Firm-level effects of AI attention on wages and productivity: A global comparative study

This paper studies how wages and productivity among publicly listed firms vary with AI attention during a period of uneven AI adoption. AI attention measures the prominence of AI in the country-level information environment. It is distinct from AI adoption. The wage measure captures average staff expenditure on wages and salaries per employee. Using data for 69,783 listed firms in 83 countries over 2004–2023, I measure this attention with Google Trends and combine TWFE, IV–2SLS, and local projections to estimate static and dynamic relationships. Among listed firms in advanced economies (AEs), higher AI attention is associated with lower wages but higher labour productivity and TFP. Listed firms in emerging market and developing economies (EMDEs) show short-run wage gains and positive productivity responses. These patterns also vary across firms, with wage declines more pronounced among smaller, lower-productivity, and goods-producing listed firms. The sample represents the organised listed corporate sector, which constitutes a particularly selective segment of the firm population in EMDEs; the wider SME, informal, and unlisted sectors remain outside the scope of the analysis. The patterns are consistent with expectations, perceived substitution risk, organisational investment, and workforce composition playing different roles in listed-firm adjustment. • Google Trends measures AI attention for 69,783 listed firms across 83 countries. • Higher AI attention relates to lower wages and higher productivity in AEs. • EMDE listed firms show short-run wage gains and positive productivity responses. • Local projections show persistent and heterogeneous responses to AI attention. • Responses vary by firm size, productivity, sector, and country development level.