China AI Workforce Exposure & Policy Transition Simulator

Macroeconomic Model 2025–2035

1. Baseline Scenario Presets

2. AI Exposure & Automation Velocity

GenAI White-Collar Velocity ? 65%
Exposure across graduate desk jobs & IT platforms
Coastal Industrial Robotics ? 55%
Displacement velocity for mid-career assembly labor
Service & Logistics Automation ? 40%
Impact on flexible gig, delivery, and migrant hospitality roles
Demographic Aging Drag ? 1.2x
Natural working-age cohort contraction multiplier
Youth Unemp. (2030 Peak)
24.2%
▲ +6.4% vs Baseline
Displaced Workers
58.4M
7.6% of Workforce
Fiscal Rebalance Drag
2.8%
of annual GDP
Reskilled Transition Rate
42.5%
Absorbed by New Tech

Demographic Labor Flow & Displacement Model (770M Total Labor Pool)

Status: Critical Stress in Graduate Cohort

3. Macroeconomic Impact Trajectories (2025–2035)

Projections updated in real-time

Cohort Vulnerability & Exposure Matrix

Cohort / Segment Est. Pool Automation Exposure Friction Risk State Buffer Cushion
Institutional Labor Context: China's 11.8 million annual college graduates encounter severe mismatch as GenAI compresses traditional junior white-collar ladders (coding, audit, clerical). Meanwhile, inland hukou barriers and SOE pension fiscal pressures limit natural geographic labor mobility.

4. State Policy Lever Matrix

SOE Hiring & Retention Mandate ? 30%
Absorbs graduate friction; increases SOE fiscal debt ratio
National Reskilling Subsidies ? 45%
Direct transition pipeline to high-end manufacturing
Algorithmic Automation Tax ? 15%
Funds social security pool; slightly cools enterprise AI velocity
Hukou-Decoupled UI Safety Net ? 40%
Prevents rural-migrant slide into long-term poverty

Executive Briefing Output