ABSTRACT
Digital inequality is a burgeoning field of study across disciplines, yet few papers address categorical digital inequality in cross-national samples. Using the only available cross-national data on access to Information and Communications Technologies (ICTs) disaggregated by gender from the International Telecommunications Union (ITU), I add to existing literature by examining factors associated with women’s unequal access to mobile phones across 51 countries. The largest of such samples, the ITU data demonstrates that the type and levels of gender inequality in mobile phone use are not consistent throughout countries. In fact, the distribution is quite variable, from incorporating a small-sample of nations where women have marginally higher access, to sub-samples close to parity, to a larger sub-sample where women are at a substantial disadvantage. Using Quantile Regression Methods to assess these variations, I test how major gender and international development theories inform inequality patterns. My findings suggest that women’s wellbeing, as measured by their access to modern contraception (i.e., reproductive autonomy), overwhelmingly promotes women’s relative access to mobile phones, regardless of preexisting levels of access. Other perspectives like the growth imperative and world polity theory show some staggered associations along the distribution that remain substantively inconclusive.
Disclosure statement
No potential conflict of interest was reported by the author.
Notes on contributor
Aarushi Bhandari is a PhD candidate at Stony Brook University. Her research focuses on the intersections between gender, technology and global development. Aarushi is from Nepal and has previously published on gender and food security in developing countries [email: [email protected]].
Notes
* Presented at the ASA 2018 Annual Meeting under a previous title: ‘Women’s Agency and the Gender Digital Divide: A Cross-National Quantile Regression Analysis’.
1 These data are collected directly from the countries of origin. ICT data is collected from two sources: (1) National telecommunication/ICT ministries and regulatory authorities and (2) Household ICT data collected from national statistical offices.
2 This is owing to ITU to data constraints. Some countries only have data for 2012, and others only for 2015. As a robustness check, year dummies were included in alternative model specifications, and the year the DV is reported for does not have any statistically significant impact on the findings.
3 In additional analyses not presented here, I also controlled for urban population as per the literature, and various other measures of women’s status including female labor force participation, female legislative representation, property rights and fertility rates. None of these variables reached levels of significance in any of the quantiles. Simultaneously, my substantive findings were unaffected when these variables were dropped from the equations.
4 The Pew report identifies four categories: 1. ‘Official State religion’ 2. ‘Preferred/State Religion’ 3. ‘No official or Favored Religion’ 4. ‘Hostile.’ Nations categorized as both 1 and 2 are coded as religious in this analysis.
5 To ensure that high GDP nations did not skew these results, robustness checks were conducted. Removing the top 25% (N = 38) and 10% (N = 46) highest GDP nations from the sample subsequently replicated these results.