–712. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–120.

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