Stratifying the Digital Divide: Analysis of Socio-Economic Influences on Internet Performance

2026-05-28

AI Summary

This study investigates how socioeconomic factors influence internet performance across regions with varying population densities, thereby elucidating localized drivers of the digital divide. Integrating 170 million crowdsourced speed tests with U.S. Census Block Group–level demographic data, the authors employ hierarchical modeling based on random forest regression, permutation importance analysis, and sampling bias correction. Findings reveal that population density significantly affects network performance only at macro scales; once density is controlled for, household income and racial composition emerge as dominant predictors, with race exhibiting greater explanatory power for download speeds than either income or education. These results underscore the highly localized nature of internet inequality and highlight the urgent need for place-specific policy interventions rather than reliance on monolithic national narratives.

Abstract

Despite numerous technological advancements, the digital divide remains a pressing issue affecting millions worldwide. We present a framework for diagnosing internet inequality at the Census Block Group level by pairing approximately 170 million crowdsourced Ookla speed tests (2021--2025) with U.S. Census demographics across six metropolitan regions. After quantifying and correcting for sampling bias, we use Random Forest regression with permutation importance to identify the socio-economic drivers of download speed, upload speed, and latency. Population density dominates all three metrics at the regional level, but this dominance is an artifact of scale: once areas are stratified into density bins, its influence vanishes in medium- and higher-density neighborhoods, revealing that socio-economic conditions are the true differentiators of internet quality in most urban settings. After controlling for density, income and racial composition emerge as the primary drivers, income consistently dictating upload speed and racial composition proving to be a stronger predictor of download speed than either income or education. Our findings demonstrate that internet inequality is locally configured: no single national narrative explains it, and effective policy demands region-specific intervention.

Problem

• digital divide
• internet performance
• socio-economic factors
• internet inequality
• urban broadband access

Innovation

• digital divide
• internet performance
• socio-economic stratification
• Random Forest regression
• crowdsourced speed tests