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TRADE IMPACTFOR GOOD
ITC WORKING PAPER SERIES
EXPLORING FIRM COMPETITIVENESS: A FACTOR ANALYSIS APPROACH
WP-04-2017.E
December 2017
Justine FalciolaUniversity of Geneva
Marion Jansen International Trade Centre
Valentina RolloInternational Trade Centre
Disclaimer
Views expressed in this paper are those of the authors and do
not necessarily coincide with those of ITC, UN or WTO. The
designations employed and the presentation of material in this
paper do not imply the expression of any opinion whatsoever on the
part of the International Trade Centre or the World Trade
Organization concerning the legal status of any country, territory,
city or area or of its authorities, or concerning the delimitation
of its frontiers or boundaries. Mention of firms, products and
product brands does not imply the endorsement of ITC or the WTO.
This is a working paper, and hence it represents research in
progress and is published to elicit comments and keep further
debate.
-
© International Trade Centre WP-04-2017.E
ITC Working Paper Series
EXPLORING FIRM COMPETITIVENESS: A
FACTOR ANALYSIS APPROACH
December 2017
Justine Falciola
University of Geneva
Marion Jansen
International Trade Centre
Valentina Rollo
International Trade Centre
Disclaimer
Views expressed in this paper are those of the authors and do
not necessarily coincide
with those of ITC, UN or WTO. The designations employed and the
presentation of
material in this paper do not imply the expression of any
opinion whatsoever on the
part of the International Trade Centre or the World Trade
Organization concerning the
legal status of any country, territory, city or area or of its
authorities, or concerning the
delimitation of its frontiers or boundaries. Mention of firms,
products and product
brands does not imply the endorsement of ITC or the WTO. This is
a working paper,
and hence it represents research in progress and is published to
elicit comments and
keep further debate.
-
Exploring firm competitiveness: a factor analysis approach
Justine Falciola1 Marion Jansen2 Valentina Rollo3
UNIGE ITC ITC
Abstract
This paper uses confirmatory factor analysis (CFA) to build an
index of firm competitiveness and fill
a gap in the literature. The proposed competitiveness framework
and its subcomponents, tested
by the CFA, are identified according to the review of the
economic and management literature and
related empirical evidence. We use data from the World Bank
Enterprise Surveys for 100 countries
of different income and development status. Our results suggest
that the competitiveness index 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. Moreover, the competitiveness
framework proves to apply to
firms of different sizes and to both exporting and non-exporting
firms.
Keywords: competitiveness, factor analysis, latent variable
models, multi-dimensional index, firm heterogeneity
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;
[email protected].
2 Marion Jansen, Chief Economist, Office of the Chief Economist,
International Trade Centre, Palais des Nations,
1211 Geneva 10, Switzerland; e-mail: [email protected].
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: [email protected].
The authors thank Jaya Krishnakumar, Stephan Sperlich, Virginie
Trachel, Olga Solleder and the participants of the UNIGE BBL in
Geneva and the 2016 ETSG Conference in Helsinki for useful comments
and discussions. We thank Yuliya Burgunder for help with the review
of the literature.
mailto:[email protected]:[email protected]:[email protected]
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1. Introduction
This paper uses multilevel confirmatory factor analysis to build
an index of firm
competitiveness across countries.4 This is relevant and
necessary because productivity
remains to date the most commonly used indicator of good
performance and
competitiveness, at both the macro and micro level. Most
importantly, whether productivity
fully represents the performance or competitive strength of
contemporary organizations
remains a subject of discussion: a consensus on what is a good
definition of productivity is still
missing, since no definition really captures all aspects of
production, especially its dynamic
nature.
Production includes both tangible and non-tangible assets, such
as knowledge work and
services (Oeij et al, 2011). This has been taken into account in
work where non-tangible assets
are included in the definition 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
Paulavičiene, 2005) and capacity planning
(McLaughlin and Coffey, 1990; Jääskeläinen and Lönnqvist,
2009).
A part from access to knowledge, firms also need to be able to
absorb capacity (Cohen &
Levinthal, 1990; Kim, 1997). To this end, R&D (Griffith,
Redding, and Van Reenen, 2004,
Fagerberg and Verspagen, 2002), education (or human capital)
(Barro, 1991; Benhabib &
Spiegel, 1994), finance (King & Levine, 1993; Levine, 1997;
Levine & Zervos, 1998), and
governance (Acemoglu, Johnson, & Robinson, 2001; Glaeser et
al., 2004; Rodrik et al., 2004)
play an important role.
The result is a proliferation of combinations of variables to
define productivity.
The concept of competitiveness is not new; it 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 business
ecosystem, remain relatively uniform across all competing firms,
but are crucial to the
4 The use of composite indicators in economics and business is
very commmon, especially in industrial competitiveness, sustainable
development, quality of life assessment, globalisation, innovation
or academic performance (see Cox et al 1992, Cribari-Neto et al
1999, Färe et al. 1994, Griliches 1990, Forni et al. 2001, Huggins
2003, Grupp and Mogee 2004, Lovell et al. 1995, Author 2005, Author
et al. 2005, Saisana and Tarantola 2002, and Wilson and Jones 2002,
among others).
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competitiveness of the firm.5 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 several
dimensions 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 into an index of firm
competitiveness. Since competitiveness is a latent concept, we
use a latent variable model.
The choice of confirmatory factor analysis (CFA) is motivated by
the fact that, based on the
review of the literature and empirical evidence, we hypothesise
a competitiveness framework
that CFA allows us to tests statistically.
CFA differs in 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 known independent variables, 𝑥𝑖, and the
known dependent variable
𝑦𝑖, factor analysis focuses on uncovering and making use of the
relationship (and consequently
correlation) among observed indicators (the independent
variables) in order to measure a
latent concept: competitiveness. Since most of the indicators
included in the CFA are highly
correlated, a multivariate regression analysis would suffer from
multi-collinearity. On the
contrary, CFA explicitly make use of the high correlation
between indicators and is therefore
particularly suited for the construction of our competitiveness
index.
The results suggest that our competitiveness index 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 competitiveness 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 contribution of this paper is therefore twofold. On one side
it contributes to filling a gap
in the attempt to measure competitiveness, until now mainly
proxied with several and open
to discussion measures of productivity. It proposes to measure
competitiveness by building a
composite indicator, using confirmatory factor analysis, so
assuming that competitiveness is
a latent concept that is unknown a priory. On the other side,
this paper provides a first attempt
to measure competitiveness with factor analysis at the firm
level, and it does so by proposing
and testing a competitiveness framework, based on the review of
the economic and
management literature.
The rest of the paper is structured as follows. Section 2
provides a review of the literature,
while Section 3 introduces the confirmatory factor analysis,
including the competitiveness
5 McGahan (1999) argues that only 36 per cent of the variance in
firms’ profitability should be attributed to the characteristics
and actions of firms. This is also argued and shown by Bartlett and
Ghoshal (1989), Prahalad and Doz (1987) and Prahalad and Hamel
(1990).
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framework to be tested and the data. Section 4 test the
relevance of the index in regression
analysis and finally Section 5 concludes.
2. Review of the literature
a. Components of firm competitiveness A multitude of components
can influence the ability of a firm to perform well. These
components (highlighted either in the economic or in the
business literature) can be directly
related to the characteristics of the firm (its innovativeness,
its export status, access to a bank
account, the ability of the manager, etc.), or indirectly affect
the firm through its business
environment. The latest can be further separated into immediate
and macroeconomic
environment, according to whether it is close to the firm
(clients, suppliers, competitors, etc)
or further away (national infrastructure, governance, trade
policy, etc) in terms of connection
and ability to influence.
Moreover, since firms do not only need to compete today, but
rather need to stay competitive
over time, it is important to take into account not only the
static but also the dynamic
components of competitiveness.6 “Productive efficiency” and
“dynamic efficiency” are
increasingly highlighted by the theoretical and empirical
literature on gains from competition.7
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).
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.8
Managerial competence
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.9 The subject has been
extensively developed by management, institutional and
organizational studies, since
Hambrick and Mason (1984) discussed the relevance of managerial
characteristics for
organisational outcomes. 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).
Learning even elementary
management skills in planning, marketing and financial literacy
can lead to an accelerated
6 Especially in more “dynamically competitive” industries
(Bresnahan, 1998; Evans and Schmalensee, 2001; Ellig and Lin,
2001). 7 Spence (1984), Ahn, (2002), Feurer and Chaharbaghi (1994)
8 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 revolutions nor the phenomena which accompany them. It
can only investigate the new equilibrium position after the changes
have occurred”. 9 Porter (1990) defines entrepreneurial and
management skills as the ability to capitalize on ideas and
opportunities by successfully implementing a business strategy.
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adoption of improved management practices, increased willingness
of owners to pay for
follow-up training and increased survival (Sonobe and Otsuka,
2006, 2011). However, variance
in organisational outcomes may be better explained by managers’
characteristics when there
is a higher degree of managerial discretion (Hambrick and
Finkelstein, 1987).10
Years of managers’ experience are found to affect performance as
well. Empirical evidence
shows that managers from an older generation are: more
conservative in terms of investment
choices and use of financial leverage; more likely to undertake
diversification moves and R&D
activities; more associated with higher returns on assets. At
the same time, having an MBA
degree is related to more aggressive strategies and is also
positively associated with higher
firm performance (Bertrand and Schoar, 2003).
Managerial skills also influence the firm’s capacity to
internationalize. The effort to learn
internationally, together with previously acquired international
experience and an open-
minded attitude towards global markets, all positively relate to
internationalization (De Clerq
et al., 2005, Reuber and Fischer, 1997, Kyvik et al., 2013),
being it entry into exporting (Wood
et al., 2015), or the capacity to diversify geographically
(Ciravegna et al., 2014). The structure
of ownership also influences the decision to internationalize.
Fernandez and Nieto (2005)
show that family-owned firms (commonly but not exclusively SMEs)
engage less in
commitment-intensive internationalization activities. However,
when SMEs are managed by
a group of shareholders, which include foreign shareholders,
export propensity increases.
Quality and sustainability standards
Standards, weather national or international, affect the basic
functioning of the firm (ITC,
2016). Adopting standards may increase sales on foreign markets,
improve the image of a
company, or even decrease associate trade costs due to
facilitated custom control regime
(Masakure, Cranfield and Henson, 2011; Latouche and
Chevassus-Lozza, 2015; Volpe
Martincus, Carballo and Graziano, 2015). However, compliance
with resource demanding
standards can require additional investment and financing in
order to adjust the production
process, product labelling, packaging, etc. Consequently,
certification may 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
Nordås van Tongeren, 2007;
Beghin and Marette, 2009).
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. A
review of the literature by Heras, Molina-Azorín and Tarí
(2012)11 shows that the positive
effect of ISO 9001 and ISO 14001 standards are related to:
improved efficiency and
effectiveness of the organization; a reduction of bureaucracy; a
reduction in the costs of
10 A review of the literature devoted to the studies analysing
managerial discretion can be found in Wangrow, Schepker and Barker
(2015). 11
http://upcommons.upc.edu/bitstream/handle/2099/12955/tari.pdf
http://upcommons.upc.edu/bitstream/handle/2099/12955/tari.pdf
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internal and external audits; and the availability of joint
training and improved communication
between all organizational levels.12
However, the decision and possibility to comply with national or
international standards only
partly depends on the firm’s capacities. Compliance also depends
on the infrastructure
available in a country, being it terms of access to finance or
physical infrastructure, or being it
in terms of supportive local or national institutions to provide
information and guidance.
Access to finance
At the macroeconomic level, evidence shows that financial
development matters for output
growth of the economy (Levine and Zervos, 1998), as it also
affects growth potential of credit-
constrained firms (Rajan and Zingales, 1998). In order to
operate, firms need to have a bank-
account to settle accounts with clients and providers quickly
and smoothly. Investment in new
activities further requires access to finance. 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. Access to finance is consistently cited as
one of the primary obstacles
affecting SMEs more than large firms (Ayyagari, Demirgüç-Kunt
and Maksimovic, 2012).
Access to finance is proved to be an important determinant of
firm performance along a number of distinct aspects, including
investment, growth, firm size distribution (Ayyagari,
Demirgüç-Kunt; and Maksimovic, 2011), and innovation
(Demirgüç-Kunt, Beck, and Honohan 2008). It also determines the
firm’s ability to enter export markets and expand abroad (Bellone
et al., 2010; and Berman and Héricourt, 2010), which are capital
intensive efforts, involving high up-front costs (i.e. needed to
create distributor networks) and high variable costs (related to
shipping, logistics and trade compliance).
However, firms’ abilities and capacities are not the only
element determining access to finance. The access to and extension
of credit greatly depends on a supportive legal and regulatory
framework. Coricelli et al. (2010) shows that in countries
characterized by weak financial market institutions and limited
market capitalization, a significant proportion of firms have no
access to bank loans.
Access to talent
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).
Backman (2014) provides
evidence of the link between work force education, experience
and cognitive skills and firm
productivity. Local availability of talented workforce is not
only a strong predictor of
productivity, but also of export diversification (Cadot, Carrère
and Strauss-Kahn, 2011).
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).
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
12 Wilkinson & Dale, 1999a, 1999b; Poksinska et al. 2003;
Zeng, Tian & Shi, 2005; Zutshi & Sohal, 2005
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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 (high) skill levels receive low
(high) skill-intensive tasks (Khalifa
and Mengova, 2012).
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. The business ecosystem and the national environment
can strengthen the
engagement of SMEs in training through cooperation via
horizontal networks. 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
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 customers
(access to market). 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). As a consequence importing can
have a positive effect on
the decision to start exporting and also on the variety of
products exported and success as an
exporter (Kasahara and Lapham, 2006; Bas and Strauss-Kahn,
2014).13
Even though the decision to export depends on the firm, access
to market remains outside of
the firm’s control, as it 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 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, 2003; Melitz
and Ottaviano, 2008).
Firms’ ability to import or export might also be constrained by
logistics. 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
13 The trade literature has vastly proved exporting firm to be
larger, more productive, more capital-intensive, more
technology-intensive and pay higher wages than non-exporting firms
(Bernard, A. B., & Jensen, J. B., 1999; Delgado, Farinas and
Ruano, 2002).
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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. For example, the
quality of roads and transport infrastructure is hardly
attributable to the action of the firm,
but rather to the national or even business ecosystem. 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).
Innovation
Innovative firms have higher levels of productivity and economic
growth (Cainelli, Evangelista
and Savona, 2004). They are also more likely to export, and to
do it successfully (Love and
Roper, 2013; Cassiman, Golovko and Martínez-Ros, 2010). The
capacity to innovate is defined
in different ways: as the ability to generate innovative outputs
(Neely et al., 2001) or as the
ability to continuously transform knowledge and ideas into new
products, processes and
systems (Lawson and Samson, 2001). In both cases, the capacity
to innovate is closely related
to the capability to change.
Innovation, and a firm ability to innovate, is closely related
with the technological capacities
of firms. 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.
Access to networks, platforms, institutions
In all previous areas of firm’s competency, we have highlighted
how forces/determinants
outside the influence of the firm also affect the way firms
performs. Management research
highlights the importance of business-to-business networks
(Schoonjans, Van Cauwenberge
and Vander Bauwhede, 2013), knowledge sharing, complementarity
of resources (Dyer and
Singh, 1998), and effective governance.
Clusters 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) or
between firms and external actors, such as universities or
R&D institutes (Acs, Audretsch and
Feldman, 1994). The use of technology in the firm’s network can
have positive spillovers on
firms’ performance (Paunov and Rollo, 2016).
Firms also 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. This can be resumed in one
word: connection, the ability to be informed about the nature of
and changes in the
competitive environment.
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A good connection to the business ecosystem is particularly
important for SMEs, which
oftentimes are unable to gather relevant business information
(Kitching, Hart and Wilson,
2015; Reid, 1984; Seringhaus, 1987; Christensen, 1991). Help to
gather this information
usually comes from public institutions or private associations.
But it can also come 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).
b. 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
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 membership to a
subgroup is determined by a
function allowing for fuzziness (i.e. it may take any value
between 0 and 1, rather than 0 or 1
only). Later, the grades of membership in each dimension need to
be aggregated, generally
through a weighted arithmetic mean (see for instance
Chakravarty, 2006). Several applications
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. When modelling multivariate data, researchers tend to
think in terms of individual
observations. Taking the regression approach and for instance
the least square methodology,
the aim is 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,𝑦𝑖, and the
dependent variables, 𝑥𝑖.
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 pillars14 of
economic competitiveness. 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
14 The pillars are 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.
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according to different levels of development15: 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 the 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.
Belonging to the literature on latent variables, factor analysis
is a well-known statistical
method to handle multivariate data. The aim of factor analysis
is to explain a set of observed
variables (i.e. indicators) in terms of a lower number of latent
– or unobserved - 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.
The aim is to uncover
information about the unobserved independent variable that
underlies them. In other words,
the aim is to make use of the relationships among observed
indicators to infer something on
the unobserved concept that influences 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 pure exploration of the data.
Additionally, EFA imposes the
measurement errors to be uncorrelated among them and each
indicator to relate to each
latent factor. In contrast, the Confirmatory Factor Analysis
(CFA) is based on a pre-specified
theoretical model. CFA allows the research to set in advance the
number of latent concepts
as well as which observed indicators are influenced by a
specific latent variable. This paper
focuses on CFA, further explained in the next section and in
Appendix I.
15 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.
-
3. Confirmatory Factor Analysis
a. Data 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 standardized World Bank Enterprise Surveys (WBES) is the
main source of data for our
paper.16 The WBES dataset reports the answers from enterprise
surveys deployed on a
representative sample of formal firms in the non-agricultural
sector, by 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. Table 1 reports information on country coverage,
while Table 2 summarizes data
coverage across firm size categories, world regions and income
levels. It shows that the vast
majority of the countries included in the data we analyse are
low and middle income
countries, from all geographic regions. Most firms in the sample
are small firms, firms that
report employing less than 20 full-time workers.
The WBES reports the answers to a wide number of questions on
firms’ characteristics and
obstacles faced by firms in their activities. We use firm level
variables to account for the
capacities of firms to be competitive, and we build proxies for
the quality of the business
ecosystem using firm level variables. We build these variables
from the WBES, as averages or
shares (depending on the type of variable we use) of firm level
answers at the industry j
country c cell, for the latest available year. 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. The industry j is defined using the ISIC code
provided in the WBES dataset.
Table 3 in the Appendix provides a description of the variables
included in the analysis as well
as their source.
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
World Intellectual Property Organization (WIPO). All trade
statistics and customs tariff data derive from the ITC Market
Analysis Tools.
b. The competitiveness framework Confirmatory factor analysis
allows researchers to confirm a model defined a priory. In this
paper, we set up a competitiveness framework based on the review
of the literature
conducted in Section 2, which shows how different criteria of
competitiveness depend on time
and context (Ambastha and Momaya, 2004). Hence, we organise the
different dimensions of
16 Downloaded on January 2016 from
http://www.enterprisesurveys.org/data/survey-datasets
http://www.enterprisesurveys.org/data/survey-datasets
-
competitiveness in the “Competitiveness Grid” (see Figure 1),
where we classify the
components of firm competitiveness according to:
How they affect competitiveness: compete, connect and change.
These three pillars
reflect traditional static and dynamic notions of
competitiveness. The pillars are
reflected in the vertical axis of the grid.
The three layer of the economy at which these components
intervene: firm
capabilities, the business ecosystem and the national
environment. 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.
a) Compete
i. Firm level: the literature has 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 WBES:
a dummy
indicating if a firm has a quality certification, another dummy
for using a bank
account and the years of manager’s experience.
ii. Business ecosystem: 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 of electricity and of a reliable
network of suppliers to
be able to operate and timely buy inputs.
iii. National Environment: it provides to the business ecosystem
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.
b) Connect
i. Firm level: 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.
ii. Business ecosystem: we proxy for its quality 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’s ability to use ICT.
iii. National environment: we proxy the institutional support
provided to
connectivity at the national level with the ITC access score and
with the
Government online service score.
c) Change
-
i. Firm level: 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 this
with several dummies, indicating if the firm provides training
to its employees,
if the firms has financial audit, bank financing and a foreign
license.
ii. Business ecosystem: we proxy for its quality with the
percentage share of firms
reporting access to finance, business licensing, and an
inadequately educated
workforce as an obstacle to their operations.
iii. National environment: 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.
c. Empirical framework We specify our econometric model as a
Confirmatory Factor Analysis (CFA), as described in (Bollen, 1989)
and (Muthén, 1984). 17 The underlying model is presented in Figure
2. In line with our competitiveness framework, we hypothesize a
second-order CFA, where the first latent factor is Competitiveness
itself measured by three latent sub-concepts: Compete, Connect and
Change. We estimate the model following a two-step procedure.
First, each pillar (Compete, Connect and Change) is estimated
separately, through linear factor analysis. Then, we aggregate the
predicted values for each estimated latent pillar (Compete, Connect
and Change) into one single measure of competitiveness, through
arithmetic mean. As traditional in the factor analysis literature,
we estimate the unknown parameters of the model by maximum
likelihood. To identify the model, we constrain the factor loading
of the first observed indicator to be one. 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 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 best. Since each
method 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
it suffers from being less
accurate in terms of average prediction error, compared to the
Thompson’s score.
When we apply the nonlinear factor analysis, we use the
empirical Bayes method to predict
latent factor scores.
d. Results We report the results from the estimation of the
factor analysis specified as for Figure 2. We
estimate each pillar (Compete, Connect and Change) separately,
through linear factor
17 For more details see Appendix.
-
analysis. We then predict values for Compete, Connect and Change
and aggregate them into
one index of competitiveness through an arithmetic mean.
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 4. 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
except for: the share of firms
experiencing power outages (Power Outages), the share of firms
affected by losses when
shipping to domestic markets (Shipping losses) or the rate of
tariff on imports (Applied tariff
rate). This is an indication that the results are in line with
expectations, because increase in
the indicators that are negatively associated with Compete (like
Power Outages) means that
more firms complain about experiencing problems with the
business ecosystem, like having
power outages, an element which usually cuts or reduces
production and daily activities at
the level of any enterprise. Since all indicators related with
the business ecosystem identify
obstacles or constraints, these indicators should not positively
be associated with any of the
pillars of competitiveness. The coefficient of Applied tariffs
is also negative as expected: higher
tariffs on imported goods are an obstacle to the purchase of
inputs.
With regard to the second latent concept, Connect, the variables
measuring an enhanced
connectivity – for instance whether a firm uses emails or a
website to communicate with
suppliers or clients - are positively associated with the 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. Once again this indicates
that the framework proposed is
working in line with expectations and economic literature and
intuition.
Finally, the last column of Table 4 summarizes the estimation
results associated with the third
pillar, Change. Again, we see that all the coefficients are of
expected sign and significant at the
1% level.
As a robustness check, we also estimate the whole model at once,
instead of estimating it using a two steps procedure. The
coefficients, in line with previous results, are reported in Table
5. Finally, to account for the fact that the model includes both
continuous and binary variables, we also perform a nonlinear factor
analysis, as described in (Muthén, 1984). The results,
qualitatively similar to those from the linear factor analysis, are
reported in Table 6.
-
Based on the sign of the coefficients as well as their
significance in Tables 4 to 6, we can
conclude that the variables chosen in each pillars are measuring
our concept of Compete,
Connect and Change.
4. 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 firm i proxies
of competitiveness (𝑧𝑖), those mainly used in the literature:
labour productivity (windsorized,
so as to reduce the outlier bias), the percentage of inputs of
foreign origin used by the firm,
the share of total sales that are exported, and the exporting
status.
Table 7 presents the estimation results from the regression of
the predicted values for
Compete (𝐶𝑖1), Connect (𝐶𝑖
2) and Change (𝐶𝑖3) (obtained through CFA as described in
Section
3), on the proxies of competitiveness.
Equation 1 𝒛𝒊 = 𝜶 + 𝜷𝟏 ∗ 𝑪𝒊𝟏 + 𝜷𝟐 ∗ 𝑪𝒊
𝟐 + 𝜷𝟑 ∗ 𝑪𝒊𝟑 + 𝜸𝒋 + 𝜸𝒄 + 𝜺𝒊
The regression (as per Equation 1) includes country (𝛾𝑐) and
sector (𝛾𝑗) fixed effects, to control
for country c and sector j characteristics that affect all firms
within the same country or sector
equally, and has robust standard errors. We find a positive and
significant correlation between
the three predicted values for Compete, Connect and Change and
the main proxies of
competitiveness (𝑧𝑖).
We then regress the Competitiveness index (𝐶𝐼𝑖) (built as the
arithmetic mean of the three
pillars) on the main proxies of competitiveness.
Equation 2 𝒛𝒊 = 𝜶 + 𝜹𝟏 ∗ 𝑪𝑰𝒊 + 𝜸𝒋 + 𝜸𝒄 + 𝜺𝒊
Equation 3 𝒛𝒊 = 𝜶 + 𝜹𝒆𝒙𝒑 ∗ 𝑪𝑰𝒊 ∗ 𝒆𝒙𝒑 + 𝜹𝒏𝒆𝒙𝒑 ∗ 𝑪𝑰𝒊 ∗ 𝒏𝒆𝒙𝒑 + 𝜸𝒋 +
𝜸𝒄 + 𝜺𝒊
Equation 4 𝒛𝒊 = 𝜶 + 𝜹𝑺 ∗ 𝑪𝑰𝒊 ∗ 𝑺 + 𝜹𝑴 ∗ 𝑪𝑰𝒊 ∗ 𝑴 + 𝜹𝑳 ∗ 𝑪𝑰𝒊 ∗ 𝑳 +
𝜸𝒋 + 𝜸𝒄 + 𝜺𝒊
Once again, we include country and sector fixed effects, and
standard errors are robust (as
per Equation 2). Table 8 shows that the index is positively and
significantly correlated with all
proxies. Interestingly, when in column (7) we differentiate
between exporting and non-
exporting firms (as per Equation 3), results are maintained for
both types of firms. Similarly,
when we split firms by size (as per Equation 4) in columns
(8-10), 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.
Most importantly, Figure 4 shows that the performance gap
between large and small firms is
higher in lower income countries than in richer countries. This
finding is supported by several
reports18, 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
paper19.
The positive relationship between our index of Competitiveness
and labour productivity, the
classic proxy for competitiveness is confirmed in the plot in
Figure 5.
5. Conclusive remarks
Competitiveness is a multidimensional concept, not easy to
define or calculate. Summarizing
several dimensions of competitiveness into one single measure is
a challenging task, but
important and worth trying, since it can allow policy makers to
monitor not only the health of
their firms but also the efficiency of the policies put in place
to help them.
Competitiveness is not a new concept, but to date productivity
remains the most commonly
used way to measure it, at both the macro and micro level.
However, whether productivity
fully represents the performance or competitive strength of
firms or countries remains a
subject of discussion.
This paper proposes to measure competitiveness by shaping its
multi-dimensionality into an
index of firm competitiveness. The first contribution of this
paper is therefore of filling a gap
in the attempt to measure competitiveness, until now mainly
proxied with several and open
to discussion measures of productivity. It does so by proposing
to measure competitiveness
using confirmatory factor analysis.
In order to summarize multidimensional realities into one single
measure of competitiveness,
we conceptualise a framework to capture this
multi-dimensionality. Therefore, the second
contribution of this paper stays in proposing and testing a
competitiveness framework, based
on the review of the economic and management literature.
Our results suggest that the Competitiveness Index from our
confirmatory factor analysis is
positively correlated with commonly used proxies of
competitiveness, such as labour
18 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. 19 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
-
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. As expected, firms in richer
countries perform better than firms in low income countries,
independently of firm’s size.
Interestingly, the performance gap between small and large firms
is higher in lower income
countries than in richer countries.
Even though further research on measuring competitiveness is
needed, our paper proposes
an alternative framework of competitiveness and a way to test
for it. It is the starting point
for further research, on both the empirical and the theoretical
side. In fact, future research
could focus on assessing whether the framework proposed in our
paper can be tested using
different statistical techniques. Finally, theoretical work in
the area of firm competitiveness
should be developed to combine the dynamic and static nature of
competitiveness, as well as
to integrate the business environment of the firm into the
complex and multidimensional
system of forces that shape firms’ performance, position and
direction.
References
Basu, K. (1987). Axioms for fuzzy measure of inequality.
(Elsevier, Ed.) Mathematical Social
Sciences, 14(3), 275--288.
Bollen, A. K. (1989). Structural equations with latent
variables. (W. Interscience, Ed.) Wiley
series in probability and mathematical statistics.
Cerioli, A., & Zani, S. (1990). A fuzzy approach to the
measurement of poverty. (Springer, Ed.)
Income and wealth distribution, inequality and poverty,
272--284.
Chakravarty, S. R. (2006). An axiomatic approach to
multidimensional poverty measurement
via fuzzy sets. A. Lemmi and G. Betti (eds.) Fuzzy Set Approach
to Multidimensional
Poverty Measurement, Springer-Vrlag, New York.
Muthén, B. (1984). A general structural equation model with
dichotomous, ordered
categorical, and continuous latent variable indicators.
(Springer, Ed.) Psychometrika,
49(1), 115--132.
Shorrocks, A. F., & Subramanian, S. (1994). Fuzzy poverty
indices. University of Essex.
StataCorp. (2015). Stata Structural Equation Modeling. (C. S.
Press, Ed.) Reference Manual
Release 13.
UNDP. (1990-2014). Statistics - Huamn Development Reports. Human
Development Reports.
Retrieved from http://hdr.undp.org/en/statistics/
-
Zadeh, L. A. (1965). Fuzzy sets. Information and control,
338--353.
ITC (2015). SME Competitiveness Outlook: Connect, compete and
change for inclusive growth.
International Trade Centre. Geneva.
Garelli, Stephane (2006). Top Class Competitors. How nations,
firms, and individuals succeed in the new
world of competitiveness. John Wiley & Sons, Ltd.
McMillan, John (2008). Market institutions. In The New Palgrave
Dictionary of Economics, 2nd edn,
Larry Blume and Steven Durlauf, ed. Palgrave Macmillan UK.
Biggs, Tayler ( ??). Is Small Beautiful and Worthy of Subsidy?
Leterature review. Mimeo. Available at
http://www.enterprise-development.org/wp-
content/uploads/Is_Small_Beautiful_and_Worthy_of_Subsidy.pdf
Fafchamps, Marcel (2004). Market Institutions in Sub-Saharan
Africa. Theory and Evidence. Cambridge:
MIT Press.
Khanna, Tarun, Krishna G. Palepu and Jayant Sinha (2005).
Strategies that fit emerging markets.
Harvard Business Review, Issue 83 (June), pp. 4–19.
World Economic Forum (2008). The Global Competitiveness Report
2008-2009. Geneva. Available at
https://www.weforum.org/reports/global-competitiveness-report-2008-2009/
Álvarez, Isabel, Raquel Marín and Georgina Maldonado (2009).
Internal and external factors of
competitiveness in the middle-income countries. Working Papers
Series; No. 08/09. Complutense
University of Madrid. Available from:
http://eprints.ucm.es/9570/
Porter, Michael E. The Five Competitive Forces That Shape
Strategy. Harvard Business Review, vol. 86, No. 1 (January), pp.
78–93. Barney, Jay (1991). Firm Resources and Sustained Competitive
Advantage. Journal of Management, vol. 17, No. 1, pp. 99-120.
Dunning, John H. and Sarianna M. Lundan (2010). The institutional
origins of dynamic capabilities in multinational enterprises.
Industrial and Corporate Change, vol. 19, No. 4, pp. 1225–1246.
Augier, Mie and David J. Teece (2007). Dynamic capabilities and
multinational enterprise: Penrosean insights and omissions.
Management International Review, 47(2), pp. 175–192.
Paunov, Caroline and Valentina Rollo (2016). Has the Internet
Fostered Inclusive Innovation in the
Developing World? World Development, vol. 78, pp. 587-609.
Dyer, Jefrey H. and Harbir Singh (1998). The Relational View:
Cooperative Strategy and Sources of
Interorganizational Competitive Advantage. Academy of Management
Review, vol. 23, No. 4, pp. 660-
679.
Schoonjans, Bilitis, Philippe Van Cauwenberge and Heidi Vander
Bauwhede (2013). Formal Business
Networking and SME growth. Small Business Economics, vol. 41,
Issue 1 (June), pp.169-181.
-
Harinder, Singh, Jaideep Motwani and Ashok Kumar (2000). A
review and analysis of the state-of-the-art research on
productivity measurement. Industrial Management & Data Systems,
vol. 100, Issue 5, pp. 234 – 241. Tangen, Stefan (2005),
Demystifying productivity and performance. International Journal of
Productivity and Performance Management, vol. 54, Issue 1, pp. 34 –
46. OECD (2001). Measuring Productivity. Measurement of Aggregated
and Industry-Level Productivity Growth. OECD Manual, OECD
Publishing, Paris. Available at
https://www.oecd.org/std/productivity-stats/2352458.pdf. OECD
(2016). OECD Compendium of Productivity Indicators 2016. OECD
Publishing, Paris. Available at
http://www.oecd.org/std/productivity-stats/oecd-compendium-of-productivity-indicators-22252126.htm.
Bris, Arturo and José Caballero (2016). Revisiting the Fundamentals
of Competitiveness: A proposal. In IMD World Competitiveness
Yearbook 2016. IMD World Competitiveness Center, Lausanne. Feurer,
Rainer and Kazem Chaharbaghi (1994). Defining Competitiveness: A
holistic approach. Management Decision, vol. 32, No. 2, pp. 49-58.
Pakes, Ariel (2015). Empirical Tools and Competition Analysis: Past
Progress and Current Problems. Mimeo. Available at
http://scholar.harvard.edu/files/pakes/files/empiricaltools-10-2015.pdf?m=1444080861.
Nelson, Richard, R. (1996). The source of Economic Growth.
Harvard University Press. Cambridge, Massachusttes/London, England.
Bertrand, Marianne and Antoinette Schoar (2003). Managing with
Style: The effect of managers on
firm policies. The Quarterly Journal of Economics, vol. 118,
Issue 4 (November), pp. 1169-1208.
Brunninge, Olof, Mattias Nordqvist and Johan Wiklund (2007).
Corporate Governance and Strategic
Change in SMEs: The Effects of Ownership, Board Composition and
Top Management Teams. Small
Business Economics, vol. 29, Issue 3 (October), pp. 295-308.
Carpenter, Mason A. and James W. Fredrickson (2001). Top
Management Teams, Global Strategic
Posture, and the Moderate Role of Uncertainty. Academy of
Management Journal, vol. 44, No. 3, pp.
533-545.
Hambrick, Donald C. and Phyllis A. Mason (1984). Upper Echelons:
The Organization as a Reflection of
Its Top Managers. Academy of Management Review, vol. 9, No. 2,
pp. 193-206.
Hambrick, Donald C (2007). Upper Echelons Theory: An update.
Academy of Management Review, vol.
32, No. 2, pp. 334-343.
Goedhuysa, Micheline and Leo Sleuwaegen (2016). International
standards certification, institutional
voids and exports from developing country firms. International
Business Review, in press, available
online 6 May 2016.
http://www.oecd.org/std/productivity-stats/oecd-compendium-of-productivity-indicators-22252126.htmhttp://www.oecd.org/std/productivity-stats/oecd-compendium-of-productivity-indicators-22252126.htm
-
Hudson, John and Philip Jones (2003). International trade in
“Quality Goods”? Signalling Problems for
Developing Countries. Journal of International development, vol.
15, Issue 8, pp. 999–1013.
Berman, Nicolas and Jérôme Héricourt (2010). Financial factors
and the margins of trade: Evidence from cross-country firm-level
data. Journal of Development Economics, vol. 93, Issue 2 (Novmber),
pp. 206-217.
Medina, Leonardo (2012). Spring Forward or Fall Back? The
Post-Crisis Recovery of Firms. IMF Working Paper, No. 12/292,
International Monetary Fund. Available at
https://www.imf.org/external/pubs/ft/wp/2012/wp12292.pdf
Braun, Matias and Borja Larrain (2005). Finance and the Business
Cycle: International, Inter-Industry Evidence. The Journal of
finance, vol. 60, No. 3, pp. 1097-1128.
Levine, Ross and Zervos, Sara. "Stock Markets and Economic
Growth." American Eco- nomic Review, June 1998, 88(3), pp. 537-
58.
Rajan, Raghuram G. and Luigi Zingales (1998). Financial
development and Growth. American Economic Review, vol. 88, No. 3
(June), pp. 559-586.
Coricelli, Fabrizio, Nigel Driffield, Sarmistha Pal and Isabelle
Roland (2010). Excess Leverage and Productivity Growth in Emerging
Economies : Is There a Threshold Effect ? Discussion Paper, No.
4834 (March). The Institue for the Study of Labour (IZA), Bonn,
Germany. Available at http://ftp.iza.org/dp4834.pdf.
Backman, Mikaela (2014). Human capital in firms and regions:
Impact on firm productivity. Papers in Regional Science, vol. 93,
Issue 3 (August), pp. 557–575.
OECD (2013), Skills Development and Training in SMEs, Local
Economic and Employment Development
(LEED), OECD Publishing. Available at
http://www.skillsforemployment.org/wcmstest4/groups/skills/documents/skpcontent/ddrf/mdu0/~
edisp/wcmstest4_054646.pdf
Amiti, Mary and Josef Konings (2007), Trade Liberalization,
Intermediate Inputs, and Productivity:
Evidence from Indonesia. The American Economic Review, vol. 97,
No. 5 (December), pp. 1611-1638.
Şeker, Murat (2012). Importing, Exporting, and Innovation in
Developing Countries. Review of
International Economics, vol. 20, Issue 2 (May), pp.
299-314.
Atalay, Murat, Nilgün Anafarta and Fulya Sarvan (2013). The
relationship between innovation and firm
performance: An empirical evidence from Turkish automotive
supplier industry. Procedia - Social and
Behavioral Science, vol. 75, pp. 226-235.
Cassiman, Bruno, Golovko, Elena and Ester Martínez-Ros (2010).
Innovation, exports and productivity.
International Journal of Industrial Organization, vol. 28, Issue
4 (July), pp. 372-376.
Rubera, G. and Kirca, A., (2012), Firm innovativeness and its
performance outcomes: A meta-analytic
review and theoretical integration, Journal of Marketing, 76(3),
pp.130-147.
http://onlinelibrary.wiley.com/doi/10.1111/pirs.2014.93.issue-3/issuetoc
-
Appendix I: Tables and Figures
Figures Figure 1: The Competitiveness Grid
Competitiveness Grid
Pillars
Capacity to compete Capacity to connect Capacity to change
Laye
rs
‘Firm level’ capabilities
Business ecosystem
National environment
Figure 2: Competitiveness Path Diagram where observed variables
are indicated by rectangles, latent variables by ellipses and
measurement errors by circles.
-
Figure 3: Competitiveness Indices by income
020
40
60
80
10
0
Co
mpe
titiven
ess
0 10000 20000 30000GDP per capita (PPP$)
avg_scoreM Fitted values
Competitiveness vs GDP
02
04
06
08
01
00
Com
pete
0 10000 20000 30000GDP per capita (PPP$)
avg_scorecomp Fitted values
Compete vs GDP
02
04
06
08
01
00
Con
ne
ct
0 10000 20000 30000GDP per capita (PPP$)
avg_scoreconn Fitted values
Connect vs GDP
02
04
06
08
01
00
Cha
ng
e
0 10000 20000 30000GDP per capita (PPP$)
avg_scorech Fitted values
Change vs GDP
-
Figure 4: Competitiveness Indices by income: Gap between Large
and Small firms
Figure 5: Competitiveness Indices versus Labour Productivity
.51
1.5
22.5
3
Co
mpe
titiven
ess G
ap (
Larg
e m
inus S
mall)
0 10000 20000 30000GDP per capita (PPP$)
m_gapLS Fitted values
Competitiveness Gap vs GDP
020
40
60
80
10
0
Co
mpe
titiven
ess
0 1 2 3 4 5Labour Productivity
avg_scoreM Fitted values
Competitiveness vs Labour Productivity
-
Tables Table 1: Data coverage by country and year
Country Year Observations Percentage share
in tota l
Country Year Observations Percentage share
in tota l
Country Year Observations Percentage share
in tota l
Angola 2010 360 0.509 Indones ia 2009 1444 2.042 Poland 2013 542
0.766
Albania 2013 360 0.509 India 2014 9281 13.123 Paraguay 2010 361
0.51
Argentina 2010 1054 1.49 Israel 2013 483 0.683 Romania 2013 540
0.764
Armenia 2013 360 0.509 Jamaica 2010 376 0.532 Russ ian
Federation 2012 4220 5.967
Azerbai jan 2013 390 0.551 Jordan 2013 573 0.81 Rwanda 2011 241
0.341
Burundi 2014 157 0.222 Kazakhstan 2013 600 0.848 Senegal 2014
601 0.85
Burkina Faso 2009 394 0.557 Kenya 2013 781 1.104 Sierra Leone
2009 150 0.212
Bangladesh 2013 1442 2.039 Kyrgyz Republ ic 2013 270 0.382 El Sa
lvador 2010 360 0.509
Bulgaria 2013 293 0.414 Cambodia 2013 472 0.667 Serbia 2013 360
0.509
Bol ivia 2010 362 0.512 Lao PDR 2012 270 0.382 Suriname 2010 152
0.215
Brazi l 2009 1802 2.548 Lebanon 2013 561 0.793 Slovak Republ ic
2013 268 0.379
Barbados 2010 150 0.212 Sri Lanka 2011 610 0.863 Slovenia 2013
270 0.382
Botswana 2010 268 0.379 Lesotho 2009 151 0.214 Sweden 2014 600
0.848
Chi le 2010 1033 1.461 Li thuania 2013 270 0.382 Swazi land 2006
307 0.434
China 2012 2700 3.818 Latvia 2013 336 0.475 Chad 2009 150
0.212
Cote d'Ivoire 2009 526 0.744 Morocco 2013 407 0.575 Tajikis tan
2013 359 0.508
Cameroon 2009 363 0.513 Moldova 2013 360 0.509 Timor-Leste 2009
150 0.212
Colombia 2010 942 1.332 Madagascar 2013 532 0.752 Trinidad and
Tobago 2010 370 0.523
Cape Verde 2009 156 0.221 Mexico 2010 1480 2.093 Tunis ia 2013
592 0.837
Costa Rica 2010 538 0.761 Macedonia 2013 360 0.509 Turkey 2013
1344 1.9
Czech Republ ic 2013 254 0.359 Mal i 2010 360 0.509 Tanzania
2013 813 1.15
Dominican Republ ic 2010 360 0.509 Myanmar 2014 632 0.894 Uganda
2013 762 1.077
Egypt 2013 2897 4.096 Montenegro 2013 150 0.212 Ukra ine 2013
1002 1.417
Estonia 2013 273 0.386 Mongol ia 2013 360 0.509 Uruguay 2010 607
0.858
Ethiopia 2011 644 0.911 Mozambique 2007 479 0.677 Venezuela 2010
320 0.452
Gabon 2009 179 0.253 Mauri tania 2014 150 0.212 Vietnam 2009
1053 1.489
Georgia 2013 360 0.509 Mauri tius 2009 398 0.563 Yemen 2013 353
0.499
Ghana 2013 720 1.018 Malawi 2014 523 0.74 South Africa 2007 937
1.325
Guinea 2006 223 0.315 Nigeria 2014 2676 3.784 Zambia 2013 720
1.018
Gambia 2006 174 0.246 Nicaragua 2010 336 0.475 Zimbabwe 2011 599
0.847
Guatemala 2010 590 0.834 Nepal 2013 482 0.682
Guyana 2010 165 0.233 Pakis tan 2013 1247 1.763
Honduras 2010 360 0.509 Panama 2010 365 0.516
Croatia 2013 360 0.509 Peru 2010 1000 1.414
Hungary 2013 310 0.438 Phi l ippines 2009 1326 1.875
-
Table 2: Data coverage by firm size, sector, income level and
world region
Group Observations Percentage share in total
Size Category
small (
-
Table 3: Description of variables used in the confirmatory
factor analysis
Variable name Mean sd Source
Firm-level capabilities
Qual i ty certi fication 0.26
Bank account 0.87
Manager's experience 2.68
[17]
0.67
Emai l 0.74
Webs ite 0.50
Tra ining 0.40
Fiancia l audit 0.55
Bank financing 0.35
Foreign l icences 0.14
Power outages 59.16 22.16
Shipping losses 17.19 11.86
Obstacle: electrici ty 47.40 21.18
Access to finance constra int 45.22 17.81
Licens ing constra int 30.55 16.44
Inadequate workforce
education
39.14 20.48
Getting electrici ty 63.72
Trading across boarders 58.00
Appl ied tari ff rate 0.09 0.04 ITC, based on data from ITC
Market Analys is
Tools , 2006–2015
(www.intracen.org/marketanalys is ).
Logis tic performance 2.89 World Bank and Turku School of
Economics ,
Logis tics Performance Index 2014,
http://lpi .worldbank.org/
Immediate buisness environment
National environment
Appl ied tari ff rate, trade-weighted mean, a l l products (%).A
tari ff i s a customs duty that i s levied by
the destination country on imports of merchandise goods .
Trade-weighted average tari ff i s ca lculated for each
importing country us ing the trade patterns of
the importing country’s reference group (based on 2013 trade s
tatis tics ). To the extent poss ible,
speci fic rates have been converted to their ad va lorem equiva
lent rates and included in the
ca lculation of weighted mean tari ffs . Preferentia l tari ff
arrangements (tari ff preferences) have been
taken into account.
A multidimens ional assessment of logis tics performance, the
Logis tics Performance Index (LPI),
compares the trade logis tics profi les of 160 countries and
rates them on a sca le of 1 (worst) to 5
(best). The ratings are based on 6,000 individual country
assessments by nearly 1,000 international
freight forwarders , who rated the eight foreign countries their
company serves most frequently.
Percentage share of fi rms identi fying an inadequately educates
workforce as an obstacle to their
current operations .
Doing Bus iness ‘Ease of getting electrici ty’ score (0–100). Al
l procedures required for a bus iness to
obtain a permanent electrici ty connection and supply for a s
tandardized warehouse.
World Bank, International Finance Corporation,
Doing Bus iness 2014:
Understanding Regulations for Smal l and
Medium-Size Enterprises ,
http://www.doingbus iness .org/
methodologysurveys/Doing Bus iness ‘Ease of trading across
borders ’ score (0–100). The indcator measures the time and
cost (excluding tari ffs ) associated with exporting and
importing a s tandardized cargo of goods by sea transport.
A dummy equals to one i f the fi rm has a l ine of credit or a
loan from a financia l insti tution.
A dummy equals to one i f the fi rm uses technology l icensed
from a foreign-owned company,
Percentage share of fi rms experiencing power outages in
industry j of country c. Authors ' own ca lculation;
Fi rm level data source:
Enterprise Surveys
(http://www.enterprisesurveys .org),
The World Bank (2005–2014)
Percentage share of fi rms experiencing losses when shipping to
domestic markets in industry j of
Percentage share of fi rms experiencing electrici ty as being an
obstacle to their current operations .
Percentage share of fi rms reporting access to finance as an
obstacle to their current operations .
Percentage share of fi rms identi fying buisness l icens ing and
permits as an obstacle to their current
Description
A dummy equals to one i f the fi rm has an international
ly-recognized qual i ty certi fication. Enterprise Surveys
(http://www.enterprisesurveys .org),
The World Bank (2005–2014)
A dummy equals to one i f the fi rm has a checking or savings
account.
Logari thm of years of the managers ’ experience [years of
managers experience]
A dummy equals to one i f the fi rm uses emai l to communicate
with cl ients or suppl iers
A dummy equals to one i f the fi rm has i ts own webs ite.
A dummy equals to one i f the fi rm offers formal tra ining
programs for i ts permanent, ful l -time
A dummy equals to one i f the fi rm had i ts annual financia l s
tatements checked and certi fied by an
external auditor.
-
ISO qual i ty s tandards 21,386 65,497 ISO, The ISO Survey of
Management System
Standard Certi fications , 2013, www.iso.org
Governance -0.37 World Bank, Worldwide Governance Indicators
(2014),
http://info.worldbank.org/governance/wgi/inde
x.aspx#home
ICT Access 4.73 ITU, Measuring the Information Society 2014,
ICT
Development Index 2014 (2013 data except for
Ta jikis tan, 2008),
http://www.itu.int/en/ITU-
D/Statis tics/Pages/publ ications/mis2014.aspx
Government onl ine service 0.47 UNPAN, e-Government Survey 2014,
http://
www2.unpan.org/egovkb/
Getting credit 19.87 World Bank, Ease of Doing Bus iness Index
2014,
Doing Bus iness 2014,
http://www.doingbus iness .org/reports/global -
reports/doing-bus iness-2014
School l i fe expectancy 12.56 2.46 UNESCO Insti tute for Statis
tics (UIS), 2001–2013,
http://stats .uis .unesco.org
Starting a bus iness 80.36 World Bank, Ease of Doing Bus iness
Index 2014,
Doing Bus iness 2014,
http://www.doingbus iness .org/methodology/st
arting-a-bus iness
Patent appl ications 65.47 141.44 WIPO, 2000–2013,
http://www.wipo.int/porta l/en/index.html
Trademark regulations 611.93 654.1 WIPO, 2004–2013,
http://www.wipo.int/porta l/en/index.html
Doing Bus iness ‘Ease of getting credit’ score (0–100). The
index measures the legal rights of
borrowers and lenders with respect to secured transactions
through one set of indicators and the
sharing of credit information through another.
School l i fe expectancy, primary to tertiary education (years
). Total number of years of school ing that
a chi ld of a certa in age can expect to receive in the future,
assuming that the probabi l i ty of his or
her being enrol led in school at any particular age is equal to
the current enrolment ratio for that
age.
Doing Bus iness ‘Ease of s tarting a bus iness ’ score (0–100).
The index measures the number of
procedures , time and cost for a smal l and medium-s ize l
imited l iabi l i ty company to s tart up and
formal ly operate.
Res ident patent appl ications , equiva lent count by appl
icant’s origin (per mi l l ion people). Patent
fi l ings made by appl icants at their home office (national or
regional ), a lso ca l led domestic
appl ications . Appl ications at regional offices are equiva
lent to multiple appl ications , one in each
of the s tate members of those offices , therefore each appl
ication is multipl ied by the
corresponding number of member s tates , except for the European
patent Office (EPO) and the
African Regional Intel lectual Property Organization (ARIPO),
for which des ignated countries are not
known, in which case each appl ication is counted as one appl
ication abroad i f the appl icant does
not res ide in a member s tate; or as one res ident and one appl
ication abroad i f the appl icant
res ides in a member s tate.
Res ident trademark regis trations , equiva lent class count by
appl icant’s origin (per mi l l ion people).
Number of "ISO 9001:2008 Qual i ty management systems" certi
ficates i ssued (per mi l l ion people).
Governance index. Average score over s ix dimens ions of
governance: voice and accountabi l i ty,
pol i tica l s tabi l i ty and absence of violence, government
effectiveness , regulatory qual i ty, rule of law,
and control of corruption.
ICT access sub-index score (0–10). Compos ite index that weights
five ICT indicators (20% each): (1)
Fixed-telephone subscriptions per 100 inhabitants ; (2) Mobi
le-cel lular telephone subscriptions per
100 inhabitants ; (3) International Internet bandwidth (bi t/s )
per Internet user; (4) Percentage of
households with a computer; and (5) Percentage of households
with Internet access .
Government’s onl ine service index score (0-1). Each country’s
national webs ite i s assessed for
content, features , access ibi l i ty and uptake, including the
national centra l porta l , e-services porta l ,
and e-participation porta l as wel l as the webs ites of the
related minis tries of education, labour,
socia l services , health, finance, and environment, as appl
icable.
-
29
Table 4: Estimation results for the linear factor analysis by
pillar.
Components of Competitveness by Pillar
Compete Connect Change
Firm
leve
l
Quality certification 0.130*** (0.0042 )
Email 0.426*** (0.0044)
Training 0.169*** (0.0043)
Bank account 0.166*** (0.0045)
Website 0.369*** (0.0045)
Financial audit 0.022*** (0.0044)
Manager's experience 0.149*** (0.0041)
Bank financing 0.154*** (0.0042)
Foreign licences 0.030*** (0.0054)
Bu
sin
ess
eco
syst
em
Power outages -0.636*** (0.0031)
Obstacle: electricity
-0.593*** (0.0029)
Access to finance constraint
-0.501*** (0.0047)
Shipping losses -0.111*** (0.0060)
Licensing constraint
-0.458*** (0.0053)
Inadequate workforce education
-0.049*** (0.0066)
Nat
ion
al E
nvi
ron
me
nt
Getting electricity 0.721*** (0.0029)
ICT Access 0.802*** (0.0026)
Getting credit 0.421*** (0.0039)
Trading across boarders
0.745*** (0.0033)
Government online service
0.712*** (0.0027)
School life expectancy
0.838*** (0.0021)
Applied tariff rate -0.562*** (0.0030)
Starting a business
0.447*** (0.0039)
Logistic performance 0.562*** (0.0032)
Patent applications
0.604*** (0.0035)
ISO quality standards 0.093*** (0.0020)
Trademark regulations
0.839*** (0.0030)
Governance 0.842*** (0.0022)
Observations 70723 70723 70723
Robust standard errors in parentheses
*** p
-
30
Table 5 : Estimation results linear factor analysis on the whole
model
Components of Competitiveness
Compete Connect Change
Firm
leve
l
Quality certification 0.130*** (0.0041 )
Email 0.426*** (0.0038)
Training 0.169*** (0.0041)
Bank account 0.166*** (0.0042)
Website 0.369*** (0.0039)
Financial audit 0.022*** (0.0042)
Manager's experience 0.149*** (0.0041)
Bank financing 0.154*** (0.0041)
Foreign licences 0.029*** (0.0049)
Bu
sin
ess
eco
syst
em
Power outages -0.636*** (0.0030)
Obstacle: electricity
-0.593*** (0.0032)
Access to finance constraint
-0.501*** (0.0035)
Shipping losses -0.111*** (0.0050)
Licensing constraint
-0.458*** (0.0038)
Inadequate workforce education
-0.049*** (0.0045)
Nat
ion
al E
nvi
ron
me
nt
Getting electricity 0.721*** (0.0027)
ICT Access 0.802*** (0.0026)
Getting credit 0.421*** (0.0039)
Trading across boarders
0.745*** (0.0026)
Government online service
0.712*** (0.0027)
School life expectancy
0.838*** (0.0019)
Applied tariff rate -0.562*** (0.0030)
Starting a business
0.447*** (0.0038)
Logistic performance 0.562*** (0.0030)
Patent applications
0.604*** (0.0035)
ISO quality standards 0.093*** (0.0041)
Trademark regulations
0.839*** (0.0021)
Governance 0.842*** (0.0018)
Observations 70723 70723 70723
Robust standard errors in parentheses
*** p
-
31
Table 6: Estimation results of non-linear factor analysis by
pillar
Components of Competitveness by Pillar
Compete Connect Change
Firm
leve
l
Quality certification 1 (constrained) Email 1 (constrained)
Training 1 (constrained)
Bank account 1.831*** (0.0741)
Website 0.369*** (0.0045)
Financial audit
0.128*** (0.0244)
Manager's experience 0.339*** (0.0143)
Bank financing
0.931*** (0.0335)
Foreign licences
0.237*** (0.0429)
Bu
sin
ess
eco
syst
em
Power outages -49.31*** (1.5866)
Obstacle: electricity
-10.92*** (0.1719)
Access to finance constraint
-25.62*** (0.7346)
Shipping losses -4.50*** (0.2731)
Licensing constraint
-21.60*** (0.6609)
Inadequate workforce education
-2.912*** (0.4037)
Nat
ion
al E
nvi
ron
me
nt
Getting electricity 47.67*** (1.5200)
ICT Access 1.197*** (0.0187)
Getting credit
24.32*** (0.7090)
Trading across boarders
53.86*** (1.7674)
Government online service
0.118*** (0.0017)
School life expectancy
5.851*** (0.1604)
Applied tariff rate -0.082*** (0.0028)
Starting a business
13.10*** (0.4006)
Logistic performance 0.653*** (0.0204)
Patent applications
254.2*** (6.6163)
ISO quality standards 20597.2*** (775.55)
Trademark regulations
1622.3*** (43.378)
Governance 1.611*** (0.0518)
Observations 70723 70723 70723
Robust standard errors in parentheses
*** p
-
32
Table 7 : Regression results by pillar, with country and sector
fixed effects
(1) (2) (3) (4) (5) (6)
VARIABLES
ln(Lab Prod usd) wind
Percentage of imported
inputs
Percentage of sales
exported
Exporter Exporter Exporter
LPM Logit Margin
Compete 0.041*** 1.112*** 0.681*** 0.021*** 0.127***
0.019***
(0.007) (0.206) (0.128) (0.002) (0.014) (0.002)
Connect 0.062*** 1.107*** 1.028*** 0.023*** 0.183***
0.027***
(0.003) (0.069) (0.042) (0.001) (0.006) (0.001)
Change 0.087*** 1.439*** 1.096*** 0.032*** 0.215*** 0.032***
(0.006) (0.159) (0.100) (0.002) (0.011) (0.002)
Observations 23,351 16,248 26,453 26,546 26,546 26,546
R-squared 0.226 0.254 0.126 0.175
Robust standard errors in parentheses
*** p
-
33
Table 8 : Regression results for the competitiveness index
(arithmetic mean) country and sector fixed effects.
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
VARIABLES
ln(Lab Prod usd)
wind
Percentage of imported
inputs
Percentage of sales
exported
Exporter Exporter Exporter ln(Lab Prod usd)
wind
ln(Lab Prod usd)
wind
Percentage of
imported inputs
Percentage of sales
exported
LPM Logit Margin
Competitivness 0.191*** 3.493*** 2.981*** 0.073*** 0.541***
0.080***
(0.005) (0.143) (0.092) (0.001) (0.013) (0.002)
Competitivness*(Exporter) 0.177***
(0.006)
Competitivness*(Non Exporter) 0.172***
(0.006)
Competitivness*(Small) 0.169*** 3.039*** 1.952***
(0.006) (0.158) (0.095)
Competitivness*(Medium) 0.171*** 3.065*** 2.008***
(0.006) (0.155) (0.094)
Competitivness*(Large) 0.174*** 3.125*** 2.181***
(0.006) (0.154) (0.093)
Prob > F 0.00 0.00 0.00 0.00
Observations 23,351 16,248 26,453 26,546 26,546 26,546 23,351
23,351 16,248 26,453
R-squared 0.225 0.254 0.126 0.174 0.232 0.229 0.257 0.157
Robust standard errors in parentheses
*** p
-
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