||(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***|

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