From digital inequality to income inequality: exploring the multifaceted impact of digital literacy on income

Purpose This study aims to examine the impact of digital literacy as a major component of digital inequality on the total income of individuals (a major component of economic inequality) in Sri Lanka, emphasizing the variations observed due to several demographic and geographical factors. Design/methodology/approach A baseline regression analysis using ordinary least squares was conducted, followed by an instrumental variable approach to address potential endogeneity. Variables such as age, education, gender, sector and employment status were included alongside digital literacy to examine their combined effect on total income. A logit model assessed digital literacy’s impact across income levels, while heterogeneity analysis explored how digital literacy influenced total income in diverse groups with distinct attributes. Findings This study reveals that digital literacy significantly increases total income for individuals in Sri Lanka, as affirmed by various analyses used in the study. Results further indicate that demographic characteristics, such as age, gender, level of education, location, employment status and digital literacy, significantly affect individuals’ total income. Originality/value This study contributes to the literature in multiple ways by exploring the impact of digital literacy on income inequalities by concentrating on different strata of society without focusing on one segment of society, focusing on individuals in a developing country in South Asia to investigate the income inequalities based on their level of digital literacy and using Labour Force Survey data to conduct research on digital inclusion and digital inequality.

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Publication Details

Journal
Digital Policy Regulation and Governance
Published
2025-05-09
DOI
https://doi.org/10.1108/dprg-11-2024-0278
Citations
3
Primary Topic
Youth Education and Societal Dynamics
Type
article
Field-Weighted Citation Impact
12.60
Controls
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article

From digital inequality to income inequality: exploring the multifaceted impact of digital literacy on income

Maneka Jayasinghe, Jen‐Je Su, Saroja Selvanathan, Selva Selvanathan et al.
3 citations
Digital Policy Regulation and Governance
Youth Education and Societal Dynamics
12.60
article

From digital inequality to income inequality: exploring the multifaceted impact of digital literacy on income

Maneka Jayasinghe, Jen‐Je Su, Saroja Selvanathan, Selva Selvanathan, Prabath Perera
article en
3 citations

Abstract

Purpose This study aims to examine the impact of digital literacy as a major component of digital inequality on the total income of individuals (a major component of economic inequality) in Sri Lanka, emphasizing the variations observed due to several demographic and geographical factors. Design/methodology/approach A baseline regression analysis using ordinary least squares was conducted, followed by an instrumental variable approach to address potential endogeneity. Variables such as age, education, gender, sector and employment status were included alongside digital literacy to examine their combined effect on total income. A logit model assessed digital literacy’s impact across income levels, while heterogeneity analysis explored how digital literacy influenced total income in diverse groups with distinct attributes. Findings This study reveals that digital literacy significantly increases total income for individuals in Sri Lanka, as affirmed by various analyses used in the study. Results further indicate that demographic characteristics, such as age, gender, level of education, location, employment status and digital literacy, significantly affect individuals’ total income. Originality/value This study contributes to the literature in multiple ways by exploring the impact of digital literacy on income inequalities by concentrating on different strata of society without focusing on one segment of society, focusing on individuals in a developing country in South Asia to investigate the income inequalities based on their level of digital literacy and using Labour Force Survey data to conduct research on digital inclusion and digital inequality.

Digital Policy Regulation and GovernanceVol. 28(2)
Griffith University (AU), University of Kelaniya (LK), Charles Darwin University (AU)
Openalex Percentile: Top 2%
Youth Education and Societal Dynamics
12.60
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