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Modern Economy, 2011, 2, 181-193 doi:10.4236/me.2011.23024 Published Online July 2011 (http://www.SciRP.org/journal/me) Copyright © 2011 SciRes. ME The Optimal Level and Impact of Internal Factors on Growth Kui-Wai Li Department of Economics and Finance, and APEC Study Center, Col le ge of Business, City University of Hong Kong, Hong Kong, China E-mail: efkwli@cityu.edu.hk Received May 5, 2011; revised June 15, 2011; accepted June 25, 2011 Abstract This paper empirically uses data from the world economy to show that performance of domestic factors are equally important to external factors when comes to growth. Various external and domestic factors are used to construct two separate indices and the principal component method is applied in the analysis. The empiri- cal results show that given a different level of performance in the economy’s external factors, a higher per- formance in the internal factors will produce a higher growth rate. When the performance of an economy’s internal factors is extremely low, it would be appropriate for that economy first to improve its internal factors. Keywords: Economic Openness, Domestic Factors, Globalization Index, Principle Component Analysis, World Growth 1. Introduction In the debate on economic growth, the neo-classical school argues that capital and labor are exogenous fac- tors in production, while technological advancement em- braces a number of non-measurable factors. In contrast, the “new growth theory” advocates the importance of endogenous factors that incorporates human capital and a number of institutional and domestic factors, such as the level of corruption and protection of property rights.[1-3] Exogenous factors are quantifiable and are derived from outside the economic system, and examples include such external factors as export, foreign direct investment, tourism and international transfers. Endogenous factors are unquantifiable and are derived from within the eco- nomic system; examples include such internal factors as education spending, political stability, rule of law and other institutional factors. Empirical growth studies have produced a mixed re- sult in the impact of different factors on growth and glo- balization. While external factors are considered crucial gains in the gains process of globalization, critics have considered the costs of globalization in terms of domes- tic factors. [4-14] Similarly, the construction of globali- zation indices have popularly been based on a mixture of external and domestic factors. [15-18] Other cross-coun- try empirical studies have identified a great number of domestic and geographical factors that have various de- grees of impact on growth.[19-26] Although the performance of the external factors con- tribute to economic growth, the link between the external factors and an economy’s growth performance in the global community depend to a considerable extent on how well internal factors have performed.[27] A more matured capital market, for example, will facilitate a greater capital flow, while a more transparent, corrup- tion-free investment environment could attract more for- eign direct investment. Successful performance of inter- nal factors can complement the performance of external factors in the growth and globalization process. The performance of both external factors and internal factors can impact on growth directly, but there is also an indirect link between internal factors and growth. The good performance in internal factors can exercise an ad- ditional and positive influence on the performance of external factors, which in turn can have a greater impact on growth. It is probable that internal factors can influ- ence economic growth directly, and can indirectly impact on growth through a better performance in external fac- tors. As such, the internal factors can be the more fun- damental factors to growth than external factors.[25-26] This paper empirically investigates the hypothesis that internal factors are the more fundamental factors to growth than external factors. Two separate indices for K.-W. LI Copyright © 2011 SciRes. ME 182 external factors and internal factors will first be con- structed. Instead of examining the impact of a single factor each of the two indices is constructed from a total of 17 factors.[22,24,28] Regression analysis shows how the two groups of factors can independently impact on growth. To show how the internal factors can exert in- dependent influence on growth, a simulation analysis is used to find the optimal level of performance in each economy’s internal factors. Lastly, the sample world economies are mapped according to their performance in the external factors and internal factors. The empirical result shows that economies will have to achieve a cer- tain level in the performance of the internal factors be- fore they can take advantage of the performance in ex- ternal factors. The data sources of the 34 factors for the 62 world economies for the period 1998-2002 are given in the Appendix. Section II uses the principal component analysis me- thod to work out the two indices for the external factors and internal factors. Section III gives the regression es- timates, while section IV compiles an optimal level of performance in each economy’s internal factors and a simulation study is conducted to show how the 62 world economies performed in the two types of factors. Section V concludes the paper. 2. The Two Indices Instead of pulling different factors into a single globali- zation index, this section constructs two separate indices on the external factor and internal factors. Kearney [15] grouped the external factors into the four categories of economic integration, technological connectivity, per- sonal contacts and international engagement. Kearney’s selection of external factors and categories can be im- proved by incorporating the inter-industry trade and in- tra-industry trade indicators. In theory, trade statistics are post-trade data that reflect the outcome of trade policies and show the actual quantity of export and import. An economy’s inter-industry trade is traditionally based on comparative advantage. In intra-industry trade, eco- no- mies export and import the same good or service in a given period. Thus, intra-industry trade reflects more on the varieties of goods the economy enjoys due to indus- trial diversity and technological advancement than sim- ply trade flows based on comparative advantages. The calculation of the inter-industry and intra-industry trade indicators is shown in the Appendix. The External Fac- tors Index (EFI) is constructed from a total of 17 ex- ternal economic openness factors grouped under six categories. The choice of internal factors used to construct the In- ternal Factors Index (IFI) is chosen from the list in Dur- lauf. [19] The 17 internal factors are classified into three broad categories. While the first category of institutional establishment is considered as proxy measures for civili- ty, security and protection of individuals, the other two categories provide indicators on the quality of life. Ap- pendix Table 1 summarizes the categories of external factors and internal factors. Both the external and inter- nal factors are normalized on a yearly basis before they are used to construct the two indices.1 [29] The principal component analysis (PCA) is used to construct the two indices. There are several advantages in using the PCA method. First, the PCA is meant to give weightings that maximize the variance of the indices. Since the factors are likely to be correlated, the PCA reduces the number of factors to capture the maximum variation. Secondly, the PCA method can commensurate on the different measurement units of these factors. Most importantly, the PCA method selects the weights by the data itself. [30] The principal components are extracted from the correlation matrix of the variables, in a way that they accounted for the highest percentage of variation. The PCA is applied to each individual year instead of applying one PCA to the whole sample period. This has the advantage of incorporating various changes in the sample period, and can eliminate the impact of a sudden change in any particular year that could affect other sample years.2 We adopt a latent variable model and postulate that the index is linearly dependent on a set of observable factors (V) and an error term. The principal components (PCs) are computed from the following procedure: 1111 1 2211 2 11 ,, ,, ,, LL L PC VV PC VV PC VV , (1) where 11 121 ,,, are elements of eigenvector 111 1 ,, , and there are a total of L eigenvectors, which are determined by the data. A total of L principal components are computed using successive eigenvectors elements, 12 ,,, L , corresponding to the largest L eigenvalues, 12 L , of the factor correlation matrix. The first principal component, PC1, of the linear combination with maximal variance becomes our EFI, 1The normalization formulas for the high and low value variables are: 111 min, ,max(, ,)min(, ,) itiNNN t Vv vvvvvv , and 111 max, ,max, ,min, , itNiNN t Vvvv vvvv . Vit is variable V of economy i at time t. 2This is seen as an improvement to Andersen and Herbertsson[16] who m use a single principle component for all years, and to Dreher[18] whom uses weightings of year 2000 for the calculation of indices fo r all years from 1970 to 2000. K.-W. LI Copyright © 2011 SciRes. ME 183 Table 1. Pooled-GLS Estimates of 62 World Economies, 1998-2002. Coefficients k = 3 k = 4 k = 8 k = 10 α 7.5159 (0.0722)* 7.3161 (0.0861)* 7.5144 (0.0967)* 7.5269 (0.0956)* β1 0.2904 (0.0270)* 0.3591 (0.0360)* -0.0324 (0.0911) -0.0868 (0.0920) β2 0.3036 (0.0073)* 0.2260 (0.0163)* 0.3593 (0.0729)* 0.3916 (0.0739)* β3 0.3690 (0.0097)* 0.3472 (0.0174)* 0.4956 (0.0730)* 0.5224 (0.0731)* β4 0.3421 (0.0188)* 0.5961 (0.0750)* 0.5561 (0.0749)* β5 0.6334 (0.0762)* 0.6447 (0.0759)* β6 0.7027 (0.0766)* 0.6757 (0.0770)* β7 0.6847 (0.0777)* 0.7346 (0.0771)* β8 0.6894 (0.0779)* 0.7523 (0.0782)* β9 0.7342 (0.0787)* β10 0.7427 (0.0788)* F-test† Adj. R2 Wald Test† 0.0000 0.999704 0.0000 0.0000 0.999624 0.0000 0.0000 0.999670 0.0000 0.0000 0.999745 0.0000 Notes: Figures in parentheses are standard errors. * and † = significance at 1% and 5% levels, respectively. which is then normalized or scaled.3 The scaled EFI will take a value of unity when an economy has the best per- formance in its external environment. The same proce- dures are applied to the construction of the Internal Fac- tors Index (IFI). Appendix Table 2 gives the five-year (1998-2002) average of the EFI and IFI. The ranking based on the five-year average shows that the top 10 economies in the two indices are mainly advanced economies in North America and Western Europe. Most of the remaining European Union economies are included when the scores are extended to the top 20. Singapore and Hong Kong are the only two Asian economies in the top 20 of both indi- cators. We observe that an economy can vary between the two indices. For example, Japan ranked 18th in the IFI, but ranked 26th in the EFI, while Indonesia ranked 44th and 55th in EFI and IFI, respectively. Economically weaker economies tend to rank lower in the two indica- tors. Effectively, economies that ranked below 30th are all developing economies. 3. Regression Estimates The hypothesis that economies with strong performance in internal factors enjoy a higher rate of per capita GDP growth at different level of performance in the external factors is examined. The IFI is divided into k portions using percentiles, shown in Equation (2), with N being the number of economies. For example, the IFI of year t is divided into three portions, so k = 3, with 33.33 percent of the economies in each portion. The first portion is made up of the min- imum IFI in year t to the 33rd IFI in year t. A dummy variable, D , where 1,k ,, is assigned to each of the last (k-1) portions of IFI, namely2,k DD. The D dummy takes a value of unity if IFIit falls into the th portion, otherwise it takes a value of zero. An economy with 1D has a better internal environment than an economy with 11D . The following model is used to examine how internal factors can affect the outcome of external factors: 122, , lnlnln * ln *, ititit it kitk itit yEFIEFI D EFI D (3) where yit is the real GDP per capita deflated by the pur- chasing power parity of economy i at time t. For economy i who has the dummy 1D , the regression equation become: 1 lnln . itit it y EFI (4) For another economy j has the dummy 1, c D for any c > 0. In other words, when economy j’s internal environment is not as good as economy i’s, the regres- sion equation become: 1 lnln . j tcjtjt y EFI (5) If a higher performance in internal factors brings a 100 100100 min, ,%*,1%*, ,2%*,, 100 100 11 %*,,%*. th th th titt th th t IFIIFIN IFIN IFINIFI kk k kNIFIkNIFI kk (2) 3Scaled min maxmin itiii it EFI EFIEFIEFIEFI K.-W. LI Copyright © 2011 SciRes. ME 184 Table 2. The EFI – IFI matrix of world economies, 1998-2002 average. Range Internal Factors Index (IFI) 0.00 - 0.20 0.21 - 0.40 0.41 - 0.60 0.61 - 0.80 0.81 - 1.00 External Factors Index (EFI) 0.00 - 0.20 Uganda (4.049) Bangladesh (3.025)* Senegal (2.322) N igeria (1.575)* Indonesia (1.408) Pakistan (1.398) Kenya (–1.343) China (6.749) Russian Fed. (6.381) Ukraine (5.692) India (3.287) Romania (3.071) Egypt (2.932) Iran (2.786) Sri Lanka (1.928) Philippines (1.239) Brazil (1.229) S. Africa (1.227) Mexico (1.001) Peru (0.768) Turkey (–0.096) Colombia (–0.807) Venezuela (–3.697) Botswana (8.615) Tunisia (3.198) Thailand (2.911) Chile (1.072) Morocco (0.720) Saudi Arab. (–0.938) Argentina (–5.887) 0.21 - 0.40 Croatia (3.654) Korea (5.957) Greece (4.207) Slovak Rep. (3.341) Poland (2.981) Malaysia (2.945) Panama (0.661) Hungary (3.869) Slovenia (3.858) Czech Rep. (3.354) Spain (2.671) Portugal (1.945) Italy (1.590) Japan (0.477) Israel (–0.096) 0.41 - 0.60 Hong Kong (3.346) France (2.201) N ew Zealand (3.150) Canada (2.829) Australia (1.821) N orway (1.374) Germany (1.175) 0.61 - 0.80 Singapore (4.082) Sweden (2.500) Finland (2.161) U.K. (2.102) Denmark (1.788) Austria (1.723) N etherlands (1.617) USA (1.455) Switzerland (1.095) 0.81 - 1.00 Ireland (9.737) Note: Figures in parenthesis are the percentage growth rates of the average 1999-2002 GDP per capita (purchasing power parity in constant 2000 price). *Countries with IFI < 0. higher marginal effect of external factors on economic growth, we expect to see 11c . Thus, ge- neralizing all the k dummy variables, and if a better in- ternal environment has a positive impact of external fac- tors on growth, we expect to see 112 13 1k , suggesting that a strong perfor- mance in an economy’s internal factors enables an economy to benefit more from performance in external factors. Two Wald tests are conducted to show the signific- ance of the coefficient estimates. The first Wald test is to see if a low performance in the internal factors will con- strain economic growth. An alternative hypothesis with 10 implies that if an economy has an extremely weak performance in its internal factors (reflected in the IFI value falling into the first partition of the index), ex- ternal factors would bring negative effects on economic growth, namely: 1 1 1 1 :0 :0. Ho Ha (6) The second Wald test shows that an economy’s IFI can significantly affect the marginal effect of an econo- my’s external factors on its real per capita GDP growth rate: 2 1 2 111 :02,,. :3,,. Hofor k H afork (7) The alternative hypothesis, 2 H a, states that econo- mies that have a better performance in their internal fac- tors should benefit more from performance in external factors. The pooled-GLS with White-Heteroskedasticity con- sistent standard error and covariance is applied to esti- mate Equation (3), which is estimated with k = 3, 4, 8 and 10. Table 1 shows the empirical estimation of the K.-W. LI Copyright © 2011 SciRes. ME 185 pooled-GLS results for the 62 countries for the sample period of 1998-2002. All estimates with k = 3 and k = 4 in Tab le 1 are significant at 1 percent level. In these two cases, the estimate for 1 is not negative, but is signifi- cantly different from zero, suggesting that a low perfor- mance in internal factors does not adversely affect the effect on economic growth, though this may be due to the small size of k. When the size of k is small, the mar- ginal effect of internal factors on economic growth may not be obvious. The F-tests reject the null hypothesis of Equation (3), suggesting that as economies improve their performance in internal factors, the marginal effect on growth increases. For estimates with k = 8 and k = 10, and with the ex- ception of the insignificant estimate for 1, all the esti- mates are significance at 1 percent level. For these esti- mated values of k, the estimate of 1 is negative, which means that a low performance in internal factors of an economy can adversely affect growth. Similarly, the F-tests also reject the null hypothesis as in the cases of k = 3 and k = 4. 4. Optimal Performance in Internal Factors This section uses a simulation method to work out the optimal performance in the internal factors in order to achieve a maximum gain in economic growth. From the estimation result of k=4, 8 and 10 in Table 1, we first examine economies with top scores in IFI to see if there is diminishing returns in the external factors. Hypotheti- cal economies are compared in order to see how their growth performs given a different level of performance in internal factors. Two hypotheses are postulated. First, given two ex- ternally homogeneous economies (namely, economies with same performance in the EFI), heterogeneity in the performance of IFI will lead to differences in economic growth. Secondly, given homogeneity in the perfor- mance of IFI among different economies, those econo- mies with a better performance in EFI will result in higher economic growth. The empirical result with k = 10 in Table 1 is used to simulate the growth of per capita GDP for a total of 100 hypothetical economies with an incremental change of 0.01 in the IFI that ranged from zero to one. The differ- ent values of the EFI are either below or above the me- dian value. A simulated series of per capita GDP figures are generated from the empirical results with k = 10 in Table 1.4 The simulated per capita GDP growth rates are plotted against the IFI, and a step function is presented separately for the four values of EFI (at 0.25, 0.45. 0.75 and 0.95) as shown in Figure 1. The first observation in Figure 1 is that economies with a higher performance in external factors (with high- er EFI) produce a higher level of per capita GDP growth at all level of IFI above 0.1. In economies with IFI below the median, a higher performance in EFI always produc- es a higher economic growth, except when IFI is below 0.1. The second observation is that, when the IFI is above median, economic growth keeps rising regardless of the performance in the EFI until an economy’s IFI reaches the range of 0.7 and 0.8, beyond which the growth rate of per capita GDP declines. This suggests that the 0.7 to 0.8 range of the IFI is the optimal level, and economies will reach their highest possible growth rates given their EFI. When the value of EFI lies be- tween 0 and 1, the marginal contribution of IFI to the per capita GDP growth of an economy is positive if the value of IFI lies between 0 and the optimal level. When the value of IFI is above its optimal level, the marginal con- tribution of IFI to an economy’s GDP per capita growth is negative.5 In short, if an economy has an IFI value below 0.1, a lower value of EFI actually produces a higher per capital GDP growth. So long as the value of IFI lies above 0.1, the marginal contribution by the different level of EFI to per capita GDP growth is positive. On the contrary, when IFI lies between 0 and 0.1, the marginal contribution of EFI to per capita GDP growth is negative.6 The marginal effect of both EFI and IFI can be ex- amined from plotting the change in the per capita GDP growth rate against the IFI at different level of the EFI, Figure 2 shows that a higher EFI can lead to a larger change in the growth rate of per capita GDP at different level of IFI.7 However, as shown in Figure 3, the mar- ginal effect of IFI on the change in growth rate of per capita GDP at different level of EFI is increasing at a decreasing rate. Furthermore, Figure 3 shows that when the EFI value is below the median, its marginal contribu- tion to growth is larger than that when EFI is above the 5This can also be seen if Equation (3) is modeled as a continuous o r differentiable function, where 0< i < 1, and IFI* represents the optimal value: ** 0, ; ; lnlnln 0; 0; 0 iii IFI MedianEFI EFIIFI IFIEFI EFIIFI IFIEFI EFI yyy IFIIFI IFI 6When the function is a differentiable, the results can be summarized as follows: 0.5 10.10.500.1 ln lnln 0; 0; 0 ln lnln IFI IFIIFI yy y EFI EFIEFI 7The marginal effect can be summarized as follows when a differentia- ble equation is used: 0.25 0.450.95 0.75 ln ln ln ln EFI EFIEFI EFI yyyy IFI IFI IFI IFI 4For example, when EFI = 0.25, and with 3, 1 it D (namely, the range of IFI is between 0.2 and 0.3, and other dummies take a zero value), the simulated GDP per capita growth is 8.92904 (i.e. 7.52687 + (–0.08675)*ln(0.25*100) + (0.522359)*ln(0.25*100)* 1). K.-W. LI Copyright © 2011 SciRes. ME 186 Figure 1. Effect of external factors on growth. Figure 2. Marginal effect of EFI on growth. K.-W. LI Copyright © 2011 SciRes. ME 187 median.8 With the construction of the two indices that look sepa- rately at internal factors and external factors, the regres- sion and simulation results can provide additional evi- dence to support other studies that internal factors can have independent influence on growth. [17] Various pol- icy recommendations can be suggested from the empiri- cal and simulation analysis. Firstly, a more global eco- nomy indicated by the higher performance in the external factors does not always lead to higher economic growth. Instead, those economies with 0 < IFI < 0.1 should im- prove their IFI in order to reap additional gain from economic openness and globalization. Secondly, econo- mies whose IFI is above 0.1, but below the optimal range (0.7 to 0.8), should aim to improve the performance of the internal factors. A summary pattern of relationship between economic growth and the performance in the external factors and internal factors seems to have emerged from the simula- tion analysis. Figure 4 shows that once the performance in the internal factors has reached a minimum level, im- provement in internal factors will lead to a larger per capita GDP growth rate at every higher level of EFI. Thus, at a high level of external factors, EFI3 for exam- ple, a higher level of per capita GDP growth rate can be achieved. To see how the 62 world economies perform in the 1998-2002 period, Table 2 maps out the sample period average in five different ranges of EFI and IFI. Individu- al economies can consider their own positions in the ranking of the two indices, and compare their perfor- mance with other economies, including the periodic av- erage in the per capita GDP growth rates. There are sev- en mainly poor developing economies (Bangladesh, In- donesia, Kenya, Nigeria, Pakistan, Senegal and Uganda) that have the lowest rankings in both indices. On the contrary, those economies that performed strongly in both EFI and IFI are mainly developed economies (Aus- tria, Denmark, Finland, Netherlands, Singapore, Sweden, Switzerland, United Kingdom and USA). Most devel- oped economies have performed stronger in IFI than in EFI. Ireland is the only economy that has a stronger per- formance in EFI than in IFI in the sample period.9 One observation from Table 2 is that performance of internal factors is the relevant constraint in the growth of any economy. Most economies that are strong in the performance of IFI are also strong in the performance of EFI, but not the reverse. In other words, it would be ap- propriate for economies to improve their internal condi- tions and environment before they can gain from open- ness and globalization. A good performance in internal factors is essential to growth and development. There are a number of economies (Argentina, Botswana and so on) that have achieved the median in IFI, but show low per- formance in EFI. The 0.61 to 0.80 range of the IFI seems to be the critical range, as virtually all industrialized ad- vanced economies achieved an IFI score above 0.61. Table 2 shows that a number of economies in the second lowest (0.21 – 0.40) range of IFI experience a relative high growth rate in the sample period. For ex- ample, China has a growth rate of 6.749 percent and the Russian Federation had 6.381 percent and so on. This suggests that these economies have to improve their IFI before further reaping the gain from economic openness and globalization. Among the developing economies, African economies (e.g. Uganda, Kenya and Senegal) are the weakest performers in both the EFI and IFI, while the middle-ranking economies are the few Asian (e.g. Thailand and Malaysia) and Latin American (e.g. Pana- ma and Chile) economies. Other Asian economies (e.g. India, Indonesia, Philippines and Sri Lanka) have per- formed poorly in both EFI and IFI. The group of devel- oping economies that have reached the range of 0.61 – 0.80 in the IFI are mostly Eastern European economies (e.g. Hungary, Slovenia and Czech Republic), which will probably be the next group of countries that would bene- fit from globalization. The lesson is that sound perfor- mance in the various internal factors will facilitate good performance of external factors. In short, advancement in the performance of internal factors will help promoting economic openness. 5. Conclusions The empirical results in the paper clarify the importance of internal factors in growth and globalization. The rea- sonable large number of factors used in the construction of the two indices provides a comprehensive picture on the performance of different economies. The regression result that internal factors are important in promoting an economy’s growth has led to further investigation and analysis in the relationship of the two types of factors. Given a different level of performance in the economy’s external factors, a higher performance in the internal factors will produce a higher growth rate. When the per- formance of an economy’s internal factors is extremely low, it would be appropriate for that economy first to improve its internal factors. In short, performance in the internal factors is the more fundamental condition than performance in the external factors. Before the “optimal” level of internal 8The marginal effect can be summarized as follows when a differentia- ble equation is used: ln ln ln ln ii Below MedianAbove Median I FI IFIIFI IFI YY EFI EFI 9Measured in purchasing power parity constant 2000 price, Ireland’s GDP per capita is highest among the 62 world economies. K.-W. LI Copyright © 2011 SciRes. ME 188 Figure 3. Effect of a change in EFI. Figure 4. Relationships between growth, external and internal factors. factors is reached, economies will experience a rise in their per capita GDP as their performance of internal factors improve. The empirical results in this paper lend further support on the importance of a sound perfor- mance in domestic factors.[31-32] Economies with strong performance in external factors and globalization have sound performance in their internal factors. For those world economies that are ranked low in the Internal Factor Index, appropriate economic policies should be conducted to improve the performance of internal factors. The conclusion that the internal or institutional factors are more fundamental to growth than external factors adds to the debate on the difference between the two types of factors, or the contribution of single factors.[24, 28] Despite the useful empirical findings and the policy lessons economies can draw on, there can be a number of possible drawbacks in this paper. One is the selection of factors in the two categories. It is possible that different factors selection would produce different empirical re- sults, and the use of 34 external and internal factors can provide sufficient representation. The problem of possi- K.-W. LI Copyright © 2011 SciRes. ME 189 ble overlap among factors can partly be alleviated by the advantages of the principal component analysis. [33-34] 6. Acknowledgements The author is indebted to comments from Barbara Stal- lings, Neantro Saavedra-Rivano, Gianluca F. Grimalda, Peter J. Newell, Eden Yu, participants in the 2006 meet- ing of the APEC Study Center Consortium and col- leagues in the brown-bag seminar of the Department of Economics and Finance, City University of Hong Kong. An earlier version of this paper has been published as a Working Paper in the Center for the Study of Globalisa- tion and Regionalism, University of Warwick. The two research assistants, Iris Pang and Michael Ng, have pro- vided excellent research support. The funding support from the City University of Hong Kong under the Stra- tegic Research Grant (Number 7002433) is gratefully acknowledged. The author is solely responsible for the remaining errors. 7. References [1] P. M. Romer, “Increasing Returns and Long Run Growth,” Journal of Political Economy, Vol. 94, No. 5, 1986, pp. 1002-1037. doi:10.1086/261420 [2] P. M. 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[43] International Telecommunication Union, “Netcraft Se- cure Server Surveys,” International Telecommunication Union, Geneva, 1998-2002. [44] World Tourism Organization, “Compendium of Tourism Statistics,” New York, 1998-2002. [45] Central Intelligent Agency, “The World Factbook,” The Central Intelligent Agency, Washington D.C., 1998-2002. [46] United Nations, “United Nations Development Program Indicators,” New York, 1998-2002. [47] World Bank, “World Development Indicators,” The World Bank, Washington D.C., 1998-2002. [48] Transparency House, “Corruption Index,” Transparency House, Washington D.C., 1996-2003. [49] World Bank, “Aggregate Governance Indicators,” The World Bank, Washington D.C., 1998-2002. [50] Heritage Foundation, “Index of Economic Freedom,” The Heritage Foundation, Washington D.C., 1998-2002. K.-W. LI Copyright © 2011 SciRes. ME 191 Appendix Data All data are obtained from established international sources. The per capita GDP data are obtained from the World Development Indicators, The World Bank. The inter-industry trade index and the intra-industry trade index are compiled using the UN Comtrade Database, SITC Rev.3, for all the 62 economies with all commodi- ties up to two-digit level.[35] The performance of in- ter-industry trade is estimated from an economy’s re- vealed comparative advantage (RCA) index.[36-39] An economy’s RCA index can be calculated by: ,it gigwgiwt RCAXXX X, (A1) where ig X denotes economy i’s export of commodity g, wg X is world export of commodity g, i X is economy i’s total export and w X is total world exports, where 1,iN, 1,,tT and 1,, g G . When the val- ue of ,it g RCA exceeds unity, economy i is said to have a revealed comparative advantage in good g at time t. The total number of export industries of individual economies with revealed comparative advantage greater than unity is selected and normalized (NRCA) to form an indicator for the economy’s inter-industry trade performance (it TRCA): iti it TRCANRCA MAXNRCA. (A2) The intra-industry trade index (IIT) can be calculated as: ,, 1,, ,, 1,, 1 *100 1*100 j j nijgijg g jij gij g g it nijgijg g ijij gij g gt XM XM IIT XM MAX XM ,(A3) where Xij,g is the export value of good g from country i to country j, Mij,g is the import value of good g to country i from country j, and j n= total number of economy i’s trading partners. Equation (A3) shows the weighted av- erage of individual industry indices, where the weights are the shares of industries in total trade. The sources of data for the 17 factors in each of the Internal Factors Index and the External Factor Index shown in Appendix Table 1 are: Table 1. The classification of external factors and internal factors. External Factors Data SourceInternal Factors Data Source Economic integration (% GDP): 1) Total trade flows 2) Foreign direct investment 3) Portfolio capital flows 4) Investment income Inter-industry trade (SITC, 2-digit): 5) Revealed comparative advantage Intra-industry trade (SITC, 2-digit): 6) Export and import: same product Technology connectivity: 7) Internet users (% population) 8) Internet hosts (per capita) 9) Secure servers (per capita) Personal contact: 10) International travel & tourism (% population) 11) International telephone traffic (minutes per capita) 12) Remittances (% GDP) 13) Personal transfers (% GDP) International engagement: 14) Membership in international organizations 15) Government transfer (% GDP) 16) International treaties ratified 17) Personnel and financial contribution to United Nations Security Council missions (% population) IFS IFS IFS BOPS UN UN ITU ITU Net SSCT ITU BOPS BOPS WFB BOPS OFW UNDPI Institutional establishment: 1) Patent applications 2) Corruption Perception Index 3) Voice and accountability 4) Political stability 5) Government effectiveness 6) Regulatory quality 8) Rule of law 8) Control of corruption 9) Property right protection 10) Regulatory scores Education and health: 11) Public spending on education (% of GDP) 12) Primary school pupil-teacher ratio 13) Total health expenditure (% of GDP) 14) Physicians per thousand people 15) Primary school enrolment (% gross) Quality of labor force: 16) Youth unemployment (% of labor force ages 15-24) 17) Labor force, children 10-14 (% of age group) WDI CI AGI AGI AGI AGI AGI AGI IEF IEF WDI WDI WDI WDI WDI WDI WDI K.-W. LI Copyright © 2011 SciRes. ME 192 Table 2. External factors and internal factors indices: 1998-2002 average. Ranking External Factors Index Internal Factors Index Economies Index Economies Index 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 Ireland United States Netherlands Switzerland Sweden Finland Singapore Denmark Austria United Kingdom Canada New Zealand Australia Norway Germany France Hong Kong Portugal Spain Italy Czech Republic Israel Slovenia Hungary Slovak Republic Japan Malaysia Panama Greece Poland Korea Croatia Argentina Chile Philippine Brazil Russian Thailand Mexico China Turkey Romania South Africa Indonesia Ukraine Botswana India Tunisia Colombia Peru Senegal Venezuela Nigeria Egypt Kenya Morocco Pakistan Sri Lanka Uganda Saudi Arabic Iran Bangladesh 1.00 0.70 0.72 0.65 0.65 0.62 0.64 0.61 0.60 0.60 0.60 0.56 0.50 0.48 0.49 0.48 0.47 0.40 0.38 0.37 0.35 0.32 0.30 0.27 0.28 0.27 0.26 0.25 0.24 0.23 0.23 0.20 0.19 0.17 0.16 0.15 0.15 0.15 0.14 0.14 0.13 0.13 0.14 0.12 0.12 0.10 0.11 0.11 0.10 0.08 0.08 0.07 0.07 0.07 0.06 0.05 0.05 0.04 0.04 0.03 0.03 0.01 Sweden Switzerland Finland Denmark United States Norway Canada Germany Singapore Netherlands New Zealand Austria United Kingdom Australia Ireland Spain France Japan Portugal Hong Kong Slovenia Italy Israel Czech Republic Hungary Malaysia Chile Greece Poland Saudi Arabic Tunisia Korea Panama Slovak Republic Argentina Morocco Botswana Brazil Thailand Romania Egypt South Africa Croatia Sri Lanka Turkey Peru Mexico Venezuela Colombia Russian Philippine India Iran China Indonesia Ukraine Senegal Kenya Pakistan Uganda Bangladesh Nigeria 0.93 0.91 0.90 0.93 0.89 0.87 0.88 0.88 0.86 0.84 0.83 0.86 0.84 0.85 0.80 0.74 0.73 0.73 0.72 0.71 0.71 0.70 0.66 0.63 0.63 0.53 0.60 0.59 0.56 0.52 0.48 0.48 0.47 0.47 0.44 0.41 0.43 0.39 0.40 0.37 0.36 0.38 0.37 0.34 0.32 0.32 0.30 0.30 0.30 0.29 0.28 0.26 0.21 0.22 0.16 0.21 0.19 0.13 0.12 0.10 0.03 0.00 K.-W. LI Copyright © 2011 SciRes. ME 193 IFS = International Financial Statistics, International Monetary Fund;[40] BOPS = Balance of Payment Statistics, United Na- tions;[41] UN = United Nations Co mtr ade, United Nations;[35] ITU = International Telecommunication Union Data- base, International Telecommunication Union;[42] Net = Netcraft Secure, International Telecommunica- tion Union;[43] SSCT = Server Surveys Compendium of Tourism Sta- tistics, World Tourism Organization;[44] WFB = The World Factbook, Central Intelligence Agency;[45] OFW = Official websites of selected basket of treaties; UNDPI = United National Development Program In- dicators, United Nations;[46] WDI = World Development Indicators, World Bank;[47] CI = Corruption Index 1996-2002, Transparency House; [48] AGI = Aggregating Governance Indicators 1996-2004, World Bank;[49] IEF = Index of Economic Freedom, Heritage Founda- tion.[50] There are few exceptions. For example, Hong Kong has probably little international engagement in govern- ment transfer and does not engage in financial contribu- tion to the United Nations Security Council missions. The few missing values in the country series are replaced by the average of the immediate past and future years. In the EFI, the maximum number of missing economies in the 1998-2002 sample periods is 4, and their percentage ranged between 5.9% and 11.8%. For the IFI, the cor- responding figures for the maximum number of missing economies are 40, and the percentage ranged between 5.9% and 35.3%. A complete set of data is obtained for the three years of 1998-2001, while some data in 2002 are either provisional or unavailable. In the case of IFI, the few provisional data of 2002 are replaced by the cor- responding figures in 2001. |