Determinants of mobile phone ownership in Nigeria

Telecommunications Policy 43 (2019) 101812

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Determinants of mobile phone ownership in Nigeria Ivan Forenbacher∗, Siniša Husnjak, Ivan Cvitić, Ivan Jovović

T

Department of Information and Communication Traffic, Faculty of Transport and Traffic Sciences, University of Zagreb, Vukelićeva 4, Zagreb, Croatia

ARTICLE INFO

ABSTRACT

Keywords: Digital divide Mobile phone Socio-economic factors Policy implications Nigeria Africa

Mobile phones are recognized as a primary platform for mitigating the digital divide and increasing economic growth, and the same appears to be true for Nigeria, the largest economy in Africa. Since 2012, mobile phone penetration has shown nearly linear growth, reaching 83% in 2016. However, this statistic falls to only 46% after correcting for ownership of multiple SIM cards and sharing of mobile phones among multiple users. The determinants of mobile phone ownership in Nigeria are poorly understood, which hinders research that could inform policies capable of increasing mobile phone penetration and eliminating the digital divide. To begin to fill this research gap, we have analyzed socio-economic factors related to mobile phone ownership in the country. We used a logit model and the latest national-level Datafirst ICT dataset (2012) about mobile phone adoption from 1552 individuals. The sample was stratified, clustered, and probability-weighted to make it representative of the situation at the national level. The results suggest that factors such as geographic location and income may not strongly influence mobile phone ownership, in contrast to what was previously thought. Instead, the strongest factors appeared to be education level, informal work, social engagement, type of electricity supply and employment status. Our analysis suggests that to increase mobile phone ownership and close the digital divide, policy makers should target younger adults, provide training in digital literacy specifically for mobile phone use, invest in electricity supply infrastructure, and develop content and applications in non-English languages. These findings may contribute to understanding mobile phone distribution in Nigeria as well as inform implementation of the country's ICT Roadmap 2017–2020 and Vision 2020.

1. Introduction Africa has the largest and fastest-growing number of mobile users, which probably reflects significant investment in the telecommunications sector (Patterson, 2016; Schoentgen & Gille, 2017). Nigeria is no exception as the most populous African country with approximately 180 million people: it has the largest economy and mobile market on the continent (Onyeajuwa, 2017). With fixed line penetration below 0.2%, many Nigerians “leap-frogged” past more expensive fixed-line technology to adopt mobile technologies, which are provided by four main operators: MTN, Globacom, Airtel, and 9mobile, formerly known as Etisalat (Gillwald, Odufuwa, & Mothobi, 2018; GSMA 2015; ITU, 2018; Onyeajuwa, 2017; Ragnedda & Muschert, 2015). As a result, data for 2017 indicate that 71% of the population use mobile phones as a primary platform for communication and accessing the Internet, with 89.79% of the population covered by 2G signal, 62.05% by 3G signal, and 11.04% by 4G signal (Gillwald et al., 2018). The popularity of mobile technologies reflects their ability to serve as a “bridge” across the digital divide and accelerate economic growth in developing countries (Aker & Mbiti, 2010; Avilés, Larghi, & Aguayo, 2016; Fife & Pereira, 2016; Kpodar & Andrianaivo, 2011; Lee,

∗

Corresponding author. E-mail address: [email protected] (I. Forenbacher).

https://doi.org/10.1016/j.telpol.2019.03.001 Received 17 October 2018; Received in revised form 21 February 2019; Accepted 3 March 2019 Available online 14 March 2019 0308-5961/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Levendis, & Gutierrez, 2012; Ragnedda & Muschert, 2015; Thompson & Garbacz, 2007; Waverman, Meschi, & Fuss, 2005, pp. 10–23). Mobile phone penetration, since mobile services were introduced in Nigeria in 2001, is still at initial stages in Nigeria and is inaccurately represented by global indicators from the Nigerian Communication Commission (NCC) and the International Telecommunication Union (ITU), which rely on supply-side data from operators (Gillwald et al., 2018). Analysis from the NCC (2018) suggests that mobile penetration grew from 80.85% in 2012 to 110.38% in 2016, while analysis from the ITU (2018) suggests growth from 67.41% to 83% over the same period. According to Gillwald et al. (2018) and GSMA (2014), these figures may be inaccurate because a significant portion of users with more than one active SIM card are counted as unique subscribers, and many have access to mobile phone through sharing. Those researchers suggest that mobile penetration per unique user, a better proxy for mobile phone ownership, is substantially lower, 46% in 2016 and 64% in 2017, leading them to classify the country as “underpenetrated”, contrary to other countries in the sub-Saharan region such as Kenya (87%), South Africa (84%), and Ghana (74%). Those researchers argue that proper analysis of determinants of mobile phone ownership and policy interventions could help large and underpenetrated markets like Nigeria increase mobile penetration in the future (Gillwald et al., 2018; GSMA, 2018; GSMA, 2017). Various policy initiatives in Nigeria have sought to reduce the digital divide in the past (Arikpo, Osofisan, & Usoro, 2009), but to our knowledge, only Growth Enhancement Support Group (GES) policy in 2012 aimed to reduce the digital divide in mobile ownership among rural users. The GES policy was based on the idea that poverty was the main reason for not owning a mobile phone, but this conclusion was never supported with detailed ex ante analysis of socio-economic factors affecting mobile phone ownership (Federal Government of Nigeria, 2017; Ojameruaye, 2013; Yeboah-Boateng, Osei-Owusu, & Henten, 2017). Rather than top-down decisions to impose mobile phone distribution, solutions are needed based on complete analysis of socio-economic determinants of mobile phone ownership (Yeboah-Boateng et al., 2017). Such analysis, to our knowledge, has not yet been conducted for the Nigerian market. A major obstacle to such analysis is the availability of high-quality datasets, especially from the demand-side. It is difficult to analyze the digital divide based on gender, urban/rural population and other socio-economic factors using supply-side data from operators (Gillwald et al., 2018; Fife & Pereira, 2016). It is usually possible to collect such data from more developed countries to support national-level analyses, but this is rarely possible for developing countries because sampling methods are usually inadequate (Fife & Pereira, 2016; Haughton & Khandker, 2009). This has hindered implementation of the two current national telecommunication policies in Nigeria, Vision 2020 and the ICT Roadmap 2017–2020 (Federal Government of Nigeria, 2017). These policies reflect the government's intention to mitigate the digital divide by increasing mobile penetration, while avoiding some recent policy mistakes and inefficiencies. As a result, the government has prioritized a national-level analysis of socio-economic factors affecting mobile phone ownership in the country (Federal Government of Nigeria, 2017). Therefore, we conducted the first detailed analysis of socio-economic factors affecting mobile phone ownership at the individual level in Nigeria. We estimated a logit model and considered factors from the literature as well as new factors that could be queried with available data but have traditionally been neglected in studies of the digital divide, such as type of electricity supply (Armey & Hosman, 2016). To solve the perennial problem of scarcity of high-quality data in developing country research (Fife & Pereira, 2016; Haughton & Khandker, 2009), we used the latest national-level DataFirst dataset from Statistics South Africa (2012) about ICT access at the individual level in 2012. 2. Literature review 2.1. Determinants of ICT adoption and the digital divide The digital divide, according to the Organization for Economic Cooperation and Development (OECD, 2018), refers to the “gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard to both their opportunities to access information and communication technologies (ICTs) and to their use of the Internet for a wide variety of activities”. To address the digital divide, the problem must be approached from multiple perspectives. Srinuan and Bohlin (2011) as well as Helbig, Gil-Garcia, and Ferro (2009) suggest identifying determinants of the digital divide on three levels: (1) level of access, (2) multi-dimensional level and (3) multi-perspective level. The level of access considers the divide as the dichotomous problem of a gap between those who “have” access to technology and information and those who “do not”. Since the digital divide lacks specific ethical and political meaning, its determinants on this level are from the supply side: access to infrastructure and access to investments in network infrastructure (Srinuan & Bohlin, 2011). Theories at the level of access stipulate that access to infrastructure can predict the probability that ICT technology is adopted and the probability of ICT coverage. Greater ICT infrastructure should lead to greater adoption of ICT technology, including mobile technology, and thereby to a narrowing of the digital divide. From the perspective of access, the digital divide should disappear when everyone has access (Srinuan & Bohlin, 2011). However, various studies (Choudrie, Weerakkody, & Jones, 2005; Mwin & Kritzinger, 2016; Rooksby, Weckert, & Lucas, 2002; Srinuan & Bohlin, 2011; Szeles, 2018) have demonstrated that the digital divide problem cannot be approached dichotomously because it is influenced not only by access to technical infrastructure (e.g. mobile phone coverage in a certain area) but by other dimensions as well. The multi-dimensional level of the digital divide, according to Srinuan and Bohlin (2011) as well as Helbig et al. (2009), involves categorical factors that support ICT, such as socio-economic factors (e.g. income or GDP per capita), geographic location, education level, digital skills and age. For example, a widespread theory holds that higher-income countries have narrower digital divides. Reducing the digital divide 2

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can lead to economic growth in developing countries such as in Africa. Waverman et al. (2005, pp. 10–23) concluded that increasing mobile penetration to 10 mobile phones per 100 people can increase GDP per capita by 0.8–1.2 percentage points in developing countries and by 0.6 percentage points in developed countries. Similarly, the results of Lee et al. (2012) suggest that mobile penetration is an important determinant of economic growth of countries in sub-Saharan Africa. Their results further suggest that the effects of increasing mobile penetration are even greater if the country has small fixed-line penetration, which is the case in Nigeria (Gillwald et al., 2018; ITU, 2018; Onyeajuwa, 2017). The digital divide can also be examined from the geographic dimension: whether a population is urban or rural, for example, is often a key predictor of the digital divide (Billon, Marco, & Lera-Lopez, 2009; Salemink, Strijker, & Bosworth, 2017; Schleife, 2010; Srinuan & Bohlin, 2011). Those studies show that urban environment is associated with a narrower digital divide because individuals have easier and cheaper access to ICT and supporting technology, and that the costs of adopting ICT infrastructure fall as the population grows and ICT penetration increases. Education can play an important role in bridging the digital divide, based on studies by Szeles (2018), Mwin and Kritzinger (2016), as well as Srinuan and Bohlin (2011). Generally, more educated individuals are more inclined to use and adopt ICT (Szeles, 2018). Other researchers, such as Moon, Park, Jung, and Choe (2010), argue that literacy is the fundamental factor to address when trying to narrow the digital divide: greater literacy translates to a narrower digital divide. On the other hand, at least one study (Knoche & Huang, 2012) suggests that illiteracy may not be such an obstacle to using mobile technology because the subjects in their study remembered what path to navigate through menus, they were able to identify unwanted SMS messages because the sender's number was too long or short, and they would ask someone else to read their SMS messages for them. Education-related determinants of the digital divide include digital skills and digital knowledge (Bagchi, 2005; Cullen, 2003; Mwin & Kritzinger, 2016; Tirado-Morueta, Aguaded-Gómez, & Hernando-Gómez, 2018; Wamuyu, 2017). Some researchers see the lack of these skills and knowledge as key contributors to the digital divide in Africa and elsewhere (Goncalves, Oliveira, & Cruz-Jesus, 2018; Mwin & Kritzinger, 2016). Nigeria, for example, features a relatively low-quality education system, low rate of literacy and low rate of digital skills, which can be captured to some extent by the proxy variable of education level (Nishijima, Ivanauskas, & Sarti, 2017). Age may be another factor that strongly affects the digital divide (Salajan, Schonwetter, & Cleghorn, 2010): several studies show that older people are less likely to adopt new technologies than teenagers (Akhter, 2003; Billon et al., 2009; Middleton & Chambers, 2010; Schleife, 2010; Srinuan & Bohlin, 2011) and are more likely to find excuses to avoid using new technologies (Abbey & Hyde, 2009; van Deursen & van Dijk, 2009). At the same time, at least one study suggests that once older people accept technology and possess the necessary skills, they may own more devices for using ICT (Mwin & Kritzinger, 2016). The multi-perspective level of the digital divide, according to Srinuan and Bohlin (2011) as well as Helbig et al. (2009), holds that no group of people inherently uses technology differently from other groups, yet different groups use technologies for particular goals usually related to their history and social location (Hines, Nelson, & Tu, 2001). This implies the need to consider the problem of the digital divide from diverse perspectives through analysis of such factors as gender, profession, culture, social engagement, language and content, as well as attitudes toward ICT (Srinuan & Bohlin, 2011). For example, several studies suggest that males are generally more inclined to use ICT than females (Akhter, 2003; Orviska & Hudson, 2009; Penard, Poussing, Zomo Yebe, & Nsi Ella, 2012). Profession can also play a role in the divide (Mwin & Kritzinger, 2016; Schleife, 2010): workers in scientific, technical and research fields, as well as various professionals, are more inclined to use ICT than other types of professions (Mwin & Kritzinger, 2016; Schleife, 2010). The digital divide can also be influenced by network effects arising through membership in religious, cultural, economic and other communities (Al-Jaghoub & Westrup, 2009; Mwin & Kritzinger, 2016; Srinuan & Bohlin, 2011; Zhao, Kim, Suh, & Du, 2007). Studies suggest that different communities can have different perceptions of ICT use, which can lead to differences in the adoption of new technologies and thereby affect ICT penetration. Mwin and Kritzinger (2016) as well as Wetzl (2010) argue that language can be a determinant of the digital divide, since it conditions an individual's readiness to use ICT, and the content of the language can repel or attract people to ICT (Vie, 2008). Kende and Quast (2016) emphasize that non-English content can further increase mobile phone adoption and narrow the digital divide. An individual's attitude toward evolving ICT technologies is especially important to the digital divide (Chen, Lin, & Lai, 2010; Srinuan & Bohlin, 2011). A positive attitude toward ICT can help narrow the divide (Mwin & Kritzinger, 2016; Srinuan & Bohlin, 2011). Waycott, Bennett, Kennedy, Dalgarno, and Gray (2010) suggest that confidence in the advances of ICT as well as a positive attitude toward ICT use can lead to rapid adoption, narrowing the divide. Indeed, an understanding of the advantages of using certain technologies can accelerate the adoption of certain devices and use of certain services (Mwin & Kritzinger, 2016; Srinuan & Bohlin, 2011). All three levels of the digital divide should be considered together in a holistic approach (Mwin & Kritzinger, 2016; Srinuan & Bohlin, 2011). Experts should focus not only on access to technology but also on myriad additional factors, particularly from the demand side, in order to understand the digital divide comprehensively and to formulate policies that narrow it (Gillwald et al., 2018; Mwin & Kritzinger, 2016; Srinuan & Bohlin, 2011). 2.2. Socio-economic factors that affect mobile phone ownership in developing countries Several studies have examined socio-economic factors that affect the likelihood that a given individual will own a mobile phone. These studies have applied quantitative and qualitative methods to datasets of lower or higher quality. For example, Zhang (2017) examined determinants of mobile phone use at the global level using data from more than 150 countries, and they found income to be 3

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among the most significant. Using a logit model and national sampling data from 2005 to 2013, Nishijima et al. (2017) identified age, gender and income as significant determinants among Brazilians. Gupta and Jain (2015) researched mobile phone adoption in rural areas of India using the Technology Acceptance Model and partial least-squares regression. Age, gender, and geographical region were found to be significant determinants. Fewer such studies have been performed in Africa. In their exhaustive review, Srinuan and Bohlin (2011) examined 195 studies of the digital divide. They found that 71% came from Asia/Pacific, Canada, USA and Europe, while 6.7% came from Africa. In addition, 73.5% of studies examined computer, Internet and broadband, while only 4.1% examined mobile technology. Among the few studies from Africa, Grzybowsky (2015) used national-level data from South Africa and a logit model to identify network effects, education level, literacy, type of employment and income as significant determinants. Sekabira and Qaim (2017) used panel data and regression to identify gender and household income as determinants of mobile phone use in rural Uganda. In contrast, Kabbiri, Dora, Kumar, Elepu, and Gellynck (2018) used structural equation modeling to identify calendar year as a significant determinant of mobile phone ownership among 300 farmers in Uganda. A study in Cameroon based on a logit model identified income as the most significant determinant of mobile phone ownership (Honoré, 2018). Penard et al. (2012) used a logit model to identify gender, year, income, literacy and education level as significant factors affecting mobile phone ownership in urban Gabon. Some studies in Africa have used macro-data to analyze the mobile digital divide, such as one analysis of Kenya and Somalia based on official statistics for 2000–2008 (Brännström, 2012) and another analysis of Ghana and South Africa (Fuchs & Horak, 2008). Still other studies have used qualitative methods such as interviews to understand the effects of gender on the mobile digital divide in Rwanda (Mumporeze & Prieler, 2017); this work suggests that education level and anxiety towards technology are key determinants. Much less is known about the digital divide in Nigeria, apart from a few studies in rural areas (Chiemeke & Daodu, 2006) or among users of e-government websites (Okunola, Rowley, & Johnson, 2017). Many of these studies rely on extremely limited datasets, such as from statistical or consulting reports that examine standard statistical indicators. We are unaware of published analyses of mobile phone ownership at the national level. Efforts to identify factors linked to mobile phone use among specific segments of the population, such as university students (Adegbija & Bola, 2015) or women using maternal health services (Jennings, Omoni, Akerele, Ibrahim, & Ekanem, 2015), cannot provide insights at the national level. A limitation of studies in Nigeria and elsewhere is the potential influence of type of electricity supply of an individual or household (e.g. main electricity grid, generator, or alternative sources such as solar) and the likelihood that they own a mobile phone (Armey & Hosman, 2016). Electricity plays a central role in any ICT4D initiative: if individuals lack access to a reliable electricity supply for powering ICT technology, including a network, then they are less likely to own a mobile phone. To fill these research gaps for Nigeria and provide analyses for that country and beyond, we assessed a range of socio-economic factors for their influence on mobile phone ownership. Factors included those well-studied in the conventional literature on the digital divide, as well as type of electricity supply, a factor whose potential influence has been neglected. 3. Data and methodology 3.1. Data source We relied on DataFirst data from Statistics South Africa (2012), which came from the Research ICT Africa Household and Small Business Access and Usage Survey 2011–2012 (Research ICT Africa, 2012). Data were collected through face-to-face interviews. The unit of analysis was individuals at least 15 years old. The survey sample was obtained through a random sampling procedure.1 The final sample was 1552 individuals, which was weighted to allow results to be generalized to the national population of people aged 15 years and older (90.6 million in 2011–2012 according to World Bank Data). Weighting factors were calculated at the individual level based on inverse choice probabilities. 3.2. Model specification The dependent variable is mobile phone ownership (mpo), which is equal to 1 if the individual owns a mobile phone, or 0 if he or she does not. We do not differentiate whether the mobile phone is purchased new, second-hand or acquired illegally, since this information was not collected in the dataset. The independent (explanatory) variables are split into two groups, ratio and categorical (Table 1). The only ratio variables are age and disposable_income, defined as the income available to an individual every month. Categorical variables cover type of living environment (rural or urban), type of electricity supply (none, main electricity grid, generator, other [e.g. solar]), gender (male or female), highest level of completed education (none, primary, secondary, tertiary with diploma, tertiary with BSc/BA degree), current employment status (student, unpaid house work [e.g. housewife], retired, unemployed, disabled and unable to work, employed, or self-employed), reading and writing ability in the mother tongue (no ability, with difficulty, easily), reading and writing ability in English, reading and writing ability in English for those younger than 25 years, informal work (participation in informal savings clubs or microfinance groups such as tontines2), as well as social capital (belonging to social networks such as sports or reading clubs). 1

For more details about sampling procedure steps, please see Research ICT Africa (2012). A tontine, or esusu in Nigeria, is an informal association of relatives, friends, and co-workers. Members pool a certain amount of money and then receive annual dividends depending on the capital invested. 2

4

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Table 1 Socio-economic factors and variables used to specify the empirical model. Socio-economic factor

Variable name

Description and possible values

Mobile phone ownership

mpo

Mobile phone owner (1 = yes, 0 = no)

Geographical location

rural

Rural area (1 = yes, 0 = no)

Type of electricity supply

main electricity grid generator other

Household is connected to main electricity grid (1 = yes, 0 = no) Household has a generator (1 = yes, 0 = no) Household uses other electricity sources, e.g. solar (1 = yes, 0 = no)

Gender Age

female age

Individual is a female (1 = yes, 0 = no) Age in years

Highest completed education level

primary secondary tertiary: diploma/certificate tertiary: BSc/BA

Individual Individual Individual Individual

has has has has

Employment status

unpaid house work retired unemployed disabled and unable to work employed self-employed

Individual Individual Individual Individual Individual Individual

is is is is is is

Mother-tongue reading skills

with difficulty not at all with difficulty not at all

Reading difficulties (1 = yes, 0 = no) Does not know how to read (1 = yes, 0 = no) Writing difficulties (1 = yes, 0 = no) Does not know how to write (1 = yes, 0 = no)

English reading and writing skills English reading/writing skills & age < 25

eng_yes youth_eng_yes

Knows to read and write in English (1 = yes, 0 = no) Knows to read and write in English and is younger than 25 years (1 = yes, 0 = no)

Informal work

Informal_acitivities_yes

Individual participates in a savings club, e.g. tontine (1 = yes, 0 = no)

Social capital

social_engagement_yes

Individual is a member of a sports or reading group (1 = yes, 0 = no)

Income

disposable income

Amount of disposable earnings per month

Mother-tongue writing skills

primary degree (1 = yes, 0 = no) secondary degree (1 = yes, 0 = no) diploma degree (1 = yes, 0 = no) BSc/BA degree (1 = yes, 0 = no)

at home, e.g. housewife (1 = yes, 0 = no) retired (1 = yes, 0 = no) unemployed (1 = yes, 0 = no) unable to work because of disability (1 = yes, 0 = no) employed (1 = yes, 0 = no) self-employed (1 = yes, 0 = no)

Well-studied socio-economic variables that may affect the digital divide are taken from the literature (e.g. Nishijima et al., 2017; Penard et al., 2012; Gillwald et al., 2010). We also include type of electricity supply as a neglected but potentially important factor (Armey & Hosman, 2016). Tables 2–3 show basic descriptive statistics for the variables used in the model. 3.3. Empirical model Data were analyzed using an econometric binary logit model, which was derived from a latent-variable model:

mpoi =

1 yi = 0

+ x+

>0 (1)

otherwise

where yi is an unobserved, latent variable that demonstrates the utility of possessing a mobile phone. If yi is greater than zero, observation i is associated with positive utility of possessing a mobile phone, and in this case, the individual is likely to possess a phone (mpoi = 1). As a result, mpo is a limited dependent variable. The error term is unobserved and follows the standard logistic distribution. The is a constant term and x refers to a vector of m explanatory variables presented in Tables 1 and 2 for a single observation i from a sample of total n observations, denoted as x i with corresponding coefficient k . This can be rewritten as x = 1 x ik + + k xik for k = 1,2, …, m . The goal is to determine the probability that

Prob (mpoi = 1|x ) = Prob (mpoi = 1|x1, x2 ,

(2)

xk )

To avoid the limitations of the linear probability model, we consider the binary response model (3)

Prob (mpoi = 1|x ) = L( + x ) = L (z )

where z is a linear function of our explanatory variables and L is a non-linear function that takes on values in the range 0 < L (z ) < 1 5

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Table 2 Descriptive statistics for categorical variables in the national-level logit model. Socio-economic factor

Variable

Frequency

Mobile phone ownership (mpo)

no yes

0.3363 0.6637

Geographical location

rural urban

0.498 0.502

Type of electricity

no main electricity grid generator other

0.2993 0.5747 0.1025 0.0235

Gender

male female

0.5315 0.4385

Educational attainment

none primary secondary tertiary: diploma/certificate tertiary: BSc/BA

0.2872 0.1867 0.3777 0.0972 0.0512

Employment status

student/pupil unpaid house work retired unemployed disabled employed self-employed

0.1548 0.2093 0.0116 0.0603 0.0016 0.1469 0.4156

Mother-tongue reading skills

easily with difficulties not at all

0.4619 0.2108 0.3273

Mother-tongue writing skills

easily with difficulties not at all

0.473 0.2014 0.3256

English reading/writing skills

eng_yes eng_no

0.5171 0.4829

English reading/writing skills & age < 25

youth_eng_yes youth_eng_no

0.1603 0.8397

Informal work

informal_activities_yes informal_activities_no

0.2148 0.7852

Social capital

social_engagement_yes social_engagement_no

0.1046 0.8953

Number of strata Number of primary sampling units

2 63

Number of observations Population size Design degrees of freedom

1552 90 595 137 61

Table 3 Descriptive statistic for ratio variables in the national-level logit model. Variable

Mean

Std. error

[95% Conf. Interval]

age

34.263

0.646

32.970

disposable income

6062.294

681.510

4699.53

2 63

Number of observations Population size Design degrees of freedom

Number of strata Number of primary sampling units

6

Min

Max

35.556

15

99

7425.058

0

200000

1552 90 595 137 61

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Table 4 Estimated coefficients of the logit model for the Nigerian case. k

Variable (xk)

Coef. (βk)

Lin. Std. Err.

t

P>t

[95% Conf. Interval]

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

constant rural main electricity grid generator other female age primary secondary tertiary: diploma/certificate tertiary: BSc/BA unpaid house work retired unemployed disabled employed self-employed reading_with difficulty reading_not at all writing_with difficulty writing_not at all eng_yes youth_eng_yes informal_activities_yes social_engagement_yes disposable_income

−1.194 −0.138 0.916 1.810 −0.231 −0.474 −0.022 0.725 1.586 1.601 1.744 0.776 2.329 0.878 0.858 1.082 1.025 1.082 1.016 −0.190 −1.738 0.696 −0.703 1.078 0.987 0.00008

0.696 0.370 0.331 0.428 0.586 0.270 0.009 0.489 0.542 0.468 0.964 0.337 0.887 0.475 1.025 0.330 0.309 0.554 0.932 0.428 0.880 0.433 0.450 0.266 0.432 0.00001

−1.71 −0.37 2.76 4.22 −0.40 −1.76 −2.21 1.48 2.92 3.42 1.81 2.30 2.62 1.85 0.84 3.28 3.32 1.95 1.09 −0.44 −1.97 1.60 −1.56 4.04 2.28 4.70

0.092*** 0.711 0.008* 0.000* 0.694 0.084*** 0.031** 0.144 0.005* 0.001* 0.075*** 0.025** 0.011** 0.070*** 0.406 0.002* 0.002* 0.055*** 0.280 0.659 0.053*** 0.114 0.124 0.000* 0.026** 0.000*

−2.587 −0.878 0.252 0.953 −1.403 −1.014 −0.041 −0.253 0.500 0.665 −0.184 0.101 0.544 −0.073 −1.191 0.421 0.407 −0.025 −0.848 −1.047 −3.498 −0.171 −1.605 0.544 0.121 0.00004

0.198 0.602 1.579 2.668 0.940 0.065 −0.002 1.705 2.671 2.537 3.674 1.452 4.105 1.830 2.908 1.742 1.644 2.190 2.880 0.667 0.022 1.564 0.198 1.611 1.853 0.00011

Model summary Number of strata

2

no. of observations

1552

Number of primary sampling units

63

Population size

90 595 137

Design degrees of freedom Adjusted Wald (model) (25, 37) Probability of > adjusted Wald (model)

61 10.11 < 0.001

Note: Variables with “*", "**", & "***" are significant at 1%, 5% and 10% significance levels.

for all real numbers z . In our case, the non-linear function L is from the family of logistic functions defined as follows:

Prob (mpoi = 1|x ) = L (z ) =

exp(z ) = 1 + exp(z )

(z )

(4)

which lies in the interval between 0 and 1 for all real numbers z . (z ) is a cumulative logistic function for a standard logistic random variable. Equation (4) can be interpreted as the probability that mpo = 1 (owning a mobile phone). From this we can now derive the inverse of the logistic function (z) logit or ln (odds) to obtain the linear expression:

logit [ (z )] = ln

1

(z ) = (z )

0

+ x

(5)

Rewriting Equation (5) leads to the final econometric logit model to be estimated:

ln (odds )i =

+

k x ik

+

+ k xik ,

k = 0,1, …, m i = 0,1, …, n

(6)

Equation (6) states that the dependent variable refers to the logit of mobile phone ownership for a particular observation i in the sample. The coefficient k measures ceteris paribus the effect of a one-unit change in x ik on the dependent variable. Predicted probabilities can be calculated using equation (4). 4. Results Using equations (1)–(6), we adopted the approach of Heeringa, West, and Berglund (2010) and Chambers and Skinner (2003), such that coefficients were determined using a maximum pseudo-likelihood estimator in order to take the complex sample design, which is stratified, clustered, and probability-weighted, into account for the Nigerian case (Table 4). The final model contained 22 variables and constants, and weighted analysis was performed for 1552 observations. Most variables in the model were associated with p values indicating statistical significance at the levels of 1%, 5% or 10%. 7

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Since the dependent variable mpo was defined at the level of the individual, we used individual-level weighting to allow extrapolation to the national level. Since the complex survey data in our study were stratified, clustered, and probability-weighted, the assumptions that underlie the likelihood ratio statistic are invalid (Heerigna et al., 2010; Chambers & Skinner, 2003), and the adjusted Wald test was performed instead in order to test the null hypothesis that all coefficients were zero (Heeringa et al., 2010). The adjusted Wald value for the overall model was 10.11 with (25, 37) degrees of freedom. The probability of observing a greater adjusted Wald value was < 0.001, suggesting that the model was statistically significant. To evaluate the validity of the model, we adopted the approach of Hosmer, Lemeshow, and Sturdivant (2013) and Heeringa at al. (2010) and conducted two post-estimation tests. First, we conducted a Hosmer–Lemeshow goodness-of-fit test with 10 groups, obtaining F (9, 53) = 1.71 with p > F = 0.1094. Since p > 0.05, the null hypothesis that there is no significant difference between observed and estimated probabilities cannot be rejected, nor can the model. Second, we cross-classified observed vs predicted outcomes of the dependent variable. The model predicted correctly 1261 out of 1552 outcomes (p cut-off value = 0.5), or 81.30% of the time. This suggests that overall model validity is very good. 5. Discussion 5.1. Impact of socio-economic factors on mobile phone ownership in Nigeria This analysis, apparently the first national-level comprehensive assessment of socio-economic factors that may affect mobile phone ownership in Nigeria, suggests that the most significant determinants of ownership among individuals at least 15 years old are informal work, social engagement, education, employment status and type of electricity. Type of electricity supply has traditionally been neglected in the literature on the digital divide. Our findings may help guide more effective government policies and interventions for closing this divide. Our results provide a much more detailed picture of socio-economic factors that influence mobile phone ownership than the government-sponsored analysis that led to the conclusion that poverty was the single most important determinant. This led to a government subsidy to farmers to facilitate their purchase of mobile phones, but the results have been less than impressive (GSMA, 2014). Our findings may contribute to more sophisticated measures that may be more effective in the long run. For example, we found that rural or urban location did not significantly contribute to probability of mobile phone ownership in our sample (p = 0.711), which contradicts previous assumptions of GES programs. Instead, our results are consistent with the claim of Ojameruaye (2013) that rural residents will not purchase mobile phones unless they perceive a direct benefit that compensates for mobile telephony costs. This may help explain why disposable_income only weakly influenced the likelihood of mobile phone ownership in our sample (coefficient, 0.00008). One possible explanation is network effects: the variable social_engagement_yes was statistically significant (p = 0.026) and positively related to mobile phone ownership (coefficient, 0.987), meaning that individuals who are engaged in social networks (e.g. sports and reading clubs) probably know others who have purchased mobile phones and are more likely to purchase them for themselves, independently of disposable income. A similar social engagement effect was observed in Gabon and South Africa (Grzybowski, 2015; Penard et al., 2012). In addition, because of these same network effects, individuals – especially those with lower incomes – who know others who have purchased mobile phones may be more aware of how they can purchase less expensive phones, such as ones with fewer features (Gillwald at al., 2010), or obtain phones illegally. Another possible explanation is the “early adopter” effect: people with lower income make great sacrifices to purchase mobile phones, primarily to remain in contact with family members in case of danger or accident (Gillwald at al., 2010). Since the variable informal_activities_yes showed a statistically significant (p < 0.001) positive association with the likelihood of mobile phone ownership (coefficient, 1.078), we cannot exclude the possibility that a substantial proportion of survey respondents had higher incomes than they reported, such as through informal work. Indeed, informal work is estimated to make up 60% of the gross domestic product in Nigeria (Benjamin & Mbaye, 2012; Lindell et al., 2013). We found that a key driver of mobile phone ownership in Nigeria is the type of electricity supply to the user, which is an important finding given that this variable has been neglected in most studies of the digital divide in developing countries. Access to electricity supplied by a generator (coefficient 1.810, p < 0.001) or through a main electricity grid (coefficient 0.916, p = 0.008) was associated with significantly higher likelihood of owning a mobile phone, which is consistent with a link between the digital divide and access to a stable electricity supply (Armey & Hosman, 2016). Access to “other” types of electricity supply (p = 0.694) did not contribute significantly to the probability of owning a mobile phone. This implies that closing the digital divide requires taking a holistic approach that includes analysis of electricity infrastructure. Our result that the female gender was associated with lower likelihood of owning a mobile phone (coefficient, −0.474) is consistent with similar studies in Gabon, Senegal, and Tanzania (Penard et al., 2012; Gillwald, Milek, & Stork, 2010; Chabossou, Stork, Stork, & Zahonogo, 2009). It contrasts, however, with the lack of a gender effect in similar studies in South Africa and Mozambique (Bimber, 2000; Chabossou et al., 2009; Gillwald et al., 2010; Penard et al., 2012; Schumacher & Morahan-Martin, 2001). One explanation is that early adopters of new technology tend to be men. Another possible explanation is that men prefer to purchase second-hand mobile phones, whereas women tend to prefer new phones in order to avoid acquiring a stolen device (Gillwald et al., 2010). A third explanation is that women tend to receive mobile phones as gifts rather than purchase them for themselves, since they often do not have purchasing power in the household (Gillwald et al., 2010). Other factors may contribute to the gender bias and should be investigated. For example, one study has shown that both genders are more likely to purchase mobile phones as technology penetrates more into society, such that gender differences shrink over time (Penard et al., 2012). In addition, Adegbija and Bola (2015) showed that gender differences do not exist among university students. This implies that the government 8

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should target the younger population to inform them about the benefits of mobile phone use. We found that age (coefficient −0.022) had a negative effect on mobile phone ownership, with older individuals less likely to own. However, employment status suggested a different story: retired individuals were most likely to own a mobile phone (coefficient 2.329, p = 0.011), followed by employed (coefficient 1.082, p = 0.002) and self-employed (coefficient 1.025, p = 0.002). This does not have to be a paradox, since the individuals who self-reported as “retired” were relatively young, with a median age of 65 yr, mean age of 68 and age range from 32 to 98 yr. Similarly young “retired” populations have been reported in other countries, including Gabon and Brazil (Nishijima et al., 2017; Penard et al., 2012). Such relatively young individuals are even more likely to derive benefits from mobile phone ownership. We found that individuals who were employed and unemployed were also significantly more likely than students to own mobile phones, suggesting that they use them for business and job-seeking. We found that higher education level was associated with greater likelihood of mobile phone ownership, as reported in studies in Brazil, Gabon and Poland (Nishijima et al., 2017; Penard et al., 2012; Dudek, 2007). Greater education may translate not only to greater understanding of the benefits of ownership but also to a shorter “learning curve” until those benefits can be enjoyed (Penard et al., 2012). Although English is an official language in Nigeria, reading and writing skills in that language do not appear to significantly influence mobile phone ownership in Nigeria. Other studies in Rwanda and Gabon (Chair & De Lannoy, 2018; Penard et al., 2012) have come to a similar conclusion. This suggests that proficiency in reading and writing other languages, foremost one's mother tongue, may be more relevant factors in predicting ownership. Indeed, we found that writing or reading one's mother tongue with difficulty was associated with significantly higher likelihood of mobile phone ownership than not being able to read or write in the mother tongue at all. Consistent with the importance of local language, our variable eng_youth, which controls for possible interaction between young age and English literacy, tended (p = 0.124) to be negatively associated with mobile phone ownership (coefficient, −0.703). This aligns with previous analyses (GSMA, 2016; Kende & Quast, 2016) suggesting that language barriers and lack of locally relevant mobile content can thwart efforts to increase mobile phone uptake, leading those authors to conclude that “content is most relevant when it is in the local language” in the case of developing African countries. Taken together, our analyses suggest that the strongest hindrances to mobile phone ownership are female gender, older age and illiteracy in one's mother tongue, while the strongest promoters of ownership are education level, employment status, informal work, social engagement, type of electricity, and partial literacy. It may be that learning to use mobile phones is more complex than learning to use other technologies such as the Internet. This suggests that closing the digital divide may depend on focused training in literacy skills specifically tailored to mobile phone use. Our analysis is based on a binary logit model, which has proven reliable in previous studies of factors affecting mobile and ICT use (Nishijima et al., 2017; Tran et al., 2015; Penard et al., 2012). The quality indicators of our model suggest a good fit to the data. The ratio of 60 observations per variable in our model exceeds the minimum ratio of 10–20 suggested to ensure empirical validity of logit models (Hosmer at al., 2013). 5.2. Policy implications Our results suggest several directions and measures that regulators and policy makers could consider to increase the number of mobile phone owners in Nigeria and thereby reduce the digital divide. One measure is to ensure that mobile phones are available to young girls and women, which may help reduce the observed gap between the rates of mobile phone ownership by women. Our observation that English literacy does not influence mobile phone ownership suggests that mobile content in users' mother tongue is key for them to perceive benefits to owning a mobile phone. Efforts to inform the public about the benefits of mobile phone ownership should target young people in particular. These efforts should include training in digital literacy, which has become key to success on the job market. Thus, national policies and programs are needed to integrate mobile technology into the educational system. These and other efforts targeting youth should provide men and women with similar possibilities and should present mobile technology as a support for youth development, as a way to break out of the poverty cycle and as a tool for social inclusion (ITU & UN-Habitat, 2012). For example, mobile phones can support and drive effective networking, idea exchange and social entrepreneurship, which is particularly important for an economy such as Nigeria's, where the traditional job market cannot accommodate all eligible jobseekers. The professional benefits of mobile phone ownership probably help explain why employment status significantly influenced likelihood of mobile phone ownership in our sample. Our results support a holistic approach to promoting mobile phone ownership through attention to the well-studied socio-economic factors examined in the present study, as well as to the neglected factor of electricity supply. This highlights the importance of contextualizing digital divide solutions to the particular country rather than applying “one size fits all” solutions (Chair & De Lannoy, 2018; Yeboah-Boateng et al., 2017). Future studies should examine in what other countries or contexts electricity supply is important for closing the digital divide. Future work on the digital divide may also look deeper than mobile phone ownership, to how individuals use their mobile phones (Dewan & Riggins, 2005; Kilenthong & Odton, 2014; UN, 2014; van Dijk, 2006) and to how these uses translate to success in education or job seeking (Wei, Teo, Chan, & Tan, 2010). Acknowledgments The authors would like to thank the DataFirst team at the University of Cape Town in South Africa for providing valuable data 9

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