Economic Dependency and Rural Exclusion

Abstract

The global digital divide is a pressing contemporary form of inequality, especially considering the increased salience of information and communication technologies (ICTs) in the post-pandemic world. Despite unequal rates of expansion across the developing world, processes that explain disparities in digital technology access are under-studied from macro, cross-national sociological perspectives. This longitudinal study evaluates factors associated with access to mobile phones across 133 developing countries from 1995 to 2014, the key period of rapid ICT expansion worldwide. We investigate two major global sociological theories, dependency and world society, to determine the processes that best explain unequal access between developing states. Using negative binomial count models within a generalized estimating equation framework, we find that the global digital divide is exacerbated by (1) larger rural populations at the state level and (2) higher levels of dependence, as measured by foreign direct investment, export concentration, and domestic fiscal independence. We also find that internal characteristics of economically dependent states work in combination to worsen the digital divide; specifically, we find that the negative effect of rurality on subscriptions is heightened in states with less domestic fiscal independence. Finally, we find limited evidence that world society institutionalization of ICT norms has a positive effect on access to mobile phones globally; however, the relationship between state-level measures of world society penetration and mobile phone subscriptions is unclear.

Anti-Systemic Movements in the Attention Economy

“…The negative binomial model is assessed using total population counts as exposure to account for the maximum possibility of protest participation count for each country-year (Hutchinson and Holtman 2005). I further apply a population-averaged Generalized Estimating Equations (GEE) approach, which describes the average effect of an independent variable on an outcome across a population (Ghisletta and Spini 2004;Bhandari and Shirazi 2022). This approach is favored against alternative estimation techniques when analyzing longitudinal count data outcomes (Zhang et al 2012;Bhandari and Shirazi 2022).…”

“…I further apply a population-averaged Generalized Estimating Equations (GEE) approach, which describes the average effect of an independent variable on an outcome across a population (Ghisletta and Spini 2004;Bhandari and Shirazi 2022). This approach is favored against alternative estimation techniques when analyzing longitudinal count data outcomes (Zhang et al 2012;Bhandari and Shirazi 2022).…”