1. Trade Diversification and Economic Growth: A Comparative Study between East Asian and Latin American Countries Peter C.Y. Chow

2. Abstract While many East Asian and Latin American economies pursued their export-led growth strategy, the paths of their export growth differs across countries. This study analyzes whether there is a uniform pattern of export diversification in the process of their respective economic developments among the top 10 major trader dependent economies in both hemispheres in East Asia and Latin America.

3. Abstract If the minimum score of the Theil index of concentration can be considered as the threshold of turning point from export diversification to specialization, then one can identify various patterns of export diversification in those countries in East Asia and Latin America.

4. Introduction Latin American countries which are relatively well endowed with natural resources than most East Asian countries, engaged in import-substitution (IS) rather than export-promotion (EP) until the mid-1970’s. Balassa (1979) argued that a country may focus on a few industries in which it has comparative advantage in world markets at the early stage of development. But as a country becomes more industrialized, it would gradually diversify the composition of its export commodities.

5. Introduction Cadotet al ( 2011) argued that there is a U-shaped hump in export diversification with a threshold of re-concentration after reaching the turning point around $ 25,000 GDP per capita at purchasing power parity. This study analyzes export diversification (or lack of) on all export commodities and manufacturing sector in East Asia and Latin America. Ten top trading countries in East Asia and Latin America are selected from each hemisphere respectively.

6. Introduction The study include China, Japan, the 4 little tigers and ASEAN-4. These 10 Asian countries accounted for 86.65% of total exports from Asia in 2012. The top 10 trading countries in Latin America are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Mexico, Peru and Venezuela. Other than Mexico which is generally classified as one of the North America countries, the other nine countries accounted for 82.21% of exports from Latin American countries.

7. Introduction There indexes of export concentration ( diversification) are tested in this study. They are ; 1.the Gini coefficient, 2.the Herfindahl-Hirschman (HH) index and 3.the Theil index which is decomposed into “between” and “within” industries. Regression analyses on the panel data shows that there is a U- shaped of export concentration when the Hirschman -Herfindahl ( HH) index is served as the measurement of export concentration. But, there are robust results from GMM on the U- shaped of manufacturing exports in all three indexes of export concentration.

8. Review of Literature Chow (2012) found that, with few exceptions, “most East Asian countries tended to diversify their export structures over time as their economies became more developed in the past four decades. Kellman and Shachmurove (2011), found from different sample countries, that there is a significant export diversification in the overall exports with a tendency of specialization within the machinery sector after their economies took off in various time periods.

9. Review of Literature Ferdous ( 2011) found that “export diversification had been almost steady over the years in East Asian economies while all the countries in the study have concentrated trade in manufacturing products.” Klinger and Lederman (2006) found that there is an inverted U-shaped curve in related to income per capita, though the turning point of export basket of commodities occurred at the per capita income of $ 22,500 in 2000 US dollars adjusted by PPP, which is higher that of Imbs and Wacziarg (2003).

10. Review of Literature Cadot et al (2011, p.596) found that there is a lump-shape of export diversification in relating to level of development. On the determinants of trade specialization, Cadot et al ( 2011) only used the GDP per capita and its squared term to detect what was behind “ lump” of the export diversification without any control variable. Ferdous (2011) found that GDP in the exporting country and economic integration had positive effect whereas tariff and exchange rates had negative effect on trade specialization in that country. Parteka and Tamberi (2011) added the number of regional trading agreements (RTAs) as additional explanatory variable in their regression models.

11. Measurements of Export Diversification, Data Specifications and Descriptive Statistics In this study, the Herfindahl –Hirschman index (HH index), the Gini coefficient, and the Theil index will be adopted; The Gini coefficient of concentration is generally used to measure the income distribution. But, one can also use it to measure the export concentration as well. The Herfindahl –Hirschman index (HH index) of export concentration is calculated in the following way:

12. Measurements of Export Diversification, Data Specifications and Descriptive Statistics

13. The Theil’s index (T) is the sum of “between industry” (TB) and “within industry (Tw), T=Tb + Tw where = 1/∑/u ln(/), where =1/∑ =1 b=∑/./ ln(/), =1 w=∑/./. =∑ /./ {1/∑/.ln(/ ) }, ∈ =1 =∑ℓ/ℓ=1∑ℓ/ℓ

14. Measurements of Export Diversification, Data Specifications and Descriptive Statistics Data Specification Data source on export are from NBER-UN trade data. Manufactured commodities are for SITC 5 to 8 excluding SITC 68 (non-ferrous metals) as is commonly defined. Data on effective exchange rate are from the International Bank of Settlement at the Website http://www.bis.org/statistics/eer/index.htm. There are two sets of real effective exchange rates based on either 27 or 48 basket of currencies in each of the exporting countries. http://www.bis.org/statistics/eer/index.htm

15. Measurements of Export Diversification, Data Specifications and Descriptive Statistics Data Specification Data on GDP per capita is from Penn World Table 7.1 at constant international dollars, and the World Development Report on Purchasing Power Parity (PPP). Data on inward FDI stock as percentage of GDP is derived from the “World Investment Report” from the website of the United Nations Conference on Trade and Development (UNCTD) at http://unctad.org/en/pages/PublicationWebflyer.aspx?public ationid=588

16. Within and between components of Theil’s index Source: Cadot et al ( 2007.p.40) using COMTRADE (quadratic estimates) at the HS6 level of disaggregation.

17. Argentina Figure 1: Within and Between Components of Theil Index with GDP Per capita in Argentina

18. Bolivia Figure 2: Within and Between Components of Theil Index with GDP Per capita in Bolivia

19. Brazil Figure 3: Within and Between Components of Theil Index with GDP Per capita in Brazil

20. Chile Figure 4: Within and Between Components of Theil Index with GDP Per capita in Chile

21. China Figure 5: Within and Between Components of Theil Index with GDP Per capita in China

22. Colombia Figure 6: Within and Between Components of Theil Index with GDP Per capita in Colombia

23. Costa Rica Figure 7: Within and Between Components of Theil Index with GDP Per capita in Costa Rica

24. Ecuador Figure 8: Within and Between Components of Theil Index with GDP Per capita in Ecuador

25. Hong Kong Figure 9: Within and Between Components of Theil Index with GDP Per capita in Hong Kong

26. Indonesia Figure 10: Within and Between Components of Theil Index with GDP Per capita in Indonesia

27. Japan Figure 11: Within and Between Components of Theil Index with GDP Per capita in Japan

28. Korea Figure 12: Within and Between Components of Theil Index with GDP Per capita in Korea

29. Malaysia Figure 13: Within and Between Components of Theil Index with GDP Per capita in Malaysia

30. Mexico Figure 14: Within and Between Components of Theil Index with GDP Per capita in Mexico

31. Peru Figure 15: Within and Between Components of Theil Index with GDP Per capita in Peru

32. The Philippines Figure 16: Within and Between Components of Theil Index with GDP Per capita in the Philippines

33. Singapore Figure 17: Within and Between Components of Theil Index with GDP Per capita in Singapore

34. Taiwan Figure 18: Within and Between Components of Theil Index with GDP Per capita in Taiwan

35. Thailand Figure 19: Within and Between Components of Theil Index with GDP Per capita in Thailand

36. Venezuela Figure 20: Within and Between Components of Theil Index with GDP Per capita in Venezuela

38. Regression Models on Panel Data The regression model is formulated in the following way: 1.Gini it = α +β1 log yit +β2 log yit sq +β3 log openness it ) +β4 log REER it + β5 log (FDI/GDP) it +u + ε 2.HHIit = α + β1 log yit +β2 log yit sq +β3 log openness it ) +β4 log REER it + β5 log (FDI/GDP) it + u + ε 3.Theil it = α + β1 yit +β2 yit sq +β3 openness it ) +β4 FTA it + β5 REER it + β6 (FDI/GDP) it +u + ε

39. Regression Models on Panel Data Where HHit, Gini it, Theil it : the export concentration ratio measured by HH index, Gini coefficient and the Theil Index of country i in year t Yit : the GDP per capita for country i in period t Openness: the percentage of export and import in total GDP for country i in period t. REER : the real effective exchange rate for country i in period t. FDI/ GDP: The percentages of inward FDI stock in total GDP for country i in period t.

40. Regression Models on Panel Data The regression analyses for the three indexes for all commodities and manufactured goods are conducted for both random and fixed effect. The Hauseman test is followed to determine whether the random effect model or the fixed effect model is to be chosen. The Hausement is conduct in the following way; Ho : Random effect is consistent ( efficient) Ha : Random effect is not consistent ( efficient) If the calculated Chi-square is significantly larger than the critical value, then reject the Ho, in favor of Ha and choose the fixed effect model. Otherwise, it is said to fail to reject the H0, and choose the random effect model.

41. Table 2 A: Regression on Transformed Gini index of export concentration for all countries.

42. Table 2B: Regression on Theil Index of export concentration for all countries

43. *** = p-value<.01 ** = p-value<.05 *** = p-value<.10 Table 2C: Regression on Theil Index of export concentration for all countries

44. For the HH index, the signs of GDP per capita (log y) and its square are statistically significant at the 10% and 5% levels of significance for all exports and at the 5% level for manufacturing exports. Meanwhile, the signs for GDP per capita (log y) are negative in the regression models for all exports and manufacturing exports,but are positive for GDP per capita square in all regression models. Hence, if one uses the HH index as a measurement for concentration, then exports for all and manufacturing commodities will diversify as economic growth takes place. But, they will become more diversified after pass a threshold.

45. GMM Regressions For All Countries Table 5: GMM for all countries

47. Table 6: GMM for 10 Asian Countries

48. From the results reported on Table 3, one can find that the signs for GDP per capita (ln y ) and its square behave as what is expected for a “ U-shaped” in manufacturing export ; The signs of GDP per capita ( log y) are statistically significant negative when the Gini, Theil and the HH index are the dependent variables in all GMM models. It implies as economic growth takes place, export of manufacturing commodities will be more diversified. However, after pass the turning point, export of manufacturing commodities will become more concentrated as the signs of GDP per capita square become positive in all regressions.

49. However, the evolution process of overall exports is different from that of the export of manufacturing commodities; the signs of GDP per capita are positive at various levels of statistical significance in the three GMM regressions on the Gini, Theil and HH indexes. The signs of GDP per capita square are negative and significant at various levels of statistical significance. Therefore, by including primary exports in the overall export in the GMM regressions, the results show that pathway of export growth become more concentrated as economic growth take places.

50. Summary and Conclusions Though there is a general tendency of non- monotonic paths in export diversification, a decomposed Theil index show various patterns of export diversification and GDP growth in each country, which cannot be generalized from the panel data study concluded by Cadot et al (2011).

51. Summary and Conclusions The regression models from the pooled data show that export growth in those East Asian and Latin American countries is subject to a two-stage U-shaped development when HH index is the measurement of export concentration. In the first stage of development, the pattern of export growth becomes more diversified. After passing the threshold, export becomes more concentrated. This phenomenon is more significant in the overall export commodities than on the export of manufactured commodities.

52. Summary and Conclusions In development literature, successful development models of export-led growth strategy are rather limited to a handful of countries in the world. Hence, this study only focuses on the selected trade dependent economies in eastern and western hemisphere and to identify the path ways of export growth in those economies after decades of their respective outward-looking, export-led growth strategies. Therefore, the paths of export diversification in those economies are not expected to be exactly the same as ones generalized from large samples in existing literature.