Prague University of Economics and Business
Faculty of International Relations
Bachelor’s Thesis
2021 Aleksandra Parshina
Prague University of Economics and Business Faculty of International Relations
Bachelor’s Field: International Business
Title of the Bachelor’s Thesis:
Export opportunities and unrealized export potential of the Czech Republic
in Argentina
Author: Aleksandra Parshina
Supervisor: Ing. Ondřej Sankot, Ph.D.
Declaration of Authenticity
I hereby declare that the Bachelor´s Thesis presented herein is my own work, or fully and specifically acknowledged wherever adapted from other sources.
This work has not been published or submitted elsewhere for the requirement of a degree programme.
Prague, December 10, 2021 Aleksandra Parshina
Acknowledgments
I would like to express my gratitude to my supervisor Ing. Ondřej Sankot, Ph.D.
for his patience, expertise, and guidance throughout the writing of this thesis.
I would like to thank my family for their constant support during the whole
studies.
Table of Contents
Introduction ... 1
1 Overview of the current economic situation in Argentina and its involvement in the international trade ... 4
1.1 Argentina’s economy ... 4
1.2 Involvement of Argentina in the international trade ... 5
1.3 The EU – Argentina trade ... 6
1.4 Argentina’s trade with the Czech Republic and its prospects ... 6
2 Identification of export opportunities ... 9
2.1 Theoretical concept of comparative advantage ... 9
2.2 Empirical usage of comparative advantage law for detecting export opportunities .. 11
2.3 Models for calculating export potential ... 16
3 Calculation of export potential ... 21
3.1 Supply estimation ... 22
3.2 Demand estimation ... 28
3.3 Easiness to trade ... 35
3.4 Export Potential Indicator ... 35
Conclusion ... 40
References ... 43
Annex ... 47
List of tables
Table 1: Exports of the Czech Republic to the world (first ten product groups) ... 23
Table 2: Projection of the GDP growth in the Czech Republic (2020–2025) ... 23
Table 3: Descriptive statistics for Projected MS, GTA and Supply indicators ... 27
Table 4: Projection of the GDP growth in Argentina (2020–2025) ... 29
Table 5: Descriptive statistics for Projected M, MTA and Demand indicators ... 34
Table 6: Descriptive statistics for unrealized EP ... 36
Table 7: Product groups with the highest unrealized EP ... 37
Table 8: Supply, Demand, and Easiness to trade estimations for five product groups with the highest unrealized EP ... 38
List of graphs
Graph 1: Histogram of the CZ market shares (supply indicators) ... 27
Graph 2: Income elasticities for each HS 2-digit level (Argentina) ... 30
Graph 3: Histogram of Argentina's Projected M calculations... 31
Graph 4: Data distribution for GTA and MTA ... 33
Graph 5: Comparison of the actual exports with the EP for the first five product groups ... 39
List of abbreviations
BI Balassa index
CA Comparative advantage
CEECs Central and Eastern European countries
DSM Decision Support Model
EC Economic conditions
EP Export potential
EPI Export Potential Indicator
EU European Union
GDP Gross Domestic Product
GNP Gross National Product
GTA Global margin of preference
GTAP Global Trade Agreement project
HS Harmonised System
ICT Information and Communication Technologies
IMF International Monetary Fund
IT Information technology
ITC International Trade Centre
LFI Lafay index
M Import
MG Market growth
MS Market share
MTA Margin of preference in the target market
OLS Ordinary Least Squares
RCA Revealed comparative advantage
TB Export-import ratio
TPC Trade, production, and consumption
WEF World Economic Forum
WTO World Trade Organisation
UN United Nations
US, USA United States of America
1
Introduction
The Czech Republic is an open medium-sized economy with a 71% share of exports of goods in Gross Domestic Product (GDP). International merchandise trade plays a crucial role in the economic development of the Czech Republic as an export-led economy, binding Czech producers and consumers from all over the world into a global economic system consisting of various global value chains. Thanks to its active involvement in the foreign trade, strengthening competitiveness on world markets, and state export support, the country has shown the best economic performance among Central and Eastern European countries (CEECs) that adhered to the European Union (EU) (Silaghi, 2009).
At the same time, the Czech Republic has become dependent on the EU internal market, especially on Germany, which is mainly due to its geographical proximity and economic performance. According to the Czech Ministry of Foreign Affairs, the main task for the Czech Republic’s foreign trade is not only to contribute to barrier-free functioning of the internal market but also to broaden the horizons of the Czech exporters’ knowledge of the world outside Europe so that the risks of concentration in the event of a crisis are expanded. Given the current structure of the economy and external trade relations, Czech trade faces the risk of exploitation of its trade potential with nearby export markets (both territorially and sectorally), which is likely to make Czech exports hit the ceiling of the export potential (EP). It is worth mentioning that a significant part of Czech exports is re-exported to third countries. If Czech exporters establish themselves directly as final suppliers on non-European markets, the country will significantly increase its export profits. Therefore, establishing trade relations with non- European markets is currently a major challenge for the Czech Republic (Tlapa et al., 2019).
Argentina is one of the many markets where the Czech potential is not fully exploited, and hence represents a long-term partner of medium importance for the Czech Republic in Latin America. Since the end of the 20th century, the Czech Republic has shown interest in establishing closer relations with Argentina. Already in 1998, the Agreement on the Promotion and Mutual Protection of Investments entered into force, the main purpose of which was to create and maintain favourable conditions for the investments for both countries. In the period between 2001 and 2013, Czech exports of goods to Argentina have recorded promising quantitative growth, which in 2013 reached 97.933 million USD. However, the rapid growth of Czech exports was replaced by a gradual decline and the value of the products exported in 2020 almost halved compared to 2013. With more than
2 48 million USD worth of trade with goods in 2020, Argentina remains Czech Republic’s 5th largest trading partner in Latin America (ITC, 2021), although according to the World Bank (2021b), it is the 3rd largest market in the region in terms of consumer spending (sorted by the Household final consumption expenditure). Restrictive measures against imports of goods to Argentina are mostly to blame for the trade decline by the majority of Czech authorities (Ministry of Foreign Affairs, Ministry of Industry and Trade). Nevertheless, despite the current economic situation and Argentina’s protectionist measures, Czech companies that want to establish themselves in Argentina have numerous open possibilities (CzechTrade, 2021).
Despite the fact that Argentina is a huge export market, not enough research on the EP of the Czech Republic in Argentina has been carried out so far. Moreover, the region itself has not yet been sufficiently explored by Czech exporters, which offers the possibility of finding a prospective market for Czech products. This is what makes the topic in question particularly relevant.
This bachelor thesis aims to find out whether there exists an unrealized EP of the Czech Republic in Argentina and if so, to subsequently calculate the overall amount of the unrealized EP (in USD) of the Czech Republic in Argentina, as well as to identify potentially prospective product groups for the Czech export in Argentina through calculating the unrealized EP for each product group. A partial goal of the thesis is to determine which factors have the most influence on the unrealized EP of the Czech Republic in the selected target market.
The first part of the thesis provides a brief overview of the Argentina’s economy and highlights the general pattern of Argentina’s integration in the international trade. In other terms, the first section describes the current economic context of Argentina’s trade relations with the rest of the world, with a deeper focus on the EU and the Czech Republic.
The next part summarizes the theoretical knowledge about the comparative advantage (CA) theory, followed by the process of comparing theoretical principles with its practical application within the calculation of export opportunities and the EP. The focus is then moved to the comparison of existing methods used to measure the EP, which is crucial to achieve the aims of the thesis. Furthermore, the second part describes the International Trade Centre (ITC) methodology, including a detailed description of the chosen methodology, the extraction of data, and the process of calculation of the unrealized EP.
3 The final part is devoted to the main output of this work – the calculation of the unrealized EP of the Czech Republic in Argentina with the use of the ITC methodology. To achieve the goals of the thesis, the chapter is divided into four subchapters, three of which consist of a detailed calculation of the Export Potential Indicator’s (EPI’s) components, while the fourth is dedicated to the results of the EP calculations. The first component of the EPI is supply capacity of the exporter, expressed as projected market share corrected for the rise (or decline) in GDP, re-exports, and tariff barriers to trade. The second component is demand of the target market, which estimates the amount of products that the target market can absorb, adjusted for the GDP and population growth (or decline), tariff barriers to trade, and distance between the countries.
The last component of EPI is easiness to trade, which is a ratio of actual trade between the Czech Republic and Argentina to hypothetical trade between them. It reflects intangible factors that are not captured by supply and demand calculations. The trade data for the Czech Republic and Argentina were extracted from the UN (United Nations) Comtrade Database (2021), the GDP growth projections were taken from the World Economic Outlook Database (IMF, 2021), population growth data come from UN (2021), tariff data (subsequently modified by the price elasticity to imports estimated by Dimaranan et al. (2006) for 43 sectors) and the average distances between Argentina and its suppliers for all the product groups were taken from ITC (2021). The calculation is restricted to the Harmonised System (HS) 4-digit level due to the lack of data required to calculate the EP for a more specific HS 6-digit level.
To describe large datasets, descriptive statistics with selected characteristics are used, with a focus on the arithmetic mean, median, maximum, minimum, first and third quartiles.
The median provides a measure of the centre of the datasets, and through its comparison with the arithmetic mean, the overview of the distribution of the datasets is obtained. Maximum and minimum show the range of data in the datasets. Lower and upper quartiles along with the median allow to divide the datasets into four parts, making it easy to discover which of the four intervals a particular data point is in. The ITC methodology is the most complex when it comes to the calculation of the unrealized EP since it takes into account growth projection of the target market, economic development of the exporter, tariff barriers to trade, distance factor, and intangible factors, omitted by most methodologies.
4
1 Overview of the current economic situation in Argentina and its involvement in the international trade
The first part of the thesis presents a general economic overview of Argentina and comments on the country’s integration in the international trade, while it brings into focus the current trade between Argentina and the EU, specifically the Czech Republic. At the end of this section, the prospects of trade between Argentina and the Czech Republic, identified by the Ministry of Industry and Trade, are discussed.
1.1 Argentina’s economy
Argentina’s economy is relatively developed and well diversified. It is currently the 4th largest economy in Latin America. GDP of Argentina (constant 2015 prices) in 2020 reached 514,664 billion USD (constant GDP per capita reached the level of 11,342 USD), which showed the economic downturn of 9.9 % in 2020 compared to contractions of 3.8 % in the world economy (World Bank, 2021). According to the International Monetary Fund (IMF) predictions, only a minor rebound is projected in 2021. In 2020, Argentina implemented one of the world’s longest and tightest lockdowns in response to the onset of the coronavirus pandemic, which explains such an economic collapse. In general, the most problematic and volatile factors in the Argentina’s economy are high inflation exceeding 30 % (reaching 31.8 % in 2018) and a rigid labour market, which the government has failed to reform.
In addition to high inflation, which hit 36 % year-on-year in 2020 despite a steep economic downturn and is one of the highest in the world, the country is grappling with rising unemployment (11.8 % in 2020), a weakening local currency, and a restrictive foreign trade system. Furthermore, government debt is growing every year, reaching 102 % of the GDP in 2020. The main reason for the growing debt is the long-term deficit budget. In 2019, it arrived at 4.47 % of the GDP, while in 2020, it is expected to grow to almost 9 % of the GDP. The key to debt service is financial assistance provided by the IMF, which in September 2018 borrowed 57 billion USD to help Argentina recover from economic crisis, performing thus the biggest IMF loan in the history (CzechTrade, 2021). In the ranking of competitiveness, which is compiled by the World Economic Forum (WEF), Argentina ranked 83rd out of 140 countries under comparison (WEF, 2019)
5 As for the structure of the country’s economy, industry accounts for more than a quarter of GDP, agriculture for about 10 % of GDP, while trade and tourism account for 17 % of GDP (CzechTrade, 2021).
Considering the trade indicators, it seems more relevant to take into account data for 2019, when the world’s economy was not yet drastically affected by the COVID-19 pandemic.
Economic crisis led to unprecedented swings in the volume traded, which could create a distorted image of Argentina’s trade structure.
1.2 Involvement of Argentina in the international trade
In 2019, Argentina exported goods worth 64 billion USD and reached the 46th position among 225 countries of the world in total exports. The top 5 Argentina’s export markets in 2019 were:
Brazil (10.3 billion USD), China (6.9 billion USD), United States of America (USA) (4.4 billion USD), Chile (3.2 billion USD), and Vietnam (2.8 billion USD) (OEC, 2021).
Argentina is a mineral-rich country with an agrarian economy, which is reflected in the export structure: primary products exports account for 25 % of sales, processed agriculture or food items account for 45 % of exports, and industrial production for 29 % of exports. Top export categories in 2019 were: soybean meal (8.8 billion USD), corn (6.2 billion USD), delivery trucks (3.8 billion USD), soybeans (3.5 billion USD), and soybean oil (3.4 billion USD).
It is worth mentioning that in 2019 Argentina was the largest exporter of soybean meal, soybean oil and bran in the world (OEC, 2021).
Argentina’s imports of goods in 2019 reached almost 48 billion USD and placed the country on the 54th place in the world in total imports. The top 5 Argentina’s import suppliers in 2019 were: Brazil (9.9 billion USD), China (8.5 billion USD), USA (6.8 billion USD), Germany (2.7 billion USD), and Paraguay (1.7 billion USD). In order to thrive, Argentina must import primarily capital goods because its domestic industry produces very few capital goods and machine equipment. As a result, capital items account for 19 % of all imports. Imports of items for further processing account for 35 % of total imports, consumer goods for 13 %, and fuels and lubricants for 10 %. According to OEC (2021), top import categories in 2019 were: cars (2.3 billion USD), refined petroleum (2.3 billion USD), vehicle parts (2.1 billion USD), petroleum gas (1.5 billion USD), and soybeans (1.4 billion USD).
6
1.3 The EU – Argentina trade
The EU represents an important trade partner for Argentina. Its share in total trade lies between 15 and 20 %. In 2019, Argentina’s main trade partners from the EU were Germany, Italy, and Spain, while the value of export from Argentina to the EU reached 7,029 million USD.
The main products exported to the EU were soybeans and their derivatives, meat derivatives and products used for feeding animals. On the other hand, imports from the EU to Argentina accounted for 7,099 million USD in 2019. The main imported products were machinery, mechanical equipment, pharmaceutical products, and vehicles (CzechTrade, 2021).
The EU has long been negotiating with the Mercosur trade bloc, which Argentina is a member of. Currently, there is no preferential trade regime between the EU and Argentina, which means that trade relations abide by the rules of the World Trade Organization (WTO) and countries’
commitments in it (CzechTrade, 2021).
1.4 Argentina’s trade with the Czech Republic and its prospects
Argentina has long been a medium-term partner for the Czech Republic in Latin America.
Its market is the fifth trading partner in South America for Czech enterprises. Strong protectionist policies against imports are mainly to blame for the unfulfilled potential in commerce (CzechTrade, 2021).
In 2019, Argentina’s imports from the Czech Republic totalled 130.7 million USD, down 31 % (58.8 million USD) since 2018 and accounted for 0.27 % of overall Argentina’s imports. The top import categories (HS 2-digit level) from the Czech Republic to Argentina in 2019 were: machinery and mechanical appliances (44.8 million USD), electrical machinery and equipment (28.1 million USD), vehicles and their parts (15.2 million USD), plastics and articles thereof (4.0 million USD), and miscellaneous products of base metal (3.9 million USD) (UN Comtrade, 2021).
In 1998, the Agreement on the Promotion and Mutual Protection of Investments between the Czech Republic and Argentina came into force, but due to the harmonisation with the EU law, the Czech side is now trying to renegotiate the text. Moreover, in 2008 Argentina ratified the Agreement on Economic and Industrial Cooperation, which was signed by the Prime Minister of the Czech Republic during his visit on March 6, 2006. According to this Agreement, the two countries shall encourage the development of bilateral economic relations and promote
7 economic, industrial, technical, and technological cooperation, as well as the flow of investments (zákony.cz, 2008).
In the first half of 2021, the Treaty on Mutual Administrative Assistance in Customs Matters came into operation. According to it, the parties agreed to provide administrative assistance for the purpose of enforcing customs legislation, preventing, investigating and fighting customs offense, therefore ensuring the security of the international supply chain (zakonyprolidi.cz, 2021).
The potential for export to Argentina is unquestionably significant, nevertheless, current economic and political circumstances prevent it from being realized. As stated by Tlapa et al. (2021), Czech companies still have prospects to establish themselves in Argentina. Firstly, Argentina has vast mineral reserves (copper, gold, silver, lithium, molybdenum, potassium), and much of these deposits have yet to be discovered, which presents a huge opportunity for international mining and quarrying firms. The government is attracting foreign investments to mining projects in more than 30 potential large deposits that are currently under development and exploration. Second promising area are Information and Communication Technologies (ICT). Mobile and broadband internet connections in Argentina are about 15 % slower than in neighbouring countries, and the government aims to resolve this situation. The geography of the country also poses problems, as the population is concentrated in several cities with large distances between them. Opportunities therefore occur in the construction of the now missing mobile signal transmitters, which are crucial for the improvement of the situation. Thirdly, Argentina has a deficit in the production and distribution of electricity. In particular, solar and wind power plants have recently become popular among investors. However, the large distance between the source and the areas where electricity is consumed tends to be problematic. Moreover, the distribution networks are obsolete and large sums of money must be spent on their modernisation. Last but not least, transport is another area with great EP. The government of Argentina recently began investing in the modernisation of passenger and freight transport. While railway appears to be the best solution for the development of new deposits, the country is also actively importing railway vehicles, machinery and new technologies.
In general, the main article Czech enterprises could offer to Argentina’s market are technological advancements, which the entire region is in deficiency of. In addition to the areas mentioned above, such innovations and new technologies are needed also in sectors
8 that will be necessary for the country’s economic growth in the short term, such as biotechnology, healthcare, information technology (IT), etc. (Tlapa et al., 2021).
9
2 Identification of export opportunities
The following part of the thesis first explains the theory of CA and its modifications throughout the history. Afterwards, the focus is moved to the detection of export opportunities based on the theoretical patterns explained above. The last part of this chapter is dedicated to the calculation of the volume of unrealized export with the use of ITC methodology.
The estimate is then going to be applied in the analytical part of the thesis, within the calculations of the EP of the Czech Republic in Argentina.
2.1 Theoretical concept of comparative advantage
In general, theories of international trade attempt to answer three main questions: 1. Whether and what are the gains from international trade? 2. How should the international trade structure be composed? 3. How free should the international trade be? Historically, such theories had progressed through three stages, in response to the development of trade: classical, neoclassical, and modern (Štěrbová et al., 2013). Nowadays, despite its extreme simplifications, theories of international trade originate from the concept of CA, following the common idea that CA generally determines the structure of trade (exports and imports) of a given country (Golub & Hsieh, 2000). Nevertheless, the explanation of the international trade based on the theory of CA has started to be gradually replaced by theories focusing on differences in countries’ ability to deal with factors of production, human capital, and similarity of preferences. Today’s theories thus highlight global economic shifts, economic models, and the link of international trade with the external economic balance (Štěrbová et al., 2013).
CA theory was developed by David Ricardo, following Adam Smith’s theory of absolute advantage. According to Smith’s theory, a country should specialise in production of such goods that it can produce with absolutely lower costs than other countries. In other words, a country should produce the goods which it is able to produce using less work than others. These goods should then be exported to countries in which their production is more expensive. Conversely, it should import the products which these countries are able to produce at the cheapest price (Neumann et al., 2010). Ricardo’s approach tries to eliminate one of the most significant defects of Smith’s theory pointing to the fact that a country does not necessarily have to possess an absolute advantage (i.e. not all countries are able to produce goods cheaper than the rest of the world). The theory of CA, on the other hand, states that even if this is the case, the country’s participation in international trade pays off
10 (Štěrbová et al., 2013). According to Ricardo’s theory, a country with a CA exports the goods it produces with relatively lower opportunity costs than its trading partners. By focusing on these products, the country makes its international trade most efficient (Ricardo, 1891).
CA can be also defined as the relatively largest absolute advantage if the country has an absolute advantage in the production of both products, or conversely, as the relatively smallest absolute disadvantage if the country has an absolute disadvantage in the production of both products (Neumann et al., 2010).
Neo-classical economists, for example Heckscher and Ohlin, have defined the shortcomings of the classical theory of international trade and further developed it, thus eliminating the weaknesses of orthodox theories. Among others, they stood against disregarding the movements of factors of production and overemphasis on the value of labour, which neglected the changes in incomes from other production factors (Ohlin, 1935). Building on these shortcomings, Ohlin (1935) came up with a theory according to which a country that is relatively better endowed with capital should specialise in the production of capital- intensive goods. Similarly, a country relatively better endowed with labour and possessing relatively limited capital should produce and export labour-intensive goods (Ohlin, 1935).
Nevertheless, this theory has been criticised basing on the fact that not all of its assumptions correspond with the state of reality. For example, the assumption of very limited mobility of production factors is misguided, especially in case of the capital. In the 21st century, we can even state that the situation is close to perfect capital mobility. Thus, a country with a relative shortage of capital has the possibility to import capital and still focus on the production of capital-intensive goods. Furthermore, Ohlin’s theory states that it is impossible to replace capital with labour and vice versa, which, in reality, is indeed feasible. This means that the same product can be capital-intensive in one country and labour- intensive in another (Neumann et al., 2010).
Samuelson (1948) broadened Heckscher-Ohlin’s theory by stating that international trade results in the equalisation of factor prices. This is due to the impact of demand for factors of production on their prices, which leads to changes in the structure of production and trade.
While verifying the Heckscher-Ohlin model, Leontief (1953) discovered that the USA exported labour-intensive goods, although it was relatively better endowed with capital. The author himself explained the paradox by the character of human capital, which was more educated, experienced and as a result more productive. Large investments in human capital resulted in a highly skilled labour force which created the efficient US labour supply, much larger
11 than the mere numbers of workers used to calculate the relative endowment of labour in a given country within the Heckscher-Ohlin’s model (Leontief, 1953).
With the rise of modern tendencies in the world economy, classical and neo-classical theories of international trade based on the concept of CA have been re-examined. The international trade is now exposed to unprecedented challenges, which has an impact on how the CA theory is perceived. Globalisation changes the way gains from trade are redistributed and factors such as research and development policy, corporate governance rules, exchange rate manipulations, government procurement policy, and policies improving global competitiveness of firms can influence the trade flows which used to be based mainly on distribution of factors of production and climate differences (Palley, 2008). Basing on macroeconomic policies, Comory and Baumol (2000) analysed how trade patterns change. They concluded that policies such as undervalued exchange rates or industrial subsidies could change a country’s export profile, cause market distortion and help some industries acquire CA they would not have obtained without government assistance (Comory & Baumol, 2000). Volatility of trade costs associated with automation, price shocks, etc. is nowadays perceived to have a significant impact on trade, although CA has been proven to still be meaningful in determining the direction of trade flows (Deardorff, 2014).
2.2 Empirical usage of comparative advantage law for detecting export opportunities
If the concept of CA is to be applied empirically (to estimate export opportunities of a given country), some methods of quantification need to be used. Classical and neo-classical theories are based on relative autarkic prices which are almost impossible to calculate in today’s interrelated economic world (Ballance et al., 1987). In view of this, Ballance et al. (1987) proposed a way to link the theoretical concept of CA with empirical application using the following scheme:
EC → CA → TPC → RCA
Economic conditions (EC) of a given country have a determinative impact on the CA the country holds, which copies the structure of international trade, production, and consumption (TPC). Revealed comparative advantage (RCA) is based on the indices used to calculate TPC. Ballance et al. (1987) state that it is appropriate to use post-trade indicators
12 to determine RCA. Some limitations were incorporated into the model, including: i) relating the amount of export, production, and consumption to the country size and importance of the good; ii) taking into consideration the impact of government policies that distort trade relations; iii) dealing with the aggregation of the data. Despite them, Ballance et al. (1987) state that using post-trade data will reveal the general pattern behind the export flows.
The most used index measured to reveal RCA on the basis of ex-post data is Balassa index (BI).
BI is based on available data on the exports of a certain commodity (a good) by a country, total exports of the country, world’s exports of the commodity (a good) and world’s total exports, and is measured as follows:
𝐵𝐼 (𝑐, 𝑖) =
𝑥 (𝑐,𝑖)
∑ 𝑥 (𝑐,𝑖)𝑖
∑ 𝑥 (𝑐,𝑖)𝑐
∑𝑐,𝑖𝑥 (𝑐,𝑖)
,
where x (c, i) is the amount of exports of product i from country c. The numerator expresses the share of exports of a certain good in the total exports of the country. The denominator indicates the share of exports of the same good in the world in the total world’s exports. If BI is higher than 1, the share of the exports of this good in the country is higher than the share of exports of the same good in the world’s exports, meaning the country has RCA and exports the product efficiently. BI enables not only to detect RCA, but also to compare the indexes between countries (Balassa, 1965).
BI was criticized in some respects in relation to its inability to precisely indicate CAs between countries. The demand bias in favour of a particular good can change the structure of net trade in a positive or negative way, while national preferences can only have a positive effect on net exports (Lundbäck & Torstensson, 1998). Trade and other policy measures (especially tariffs and non-tariff measures) can distort the results obtained by using RCA leading to lower possible efficiency that could have been achieved without introducing protectionist measures (Bojnec, 2001). When it comes to large economies that benefit from the economies of scale, trade liberalisation can significantly contribute to further anchoring of existing trade specialisation, thereby increasing return to scale (Bastos & Cabral, 2007).
Some researchers built on BI to define export opportunities of a given country. Russow and Okoroafo (1996), while working on their global screening model, defined three areas that have a significant impact on countries’ export opportunities and are omitted by BI:
13 i) market-size and growth of a country of destination; ii) costs and availability of factors of production; iii) economic developments of exporting country. These areas were further incorporated in other models estimating export opportunities of potential markets. While working on Decision Support Model (DSM), Cuyvers et al. (1995) decided to broaden the Balassa’s model by incorporating all above-mentioned areas in their research. After eliminating countries that are not sufficiently economically and politically stable (using macroeconomic indicators, such as credit risk ratings), the markets that do not show an adequate size or a sufficiently large growth of their economies to provide possibilities for exports are left out of the analysis (mainly based on the Gross National Product (GNP) and GNP per capita).
The second step is to eliminate markets that do not show adequate market size or sufficient growth using the following destination market indicators: short-term import growth (percentage growth of imports between two most recent years), long-term import growth (average annual percentage of growth of imports over a period of five years), and relative import market size (share of imports of country i for product j from a total import of the product j to country i).
After eliminating unpromising product groups in terms of the target market using cut-off variables, the analysis of trade restrictions is conducted to define realistic export opportunities.
The analysis contains two categories of barriers: the degree of concentration (since a partial analysis revealed that there is a significantly negative correlation between export performance and market concentration) and trade restrictions. The last step is assessing the export position of the exporting country for each of the remaining realistic export opportunities through BI.
The main shortcoming of the model is the high aggregation of data. The calculation is done at two-digit levels, which are very heterogeneous and the actual products they encompass may vary in terms of market attractiveness. Moreover, the assessment of target market is based on past developments in imports, omitting their predictions in the future (Cuyvers et al., 1995).
Another problem of BI, addressed by Lafay (1992), is that it is time invariant, which means it cannot show the evolution of CA over time. To monitor the development of CA over time, the Lafay index (LFI) was proposed. LFI is calculated as follows:
𝐿𝐹𝐼 (𝑐, 𝑖) = 100 ∗ (𝑥𝑐,𝑖−𝑚𝑐,𝑖
𝑥𝑐,𝑖+𝑚𝑐,𝑖−∑ (𝑥𝑖 𝑐,𝑖−𝑚𝑐,𝑖)
∑ (𝑥𝑖 𝑐,𝑖+𝑚𝑐,𝑖)) ∗ 𝑥𝑐,𝑖+𝑚𝑐,𝑖
∑ (𝑥𝑖 𝑐,𝑖+𝑚𝑐,𝑖),
where x (c, i) is the amount of exports of product i from country c, m (c, i) is the amount of imports of product i to country c (Tlapa et al., 2019). LFI quantifies a country’s CA for a particular product by comparing the share of the trade balance of the product
14 in the turnover of the item with the share of the total trade balance in the turnover of the country.
The weight here is the share of turnover of this item in the total turnover of trade. LFI does not measure CA in relation to other countries but shows a CA above the overall trade structure of the country. Therefore, positive LFI values show that a country has a CA and indicate the degree of specialisation of the item (the higher the value of the index, the higher the degree of specialisation). Negative values indicate a comparative disadvantage (degree of non-specialisation) of the given item. LFI average is therefore always zero. To compare the items according to their CA, calculated LFIs for all items are sorted in descending order.
In this way, it is easy to find out which of these items show the largest CA in a given country.
From the calculated values of the LFI index for individual items, it is also possible to calculate the cumulative value of the LFI (Lafay, 1992).
In 2016, the Ministry of Foreign Affairs of the Czech Republic prepared an input analysis to identify export opportunities. The analysis includes the intersection of CA development over time (using LFI), the growth dynamics of the target market, and the untapped EP of the exporter in the partner market with a two- to three-year outlook. The results were published in the Map of global industry opportunities and in the Map of strategic opportunities to provide Czech exporters with an overview of promising markets. The first step of the analysis is the selection of commodities in which the exporter shows RCA and the partner a comparative disadvantage (called export competence). The exporter’s export competence is measured by calculating CAs for individual product groups (or items) using LFI. An exporter has a CA in item A if the share of its net exports of item A in the turnover of item A is greater than the share of its total net exports in the turnover of foreign trade. Otherwise, item A has a comparative disadvantage.
According to the model, those items where the exporter has a CA and the importer comparative disadvantage are selected. The second factor is the target market’s imports growth dynamics.
When following the imports growth dynamics of the target market, it is important for the methodology that the growth rate does not slow down in the period under review.
The dynamics of the target market growth (MG) is calculated as a geometric mean as follows:
𝑀𝐺 (𝑐, 𝑖) = ∏ ( 𝑚𝑡,𝑐,𝑖
𝑚𝑡−1,𝑐,𝑖)
1 𝑛 𝑛−1
𝑡=2 ,
where mt,c,i is the amount of imports of product i to country c in year t. The model is interested in such items that have at least 10% year-on-year growth in imports in the target market over the last 4 years. Items whose import growth has been faster in the last two years than in the first
15 two years are selected in order to exclude the items for which import growth is saturated.
The third factor in the methodology is EP of the Czech Republic on a global scale. In this step, items that do not meet the expected EP of the Czech Republic in the target market are selected.
This means that the share of Czech exports of the item in the partner market is at least twice as small as its share in the world market. The final selection of items with export opportunities then includes those items that meet all three conditions (Tlapa et al., 2019).
The methodology presented in the Map of global industry opportunities has several limitations.
Firstly, its ambition is not an in-depth analysis of the competitive environment, which would actually affect the extent to which companies could establish themselves in foreign markets.
Moreover, the methodology is not capable of estimating to which extent the item is prospective, which does not allow the comparison of given items and choice of the most promising ones.
Another aspect is strictly defined criteria for import growth, which includes the minimum rate of 10% year-on-year growth in import in the target market over the last 4 years and the condition of higher imports growth in the last two years compared to the previous two. These conditions create high barriers, which impede most of the items from entering the category of products with export opportunities. It is also emphasised by the Ministry of Foreign Affairs that the model is not capable of capturing many of the factors affecting trade between the exporter and the target market (such as security and political risks, trade agreements, tariff advantages, cultural proximity, etc.). The model relies mostly on experts from each target territory to identify significant technical and non-technical barriers to entry into the target market for each promising item identified (Tlapa et al., 2019).
Several successful alternative attempts to create a model that would identify export opportunities have also been made, for example, by Green and Allaway (1985). They used the shift-share model, the core of which resides in the calculation of market share changes over a selected period of time. Data on imports of a certain product to a given country are used to calculate average market share change for each product-country combination (actual growth). Expected import growth is calculated based on the average growth of all importing countries. The difference between the actual and expected growth of each market is defined as a net shift and will be positive for markets that have gained market share in that country for a defined period of time. For comparison between product groups, the percentage net shift is divided by the total net shift of all markets included in the analysis. The main weakness of the analysis is the focus on import-only measures. What is more, the results may be biased
16 based on the years chosen since the model is based on only two points in time (Papadopoulos et al., 2002).
2.3 Models for calculating export potential
The theory of CA is not capable of predicting the volume of trade for each product. According to the Ricardian model, a country will specialise in exporting the goods with higher comparative labour productivity than its trading partner. On the contrary, it will import those goods where its labour productivity is lower than of its trading partner. Nevertheless, the amount of trade is not influenced by the degree of the country’s CA or disadvantage, because the theory of CA is based on the assumption of a country’s full specialisation in its exported products.
That is why the models of the EP (where the volume of trade is predicted) are not based on the CA theories (Rauch, 1991).
A few theories went on not only to estimate trade opportunities, but also to calculate trade potential between countries. Gravity model is a model used to estimate (and predict) the volume of trade between two countries based on GNP of an exporter and an importer and the geographic destination between them (Tinbergen, 1962). Tinbergen (1962) measured the exports from country i to country j by means of the following formula:
𝐸
𝑖𝑗= 𝛽
𝑜𝑌
𝑖𝛽1𝑌
𝑗𝛽2𝐷
𝑖𝑗𝛽3,
where Eij are exports from country i to country j, Yi – GNP of exporter i, Yj – GNP of importer j, Dij is the distance between two countries. β0 is regression constant, β1 till β3 are dummy variables that test for specific effects, such as cultural proximity, trade agreements, sharing a common land border, etc. (Rahman, 2003). The model implies that there is a positive correlation between income growth (GNP growth) of trade partners and trade flows between them, and a negative correlation between distance and volume of trade.
The gravity model was widely criticized mainly because of omitting the effect of trade barriers on trade (Hummels, 2001). Further studies proved the possibility to estimate trade potential on the basis of gravity model by adding an estimation of trade barriers impact on trade and measuring the impact of change in tariffs on future trade flows (Melchior et al., 2009).
Other revisions included administrative costs associated with trade procedures, institutional inefficiency and lack of transparency, differences in transportation costs or increased exposure
17 to misappropriation when the distance increase (since the impact of distance is different when considering close trade and trade on longer distances – as opposed to the gravity model, according to which the impact is constant) (Anderson & Marcouiller, 2002). Anderson and Marcouiller (2002) have also proved that income rise does not necessarily mean increase in the expenditure devoted to traded goods. Considering all these implications, Egger (2002) claims that trade potential cannot be calculated by the gravity model and the difference between observed and predicted trade represents misspecification of the model instead of unused trade potential.
The model of ITC for identifying EP considers all three general factors indicated as essential by Russow and Okoroafo (1996) in the global screening model and subsequently used in DSM by Cuyvers et al. (1995), although it does not rely on filters (cut-off points) but rather calculates all the possible product-market combinations and establishes a basis for their comparison. EPI was inspired by the gravity model, but it is focused on specific product categories, includes estimation of tariff barriers to trade, and eliminates some of the shortcomings of the gravity model. The indicator was designed to help governments promote exports to new or already existing export destinations by identifying products that have already demonstrated worldwide competitiveness. The result of the calculation helps to detect product items that have a good chance of succeeding in specific target markets.
The assumption behind the EPI estimates that in a world without frictions, trade flows might be characterized as a mix of the possible supply of the exporting country, the demand by the importing country and the easiness of trade between them. Therefore, the calculations are based on the separate estimation of the supply capacities of the exporting country, demand conditions on the destination market and easiness of trade between two countries (Decreux & Spies, 2016).
The supply capacity of the exporter is measured the following way:
𝑆𝑢𝑝𝑝𝑙𝑦𝑖𝑘𝐸𝑃 = 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑀𝑆𝑖𝑘∗ 𝑇𝐵𝑖𝑘∗ 𝐺𝑇𝐴𝑖𝑘 ,
where Projected MSik is projected market share of exporter i for good k (based on the projected increase in GDP), TBik is export-import ratio of exporter i (used to eliminate re-export impact), GTAik is global margin of preference that exporter i faces when exporting product k (based on average tariffs).
18 The first component of the supply estimation is projected market share (Projected MSik) corrected for the rise (or decline) in GDP in five years. It is worth mentioning that the model including correction for the GDP growth is highly relevant especially for the Czech Republic since it has been proved that the Czech Republic is a country with not only export-led growth, but also with growth-led exports. The Czech Republic spurs growth through supporting exports and engaging in global supply chains, which means that participating in international trade is crucial for its economic development. The Czech Republic proved to be one of the CEECs to experience growth-led exports given that it actively encourages exports, enters in trade agreements, opens up the economy and supports technological change to make its exporters globally competitive (Silaghi, 2009). The export-import ratio (TBik) is the second factor in the supply calculation. Through the export-import ratio re-exports are eliminated from the calculation since they do not reflect the capacity of a country to manufacture the given good. The last but not least component in the supply formula is the global margin of preference (GTAik), which reflects tariff advantages a country faces when selling the product, modified by the price elasticity to imports. Price elasticity reflects sensitivity of imports to changes in price, which differs significantly according to the product item considered.
Demand conditions in the distance market are measured by means of the following formula:
𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑗𝑘 = 𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑀𝑗𝑘∗ 𝑀𝑇𝐴𝑖𝑗𝑘 ∗ 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑓𝑎𝑐𝑡𝑜𝑟𝑖𝑗𝑘 ,
where Projected Mjk is projected import of importer j (based on GDP per capita growth), MTAijk
is the margin of preference in the target market (calculated on the basis of average tariffs), Distance factorijk refers to the product specific distance advantage indicator, comparing distance between exporter i and importer j to distance to other suppliers of product k.
The first component of the demand estimations is projected import (Projected Mjk), which is calculated as current imports adjusted by the population growth, GDP per capita growth, and the changes in demand caused by the sensitivity of demand to the changes in revenue. The second factor is the margin preference (MTAijk) in the target market which shows whether the exporter has tariff advantage or disadvantage in the target market. If tariffs faced by exporter i are lower than the average tariff applied by importer j, the exporter has a tariff advantage when exporting to this target market which increases the overall import potential. The tariff advantage is again modified by the price sensitivity of trade (increasing the magnitude of the advantage). The third component of the formula is the distance advantage
19 indicator (Distance factorijk), which shows the difference among the distances between the importer j and exporter i and the average distance between the importer j and other countries that supply product k. If the exporter i is far from the target market, it will get a distance disadvantage compared to other suppliers located near the importer, which will be reflected in the product- and country-specific demand calculations.
Easiness to trade is a ratio of actual trade between the exporter i and the importer j to a hypothetical trade between them. It reflects all the factors that are not captured by the previous calculations. It is calculated as follows:
𝐸𝑎𝑠𝑖𝑛𝑒𝑠𝑠𝑖𝑗 = 𝑣𝑖𝑗
∑ (𝑆𝑢𝑝𝑝𝑙𝑦𝑘 𝑖𝑘𝐸𝑃,𝑆𝑡𝑎𝑡𝑖𝑐∗𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑗𝑘𝑆𝑡𝑎𝑡𝑖𝑐), where vij are actual exports from country i to target market j.
In case easiness to trade is higher than 1, exporter i sees market j as a promising trading partner since the indicator shows that it is easier for exporter i to trade with market j than with global markets on average. Reasons for that vary significantly and can include cultural proximity, such as a common language, or already established business ties and trading agreements.
Conversely, if easiness to trade is lower than 1, exporter i finds it harder to trade with importer j than with global markets on average.
As mentioned above, EPI is calculated based on the combination of supply, demand, and easiness to trade components:
𝐸𝑃𝑖𝑗𝑘 = 𝑆𝑢𝑝𝑝𝑙𝑦𝑖𝑘𝐸𝑃∗ 𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑗𝑘∗ 𝐸𝑎𝑠𝑖𝑛𝑒𝑠𝑠𝑖𝑗
Unrealized EP is then calculated the following way (in case of vijk (actual exports from country i to country j of product k) > EPijk,, the unrealized potential equals to zero):
𝑈𝑛𝑟𝑒𝑎𝑙𝑖𝑧𝑒𝑑 𝑝𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙𝑖𝑗𝑘 = 𝐸𝑃𝑗𝑘− min(𝑣𝑖𝑗𝑘, 𝐸𝑃𝑖𝑗𝑘) =
= 𝑆𝑢𝑝𝑝𝑙𝑦𝑖𝑘𝐸𝑃∗ 𝐸𝑎𝑠𝑖𝑛𝑒𝑠𝑠𝑖𝑗 ∗ 𝐷𝑒𝑚𝑎𝑛𝑑𝑖𝑗𝑘− min(𝑣𝑖𝑗𝑘, 𝐸𝑃𝑖𝑗𝑘)
Although the model is complex, it still shows distorted unrealized EP. On one hand, this is due to the exclusion of intangible factors that can influence structural decomposition of bilateral trade, such as the possibilities of marketing, knowledge about the product on the foreign market, branding, trade linkages established before, cultural differences, etc.
20 On the other hand, it is due to several erroneous assumptions of the model, which comprise:
i) perfect competition within each country; ii) ad valorem bilateral trade costs that do not depend on products; iii) the same quality of imported products from all the exporters; iv) the average import price being modified from market to market in the same proportion; v) invariable preferences of a supplier (Decreux & Spies, 2016).
21
3 Calculation of export potential
This part of the thesis is dedicated to the calculation of the overall EP of the Czech Republic in Argentina and to the detection of product groups with the largest EP. The EP is calculated with the use of the ITC methodology since it is the most complex and comprehensive in comparison to other methods explained above. The calculation is restricted to HS 4-digit level because of the availability of data needed for the calculation of the EPI for this level.
The data evaluation is done with use of the Microsoft Excel program, which is provided by the Prague University of Economics and Business to all students.
To describe large datasets, selected characteristics of descriptive statistics are used, focusing mainly on arithmetic mean, median, maximum, minimum, lower and upper quartiles.
The arithmetic mean can be defined as the ratio of the sum of the values of the variable to the number of these values. However, if the dataset contains extreme values, the arithmetic mean represents a biased result. In order not to obtain a distorted picture of my estimations, the median, which is the value separating two halves of the data sample, is calculated. In other words, the median is the value lying in the middle of the dataset sorted in descending or ascending manner. Since it is possible that my calculations will comprise skewed datasets, it is important to obtain also the median value, which serves as a more suitable representative of the central location of data. Comparing the median to the arithmetic mean also provides an overview of the distribution of the dataset. Next, the values of maximum and minimum show the range of data in the dataset. Another descriptive statistical characteristic are quartiles, which divide the datasets into two parts. While the first part of the dataset divided by a lower quartile is made up of 25 % of the lowest values of the dataset, the first part of the dataset divided by an upper quartile is made up of 75 % of the lowest values of the dataset. Thus, quartiles show how the data are spread in the statistical set (Hindls et al., 2018).
To achieve the above set goals of my survey, this chapter has been divided into four parts, three of which contain a detailed calculation of the components of the EPI formula (Supply, Demand and Easiness to trade), while in the last one the results of the EPI calculations are presented.
22
3.1 Supply estimation
The supply part of the calculation gives us an overview of the Czech Republic’s world market shares for each product (restricted to HS 4-digit level), corrected for some of the factors that distort the measure of the true EP (mainly re-exports and tariffs).
170 product groups (13.5 % of the total) were left out from the calculation because of the unavailability of data for Argentina or the Czech Republic. In overall, 1090 product groups were included into the calculation.
The first component of the supply estimation, which is projected market share, is calculated as follows:
𝑃𝑟𝑜𝑗𝑒𝑐𝑡𝑒𝑑 𝑀𝑆𝑖𝑘 = ∑ (𝑣𝑣𝑖𝑘∗∆𝐺𝐷𝑃𝑖
𝑖𝑘∗∆𝐺𝐷𝑃𝑖),
𝑖 ,
where vik corresponds to exports from exporter i of product k to the rest of the world and ∆GDPi
is the difference between the predicted GDP of the exporter in five years (2025) and the exporter’s current GDP (2020). The expected GDP rise for the next five years starting from the most recent time period (2020) is incorporated into the formula to estimate the growth of the exporter’s supply potential with the GDP increase. The Czech Republic’s exports to the rest of the world were taken from the UN Comtrade Database (2021) and were restricted to the HS 4-digit level because of the availability of data needed for supply estimation. The data extraction for the first 10 product groups (restricted to HS 4-digit level) is represented in Table 1.
23
Table 1: Exports of the Czech Republic to the world (first ten product groups)
Product group
code
Description of the product group CZ exports to the world, USD (thousands)
0101 Live horses, asses, mules and hinnies 2,290,000,000
0102 Live bovine animals 189,409,000,000
0103 Live swine 65,130,000,000
0104 Live sheep and goats 193,000,000
0105 Live poultry, fowls of the species Gallus domesticus,
ducks, geese, turkeys and guinea fowls 76,842,000,000 0106 Live animals (excluding horses, asses, mules,
hinnies, bovine animals, swine, sheep, goats, etc.) 16,845,000,000 0201 Meat of bovine animals, fresh or chilled 54,982,000,000
0202 Meat of bovine animals, frozen 3,963,000,000
0203 Meat of swine, fresh, chilled or frozen 74,010,000,000 0204 Meat of sheep or goats, fresh, chilled or frozen 1,539,000,000
Source: UN Comtrade (2021)
Data for the GDP growth projection were extracted from the World Economic Outlook Database (IMF, 2021). For all product groups considered, the GDP growth is the same constant number, which corrects current market shares for the overall growth (or decline) of the Czech economy in the future.
Table 2: Projection of the GDP growth in the Czech Republic (2020–2025)
GDP, constant prices, national currency (billion)
2020 2021 2022 2023 2024 2025
∆GDPi
= GDP2025/
GDP2020
Czech Republic 4996.11 5186.29 5418.04 5641.66 5842.95 6027.60 1.2065
Source: IMF (2021)
24 According to the projections of the IMF (Table 2), the GDP of the Czech Republic will grow by 20.65 % in the period from 2020 to 2025. Therefore, for the upcoming calculation of market shares, exports are corrected by the 20.65% GDP growth to prevent the distortion of the Projected MS indicator.
After correcting the exports from the Czech Republic to the rest of the world for the GDP growth, Projected MS for each product group (restricted to HS 4-digit level) was calculated.
The characteristics of descriptive statistics were used for a clearer description of the data.
Not all the characteristics were used; the focus was made on the most relevant ones, which are arithmetic mean, median, first and third quartiles, minimum and maximum.
The descriptive statistics for Projected MS indicator are presented in Table 3. The average Projected MS, which is 0.0904 %, differs significantly from the median (0.0097 %). The median is lower than the arithmetic mean, which shows that the distribution of the data is skewed to the right. Moreover, there are more product groups whose Projected MS is lower than the arithmetic mean, so in general most product groups have Projected MS lower than 0.0904 %. Product group 8703 (Motor cars and other motor vehicles principally designed for the transport of persons, incl. station wagons and racing cars (excluding motor vehicles of heading 8702)) has the maximum Projected MS, which is 10.7576 %. Product group 3807 (Wood tar; wood tar oils; wood creosote; wood naphtha; vegetable pitch; brewer's pitch and similar preparations based on rosin, resin acids or vegetable pitch (excluding Burgundy pitch, yellow pitch, stearin pitch, fatty acid pitch, fatty tar and glycerin pitch)) has the minimum Projected MS, approaching 0 %. First 25 % of Projected MS lie under 0.0015 %, last 25 % of data are larger than 0.0463 %.
The export-import ratio is the second factor in the supply estimation. It is defined as 𝑇𝐵𝑖𝑘 = min (1,𝑥𝑖𝑘
𝑚𝑖𝑘),
where xik refers to exports from exporter i of product k to the rest of the world and mik states imports of exporter i of the product k from the rest of the world. Through the export-import ratio, re-exports are eliminated from the calculation since they do not reflect the capacity of a country to produce a given good. As a result of incorporating the second factor in the supply estimation, the product that has the potential to be competitively exported is downgraded in case its imports are higher than its exports. However, in case of an opposite situation (when exports exceed imports) the supply potential stays without changes.
25 The data for the Czech Republic’s exports and imports were extracted from UN Comtrade (2021). The results of the calculations are the following: 408 product groups in the Czech Republic are not going to be corrected for re-exports because the exports exceed the imports, which means that the capacity of the Czech Republic to produce these goods are not distorted by re-exports. The potential supply of other 682 product groups will be decreased since the imports exceed the exports. The product group mostly affected by re-exports is 2601 (Iron ores and concentrates, incl. roasted iron pyrites), where the imports of the goods from this product group to the Czech Republic exceed almost 82,000 times the exports.
The last component in the supply formula is the global margin of preference, which reflects tariff advantages a country faces when selling the product. This indicator is calculated as follows:
𝐺𝑇𝐴𝑖𝑘 = (1+𝑎𝑣.𝑡𝑎𝑟𝑖𝑓𝑓𝑖𝑘 1+𝑎𝑣.𝑡𝑎𝑟𝑖𝑓𝑓𝑘)𝜎𝑘,
where av.tariffik is the weighted average tariff charged by importer j on product k (importer’s j imports of product k from suppliers i are utilized as weights), av.tariffk refers to the weighted average tariff the exporter i faces when exporting product k, σk >0 constitutes the substitution elasticity among imports from different suppliers assigned to product k. If the numerator exceeds the denominator, the Czech Republic’s supply estimation will grow since the GTA>1 reflects that the exporter has a tariff advantage on the target market. The stronger is the tariff advantage, the higher is the increase of the estimated market share. The tariff advantage is then modified by the sensitivity of trade to prices, which tells us how easy it is for the market to switch from one supplying country to another if prices increase. It is also more possible that the GTA is larger if the Czech Republic enjoys significant tariff advantages on certain goods in world markets.
To calculate the average tariff Argentina imposes on its suppliers and the average tariff the Czech Republic faces when exporting its goods to the rest of the world, the data from ITC (2021) was used. Data for price elasticities were taken from Global Trade Analytics project (GTAP) database that distinguishes 43 sectors (Dimaranan et al., 2006). Price elasticity for each product group was assigned manually on the basis of the sector name. Prices elasticities for all 43 sectors are presented in Annex 1.
