Explaining The Territorial Adoption Of New Technologies - A Spatial Econometric Approach
The notion that Information and Communication Technology would have reduced the economic importance of geographic distance has been proposed with energy in the post-Internet literature (Cairncross, 2001). According to this view, the New Economy would work in a space rather than a place, cost of transport would be drastically reduced, distance would be less important, and peripheral regions would benefit from opportunities that were not available in the economy based on manufacturing industry (Negroponte, 1995; Cairncross, 1997; Kelly, 1998; Compaine, 2001). Since ICT are mostly based on immaterial and human capital investment, regions or areas that have historically suffered from isolation, large cost of transportation, or lack of physical private and public infrastructure might find new paths for growth. Consequently, according to this view, the concentration of income opportunities and wealth should decrease over time. Although other predictions were also present in the debate over the impact of the digital economy (e.g. Norris, 2001; UNDP; 2001), this view was largely dominant. The reality is not so rosy. Not only there are huge disparities in the intensity with which ICT are adopted and used across countries, but also there are still large differences within industrialized countries. Indeed, differences in economic development still shape the rate of the adoption of these technologies, at the firm, regional and country level. The reasons behind these stylized facts have been investigated at length in recent times. This paper contributes to the literature in several ways. First, it focuses on intra-national or regional differences, which is a much less explored dimension of the digital divide. Second, it uses a new metric for the adoption of ICT, namely the number of second level Internet domain names, registered under the ccTLD Â“.it.Â”. Finally, it explicitly combines the analysis of determinants with a spatial econometric approach. Thanks to the availability of panel data for both the dependent and the explanatory variables (time period: 1990-2001), spatial and temporal effect are simultaneously taken into account. Panel data techniques that account for temporal correlations are in widespread use while there have been a variety of studies accounting for spatial autocorrelation (see for instance Coughlin et al. 2003; Dubin, 1992; McMillan, 2004). However one of the major drawbacks to many analyses is that they fail to integrate the spatial and temporal correlations that are present in geographical systems (Elhorst, 2003).
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