LITHUANIA S AGRI-FOOD PRODUCTS EXPORT 1 Evaldas Serva 2, MA, Aleksandras Stulginskis University Vlada Vitunskiene, Dr., prof., Aleksandras Stulginskis University Abstract. The article focuses on the evolution of Lithuania s Agri-Food products market shares in the world exports and main markets over the period of 2001-2012. Since the authors are interested in the shift of export of Lithuania within the main markets context, this study aims to analyse shifts in specialisation of Lithuania s Agri-Food products export and to investigate the link between competitiveness in major markets and specialisation. A world market share methodology was used for this purpose: changes of share in the main partners markets and two specialisation indices RCA, proposed by Ballasa (1965) and Local competitiveness, proposed by Imagawa (2004). The shift level in pattern of geographical and product specialisation was calculated as ratio of Local competitiveness (LCI) and RCA indices. Key words: foreign trade, shift, revealed comparative advantage. JEL code: F14 Introduction The debates on the export specialisation and shifting in patterns of trade measures have been continuing for more than 50 years since Bela Balassa published a study using a measure of revealed comparative advantage. In recent academic literature, the pattern of foreign trade specialisation has been measured using various variations of Balassa index. Alessandrini et al. (2007) examined the pattern of specialisation analysing the growth in the world demand using Lafay s index like a signal that trade specialisation has improved precisely among those sectors that could bring the largest benefits to the economy, in terms of their export potential. Amador et al. (2008) investigated the evolution of Portuguese market shares in the world exports using decomposed market share technique, which consists of market share effect, taking into account the effective changes of share in each product/geographical market and two additional terms that show how the geographical and product composition of Portuguese exports affected developments in the overall market share. Widodo (2008) employs statistical hypothesis test procedure of correlation on the Revealed Symmetric Comparative Advantage (RSCA) for the shift analysis. Del Gato et al. (2012) uses ratio of the change in world exports decomposed as the sum of changes across product categories to clarify the changes in total export shifts. This study aims to examine empirically the patterns and dynamics of Lithuania s Agri-food export specialisation in main markets over the period of 2001-2012. Seeking for this aim, the authors focus on the following research tasks: to assess the patterns and dynamics of Lithuania s Agri-food export specialisation; 1 The article is presented under the financing of Research Council of Lithuania 2 Corresponding author. Tel.: + 370 611 30817 E-mail address: Evaldas.Serva@asu.lt 135 ISSN 1691-3078; ISBN 978-9934-8466-1-8
to provide methodology for analysing shifts in trade patterns in the main partners markets. The structure of the article is organised as follows: the methodology for shift changes is presented in the first section. Section 2 presents the results of the Lithuania s Agri-food export analysis over the period of 2001-2012. The final section provides the outcomes and conclusions of the study. Research results and discussion 1. Measuring shifts in the pattern of specialisation of Lithuania s agri-food products export Based on the integrated evaluation method, which is defined as integrated shift of export specialisation indicator, the authors performed the measurement of the country s specialisation and shift changes. Specialisation and shifts were measured according to the following categories. The first the significance of indicators (SI) is determined and significance for 2-digit of Harmonised Nomenclature (HS4) agriculture and food products over the period 2001-2012 is computed employing the following equation: where (1) ; Major export category (Mx) defines the values of the largest sectoral (HS4 01-24) export share in total exports of a k economy: where Mx major export; x - export; k any specified commodity; i exporting country. (2) Export market share (EMS) measures the degree of importance of a country within the total exports of the world: (3) where Ms market share; x export; m import; k any specified commodity; i exporting country; j importing country; s set of countries. The second the level of specialisation. RCA being determined as the main indicator of specialisation is measured. The concept of RCA is widely used in practice and linked to the analysis of a country s capabilities and its potential productive capacity (Ferrarini et al., 2011). Imagawa (2004) and Cai et al. (2009) indicate that there are wide variations in measuring the index of export specialisation as well as in asymmetric and symmetric ways (Laursen, 1998). The RCA index shifts from 1 to +. Another method or matrix method is suggested by Hinloopen et al. (2001). They divide the Balassa s index into 4 classes which can be readily interpreted: class a: 0 < Balassa s Index <1; class b: 1 < Balassa s Index <2; class c: 2 < Balassa s Index <4; class d: 4 < Balassa s Index. Class a captures all the products / industries / 136 ISSN 1691-3078; ISBN 978-9934-8466-1-8
sectors without a comparative advantage. The other three classes, b, c, and d, include products / industries / sectors with a comparative advantage, roughly divided into weak comparative advantage (class b), medium comparative advantage (class c), and strong comparative advantage (class d). Specialisation in our research is determined by the following equation: where: RCA revealed comparative advantage; k any specified commodity; i exporting country; j importing countries. (4) As pointed by Cai et al. (2009), Balassa s RCA index is a measure of comparative advantage at a point in time, it seems natural to use the difference between RCA indexes at the beginning and the end of a period to measure the change of comparative advantage during the period. Although, this has been a common practice, the direct use of the difference between RCA indices at different time periods measures specialisation shifts. The third the level of specialisation in major markets is identified. For this purpose, the Balassa s RCA index variation composed by Imagawa (2004) is used to study the international competitiveness in the specific (local) region. If international competitiveness index exceeds 1, the sector of the considered country is competitive on the partners market and it is non-competitive when it is lower than 1. Specialisation on the specific market is determined employing the following equation: (5) where ICM international competitiveness on a specific market x export; m- import; k any specified commodity; i exporting country; j importing country. The last shift level (SL) is identified. The shift level was calculated as a ratio of LCI and RCA indices: (6) To determine the Lithuania s Agri-Food products specialisation and competitiveness in the main markets shifts, the LCI was used as the numerator and RCA as the denominator. If calculated index SL 137 ISSN 1691-3078; ISBN 978-9934-8466-1-8
exceeds 1 over the period, the relative significance of a particular market is greater than specialisation of k product export, and the relative significance of a particular market is lower when the calculated index is lower than 1. This method is particularly useful when linking the competitiveness at a geographical level and trade specialisation of the country. The findings of the authors study allowed choosing the evaluation scale (Table 1). Division of shifts by classes in terms of specialisation Table 1 Class Criterion Description a 0<SL<1 class a captures products without shifts in b 1<SL<2 patterns class a captures of trade products with weak shifts in c 2<SL<4 patterns class a captures of trade products with medium shifts in d SL>4 patterns class a captures of trade products with strong shifts in Source: authors construction based on Balassa, B. (1965) patterns Trade of trade Liberalisation and Revealed Comparative Advantage, 2013 2. Data and empirical findings Having presented the theoretical background in the previous section, in this section, an empirical examination of Lithuania s agricultural products export shifts on the main partners market will be presented by the authors. As it has already been indicated in Section 2, the empirical analysis is based on major export (Mx, results presented in chart 1), export markets share (EMS, results are presented in chart 2), revealed the comparative advantage (RCA, results are presented in Table 1), local international competitiveness (LCI, results are presented in Appendix 1), ratio between LCI and RCA, and shifts on the main partners markets (results are presented in Table 4). The empirical analysis is based on the annual time series data on agricultural exports, extracted from TRADEMAP database. Shift indices are calculated as aggregated 2-digit of Harmonised Nomenclature (HS4) over the period of 2001-2012. There are 24 two-digit headlines in the HS4 categories: 01 Live animals; 02 Meat and edible meat offal; 03 Fish, crustaceans, molluscs, aquatic invertebrates nes; 04 Dairy products, eggs, honey, edible animal product nes; 05 Products of animal origin, nes; 06 Live trees, plants, bulbs, roots, cut flowers etc; 07 Edible vegetables and certain roots and tubers; 08 Edible fruit, nuts, peel of citrus fruit, melons; 09 Coffee, tea, mate and spices; 10 Cereals; 11 Milling products, malt, starches, inulin, wheat gluten; 12 Oil seed, oleagic fruits, grain, seed, fruit, etc, nes; 13 Lac, gums, resins, vegetable saps and extracts nes; 14 Vegetable plaiting materials, vegetable products nes; 15 Animal, vegetable fats and oils, cleavage products, etc; 16 Meat, fish and seafood food preparations nes; 17 Sugars and sugar confectionery; 18 Cocoa and cocoa preparations; 19 Cereal, flour, starch, milk preparations and products; 20 Vegetable, fruit, nut, etc food preparations; 21 Miscellaneous edible preparations; 22 Beverages, spirits and vinegar; 23 Residues, wastes of food industry, animal fodder; 24 Tobacco and manufactured tobacco substitutes. Table 2 shows the changes of agricultural and total products export to the world. From 2001 to 2012, the Agri-Food products export to the World increased almost tenfold from 567007 to 5451343 thousand US dollars. It is worth mentioning, that the growth of export was declining only in 2009 (both totally and in agricultural products). 138 ISSN 1691-3078; ISBN 978-9934-8466-1-8
Year Lithuania s trade (HS4 01-99) from 2001 to 2012 (USD, thousand) Export to the World (Total, HS4 01-99) Change (%) Export to the World (Total, HS4 01-24) Change (%) 2001 4583050-567007 - 2002 5475632 19.48 587543 3.62 2003 7162433 30.81 833677 41.89 2004 9302609 29.88 1066592 27.94 2005 12070444 29.75 1524285 42.91 2006 14135190 17.11 1974431 29.53 2007 17162396 21.42 2925245 48.16 2008 23769895 38.50 3776070 29.09 2009 16496339-30.60 3232880-14.39 2010 20813923 26.17 3764567 16.45 2011 28068648 34.86 4596398 22.10 2012 29652662 5.64 5451343 18.60 Source: authors calculations based on Trade Statistics from Trademap.org database, 2013 Table 2 Significance for 2-digit of Harmonised Nomenclature (HS4) agriculture and food products over the period of 2001-2012 were calculated using Equation 1, taking into further analysis product groups with export share greater than 0.8 (average share in the total export in 2001-2012 and world market share greater than 0.003 (average world market share in 2001-2012). % % Fig. 1. Constant export share analysis (share in the total export) of Lithuania Agri-food products exports, 2001-2012 Product Code Fig. 2. Constant market share analysis (percentage in the world export) of Lithuania Agri-food products exports, 2001-2012 Source: authors calculations based on Trade Statistics from Trademap.org database, 2013 Live animals (code 01) and Tobacco and manufactured tobacco substitutes (code 24) group products were excluded from further specialisation and market analysis. Hence, those product groups have significant world market share (Chart 2, Live animals 0.0038 and Tobacco and manufactured tobacco substitutes 0.0049 percent), Live animals product group were excluded because of a low export share in a total Lithuania s export (average export share in 2001-2012 0.37 percent), Tobacco and manufactured tobacco substitutes group products were excluded because of lack of markets (main export goes to one market the Netherlands). Finally, Dairy (code 04) products (2.7 average share in the total export in 2001-2012 and the world market share 0.0072), Edible vegetables and certain roots and tubers (code 07) group products (0.87 average share in the total export in 2001-2012 and the world market share 0.0034), Cereals (code 10) 139 ISSN 1691-3078; ISBN 978-9934-8466-1-8
group products (1.36 average share in the total export in 2001-2012 and the world market share 0.0031), Meat, fish and seafood food preparations (code 16) group products (0.80 average share in the total export in 2001-2012 and the world market share 0,0039), Residues, wastes of food industry, animal fodder (code 23) group products (1.37 average share in the total export in 2001-2012 and the world market share 0.0048) were included into analysis. Table 3 presents RCA indices for five main export groups in 2001-2012. As indicated, during 2001-2012, there was a shift towards comparative advantage ( RCA - 6.05) exporting Dairy products that indicates greater specialisation in Lithuania s 04 category group products exports. As expected from the previous analysis, the results for other four product groups (07, 10, 16, 23), also revealed comparative advantages, while the variation of RCA indices for each category was greater than in 04 group (Table 3). Also, the decline in RCA values for 07, 10, 16, 23 category group products was greater after crisis in 2008-2009 than for 04 category group products. The Balassa RCA index in agri-food products, 2001-2012 Code 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 RCA St. devi atio n Table 3 04 7.21 5.85 5.26 6.59 6.12 6.72 7.26 5.39 6.04 5.70 5.10 5.38 6.05 0.75 12.3 07 1.39 0.76 1.34 1.45 1.24 2.36 3.13 3.87 2.79 3.51 3.94 5.01 2.57 1.35 52.5 10 1.91 1.29 2.40 1.98 2.85 1.95 2.41 3.02 3.39 2.74 1.87 3.42 2.44 0.66 27.2 16 2.30 2.66 2.74 3.03 3.49 3.62 3.52 3.25 4.45 4.45 2.36 2.49 3.20 0.74 23.2 23 5.40 5.08 5.41 4.58 4.05 4.59 4.55 3.45 3.74 3.45 2.73 2.49 4.13 0.98 23.7 Source: authors calculations based on Trade Statistics from Trademap.org database, 2013 V RCA Final step of the analysis was to identify the link between competitiveness at a geographical level and the country s trade specialisation. This ratio method is useful for analysing stable and growing export markets. During the period from 2001 to 2012, the export of the 04 group products was found unstable at international markets positions - 6 of 10 top markets upon the ratio between LIC and RCA were in the first group (products without shifts in the patterns of trade). The increases in the export share in Italy (as indicated by LIC and RCA ratio raised from 0.15 in 2001 to 5.32 in 2008 and 3.80 in 2012) and in Poland (as indicated by LIC and RCA the ratio in last five considered years raised from 2.00 to 2.67) was the major explanation for the observed positive SL effect. During the period from 2001 to 2012, all 07 group products markets (except for Switzerland) were without shifts in patterns of trade. However, it is worth mentioning the decline on a German market (as indicated by LIC and RCA ratio declined from 1.90 in 2001 to 0.24 in 2012). During the period from 2006 to 2012, the increases in the export share of 10 group products in the Saudi Arabia (as indicated by LIC and RCA ratio during the period of 2006-2012 increased from 5.15 to 14.11), Turkey, Iran (as indicated by LIC and RCA ratio in 2012 3.52, no export before 2012), German, Spain were the major explanation for the observed positive SL effect. The SL on the mentioned markets was either in the third or the forth class. 140 ISSN 1691-3078; ISBN 978-9934-8466-1-8
Table 4 Shifts in geographical specialisation of Lithuania agri-food products export, 2001-2012 Product 0<SL<1 1<SL<2 2<SL<4 SL>4 HS4 04, Dairy products HS4 07, Edible vegetables HS4 10, Cereals Russian Federation, Germany, Latvia, the United States of America, Estonia, the United Kingdom Russian Federation, Germany, Latvia, Sweden, France, Estonia, Italy, Belarus, Poland Belarus, the Netherlands, Poland the Netherlands, Spain Latvia Italy, Poland Iran, Germany, Spain Switzerland Saudi Arabia, Turkey, Algeria HS4 16, Meat, fish and seafood food preparations HS4 23, Residues, wastes of food industry, animal fodder Germany, Estonia, Belgium, the United Kingdom, Poland Russian Federation, Poland, Belarus, Latvia, the Netherlands, Denmark, Italy, Norway Latvia, Russian Federation Germany France, Spain, Italy Source: authors calculations based on Trade Statistics from Trademap.org database, 2013 the United Kingdom Increases in export of 16 group products in France (observed LIC and RCA ratio raised from 2.12 in 2001 to 3.85 in 2012), Italy (observed LIC and RCA ratio raised from 0.15 in 2001 to 5.32 in 2012) and Spain (observed LIC and RCA ratio raised from 0.79 in 2001 to 2.02 in 2012) explain the shifts on the mentioned markets. During 2001 and 2012, all the 23 group products market (except the United Kingdom) was without shifts in patterns of trade (Table 4). Conclusions This study employs a new analytical tool to investigate the patterns of shift in trade in Lithuania s Agri-Food products export in the period from 2001 to 2012. The findings of the empirical study allow making the following conclusions: 1) regarding the significance of export and market share for 2-digit of Harmonised Nomenclature (HS4), agriculture and food products over the period of 2001-2012, 04, 07, 10, 16 and 23 group products were included into analysis; 2) regarding the stability of the distribution of RCA, for all 5 group products results revealed comparative advantages with high variation of RCA indices for each category (except for the class 04); 3) regarding competitiveness on the international markets and shifts to/from new/old markets, there is a relatively low degree of shifting, except for the export of 10 group products and growing position in new markets (Saudi Arabia, Iran, Turkey); 4) despite the shortcomings of the new analytical tool, the ratio between LCI and RCA indices still provides a useful tool to detect shifts in market changes and also offers additional information 141 ISSN 1691-3078; ISBN 978-9934-8466-1-8
on the specialisation and competitiveness of Lithuania s Agri-food products on international markets. Bibliography 1. Balassa, B. (1965). Trade Liberalisation and Revealed Comparative Advantage. The Manchester School of Economic and Social Studies, 33. Assessment. New York: Cambridge University Press, pp. 99-123. 2. Laursen, K. (1998). Revealed Comparative Advantage and the Alternatives as Measures of International Specialisation. Working Paper No. 98-30. DRUID. 3. Hinloopen, J., Van Marrewijk, Ch. (2000). On the Empirical Distribution of the Balassa Index. Rotterdam. 4. Ferto, I., Hubbard, L.J. (2003). Revealed Comparative Advantage and Competitiveness in Hungarian Agri-Food Sectors. The World Economy, No 26, pp. 247-259. 5. Havrila, I., Gunawardana, P. (2003). Analysing Comparative Advantage and Competitiveness: An Application to Australia's Textile and Clothing Industries. Australian Economic Papers, Volume 42, Issue 1, pp. 103-117. 6. Imagawa, T. (2004). Use of BTM for Measuring International Competitiveness - Japanese Manufacturing Exports in the US Market. Prepared for 12 th INFORUM World Conference Held at University of Florence, Italy September 5-11, 2004. Retrieved: http://www.inforum.umd.edu/papers/conferences/2004/imagawa.pdf. Access: 16.11.2013. 7. Alessandrini, M., Fattouh, B., Scaramozzino, P. (2007). The Changing Pattern of Foreign Trade Specialisation in Indian Manufacturing. Oxford Review of Economic Policy, Volume 23, Number 2, 2007, pp. 270 291. 8. Amador, J., Cabral, S. (2008). The Portuguese Export Performance in Perspective: a Constant Market Share Analysis. Retrieved: http://www.bportugal.pt/en-us/bdp%20publications%20research/ab200813_e.pdf. Access: 11.11.2013. 9. Widodo, Tri. (2008). Shifts in Pattern of Specialization. Case Studies of India and China. Gadjah Mada International Journal of Business, January-April 2008, Vol. 10, No. 1, pp. 47 75. 10. Cai, J., Leung Ping, S., Hishamunda, N. (2009). Assessment of Comparative Advantage in Aquaculture. Framework and Application on Selected Species in Developing Countries. Retrieved: http://www.fao.org/docrep/012/i1214e/i1214e.pdf. Access: 29.12.2013. 11. Ferrarini, B., and Scaramozzino, P. (2010). Indicators and Patterns of Specialization in International Trade. Retrieved: http://www.wti.org/fileadmin/user_upload/nccrtrade.ch/wp6/indicators_and_patterns_of_specialization.pdf 12. Lee, J. (2010). Export Specialisation and Economic Growth around the World. Retrieved: http://www.sciencedirect.com/science/article/pii/s0939362510000749. Access: 28.12.2013. 13. Del Gatto, M., di Mauro, F., Gruber, J., Mandel, B. (2012). The Structural Determinants of the US Competitiveness in the Last Decades a Trade-Revealing Analysis. European Central Bank. Working Paper Series No 1443. 14. Gathani, S., Stoelinga, D. (2012). Export Similarity Networks and Proximity Control Methods for Comparative Case Studies. Retrieved: http://www.laterite-africa.com/wp-content/uploads/2013/04/gathani-and-stoelinga-_- Export-Similarity-Networks-and-Proximity-Control-Methods-for-Comparative-Case-Studies-_-Published-version.pdf. Access: 19.11.2013. 142 ISSN 1691-3078; ISBN 978-9934-8466-1-8
Lithuanian LC Index for HS4 04 group products, 2001-2012 Appendix 1 Ran Market 2001 2002 2003 200 200 200 200 200 200 201 201 201 1 Russian 6.39 5.05 4.98 6.23 6.09 10.7 9.70 8.50 8.44 5.93 6.13 3.57 2 Federation Italy 1.05 2.07 2.64 18.2 27.8 23.93 20.6 28.6 26.3 22.5 20.3 20.4 3 Germany 2.51 1.08 3.29 8.47 9 6.94 2 7.31 7 8.50 2 6.27 9 4.11 8 4.17 9 4.78 9 5.05 8 4 Poland 14.63 7.46 21.31 13.2 10.8 13.5 19.3 10.7 13.4 14.1 12.8 11.7 5 Latvia 3.25 4.44 4.49 4.86 6 3.29 9 2.26 8 2.35 7 2.39 8 2.47 7 1.73 0 1.72 0 1.87 3 6 Netherland 22.27 11.18 6.09 6.95 6.29 2.91 11.0 6.19 4.44 3.00 2.52 1.82 7 s USA 209.0 229.3 199.6 37.0 5.79 1.67 2.89 0 4.12 5.28 2.22 2.66 7.02 8 Estonia 3.24 0 4.63 9 4.24 5 6.15 5 4.72 4.36 3.48 2.21 1.63 2.24 2.63 1.93 9 Spain 0.00 0.00 0.13 3.92 3.56 7.46 9.32 6.50 6.91 8.39 5.85 9.12 10 United Kingdom 0.11 0.01 0.03 1.43 4.48 5.23 2.34 0.95 1.09 0.99 1.59 1.44 Lithuanian LC Index for HS4 07 group products, 2001-2012 Rank Market 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1 Russian Federation 0.30 0.24 0.26 0.37 0.64 4.54 6.60 8.42 5.86 6.64 6.55 8.85 2 Germany 2.64 1.60 2.90 2.88 2.58 2.77 1.52 2.69 1.54 1.24 1.49 1.21 3 Latvia 0.89 0.89 0.97 1.07 0.85 1.22 1.07 0.90 0.97 1.07 0.89 0.80 4 Sweden 1.05 0.51 0.97 1.54 1.28 1.72 2.00 2.44 3.02 2.71 2.32 2.27 5 France 4.66 1.06 2.65 1.63 0.77 1.58 1.61 0.86 0.91 0.73 0.93 1.30 6 Estonia 0.89 0.68 1.00 0.93 0.87 1.46 3.64 2.57 1.36 0.58 0.51 0.59 7 Italy 6.04 1.52 4.63 5.98 3.58 3.24 1.30 2.41 1.81 1.84 1.87 2.02 8 Belarus 0.36 1.06 0.52 0.52 0.59 1.77 1.39 1.11 1.16 1.71 2.28 2.93 9 Switzerland 5.96 1.65 0.29 1.17 7.72 6.63 13.97 4.06 10.76 16.50 23.04 18.09 10 Poland 0.36 0.47 2.12 1.42 0.62 0.55 0.38 0.46 0.26 0.34 0.52 0.88 Lithuanian LC Index for HS4 10 group products, 2001-2012 Ran 200 Market 2001 2002 k 3 2004 2005 2006 2007 2008 2009 2010 2011 2012 1 Latvia 0.35 0.18 2.05 4.16 3.09 4.47 3.50 4.31 5.00 4.97 2.75 2.97 2 Saudi Arabia 0.00 0.00 0.00 0.00 0.00 27.5 5 24.6 1-17.4 2 23.0 9 25.9 8 25.3 6 3 Iran 0.00 0.00 0.00 0.00 0.00 0.00 - - - 0.00 0.00 12.0 7 4 Germany 2.51 19.0 1 1.81 0.13 4.62 0.13 8.94 6.86 4.01 7.35 6.36 9.15 5 Spain 0.00 1.80 0.00 6.92 21.5 4 14.9 5 3.22 1.85 18.9 9 7.71 5.79 7.80 6 Belarus 11.3 8 1.51 7.99 15.6 1 7.86 5.92 1.19 3.40 1.41 0.05 1.38 0.58 7 Turkey 0.00 0.00 3.68 0.00 0.00 0.00 0.00 20.1 9 26.1 6 44.9 0 18.6 0 7.62 8 Netherland s 0.00 0.39 3.58 0.71 16.9 6 6.78 0.21 0.29 2.81 1.06 1.58 2.78 9 Poland 0.43 1.36 2.01 0.78 0.06 0.37 2.16 8.82 0.69 1.79 2.23 5.77 10 Algeria 0.66 0.00 0.00 0.00 0.00 0.00 13.9 3 9.70 Lithuanian LC Index for HS4 16 group products, 2001-2012 15.6 8 0.00 0.00 0.00 Rank Market 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 1 Germany 4.18 2.65 1.54 3.32 2.63 1.73 1.47 0.99 1.00 1.21 2.68 2.59 2 France 4.88 5.87 9.41 9.04 7.68 11.16 11.80 7.37 14.58 12.28 8.75 9.61 3 Latvia 3.71 3.28 2.91 3.64 6.77 9.16 7.80 12.72 15.71 15.14 2.50 4.26 4 Estonia 1.84 2.68 2.18 2.63 3.29 3.49 2.32 2.14 2.23 2.02 1.65 1.71 5 Russian Federation 3.13 5.17 8.83 9.06 7.72 5.85 5.64 3.58 3.21 3.71 3.42 2.78 6 Spain 2.29 40.45 37.35 15.93 8.96 8.68 12.50 8.58 11.75 15.78 7.89 7.43 7 Belgium 0.05 0.00 0.00 0.20 0.25 6.33 0.44 0.47 0.24 0.08 0.06 1.01 8 Italy 1.82 4.92 7.86 9.83 10.75 7.23 5.86 5.07 6.44 14.43 4.62 5.03 9 United Kingdom 0.09 0.21 0.43 0.43 0.46 1.07 1.34 1.04 0.83 0.85 1.21 0.95 10 Poland 0.11 0.26 0.25 1.65 2.35 2.85 3.94 3.85 3.05 2.90 2.53 2.71 143 ISSN 1691-3078; ISBN 978-9934-8466-1-8
Lithuanian LC Index for HS4 23 group products, 2001-2012 Ran 201 Market 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 k 2 1 United Kingdom 6.17 10.6 0 17.3 2 16.6 3 20.8 0 32.9 7 29.0 8 17.2 8 22.0 5 15.3 3 12.4 1 7.78 2 Russian Federation 5.36 4.38 7.80 5.06 3.78 2.21 1.91 2.93 3.26 2.61 2.16 1.03 3 Germany 13.6 8 13.1 0 11.4 7 8.87 5.55 6.58 7.49 4.42 4.61 3.05 1.94 2.95 4 Poland 0.76 0.24 1.98 2.24 1.40 1.28 1.93 2.44 1.35 2.22 1.71 1.87 5 Belarus 5.37 3.90 4.11 4.63 3.12 2.78 2.47 1.83 1.06 0.94 0.83 1.08 6 Latvia 0.34 0.31 0.39 0.69 0.51 0.72 0.84 0.72 0.66 0.91 1.21 0.90 7 Netherland s 3.24 3.48 3.40 2.35 3.08 2.17 3.25 1.69 1.88 1.14 0.68 0.90 8 Denmark 0.93 0.72 0.53 0.36 0.44 0.28 0.63 0.93 1.05 1.36 1.97 0.90 9 Italy 2.73 0.57 0.65 3.57 5.90 8.74 3.86 2.84 4.31 6.48 5.77 5.11 10 Norway 3.06 1.64 1.80 1.86 1.64 0.85 0.97 4.41 2.33 2.51 1.89 1.80 Source: authors calculations based on Trade Statistics from Trademap.org database, 2013 144 ISSN 1691-3078; ISBN 978-9934-8466-1-8