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Numerous studies handle analyses of revealed comparative advantages of global trade (especially in agriculture sector) using Balassa index, but the selected automobile industry represents new potentials to study. This study focuses on the competitiveness of automobile industry, which is a key sector due to its high value-added activities, a competitive market, with increasing technology requirements and high employment characteristics. The aim of our paper is to analyse the revealed comparative advantages of global automobile trade as well as the duration and stability of Balassa indices by applying Markov transition probability matrices and Kaplan-Meier survival function. The source of data is global automobile exports at HS6 level for 1997-2016. The paper has reached numerous conclusions. First, by analysing characteristics of global automobile trade, it turned out that China, USA, Japan and Germany were the biggest producers of cars, however the top exporters were Germany, Japan and Canada in the period analysed, together giving 40% of all products exported - the top10 countries, however, gave 71% of concentration. Second, our analysis has made it clear that the most traded/exported automobile product is vehicle with only sparkling ignition internal combustion (1500-300cm3) (870323) globally, giving more than 40% of all vehicle exports between 1997 and 2016. Third, the calculation of Balassa indices showed that Spain and Japan had highest comparative advantages in all periods analysed among the most important automobile exporters in the world.
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ECONOMIC ANNALS, Volume LXIII, No. 218 / July – September 2018
UDC: 3.33 ISSN: 0013-3264
* Corvinus University of Budapest, Hungary, email:
** Corvinus University of Budapest, Hungary, email: zso
ABSTRACT: Numerous studies handle
analyses of revealed comparative advan-
tages of global trade (especially in agricul-
ture sector) using Balassa index, but the
selected automobile industry represents
new potentials to study. is study focuses
on the competitiveness of automobile in-
dustry, which is a key sector due to its high
value-added activities, a competitive mar-
ket, with increasing technology require-
ments and high employment characteris-
tics. e aim of our paper is to analyse the
revealed comparative advantages of global
automobile trade as well as the duration
and stability of Balassa indices by apply-
ing Markov transition probability matrices
and Kaplan-Meier survival function. e
source of data is global automobile exports
at HS6 level for 1997-2016. e paper has
reached numerous conclusions. First, by
analysing characteristics of global automo-
bile trade, it turned out that China, USA,
Japan and Germany were the biggest pro-
ducers of cars, however the top exporters
were Germany, Japan and Canada in the
period analysed, together giving 40% of all
products exported – the top10 countries,
however, gave 71% of concentration. Sec-
ond, our analysis has made it clear that the
most traded/exported automobile product
is vehicle with only sparkling ignition in-
ternal combustion (1500-300cm3) (870323)
globally, giving more than 40% of all vehicle
exports between 1997 and 2016. ird, the
calculation of Balassa indices showed that
Spain and Japan had highest comparative
advantages in all periods analysed among
the most important automobile exporters
in the world.
KEY WORDS: revealed comparative ad-
vantage, automobiles, trade, stability, du-
Judit Nagy*
Zsóa Jámbor**
In the modern world the topic of competitiveness has been a focus of attention
for decades, and is based on numerous definitions, interpretations, and
approaches. Competitiveness can be interpreted according to level; for example,
it has been grouped according to micro, meso, and macro levels. Micro-level
competitiveness exists at the firm level and is “the ability to produce
products/services that people will purchase over those of competitors
(Wijnands, Bremmers, Van Der Meulen and Poppe 2008, p.3). According to
Domazet (2012, p.294–295), competitiveness “resides in the ability of firms to
consistently and profitably produce products that meet the requirements of an
open market in terms of price, quality, etc.” In summary, we can conclude that
at the micro-level the meaning of competitiveness is very closely linked to the
creation of dual value, a process that includes both the consumer’s and the
owner’s value (Chikán 2008a, p.28). Meso-level competitiveness is mainly linked
to regional competitiveness. According to Kitson, Martin, and Tyler (2004,
p.992), “regional (and urban) competitiveness might be defined as the success
with which regions and cities compete with one another in some way”. Gorton,
Hubbard, and Fertő (2013, p.4) define regional competitiveness as “the ability to
offer an attractive and sustainable environment for firms and residents to live
and work”. Thus, meso-level competitiveness is situated between micro- and
macro-level competitiveness and cannot be interpreted as the aggregation of
several firms in a given area, but is a more complex phenomenon. Macro-level
competitiveness, or national competitiveness, is “the capability of a national
economy to operate ensuring the increasing welfare of its citizens and the
sustainable growth of its factor productivity. This capability is realized through
maintaining an environment for its companies and other institutions to create,
utilize and sell goods and services meeting the requirements of global
competition and changing social norms” (Chikán 2008b, p. 25).
According to Porter (1990), a nation’s competitiveness depends on four
interconnected factors (diamond model): factor conditions, demand conditions,
related and supporting industries, and firm strategy, structure, and rivalry.
‘Factor conditions’ refer to the labour pool, local raw material sources, and
everything that is important for effective production. ‘Demand conditions’
cover the characteristics of demand for the end product in the domestic market:
if customer needs are sophisticated they can pressure companies to higher
standards. ‘Related and supporting industries’ that are globally competitive
constitute a stable background for downstream industries. They can supply
Economic Annals, Volume LXIII, No. 218 / July – September 2018
cost-effective, high-quality inputs and are sources of innovation. ‘Firm strategy,
structure, and domestic rivalry’ can also determine a nation’s competitiveness.
National circumstances specify how a company is created; domestic traditions
define management style; and domestic rivalry pressurizes firms to be cost
effective, innovative, and customer-oriented. Domestic rivalry can be even
greater when geographic concentration is high. In Porter’s opinion, government
has a role in creating an environment in which companies can achieve
competitive advantage. Direct state development programmes can only succeed
in nations which are in the early stages of the development process.
The national level of competitiveness is strongly tied to international trade
theory, which aims to explain why nations trade with each other. The most
influential work in this area was by David Ricardo in 1817, who produced the
theory whereby nations focus on the production and trade of products with
comparative advantage.
As evident from the literature, there are various different methods for
measuring competitiveness. Bhawsar and Chattopadhyay (2015) provide an
excellent review on the different methods of measuring competitiveness. On the
national level, competitiveness can be measured by national productivity,
balance of trade, labour productivity, and foreign exchange rate. As our paper
concentrates on trade-based macro-level competitiveness we have opted to use
the Balassa index, which captures competitiveness through trade flows.
Based on the Ricardian theory of comparative advantage, Balassa (1965) created
an index of revealed comparative advantage. This study analyses revealed
comparative advantage in the global automobile trade, which, to the best to our
knowledge, is currently missing from the literature. It contributes to the existing
literature in three ways. First, it applies the theory of revealed comparative
advantage to a key manufacturing industry. Second, it analyses a product which
is economically important, as automobiles represent a key sector in the global
economy. Third, the paper identifies the factors lying behind comparative
advantage in car manufacturing.
The paper is structured as follows. After this short introduction, Section 2
presents a short review of the automobile sector, including the relevant
empirical research. Section 3 describes the methodology and the data used.
Section 4 introduces the key findings regarding the global automotive trade’s
descriptive statistics. Section 5 summarizes the patterns and stability of the
comparative advantage, presents the main conclusions, and lists future research
In our paper we investigate the global competitiveness factors of the automotive
industry and identify which countries have a comparative export advantage and
what this advantage is based on.
In the past three decades there have been huge changes in both the organisation
and the geography of production (Pavlinek 2015). The industry can be broken
down geographically into the following regions: Europe (excluding Russia);
North America (Canada, USA, Mexico); Japan and South Korea; the BRICs
(Brazil, Russia, India, China); and the rest of the world (RoW). In 2007 the
BRICs and RoW accounted for approximately 30% of global profits, rising to
60% in 2012 (Mohr et al. 2013).
In the last decades the most important tendencies in the industry have been the
introduction of modular production, a reduction in the number of direct
suppliers, and organising the production network on a macro-regional scale
(Pavlinek 2015). Different regions used to have different competitive potentials.
In North America, automotive manufacturers and their suppliers tended to
cluster together in industrial cities such as Detroit, which was advantageous
because of the strong local base of specialized suppliers (Krugman 1993;
Sturgeon, Van Biesebroeck and Gereffi 2008), while demand was also close.
Now North American car manufacturers can exploit low-cost production in
Mexico, which is still close to the end market.
In Europe car manufacturers face fierce competition, which at the end of the
20th century forced them to expand into former socialist countries in Central
and Eastern Europe. While Western European automakers kept innovation
activity and high value-added production for themselves, the CEE region
attracted manufacturers with its skilled workers, cheap wages, and close
geographic proximity to Western European markets. Also, as living standards
grew, Central and Eastern Europe became a relevant market (Pavlínek 2015;
Jürgen-Krzywdzinski 2009).
In the automotive industry, reaching economies of scale is a prerequisite for
keeping production costs as low as possible. Therefore, many developing
Economic Annals, Volume LXIII, No. 218 / July – September 2018
countries try to protect the industry with direct or indirect import barriers
and/or state-initiated development programmes, which is how the Japanese and
South Korean automotive industries were established.
The Japanese car industry was developed after WWII. Thanks to its production
methods, specific company structure, and state subsidies, exports to the USA
rose from 300,000 cars in the 1960s to 11 million cars in the 1980s (Sturgeon et
al. 2008). Dyer (1996) finds that Japanese automakers have more specialized
suppliers than in the US, and Toyota assembly plants, for example, are
geographically concentrated, which results in a more specialized and productive
value chain than at Nissan or General Motors.
South Korea’s government-led economic development programme was one of
the most successful. In 1962 South Korea introduced the Automotive Industry
Protection Law, which prohibited the importation of complete cars but
exempted parts and components. In the 1970s the oil crisis endangered the
entire industry and the escape route was to restructure and reorient the industry
and expand production to reach economies of scale. This was not feasible
without exports, which South Korea targeted at the most lucrative market, the
USA. At the end of the 20th century, Korea was selling 80% of its exports in the
U.S. and was exporting half of the cars it manufactured (Green 1992).
In the early 1990s, India also started a state development programme for its
automotive industry. The country has the advantage of a skilled and educated
English-speaking workforce, but it also has poor infrastructure, a complicated
tax structure, and inflexible labour laws, which outmatch the labour cost
advantage (Narayanan-Vashisht 2008).
Chinese economic growth was driven by FDI and labour productivity growth,
which were 1.5 times higher than in India. Car-makers and Tier 1 suppliers very
quickly reached world standard; however, the lower stages (Tiers 2 and 3) are
still below global standards. China also uses tariff and quota policies, coupled
with local content regulation. The Chinese auto industry is very fragmented, the
quality is low, and improvement is needed in technological and managerial
skills (Narayanan-Vashisht 2008). However, internal demand for cars is
increasing, the price of the final product is cost-competitive, and in 2012 half
the global sales growth came from China (Mohr et al. 2013).
In the 1970s the Philippine automotive industry was also supported by the state,
which implemented several development programmes (Aldaba 2007). Thanks to
these, the Philippine automotive sector is competitive with other ASEAN
countries in terms of product quality and delivery, but not in terms of price. A
skilled and cheap workforce cannot offset the unavailability of raw materials
(resulting in expensive imports), strong unions, strikes, and weak internal
Although the Republic of South Africa is a relatively small market for
automakers, the state recognised its potential and promoted exports through
subsidies. Exports started to grow dynamically, new investors entered the
industry, and the parts and components industry also grew. Thanks to the
state’s development strategy the sector is competitive with Western Europe,
except regarding delivery flexibility because of the large geographical distance
(Barnes, Kaplinsky, and Morris 2004).
Porter’s ‘diamond’ model says that factors of competitive advantage depend on
each other. From the previous synopsis we can see that nations try to exploit
more than one factor at the same time. In North America, factor conditions,
supporting industries, and domestic rivalry force companies to fight for
competitive advantage. In Europe the drivers of competitive advantage are
factor conditions, demand conditions, and supporting industries. Japan and
South Korea have a strong supporting industrial base, specific and successful
management methods, and considerable domestic demand, while domestic
factors are also important. The BRICs and the RoW base their competitive
advantage on factor conditions, while state programmes to develop supporting
industries and domestic demand are tending to increase in these countries.
Previous empirical research on revealed comparative advantage in trade is vast
and varies in scope and focus. Abidin and Loke (2008) examine Malaysian
export data (for several sectors) and conclude that the country has a
comparative advantage in electric, electronic, and machinery products but not
in automobile goods. Cinicioglu, Önsel and Ülengin (2012) use the Bayesian
network model to identify Turkey’s competitiveness. They find that the
competitiveness of countries that develop technology is higher than that of
countries that only buy technology. Spatz and Nunnenkamp (2002) analyse the
revealed comparative advantage of Germany, Japan, and US as key automobile
manufacturing countries globally, and conclude that globalisation negatively
affected the comparative advantage of these countries, especially of the US.
Economic Annals, Volume LXIII, No. 218 / July – September 2018
The paper is based on the revealed comparative advantage index, originally
defined by Balassa (1965) as follows:
where X indicates exports, i means a given country, j is a given product, t is a
group of products, and n stands for a group of countries. Consequently, the
index can be calculated by comparing a given country’s export share of a certain
commodity in its total exports with the export share of the commodity in total
exports of a reference group of countries. If the value of the RCA index is higher
than 1 the country has a comparative advantage compared to the reference
countries; if it is 1 or lower it has a revealed comparative disadvantage.
The original index is criticised for many reasons, especially because of its
asymmetry and because it neglects the various effects of economic policies. The
problem of asymmetry stems from the fact that its value varies from only zero to
one for products/sectors in which the country has a revealed comparative
disadvantage, but from one to infinity for those with comparative advantage,
thereby overestimating sectors’ relative weight. Moreover, government
intervention – especially protectionist policies – strongly affects international
trade and associated markets, but the RCA index does not measure this impact.
Vollrath (1991) suggested three other specifications of comparative advantage to
solve the above problems. First, he introduced a revealed comparative advantage
index for imports (RMA), substituting export values with import values in the
original index as follows:
As opposed to the RCA index, RMA values below one mean comparative
advantage, thereby solving the problem of asymmetry. The second index
suggested by Vollrath (1991) is the revealed trade advantage index (RTA), which
is a simple transformation of the first and second equations:
RTAij = RXAij – RMAij
Positive values here imply comparative advantage. Third, Vollrath (1991)
created a revealed competitiveness index (RC) by taking the natural log of the
RCA and RMA indices as follows:
RCij = ln RXAij – ln RMAij (4)
The RC index is symmetric to zero and positive values mean revealed
competitiveness. Dalum et al. (1998) also tried to solve the asymmetric value
problem of the original Balassa index and created the Symmetric Comparative
Advantage (RSCA) index:
The RSCA takes values between –1 and 1, where positive values indicate a
comparative advantage in exports, and values between –1 and 0 a comparative
Proudman and Redding (1998) suggested weighting the original Balassa index
by the number of products (N) as follows:
Following their idea, if the value of a product’s RCA index is higher than the
RCA index for all products, country j has a comparative advantage in product i.
Hoen and Oosterhaven (2006) provided another transformation of the original
where ARCA is the additive revealed comparative advantage index, with values
above zero meaning a comparative advantage. Research by Yu, Cai, and Leung
  
ij XX
Economic Annals, Volume LXIII, No. 218 / July – September 2018
(2009) and by Yu, Cai, Loke, and Leung (2010) adopts an alternative measure to
analyse the dynamics of comparative advantage, the Normalised Comparative
Advantage (NRCA) index, defined as follows:
where Xij represents actual exports and stands for the
comparative-average-neutral level in exports of commodity j for country i. If
NRCA > 0, a country’s comparative advantage in the world market is positive.
As well as calculating static comparative advantage indices we also measure
stability and duration, following the logic of Bojnec and Fertő (2008). First,
Markov transition probability matrices are calculated to analyse the stability of
the SRCA index, evaluating the mobility of revealed comparative advantage
across space and time. In addition, Bojnec and Fertő (2008) use the non-
parametric Kaplan-Meier product limit estimator as a measure of the duration
of indices. According to Bojnec and Fertő, a sample contains n independent
observations (denoted as ti; ci), where i = 1, 2, . . . , n; ti is the survival time, while
ci is the censoring indicator variable C of observation i (C = 1 if a failure
occurred, and 0 otherwise). Moreover, it is assumed that there are m < n times
recorded failures. Then we denote the rank-ordered survival times as t(1) < t(2)
< … < t(m), nj indicates the number of subjects at risk of failing at t(j), and dj
denotes the number of observed failures. The Kaplan-Meier estimator of the
survival function is then (with the convention that ˆS(t) = 1 if t < t(1)):
This paper employs global automobile trade data from the World Bank’s World
Integrated Trade Solution database. Data was retrieved at the HS-6 level for all
countries of the world from 1997 to 2016 for nine products (see product list in
the Appendix).
Note that the methodology presented above has a number of limitations. First,
the trade data is not totally trustworthy, for a number of reasons: disaggregated
values might not add up; missing value problems exist; data varies by
ij XEE
ijjiji XEXE
tit n
classification; and export and respective import values for the same destination
might not be equal. Second, Balassa-based indices are sensible to zero values.
Third, database cleaning might result in the loss of useful information. Fourth,
for space and correlation reasons the paper mainly concentrates on the
presentation of the original Balassa index. Last but not least, the phenomenon of
double counting appears. Double counting arises during Balassa index
calculation when a given export product is also part of a group of export
products and when the analysed country belongs to a given group of countries.
The solution would be to exclude the analysed item from the group when
calculating. As, in our case, the analysed products and countries appear in the
selected groups of products and countries with a marginal ratio, we have made
an exception and not changed the original indices.
The automotive industry is one of the most globalised industries. It successfully
recovered from the economic crisis, and industry profits were 31% higher in
2012 than in the last pre-crisis year, 2007 (Mohr et al. 2013). According to
WTEx (Workman 2018) information, in 2016 the automobile industry led in
terms of exports, exceeding even crude oil earnings (due to relatively low oil
prices). According to the news4business website (2017), in 2017 it grew globally,
with automobile companies and their supplier networks having a record
income. Based on Forbes’ 2017 Global 2000 ranking report, the world’s top ten
automobile and truck companies (in terms of production) are as follows: Toyota
Motor (Japan), Volkswagen Group (Germany), Daimler (Germany), Ford
Motor (United States of America), BMW Group (Germany), General Motors
(Unites States of America), Honda Motor (Japan), Hyundai Motor (South
Korea), Nissan Motor (Japan), and SAIC Motor (China) (Schmitt 2017). New
trends in the car business are clearly defined as electric cars, safety
requirements, environmental regulations, and increasing focus on emerging
Regarding the production of cars and commercial vehicles, the top ten countries
are China (annual production of more than 28 million vehicles in 2016), the
USA, Japan, Germany, India, South Korea, Mexico, Spain, Canada, and Brazil
( 2017), representing more than 77% of global personal and
commercial vehicle production.
Economic Annals, Volume LXIII, No. 218 / July – September 2018
As for global automobile trade, Germany, Japan, and Canada were the biggest
exporters in the period analysed, together constituting 40% of all exports, while
the top ten countries comprised 71% of the concentration (Table 1): together
Germany, Japan, Canada, the USA, South Korea, the United Kingdom, Spain,
Belgium, France, and Mexico represented 81.74%, 73.19%, 68.82%, and 68.37%
of total global automobile product exports for the periods 1997–2001, 2002–
2006, 2007–2011, and 2012–2016, respectively (WITS 2017).
Thus, the top producers of automobiles are not necessarily the top exporters.
The top-ranking exporters are countries that have less domestic need for cars
than their annual production. Consequently, if they want to sustain production
and economies of scale they need to export. Huge car producers like China and
India have a fast-developing market and can sell most of their automobile
products domestically.
Table 1: Top 10 automobile exporters in the world, 1997–2016, by country
(in 1000 US$)
Country 1997–2001 2002–2006 2007–2011 2012–2016 1997–2016
Germany 60,717,149 98,579,229 132,868,350 152,165,113 111,082,460
Japan 52,791,714 76,034,334 92,713,321 91,127,746 78,166,779
Canada 31,289,701 34,929,224 33,830,236 45,720,822 36,442,496
U.S.A. 16,166,998 24,146,296 38,645,973 53,265,488 33,056,189
South Korea 10,352,495 22,697,598 32,172,230 42,140,865 26,840,797
U K 14,164,343 21,230,890 27,232,504 38,095,704 25,180,860
Spain 16,344,542 23,035,898 28,507,158 30,984,570 24,718,042
Belgium 10,739,725 26,517,532 27,716,977 29,586,107 23,640,085
France 19,224,543 31,027,391 24,624,749 18,832,853 23,427,384
Mexico 12,974,355 13,829,225 21,065,940 31,642,182 19,877,926
Concentration 81.74% 73.19% 68.82% 68.37% 71.20%
Note: Countries are listed in decreasing order based on their 1997–2016 averages.
Source: Own calculation based on WITS (2017) data.
On a product basis, the category of vehicles with spark-ignition internal
combustion reciprocating piston engines and a cylinder capacity between
1500cc and 3000cc comprised 40% of global automobile exports in the period
analysed (Figure 1). This category was followed by over-3000cc cars (code
870324), accounting for another 20% of global exports. Third came diesel
engines between 1500cc and 2500cc (code 870332), with another 20%. All in all,
these three categories constituted more than 80% of global automobile exports,
implying a high rate of concentration.
Figure 1: Top 10 automobile exports in the world, 1997–2016, by product (%)
Note: See detailed product codes and meanings in the Appendix.
Source: Own calculation based on WITS (2017) data.
As for global automobile imports, the vast majority of automobile exporters are
also importers of the same product (Table 2). The USA, for instance, which was
the 4th major exporter of automobiles, was also by far the leading importer of
cars (Table 2). Germany, the United Kingdom, France, Canada, Belgium, and
Spain were also among the top ten exporters and importers of automobiles,
showing signs of intra-industry trade. Italy, China, and Australia are not top
exporters but take a large share of total global imports. Note that the
concentration of the 10 biggest car importers was 72%, 67%, 59%, and 62% in
the respective sub-periods analysed (WIT, 2017).
1997‐2001 2002‐2006 2007‐2011 2012‐2016 1997‐2016
870310 870321 870322 870323 870324
870331 870332 870333 870390
Economic Annals, Volume LXIII, No. 218 / July – September 2018
Table 2: Top 10 automobile importers in the world, 1997–2016, by country (in
1000 US$)
Country 1997–2001 2002–2006 2007–2011 2012–2016 1997–2016
United States 95,061,394 123,887,709 117,575,403 159,945,708 124,117,553
Germany 24,083,525 35,139,400 41,198,893 45,440,672 36,465,622
United Kingdom 24,403,259 39,309,304 38,965,753 42,725,042 36,350,840
France 15,091,623 23,570,259 33,619,184 30,149,391 25,607,614
Italy 17,967,372 27,861,249 32,015,057 22,438,077 25,070,439
Canada 13,733,718 19,165,540 22,676,846 26,369,022 20,486,282
Belgium 6,892,081 18,665,714 25,962,873 28,472,122 19,998,197
China 645,944 4,659,336 21,631,114 48,180,591 18,779,246
Spain 10,489,883 19,448,298 17,902,248 14,175,234 15,503,916
Australia 4,417,385 8,231,918 12,952,107 16,452,391 10,513,450
Concentration 72.17% 67.33% 59.57% 62.48% 64.11%
Note: Countries are listed in decreasing order based on their 1997–2016 averages.
Source: Own calculation based on WITS (2017) data.
Calculating the Balassa indices (Table 3) reveals the specialisation of the global
automobile trade. Spain and Japan had the highest comparative advantage in all
periods analysed, suggesting high potential competitiveness France, Canada,
and Germany also had relatively high comparative advantage in global
automobile exports, while similar numbers for other analysed countries varied
significantly. Mexico, despite being one of the biggest global automobile
exporters, generally had low comparative advantage.
Table 3: Original Balassa index for the most important global automobile
exporters, 1997–2016
Country 1997–2001 2002–2006 2007–2011 2012–2016 1997–2016
Germany 1.15 1.18 1.25 1.54 1.28
Japan 1.85 2.03 1.76 1.42 1.77
Canada 1.43 1.18 1.17 1.39 1.29
USA 0.67 0.94 1.26 1.19 1.01
South Kore
1.17 1.16 0.95 0.92 1.05
UK 0.81 0.77 1.12 1.39 1.02
Spain 3.70 2.39 2.47 2.35 2.73
Belgium 0.92 1.20 1.12 1.31 1.14
France 1.26 1.55 1.37 1.22 1.35
Mexico 0.56 0.62 0.92 1.35 0.86
Source: Own calculation based on WITS (2017) data
However, results vary significantly by the method used (Table 4). Only Japan
and Spain had a comparative advantage in all period and all indices. According
to the RTA index, and similar to the lnRCA and SRCA indices, Japan and Spain
were the countries with the highest comparative advantage. However, the RC
index found Japan and South Korea to be the most competitive nations in the
global automobile trade.
Table 4: Balassa-based indices for the most important global automobile
exporters, 1997–2016
Germany 1.28 0.26 –0.42 0.02 –0.07
Japan 1.77 1.49 0.23 2.50 0.12
Canada 1.29 –0.19 –2.12 –1.34 –0.36
USA 1.01 0.06 –1.02 0.36 –0.24
South Kore
1.05 0.77 –0.79 2.24 –0.19
UK 1.02 –0.42 –0.31 –0.40 –0.11
Spain 2.73 1.44 0.04 0.24 0.05
Belgium 1.17 –0.58 –0.16 –0.33 –0.05
France 1.35 –0.27 –0.47 –0.48 –0.11
Mexico 0.86 0.46 –2.49 –0.12 –0.38
Note: WRCA, ARCA, and NRCA indices gave inconsistent results for our sample and therefore
are omitted.
Source: Own calculation based on WITS (2017) data.
Economic Annals, Volume LXIII, No. 218 / July – September 2018
At the product level, diesel engine cars below 1500cc capacity (870331) were the
product with the highest competitive potential globally, followed by vehicles
with only spark-ignition internal combustion reciprocating piston engines and a
cylinder capacity of 1000cc–1500cc (870322). These types of car were mainly
produced in Spain and Japan, respectively.
Table 5: The original Balassa index for the most important global automobile
products, 1997–2016
Product 1997–2001 2002–2006 2007–2011 2012–2016 1997–2016
870310 1.01 0.96 1.17 1.29 1.11
870321 1.69 1.41 1.24 1.23 1.39
870322 1.68 1.50 1.40 1.49 1.52
870323 1.27 1.34 1.46 1.50 1.39
870324 1.29 1.32 1.44 1.51 1.39
870331 2.03 1.67 1.93 1.77 1.85
870332 1.35 1.34 1.39 1.42 1.38
870333 1.17 1.03 1.00 1.15 1.09
870390 0.85 1.07 0.95 1.25 1.03
Note: Product names in the Appendix.
Source: Own calculation based on WITS (2017) data.
The degree of mobility in the SRCA index is estimated by using Markov
transition probability matrices (Figure 2). The results show a relatively low
mobility of the SRCA index for the global automobile trade in most of the
countries, suggesting stable competitive potential. Seven countries maintained a
comparative advantage in more than 70% of product groups, while the UK,
South Korea, and Spain had the lowest mobility measures, implying heavy
competition in the sector.
Figure 2: The mobility of the SRCA index, 1997–2016, by country, %
Source: Own calculation based on WITS (2017) data.
The non-parametric Kaplan-Meier product limit estimator (Equation 9) was
estimated to determine the duration of revealed comparative advantage in
global automobile exports. The results confirm that, in general, survival times
are inconsistent over the period analysed (Table 6 & Table 7). Survival chances
of 98% at the beginning of the period fell to 7%–26% by the end of the period,
suggesting fierce competition in the global automobile trade. The results vary by
country (Table 6) – Germany, the UK, and Belgium had the highest survival
times – and by product group (Table 7) – the highest survival times were for
cars with 1000–1500cc (870322) and 1500–3000 cc (870323) petrol engines.
64% 66% 68% 70% 72% 74% 76%
Economic Annals, Volume LXIII, No. 218 / July – September 2018
Table 6: Kaplan-Meier survival rates for Balassa indices and tests for equality of
survival function in global automobile trade, by country, 1997–2016
1997 0.97 0.97 0.98 0.96 0.96 0.97 0.97 0.98 0.98 0.97 0.97
1998 0.94 0.94 0.96 0.92 0.92 0.94 0.94 0.96 0.97 0.94 0.93
1999 0.91 0.92 0.94 0.89 0.88 0.91 0.91 0.94 0.96 0.91 0.89
2000 0.88 0.90 0.93 0.85 0.84 0.88 0.88 0.92 0.93 0.88 0.85
2001 0.85 0.88 0.92 0.81 0.80 0.86 0.83 0.91 0.90 0.85 0.81
2002 0.81 0.86 0.91 0.77 0.76 0.83 0.77 0.89 0.87 0.82 0.77
2003 0.78 0.83 0.90 0.73 0.72 0.80 0.73 0.86 0.84 0.79 0.73
2004 0.74 0.80 0.88 0.68 0.67 0.78 0.70 0.83 0.82 0.76 0.68
2005 0.71 0.77 0.86 0.64 0.64 0.75 0.67 0.80 0.80 0.73 0.63
2006 0.67 0.73 0.85 0.59 0.60 0.72 0.63 0.76 0.78 0.69 0.58
2007 0.63 0.69 0.83 0.55 0.56 0.68 0.60 0.73 0.75 0.65 0.53
2008 0.59 0.64 0.81 0.50 0.54 0.64 0.57 0.69 0.71 0.61 0.48
2009 0.55 0.60 0.77 0.46 0.50 0.58 0.55 0.65 0.67 0.57 0.44
2010 0.50 0.56 0.75 0.42 0.46 0.53 0.51 0.58 0.63 0.53 0.39
2011 0.46 0.52 0.71 0.37 0.42 0.48 0.47 0.52 0.60 0.49 0.35
2012 0.41 0.47 0.66 0.32 0.38 0.43 0.44 0.46 0.56 0.45 0.32
2013 0.36 0.43 0.59 0.27 0.33 0.37 0.41 0.40 0.51 0.40 0.29
2014 0.30 0.42 0.48 0.21 0.27 0.28 0.38 0.34 0.43 0.34 0.24
2015 0.22 0.39 0.35 0.14 0.19 0.21 0.33 0.24 0.34 0.27 0.19
2016 0.11 0.31 0.15 0.05 0.09 0.07 0.26 0.11 0.23 0.15 0.08
Source: Own calculation based on WITS (2017) data.
Table 7: Kaplan-Meier survival rates for Balassa indices and tests for equality of
survival function in global automobile trade, by product, 1997–2016
Year/Product 870310 870321 870322 870323 870324 870331 870332 870333 870390
1997 0.97 0.97 0.98 0.97 0.97 0.97 0.97 0.97 0.96
1998 0.94 0.94 0.95 0.95 0.94 0.94 0.94 0.94 0.93
1999 0.90 0.92 0.93 0.93 0.91 0.90 0.92 0.91 0.89
2000 0.86 0.89 0.90 0.90 0.88 0.86 0.89 0.88 0.85
2001 0.82 0.86 0.88 0.87 0.84 0.83 0.85 0.85 0.81
2002 0.78 0.83 0.85 0.85 0.81 0.80 0.82 0.82 0.77
2003 0.74 0.79 0.82 0.83 0.77 0.77 0.78 0.78 0.73
2004 0.70 0.77 0.79 0.81 0.73 0.74 0.74 0.74 0.69
2005 0.66 0.74 0.76 0.78 0.69 0.70 0.71 0.71 0.64
2006 0.62 0.71 0.73 0.75 0.64 0.66 0.67 0.67 0.59
2007 0.57 0.67 0.69 0.72 0.61 0.61 0.64 0.63 0.54
2008 0.53 0.64 0.65 0.70 0.57 0.57 0.61 0.59 0.49
2009 0.49 0.59 0.61 0.67 0.53 0.53 0.56 0.54 0.44
2010 0.45 0.54 0.57 0.63 0.49 0.48 0.51 0.49 0.40
2011 0.40 0.49 0.53 0.58 0.45 0.43 0.48 0.44 0.36
2012 0.35 0.44 0.49 0.53 0.40 0.37 0.44 0.39 0.33
2013 0.29 0.39 0.44 0.47 0.34 0.31 0.39 0.34 0.28
2014 0.23 0.33 0.40 0.40 0.28 0.26 0.32 0.28 0.23
2015 0.15 0.26 0.35 0.31 0.20 0.19 0.23 0.20 0.18
2016 0.03 0.14 0.25 0.17 0.07 0.08 0.11 0.11 0.08
Log-rank test 0.00
Wilcoxon test 0.00
Source: Own calculations based on WITS (2017) data.
The equality of the survival function is checked using Wilcoxon and log-rank
tests. The results show that the hypothesis of equality across survivor functions
can be rejected at all levels of significance, meaning that similarities in the
duration of comparative advantage across the most important global
automobile exporters are absent (Tables 6 and 7).
Last but not least, following Widodo (2009) and Lafay (1992), this paper uses
product maps based on Balassa indices and the Trade Balance Index (TBI) to
Economic Annals, Volume LXIII, No. 218 / July – September 2018
calculate the dynamics of export specialisation. The TBI index is defined as
follows (Lafay 1992):
where X means exports, M means imports, and i indicates a given country.
Using the method of Widodo (2009), Balassa indices and the TBI index can be
matched to create a product map based on a simple matrix (Table 8). Using
these maps in time allows for the analysis of trade patterns in a dynamic context.
Table 8: Product map categories
Group D
Revealed comparative
disadvantage and net
Group C
Revealed comparative
disadvantage and net
Group B
Revealed comparative
advantage and net
Group A
Revealed comparative
advantage and net
Source: Own composition based on Widodo (2009).
Due to the large number of products and countries, an alternative way of
visualizing the product maps is used here, counting the frequencies of products
that belong to the four groups mentioned in Table 8 and comparing them to the
total number of observations. Thus, the percentage of products belonging to
each category becomes apparent.
We find that most of the products of the biggest global automobile exporters
belong to Category A or D, suggesting a high share of cases where a country
exports (imports) products with a comparative advantage (disadvantage) (Table
9). However, there are huge differences between countries in this respect.
Almost every second German car exported in 2012–2016 had a comparative
advantage, while this share was only 2% for France and 6% for Belgium.
Moreover, 77% of Canadian cars with comparative disadvantage were imported,
while this was only 30% for Mexico (and 0% for Japan).
  
iiii MXMXTBI /
There are also large differences in the other two categories of the product maps.
Almost 75% of Japanese automobile products were classified as Category C,
meaning that they were exported without any comparative advantage,
suggesting a very offensive strategy in the global market. At the other end of the
spectrum, France and Belgium had a high and increasing share of Category B
products, suggesting that almost a third of such cars were imported, despite
these countries having a comparative advantage in car manufacture. On the
whole, there is a general trend of German, British, and Mexican products having
improved comparative advantage and potential, while cars from the Asian
market appear to have suffered from a loss of competitiveness recently.
Table 9: Product map of top ten automobile exporters in the world, 1997–2016
Category A Category B Category C Category D
SRCA > 0 &
TBI > 0
SRCA>0 &
TBI < 0
SRCA < 0 &
TBI > 0
SRCA < 0 &
TBI < 0
Germany 21% 45% 4% 0% 11% 8% 64% 47%
Japan 42% 26% 0% 0% 58% 74% 0% 0%
Canada 7% 8% 3% 6% 17% 9% 72% 77%
USA 5% 12% 4% 14% 19% 32% 72% 42%
Korea 19% 16% 0% 0% 50% 35% 31% 50%
UK 6% 21% 12% 28% 21% 0% 61% 51%
Spain 24% 21% 15% 0% 23% 48% 39% 32%
Belgium 13% 6% 6% 38% 21% 19% 61% 36%
France 13% 2% 9% 36% 21% 0% 58% 63%
Mexico 8% 26% 0% 0% 32% 45% 61% 30%
Source: Own composition based on Widodo (2009).
This paper analyses the global trade competitiveness of automobile production,
paying special attention to exports. The paper reaches a number of conclusions.
First, analysis of the characteristics of the global automobile trade shows that
China, the USA, Japan, and Germany were the biggest producers of cars.
However, in the period analysed the top exporters were Germany, Japan, and
Economic Annals, Volume LXIII, No. 218 / July – September 2018
Canada, together producing 40% of all exports, while the top ten countries
provided 71% of the concentration. On the other hand, the USA, Germany, and
the United Kingdom were the biggest importers, mainly for the purpose of re-
exporting. Second, our analysis revealed that globally the most traded/exported
automobiles were vehicles with spark-ignition internal combustion engines
(1500–3000cc) (870323), comprising more than 40% of all vehicle exports
between 1997 and 2016. Third, the calculation of Balassa indices showed that of
the most important automobile exporters in the world, Spain and Japan had the
highest comparative advantage in all periods analysed. Analyses using the
various indices showed similar results. According to RTA, lnRCA, and SRCA
indices, Japan and Spain had the largest comparative advantage in automobile
production; however, the RC index found Japan and South Korea to be the most
competitive. The relatively low mobility of the SRCA index for the global
automobile trade for most of the countries suggests stable competitive potential.
Regarding the duration of revealed comparative advantage in global automobile
exports in general, the survival times are inconsistent over the period analysed,
and their decline suggests fierce competition in the global automobile trade.
Future research might investigate why Japan and Spain are so competitive,
which requires a thorough investigation of Japanese and Spanish automobile
production and export strategies.
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Received: June 25, 2018
Accepted: August 20, 2018
Automobile product codes and associated descriptions at HS6 level
870310 Vehicles specially designed for travelling on snow, golf cars and
similar vehicles
870321 Vehicles with only spark-ignition internal combustion reciprocating
piston engine, cylinder capacity not over 1000cc
870322 Vehicles with only spark-ignition internal combustion reciprocating
piston engine, cylinder capacity over 1000 but not over 1500cc
870323 Vehicles with only spark-ignition internal combustion reciprocating
piston engine, cylinder capacity over 1500 but not over 3000cc
870324 Vehicles with only spark-ignition internal combustion reciprocating
piston engine, cylinder capacity over 3000cc
870331 Vehicles with only compression-ignition internal combustion piston
engine (diesel or semi-diesel), cylinder capacity not over 1500cc
Vehicles with only compression-ignition internal combustion piston
engine (diesel or semi-diesel), cylinder capacity over 1500 but not over
870333 Vehicles with only compression-ignition internal combustion piston
engine (diesel or semi-diesel), cylinder capacity over 2500cc
870390 Vehicles for transport of persons (other than those of heading no.
8702) n.e.c. in heading no. 8703
Source: Own composition based on World Bank database (2017).
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Bu çalışmada Türk tekstil ve hazır-giyim sektörünün uluslararası pazarlardaki rekabet gücünün araştırılması amaçlanmaktadır. Türkiye’nin lokomotif sektörlerinden olan Türk tekstil ve hazır-giyim sektöründeki rekabet olgusu, diğer sektörlerle kıyaslandığında ülke ekonomisindeki yeri, istihdam ve ihracattaki payı nedeni ile son derecede önemli bir konumda bulunmaktadır. İlk olarak, tekstil ve hazır giyim sektörünün hem ulusal düzeyde hem de uluslararası düzeydeki mevcut durumunun belirlenmesi amaçlanmaktadır. Daha sonra ise, sektörün rekabet gücünü oluşturan göstergeler Porter’ın ortaya koyduğu “elmas modeli” üzerinden incelenmiştir. Türk tekstil ve hazır-giyim sektörünün uluslararası rekabet gücü, girdi ve talep koşulları, ilgili ve destekleyici endüstriler, firma stratejisi/rekabet yapısı ve devletin rolü boyutları üzerinden incelenecektir.
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Girişimcilik, ekonominin büyümesinde ve gelişmesinde önemli rolü olan ve toplumsal yenilikçiliği ön plana çıkaran bir harekettir. Bireylerin yaşamlarında edindikleri yenilikçi fikirlerin ekonomik bir amaçla iş haline dönüşmesinin sonucu olarak tanımlanabilecek bir terimdir. Bu hareket, toplumların girişimcilik düzeylerini ölçmek için geliştirilen “Girişimcilik Endeksi” verilerine göre gelişmiş ekonomilerde çok daha yaygın bir şekilde görülmektedir. Bunun nedeni, gelişmiş ekonomilerdeki güçlü finansal yapı, iş yapabilme kolaylığı, altyapı gibi gerekli ortamı oluşturmayı teşvik edici unsular olduğu düşünülebilir. Ancak girişimciliğe yatkınlık sadece somut ekonomik yatırımlarla değil aynı zamanda karakteristik insan özelliklerine göre de belirleyici olabilir. Yaşayış biçimleri, kültürel yatkınlıklar, sosyal ilişkiler gibi topluma ve bireye özgü özellikler de girişimciliğin temelini oluşturabilmektedir. Örneğin, dünyada her yıl en fazla yeni işletme kurulan ülkelerden biri olan ABD’de sosyal etkileşimin yoğun olduğu yaşayış biçimlerinin iyi iş ilişkileri kurmayı ve girişimciliği etkilediği ya da dünyada en fazla teknolojik yeniliklerin yapıldığı ülkelerden biri olan Japonya’da kültürel yatkınlıkların yeni fikirler geliştirilmesinde etkili olduğu varsayılabilir. Bu kitabın amacı, Türk toplumunun eskiden beri en iyi oldukları sektörlerden biri olan tekstil ve giyim sektörünü girişimcilik açısından ele almak ve bu konuda sektör temsilcilerine rehberlik etmektir. Kitap altı bölümden oluşmaktadır. Birinci bölümde, Türk tekstil sektörü ve ekosistemi hakkında bilgi verilmiştir. İkinci bölümde, girişim sermayesi, melek yatırımcı ve kitlesel fonlama gibi üç farklı finansman tekniği hakkında bilgiler verilmiştir. Üçüncü bölümde, girişimcilerin yeni fikirlerini bir ürün olarak hayata geçirmesi, hizmet düzeyini ve müşteri potansiyelini belirlemesi, tanıtma ve markalaşması amacıyla girişimci pazarlamasına yer verilmiştir. Dördüncü bölümde, Türkiye’de genç girişimciliğin desteklenmesi adına genç girişimci kazanç istisnası ve prim teşviki uygulamalarına yer verilmiştir. Beşinci bölümde, tekstil mühendisliği öğrencilerinin nazarından tekstil ve giyim sektöründeki çalışma koşulları, öğrencilerin kariyer beklentileri ve sektörün durumu incelenmiştir. Son bölümde ise Türk tekstil ve hazır-giyim sektörünün uluslararası rekabet düzeyi, girdi ve talep koşulları, ilgili ve destekleyici endüstriler, firma stratejisi/rekabet yapısı ve devletin rolü boyutları üzerinden incelenmiştir.
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Competitiveness has now become a buzzword like globalization. It has received attention from researchers, governments and business organizations because of its close association with the success of an entity. In the past decades, many works on competitiveness with different perspectives have been published. But competitiveness is yet an elusive concept, the relevance of which is changing with time. There is a need for a comprehensive review of extant literature on the subject. This review article presents the state-of-the-art development of competitiveness research. To begin with, the article lays the foundation for basic understanding on competitiveness at various levels, such as nation, industry and the firm. After elaborating on the theories of competitiveness that have evolved over the years, it gives insight on the measurement models. The plethora of studies that signify different approaches to measure competitiveness are discussed at length. The future direction of competitiveness research is also suggested.
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The article deals with the impact of the emerging new division of labour between Western and Central and Eastern Europe (CEE) on work and employment, both in the Western and CEE countries. Major points of discussion will be the hypothesis of a `hollowingout' of the Western European auto industry, and the hypothesis of a `regime flight'; that is, the claim that companies use CEE locations to escape the collectively regulated work models of Western Europe. The article draws from our own empirical research, including company case-studies in Western and Eastern auto plants, and on statistical analysis. The main conclusions are: in CEE countries, an upgrading process of production sites can be observed, which challenges the view of an emerging `high end/low end' division of labour between the West and the East.While relocation has led to some losses of low-skill jobs in Western Europe, the overall effect of the expansion of the automotive industry to CEE on growth and employment in Western Europe was positive.The impact of low-cost component imports from CEE countries has increased the competitiveness of the German firms, which are by far the main investor in CEE countries. Our case-studies reveal no trend towards regime flight from Western European work models, but management threats of relocation have become commonplace and have led to a renegotiation of work models in Western European countries. In CEE countries, the work models of automobile companies more and more are oriented at a high-road path.This development is fostered by the companies' responses to the problems of migration and the increasing shortage of skilled labour.
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Purpose The purpose of this paper is to provide a framework for connecting macro‐ and micro‐level research on competitiveness. Design/methodology/approach Based on some major international survey research reports (by the WEF, IMD, OECD, and the EU) and research experience, a gap is described between national and firm level competitiveness studies. A reasoning why filling this gap is discussed and a general research model connecting the two levels by using Porter's diamond model is developed. Findings By using appropriate definitions of national and firm competitiveness and the diamond model a meaningful connection of the two levels can be created, which is useful both for connecting recent research results and as a foundation for further research. Research limitations/implications Though the model is based on actual research experiences, its real value will become apparent after having it applied in concrete projects. This process is ongoing. Practical implications The model is a very useful tool in analysing real world situations, from economic policy issues to strategic management. Originality/value The paper is a result of extended research on competitiveness and provides a new model for further analyses in a very important field.
SEE economies are faced with solving current crisis and establishing the economic system based on growth and sustainable development. A challenge for the economics has been to find metrics to gauge the extent to which society has become more dependent on knowledge production. Although there is wide recognition of the importance of knowledge and intangible capital in fostering economic growth and social change, devising useful measures of these assets has been difficult. The primary task of each SEE economy is to establish a new economic model within a new economic system, though there is a need to put theory into practice by moving sustainable development into mainstream economics where that knowledge creates relations across sectors and institutions. Developing, sustaining, and managing knowledge will also require a significant investment in education and knowledge, which indeed constitutes a new economics and development paradigm that should prevail in the global society in the time ahead. In order to achieve wealthier status by all the people of each SEE country, it is necessary to leave the current economic model and to establish a new one as a new economic paradigm. Economic growth as one part of the new economic model should account for at least 5% of GDP annually in the long term. The precondition for achieving the above-mentioned is to increase competitiveness of products that should be realized through regional cooperation. How to finance regional cooperation is, however, the next more important issue.
This study examines the relationship between interfirm asset specificity and performance in the auto industry. More specifically, I examine the extent to which differences in supplier–automaker asset specialization may explain performance differences between Japanese automakers (Nissan and Toyota) and U.S. automakers (Chrysler, Ford, General Motors). The findings indicate a positive relationship between supplier–automaker specialization and performance. In particular, the data suggest a positive relationship between interfirm human asset cospecialization and both quality and new model cycle time. Moreover, site specialization is found to be positively associated with lower inventory costs. The findings suggest that in the auto industry a tightly integrated production network characterized by proximity and a high level of human cospecialization will outperform a loosely integrated production network characterized by low levels of interfirm specialization.