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ICT, Growth and Happiness

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Abstract

This chapter reviews two strands of literature. The first is on Information and Communication Technology (ICT) and growth. The increasing role of ICTs came together with stagnating growth rates in many countries. This has been denoted the Solow paradox. During the dot-com era from the mid-1990s, many believed that the paradox was solved. Growth rates increased, and the Internet became pervasive. The great recession has been followed by lower growth in Europe and the USA and a return of the Solow paradox. Evidence indicates that the share of Internet users in countries' populations had a positive effect on growth in the 1990s, but that this effect vanished for developed countries after 2000. The second strand of literature is a heterogeneous research tradition that relates ICT not to income and growth, but to human well-being. That literature indicates positive (as well as some negative) effects of ICT and the Internet on people's happiness. Some new evidence indicates that the share of Internet users in populations in a panel of countries is positively related to average happiness.

An earlier and preliminary version of this chapter was pre-published as a working paper, Maurseth (2017).

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Notes

  1. 1.

    Gordon (2016, p. 430).

  2. 2.

    Gordon (2016) describes diffusion of television in the USA, which was even faster than diffusion of mobile phones and the internet. In 1950, 9% of American households owned a TV set. In 1955, this number had increased to 65%.

  3. 3.

    By 2017, therefore, probably most people in the world are internet users.

  4. 4.

    For hedonic price indexes for computers, also see Chow (1967) and Berndt and Griliches (1993).

  5. 5.

    The agreement has weakened somewhat recently. In the wake of the literature about endogenous growth, technological change is the result of economic mechanisms and in need of explanation itself. As primary explanations for growth have geography and institutions emerged as candidates (see, e.g., Diamond 1997; Acemoglu et al. 2005 or Rodrik et al. 2004).

  6. 6.

    Note that several contributions have relaxed the assumptions of constant returns to scale and perfect competition. See, e.g., Feenstra (2004, Chap. 10).

  7. 7.

    In patent documents, patents are assigned a technology class (IPC). Patents are sometimes assigned to several IPC classes.

  8. 8.

    Network effects can be hard to identify. Brynjolfsson and Kemerer (1996) analyse the market for spreadsheets in the 1987–1991 period. They find that prices for spreadsheets depend on product characteristics, a time trend and the accumulated number of the particular spreadsheet sold. They find positive effects of the latter and interpret it a network effect. They acknowledge, however, that also strategic pricing may play a role.

  9. 9.

    Broadband includes connections with data speed of 256 kbit/s or more (OECD 2014a).

  10. 10.

    ICT service exports include computer and communications services (telecommunications and postal and courier services) and information services (computer data and news-related service transactions). ICT goods exports include computers and peripheral equipment, communication equipment, consumer electronic equipment, electronic components and other information and technology goods (miscellaneous). The definitions are explained in World Development Indicators (2017).

  11. 11.

    Note that European and US trend growth rates intersect in the early 1990s in Fig. 3.

  12. 12.

    They define the knowledge economy as changes in labour composition, ICT capital per hours worked and TFP.

  13. 13.

    The share of internet users is measured as the share of the population that have used the internet during the last 12 months. Investments shares are gross fixed capital formation as share of GDP. Government expenditures are included. Choi and Li (2009, p. 40) expect this variable to negatively influence on growth since “the government distorts the private decisions”. I expect its coefficient to be negative because government expenditures are often more stable than the more varying marked-based private sectors (and therefore serve as automatic stabilizers). High rates of inflation are known to retard growth. A priori, I don’t have any expectations about the coefficient when inflation is low.

  14. 14.

    For an introduction to consumer theory, see Kreps (1990) or Gravelle and Rees (1992).

  15. 15.

    The eminent Norwegian economist Asbjørn Rødseth writes (p. 46, my translations) “Most modern economists make use of such a utility notion (ordinal)” (Rødseth 1992). Gravelle and Rees writes (p. 182) “The utility function of consumer theory is an ordinal function …”.

  16. 16.

    Deaton (2008) regresses happiness on log of income in the same year as well as average yearly growth rates in for two alternative time periods.

  17. 17.

    Data coverage increases much from 2006 (89 countries) to 2007 (102 countries).

  18. 18.

    Note that a regression of happiness level on current income and previous income growth is indistinguishable from a regression on current and past income levels.

  19. 19.

    The term Social capital is used with different definitions in social sciences. It can be used at group level as “informal values or norms shared among members of a group that permits them to co-operate with one another” (Fukuyama 1999). The term is also used as individual characteristics as “the number of trusting relationships and social ties in which she is involved and where she has access” (Laumann and Sandefur 1998). See the discussion in Pénard and Poussing 2010.

  20. 20.

    A plot of relative standard deviation shows an even closer relationship.

  21. 21.

    Results were not significant. But the signs of the coefficient indicated that ICT imports increase happiness and that ICT export (as share of total exports) decreases happiness.

  22. 22.

    I also experimented with using the Gini coefficient. Use of the Gini coeffcients produced qualitatively similar results, but reduced sample size and significance of several variables (internet users included). In fixed effects regressions, internet users were no longer significant. In pooled cross-country regressions, internet users significantly and positively correlate with average happiness scores.

  23. 23.

    Using coefficient of variation (relative standard deviation) as a measure of inequality leaves several estimated coefficients insignficant, including internet users.

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Acknowledgements

I thank Arne Melchior, Jens C. Andvig, Hege Medin and Fulvio Castellacci for comments on a previous version of this paper. This paper was written with financial support from the project Responsible Innovation and Happiness: A New Approach to the Effects of ICTs, founded by the Norwegian Research Council.

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  1. BI—Norwegian Business School, Oslo, Norway

    Per Botolf Maurseth

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Correspondence to Per Botolf Maurseth .

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  1. Delhi School of Economics, Delhi, India

    Dibyendu Maiti

  2. Head of Department, Centre for Technology, Innovation and Culture, Oslo, Norway

    Fulvio Castellacci

  3. Senior Research Fellow, Norwegian Institute of International Affairs, Oslo, Norway

    Arne Melchior

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Maurseth, P.B. (2020). ICT, Growth and Happiness. In: Maiti, D., Castellacci, F., Melchior, A. (eds) Digitalisation and Development. Springer, Singapore. https://doi.org/10.1007/978-981-13-9996-1_2

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