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Table 8. Correlation matrix for significant regressors of specification S3 (n = 225, Notes: Test-¢ in parentheses. ” Significant at the 90% level. cawignificant at the 95% level. Significant at the 99% level.

Table 8 Correlation matrix for significant regressors of specification S3 (n = 225, Notes: Test-¢ in parentheses. ” Significant at the 90% level. cawignificant at the 95% level. Significant at the 99% level.

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Table 1. Annual rate of growth for world shares of patents and exports, by industry Note: the percentage change is relative to the overall average annual rate of growth. Source: Authors’ calculation on COMTRADE and USPTO data (see Appendix A for data description and sectora definitions and concordance).
Table 1. Annual rate of growth for world shares of patents and exports, by industry Note: the percentage change is relative to the overall average annual rate of growth. Source: Authors’ calculation on COMTRADE and USPTO data (see Appendix A for data description and sectora definitions and concordance).
Source: Authors’ calculation on COMTRADE and USPTO data (see Appendix A for data description). * The technological classification of SITCrev2 exports is from the UNIDO Scoreboard Database. > The percentage change is relative to the overall world rate of growth. Table 2. Descriptive statistics on technological and export performance and structural change
Source: Authors’ calculation on COMTRADE and USPTO data (see Appendix A for data description). * The technological classification of SITCrev2 exports is from the UNIDO Scoreboard Database. > The percentage change is relative to the overall world rate of growth. Table 2. Descriptive statistics on technological and export performance and structural change
Table 3. Structural decomposition analysis for world shares of patents Source: Authors’ calculation on USPTO data.
Table 3. Structural decomposition analysis for world shares of patents Source: Authors’ calculation on USPTO data.
Source: Authors’ calculation on COMTRADE data. Table 4. Structural decomposition analysis for world shares of exports To conclude, the structural decomposition analysis provides preliminary evidence in favor of the existence of a positive relationship be- tween technological activities and competitive- ness. There is a remarkable similarity between the evolution of patent and export shares: Chi- na is far ahead in the two rankings, followed by East-Asian economies. Brazil, Argentina, and Colombia lag behind. Moreover, rates of growth in both patent and export shares are All sample countries, apart from Brazil, in- crease their share of world trade. However, there are differences in the rates of change, aera eneieenaaie easement al Table 4s summarizes the results of this decom- position. MOMS Teel, Seep Pere ee SEs ee As thoroughly explained above, these effects represent, respectively, the change of export shares assuming out changes in the structure of world export (MSH;); the impact of trade specialization assuming it constant throughout the period (MST;); the move toward sectors growing above average or out of those that are stagnating at the international level (respec- tively MGR; and MSG)).
Source: Authors’ calculation on COMTRADE data. Table 4. Structural decomposition analysis for world shares of exports To conclude, the structural decomposition analysis provides preliminary evidence in favor of the existence of a positive relationship be- tween technological activities and competitive- ness. There is a remarkable similarity between the evolution of patent and export shares: Chi- na is far ahead in the two rankings, followed by East-Asian economies. Brazil, Argentina, and Colombia lag behind. Moreover, rates of growth in both patent and export shares are All sample countries, apart from Brazil, in- crease their share of world trade. However, there are differences in the rates of change, aera eneieenaaie easement al Table 4s summarizes the results of this decom- position. MOMS Teel, Seep Pere ee SEs ee As thoroughly explained above, these effects represent, respectively, the change of export shares assuming out changes in the structure of world export (MSH;); the impact of trade specialization assuming it constant throughout the period (MST;); the move toward sectors growing above average or out of those that are stagnating at the international level (respec- tively MGR; and MSG)).
Table 5. Regression results for specification 1: technological activities on export performance (for 25 sectors and nine countries ) Notes: Test-t in parentheses. Heteroskedasticity consistent estimator are used (White estimator), F(u; = 0) an Fa; = = 0) test the hypothesis of joint insignificance of sector and country-specific dummies. _ Significant at the 90% level. caeignificant at the 95% level. Significant at the 99% level.
Table 5. Regression results for specification 1: technological activities on export performance (for 25 sectors and nine countries ) Notes: Test-t in parentheses. Heteroskedasticity consistent estimator are used (White estimator), F(u; = 0) an Fa; = = 0) test the hypothesis of joint insignificance of sector and country-specific dummies. _ Significant at the 90% level. caeignificant at the 95% level. Significant at the 99% level.
Table 6. Regression results for specifications 2 and 3: the determinants of export Votes: Test-t in parentheses. Heteroskedasticity consistent estimator are used (White estimator). F(u; = 0) and F(a; = 0) test the hypothesis of joint s ectoral and country dummies. Significant at the 90% level. significant at the 95% level. Significant at the 99% level.
Table 6. Regression results for specifications 2 and 3: the determinants of export Votes: Test-t in parentheses. Heteroskedasticity consistent estimator are used (White estimator). F(u; = 0) and F(a; = 0) test the hypothesis of joint s ectoral and country dummies. Significant at the 90% level. significant at the 95% level. Significant at the 99% level.
empirical analysis. The remaining countries con- versely are satisfactorily covered in the UNIDO Database. Nonetheless, the available years for the three variables used to compute ULC (Employment, Wages and salaries, and Value added) are different for the different economies considered: Colombia 1985-98, India 1985-97, Malaysia 1985-97, Mexico 1987-94, Singapore 1985-94, Thailand 1986-94. Unit labor costs measure the cost of labor per unit of value added. We used the UNIDO Indus- trial Statistics Database which contains data on industries classified at the 4-digit level of ISIC (Revision 2). Argentina, Brazil, and China have an insufficient number of observations on differ- ent industries and years to be included in the
empirical analysis. The remaining countries con- versely are satisfactorily covered in the UNIDO Database. Nonetheless, the available years for the three variables used to compute ULC (Employment, Wages and salaries, and Value added) are different for the different economies considered: Colombia 1985-98, India 1985-97, Malaysia 1985-97, Mexico 1987-94, Singapore 1985-94, Thailand 1986-94. Unit labor costs measure the cost of labor per unit of value added. We used the UNIDO Indus- trial Statistics Database which contains data on industries classified at the 4-digit level of ISIC (Revision 2). Argentina, Brazil, and China have an insufficient number of observations on differ- ent industries and years to be included in the
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