China is ageing rapidly and is expected to become a super-aged society around 2035. As long-term care insurance expands nationwide, the coming decade will be critical for long-term care development and workforce planning. China is building a home- and community-based elderly care system with professional institutional support and medical-care integration, yet workforce shortages remain a constraint. Care needs and arrangements vary by disability level, and the two settings differ in recipient composition, service organization, and staffing mechanisms. Existing studies often treat care as homogeneous, obscuring the scale and structure of workforce demand.

This study addresses two questions: (1) What will be the scale of demand for China's long-term care workforce (LTC workforce) from 2020 to 2035? (2) How will this demand differ between institutional and home- and community-based elderly care settings? To address these questions, data are drawn from the China Longitudinal Aging Social Survey (CLASS), the Chinese Longitudinal Healthy Longevity Survey (CLHLS), the Seventh National Population Census, World Population Prospects 2024, and the China Civil Affairs Statistical Yearbook. Disability transition probabilities are estimated from 2014 and 2018 CLASS and CLHLS panel data and combined with census and population projection data to project disabled older adults by disability level. Two scenarios are constructed: a steady-state distribution scenario and a policy development scenario. Demand for the LTC workforce is estimated using staffing ratios for institutional elderly care and service time requirements for home- and community-based elderly care.

The results show that the number of disabled older adults will increase from 22.89 million in 2020 to 65.97 million in 2035, with older adults with mild disabilities remaining the largest group and those with severe disabilities experiencing the fastest growth. This expansion and changing composition increase LTC workforce demand, with distinct patterns across care settings. Under the medium specification, demand for the institutional LTC workforce reaches 0.68(steady-state distribution scenario)~1.53(policy development scenario)million in 2035; in the latter, care for older adults with severe disabilities accounts for more than 80% of institutional demand. Home- and community-based elderly care accounts for most LTC workforce demand, requiring 6.24(policy development scenario)~7.01(steady-state distribution scenario)million workers in 2035, with demand increasingly concentrated among older adults with mild and severe disabilities. Total demand for the LTC workforce reaches 7.69(steady-state distribution scenario)~7.78(policy development scenario)million in 2035. Although the policy development scenario raises institutional demand, the corresponding reduction in home- and community-based demand keeps the difference in total demand relatively small. Workforce supply growth lags markedly behind demand, with the supply-demand gap under the adjusted measure widening from 1.58(steady-state distribution scenario)~1.60(policy development scenario)million in 2020 to 3.12(steady-state distribution scenario)~3.16(policy development scenario)million in 2023.

These findings show that LTC workforce pressures reflect both expanding demand and structural differentiation across disability levels and care settings. Future workforce planning should therefore balance supply expansion with structural optimization: while broadening training and employment channels and improving occupational protection to enhance supply capacity, it should also allocate human resources rationally across disability levels and care settings, and improve service efficiency through digital and intelligent technologies. By distinguishing institutional elderly care from home- and community-based elderly care and their staffing mechanisms, this study extends workforce projection from aggregate estimation to structural analysis and provides targeted empirical support for long-term care system development and workforce planning in China.