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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.

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 [

However, the growth effects found for developed economies often cannot be generalized to less developed countries (LDCs). For example, Kraemer and Dedrick [

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 [

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, [

Following Bosworth and Collins [^{α}·(LE)^{1−α}

We divide both sides of the production function by labor L:
^{α}·(E)^{1−α}

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 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,
_{i}_{0} + β_{1}·GDP80_{i}_{2}·SCHOOL_{i}_{4}·INTNET_{i}_{i}_{i}

As noted earlier, residuals ε_{i}

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, [

Partial association between per capita GDP growth rates and 1980 per capita GDP.

Data source:

Hanushek and Kimko [

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% (

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

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.

Per capita GDP in 1980

Data Source:

Prior to estimation of the regression model, correlation coefficients among independent variables are reported to check with a potential multicollinearity problem. ^{2}. 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.

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

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 |
0.060 (0.020)** | 0.048 |
|||

Observations | 36 | 36 | 36 | 36 | 36 | 36 |

Adjusted ^{2} |
0.292 | 0.475 | 0.535 | 0.503 | 0.582 | 0.583 |

See

Regression (2) replicates Hanushek and Kimko [

For the robustness of our results, Regression (3) further employs Hanushek and Kimko’s [

Regression (4) further includes Internet usage rates (INTNET) as an alternative quality measure of labor force. The growth effect of INTNET appears to be significant, and an increase in computer proficiency by one standard deviation enhances GDP growth rates by 0.92 percentage points on average (i.e., 0.079 × 11.8 = 0.92). The size of the effect is greater than the growth effect of SCHOOL (i.e., 0.058 × 10.7 = 0.62).

Partial association between per capita GDP growth and the computer proficiency of the labor force.

Data source:

Furthermore, Regression (5) directly compares the relative size of the growth effects of INTNET and MATH&SCI within a model. Both quality measures are strongly related to GDP growth rates. A one standard deviation rise in MATH&SCI will generate 0.53 additional percentage points of GDP growth on average, whereas a one standard deviation increase in INTNET will improve the GDP growth rates by 0.71 percentage points. The growth effect of computer proficiency is even greater than the one with math and science skills if one standard deviation is raised in each variable. Similar results are found in Regression (6), which uses the second measure of math and science skills.

For the robustness of the results,

Further estimation.

(7) | (8) | (9) | (10) | (11) | (12) | |
---|---|---|---|---|---|---|

Dependent variable | GR95-05 | GR95-05 | GR80-05 | GR80-05 | GR80-05 | GR80-05 |

Constant | −3.348 (1.798) | −1.178 (1.170) | −3.132 (1.578) | −7.630 (7.441) | −2.122 |
−3.195 (1.296) |

GDP80 | −0.142 (0.038)** | −0.118 (0.024)** | −0.342 (0.118)** | −0.156 (0.035)** | −0.163 (0.034)** | −0.137 (0.028)** |

GDP80SQ | 0.595 (0.375) | |||||

SCHOOL | 0.081 (0.025)** | 0.043 (0.017)** | 0.073 (0.024)** | 0.185 (0.193) | 0.052 (0.025)* | 0.062 (0.018)** |

SCHOOLSQ | −0.083 (0.125) | |||||

INTNET | 0.016 (0.023) | 0.046 (0.015)** | 0.083 (0.020)** | 0.080 (0.021)** | 0.075 (0.021)** | 0.054 (0.017)** |

LATIN | −0.376 (0.569) | |||||

ASIA | 1.870 (0.438)** | |||||

Observations | 36 | 34 | 36 | 36 | 36 | 36 |

Adjusted ^{2} |
0.307 | 0.479 | 0.525 | 0.494 | 0.494 | 0.677 |

See

Regression (7) changes the sample period for GDP growth rates from 1980–2005 to more recent years, 1995–2005. In this way, we can examine the notion of causality that may run from schooling one decade earlier to the growth of GDP afterwards (Barro, [

Partial association between per capita GDP growth and the computer proficiency of the labor force.

Data source:

Ireland, once referred to as a Celtic Tiger, experienced an exceptionally rapid economic growth between 1995 and 2005. The average annual GDP growth rate of Ireland over the period 1995–2005 was 6.2%, compared with 4.4% over the whole sample period 1980–2005. While this economic boom was once credited to effective government policies and economic reform in the early 1990s, a recent collapse of the Irish economy following the 2008 global financial crisis suggests that the previous growth rates were driven by factors other than ‘real’ economic development, such as housing and credit bubbles.

Therefore, Regression (8) further estimates without using the two outliers mentioned above. As expected, the computer skills of the labor force are found to be significantly related to GDP growth, and a one standard deviation rise in INTNET is now associated with 0.54 percentage points of GDP growth rates on average.

Although the convergence hypothesis discussed earlier is generally supported in Regressions (1)–(8), measurement errors in GDP may bring about the negative correlation between the initial level of income and a subsequent growth rate (Romer, [

Residual plot.

Data source:

The negative relationship between the initial level of income and a subsequent growth rate may attenuate higher income (Barro, [

Partial association between per capita GDP growth and 1980 GDP per capita.

Data source: World Development Indicators (World Bank) [

Regression (10) estimates a quadratic relationship between school enrollment rates and GDP growth. Since the enrollment rates are measured in percentages, their effects on GDP growth may be non-linear, that is, the marginal growth effects of SCHOOL may diminish as schooling approaches 100%. To allow for this possibility of non-linearity, SCHOOL has been squared. The estimated coefficient of SCHOOLSQ is found negative, but appears to be insignificant. In other words, the growth effect of schooling increases at a decreasing rate, but the diminishing non-linear relationship due to schooling is not prominent.

To observe regional differences in economic performances, Regression (11) includes a dummy variable for seven Latin American countries (LATIN): Argentina, Brazil, Costa Rica, Panama, Trinidad and Tobago, Uruguay and Venezuela. The estimated coefficient of the dummy variable is negative, but not significant. This means that Latin American countries have GDP growth rates slightly less than the world average, but the difference is not large enough to reject the null hypothesis of a zero difference.

Similarly, Regression (12) includes an Asian dummy (ASIA): Hong Kong, Japan, Singapore and South Korea. These economies had notably higher growth rates in the past decades. For instance, Hong Kong’s actual growth rate over the sample period used was 4.1%, but the predicted growth rate was 1.8% worldwide. Likewise, the actual growth rates of Japan, Singapore and South Korea exceeded the predicted growth rate by 1.0%, 2.1% and 2.0%, respectively.

Per capita GDP growth

Data source: World Development Indicators (World Bank) [

This paper empirically investigates the growth effects of Internet education using a cross-section of 36 high-income countries. Following Barro [

Regression results show that both schooling and math and science skills are significantly related to the growth rates of real GDP. Internet usage rates, a proxy for different levels of Internet education across countries, also have a positive and significant effect on real GDP growth. The estimated growth effects remain significant when other relevant variables are included in the model. The relative size of the growth effects caused by the two quality measures of education shows that, if one standard deviation is increased in each variable, the growth effect of Internet skills is even greater than the growth effect of math and science skills. The results are, in general, robust across different model specifications.

One policy implication is that Internet education, as well as math and science education, will improve labor productivity which in turn helps promote economic growth. In particular, students in high-income countries are more exposed to computer facilities, and hence the future labor force will be well equipped with Internet skills. Middle-income economies especially in Asia also have been able to absorb new technologies overseas within a short period of time by increasing the Internet skills of the workforce.

Besides, Internet education can be enhanced without many trade-offs. As long as universities and colleges are well equipped with high-tech Internet facilities on campus, students are more exposed to computer-related advanced technologies, and more students will have an opportunity to enhance their 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.

The authors declare no conflict of interest.

Another paper by Miranda and Lima [

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, [

A similar criterion has been used by Roller and Waverman [

To ensure that our results were not significantly influenced by this outlier, we estimated our landmark regression (4) after excluding Kuwait from our sample:

Likewise, the economic growth of Greece over the period 1995–2005 could not have been driven by true improvements in human capital. The global financial crisis in 2008 hit the Greek economy worse than other EU member countries. This might be due to the misallocation of resources and corruption in public policies. In