Digital exclusion among middle-aged and older adults in China: age-period-cohort evidence from three national surveys, 2011-2022

2026-02-08

AI Summary

This study addresses digital exclusion among middle-aged and older adults in China, systematically disentangling age, period, and birth cohort effects for the first time within this population. Leveraging nationally representative data from CHARLS, CFPS, and CGSS, the authors employ a hierarchical age–period–cohort (HAPC) model, incorporating cross-sectional weights and inverse probability weighting to uncover dynamic trends and heterogeneity across urban–rural divides, geographic regions, and health dimensions. Findings reveal that digital exclusion rises significantly with age; despite an overall attenuation of period effects over time, the urban–rural gap persists. Individuals with poorer cognitive function experience heightened exclusion, and those born in the 1950s face elevated risk—highlighting enduring structural inequalities rooted in historical context.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Knowledge Representation and Reasoning: Preferences
• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Web Mining and Content Analysis: Models for Web evolution
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Amid China's ageing and digital shift, digital exclusion among older adults poses an urgent challenge. To unpack this phenomenon, this study disentangles age, period, and cohort effects on digital exclusion among middle-aged and older Chinese adults. Using three nationally representative surveys (CHARLS 2011-2020, CFPS 2010-2022, and CGSS 2010-2021), we fitted hierarchical age-period-cohort (HAPC) models weighted by cross-sectional survey weights and stabilized inverse probability weights for item response. We further assessed heterogeneity by urban-rural residence, region, multimorbidity, and cognitive risk, and evaluated robustness with APC bounding analyses. Across datasets, digital exclusion increased with age and displayed mild non-linearity, with a small midlife easing followed by a sharper rise at older ages. Period effects declined over the 2010s and early 2020s, although the pace of improvement differed across survey windows. Cohort deviations were present but less consistent than age and period patterns, with an additional excess risk concentrated among cohorts born in the 1950s. Rural and western residents, as well as adults with multimorbidity or cognitive risk, remained consistently more excluded. Over the study period, the urban-rural divide showed no evidence of narrowing, whereas the cognitive-risk gap widened. These findings highlight digital inclusion as a vital pathway for older adults to remain integral participants in an evolving digital society.

Problem

• digital exclusion
• ageing
• digital divide
• older adults
• China

Innovation

• age-period-cohort analysis
• digital exclusion
• HAPC model
• inverse probability weighting
• heterogeneity analysis