Internet education and economic growth: Evidence from cross-country regressions

Jin, Lawrence; Jin, Jang C. Article Internet education and economic growth: Evidence from cross-country regressions Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Jin, Lawrence; Jin, Jang C. (2014) : Internet education and economic growth: Evidence from cross-country regressions, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 2, Iss. 1, pp. 78-94, https://doi.org/10.3390/economies2010078 This Version is available at: https://hdl.handle.net/10419/98461 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/ Economies 2014, 2, 78-94; doi:10.3390/economies2010078 economies ISSN 2227-7099 www.mdpi.com/journal/economies Article Internet Education and Economic Growth: Evidence from Cross-Country Regressions Lawrence Jin 1 and Jang C. Jin 2,* 1 College of Arts and Sciences, Cornell University, Ithaca, NY 14853, USA; E-Mail: [email protected] 2 Department of Economics, George Mason University-Korea, Songdo Global Campus, Incheon 406-840, Korea * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +82-32-626-1823. Received: 26 September 2013; in revised form: 31 December 2013 / Accepted: 20 February 2014 / Published: 20 March 2014 Abstract: The effects of Internet education on economic growth are examined using a cross-section of 36 high-income countries. Internet usage rates are employed as a proxy for Internet education across countries. Regression results show that the frequent usage of the Internet has a positive and significant effect on economic growth. The estimated growth effect of Internet skills is also found to be greater than the growth effect of math and science skills. The results are, in general, robust across model specifications. Keywords: Internet usage rates; economic growth; human capital; schooling; convergence hypothesis 1. Introduction That the information technology (IT) can importantly influence the domestic economy is now widely recognized. The ‘IT-led growth’ suggests that investments in IT industries enhance labor productivity, and hence, the economies equipped with computer-related technologies grow faster than other countries. Oliner and Sichel [1] and Jorgenson [2] find that the sustained growth of the U.S. economy in the late 1990s is largely related to the increased investment in IT industries. Schreyer [3] and Daveri [4] also find similar results for other developed economies. OPEN ACCESS Economies 2014, 2 79 However, the growth effects found for developed economies often cannot be generalized to less developed countries (LDCs). For example, Kraemer and Dedrick [5], Dewan and Kraemer [6,7] and Pohjola [8] find that the growth effects of IT are small and insignificant for LDCs, perhaps due to inadequate IT investments in developing countries. Surveys of other empirical evidence are found in Dedrick, et al. [9], Indjikian and Siegel [10] and Shih, et al. [11]. All these studies use the amount of investment in computer hardware as a separate form of physical capital within an aggregate production function framework, which is a sharp contrast to our use of Internet education as human capital in growth models. 2. Literature on Internet Education and Economic Growth Education in Internet skills enhances the productivity of the future labor force, which, in turn, helps promote economic growth. These days, more countries are increasing investments in information and communication technologies (ICT). New investments in such high-tech industries may require more capital than labor, and hence, the marginal rate of technical substitution (MRTS), which is the ratio of marginal productivities of labor and capital, would be relatively high. The high MRTS suggests that labor is more productive in such capital-intensive countries. The high productivity of labor is largely due to human capital, such as being educated Internet skills. In this case, marketing and management skills are also highly developed in industrialized countries, and thus the production process will be more efficient. The Internet skills of the labor force are thus treated here as human capital which is regarded as an advancement in total factor productivity. More specifically, average Internet usage rates can be used as a measure of Internet skills in each country. A rapid increase of computer-related technology in recent years makes the Internet a central force of technology advancement. For example, most industrialized countries that have a rapid growth in frontier technologies are closely related to higher usage rates of the Internet. Late comers, such as East Asian economies with a higher percentage of the labor force that utilizes the Internet, also tend to learn advanced technologies overseas faster than other countries. This suggests that the percentage of Internet users in each country can be used as a quality measure of human capital alongside Hanushek and Kimko’s [12] index of math and science skills. Litan and Rivlin [13] described the benefits of the Internet at an industry level; Czernich, et al. [14] investigated the significant effects of broadband infrastructure on economic growth; Miranda and Lima [15] quantitatively analyzed the evolution of the Internet over the past two decades. No studies have discussed the role of Internet education in the growth of nations.1 This paper thus investigates empirically the effects of Internet skills on economic growth employing a cross-section of 36 high-income countries. The average annual usage rates of Internet over time are employed here as a proxy for different levels of Internet skills of the labor force across countries. Employing the Internet usage rates as a quality measure of human capital offers two large advantages. First, most empirical studies in the growth literature used schooling—the quantity measure of education, assuming that the quality of education is constant across countries (Barro, [17]; Mankiw, et al. [18]; 1 Another paper by Miranda and Lima [16] discussed the exponential relationship between the development of science and technology and the world GDP growth over the past five centuries. However, the economic role of Internet usage has not been discussed in the literature. Economies 2014, 2 80 Islam, [19], among others). However, Internet skills—the quality measure of education—differ across countries and suggest why some countries have a rapid growth in advanced technologies and thus a subsequent economic growth faster than other countries. Second, we can directly compare a relative size of the growth effects of two different quality measures of human capital: one is the growth effect of proficiency in high school math and science (Hanushek and Kimko [12]; Hanushek and Woessmann [20,21]); the other is the growth effect of Internet usage rates that are used here as a measure of the Internet skills of the future labor force. 3. Empirical Model and Dataset Following Bosworth and Collins [22], our empirical model is specified based on a Cobb-Douglas production function in which output is a function of capital, labor, and a term for total factor productivity: Y = AKα·(LE)1−α (1) Y is a measure of output. A represents total factor productivity. K stands for physical capital with its share of income, α. Labor L is now adjusted for improvements in educational attainment E, and hence, LE represents the educated labor force. The educated labor force’s share of income is assumed equal to (1 − α). With this assumption, along with capital share α, the output Y shows the constant returns to scale of all inputs used. We divide both sides of the production function by labor L: Y/L = A·(K/L)α·(E)1−α (2) After taking logarithms of both sides, we take first the differences of the variables to report the results in terms of growth rates: ∆ln(Y/L) = ∆ln A + α·∆ln(K/L) + (1 − α)·∆ln E (3) With this framework, the growth in output per worker is decomposed of the contributions of an improvement in total factor productivity, growth in capital per worker and increases in educational attainment per worker. Because total factor productivity is not directly observable, it is measured indirectly. That is, total factor productivity is the amount of output growth that remains after we have accounted for the determinants of growth that we can measure directly. This is sometimes called the Solow residual. The capital-labor ratio is replaced by the initial level of income that determines the speed of convergence. The convergence hypothesis is based upon diminishing returns to reproducible capital. In other words, poor countries with low capital-labor ratios are able to grow faster than rich countries, because the marginal productivity of capital is relatively high in lower-income economies. The higher productivity of capital induces the low-income countries to grow faster than rich countries. Therefore, a country’s growth rate of real per capita GDP has a negative relationship with its initial level of income per person. In addition, the educational attainment E is not only achieved by the number of years of schooling (i.e., educational quantity), but also improved by the quality of the future labor force (i.e., educational quality). Therefore, our empirical model includes two types of educational attainments (quantity of Economies 2014, 2 81 schooling and quality of education), in addition to the initial level of income which replaces the capital-labor ratio that determines the speed of convergence. That is, GR80-05i = β0 + β1·GDP80i + β2·SCHOOLi + β4·INTNETi + εi (4) where GR80-05 = the average annual growth rates of real per capita GDP over the period 1980–2005; GDP80 = the real per capita GDP of the initial year 1980; SCHOOL = the arithmetic mean of primary and secondary school net enrollment rates in 1980; INTNET = the average annual percentage of Internet users over the period 1995–2004;2 and εi represents serially uncorrelated white noise residuals. As noted earlier, residuals εi are treated as the estimates of total factor productivity, which are best interpreted as gains in efficiency when all factor inputs are used. Thus, regression models may include all other factors that determine the efficiency of factor usage: government spending on R&D, government policies on economic openness and property rights, tax burden, life expectancies and even geographical variables (Sala-i-Martin [23]). If all these variables are included, our model clearly suffers from the degrees of freedom problem. Rather than controlling for all possible determinants of growth, we merely focus on two important aspects of educational attainments: one is the quantity measure of schooling (SCHOOL) and another the quality measure of computer proficiency (INTNET). The initial level of income (GDP80) is included in the model to test for the convergence hypothesis in which a country’s growth rate of real per capita GDP has a negative relationship with its initial level of income per person (Solow, [24]; Cass, [25]; and Koopmans, [26]). The correlation coefficient between GDP growth and the initial level of GDP appears to be negative, but virtually zero (r = −0.06). The insignificant relationship found here is perhaps due to the fact that economic growth is, in fact, explained by other relevant variables, as well. For example, the GDP growth rates are first allowed to be explained by school enrollment rates (SCHOOL). The predicted GDP growth rates are subtracted from actual growth rates, and the remaining unexplained portion of GDP growth is plotted with the initial level of income (GDP80). Figure 1 shows that the inclusion of school enrollment rates as an explanatory variable for GDP growth significantly strengthens the convergence hypothesis. The partial correlation coefficient between GDP growth rates and the initial level of income appears to be −0.57. The results are, in general, consistent with the convergence hypothesis found in Barro [17], among others. 2 Internet usage rates have been averaged out over the period 1995–2004, in which the revolution of the Internet began in the early 1990s (Miranda and Lima, [15]). However, this measure has lagged fifteen years behind since 1980—the initial year of our sample—and thus, strictly speaking, it is not a measure of the initial Internet skills in the 1980s. However, economies grow slowly over time, especially in developed countries, and thus, the average growth rate of GDP over the entire sample period would be similar to the growth rate of more recent years. Regressions (7) and (8) in Table 4 employed a shorter sample period, 1995–2005, for GDP growth, but the growth effect of Internet usage was not materially changed. Economies 2014, 2 82 Figure 1. Partial association between per capita GDP growth rates and 1980 per capita GDP. -6.0 -5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 1980 GDP per capita ($1,000) per capita Growth Unexplained by SCHOOL (%) Data source: World Development Indicators (World Bank) [27]. Hanushek and Kimko [12] further included in the model international math and science exam scores (MATH&SCI) as a proxy for the quality measure of labor force. While math and science are important for the development of science and technology in a hardware context, computer-related technologies these days prevail in all types of industries, especially in the service sector, which uses technologies in a soft form, such as management and marketing skills, as well as logistics. Internet usage rates (INTNET) are, therefore, used here as a proxy for another quality measure of human capital, which is our major contribution to the literature. It is, however, noted that measuring a country’s computer proficiency is relevant only for the countries to which the use of the Internet has been influential. For less developed countries that are still using lower-level technologies, computer-related advanced technologies may not be sufficiently used to develop light industries, such as textiles and home appliances, compared to heavy chemical industries that use high tech intensely. This is similar to the case of an early stage in the process of economic development. For low-income countries, the standard deviation of Internet usage rates was also observed to be greater than the mean value, and thus, measurement errors were unavoidable if low-income countries were included. This contrasts with the use of 36 high-income countries, in which the mean value appears to be 20.1% and its standard deviation 11.8% (Table 1). In addition, the Internet these days has been used extensively in low-income countries, like Nigeria, to generate spam emails and defraud money online. According to a report by the Message Anti-Abuse Working Group [28], about 88%–90% of emails are estimated to be spam. These harmful Internet activities may incorrectly show up as a positive correlation with economic growth in such low-income countries. This paper thus includes 36 high-income countries only, in which an initial income level is higher than US$3000 in the year 1980.3 Table 1 reports the summary statistics of the key variables used. The mean growth rate of real GDP per capita across countries in our sample appears to be 1.76% on average. South Korea had the highest growth rate of GDP (5.52%) in our sample over the period, while Kuwait had a negative annual 3 A similar criterion has been used by Roller and Waverman [29] in which rich countries that have enough telecommunication infrastructure could bring about higher growth effects than in poor countries. Economies 2014, 2 83 average growth rate (−2.15%). It is also noted that real GDP per capita of the initial year 1980 ranges from the lowest of US$3180 in Panama to the highest of US$28,210 in Switzerland. The average income in our sample at that time appears to be US$12,130. For Barro’s [17] schooling, Belgium has the highest average of primary and secondary school enrollment rates with 98% (100% in a primary and 96% in a secondary school). The lowest school enrollment rate is found in Venezuela, with an average of 53.5%. Hanushek and Kimko’s [12] math and science quality measure appears to be 48.4 points on average. The lowest score is found in Kuwait with 28.4 points, while Japan has the highest score of 60.7 points. Japan is also among the highest in school enrollment rates with an average of 96.5%. Table 1. Descriptive Statistics. Variables Mean Median SD Minimum Maximum GR80-05 (%) 1.76 1.68 1.36 −2.15 5.52 GDP80 (US$1000) 12.13 11.86 6.83 3.18 28.21 SCHOOL (%) 85.3 88.8 10.7 53.5 98.0 MATH&SCI (Score) 48.4 48.9 7.5 28.4 60.7 INTNET (%) 20.1 20.0 11.8 3.4 40.6 See Appendix for the definition of variables. In addition, the level of computer proficiency fluctuates about the mean value of 20.1%, ranging from the lowest of 3.4% in Venezuela to the highest of 40.6% in Sweden. Figure 2 further shows that rich countries were closely related to higher usage rates of the Internet, while the Internet usage rates of lower-income countries were relatively low. Internet usage rates would be even lower if low-income countries were included in our sample. Notice, however, that South Korea’s real per capita GDP in 1980 (US$3220) was among the lowest in our sample, but the average Internet usage rate was notably high (31.0%). South Korea is thus a seemingly extreme value in our sample, but the high Internet usage rate reflects South Korea’s early investment in information technology, which facilitated the country in quickly catching up with the advanced technologies of the U.S. and Japan in recent decades. In contrast, Kuwait had a high income level in 1980 (US$20,300), but was unusually low in its Internet usage rate (8.5%). As an oil producing country, Kuwait could be exclusively driven by exports of oil, and its high income would be mainly due to oil revenue. Its low rate of Internet usage indicates that computer proficiency in Kuwait might have little impact on its economy, and hence, Kuwait could be regarded as a potential outlier. Except for these few extremes, rich countries generally show high computer proficiencies.4 4 To ensure that our results were not significantly influenced by this outlier, we estimated our landmark regression (4) after excluding Kuwait from our sample: GR80-05i = −2.617 − 0.133 GDP80i + 0.056 SCHOOLi + 0.065 INTNETi (0.036)** (0.021)** (0.021)** Adjusted R2 = 0.41 N = 35 observations. ** Significant at the 1% significant level. The estimated coefficients of GDP80 and SCHOOL remained largely intact. The magnitude of the estimated coefficient of INTNET fell slightly, but it remained statistically significant at the 1% significance level. Thus, we conclude that Kuwait is not an influential outlier, and hence we keep Kuwait in our sample. Economies 2014, 2 84 Figure 2. Per capita GDP in 1980 versus computer proficiency. Data Source: World Development Indicators (World Bank) [27]. 4. Basic Results Prior to estimation of the regression model, correlation coefficients among independent variables are reported to check with a potential multicollinearity problem. Table 2 shows that the quantity measure of schooling is moderately correlated with the quality measure of math and science skills; the quality measure is also moderately correlated with another quality measure of computer skills in the Internet. However, the degree of a linear relationship is less than 0.8, a rule of thumb, and hence, a multicollinearity problem may not be serious. The symptom of multicollinearity has been further checked with the regression results. All parameter estimates appear to have correct signs, and t-values are generally consistent with the size of the adjusted R2. The standard error estimates also remain relatively stable when other explanatory variables are included in the model. We therefore conclude that multicollinearity problems may not be serious in this model specification. Economies 2014, 2 85 Table 2. Correlation coefficients among independent variables. GDP80 SCHOOL MATH&SCI INTNET GDP80 1 SCHOOL 0.657 1 MATH&SCI 0.357 0.606 1 INTNET 0.656 0.681 0.587 1 See Appendix for the definition of variables. Table 3 shows basic regression results. In Regression (1), GDP growth rates are explained by the initial level of income (GDP80) and school enrollment rates (SCHOOL). This is simply a replication of Barro [17]. Although slightly different sample periods and a smaller sample size of countries are used, our findings are similar to Barro [17]. The estimated coefficient of GDP80 appears to be negative and significant at the 1% significance level. The convergence hypothesis is well supported, in which an increase in per capita income by US$1000 will reduce the subsequent growth rates by approximately 0.11 percentage points on average. The quantity measure of education, SCHOOL, is also positively and significantly related to GDP growth rates. The parameter estimate of schooling suggests that an increase of one standard deviation in both primary and secondary school enrollment rates will generate 1.03 percentage points of GDP growth on average (i.e., 0.096 × 10.7 = 1.03). The results are generally consistent with the findings in Barro [17]. Table 3. Basic regression results. (1) (2) (3) (4) (5) (6) Dependent variable GR80-05 GR80-05 GR80-05 GR80-05 GR80-05 GR80-05 Constant −5.089 (1.783) −6.111 (1.561) −5.902 (1.458) −2.828 (1.603) −4.090 (1.546) −4.291 (1.568) GDP80 −0.112 (0.038)** −0.104 (0.032)** −0.103 (0.031)** −0.162 (0.034)** −0.144 (0.032)** −0.136 (0.033)** SCHOOL 0.096 (0.024)** 0.051 (0.024)* 0.057 (0.021)** 0.058 (0.022)** 0.035 (0.022) 0.046 (0.021)* MATH&SCI 0.099 (0.028)** 0.071 (0.027)** MATH&SCI 2 0.076 (0.018)** 0.053 (0.020)** INTNET 0.079 (0.020)** 0.060 (0.020)** 0.048 (0.022)* Observations 36 36 36 36 36 36 Adjusted R2 0.292 0.475 0.535 0.503 0.582 0.583 See Appendix for the definition of variables. ** Significant at the 1% significance level; * significant at the 5% significance level. Regression (2) replicates Hanushek and Kimko [12], who additionally include a math and science quality measure (MATH&SCI). The effect of math and science skills on GDP growth appears to be positive and significant. The parameter estimate of MATH&SCI suggests that an increase of one standard deviation in MATH&SCI improves GDP growth rates by 0.74 percentage points on average Economies 2014, 2 92 Internet skills. It is, however, noted that this policy implication may not be appropriate for generalizing to low-income countries, because high technologies are not sufficiently used in the process of economic development. For low-income countries, increasing school enrollment rates would rather be more effective in economic growth. Conflicts of Interest The authors declare no conflict of interest. Appendix: The Definition of Variables and Data Sources GR80-05: the average annual growth rate of real GDP per capita over the period of 1980–2005, measured in percentages (World Bank) [27]. GR95-05: the average annual growth rate of real GDP per capita over the period of 1995–2005, measured in percentages (World Bank) [27]. GDP80: the real GDP per capita in the initial year, 1980, measured in US$1000 (World Bank) [27]. GDP80SQ: the square of GDP80, measured in US$100 million. SCHOOL: the arithmetic mean of primary and secondary school net enrollment rates in the initial year, 1980, measured in percentages (World Bank) [27]. SCHOOLSQ: the square of SCHOOL, measured in percentages. MATH&SCI: the international test scores in math and science, equivalent to QL1 in Hanushek and Kimko [12]. MATH&SCI 2: the international test scores in math and science normalized to yearly U.S. performances, equivalent to QL2 in Hanushek and Kimko [12]. INTNET: the average annual percentage of Internet users over the period of 1995–2004, measured in percentages (World Bank) [27]. LATIN: the dummy variable for Latin American countries (Argentina, Brazil, Costa Rica, Panama, Trinidad and Tobago, Uruguay and Venezuela). ASIA: the dummy variable for Asian countries (Hong Kong, Japan, Singapore and South Korea). References 1. Oliner, S.D.; Sichel, D.E. The resurgence of growth in the late 1990s: Is information technology the story? J. Econ. Perspect. 2000, 14, 3–22. 2. Jorgenson, D.W. Information technology and the U.S. economy (presidential address to the American Economic Association). Am. Econ. Rev. 2001, 91, 1–32. 3. Schreyer, P. The Contribution of Information and Communication Technology to Output Growth: A Study of the G-7 Countries. Available online: http://www.oecd-ilibrary.org/science-andtechnology/the-contribution-of-information-and-communication-technology-to-outputgrowth_151634666253 (accessed on 26 September 2013). 4. Daveri, F. Is Growth an Information Technology Story in Europe Too. IGIER Working Paper No. 168. Available online: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=249115 (accessed on 26 September 2013). Economies 2014, 2 93 5. Kraemer, K.L.; Dedrick, J. Payoffs from investment in information technology: Lessons from the Asia-Pacific region. World Dev. 1994, 22, 1921–1931. 6. Dewan, S.; Kraemer, K.L. International dimensions of the productivity paradox. Commun. ACM 1998, 41, 56–62. 7. Dewan, S.; Kraemer, K.L. Information technology and productivity: Preliminary evidence from country-level data. Manag. Sci. 2000, 46, 548–562. 8. Pohjola, M. Information technology and economic growth: A cross-country analysis. In Information Technology and Economic Development; Pohjola, M., Ed.; Oxford University Press: Cambridge, UK, 2001; pp. 242–256. 9. Dedrick, J.; Gurbasani, V.; Kraemer, K.L. Information technology and economic performance: A critical review of the empirical evidence. ACM Comput. Surv. 2003, 35, 1–28. 10. Indjikian, R.; Siegel, D.S. The impact of investment in IT on economic perormance: Implications for developing countries. World Dev. 2005, 33, 681–700. 11. Shih, E.; Kraemer, K.L.; Dedrick, J. Determinants of country-level investment in information technology. Manag. Sci. 2007, 53, 521–528. 12. Hanushek, E.A.; Kimko, D.D. Schooling, labor force quality, and the growth of nations. Am. Econ. Rev. 2000, 90, 1184–1208. 13. Litan, R.E.; Rivlin, A.M. Projecting the economic impact of the internet. Am. Econ. Rev. 2001, 91, 313–317. 14. Czernich, N.; Falck, O.; Kretschmer, T.; Woessmann, L. Broadband infrastructure and economic growth. Econ. J. 2011, 121, 505–532. 15. Miranda, L.C.M.; Carlos A.S.L. Trends and cycles of the internet evolution and worldwide impacts. Technol. Forecast. Soc. Change 2012, 79, 744–765. 16. Miranda, L.C.M.; Carlos, A.S.L. On trends and rhythms in scientific and technological knowledge: A quantitative analysis. Int. J. Technol. Intell. Plan. 2010, 6, 76–109. 17. Barro, R.J. Economic growth in a cross section of countries. Q. J. Econ. 1991, 106, 407–443. 18. Mankiw, N.; Gregory, D.R.; Weil, D. A contribution to the empirics of economic growth. Q. J. Econ. 1992, 107, 407–437. 19. Islam, N. Growth empirics: A panel data approach. Q. J. Econ. 1995, 110, 1127–1170. 20. Hanushek, E.A.; Woessmann, L. Do Better Schools Lead to More Growth? Cognitive Skills, Economic Outcomes, and Causation; NBER Working Paper 14633, National Bureau of Economic Research: Cambridge, MA, USA, 2009. 21. Hanushek, E.A.; Woessmann, L. The Economics of International Differences in Educational Achievement; NBER Working Paper 15949, National Bureau of Economic Research: Cambridge, MA, USA, 2010. 22. Bosworth, B.; Collins, S.M. The empirics of growth: An update. Brook. Pap. Econ. Act. 2003, 1, 113–206. 23. Sala-i-Martin, X. I just ran two million regressions. Am. Econ. Rev. 1997, 87, 178–183. 24. Solow, R.M. A Contribution to the theory of economic growth. Q. J. Econ. 1956, 70, 65–94. 25. Cass, D. Optimum growth in an aggregative model of capital accumulation. Rev. Econ. Stud. 1965, 32, 233–240. Economies 2014, 2 94 26. Koopmans, T.C. On the concept of optimal economic growth. In The Econometric Approach to Development Planning; North-Holland: Amsterdam, The Netherlands, 1965. 27. World Bank. World Development Indicators. Available online: http://data.worldbank.org/datacatalog/world-development-indicators (accessed on 26 September 2013). 28. Message Anti-Abuse Working Group (MAAWG). Email Metrics Program: The Network Operators’ Perspective. Report #15—First, Second and Third Quarter 2011 (Issued November 2011). Available online: http://www.maawg.org/sites/maawg/files/news/ MAAWG_2011_Q1Q2Q3_Metrics_Report_15.pdf (accessed on 26 September 2013). 29. Roller, L.H.; Waverman, L. Telecommunications infrastructure and economic development: A simultaneous approach. Am. Econ. Rev. 2001, 91, 909–923. 30. Romer, P.M. Human capital and growth: Theory and evidence. Carnegie-Rochester Conf. Ser. Pub. Policy 1990, 32, 251–286. 31. Breusch, T.S.; Pagan, A.R. A simple test for heteroskedasticity and random coefficient variation. Econometrica 1979, 47, 1287–1294. 32. White, H. A Heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 1980, 48, 817–838. © 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).