# **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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