Some studies consider the factors affecting fixed-internet access and usage and other aspects of the digital divide. For instance, income, installation fees, and age are shown to be significant factors predicting fixedinternet usage (Birba and Diagne 2012; Cerno and Amaral 2006; Katz and Rice 2003). Other socioeconomic variables such as education, gender, locality and household size are also found to be drivers for internet adoption and usage in general. For instance, Goldfarb and Prince (2008) find that low income, less-educated people spend more time online. Similarly, Penard et al. (2012) and Penard et al (2015) find that education and computer literacy increase internet usage. Gilwald et al. (2018) provide evidence on lower internet adoption rate among women and those who live in rural areas. Other infrastructure factors such as increasing the distribution of electricity and improving competition in digital infrastructure are found to help promote the adoption of internet services (Armey and Hosman 2016; Rodriguez-Castelan et al. 2019).

However, with regards to mobile internet, existing studies have a few limitations. First, previous research has mainly focused on understanding the determinants of mobile phone ownership in developing countries. Education, employment status, and type of electricity are found to be important factors (Aker and Mbiti 2010; Björkegren 2019; Forenbacher et al. 2019; van Biljon and Kotzé 2007). Second, studies on mobile internet adoption tend to focus on the supply side, emphasizing technological aspects such as the efficient compression of images or data delivery (Kim and Kim 2002). Funk (2005) presents how certain technological products such as the push mail service and micropayment systems promote mobile internet adoption. Third, only specific country or regional data are used in the few studies analyzing the demandside factors affecting mobile internet adoption. For instance, Srinuan, Srinuan, and Bohlin (2012) use data from Thailand to find that price, the availability of fixed telephony, and individuals’ age and location are strong determinants of mobile internet adoption. Consumers’ perspectives on service applications are also found to affect mobile internet adoption in Taiwan, China (Hsu, Lu, and Hsu 2007). Hasbi and Dubus (2020) provide evidence of the positive impact of being part of an online social community on mobile broadband use in Sub-Saharan Africa. Meanwhile, a cross-country/region analysis that presents commonalities and differences in how specific demand-side factors affect mobile internet adoption across developing countries is missing.

This study aims to (1) examine how socioeconomic variables and perception factors affect mobile internet adoption; and (2) compare differences in mobile internet adoption across different countries, regions, and demographic profiles from the demand side. The study adds to the literature in the following ways: (1) it is one of the first studies to explore determinants of mobile internet adoption in developing countries, (2) it uses nationally representative household-level data that follow a consistent methodology across countries allowing for cross-country comparisons, which is a rarity in the literature, and (3) it establishes a significant relationship between a few demand side variables and mobile internet adoption, which present significant policy implications. The study finds that demand-side factors are of critical importance for mobile internet adoption. Across the developing world, females, the elderly, those who live in rural areas, and those who have a relatively low level of income or education are less likely to adopt mobile internet. Social network effects are found to have a significant positive impact on the usage of mobile internet. In addition, across regions, although the factors affecting the adoption of mobile internet remain largely the same, the magnitude of their impacts vary.

The paper is organized as follows: section 2 outlines the study’s conceptual framework. Section 3 describes the data used. Section 4 lays out the study’s empirical strategy. Section 5 presents the results of several modeling exercises and explores alternate scenarios. The last section concludes and discusses policy implications.

**2. Conceptual Framework**

3