Does the ownership of a mobile phone contribute to a better quality of life? A comparison between Sweden and Indonesia

Paper i proceeding, 2011

Empirical studies suggest that future economic development will be driven, to a great extent, by the Information and Communication (ICT) sector. The sector has been identified as having the ability to stimulate further growth of the economy, for example, through mobile phone penetration (Sridhar and Sridhar, 2004; Lee, Levendis and Guiterez, 2009), broadband (among others, Crandall et al., 2003; Katz et al., 2008; Atkinson et al., 2009; Libenau et al., 2009), and telecommunication investment in general (Cronin et al., 1991; Madden and Savage, 1998; Dutta, 2001; Chakraborty and Nandi, 2003; Shiu and Lam, 2008). Telecommunication undoubtedly plays an important role in accelerating economic growth by providing new employment and increasing the efficiency and productivity of firms by reducing the cost of doing business. While most of the study has been carried out on the aggregation of the impact on the economic indicators, such as GDP and employment, little attention has actually been paid to investigating the black box of the penetration rate affecting the socio-economic variables and the quality of life indicators. Rohman (2010) found that the current penetration rate of ICT devices in Asia has no cointegration (long-term relationship) with the performance of the socio-economic variables. Some studies of African countries (Rettie, 2008; Diga, 2007; Samuel et al, 2005; Donner, 2005; Hahn and Kibora, 2008) also found that the main aim of the adoption of mobile phones was to maintain social networks and, only to a small degree, connect to business and economic activities. This paper investigates two survey datasets in Indonesia and Sweden, observing the relationship between mobile phone adoption and quality of life in terms of economic indicators and socio-economic measurements on an individual level. To operationalize these aims, the first model identifies the return on the education-type equation (Card, 2005) to see the impact of ICT adoption (denoted by the length of mobile phone adoption and the intensity of usage) on income by controlling the other independent variables. The second model also scrutinizes some components of the quality of life indicators (self-reported personal values) that are a priority in the hypothesis of the influence of mobile phone adoption. The results are quite surprising: while a return to education indicates an even higher impact of ICT adoption on income in Indonesia than in Sweden, the quality of life indicators differ between the two countries in terms of the effect of mobile phone adoption, with a higher impact from the adoption process in Sweden. The paper concludes that the link between adoption and the quality of life indicators at micro level will continue to grow in the developing countries, and African countries, in particular, could learn from this.