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Technology Trade José L. Groizard University of the Balearic Islands√ December, 2007 Abstract This study addresses the question of why some countries import more R&D intensive goods than others. Using a panel data set of countries for the period 1970 to 1995, results indicate that trade openness, domestic investment, FDI and the quality of intellectual property rights (IPR) systems positively affect technology imports. However, the higher the percentage of the workforce with primary studies and the more developed the country, the lower technology imports are. JEL Classifications: F10, F13, O33, O34 Keywords: R&D, technology diffusion, absorptive capacity, complementarities, intellectual property rights, technology imports. √ Department of Applied Economics, University of the Balearic Islands, Ctra de Valldemossa km 7,5, 07122 Palma de Mallorca, Spain. Tel.: +34-971-172-784; fax: +34-971-172-389. E-mail: [email protected] I. INTRODUCTION Differences in technology levels account for a major portion of cross-country income and growth disparities (Klenow and Rodríguez-Clare, 1997; Hall and Jones, 1999; Caselli, 2005). While industrialized economies have the advantage in terms of innovation, the majority of the world, which operates below the technological frontier, adopts technology developed by other countries. This fact brings to backward economies an important advantage because imitation and adaptation of new technologies is less expensive and risky than creating them (Gerschenkron, 1962). Importing foreign technology is, then, a mechanism with which to increase growth and reduce the income gap across countries. Technology crosses borders via a variety of formal and informal channels, such as contracts for technology transfer, trade, foreign direct investment (FDI), the migration of skilled labour and imitation. A significant amount of recent evidence has supported the idea that trade is an important channel in technology diffusion both among developed countries (Coe and Helpman, 1995) and between developed and developing countries (Coe, Helpman and Hoffmaister, 1997, Busse and Groizard, 2007)) and among different potential channels, trade is perhaps the most consistent one (Keller, 2004). Hence, understanding the technology adoption process through trade is an open debate. The factors that influence technology diffusion through trade and the role of development in this process are less straightforward. For instance, there are several studies showing that stronger intellectual property rights (IPR) encourage trade (e.g. Maskus and Penubarti, 1995 and Falvey et al., 2006), but there is no clear evidence that IPRs encourage technology imports. Additionally, human capital, domestic investment or 2 openness are somehow perceived in the literature as factors enhancing technology diffusion, however evidence on their respective contributions is scarce. This study investigates the determinants of technology imports in a cross-section of countries between 1970 and 1995, using panel data techniques. Our goal is to investigate the precise role played by different factors that exert a potential influence on technology adoption decision-making. Results suggest that domestic and foreign investment positively influences high-tech imports. Moreover, importer countries may increase technology inflows improving the quality of IPR systems. Other factors exert a positive effect, such as exports of government expenditure. In contrast, countries with a human capital base strongly skewed towards unskilled workers, import less technology from abroad, while skill-biased economies tend to import more. Finally, GDP per worker negatively affects technology imports, suggesting that the role of R&D intensive imports in fostering productivity growth changes with the development process. The study is organized as follows. The second section reviews the theoretical and empirical literature on the determinants of technology adoption through trade. The third section examines the technology gap and describes the data used to test the main hypothesis. The fourth section presents the model, the estimation techniques and discusses the results. Finally, section five summarizes the main conclusions of the study. II. BACKGROUND The neoclassical trade model emphasizes the role played by factor endowments and technology levels to predict trade pattern. Leamer (1984) estimated a Heckscher-OhlinVanek (HOV) model and found support for the idea that countries import relatively larger amounts of those goods that are intensive in the factor relatively scarce in the country. 3 However, Trefler (1995) and Harrigan (1997) have evaluated that HOV performs poorly in a cross-section of countries. Nevertheless, when the assumption that technology is the same for all the countries the model gains in explanatory power. In the latter studies there is no role for factor complementarities in the determination of trade patterns, because they ignore the case of trade in intermediate goods and product differentiation. Neoclassical theory neglects the idea that countries tend to import more of those goods in which the agents have a special advantage to use them more efficiently. Surprisingly, there is a lack of theoretical literature concerning the specific determinants of technology imports. The only two exceptions are Eaton and Kortum (2001) and Caselli and Wilson (2004). The former proposes a comparative advantage model with heterogeneous capital goods, in which imports of capital goods (as a share of GDP) depend on investment rate and trade cost parameters that capture home-bias and other geographic factors1. The latter proposes a model of investment based on heterogeneous forms of capital, suggesting that shares in different types of capital goods in investment depend on their intrinsic efficiency (embodied technology) and how much they are complementary to other inputs, the abundance of which may vary across countries. On the other hand, the literature on technology diffusion points out that technology is specific to a particular combination of inputs (Atkinson and Stiglitz, 1969; Basu and Weil, 1998) making its adoption more difficult, even with non-existent adoption costs. Human capital is one of the most important factors likely to restrict imports of R&D intensive goods, because the implementation of foreign technology 1 For instance, Eaton and Kortum (2001) find that a country’s level of human capital (measured by average years of schooling) is associated with lower trade barriers, and that once human capital is accounted for, tariff barriers remain an insignificant trade cost parameter. 4 might require skills and capabilities that are embodied in workers. Other studies emphasize that technology developed in advanced countries is more likely to be skillbiased (Acemoglu and Zilibotti, 1999), and, consequently, technology adoption and human capital specific investment needs to be undertaken simultaneously. Empirical studies on the role of complementarities on trade are scant. Several studies have detected that human capital plays a positive role in a country's absorptive capacity. These findings suggest that the imitative ability acquired through trade is also likely to be constrained by certain complementary factors. For instance, Falvey et al. (2006) explain that intellectual property rights affect trade according to level of development, imitative ability (human capital) and the size of the importing country. On the other hand, the empirical evidence provided by Caselli and Wilson (2004) suggests that imports of equipment are positively related to the R&D content of imports interacting with a time trend, outward FDI, remoteness and property rights. But no clear evidence has been found detecting any influence of human capital levels, intellectual property rights or per capita income. However, a negative and robust inward FDI effect has been found, suggesting that FDI flows skew the composition of capital stock towards low-tech equipment. By looking at different types of capital goods, Caselli and Coleman (2001) found that imports of computers increase according to human capital stock size and the level of openness to trade, suggesting that technological spillovers are transmitted through trade. In summary, the ambiguity that arises in the literature concerning the role played by openness, domestic and foreign investment, IPR and human capital in the process of importing technology has driven this research. 5 III. DATA AND MEASUREMENT A fundamental question approached in this study is how to measure technology. Technology is defined as the quality of capital and intermediate goods used in the production process at any given time. Technology adoption, hence, is investment dedicated to updating and upgrading the quality of installed capital. Capital is a heterogeneous composite of different kinds of goods, such as machinery and instruments. To our knowledge there is no international data on disaggregate production or investment for a large number of countries and periods and no index to compare capital quality across countries2. Furthermore, one advantage of this definition of technology is that while the production of capital goods is highly concentrated in a few countries, as Eaton and Kortum (2001) have shown the imports of capital and intermediate goods emerge as a reasonable proxy for technology adoption investment. In the absence of detailed international production data on capital and intermediate goods, the analysis has focused on the trade flows of machinery, instruments and fine chemical products because these sectors are R&D intensive in the developed world3. The theory suggests that a country with comparative advantage in the production of knowledge intensive goods should show a significant presence of such products in the trade pattern. The revealed comparative advantage index (RCA) in product i and country j is described by the expression 2 Existing data matching production and trade data cover a shorter period of time (1980-1997) and is restricted to a lesser number of countries. See Caselli and Wilson (2004) for an explanation. 3 This taxonomy is based on Pavitt (1984). In Table A1 in the Appendix, the reader can find a brief description of the capital and intermediate goods selected according to R&D intensity. 6 (1) RCAij = ∑ X ij / ∑ X ij X ij / ∑ ∑ X ij where X ij is the value of R&D intensive exports (in US dollars) in country j at a given time. A value of 1.1 shows that country j’s technology exports relative to its total exports are 10 percent higher than the share of world technology exports relative to total world exports. Hence, an RCA index above one for a country can be interpreted as a technological advantage, and conversely, an RCA below one can be interpreted as a technological disadvantage. Figure 1 shows worldwide technological change since 1965, as revealed in the export pattern among country groups. In 1965, the technology gap between the top 8 R&D providers and the developing economies was tremendous, but has diminished significantly in the period studied. Furthermore, the data reveals a technology gap between technological leaders and OECD economies4. During these years, the ability to export technology to world markets has been concentrated in OECD countries as a sign of technological superiority and in particular, among a small group of top 8 R&D performer countries. INSERT FIGURE 1 These types of goods are by definition R&D intensive in R&D performing countries. Figure 2 shows a strongly positive relationship between RCA in technological products and R&D expenditure. The larger a country's investment in R&D, the greater its 4 The data for OECD is an average that includes the top 8 R&D performer countries, when we exclude them the gap also is magnified. We plotted OECD average, since the aim of this exercise was to compare technology gaps between developing economies and technological leaders and not between technological leaders. 7 technological advantage is in the export pattern5. The data clearly shows that technological advantage and R&D effort are positively related in a cross-section of countries, which means that this type of product embodies technology that is based on R&D activities at country level. Since data on R&D stock for several years and multiple countries is not available we have only considered countries with an RCA index above one in the initial year as technology leaders. The group of countries selected are Germany, Finland, France, Japan, Sweden, Switzerland, United Kingdom and USA6. IV. MODEL AND ESTIMATION The goal of this study is to estimate the determinants of R&D intensive goods imports exported by the top 8 technology leaders. We used the following model (2) log M jt =α+X jt β+u j +vt +ε jt jt where M is per worker technology imports, measured in current US dollars in country j jt and time t. X is a set of macroeconomic and institutional explanatory variables that j capture the social capability to absorb advanced technology, u is a fixed country effect, v t jt is a set of time dummies, and ε is an error term identically and independently distributed across countries and time. Technology is adopted because agents find that expected profits overcome expected costs. Although this decision is taken on the demand side, expected costs and 5 Thailand and Mexico are clear outliers, since neither of these economies would be expected to show a technological advantage in the RCA index, due to their low level of R&D expenditure. However, they still have an RCA index greater than one due to the fact that they are importers and assemblers of technological components. 6 Eaton and Kortum (2001) found similar trade patterns comparing more recent production data of capital goods. 8 benefits are affected by the supply side and by the institutional environment. At national level, the demand for technology imports is constrained by an adequate supply of complementary inputs, such as skilled workers. To control for this issue we have introduced several educational variables relating to workforce. On the one hand, we constructed an aggregated measure of human capital based on the adult population's years of education, taken from Hall and Jones (1999).7 On the other hand, we disaggregated human capital at different levels of education according to attainment measures calculated by Barro and Lee (2000). There are also relatively important potential technology adopters, such as domestic companies, the government and multinational companies. To control for these we included three proxies, domestic investment, government consumption and FDI inflows8. There are several factors that affect technology adoption from the supply side. Rosenberg (1972) emphasized three factors, such as the novelties introduced into technology after creation, the invention of new uses for technology and the provision of complementary inputs by technology providers. At country level we controlled for these factors by including investment per worker as an independent variable. Finally, another set of control variables were introduced to take the institutional and policy environment into account. The increasing literature on intellectual property rights and trade concludes that a stronger IPR system has two opposite effects (Maskus and Penubarti, 1995). On the one hand, there is a positive market expansion effect, since companies should be encouraged to export into that market because protection reduces 7 Hall and Jones (1999) measured human capital by imposing a discontinuous linear function of schooling year per worker. 8 Data extracted from WDI (2005). 9 the risk of piracy and protects company profitability in that market. However, there is a market power effect of negative sign, since stronger IPR protection reduces the ability of domestic companies to imitate and this increases the market power of the exporter firm, which might lead to reduced sales ex-post in that market. The final result will depend on the relative importance of market power and market expansion. To measure IPR we introduced an index of patent protection ranked from less (0) to more (5) patent protective legal systems as calculated from Ginarte and Park (1997). According to the conventional view, trade yields static gains because exporting the goods a country produces more efficiently, allows the importation of more of those goods the country produces more inefficiently. In other words, exports are what an economy pays for its imports. More recent theories bring attention to trade openness as a channel of knowledge regarding spillovers9. According to this view, more open economies tend to benefit more in terms of access to public world knowledge, which might lead to dynamic gains from trade. To control for these issues we introduced exports per worker as a measure of trade openness10. We added GDP per worker as a control variable, since there are possible omitted factors determining technology imports not clearly captured by the variables already described. The data was obtained from an updated version of the Summers and Heston (1991) data set. 9 Grossman and Helpman (1991) provide an excellent overview of models. 10 Data on exports comes from Easterly and Sewadeh (2005). 10 Several X variables, such as education and the intellectual property rights protection index are not available on a yearly basis. Therefore, a panel with a crosssection of a maximum of 79 countries, from 1970 to 1995, at five year intervals was used. Table 1 displays summary statistics of the variables used in the empirical analysis. Each variable is represented as an average for the countries and periods employed. In 1970, technology imports represented 12 US dollars per worker while total exports represented 280 US dollars per worker, that is 23 times larger than the former. However, in 1995, the average country imported technology to the sum of 136 US dollars and exported 1876 US dollars both in per worker terms. This means that during this period technology imports grew at a faster rate than exports for the country sample. [INSERT TABLE 1 ABOUT HERE] A major concern would be the potential bias arising from reverse causality in several explanatory variables. For instance, technology imports increase GDP per worker rather than the other way around. Since GDP per worker is not a variable of interest and only a control variable, we minimized the endogeneity issue by lagging it five years. Other variables with similar problems are domestic investment and exports. It is likely that technology imports and domestic investment and imports and exports are determined simultaneously because they may have similar underlying factors. To deal with this issue we introduced them into the regression analysis as predetermined, lagged five years. 4.1 Results Table 2 summarizes the basic results derived from the total sample estimation. Each column represents a different specification of the model. The first one includes the main set of predictors, the second one shows the role of education level variables, the third is 11 an extension to decompose the influences of trade openness in technology imports, and the forth and fifth are robustness checks. Each specification was estimated by fixed effects (FE) and random effects (RE) including year time dummies. The dummy coefficients have been omitted due to space restrictions. A test of FE versus RE was performed to evaluate the efficiency and consistency of the estimators. The Chi-squared statistic and its p-value are displayed at the bottom of the table, and as we can see the RE estimators have been discarded in all cases11. [INSERT TABLE 2 ABOUT HERE] In column 1, six out of seven predictors have a significant effect on technology imports at conventional levels: income, investment, government consumption, IPR protection index, FDI and exports. All the variables have the expected sign with the exception of the non-significant Hall-Jones human capital index, which is discussed below. In column 2, human capital was broken down into three different levels: percentage of the labour force with completed primary education and no more, percentage of the labour force with completed secondary education and no more, and percentage of the labour force with completed tertiary education. The effect of the educational levels on technology imports is negative at primary level and null at secondary and higher levels of education. The remaining variables maintain the same sign 11 The Sargan-Hansen test is a test of overidentifying restrictions that is designed to generate non- negative test statistics. See Arellano (1993) for a precise explanation. 12 and significance with the exception of the FDI and IPR protection index which are not significant. Column 3 shows the estimates of the model once openness to trade is divided into manufacturing and non-manufacturing exports and the composition of the labour force education is accounted for. The manufacturing export coefficient is negative and insignificant and the non-manufacturing export coefficient is positive and significant at the 1 percent level. When splitting exports, all the other predictors are significant at conventional levels with the exception of income per worker and educational attainments at the secondary and tertiary levels. Moreover, IPR and FDI turned out to be positive and significant. The specification in column 4 checks the robustness of previous results from different measures of labour force education. Existing data allows discrimination between the number of individuals that start a certain level of education but who do not finish it. We employed measures of portion of individuals attaining some level of education (i.e. primary, secondary and tertiary). Comparing columns 3 and 4 we can see two changes in the significance level of coefficients. On the one hand, the percentage of individuals with some secondary education affects technology imports negatively. On the other hand, income becomes significant while IPR loses its significance level. Finally, the specification in column 5 replaces income per worker with shares of agriculture and industry in GDP. The only noticeable change is that exports of manufactured goods are now significant, while the significance levels and magnitude of the other estimated coefficients are maintained. 13 4.2 Discussion Why do some countries import more R&D intensive goods per worker? According to the literature, a broad set of variables has been tested as potential explanations. Rising domestic investment, government consumption, exports of non-manufacturing goods, FDI inflows and improving the institutional Intellectual Property Rights (IPR) system stimulate technology imports. Meanwhile, increases in income per worker and in attainment of basic education seem to constrain access to embodied foreign technology. The estimated elasticity of their respective contributions vary slightly across specifications, but, in general, parameters seem to be stable. Economies investing more resources domestically also invest more in foreign technology. The estimated elasticity suggests that increasing domestic investment by 1 per cent results in an increase in imports of R&D intensive goods of 0.5 per cent, five years after. This variable, basically aimed at capturing the effort of the local economy to provide supply side stimulus for the adoption of new technology, such as the adequate provision of complementary physical capital inputs or expenditure to adapt foreign technology to local conditions. Moreover, level of public expenditure also raises the imports of technology, meaning that the government is an important technology adopter, either acting directly or generating favourable conditions relating to the level of public consumption. When computing the effect of a general measure of human capital, the elasticity becomes negative but insignificant. When splitting the human capital effect into three educational categories, economies with a low percentage of primary educated workers tend to import more technology, while countries with a higher percentage of tertiary 14 educated workers tend to import more R&D intensive goods12. This suggests that unskilled workers and technological goods are substitutes, an idea which is consistent with the international skill-biased technological progress evidenced by Berman and Machin (2000) and Gancia and Epifani (2007). A striking implication of these findings is that low income countries needing foreign technology to catch-up with the leading economies, but which do not have enough human capital are stuck in a form of poverty trap. In other words, policies aiming at extending primary education at the expense of tertiary education will perpetuate this state. A robust link exists between exports and technology imports. In the first two specifications, total exports have a positive elasticity estimated with great precision. A 1 percent increase in exports per worker generates an increase in technology imports ranging from 0.22 to 0.26 percent five years after. Disaggregating between manufacturing and non-manufacturing exports results shows that non-manufacturing exports stimulate technology imports, and that manufacturing exports have no clear impact on imports of R&D intensive goods. These findings can be interpreted as evidence of both, gains from trade, and international knowledge spillovers linked to trade. On the one hand, trade theory establishes that all countries experience welfare gains when they specialize according to comparative advantage. One of these welfare enhancing channels is that exporting countries are able to import those goods that are difficult to substitute by domestic products, such as R&D intensive goods. The fact that countries that export more manufactured goods per worker tend to import less technology and countries that export 12 However, the effect was measured with a low level of precision. 15 more non-manufactured goods import more technology, is conceivable within the comparative advantage framework. On the other hand, there is a great deal of new theoretical13 and empirical14 literature stressing the role of international trade as a channel for knowledge and technology diffusion around the world. Trade allows the flow of ideas beyond national borders and learning from the experience of others in the production and use of new technology. So, when economies are open to trade they are not simply trading products; they are establishing information channels that are extremely useful for the transfer of free knowledge. The kind of knowledge that flows from other countries is likely to result from experience rather than from R&D investment, so it is a complement to the knowledge embodied in the capital and intermediate goods traded. The interaction between embodied and disembodied knowledge has to be carefully considered because it may be the origin of innovative processes in early stages of development. This is possible because domestic and foreign knowledge is more complementary when differences in development are wider, that is when the presence of non-manufactured products in the export pattern is larger. This outcome is similar to that found by Caselli and Coleman (2001) in the case of computers. Multinational companies are also complements in the adoption of technology because they demand R&D intensive goods. A 1 percent increase in FDI raises imports of R&D intensive goods by 0.04 per cent. The magnitude of this elasticity is far below the contribution of domestic investment to technology imports which is around 0.5. 13 Grossman and Helpman (1991) provide a good discussion on a collection of theoretical models. 14 Coe and Helpman (1995) for OECD countries, and Coe et al. (1997) for Non-OECD countries, among others. 16 Another important determinant of technology imports is the protection of intellectual property rights. Our findings suggest that increasing the level of patent right protection may increase technology imports. This implies that incentives to export technology are stronger when institutions guarantee appropiability from technology providers. This empirical finding is in sharp contrast with other study results which never found that the market power effect existed in R&D type goods (Caselli and Wilson, 2004). Finally, by increasing GDP per worker, economies tend to reduce imports of technology. This is a sensible result since a higher level of development means an increased ability to produce more sophisticated goods and as a consequence to facilitate the use of domestic technology. Several studies have documented the role of trade, and R&D intensive trade as a channel for increasing the level of GDP per worker. Results reported in this study suggest that the role of trade in promoting technology diffusion is greater the lower the level of development is in the country. V. CONCLUSION In this study some macro-determinants of technology adoption in a cross-section of countries between 1970 and 1995 have been explained. Technology imports have been measured in terms of R&D intensive goods imports exported by top R&D performing countries. The findings suggest that countries can increase imports of R&D intensive goods through domestic and foreign investment, increasing human capital levels, opening up the economy to trade and improving the IPR system. However, economies tend to substitute imports of foreign technology by increasing the number of non-skilled individuals into the workforce and exporting manufactured goods. Moreover, increasing 17 the level of development tends to reduce technology imports. The cross-country variability in the factors that enhance or restrict technology adoption through imports also implies that those factors will affect investment in R&D intensive sectors. References Acemoglu, D. and F. Zilibotti (2001). “Productivity Differences”, Quarterly Journal of Economics, 116(2): 563-606. 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Rosenberg, Nathan (1972). “Factors Affecting the Diffusion of Technology”, Explorations in Economic History, 10(1): 3-33. Summers R. and A. Heston (1991). “The Penn World Table (Mark 5): an expanded set of international comparison”, Quarterly Journal of Economics, 106, 1991, 327-368. Treffler, Daniel (1995). “The Case of Missing Trade and other Misteries”, American Economic Review, 85: 1029-46. UNCTAD, UN COMTRADE. Online Access to the Trade Database of the UNCTAD, Internet Posting: http://comtrade.un.org/, 2005. UNESCO (1999). UNESCO Statistical Yearbook 1999, Paris. World Bank (2005). World Development Indicators 2005, CD-ROM, Washington. 20 Table A1: Technological goods SITC Code rev.1 Description 541, 553 Medicinal, pharmaceutical products and fine chemical products 7111, 7112, 7113, 7114, 7115, 7116, 7117, 7118 Various machinery and equipment (excluding internal combustion engines) 722, 7231, 7249, 726, 729, 734 Specialized machinery for several industries (excluding machinery for manufacturing paper and food processing) 861, 862, 864 Specialised machinery and instruments for particular industries 9510 Other goods Source: Standard International Trade Classification (SITC), revision 1 and ECLAC (1996). 21 Figure 1: Revealed Comparative Advantage in R&D inventive goods 1.6 0 1.4 0 1.2 0 1.0 0 0 .8 0 0 .6 0 0 .4 0 0 .2 0 0 .0 0 1965 1995 OCDE No-OCDE Top 8 Figure 2: Technology Advantage and R&D Effort, 1995 JPN 2 Revealed Comparative Advantage SGP IRL GBR DEU MEX USA KOR SWE FRA FIN 1 THA ITA AUT CHN CZE HUN 0 BRA PRT ESP SVK POL EST LTU HRV ROM KGZ TUN LVA ARG NZL MDA KAZ TUR BGD SEN CHL ECU EGY KWT MDG UGA 0 1 DNK NLD ISR SVN CAN BEL NOR ISL 2 3 R&D expenditure (% of GDP) 22 4 Table 1: Summary Statistics Variable Obs Mean Std. Dev. Log of Technology Imports per worker 329 4.198 1.654 Log de GDP per worker 329 9.072 0.962 Log Investment per worker 329 7.336 1.496 Government Consumption (% GDP) 329 15.124 5.956 Intellectual Property Rights Protection Index 329 2.571 0.921 Log FDI per worker 329 3.452 2.216 Log of human capital 329 0.626 0.300 Primary Education Completed 327 14.493 8.223 Secondary Education Completed 327 9.818 9.214 Higher Education 329 8.005 8.494 Log Exports per worker 329 6.747 1.756 Log Manuf. Exports per worker 328 4.829 2.603 Log Non-Manuf. Exports per worker 328 6.364 1.611 Note: all variables refer to country averages for the period considered 23 Min -0.499 6.737 3.939 3.135 0.000 -3.878 0.000 1.200 0.100 0.000 2.194 -1.301 1.531 Max 8.106 10.530 10.040 43.406 4.860 8.318 1.224 45.100 49.300 50.300 10.779 10.105 10.067 Table 2: Results (1) -0.388 [0.127]*** 0.538 [0.060]*** 0.019 [0.008]** 0.168 [0.075]** 0.032 [0.018]* -0.614 [0.380] 0.260 [0.061]*** Log GDP per worker Log Investment per worker Government Consumption (% GDP) Intellectual Property Rights Protection Index Log FDI per worker Log of Human Capital Log Exports per worker (2) -0.322 [0.127]** 0.570 [0.059]*** 0.020 [0.008]** 0.119 [0.075] 0.027 [0.018] Log Non-Manufacturing Exports per worker -0.011 [0.004]*** -0.001 [0.005] 0.016 [0.018] Secondary Education Completed Higher Education Completed (4) -0.208 [0.115]* 0.509 [0.059]*** 0.019 [0.008]** 0.122 [0.075] 0.042 [0.018]** -0.037 [0.026] 0.245 [0.062]*** -0.012 [0.004]*** -0.002 [0.005] 0.014 [0.018] -0.040 [0.026] 0.261 [0.060]*** (5) 0.481 [0.066]*** 0.027 [0.010]*** 0.146 [0.080]* 0.035 [0.019]* 0.215 [0.063]*** Log Manufacturing Exports per worker Primary Education Completed (3) -0.182 [0.116] 0.560 [0.059]*** 0.020 [0.008]** 0.133 [0.075]* 0.037 [0.018]** Some Primary Education -0.054 [0.025]** 0.173 [0.055]*** -0.012 [0.004]*** 0.000 [0.006] -0.002 [0.024] -0.012 [0.004]*** -0.014 [0.005]*** -0.005 [0.011] Some Secondary Education Some Higher Education Agriculture share (% GDP) Industrial share (% GDP) Constant 0.550 [0.889] 328 79 0.83 Observations Number of countries R-squared 0.108 [0.879] 326 79 0.84 -1.008 [0.844] 325 78 0.84 -0.009 [0.913] 325 78 0.84 -0.010 [0.006] 0.002 [0.007] -1.541 [0.561]*** 286 73 0.82 63.031 71.746 72.698 76.439 62.963 Sargan-Hansen Statitic (χ2) p-value 0.000 0.000 0.000 0.000 0.000 Standard errors in brackets. Significance levels: * significant at 10%; ** significant at 5%; *** significant at 1%. The dependent variable is the log of technology imports per worker. The following variables are lagged five years: GDP per worker, Investment per worker, Total exports per worker, Manufacturing Exports per worker and Non-Manufacturing Exports per worker. All the specifications are estimated using the fixed effect estimator and include a set of time dummy variables. 24