Education for Technology Readiness: Prospects for Developing Countries*

Transcription

1 Journal of Human Development, Vol. 2, No. 1, 2001 Education for Technology Readiness: Prospects for Developing Countries* JONG-WHA LEE Korea University The technology gap between developing and advanced countries has been increasing during the last few decades. In the process of technology development human capital plays a critical role as an absorption capacity for new technologies in developing countries. The cross-country regression shows that human capital interacts with in ows of foreign technology embodied in machinery imports as well as FDI, and thereby contributes to technology growth in developing countries. We also nd that the stock of human capital, at the secondary and tertiary levels of education in particular, plays a key role in determining the development of information and communication technology. This paper discusses the measures in building appropriate human capacities for the adaptation of new technologies in developing countries by focusing on the education strategies of East Asian economies. 1. Introduction Technology is widely considered to be a crucial factor of input in the industrialization and development of countries. Recent studies on economic growth, such as those by Romer (1986, 1990), Grossman and Helpman (1991), and Aghion and Howitt (1992), emphasize the argument that technological progress plays a pivotal role in sustained long-term growth. Empirical evidence has also shown that technology accounts for a major portion of cross-country differences in both the level and growth rates of income. 1 During the last two decades, technological progress has greatly accelerated with the spread of new technologies such as information and communication technology (ICT). Such rapid technological progress is anticipated to reduce the gap in income among countries. While advanced countries have advantages in technology creations, developing countries, on the other hand, can imitate and adopt the new technologies. The imitation and adoption of existing technologies are less costly in comparison to inventing new ones. Hence, if developing countries tap global technologies successfully with little cost and time, they would be able to have faster technology growth than advanced countries. *The author is grateful to Nancy Birdsall, Sakiko Fukuda-Parr, and Jeffrey Sachs for helpful suggestions, and Si-Yeon Lee for the excellent research assistance. ISSN print/issn online/01/ United Nations Development Programme DOI: /

2 Jong-Wha Lee However, despite such theoretical predictions, in reality, income disparities between developing countries and advanced countries have been persistent and have even been increasing in recent decades. The discrepancies in the performance of output and technology progress are substantial among developing countries as well. While some developing countries have successfully caught up with advanced countries, others have drastically fallen behind. The persisting global income disparities indicate that technology is not diffused automatically. The extent and speed of diffusion depend on the domestic capacity of absorbing the advanced technology. The absorption capacity also differs substantially across countries. Global technology advances would not provide bene ts for the countries that lack in absorption capabilities. The adaptation of new technologies requires human efforts and capabilities that cannot be just granted. New technologies often require new skills. As such, lack of human capacity to utilize advanced technologies has been one explanation for the failure of many developing countries to fully exploit the existing global technologies. For instance, even if computers are completely free, they are not applicable to the countries in which the majority of the people are illiterate. That is, without the appropriate investment in human capital, advanced technologies cannot make developing countries grow at a rapid pace. Thus, the purpose of this paper is to examine the role of human capital in the process of technology diffusion. We construct a measure of technology gap between developing and advanced countries, and then examine the role played by human capital in technological catch-up by developing countries. Empirical tests focus on examining the complementary effect between technology imports measured by foreign direct investment (FDI) in ows and machinery imports and educational stock on technology progress, based on cross-country data. The complementary effect indicates that human capital works as a country s capability to absorb new technologies that are embodied in FDI and machinery imports. We also test the role of human capital in the development of information and communication technology, by estimating the relationship between various ICT indicators and human capital. The importance of human capital in technology diffusion implies that promoting education is a key factor in eliminating global technology disparities. Consequently, based on the empirical results obtained, we discuss the measures in building appropriate human capacities for the adaptation of new technologies in developing countries by focusing on the education strategies of East Asian economies. 2. The global gap in technology This section examines the gap of per capita income and technology level among countries. We investigate the evolution of income and technology gap during the period of in developing countries and examine to what extent the disparities in technology are related to the divergence of income across countries. 116

3 Education for Technology Readiness 2.1. Changes of income and the technology gap in developing countries In order to see the evolutionary process of the gap in income and technology among countries, we need to initially measure the level of technology. We use the aggregate production function approach and decompose the level of output per worker into the levels of inputs and total factor productivity (TFP). In this framework, the state of technology is measured by TFP. Then, by comparing the results across countries, we can measure the extent to which the aspect of cross-country differences in output per capita is attributed to differences in technology level and see how the income and technology gap evolve over time. Based on a Cob Douglas production function, we present this approach. 2 The production function is written as Y 5 K a (AhL) 1 2 a (1) where K denotes the stock of physical capital, h is the amount of human capital per worker, L is the number of workers, and A denotes a measure of total factor productivity. The production function is rewritten in terms of output per worker, y 5 Y/L, as y 5 ( K ) a 1 2 a h A (2) Y Let s assume that the capital share ( is constant across countries. Then, the ratio of output per worker between two countries, i and j is written as y i y j 5 ( j j) a 1 2 a i j ( h i h j ) ( A i A j) (3) j where K/Y. By taking log, the gap in output per worker between country i and country j: log( y i ) log( y j ) ( y i y j )/y j is written as an additive sum of three components, ln( y i y j) 5 a (1 2 a ) * ln ( j i j j) + ln ( h i h j ) + ln ( A i A j) (4) The equation allows us to decompose the differences in GDP per worker across countries into differences in the capital-output ratio, differences in educational attainment, and differences in total factor productivity. In this equation, we consider country i as a reference country that has a higher income than country j. Thus, the higher value of each term in equation (4) indicates that the gap of income of a developing country j from that of the reference country i becomes larger. To conduct the decomposition of the output, we measure the output per worker by the level of GDP per working age population aged between 15 and 64. We have used the working age population as a measure for the 117

4 Jong-Wha Lee size of workers, considering that the available cross-country sources of labor force or employees are less reliable than those of the working-age population. The physical capital stock data are constructed using the perpetual inventory method based on aggregate investment data in the Penn-World Tables (PWT) Regarding the capital share parameter, we assume a Human capital per worker is assumed to have relations to years of schooling as follows: h 5 e u (E) (5) In this equation, u (E) measures the ef ciency of a unit of labor with E years of education relative to one with no schooling. The derivative, u (E) is the marginal return to an additional year of schooling. We assume that u (E) is linear, and the average marginal return to an additional year of schooling is 11%, corresponding to the average number in Psacharopoulos (1994) survey. The years of schooling are measured by average years of schooling for the population aged 15 and over, as constructed by Barro and Lee (2000). Figure 1 summarizes the results. It shows the gap of output per worker in all 59 developing countries at 5-year intervals for the period from 1970 to For comparison, the group of 22 advanced countries is selected as a reference group 2 i in equation (4). Hence, the gap in output per worker, capital-output ratio, human capital, and technology level is expressed as a log difference in each component between a developing country and the advanced country group. The gure shows that the income gap between advanced and developing countries, on average, has increased over time from 1.52 in 1970 to 1.73 in The gap became much wider particularly FIGURE 1. Changes in the gap of output per worker and its components, (Average of all developing countries) Note: The gap is expressed as the log difference between the value of each term for individual developing country and the corresponding average value for 22 advanced countries. 118

5 Education for Technology Readiness FIGURE 2. Technology gap and output per worker gap, 1985 after the 1980s. The gure also shows the results of the decomposition of the differences in GDP per worker into differences in three factors capitaloutput ratio, educational attainment, and TFP, as in equation (4). TFP differences are indicated by the clear part of the bar. As shown, TFP accounts for a substantial fraction of the income differences between the advanced and the developing country groups. The contribution of TFP to per worker output gap has been increasing over time from about 34% in 1970 to 47% in Thus, technology gap plays an important and increasing role in explaining income disparities. Among developing countries, differences in technology are also an important factor for the differences in output per worker. Figure 2 plots the TFP gap against the GDP per worker gap across the 59 developing countries for the year It illustrates that the differences in GDP per worker are highly correlated to differences in TFP; the correlation between the two series is Table 1 summarizes the level of income gap and its change over the period by the developing countries as a whole and by speci c regions. For the developing region as a whole, the income has been diverged over time. The divergence in technology has been a dominant factor that in turn caused the divergence in income gap. While K/Y of developing countries on average converged to the level of advanced countries over the period between 1970 and 1995, TFP showed a strong divergence that offset the converging effect of capital accumulation. There also exist signi cant variations in the income and technology gap among developing regions. The income gap of Sub-Saharan Africa on average reached 2.47 in 1995 compared to 0.77 of East Asia. More than half of the 119

6 Jong-Wha Lee TABLE 1. Change of income and technology gap, by region Group Changes (No. of Gap(log) countries) All developing countries(59) GDP per worker Capital-output Human capital Technology (TFP) Sub-Saharan Africa(21) GDP per worker Capital-output Human capital Technology (TFP) East Asia(9) GDP per worker Capital-output Human capital Technology (TFP) Latin America(21) GDP per worker Capital-output Human capital Technology (TFP) Other developing(8) GDP per worker Capital-output Human capital Technology (TFP) Note: The gap is expressed as the log difference between the value of each term for individual developing country and the corresponding average value for 22 advanced countries. Regional averages are simple averages. The gap in GDP per worker is the sum of the gap in the capital-output ratio, gap in educational attainment, and gap in technology (see equation (4) in the text). differences in income between the two regions are attributed to the gap in technology: 1.22 of Sub-Saharan Africa vs of East Asia. The income disparities across developing regions appear to have increased over time. Both the income and technology gap increased in Sub-Saharan Africa and Latin American countries between 1970 and On the contrary, they decreased substantially in the East Asia region The gap in technology investment levels The empirical results, based on the aggregate cross-country data, show that income disparities across countries have been widening in recent decades. The main factor of the global income gap is technology divide. As Sachs (2000) puts it, at the core of the global divide is the vast inequality in innovation and diffusion of technology (p. 97). 120

7 Education for Technology Readiness TABLE 2. Trends in ownership of US patents, Patents granted during Share Share Share US Japan Germany France United Kingdom Canada Taiwan Italy Switzerland Netherlands South Korea Sweden Israel Hong Kong South Africa Brazil China Peoples Rep Singapore Mexico India Venezuela Argentina Others Total Source: US Patents and Trademarks Of ce (1997) TAF Special Report: All Patents, All Types January 1977 December Adapted from Table 1 and 2 in Kumar (1997). The signi cant cross-national differences in technology level imply that technology inputs for technology creation and adaptation substantially vary across countries. The US patents ownership data, which is often used for international comparisons of innovative activities, bears witness to the substantial gap in technology creation between developing and advanced countries. Table 2 presents the data on the number of patents obtained by inventors from different countries in the US and reveals that over the past two decades, the bulk of innovative activities concentrated in only a few countries. G5 countries combined, produced as much as 90% of the US patents. While the share of the top ve countries in total patents has been constant, Japan has shown a big increase as a leading foreign holder of US patents by improving her share from 11% during the period to about 21% during the period The data show that only a few developing countries emerge as new inventors. The four East Asian economies including Taiwan, South Korea, Hong Kong SAR, and Singapore accounted for 2% of US patents granted during , compared to 0.2% during However, developing countries, other than the Asian Newly-Industrialized Economies (NIES), play a negligible role in innovative activities. Hence, in the area of 121

8 Jong-Wha Lee technology generation, most developing countries are accordingly lagging behind. There are no adequate measures for domestic technological inputs for innovative activities. Data on investment in technology and science are of poor quality for developing countries. R&D expenditures and personnel are often used as measures of technological investment, although the concept itself is hard to de ne and some national speci cations vary from international classi cations. The available R&D data underscores the existence of a substantial gap in the technological investment between innovating industrialized countries and non-innovators. In terms of R&D scientists and technicians, industrialized countries had on average 4.1 persons per 1,000 in population during the early 1990s. 4 In contrast, developing countries as a whole had 0.4 persons. Also there is an enormous differentiation in the developing world. While East Asia had 2.5 R&D personnel per 1,000 people, most Sub-Saharan African countries have very few scientists and technicians. In terms of R&D expenditures, industrial countries account for over 85% of the total world expenditures (Kumar, 1997). As a percentage of GNP, industrialized countries spent over 3%, compared to less than 1% by developing countries. The current disparities in the R&D expenditures and personnel across countries will inevitably force the global gap in technology level to rise further in the future. Technology capacity of an economy depends not only on the capability of its own innovations, but also on its ability to adopt the technologies produced elsewhere. For developing countries, a considerable portion of technological inputs is sourced from abroad. Despite the disadvantages in technology generation, developing countries can bene t from global technologies by adapting or imitating them. Technology is often transferred to developing countries as embodied in imported machinery and equipment. The transfer of disembodied technology occurs by contracts under which rights to use patented knowledge are granted by their owner to the rms in developing countries at a fee. FDI by multinational corporations also works as a channel of technology transfer to developing countries. FDI ows to developing countries have surged rapidly in the 1990s and now account for roughly 40% of all global FDI ows. However, the recent sharp rise in the share of developing countries is to a large extent attributed to a rather heavy concentration of FDI in ows into China. As shown in Table 3, the regional disparities in net FDI in ows are enormous. Sub-Saharan African regions received only about 14 billion dollars during the period of , compared to roughly 240 billion dollars in East Asia. 3. The role of human capital in technology diffusion In the previous section, we found that there exists a large gap in the technology between developing and advanced countries. On average, the gap has been increasing over time. Even among the developing countries, 122

9 Education for Technology Readiness TABLE 3. Net foreign direct investment in developing countries, (Billions of $US) Country or country group All developing countries Sub-Saharan Africa Middle East and North Africa East Asia South Asia Europe and Central Asia Latin America Major recipient countries China Mexico Malaysia Brazil Indonesia Thailand Argentina Hungary Poland Chile Source: World Bank, Global Development Finance there are substantial discrepancies. Few developing countries have been successful in reducing the technology gap and catching up with advanced countries, while many others have fallen further behind. Hence, this section investigates the factors that determine technological catch-up of developing countries, focusing on the role of human capital. We review the existing literature and then present the results of an empirical investigation based on cross-country data Human capital and technology diffusion: theory and evidence The traditional neoclassical growth theory views technology as public goods that are freely available to anyone. Thus, technology is not considered to be an important explanatory factor for differences in economic growth across countries. On the contrary, other strands of literature emphasize technology as a crucial factor in explaining cross-national differences in income and growth. Recent endogenous growth theory highlights the role of technology progress in long-term economic growth. Adaptation, rather than production, of technology is considered to be more important for low-income, low-technology countries to catch up to advanced countries. For developing countries lagging behind in the technology ladder, imitation and adaptation of advanced technology created by advanced countries can provide better opportunities to catch up to more technologically advanced countries. This insight is highlighted by Gerschenkron (1962) as an advantage of backwardness. Recent growth models, such 123

10 Jong-Wha Lee as those of Grossman and Helpman (1991, chapters 11 and 12), and Barro and Sala-i-Martin (1997), provide more concrete models that analyze the role of technology diffusion in economic growth. One important issue in the literature of technology diffusion is what determines the extent and speed of technology spillovers. If technology spillover is completely global and swift, no gap in technology persists in the long run. Then, technology is not the important factor that can explain the existing vast gap in per capita income across countries. But, implementation of new technologies is not free. The imitation and adaptation of advanced technologies requires effort and capabilities. Thus, diffusion of technology can not be taken for granted. Abramovitz (1979) used the term absorptive capacity to denote domestic capabilities to absorb the spillovers of foreign technologies. Hence, both access to advanced technology and absorption capability are important determinants of technology progress of technologically-backward countries. Recent growth models such as the one presented by Parente and Prescott (1994) highlight the role of domestic capacity in technology adoption. Full exploitation of global technologies often requires highly-trained human skills. Lack of human capacity to adapt new technologies is considered to be a crucial factor that limits the absorption capability of a nation. Nelson and Phelps (1966) show that the facilitation of new knowledge is only possible with a suf cient level of human capital present in a developing country. Acemoglu and Zilibotti (1999) point out that technologies invented in advanced countries are more skill-complementary. Hence, the mismatch between skills and technology lead to differences in productivity even when all countries have equal access to new technologies. Recent papers highlight the complementary effects between human capital and technology, as both human capital and technology investment are endogenous choices of society. Redding (1996), for instance, assumes that both forms of investments in human capital and technology (R&D) exhibit pecuniary externalities and are strategic complements. In his model, the incentives to invest in each are interdependent, and thus multiple equilibria exist: an economy can be trapped in either a low-education, lowtechnology or a high-education, high-technology trap. Empirical work based on technology diffusion focused on testing the extent of spillovers and the signi cance of absorptive capacity in the diffusion of technology. 5 Eaton and Kortum (1996, 1999), by using patents applied for or granted by foreigners as a measure of foreign technology ows, nd spillovers of technology across OECD countries. In addition, Coe and Helpman (1995), and Coe, Helpman and Hoffmaister (1997) nd evidence for international R&D spillovers from OECD countries to developing countries. They nd that international trade plays a key role in these spillovers. The foreign R&D capital stock only affects developing country productivity through its interaction with imports. Jaumotte (2000) also con rms the role of trade in technology diffusion, in which a developing country s trade with OECD countries has a positive effect on the TFP growth of developing countries. A recent study by Caselli and Coleman (2001) shows that the 124

11 Education for Technology Readiness adoption of computer technology, measured by imports of computer equipment, is associated with higher level of human capital. In the ndings of Coe, Helpman and Hoffmaister (1997) and Jaumotte (2000), human capital does not play a clear role in technology diffusion. They nd human capital to be positively related to TFP growth, but the independent effect of human capital on productivity growth may come from other factors, other than technology diffusion. Benhabib and Spiegel (1994) nd that a higher level of human capital raises the responsiveness of the growth rate to the initial income gap. Hence, human capital stock plays an important role as an absorptive capacity for a country, which has a larger gap in income per worker and thus is technologically lagging behind in catching up to the world technology frontier. Borensztein et al. (1998) nd that there is a strong complementary effect between FDI and human capital: FDI contributes to economic growth only when the host country has a minimum threshold stock of human capital. 3.2 Human capital constraints for technology diffusion: an empirical analysis This section examines the role of human capital in the process of technological progress in developing countries, and tests whether the complementary effect between technology imports and human capital are an important determinant of technology diffusion. The empirical framework is based on Borensztein et al. (1998) which tests a complementary effect between FDI and human capital in economic growth. But, while they have used GDP growth as an indicator for technology progress, we use direct measures of technology gap and technology progress. We also include imports of machinery and transportation equipment in addition to FDI in ows as another channel of technology spillovers from advanced countries into developing countries. For the empirical investigation, we set up the following basic speci cations: D(TFP) 5 c 0 + c 1 *GTFP 0 + c 2 *H 0 + c 3 *TIM + c 4 *TIM*H (6) where D(TFP) is the growth of technology, GTFP 0 the initial gap of technology level, H 0 the initial stock of human capital, and TIM a measure of technology imports such as foreign direct investments and machinery imports over the period. The framework allows us to test the role of human capital as an absorption capacity to facilitate imported technology in developing countries. If the interactive term (TIM*H) is signi cant in the regressions, it indicates that the positive technology spillover effects depend on the stock of human capital owned by technology importing countries. All regressions are based on cross-section data for the period from 1970 to The technology growth measure is the average annual rate of TFP 125

12 Jong-Wha Lee growth. The gap of technology is measured by the log difference level of TFP between each developing country and the advanced countries group, which are constructed in section 2. Foreign direct investment refers to net FDI ows as a ratio to GDP, averaged for the period from 1970 to The data come from OECD sources and measure FDI ows, originating in OECD countries going into developing countries. In our framework, this measure, which does not include FDI ows originating from developing countries, is a more appropriate concept, for the technology ows embodied in the FDI coming from more advanced countries into less developed countries. The machinery import is the sum of machinery and transport equipment (SITC 7 in Rev. 2) imported by each developing country from the OECD countries as a ratio to GDP, averaged for the period from 1970 to For the human capital stock variables, we use the initial-year level of average years of total schooling for the population aged 15 and above. For the human capital variable in the interaction term, we use the level of secondary and higher schooling, averaged over the sample period. 6 The nal sample consists of 57 developing countries, for which data on all the variables are available. Table 4 summarizes the regression results. Regression 4.1 shows that the technology gap and initial human capital stock have a positive impact on economic growth. The coef cient on technology gap, which is positive and marginally signi cant, indicates that more technologically backward countries experienced faster technology progress over time. Regression 4.2 includes FDI in its regression. FDI has a positive effect on technology growth, but the coef cient is not statistically signi cant. The theory outlined above suggests that the effect of FDI on technology growth interacts with the level of human capital. The speci cation in TABLE 4. Human capital and technology diffusion: cross-country regression for the sample of 57 developing countries (Dependent variable: Annual average growth rate of TFP, ) Average Machinery Regression Technology schooling Machinery FDI* imports* no. gap, 1970 years, 1970 FDI import schooling schooling R 2 (4.1) 1.12** 0.47* 0.17 (0.58) (0.15) (4.2) 1.16* 0.41* (0.57) (0.16) (0.58) 2 (4.3) 1.33* * 0.28 (0.56) (0.17) (1.17) (0.62) (4.4) 1.04** 0.39* 0.09** 0.22 (0.57) (0.16) (0.05) 2 (4.5) 1.50* * 0.33 (0.56) (0.20) (0.12) (0.06) Notes: Standard errors are reported in parentheses. * signi cant at 5 percent level. ** signi cant at 10 percent level. 126

13 Education for Technology Readiness regression 4.3 adds the interactive term between FDI and human capital. The result shows that the interaction term is positive and statistically signi cant, while the coef cient on FDI is still insigni cant. The coef cient on the interactive term indicates that, for instance, in an economy with postprimary attainment of 1.0 which is the average value for the sample countries, an increase in FDI by 0.5 percentage point of GDP (equivalent to one standard deviation) raises the TFP growth rate of the host economy by 0.7 percentage points per year. We have also explored the effect of machinery and transport equipment imports on technology progress in the same way. In regression 4.4, the coef cient on machinery imports is positive, though marginally signi cant. It implies that an increase in machinery imports by 5.2 percentage points of GDP (equivalent to one standard deviation) leads to an increase in the TFP growth rate of about 0.5 percentage points per year. An interaction term between machinery imports and human capital is added in the speci cation of regression 4.5. As with the case for FDI, the result shows that the interaction term is signi cantly positive, while the coef cient on machinery imports is insigni cant. Hence, machinery import has a positive effect on economic growth and the magnitude of the effect depends on the stock of human capital available in the economy. 7 Overall, the results from the regressions displayed in Table 4 show strong complementary effects between technology imports and human capital on technology progress in developing countries. Human capacity is a critical factor necessary for global technologies to be effective as a tool of economic growth in developing countries. The access to advanced technologies alone is not enough and less ef cient for technology progress. It should also be combined with the ability to absorb the advanced technology. 4. Human capital and digital divide The revolutionary development of modern information and communication technologies has had a powerful impact on productivity growth in an economy. The ICT industry itself has developed at the highest rate of innovation. This in turn has made rms and markets work more ef ciently by fostering greater networking in all sectors of the economy, thereby contributing to productivity growth in the overall economy as well. Several studies provide evidence in supporting the positive impact of ICT investment on productivity growth. Recent data on the United States show that TFP growth rates doubled from about 0.6% over the period to 1.25% during (Oliner and Sichel, 2000). It is believed that the longest-ever sustained growth of the US economy was contributed by the development of ICT. Jorgenson and Stiroh (2000) observe that technological progress in the ICT industry is the primary factor of the acceleration in US productivity growth. In addition, the diffusion of new ICT technologies accelerated worldwide in the 1990s, especially after 1995 as the Internet-based technologies such as the World Wide Web and the browser spread rapidly. Yet at 127

14 Jong-Wha Lee present, the developments are heavily concentrated on a few countries. The United States has more computers than the rest of the world combined. A majority of Sub-Saharan African people have never made even a telephone call, not to mention having access to Internet. Since ICT is not spread out on a world-wide scale, the impacts of ICT on growth are still limited in developing countries. Recent studies based on aggregate data found that the impact of ICT investment on economic growth was signi cant in developed countries, but not signi cant in developing countries (Kraemer and Dedrick, 1999, and Pohjola, 2000). Hence, the development of the ICT industry has worked as a force increasing the technology gap between developed and developing countries. The widening gap in ICT represents a major challenge to developing countries. ICT itself not only grows at a fast rate, but also helps to expedite the international diffusion of new technologies. Therefore, the successful development of ICT could provide developing countries with the opportunities to leapfrog on the technology ladder. Recent data shows that the gap in the access to ICT is increasing even among developing countries. While some developing countries including Singapore, Hong Kong SAR, Korea and Taiwan successfully entered the ICT industry, most developing countries are still lagging behind. There are several constraints that prevent a developing country from reaping the bene ts from the new ICT technologies. Lack of nancial resources is one major factor that restrains information and communication investment in developing countries. Building ICT infrastructures require vast amounts of investment. To illustrate, investment in the telecommunications infrastructure in OECD countries reached a record US$151 billion in 1997 (OECD, 2000a). A large infrastructure provides low access cost to ICT products and services, thereby stimulating demand for ICT. Consequently, the ICT industry develops, leading to a high productivity growth. Hence, high-income countries can enjoy a virtuous circle. On the contrary, low-income developing countries such as those in Sub-Saharan Africa have obvious dif culties in escaping from the low-income, low-technology equilibrium to enter into the ICT realm. In addition to nancial constraints, human capacity is a key factor that restrains the development of ICT. Even if free computers and free Internet access are available, they are useless to those individuals who are illiterate or lack the know-how. The application of ICT technologies requires human capabilities to handle such technologies. The Internet requires literacy and at least a minimal amount of ICT technology knowledge. Unless the new technologies are applicable and productive, access to the technologies alone can not lead to sustained growth of ICT in the economy. As such, we put to the test the role of human capital in the development of ICT. We estimate the relationship between ICT indicators and human capital, based on cross-country data. For ICT indicators, we have used main telephone lines, cellular phone subscribers, cable TV subscribers, number of personal computers, and Internet hosts. All data are expressed as a ratio to 1,000 persons and are averaged over the period. For the human capital stock variable, we use average years of total schooling for the 128

15 Education for Technology Readiness TABLE 5. Education and technology indicators, cross-country regression Total Primary Secondary Tertiary R 2 Regression Dependent log(gdp school school school school (no. of no. variable per capita) years years year year sample) (5.1) Telephone lines 1.17* 0.24* 0.78(94) (0.19) (0.08) 2 (5.2) 0.99* * (94) (0.18) (0.12) (0.16) (0.64) (5.3) Cellular phone 35.57* 10.72* 0.58(94) subscribers (10.89) (4.54) 2 (5.4) 27.18* * (94) (10.61) (7.01) (9.87) (38.36) (5.5) Cable TV * 0.40(62) subscribers (19.18) (7.17) (5.6) * (62) (19.47) (11.89) (14.90) (58.77) (5.7) Personal 40.50* 18.30* 0.66(83) Computers (13.33) (5.46) 2 (5.8) 34.42* * 92.37* 0.71(83) (12.96) (9.08) (11.81) (45.23) (5.9) Internet hosts * 0.39(94) (2.03) (0.85) 2 2 (5.10) * 17.7* 0.47(94) (1.98) (1.31) (1.84) (7.15) Note: All dependent variables are expressed as a ratio to 1,000 population in each country. Standard errors are reported in parentheses. * signi cant at 5 percent level. population aged 15 or above in Also, we set up another speci cation by including the average years of schooling at primary, secondary, or higher levels individually to see the separate effect of each schooling in ICT. The regressions also include per capita GDP that represents the national constraints in nancial resources. Table 5 presents the regression results. Regression (5.1) shows that human capital stock measured by average years of total education in 1995 is the signi cant factor determining telephone use across countries. The coef cient, 0.24 means that one standard-deviation increase (2.8) of total education raises a measure of teledensity (main telephone lines per 1,000 people) by about 0.7. The mean and standard deviation of the dependent variable are 1.9 and 2.2. In the regression, the income variable also appears to be signi cantly positive, implying that income is an important factor in determining the spread of telephones across countries. The income and education variables explain most of the cross-national variations in teledensity: the overall R 2 is Regression (5.2) reports the result of regression for 129

16 Jong-Wha Lee the same dependent variable while including primary, secondary, and tertiary level of education separately as independent variables. It turns out that only the secondary education variable is statistically signi cant in the regression. The result indicates that schooling at secondary levels, but not at primary or higher levels, have a signi cant impact on the use of telephones. Regressions (5.3) (5.6) report the estimation results, when either the cellular phone subscriber variable or the cable TV subscriber variable is used as a dependent variable. The results also con rm that education plays an important role in explaining the cross-country variations of these technology indicators. We also carry out regressions with primary, secondary and higher schooling separately as independent variables. Again, as in telephone lines, secondary education plays the most important role in determining the use of cellular phones and cable TV. Cross-national disparities in the spread of personal computers are also explained by the differences in educational attainment of adults across countries. Regression 5.7 shows that countries with higher educational stock appear to have more personal computers. The estimated coef cient, 18.3, indicates that an increase in one standard-deviation (2.8) of total education raises the number of PCs (per 1,000 persons) by about 50. Regression 6.8 shows that the estimated effect of education on computers come from both secondary and tertiary education, in contrast to the previous regressions in which only secondary education was signi cant in explaining telephone lines, cellular phone subscribers, and cable TV viewers. This result may come from the notion that a college level of education is needed for computer-based technologies. The last two rows of Table 5 show the results of estimating the regressions for Internet hosts. The variable on the average year of total education has a coef cient that is positive and statistically signi cant. The estimated coef cient implies that a one-year increase of average schooling raises the number of Internet hosts by 2,900. As in the case of computers, both secondary and tertiary education are important for the expansion of the Internet. Interestingly, income variable appears to be statistically insigni cant for Internet hosts. This may indicate that education, instead of income, becomes more important in the use of Internet technologies. In sum, the signi cance of human capital in the regressions shows the important role of human capital in the diffusion of ICT. A number of national surveys also support the argument that education lies at the heart of the problem of digital divide within a country. National Internet surveys in 1998 and 1999 show that access to the Internet is signi cantly related to income, race, sex, and education. The typical Internet users worldwide are male, under 35 years old, with a college education and high income (UNDP, 1999). Globally, 30% of the users have a university degree. NTIA (1999) reports that in America, those who possess a college degree are more than eight times likely to own home computer and nearly sixteen times likely to have home Internet access, in comparison to those with only secondary school education or less. Education plays a pivotal role in distinguishing between technology haves and have-nots in the era of new digital technologies. 130

17 Education for Technology Readiness 5. Building human capital for technology readiness Human capital is a key factor in technology adaptation for developing countries. In section 3, we found that technology in ows through FDI and machinery imports of developing countries from advanced countries alone have an insigni cant or minimal impact on productivity growth. But, the impact of technology in ows on technology progress becomes signi cant in the countries that have adequate human capability. Therefore, to promote technology diffusion, developing countries should implement a combination of two policies one that increases access to advanced technologies and another that nourishes human resources to utilize the new technologies. Thus, building human skills are a key agenda for developing countries to catch up to the technology level of advanced countries. Education policy will become more important for technology improvement as new technologies become more human capital supplementary. Improving human capital can cause a virtuous circle as human capital facilitates technology adaptation and in turn technology progress induces further increase in demand for human skills. 8 Evidence indicates that the demand for high skills is increasing with the progress of skill-complementary technologies. Studies in OECD countries show that employment growth is higher in white-collar high-skilled occupations, while unemployment rates are much higher and increasing at a faster rate for low-educated people (OECD, 2000c). The fall in the real wages of people with low skills and widening earning gap is also considered as an evidence for upskilling. In this section, we rst summarize some of the important strategies for developing countries in building human skills both in formal and informal schooling in order to meet the challenges of changing demands and skill requirements in the technology-dominated economy. Then, we discuss how the policies for education and training were actually designed and implemented by the most successful group of economies in East Asia Strategies for building human capital A. Expansion of secondary and tertiary education Human skills are primarily obtained in schools. Formal schooling provides future workers with opportunities to learn basic skills in literacy, numeracy, scienti c reasoning, and problem-solving. Educational attainment at formal schools showed strong progress worldwide during the last few decades. Table 6 shows the changes in educational attainment at the seven levels of schooling for the overall population of age 15 or older, summarized by region from 1980 to For the 23 advanced countries, the average years of schooling increased from 8.9 years in 1980 to 9.5 years in For the 73 developing countries as a whole, the average years of schooling grew more rapidly from 3.6 years in 1980 to 4.8 years in Hence, the education gap between advanced countries and developing countries has narrowed down since the 1980s. 131

18 Jong-Wha Lee TABLE 6. Trends of educational attainment of the total population aged 15 and over by region Highest level attained (percentage of population aged 15 and over) Primary Secondary Higher Average Region Pop. over years of (no. of countries) Year 15 (mill.) No school Total Full Total Full Total Full school Advanced (23) All developing (73) Middle East/ North Africa (11) Sub-Saharan Africa (22) Latin Ameri./ Caribbean (23) East Asia/ Paci c (10) South Asia (7) Transitional economies (13) Notes: Regional averages are weighted by each country s population. Total refers to the percentage of the population for whom the indicated level is the highest attained. Full refers to those who had the completion of the indicated level as the highest attained. Source: Barro and Lee (2000). 132

19 Education for Technology Readiness Although the educational progress of the developing regions has been relatively strong over the last few decades, the educational level in this region still lags behind the advanced regions. For developing countries, 38% of the population aged 15 and over still had no formal schooling and only 29% had some secondary level of education. In contrast, for advanced countries, about 70% of the population of age 15 or more had at least a few years of secondary schooling. Among developing countries, there exist signi cant variations in education attainment. For example, in 1995, East Asia had an average educational attainment of 6.4 years, compared to 3.4 years in Sub-Saharan Africa. While 41 % of the population aged 15 or more in East Asia had some form of secondary schooling, only 18% attained the same level of education in Sub- Saharan Africa. Since secondary and tertiary levels of education become more important for technology development, reducing the gap in the educational attainment at these higher levels are critical for developing countries in reducing the technology gap. Evidence presented in the last section showed that secondary and tertiary levels of education, but not primary education, explain the cross-country differences of the development of ICT. Hence, it is necessary to expand the opportunities for second-level education in developing countries. Enrollment data in Table 7 depict the substantial gap in secondary and tertiary enrollments between advanced countries and developing countries. In 1997, gross enrollment ratios at secondary and tertiary levels reached 108% and 62% respectively for advanced regions, while they were 52% and 10% for developing regions as a whole. On the contrary, gross primary enrollment ratio reached over 100% in 1997 in most regions except Sub- Saharan Africa (77%) and the Middle East (85%). The low attainment and enrollment in secondary and tertiary schooling in developing countries re ect the low continuation of education at the post-elementary level due to the low rates of return to secondary and tertiary TABLE 7. Gross enrollment ratios, 1990 and 1997 (Average, by region) Primary Secondary Tertiary World total Advanced countries Developing countries Sub-Saharan Africa Middle East Latin America East Asia South Asia Transitional countries Source: UNESCO, World Education Report