WP-04-2017.E
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
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.
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; 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 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.
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).
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).
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.
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
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
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).
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.
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.
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
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 (𝐶𝑖 1), Connect (𝐶𝑖
2) and Change (𝐶𝑖 3) (obtained through CFA as described in Section
3), on the proxies of competitiveness.
Equation 1 𝒛𝒊 = 𝜶 + 𝜷𝟏 ∗ 𝑪𝒊 𝟏 + 𝜷𝟐 ∗ 𝑪𝒊
𝟐 + 𝜷𝟑 ∗ 𝑪𝒊 𝟑 + 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.
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Figures Figure 1: The Competitiveness Grid
Competitiveness Grid
La ye
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
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 4: Competitiveness Indices by income: Gap between Large and Small firms
Figure 5: Competitiveness Indices versus Labour Productivity
.5 1
1 .5
e s s G
m_gapLS Fitted values
e s s
avg_scoreM Fitted values
Country Year Observations Percentage share
in tota l
in tota l
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
Sector
Middle East & North Africa 5866 8.29
South Asia 13062 18.47
Sub-Saharan Africa 15494 21.91
Total 70723 100
Table 3: Description of variables used in the confirmatory factor analysis
Variable name Mean sd Source
Firm-level capabilities
Bank account 0.87
Manager's experience 2.68
Access to finance constra int 45.22 17.81
Licens ing constra int 30.55 16.44
Inadequate workforce
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/
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:
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
ICT Access 4.73 ITU, Measuring the Information Society 2014, ICT
Development Index 2014 (2013 data except for
Ta jikis tan, 2008),
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,
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.wipo.int/porta l/en/index.html
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
Starting a business
Trademark regulations
0.839*** (0.0030)
*** p<0.01, ** p<0.05, * p<0.1
30
Table 5 : Estimation results linear factor analysis on the whole model
Components of Competitiveness
Compete Connect Change
Starting a business
Trademark regulations
0.839*** (0.0021)
*** p<0.01, ** p<0.05, * p<0.1
31
Table 6: Estimation results of non-linear factor analysis by pillar
Components of Competitveness by Pillar
Compete Connect Change
Training 1 (constrained)
Starting a business
Trademark regulations
1622.3*** (43.378)
*** p<0.01, ** p<0.05, * p<0.1
32
Table 7 : Regression results by pillar, with country and sector fixed effects
(1) (2) (3) (4) (5) (6)
VARIABLES
(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<0.01, ** p<0.05, * p<0.1
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
wind
(0.005) (0.143) (0.092) (0.001) (0.013) (0.002)
Competitivness*(Exporter) 0.177***
(0.006) (0.158) (0.095)
(0.006) (0.155) (0.094)
(0.006) (0.154) (0.093)
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<0.01, ** p<0.05, * p<0.1
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Street address International Trade Centre 54-56 Rue de Montbrillant 1202 Geneva, Switzerland
P: +41 22 730 0111 F: +41 22 733 4439 E: itcreg@intracen.org www.intracen.org
Postal address International Trade Centre Palais des Nations 1211 Geneva 10, Switzerland
Street address International Trade Centre 54-56 Rue de Montbrillant 1202 Geneva, Switzerland
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