Global Automation Shock Index (GASI)

The Global Automation Shock Index (GASI) is a composite macro-structural indicator designed to measure the intensity, diffusion, and systemic labor-market impact of artificial intelligence and automation technologies across economies. It captures sectoral exposure to task displacement, occupational substitution, and productivity reconfiguration by integrating measures of technological adoption, industry-level automation intensity, and workforce composition. By situating these variables within a unified global framework, the GASI provides a consistent and comparable measure of automation pressure across countries and sectors. Methodologically, the GASI incorporates heterogeneous data on AI deployment, capital deepening in automation technologies, and labor market adjustments, alongside macroeconomic indicators such as wage dynamics, employment elasticity, and sectoral output shifts. The index is structured to reflect both direct substitution effects and indirect equilibrium adjustments, including wage compression, labor reallocation, and productivity spillovers. This allows the GASI to capture nonlinear adjustment dynamics, where incremental technological adoption can produce disproportionate changes in labor demand and income distribution depending on sectoral vulnerability and institutional labor market rigidity. The resulting index generates forward-looking signals of structural labor market disruption and macroeconomic transition risk, with applications in policy design, workforce planning, and financial stability assessment. It is relevant to institutions such as the International Monetary Fund, the World Bank, and the Bank for International Settlements, where understanding technology-driven structural change is critical for macroprudential surveillance and inclusive growth strategies. By translating heterogeneous automation dynamics into a single interpretable metric, the GASI enables early identification of systemic labor market pressures and supports proactive policy responses to mitigate distributional and macroeconomic risks.