| --- |
| license: mit |
| pretty_name: EconomicIndex |
| tags: |
| - text |
| viewer: true |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "onet_task_mappings.csv" |
| --- |
| ## Overview |
| This directory contains O*NET task mapping and automation vs. augmentation data from "Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations." The data and provided analysis are described below. |
| |
| **Please see our [blog post](https://www.anthropic.com/news/the-anthropic-economic-index) and [paper](https://assets.anthropic.com/m/2e23255f1e84ca97/original/Economic_Tasks_AI_Paper.pdf) for further visualizations and complete analysis.** |
| |
| ## Data |
| |
| - `SOC_Structure.csv` - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database |
| - `automation_vs_augmentation.csv` - Data on automation vs augmentation patterns, with columns: |
| - interaction_type: Type of human-AI interaction (directive, feedback loop, task iteration, learning, validation) |
| - pct: Percentage of conversations showing this interaction pattern |
| Data obtained using Clio (Tamkin et al. 2024) |
| - `bls_employment_may_2023.csv` - Employment statistics from U.S. Bureau of Labor Statistics, May 2023 |
| - `onet_task_mappings.csv` - Mappings between tasks and O*NET categories, with columns: |
| - task_name: Task description |
| - pct: Percentage of conversations involving this task |
| Data obtained using Clio (Tamkin et al. 2024) |
| - `onet_task_statements.csv` - Task descriptions and metadata from the U.S. Department of Labor O*NET database |
| - `wage_data.csv` - Occupational wage data scraped from O*NET website using open source tools from https://github.com/adamkq/onet-dataviz |
| |
| ## Analysis |
| |
| The `plots.ipynb` notebook provides visualizations and analysis including: |
| |
| ### Task Analysis |
| - Top tasks by percentage of conversations |
| - Task distribution across occupational categories |
| - Comparison with BLS employment data |
| |
| ### Occupational Analysis |
| - Top occupations by conversation percentage |
| - Occupational category distributions |
| - Occupational category distributions compared to BLS employment data |
| |
| ### Wage Analysis |
| - Occupational usage by wage |
| |
| ### Automation vs Augmentation Analysis |
| - Distribution across interaction modes |
| |
| ## Usage |
| To generate the analysis: |
| |
| 1. Ensure all data files are present in this directory |
| 2. Open `plots.ipynb` in Jupyter |
| 3. Run all cells to generate visualizations |
| 4. Plots will be saved to the notebook and can be exported |
| |
| The notebook uses pandas for data manipulation and seaborn/matplotlib for visualization. Example outputs are contained in the `plots\` folder. |
| |
| **Data released under CC-BY, code released under MIT License** |
| |
| ## Contact |
| You can submit inquires to kunal@anthropic.com or atamkin@anthropic.com. We invite researchers to provide input on potential future data releases using [this form](https://docs.google.com/forms/d/e/1FAIpQLSfDEdY-mT5lcXPaDSv-0Ci1rSXGlbIJierxkUbNB7_07-kddw/viewform?usp=dialog). |
