Global Automation Atlas

1. 1Develop a task-based framework to classify automation exposure
2. 2Collect country-specific data on labor markets and automation technologies
3. 3Apply machine learning algorithms to disentangle labor-substituting from labor-augmenting automation
4. 4Analyze the results to identify relevant technology channels and material impacts
5. 5Visualize the findings using data visualization tools to facilitate understanding and communication

Data scientists and researchers on a team benefit from this approach as it allows for more accurate comparisons of automation exposure across countries, enabling better decision-making for businesses and policymakers

💡 Automation exposure can be accurately measured and compared across countries using a task-based approach, revealing differences in labor-substituting and labor-augmenting effects

🤖 Automation exposure varies globally. New task-based approach helps compare labor impacts across countries #automation #labourmarket