Justine Falciola1 Marion Jansen2 Valentina Rollo3
UNIGE ITC ITC
This paper contributes to defining firm competitiveness by identifying its components and
studying how they belong to different pillars of competitiveness, through confirmatory factor
analysis. We use data from the World Bank Enterprise Surveys for the 2006-2014 period for 100
countries of different income and development status. Since firm competitiveness cannot be
captured by a single proxy on its own, we build a multi-dimensional index, the Competitiveness
Index. Our results suggest that the Competitiveness Index from our analysis is positively
correlated with commonly used proxies of competitiveness, such as labour productivity, the
probability to export, the percentage of inputs of foreign origin used by the firm and the share of
total sales that were exported. The multidimensional framework proves to apply to firms of
different sizes and to both exporting and non-exporting firms.
Keywords: competitiveness, firm heterogeneity, factor analysis, latent variable models, multi-dimensional
index
JEL classification: F23, C38, M21, L11
1 Justine Falciola, Doctoral Student, Geneva School of Economics and Management, University of Geneva,
Uni-Mail, 1221 Geneva 4, Switzerland; justine.falciola@unige.ch.
2 Marion Jansen, Chief Economist, Office of the Chief Economist, International Trade Centre, Palais des Nations,
1211 Geneva 10, Switzerland; e-mail: jansen@intracen.org.
3 Corresponding author: Valentina Rollo, Economist, Office of the Chief Economist, International Trade Centre,
Palais des Nations, 1211 Geneva 10, Switzerland; tel. +41-22-730.0331 ; e-mail: rollo@intracen.org.
The authors thank Jaya Krishnakumar, Stephan Sperlich, Virginie Trachel, Olga Solleder and the participants of the UNIGE BBL for useful comments and discussions.
The most commonly used indicator of good performance and competitiveness, at both the
macro and micro levels, is the level of productivity, normally described as the ratio between
outputs and inputs, where the inputs comprise all factors utilized to produce the output
demand. The use of productivity as a measure of performance finds its origin in the
traditional manufacturing industry (e.g. Sink, 1983), where inputs and outputs can be
quantified. Nevertheless, it is reasonable to question whether this concept fully represents
the performance or competitive strength of contemporary organizations.
In fact, production includes a large amount of non-tangible assets, such as knowledge work
and services (Oeij et al, 2011). Even though more recent work has tried to propose more
realistic definitions of productivity - including the time factor (Johnston and Jones, 2004),
quality (Drucker, 1999; Grönroos and Ojasalo, 2004), the role of clients or customers (Martin
et al., 2001), value creation (Rutkauskas and Paulaviiene, 2005) and capacity planning
(McLaughlin and Coffey, 1990; Jääskeläinen and Lönnqvist, 2009) - this has resulted in a
proliferation of combinations of variables to define productivity.
Moreover, access to knowledge is a necessary but not sufficient element of good
performance. Firms also need to be able to absorb capacity (Cohen & Levinthal, 1990; Kim,
1997). In this sense, even if the economic literature shows large support for the importance
of R&D and innovation for economic growth (Griffith, Redding, and Van Reenen, 2004,
Fagerberg and Verspagen, 2002), this cannot be delinked from the role played by education
(or human capital) (Barro, 1991; Benhabib & Spiegel, 1994), finance (King & Levine, 1993;
Levine, 1997; Levine & Zervos, 1998), or governance (Acemoglu, Johnson, & Robinson, 2001;
Glaeser et al., 2004; Rodrik et al., 2004).
Competitiveness has been described in the economic and business literature as a
multidimensional concept, where different criteria of competitiveness depend on time and
context (Ambastha and Momaya, 2004). Porter (1998) states that “it is the firms, not
nations, which compete in international markets”. Empirical evidence shows that 36 per cent
of the variance in firms’ profitability should be attributed to the characteristics and actions
of firms (McGahan, 1999), while other works focus on firms’ strategies and resource
positions (Bartlett and Ghoshal, 1989; Prahalad and Doz, and 1987; Prahalad and Hamel,
1990) as the real sources of competitiveness. The environmental factors, in this paper
divided between the national and the immediate business environment, remain relatively
uniform across all competing firms, but are crucial to the competitiveness of the firm. In fact,
competitiveness arises from an integral process that goes beyond the boundaries of the
single firm and connects employees and clients/customers in many ways (Oeij et al, 2011).
The challenging task to be tackled is to summarize multidimensional realities into one single
measure of competitiveness that would allow policy makers to monitor progress efficiently.
This paper tries to achieve this objective by shaping this multi-dimensionality and building an
index of firm competitiveness. The methodology used in this paper, factor analysis, differs in
3
spirit from classical regression analysis as it emphasizes covariances rather than individual
variables. In fact, while multivariate regression analysis focus on the relation between one or
more independent variables, , and the dependent variable , factor analysis focuses on
uncovering the relationship among observed indicators (the independent variables) in order
to measure a latent concept: Competitiveness. Each observed indicator is treated as a partial
manifestation of a postulated broader latent variable. Factor analysis is particularly well
suited for the construction of the SME Competitiveness Index, a multi-dimensional index.
Our results suggest that the Competitiveness Index from our confirmatory factor analysis is
positively correlated with commonly used proxies of competitiveness, such as labour
productivity, the probability to export, the percentage of inputs of foreign origin used by the
firm and the share of total sales that were exported. Finally, the multidimensional
framework we build is applicable to firms of different size and to both exporting and non-
exporting firms, as shown by the positive relationship between labour productivity and the
index for the different types of firms.
The rest of the paper is structured as follows. Section 2 provides a review of the literature,
while Section 3 introduces the competitiveness framework we use. Section 4 describes the
databases used in the analysis and Section 5 outlines the empirical model and shows the
results. Finally Section 6 concludes.
2. Review of the literature
a. Components of firm competitiveness
Successful firms are directed by strong managers
One of the important components of firm competitiveness and a good predictor of how well
a firm will perform in the market is the competency of its manager. Porter (1990) defines
entrepreneurial and management skills as the ability to capitalize on ideas and opportunities
by successfully implementing a business strategy.
The subject has been extensively developed by management, institutional and
organizational studies, since the widely cited paper by Hambrick and Mason (1984) discussed
the relevance of managerial characteristics for organisational outcomes. This work
stimulated fruitful empirical research on the links between managerial experience and firms’
performance. The main idea of this strand of literature is that managers’ strategic choices,
crucial for firms’ performance, may be predicted by personal traits, values and executive’s
experience. For example, management practices can improve productivity, through their
impact on marginal productivity of inputs and resource constraints (e.g. Syverson, 2011), as
well as growth and longevity (Bloom and Van Reenen, 2010). Empirical evidence has shown
how learning management skills can improve firm performance; for example, learning even
elementary management skills in planning, marketing and financial literacy can lead to an
accelerated adoption of improved management practices, increased willingness of owners to
pay for follow-up training and increased survival (Sonobe and Otsuka, 2006, 2011)
4
However, variance in organisational outcomes may be better explained by managers
characteristics when there is a higher degree of managerial discretion, i.e. there are fewer
constraints and multiple plausible alternatives available to a decision maker (Hambrick and
Finkelstein, 1987). Moreover, degree of managerial discretion varies across firms and
sectors, and it may depend on the environment, internal organization and manager’s
characteristics, including psychological traits.4
The early literature on the importance of managerial characteristics for organisational
outcomes has later evolved into two streams: on one side studies looking at individual CEO
background, and on the other studies evaluating top management teams (Hambrick, 2007).
The latest, the group leadership perspective, allows a complex view on the effect of top
management teams diversity and heterogeneity on performance of firms. The literature has
shown that higher heterogeneity in terms of international experience, functional, tenure and
education may affect strategic choices for global expansion in highly uncertain business
environment (Carpenter and Fredrickson, 2001). Similarly, small firm governance structure
may be crucial for making strategic choices, which not only influence the ability to
“Compete” but also willingness to “Change”. In SMEs, where, commonly, only one person is
responsible for managerial choices, strategic change may be less likely to occur (Brunninge,
Nordqvist and Wiklund, 2007).
Empirical evidence also shows that a manager from an older generation, which is more likely
to have more years of experience, is more conservative in terms of investment choices and
use of financial leverage. At the same time, an older manager is more likely to undertake
diversification moves and R&D activities. Finally, the earlier age of managers is associated
with higher returns on assets. On the other hand, a manager with an MBA degree follows, on
average, more aggressive strategies and is also positively associated with higher firm
performance. Most importantly, using panel data estimation, this study confirms that
heterogeneity in financial and organizational practices is associated with managers’ specific
traits (or fixed effects). Therefore, the empirical research corroborates the assumption that
traits of individual manager do matter for corporate behaviour and therefore performance
(Bertrand and Schoar, 2003).
Managerial skills are also found to influence the capacity of firms to internationalize. More
specifically, the effort to learn internationally, previously acquired international experience
and an open minded attitude towards global markets, all seem to be positively related to
internationalization (De Clerq et al., 2005, Reuber and Fischer, 1997, Kyvik et al., 2013). In
fact, more detailed research shows that a positive attitude and managerial motivation
positively influences not only entry into exporting (Wood et al., 2015), but also the capacity
to diversify geographically (Ciravegna et al., 2014). The structure of ownership also seems to
influence the decision to internationalize. For example, Fernandez and Nieto (2005) show
that family-owned firms (commonly but not exclusively SMEs) engage less in commitment-
intensive internationalization activities. The reason is explained not only by limited financial
4 A review of the literature devoted to the studies analysing managerial discretion can be found in Wangrow,
Schepker and Barker (2015).
5
resources, but also by limited willingness to establish relations with new partners and
interest in international expansion. On the other side, when SMEs are managed by a group
of shareholders which include foreign shareholders, export propensity increases.
Meeting quality and sustainability standards is becoming a competitive ‘must’
Standards, weather national or international, affect the basic functioning of the firm.
Standards in finance, human resource management or operations or logistics, for instance,
affect firms’ efficiency through their cost structure. In fact, by affecting core operations,
standards and regulations fundamentally affect the nature of final or intermediate products,
whether they enter directly or indirectly than international value chain. Also regulations
applied in post-sale services affect firms, in that they affect how customers experience the
consumption of an acquired good or service, and consequently affect the product’s demand
(ITC, 2016 forthcoming).
Adopting standards may increase sales on the foreign markets (Masakure, Cranfield and
Henson, 2011). Compliance with specifically targeted standards, especially private ones,
might improve the image of a company, or may decrease associate trade costs due to
facilitated custom control regime (Latouche and Chevassus-Lozza, 2015; Volpe Martincus,
Carballo and Graziano, 2015). However, compliance with resource demanding standards also
requires additional investment and financing in order to adjust the production process,
product labelling, packaging, etc. Certification may indeed restrain producers in accessing
foreign markets since they incur in extra costs, both fixed and variable, which ultimately
increase the product price (World Bank, 2005; Kox and Nordas van Tongeren, 2007; Beghin
and Marette, 2009).
In summary, empirical research using firm-level data has shown, on one side, evidence of the
positive effect of compliance with quality and sustainability standards on firms’ performance
and competitiveness. However, this positive effect is conditional on the affordability of
certification for entrepreneurs, given both the sometimes limited access to finance and
technology, and to insufficient development of trade related infrastructure in some
countries.
The exhaustive available literature on the effect of ISO 9001 standards shows that
management system standards (MSSs) have enjoyed enormous success over the last years.
By the end of 2010 at least 1.109.905 ISO 9001 certificates had been granted in a total of 178
countries worldwide, which nearly tripled the number of certificates at the end of 2000 (ISO,
2011). A review of the literature by Heras, Molina-Azorín and Tarí (2012)5 shows the positive
effect of ISO 9001 and ISO 14001 standards are related to:
an improvement in the efficiency and effectiveness of the organization, avoiding the
duplication of efforts;
a reduction of bureaucracy by eliminating duplication of policies, procedures and
registers,
5 http://upcommons.upc.edu/bitstream/handle/2099/12955/tari.pdf
a reduction in the costs of internal and external audits, and
the availability of joint training and improved communication between all
organizational levels.6
However, the effect on financial performance seems to be inconclusive.
Growing evidence also shows the positive effect of adopting international standards on
competitiveness of firms from developing countries in international markets. Certified firms
participate more in exporting activities compared to non-certified firms. The channels that
lead to these results are linked to productivity and costs: international quality standards
affect competitiveness via higher productivity and lower transaction costs channels.
Moreover, compliance with standards affects more the export performance of domestic
firms, compared to foreign firms, especially in countries characterised by weak institutional
development (Gedhuys and Sleuwaegen, 2016). Their analysis, based on World Bank
Enterprise Survey of manufacturing firms from 89 developing and transition countries,
reveals that holding international standards would increase firms participation in export
activities, both at the extensive and intensive margins, more likely indicating the transaction
costs reduction effect. Productivity is affected by the adoption of standards, through
production quality improvement and net costs optimization. A possible explanation for such
effect is that acceptance of common rules, formalized by international standards, reduces
uncertainty and information asymmetry arising from economic and institutional distances.
The decision and possibility to comply with national or international standards only partly
depends the firm’s capacities. The firm’s immediate business environment is in fact a
determinant of compliance, which does not depend on the firm’s abilities or competencies.
The ‘cold chain’ example can explain this. Good refrigerators and good infrastructures help
perishable goods to stay fresh and safe for consumption: firms active in the food industry
needs cooling and storage facilities from the point of slaughter or harvest through to the
final consumer. Such infrastructure is key to determine whether firms in this sector are
competitive in markets. Supportive local and sectoral business environments are crucial
determinants of firm competitiveness, but firms often have limited influence on them.
Access to finance determines daily operational efficiency and the ability to make
investments for the future
At the macroeconomic level, evidence shows that financial development matters for output growth of economy. Levine and Zervos (1998), using cross-country analysis, provide evidence of the impact of stock market liquidity and banking development on long-run economic growth, capital accumulation and productivity growth. A possible explanation for this is that level of financial development of host countries affects growth potential of credit- constrained firms via cost of external finance reduction mechanisms (Rajan and Zingales, 1998). This indicates the importance of finance for firms, and not only for countries. In fact, in order to operate, firms need to have a bank-account, which allows them to settle accounts with clients and providers quickly and smoothly. However, a simple bank-account
6 Wilkinson & Dale, 1999a, 1999b; Poksinska et al. 2003; Zeng, Tian & Shi, 2005; Zutshi & Sohal, 2005
7
might not be enough to adjust to changing market conditions. In order to be able to invest in new activities and remain competitive, firms need access to finance, which is consistently cited as one of the primary obstacles affecting SMEs more than large firms (Ayyagari, Demirgüç-Kunt and Maksimovic, 2012).
Analysis of cross-section of industries in both developed and developing countries suggest
that financial development plays a role for the rise of new firms, which are usually more
finance constrained at initial stages, but which are likely to bring innovation much needed
for growth. Musso and Schiavo (2008) show how access to external finance in France has a
positive effect on firm performance in terms of sales, capital stock and employment.
Cross-sectional firm-level analysis of the corporate performance in developed and developing countries also reveals that firm-specific financial characteristics are important determinants in post-crises periods (Medina, 2012). Specifically, the pre-crises financial leverage and short-term debt affect negatively the post-crises real sales growth - the negative effect of leverage being higher for the emerging market economies - while tangibility, measured as fixed assets over total assets, positively affects firm growth. Coricelli et al. (2010) finds that the relationship between financial leverage and growth in central and eastern European countries over 2001-2005 period is positive only until a certain threshold (around 40 per cent of total assets) after which financial instability may result. Also, the threshold point varies across countries. The work shows that in the countries of the sample, characterized by weak financial market institutions and limited market capitalization, a significant proportion of firms have no access to bank loans. At the same time, excess leverage is common among firm having access to credit, and seems to affect more negatively the TFP of less-profitable firms.
Access to finance is also an important determinant of innovation, as proved by Ayyagari, Demirgüç-Kunt, and Maksimovic (2011) in a study including over 19,000 firms across 47 developing economies. The results show how external finance is associated with greater innovation by all private firms. Firm level and cross-country evidence are unambiguous (Demirgüç-Kunt, Beck, and Honohan 2008): access to and use of finance are positively associated with firm performance along a number of distinct aspects, including investment, growth, innovation and firm size distribution.
Access to finance is also proved to be a determinant of the firm ability to enter export markets and expand abroad. The reason is in the nature of trade, a highly capital intensive effort, involving high up-front costs (i.e. needed to create distributor networks) and high variable costs (related to shipping, logistics and trade compliance). The ability to access working capital and sufficient cash flow timely permits to mitigate the usual risks associated with trade, like customer non-payment and exchange-rate volatility. The literature shows how improved access to external finance positively affects the probability of starting to export and reduces the time firms need to decide serving foreign customers (Bellone et al., 2010). Berman and Héricourt (2010) confirm the importance of access to finance as a determinant of the extensive margins, contrary to the survival and intensive margins: better financial development is associated with the number of exporters and probability to become an exporter.
8
Finally, access to finance includes trade finance, which is mentioned as one of the primary
export constraints for SMEs. Why is trade finance so crucial? Banks are intermediaries that
reduce the payment and supply risk of transactions, while providing the exporter with
accelerated receivables and the importer with extended credit (Niepmann and Schmidt-
Eisenlohr, 2014). However, even in this case firms’ abilities and capacities are not the only
element determining access to finance. The access and extension of credit greatly depends
on a supportive legal and regulatory framework, so on the immediate business environment
and on the national environment. In fact, the tax and administrative and regulatory
environments affect the entry into a market of various financial institutions – foreign, state-
owned, large and small – as well as their market share ability to compete and corporate
governance structure.
Access to talent is required at all levels of operations
Skills gained over life, through education, experience and training, are an asset to firms and
countries, and identify patterns of human capital accumulation, an essential determinant of
productivity and ultimately economic growth. A skilled workforce is central to the ability of
firms to anticipate change or to adjust to it, and an important determinant of economic
growth (Woessman, 2011). Using data on Swedish firms in business service sector, Backman
(2014) studies the link between human capital and firm productivity at different levels:
firms, industry and locality. The results prove a strong evidence of the link between work
force education, experience and cognitive skills and firm productivity. Further, local
availability of talented workforce is a strong predictor of productivity, and education
increases export diversification (Cadot, Carrère and Strauss-Kahn, 2011). As a matter of fact,
internationalization can require soft skills such as presentation, communication and
language skills (CEDEFOP, 2012).
Talent is even more important in developing countries, where firms need
absorptive capacities to internalize foreign technologies, and where workers with education
and training are in high needs for this task. Firms that adopt new foreign technologies need
educated staff to innovate as they enter more knowledge-intensive activities. This is even
more relevant when firms want to enter Global Value Chains. Evidence shows that firms in
countries with relatively low skill levels receive low skill-intensive tasks and firms in countries
with high skill levels receive higher skill-intensive tasks (Khalifa and Mengova, 2012). Skilled
workers are also crucial for firms dealing with foreign clients’ quality standards, especially
SMEs (Jansen and Lanz, 2013).
Matching the skills needs firms have with the skills supplied by countries’ education systems
is not always an easy task, and a usual source of inefficiency (Jansen and Lanz, 2013). This
becomes even more important in a very dynamic environment and changing market
conditions, where employers have to hire workers with the right skills or to regularly adjust
skills through in-house training. The mismatch between labor needs and offer often result in
high rates of unemployment among young graduates, where employers complain about the
difficulty in finding workers with specific skills (Almeida, Behrman and Robalino, 2012).
9
Some firms, especially SMEs, might need to invest in training but do not simply because the
expected rate of return associated with training is smaller than the return on other
investments (Almeida, Behrman and Robalino, 2012). This is related to SMEs being more
resource constrained than larger firms (Okada, 2004), and to the difficulty for very small
firms to handle the drop in production that results from the absence of an employee in
formal training. At the same time, the reluctance from providing training can be explained by
the lack of capacity to both assess future skills needs (Cedefop, 2012), and foresee the future
returns from such training (Adams, 2007). The resulting underinvestment in knowledge
upgrading negatively affect firms by making them weaker and more vulnerable in the face of
new market challenges. The immediate business environment and the national environment
can strengthen the engagement of SMEs in training through cooperation via horizontal
networks, including actors from the public and private sectors. These networks can in fact
create opportunities for knowledge exchange, resulting in collaborative research and
development (Bosworth and Stanfield, 2009).
Access to inputs and customers abroad matters for competitiveness
In order to produce their final goods, firms need access to a varied range of inputs and
suppliers, and in order to sell, firms need access to many customers, or, in other words,
access to markets. Empirical evidence shows that access to foreign intermediate inputs can
increase firms' efficiency by providing more diverse and higher quality inputs (Bas and
Strauss-Kahn, 2014), especially for SMEs since they are able to raise their productivity via
learning, variety and quality effects (Amiti and Konings, 2007). Importing also improves firm
productivity (Vogel and Wagner, 2010 and Kasahara and Rodrigue, 2008), and as a
consequence importing can have a positive effect on the decision to start exporting (since a
higher productivity helps firms to more comfortably bear the cost of starting to export) and
also on the variety of products exported and success as an exporter (Kasahara and Lapham,
2006; Bas and Strauss-Kahn, 2014).
As a consequence, it is not surprising to expect exporting firm to be more productive than
non-exporting firms (Delgado, Farinas and Ruano, 2002), as the trade literature has vastly
proved. Exporters are larger, more productive, more capital-intensive, more technology-
intensive and pay higher wages (Bernard, A. B., & Jensen, J. B., 1999). Moreover, empirical
analysis show that two-ways traders (firms that export and import) grow faster and more
likely engage in both product and process innovative activities, than only exporting firms,
and both groups outperform non-trading firms (eker, 2012, Bagdhadi et al, 2015). Finally,
productivity differences across firms are affected not only by simply exporting but also by
the export destination. Cebeci (2014) shows that exporting to higher-income countries
results in significantly higher productivity and wages, in contrast to exports from the same
sector towards lower-income destinations, with no difference on employment effects.
Even though the decision to export depends on the firm, access to market remains outside
of the firm’s control, as is determined by the trade policy of home or destination countries.
Ample evidence shows that trade liberalization - lower tariffs and fewer barriers to trade -
leads to better economic outcomes (Wacziarq and Welch, 2008). Amiti and Konings (2007)
even show that reducing input tariffs increases productivity three times more than a
10
reduction in output tariffs. Trade liberalization does not only affect the capacity of a single
firm to export or import, it affects the degree of competitiveness firms face in a market.
Melitz and Ottaviano (2008) show how allowing foreign firms to sell in the domestic market
increases the number of more productive firms, that in turn reduces mark-ups and affect the
selection of more productive producers into export markets. As a result, the gains from
trade liberalizations come from higher productivity level, lower prices (via decreased marks-
up due to higher competition), as well as increased product variety.
Firms’ ability to import or export might also be constrained by logistics. Traditionally,
logistics management has been associated with large manufacturing firms, which have
pioneered innovations in the field, such as ‘just-in-time’ production and delivery. Logistics
management, however, is an important part of any business, whether large or small. Poor
logistics management can render firms uncompetitive, impeding their access to suppliers
and buyers, and their participation in international value chains.
Logistics costs are an important share of the value of final goods produced, especially for
SMEs, and in developing countries: for example, in LAC logistics costs represent 18% to 35%
of the final value of goods, while in OECD countries it remains close to 8%. For small
companies, the share may be over 42%, mainly due to high inventory and warehousing costs
(Schwartz et al., 2009). However, logistics are not always in the firm’s control, especially for
SMEs. In the context of international trade, SMEs cannot reap the benefits of
containerization. When firms or countries trade small quantities of goods, they do not
manage to entirely fill containers (which have standard sizes) with their goods, and
consequently have to pay higher unit costs.7 An option, when trading volumes are low, is to
face longer travel times, so that the international carriers can make more stops to fill
containers or vessels.8 In both cases, the increase in costs for small firms or small countries is
inevitable. An impact assessment study of the Peruvian road network’s expansion between
2003 and 2010 estimates that total Peruvian exports would have been roughly 20% smaller
in 2010 without the road development programme (Carballo, Volpe Martincus and Cusolito,
2013).
Innovative firms are more productive and more likely to export
Innovation is crucial for firms’ good performance. This is widely shown by the literature on
innovative firms, which are shown to have higher levels of productivity and economic growth
(Cainelli, Evangelista and Savona, 2004) as well as to be more likely to export, and to do it
successfully (Love and Roper, 2013; Cassiman, Golovko and Martínez-Ros, 2010). Atalay,
Anafarta and Sarvan (2013) define innovation as the ‘implementation of a new or
significantly improved product (good or service), a new process, a new marketing method, or
a new organizational method in business practices, workplace organization or external
relations’.
7 UNCTAD (2012) The way to the ocean: Transit corridors servicing the trade of landlocked developing
countries. http://unctad.org/en/PublicationsLibrary/dtltlb2012d1_en.pdf 8 World Bank (2002) Global Economic Prospects and the Developing Countries
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Innovation, and a firm ability to innovate, is closely related with the technological capacities
of firms. The capacity to innovate is defined in different ways. On one side Neely et al. (2001)
defines it as the ability to generate innovative outputs, while Lawson and Samson (2001)
provide a broader concept: the ability to continuously transform knowledge and ideas into
new products, processes and systems for the benefit of the firm and its stakeholders. The
ability to innovate is particularly important for SMEs (Simon, Houghton and Aquino, 2000),
that are increasingly required to catch up with the rapid advances in new technologies
(Awazu et al., 2009). The wide digitization has also helped SMEs to become more
competitive, as shown by Tanabe and Watanabe (2003) for Japan.
Once again, the immediate business and national environment are crucial to support firms’
innovation capacities. In fact, firms can innovate by engaging in open innovation via external
sources. Clusters or Global Value Chains can create links between firms and boost
knowledge sharing and positive synergies, either between firms (business-to-business
networks, as for Winters and Stam, 2007) of between firms and external actors, such as
universities or R&D institutes (Acs, Audretsch and Feldman, 1994). Further evidence of the
spillovers from the use of technology in the firm’s network comes from Paunov and Rollo
(2016), who show not only that the industries’ use of the Internet positively affects the
average firm’s productivity and its investment in equipment, but also that the returns to
productivity are larger for firms that commonly engage less in innovation, including single-
plant establishments, non-exporters, and firms located in small agglomerations.
Research shows that the strength of innovation systems positively affects the rate and
direction of technological change (Lundvall, 1992; Nelson, 1993). While it is common for
these systems to focus on the creation of new technology in developed countries, in
developing countries innovation systems have to focus on strengthening the absorption
capacities: such a system is defined as a national technology system in Lall and Pietrobelli
(2002, 2003, 2005).
b. The immediate business environment
The ability of a firm to perform well does not depend only on the characteristics of the firm
(its innovativeness, its export status, access to a bank account, the ability of the manager,
etc.), because the firm operates in a broader context, its competitive environment. This was
firstly advanced in the pioneering book on competitive strategy by Porter (1985), who
distinguished five competitive forces at play in the micro-environment of a firm:
1. threat of new competitors (entrants);
2. threat of substitute products;
3. bargaining power of customers (buyers);
4. bargaining power of suppliers;
5. intensity of industry rivalry.
The industry-structure stream of research highlights how the structure of the industry
affects firms’ performance (Porter, 2008). Management research also highlights the
12
importance of networking, knowledge sharing, complementarity of resources, and effective
governance. The relationships that become beneficial and create partner-specific values are
difficult to imitate, as they are based on the complementarity of resources and assets, as
well as build overtime trust and knowledge, and reinforce the already established
agreements enforcement (Dyer and Singh, 1998). The importance of business-to-business
networks, especially for SMEs, is shown by Schoonjans, Van Cauwenberge and Vander
Bauwhede (2013), who show how a networking programme subsidized by the Belgian
government helped participating SMEs. More specifically the creation of formal business
network improved firm growth (in terms of net asset and added value).
But this is not the whole story. Firms need to be informed about consumers’ needs,
demographics and habits, about the legal requirements they have to comply with, about the
status of trade agreements their country is a signatory of, about the consequences of not
being a signatory and the visible and less visible trade barriers they could encounter if willing
to trade. All these elements (and others) shape the competitive forces firms have to deal
with, and as soon as one piece of the combination changes all the combination has to adjust,
so the competitive forces are constantly changing. As a consequence, connection, seen as
the ability to be informed about the nature of and changes in the competitive environment,
is crucial for firm competitiveness.
Connecting enables the firm on one side to acquire information about customers, suppliers,
competitors, products, technologies and government policies, and on the other side to
better advertise its products and services. A good connection to the immediate business
environment is particularly important for SMEs, which oftentimes are unable to gather
relevant business information. Help to gather this information usually comes from public
institutions or private associations, or from informal institutions, as it is shown in a study
conducted in Northern Uganda, where SMEs lack awareness or the capability to access
information from formal trade and investment support institutions (TISIs) (Okello-Obura et
al., 2008). Lack of information or inability to acquire such information are a clear barrier to
entry for SMEs (Kitching, Hart and Wilson, 2015), especially on international market (Reid,
1984; Seringhaus, 1987; Christensen, 1991).
c. Static and dynamic aspects of competitiveness A prerequisite to design a successful business strategy is to be aware of the competitive
forces shaping a firm’s environment. The type of competition faced in a market greatly
affects how the firm has to plan its strategy. Competitive forces include a dynamic element
in the run for competitiveness, as also highlighted by the theoretical and empirical literature
on gains from competition, where “productive efficiency” and “dynamic efficiency” are
increasingly highlighted. This strand of literature shows how productive efficiency gains arise
from productivity-enhancing innovations though the introduction of new and better
production methods, which can in turn ameliorate the level and growth rate of productivity
in the long run (i.e., “dynamic efficiency” gains). One example is found in Spence (1984),
where the links between market structure and industry performance are considered in
terms of both “static allocative efficiency” and “dynamic technical efficiency” channels (Ahn,
2002).
13
Firms need to compete today: in the present time they only look at the static components of
competitiveness. However, in order to remain competitive they need to change, innovate, so
they have to concentrate on the dynamic components of competitiveness. Taking into
account the dynamic aspect of competitiveness also allows better understanding new modes
of competition, especially in more “dynamically competitive” industries (Bresnahan, 1998;
Evans and Schmalensee, 2001; Ellig and Lin, 2001).
In the literature on the gains from competition, the static concept of competitiveness
focuses on comparing relative competitive positions of organisations at a particular point in
time, as per Feurer and Chaharbaghi (1994). The authors propose to consider competition
being constituent of customer values (satisfaction with offerings), shareholder values (profit
potential) and organisations’ ability to act and react within its changing competitive
environment. The later implies financial strength, both short- and long-term, in order to
pursue investment into technology and human capital. Therefore, business strategy
determining choices when moving from current to a stronger competitive position would
imply continuous change, while preserving equilibrium of the whole system, where elements
are of a conflicting nature (consumers and shareholders, for example).
Therefore, considering competitiveness exclusively from a static point of view would neglect
ability of an organization to survive in the long run. For example, lowering prices after a
competitor introduced a new product allow preserving market share. Moreover, neglecting
investment into product improvement via introduction of new technologies and attracting
skilled people would not allow keeping sustainable competitive position high over a longer
period of time.
Research in evolutionary economics, behavioural theory of the firm and transaction costs
economics has led to formulate the concept of dynamic capabilities of firms, which are
tightly related to the ability of firms active in international markets to shape international
environment, thus influencing a nation institutional framework (Dunning and Lundan, 2010).
As the authors adapt from Augier and Teece (2007): “Dynamic capabilities refer to the
(inimitable) capacity firms have to shape, reshape, configure and reconfigure the firm’s asset
base so as to respond to changing technologies and markets…the organization’s (non-
inimitable) ability to sense changing customer needs, technological opportunities, and
competitive developments; but also its ability to adapt to—and possibly even to shape—the
business environment in a timely and efficient manner”.
Therefore, the main difference compared to the static perspective of firms’ competitiveness
is that, even though at a certain point of time a firm may possess superior resources and
competences, it may lead to sustainable growth only if capacity for continuous innovation is
adapted within the organizational structure, which is in continuous interaction with the
business environment.
As Nelson (1996) reminded, Schumpeter’s idea from his Theory of Economic Development
“Static analysis is not only unable to predict the consequences of discretionary changes in
the traditional ways of doing things; it can neither explain the occurrence of such productive
14
revolutions nor the phenomena which accompany them. It can only investigate the new
equilibrium position after the changes have occurred”.
Firms operating in a global environment are constantly exposed to change, and adequate
returns can only be achieved in a sustained manner if the firm is able to adjust to, or to
embrace, change.
d. Construction of indices There exist many methodologies to build multidimensional indices ranging from axiomatic
approaches to multivariate methods. This section reviews some of the most widely used
techniques for index construction.
The most common types of multi-dimensional indices are composite indices. A well-known
example is the Human Development Index (UNDP, 1990-2014) which aggregates through a
geometric mean three dimensions (i.e. life expectancy, education and per capita income)
previously scaled (i.e. by projecting each dimension on a scale ranging from 0 to 1).
Looking at axiomatic approaches, fuzzy sets theory (Zadeh, 1965) has been widely used to
construct indices. The general idea is that the membership to a subgroup is determined by a
function allowing for fuzziness (i.e. it may take any values between 0 and 1 rather than 0 or 1
only). The grades of membership in each dimension need to be then aggregated, generally
through a weighted arithmetic mean (see for instance (Chakravarty, 2006)). Several
application of fuzzy sets theory can be found in the development literature through the
measure of inequality and poverty (see for instance (Basu, 1987); (Chakravarty, 2006);
(Shorrocks & Subramanian, 1994); (Cerioli & Zani, 1990)).
Multivariate methods are another cornerstone to the construction of multidimensional
indices.
In general, when modelling multivariate data, researchers tend to think in terms of individual
observations. Taking the regression approach and for instance the least square
methodology, we seek to minimize the sum of the squared distances between the observed
and the predicted dependent variable for each individual observation. The focus is set on
individual cases and the relation under study is between the independent variable,, and
the dependent variables, .
The Global Competitiveness Index (World Economic Forum 2008-2009) is a good example of
an index that relies on regression methodology. The index incorporates twelve pillars of
economic competitiveness (i.e. institutions, infrastructure, macroeconomic stability, health
and primary education, higher education and training, good market efficiency, labour market
efficiency, financial market sophistication, technological readiness, market size, innovation,
business sophistication). Although the pillars are all meaningful determinants of
competitiveness, their relative importance in explaining competitiveness can vary according
to the specific level of development of each country. To incorporate this fact in the
construction of the final index, the twelve pillars are further regrouped into three sub-pillars
15
according to different levels of development9: the basic requirements subindex, the
efficiency enhancers subindex and the innovation and sophistication factors subindex. First,
specific weights for each subindex are estimated using maximum likelihood by regressing the
level of GDP per capita on the past values of subindices. Then, the final index is built from
aggregating through a weighted average the three sub-pillars for which specific weights have
been estimated according to the stage of development.
Factor analysis is among famous statistical methods to handle multivariate data. Belonging
to the literature on latent variables, the core idea of factor analysis is that it may be possible
to explain a set of observed variables (i.e. indicators) in terms of a lower number of latent
variables (i.e. factors). Each observed indicator is treated as a partial manifestation of a
postulated broader latent variable. Uncovering the relationship among the observed
indicators allows for the measurement of the latent concept.
This methodology differs in spirit from classical regression analysis as it emphasizes
covariances rather than individual cases. Additionally, the relationship under study is the one
linking many dependent observed variables with the objective to uncover something about
the unobserved independent variable that underlies them.
Factor analysis is particularly well suited for the construction of multi-dimensional indices for
various reasons. First, since no indicator is sufficient on its own to predict the underlying
latent variable, factor analysis truly acknowledges multi-dimensionality as essential in the
construction of the final index. Second, factor analysis allows estimating weights (i.e. factor
loadings) associated to each observed indicator in the measurement of the latent factor.
These estimated factor loadings relieve the researcher from subjectively designing the
weighting scheme to follow in the final aggregation step.
Two main types of factor analysis models exist. The first one, Exploratory Factor Analysis
(EFA) does not rely on a particular theoretical model and thus the number of latent variables
present in the data is determined by the estimation (generally using a Kaiser’s criterion).
Additionally, EFA imposes the measurement errors to be uncorrelated among them and that
each indicator relates to each latent factor.
In contrast, the Confirmatory Factor Analysis (CFA) is based on a pre-specified theoretical
model. Through this model, the research sets in advance the number of latent concepts as
well as which observed indicator is influenced by a specific latent variable.
3. Defining Competitiveness
As the review of the literature shows, competitiveness is a multidimensional concept, where
different criteria of competitiveness depend on time and context (Ambastha and Momaya,
2004). Interestingly, only 36 per cent of the variance in firms’ profitability should be
9 Countries are classified in three stages of development according to two criteria. The first criterion is based on
the level of GDP per capita at market exchange rates and the second one is the share of exports of primary goods in total exports to measure the degree to which the economies are factor driven.
16
attributed to the characteristics and actions of firms (McGahan, 1999), as shown by a strand
of the literature focusing on firms’ strategies and resource positions (Bartlett and Ghoshal,
1989; Prahalad and Doz, and 1987; Prahalad and Hamel, 1990) as the real sources of
competitiveness. The environmental factors are also crucial to the competitiveness of the
firm, as shown by Oeij et al (2011), where it is explained that competitiveness arises from an
integral process that goes beyond the boundaries of the single firm and connects employees
and clients/customers in many ways.
In order to summarize multidimensional realities into one single measure of competitiveness
we conceptualise a framework that includes dimensions such as time (punctual or
sustainable), scale (optimal firm size), space (e.g. national or international) and scope. We
organise the different dimensions of competitiveness in the “SME Competitiveness Grid”
(see Figure 1), where we classify the components of firm competitiveness according to how
they affect competitiveness (three pillars of competitiveness) and according to the layer of
the economy at which they intervene (three layers of competitiveness). The structure
proposed in the Competitiveness Grid bridges a gap in existing composite indicators of
competitiveness, that focus on macroeconomic determinants of competitiveness rather than
microeconomic determinants. The importance of macroeconomic determinants is, however,
fully recognized and reflected in the competitiveness grid.
The SME Competitiveness Grid has two core dimensions (Figure 1):
The components of competitiveness, identified as the three pillars: compete,
connect and change. These three pillars reflect traditional static and dynamic notions
of competitiveness. They emphasize the importance of connectivity for
competitiveness in modern economies. The pillars are reflected in the vertical axis of
the grid.
The layer of the economy at which these determinants intervene: firm capabilities,
the immediate business environment and the national environment. These layers are
in line with those identified in related work on competitiveness, but put an explicit
focus on the micro, or firm level, dimension. The layers are reflected in the horizontal
axis of the grid.
How do we populate each cell of the grid in view of the empirical analysis? We draw from
the review of the literature, where we have shown the importance of strong managers, of
meeting quality and sustainability standards and of access to banking services and inputs for
firms to be able to compete and operate today. We proxy these concepts with the following
firm level variables (from the World Bank Enterprise Surveys, data sources are described in
Section 4): a dummy indicating if a firm has a quality certification, another dummy for using
a bank account and the years of manager’s experience. Firms’ ability to compete today is
also determined by its immediate business environment, which determines if the firm can
operate efficiently. We build the immediate business environment variables from the WBES,
as averages/shares within an industry j country c cell. The two proxies included in the IBE to
enable firms to compete are the percentage share of firms experiencing power outages and
the percentage share of firms experiencing losses when shipping to domestic markets in
industry j from country c. These proxies indicate the importance of a reliable administration
17
of electricity and of a reliable network of suppliers to be able to operate and timely buy
inputs. The choice of the industry-country combination is motivated by the possibility that
within the same country different industries are affected differently by similar issues, and
also by the fact that different sectors might perceive the same issue differently. Robustness
checks will be performed to try different combinations, such as location-country. Finally, we
also look at the national environment, which provides to the immediate business
environment the macroeconomic framework to operate. We proxy for it with several
macroeconomic indicators from different sources: the ease of getting electricity (in terms of
procedures required), the ease of trading across the border, the applied tariff rate (to assess
how costly it is to import inputs for production), the logistic performance, the number of
quality standards issued in the country, and the governance index.
With regard to the connect pillar, the review of the literature stresses the importance of
technology to be connected with clients and suppliers, and to be aware of the competitors.
At the firm level, we proxy for firm’s capacity to connect with a dummy indicating if the firm
uses email and another dummy for the use of website. At the IBE level we proxy for the
quality of the IBE to support firms’ connectivity with the share of firms experiencing power
outages in industry j in country c. Power outages, in fact, can hamper the firm ability to use
ICT. Finally, at the macroeconomic level we proxy the institutional support provided to
connectivity at the national level, with the ITC access score and with the Government online
service score.
Finally, the review of the literature also motivates our choice of indicators for the change
pillar. More specifically, it shows how access to credit, talent and innovation affect the
capacity of firms to change and remain competitive over time. At the firm level, we proxy for
firm’s capacity to change with several dummies indicating if the firm provide training to its
employees, if the firms has financial audit, bank financing and a foreign license. At the IBE
level we proxy for the quality of the IBE to support firms’ ability to change to remain
competitive with the percentage share of firms reporting access to finance, business
licensing, and an inadequately educated workforce as an obstacle to their operations.
Finally, to capture how the national framework supports the business environment, and the
firm, we use the ease of getting credit score, the school life expectancy, the ease of starting
a business score, and the resident patent applications and trademark registrations by
country.
The confirmatory factor analysis in this paper requires multidimensional data, which cannot
be sourced by a single dataset. Henceforth, this paper uses several datasets. The firm level
data and the proxies for the immediate business environment are sourced from the World
Bank Enterprise Surveys (WBES)10. This dataset reports the answers from enterprise surveys
deployed on a representative sample of formal firms in the non-agricultural sector, by
10
country. Firms are selected through stratified random sampling (more information on the
data can be found in Dethier, Hirn, & Straub, 2011).
Our analysis retains only the last year available for each country from the cross-section of
firms. We analyse information for 70723 firm observations across 100 countries for the
2006–14 period.
This data is then merged with other macroeconomic datasets from several sources: the World Bank Doing Business Indicators, the World Bank and Turku School of Economics’ Logistics Performance Index, the ISO Survey of Management System Standard Certifications, the World Bank Worldwide Governance Indicators, ITU’s ICT Development Index, UNESCO Institute for Statistics (UIS) and the WIPO. All trade statistics and customs tariff data derive from the ITC Market Analysis Tools. In the appendix, Table 4 provides a description of the variables included in the analysis as well as their source. Additionally, Table 5 summarizes data coverage across world regions, firm size categories, and years.
5. Confirmatory Factor Analysis
a. Empirical framework
We specify our econometric model as a Confirmatory Factor Analysis (CFA) as described in (Bollen, 1989) and (Muthén, 1984).
Equation 1 = + () + = , … ,
where denotes the country, is a ( × 1) vector of intercepts, is a ( × 1) vector of indicators, is a ( × 1) vector of latent factors and is a ( × 1) measurement error vector. As in practice, the model may include both continuous and binary or ordered categorical variables, we specify a function h to link the vector of observed indicators xi to the vector latent factors . The relationship between our vector of latent factors contained in () and the vector of observed indicators is linear when the latter are continuous. In this case, Equation 1 becomes:
Equation 2 = + +
where Λ is a ( × ) matrix of factor loadings. Each particular , where denotes the indicator, contained in the matrix Λ can be seen in
two ways. First, paralleling the traditional regression framework, is the regression
coefficient of interest for the indicator obtained from = + λij + . It can be
interpreted as the expected change in for one unit of change in considering all the
other latent factors as constant. Second, as traditionally in the factor analysis literature, is
called the factor loading of the latent factor on the indicator.
19
In the case of binary or ordered categorical indicators, the relationship between the vector of latent factors and the vector of indicator described in Equation 1 by the function becomes nonlinear. Considering one particular binary indicator , the measurement model is as follows:
Equation 3 = { ≥
<
Equation 4 = + +
The variable is a binary indicator, for instance it could take the value 1 when a firm uses
email in order to communicate with client and suppliers and 0 otherwise. In this specific example, the continuous latent response variable can be interpreted as the propensity of
this specific firm to use emails in its current operations. Finally, the relationship between our latent variable of interest ξj and the continuous latent response variable is linear with the
being the factor loadings.
The stochastic assumptions of the model are the following:
1. () = 0 2. () = 0 3. () = Φ 4. () = Θ 5. (, ) = 0
For the model to be identified, we need to introduce some constraints on the parameters. In the baseline specification, both the covariance matrix of the latent variables Φ as well as Θ are constrained to be identity. In order words, we impose indicators to be orthogonal to each other as well as latent factors to be uncorrelated. Resting on the latter set of stochastic assumptions, a theoretical expression for the variance- covariance matrix of is defined as:
Equation 5 () = ′ +
where is a vector containing all the unknown parameter matrices of the model namely Λ the matrix of factors loadings, Φ the covariance matrix for the latent factors and Θ the covariance matrix for the measurement errors. Estimation: As traditional in the factor analysis literature, we estimate the unknown parameters of the model by maximum likelihood. The fitting function to be maximized takes the form:
Equation 6 = −
20
where is the vector of the sample mean and is the sample covariance matrix and and () are their theoretical counterparts. In order to solve the maximization problem, the estimation method chooses the value of the parameters that best reproduce the sample mean and covariance matrix. Finally, we are constraining the factor loading of the first observed indicator to be one in order to provide a scale for the latent variable in the same units as this particular indicator. In other words, one unit of change in the latent variable leads to one unit of change in the first observed indicator. Prediction of the latent scores:
In the case of linear factor analysis, we use the regression method also known as the
Thompson method to predict the factor scores. Another method often used in the literature
to predict latent variables in the context of factor analysis is the Bartlett’s factor score.
There has been a long debate in the literature on which prediction method is the best. Since
each has some desirable properties, there is no clear answer. For instance, the Bartlett’s
factor score is an unbiased estimate of the latent variable but suffers from being less
accurate in terms of average prediction error compared to the Thompson’s score.
In the case of nonlinear factor analysis, the empirical Bayes method is used to predict latent
factor scores.
b. Results We report the results of the estimation of the factor analysis as specified in Figure 2. We
estimate each pillar (Compete, Connect and Change) separately, through linear factor
analysis. We then predict values for Compete, Connect and Change and aggregate them into
one index of competitiveness through an arithmetic mean.
As discussed previously, we estimate the model by maximum likelihood. To deal with the
substantial amount of missing values, we propose to use a full information maximum
likelihood method implemented in Stata 14 (StataCorp, 2015) as an option to the sem
command. This technique assumes joint normality of all variables as well as the missing
values to be missing at random (MAR) so that maximum likelihood can be coupled with a
simple imputation procedure.
The estimation results of the Competitiveness path diagram are displayed in Table 1. All the
coefficients are reported in their standardized forms with their corresponding robust
standard errors in parenthesis.
Focusing on our first latent concept Compete, we see that all the estimated coefficients (i.e.
the factor loadings) are of expected sign and significant at the 1% level. Notably, all the
variables are positively associated with the Compete pillar expect for the share of firms
experiencing power outages (Power Outages), the share of firms affected by losses when
21
shipping to domestic markets (Shipping losses) or the rate of tariff on imports (Applied tariff
rate).
The variables measuring an enhanced connectivity, as for instance whether a firm uses
emails or a website to communicate with suppliers or clients, are positively associated with
our second latent variable whereas the share of firms reporting to have experienced
electricity as an obstacle to their operations is negatively correlated with our Connect pillar.
Finally, the last column of Table 1 summarizes the estimation results associated with the
Change pillar. Again, we see that all the coefficients are of expected sign and significant at
the 1% level.
Based on the sign of the coefficient as well as their significance, we can conclude that the
variables chosen in each pillars are measuring our concept of Compete, Connect and Change.
c. Relevance of the Competitiveness Index In order to verify that our indices for Compete, Connect and Change, as well as our final
index of Competitiveness, are good measures, we regress each index on a battery of proxies
of competitiveness, those mainly used in the literature: labour productivity (windsorized and
not windsorized), the percentage of inputs of foreign origin used by the firm, the share of total
sales that are exported, and the exporting status.
Table 2 presents the estimation results from the regression of the predicted values for
Compete, Connect and Change (obtained through a factor analysis), on the proxies of
competitiveness. The regression includes country and sector fixed effects, and has robust
standard errors. We find a positive and significant correlation between the three predicted
values and the main proxies of competitiveness.
We then regress the Competitiveness index (built as the arithmetic mean of the three pillars)
on the main proxies of competitiveness. Table 3 shows that the index is positively and
significantly correlated with all proxies. Interestingly, when in column (8) we differentiate
between exporting and non-importing firms, results are maintained for both types of firms.
Similarly, when we split firms by size in column (10), results apply to firms of all sizes. These
results provide further evidence both of the fact that our index is a valid measure of
competitiveness, and that our proposed framework of competitiveness applies to all firms,
independently of their exporting status and of their size.
Finally, we try to verify the quality of our indices by conducting some graphic analysis. Since
it is reasonable to assume that firms in low income countries will be less competitive than
firms in high income countries, on average, we plot the predicted values for Compete,
Connect and Change, as well as the Competitiveness Index (normalized between 0-100 and
averaged by country) on GDP per capita, as for Figure 3. The plots confirm that firms in richer
countries perform better, as expected. More interestingly, Figure 4 shows that this is true for
firms of all size, where the performance of small firms is always lower than that of medium
and large firms, as expected.
22
Most importantly, Figure 4 shows that the performance gap between SMEs and large firms is
higher in lower income countries than in richer countries. This finding is supported by
several reports11, and notably by data available for Latin American and European countries
that have been reported by McDermott, Gerald A. and Pietrobelli, Carlo (2015) in an ITC
working paper12. We can also see that the slope for large firms is flatter than that for SMEs,
suggesting that large firms from poor countries are in a better position to compete with
large firms from developed countries – however – small firms from developing countries are
in no position to compete with small firms from developed countries.
Finally, the positive relationship between our index of Competitiveness and labour
productivity, the classic proxy for competitiveness is confirmed in the plot in Figure 5.
6. Robustness checks (to be completed)
Conclusive remarks
Competitiveness is a multidimensional concept, where different criteria of competitiveness
depend on time and context. In order to summarize multidimensional realities into one
single measure of competitiveness, we conceptualise a framework to capture this multi-
dimensionality. We then tackle the challenging task of summarizing multidimensional
realities into one single measure of competitiveness by building an index of firm
competitiveness. The methodology used in this paper, factor analysis, is particularly well
suited for the construction of the Competitiveness Index, a multi-dimensional index.
Our results suggest that the Competitiveness Index from our confirmatory factor analysis is
positively correlated with commonly used proxies of competitiveness, such as labour
productivity, the probability to export, the percentage of inputs of foreign origin used by the
firm and the share of total sales that were exported. The multidimensional framework we
build proves to be applicable to firms of different size and to both exporting and non-
exporting firms, as shown by the positive relationship between labour productivity and the
index for the different types of firms.
Finally, graphic analysis confirms that firms in richer countries perform better than firms in
low income countries, independently of firm’s size. Interestingly, not only the performance
gap between SMEs and large firms is higher in lower income countries than in richer
countries. The flatter slope of the relationship between competitiveness and GDP for small
11
a) SME Competitiveness Outlook: Connect, Compete and Change for Inclusive Growth (2015). International Trade Centre, Geneva b) Perspectives on Global Development: Boosting Productivity to meet the middle-income challenge (2014). OECD, Paris. c) On the role of productivity and factor accumulation in economic development in Latin America and the Caribbean (2010). Inter-American Development Bank. 12
McDermott, Gerald A. and Pietrobelli, Carlo (2015). SMEs, Trade and Development in Latin America: Toward a new approach on Global Value Chain Integration and Capabilities Upgrading. ITC Working paper. International Trade Centre, Geneva
23
firms indicates that large firms from poor countries are in a better position to compete with
large firms from developed countries, while small firms from developing countries are in no
position to compete with small firms from developed countries.
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SME Competitiveness Grid
La ye
National environment
Figure 2: Competitiveness Path Diagram where observed variables are indicated by rectangles, latent variables by ellipses and measurement errors by circles.
28
0 2 0
e s s
avg_scoreM Fitted values
Competitiveness vs GDP
avg_scorecomp Fitted values
Compete vs GDP
avg_scoreconn Fitted values
Connect vs GDP
avg_scorech Fitted values
Change vs GDP
Figure 5: Competitiveness Indices versus Labour Productivity
0 2 0
e s s
Small Fitted values
Medium Fitted values
Large Fitted values
Competitiveness by size
0 2 0
e s s
avg_scoreM Fitted values
Table 1 : Estimation results for the factor analysis by pillar.
Quality certification 0.133***
(0.0047)
*** p<0.01, ** p<0.05, * p<0.1
Components of Competitveness by Pillar
Compete Connect Change
31
Table 2 : Regression results by pillar, with country and sector fixed effects
(1) (2) (3) (4) (5) (6) (7) (8)
VARIABLES
inputs
LPM Logit Margin
(0.007) (0.007) (0.193) (0.122) (0.002) (0.014) (0.002) (0.009)
Connect 0.062*** 0.063*** 1.104*** 1.026*** 0.023*** 0.182*** 0.027*** 0.065***
(0.002) (0.003) (0.067) (0.043) (0.001) (0.006) (0.001) (0.003)
Change 0.087*** 0.089*** 1.437*** 1.095*** 0.032*** 0.215*** 0.032*** 0.126***
(0.005) (0.006) (0.150) (0.096) (0.002) (0.011) (0.002) (0.007)
Observations 23,351 23,351 16,248 26,453 26,546 26,546 26,546 23,351
R-squared 0.226 0.213 0.254 0.126 0.175
Number of groups 45
Standard errors in parentheses
32
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
VARIABLES
inputs
ln(Lab Prod usd) wind
ln(Lab Prod usd) wind
(0.002) (0.002) (0.050) (0.024) (0.000) (0.003) (0.000) (0.002)
Competitivness*(Exporter) 0.047***
(0.002)
Observations 57,136 57,136 32,987 65,857 66,935 66,935 66,935 57,136 57,136 57,136
R-squared 0.465 0.448 0.214 0.118 0.126 0.470 0.469
Number of groups 96
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
33
Table 4: Description of variables used in the confirmatory factor analysis
34