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--- |
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license: mit |
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pretty_name: EconomicIndex |
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tags: |
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- text |
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viewer: true |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "onet_task_mappings.csv" |
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--- |
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## Overview |
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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. |
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**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.** |
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## Data |
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- `SOC_Structure.csv` - Standard Occupational Classification (SOC) system hierarchy from the U.S. Department of Labor O*NET database |
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- `automation_vs_augmentation.csv` - Data on automation vs augmentation patterns, with columns: |
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- interaction_type: Type of human-AI interaction (directive, feedback loop, task iteration, learning, validation) |
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- pct: Percentage of conversations showing this interaction pattern |
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Data obtained using Clio (Tamkin et al. 2024) |
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- `bls_employment_may_2023.csv` - Employment statistics from U.S. Bureau of Labor Statistics, May 2023 |
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- `onet_task_mappings.csv` - Mappings between tasks and O*NET categories, with columns: |
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- task_name: Task description |
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- pct: Percentage of conversations involving this task |
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Data obtained using Clio (Tamkin et al. 2024) |
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- `onet_task_statements.csv` - Task descriptions and metadata from the U.S. Department of Labor O*NET database |
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- `wage_data.csv` - Occupational wage data scraped from O*NET website using open source tools from https://github.com/adamkq/onet-dataviz |
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## Analysis |
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The `plots.ipynb` notebook provides visualizations and analysis including: |
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### Task Analysis |
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- Top tasks by percentage of conversations |
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- Task distribution across occupational categories |
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- Comparison with BLS employment data |
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### Occupational Analysis |
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- Top occupations by conversation percentage |
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- Occupational category distributions |
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- Occupational category distributions compared to BLS employment data |
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### Wage Analysis |
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- Occupational usage by wage |
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### Automation vs Augmentation Analysis |
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- Distribution across interaction modes |
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## Usage |
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To generate the analysis: |
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1. Ensure all data files are present in this directory |
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2. Open `plots.ipynb` in Jupyter |
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3. Run all cells to generate visualizations |
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4. Plots will be saved to the notebook and can be exported |
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The notebook uses pandas for data manipulation and seaborn/matplotlib for visualization. Example outputs are contained in the `plots\` folder. |
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**Data released under CC-BY, code released under MIT License** |
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## Contact |
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You can submit inquires to [email protected] or [email protected]. 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). |
