metadata: authors: 0: author: Chen,Rong - DECIG
count: World
docna: 0: docna: A Demand-Side View of Mobile Internet
            Adoption in the Global South
docty: Policy Research Working Paper
subtopic: Educational Sciences,Information Technology,Telecommunications Infrastructure,Energy Policies & Economics,Gender and Development
teratopic: Information and Communication Technologies,Education,Energy,Gender
abstracts: cdata!: Mobile technologies show great potential
            to accelerate internet access and usage, especially in
            developing countries. A better understanding of key drivers
            and main constraints for mobile internet access is the first
            prerequisite for governments to design targeted policy
            solutions. This study exploits a household survey that
            collects information on information and communications
            technology access and usage at the household and individual
            levels in 22 countries in the Global South. The study finds
            that in addition to infrastructure investment, which has
            been the main focus of many developing countries, other
            demand-side factors are of critical importance. 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.
            Those who have more close friends using an online social
            network are more likely to adopt mobile internet.
            Individuals whose five closest friends are using an online
            social network (such as Facebook or Twitter) are 63.1
            percent more likely to adopt it than those without any close
            friends using such online social network sites/apps. Across
            regions, although the factors affecting the adoption of
            mobile internet remain largely the same, the magnitudes of
            their impacts vary. In Asia, gender differences are
            negatively associated with mobile internet. In Africa, the
            impact of education level is more salient than in the other
            two regions, implying an urgent need to improve digital literacy.
repnme: repnme: A Demand-Side View of Mobile Internet
            Adoption in the Global South
display_title: A Demand-Side View of Mobile
            Internet Adoption in the Global South
listing_relative_url: /research/2021/03/32931054/demand-side-view-mobile-internet-adoption-global-south
topicv3: Information and Communication Technologies
            m1326624 133,Education m1327048 470,Power and Electricity Sector m1327094 515,Gender m1327621 615,Mobile Phone m1505675 3092
publishtoextweb_dt: 2021-03-22T00:00:00Z
dois: 10.1596/1813-9450-9590
historic_topic: Information and Communication Technologies,Education,Energy,Gender
origu: World Development Report (DECWD)
no_of_pages: 30
model_extractions: Policy Research Working Paper 9590 World Development Report 202 <mark>1</mark> _Background Paper_ A Demand-Side View of Mobile Internet Adoption in the Global South _Rong Chen_ Development Economics World Development Report 2021 Team March 2021
Policy Research Working Paper 9590

# **Abstract**

Mobile technologies show great potential to accelerate internet access and usage, especially in developing countries. A better understanding of key drivers and main constraints for mobile internet access is the first prerequisite for governments to design targeted policy solutions. This study exploits a household survey that collects information on information and communications technology access and usage at the household and individual levels in 22 countries in the Global South. The study finds that in addition to infrastructure investment, which has been the main focus of many developing countries, other demand-side factors are of critical importance. 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. Those who have more close friends using an online social network are more likely to adopt mobile internet. Individuals whose five closest friends are using an online social network (such as Facebook or Twitter) are 63.1 percent more likely to adopt it than those without any close friends using such online social network sites/apps. Across regions, although the factors affecting the adoption of mobile internet remain largely the same, the magnitudes of their impacts vary. In Asia, gender differences are negatively associated with mobile internet. In Africa, the impact of education level is more salient than in the other two regions, implying an urgent need to improve digital literacy.

This paper is a product of the World Bank’s _World Development Report 2021_ Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The author may be contacted at rchen5@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **A Demand-Side View of Mobile Internet Adoption in the Global South**<sup>**1**</sup>

Rong Chen Keywords: Internet adoption, mobile broadband, developing economy, digital gap. JEL codes: O12, I30, L96, O55.

1 This research was undertaken as a Background Paper prepared for Chapter 5 of the World Development Report 2021 _Data for Better Lives_ . The author would like to thank the following colleagues from the World Bank Group for their review and valuable feedback on the paper: Mark Williams (Practice Manager, Digital Development Global Practice), Carlos Rodriguez Castelan (Lead Economist, Poverty and Equity Global Practice) and Takaaki Masaki (Economist, Poverty and Equity Global Practice). The author is also grateful to participants to the InfraXchange seminar: A Demand Side View of Mobile Internet Adoption in the Global South, on November 19<sup>th</sup> , 2020 for their comments and suggestions. The author appreciates RIA (Research ICT Africa), LIRNEasia (Learning Initiative for Network Economies in Asia), and DIRSI (el Diálogo Regional sobre la Sociedad de la Información / Regional Dialogue on the Information Society) for sharing survey data to support the analyses.
# **1. Introduction**

Since its inception in the 1990s, the internet has transformed the ways people interact, businesses conduct commercial transactions, and governments deliver public services. Internet penetration—on both the supply side (infrastructure deployment) and demand side (user adoption)—continues to improve. International internet bandwidth nearly tripled between 2015 and 2019, to 466 terabytes per second.<sup>2</sup> The share of the global population using the internet increased from 8.0 percent in 2001 to 51.4 percent in 2018.<sup>3</sup> However, the penetration of fixed broadband internet is far from satisfactory in low-income countries, where, in 2018, fixed broadband subscriptions averaged 0.74 per 100 people.<sup>4</sup> In this context, mobile technologies show great potential to accelerate internet access and usage. Global mobile cellular subscriptions increased more than 10 times—from 0.74 billion in 2000 to 7.9 billion in 2018.<sup>5</sup> In low- and middle-income countries, mobile cellular subscriptions also reached a relatively high level of 102 per 100 people in 2018.<sup>6</sup> (However, it is worth noting that the unique mobile subscriber penetration rate, as a share of population, was still as low as 45 percent in the Sub-Saharan Africa region in 2018.<sup>7</sup> ) It is estimated that by 2020, 9 in 10 people will be covered by 3G networks that enable internet access from the palms of their hands (World Bank 2018). The increasing rate of mobile phone ownership in combination with expanding 3G network coverage promises to promote access to and usage of mobile internet.

Research findings have provided evidence on the development impacts of mobile internet access and usage. Katz and Callorda (2018) estimate that a 10 percent increase in mobile broadband penetration is associated with a 1.8 percent increase in gross domestic product (GDP) in middle-income countries, and a 2 percent increase in GDP in low-income countries. Mobile broadband coverage is also shown to have large and positive impacts on household consumption levels (Bahia et al. 2020). Mobile internet access and usage are also found to have a positive impact on people’s happiness and well-being (GSMA and Gallup 2018) and women’s empowerment (Bailur and Masiero 2017). It is argued that access to and usage of information and communication technology (ICT) reduce poverty, by fostering access to and exchange of information, and improving the transparency and accessibility of public services—all benefits that would also apply to mobile internet (Cecchini and Scott 2003; Roller and Waverman 2001; Waverman, Meschi, and Fuss 2005).

Despite the potential of mobile internet to help achieve development goals, there is a significant digital divide among and within countries. According to the Organisation for Economic Co-operation and Development, the digital divide refers to the “gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard to both their opportunities to access ICTs and to their use of the Internet for a wide variety of activities”. According to the Global System for Mobile Communications (GSMA 2019a), those who are not connected through mobile internet are disproportionately rural, women, or illiterate. For instance, in developing countries, women still remain 10 percent less likely than men to own a mobile phone, and 23 percent less likely than men to use mobile internet services. Similarly, rural populations are 40 percent less likely to use mobile internet than urban populations (GSMA 2019b).

> 2 TeleGeography. 3 ITU statistics. 4 World Development Indicators. 5 World Development Indicators.

> 6 World Development Indicators. 7 Statista.com.

2
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
Information is critical to social and economic activities that constitute the development process. Efficiency, effectiveness, and equity all matter in leveraging information to promote development. Access and usage are among the first prerequisites to exploiting the potential of ICTs. Unequal levels of access and usage create a digital divide between individuals, households, businesses, and geographic areas at different socioeconomic levels.

The world’s significant gap in ICT access and usage, despite broad awareness of their associated benefits, has driven researchers to examine determinants of the digital divide. As shown by Srinuan and Bohlin (2011) and Helbig, Gil-Garcia, and Ferro (2005), the determinants can be grouped into three categories: (1) physical infrastructure on the supply side; (2) socioeconomic factors on the demand side, such as income and education level; and (3) perspective factors on the demand side shaped by the institutional environment, culture, language, and network effects. This is also consistent with the framework proposed by van Dijk (2006), who argues that there is a cumulative and recursive model of digital technology adoption, starting with motivation, followed by physical/material access, subsequently requiring digital skills and complementary services (such as electricity) to achieve usage, leading to strengthened motivation and increased usage over time. A higher level of skills and more customized products could strengthen the initial motivation.

Much attention was devoted to the importance of physical infrastructure access following the technological determinism of the early 2000s (Jerome Lim 2002; Lentz and Oden 2001; Moss 2002). However, as the digital divide widened, despite expanding service coverage, many researchers explored other underlying socioeconomic factors affecting demand, such as literacy, education, income, and geography (Bagchi 2005; Gauld, Goldfinch, and Horsburgh 2010; Salajan, Schönwetter, and Cleghorn 2010). At a later stage, other social science disciplines came into play as the research on the digital divide evolved. One strand of the literature argues that psychological attitudes toward ICTs affect their adoption (Srinuan and Bohlin 2011; van Dijk 2006), while a second strand emphasizes the role of social networks in shaping perceptions and driving adoption, such as membership in occupational, religious, or cultural communities (Al‐Jaghoub and Westrup 2009; Andrés et al. 2010).

The abovementioned studies focus on assessing the determinants of fixed-internet access and usage, many in developed countries. Nevertheless, the fixed internet and mobile internet are different in a variety of aspects such as the price of access devices, convenience of access, coverage and reliability of signal, connection speed, and so on. This paper will explore whether the three main determining factors behind the adoption of general ICTs apply to mobile internet as well. First, the physical infrastructure needed to access mobile internet at the level of 3G or above differs from the telephone or fixed-line broadband connections needed for fixed internet. Household access to electricity is essential for laptop usage with fixed connections, but people can charge mobile phones at other places outside the home, such as offices, community hotspots, or with neighbors. Second, socioeconomic factors such as income and education level might affect access to and usage of mobile internet differently than they do other types of ICTs. For instance, the cost of a mobile phone device is often less than that of a desktop computer. Many mobile network operators and technology firms (e.g., social media platforms) offer a variety of data promotion packages to attract potential customers, and mobile systems can be more intuitive and easier to learn. Third, in terms of perspective factors, with the recent booming of social media, it is argued that social network effects such as the desire to follow friends or family on social media mobile apps are of particular relevance to the access and usage of mobile internet.

Within a framework commonly used in the literature to assess the determinants of general ICT access and usage, this paper examines three sets of determining factors behind mobile internet adoption: (1) physical infrastructure (coverage by a 3G or above mobile network, and electricity access); (2) socioeconomic 4
factors such as gender, income level, location (urban/rural), education level, price of mobile data packages, price of internet-enabled phones, and so on; and (3) perspective factors shaped by the institutional environment, culture, and network effects.

# **3. Data**

In 2017–18, ICT policy think tanks in the Global South, including RIA (Research ICT Africa), LIRNEasia (Learning Initiative for Network Economies in Asia), and DIRSI (el Diálogo Regional sobre la Sociedad de la Información / Regional Dialogue on the Information Society), coordinated a global initiative, conducting household surveys to collect information about ICT access and usage at household and individual levels. Ten African countries (Ghana, Kenya, Lesotho, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Tanzania, and Uganda), six Asian<sup>8</sup> countries (India, Sri Lanka, Pakistan, Bangladesh, Nepal, and Cambodia), and six Latin American countries (Argentina, Colombia, Ecuador, Guatemala, Paraguay, and Peru) were covered (see Annex 1 for more survey methodology details).

Going beyond other microlevel data sets such as the Living Standards Measurement Study and the Global Findex database, this data set collects rich, in-depth information on ICT access and usage from the demand side. It provides valuable information on mobile phone ownership and usage, internet access and usage, social media activities, digital finance, participation in the gig/sharing economy, as well as the reasons for engaging in those ICT-enabled activities. The surveys are nationally representative, and the data can be disaggregated on the basis of gender, age, location (urban or rural), and income level.

Unfortunately, there is no direct question on mobile internet adoption in the surveys. However, such usage can be captured through combining information gained through multiple survey questions regarding whether respondents: (1) have used the internet or not, (2) own a mobile phone, and (3) whether the type of mobile phone they own allows internet access. An individual is counted as using mobile internet if he or she has used the internet and also owns an internet-enabled phone.<sup>9</sup> Results show that Latin American countries have the highest rate of mobile internet adoption: 65.6 percent of the total population. The rural and urban divide is significant across all regions (figure 1), especially in Africa, where the adoption rate among urban residents is more than twice that among rural residents. Asian countries in the sample have the widest gender gap: only 8.4 percent of females used mobile internet in 2017. Those who are at the bottom 40 percent of the national income distribution have a lower adoption rate across regions, while the gap is minimal in Latin America.

**Figure 1. Mobile internet adoption, by subgroup, across regions (% of population) a. Rural vs. urban and male vs. female b. Poor vs. not and educated vs. not**

> 8 The majority of the Asian countries covered in the sample are in South Asia.

> 9 Internet-enabled phones include smartphones and feature phones. Admittedly, this constructed variable cannot completely rule out cases of individuals owning an internet-enabled phone who in fact accessed the internet through Wi-Fi–enabled fixed broadband. However, data from Telegeography show that household fixed-broadband penetration is low (at 13.5 percent) across countries in the sample; thus, such cases should be minimal. The estimated rate is as low as 3.6 percent among African countries, and 7.7 percent among the Asian countries covered.

5
<!-- Start of picture text --> 100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Asia Latin America Sub-Saharan Average Asia Latin America Sub-Saharan Average<br>Africa Africa<br>Rural Urban Male Female Poor Non-poor Educated Non-educated<br><!-- End of picture text --> _Note:_ Individuals who have completed at least primary education are grouped as “educated”; individuals who are in the bottom 40 percent of the national income distribution are classified as “poor.” Individual weights are applied in the calculation.

The demographic profiles of mobile internet users and nonusers are notably different (figure 2). Across the countries covered in the sample, 54 percent of the nonusers are female, and 68 percent of them are living in rural areas, while the majority of mobile internet users are male and urban residents. The average age of mobile internet users is 29.6 while that of the nonusers is 37.1, indicating that the elderly lag behind in adopting new technologies. Mobile internet users tend to be more educated and wealthier than nonusers. The average number of years of formal education (12.0 years) of mobile internet users is almost twice that of the nonusers (6.1 years). Furthermore, 92 percent of mobile internet users have at least completed primary school, while more than half of the nonusers have either no formal education or have not completed their primary education. The monthly income of mobile internet users is $303.4, more than double that of nonusers at $123.6.

# **Figure 2. Demographic profiles of mobile internet users and nonusers**

<!-- Start of picture text --> 100% 40.0 350.0<br>90% 35.0 300.0<br>39%<br>80% Male46% rural 30.0 250.0<br>70% 65% 68% 25.0<br>Male rural 200.0<br>60%<br>20.0<br>50% 150.0<br>15.0<br>40% 100.0<br>10.0<br>61%<br>30% 54% urban 5.0 50.0<br>female<br>20% 35% 32%<br>0.0 0.0<br>female urban<br>10% mobile internet user non-mobile internet user<br>0%<br>years of formal educaiton age<br>mobile non-mobile mobile non-mobile<br>internet userinternet user internet userinternet user monthly income (usd)<br>usd<br><!-- End of picture text --> _Note:_ Individual weights are applied in the calculation.

6
The analysis examines decision factors from two perspectives: owning an internet-enabled phone and using internet through the internet-enabled phone. Having an access device, or internet-enabled phone, is the first prerequisite to using mobile internet. More than half of the total population own a mobile phone in almost all the countries covered except for Mozambique, Rwanda, and Uganda (figure 3). There remains a significant divide between men (77.8 percent) versus women (50.4 percent), and urban (73.6 percent) versus rural residents (58.1 percent).

**Figure 3. Mobile phone ownership by type across selected countries (% of population)**

<!-- Start of picture text --> Africa Asia Latin America<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No mobile phone Basic phone Feature phone Smartphone<br>population)<br>Mobile phone type ownership (% of total<br><!-- End of picture text --> _Note:_ Individual weights are applied in the calculation.

With regard to obstacles to owning a mobile phone, different reasons are cited such as affordability (39.3 percent), no mobile coverage (16.3 percent), and no electricity at home (17.7 percent). For those who do not have a mobile phone, affordability is a bottleneck across countries: 68.5 percent of those without a mobile phone in Cambodia cite affordability as such. The obstacle of electricity access is particularly severe in Africa (figure 4), especially in Rwanda (50.9 percent) and Senegal (40.4 percent). A lack of mobile coverage is challenging in Cambodia (32.3 percent), Peru (31.2 percent), and Senegal (31.5 percent).

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**Figure 4. Reasons cited for not having a mobile phone (respondents can select multiple options)**

<!-- Start of picture text --> 80 Africa Asia Latin America<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No mobile coverage No electricity at home Can't afford it<br>Note: Individual weights are applied in the calculation.<br>Around 70 percent of those who own a mobile phone only have a basic or feature phone. 10 Overall,<br>smartphone penetration is higher in Latin American and Asian countries than in African countries. For<br>instance, among those who own a mobile phone, the percentage with a smartphone is above half in all Latin<br>American countries in the sample, and close to half in Asian countries (48.5 percent in Cambodia, 51.8<br>percent in Nepal, and 47 percent in Sri Lanka). That a smartphone is not necessary and not affordable are<br>the two most cited reasons for not having one (figure 5). The affordability obstacle is especially severe in<br>Mozambique, Rwanda, and Tanzania.<br>Figure 5. Main reason cited for not choosing a smartphone among owners of a basic mobile phone<br>(respondents can choose only one main reason)<br>Africa Asia Latin America<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>No necessity Can't afford Too complicated Other<br>having a mobile phone)<br>Percentage points (% of adults not<br>phone)<br>own a basic phone or a feature<br>Percentage points (% of those who<br><!-- End of picture text --> _Note:_ Individual weights are applied in the calculation.

Around 70 percent of those who own a mobile phone only have a basic or feature phone.<sup>10</sup> Overall, smartphone penetration is higher in Latin American and Asian countries than in African countries. For instance, among those who own a mobile phone, the percentage with a smartphone is above half in all Latin American countries in the sample, and close to half in Asian countries (48.5 percent in Cambodia, 51.8 percent in Nepal, and 47 percent in Sri Lanka). That a smartphone is not necessary and not affordable are the two most cited reasons for not having one (figure 5). The affordability obstacle is especially severe in Mozambique, Rwanda, and Tanzania.

**Figure 5. Main reason cited for not choosing a smartphone among owners of a basic mobile phone (respondents can choose only one main reason)**

_Note:_ Individual weights are applied in the calculation.

10 Annex 2 contains detailed country-level information on smartphone ownership and smartphone uptake rates.

8
Besides owning an internet-enabled phone, individuals face challenges in accessing and using the internet. For those who do not use the internet at all, the most cited reasons relate to digital literacy, including “do not know what internet is” (58.9 percent) and “do not know how to use the internet” (10.1 percent) (figure 6). No access to a device (computer or mobile phone) is another internet usage constraint (11.5 percent). Other factors such as lack of content in the local language or data privacy concerns do not appear to be key obstacles. Among those who do actually use the internet, the extent of their usage is constrained primarily by lack of time, high data costs, and internet speed.

**Figure 6. Main reason for not using the internet across the population as a whole (in percentage points)**

<!-- Start of picture text --> Africa Asia Latin America<br>100.00<br>90.00<br>80.00<br>70.00<br>60.00<br>50.00<br>40.00<br>30.00<br>20.00<br>10.00<br>0.00<br>Digital literacy Affordability Relevance Other<br>use Internet)<br>Percentage points (% of those who do not<br><!-- End of picture text --> _Note:_ The digital literacy category includes “do not know what internet is” and “do not know how to use internet”; affordability includes “no access device” and “too expensive”; the relevance category includes “no interest/not useful” and “no relevant content in local language.” Individual weights are applied in the calculation.

People also engage in various mobile internet activities. Survey results show that social networking apps are the most often used app type among people who have an internet-enabled phone. More than a quarter of internet-enabled phone owners use social networking apps daily. Average monthly expenditure on mobile phone data, as a percentage of total income, is 0.9 percent in all the countries covered. In a few countries, such as Sri Lanka, South Africa, and Ghana, monthly mobile data expenditure is more than 2 percent of monthly income—an affordability threshold set by the United Nations.<sup>11</sup> Meanwhile, male, urban residents and young people tend to spend more, which may be associated with their income and level of digital awareness.

Those who use the internet adopt different methods to save data charges, such as accessing the internet in a free Wi-Fi area (37.2 percent) or at home or at work (41.9 percent), and taking advantage of special data promotions (49.6 percent). Using special data promotions is the most popular method to save on data charges across countries. This may be related to not only the various promotion packages offered by mobile network operators, but also zero-rating plans offered by content providers such as Facebook.

> 11 The United Nation’s “1 for 2” threshold of internet affordability is defined as 1 gigabyte (GB) for no more than 2 percent of average monthly income. https://a4ai.org/affordable-internet-is-1-for-2.

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# **4. Empirical Strategy**

To further unravel the factors affecting mobile internet adoption, following the approach of Forenbacher et al. (2019) and Hasbi and Dubus (2020), this analysis tries to estimate the probability of using mobile internet through the following model: where _Mobile Interneti_ is a binary variable that equals 1 if individual _i_ uses mobile internet, and 0 if not. Given that the dependent variable is a binary variable, a logit model is adopted for the estimation to predict the probability of an individual adopting mobile internet. By logarithmically transforming the outcome variable, it allows the examination of a nonlinear association in a linear way. The coefficient βk measures, ceteris paribus, the effect of a one-unit change in xik on the dependent variable.

Following the conceptual framework, which categorizes factors affecting the digital divide into three groups, variables in the model include: (1) infrastructure factors (3G network coverage and household access to electricity); (2) socioeconomic factors such as gender, income level, education level, and rural/urban location; and (3) social network effects (table 1). Those factors also reflect what the descriptive statistics show to be the main obstacles to owning an internet-enabled phone or using internet, as outlined in the data section above.

Infrastructure factors include whether an individual’s location is covered by a 3G mobile network signal, and whether the individual’s household has access to electricity. Whether an address is covered by a 3G mobile signal is identified by mapping the household survey data (which feature GPS location data at the individual level) with the global mobile network coverage data from the Collins Bartholomew data set.<sup>12</sup> Socioeconomic factors include gender, age, education level, income level, marital status, and urban/rural location. Demographic information at the individual level is taken directly from the household survey. Lastly, the number of close friends using an online social network like Facebook or Twitter is used as a proxy for social network effects.

Though three waves of data were collected in years 2008, 2012, and 2017, the survey samples were different in each wave, so no panel is available. Given the rapid pace of change in the ICT sector, analysis is confined to the most recently available data for the year 2017, based on cross-sectional estimation.

**<u>Table 1. Data description</u>**

|**Variable**|**Definition, Year**|**Source**|
|---|---|---|
|_Dependent variable_|||
|Mobile internet|Accessing the internet through a smartphone<br>or feature phone|After Access data, 2017|
|_Infrastructure variable_|||
|3G network coverage|<sup>The individual’s household is covered by a</sup><br>3G mobile network signal|After Access data, 2017; Collins<br>Bartholomew data, 2017|

12 Not all countries have geolocation data at the individual/household level in the ICT household survey. For countries that do not have geolocation data at the individual/household level, a household is considered as not having a 3G mobile network coverage if anyone from the same survey enumerator area identified a lack of mobile coverage as an obstacle to accessing the internet.

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|Electricity access|The individual’s household has access to<br>|After Access data, 2017|
|---|---|---|
||electricity||
|_Socioeconomic variabl_<br>|<br>_es_<br>||
|Female|Respondent is female, 2017||
|Age group (1-3)|Respondent’s age group, 2017 (1 stands for<br>young people, 15-30 years old; 2 stands for<br>middle-age people, 30-60 years old; 3 stands<br>for elderly people,>60 years old)||
|Education (1–5)|Respondent’s education level, 2017 (1 stands<br>for no formal education; 2 stands for less than<br>6 years of formal education; 3 stands for more<br>than 6 but less than 12 years of formal<br>education; 4 stands for more than 12 but less<br>than 16 years of formal education; 5 stands for<br>more than 16 years of formal education)|After Access data, 2017|
|Log value of monthly<br>income|Log value of the individual’s monthly income<br>in USD||
|Married|Individual is married||
|Urban|Individual resides in an urban area||
|_Social network effect_|||
|Close friend (0–5)|The number (up to 5) of the respondents’<br>closest friends who use an online social<br>network like Facebook or Twitter (0 refers to<br>no close friend and 5 refers to all five closest<br>friends)|After Access Data, 2017|

**Table 2. Data summary**

|Mobile internet|Mean<br>0.338|SD<br>0.473|Min<br>0|Max<br>1|
|---|---|---|---|---|
|Household 3G network coverage|0.909|0.288|0|1|
|Household having access to electricity|0.843|0.364|0|1|
|Female|0.563|0.496|0|1|
|Age group (1-3)|1.695|0.632|1|3|
|Log value of monthly income|4.709|1.411|-6.274|11.353|
|Married|0.558|0.497|0|1|
|Urban residence|0.541|0.510|0|2|
|Education level (1-5)|2.740|1.035|1|5|
|Number of closest friends using online social<br>networks (e.g.Facebook/Twitter(0–5)|2.072|2.211|0|5|

# **5. Results**

The results section first presents the results from the basic model (1) specification applied to the entire sample and conducts the checks for robustness. The impact of several alternative modeling strategies is then explored, including disaggregation of the sample by geographic region, and the impact of alternative ways of modeling the influence of income on uptake decisions.

# _5.1 Factors affecting mobile internet adoption in the Global South_

Estimations show that factors affecting the adoption of mobile internet are largely consistent with those that affect other types of digital divides (table 3). Among infrastructure factors, having access to electricity at 11
the household level is found to significantly improve the likelihood of mobile internet adoption. Socioeconomic factors such as gender, age, income, and education level all have a significant impact on the usage of mobile internet. Females, the elderly, those who live in rural areas, and those who have lower levels of income or education are less likely to adopt mobile internet. Social network effects have a significant positive impact on the usage of mobile internet.<sup>13</sup> Individuals whose five closest friends are using an online social network (e.g., Facebook, Twitter) are 63.1 percent more likely to use the mobile internet than those who do not have any close friends using such online social network sites/apps.

**<u>Table 3. Logit regression results on mobile internet adoption</u>**

|Model|Lo|git|
|---|---|---|
||Mobile i<br>adop|nternet<br>tion<br>|
||Coefficient|Marginal<br>effects|
|Infrastructure factors|||
|Household 3G network coverage|0.188**|0.039**|
||(0.085)|(0.017)|
|Household having access to<br>electricity|0.759***|0.143***|
||(0.110)|(0.017)|
|Social-economic factors|||
|Female|-0.142***|-0.031***|
||(0.044)|(0.010)|
|Age group (middle age, 30-60)|-0.695***|-0.150***|
||(0.048)|(0.010)|
|Age group (old people, >60)|-1.985***|-0.282***|
||(0.109)|(0.009)|
|Log value of monthly income|0.195***|0.042***|
||(0.018)|(0.004)|
|Married|-0.138***|-0.030***|
||(0.049)|(0.011)|
|Urban|0.378***|0.081***|
||(0.046)|(0.010)|
|Years of formal education (less<br>than 6)|0.676***|0.155***|
||(0.124)|(0.029)|
|Years of formal education (6-12)|1.603***|0.347***|
||(0.118)|(0.024)|
|Years of formal education (12-16)|2.388***|0.535***|
||(0.125)|(0.022)|
|Years of formal education (>16)|2.551***|0.552***|
||(0.139)|(0.020)|
|Network effects|||
|1 close friend uses an online social<br>network|1.461***|0.349***|

13 It is also worth noting that the adjusted R2 decreases from 0.448 to 0.346 if the variable “how many close friends are using an online social network” is removed from the model.

12
||(0.087)|(0.020)|
|---|---|---|
|2 close friends use an online social<br>network|1.335***|0.319***|
||(0.076)|(0.018)|
|3 close friends use an online social<br>network|1.791***|0.420***|
||(0.076)|(0.016)|
|4 close friends use an online social<br>network|2.115***|0.480***|
||(0.096)|(0.017)|
|5 close friends use an online social<br>network|3.049***|0.631***|
||(0.064)|(0.010)|
|_cons|-4.287***||
||(0.225)||
|Country fixed effects|Yes|Yes|
|Number of observations|19,979|19,979|
|R2|||
|Adjusted R2|0.448|0.448|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Marginal effects at mean are presented.

To evaluate the validity of model (1), a Hosmer–Lemeshow goodness-of-fit test is conducted after the estimation. Given the large number of observations, the test is conducted with 100 groups. A p-value of 0.7387 of the Pearson chi-square from the Hosmer and Lemeshow’s goodness-of-fit test indicates that the model fits the data well. Furthermore, to detect if any potential observations have a significant impact on the model, model (1) is retested by excluding observations with Pearson residual value more than 2, deviance residual value more than 2, and leverage value more than 3 times the average leverage. The sign and significance of the coefficient of different factors affecting mobile internet adoption are still retained after excluding those observations.

# _5.2 Differences in factors affecting mobile internet adoption across regions_

Results are slightly different if model (1) is estimated for each region separately (table 4). Among the infrastructure factors, 3G network coverage’s impact is significant in Asia, and the impact of household access to electricity is particularly high in Africa and Latin America. In terms of socioeconomic factors, gender significantly affects the adoption of mobile internet in Asian countries, showing potential gender inequality in digital access and usage in the region. In Asia, married people tend not to use mobile internet as much as others. Residence in a rural or urban location has a significant impact in African and Latin American countries. Income level’s impact is significant in Africa and Asia. The effects of education level are particularly salient in Africa, implying the region’s needs to improve digital literacy.

13
**<u>Table 4. Logit regression results for mobile internet adoption by region</u>**

|Model||Lo|git||
|---|---|---|---|---|
|||Margina|l effects<br>Lti||
||All region|Africa|an<br>America|Asia|
|Infrastructure factors|||||
|Household 3G network coverage|0.039**|0.012|0.053|0.037**|
||(0.017)|(0.021)|(0.046)|(0.018)|
|Household having access to<br>electricity|0.143***|0.106***|0.229|0.070**|
||(0.017)|(0.016)|(0.149)|(0.028)|
|Social-economic factors|||||
|Female|-0.031***|-0.020*|-0.002|-0.042***|
||(0.010)|(0.012)|(0.016)|(0.012)|
|Age group (middle age, 30-60)|-0.150***|-0.112***|-0.174***|-0.099***|
||(0.010)|(0.014)|(0.018)|(0.014)|
|Age group (old people, >60)|-0.282***|-0.153***|-0.538***|-0.148***|
||(0.009)|(0.016)|(0.025)|(0.013)|
|Log value of monthly income|0.042***|0.037***|0.015***|0.048***|
||(0.004)|(0.005)|(0.006)|(0.006)|
|Married|-0.030***|0.000|-0.034*|-0.062***|
||(0.011)|(0.013)|(0.018)|(0.017)|
|Urban|0.081***|0.069***|0.084***|0.049***|
||(0.010)|(0.013)|(0.019)|(0.011)|
|Years of formal education (less<br>than 6)|0.155***|0.093**|0.119**|0.142***|
||(0.029)|(0.037)|(0.057)|(0.039)|
|Years of formal education (6-12)|0.347***|0.269***|0.321***|0.267***|
||(0.024)|(0.029)|(0.060)|(0.034)|
|Years of formal education (12-16)|0.535***|0.491***|0.344***|0.511***|
||(0.022)|(0.035)|(0.034)|(0.047)|
|Years of formal education (>16)|0.552***|0.583***|0.296***|0.466***|
||(0.020)|(0.035)|(0.019)|(0.052)|
|Network effects|||||
|1 close friend uses an online social<br>network|0.349***|0.391***|0.156***|0.257***|
||(0.020)|(0.030)|(0.028)|(0.034)|
|2 close friends use an online social<br>network|0.319***|0.298***|0.167***|0.272***|
||(0.018)|(0.027)|(0.024)|(0.029)|
|3 close friends use an online social<br>network|0.420***|0.427***|0.202***|0.384***|
||(0.016)|(0.026)|(0.020)|(0.030)|
|4 close friends use an online social<br>network|0.480***|0.492***|0.227***|0.480***|
||(0.017)|(0.032)|(0.018)|(0.036)|
|5 close friends use an online social<br>network|0.631***|0.666***|0.555***|0.553***|

14
||(0.010)|(0.015)|(0.027)|(0.020)|
|---|---|---|---|---|
|Number of observations|19,979|8,068|5,469|6,442|
|R2|||||
|Adjusted R2|0.448|0.454|0.366|0.394|

_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Marginal effects at mean are presented.

# _5.3 Alternative way of modeling the influence of income_

Model (1) includes the income variable, which is an important explanatory factor for ICT adoption in general according to the literature. Individuals’ income level could affect their decisions to purchase an Internet-enabled phone, and afford mobile data packages to access internet, which jointly have an impact on the decision of mobile internet adoption. To further unpack the impact of income on affordability, the analysis tries an alternative specification with inclusion of a few affordability dummies while other covariates remain the same. To understand how the prices of mobile data packages affect the adoption of mobile internet, the analysis considers a country’s average cost per 1 gigabyte (GB) of mobile data, and constructs a dummy variable that equals 1 if the country’s average cost per 1 GB of mobile data is more than 2 percent of the individual’s monthly income. Moreover, since the affordability of an access device is often cited as an obstacle to internet usage, the analysis uses data about a country’s average cost of an internet-enabled phone from International Data Corporation (IDC) and constructs a dummy variable that equals 1 if the average phone cost is more than the individual’s monthly income. Due to collinearity concerns, the inclusion of these affordability measures precludes the incorporation of the income variable directly.

Results confirm the important role of affordability issues in driving mobile internet adoption. The high cost of mobile data packages and expensive mobile phones negatively affect the adoption of mobile internet. If the country’s average cost per 1 GB of mobile data is more than 2 percent of an individual’s monthly income, that individual is 5 percent less likely to adopt mobile internet. If the country’s average cost of an internetenabled phone is more than an individual’s monthly income, that individual is 4 percent less likely to adopt mobile internet. Meanwhile, the sign and level of significance of other variables in model (1) remain largely similar (see tables 5 and 3). For instance, under both the original and new model specifications, if the individual’s residence has access to electricity, he or she is 14 percent more likely to use mobile internet; and urban residents are 8 percent more likely to use mobile internet. With regard to education level, the impacts of completing primary and secondary education remain largely the same, while the impact of completing tertiary education is at a higher level in the new model specification. Individuals who have completed tertiary education are 61.2 percent more likely than those with no formal education to use mobile internet, while the impact level is 55.2 percent in model (1). Finally, the overall explanatory power of the regression is slightly lower when income is used (0.45) than when the two affordability dummies are used (0.47).

**<u>Table 5. Logit regression results with different treatment of the income variable</u>**

|Model|Log|it|
|---|---|---|
||Mobile inte<br>Coefficient|rnet usage<br>Marginal<br>effects|
|Infrastructure factors|||
|Household 3G network coverage|0.180*|0.037*|
||(0.108)|(0.022)|
|Household having access to<br>electricity|0.770***|0.142***|

15
||(0.121)|(0.018)|
|---|---|---|
|Social-economic factors|||
|Female|-0.235***|-0.050***|
||(0.049)|(0.010)|
|Age group (middle age, 30-60)|-0.709***|-0.150***|
||(0.054)|(0.011)|
|Age group (old people, >60)|-1.914***|-0.268***|
||(0.118)|(0.010)|
|Married|-0.130**|-0.028**|
||(0.055)|(0.012)|
|Urban|0.362***|0.076***|
||(0.052)|(0.011)|
|Years of formal education (less<br>than 6)|0.801***|0.185***|
||(0.164)|(0.039)|
|Years of formal education (6-12)|1.743***|0.365***|
||(0.154)|(0.029)|
|Years of formal education (12-16)|2.541***|0.561***|
||(0.161)|(0.028)|
|Years of formal education (>16)|3.016***|0.612***|
||(0.182)|(0.021)|
|High cost of internet-enabled<br>phone|-0.189***|-0.040***|
||(0.071)|(0.015)|
|High cost of mobile data package|-0.226***|-0.048***|
||(0.074)|(0.016)|
|Network effects|||
|1 close friend uses an online social<br>network|1.577***|0.374***|
||(0.097)|(0.021)|
|2 close friends use an online social<br>network|1.416***|0.337***|
||(0.086)|(0.020)|
|3 close friends use an online social<br>network|1.797***|0.421***|
||(0.086)|(0.018)|
|4 close friends use an online social<br>network|2.205***|0.497***|
||(0.110)|(0.019)|
|5 close friends use an online social<br>network|3.073***|0.631***|
||(0.072)|(0.011)|
|_cons|-3.119***||
||(0.250)||
|Number of observations|16,591|16,591|
|R2|||
|Adjusted R2|0.466|0.466|

16
_Note:_ *** p<0.01, ** p<0.05, * p<0.1. Marginal effects at mean are presented.

# **6. Conclusion**

As digital technologies penetrate various aspects of social and economic life, having access to affordable and reliable internet becomes essential for individuals to stay connected with their social networks, efficiently engage in online economic activities, and better receive public services. The United Nations Sustainable Development Goals set the target (9.c) to “significantly increase access to information and communications technology and strive to provide universal and affordable access to the Internet in least developed countries by 2020.” A better understanding of key drivers and main constraints for internet access is the first prerequisite for governments to design targeted policy solutions.

Results from the study show that besides infrastructure investment, which has been the main focus of many developing countries, other demand-side factors are of critical importance. 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. Therefore, policy measures targeted at reducing gender inequality and the urban/rural divide could support wider adoption of mobile internet. Enhancing education levels to increase people’s awareness of the benefits of being digitally connected could also positively affect mobile internet adoption. Among infrastructure factors, having access to electricity at the household level is significantly associated with mobile internet adoption, particularly owning an internet-enabled phone. 3G network coverage remains to be a significant factor, especially in Asia.

Moreover, social network effects are found to have a significant positive impact on the usage of mobile internet. Those who have more close friends using an online social network are more likely to adopt mobile internet. Individuals whose five closest friends are using an online social network (e.g., Facebook, Twitter) are 63.1 percent more likely to adopt it than those without any close friends using such online social network sites/apps. Collaborating with different firms across social media platforms to leverage these positive social network effects is one route to reduce the digital divide in mobile internet connections.

Lastly, income is a strong driver of mobile internet adoption in its own right. Moreover, the high cost of smartphones and mobile data packages negatively affects the adoption of mobile internet. It is estimated that if a country’s average cost for 1 GB of mobile data is more than 2 percent of an individual’s monthly income, that individual is 5 percent less likely to adopt mobile internet. If the country’s average cost of an internet-enabled phone is more than the individual’s monthly income, that individual is 4 percent less likely to adopt mobile internet. This implies the necessity to create a competitive market with multiple players offering data packages at affordable prices, and to take measures to reduce the retail cost of smartphones.

Across regions, although the factors affecting the adoption of mobile internet remain largely the same, the magnitude of their impacts vary. In Asia, gender differences are negatively associated with mobile internet in the region. In Africa, the impact of education level is more salient than the other two regions, implying an urgent need to improve digital literacy.

17