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A random forest-based analysis of household survey data to infer insights on digital inequality

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Abstract

This paper examines digital inequalities in Nepal based on a publicly available dataset. We build different random forest classification models and apply sensitivity analysis, permutation variable test, and partial dependence analysis to characterize digital inequality on access, skill, and use at household and individual levels. Our analysis reveals important Nepal-specific findings about digital inequality. In addition, our random forest-based analysis illustrates how non-parametric methods can explicate complex nonlinear relationships that prevail between demographic variables. This paper also illustrates how sensitivity and partial dependence analysis can aid in interpreting the so-called ‘black box’ models like random forests. One of our notable findings is that caste has very little explanatory power in explaining the adoption of digital technologies. Gender, on the other hand, is still a strong predictor of an individual’s computer skills. Although the analysis in this paper is limited to Nepal, the methodology applies to similar datasets for other countries too.

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Notes

  1. There could be other measures of sensitivity as well. We restrict to AAD as our objective is only to triangulate the variable importance inferred from permutation and sensitivity-based analyses.

  2. This can be observed through the Management Information Report series provided on the website of Nepal Telecommunication Authority, the telecom regulator of Nepal.

  3. Literally, ‘Sudoorpaschim’ literally translates to ‘far-western’. The far-western region is the most underdeveloped in terms of ICT access, income, education, and other indicators as per the census data of 2011. This fact has been explored in [7]. Recent reports on human development by the Central Bureau of Statistics Nepal, available on its official website, show that the situation is still similar.

  4. Around 67 percent of the population had access to electricity in 2011, which increased to around 90 percent in 2019. This data is provided online by the World Bank.

  5. A good review of the socioeconomic impacts of the caste system can be found in [15].

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  1. Everest Engineering College, Lalitpur, Nepal

    Nischal Regmi

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  1. Nischal Regmi
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Regmi, N. A random forest-based analysis of household survey data to infer insights on digital inequality. Iran J Comput Sci 6, 333–344 (2023). https://doi.org/10.1007/s42044-023-00143-y

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