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Does the internet generate economic growth, international trade, or both?

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Abstract

Recent cross country panel data studies find a positive impact of internet use on economic growth and a positive impact of internet use on trade. The present study challenges the first finding by showing that internet use does not explain economic growth directly in a fully specified growth model. In particular openness to international trade variables seem to be highly correlated with internet use and the findings in the literature that internet use causes trade is confirmed here suggesting that internet use impacts trade and that trade impacts economic growth. A simultaneous equations model confirms the positive and significant role of internet use to openness and the importance of openness to economic growth. Internet use shows to be more impacting trade in non-high income countries than in high income countries whereas the impact of trade on economic growth is the same for both income groups.

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Notes

  1. The term internet used here refers not only to the physical infrastructure but also to the applications running on top of this infrastructure such as world wide web, email, and file transfer.

  2. Initially also net barter terms of trade, life expectancy, fertility rates are employed but these variables did not yield satisfactory results and are not displayed in the table.

  3. These 32 countries are BGD, BEN, BFA, BDI, KHM, CMR, CAF, TCD, COG, CIV, DJI, GNQ, ETH, GHA, GIN, GNB, IND, LSO, MDG, MWI, MLI, MRT, MOZ, NPL, NIC, NER, PNG, RWA, SLE, SLB, TZA, and YEM.

  4. In 2010 the percentage of internet users has increased to 30.5% and the annual growth rate is still increasing.

  5. A regression of the first differences of the growth rate of per capita internet users on the log of per capita GDP indeed shows a positive and significant slope in 1994, 1995, 1996, 1997, 1998 and in 2002 and a negative and significant slope in 2003 and 2005 (and non-significant in the other years between 1992 and 2008).

  6. Next to the variable indicated here also life expectancy at birth, fertility rate and some institutional factors such as corruption index and rules of law were initially included but did not give significant results.

  7. To check for multicollinearity problems initially all independent variables are regressed on all (remaining) independent variables in a fixed effects model and no adjusted r-squared proved to exceed 0.9.

  8. The robust test on over identifying restrictions as proposed by (Wooldridge 2002) p 190–191 is displayed as Sargan-Hansen Chi-squared statistics including the corresponding p-value and shows that fixed effects are never redundant and is to be preferred over the random effects model. In all cases the standard Hausman tests on non-robust estimates of the equivalent models maintain the same conclusions (not shown in tables).

  9. Autocorrelation is computed using the test for serial correlation in panel data as described by (Wooldridge 2002) and (Drukker 2003).

  10. A Newey-West estimate of model (c) using STATA’s newey2 command shows standard errors which are all slightly below the ones as reported in the table such that both internet use and schooling become significant at 10% level.

  11. Minimizing the Bayesian information criterion (BIC) resulted in exactly the same lag structures.

  12. The non-reported Breusch-Pagan test on the null of independent equations indicates with p-values of 0.0000 and 0.0077 for the upper right and lower right model, respectively, that the equations are not independent and that the SUR results are to be preferred.

  13. As reported in the description of the data the statistics on area and not exactly time invariant as there are some very minor changes for a very limited number of countries. To allow for the Hausman-Taylor analysis what follows in this section these minor changes are averaged out.

  14. In the system GMM approach the current and lagged values of the number of telephones lines and the number of mobile phone users, both per capita, are used as additional instruments.

  15. Simulation using the obtained coefficients on area and using actual area size distribution indeed shows a U-shaped relation where the effect is larger for very small as well as for very large countries.

  16. See (Ziesemer 2011) for a simultaneous equation system GMM approach.

  17. Estimating the model with SUR and with 2SLS leads to the same order of magnitude.

  18. The model is also estimated using mobile phone per capita and number of fixed telephone lines per capita as instruments for internet use (instead of lagged internet use) and the results where the same and not reported here.

  19. For instance in adding internet use to the growth equation of model (c) in Table 5 yields a coefficient for internet use of −0.002 (0.014) with a p-value of 0.88, so highly insignificant which again confirms the belief that internet use is not directly impacting economic growth because otherwise 3SLS would have improved the efficiency.

  20. The group of high income countries are the 47 countries listed below Table 1. The group of non-high income countries comprise low income, lower middle income and upper middle income countries.

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Acknowledgments

The author is grateful to the participants of the ICTNET workshop in Parma and the Digital EU-Integration and Globalization workshop in Frankfurt for useful discussions and to Thomas Ziesemer for his critical comments and suggestions. The usual disclaimer applies.

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  1. UNU-MERIT and Department of Economics, Maastricht University, Maastricht, The Netherlands

    Huub Meijers

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Meijers, H. Does the internet generate economic growth, international trade, or both?. Int Econ Econ Policy 11, 137–163 (2014). https://doi.org/10.1007/s10368-013-0251-x

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