A technical paper published by the Research Institute of Economy, Trade and Industry (RIETI) on "Construction Methodology for Japan's Automation Risk Index (ARI) Database". This paper details the methodology for creating a new indicator to assess the automation risk of occupations due to AI and robotics.
Key Points
1. Research Overview
- Authors: Kyoji Fukao (RIETI Chairman), Kenta Ikeuchi (Senior Fellow), Yoshiaki Hase (Nomura Research Institute), Cristiano PERUGINI (University of Perugia), Fabrizio POMPEI (University of Perugia)
- Publication Date: June 2025
- Research Project: East Asian Industrial Productivity
- Document Number: 25-T-001
2. Characteristics of the Automation Risk Index (ARI)
- Novelty: Japan's first comprehensive automation risk assessment indicator specialized for Japanese occupations
- Assessment Target: Substitutability of occupations by AI and robotics
- Time-series Evaluation: Predictions for three points in time - 2024, 2030, and 2040
- Building on Previous Research: Based on research by Frey and Osborne (2017) and Paolillo et al. (2022)
3. Database Construction Methodology
- Base Data:
- Occupational information database from Japan's occupation information website "job tag"
- Includes 39 skills, 5 abilities, and 9 occupational attributes
- Expert Survey:
- Jointly conducted by RIETI and Nomura Research Institute in 2024
- Experts evaluated the technical substitutability of each skill, ability, and attribute
- Integration Method: Combining occupational data with expert evaluations to create an index
4. ARI Calculation Method
- Basic Principle:
- Identify the level of skills, abilities, and attributes required for each occupation
- Evaluate the technically feasible substitution levels predicted by experts
- Quantify automation risk based on the differences between the two
- Evaluation Precision: Enables detailed analysis at the occupational level
5. Three Evaluation Time Points
- 2024 (Present): Assessment of current automation risk
- 2030 (Medium-term): Medium-term predictions considering technological progress
- 2040 (Long-term): Long-term outlook assuming further technological innovation
6. Research Significance
- Contribution to Policy Making:
- Provides evidence necessary for employment policy formulation
- Basic data for predicting changes in industrial structure
- Labor Market Analysis:
- Visualizes automation impacts by occupation and industry
- New data foundation for labor market research
- Social Impact: Quantitative understanding of the impact of technological progress on employment
7. Potential Database Applications
- Research Applications:
- Use in labor economics research
- Analysis of the relationship between technological progress and employment
- Policy Applications:
- Design of vocational training and retraining programs
- Industrial policy formulation
- Corporate Applications:
- Human resource strategy development
- Reference material for investment decisions
8. International Positioning
- Japan's Unique Contribution: First comprehensive database specialized for Japan's occupational structure
- International Comparison Potential: Enables comparative analysis with similar research from other countries
- Methodological Development: New approach building on existing research
This research is an extremely important foundational resource for considering the future of Japan's labor market in the AI era. In particular, by evaluating automation risk by occupation over time, it provides valuable information for policymakers, researchers, and companies to prepare for future labor market changes.