Duplicate from Anthropic/EconomicIndex

Co-authored-by: kunalhanda <kunal-anthropic@users.noreply.huggingface.co>

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+.DS_Store

README.md

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+---
+language: en
+pretty_name: EconomicIndex
+tags:
+- AI
+- LLM
+- Economic Impacts
+- Anthropic
+viewer: true
+license: mit
+configs:
+- config_name: release_2025_09_15
+  data_files:
+  - split: raw_claude_ai
+    path: "release_2025_09_15/data/intermediate/aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv"
+  - split: raw_1p_api
+    path: "release_2025_09_15/data/intermediate/aei_raw_1p_api_2025-08-04_to_2025-08-11.csv"
+  - split: enriched_claude_ai
+    path: "release_2025_09_15/data/output/aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv"
+---
+
+# The Anthropic Economic Index
+
+## Overview
+
+The Anthropic Economic Index provides insights into how AI is being incorporated into real-world tasks across the modern economy.
+
+## Data Releases
+
+This repository contains multiple data releases, each with its own documentation:
+
+- **[2025-09-15 Release](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/release_2025_09_15)**: Updated analysis with geographic and first-party API data using Sonnet 4
+- **[2025-03-27 Release](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/release_2025_03_27)**: Updated analysis with Claude 3.7 Sonnet data and cluster-level insights
+- **[2025-02-10 Release](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/release_2025_02_10)**: Initial release with O*NET task mappings, automation vs. augmentation data, and more
+
+
+## Resources
+
+- [Index Home Page](https://www.anthropic.com/economic-index)
+- [3rd report](https://www.anthropic.com/research/anthropic-economic-index-september-2025-report)
+- [2nd report](https://www.anthropic.com/news/anthropic-economic-index-insights-from-claude-sonnet-3-7)
+- [1st report](https://www.anthropic.com/news/the-anthropic-economic-index)
+
+
+## License
+
+Data released under CC-BY, code released under MIT License
+
+## Contact
+
+For inquiries, contact econ-research@anthropic.com.
+
+## Citation
+
+### Third release
+
+```
+@online{appelmccrorytamkin2025geoapi,
+        author = {Ruth Appel and Peter McCrory and Alex Tamkin and Michael Stern and Miles McCain and Tyler Neylon},
+        title = {Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption},
+        date = {2025-09-15},
+        year = {2025},
+        url = {www.anthropic.com/research/anthropic-economic-index-september-2025-report},
+}
+```
+
+### Second release
+
+```
+@misc{handa2025economictasksperformedai,
+      title={Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations}, 
+      author={Kunal Handa and Alex Tamkin and Miles McCain and Saffron Huang and Esin Durmus and Sarah Heck and Jared Mueller and Jerry Hong and Stuart Ritchie and Tim Belonax and Kevin K. Troy and Dario Amodei and Jared Kaplan and Jack Clark and Deep Ganguli},
+      year={2025},
+      eprint={2503.04761},
+      archivePrefix={arXiv},
+      primaryClass={cs.CY},
+      url={https://arxiv.org/abs/2503.04761}, 
+}
+```

release_2025_02_10/README.md

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+---
+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).

release_2025_02_10/SOC_Structure.csv

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+Major Group,Minor Group,Broad Occupation,Detailed Occupation,Detailed O*NET-SOC,SOC or O*NET-SOC 2019 Title
+11-0000,,,,,Management Occupations
+,11-1000,,,,Top Executives
+,,11-1010,,,Chief Executives
+,,,11-1011,,Chief Executives
+,,,,11-1011.03,Chief Sustainability Officers
+,,11-1020,,,General and Operations Managers
+,,,11-1021,,General and Operations Managers
+,,11-1030,,,Legislators
+,,,11-1031,,Legislators
+,11-2000,,,,"Advertising, Marketing, Promotions, Public Relations, and Sales Managers"
+,,11-2010,,,Advertising and Promotions Managers
+,,,11-2011,,Advertising and Promotions Managers
+,,11-2020,,,Marketing and Sales Managers
+,,,11-2021,,Marketing Managers
+,,,11-2022,,Sales Managers
+,,11-2030,,,Public Relations and Fundraising Managers
+,,,11-2032,,Public Relations Managers
+,,,11-2033,,Fundraising Managers
+,11-3000,,,,Operations Specialties Managers
+,,11-3010,,,Administrative Services and Facilities Managers
+,,,11-3012,,Administrative Services Managers
+,,,11-3013,,Facilities Managers
+,,,,11-3013.01,Security Managers
+,,11-3020,,,Computer and Information Systems Managers
+,,,11-3021,,Computer and Information Systems Managers
+,,11-3030,,,Financial Managers
+,,,11-3031,,Financial Managers
+,,,,11-3031.01,Treasurers and Controllers
+,,,,11-3031.03,Investment Fund Managers
+,,11-3050,,,Industrial Production Managers
+,,,11-3051,,Industrial Production Managers
+,,,,11-3051.01,Quality Control Systems Managers
+,,,,11-3051.02,Geothermal Production Managers
+,,,,11-3051.03,Biofuels Production Managers
+,,,,11-3051.04,Biomass Power Plant Managers
+,,,,11-3051.06,Hydroelectric Production Managers
+,,11-3060,,,Purchasing Managers
+,,,11-3061,,Purchasing Managers
+,,11-3070,,,"Transportation, Storage, and Distribution Managers"
+,,,11-3071,,"Transportation, Storage, and Distribution Managers"
+,,,,11-3071.04,Supply Chain Managers
+,,11-3110,,,Compensation and Benefits Managers
+,,,11-3111,,Compensation and Benefits Managers
+,,11-3120,,,Human Resources Managers
+,,,11-3121,,Human Resources Managers
+,,11-3130,,,Training and Development Managers
+,,,11-3131,,Training and Development Managers
+,11-9000,,,,Other Management Occupations
+,,11-9010,,,"Farmers, Ranchers, and Other Agricultural Managers"
+,,,11-9013,,"Farmers, Ranchers, and Other Agricultural Managers"
+,,11-9020,,,Construction Managers
+,,,11-9021,,Construction Managers
+,,11-9030,,,Education and Childcare Administrators
+,,,11-9031,,"Education and Childcare Administrators, Preschool and Daycare"
+,,,11-9032,,"Education Administrators, Kindergarten through Secondary"
+,,,11-9033,,"Education Administrators, Postsecondary"
+,,,11-9039,,"Education Administrators, All Other"
+,,11-9040,,,Architectural and Engineering Managers
+,,,11-9041,,Architectural and Engineering Managers
+,,,,11-9041.01,Biofuels/Biodiesel Technology and Product Development Managers
+,,11-9050,,,Food Service Managers
+,,,11-9051,,Food Service Managers
+,,11-9070,,,Entertainment and Recreation Managers
+,,,11-9071,,Gambling Managers
+,,,11-9072,,"Entertainment and Recreation Managers, Except Gambling"
+,,11-9080,,,Lodging Managers
+,,,11-9081,,Lodging Managers
+,,11-9110,,,Medical and Health Services Managers
+,,,11-9111,,Medical and Health Services Managers
+,,11-9120,,,Natural Sciences Managers
+,,,11-9121,,Natural Sciences Managers
+,,,,11-9121.01,Clinical Research Coordinators
+,,,,11-9121.02,Water Resource Specialists
+,,11-9130,,,Postmasters and Mail Superintendents
+,,,11-9131,,Postmasters and Mail Superintendents
+,,11-9140,,,"Property, Real Estate, and Community Association Managers"
+,,,11-9141,,"Property, Real Estate, and Community Association Managers"
+,,11-9150,,,Social and Community Service Managers
+,,,11-9151,,Social and Community Service Managers
+,,11-9160,,,Emergency Management Directors
+,,,11-9161,,Emergency Management Directors
+,,11-9170,,,Personal Service Managers
+,,,11-9171,,Funeral Home Managers
+,,,11-9179,,"Personal Service Managers, All Other"
+,,,,11-9179.01,Fitness and Wellness Coordinators
+,,,,11-9179.02,Spa Managers
+,,11-9190,,,Miscellaneous Managers
+,,,11-9199,,"Managers, All Other"
+,,,,11-9199.01,Regulatory Affairs Managers
+,,,,11-9199.02,Compliance Managers
+,,,,11-9199.08,Loss Prevention Managers
+,,,,11-9199.09,Wind Energy Operations Managers
+,,,,11-9199.10,Wind Energy Development Managers
+,,,,11-9199.11,Brownfield Redevelopment Specialists and Site Managers
+13-0000,,,,,Business and Financial Operations Occupations
+,13-1000,,,,Business Operations Specialists
+,,13-1010,,,"Agents and Business Managers of Artists, Performers, and Athletes"
+,,,13-1011,,"Agents and Business Managers of Artists, Performers, and Athletes"
+,,13-1020,,,Buyers and Purchasing Agents
+,,,13-1021,,"Buyers and Purchasing Agents, Farm Products"
+,,,13-1022,,"Wholesale and Retail Buyers, Except Farm Products"
+,,,13-1023,,"Purchasing Agents, Except Wholesale, Retail, and Farm Products"
+,,13-1030,,,"Claims Adjusters, Appraisers, Examiners, and Investigators"
+,,,13-1031,,"Claims Adjusters, Examiners, and Investigators"
+,,,13-1032,,"Insurance Appraisers, Auto Damage"
+,,13-1040,,,Compliance Officers
+,,,13-1041,,Compliance Officers
+,,,,13-1041.01,Environmental Compliance Inspectors
+,,,,13-1041.03,Equal Opportunity Representatives and Officers
+,,,,13-1041.04,Government Property Inspectors and Investigators
+,,,,13-1041.06,Coroners
+,,,,13-1041.07,Regulatory Affairs Specialists
+,,,,13-1041.08,Customs Brokers
+,,13-1050,,,Cost Estimators
+,,,13-1051,,Cost Estimators
+,,13-1070,,,Human Resources Workers
+,,,13-1071,,Human Resources Specialists
+,,,13-1074,,Farm Labor Contractors
+,,,13-1075,,Labor Relations Specialists
+,,13-1080,,,Logisticians and Project Management Specialists
+,,,13-1081,,Logisticians
+,,,,13-1081.01,Logistics Engineers
+,,,,13-1081.02,Logistics Analysts
+,,,13-1082,,Project Management Specialists
+,,13-1110,,,Management Analysts
+,,,13-1111,,Management Analysts
+,,13-1120,,,"Meeting, Convention, and Event Planners"
+,,,13-1121,,"Meeting, Convention, and Event Planners"
+,,13-1130,,,Fundraisers
+,,,13-1131,,Fundraisers
+,,13-1140,,,"Compensation, Benefits, and Job Analysis Specialists"
+,,,13-1141,,"Compensation, Benefits, and Job Analysis Specialists"
+,,13-1150,,,Training and Development Specialists
+,,,13-1151,,Training and Development Specialists
+,,13-1160,,,Market Research Analysts and Marketing Specialists
+,,,13-1161,,Market Research Analysts and Marketing Specialists
+,,,,13-1161.01,Search Marketing Strategists
+,,13-1190,,,Miscellaneous Business Operations Specialists
+,,,13-1199,,"Business Operations Specialists, All Other"
+,,,,13-1199.04,Business Continuity Planners
+,,,,13-1199.05,Sustainability Specialists
+,,,,13-1199.06,Online Merchants
+,,,,13-1199.07,Security Management Specialists
+,13-2000,,,,Financial Specialists
+,,13-2010,,,Accountants and Auditors
+,,,13-2011,,Accountants and Auditors
+,,13-2020,,,Property Appraisers and Assessors
+,,,13-2022,,Appraisers of Personal and Business Property
+,,,13-2023,,Appraisers and Assessors of Real Estate
+,,13-2030,,,Budget Analysts
+,,,13-2031,,Budget Analysts
+,,13-2040,,,Credit Analysts
+,,,13-2041,,Credit Analysts
+,,13-2050,,,Financial Analysts and Advisors
+,,,13-2051,,Financial and Investment Analysts
+,,,13-2052,,Personal Financial Advisors
+,,,13-2053,,Insurance Underwriters
+,,,13-2054,,Financial Risk Specialists
+,,13-2060,,,Financial Examiners
+,,,13-2061,,Financial Examiners
+,,13-2070,,,Credit Counselors and Loan Officers
+,,,13-2071,,Credit Counselors
+,,,13-2072,,Loan Officers
+,,13-2080,,,"Tax Examiners, Collectors and Preparers, and Revenue Agents"
+,,,13-2081,,"Tax Examiners and Collectors, and Revenue Agents"
+,,,13-2082,,Tax Preparers
+,,13-2090,,,Miscellaneous Financial Specialists
+,,,13-2099,,"Financial Specialists, All Other"
+,,,,13-2099.01,Financial Quantitative Analysts
+,,,,13-2099.04,"Fraud Examiners, Investigators and Analysts"
+15-0000,,,,,Computer and Mathematical Occupations
+,15-1200,,,,Computer Occupations
+,,15-1210,,,Computer and Information Analysts
+,,,15-1211,,Computer Systems Analysts
+,,,,15-1211.01,Health Informatics Specialists
+,,,15-1212,,Information Security Analysts
+,,15-1220,,,Computer and Information Research Scientists
+,,,15-1221,,Computer and Information Research Scientists
+,,15-1230,,,Computer Support Specialists
+,,,15-1231,,Computer Network Support Specialists
+,,,15-1232,,Computer User Support Specialists
+,,15-1240,,,Database and Network Administrators and Architects
+,,,15-1241,,Computer Network Architects
+,,,,15-1241.01,Telecommunications Engineering Specialists
+,,,15-1242,,Database Administrators
+,,,15-1243,,Database Architects
+,,,,15-1243.01,Data Warehousing Specialists
+,,,15-1244,,Network and Computer Systems Administrators
+,,15-1250,,,"Software and Web Developers, Programmers, and Testers"
+,,,15-1251,,Computer Programmers
+,,,15-1252,,Software Developers
+,,,15-1253,,Software Quality Assurance Analysts and Testers
+,,,15-1254,,Web Developers
+,,,15-1255,,Web and Digital Interface Designers
+,,,,15-1255.01,Video Game Designers
+,,15-1290,,,Miscellaneous Computer Occupations
+,,,15-1299,,"Computer Occupations, All Other"
+,,,,15-1299.01,Web Administrators
+,,,,15-1299.02,Geographic Information Systems Technologists and Technicians
+,,,,15-1299.03,Document Management Specialists
+,,,,15-1299.04,Penetration Testers
+,,,,15-1299.05,Information Security Engineers
+,,,,15-1299.06,Digital Forensics Analysts
+,,,,15-1299.07,Blockchain Engineers
+,,,,15-1299.08,Computer Systems Engineers/Architects
+,,,,15-1299.09,Information Technology Project Managers
+,15-2000,,,,Mathematical Science Occupations
+,,15-2010,,,Actuaries
+,,,15-2011,,Actuaries
+,,15-2020,,,Mathematicians
+,,,15-2021,,Mathematicians
+,,15-2030,,,Operations Research Analysts
+,,,15-2031,,Operations Research Analysts
+,,15-2040,,,Statisticians
+,,,15-2041,,Statisticians
+,,,,15-2041.01,Biostatisticians
+,,15-2050,,,Data Scientists
+,,,15-2051,,Data Scientists
+,,,,15-2051.01,Business Intelligence Analysts
+,,,,15-2051.02,Clinical Data Managers
+,,15-2090,,,Miscellaneous Mathematical Science Occupations
+,,,15-2099,,"Mathematical Science Occupations, All Other"
+,,,,15-2099.01,Bioinformatics Technicians
+17-0000,,,,,Architecture and Engineering Occupations
+,17-1000,,,,"Architects, Surveyors, and Cartographers"
+,,17-1010,,,"Architects, Except Naval"
+,,,17-1011,,"Architects, Except Landscape and Naval"
+,,,17-1012,,Landscape Architects
+,,17-1020,,,"Surveyors, Cartographers, and Photogrammetrists"
+,,,17-1021,,Cartographers and Photogrammetrists
+,,,17-1022,,Surveyors
+,,,,17-1022.01,Geodetic Surveyors
+,17-2000,,,,Engineers
+,,17-2010,,,Aerospace Engineers
+,,,17-2011,,Aerospace Engineers
+,,17-2020,,,Agricultural Engineers
+,,,17-2021,,Agricultural Engineers
+,,17-2030,,,Bioengineers and Biomedical Engineers
+,,,17-2031,,Bioengineers and Biomedical Engineers
+,,17-2040,,,Chemical Engineers
+,,,17-2041,,Chemical Engineers
+,,17-2050,,,Civil Engineers
+,,,17-2051,,Civil Engineers
+,,,,17-2051.01,Transportation Engineers
+,,,,17-2051.02,Water/Wastewater Engineers
+,,17-2060,,,Computer Hardware Engineers
+,,,17-2061,,Computer Hardware Engineers
+,,17-2070,,,Electrical and Electronics Engineers
+,,,17-2071,,Electrical Engineers
+,,,17-2072,,"Electronics Engineers, Except Computer"
+,,,,17-2072.01,Radio Frequency Identification Device Specialists
+,,17-2080,,,Environmental Engineers
+,,,17-2081,,Environmental Engineers
+,,17-2110,,,"Industrial Engineers, Including Health and Safety"
+,,,17-2111,,"Health and Safety Engineers, Except Mining Safety Engineers and Inspectors"
+,,,,17-2111.02,Fire-Prevention and Protection Engineers
+,,,17-2112,,Industrial Engineers
+,,,,17-2112.01,Human Factors Engineers and Ergonomists
+,,,,17-2112.02,Validation Engineers
+,,,,17-2112.03,Manufacturing Engineers
+,,17-2120,,,Marine Engineers and Naval Architects
+,,,17-2121,,Marine Engineers and Naval Architects
+,,17-2130,,,Materials Engineers
+,,,17-2131,,Materials Engineers
+,,17-2140,,,Mechanical Engineers
+,,,17-2141,,Mechanical Engineers
+,,,,17-2141.01,Fuel Cell Engineers
+,,,,17-2141.02,Automotive Engineers
+,,17-2150,,,"Mining and Geological Engineers, Including Mining Safety Engineers"
+,,,17-2151,,"Mining and Geological Engineers, Including Mining Safety Engineers"
+,,17-2160,,,Nuclear Engineers
+,,,17-2161,,Nuclear Engineers
+,,17-2170,,,Petroleum Engineers
+,,,17-2171,,Petroleum Engineers
+,,17-2190,,,Miscellaneous Engineers
+,,,17-2199,,"Engineers, All Other"
+,,,,17-2199.03,"Energy Engineers, Except Wind and Solar"
+,,,,17-2199.05,Mechatronics Engineers
+,,,,17-2199.06,Microsystems Engineers
+,,,,17-2199.07,Photonics Engineers
+,,,,17-2199.08,Robotics Engineers
+,,,,17-2199.09,Nanosystems Engineers
+,,,,17-2199.10,Wind Energy Engineers
+,,,,17-2199.11,Solar Energy Systems Engineers
+,17-3000,,,,"Drafters, Engineering Technicians, and Mapping Technicians"
+,,17-3010,,,Drafters
+,,,17-3011,,Architectural and Civil Drafters
+,,,17-3012,,Electrical and Electronics Drafters
+,,,17-3013,,Mechanical Drafters
+,,,17-3019,,"Drafters, All Other"
+,,17-3020,,,"Engineering Technologists and Technicians, Except Drafters"
+,,,17-3021,,Aerospace Engineering and Operations Technologists and Technicians
+,,,17-3022,,Civil Engineering Technologists and Technicians
+,,,17-3023,,Electrical and Electronic Engineering Technologists and Technicians
+,,,17-3024,,Electro-Mechanical and Mechatronics Technologists and Technicians
+,,,,17-3024.01,Robotics Technicians
+,,,17-3025,,Environmental Engineering Technologists and Technicians
+,,,17-3026,,Industrial Engineering Technologists and Technicians
+,,,,17-3026.01,Nanotechnology Engineering Technologists and Technicians
+,,,17-3027,,Mechanical Engineering Technologists and Technicians
+,,,,17-3027.01,Automotive Engineering Technicians
+,,,17-3028,,Calibration Technologists and Technicians
+,,,17-3029,,"Engineering Technologists and Technicians, Except Drafters, All Other"
+,,,,17-3029.01,Non-Destructive Testing Specialists
+,,,,17-3029.08,Photonics Technicians
+,,17-3030,,,Surveying and Mapping Technicians
+,,,17-3031,,Surveying and Mapping Technicians
+19-0000,,,,,"Life, Physical, and Social Science Occupations"
+,19-1000,,,,Life Scientists
+,,19-1010,,,Agricultural and Food Scientists
+,,,19-1011,,Animal Scientists
+,,,19-1012,,Food Scientists and Technologists
+,,,19-1013,,Soil and Plant Scientists
+,,19-1020,,,Biological Scientists
+,,,19-1021,,Biochemists and Biophysicists
+,,,19-1022,,Microbiologists
+,,,19-1023,,Zoologists and Wildlife Biologists
+,,,19-1029,,"Biological Scientists, All Other"
+,,,,19-1029.01,Bioinformatics Scientists
+,,,,19-1029.02,Molecular and Cellular Biologists
+,,,,19-1029.03,Geneticists
+,,,,19-1029.04,Biologists
+,,19-1030,,,Conservation Scientists and Foresters
+,,,19-1031,,Conservation Scientists
+,,,,19-1031.02,Range Managers
+,,,,19-1031.03,Park Naturalists
+,,,19-1032,,Foresters
+,,19-1040,,,Medical Scientists
+,,,19-1041,,Epidemiologists
+,,,19-1042,,"Medical Scientists, Except Epidemiologists"
+,,19-1090,,,Miscellaneous Life Scientists
+,,,19-1099,,"Life Scientists, All Other"
+,19-2000,,,,Physical Scientists
+,,19-2010,,,Astronomers and Physicists
+,,,19-2011,,Astronomers
+,,,19-2012,,Physicists
+,,19-2020,,,Atmospheric and Space Scientists
+,,,19-2021,,Atmospheric and Space Scientists
+,,19-2030,,,Chemists and Materials Scientists
+,,,19-2031,,Chemists
+,,,19-2032,,Materials Scientists
+,,19-2040,,,Environmental Scientists and Geoscientists
+,,,19-2041,,"Environmental Scientists and Specialists, Including Health"
+,,,,19-2041.01,Climate Change Policy Analysts
+,,,,19-2041.02,Environmental Restoration Planners
+,,,,19-2041.03,Industrial Ecologists
+,,,19-2042,,"Geoscientists, Except Hydrologists and Geographers"
+,,,19-2043,,Hydrologists
+,,19-2090,,,Miscellaneous Physical Scientists
+,,,19-2099,,"Physical Scientists, All Other"
+,,,,19-2099.01,Remote Sensing Scientists and Technologists
+,19-3000,,,,Social Scientists and Related Workers
+,,19-3010,,,Economists
+,,,19-3011,,Economists
+,,,,19-3011.01,Environmental Economists
+,,19-3020,,,Survey Researchers
+,,,19-3022,,Survey Researchers
+,,19-3030,,,Psychologists
+,,,19-3032,,Industrial-Organizational Psychologists
+,,,19-3033,,Clinical and Counseling Psychologists
+,,,19-3034,,School Psychologists
+,,,19-3039,,"Psychologists, All Other"
+,,,,19-3039.02,Neuropsychologists
+,,,,19-3039.03,Clinical Neuropsychologists
+,,19-3040,,,Sociologists
+,,,19-3041,,Sociologists
+,,19-3050,,,Urban and Regional Planners
+,,,19-3051,,Urban and Regional Planners
+,,19-3090,,,Miscellaneous Social Scientists and Related Workers
+,,,19-3091,,Anthropologists and Archeologists
+,,,19-3092,,Geographers
+,,,19-3093,,Historians
+,,,19-3094,,Political Scientists
+,,,19-3099,,"Social Scientists and Related Workers, All Other"
+,,,,19-3099.01,Transportation Planners
+,19-4000,,,,"Life, Physical, and Social Science Technicians"
+,,19-4010,,,Agricultural and Food Science Technicians
+,,,19-4012,,Agricultural Technicians
+,,,,19-4012.01,Precision Agriculture Technicians
+,,,19-4013,,Food Science Technicians
+,,19-4020,,,Biological Technicians
+,,,19-4021,,Biological Technicians
+,,19-4030,,,Chemical Technicians
+,,,19-4031,,Chemical Technicians
+,,19-4040,,,Environmental Science and Geoscience Technicians
+,,,19-4042,,"Environmental Science and Protection Technicians, Including Health"
+,,,19-4043,,"Geological Technicians, Except Hydrologic Technicians"
+,,,19-4044,,Hydrologic Technicians
+,,19-4050,,,Nuclear Technicians
+,,,19-4051,,Nuclear Technicians
+,,,,19-4051.02,Nuclear Monitoring Technicians
+,,19-4060,,,Social Science Research Assistants
+,,,19-4061,,Social Science Research Assistants
+,,19-4070,,,Forest and Conservation Technicians
+,,,19-4071,,Forest and Conservation Technicians
+,,19-4090,,,"Miscellaneous Life, Physical, and Social Science Technicians"
+,,,19-4092,,Forensic Science Technicians
+,,,19-4099,,"Life, Physical, and Social Science Technicians, All Other"
+,,,,19-4099.01,Quality Control Analysts
+,,,,19-4099.03,Remote Sensing Technicians
+,19-5000,,,,Occupational Health and Safety Specialists and Technicians
+,,19-5010,,,Occupational Health and Safety Specialists and Technicians
+,,,19-5011,,Occupational Health and Safety Specialists
+,,,19-5012,,Occupational Health and Safety Technicians
+21-0000,,,,,Community and Social Service Occupations
+,21-1000,,,,"Counselors, Social Workers, and Other Community and Social Service Specialists"
+,,21-1010,,,Counselors
+,,,21-1011,,Substance Abuse and Behavioral Disorder Counselors
+,,,21-1012,,"Educational, Guidance, and Career Counselors and Advisors"
+,,,21-1013,,Marriage and Family Therapists
+,,,21-1014,,Mental Health Counselors
+,,,21-1015,,Rehabilitation Counselors
+,,,21-1019,,"Counselors, All Other"
+,,21-1020,,,Social Workers
+,,,21-1021,,"Child, Family, and School Social Workers"
+,,,21-1022,,Healthcare Social Workers
+,,,21-1023,,Mental Health and Substance Abuse Social Workers
+,,,21-1029,,"Social Workers, All Other"
+,,21-1090,,,Miscellaneous Community and Social Service Specialists
+,,,21-1091,,Health Education Specialists
+,,,21-1092,,Probation Officers and Correctional Treatment Specialists
+,,,21-1093,,Social and Human Service Assistants
+,,,21-1094,,Community Health Workers
+,,,21-1099,,"Community and Social Service Specialists, All Other"
+,21-2000,,,,Religious Workers
+,,21-2010,,,Clergy
+,,,21-2011,,Clergy
+,,21-2020,,,"Directors, Religious Activities and Education"
+,,,21-2021,,"Directors, Religious Activities and Education"
+,,21-2090,,,Miscellaneous Religious Workers
+,,,21-2099,,"Religious Workers, All Other"
+23-0000,,,,,Legal Occupations
+,23-1000,,,,"Lawyers, Judges, and Related Workers"
+,,23-1010,,,Lawyers and Judicial Law Clerks
+,,,23-1011,,Lawyers
+,,,23-1012,,Judicial Law Clerks
+,,23-1020,,,"Judges, Magistrates, and Other Judicial Workers"
+,,,23-1021,,"Administrative Law Judges, Adjudicators, and Hearing Officers"
+,,,23-1022,,"Arbitrators, Mediators, and Conciliators"
+,,,23-1023,,"Judges, Magistrate Judges, and Magistrates"
+,23-2000,,,,Legal Support Workers
+,,23-2010,,,Paralegals and Legal Assistants
+,,,23-2011,,Paralegals and Legal Assistants
+,,23-2090,,,Miscellaneous Legal Support Workers
+,,,23-2093,,"Title Examiners, Abstractors, and Searchers"
+,,,23-2099,,"Legal Support Workers, All Other"
+25-0000,,,,,Educational Instruction and Library Occupations
+,25-1000,,,,Postsecondary Teachers
+,,25-1010,,,"Business Teachers, Postsecondary"
+,,,25-1011,,"Business Teachers, Postsecondary"
+,,25-1020,,,"Math and Computer Science Teachers, Postsecondary"
+,,,25-1021,,"Computer Science Teachers, Postsecondary"
+,,,25-1022,,"Mathematical Science Teachers, Postsecondary"
+,,25-1030,,,"Engineering and Architecture Teachers, Postsecondary"
+,,,25-1031,,"Architecture Teachers, Postsecondary"
+,,,25-1032,,"Engineering Teachers, Postsecondary"
+,,25-1040,,,"Life Sciences Teachers, Postsecondary"
+,,,25-1041,,"Agricultural Sciences Teachers, Postsecondary"
+,,,25-1042,,"Biological Science Teachers, Postsecondary"
+,,,25-1043,,"Forestry and Conservation Science Teachers, Postsecondary"
+,,25-1050,,,"Physical Sciences Teachers, Postsecondary"
+,,,25-1051,,"Atmospheric, Earth, Marine, and Space Sciences Teachers, Postsecondary"
+,,,25-1052,,"Chemistry Teachers, Postsecondary"
+,,,25-1053,,"Environmental Science Teachers, Postsecondary"
+,,,25-1054,,"Physics Teachers, Postsecondary"
+,,25-1060,,,"Social Sciences Teachers, Postsecondary"
+,,,25-1061,,"Anthropology and Archeology Teachers, Postsecondary"
+,,,25-1062,,"Area, Ethnic, and Cultural Studies Teachers, Postsecondary"
+,,,25-1063,,"Economics Teachers, Postsecondary"
+,,,25-1064,,"Geography Teachers, Postsecondary"
+,,,25-1065,,"Political Science Teachers, Postsecondary"
+,,,25-1066,,"Psychology Teachers, Postsecondary"
+,,,25-1067,,"Sociology Teachers, Postsecondary"
+,,,25-1069,,"Social Sciences Teachers, Postsecondary, All Other"
+,,25-1070,,,"Health Teachers, Postsecondary"
+,,,25-1071,,"Health Specialties Teachers, Postsecondary"
+,,,25-1072,,"Nursing Instructors and Teachers, Postsecondary"
+,,25-1080,,,"Education and Library Science Teachers, Postsecondary"
+,,,25-1081,,"Education Teachers, Postsecondary"
+,,,25-1082,,"Library Science Teachers, Postsecondary"
+,,25-1110,,,"Law, Criminal Justice, and Social Work Teachers, Postsecondary"
+,,,25-1111,,"Criminal Justice and Law Enforcement Teachers, Postsecondary"
+,,,25-1112,,"Law Teachers, Postsecondary"
+,,,25-1113,,"Social Work Teachers, Postsecondary"
+,,25-1120,,,"Arts, Communications, History, and Humanities Teachers, Postsecondary"
+,,,25-1121,,"Art, Drama, and Music Teachers, Postsecondary"
+,,,25-1122,,"Communications Teachers, Postsecondary"
+,,,25-1123,,"English Language and Literature Teachers, Postsecondary"
+,,,25-1124,,"Foreign Language and Literature Teachers, Postsecondary"
+,,,25-1125,,"History Teachers, Postsecondary"
+,,,25-1126,,"Philosophy and Religion Teachers, Postsecondary"
+,,25-1190,,,Miscellaneous Postsecondary Teachers
+,,,25-1192,,"Family and Consumer Sciences Teachers, Postsecondary"
+,,,25-1193,,"Recreation and Fitness Studies Teachers, Postsecondary"
+,,,25-1194,,"Career/Technical Education Teachers, Postsecondary"
+,,,25-1199,,"Postsecondary Teachers, All Other"
+,25-2000,,,,"Preschool, Elementary, Middle, Secondary, and Special Education Teachers"
+,,25-2010,,,Preschool and Kindergarten Teachers
+,,,25-2011,,"Preschool Teachers, Except Special Education"
+,,,25-2012,,"Kindergarten Teachers, Except Special Education"
+,,25-2020,,,Elementary and Middle School Teachers
+,,,25-2021,,"Elementary School Teachers, Except Special Education"
+,,,25-2022,,"Middle School Teachers, Except Special and Career/Technical Education"
+,,,25-2023,,"Career/Technical Education Teachers, Middle School"
+,,25-2030,,,Secondary School Teachers
+,,,25-2031,,"Secondary School Teachers, Except Special and Career/Technical Education"
+,,,25-2032,,"Career/Technical Education Teachers, Secondary School"
+,,25-2050,,,Special Education Teachers
+,,,25-2051,,"Special Education Teachers, Preschool"
+,,,25-2055,,"Special Education Teachers, Kindergarten"
+,,,25-2056,,"Special Education Teachers, Elementary School"
+,,,25-2057,,"Special Education Teachers, Middle School"
+,,,25-2058,,"Special Education Teachers, Secondary School"
+,,,25-2059,,"Special Education Teachers, All Other"
+,,,,25-2059.01,Adapted Physical Education Specialists
+,25-3000,,,,Other Teachers and Instructors
+,,25-3010,,,"Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors"
+,,,25-3011,,"Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors"
+,,25-3020,,,Self-Enrichment Teachers
+,,,25-3021,,Self-Enrichment Teachers
+,,25-3030,,,"Substitute Teachers, Short-Term"
+,,,25-3031,,"Substitute Teachers, Short-Term"
+,,25-3040,,,Tutors
+,,,25-3041,,Tutors
+,,25-3090,,,Miscellaneous Teachers and Instructors
+,,,25-3099,,"Teachers and Instructors, All Other"
+,25-4000,,,,"Librarians, Curators, and Archivists"
+,,25-4010,,,"Archivists, Curators, and Museum Technicians"
+,,,25-4011,,Archivists
+,,,25-4012,,Curators
+,,,25-4013,,Museum Technicians and Conservators
+,,25-4020,,,Librarians and Media Collections Specialists
+,,,25-4022,,Librarians and Media Collections Specialists
+,,25-4030,,,Library Technicians
+,,,25-4031,,Library Technicians
+,25-9000,,,,Other Educational Instruction and Library Occupations
+,,25-9020,,,Farm and Home Management Educators
+,,,25-9021,,Farm and Home Management Educators
+,,25-9030,,,Instructional Coordinators
+,,,25-9031,,Instructional Coordinators
+,,25-9040,,,Teaching Assistants
+,,,25-9042,,"Teaching Assistants, Preschool, Elementary, Middle, and Secondary School, Except Special Education"
+,,,25-9043,,"Teaching Assistants, Special Education"
+,,,25-9044,,"Teaching Assistants, Postsecondary"
+,,,25-9049,,"Teaching Assistants, All Other"
+,,25-9090,,,Miscellaneous Educational Instruction and Library Workers
+,,,25-9099,,"Educational Instruction and Library Workers, All Other"
+27-0000,,,,,"Arts, Design, Entertainment, Sports, and Media Occupations"
+,27-1000,,,,Art and Design Workers
+,,27-1010,,,Artists and Related Workers
+,,,27-1011,,Art Directors
+,,,27-1012,,Craft Artists
+,,,27-1013,,"Fine Artists, Including Painters, Sculptors, and Illustrators"
+,,,27-1014,,Special Effects Artists and Animators
+,,,27-1019,,"Artists and Related Workers, All Other"
+,,27-1020,,,Designers
+,,,27-1021,,Commercial and Industrial Designers
+,,,27-1022,,Fashion Designers
+,,,27-1023,,Floral Designers
+,,,27-1024,,Graphic Designers
+,,,27-1025,,Interior Designers
+,,,27-1026,,Merchandise Displayers and Window Trimmers
+,,,27-1027,,Set and Exhibit Designers
+,,,27-1029,,"Designers, All Other"
+,27-2000,,,,"Entertainers and Performers, Sports and Related Workers"
+,,27-2010,,,"Actors, Producers, and Directors"
+,,,27-2011,,Actors
+,,,27-2012,,Producers and Directors
+,,,,27-2012.03,Media Programming Directors
+,,,,27-2012.04,Talent Directors
+,,,,27-2012.05,Media Technical Directors/Managers
+,,27-2020,,,"Athletes, Coaches, Umpires, and Related Workers"
+,,,27-2021,,Athletes and Sports Competitors
+,,,27-2022,,Coaches and Scouts
+,,,27-2023,,"Umpires, Referees, and Other Sports Officials"
+,,27-2030,,,Dancers and Choreographers
+,,,27-2031,,Dancers
+,,,27-2032,,Choreographers
+,,27-2040,,,"Musicians, Singers, and Related Workers"
+,,,27-2041,,Music Directors and Composers
+,,,27-2042,,Musicians and Singers
+,,27-2090,,,"Miscellaneous Entertainers and Performers, Sports and Related Workers"
+,,,27-2091,,"Disc Jockeys, Except Radio"
+,,,27-2099,,"Entertainers and Performers, Sports and Related Workers, All Other"
+,27-3000,,,,Media and Communication Workers
+,,27-3010,,,Broadcast Announcers and Radio Disc Jockeys
+,,,27-3011,,Broadcast Announcers and Radio Disc Jockeys
+,,27-3020,,,"News Analysts, Reporters and Journalists"
+,,,27-3023,,"News Analysts, Reporters, and Journalists"
+,,27-3030,,,Public Relations Specialists
+,,,27-3031,,Public Relations Specialists
+,,27-3040,,,Writers and Editors
+,,,27-3041,,Editors
+,,,27-3042,,Technical Writers
+,,,27-3043,,Writers and Authors
+,,,,27-3043.05,"Poets, Lyricists and Creative Writers"
+,,27-3090,,,Miscellaneous Media and Communication Workers
+,,,27-3091,,Interpreters and Translators
+,,,27-3092,,Court Reporters and Simultaneous Captioners
+,,,27-3099,,"Media and Communication Workers, All Other"
+,27-4000,,,,Media and Communication Equipment Workers
+,,27-4010,,,"Broadcast, Sound, and Lighting Technicians"
+,,,27-4011,,Audio and Video Technicians
+,,,27-4012,,Broadcast Technicians
+,,,27-4014,,Sound Engineering Technicians
+,,,27-4015,,Lighting Technicians
+,,27-4020,,,Photographers
+,,,27-4021,,Photographers
+,,27-4030,,,"Television, Video, and Film Camera Operators and Editors"
+,,,27-4031,,"Camera Operators, Television, Video, and Film"
+,,,27-4032,,Film and Video Editors
+,,27-4090,,,Miscellaneous Media and Communication Equipment Workers
+,,,27-4099,,"Media and Communication Equipment Workers, All Other"
+29-0000,,,,,Healthcare Practitioners and Technical Occupations
+,29-1000,,,,Healthcare Diagnosing or Treating Practitioners
+,,29-1010,,,Chiropractors
+,,,29-1011,,Chiropractors
+,,29-1020,,,Dentists
+,,,29-1021,,"Dentists, General"
+,,,29-1022,,Oral and Maxillofacial Surgeons
+,,,29-1023,,Orthodontists
+,,,29-1024,,Prosthodontists
+,,,29-1029,,"Dentists, All Other Specialists"
+,,29-1030,,,Dietitians and Nutritionists
+,,,29-1031,,Dietitians and Nutritionists
+,,29-1040,,,Optometrists
+,,,29-1041,,Optometrists
+,,29-1050,,,Pharmacists
+,,,29-1051,,Pharmacists
+,,29-1070,,,Physician Assistants
+,,,29-1071,,Physician Assistants
+,,,,29-1071.01,Anesthesiologist Assistants
+,,29-1080,,,Podiatrists
+,,,29-1081,,Podiatrists
+,,29-1120,,,Therapists
+,,,29-1122,,Occupational Therapists
+,,,,29-1122.01,"Low Vision Therapists, Orientation and Mobility Specialists, and Vision Rehabilitation Therapists"
+,,,29-1123,,Physical Therapists
+,,,29-1124,,Radiation Therapists
+,,,29-1125,,Recreational Therapists
+,,,29-1126,,Respiratory Therapists
+,,,29-1127,,Speech-Language Pathologists
+,,,29-1128,,Exercise Physiologists
+,,,29-1129,,"Therapists, All Other"
+,,,,29-1129.01,Art Therapists
+,,,,29-1129.02,Music Therapists
+,,29-1130,,,Veterinarians
+,,,29-1131,,Veterinarians
+,,29-1140,,,Registered Nurses
+,,,29-1141,,Registered Nurses
+,,,,29-1141.01,Acute Care Nurses
+,,,,29-1141.02,Advanced Practice Psychiatric Nurses
+,,,,29-1141.03,Critical Care Nurses
+,,,,29-1141.04,Clinical Nurse Specialists
+,,29-1150,,,Nurse Anesthetists
+,,,29-1151,,Nurse Anesthetists
+,,29-1160,,,Nurse Midwives
+,,,29-1161,,Nurse Midwives
+,,29-1170,,,Nurse Practitioners
+,,,29-1171,,Nurse Practitioners
+,,29-1180,,,Audiologists
+,,,29-1181,,Audiologists
+,,29-1210,,,Physicians
+,,,29-1211,,Anesthesiologists
+,,,29-1212,,Cardiologists
+,,,29-1213,,Dermatologists
+,,,29-1214,,Emergency Medicine Physicians
+,,,29-1215,,Family Medicine Physicians
+,,,29-1216,,General Internal Medicine Physicians
+,,,29-1217,,Neurologists
+,,,29-1218,,Obstetricians and Gynecologists
+,,,29-1221,,"Pediatricians, General"
+,,,29-1222,,"Physicians, Pathologists"
+,,,29-1223,,Psychiatrists
+,,,29-1224,,Radiologists
+,,,29-1229,,"Physicians, All Other"
+,,,,29-1229.01,Allergists and Immunologists
+,,,,29-1229.02,Hospitalists
+,,,,29-1229.03,Urologists
+,,,,29-1229.04,Physical Medicine and Rehabilitation Physicians
+,,,,29-1229.05,Preventive Medicine Physicians
+,,,,29-1229.06,Sports Medicine Physicians
+,,29-1240,,,Surgeons
+,,,29-1241,,"Ophthalmologists, Except Pediatric"
+,,,29-1242,,"Orthopedic Surgeons, Except Pediatric"
+,,,29-1243,,Pediatric Surgeons
+,,,29-1249,,"Surgeons, All Other"
+,,29-1290,,,Miscellaneous Healthcare Diagnosing or Treating Practitioners
+,,,29-1291,,Acupuncturists
+,,,29-1292,,Dental Hygienists
+,,,29-1299,,"Healthcare Diagnosing or Treating Practitioners, All Other"
+,,,,29-1299.01,Naturopathic Physicians
+,,,,29-1299.02,Orthoptists
+,29-2000,,,,Health Technologists and Technicians
+,,29-2010,,,Clinical Laboratory Technologists and Technicians
+,,,29-2011,,Medical and Clinical Laboratory Technologists
+,,,,29-2011.01,Cytogenetic Technologists
+,,,,29-2011.02,Cytotechnologists
+,,,,29-2011.04,Histotechnologists
+,,,29-2012,,Medical and Clinical Laboratory Technicians
+,,,,29-2012.01,Histology Technicians
+,,29-2030,,,Diagnostic Related Technologists and Technicians
+,,,29-2031,,Cardiovascular Technologists and Technicians
+,,,29-2032,,Diagnostic Medical Sonographers
+,,,29-2033,,Nuclear Medicine Technologists
+,,,29-2034,,Radiologic Technologists and Technicians
+,,,29-2035,,Magnetic Resonance Imaging Technologists
+,,,29-2036,,Medical Dosimetrists
+,,29-2040,,,Emergency Medical Technicians and Paramedics
+,,,29-2042,,Emergency Medical Technicians
+,,,29-2043,,Paramedics
+,,29-2050,,,Health Practitioner Support Technologists and Technicians
+,,,29-2051,,Dietetic Technicians
+,,,29-2052,,Pharmacy Technicians
+,,,29-2053,,Psychiatric Technicians
+,,,29-2055,,Surgical Technologists
+,,,29-2056,,Veterinary Technologists and Technicians
+,,,29-2057,,Ophthalmic Medical Technicians
+,,29-2060,,,Licensed Practical and Licensed Vocational Nurses
+,,,29-2061,,Licensed Practical and Licensed Vocational Nurses
+,,29-2070,,,Medical Records Specialists
+,,,29-2072,,Medical Records Specialists
+,,29-2080,,,"Opticians, Dispensing"
+,,,29-2081,,"Opticians, Dispensing"
+,,29-2090,,,Miscellaneous Health Technologists and Technicians
+,,,29-2091,,Orthotists and Prosthetists
+,,,29-2092,,Hearing Aid Specialists
+,,,29-2099,,"Health Technologists and Technicians, All Other"
+,,,,29-2099.01,Neurodiagnostic Technologists
+,,,,29-2099.05,Ophthalmic Medical Technologists
+,,,,29-2099.08,Patient Representatives
+,29-9000,,,,Other Healthcare Practitioners and Technical Occupations
+,,29-9020,,,Health Information Technologists and Medical Registrars
+,,,29-9021,,Health Information Technologists and Medical Registrars
+,,29-9090,,,Miscellaneous Health Practitioners and Technical Workers
+,,,29-9091,,Athletic Trainers
+,,,29-9092,,Genetic Counselors
+,,,29-9093,,Surgical Assistants
+,,,29-9099,,"Healthcare Practitioners and Technical Workers, All Other"
+,,,,29-9099.01,Midwives
+31-0000,,,,,Healthcare Support Occupations
+,31-1100,,,,"Home Health and Personal Care Aides; and Nursing Assistants, Orderlies, and Psychiatric Aides"
+,,31-1120,,,Home Health and Personal Care Aides
+,,,31-1121,,Home Health Aides
+,,,31-1122,,Personal Care Aides
+,,31-1130,,,"Nursing Assistants, Orderlies, and Psychiatric Aides"
+,,,31-1131,,Nursing Assistants
+,,,31-1132,,Orderlies
+,,,31-1133,,Psychiatric Aides
+,31-2000,,,,Occupational Therapy and Physical Therapist Assistants and Aides
+,,31-2010,,,Occupational Therapy Assistants and Aides
+,,,31-2011,,Occupational Therapy Assistants
+,,,31-2012,,Occupational Therapy Aides
+,,31-2020,,,Physical Therapist Assistants and Aides
+,,,31-2021,,Physical Therapist Assistants
+,,,31-2022,,Physical Therapist Aides
+,31-9000,,,,Other Healthcare Support Occupations
+,,31-9010,,,Massage Therapists
+,,,31-9011,,Massage Therapists
+,,31-9090,,,Miscellaneous Healthcare Support Occupations
+,,,31-9091,,Dental Assistants
+,,,31-9092,,Medical Assistants
+,,,31-9093,,Medical Equipment Preparers
+,,,31-9094,,Medical Transcriptionists
+,,,31-9095,,Pharmacy Aides
+,,,31-9096,,Veterinary Assistants and Laboratory Animal Caretakers
+,,,31-9097,,Phlebotomists
+,,,31-9099,,"Healthcare Support Workers, All Other"
+,,,,31-9099.01,Speech-Language Pathology Assistants
+,,,,31-9099.02,Endoscopy Technicians
+33-0000,,,,,Protective Service Occupations
+,33-1000,,,,Supervisors of Protective Service Workers
+,,33-1010,,,First-Line Supervisors of Law Enforcement Workers
+,,,33-1011,,First-Line Supervisors of Correctional Officers
+,,,33-1012,,First-Line Supervisors of Police and Detectives
+,,33-1020,,,First-Line Supervisors of Firefighting and Prevention Workers
+,,,33-1021,,First-Line Supervisors of Firefighting and Prevention Workers
+,,33-1090,,,"Miscellaneous First-Line Supervisors, Protective Service Workers"
+,,,33-1091,,First-Line Supervisors of Security Workers
+,,,33-1099,,"First-Line Supervisors of Protective Service Workers, All Other"
+,33-2000,,,,Firefighting and Prevention Workers
+,,33-2010,,,Firefighters
+,,,33-2011,,Firefighters
+,,33-2020,,,Fire Inspectors
+,,,33-2021,,Fire Inspectors and Investigators
+,,,33-2022,,Forest Fire Inspectors and Prevention Specialists
+,33-3000,,,,Law Enforcement Workers
+,,33-3010,,,"Bailiffs, Correctional Officers, and Jailers"
+,,,33-3011,,Bailiffs
+,,,33-3012,,Correctional Officers and Jailers
+,,33-3020,,,Detectives and Criminal Investigators
+,,,33-3021,,Detectives and Criminal Investigators
+,,,,33-3021.02,Police Identification and Records Officers
+,,,,33-3021.06,Intelligence Analysts
+,,33-3030,,,Fish and Game Wardens
+,,,33-3031,,Fish and Game Wardens
+,,33-3040,,,Parking Enforcement Workers
+,,,33-3041,,Parking Enforcement Workers
+,,33-3050,,,Police Officers
+,,,33-3051,,Police and Sheriff's Patrol Officers
+,,,,33-3051.04,Customs and Border Protection Officers
+,,,33-3052,,Transit and Railroad Police
+,33-9000,,,,Other Protective Service Workers
+,,33-9010,,,Animal Control Workers
+,,,33-9011,,Animal Control Workers
+,,33-9020,,,Private Detectives and Investigators
+,,,33-9021,,Private Detectives and Investigators
+,,33-9030,,,Security Guards and Gambling Surveillance Officers
+,,,33-9031,,Gambling Surveillance Officers and Gambling Investigators
+,,,33-9032,,Security Guards
+,,33-9090,,,Miscellaneous Protective Service Workers
+,,,33-9091,,Crossing Guards and Flaggers
+,,,33-9092,,"Lifeguards, Ski Patrol, and Other Recreational Protective Service Workers"
+,,,33-9093,,Transportation Security Screeners
+,,,33-9094,,School Bus Monitors
+,,,33-9099,,"Protective Service Workers, All Other"
+,,,,33-9099.02,Retail Loss Prevention Specialists
+35-0000,,,,,Food Preparation and Serving Related Occupations
+,35-1000,,,,Supervisors of Food Preparation and Serving Workers
+,,35-1010,,,Supervisors of Food Preparation and Serving Workers
+,,,35-1011,,Chefs and Head Cooks
+,,,35-1012,,First-Line Supervisors of Food Preparation and Serving Workers
+,35-2000,,,,Cooks and Food Preparation Workers
+,,35-2010,,,Cooks
+,,,35-2011,,"Cooks, Fast Food"
+,,,35-2012,,"Cooks, Institution and Cafeteria"
+,,,35-2013,,"Cooks, Private Household"
+,,,35-2014,,"Cooks, Restaurant"
+,,,35-2015,,"Cooks, Short Order"
+,,,35-2019,,"Cooks, All Other"
+,,35-2020,,,Food Preparation Workers
+,,,35-2021,,Food Preparation Workers
+,35-3000,,,,Food and Beverage Serving Workers
+,,35-3010,,,Bartenders
+,,,35-3011,,Bartenders
+,,35-3020,,,Fast Food and Counter Workers
+,,,35-3023,,Fast Food and Counter Workers
+,,,,35-3023.01,Baristas
+,,35-3030,,,Waiters and Waitresses
+,,,35-3031,,Waiters and Waitresses
+,,35-3040,,,"Food Servers, Nonrestaurant"
+,,,35-3041,,"Food Servers, Nonrestaurant"
+,35-9000,,,,Other Food Preparation and Serving Related Workers
+,,35-9010,,,Dining Room and Cafeteria Attendants and Bartender Helpers
+,,,35-9011,,Dining Room and Cafeteria Attendants and Bartender Helpers
+,,35-9020,,,Dishwashers
+,,,35-9021,,Dishwashers
+,,35-9030,,,"Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop"
+,,,35-9031,,"Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop"
+,,35-9090,,,Miscellaneous Food Preparation and Serving Related Workers
+,,,35-9099,,"Food Preparation and Serving Related Workers, All Other"
+37-0000,,,,,Building and Grounds Cleaning and Maintenance Occupations
+,37-1000,,,,Supervisors of Building and Grounds Cleaning and Maintenance Workers
+,,37-1010,,,First-Line Supervisors of Building and Grounds Cleaning and Maintenance Workers
+,,,37-1011,,First-Line Supervisors of Housekeeping and Janitorial Workers
+,,,37-1012,,"First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers"
+,37-2000,,,,Building Cleaning and Pest Control Workers
+,,37-2010,,,Building Cleaning Workers
+,,,37-2011,,"Janitors and Cleaners, Except Maids and Housekeeping Cleaners"
+,,,37-2012,,Maids and Housekeeping Cleaners
+,,,37-2019,,"Building Cleaning Workers, All Other"
+,,37-2020,,,Pest Control Workers
+,,,37-2021,,Pest Control Workers
+,37-3000,,,,Grounds Maintenance Workers
+,,37-3010,,,Grounds Maintenance Workers
+,,,37-3011,,Landscaping and Groundskeeping Workers
+,,,37-3012,,"Pesticide Handlers, Sprayers, and Applicators, Vegetation"
+,,,37-3013,,Tree Trimmers and Pruners
+,,,37-3019,,"Grounds Maintenance Workers, All Other"
+39-0000,,,,,Personal Care and Service Occupations
+,39-1000,,,,Supervisors of Personal Care and Service Workers
+,,39-1010,,,First-Line Supervisors of Entertainment and Recreation Workers
+,,,39-1013,,First-Line Supervisors of Gambling Services Workers
+,,,39-1014,,"First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services"
+,,39-1020,,,First-Line Supervisors of Personal Service Workers
+,,,39-1022,,First-Line Supervisors of Personal Service Workers
+,39-2000,,,,Animal Care and Service Workers
+,,39-2010,,,Animal Trainers
+,,,39-2011,,Animal Trainers
+,,39-2020,,,Animal Caretakers
+,,,39-2021,,Animal Caretakers
+,39-3000,,,,Entertainment Attendants and Related Workers
+,,39-3010,,,Gambling Services Workers
+,,,39-3011,,Gambling Dealers
+,,,39-3012,,Gambling and Sports Book Writers and Runners
+,,,39-3019,,"Gambling Service Workers, All Other"
+,,39-3020,,,Motion Picture Projectionists
+,,,39-3021,,Motion Picture Projectionists
+,,39-3030,,,"Ushers, Lobby Attendants, and Ticket Takers"
+,,,39-3031,,"Ushers, Lobby Attendants, and Ticket Takers"
+,,39-3090,,,Miscellaneous Entertainment Attendants and Related Workers
+,,,39-3091,,Amusement and Recreation Attendants
+,,,39-3092,,Costume Attendants
+,,,39-3093,,"Locker Room, Coatroom, and Dressing Room Attendants"
+,,,39-3099,,"Entertainment Attendants and Related Workers, All Other"
+,39-4000,,,,Funeral Service Workers
+,,39-4010,,,Embalmers and Crematory Operators
+,,,39-4011,,Embalmers
+,,,39-4012,,Crematory Operators
+,,39-4020,,,Funeral Attendants
+,,,39-4021,,Funeral Attendants
+,,39-4030,,,"Morticians, Undertakers, and Funeral Arrangers"
+,,,39-4031,,"Morticians, Undertakers, and Funeral Arrangers"
+,39-5000,,,,Personal Appearance Workers
+,,39-5010,,,"Barbers, Hairdressers, Hairstylists and Cosmetologists"
+,,,39-5011,,Barbers
+,,,39-5012,,"Hairdressers, Hairstylists, and Cosmetologists"
+,,39-5090,,,Miscellaneous Personal Appearance Workers
+,,,39-5091,,"Makeup Artists, Theatrical and Performance"
+,,,39-5092,,Manicurists and Pedicurists
+,,,39-5093,,Shampooers
+,,,39-5094,,Skincare Specialists
+,39-6000,,,,"Baggage Porters, Bellhops, and Concierges"
+,,39-6010,,,"Baggage Porters, Bellhops, and Concierges"
+,,,39-6011,,Baggage Porters and Bellhops
+,,,39-6012,,Concierges
+,39-7000,,,,Tour and Travel Guides
+,,39-7010,,,Tour and Travel Guides
+,,,39-7011,,Tour Guides and Escorts
+,,,39-7012,,Travel Guides
+,39-9000,,,,Other Personal Care and Service Workers
+,,39-9010,,,Childcare Workers
+,,,39-9011,,Childcare Workers
+,,,,39-9011.01,Nannies
+,,39-9030,,,Recreation and Fitness Workers
+,,,39-9031,,Exercise Trainers and Group Fitness Instructors
+,,,39-9032,,Recreation Workers
+,,39-9040,,,Residential Advisors
+,,,39-9041,,Residential Advisors
+,,39-9090,,,Miscellaneous Personal Care and Service Workers
+,,,39-9099,,"Personal Care and Service Workers, All Other"
+41-0000,,,,,Sales and Related Occupations
+,41-1000,,,,Supervisors of Sales Workers
+,,41-1010,,,First-Line Supervisors of Sales Workers
+,,,41-1011,,First-Line Supervisors of Retail Sales Workers
+,,,41-1012,,First-Line Supervisors of Non-Retail Sales Workers
+,41-2000,,,,Retail Sales Workers
+,,41-2010,,,Cashiers
+,,,41-2011,,Cashiers
+,,,41-2012,,Gambling Change Persons and Booth Cashiers
+,,41-2020,,,Counter and Rental Clerks and Parts Salespersons
+,,,41-2021,,Counter and Rental Clerks
+,,,41-2022,,Parts Salespersons
+,,41-2030,,,Retail Salespersons
+,,,41-2031,,Retail Salespersons
+,41-3000,,,,"Sales Representatives, Services"
+,,41-3010,,,Advertising Sales Agents
+,,,41-3011,,Advertising Sales Agents
+,,41-3020,,,Insurance Sales Agents
+,,,41-3021,,Insurance Sales Agents
+,,41-3030,,,"Securities, Commodities, and Financial Services Sales Agents"
+,,,41-3031,,"Securities, Commodities, and Financial Services Sales Agents"
+,,41-3040,,,Travel Agents
+,,,41-3041,,Travel Agents
+,,41-3090,,,"Miscellaneous Sales Representatives, Services"
+,,,41-3091,,"Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel"
+,41-4000,,,,"Sales Representatives, Wholesale and Manufacturing"
+,,41-4010,,,"Sales Representatives, Wholesale and Manufacturing"
+,,,41-4011,,"Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products"
+,,,,41-4011.07,Solar Sales Representatives and Assessors
+,,,41-4012,,"Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products"
+,41-9000,,,,Other Sales and Related Workers
+,,41-9010,,,"Models, Demonstrators, and Product Promoters"
+,,,41-9011,,Demonstrators and Product Promoters
+,,,41-9012,,Models
+,,41-9020,,,Real Estate Brokers and Sales Agents
+,,,41-9021,,Real Estate Brokers
+,,,41-9022,,Real Estate Sales Agents
+,,41-9030,,,Sales Engineers
+,,,41-9031,,Sales Engineers
+,,41-9040,,,Telemarketers
+,,,41-9041,,Telemarketers
+,,41-9090,,,Miscellaneous Sales and Related Workers
+,,,41-9091,,"Door-to-Door Sales Workers, News and Street Vendors, and Related Workers"
+,,,41-9099,,"Sales and Related Workers, All Other"
+43-0000,,,,,Office and Administrative Support Occupations
+,43-1000,,,,Supervisors of Office and Administrative Support Workers
+,,43-1010,,,First-Line Supervisors of Office and Administrative Support Workers
+,,,43-1011,,First-Line Supervisors of Office and Administrative Support Workers
+,43-2000,,,,Communications Equipment Operators
+,,43-2010,,,"Switchboard Operators, Including Answering Service"
+,,,43-2011,,"Switchboard Operators, Including Answering Service"
+,,43-2020,,,Telephone Operators
+,,,43-2021,,Telephone Operators
+,,43-2090,,,Miscellaneous Communications Equipment Operators
+,,,43-2099,,"Communications Equipment Operators, All Other"
+,43-3000,,,,Financial Clerks
+,,43-3010,,,Bill and Account Collectors
+,,,43-3011,,Bill and Account Collectors
+,,43-3020,,,Billing and Posting Clerks
+,,,43-3021,,Billing and Posting Clerks
+,,43-3030,,,"Bookkeeping, Accounting, and Auditing Clerks"
+,,,43-3031,,"Bookkeeping, Accounting, and Auditing Clerks"
+,,43-3040,,,Gambling Cage Workers
+,,,43-3041,,Gambling Cage Workers
+,,43-3050,,,Payroll and Timekeeping Clerks
+,,,43-3051,,Payroll and Timekeeping Clerks
+,,43-3060,,,Procurement Clerks
+,,,43-3061,,Procurement Clerks
+,,43-3070,,,Tellers
+,,,43-3071,,Tellers
+,,43-3090,,,Miscellaneous Financial Clerks
+,,,43-3099,,"Financial Clerks, All Other"
+,43-4000,,,,Information and Record Clerks
+,,43-4010,,,Brokerage Clerks
+,,,43-4011,,Brokerage Clerks
+,,43-4020,,,Correspondence Clerks
+,,,43-4021,,Correspondence Clerks
+,,43-4030,,,"Court, Municipal, and License Clerks"
+,,,43-4031,,"Court, Municipal, and License Clerks"
+,,43-4040,,,"Credit Authorizers, Checkers, and Clerks"
+,,,43-4041,,"Credit Authorizers, Checkers, and Clerks"
+,,43-4050,,,Customer Service Representatives
+,,,43-4051,,Customer Service Representatives
+,,43-4060,,,"Eligibility Interviewers, Government Programs"
+,,,43-4061,,"Eligibility Interviewers, Government Programs"
+,,43-4070,,,File Clerks
+,,,43-4071,,File Clerks
+,,43-4080,,,"Hotel, Motel, and Resort Desk Clerks"
+,,,43-4081,,"Hotel, Motel, and Resort Desk Clerks"
+,,43-4110,,,"Interviewers, Except Eligibility and Loan"
+,,,43-4111,,"Interviewers, Except Eligibility and Loan"
+,,43-4120,,,"Library Assistants, Clerical"
+,,,43-4121,,"Library Assistants, Clerical"
+,,43-4130,,,Loan Interviewers and Clerks
+,,,43-4131,,Loan Interviewers and Clerks
+,,43-4140,,,New Accounts Clerks
+,,,43-4141,,New Accounts Clerks
+,,43-4150,,,Order Clerks
+,,,43-4151,,Order Clerks
+,,43-4160,,,"Human Resources Assistants, Except Payroll and Timekeeping"
+,,,43-4161,,"Human Resources Assistants, Except Payroll and Timekeeping"
+,,43-4170,,,Receptionists and Information Clerks
+,,,43-4171,,Receptionists and Information Clerks
+,,43-4180,,,Reservation and Transportation Ticket Agents and Travel Clerks
+,,,43-4181,,Reservation and Transportation Ticket Agents and Travel Clerks
+,,43-4190,,,Miscellaneous Information and Record Clerks
+,,,43-4199,,"Information and Record Clerks, All Other"
+,43-5000,,,,"Material Recording, Scheduling, Dispatching, and Distributing Workers"
+,,43-5010,,,Cargo and Freight Agents
+,,,43-5011,,Cargo and Freight Agents
+,,,,43-5011.01,Freight Forwarders
+,,43-5020,,,Couriers and Messengers
+,,,43-5021,,Couriers and Messengers
+,,43-5030,,,Dispatchers
+,,,43-5031,,Public Safety Telecommunicators
+,,,43-5032,,"Dispatchers, Except Police, Fire, and Ambulance"
+,,43-5040,,,"Meter Readers, Utilities"
+,,,43-5041,,"Meter Readers, Utilities"
+,,43-5050,,,Postal Service Workers
+,,,43-5051,,Postal Service Clerks
+,,,43-5052,,Postal Service Mail Carriers
+,,,43-5053,,"Postal Service Mail Sorters, Processors, and Processing Machine Operators"
+,,43-5060,,,"Production, Planning, and Expediting Clerks"
+,,,43-5061,,"Production, Planning, and Expediting Clerks"
+,,43-5070,,,"Shipping, Receiving, and Inventory Clerks"
+,,,43-5071,,"Shipping, Receiving, and Inventory Clerks"
+,,43-5110,,,"Weighers, Measurers, Checkers, and Samplers, Recordkeeping"
+,,,43-5111,,"Weighers, Measurers, Checkers, and Samplers, Recordkeeping"
+,43-6000,,,,Secretaries and Administrative Assistants
+,,43-6010,,,Secretaries and Administrative Assistants
+,,,43-6011,,Executive Secretaries and Executive Administrative Assistants
+,,,43-6012,,Legal Secretaries and Administrative Assistants
+,,,43-6013,,Medical Secretaries and Administrative Assistants
+,,,43-6014,,"Secretaries and Administrative Assistants, Except Legal, Medical, and Executive"
+,43-9000,,,,Other Office and Administrative Support Workers
+,,43-9020,,,Data Entry and Information Processing Workers
+,,,43-9021,,Data Entry Keyers
+,,,43-9022,,Word Processors and Typists
+,,43-9030,,,Desktop Publishers
+,,,43-9031,,Desktop Publishers
+,,43-9040,,,Insurance Claims and Policy Processing Clerks
+,,,43-9041,,Insurance Claims and Policy Processing Clerks
+,,43-9050,,,"Mail Clerks and Mail Machine Operators, Except Postal Service"
+,,,43-9051,,"Mail Clerks and Mail Machine Operators, Except Postal Service"
+,,43-9060,,,"Office Clerks, General"
+,,,43-9061,,"Office Clerks, General"
+,,43-9070,,,"Office Machine Operators, Except Computer"
+,,,43-9071,,"Office Machine Operators, Except Computer"
+,,43-9080,,,Proofreaders and Copy Markers
+,,,43-9081,,Proofreaders and Copy Markers
+,,43-9110,,,Statistical Assistants
+,,,43-9111,,Statistical Assistants
+,,43-9190,,,Miscellaneous Office and Administrative Support Workers
+,,,43-9199,,"Office and Administrative Support Workers, All Other"
+45-0000,,,,,"Farming, Fishing, and Forestry Occupations"
+,45-1000,,,,"Supervisors of Farming, Fishing, and Forestry Workers"
+,,45-1010,,,"First-Line Supervisors of Farming, Fishing, and Forestry Workers"
+,,,45-1011,,"First-Line Supervisors of Farming, Fishing, and Forestry Workers"
+,45-2000,,,,Agricultural Workers
+,,45-2010,,,Agricultural Inspectors
+,,,45-2011,,Agricultural Inspectors
+,,45-2020,,,Animal Breeders
+,,,45-2021,,Animal Breeders
+,,45-2040,,,"Graders and Sorters, Agricultural Products"
+,,,45-2041,,"Graders and Sorters, Agricultural Products"
+,,45-2090,,,Miscellaneous Agricultural Workers
+,,,45-2091,,Agricultural Equipment Operators
+,,,45-2092,,"Farmworkers and Laborers, Crop, Nursery, and Greenhouse"
+,,,45-2093,,"Farmworkers, Farm, Ranch, and Aquacultural Animals"
+,,,45-2099,,"Agricultural Workers, All Other"
+,45-3000,,,,Fishing and Hunting Workers
+,,45-3030,,,Fishing and Hunting Workers
+,,,45-3031,,Fishing and Hunting Workers
+,45-4000,,,,"Forest, Conservation, and Logging Workers"
+,,45-4010,,,Forest and Conservation Workers
+,,,45-4011,,Forest and Conservation Workers
+,,45-4020,,,Logging Workers
+,,,45-4021,,Fallers
+,,,45-4022,,Logging Equipment Operators
+,,,45-4023,,Log Graders and Scalers
+,,,45-4029,,"Logging Workers, All Other"
+47-0000,,,,,Construction and Extraction Occupations
+,47-1000,,,,Supervisors of Construction and Extraction Workers
+,,47-1010,,,First-Line Supervisors of Construction Trades and Extraction Workers
+,,,47-1011,,First-Line Supervisors of Construction Trades and Extraction Workers
+,,,,47-1011.03,Solar Energy Installation Managers
+,47-2000,,,,Construction Trades Workers
+,,47-2010,,,Boilermakers
+,,,47-2011,,Boilermakers
+,,47-2020,,,"Brickmasons, Blockmasons, and Stonemasons"
+,,,47-2021,,Brickmasons and Blockmasons
+,,,47-2022,,Stonemasons
+,,47-2030,,,Carpenters
+,,,47-2031,,Carpenters
+,,47-2040,,,"Carpet, Floor, and Tile Installers and Finishers"
+,,,47-2041,,Carpet Installers
+,,,47-2042,,"Floor Layers, Except Carpet, Wood, and Hard Tiles"
+,,,47-2043,,Floor Sanders and Finishers
+,,,47-2044,,Tile and Stone Setters
+,,47-2050,,,"Cement Masons, Concrete Finishers, and Terrazzo Workers"
+,,,47-2051,,Cement Masons and Concrete Finishers
+,,,47-2053,,Terrazzo Workers and Finishers
+,,47-2060,,,Construction Laborers
+,,,47-2061,,Construction Laborers
+,,47-2070,,,Construction Equipment Operators
+,,,47-2071,,"Paving, Surfacing, and Tamping Equipment Operators"
+,,,47-2072,,Pile Driver Operators
+,,,47-2073,,Operating Engineers and Other Construction Equipment Operators
+,,47-2080,,,"Drywall Installers, Ceiling Tile Installers, and Tapers"
+,,,47-2081,,Drywall and Ceiling Tile Installers
+,,,47-2082,,Tapers
+,,47-2110,,,Electricians
+,,,47-2111,,Electricians
+,,47-2120,,,Glaziers
+,,,47-2121,,Glaziers
+,,47-2130,,,Insulation Workers
+,,,47-2131,,"Insulation Workers, Floor, Ceiling, and Wall"
+,,,47-2132,,"Insulation Workers, Mechanical"
+,,47-2140,,,Painters and Paperhangers
+,,,47-2141,,"Painters, Construction and Maintenance"
+,,,47-2142,,Paperhangers
+,,47-2150,,,"Pipelayers, Plumbers, Pipefitters, and Steamfitters"
+,,,47-2151,,Pipelayers
+,,,47-2152,,"Plumbers, Pipefitters, and Steamfitters"
+,,,,47-2152.04,Solar Thermal Installers and Technicians
+,,47-2160,,,Plasterers and Stucco Masons
+,,,47-2161,,Plasterers and Stucco Masons
+,,47-2170,,,Reinforcing Iron and Rebar Workers
+,,,47-2171,,Reinforcing Iron and Rebar Workers
+,,47-2180,,,Roofers
+,,,47-2181,,Roofers
+,,47-2210,,,Sheet Metal Workers
+,,,47-2211,,Sheet Metal Workers
+,,47-2220,,,Structural Iron and Steel Workers
+,,,47-2221,,Structural Iron and Steel Workers
+,,47-2230,,,Solar Photovoltaic Installers
+,,,47-2231,,Solar Photovoltaic Installers
+,47-3000,,,,"Helpers, Construction Trades"
+,,47-3010,,,"Helpers, Construction Trades"
+,,,47-3011,,"Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters"
+,,,47-3012,,Helpers--Carpenters
+,,,47-3013,,Helpers--Electricians
+,,,47-3014,,"Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons"
+,,,47-3015,,"Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters"
+,,,47-3016,,Helpers--Roofers
+,,,47-3019,,"Helpers, Construction Trades, All Other"
+,47-4000,,,,Other Construction and Related Workers
+,,47-4010,,,Construction and Building Inspectors
+,,,47-4011,,Construction and Building Inspectors
+,,,,47-4011.01,Energy Auditors
+,,47-4020,,,Elevator and Escalator Installers and Repairers
+,,,47-4021,,Elevator and Escalator Installers and Repairers
+,,47-4030,,,Fence Erectors
+,,,47-4031,,Fence Erectors
+,,47-4040,,,Hazardous Materials Removal Workers
+,,,47-4041,,Hazardous Materials Removal Workers
+,,47-4050,,,Highway Maintenance Workers
+,,,47-4051,,Highway Maintenance Workers
+,,47-4060,,,Rail-Track Laying and Maintenance Equipment Operators
+,,,47-4061,,Rail-Track Laying and Maintenance Equipment Operators
+,,47-4070,,,Septic Tank Servicers and Sewer Pipe Cleaners
+,,,47-4071,,Septic Tank Servicers and Sewer Pipe Cleaners
+,,47-4090,,,Miscellaneous Construction and Related Workers
+,,,47-4091,,Segmental Pavers
+,,,47-4099,,"Construction and Related Workers, All Other"
+,,,,47-4099.03,Weatherization Installers and Technicians
+,47-5000,,,,Extraction Workers
+,,47-5010,,,"Derrick, Rotary Drill, and Service Unit Operators, Oil and Gas"
+,,,47-5011,,"Derrick Operators, Oil and Gas"
+,,,47-5012,,"Rotary Drill Operators, Oil and Gas"
+,,,47-5013,,"Service Unit Operators, Oil and Gas"
+,,47-5020,,,Surface Mining Machine Operators and Earth Drillers
+,,,47-5022,,"Excavating and Loading Machine and Dragline Operators, Surface Mining"
+,,,47-5023,,"Earth Drillers, Except Oil and Gas"
+,,47-5030,,,"Explosives Workers, Ordnance Handling Experts, and Blasters"
+,,,47-5032,,"Explosives Workers, Ordnance Handling Experts, and Blasters"
+,,47-5040,,,Underground Mining Machine Operators
+,,,47-5041,,Continuous Mining Machine Operators
+,,,47-5043,,"Roof Bolters, Mining"
+,,,47-5044,,"Loading and Moving Machine Operators, Underground Mining"
+,,,47-5049,,"Underground Mining Machine Operators, All Other"
+,,47-5050,,,"Rock Splitters, Quarry"
+,,,47-5051,,"Rock Splitters, Quarry"
+,,47-5070,,,"Roustabouts, Oil and Gas"
+,,,47-5071,,"Roustabouts, Oil and Gas"
+,,47-5080,,,Helpers--Extraction Workers
+,,,47-5081,,Helpers--Extraction Workers
+,,47-5090,,,Miscellaneous Extraction Workers
+,,,47-5099,,"Extraction Workers, All Other"
+49-0000,,,,,"Installation, Maintenance, and Repair Occupations"
+,49-1000,,,,"Supervisors of Installation, Maintenance, and Repair Workers"
+,,49-1010,,,"First-Line Supervisors of Mechanics, Installers, and Repairers"
+,,,49-1011,,"First-Line Supervisors of Mechanics, Installers, and Repairers"
+,49-2000,,,,"Electrical and Electronic Equipment Mechanics, Installers, and Repairers"
+,,49-2010,,,"Computer, Automated Teller, and Office Machine Repairers"
+,,,49-2011,,"Computer, Automated Teller, and Office Machine Repairers"
+,,49-2020,,,Radio and Telecommunications Equipment Installers and Repairers
+,,,49-2021,,"Radio, Cellular, and Tower Equipment Installers and Repairers"
+,,,49-2022,,"Telecommunications Equipment Installers and Repairers, Except Line Installers"
+,,49-2090,,,"Miscellaneous Electrical and Electronic Equipment Mechanics, Installers, and Repairers"
+,,,49-2091,,Avionics Technicians
+,,,49-2092,,"Electric Motor, Power Tool, and Related Repairers"
+,,,49-2093,,"Electrical and Electronics Installers and Repairers, Transportation Equipment"
+,,,49-2094,,"Electrical and Electronics Repairers, Commercial and Industrial Equipment"
+,,,49-2095,,"Electrical and Electronics Repairers, Powerhouse, Substation, and Relay"
+,,,49-2096,,"Electronic Equipment Installers and Repairers, Motor Vehicles"
+,,,49-2097,,Audiovisual Equipment Installers and Repairers
+,,,49-2098,,Security and Fire Alarm Systems Installers
+,49-3000,,,,"Vehicle and Mobile Equipment Mechanics, Installers, and Repairers"
+,,49-3010,,,Aircraft Mechanics and Service Technicians
+,,,49-3011,,Aircraft Mechanics and Service Technicians
+,,49-3020,,,Automotive Technicians and Repairers
+,,,49-3021,,Automotive Body and Related Repairers
+,,,49-3022,,Automotive Glass Installers and Repairers
+,,,49-3023,,Automotive Service Technicians and Mechanics
+,,49-3030,,,Bus and Truck Mechanics and Diesel Engine Specialists
+,,,49-3031,,Bus and Truck Mechanics and Diesel Engine Specialists
+,,49-3040,,,Heavy Vehicle and Mobile Equipment Service Technicians and Mechanics
+,,,49-3041,,Farm Equipment Mechanics and Service Technicians
+,,,49-3042,,"Mobile Heavy Equipment Mechanics, Except Engines"
+,,,49-3043,,Rail Car Repairers
+,,49-3050,,,Small Engine Mechanics
+,,,49-3051,,Motorboat Mechanics and Service Technicians
+,,,49-3052,,Motorcycle Mechanics
+,,,49-3053,,Outdoor Power Equipment and Other Small Engine Mechanics
+,,49-3090,,,"Miscellaneous Vehicle and Mobile Equipment Mechanics, Installers, and Repairers"
+,,,49-3091,,Bicycle Repairers
+,,,49-3092,,Recreational Vehicle Service Technicians
+,,,49-3093,,Tire Repairers and Changers
+,49-9000,,,,"Other Installation, Maintenance, and Repair Occupations"
+,,49-9010,,,Control and Valve Installers and Repairers
+,,,49-9011,,Mechanical Door Repairers
+,,,49-9012,,"Control and Valve Installers and Repairers, Except Mechanical Door"
+,,49-9020,,,"Heating, Air Conditioning, and Refrigeration Mechanics and Installers"
+,,,49-9021,,"Heating, Air Conditioning, and Refrigeration Mechanics and Installers"
+,,49-9030,,,Home Appliance Repairers
+,,,49-9031,,Home Appliance Repairers
+,,49-9040,,,"Industrial Machinery Installation, Repair, and Maintenance Workers"
+,,,49-9041,,Industrial Machinery Mechanics
+,,,49-9043,,"Maintenance Workers, Machinery"
+,,,49-9044,,Millwrights
+,,,49-9045,,"Refractory Materials Repairers, Except Brickmasons"
+,,49-9050,,,Line Installers and Repairers
+,,,49-9051,,Electrical Power-Line Installers and Repairers
+,,,49-9052,,Telecommunications Line Installers and Repairers
+,,49-9060,,,Precision Instrument and Equipment Repairers
+,,,49-9061,,Camera and Photographic Equipment Repairers
+,,,49-9062,,Medical Equipment Repairers
+,,,49-9063,,Musical Instrument Repairers and Tuners
+,,,49-9064,,Watch and Clock Repairers
+,,,49-9069,,"Precision Instrument and Equipment Repairers, All Other"
+,,49-9070,,,"Maintenance and Repair Workers, General"
+,,,49-9071,,"Maintenance and Repair Workers, General"
+,,49-9080,,,Wind Turbine Service Technicians
+,,,49-9081,,Wind Turbine Service Technicians
+,,49-9090,,,"Miscellaneous Installation, Maintenance, and Repair Workers"
+,,,49-9091,,"Coin, Vending, and Amusement Machine Servicers and Repairers"
+,,,49-9092,,Commercial Divers
+,,,49-9094,,Locksmiths and Safe Repairers
+,,,49-9095,,Manufactured Building and Mobile Home Installers
+,,,49-9096,,Riggers
+,,,49-9097,,Signal and Track Switch Repairers
+,,,49-9098,,"Helpers--Installation, Maintenance, and Repair Workers"
+,,,49-9099,,"Installation, Maintenance, and Repair Workers, All Other"
+,,,,49-9099.01,Geothermal Technicians
+51-0000,,,,,Production Occupations
+,51-1000,,,,Supervisors of Production Workers
+,,51-1010,,,First-Line Supervisors of Production and Operating Workers
+,,,51-1011,,First-Line Supervisors of Production and Operating Workers
+,51-2000,,,,Assemblers and Fabricators
+,,51-2010,,,"Aircraft Structure, Surfaces, Rigging, and Systems Assemblers"
+,,,51-2011,,"Aircraft Structure, Surfaces, Rigging, and Systems Assemblers"
+,,51-2020,,,"Electrical, Electronics, and Electromechanical Assemblers"
+,,,51-2021,,"Coil Winders, Tapers, and Finishers"
+,,,51-2022,,Electrical and Electronic Equipment Assemblers
+,,,51-2023,,Electromechanical Equipment Assemblers
+,,51-2030,,,Engine and Other Machine Assemblers
+,,,51-2031,,Engine and Other Machine Assemblers
+,,51-2040,,,Structural Metal Fabricators and Fitters
+,,,51-2041,,Structural Metal Fabricators and Fitters
+,,51-2050,,,Fiberglass Laminators and Fabricators
+,,,51-2051,,Fiberglass Laminators and Fabricators
+,,51-2060,,,Timing Device Assemblers and Adjusters
+,,,51-2061,,Timing Device Assemblers and Adjusters
+,,51-2090,,,Miscellaneous Assemblers and Fabricators
+,,,51-2092,,Team Assemblers
+,,,51-2099,,"Assemblers and Fabricators, All Other"
+,51-3000,,,,Food Processing Workers
+,,51-3010,,,Bakers
+,,,51-3011,,Bakers
+,,51-3020,,,"Butchers and Other Meat, Poultry, and Fish Processing Workers"
+,,,51-3021,,Butchers and Meat Cutters
+,,,51-3022,,"Meat, Poultry, and Fish Cutters and Trimmers"
+,,,51-3023,,Slaughterers and Meat Packers
+,,51-3090,,,Miscellaneous Food Processing Workers
+,,,51-3091,,"Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders"
+,,,51-3092,,Food Batchmakers
+,,,51-3093,,Food Cooking Machine Operators and Tenders
+,,,51-3099,,"Food Processing Workers, All Other"
+,51-4000,,,,Metal Workers and Plastic Workers
+,,51-4020,,,"Forming Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4021,,"Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4022,,"Forging Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4023,,"Rolling Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4030,,,"Machine Tool Cutting Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4031,,"Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4032,,"Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4033,,"Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4034,,"Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4035,,"Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4040,,,Machinists
+,,,51-4041,,Machinists
+,,51-4050,,,"Metal Furnace Operators, Tenders, Pourers, and Casters"
+,,,51-4051,,Metal-Refining Furnace Operators and Tenders
+,,,51-4052,,"Pourers and Casters, Metal"
+,,51-4060,,,"Model Makers and Patternmakers, Metal and Plastic"
+,,,51-4061,,"Model Makers, Metal and Plastic"
+,,,51-4062,,"Patternmakers, Metal and Plastic"
+,,51-4070,,,"Molders and Molding Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4071,,Foundry Mold and Coremakers
+,,,51-4072,,"Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4080,,,"Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4081,,"Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4110,,,Tool and Die Makers
+,,,51-4111,,Tool and Die Makers
+,,51-4120,,,"Welding, Soldering, and Brazing Workers"
+,,,51-4121,,"Welders, Cutters, Solderers, and Brazers"
+,,,51-4122,,"Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders"
+,,51-4190,,,Miscellaneous Metal Workers and Plastic Workers
+,,,51-4191,,"Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4192,,"Layout Workers, Metal and Plastic"
+,,,51-4193,,"Plating Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4194,,"Tool Grinders, Filers, and Sharpeners"
+,,,51-4199,,"Metal Workers and Plastic Workers, All Other"
+,51-5100,,,,Printing Workers
+,,51-5110,,,Printing Workers
+,,,51-5111,,Prepress Technicians and Workers
+,,,51-5112,,Printing Press Operators
+,,,51-5113,,Print Binding and Finishing Workers
+,51-6000,,,,"Textile, Apparel, and Furnishings Workers"
+,,51-6010,,,Laundry and Dry-Cleaning Workers
+,,,51-6011,,Laundry and Dry-Cleaning Workers
+,,51-6020,,,"Pressers, Textile, Garment, and Related Materials"
+,,,51-6021,,"Pressers, Textile, Garment, and Related Materials"
+,,51-6030,,,Sewing Machine Operators
+,,,51-6031,,Sewing Machine Operators
+,,51-6040,,,Shoe and Leather Workers
+,,,51-6041,,Shoe and Leather Workers and Repairers
+,,,51-6042,,Shoe Machine Operators and Tenders
+,,51-6050,,,"Tailors, Dressmakers, and Sewers"
+,,,51-6051,,"Sewers, Hand"
+,,,51-6052,,"Tailors, Dressmakers, and Custom Sewers"
+,,51-6060,,,"Textile Machine Setters, Operators, and Tenders"
+,,,51-6061,,Textile Bleaching and Dyeing Machine Operators and Tenders
+,,,51-6062,,"Textile Cutting Machine Setters, Operators, and Tenders"
+,,,51-6063,,"Textile Knitting and Weaving Machine Setters, Operators, and Tenders"
+,,,51-6064,,"Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders"
+,,51-6090,,,"Miscellaneous Textile, Apparel, and Furnishings Workers"
+,,,51-6091,,"Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers"
+,,,51-6092,,Fabric and Apparel Patternmakers
+,,,51-6093,,Upholsterers
+,,,51-6099,,"Textile, Apparel, and Furnishings Workers, All Other"
+,51-7000,,,,Woodworkers
+,,51-7010,,,Cabinetmakers and Bench Carpenters
+,,,51-7011,,Cabinetmakers and Bench Carpenters
+,,51-7020,,,Furniture Finishers
+,,,51-7021,,Furniture Finishers
+,,51-7030,,,"Model Makers and Patternmakers, Wood"
+,,,51-7031,,"Model Makers, Wood"
+,,,51-7032,,"Patternmakers, Wood"
+,,51-7040,,,"Woodworking Machine Setters, Operators, and Tenders"
+,,,51-7041,,"Sawing Machine Setters, Operators, and Tenders, Wood"
+,,,51-7042,,"Woodworking Machine Setters, Operators, and Tenders, Except Sawing"
+,,51-7090,,,Miscellaneous Woodworkers
+,,,51-7099,,"Woodworkers, All Other"
+,51-8000,,,,Plant and System Operators
+,,51-8010,,,"Power Plant Operators, Distributors, and Dispatchers"
+,,,51-8011,,Nuclear Power Reactor Operators
+,,,51-8012,,Power Distributors and Dispatchers
+,,,51-8013,,Power Plant Operators
+,,,,51-8013.03,Biomass Plant Technicians
+,,,,51-8013.04,Hydroelectric Plant Technicians
+,,51-8020,,,Stationary Engineers and Boiler Operators
+,,,51-8021,,Stationary Engineers and Boiler Operators
+,,51-8030,,,Water and Wastewater Treatment Plant and System Operators
+,,,51-8031,,Water and Wastewater Treatment Plant and System Operators
+,,51-8090,,,Miscellaneous Plant and System Operators
+,,,51-8091,,Chemical Plant and System Operators
+,,,51-8092,,Gas Plant Operators
+,,,51-8093,,"Petroleum Pump System Operators, Refinery Operators, and Gaugers"
+,,,51-8099,,"Plant and System Operators, All Other"
+,,,,51-8099.01,Biofuels Processing Technicians
+,51-9000,,,,Other Production Occupations
+,,51-9010,,,"Chemical Processing Machine Setters, Operators, and Tenders"
+,,,51-9011,,Chemical Equipment Operators and Tenders
+,,,51-9012,,"Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders"
+,,51-9020,,,"Crushing, Grinding, Polishing, Mixing, and Blending Workers"
+,,,51-9021,,"Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders"
+,,,51-9022,,"Grinding and Polishing Workers, Hand"
+,,,51-9023,,"Mixing and Blending Machine Setters, Operators, and Tenders"
+,,51-9030,,,Cutting Workers
+,,,51-9031,,"Cutters and Trimmers, Hand"
+,,,51-9032,,"Cutting and Slicing Machine Setters, Operators, and Tenders"
+,,51-9040,,,"Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders"
+,,,51-9041,,"Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders"
+,,51-9050,,,"Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders"
+,,,51-9051,,"Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders"
+,,51-9060,,,"Inspectors, Testers, Sorters, Samplers, and Weighers"
+,,,51-9061,,"Inspectors, Testers, Sorters, Samplers, and Weighers"
+,,51-9070,,,Jewelers and Precious Stone and Metal Workers
+,,,51-9071,,Jewelers and Precious Stone and Metal Workers
+,,,,51-9071.06,Gem and Diamond Workers
+,,51-9080,,,Dental and Ophthalmic Laboratory Technicians and Medical Appliance Technicians
+,,,51-9081,,Dental Laboratory Technicians
+,,,51-9082,,Medical Appliance Technicians
+,,,51-9083,,Ophthalmic Laboratory Technicians
+,,51-9110,,,Packaging and Filling Machine Operators and Tenders
+,,,51-9111,,Packaging and Filling Machine Operators and Tenders
+,,51-9120,,,Painting Workers
+,,,51-9123,,"Painting, Coating, and Decorating Workers"
+,,,51-9124,,"Coating, Painting, and Spraying Machine Setters, Operators, and Tenders"
+,,51-9140,,,Semiconductor Processing Technicians
+,,,51-9141,,Semiconductor Processing Technicians
+,,51-9150,,,Photographic Process Workers and Processing Machine Operators
+,,,51-9151,,Photographic Process Workers and Processing Machine Operators
+,,51-9160,,,Computer Numerically Controlled Tool Operators and Programmers
+,,,51-9161,,Computer Numerically Controlled Tool Operators
+,,,51-9162,,Computer Numerically Controlled Tool Programmers
+,,51-9190,,,Miscellaneous Production Workers
+,,,51-9191,,Adhesive Bonding Machine Operators and Tenders
+,,,51-9192,,"Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders"
+,,,51-9193,,Cooling and Freezing Equipment Operators and Tenders
+,,,51-9194,,Etchers and Engravers
+,,,51-9195,,"Molders, Shapers, and Casters, Except Metal and Plastic"
+,,,,51-9195.03,"Stone Cutters and Carvers, Manufacturing"
+,,,,51-9195.04,"Glass Blowers, Molders, Benders, and Finishers"
+,,,,51-9195.05,"Potters, Manufacturing"
+,,,51-9196,,"Paper Goods Machine Setters, Operators, and Tenders"
+,,,51-9197,,Tire Builders
+,,,51-9198,,Helpers--Production Workers
+,,,51-9199,,"Production Workers, All Other"
+53-0000,,,,,Transportation and Material Moving Occupations
+,53-1000,,,,Supervisors of Transportation and Material Moving Workers
+,,53-1040,,,First-Line Supervisors of Transportation and Material Moving Workers
+,,,53-1041,,Aircraft Cargo Handling Supervisors
+,,,53-1042,,"First-Line Supervisors of Helpers, Laborers, and Material Movers, Hand"
+,,,,53-1042.01,Recycling Coordinators
+,,,53-1043,,First-Line Supervisors of Material-Moving Machine and Vehicle Operators
+,,,53-1044,,First-Line Supervisors of Passenger Attendants
+,,,53-1049,,"First-Line Supervisors of Transportation Workers, All Other"
+,53-2000,,,,Air Transportation Workers
+,,53-2010,,,Aircraft Pilots and Flight Engineers
+,,,53-2011,,"Airline Pilots, Copilots, and Flight Engineers"
+,,,53-2012,,Commercial Pilots
+,,53-2020,,,Air Traffic Controllers and Airfield Operations Specialists
+,,,53-2021,,Air Traffic Controllers
+,,,53-2022,,Airfield Operations Specialists
+,,53-2030,,,Flight Attendants
+,,,53-2031,,Flight Attendants
+,53-3000,,,,Motor Vehicle Operators
+,,53-3010,,,"Ambulance Drivers and Attendants, Except Emergency Medical Technicians"
+,,,53-3011,,"Ambulance Drivers and Attendants, Except Emergency Medical Technicians"
+,,53-3030,,,Driver/Sales Workers and Truck Drivers
+,,,53-3031,,Driver/Sales Workers
+,,,53-3032,,Heavy and Tractor-Trailer Truck Drivers
+,,,53-3033,,Light Truck Drivers
+,,53-3050,,,Passenger Vehicle Drivers
+,,,53-3051,,"Bus Drivers, School"
+,,,53-3052,,"Bus Drivers, Transit and Intercity"
+,,,53-3053,,Shuttle Drivers and Chauffeurs
+,,,53-3054,,Taxi Drivers
+,,53-3090,,,Miscellaneous Motor Vehicle Operators
+,,,53-3099,,"Motor Vehicle Operators, All Other"
+,53-4000,,,,Rail Transportation Workers
+,,53-4010,,,Locomotive Engineers and Operators
+,,,53-4011,,Locomotive Engineers
+,,,53-4013,,"Rail Yard Engineers, Dinkey Operators, and Hostlers"
+,,53-4020,,,"Railroad Brake, Signal, and Switch Operators and Locomotive Firers"
+,,,53-4022,,"Railroad Brake, Signal, and Switch Operators and Locomotive Firers"
+,,53-4030,,,Railroad Conductors and Yardmasters
+,,,53-4031,,Railroad Conductors and Yardmasters
+,,53-4040,,,Subway and Streetcar Operators
+,,,53-4041,,Subway and Streetcar Operators
+,,53-4090,,,Miscellaneous Rail Transportation Workers
+,,,53-4099,,"Rail Transportation Workers, All Other"
+,53-5000,,,,Water Transportation Workers
+,,53-5010,,,Sailors and Marine Oilers
+,,,53-5011,,Sailors and Marine Oilers
+,,53-5020,,,Ship and Boat Captains and Operators
+,,,53-5021,,"Captains, Mates, and Pilots of Water Vessels"
+,,,53-5022,,Motorboat Operators
+,,53-5030,,,Ship Engineers
+,,,53-5031,,Ship Engineers
+,53-6000,,,,Other Transportation Workers
+,,53-6010,,,Bridge and Lock Tenders
+,,,53-6011,,Bridge and Lock Tenders
+,,53-6020,,,Parking Attendants
+,,,53-6021,,Parking Attendants
+,,53-6030,,,Transportation Service Attendants
+,,,53-6031,,Automotive and Watercraft Service Attendants
+,,,53-6032,,Aircraft Service Attendants
+,,53-6040,,,Traffic Technicians
+,,,53-6041,,Traffic Technicians
+,,53-6050,,,Transportation Inspectors
+,,,53-6051,,Transportation Inspectors
+,,,,53-6051.01,Aviation Inspectors
+,,,,53-6051.07,"Transportation Vehicle, Equipment and Systems Inspectors, Except Aviation"
+,,53-6060,,,Passenger Attendants
+,,,53-6061,,Passenger Attendants
+,,53-6090,,,Miscellaneous Transportation Workers
+,,,53-6099,,"Transportation Workers, All Other"
+,53-7000,,,,Material Moving Workers
+,,53-7010,,,Conveyor Operators and Tenders
+,,,53-7011,,Conveyor Operators and Tenders
+,,53-7020,,,Crane and Tower Operators
+,,,53-7021,,Crane and Tower Operators
+,,53-7030,,,Dredge Operators
+,,,53-7031,,Dredge Operators
+,,53-7040,,,Hoist and Winch Operators
+,,,53-7041,,Hoist and Winch Operators
+,,53-7050,,,Industrial Truck and Tractor Operators
+,,,53-7051,,Industrial Truck and Tractor Operators
+,,53-7060,,,Laborers and Material Movers
+,,,53-7061,,Cleaners of Vehicles and Equipment
+,,,53-7062,,"Laborers and Freight, Stock, and Material Movers, Hand"
+,,,,53-7062.04,Recycling and Reclamation Workers
+,,,53-7063,,Machine Feeders and Offbearers
+,,,53-7064,,"Packers and Packagers, Hand"
+,,,53-7065,,Stockers and Order Fillers
+,,53-7070,,,Pumping Station Operators
+,,,53-7071,,Gas Compressor and Gas Pumping Station Operators
+,,,53-7072,,"Pump Operators, Except Wellhead Pumpers"
+,,,53-7073,,Wellhead Pumpers
+,,53-7080,,,Refuse and Recyclable Material Collectors
+,,,53-7081,,Refuse and Recyclable Material Collectors
+,,53-7120,,,"Tank Car, Truck, and Ship Loaders"
+,,,53-7121,,"Tank Car, Truck, and Ship Loaders"
+,,53-7190,,,Miscellaneous Material Moving Workers
+,,,53-7199,,"Material Moving Workers, All Other"
+55-0000,,,,,Military Specific Occupations
+,55-1000,,,,Military Officer Special and Tactical Operations Leaders
+,,55-1010,,,Military Officer Special and Tactical Operations Leaders
+,,,55-1011,,Air Crew Officers
+,,,55-1012,,Aircraft Launch and Recovery Officers
+,,,55-1013,,Armored Assault Vehicle Officers
+,,,55-1014,,Artillery and Missile Officers
+,,,55-1015,,Command and Control Center Officers
+,,,55-1016,,Infantry Officers
+,,,55-1017,,Special Forces Officers
+,,,55-1019,,"Military Officer Special and Tactical Operations Leaders, All Other"
+,55-2000,,,,First-Line Enlisted Military Supervisors
+,,55-2010,,,First-Line Enlisted Military Supervisors
+,,,55-2011,,First-Line Supervisors of Air Crew Members
+,,,55-2012,,First-Line Supervisors of Weapons Specialists/Crew Members
+,,,55-2013,,First-Line Supervisors of All Other Tactical Operations Specialists
+,55-3000,,,,Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members
+,,55-3010,,,Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members
+,,,55-3011,,Air Crew Members
+,,,55-3012,,Aircraft Launch and Recovery Specialists
+,,,55-3013,,Armored Assault Vehicle Crew Members
+,,,55-3014,,Artillery and Missile Crew Members
+,,,55-3015,,Command and Control Center Specialists
+,,,55-3016,,Infantry
+,,,55-3018,,Special Forces
+,,,55-3019,,"Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members, All Other"

release_2025_02_10/automation_vs_augmentation.csv

@@ -0,0 +1,7 @@
+interaction_type,pct
+directive,22.563272409918948
+feedback loop,12.036303266190515
+learning,18.917648061953294
+none,2.9013020624347967
+task iteration,25.47648663831153
+validation,2.314220367546746

release_2025_02_10/bls_employment_may_2023.csv

@@ -0,0 +1,23 @@
+SOC or O*NET-SOC 2019 Title,bls_distribution
+Management Occupations,10495770
+Business and Financial Operations Occupations,10087830
+Computer and Mathematical Occupations,5177400
+Architecture and Engineering Occupations,2539660
+"Life, Physical, and Social Science Occupations",1389430
+Community and Social Service Occupations,2418130
+Legal Occupations,1240630
+Educational Instruction and Library Occupations,8744560
+"Arts, Design, Entertainment, Sports, and Media Occupations",2106490
+Healthcare Practitioners and Technical Occupations,9284210
+Healthcare Support Occupations,7063530
+Protective Service Occupations,3504330
+Food Preparation and Serving Related Occupations,13247870
+Building and Grounds Cleaning and Maintenance Occupations,4429070
+Personal Care and Service Occupations,3040630
+Sales and Related Occupations,13380660
+Office and Administrative Support Occupations,18533450
+"Farming, Fishing, and Forestry Occupations",432200
+Construction and Extraction Occupations,6225630
+"Installation, Maintenance, and Repair Occupations",5989460
+Production Occupations,8770170
+Transportation and Material Moving Occupations,13752760

release_2025_02_10/plots.ipynb

@@ -0,0 +1,767 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Which Economic Tasks are Performed with AI? Evidence from Millions of Claude Conversations\n",
+    "\n",
+    "_Handa et al., 2025_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from textwrap import wrap\n",
+    "import numpy as np\n",
+    "\n",
+    "palette = sns.color_palette(\"colorblind\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create O*NET / SOC Merged Dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_onet_soc_data() -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Merges O*NET task statements with SOC (Standard Occupational Classification) data\n",
+    "    based on major group codes.\n",
+    "    \n",
+    "    Args:\n",
+    "        onet_path (str): Path to the O*NET task statements CSV file\n",
+    "        soc_path (str): Path to the SOC structure CSV file\n",
+    "    \n",
+    "    Returns:\n",
+    "        pd.DataFrame: Merged DataFrame containing O*NET data with SOC major group titles\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # Read and process O*NET data\n",
+    "    onet_df = pd.read_csv(\"onet_task_statements.csv\")\n",
+    "    onet_df[\"soc_major_group\"] = onet_df[\"O*NET-SOC Code\"].str[:2]\n",
+    "    \n",
+    "    # Read and process SOC data\n",
+    "    soc_df = pd.read_csv(\"SOC_Structure.csv\")\n",
+    "    soc_df = soc_df.dropna(subset=['Major Group'])\n",
+    "    soc_df[\"soc_major_group\"] = soc_df[\"Major Group\"].str[:2]\n",
+    "    \n",
+    "    # Merge datasets\n",
+    "    merged_df = onet_df.merge(\n",
+    "        soc_df[['soc_major_group', 'SOC or O*NET-SOC 2019 Title']],\n",
+    "        on='soc_major_group',\n",
+    "        how='left'\n",
+    "    )\n",
+    "\n",
+    "    return merged_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_occupations_df = merge_onet_soc_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_occupations_df[\"Title\"].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Update cluster mappings to include data from the merged_df\n",
+    "task_occupations_df[\"task_normalized\"] = task_occupations_df[\"Task\"].str.lower().str.strip()\n",
+    "# Some tasks are included multiple times, so we need to count the number of occurrences per task\n",
+    "task_occupations_df[\"n_occurrences\"] = task_occupations_df.groupby(\"task_normalized\")[\"Title\"].transform(\"nunique\")\n",
+    "\n",
+    "task_occupations_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load Task Mappings and Join"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "task_mappings_df = pd.read_csv(\"onet_task_mappings.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_with_occupations = task_mappings_df.merge(\n",
+    "    task_occupations_df,\n",
+    "    left_on=\"task_name\",\n",
+    "    right_on=\"task_normalized\",\n",
+    "    how=\"left\"\n",
+    ")\n",
+    "\n",
+    "grouped_with_occupations[\"pct_occ_scaled\"] = 100 * (grouped_with_occupations[\"pct\"] / grouped_with_occupations[\"n_occurrences\"]) / (grouped_with_occupations[\"pct\"] / grouped_with_occupations[\"n_occurrences\"]).sum()\n",
+    "grouped_with_occupations[\"pct_occ_scaled\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_with_occupations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## EXPERIMENTS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### TASKS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set style and increase font sizes\n",
+    "plt.rcParams['font.size'] = 12  # Base font size\n",
+    "plt.rcParams['axes.titlesize'] = 14  # Title font size\n",
+    "plt.rcParams['axes.labelsize'] = 12  # Axis labels size\n",
+    "plt.rcParams['xtick.labelsize'] = 11  # X-axis tick labels size\n",
+    "plt.rcParams['ytick.labelsize'] = 11  # Y-axis tick labels size\n",
+    "plt.rcParams['legend.fontsize'] = 11  # Legend font size\n",
+    "plt.rcParams['figure.titlesize'] = 16  # Figure title size\n",
+    "\n",
+    "# If you're using seaborn, you can also set its context\n",
+    "sns.set_context(\"notebook\", font_scale=1.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get top 10 tasks overall to ensure consistent comparison\n",
+    "total_tasks = (grouped_with_occupations.groupby(\"Task\")[\"pct_occ_scaled\"]\n",
+    "               .sum()\n",
+    "               .sort_values(ascending=False))\n",
+    "top_10_tasks = total_tasks.head(10).index\n",
+    "\n",
+    "# Create plot dataframe with all groups\n",
+    "plot_df = (grouped_with_occupations[grouped_with_occupations[\"Task\"].isin(top_10_tasks)]\n",
+    "           .groupby([\"Task\"])[\"pct_occ_scaled\"]\n",
+    "           .sum()\n",
+    "           .reset_index())\n",
+    "\n",
+    "# Sort tasks by overall frequency\n",
+    "task_order = (plot_df.groupby(\"Task\")[\"pct_occ_scaled\"]\n",
+    "              .sum()\n",
+    "              .sort_values(ascending=False)\n",
+    "              .index)\n",
+    "plot_df[\"Task\"] = pd.Categorical(plot_df[\"Task\"], categories=task_order, ordered=True)\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(16, 12))\n",
+    "sns.barplot(\n",
+    "    data=plot_df,\n",
+    "    x=\"pct_occ_scaled\",\n",
+    "    y=\"Task\",\n",
+    "    color=palette[0],\n",
+    ")\n",
+    "\n",
+    "# Wrap task titles\n",
+    "ax = plt.gca()\n",
+    "ax.set_yticklabels(['\\n'.join(wrap(label.get_text(), width=40)) \n",
+    "                    for label in ax.get_yticklabels()])\n",
+    "\n",
+    "# Modify legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "\n",
+    "# Wrap task labels\n",
+    "ax = plt.gca()\n",
+    "ax.set_yticklabels(['\\n'.join(wrap(label.get_text(), width=40)) \n",
+    "                    for label in ax.get_yticklabels()])\n",
+    "\n",
+    "# Format x-axis as percentages\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.1f}%'))\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Top Tasks by % of Conversations')\n",
+    "plt.xlabel('Percentage of Records')\n",
+    "plt.ylabel('O*NET Task')\n",
+    "\n",
+    "# Adjust layout to prevent label cutoff\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OCCUPATIONS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_with_occupations.groupby(\"Title\")[\"pct_occ_scaled\"].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate percentages per group and occupation\n",
+    "plot_df = (grouped_with_occupations.groupby(\"Title\")[\"pct_occ_scaled\"]\n",
+    "           .sum()\n",
+    "           .reset_index())\n",
+    "\n",
+    "# Get top occupations overall\n",
+    "total_occs = (plot_df.groupby(\"Title\")[\"pct_occ_scaled\"]\n",
+    "              .sum()\n",
+    "              .sort_values(ascending=False))\n",
+    "top_occs = total_occs.head(15).index\n",
+    "\n",
+    "# Filter for top occupations\n",
+    "plot_df = plot_df[plot_df[\"Title\"].isin(top_occs)]\n",
+    "\n",
+    "# Sort occupations by overall frequency\n",
+    "occ_order = (plot_df.groupby(\"Title\")[\"pct_occ_scaled\"]\n",
+    "             .sum()\n",
+    "             .sort_values(ascending=False)\n",
+    "             .index)\n",
+    "plot_df[\"Title\"] = pd.Categorical(plot_df[\"Title\"], categories=occ_order, ordered=True)\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(18, 16))\n",
+    "sns.barplot(\n",
+    "    data=plot_df,\n",
+    "    x=\"pct_occ_scaled\",\n",
+    "    y=\"Title\",\n",
+    "    color=palette[0],\n",
+    ")\n",
+    "\n",
+    "# Wrap occupation titles\n",
+    "ax = plt.gca()\n",
+    "ax.set_yticklabels(['\\n'.join(wrap(label.get_text(), width=40)) \n",
+    "                    for label in ax.get_yticklabels()])\n",
+    "\n",
+    "# Format x-axis as percentages\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.1f}%'))\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Top Occupations by % of Conversations')\n",
+    "plt.xlabel('Percentage of Conversations')\n",
+    "plt.ylabel('Occupation')\n",
+    "\n",
+    "# Adjust layout to prevent label cutoff\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OCCUPATIONAL CATEGORIES"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate percentages per group and occupational category\n",
+    "plot_df = (grouped_with_occupations.groupby(\"SOC or O*NET-SOC 2019 Title\")[\"pct_occ_scaled\"]\n",
+    "           .sum()\n",
+    "           .reset_index())\n",
+    "\n",
+    "# Sort categories by group-1 frequency\n",
+    "cat_order = plot_df.sort_values(\"pct_occ_scaled\", ascending=False)[\"SOC or O*NET-SOC 2019 Title\"]\n",
+    "plot_df[\"SOC or O*NET-SOC 2019 Title\"] = pd.Categorical(\n",
+    "    plot_df[\"SOC or O*NET-SOC 2019 Title\"], \n",
+    "    categories=cat_order, \n",
+    "    ordered=True\n",
+    ")\n",
+    "\n",
+    "# Create the plot\n",
+    "plt.figure(figsize=(18, 16))\n",
+    "sns.barplot(\n",
+    "    data=plot_df,\n",
+    "    x=\"pct_occ_scaled\",\n",
+    "    y=\"SOC or O*NET-SOC 2019 Title\",\n",
+    "    color=palette[0],\n",
+    ")\n",
+    "\n",
+    "# Wrap category labels and remove \" Occupations\" string\n",
+    "ax = plt.gca()\n",
+    "ax.set_yticklabels(['\\n'.join(wrap(label.get_text().replace(\" Occupations\", \"\"), width=60)) \n",
+    "                    for label in ax.get_yticklabels()])\n",
+    "\n",
+    "# Format x-axis as percentages\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.1f}%'))\n",
+    "\n",
+    "# Customize the plot\n",
+    "plt.title('Occupational Categories by % of Conversations')\n",
+    "plt.xlabel('Percentage of Conversations')\n",
+    "plt.ylabel('Occupational Category')\n",
+    "\n",
+    "# Adjust layout to prevent label cutoff\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grouped_with_occupations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load employment data\n",
+    "bls_employment_df = pd.read_csv(\"bls_employment_may_2023.csv\")\n",
+    "\n",
+    "claude_employment_df = grouped_with_occupations.groupby(\"SOC or O*NET-SOC 2019 Title\")[\"pct_occ_scaled\"].sum().reset_index(name='claude_distribution')\n",
+    "\n",
+    "employment_df = claude_employment_df.merge(bls_employment_df, \n",
+    "                                     on='SOC or O*NET-SOC 2019 Title',\n",
+    "                                     how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Calculate percentages and setup data\n",
+    "plot_df = employment_df.copy()\n",
+    "\n",
+    "def get_distribution(df, value_column):\n",
+    "   total = df[value_column].sum()\n",
+    "   return (df[value_column] / total * 100).round(1)\n",
+    "\n",
+    "plot_df['bls_pct'] = get_distribution(plot_df, 'bls_distribution')\n",
+    "plot_df['claude_pct'] = get_distribution(plot_df, 'claude_distribution')\n",
+    "plot_df['clean_label'] = plot_df['SOC or O*NET-SOC 2019 Title'].str.replace(' Occupations', '')\n",
+    "plot_df['pct_difference'] = plot_df['claude_pct'] - plot_df['bls_pct']\n",
+    "plot_df = plot_df.sort_values('bls_pct', ascending=True)\n",
+    "\n",
+    "# Create the plot\n",
+    "fig, ax = plt.subplots(figsize=(20, 12))\n",
+    "\n",
+    "# Set colors\n",
+    "claude_color = palette[1] \n",
+    "bls_color = palette[0] \n",
+    "\n",
+    "# Create lines and circles\n",
+    "y_positions = range(len(plot_df))\n",
+    "for i, row in enumerate(plot_df.itertuples()):\n",
+    "   # Determine color based on which value is larger\n",
+    "   line_color = claude_color if row.claude_pct > row.bls_pct else bls_color\n",
+    "   \n",
+    "   # Draw the line between bls and claude percentages\n",
+    "   ax.plot([row.bls_pct, row.claude_pct], [i, i], \n",
+    "           color=line_color, \n",
+    "           linestyle='-', \n",
+    "           linewidth=2.5,\n",
+    "           zorder=1)\n",
+    "   \n",
+    "   # Determine label positioning\n",
+    "   if row.claude_pct > row.bls_pct:\n",
+    "       bls_ha = 'right'\n",
+    "       claude_ha = 'left'\n",
+    "       bls_offset = -0.4\n",
+    "       claude_offset = 0.4\n",
+    "   else:\n",
+    "       bls_ha = 'left'\n",
+    "       claude_ha = 'right'\n",
+    "       bls_offset = 0.4\n",
+    "       claude_offset = -0.4\n",
+    "\n",
+    "   # Plot BLS percentage\n",
+    "   ax.scatter([row.bls_pct], [i], \n",
+    "              color=bls_color,\n",
+    "              s=200,\n",
+    "              zorder=2,\n",
+    "              label='% of U.S. workers (BLS)' if i == 0 else \"\")\n",
+    "   ax.text(row.bls_pct + bls_offset,\n",
+    "           i,\n",
+    "           f'{row.bls_pct:.1f}%',\n",
+    "           ha=bls_ha,\n",
+    "           va='center',\n",
+    "           color=bls_color)\n",
+    "   \n",
+    "   # Plot Claude's percentage\n",
+    "   ax.scatter([row.claude_pct], [i], \n",
+    "              color=claude_color,\n",
+    "              s=200,\n",
+    "              zorder=2,\n",
+    "              label='% of Claude conversations' if i == 0 else \"\")\n",
+    "   ax.text(row.claude_pct + claude_offset,\n",
+    "           i,\n",
+    "           f'{row.claude_pct:.1f}%',\n",
+    "           ha=claude_ha,\n",
+    "           va='center',\n",
+    "           color=claude_color)\n",
+    "\n",
+    "# Customize the plot\n",
+    "ax.set_xlabel('Percentage')\n",
+    "ax.set_ylabel('Occupational Category')\n",
+    "\n",
+    "# Add percentage formatter to x-axis\n",
+    "ax.xaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x:.1f}%'))\n",
+    "\n",
+    "# Set y-axis labels\n",
+    "ax.set_yticks(y_positions)\n",
+    "ax.set_yticklabels(plot_df['clean_label'])\n",
+    "\n",
+    "# Add legend\n",
+    "handles, labels = ax.get_legend_handles_labels()\n",
+    "handles = handles[::-1]\n",
+    "labels = labels[::-1]\n",
+    "ax.legend(handles, labels, loc='lower right', bbox_to_anchor=(1.0, 0.0))\n",
+    "\n",
+    "# Adjust grid and layout\n",
+    "ax.grid(axis='x', linestyle='--', alpha=0.3)\n",
+    "ax.set_axisbelow(True)\n",
+    "\n",
+    "# Set axis limits with padding\n",
+    "max_val = max(plot_df['bls_pct'].max(), plot_df['claude_pct'].max())\n",
+    "min_val = min(plot_df['bls_pct'].min(), plot_df['claude_pct'].min())\n",
+    "padding = (max_val - min_val) * 0.15\n",
+    "ax.set_xlim(min_val - padding, max_val + padding)\n",
+    "ax.set_ylim(-1, len(plot_df))\n",
+    "\n",
+    "# Adjust layout\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### USAGE BY WAGE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read and process wage data\n",
+    "wage_df = pd.read_csv(\"wage_data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wage_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Join wage and occupation data\n",
+    "grouped_with_occupations_and_wage = grouped_with_occupations.merge(wage_df, left_on=\"O*NET-SOC Code\", right_on=\"SOCcode\", how=\"left\")\n",
+    "grouped_with_occupations_and_wage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def create_wage_distribution_plot(plot_df):\n",
+    "    # Create figure\n",
+    "    plt.figure(figsize=(24, 12))\n",
+    "    \n",
+    "    # Create scatter plot\n",
+    "    sns.scatterplot(data=plot_df,\n",
+    "                    x='MedianSalary',\n",
+    "                    y='pct_occ_scaled',\n",
+    "                    alpha=0.5,\n",
+    "                    size='pct_occ_scaled',\n",
+    "                    sizes=(60, 400),\n",
+    "                    color=palette[0],\n",
+    "                    legend=False)\n",
+    "    \n",
+    "    # Style the plot\n",
+    "    plt.xlabel('Median Wage ($)')\n",
+    "    plt.ylabel('Percent of Conversations')\n",
+    "    plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda y, _: '{:.1f}%'.format(y)))\n",
+    "    \n",
+    "    # Add title\n",
+    "    plt.title('Wage Distribution by % of Conversations'), \n",
+    "    \n",
+    "    # Annotate points\n",
+    "    # Top points by percentage\n",
+    "    top_n = 7\n",
+    "    for _, row in plot_df.nlargest(top_n, 'pct_occ_scaled').iterrows():\n",
+    "        plt.annotate('\\n'.join(wrap(row['Title'], width=20)), \n",
+    "                    (row['MedianSalary'], row['pct_occ_scaled']),\n",
+    "                    xytext=(5, 5), \n",
+    "                    textcoords='offset points')\n",
+    "    \n",
+    "    # Extreme salary points\n",
+    "    n_extremes = 2\n",
+    "    # Annotate lowest and highest salaries\n",
+    "    for df_subset in [plot_df.nsmallest(n_extremes, 'MedianSalary'),\n",
+    "                     plot_df.nlargest(n_extremes, 'MedianSalary')]:\n",
+    "        for i, row in enumerate(df_subset.iterrows()):\n",
+    "            if i != 0:  # Skip if already annotated in top_n\n",
+    "                plt.annotate('\\n'.join(wrap(row[1]['Title'], width=20)), \n",
+    "                            (row[1]['MedianSalary'], row[1]['pct_occ_scaled']),\n",
+    "                            xytext=(5, -15),\n",
+    "                            textcoords='offset points')\n",
+    "    \n",
+    "    # Formatting\n",
+    "    plt.ylim(bottom=0)\n",
+    "    plt.grid(True, linestyle='--', alpha=0.7)\n",
+    "    plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x:,.0f}'))\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    plt.show()\n",
+    "    plt.close()\n",
+    "\n",
+    "# Create aggregation dictionary, excluding groupby columns\n",
+    "groupby_cols = [\"Title\"]\n",
+    "agg_dict = {col: 'first' for col in grouped_with_occupations_and_wage.columns \n",
+    "            if col not in groupby_cols}\n",
+    "agg_dict['pct_occ_scaled'] = 'sum'\n",
+    "\n",
+    "\n",
+    "plot_df = (grouped_with_occupations_and_wage\n",
+    "            .groupby(groupby_cols)\n",
+    "            .agg(agg_dict)\n",
+    "            .reset_index()\n",
+    "            .copy())\n",
+    "    \n",
+    "# Filter out null values and very low salaries\n",
+    "plot_df = plot_df[plot_df[\"MedianSalary\"].notnull() & \n",
+    "                    (plot_df[\"MedianSalary\"] > 100)]\n",
+    "    \n",
+    "# Create and save plot for current group\n",
+    "create_wage_distribution_plot(plot_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### AUTOMATION VS AUGMENTATION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "automation_vs_augmentation_df = pd.read_csv(\"automation_vs_augmentation.csv\")\n",
+    "\n",
+    "def adjust_color_brightness(color, factor):\n",
+    "    \"\"\"Adjust the brightness of a color by a factor\"\"\"\n",
+    "    # Convert color to RGB if it's not already\n",
+    "    if isinstance(color, str):\n",
+    "        color = mcolors.to_rgb(color)\n",
+    "    # Make brighter by scaling RGB values\n",
+    "    return tuple(min(1.0, c * factor) for c in color)\n",
+    "\n",
+    "def plot_interaction_modes(df):\n",
+    "    # Load in dataframe\n",
+    "    plot_df = df.copy()\n",
+    "    \n",
+    "    # Convert cluster_name to lowercase first, then filter and normalize\n",
+    "    plot_df['interaction_type'] = plot_df['interaction_type'].str.lower()\n",
+    "    plot_df = plot_df[plot_df['interaction_type'] != 'none']\n",
+    "    total = plot_df['pct'].sum()\n",
+    "    plot_df['pct'] = plot_df['pct'] / total\n",
+    "    \n",
+    "    # Create category mapping\n",
+    "    category_map = {\n",
+    "        'directive': 'Automation',\n",
+    "        'feedback loop': 'Automation',\n",
+    "        'task iteration': 'Augmentation',\n",
+    "        'learning': 'Augmentation',\n",
+    "        'validation': 'Augmentation'\n",
+    "    }\n",
+    "    \n",
+    "    # Add category column\n",
+    "    plot_df['category'] = plot_df['interaction_type'].map(category_map)\n",
+    "    \n",
+    "    # Convert to title case for plotting\n",
+    "    plot_df['interaction_type'] = plot_df['interaction_type'].str.title()\n",
+    "    \n",
+    "    # Create color variants\n",
+    "    colors_a = [\n",
+    "        palette[1],\n",
+    "        adjust_color_brightness(palette[1], 1.3)\n",
+    "    ]\n",
+    "    \n",
+    "    colors_b = [\n",
+    "        palette[2],\n",
+    "        adjust_color_brightness(palette[2], 1.3),\n",
+    "        adjust_color_brightness(palette[2], 1.6)\n",
+    "    ]\n",
+    "    \n",
+    "    # Create the stacked bar plot\n",
+    "    plt.figure(figsize=(16, 6))\n",
+    "    \n",
+    "    # Create separate dataframes for each category and sort them to match visual order\n",
+    "    automation_df = plot_df[plot_df['category'] == 'Automation'].sort_values('interaction_type', ascending=False)\n",
+    "    augmentation_df = plot_df[plot_df['category'] == 'Augmentation'].sort_values('interaction_type', ascending=False)\n",
+    "    \n",
+    "    # Calculate positions for the bars\n",
+    "    bar_positions = [0, 1]\n",
+    "    bar_width = 0.8\n",
+    "    \n",
+    "    # Create the stacked bars for each category\n",
+    "    left_auto = 0\n",
+    "    handles, labels = [], []  # Initialize empty lists for legend ordering\n",
+    "    \n",
+    "    # First plot automation bars but save their handles/labels\n",
+    "    auto_handles, auto_labels = [], []\n",
+    "    for i, (_, row) in enumerate(automation_df.iterrows()):\n",
+    "        bar = plt.barh(0, row['pct'], left=left_auto, height=bar_width, \n",
+    "                color=colors_a[i])\n",
+    "        auto_handles.append(bar)\n",
+    "        auto_labels.append(row['interaction_type'])\n",
+    "        plt.text(left_auto + row['pct']/2, 0, \n",
+    "                f'{row[\"pct\"]*100:.1f}%', \n",
+    "                ha='center', va='center',\n",
+    "                color='white')\n",
+    "        left_auto += row['pct']\n",
+    "    \n",
+    "    # Plot augmentation bars and save handles/labels\n",
+    "    left_aug = 0\n",
+    "    aug_handles, aug_labels = [], []\n",
+    "    for i, (_, row) in enumerate(augmentation_df.iterrows()):\n",
+    "        bar = plt.barh(1, row['pct'], left=left_aug, height=bar_width,\n",
+    "                color=colors_b[i])\n",
+    "        aug_handles.append(bar)\n",
+    "        aug_labels.append(row['interaction_type'])\n",
+    "        plt.text(left_aug + row['pct']/2, 1, \n",
+    "                f'{row[\"pct\"]*100:.1f}%', \n",
+    "                ha='center', va='center',\n",
+    "                color='white')\n",
+    "        left_aug += row['pct']\n",
+    "    \n",
+    "    # Customize the plot\n",
+    "    plt.yticks(bar_positions, ['Automation', 'Augmentation'])\n",
+    "    plt.xlabel('Percentage of Conversations')\n",
+    "    \n",
+    "    # Create legend with custom order\n",
+    "    # Combine handles and labels in the desired order\n",
+    "    all_handles = aug_handles + auto_handles\n",
+    "    all_labels = aug_labels + auto_labels\n",
+    "    \n",
+    "    # Create legend with specified order\n",
+    "    desired_order = ['Validation', 'Task Iteration', 'Learning', 'Feedback Loop', 'Directive'] \n",
+    "    ordered_handles = []\n",
+    "    ordered_labels = []\n",
+    "    \n",
+    "    for label in desired_order:\n",
+    "        idx = all_labels.index(label)\n",
+    "        ordered_handles.append(all_handles[idx])\n",
+    "        ordered_labels.append(all_labels[idx])\n",
+    "    \n",
+    "    plt.legend(ordered_handles, ordered_labels, loc='lower right')\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "plot_interaction_modes(automation_vs_augmentation_df)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py311",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

release_2025_03_27/README.md

@@ -0,0 +1,63 @@
+# Anthropic Economic Index: Insights from Claude 3.7 Sonnet
+## Analysis Replication Notebook
+
+This notebook contains the code used to produce the visualizations and analysis for the Anthropic Economic Index report based on Claude 3.7 Sonnet data. It analyzes how different occupations interact with AI systems through automation and augmentation patterns derived from real-world usage data.
+
+## Data Files in this Directory
+
+- **cluster_level_dataset**: A folder containing data released at the cluster level, including mappings to O*NET tasks, automation vs. augmentation, and "extended thinking" mode fraction
+- **onet_task_statements.csv**: Contains O*NET task statements with their associated occupational codes
+- **SOC_Structure.csv**: Standard Occupational Classification (SOC) structure data with major group codes and titles
+- **task_pct_v1.csv**: Version 1 of task percentage data
+- **task_pct_v2.csv**: Version 2 of task percentage data (current)
+- **automation_vs_augmentation_by_task.csv**: Data on automation vs. augmentation classifications by task
+- **automation_vs_augmentation_v1.csv**: Version 1 of automation vs. augmentation interaction type data
+- **automation_vs_augmentation_v2.csv**: Version 2 of automation vs. augmentation interaction type data
+- **task_thinking_fractions.csv**: Fraction of each O*NET task with its associated "extended thinking" mode fraction
+
+## Data Dictionary
+
+### onet_task_statements.csv
+| Field | Description |
+|-------|-------------|
+| O*NET-SOC Code | Occupational code from the O*NET-SOC system |
+| Title | Occupational title |
+| Task | Description of specific occupational task |
+
+### SOC_Structure.csv
+| Field | Description |
+|-------|-------------|
+| Major Group | Two-digit code identifying major occupational group |
+| SOC or O*NET-SOC 2019 Title | Title of the major occupational group |
+
+### task_pct_v1.csv and task_pct_v2.csv
+| Field | Description |
+|-------|-------------|
+| task_name | Normalized name of the task |
+| pct | Percentage of task prevalence in dataset |
+
+### automation_vs_augmentation_by_task.csv
+| Field | Description |
+|-------|-------------|
+| task_name | Normalized name of the task |
+| directive | Ratio indicating directive automation pattern (0-1) |
+| feedback_loop | Ratio indicating feedback loop automation pattern (0-1) |
+| validation | Ratio indicating validation augmentation pattern (0-1) |
+| task_iteration | Ratio indicating task iteration augmentation pattern (0-1) |
+| learning | Ratio indicating learning augmentation pattern (0-1) |
+| filtered | Ratio indicating filtered (excluded) tasks (0-1) |
+
+### automation_vs_augmentation_v1.csv and automation_vs_augmentation_v2.csv
+| Field | Description |
+|-------|-------------|
+| interaction_type | Type of interaction (directive, feedback loop, validation, task iteration, learning, none) |
+| pct | Percentage of this interaction type in the dataset |
+
+### task_thinking_fractions.csv
+| Field | Description |
+|-------|-------------|
+| task_name | Normalized name of the task |
+| thinking_fraction | Ratio of this task that used extended thinking mode |
+
+## Running the analysis
+Open `v2_report_replication.ipynb` in a notebook editor and run the cells in order.

release_2025_03_27/SOC_Structure.csv

@@ -0,0 +1,1597 @@
+Major Group,Minor Group,Broad Occupation,Detailed Occupation,Detailed O*NET-SOC,SOC or O*NET-SOC 2019 Title
+11-0000,,,,,Management Occupations
+,11-1000,,,,Top Executives
+,,11-1010,,,Chief Executives
+,,,11-1011,,Chief Executives
+,,,,11-1011.03,Chief Sustainability Officers
+,,11-1020,,,General and Operations Managers
+,,,11-1021,,General and Operations Managers
+,,11-1030,,,Legislators
+,,,11-1031,,Legislators
+,11-2000,,,,"Advertising, Marketing, Promotions, Public Relations, and Sales Managers"
+,,11-2010,,,Advertising and Promotions Managers
+,,,11-2011,,Advertising and Promotions Managers
+,,11-2020,,,Marketing and Sales Managers
+,,,11-2021,,Marketing Managers
+,,,11-2022,,Sales Managers
+,,11-2030,,,Public Relations and Fundraising Managers
+,,,11-2032,,Public Relations Managers
+,,,11-2033,,Fundraising Managers
+,11-3000,,,,Operations Specialties Managers
+,,11-3010,,,Administrative Services and Facilities Managers
+,,,11-3012,,Administrative Services Managers
+,,,11-3013,,Facilities Managers
+,,,,11-3013.01,Security Managers
+,,11-3020,,,Computer and Information Systems Managers
+,,,11-3021,,Computer and Information Systems Managers
+,,11-3030,,,Financial Managers
+,,,11-3031,,Financial Managers
+,,,,11-3031.01,Treasurers and Controllers
+,,,,11-3031.03,Investment Fund Managers
+,,11-3050,,,Industrial Production Managers
+,,,11-3051,,Industrial Production Managers
+,,,,11-3051.01,Quality Control Systems Managers
+,,,,11-3051.02,Geothermal Production Managers
+,,,,11-3051.03,Biofuels Production Managers
+,,,,11-3051.04,Biomass Power Plant Managers
+,,,,11-3051.06,Hydroelectric Production Managers
+,,11-3060,,,Purchasing Managers
+,,,11-3061,,Purchasing Managers
+,,11-3070,,,"Transportation, Storage, and Distribution Managers"
+,,,11-3071,,"Transportation, Storage, and Distribution Managers"
+,,,,11-3071.04,Supply Chain Managers
+,,11-3110,,,Compensation and Benefits Managers
+,,,11-3111,,Compensation and Benefits Managers
+,,11-3120,,,Human Resources Managers
+,,,11-3121,,Human Resources Managers
+,,11-3130,,,Training and Development Managers
+,,,11-3131,,Training and Development Managers
+,11-9000,,,,Other Management Occupations
+,,11-9010,,,"Farmers, Ranchers, and Other Agricultural Managers"
+,,,11-9013,,"Farmers, Ranchers, and Other Agricultural Managers"
+,,11-9020,,,Construction Managers
+,,,11-9021,,Construction Managers
+,,11-9030,,,Education and Childcare Administrators
+,,,11-9031,,"Education and Childcare Administrators, Preschool and Daycare"
+,,,11-9032,,"Education Administrators, Kindergarten through Secondary"
+,,,11-9033,,"Education Administrators, Postsecondary"
+,,,11-9039,,"Education Administrators, All Other"
+,,11-9040,,,Architectural and Engineering Managers
+,,,11-9041,,Architectural and Engineering Managers
+,,,,11-9041.01,Biofuels/Biodiesel Technology and Product Development Managers
+,,11-9050,,,Food Service Managers
+,,,11-9051,,Food Service Managers
+,,11-9070,,,Entertainment and Recreation Managers
+,,,11-9071,,Gambling Managers
+,,,11-9072,,"Entertainment and Recreation Managers, Except Gambling"
+,,11-9080,,,Lodging Managers
+,,,11-9081,,Lodging Managers
+,,11-9110,,,Medical and Health Services Managers
+,,,11-9111,,Medical and Health Services Managers
+,,11-9120,,,Natural Sciences Managers
+,,,11-9121,,Natural Sciences Managers
+,,,,11-9121.01,Clinical Research Coordinators
+,,,,11-9121.02,Water Resource Specialists
+,,11-9130,,,Postmasters and Mail Superintendents
+,,,11-9131,,Postmasters and Mail Superintendents
+,,11-9140,,,"Property, Real Estate, and Community Association Managers"
+,,,11-9141,,"Property, Real Estate, and Community Association Managers"
+,,11-9150,,,Social and Community Service Managers
+,,,11-9151,,Social and Community Service Managers
+,,11-9160,,,Emergency Management Directors
+,,,11-9161,,Emergency Management Directors
+,,11-9170,,,Personal Service Managers
+,,,11-9171,,Funeral Home Managers
+,,,11-9179,,"Personal Service Managers, All Other"
+,,,,11-9179.01,Fitness and Wellness Coordinators
+,,,,11-9179.02,Spa Managers
+,,11-9190,,,Miscellaneous Managers
+,,,11-9199,,"Managers, All Other"
+,,,,11-9199.01,Regulatory Affairs Managers
+,,,,11-9199.02,Compliance Managers
+,,,,11-9199.08,Loss Prevention Managers
+,,,,11-9199.09,Wind Energy Operations Managers
+,,,,11-9199.10,Wind Energy Development Managers
+,,,,11-9199.11,Brownfield Redevelopment Specialists and Site Managers
+13-0000,,,,,Business and Financial Operations Occupations
+,13-1000,,,,Business Operations Specialists
+,,13-1010,,,"Agents and Business Managers of Artists, Performers, and Athletes"
+,,,13-1011,,"Agents and Business Managers of Artists, Performers, and Athletes"
+,,13-1020,,,Buyers and Purchasing Agents
+,,,13-1021,,"Buyers and Purchasing Agents, Farm Products"
+,,,13-1022,,"Wholesale and Retail Buyers, Except Farm Products"
+,,,13-1023,,"Purchasing Agents, Except Wholesale, Retail, and Farm Products"
+,,13-1030,,,"Claims Adjusters, Appraisers, Examiners, and Investigators"
+,,,13-1031,,"Claims Adjusters, Examiners, and Investigators"
+,,,13-1032,,"Insurance Appraisers, Auto Damage"
+,,13-1040,,,Compliance Officers
+,,,13-1041,,Compliance Officers
+,,,,13-1041.01,Environmental Compliance Inspectors
+,,,,13-1041.03,Equal Opportunity Representatives and Officers
+,,,,13-1041.04,Government Property Inspectors and Investigators
+,,,,13-1041.06,Coroners
+,,,,13-1041.07,Regulatory Affairs Specialists
+,,,,13-1041.08,Customs Brokers
+,,13-1050,,,Cost Estimators
+,,,13-1051,,Cost Estimators
+,,13-1070,,,Human Resources Workers
+,,,13-1071,,Human Resources Specialists
+,,,13-1074,,Farm Labor Contractors
+,,,13-1075,,Labor Relations Specialists
+,,13-1080,,,Logisticians and Project Management Specialists
+,,,13-1081,,Logisticians
+,,,,13-1081.01,Logistics Engineers
+,,,,13-1081.02,Logistics Analysts
+,,,13-1082,,Project Management Specialists
+,,13-1110,,,Management Analysts
+,,,13-1111,,Management Analysts
+,,13-1120,,,"Meeting, Convention, and Event Planners"
+,,,13-1121,,"Meeting, Convention, and Event Planners"
+,,13-1130,,,Fundraisers
+,,,13-1131,,Fundraisers
+,,13-1140,,,"Compensation, Benefits, and Job Analysis Specialists"
+,,,13-1141,,"Compensation, Benefits, and Job Analysis Specialists"
+,,13-1150,,,Training and Development Specialists
+,,,13-1151,,Training and Development Specialists
+,,13-1160,,,Market Research Analysts and Marketing Specialists
+,,,13-1161,,Market Research Analysts and Marketing Specialists
+,,,,13-1161.01,Search Marketing Strategists
+,,13-1190,,,Miscellaneous Business Operations Specialists
+,,,13-1199,,"Business Operations Specialists, All Other"
+,,,,13-1199.04,Business Continuity Planners
+,,,,13-1199.05,Sustainability Specialists
+,,,,13-1199.06,Online Merchants
+,,,,13-1199.07,Security Management Specialists
+,13-2000,,,,Financial Specialists
+,,13-2010,,,Accountants and Auditors
+,,,13-2011,,Accountants and Auditors
+,,13-2020,,,Property Appraisers and Assessors
+,,,13-2022,,Appraisers of Personal and Business Property
+,,,13-2023,,Appraisers and Assessors of Real Estate
+,,13-2030,,,Budget Analysts
+,,,13-2031,,Budget Analysts
+,,13-2040,,,Credit Analysts
+,,,13-2041,,Credit Analysts
+,,13-2050,,,Financial Analysts and Advisors
+,,,13-2051,,Financial and Investment Analysts
+,,,13-2052,,Personal Financial Advisors
+,,,13-2053,,Insurance Underwriters
+,,,13-2054,,Financial Risk Specialists
+,,13-2060,,,Financial Examiners
+,,,13-2061,,Financial Examiners
+,,13-2070,,,Credit Counselors and Loan Officers
+,,,13-2071,,Credit Counselors
+,,,13-2072,,Loan Officers
+,,13-2080,,,"Tax Examiners, Collectors and Preparers, and Revenue Agents"
+,,,13-2081,,"Tax Examiners and Collectors, and Revenue Agents"
+,,,13-2082,,Tax Preparers
+,,13-2090,,,Miscellaneous Financial Specialists
+,,,13-2099,,"Financial Specialists, All Other"
+,,,,13-2099.01,Financial Quantitative Analysts
+,,,,13-2099.04,"Fraud Examiners, Investigators and Analysts"
+15-0000,,,,,Computer and Mathematical Occupations
+,15-1200,,,,Computer Occupations
+,,15-1210,,,Computer and Information Analysts
+,,,15-1211,,Computer Systems Analysts
+,,,,15-1211.01,Health Informatics Specialists
+,,,15-1212,,Information Security Analysts
+,,15-1220,,,Computer and Information Research Scientists
+,,,15-1221,,Computer and Information Research Scientists
+,,15-1230,,,Computer Support Specialists
+,,,15-1231,,Computer Network Support Specialists
+,,,15-1232,,Computer User Support Specialists
+,,15-1240,,,Database and Network Administrators and Architects
+,,,15-1241,,Computer Network Architects
+,,,,15-1241.01,Telecommunications Engineering Specialists
+,,,15-1242,,Database Administrators
+,,,15-1243,,Database Architects
+,,,,15-1243.01,Data Warehousing Specialists
+,,,15-1244,,Network and Computer Systems Administrators
+,,15-1250,,,"Software and Web Developers, Programmers, and Testers"
+,,,15-1251,,Computer Programmers
+,,,15-1252,,Software Developers
+,,,15-1253,,Software Quality Assurance Analysts and Testers
+,,,15-1254,,Web Developers
+,,,15-1255,,Web and Digital Interface Designers
+,,,,15-1255.01,Video Game Designers
+,,15-1290,,,Miscellaneous Computer Occupations
+,,,15-1299,,"Computer Occupations, All Other"
+,,,,15-1299.01,Web Administrators
+,,,,15-1299.02,Geographic Information Systems Technologists and Technicians
+,,,,15-1299.03,Document Management Specialists
+,,,,15-1299.04,Penetration Testers
+,,,,15-1299.05,Information Security Engineers
+,,,,15-1299.06,Digital Forensics Analysts
+,,,,15-1299.07,Blockchain Engineers
+,,,,15-1299.08,Computer Systems Engineers/Architects
+,,,,15-1299.09,Information Technology Project Managers
+,15-2000,,,,Mathematical Science Occupations
+,,15-2010,,,Actuaries
+,,,15-2011,,Actuaries
+,,15-2020,,,Mathematicians
+,,,15-2021,,Mathematicians
+,,15-2030,,,Operations Research Analysts
+,,,15-2031,,Operations Research Analysts
+,,15-2040,,,Statisticians
+,,,15-2041,,Statisticians
+,,,,15-2041.01,Biostatisticians
+,,15-2050,,,Data Scientists
+,,,15-2051,,Data Scientists
+,,,,15-2051.01,Business Intelligence Analysts
+,,,,15-2051.02,Clinical Data Managers
+,,15-2090,,,Miscellaneous Mathematical Science Occupations
+,,,15-2099,,"Mathematical Science Occupations, All Other"
+,,,,15-2099.01,Bioinformatics Technicians
+17-0000,,,,,Architecture and Engineering Occupations
+,17-1000,,,,"Architects, Surveyors, and Cartographers"
+,,17-1010,,,"Architects, Except Naval"
+,,,17-1011,,"Architects, Except Landscape and Naval"
+,,,17-1012,,Landscape Architects
+,,17-1020,,,"Surveyors, Cartographers, and Photogrammetrists"
+,,,17-1021,,Cartographers and Photogrammetrists
+,,,17-1022,,Surveyors
+,,,,17-1022.01,Geodetic Surveyors
+,17-2000,,,,Engineers
+,,17-2010,,,Aerospace Engineers
+,,,17-2011,,Aerospace Engineers
+,,17-2020,,,Agricultural Engineers
+,,,17-2021,,Agricultural Engineers
+,,17-2030,,,Bioengineers and Biomedical Engineers
+,,,17-2031,,Bioengineers and Biomedical Engineers
+,,17-2040,,,Chemical Engineers
+,,,17-2041,,Chemical Engineers
+,,17-2050,,,Civil Engineers
+,,,17-2051,,Civil Engineers
+,,,,17-2051.01,Transportation Engineers
+,,,,17-2051.02,Water/Wastewater Engineers
+,,17-2060,,,Computer Hardware Engineers
+,,,17-2061,,Computer Hardware Engineers
+,,17-2070,,,Electrical and Electronics Engineers
+,,,17-2071,,Electrical Engineers
+,,,17-2072,,"Electronics Engineers, Except Computer"
+,,,,17-2072.01,Radio Frequency Identification Device Specialists
+,,17-2080,,,Environmental Engineers
+,,,17-2081,,Environmental Engineers
+,,17-2110,,,"Industrial Engineers, Including Health and Safety"
+,,,17-2111,,"Health and Safety Engineers, Except Mining Safety Engineers and Inspectors"
+,,,,17-2111.02,Fire-Prevention and Protection Engineers
+,,,17-2112,,Industrial Engineers
+,,,,17-2112.01,Human Factors Engineers and Ergonomists
+,,,,17-2112.02,Validation Engineers
+,,,,17-2112.03,Manufacturing Engineers
+,,17-2120,,,Marine Engineers and Naval Architects
+,,,17-2121,,Marine Engineers and Naval Architects
+,,17-2130,,,Materials Engineers
+,,,17-2131,,Materials Engineers
+,,17-2140,,,Mechanical Engineers
+,,,17-2141,,Mechanical Engineers
+,,,,17-2141.01,Fuel Cell Engineers
+,,,,17-2141.02,Automotive Engineers
+,,17-2150,,,"Mining and Geological Engineers, Including Mining Safety Engineers"
+,,,17-2151,,"Mining and Geological Engineers, Including Mining Safety Engineers"
+,,17-2160,,,Nuclear Engineers
+,,,17-2161,,Nuclear Engineers
+,,17-2170,,,Petroleum Engineers
+,,,17-2171,,Petroleum Engineers
+,,17-2190,,,Miscellaneous Engineers
+,,,17-2199,,"Engineers, All Other"
+,,,,17-2199.03,"Energy Engineers, Except Wind and Solar"
+,,,,17-2199.05,Mechatronics Engineers
+,,,,17-2199.06,Microsystems Engineers
+,,,,17-2199.07,Photonics Engineers
+,,,,17-2199.08,Robotics Engineers
+,,,,17-2199.09,Nanosystems Engineers
+,,,,17-2199.10,Wind Energy Engineers
+,,,,17-2199.11,Solar Energy Systems Engineers
+,17-3000,,,,"Drafters, Engineering Technicians, and Mapping Technicians"
+,,17-3010,,,Drafters
+,,,17-3011,,Architectural and Civil Drafters
+,,,17-3012,,Electrical and Electronics Drafters
+,,,17-3013,,Mechanical Drafters
+,,,17-3019,,"Drafters, All Other"
+,,17-3020,,,"Engineering Technologists and Technicians, Except Drafters"
+,,,17-3021,,Aerospace Engineering and Operations Technologists and Technicians
+,,,17-3022,,Civil Engineering Technologists and Technicians
+,,,17-3023,,Electrical and Electronic Engineering Technologists and Technicians
+,,,17-3024,,Electro-Mechanical and Mechatronics Technologists and Technicians
+,,,,17-3024.01,Robotics Technicians
+,,,17-3025,,Environmental Engineering Technologists and Technicians
+,,,17-3026,,Industrial Engineering Technologists and Technicians
+,,,,17-3026.01,Nanotechnology Engineering Technologists and Technicians
+,,,17-3027,,Mechanical Engineering Technologists and Technicians
+,,,,17-3027.01,Automotive Engineering Technicians
+,,,17-3028,,Calibration Technologists and Technicians
+,,,17-3029,,"Engineering Technologists and Technicians, Except Drafters, All Other"
+,,,,17-3029.01,Non-Destructive Testing Specialists
+,,,,17-3029.08,Photonics Technicians
+,,17-3030,,,Surveying and Mapping Technicians
+,,,17-3031,,Surveying and Mapping Technicians
+19-0000,,,,,"Life, Physical, and Social Science Occupations"
+,19-1000,,,,Life Scientists
+,,19-1010,,,Agricultural and Food Scientists
+,,,19-1011,,Animal Scientists
+,,,19-1012,,Food Scientists and Technologists
+,,,19-1013,,Soil and Plant Scientists
+,,19-1020,,,Biological Scientists
+,,,19-1021,,Biochemists and Biophysicists
+,,,19-1022,,Microbiologists
+,,,19-1023,,Zoologists and Wildlife Biologists
+,,,19-1029,,"Biological Scientists, All Other"
+,,,,19-1029.01,Bioinformatics Scientists
+,,,,19-1029.02,Molecular and Cellular Biologists
+,,,,19-1029.03,Geneticists
+,,,,19-1029.04,Biologists
+,,19-1030,,,Conservation Scientists and Foresters
+,,,19-1031,,Conservation Scientists
+,,,,19-1031.02,Range Managers
+,,,,19-1031.03,Park Naturalists
+,,,19-1032,,Foresters
+,,19-1040,,,Medical Scientists
+,,,19-1041,,Epidemiologists
+,,,19-1042,,"Medical Scientists, Except Epidemiologists"
+,,19-1090,,,Miscellaneous Life Scientists
+,,,19-1099,,"Life Scientists, All Other"
+,19-2000,,,,Physical Scientists
+,,19-2010,,,Astronomers and Physicists
+,,,19-2011,,Astronomers
+,,,19-2012,,Physicists
+,,19-2020,,,Atmospheric and Space Scientists
+,,,19-2021,,Atmospheric and Space Scientists
+,,19-2030,,,Chemists and Materials Scientists
+,,,19-2031,,Chemists
+,,,19-2032,,Materials Scientists
+,,19-2040,,,Environmental Scientists and Geoscientists
+,,,19-2041,,"Environmental Scientists and Specialists, Including Health"
+,,,,19-2041.01,Climate Change Policy Analysts
+,,,,19-2041.02,Environmental Restoration Planners
+,,,,19-2041.03,Industrial Ecologists
+,,,19-2042,,"Geoscientists, Except Hydrologists and Geographers"
+,,,19-2043,,Hydrologists
+,,19-2090,,,Miscellaneous Physical Scientists
+,,,19-2099,,"Physical Scientists, All Other"
+,,,,19-2099.01,Remote Sensing Scientists and Technologists
+,19-3000,,,,Social Scientists and Related Workers
+,,19-3010,,,Economists
+,,,19-3011,,Economists
+,,,,19-3011.01,Environmental Economists
+,,19-3020,,,Survey Researchers
+,,,19-3022,,Survey Researchers
+,,19-3030,,,Psychologists
+,,,19-3032,,Industrial-Organizational Psychologists
+,,,19-3033,,Clinical and Counseling Psychologists
+,,,19-3034,,School Psychologists
+,,,19-3039,,"Psychologists, All Other"
+,,,,19-3039.02,Neuropsychologists
+,,,,19-3039.03,Clinical Neuropsychologists
+,,19-3040,,,Sociologists
+,,,19-3041,,Sociologists
+,,19-3050,,,Urban and Regional Planners
+,,,19-3051,,Urban and Regional Planners
+,,19-3090,,,Miscellaneous Social Scientists and Related Workers
+,,,19-3091,,Anthropologists and Archeologists
+,,,19-3092,,Geographers
+,,,19-3093,,Historians
+,,,19-3094,,Political Scientists
+,,,19-3099,,"Social Scientists and Related Workers, All Other"
+,,,,19-3099.01,Transportation Planners
+,19-4000,,,,"Life, Physical, and Social Science Technicians"
+,,19-4010,,,Agricultural and Food Science Technicians
+,,,19-4012,,Agricultural Technicians
+,,,,19-4012.01,Precision Agriculture Technicians
+,,,19-4013,,Food Science Technicians
+,,19-4020,,,Biological Technicians
+,,,19-4021,,Biological Technicians
+,,19-4030,,,Chemical Technicians
+,,,19-4031,,Chemical Technicians
+,,19-4040,,,Environmental Science and Geoscience Technicians
+,,,19-4042,,"Environmental Science and Protection Technicians, Including Health"
+,,,19-4043,,"Geological Technicians, Except Hydrologic Technicians"
+,,,19-4044,,Hydrologic Technicians
+,,19-4050,,,Nuclear Technicians
+,,,19-4051,,Nuclear Technicians
+,,,,19-4051.02,Nuclear Monitoring Technicians
+,,19-4060,,,Social Science Research Assistants
+,,,19-4061,,Social Science Research Assistants
+,,19-4070,,,Forest and Conservation Technicians
+,,,19-4071,,Forest and Conservation Technicians
+,,19-4090,,,"Miscellaneous Life, Physical, and Social Science Technicians"
+,,,19-4092,,Forensic Science Technicians
+,,,19-4099,,"Life, Physical, and Social Science Technicians, All Other"
+,,,,19-4099.01,Quality Control Analysts
+,,,,19-4099.03,Remote Sensing Technicians
+,19-5000,,,,Occupational Health and Safety Specialists and Technicians
+,,19-5010,,,Occupational Health and Safety Specialists and Technicians
+,,,19-5011,,Occupational Health and Safety Specialists
+,,,19-5012,,Occupational Health and Safety Technicians
+21-0000,,,,,Community and Social Service Occupations
+,21-1000,,,,"Counselors, Social Workers, and Other Community and Social Service Specialists"
+,,21-1010,,,Counselors
+,,,21-1011,,Substance Abuse and Behavioral Disorder Counselors
+,,,21-1012,,"Educational, Guidance, and Career Counselors and Advisors"
+,,,21-1013,,Marriage and Family Therapists
+,,,21-1014,,Mental Health Counselors
+,,,21-1015,,Rehabilitation Counselors
+,,,21-1019,,"Counselors, All Other"
+,,21-1020,,,Social Workers
+,,,21-1021,,"Child, Family, and School Social Workers"
+,,,21-1022,,Healthcare Social Workers
+,,,21-1023,,Mental Health and Substance Abuse Social Workers
+,,,21-1029,,"Social Workers, All Other"
+,,21-1090,,,Miscellaneous Community and Social Service Specialists
+,,,21-1091,,Health Education Specialists
+,,,21-1092,,Probation Officers and Correctional Treatment Specialists
+,,,21-1093,,Social and Human Service Assistants
+,,,21-1094,,Community Health Workers
+,,,21-1099,,"Community and Social Service Specialists, All Other"
+,21-2000,,,,Religious Workers
+,,21-2010,,,Clergy
+,,,21-2011,,Clergy
+,,21-2020,,,"Directors, Religious Activities and Education"
+,,,21-2021,,"Directors, Religious Activities and Education"
+,,21-2090,,,Miscellaneous Religious Workers
+,,,21-2099,,"Religious Workers, All Other"
+23-0000,,,,,Legal Occupations
+,23-1000,,,,"Lawyers, Judges, and Related Workers"
+,,23-1010,,,Lawyers and Judicial Law Clerks
+,,,23-1011,,Lawyers
+,,,23-1012,,Judicial Law Clerks
+,,23-1020,,,"Judges, Magistrates, and Other Judicial Workers"
+,,,23-1021,,"Administrative Law Judges, Adjudicators, and Hearing Officers"
+,,,23-1022,,"Arbitrators, Mediators, and Conciliators"
+,,,23-1023,,"Judges, Magistrate Judges, and Magistrates"
+,23-2000,,,,Legal Support Workers
+,,23-2010,,,Paralegals and Legal Assistants
+,,,23-2011,,Paralegals and Legal Assistants
+,,23-2090,,,Miscellaneous Legal Support Workers
+,,,23-2093,,"Title Examiners, Abstractors, and Searchers"
+,,,23-2099,,"Legal Support Workers, All Other"
+25-0000,,,,,Educational Instruction and Library Occupations
+,25-1000,,,,Postsecondary Teachers
+,,25-1010,,,"Business Teachers, Postsecondary"
+,,,25-1011,,"Business Teachers, Postsecondary"
+,,25-1020,,,"Math and Computer Science Teachers, Postsecondary"
+,,,25-1021,,"Computer Science Teachers, Postsecondary"
+,,,25-1022,,"Mathematical Science Teachers, Postsecondary"
+,,25-1030,,,"Engineering and Architecture Teachers, Postsecondary"
+,,,25-1031,,"Architecture Teachers, Postsecondary"
+,,,25-1032,,"Engineering Teachers, Postsecondary"
+,,25-1040,,,"Life Sciences Teachers, Postsecondary"
+,,,25-1041,,"Agricultural Sciences Teachers, Postsecondary"
+,,,25-1042,,"Biological Science Teachers, Postsecondary"
+,,,25-1043,,"Forestry and Conservation Science Teachers, Postsecondary"
+,,25-1050,,,"Physical Sciences Teachers, Postsecondary"
+,,,25-1051,,"Atmospheric, Earth, Marine, and Space Sciences Teachers, Postsecondary"
+,,,25-1052,,"Chemistry Teachers, Postsecondary"
+,,,25-1053,,"Environmental Science Teachers, Postsecondary"
+,,,25-1054,,"Physics Teachers, Postsecondary"
+,,25-1060,,,"Social Sciences Teachers, Postsecondary"
+,,,25-1061,,"Anthropology and Archeology Teachers, Postsecondary"
+,,,25-1062,,"Area, Ethnic, and Cultural Studies Teachers, Postsecondary"
+,,,25-1063,,"Economics Teachers, Postsecondary"
+,,,25-1064,,"Geography Teachers, Postsecondary"
+,,,25-1065,,"Political Science Teachers, Postsecondary"
+,,,25-1066,,"Psychology Teachers, Postsecondary"
+,,,25-1067,,"Sociology Teachers, Postsecondary"
+,,,25-1069,,"Social Sciences Teachers, Postsecondary, All Other"
+,,25-1070,,,"Health Teachers, Postsecondary"
+,,,25-1071,,"Health Specialties Teachers, Postsecondary"
+,,,25-1072,,"Nursing Instructors and Teachers, Postsecondary"
+,,25-1080,,,"Education and Library Science Teachers, Postsecondary"
+,,,25-1081,,"Education Teachers, Postsecondary"
+,,,25-1082,,"Library Science Teachers, Postsecondary"
+,,25-1110,,,"Law, Criminal Justice, and Social Work Teachers, Postsecondary"
+,,,25-1111,,"Criminal Justice and Law Enforcement Teachers, Postsecondary"
+,,,25-1112,,"Law Teachers, Postsecondary"
+,,,25-1113,,"Social Work Teachers, Postsecondary"
+,,25-1120,,,"Arts, Communications, History, and Humanities Teachers, Postsecondary"
+,,,25-1121,,"Art, Drama, and Music Teachers, Postsecondary"
+,,,25-1122,,"Communications Teachers, Postsecondary"
+,,,25-1123,,"English Language and Literature Teachers, Postsecondary"
+,,,25-1124,,"Foreign Language and Literature Teachers, Postsecondary"
+,,,25-1125,,"History Teachers, Postsecondary"
+,,,25-1126,,"Philosophy and Religion Teachers, Postsecondary"
+,,25-1190,,,Miscellaneous Postsecondary Teachers
+,,,25-1192,,"Family and Consumer Sciences Teachers, Postsecondary"
+,,,25-1193,,"Recreation and Fitness Studies Teachers, Postsecondary"
+,,,25-1194,,"Career/Technical Education Teachers, Postsecondary"
+,,,25-1199,,"Postsecondary Teachers, All Other"
+,25-2000,,,,"Preschool, Elementary, Middle, Secondary, and Special Education Teachers"
+,,25-2010,,,Preschool and Kindergarten Teachers
+,,,25-2011,,"Preschool Teachers, Except Special Education"
+,,,25-2012,,"Kindergarten Teachers, Except Special Education"
+,,25-2020,,,Elementary and Middle School Teachers
+,,,25-2021,,"Elementary School Teachers, Except Special Education"
+,,,25-2022,,"Middle School Teachers, Except Special and Career/Technical Education"
+,,,25-2023,,"Career/Technical Education Teachers, Middle School"
+,,25-2030,,,Secondary School Teachers
+,,,25-2031,,"Secondary School Teachers, Except Special and Career/Technical Education"
+,,,25-2032,,"Career/Technical Education Teachers, Secondary School"
+,,25-2050,,,Special Education Teachers
+,,,25-2051,,"Special Education Teachers, Preschool"
+,,,25-2055,,"Special Education Teachers, Kindergarten"
+,,,25-2056,,"Special Education Teachers, Elementary School"
+,,,25-2057,,"Special Education Teachers, Middle School"
+,,,25-2058,,"Special Education Teachers, Secondary School"
+,,,25-2059,,"Special Education Teachers, All Other"
+,,,,25-2059.01,Adapted Physical Education Specialists
+,25-3000,,,,Other Teachers and Instructors
+,,25-3010,,,"Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors"
+,,,25-3011,,"Adult Basic Education, Adult Secondary Education, and English as a Second Language Instructors"
+,,25-3020,,,Self-Enrichment Teachers
+,,,25-3021,,Self-Enrichment Teachers
+,,25-3030,,,"Substitute Teachers, Short-Term"
+,,,25-3031,,"Substitute Teachers, Short-Term"
+,,25-3040,,,Tutors
+,,,25-3041,,Tutors
+,,25-3090,,,Miscellaneous Teachers and Instructors
+,,,25-3099,,"Teachers and Instructors, All Other"
+,25-4000,,,,"Librarians, Curators, and Archivists"
+,,25-4010,,,"Archivists, Curators, and Museum Technicians"
+,,,25-4011,,Archivists
+,,,25-4012,,Curators
+,,,25-4013,,Museum Technicians and Conservators
+,,25-4020,,,Librarians and Media Collections Specialists
+,,,25-4022,,Librarians and Media Collections Specialists
+,,25-4030,,,Library Technicians
+,,,25-4031,,Library Technicians
+,25-9000,,,,Other Educational Instruction and Library Occupations
+,,25-9020,,,Farm and Home Management Educators
+,,,25-9021,,Farm and Home Management Educators
+,,25-9030,,,Instructional Coordinators
+,,,25-9031,,Instructional Coordinators
+,,25-9040,,,Teaching Assistants
+,,,25-9042,,"Teaching Assistants, Preschool, Elementary, Middle, and Secondary School, Except Special Education"
+,,,25-9043,,"Teaching Assistants, Special Education"
+,,,25-9044,,"Teaching Assistants, Postsecondary"
+,,,25-9049,,"Teaching Assistants, All Other"
+,,25-9090,,,Miscellaneous Educational Instruction and Library Workers
+,,,25-9099,,"Educational Instruction and Library Workers, All Other"
+27-0000,,,,,"Arts, Design, Entertainment, Sports, and Media Occupations"
+,27-1000,,,,Art and Design Workers
+,,27-1010,,,Artists and Related Workers
+,,,27-1011,,Art Directors
+,,,27-1012,,Craft Artists
+,,,27-1013,,"Fine Artists, Including Painters, Sculptors, and Illustrators"
+,,,27-1014,,Special Effects Artists and Animators
+,,,27-1019,,"Artists and Related Workers, All Other"
+,,27-1020,,,Designers
+,,,27-1021,,Commercial and Industrial Designers
+,,,27-1022,,Fashion Designers
+,,,27-1023,,Floral Designers
+,,,27-1024,,Graphic Designers
+,,,27-1025,,Interior Designers
+,,,27-1026,,Merchandise Displayers and Window Trimmers
+,,,27-1027,,Set and Exhibit Designers
+,,,27-1029,,"Designers, All Other"
+,27-2000,,,,"Entertainers and Performers, Sports and Related Workers"
+,,27-2010,,,"Actors, Producers, and Directors"
+,,,27-2011,,Actors
+,,,27-2012,,Producers and Directors
+,,,,27-2012.03,Media Programming Directors
+,,,,27-2012.04,Talent Directors
+,,,,27-2012.05,Media Technical Directors/Managers
+,,27-2020,,,"Athletes, Coaches, Umpires, and Related Workers"
+,,,27-2021,,Athletes and Sports Competitors
+,,,27-2022,,Coaches and Scouts
+,,,27-2023,,"Umpires, Referees, and Other Sports Officials"
+,,27-2030,,,Dancers and Choreographers
+,,,27-2031,,Dancers
+,,,27-2032,,Choreographers
+,,27-2040,,,"Musicians, Singers, and Related Workers"
+,,,27-2041,,Music Directors and Composers
+,,,27-2042,,Musicians and Singers
+,,27-2090,,,"Miscellaneous Entertainers and Performers, Sports and Related Workers"
+,,,27-2091,,"Disc Jockeys, Except Radio"
+,,,27-2099,,"Entertainers and Performers, Sports and Related Workers, All Other"
+,27-3000,,,,Media and Communication Workers
+,,27-3010,,,Broadcast Announcers and Radio Disc Jockeys
+,,,27-3011,,Broadcast Announcers and Radio Disc Jockeys
+,,27-3020,,,"News Analysts, Reporters and Journalists"
+,,,27-3023,,"News Analysts, Reporters, and Journalists"
+,,27-3030,,,Public Relations Specialists
+,,,27-3031,,Public Relations Specialists
+,,27-3040,,,Writers and Editors
+,,,27-3041,,Editors
+,,,27-3042,,Technical Writers
+,,,27-3043,,Writers and Authors
+,,,,27-3043.05,"Poets, Lyricists and Creative Writers"
+,,27-3090,,,Miscellaneous Media and Communication Workers
+,,,27-3091,,Interpreters and Translators
+,,,27-3092,,Court Reporters and Simultaneous Captioners
+,,,27-3099,,"Media and Communication Workers, All Other"
+,27-4000,,,,Media and Communication Equipment Workers
+,,27-4010,,,"Broadcast, Sound, and Lighting Technicians"
+,,,27-4011,,Audio and Video Technicians
+,,,27-4012,,Broadcast Technicians
+,,,27-4014,,Sound Engineering Technicians
+,,,27-4015,,Lighting Technicians
+,,27-4020,,,Photographers
+,,,27-4021,,Photographers
+,,27-4030,,,"Television, Video, and Film Camera Operators and Editors"
+,,,27-4031,,"Camera Operators, Television, Video, and Film"
+,,,27-4032,,Film and Video Editors
+,,27-4090,,,Miscellaneous Media and Communication Equipment Workers
+,,,27-4099,,"Media and Communication Equipment Workers, All Other"
+29-0000,,,,,Healthcare Practitioners and Technical Occupations
+,29-1000,,,,Healthcare Diagnosing or Treating Practitioners
+,,29-1010,,,Chiropractors
+,,,29-1011,,Chiropractors
+,,29-1020,,,Dentists
+,,,29-1021,,"Dentists, General"
+,,,29-1022,,Oral and Maxillofacial Surgeons
+,,,29-1023,,Orthodontists
+,,,29-1024,,Prosthodontists
+,,,29-1029,,"Dentists, All Other Specialists"
+,,29-1030,,,Dietitians and Nutritionists
+,,,29-1031,,Dietitians and Nutritionists
+,,29-1040,,,Optometrists
+,,,29-1041,,Optometrists
+,,29-1050,,,Pharmacists
+,,,29-1051,,Pharmacists
+,,29-1070,,,Physician Assistants
+,,,29-1071,,Physician Assistants
+,,,,29-1071.01,Anesthesiologist Assistants
+,,29-1080,,,Podiatrists
+,,,29-1081,,Podiatrists
+,,29-1120,,,Therapists
+,,,29-1122,,Occupational Therapists
+,,,,29-1122.01,"Low Vision Therapists, Orientation and Mobility Specialists, and Vision Rehabilitation Therapists"
+,,,29-1123,,Physical Therapists
+,,,29-1124,,Radiation Therapists
+,,,29-1125,,Recreational Therapists
+,,,29-1126,,Respiratory Therapists
+,,,29-1127,,Speech-Language Pathologists
+,,,29-1128,,Exercise Physiologists
+,,,29-1129,,"Therapists, All Other"
+,,,,29-1129.01,Art Therapists
+,,,,29-1129.02,Music Therapists
+,,29-1130,,,Veterinarians
+,,,29-1131,,Veterinarians
+,,29-1140,,,Registered Nurses
+,,,29-1141,,Registered Nurses
+,,,,29-1141.01,Acute Care Nurses
+,,,,29-1141.02,Advanced Practice Psychiatric Nurses
+,,,,29-1141.03,Critical Care Nurses
+,,,,29-1141.04,Clinical Nurse Specialists
+,,29-1150,,,Nurse Anesthetists
+,,,29-1151,,Nurse Anesthetists
+,,29-1160,,,Nurse Midwives
+,,,29-1161,,Nurse Midwives
+,,29-1170,,,Nurse Practitioners
+,,,29-1171,,Nurse Practitioners
+,,29-1180,,,Audiologists
+,,,29-1181,,Audiologists
+,,29-1210,,,Physicians
+,,,29-1211,,Anesthesiologists
+,,,29-1212,,Cardiologists
+,,,29-1213,,Dermatologists
+,,,29-1214,,Emergency Medicine Physicians
+,,,29-1215,,Family Medicine Physicians
+,,,29-1216,,General Internal Medicine Physicians
+,,,29-1217,,Neurologists
+,,,29-1218,,Obstetricians and Gynecologists
+,,,29-1221,,"Pediatricians, General"
+,,,29-1222,,"Physicians, Pathologists"
+,,,29-1223,,Psychiatrists
+,,,29-1224,,Radiologists
+,,,29-1229,,"Physicians, All Other"
+,,,,29-1229.01,Allergists and Immunologists
+,,,,29-1229.02,Hospitalists
+,,,,29-1229.03,Urologists
+,,,,29-1229.04,Physical Medicine and Rehabilitation Physicians
+,,,,29-1229.05,Preventive Medicine Physicians
+,,,,29-1229.06,Sports Medicine Physicians
+,,29-1240,,,Surgeons
+,,,29-1241,,"Ophthalmologists, Except Pediatric"
+,,,29-1242,,"Orthopedic Surgeons, Except Pediatric"
+,,,29-1243,,Pediatric Surgeons
+,,,29-1249,,"Surgeons, All Other"
+,,29-1290,,,Miscellaneous Healthcare Diagnosing or Treating Practitioners
+,,,29-1291,,Acupuncturists
+,,,29-1292,,Dental Hygienists
+,,,29-1299,,"Healthcare Diagnosing or Treating Practitioners, All Other"
+,,,,29-1299.01,Naturopathic Physicians
+,,,,29-1299.02,Orthoptists
+,29-2000,,,,Health Technologists and Technicians
+,,29-2010,,,Clinical Laboratory Technologists and Technicians
+,,,29-2011,,Medical and Clinical Laboratory Technologists
+,,,,29-2011.01,Cytogenetic Technologists
+,,,,29-2011.02,Cytotechnologists
+,,,,29-2011.04,Histotechnologists
+,,,29-2012,,Medical and Clinical Laboratory Technicians
+,,,,29-2012.01,Histology Technicians
+,,29-2030,,,Diagnostic Related Technologists and Technicians
+,,,29-2031,,Cardiovascular Technologists and Technicians
+,,,29-2032,,Diagnostic Medical Sonographers
+,,,29-2033,,Nuclear Medicine Technologists
+,,,29-2034,,Radiologic Technologists and Technicians
+,,,29-2035,,Magnetic Resonance Imaging Technologists
+,,,29-2036,,Medical Dosimetrists
+,,29-2040,,,Emergency Medical Technicians and Paramedics
+,,,29-2042,,Emergency Medical Technicians
+,,,29-2043,,Paramedics
+,,29-2050,,,Health Practitioner Support Technologists and Technicians
+,,,29-2051,,Dietetic Technicians
+,,,29-2052,,Pharmacy Technicians
+,,,29-2053,,Psychiatric Technicians
+,,,29-2055,,Surgical Technologists
+,,,29-2056,,Veterinary Technologists and Technicians
+,,,29-2057,,Ophthalmic Medical Technicians
+,,29-2060,,,Licensed Practical and Licensed Vocational Nurses
+,,,29-2061,,Licensed Practical and Licensed Vocational Nurses
+,,29-2070,,,Medical Records Specialists
+,,,29-2072,,Medical Records Specialists
+,,29-2080,,,"Opticians, Dispensing"
+,,,29-2081,,"Opticians, Dispensing"
+,,29-2090,,,Miscellaneous Health Technologists and Technicians
+,,,29-2091,,Orthotists and Prosthetists
+,,,29-2092,,Hearing Aid Specialists
+,,,29-2099,,"Health Technologists and Technicians, All Other"
+,,,,29-2099.01,Neurodiagnostic Technologists
+,,,,29-2099.05,Ophthalmic Medical Technologists
+,,,,29-2099.08,Patient Representatives
+,29-9000,,,,Other Healthcare Practitioners and Technical Occupations
+,,29-9020,,,Health Information Technologists and Medical Registrars
+,,,29-9021,,Health Information Technologists and Medical Registrars
+,,29-9090,,,Miscellaneous Health Practitioners and Technical Workers
+,,,29-9091,,Athletic Trainers
+,,,29-9092,,Genetic Counselors
+,,,29-9093,,Surgical Assistants
+,,,29-9099,,"Healthcare Practitioners and Technical Workers, All Other"
+,,,,29-9099.01,Midwives
+31-0000,,,,,Healthcare Support Occupations
+,31-1100,,,,"Home Health and Personal Care Aides; and Nursing Assistants, Orderlies, and Psychiatric Aides"
+,,31-1120,,,Home Health and Personal Care Aides
+,,,31-1121,,Home Health Aides
+,,,31-1122,,Personal Care Aides
+,,31-1130,,,"Nursing Assistants, Orderlies, and Psychiatric Aides"
+,,,31-1131,,Nursing Assistants
+,,,31-1132,,Orderlies
+,,,31-1133,,Psychiatric Aides
+,31-2000,,,,Occupational Therapy and Physical Therapist Assistants and Aides
+,,31-2010,,,Occupational Therapy Assistants and Aides
+,,,31-2011,,Occupational Therapy Assistants
+,,,31-2012,,Occupational Therapy Aides
+,,31-2020,,,Physical Therapist Assistants and Aides
+,,,31-2021,,Physical Therapist Assistants
+,,,31-2022,,Physical Therapist Aides
+,31-9000,,,,Other Healthcare Support Occupations
+,,31-9010,,,Massage Therapists
+,,,31-9011,,Massage Therapists
+,,31-9090,,,Miscellaneous Healthcare Support Occupations
+,,,31-9091,,Dental Assistants
+,,,31-9092,,Medical Assistants
+,,,31-9093,,Medical Equipment Preparers
+,,,31-9094,,Medical Transcriptionists
+,,,31-9095,,Pharmacy Aides
+,,,31-9096,,Veterinary Assistants and Laboratory Animal Caretakers
+,,,31-9097,,Phlebotomists
+,,,31-9099,,"Healthcare Support Workers, All Other"
+,,,,31-9099.01,Speech-Language Pathology Assistants
+,,,,31-9099.02,Endoscopy Technicians
+33-0000,,,,,Protective Service Occupations
+,33-1000,,,,Supervisors of Protective Service Workers
+,,33-1010,,,First-Line Supervisors of Law Enforcement Workers
+,,,33-1011,,First-Line Supervisors of Correctional Officers
+,,,33-1012,,First-Line Supervisors of Police and Detectives
+,,33-1020,,,First-Line Supervisors of Firefighting and Prevention Workers
+,,,33-1021,,First-Line Supervisors of Firefighting and Prevention Workers
+,,33-1090,,,"Miscellaneous First-Line Supervisors, Protective Service Workers"
+,,,33-1091,,First-Line Supervisors of Security Workers
+,,,33-1099,,"First-Line Supervisors of Protective Service Workers, All Other"
+,33-2000,,,,Firefighting and Prevention Workers
+,,33-2010,,,Firefighters
+,,,33-2011,,Firefighters
+,,33-2020,,,Fire Inspectors
+,,,33-2021,,Fire Inspectors and Investigators
+,,,33-2022,,Forest Fire Inspectors and Prevention Specialists
+,33-3000,,,,Law Enforcement Workers
+,,33-3010,,,"Bailiffs, Correctional Officers, and Jailers"
+,,,33-3011,,Bailiffs
+,,,33-3012,,Correctional Officers and Jailers
+,,33-3020,,,Detectives and Criminal Investigators
+,,,33-3021,,Detectives and Criminal Investigators
+,,,,33-3021.02,Police Identification and Records Officers
+,,,,33-3021.06,Intelligence Analysts
+,,33-3030,,,Fish and Game Wardens
+,,,33-3031,,Fish and Game Wardens
+,,33-3040,,,Parking Enforcement Workers
+,,,33-3041,,Parking Enforcement Workers
+,,33-3050,,,Police Officers
+,,,33-3051,,Police and Sheriff's Patrol Officers
+,,,,33-3051.04,Customs and Border Protection Officers
+,,,33-3052,,Transit and Railroad Police
+,33-9000,,,,Other Protective Service Workers
+,,33-9010,,,Animal Control Workers
+,,,33-9011,,Animal Control Workers
+,,33-9020,,,Private Detectives and Investigators
+,,,33-9021,,Private Detectives and Investigators
+,,33-9030,,,Security Guards and Gambling Surveillance Officers
+,,,33-9031,,Gambling Surveillance Officers and Gambling Investigators
+,,,33-9032,,Security Guards
+,,33-9090,,,Miscellaneous Protective Service Workers
+,,,33-9091,,Crossing Guards and Flaggers
+,,,33-9092,,"Lifeguards, Ski Patrol, and Other Recreational Protective Service Workers"
+,,,33-9093,,Transportation Security Screeners
+,,,33-9094,,School Bus Monitors
+,,,33-9099,,"Protective Service Workers, All Other"
+,,,,33-9099.02,Retail Loss Prevention Specialists
+35-0000,,,,,Food Preparation and Serving Related Occupations
+,35-1000,,,,Supervisors of Food Preparation and Serving Workers
+,,35-1010,,,Supervisors of Food Preparation and Serving Workers
+,,,35-1011,,Chefs and Head Cooks
+,,,35-1012,,First-Line Supervisors of Food Preparation and Serving Workers
+,35-2000,,,,Cooks and Food Preparation Workers
+,,35-2010,,,Cooks
+,,,35-2011,,"Cooks, Fast Food"
+,,,35-2012,,"Cooks, Institution and Cafeteria"
+,,,35-2013,,"Cooks, Private Household"
+,,,35-2014,,"Cooks, Restaurant"
+,,,35-2015,,"Cooks, Short Order"
+,,,35-2019,,"Cooks, All Other"
+,,35-2020,,,Food Preparation Workers
+,,,35-2021,,Food Preparation Workers
+,35-3000,,,,Food and Beverage Serving Workers
+,,35-3010,,,Bartenders
+,,,35-3011,,Bartenders
+,,35-3020,,,Fast Food and Counter Workers
+,,,35-3023,,Fast Food and Counter Workers
+,,,,35-3023.01,Baristas
+,,35-3030,,,Waiters and Waitresses
+,,,35-3031,,Waiters and Waitresses
+,,35-3040,,,"Food Servers, Nonrestaurant"
+,,,35-3041,,"Food Servers, Nonrestaurant"
+,35-9000,,,,Other Food Preparation and Serving Related Workers
+,,35-9010,,,Dining Room and Cafeteria Attendants and Bartender Helpers
+,,,35-9011,,Dining Room and Cafeteria Attendants and Bartender Helpers
+,,35-9020,,,Dishwashers
+,,,35-9021,,Dishwashers
+,,35-9030,,,"Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop"
+,,,35-9031,,"Hosts and Hostesses, Restaurant, Lounge, and Coffee Shop"
+,,35-9090,,,Miscellaneous Food Preparation and Serving Related Workers
+,,,35-9099,,"Food Preparation and Serving Related Workers, All Other"
+37-0000,,,,,Building and Grounds Cleaning and Maintenance Occupations
+,37-1000,,,,Supervisors of Building and Grounds Cleaning and Maintenance Workers
+,,37-1010,,,First-Line Supervisors of Building and Grounds Cleaning and Maintenance Workers
+,,,37-1011,,First-Line Supervisors of Housekeeping and Janitorial Workers
+,,,37-1012,,"First-Line Supervisors of Landscaping, Lawn Service, and Groundskeeping Workers"
+,37-2000,,,,Building Cleaning and Pest Control Workers
+,,37-2010,,,Building Cleaning Workers
+,,,37-2011,,"Janitors and Cleaners, Except Maids and Housekeeping Cleaners"
+,,,37-2012,,Maids and Housekeeping Cleaners
+,,,37-2019,,"Building Cleaning Workers, All Other"
+,,37-2020,,,Pest Control Workers
+,,,37-2021,,Pest Control Workers
+,37-3000,,,,Grounds Maintenance Workers
+,,37-3010,,,Grounds Maintenance Workers
+,,,37-3011,,Landscaping and Groundskeeping Workers
+,,,37-3012,,"Pesticide Handlers, Sprayers, and Applicators, Vegetation"
+,,,37-3013,,Tree Trimmers and Pruners
+,,,37-3019,,"Grounds Maintenance Workers, All Other"
+39-0000,,,,,Personal Care and Service Occupations
+,39-1000,,,,Supervisors of Personal Care and Service Workers
+,,39-1010,,,First-Line Supervisors of Entertainment and Recreation Workers
+,,,39-1013,,First-Line Supervisors of Gambling Services Workers
+,,,39-1014,,"First-Line Supervisors of Entertainment and Recreation Workers, Except Gambling Services"
+,,39-1020,,,First-Line Supervisors of Personal Service Workers
+,,,39-1022,,First-Line Supervisors of Personal Service Workers
+,39-2000,,,,Animal Care and Service Workers
+,,39-2010,,,Animal Trainers
+,,,39-2011,,Animal Trainers
+,,39-2020,,,Animal Caretakers
+,,,39-2021,,Animal Caretakers
+,39-3000,,,,Entertainment Attendants and Related Workers
+,,39-3010,,,Gambling Services Workers
+,,,39-3011,,Gambling Dealers
+,,,39-3012,,Gambling and Sports Book Writers and Runners
+,,,39-3019,,"Gambling Service Workers, All Other"
+,,39-3020,,,Motion Picture Projectionists
+,,,39-3021,,Motion Picture Projectionists
+,,39-3030,,,"Ushers, Lobby Attendants, and Ticket Takers"
+,,,39-3031,,"Ushers, Lobby Attendants, and Ticket Takers"
+,,39-3090,,,Miscellaneous Entertainment Attendants and Related Workers
+,,,39-3091,,Amusement and Recreation Attendants
+,,,39-3092,,Costume Attendants
+,,,39-3093,,"Locker Room, Coatroom, and Dressing Room Attendants"
+,,,39-3099,,"Entertainment Attendants and Related Workers, All Other"
+,39-4000,,,,Funeral Service Workers
+,,39-4010,,,Embalmers and Crematory Operators
+,,,39-4011,,Embalmers
+,,,39-4012,,Crematory Operators
+,,39-4020,,,Funeral Attendants
+,,,39-4021,,Funeral Attendants
+,,39-4030,,,"Morticians, Undertakers, and Funeral Arrangers"
+,,,39-4031,,"Morticians, Undertakers, and Funeral Arrangers"
+,39-5000,,,,Personal Appearance Workers
+,,39-5010,,,"Barbers, Hairdressers, Hairstylists and Cosmetologists"
+,,,39-5011,,Barbers
+,,,39-5012,,"Hairdressers, Hairstylists, and Cosmetologists"
+,,39-5090,,,Miscellaneous Personal Appearance Workers
+,,,39-5091,,"Makeup Artists, Theatrical and Performance"
+,,,39-5092,,Manicurists and Pedicurists
+,,,39-5093,,Shampooers
+,,,39-5094,,Skincare Specialists
+,39-6000,,,,"Baggage Porters, Bellhops, and Concierges"
+,,39-6010,,,"Baggage Porters, Bellhops, and Concierges"
+,,,39-6011,,Baggage Porters and Bellhops
+,,,39-6012,,Concierges
+,39-7000,,,,Tour and Travel Guides
+,,39-7010,,,Tour and Travel Guides
+,,,39-7011,,Tour Guides and Escorts
+,,,39-7012,,Travel Guides
+,39-9000,,,,Other Personal Care and Service Workers
+,,39-9010,,,Childcare Workers
+,,,39-9011,,Childcare Workers
+,,,,39-9011.01,Nannies
+,,39-9030,,,Recreation and Fitness Workers
+,,,39-9031,,Exercise Trainers and Group Fitness Instructors
+,,,39-9032,,Recreation Workers
+,,39-9040,,,Residential Advisors
+,,,39-9041,,Residential Advisors
+,,39-9090,,,Miscellaneous Personal Care and Service Workers
+,,,39-9099,,"Personal Care and Service Workers, All Other"
+41-0000,,,,,Sales and Related Occupations
+,41-1000,,,,Supervisors of Sales Workers
+,,41-1010,,,First-Line Supervisors of Sales Workers
+,,,41-1011,,First-Line Supervisors of Retail Sales Workers
+,,,41-1012,,First-Line Supervisors of Non-Retail Sales Workers
+,41-2000,,,,Retail Sales Workers
+,,41-2010,,,Cashiers
+,,,41-2011,,Cashiers
+,,,41-2012,,Gambling Change Persons and Booth Cashiers
+,,41-2020,,,Counter and Rental Clerks and Parts Salespersons
+,,,41-2021,,Counter and Rental Clerks
+,,,41-2022,,Parts Salespersons
+,,41-2030,,,Retail Salespersons
+,,,41-2031,,Retail Salespersons
+,41-3000,,,,"Sales Representatives, Services"
+,,41-3010,,,Advertising Sales Agents
+,,,41-3011,,Advertising Sales Agents
+,,41-3020,,,Insurance Sales Agents
+,,,41-3021,,Insurance Sales Agents
+,,41-3030,,,"Securities, Commodities, and Financial Services Sales Agents"
+,,,41-3031,,"Securities, Commodities, and Financial Services Sales Agents"
+,,41-3040,,,Travel Agents
+,,,41-3041,,Travel Agents
+,,41-3090,,,"Miscellaneous Sales Representatives, Services"
+,,,41-3091,,"Sales Representatives of Services, Except Advertising, Insurance, Financial Services, and Travel"
+,41-4000,,,,"Sales Representatives, Wholesale and Manufacturing"
+,,41-4010,,,"Sales Representatives, Wholesale and Manufacturing"
+,,,41-4011,,"Sales Representatives, Wholesale and Manufacturing, Technical and Scientific Products"
+,,,,41-4011.07,Solar Sales Representatives and Assessors
+,,,41-4012,,"Sales Representatives, Wholesale and Manufacturing, Except Technical and Scientific Products"
+,41-9000,,,,Other Sales and Related Workers
+,,41-9010,,,"Models, Demonstrators, and Product Promoters"
+,,,41-9011,,Demonstrators and Product Promoters
+,,,41-9012,,Models
+,,41-9020,,,Real Estate Brokers and Sales Agents
+,,,41-9021,,Real Estate Brokers
+,,,41-9022,,Real Estate Sales Agents
+,,41-9030,,,Sales Engineers
+,,,41-9031,,Sales Engineers
+,,41-9040,,,Telemarketers
+,,,41-9041,,Telemarketers
+,,41-9090,,,Miscellaneous Sales and Related Workers
+,,,41-9091,,"Door-to-Door Sales Workers, News and Street Vendors, and Related Workers"
+,,,41-9099,,"Sales and Related Workers, All Other"
+43-0000,,,,,Office and Administrative Support Occupations
+,43-1000,,,,Supervisors of Office and Administrative Support Workers
+,,43-1010,,,First-Line Supervisors of Office and Administrative Support Workers
+,,,43-1011,,First-Line Supervisors of Office and Administrative Support Workers
+,43-2000,,,,Communications Equipment Operators
+,,43-2010,,,"Switchboard Operators, Including Answering Service"
+,,,43-2011,,"Switchboard Operators, Including Answering Service"
+,,43-2020,,,Telephone Operators
+,,,43-2021,,Telephone Operators
+,,43-2090,,,Miscellaneous Communications Equipment Operators
+,,,43-2099,,"Communications Equipment Operators, All Other"
+,43-3000,,,,Financial Clerks
+,,43-3010,,,Bill and Account Collectors
+,,,43-3011,,Bill and Account Collectors
+,,43-3020,,,Billing and Posting Clerks
+,,,43-3021,,Billing and Posting Clerks
+,,43-3030,,,"Bookkeeping, Accounting, and Auditing Clerks"
+,,,43-3031,,"Bookkeeping, Accounting, and Auditing Clerks"
+,,43-3040,,,Gambling Cage Workers
+,,,43-3041,,Gambling Cage Workers
+,,43-3050,,,Payroll and Timekeeping Clerks
+,,,43-3051,,Payroll and Timekeeping Clerks
+,,43-3060,,,Procurement Clerks
+,,,43-3061,,Procurement Clerks
+,,43-3070,,,Tellers
+,,,43-3071,,Tellers
+,,43-3090,,,Miscellaneous Financial Clerks
+,,,43-3099,,"Financial Clerks, All Other"
+,43-4000,,,,Information and Record Clerks
+,,43-4010,,,Brokerage Clerks
+,,,43-4011,,Brokerage Clerks
+,,43-4020,,,Correspondence Clerks
+,,,43-4021,,Correspondence Clerks
+,,43-4030,,,"Court, Municipal, and License Clerks"
+,,,43-4031,,"Court, Municipal, and License Clerks"
+,,43-4040,,,"Credit Authorizers, Checkers, and Clerks"
+,,,43-4041,,"Credit Authorizers, Checkers, and Clerks"
+,,43-4050,,,Customer Service Representatives
+,,,43-4051,,Customer Service Representatives
+,,43-4060,,,"Eligibility Interviewers, Government Programs"
+,,,43-4061,,"Eligibility Interviewers, Government Programs"
+,,43-4070,,,File Clerks
+,,,43-4071,,File Clerks
+,,43-4080,,,"Hotel, Motel, and Resort Desk Clerks"
+,,,43-4081,,"Hotel, Motel, and Resort Desk Clerks"
+,,43-4110,,,"Interviewers, Except Eligibility and Loan"
+,,,43-4111,,"Interviewers, Except Eligibility and Loan"
+,,43-4120,,,"Library Assistants, Clerical"
+,,,43-4121,,"Library Assistants, Clerical"
+,,43-4130,,,Loan Interviewers and Clerks
+,,,43-4131,,Loan Interviewers and Clerks
+,,43-4140,,,New Accounts Clerks
+,,,43-4141,,New Accounts Clerks
+,,43-4150,,,Order Clerks
+,,,43-4151,,Order Clerks
+,,43-4160,,,"Human Resources Assistants, Except Payroll and Timekeeping"
+,,,43-4161,,"Human Resources Assistants, Except Payroll and Timekeeping"
+,,43-4170,,,Receptionists and Information Clerks
+,,,43-4171,,Receptionists and Information Clerks
+,,43-4180,,,Reservation and Transportation Ticket Agents and Travel Clerks
+,,,43-4181,,Reservation and Transportation Ticket Agents and Travel Clerks
+,,43-4190,,,Miscellaneous Information and Record Clerks
+,,,43-4199,,"Information and Record Clerks, All Other"
+,43-5000,,,,"Material Recording, Scheduling, Dispatching, and Distributing Workers"
+,,43-5010,,,Cargo and Freight Agents
+,,,43-5011,,Cargo and Freight Agents
+,,,,43-5011.01,Freight Forwarders
+,,43-5020,,,Couriers and Messengers
+,,,43-5021,,Couriers and Messengers
+,,43-5030,,,Dispatchers
+,,,43-5031,,Public Safety Telecommunicators
+,,,43-5032,,"Dispatchers, Except Police, Fire, and Ambulance"
+,,43-5040,,,"Meter Readers, Utilities"
+,,,43-5041,,"Meter Readers, Utilities"
+,,43-5050,,,Postal Service Workers
+,,,43-5051,,Postal Service Clerks
+,,,43-5052,,Postal Service Mail Carriers
+,,,43-5053,,"Postal Service Mail Sorters, Processors, and Processing Machine Operators"
+,,43-5060,,,"Production, Planning, and Expediting Clerks"
+,,,43-5061,,"Production, Planning, and Expediting Clerks"
+,,43-5070,,,"Shipping, Receiving, and Inventory Clerks"
+,,,43-5071,,"Shipping, Receiving, and Inventory Clerks"
+,,43-5110,,,"Weighers, Measurers, Checkers, and Samplers, Recordkeeping"
+,,,43-5111,,"Weighers, Measurers, Checkers, and Samplers, Recordkeeping"
+,43-6000,,,,Secretaries and Administrative Assistants
+,,43-6010,,,Secretaries and Administrative Assistants
+,,,43-6011,,Executive Secretaries and Executive Administrative Assistants
+,,,43-6012,,Legal Secretaries and Administrative Assistants
+,,,43-6013,,Medical Secretaries and Administrative Assistants
+,,,43-6014,,"Secretaries and Administrative Assistants, Except Legal, Medical, and Executive"
+,43-9000,,,,Other Office and Administrative Support Workers
+,,43-9020,,,Data Entry and Information Processing Workers
+,,,43-9021,,Data Entry Keyers
+,,,43-9022,,Word Processors and Typists
+,,43-9030,,,Desktop Publishers
+,,,43-9031,,Desktop Publishers
+,,43-9040,,,Insurance Claims and Policy Processing Clerks
+,,,43-9041,,Insurance Claims and Policy Processing Clerks
+,,43-9050,,,"Mail Clerks and Mail Machine Operators, Except Postal Service"
+,,,43-9051,,"Mail Clerks and Mail Machine Operators, Except Postal Service"
+,,43-9060,,,"Office Clerks, General"
+,,,43-9061,,"Office Clerks, General"
+,,43-9070,,,"Office Machine Operators, Except Computer"
+,,,43-9071,,"Office Machine Operators, Except Computer"
+,,43-9080,,,Proofreaders and Copy Markers
+,,,43-9081,,Proofreaders and Copy Markers
+,,43-9110,,,Statistical Assistants
+,,,43-9111,,Statistical Assistants
+,,43-9190,,,Miscellaneous Office and Administrative Support Workers
+,,,43-9199,,"Office and Administrative Support Workers, All Other"
+45-0000,,,,,"Farming, Fishing, and Forestry Occupations"
+,45-1000,,,,"Supervisors of Farming, Fishing, and Forestry Workers"
+,,45-1010,,,"First-Line Supervisors of Farming, Fishing, and Forestry Workers"
+,,,45-1011,,"First-Line Supervisors of Farming, Fishing, and Forestry Workers"
+,45-2000,,,,Agricultural Workers
+,,45-2010,,,Agricultural Inspectors
+,,,45-2011,,Agricultural Inspectors
+,,45-2020,,,Animal Breeders
+,,,45-2021,,Animal Breeders
+,,45-2040,,,"Graders and Sorters, Agricultural Products"
+,,,45-2041,,"Graders and Sorters, Agricultural Products"
+,,45-2090,,,Miscellaneous Agricultural Workers
+,,,45-2091,,Agricultural Equipment Operators
+,,,45-2092,,"Farmworkers and Laborers, Crop, Nursery, and Greenhouse"
+,,,45-2093,,"Farmworkers, Farm, Ranch, and Aquacultural Animals"
+,,,45-2099,,"Agricultural Workers, All Other"
+,45-3000,,,,Fishing and Hunting Workers
+,,45-3030,,,Fishing and Hunting Workers
+,,,45-3031,,Fishing and Hunting Workers
+,45-4000,,,,"Forest, Conservation, and Logging Workers"
+,,45-4010,,,Forest and Conservation Workers
+,,,45-4011,,Forest and Conservation Workers
+,,45-4020,,,Logging Workers
+,,,45-4021,,Fallers
+,,,45-4022,,Logging Equipment Operators
+,,,45-4023,,Log Graders and Scalers
+,,,45-4029,,"Logging Workers, All Other"
+47-0000,,,,,Construction and Extraction Occupations
+,47-1000,,,,Supervisors of Construction and Extraction Workers
+,,47-1010,,,First-Line Supervisors of Construction Trades and Extraction Workers
+,,,47-1011,,First-Line Supervisors of Construction Trades and Extraction Workers
+,,,,47-1011.03,Solar Energy Installation Managers
+,47-2000,,,,Construction Trades Workers
+,,47-2010,,,Boilermakers
+,,,47-2011,,Boilermakers
+,,47-2020,,,"Brickmasons, Blockmasons, and Stonemasons"
+,,,47-2021,,Brickmasons and Blockmasons
+,,,47-2022,,Stonemasons
+,,47-2030,,,Carpenters
+,,,47-2031,,Carpenters
+,,47-2040,,,"Carpet, Floor, and Tile Installers and Finishers"
+,,,47-2041,,Carpet Installers
+,,,47-2042,,"Floor Layers, Except Carpet, Wood, and Hard Tiles"
+,,,47-2043,,Floor Sanders and Finishers
+,,,47-2044,,Tile and Stone Setters
+,,47-2050,,,"Cement Masons, Concrete Finishers, and Terrazzo Workers"
+,,,47-2051,,Cement Masons and Concrete Finishers
+,,,47-2053,,Terrazzo Workers and Finishers
+,,47-2060,,,Construction Laborers
+,,,47-2061,,Construction Laborers
+,,47-2070,,,Construction Equipment Operators
+,,,47-2071,,"Paving, Surfacing, and Tamping Equipment Operators"
+,,,47-2072,,Pile Driver Operators
+,,,47-2073,,Operating Engineers and Other Construction Equipment Operators
+,,47-2080,,,"Drywall Installers, Ceiling Tile Installers, and Tapers"
+,,,47-2081,,Drywall and Ceiling Tile Installers
+,,,47-2082,,Tapers
+,,47-2110,,,Electricians
+,,,47-2111,,Electricians
+,,47-2120,,,Glaziers
+,,,47-2121,,Glaziers
+,,47-2130,,,Insulation Workers
+,,,47-2131,,"Insulation Workers, Floor, Ceiling, and Wall"
+,,,47-2132,,"Insulation Workers, Mechanical"
+,,47-2140,,,Painters and Paperhangers
+,,,47-2141,,"Painters, Construction and Maintenance"
+,,,47-2142,,Paperhangers
+,,47-2150,,,"Pipelayers, Plumbers, Pipefitters, and Steamfitters"
+,,,47-2151,,Pipelayers
+,,,47-2152,,"Plumbers, Pipefitters, and Steamfitters"
+,,,,47-2152.04,Solar Thermal Installers and Technicians
+,,47-2160,,,Plasterers and Stucco Masons
+,,,47-2161,,Plasterers and Stucco Masons
+,,47-2170,,,Reinforcing Iron and Rebar Workers
+,,,47-2171,,Reinforcing Iron and Rebar Workers
+,,47-2180,,,Roofers
+,,,47-2181,,Roofers
+,,47-2210,,,Sheet Metal Workers
+,,,47-2211,,Sheet Metal Workers
+,,47-2220,,,Structural Iron and Steel Workers
+,,,47-2221,,Structural Iron and Steel Workers
+,,47-2230,,,Solar Photovoltaic Installers
+,,,47-2231,,Solar Photovoltaic Installers
+,47-3000,,,,"Helpers, Construction Trades"
+,,47-3010,,,"Helpers, Construction Trades"
+,,,47-3011,,"Helpers--Brickmasons, Blockmasons, Stonemasons, and Tile and Marble Setters"
+,,,47-3012,,Helpers--Carpenters
+,,,47-3013,,Helpers--Electricians
+,,,47-3014,,"Helpers--Painters, Paperhangers, Plasterers, and Stucco Masons"
+,,,47-3015,,"Helpers--Pipelayers, Plumbers, Pipefitters, and Steamfitters"
+,,,47-3016,,Helpers--Roofers
+,,,47-3019,,"Helpers, Construction Trades, All Other"
+,47-4000,,,,Other Construction and Related Workers
+,,47-4010,,,Construction and Building Inspectors
+,,,47-4011,,Construction and Building Inspectors
+,,,,47-4011.01,Energy Auditors
+,,47-4020,,,Elevator and Escalator Installers and Repairers
+,,,47-4021,,Elevator and Escalator Installers and Repairers
+,,47-4030,,,Fence Erectors
+,,,47-4031,,Fence Erectors
+,,47-4040,,,Hazardous Materials Removal Workers
+,,,47-4041,,Hazardous Materials Removal Workers
+,,47-4050,,,Highway Maintenance Workers
+,,,47-4051,,Highway Maintenance Workers
+,,47-4060,,,Rail-Track Laying and Maintenance Equipment Operators
+,,,47-4061,,Rail-Track Laying and Maintenance Equipment Operators
+,,47-4070,,,Septic Tank Servicers and Sewer Pipe Cleaners
+,,,47-4071,,Septic Tank Servicers and Sewer Pipe Cleaners
+,,47-4090,,,Miscellaneous Construction and Related Workers
+,,,47-4091,,Segmental Pavers
+,,,47-4099,,"Construction and Related Workers, All Other"
+,,,,47-4099.03,Weatherization Installers and Technicians
+,47-5000,,,,Extraction Workers
+,,47-5010,,,"Derrick, Rotary Drill, and Service Unit Operators, Oil and Gas"
+,,,47-5011,,"Derrick Operators, Oil and Gas"
+,,,47-5012,,"Rotary Drill Operators, Oil and Gas"
+,,,47-5013,,"Service Unit Operators, Oil and Gas"
+,,47-5020,,,Surface Mining Machine Operators and Earth Drillers
+,,,47-5022,,"Excavating and Loading Machine and Dragline Operators, Surface Mining"
+,,,47-5023,,"Earth Drillers, Except Oil and Gas"
+,,47-5030,,,"Explosives Workers, Ordnance Handling Experts, and Blasters"
+,,,47-5032,,"Explosives Workers, Ordnance Handling Experts, and Blasters"
+,,47-5040,,,Underground Mining Machine Operators
+,,,47-5041,,Continuous Mining Machine Operators
+,,,47-5043,,"Roof Bolters, Mining"
+,,,47-5044,,"Loading and Moving Machine Operators, Underground Mining"
+,,,47-5049,,"Underground Mining Machine Operators, All Other"
+,,47-5050,,,"Rock Splitters, Quarry"
+,,,47-5051,,"Rock Splitters, Quarry"
+,,47-5070,,,"Roustabouts, Oil and Gas"
+,,,47-5071,,"Roustabouts, Oil and Gas"
+,,47-5080,,,Helpers--Extraction Workers
+,,,47-5081,,Helpers--Extraction Workers
+,,47-5090,,,Miscellaneous Extraction Workers
+,,,47-5099,,"Extraction Workers, All Other"
+49-0000,,,,,"Installation, Maintenance, and Repair Occupations"
+,49-1000,,,,"Supervisors of Installation, Maintenance, and Repair Workers"
+,,49-1010,,,"First-Line Supervisors of Mechanics, Installers, and Repairers"
+,,,49-1011,,"First-Line Supervisors of Mechanics, Installers, and Repairers"
+,49-2000,,,,"Electrical and Electronic Equipment Mechanics, Installers, and Repairers"
+,,49-2010,,,"Computer, Automated Teller, and Office Machine Repairers"
+,,,49-2011,,"Computer, Automated Teller, and Office Machine Repairers"
+,,49-2020,,,Radio and Telecommunications Equipment Installers and Repairers
+,,,49-2021,,"Radio, Cellular, and Tower Equipment Installers and Repairers"
+,,,49-2022,,"Telecommunications Equipment Installers and Repairers, Except Line Installers"
+,,49-2090,,,"Miscellaneous Electrical and Electronic Equipment Mechanics, Installers, and Repairers"
+,,,49-2091,,Avionics Technicians
+,,,49-2092,,"Electric Motor, Power Tool, and Related Repairers"
+,,,49-2093,,"Electrical and Electronics Installers and Repairers, Transportation Equipment"
+,,,49-2094,,"Electrical and Electronics Repairers, Commercial and Industrial Equipment"
+,,,49-2095,,"Electrical and Electronics Repairers, Powerhouse, Substation, and Relay"
+,,,49-2096,,"Electronic Equipment Installers and Repairers, Motor Vehicles"
+,,,49-2097,,Audiovisual Equipment Installers and Repairers
+,,,49-2098,,Security and Fire Alarm Systems Installers
+,49-3000,,,,"Vehicle and Mobile Equipment Mechanics, Installers, and Repairers"
+,,49-3010,,,Aircraft Mechanics and Service Technicians
+,,,49-3011,,Aircraft Mechanics and Service Technicians
+,,49-3020,,,Automotive Technicians and Repairers
+,,,49-3021,,Automotive Body and Related Repairers
+,,,49-3022,,Automotive Glass Installers and Repairers
+,,,49-3023,,Automotive Service Technicians and Mechanics
+,,49-3030,,,Bus and Truck Mechanics and Diesel Engine Specialists
+,,,49-3031,,Bus and Truck Mechanics and Diesel Engine Specialists
+,,49-3040,,,Heavy Vehicle and Mobile Equipment Service Technicians and Mechanics
+,,,49-3041,,Farm Equipment Mechanics and Service Technicians
+,,,49-3042,,"Mobile Heavy Equipment Mechanics, Except Engines"
+,,,49-3043,,Rail Car Repairers
+,,49-3050,,,Small Engine Mechanics
+,,,49-3051,,Motorboat Mechanics and Service Technicians
+,,,49-3052,,Motorcycle Mechanics
+,,,49-3053,,Outdoor Power Equipment and Other Small Engine Mechanics
+,,49-3090,,,"Miscellaneous Vehicle and Mobile Equipment Mechanics, Installers, and Repairers"
+,,,49-3091,,Bicycle Repairers
+,,,49-3092,,Recreational Vehicle Service Technicians
+,,,49-3093,,Tire Repairers and Changers
+,49-9000,,,,"Other Installation, Maintenance, and Repair Occupations"
+,,49-9010,,,Control and Valve Installers and Repairers
+,,,49-9011,,Mechanical Door Repairers
+,,,49-9012,,"Control and Valve Installers and Repairers, Except Mechanical Door"
+,,49-9020,,,"Heating, Air Conditioning, and Refrigeration Mechanics and Installers"
+,,,49-9021,,"Heating, Air Conditioning, and Refrigeration Mechanics and Installers"
+,,49-9030,,,Home Appliance Repairers
+,,,49-9031,,Home Appliance Repairers
+,,49-9040,,,"Industrial Machinery Installation, Repair, and Maintenance Workers"
+,,,49-9041,,Industrial Machinery Mechanics
+,,,49-9043,,"Maintenance Workers, Machinery"
+,,,49-9044,,Millwrights
+,,,49-9045,,"Refractory Materials Repairers, Except Brickmasons"
+,,49-9050,,,Line Installers and Repairers
+,,,49-9051,,Electrical Power-Line Installers and Repairers
+,,,49-9052,,Telecommunications Line Installers and Repairers
+,,49-9060,,,Precision Instrument and Equipment Repairers
+,,,49-9061,,Camera and Photographic Equipment Repairers
+,,,49-9062,,Medical Equipment Repairers
+,,,49-9063,,Musical Instrument Repairers and Tuners
+,,,49-9064,,Watch and Clock Repairers
+,,,49-9069,,"Precision Instrument and Equipment Repairers, All Other"
+,,49-9070,,,"Maintenance and Repair Workers, General"
+,,,49-9071,,"Maintenance and Repair Workers, General"
+,,49-9080,,,Wind Turbine Service Technicians
+,,,49-9081,,Wind Turbine Service Technicians
+,,49-9090,,,"Miscellaneous Installation, Maintenance, and Repair Workers"
+,,,49-9091,,"Coin, Vending, and Amusement Machine Servicers and Repairers"
+,,,49-9092,,Commercial Divers
+,,,49-9094,,Locksmiths and Safe Repairers
+,,,49-9095,,Manufactured Building and Mobile Home Installers
+,,,49-9096,,Riggers
+,,,49-9097,,Signal and Track Switch Repairers
+,,,49-9098,,"Helpers--Installation, Maintenance, and Repair Workers"
+,,,49-9099,,"Installation, Maintenance, and Repair Workers, All Other"
+,,,,49-9099.01,Geothermal Technicians
+51-0000,,,,,Production Occupations
+,51-1000,,,,Supervisors of Production Workers
+,,51-1010,,,First-Line Supervisors of Production and Operating Workers
+,,,51-1011,,First-Line Supervisors of Production and Operating Workers
+,51-2000,,,,Assemblers and Fabricators
+,,51-2010,,,"Aircraft Structure, Surfaces, Rigging, and Systems Assemblers"
+,,,51-2011,,"Aircraft Structure, Surfaces, Rigging, and Systems Assemblers"
+,,51-2020,,,"Electrical, Electronics, and Electromechanical Assemblers"
+,,,51-2021,,"Coil Winders, Tapers, and Finishers"
+,,,51-2022,,Electrical and Electronic Equipment Assemblers
+,,,51-2023,,Electromechanical Equipment Assemblers
+,,51-2030,,,Engine and Other Machine Assemblers
+,,,51-2031,,Engine and Other Machine Assemblers
+,,51-2040,,,Structural Metal Fabricators and Fitters
+,,,51-2041,,Structural Metal Fabricators and Fitters
+,,51-2050,,,Fiberglass Laminators and Fabricators
+,,,51-2051,,Fiberglass Laminators and Fabricators
+,,51-2060,,,Timing Device Assemblers and Adjusters
+,,,51-2061,,Timing Device Assemblers and Adjusters
+,,51-2090,,,Miscellaneous Assemblers and Fabricators
+,,,51-2092,,Team Assemblers
+,,,51-2099,,"Assemblers and Fabricators, All Other"
+,51-3000,,,,Food Processing Workers
+,,51-3010,,,Bakers
+,,,51-3011,,Bakers
+,,51-3020,,,"Butchers and Other Meat, Poultry, and Fish Processing Workers"
+,,,51-3021,,Butchers and Meat Cutters
+,,,51-3022,,"Meat, Poultry, and Fish Cutters and Trimmers"
+,,,51-3023,,Slaughterers and Meat Packers
+,,51-3090,,,Miscellaneous Food Processing Workers
+,,,51-3091,,"Food and Tobacco Roasting, Baking, and Drying Machine Operators and Tenders"
+,,,51-3092,,Food Batchmakers
+,,,51-3093,,Food Cooking Machine Operators and Tenders
+,,,51-3099,,"Food Processing Workers, All Other"
+,51-4000,,,,Metal Workers and Plastic Workers
+,,51-4020,,,"Forming Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4021,,"Extruding and Drawing Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4022,,"Forging Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4023,,"Rolling Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4030,,,"Machine Tool Cutting Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4031,,"Cutting, Punching, and Press Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4032,,"Drilling and Boring Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4033,,"Grinding, Lapping, Polishing, and Buffing Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4034,,"Lathe and Turning Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4035,,"Milling and Planing Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4040,,,Machinists
+,,,51-4041,,Machinists
+,,51-4050,,,"Metal Furnace Operators, Tenders, Pourers, and Casters"
+,,,51-4051,,Metal-Refining Furnace Operators and Tenders
+,,,51-4052,,"Pourers and Casters, Metal"
+,,51-4060,,,"Model Makers and Patternmakers, Metal and Plastic"
+,,,51-4061,,"Model Makers, Metal and Plastic"
+,,,51-4062,,"Patternmakers, Metal and Plastic"
+,,51-4070,,,"Molders and Molding Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4071,,Foundry Mold and Coremakers
+,,,51-4072,,"Molding, Coremaking, and Casting Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4080,,,"Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4081,,"Multiple Machine Tool Setters, Operators, and Tenders, Metal and Plastic"
+,,51-4110,,,Tool and Die Makers
+,,,51-4111,,Tool and Die Makers
+,,51-4120,,,"Welding, Soldering, and Brazing Workers"
+,,,51-4121,,"Welders, Cutters, Solderers, and Brazers"
+,,,51-4122,,"Welding, Soldering, and Brazing Machine Setters, Operators, and Tenders"
+,,51-4190,,,Miscellaneous Metal Workers and Plastic Workers
+,,,51-4191,,"Heat Treating Equipment Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4192,,"Layout Workers, Metal and Plastic"
+,,,51-4193,,"Plating Machine Setters, Operators, and Tenders, Metal and Plastic"
+,,,51-4194,,"Tool Grinders, Filers, and Sharpeners"
+,,,51-4199,,"Metal Workers and Plastic Workers, All Other"
+,51-5100,,,,Printing Workers
+,,51-5110,,,Printing Workers
+,,,51-5111,,Prepress Technicians and Workers
+,,,51-5112,,Printing Press Operators
+,,,51-5113,,Print Binding and Finishing Workers
+,51-6000,,,,"Textile, Apparel, and Furnishings Workers"
+,,51-6010,,,Laundry and Dry-Cleaning Workers
+,,,51-6011,,Laundry and Dry-Cleaning Workers
+,,51-6020,,,"Pressers, Textile, Garment, and Related Materials"
+,,,51-6021,,"Pressers, Textile, Garment, and Related Materials"
+,,51-6030,,,Sewing Machine Operators
+,,,51-6031,,Sewing Machine Operators
+,,51-6040,,,Shoe and Leather Workers
+,,,51-6041,,Shoe and Leather Workers and Repairers
+,,,51-6042,,Shoe Machine Operators and Tenders
+,,51-6050,,,"Tailors, Dressmakers, and Sewers"
+,,,51-6051,,"Sewers, Hand"
+,,,51-6052,,"Tailors, Dressmakers, and Custom Sewers"
+,,51-6060,,,"Textile Machine Setters, Operators, and Tenders"
+,,,51-6061,,Textile Bleaching and Dyeing Machine Operators and Tenders
+,,,51-6062,,"Textile Cutting Machine Setters, Operators, and Tenders"
+,,,51-6063,,"Textile Knitting and Weaving Machine Setters, Operators, and Tenders"
+,,,51-6064,,"Textile Winding, Twisting, and Drawing Out Machine Setters, Operators, and Tenders"
+,,51-6090,,,"Miscellaneous Textile, Apparel, and Furnishings Workers"
+,,,51-6091,,"Extruding and Forming Machine Setters, Operators, and Tenders, Synthetic and Glass Fibers"
+,,,51-6092,,Fabric and Apparel Patternmakers
+,,,51-6093,,Upholsterers
+,,,51-6099,,"Textile, Apparel, and Furnishings Workers, All Other"
+,51-7000,,,,Woodworkers
+,,51-7010,,,Cabinetmakers and Bench Carpenters
+,,,51-7011,,Cabinetmakers and Bench Carpenters
+,,51-7020,,,Furniture Finishers
+,,,51-7021,,Furniture Finishers
+,,51-7030,,,"Model Makers and Patternmakers, Wood"
+,,,51-7031,,"Model Makers, Wood"
+,,,51-7032,,"Patternmakers, Wood"
+,,51-7040,,,"Woodworking Machine Setters, Operators, and Tenders"
+,,,51-7041,,"Sawing Machine Setters, Operators, and Tenders, Wood"
+,,,51-7042,,"Woodworking Machine Setters, Operators, and Tenders, Except Sawing"
+,,51-7090,,,Miscellaneous Woodworkers
+,,,51-7099,,"Woodworkers, All Other"
+,51-8000,,,,Plant and System Operators
+,,51-8010,,,"Power Plant Operators, Distributors, and Dispatchers"
+,,,51-8011,,Nuclear Power Reactor Operators
+,,,51-8012,,Power Distributors and Dispatchers
+,,,51-8013,,Power Plant Operators
+,,,,51-8013.03,Biomass Plant Technicians
+,,,,51-8013.04,Hydroelectric Plant Technicians
+,,51-8020,,,Stationary Engineers and Boiler Operators
+,,,51-8021,,Stationary Engineers and Boiler Operators
+,,51-8030,,,Water and Wastewater Treatment Plant and System Operators
+,,,51-8031,,Water and Wastewater Treatment Plant and System Operators
+,,51-8090,,,Miscellaneous Plant and System Operators
+,,,51-8091,,Chemical Plant and System Operators
+,,,51-8092,,Gas Plant Operators
+,,,51-8093,,"Petroleum Pump System Operators, Refinery Operators, and Gaugers"
+,,,51-8099,,"Plant and System Operators, All Other"
+,,,,51-8099.01,Biofuels Processing Technicians
+,51-9000,,,,Other Production Occupations
+,,51-9010,,,"Chemical Processing Machine Setters, Operators, and Tenders"
+,,,51-9011,,Chemical Equipment Operators and Tenders
+,,,51-9012,,"Separating, Filtering, Clarifying, Precipitating, and Still Machine Setters, Operators, and Tenders"
+,,51-9020,,,"Crushing, Grinding, Polishing, Mixing, and Blending Workers"
+,,,51-9021,,"Crushing, Grinding, and Polishing Machine Setters, Operators, and Tenders"
+,,,51-9022,,"Grinding and Polishing Workers, Hand"
+,,,51-9023,,"Mixing and Blending Machine Setters, Operators, and Tenders"
+,,51-9030,,,Cutting Workers
+,,,51-9031,,"Cutters and Trimmers, Hand"
+,,,51-9032,,"Cutting and Slicing Machine Setters, Operators, and Tenders"
+,,51-9040,,,"Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders"
+,,,51-9041,,"Extruding, Forming, Pressing, and Compacting Machine Setters, Operators, and Tenders"
+,,51-9050,,,"Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders"
+,,,51-9051,,"Furnace, Kiln, Oven, Drier, and Kettle Operators and Tenders"
+,,51-9060,,,"Inspectors, Testers, Sorters, Samplers, and Weighers"
+,,,51-9061,,"Inspectors, Testers, Sorters, Samplers, and Weighers"
+,,51-9070,,,Jewelers and Precious Stone and Metal Workers
+,,,51-9071,,Jewelers and Precious Stone and Metal Workers
+,,,,51-9071.06,Gem and Diamond Workers
+,,51-9080,,,Dental and Ophthalmic Laboratory Technicians and Medical Appliance Technicians
+,,,51-9081,,Dental Laboratory Technicians
+,,,51-9082,,Medical Appliance Technicians
+,,,51-9083,,Ophthalmic Laboratory Technicians
+,,51-9110,,,Packaging and Filling Machine Operators and Tenders
+,,,51-9111,,Packaging and Filling Machine Operators and Tenders
+,,51-9120,,,Painting Workers
+,,,51-9123,,"Painting, Coating, and Decorating Workers"
+,,,51-9124,,"Coating, Painting, and Spraying Machine Setters, Operators, and Tenders"
+,,51-9140,,,Semiconductor Processing Technicians
+,,,51-9141,,Semiconductor Processing Technicians
+,,51-9150,,,Photographic Process Workers and Processing Machine Operators
+,,,51-9151,,Photographic Process Workers and Processing Machine Operators
+,,51-9160,,,Computer Numerically Controlled Tool Operators and Programmers
+,,,51-9161,,Computer Numerically Controlled Tool Operators
+,,,51-9162,,Computer Numerically Controlled Tool Programmers
+,,51-9190,,,Miscellaneous Production Workers
+,,,51-9191,,Adhesive Bonding Machine Operators and Tenders
+,,,51-9192,,"Cleaning, Washing, and Metal Pickling Equipment Operators and Tenders"
+,,,51-9193,,Cooling and Freezing Equipment Operators and Tenders
+,,,51-9194,,Etchers and Engravers
+,,,51-9195,,"Molders, Shapers, and Casters, Except Metal and Plastic"
+,,,,51-9195.03,"Stone Cutters and Carvers, Manufacturing"
+,,,,51-9195.04,"Glass Blowers, Molders, Benders, and Finishers"
+,,,,51-9195.05,"Potters, Manufacturing"
+,,,51-9196,,"Paper Goods Machine Setters, Operators, and Tenders"
+,,,51-9197,,Tire Builders
+,,,51-9198,,Helpers--Production Workers
+,,,51-9199,,"Production Workers, All Other"
+53-0000,,,,,Transportation and Material Moving Occupations
+,53-1000,,,,Supervisors of Transportation and Material Moving Workers
+,,53-1040,,,First-Line Supervisors of Transportation and Material Moving Workers
+,,,53-1041,,Aircraft Cargo Handling Supervisors
+,,,53-1042,,"First-Line Supervisors of Helpers, Laborers, and Material Movers, Hand"
+,,,,53-1042.01,Recycling Coordinators
+,,,53-1043,,First-Line Supervisors of Material-Moving Machine and Vehicle Operators
+,,,53-1044,,First-Line Supervisors of Passenger Attendants
+,,,53-1049,,"First-Line Supervisors of Transportation Workers, All Other"
+,53-2000,,,,Air Transportation Workers
+,,53-2010,,,Aircraft Pilots and Flight Engineers
+,,,53-2011,,"Airline Pilots, Copilots, and Flight Engineers"
+,,,53-2012,,Commercial Pilots
+,,53-2020,,,Air Traffic Controllers and Airfield Operations Specialists
+,,,53-2021,,Air Traffic Controllers
+,,,53-2022,,Airfield Operations Specialists
+,,53-2030,,,Flight Attendants
+,,,53-2031,,Flight Attendants
+,53-3000,,,,Motor Vehicle Operators
+,,53-3010,,,"Ambulance Drivers and Attendants, Except Emergency Medical Technicians"
+,,,53-3011,,"Ambulance Drivers and Attendants, Except Emergency Medical Technicians"
+,,53-3030,,,Driver/Sales Workers and Truck Drivers
+,,,53-3031,,Driver/Sales Workers
+,,,53-3032,,Heavy and Tractor-Trailer Truck Drivers
+,,,53-3033,,Light Truck Drivers
+,,53-3050,,,Passenger Vehicle Drivers
+,,,53-3051,,"Bus Drivers, School"
+,,,53-3052,,"Bus Drivers, Transit and Intercity"
+,,,53-3053,,Shuttle Drivers and Chauffeurs
+,,,53-3054,,Taxi Drivers
+,,53-3090,,,Miscellaneous Motor Vehicle Operators
+,,,53-3099,,"Motor Vehicle Operators, All Other"
+,53-4000,,,,Rail Transportation Workers
+,,53-4010,,,Locomotive Engineers and Operators
+,,,53-4011,,Locomotive Engineers
+,,,53-4013,,"Rail Yard Engineers, Dinkey Operators, and Hostlers"
+,,53-4020,,,"Railroad Brake, Signal, and Switch Operators and Locomotive Firers"
+,,,53-4022,,"Railroad Brake, Signal, and Switch Operators and Locomotive Firers"
+,,53-4030,,,Railroad Conductors and Yardmasters
+,,,53-4031,,Railroad Conductors and Yardmasters
+,,53-4040,,,Subway and Streetcar Operators
+,,,53-4041,,Subway and Streetcar Operators
+,,53-4090,,,Miscellaneous Rail Transportation Workers
+,,,53-4099,,"Rail Transportation Workers, All Other"
+,53-5000,,,,Water Transportation Workers
+,,53-5010,,,Sailors and Marine Oilers
+,,,53-5011,,Sailors and Marine Oilers
+,,53-5020,,,Ship and Boat Captains and Operators
+,,,53-5021,,"Captains, Mates, and Pilots of Water Vessels"
+,,,53-5022,,Motorboat Operators
+,,53-5030,,,Ship Engineers
+,,,53-5031,,Ship Engineers
+,53-6000,,,,Other Transportation Workers
+,,53-6010,,,Bridge and Lock Tenders
+,,,53-6011,,Bridge and Lock Tenders
+,,53-6020,,,Parking Attendants
+,,,53-6021,,Parking Attendants
+,,53-6030,,,Transportation Service Attendants
+,,,53-6031,,Automotive and Watercraft Service Attendants
+,,,53-6032,,Aircraft Service Attendants
+,,53-6040,,,Traffic Technicians
+,,,53-6041,,Traffic Technicians
+,,53-6050,,,Transportation Inspectors
+,,,53-6051,,Transportation Inspectors
+,,,,53-6051.01,Aviation Inspectors
+,,,,53-6051.07,"Transportation Vehicle, Equipment and Systems Inspectors, Except Aviation"
+,,53-6060,,,Passenger Attendants
+,,,53-6061,,Passenger Attendants
+,,53-6090,,,Miscellaneous Transportation Workers
+,,,53-6099,,"Transportation Workers, All Other"
+,53-7000,,,,Material Moving Workers
+,,53-7010,,,Conveyor Operators and Tenders
+,,,53-7011,,Conveyor Operators and Tenders
+,,53-7020,,,Crane and Tower Operators
+,,,53-7021,,Crane and Tower Operators
+,,53-7030,,,Dredge Operators
+,,,53-7031,,Dredge Operators
+,,53-7040,,,Hoist and Winch Operators
+,,,53-7041,,Hoist and Winch Operators
+,,53-7050,,,Industrial Truck and Tractor Operators
+,,,53-7051,,Industrial Truck and Tractor Operators
+,,53-7060,,,Laborers and Material Movers
+,,,53-7061,,Cleaners of Vehicles and Equipment
+,,,53-7062,,"Laborers and Freight, Stock, and Material Movers, Hand"
+,,,,53-7062.04,Recycling and Reclamation Workers
+,,,53-7063,,Machine Feeders and Offbearers
+,,,53-7064,,"Packers and Packagers, Hand"
+,,,53-7065,,Stockers and Order Fillers
+,,53-7070,,,Pumping Station Operators
+,,,53-7071,,Gas Compressor and Gas Pumping Station Operators
+,,,53-7072,,"Pump Operators, Except Wellhead Pumpers"
+,,,53-7073,,Wellhead Pumpers
+,,53-7080,,,Refuse and Recyclable Material Collectors
+,,,53-7081,,Refuse and Recyclable Material Collectors
+,,53-7120,,,"Tank Car, Truck, and Ship Loaders"
+,,,53-7121,,"Tank Car, Truck, and Ship Loaders"
+,,53-7190,,,Miscellaneous Material Moving Workers
+,,,53-7199,,"Material Moving Workers, All Other"
+55-0000,,,,,Military Specific Occupations
+,55-1000,,,,Military Officer Special and Tactical Operations Leaders
+,,55-1010,,,Military Officer Special and Tactical Operations Leaders
+,,,55-1011,,Air Crew Officers
+,,,55-1012,,Aircraft Launch and Recovery Officers
+,,,55-1013,,Armored Assault Vehicle Officers
+,,,55-1014,,Artillery and Missile Officers
+,,,55-1015,,Command and Control Center Officers
+,,,55-1016,,Infantry Officers
+,,,55-1017,,Special Forces Officers
+,,,55-1019,,"Military Officer Special and Tactical Operations Leaders, All Other"
+,55-2000,,,,First-Line Enlisted Military Supervisors
+,,55-2010,,,First-Line Enlisted Military Supervisors
+,,,55-2011,,First-Line Supervisors of Air Crew Members
+,,,55-2012,,First-Line Supervisors of Weapons Specialists/Crew Members
+,,,55-2013,,First-Line Supervisors of All Other Tactical Operations Specialists
+,55-3000,,,,Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members
+,,55-3010,,,Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members
+,,,55-3011,,Air Crew Members
+,,,55-3012,,Aircraft Launch and Recovery Specialists
+,,,55-3013,,Armored Assault Vehicle Crew Members
+,,,55-3014,,Artillery and Missile Crew Members
+,,,55-3015,,Command and Control Center Specialists
+,,,55-3016,,Infantry
+,,,55-3018,,Special Forces
+,,,55-3019,,"Military Enlisted Tactical Operations and Air/Weapons Specialists and Crew Members, All Other"

release_2025_03_27/automation_vs_augmentation_v1.csv

@@ -0,0 +1,7 @@
+interaction_type,pct
+directive,22.563272409918948
+feedback loop,12.036303266190515
+learning,18.917648061953294
+none,2.9013020624347967
+task iteration,25.47648663831153
+validation,2.314220367546746

release_2025_03_27/automation_vs_augmentation_v2.csv

@@ -0,0 +1,7 @@
+interaction_type,pct
+directive,29.420541308119624
+feedback loop,12.245083676255144
+learning,27.080406206093095
+none,3.2389485842287633
+task iteration,24.038560578408678
+validation,3.972959594393916

release_2025_03_27/cluster_level_data/README.md

@@ -0,0 +1,42 @@
+# Cluster Level Data
+
+This folder contains cluster level data for the second Economic Index release associated with Claude 3.7 Sonnet Data. It contains hierarchical cluster descriptions, as well as associated prevalence metrics for each cluster (% of records, % of users). It also includes mappings to [O*NET Tasks](https://www.onetonline.org/), collaboration pattern ratios, and ratios associated with whether or not Claude Sonnet 3.7's "Thinking" feature was used during the conversation.
+
+## Files in this Directory
+
+- **cluster_level_dataset.tsv**: Tab-separated values file containing the cluster data with all fields described in the data dictionary below. This is the primary dataset file for analysis.
+
+- **cluster_level_example_analysis.ipynb**: Jupyter notebook demonstrating example analyses you can perform with the cluster level dataset. This notebook includes code for loading the data, basic exploratory analysis, and visualization techniques to help understand the cluster patterns and their relationships to O*NET tasks.
+
+## Data Dictionary
+
+| Field | Description |
+|-------|-------------|
+| cluster_name_0 | Name of the level 0 (most granular) cluster |
+| cluster_description_0 | Detailed description of the level 0 cluster |
+| cluster_name_1 | Name of the level 1 (intermediate) cluster |
+| cluster_description_1 | Detailed description of the level 1 cluster |
+| cluster_name_2 | Name of the level 2 (broadest) cluster |
+| cluster_description_2 | Detailed description of the level 2 cluster |
+| percent_records | Percentage of total records that belong to this Level 0 cluster |
+| percent_users | Percentage of total users who have used this Level 0 cluster |
+| onet_task | Description of the associated O*NET task |
+| collaboration:directive_ratio | Ratio of conversations with directive collaboration patterns |
+| collaboration:feedback loop_ratio | Ratio of conversations with feedback loop collaboration patterns |
+| collaboration:learning_ratio | Ratio of conversations with learning collaboration patterns |
+| collaboration:none_ratio | Ratio of conversations with no collaboration patterns |
+| collaboration:task iteration_ratio | Ratio of conversations with task iteration collaboration patterns |
+| collaboration:validation_ratio | Ratio of conversations with validation collaboration patterns |
+| has_thinking_ratio | Ratio of conversations where the "Thinking" feature was used |
+
+## Bucketing Adjustment
+
+For percent_records and percent_users fields, we applied a bucketing adjustment to enhance privacy while preserving the overall distribution:
+
+1. Clusters were sorted by their prevalence metrics (percent_records and percent_users).
+2. 100 buckets were created.
+3. Clusters were assigned to buckets and the average prevalence within each bucket was calculated.
+4. The original values were replaced with the bucket averages to reduce precision while maintaining the distribution.
+
+
+

release_2025_09_15/README.md

@@ -0,0 +1,72 @@
+# Anthropic Economic Index September 2025 Report Replication
+
+## Folder Structure
+
+```
+.
+├── code/                  # Analysis scripts
+├── data/
+│   ├── input/             # Raw data files (from external sources or prior releases)
+│   ├── intermediate/      # Processed data files
+│   └── output/            # Final outputs (plots, tables, etc.)
+├── data_documentation.md  # Documentation of all data sources and datasets
+└── README.md
+```
+
+## Data Processing Pipeline
+
+**Note:** Since all preprocessed data files are provided, you can skip directly to the Analysis section (Section 2) if you want to replicate the results without re-running the preprocessing steps. Please refer to `data_documentation.md` for details on the different data used.
+
+Run the following scripts in order from the `code/` directory:
+
+### 1. Data Preprocessing
+
+1. **`preprocess_iso_codes.py`**
+   - Processes ISO country codes
+   - Creates standardized country code mappings
+
+2. **`preprocess_population.py`**
+   - Processes country-level population data
+   - Processes US state-level population data
+   - Outputs working age population statistics
+
+3. **`preprocess_gdp.py`**
+   - Downloads and processes IMF country GDP data
+   - Processes BEA US state GDP data
+   - Creates standardized GDP datasets
+
+4. **`preprocess_onet.py`**
+   - Processes O*NET occupation and task data
+   - Creates SOC occupation mappings
+
+5. **`aei_report_v3_preprocessing_1p_api.ipynb`**
+   - Jupyter notebook for preprocessing API and Claude.ai usage data
+   - Prepares data for analysis
+
+### 2. Analysis
+
+#### Analysis Scripts
+
+1. **`aei_report_v3_change_over_time_claude_ai.py`**
+   - Analyzes automation trends across report versions (V1, V2, V3)
+   - Generates comparison figures showing evolution of automation estimates
+
+2. **`aei_report_v3_analysis_claude_ai.ipynb`**
+   - Analysis notebook for Claude.ai usage patterns
+   - Generates figures specific to Claude.ai usage
+   - Uses functions from `aei_analysis_functions_claude_ai.py`
+
+3. **`aei_report_v3_analysis_1p_api.ipynb`**
+   - Main analysis notebook for API usage patterns
+   - Generates figures for occupational usage, collaboration patterns, and regression analyses
+   - Uses functions from `aei_analysis_functions_1p_api.py`
+
+#### Supporting Function Files
+
+- **`aei_analysis_functions_claude_ai.py`**
+  - Core analysis functions for Claude.ai data
+  - Platform-specific analysis and visualization functions
+
+- **`aei_analysis_functions_1p_api.py`**
+  - Core analysis functions for API data
+  - Includes regression models, plotting functions, and data transformations

release_2025_09_15/code/aei_analysis_functions_1p_api.py

@@ -0,0 +1,2339 @@
+# AEI 1P API Analysis Functions
+# This module contains the core analysis functions for the AEI report API chapter
+
+from pathlib import Path
+from textwrap import wrap
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import plotly.graph_objects as go
+import statsmodels.api as sm
+from plotly.subplots import make_subplots
+
+# Define the tier colors
+CUSTOM_COLORS_LIST = ["#E6DBD0", "#E5C5AB", "#E4AF86", "#E39961", "#D97757"]
+
+# Define the color cycle for charts
+COLOR_CYCLE = [
+    "#D97757",
+    "#656565",
+    "#40668C",
+    "#E39961",
+    "#E4AF86",
+    "#C65A3F",
+    "#8778AB",
+    "#E5C5AB",
+    "#B04F35",
+]
+
+
+def setup_plot_style():
+    """Configure matplotlib for publication-quality figures."""
+    plt.style.use("default")
+    plt.rcParams.update(
+        {
+            "figure.dpi": 100,
+            "savefig.dpi": 300,
+            "font.size": 10,
+            "axes.labelsize": 11,
+            "axes.titlesize": 12,
+            "xtick.labelsize": 9,
+            "ytick.labelsize": 9,
+            "legend.fontsize": 9,
+            "figure.facecolor": "white",
+            "axes.facecolor": "white",
+            "savefig.facecolor": "white",
+            "axes.edgecolor": "#333333",
+            "axes.linewidth": 0.8,
+            "axes.grid": True,
+            "grid.alpha": 0.3,
+            "grid.linestyle": "-",
+            "grid.linewidth": 0.5,
+            "axes.axisbelow": True,
+            "text.usetex": False,
+            "mathtext.default": "regular",
+            "axes.titlecolor": "#B86046",
+            "figure.titlesize": 16,
+        }
+    )
+
+
+# Initialize style
+setup_plot_style()
+
+
+def load_preprocessed_data(input_file):
+    """
+    Load preprocessed API data from CSV or Parquet file.
+
+    Args:
+        input_file: Path to preprocessed data file
+
+    Returns:
+        DataFrame with preprocessed API data
+    """
+    input_path = Path(input_file)
+
+    if not input_path.exists():
+        raise FileNotFoundError(f"Input file not found: {input_path}")
+
+    df = pd.read_csv(input_path)
+    return df
+
+
+def create_top_requests_bar_chart(df, output_dir):
+    """
+    Create bar chart showing top 15 request categories (level 2) by count share.
+
+    Args:
+        df: Preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Get request data at level 2 (global only) using percentages
+    request_data = df[
+        (df["facet"] == "request")
+        & (df["geo_id"] == "GLOBAL")
+        & (df["level"] == 2)
+        & (df["variable"] == "request_pct")
+    ].copy()
+
+    # Filter out not_classified (but don't renormalize)
+    request_data = request_data[request_data["cluster_name"] != "not_classified"]
+
+    # Use the percentage values directly (already calculated in preprocessing)
+    request_data["request_pct"] = request_data["value"]
+
+    # Get top 15 requests by percentage share
+    top_requests = request_data.nlargest(15, "request_pct").sort_values(
+        "request_pct", ascending=True
+    )
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 10))
+
+    # Create horizontal bar chart with tier color gradient
+    y_pos = np.arange(len(top_requests))
+
+    # Use tier colors based on ranking (top categories get darker colors)
+    colors = []
+    for i in range(len(top_requests)):
+        # Map position to tier color (top bars = darker, bottom bars = lighter)
+        # Since bars are sorted ascending, higher index = higher value = darker color
+        rank_position = i / (len(top_requests) - 1)
+        tier_index = int(rank_position * (len(CUSTOM_COLORS_LIST) - 1))
+        colors.append(CUSTOM_COLORS_LIST[tier_index])
+
+    ax.barh(
+        y_pos,
+        top_requests["request_pct"],
+        color=colors,
+        alpha=0.9,
+        edgecolor="#333333",
+        linewidth=0.5,
+    )
+
+    # Add value labels on bars
+    for i, (idx, row) in enumerate(top_requests.iterrows()):
+        ax.text(
+            row["request_pct"] + 0.1,
+            i,
+            f"{row['request_pct']:.1f}%",
+            va="center",
+            fontsize=11,
+            fontweight="bold",
+        )
+
+    # Clean up request names for y-axis labels
+    labels = []
+    for name in top_requests["cluster_name"]:
+        # Truncate long names and add line breaks
+        if len(name) > 60:
+            # Find good break point around middle
+            mid = len(name) // 2
+            break_point = name.find(" ", mid)
+            if break_point == -1:  # No space found, just break at middle
+                break_point = mid
+            clean_name = name[:break_point] + "\n" + name[break_point:].strip()
+        else:
+            clean_name = name
+        labels.append(clean_name)
+
+    ax.set_yticks(y_pos)
+    ax.set_yticklabels(labels, fontsize=10)
+
+    # Formatting
+    ax.set_xlabel("Percentage of total request count", fontsize=14)
+    ax.set_title(
+        "Top use cases among 1P API transcripts by usage share \n (broad grouping, bottom-up classification)",
+        fontsize=14,
+        fontweight="bold",
+        pad=20,
+    )
+
+    # Add grid
+    ax.grid(True, alpha=0.3, axis="x")
+    ax.set_axisbelow(True)
+
+    # Remove top and right spines
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    # Increase tick label font size
+    ax.tick_params(axis="x", which="major", labelsize=12)
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "top_requests_level2_bar_chart.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def load_onet_mappings():
+    """
+    Load ONET task statements and SOC structure for occupational category mapping.
+
+    Returns:
+        Tuple of (task_statements_df, soc_structure_df)
+    """
+    # Load from local files
+    task_path = Path("../data/intermediate/onet_task_statements.csv")
+    soc_path = Path("../data/intermediate/soc_structure.csv")
+
+    # Load CSV files directly
+    task_statements = pd.read_csv(task_path)
+    soc_structure = pd.read_csv(soc_path)
+
+    return task_statements, soc_structure
+
+
+def map_to_occupational_categories(df, task_statements, soc_structure):
+    """
+    Map ONET task data to major occupational categories.
+
+    Args:
+        df: Preprocessed data DataFrame
+        task_statements: ONET task statements DataFrame
+        soc_structure: SOC structure DataFrame
+
+    Returns:
+        DataFrame with occupational category mappings
+    """
+    # Filter for ONET task data
+    onet_data = df[df["facet"] == "onet_task"].copy()
+
+    # Handle not_classified and none tasks first
+    not_classified_mask = onet_data["cluster_name"].isin(["not_classified", "none"])
+    not_classified_data = onet_data[not_classified_mask].copy()
+    not_classified_data["soc_major"] = "99"
+    not_classified_data["occupational_category"] = "Not Classified"
+
+    # Process regular tasks
+    regular_data = onet_data[~not_classified_mask].copy()
+
+    # Standardize task descriptions for matching
+    # Create standardized task mapping from ONET statements
+    task_statements["task_standardized"] = (
+        task_statements["Task"].str.strip().str.lower()
+    )
+    regular_data["cluster_name_standardized"] = (
+        regular_data["cluster_name"].str.strip().str.lower()
+    )
+
+    # Create mapping from standardized task to major groups (allowing multiple)
+    task_to_major_groups = {}
+    for _, row in task_statements.iterrows():
+        if pd.notna(row["Task"]) and pd.notna(row["soc_major_group"]):
+            std_task = row["task_standardized"]
+            major_group = str(int(row["soc_major_group"]))
+            if std_task not in task_to_major_groups:
+                task_to_major_groups[std_task] = []
+            if major_group not in task_to_major_groups[std_task]:
+                task_to_major_groups[std_task].append(major_group)
+
+    # Expand rows for tasks that belong to multiple groups
+    expanded_rows = []
+    for _, row in regular_data.iterrows():
+        std_task = row["cluster_name_standardized"]
+        if std_task in task_to_major_groups:
+            groups = task_to_major_groups[std_task]
+            # Assign full value to each group (creates duplicates)
+            for group in groups:
+                new_row = row.copy()
+                new_row["soc_major"] = group
+                new_row["value"] = row["value"]  # Keep full value for each group
+                expanded_rows.append(new_row)
+
+    # Create new dataframe from expanded rows
+    if expanded_rows:
+        regular_data = pd.DataFrame(expanded_rows)
+    else:
+        regular_data["soc_major"] = None
+
+    # Get major occupational groups from SOC structure
+    # Filter for rows where 'Major Group' is not null (these are the major groups)
+    major_groups = soc_structure[soc_structure["Major Group"].notna()].copy()
+
+    # Extract major group code and title
+    major_groups["soc_major"] = major_groups["Major Group"].astype(str).str[:2]
+    major_groups["title"] = major_groups["SOC or O*NET-SOC 2019 Title"]
+
+    # Create a clean mapping from major group code to title
+    major_group_mapping = (
+        major_groups[["soc_major", "title"]]
+        .drop_duplicates()
+        .set_index("soc_major")["title"]
+        .to_dict()
+    )
+
+    # Map major group codes to titles for regular data
+    regular_data["occupational_category"] = regular_data["soc_major"].map(
+        major_group_mapping
+    )
+
+    # Keep only successfully mapped regular data
+    regular_mapped = regular_data[regular_data["occupational_category"].notna()].copy()
+
+    # Combine regular mapped data with not_classified data
+    onet_mapped = pd.concat([regular_mapped, not_classified_data], ignore_index=True)
+
+    # Renormalize percentages to sum to 100 since we may have created duplicates
+    total = onet_mapped["value"].sum()
+
+    onet_mapped["value"] = (onet_mapped["value"] / total) * 100
+
+    return onet_mapped
+
+
+def create_platform_occupational_comparison(api_df, cai_df, output_dir):
+    """
+    Create horizontal bar chart comparing occupational categories between Claude.ai and 1P API.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Load ONET mappings for occupational categories
+    task_statements, soc_structure = load_onet_mappings()
+
+    # Process both datasets to get occupational categories
+    def get_occupational_data(df, platform_name):
+        # Get ONET task percentage data (global level only)
+        onet_data = df[
+            (df["facet"] == "onet_task")
+            & (df["geo_id"] == "GLOBAL")
+            & (df["variable"] == "onet_task_pct")
+        ].copy()
+
+        # Map to occupational categories using existing function
+        onet_mapped = map_to_occupational_categories(
+            onet_data, task_statements, soc_structure
+        )
+
+        # Sum percentages by occupational category
+        category_percentages = (
+            onet_mapped.groupby("occupational_category")["value"].sum().reset_index()
+        )
+
+        # Exclude "Not Classified" category from visualization
+        category_percentages = category_percentages[
+            category_percentages["occupational_category"] != "Not Classified"
+        ]
+
+        category_percentages.columns = ["category", f"{platform_name.lower()}_pct"]
+
+        return category_percentages
+
+    # Get data for both platforms
+    api_categories = get_occupational_data(api_df, "API")
+    claude_categories = get_occupational_data(cai_df, "Claude")
+
+    # Merge the datasets
+    category_comparison = pd.merge(
+        claude_categories, api_categories, on="category", how="outer"
+    ).fillna(0)
+
+    # Filter to substantial categories (>0.5% in either platform)
+    category_comparison = category_comparison[
+        (category_comparison["claude_pct"] > 0.5)
+        | (category_comparison["api_pct"] > 0.5)
+    ].copy()
+
+    # Calculate difference and total
+    category_comparison["difference"] = (
+        category_comparison["api_pct"] - category_comparison["claude_pct"]
+    )
+    category_comparison["total_pct"] = (
+        category_comparison["claude_pct"] + category_comparison["api_pct"]
+    )
+
+    # Get top 8 categories by total usage
+    top_categories = category_comparison.nlargest(8, "total_pct").sort_values(
+        "total_pct", ascending=True
+    )
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    y_pos = np.arange(len(top_categories))
+    bar_height = 0.35
+
+    # Create side-by-side bars
+    ax.barh(
+        y_pos - bar_height / 2,
+        top_categories["claude_pct"],
+        bar_height,
+        label="Claude.ai",
+        color=COLOR_CYCLE[2],
+        alpha=0.8,
+    )
+    ax.barh(
+        y_pos + bar_height / 2,
+        top_categories["api_pct"],
+        bar_height,
+        label="1P API",
+        color=COLOR_CYCLE[0],
+        alpha=0.8,
+    )
+
+    # Add value labels with difference percentages
+    for i, (idx, row) in enumerate(top_categories.iterrows()):
+        # Claude.ai label
+        if row["claude_pct"] > 0.1:
+            ax.text(
+                row["claude_pct"] + 0.2,
+                i - bar_height / 2,
+                f"{row['claude_pct']:.0f}%",
+                va="center",
+                fontsize=9,
+            )
+
+        # 1P API label with difference
+        if row["api_pct"] > 0.1:
+            ax.text(
+                row["api_pct"] + 0.2,
+                i + bar_height / 2,
+                f"{row['api_pct']:.0f}%",
+                va="center",
+                fontsize=9,
+                color=COLOR_CYCLE[0] if row["difference"] > 0 else COLOR_CYCLE[2],
+            )
+
+    # Clean up category labels
+    labels = []
+    for cat in top_categories["category"]:
+        # Remove "Occupations" suffix and wrap long names
+        clean_cat = cat.replace(" Occupations", "").replace(", and ", " & ")
+        wrapped = "\n".join(wrap(clean_cat, 40))
+        labels.append(wrapped)
+
+    ax.set_yticks(y_pos)
+    ax.set_yticklabels(labels, fontsize=10)
+
+    ax.set_xlabel("Percentage of usage", fontsize=12)
+    ax.set_title(
+        "Usage shares across top occupational categories: Claude.ai vs 1P API",
+        fontsize=14,
+        fontweight="bold",
+        pad=20,
+    )
+    ax.legend(loc="lower right", fontsize=11)
+    ax.grid(True, alpha=0.3, axis="x")
+    ax.set_axisbelow(True)
+
+    # Remove top and right spines
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "platform_occupational_comparison.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def create_platform_lorenz_curves(api_df, cai_df, output_dir):
+    """
+    Create Lorenz curves showing task usage concentration by platform.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+
+    def gini_coefficient(values):
+        """Calculate Gini coefficient for a series of values."""
+        sorted_values = np.sort(values)
+        n = len(sorted_values)
+        cumulative = np.cumsum(sorted_values)
+        gini = (2 * np.sum(np.arange(1, n + 1) * sorted_values)) / (
+            n * cumulative[-1]
+        ) - (n + 1) / n
+        return gini
+
+    def get_task_usage_data(df, platform_name):
+        # Get ONET task percentage data (global level only)
+        onet_data = df[
+            (df["facet"] == "onet_task")
+            & (df["geo_id"] == "GLOBAL")
+            & (df["variable"] == "onet_task_pct")
+        ].copy()
+
+        # Filter out none and not_classified
+        onet_data = onet_data[
+            ~onet_data["cluster_name"].isin(["none", "not_classified"])
+        ]
+
+        # Use the percentage values directly
+        onet_data["percentage"] = onet_data["value"]
+
+        return onet_data[["cluster_name", "percentage"]].copy()
+
+    api_tasks = get_task_usage_data(api_df, "1P API")
+    claude_tasks = get_task_usage_data(cai_df, "Claude.ai")
+
+    # Sort by percentage for each platform
+    api_tasks = api_tasks.sort_values("percentage")
+    claude_tasks = claude_tasks.sort_values("percentage")
+
+    # Calculate cumulative percentages of usage
+    api_cumulative = np.cumsum(api_tasks["percentage"])
+    claude_cumulative = np.cumsum(claude_tasks["percentage"])
+
+    # Calculate cumulative percentage of tasks
+    api_task_cumulative = np.arange(1, len(api_tasks) + 1) / len(api_tasks) * 100
+    claude_task_cumulative = (
+        np.arange(1, len(claude_tasks) + 1) / len(claude_tasks) * 100
+    )
+
+    # Interpolate to ensure curves reach 100%
+    # Add final points to reach (100, 100)
+    api_cumulative = np.append(api_cumulative, 100)
+    claude_cumulative = np.append(claude_cumulative, 100)
+    api_task_cumulative = np.append(api_task_cumulative, 100)
+    claude_task_cumulative = np.append(claude_task_cumulative, 100)
+
+    # Calculate Gini coefficients
+    api_gini = gini_coefficient(api_tasks["percentage"].values)
+    claude_gini = gini_coefficient(claude_tasks["percentage"].values)
+
+    # Create panel figure
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
+
+    # LEFT PANEL: Lorenz Curves
+    # Plot Lorenz curves
+    ax1.plot(
+        api_task_cumulative,
+        api_cumulative,
+        color=COLOR_CYCLE[1],
+        linewidth=2.5,
+        label=f"1P API (Gini = {api_gini:.3f})",
+    )
+
+    ax1.plot(
+        claude_task_cumulative,
+        claude_cumulative,
+        color=COLOR_CYCLE[0],
+        linewidth=2.5,
+        label=f"Claude.ai (Gini = {claude_gini:.3f})",
+    )
+
+    # Add perfect equality line (diagonal)
+    ax1.plot(
+        [0, 100],
+        [0, 100],
+        "k--",
+        linewidth=1.5,
+        alpha=0.7,
+        label="Perfect Equality",
+    )
+
+    # Calculate 80th percentile values
+    api_80th_usage = np.interp(80, api_task_cumulative, api_cumulative)
+    claude_80th_usage = np.interp(80, claude_task_cumulative, claude_cumulative)
+
+    # Add markers at 80th percentile
+    ax1.scatter(
+        80,
+        api_80th_usage,
+        alpha=0.5,
+        s=100,
+        color=COLOR_CYCLE[1],
+        edgecolors="white",
+        linewidth=1,
+        zorder=5,
+    )
+    ax1.scatter(
+        80,
+        claude_80th_usage,
+        alpha=0.5,
+        s=100,
+        color=COLOR_CYCLE[0],
+        edgecolors="white",
+        linewidth=1,
+        zorder=5,
+    )
+
+    # Add annotations
+    ax1.text(
+        82,
+        api_80th_usage - 2,
+        f"{api_80th_usage:.1f}% of usage",
+        ha="left",
+        va="center",
+        fontsize=10,
+        color=COLOR_CYCLE[1],
+    )
+
+    ax1.text(
+        78.5,
+        claude_80th_usage + 1,
+        f"{claude_80th_usage:.1f}% of usage",
+        ha="right",
+        va="center",
+        fontsize=10,
+        color=COLOR_CYCLE[0],
+    )
+
+    # Add text box
+    ax1.text(
+        0.05,
+        0.95,
+        f"The bottom 80% of tasks account for:\n• 1P API: {api_80th_usage:.1f}% of usage\n• Claude.ai: {claude_80th_usage:.1f}% of usage",
+        transform=ax1.transAxes,
+        va="top",
+        ha="left",
+        bbox=dict(
+            boxstyle="round,pad=0.3",
+            facecolor="white",
+            alpha=0.8,
+            edgecolor="black",
+            linewidth=1,
+        ),
+        fontsize=10,
+    )
+
+    # Styling for Lorenz curves
+    ax1.set_xlabel("Cumulative percentage of tasks", fontsize=12)
+    ax1.set_ylabel("Cumulative percentage of usage", fontsize=12)
+    ax1.set_title("Lorenz curves", fontsize=14, fontweight="bold", pad=20)
+    ax1.set_xlim(0, 100)
+    ax1.set_ylim(0, 100)
+    ax1.grid(True, alpha=0.3, linestyle="--")
+    ax1.set_axisbelow(True)
+    ax1.legend(loc=(0.05, 0.65), fontsize=11, frameon=True, facecolor="white")
+    ax1.spines["top"].set_visible(False)
+    ax1.spines["right"].set_visible(False)
+
+    # RIGHT PANEL: Zipf's Law Analysis
+    min_share = 0.1
+
+    # Filter for minimum share
+    api_filtered = api_tasks[api_tasks["percentage"] > min_share]["percentage"].copy()
+    claude_filtered = claude_tasks[claude_tasks["percentage"] > min_share][
+        "percentage"
+    ].copy()
+
+    # Calculate ranks and log transforms
+    ln_rank_api = np.log(api_filtered.rank(ascending=False))
+    ln_share_api = np.log(api_filtered)
+
+    ln_rank_claude = np.log(claude_filtered.rank(ascending=False))
+    ln_share_claude = np.log(claude_filtered)
+
+    # Fit regressions
+    api_model = sm.OLS(ln_rank_api, sm.add_constant(ln_share_api)).fit()
+    api_slope = api_model.params.iloc[1]
+    api_intercept = api_model.params.iloc[0]
+
+    claude_model = sm.OLS(ln_rank_claude, sm.add_constant(ln_share_claude)).fit()
+    claude_slope = claude_model.params.iloc[1]
+    claude_intercept = claude_model.params.iloc[0]
+
+    # Plot scatter points
+    ax2.scatter(
+        ln_share_api,
+        ln_rank_api,
+        alpha=0.5,
+        s=100,
+        color=COLOR_CYCLE[1],
+        label=f"1P API: y = {api_slope:.2f}x + {api_intercept:.2f}",
+    )
+
+    ax2.scatter(
+        ln_share_claude,
+        ln_rank_claude,
+        alpha=0.5,
+        s=100,
+        color=COLOR_CYCLE[0],
+        label=f"Claude.ai: y = {claude_slope:.2f}x + {claude_intercept:.2f}",
+    )
+
+    # Add Zipf's law reference line (slope = -1)
+    x_range = np.linspace(
+        min(ln_share_api.min(), ln_share_claude.min()),
+        max(ln_share_api.max(), ln_share_claude.max()),
+        100,
+    )
+    avg_intercept = (api_intercept + claude_intercept) / 2
+    y_line = -1 * x_range + avg_intercept
+
+    ax2.plot(
+        x_range,
+        y_line,
+        color="black",
+        linestyle="--",
+        linewidth=2,
+        label=f"Zipf's Law: y = -1.00x + {avg_intercept:.2f}",
+        zorder=0,
+    )
+
+    # Styling for Zipf's law plot
+    ax2.set_xlabel("ln(Share of usage)", fontsize=12)
+    ax2.set_ylabel("ln(Rank by usage)", fontsize=12)
+    ax2.set_title(
+        "Task rank versus usage share", fontsize=14, fontweight="bold", pad=20
+    )
+    ax2.grid(True, alpha=0.3, linestyle="--")
+    ax2.set_axisbelow(True)
+    ax2.legend(fontsize=11)
+    ax2.spines["top"].set_visible(False)
+    ax2.spines["right"].set_visible(False)
+
+    # Overall title
+    fig.suptitle(
+        "Lorenz curves and power law analysis across tasks: 1P API vs Claude.ai",
+        fontsize=16,
+        fontweight="bold",
+        y=0.95,
+        color="#B86046",
+    )
+
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.85)  # More room for suptitle
+
+    # Save plot
+    output_path = Path(output_dir) / "platform_lorenz_zipf_panel.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def create_collaboration_alluvial(api_df, cai_df, output_dir):
+    """
+    Create alluvial diagram showing collaboration pattern flows between platforms.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+
+    def get_collaboration_data(df, platform_name):
+        # Get collaboration facet data (global level only)
+        collab_data = df[
+            (df["facet"] == "collaboration")
+            & (df["geo_id"] == "GLOBAL")
+            & (df["variable"] == "collaboration_pct")
+        ].copy()
+
+        # Use cluster_name directly as the collaboration pattern
+        collab_data["pattern"] = collab_data["cluster_name"]
+
+        # Filter out not_classified
+        collab_data = collab_data[collab_data["pattern"] != "not_classified"]
+
+        # Use the percentage values directly
+        result = collab_data[["pattern", "value"]].copy()
+        result.columns = ["pattern", "percentage"]
+        result["platform"] = platform_name
+
+        return result
+
+    api_collab = get_collaboration_data(api_df, "1P API")
+    claude_collab = get_collaboration_data(cai_df, "Claude.ai")
+
+    # Combine collaboration data
+    collab_df = pd.concat([claude_collab, api_collab], ignore_index=True)
+
+    # Define categories
+    augmentation_types = ["learning", "task iteration", "validation"]
+    automation_types = ["directive", "feedback loop"]
+
+    # Colors matching the original
+    pattern_colors = {
+        "validation": "#2c3e67",
+        "task iteration": "#4f76c7",
+        "learning": "#79a7e0",
+        "feedback loop": "#614980",
+        "directive": "#8e6bb1",
+    }
+
+    # Extract flows
+    flows_claude = {}
+    flows_api = {}
+
+    for pattern in augmentation_types + automation_types:
+        claude_mask = (collab_df["pattern"] == pattern) & (
+            collab_df["platform"] == "Claude.ai"
+        )
+        if claude_mask.any():
+            flows_claude[pattern] = collab_df.loc[claude_mask, "percentage"].values[0]
+
+        api_mask = (collab_df["pattern"] == pattern) & (
+            collab_df["platform"] == "1P API"
+        )
+        if api_mask.any():
+            flows_api[pattern] = collab_df.loc[api_mask, "percentage"].values[0]
+
+    # Create figure with subplots
+    fig = make_subplots(
+        rows=2,
+        cols=1,
+        row_heights=[0.5, 0.5],
+        vertical_spacing=0.15,
+        subplot_titles=("<b>Augmentation Patterns</b>", "<b>Automation Patterns</b>"),
+    )
+
+    # Update subplot title colors and font
+    for annotation in fig.layout.annotations:
+        annotation.update(font=dict(color="#B86046", size=14, family="Styrene B LC"))
+
+    def create_alluvial_traces(patterns, row):
+        """Create traces for alluvial diagram"""
+        # Sort by size on Claude.ai side
+        patterns_sorted = sorted(
+            [p for p in patterns if p in flows_claude],
+            key=lambda p: flows_claude.get(p, 0),
+            reverse=True,
+        )
+
+        # Calculate total heights first to determine centering
+        total_claude = sum(
+            flows_claude.get(p, 0) for p in patterns if p in flows_claude
+        )
+        total_api = sum(flows_api.get(p, 0) for p in patterns if p in flows_api)
+        gap_count = max(
+            len([p for p in patterns if p in flows_claude and flows_claude[p] > 0]) - 1,
+            0,
+        )
+        gap_count_api = max(
+            len([p for p in patterns if p in flows_api and flows_api[p] > 0]) - 1, 0
+        )
+
+        total_height_claude = total_claude + (gap_count * 2)
+        total_height_api = total_api + (gap_count_api * 2)
+
+        # Calculate offset to center the smaller side
+        offset_claude = 0
+        offset_api = 0
+        if total_height_claude < total_height_api:
+            offset_claude = (total_height_api - total_height_claude) / 2
+        else:
+            offset_api = (total_height_claude - total_height_api) / 2
+
+        # Calculate positions for Claude.ai (left side)
+        y_pos_claude = offset_claude
+        claude_positions = {}
+        for pattern in patterns_sorted:
+            if pattern in flows_claude and flows_claude[pattern] > 0:
+                height = flows_claude[pattern]
+                claude_positions[pattern] = {
+                    "bottom": y_pos_claude,
+                    "top": y_pos_claude + height,
+                    "center": y_pos_claude + height / 2,
+                }
+                y_pos_claude += height + 2  # Add gap
+
+        # Calculate positions for 1P API (right side)
+        patterns_sorted_api = sorted(
+            [p for p in patterns if p in flows_api],
+            key=lambda p: flows_api.get(p, 0),
+            reverse=True,
+        )
+        y_pos_api = offset_api
+        api_positions = {}
+        for pattern in patterns_sorted_api:
+            if pattern in flows_api and flows_api[pattern] > 0:
+                height = flows_api[pattern]
+                api_positions[pattern] = {
+                    "bottom": y_pos_api,
+                    "top": y_pos_api + height,
+                    "center": y_pos_api + height / 2,
+                }
+                y_pos_api += height + 2  # Add gap
+
+        # Create shapes for flows
+        shapes = []
+        for pattern in patterns:
+            if pattern in claude_positions and pattern in api_positions:
+                # Create a quadrilateral connecting the two sides
+                x_left = 0.2
+                x_right = 0.8
+
+                claude_bottom = claude_positions[pattern]["bottom"]
+                claude_top = claude_positions[pattern]["top"]
+                api_bottom = api_positions[pattern]["bottom"]
+                api_top = api_positions[pattern]["top"]
+
+                # Create path for the flow
+                path = f"M {x_left} {claude_bottom} L {x_left} {claude_top} L {x_right} {api_top} L {x_right} {api_bottom} Z"
+
+                hex_color = pattern_colors[pattern]
+                r = int(hex_color[1:3], 16)
+                g = int(hex_color[3:5], 16)
+                b = int(hex_color[5:7], 16)
+
+                shapes.append(
+                    dict(
+                        type="path",
+                        path=path,
+                        fillcolor=f"rgba({r},{g},{b},0.5)",
+                        line=dict(color=f"rgba({r},{g},{b},1)", width=1),
+                    )
+                )
+
+        # Create text annotations
+        annotations = []
+
+        # Claude.ai labels
+        for pattern in patterns_sorted:
+            if pattern in claude_positions:
+                annotations.append(
+                    dict(
+                        x=x_left - 0.02,
+                        y=claude_positions[pattern]["center"],
+                        text=f"{pattern.replace('_', ' ').title()}<br>{flows_claude[pattern]:.1f}%",
+                        showarrow=False,
+                        xanchor="right",
+                        yanchor="middle",
+                        font=dict(size=10),
+                    )
+                )
+
+        # 1P API labels
+        for pattern in patterns_sorted_api:
+            if pattern in api_positions:
+                annotations.append(
+                    dict(
+                        x=x_right + 0.02,
+                        y=api_positions[pattern]["center"],
+                        text=f"{pattern.replace('_', ' ').title()}<br>{flows_api[pattern]:.1f}%",
+                        showarrow=False,
+                        xanchor="left",
+                        yanchor="middle",
+                        font=dict(size=10),
+                    )
+                )
+
+        # Platform labels
+        annotations.extend(
+            [
+                dict(
+                    x=x_left,
+                    y=max(y_pos_claude, y_pos_api) + 5,
+                    text="Claude.ai",
+                    showarrow=False,
+                    xanchor="center",
+                    font=dict(size=14, color="black"),
+                ),
+                dict(
+                    x=x_right,
+                    y=max(y_pos_claude, y_pos_api) + 5,
+                    text="1P API",
+                    showarrow=False,
+                    xanchor="center",
+                    font=dict(size=14, color="black"),
+                ),
+            ]
+        )
+
+        return shapes, annotations, max(y_pos_claude, y_pos_api)
+
+    # Create augmentation diagram
+    aug_shapes, aug_annotations, aug_height = create_alluvial_traces(
+        augmentation_types, 1
+    )
+
+    # Create automation diagram
+    auto_shapes, auto_annotations, auto_height = create_alluvial_traces(
+        automation_types, 2
+    )
+
+    # Add invisible traces to create subplots
+    fig.add_trace(
+        go.Scatter(x=[0], y=[0], mode="markers", marker=dict(size=0)), row=1, col=1
+    )
+    fig.add_trace(
+        go.Scatter(x=[0], y=[0], mode="markers", marker=dict(size=0)), row=2, col=1
+    )
+
+    # Update layout with shapes and annotations
+    fig.update_layout(
+        title=dict(
+            text="<b>Collaboration Modes: Claude.ai Conversations vs 1P API Transcripts</b>",
+            font=dict(size=16, family="Styrene B LC", color="#B86046"),
+            x=0.5,
+            xanchor="center",
+        ),
+        height=800,
+        width=1200,
+        paper_bgcolor="white",
+        plot_bgcolor="white",
+        showlegend=False,
+    )
+
+    # Ensure white background for both subplots
+    fig.update_xaxes(showgrid=False, zeroline=False, showticklabels=False, row=1, col=1)
+    fig.update_xaxes(showgrid=False, zeroline=False, showticklabels=False, row=2, col=1)
+    fig.update_yaxes(showgrid=False, zeroline=False, showticklabels=False, row=1, col=1)
+    fig.update_yaxes(showgrid=False, zeroline=False, showticklabels=False, row=2, col=1)
+
+    # Add shapes and annotations to each subplot
+    for shape in aug_shapes:
+        fig.add_shape(shape, row=1, col=1)
+    for shape in auto_shapes:
+        fig.add_shape(shape, row=2, col=1)
+
+    for ann in aug_annotations:
+        fig.add_annotation(ann, row=1, col=1)
+    for ann in auto_annotations:
+        fig.add_annotation(ann, row=2, col=1)
+
+    # Set axis ranges and ensure white background
+    fig.update_xaxes(
+        range=[0, 1],
+        showgrid=False,
+        zeroline=False,
+        showticklabels=False,
+        row=1,
+        col=1,
+    )
+    fig.update_xaxes(
+        range=[0, 1],
+        showgrid=False,
+        zeroline=False,
+        showticklabels=False,
+        row=2,
+        col=1,
+    )
+
+    fig.update_yaxes(
+        range=[0, aug_height + 10],
+        showgrid=False,
+        zeroline=False,
+        showticklabels=False,
+        row=1,
+        col=1,
+    )
+    fig.update_yaxes(
+        range=[0, auto_height + 10],
+        showgrid=False,
+        zeroline=False,
+        showticklabels=False,
+        row=2,
+        col=1,
+    )
+
+    # Save plot
+    output_path = Path(output_dir) / "collaboration_alluvial.png"
+    fig.write_image(str(output_path), width=1200, height=800, scale=2)
+    fig.show()
+    return str(output_path)
+
+
+def get_collaboration_shares(df):
+    """
+    Extract collaboration mode shares for each ONET task from intersection data.
+
+    Args:
+        df: Preprocessed data DataFrame
+
+    Returns:
+        dict: {task_name: {mode: percentage}}
+    """
+    # Filter to GLOBAL data only and use pre-calculated percentages
+    collab_data = df[
+        (df["geo_id"] == "GLOBAL")
+        & (df["facet"] == "onet_task::collaboration")
+        & (df["variable"] == "onet_task_collaboration_pct")
+    ].copy()
+
+    # Split the cluster_name into task and collaboration mode
+    collab_data[["task", "mode"]] = collab_data["cluster_name"].str.rsplit(
+        "::", n=1, expand=True
+    )
+
+    # Filter out 'none' and 'not_classified' modes
+    collab_data = collab_data[~collab_data["mode"].isin(["none", "not_classified"])]
+
+    # Use pre-calculated percentages directly
+    collaboration_modes = [
+        "directive",
+        "feedback loop",
+        "learning",
+        "task iteration",
+        "validation",
+    ]
+    result = {}
+
+    for _, row in collab_data.iterrows():
+        task = row["task"]
+        mode = row["mode"]
+
+        if mode in collaboration_modes:
+            if task not in result:
+                result[task] = {}
+            result[task][mode] = float(row["value"])
+
+    return result
+
+
+def create_automation_augmentation_panel(api_df, cai_df, output_dir):
+    """
+    Create combined panel figure showing automation vs augmentation for both platforms.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Create figure with subplots
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))
+
+    def create_automation_augmentation_subplot(df, ax, title, platform_name):
+        """Helper function to create one automation vs augmentation subplot"""
+        # Get collaboration shares for each task
+        collab_shares = get_collaboration_shares(df)
+
+        # Get task usage counts for bubble sizing
+        df_global = df[df["geo_id"] == "GLOBAL"]
+        task_counts = (
+            df_global[
+                (df_global["facet"] == "onet_task")
+                & (df_global["variable"] == "onet_task_count")
+                & (~df_global["cluster_name"].isin(["none", "not_classified"]))
+            ]
+            .set_index("cluster_name")["value"]
+            .to_dict()
+        )
+
+        # Prepare data for plotting
+        tasks = []
+        automation_scores = []
+        augmentation_scores = []
+        bubble_sizes = []
+
+        for task_name, shares in collab_shares.items():
+            if task_name in task_counts:
+                # Calculate automation score (directive + feedback loop)
+                automation = shares.get("directive", 0) + shares.get("feedback loop", 0)
+
+                # Calculate augmentation score (learning + task iteration + validation)
+                augmentation = (
+                    shares.get("learning", 0)
+                    + shares.get("task iteration", 0)
+                    + shares.get("validation", 0)
+                )
+
+                # Only include tasks with some collaboration data
+                if automation + augmentation > 0:
+                    tasks.append(task_name)
+                    automation_scores.append(automation)
+                    augmentation_scores.append(augmentation)
+                    bubble_sizes.append(task_counts[task_name])
+
+        # Convert to numpy arrays for plotting
+        automation_scores = np.array(automation_scores)
+        augmentation_scores = np.array(augmentation_scores)
+        bubble_sizes = np.array(bubble_sizes)
+
+        # Scale bubble sizes
+        bubble_sizes_scaled = (bubble_sizes / bubble_sizes.max()) * 800 + 40
+
+        # Color points based on whether automation or augmentation dominates
+        colors = []
+        for auto, aug in zip(automation_scores, augmentation_scores, strict=True):
+            if auto > aug:
+                colors.append("#8e6bb1")  # Automation dominant
+            else:
+                colors.append("#4f76c7")  # Augmentation dominant
+
+        # Create scatter plot
+        ax.scatter(
+            automation_scores,
+            augmentation_scores,
+            s=bubble_sizes_scaled,
+            c=colors,
+            alpha=0.7,
+            edgecolors="black",
+            linewidth=0.5,
+        )
+
+        # Add diagonal line (automation = augmentation)
+        max_val = max(automation_scores.max(), augmentation_scores.max())
+        ax.plot([0, max_val], [0, max_val], "--", color="gray", alpha=0.5, linewidth=2)
+
+        # Labels and formatting (increased font sizes)
+        ax.set_xlabel("Automation Share (%)", fontsize=14)
+        ax.set_ylabel(
+            "Augmentation Score (%)",
+            fontsize=14,
+        )
+        ax.set_title(title, fontsize=14, fontweight="bold", pad=15)
+
+        # Calculate percentages for legend
+        automation_dominant_count = sum(
+            1
+            for auto, aug in zip(automation_scores, augmentation_scores, strict=True)
+            if auto > aug
+        )
+        augmentation_dominant_count = len(automation_scores) - automation_dominant_count
+        total_tasks = len(automation_scores)
+
+        automation_pct = (automation_dominant_count / total_tasks) * 100
+        augmentation_pct = (augmentation_dominant_count / total_tasks) * 100
+
+        # Add legend with percentages centered at top
+        automation_patch = plt.scatter(
+            [],
+            [],
+            c="#8e6bb1",
+            alpha=0.7,
+            s=100,
+            label=f"Automation dominant ({automation_pct:.1f}% of Tasks)",
+        )
+        augmentation_patch = plt.scatter(
+            [],
+            [],
+            c="#4f76c7",
+            alpha=0.7,
+            s=100,
+            label=f"Augmentation dominant ({augmentation_pct:.1f}% of Tasks)",
+        )
+        ax.legend(
+            handles=[automation_patch, augmentation_patch],
+            loc="upper center",
+            bbox_to_anchor=(0.5, 0.95),
+            fontsize=12,
+            frameon=True,
+            facecolor="white",
+        )
+
+        # Grid and styling
+        ax.grid(True, alpha=0.3)
+        ax.set_axisbelow(True)
+        ax.tick_params(axis="both", which="major", labelsize=12)
+
+        return len(tasks), automation_pct, augmentation_pct
+
+    # Create API subplot
+    create_automation_augmentation_subplot(api_df, ax1, "1P API", "1P API")
+
+    # Create Claude.ai subplot
+    create_automation_augmentation_subplot(cai_df, ax2, "Claude.ai", "Claude.ai")
+
+    # Add overall title
+    fig.suptitle(
+        "Automation and augmentation dominance across tasks: Claude.ai vs. 1P API",
+        fontsize=16,
+        fontweight="bold",
+        y=0.95,
+        color="#B86046",
+    )
+
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.85)  # More room for suptitle
+
+    # Save plot
+    output_path = Path(output_dir) / "automation_vs_augmentation_panel.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def extract_token_metrics_from_intersections(df):
+    """
+    Extract token metrics from preprocessed intersection data.
+
+    Args:
+        df: Preprocessed dataframe with intersection facets
+
+    Returns:
+        DataFrame with token metrics for analysis
+    """
+    # Extract data using new variable names from mean value intersections
+    cost_data = df[
+        (df.facet == "onet_task::cost") & (df.variable == "cost_index")
+    ].copy()
+    cost_data["base_task"] = cost_data["cluster_name"].str.replace("::index", "")
+    onet_cost = cost_data.set_index("base_task")["value"].copy()
+
+    prompt_data = df[
+        (df.facet == "onet_task::prompt_tokens")
+        & (df.variable == "prompt_tokens_index")
+    ].copy()
+    prompt_data["base_task"] = prompt_data["cluster_name"].str.replace("::index", "")
+    onet_prompt = prompt_data.set_index("base_task")["value"].copy()
+
+    completion_data = df[
+        (df.facet == "onet_task::completion_tokens")
+        & (df.variable == "completion_tokens_index")
+    ].copy()
+    completion_data["base_task"] = completion_data["cluster_name"].str.replace(
+        "::index", ""
+    )
+    onet_completion = completion_data.set_index("base_task")["value"].copy()
+
+    # Get API call counts for bubble sizing and WLS weights
+    api_records_data = df[
+        (df.facet == "onet_task::prompt_tokens")
+        & (df.variable == "prompt_tokens_count")
+    ].copy()
+    api_records_data["base_task"] = api_records_data["cluster_name"].str.replace(
+        "::count", ""
+    )
+    onet_api_records = api_records_data.set_index("base_task")["value"].copy()
+
+    # Create metrics DataFrame - values are already re-indexed during preprocessing
+    metrics = pd.DataFrame(
+        {
+            "cluster_name": onet_cost.index,
+            "cost_per_record": onet_cost,  # Already indexed (1.0 = average)
+            "avg_prompt_tokens": onet_prompt.reindex(
+                onet_cost.index
+            ),  # Already indexed
+            "avg_completion_tokens": onet_completion.reindex(
+                onet_cost.index
+            ),  # Already indexed
+        }
+    )
+
+    # Get task usage percentages
+    usage_pct_data = df[
+        (df.facet == "onet_task") & (df.variable == "onet_task_pct")
+    ].copy()
+    usage_pct_data["base_task"] = usage_pct_data["cluster_name"]
+    onet_usage_pct = usage_pct_data.set_index("base_task")["value"].copy()
+
+    # Add API records and usage percentages
+    metrics["api_records"] = onet_api_records.reindex(onet_cost.index)
+    metrics["usage_pct"] = onet_usage_pct.reindex(onet_cost.index)
+
+    # Calculate derived metrics
+    metrics["output_input_ratio"] = (
+        metrics["avg_completion_tokens"] / metrics["avg_prompt_tokens"]
+    )
+    metrics["total_tokens"] = (
+        metrics["avg_prompt_tokens"] + metrics["avg_completion_tokens"]
+    )
+
+    return metrics
+
+
+def add_occupational_categories_to_metrics(
+    task_metrics, task_statements, soc_structure
+):
+    """
+    Add occupational categories to task metrics based on ONET mappings.
+
+    Args:
+        task_metrics: DataFrame with task metrics
+        task_statements: ONET task statements DataFrame
+        soc_structure: SOC structure DataFrame
+
+    Returns:
+        DataFrame with occupational categories added
+    """
+    # Standardize task descriptions for matching
+    task_statements["task_standardized"] = (
+        task_statements["Task"].str.strip().str.lower()
+    )
+    task_metrics["cluster_name_standardized"] = (
+        task_metrics["cluster_name"].str.strip().str.lower()
+    )
+
+    # Create mapping from standardized task to major group
+    task_to_major_group = {}
+    for _, row in task_statements.iterrows():
+        if pd.notna(row["Task"]) and pd.notna(row["soc_major_group"]):
+            std_task = row["task_standardized"]
+            major_group = str(int(row["soc_major_group"]))
+            task_to_major_group[std_task] = major_group
+
+    # Map cluster names to major groups
+    task_metrics["soc_major"] = task_metrics["cluster_name_standardized"].map(
+        task_to_major_group
+    )
+
+    # Get major occupational groups from SOC structure
+    major_groups = soc_structure[soc_structure["Major Group"].notna()].copy()
+    major_groups["soc_major"] = major_groups["Major Group"].astype(str).str[:2]
+    major_groups["title"] = major_groups["SOC or O*NET-SOC 2019 Title"]
+
+    # Create a clean mapping from major group code to title
+    major_group_mapping = (
+        major_groups[["soc_major", "title"]]
+        .drop_duplicates()
+        .set_index("soc_major")["title"]
+        .to_dict()
+    )
+
+    # Map major group codes to titles
+    task_metrics["occupational_category"] = task_metrics["soc_major"].map(
+        major_group_mapping
+    )
+
+    # Remove unmapped/not classified tasks from analysis
+    task_metrics = task_metrics[task_metrics["occupational_category"].notna()].copy()
+
+    # Find top 6 categories by usage share (API calls) and group others as "All Other"
+    category_usage = (
+        task_metrics.groupby("occupational_category")["api_records"]
+        .sum()
+        .sort_values(ascending=False)
+    )
+    top_6_categories = list(category_usage.head(6).index)
+
+    # Group smaller categories as "All Other"
+    task_metrics["occupational_category"] = task_metrics["occupational_category"].apply(
+        lambda x: x if x in top_6_categories else "All Other"
+    )
+
+    return task_metrics
+
+
+def create_token_output_bar_chart(df, output_dir):
+    """
+    Create bar chart showing average output (completion) tokens by occupational category.
+
+    Args:
+        df: Preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Load ONET mappings for occupational categories
+    task_statements, soc_structure = load_onet_mappings()
+
+    # Use preprocessed intersection data
+    task_metrics = extract_token_metrics_from_intersections(df)
+
+    # Add occupational categories
+    task_metrics = add_occupational_categories_to_metrics(
+        task_metrics, task_statements, soc_structure
+    )
+
+    # Calculate average output tokens by occupational category
+    category_stats = (
+        task_metrics.groupby("occupational_category")
+        .agg(
+            {
+                "avg_completion_tokens": "mean",  # Average across tasks
+                "api_records": "sum",  # Total API calls for ranking
+            }
+        )
+        .reset_index()
+    )
+
+    # Find top 6 categories by total API calls
+    top_6_categories = category_stats.nlargest(6, "api_records")[
+        "occupational_category"
+    ].tolist()
+
+    # Group smaller categories as "All Other"
+    def categorize(cat):
+        return cat if cat in top_6_categories else "All Other"
+
+    task_metrics["category_group"] = task_metrics["occupational_category"].apply(
+        categorize
+    )
+
+    # Recalculate stats with grouped categories
+    final_stats = (
+        task_metrics.groupby("category_group")
+        .agg(
+            {
+                "avg_completion_tokens": "mean",  # Average output tokens across tasks
+                "api_records": "sum",  # Total usage for reference
+            }
+        )
+        .reset_index()
+    )
+
+    # Sort by output tokens (descending)
+    final_stats = final_stats.sort_values("avg_completion_tokens", ascending=True)
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    # Create horizontal bar chart
+    y_pos = np.arange(len(final_stats))
+    colors = [COLOR_CYCLE[i % len(COLOR_CYCLE)] for i in range(len(final_stats))]
+
+    ax.barh(
+        y_pos,
+        final_stats["avg_completion_tokens"],
+        color=colors,
+        alpha=0.8,
+        edgecolor="#333333",
+        linewidth=0.5,
+    )
+
+    # Add value labels
+    for i, (idx, row) in enumerate(final_stats.iterrows()):
+        ax.text(
+            row["avg_completion_tokens"] + 0.02,
+            i,
+            f"{row['avg_completion_tokens']:.2f}",
+            va="center",
+            fontsize=11,
+            fontweight="bold",
+        )
+
+    # Clean up category labels
+    labels = []
+    for cat in final_stats["category_group"]:
+        clean_cat = cat.replace(" Occupations", "").replace(", and ", " & ")
+        labels.append(clean_cat)
+
+    ax.set_yticks(y_pos)
+    ax.set_yticklabels(labels, fontsize=10)
+
+    # Formatting
+    ax.set_xlabel(
+        "Average output token index for observed tasks in a given category",
+        fontsize=12,
+    )
+    ax.set_title(
+        "Average output token index across leading occupational categories",
+        fontsize=14,
+        fontweight="bold",
+        pad=20,
+    )
+
+    # Grid and styling
+    ax.grid(True, alpha=0.3, axis="x")
+    ax.set_axisbelow(True)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+    ax.tick_params(axis="x", which="major", labelsize=11)
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "token_output_bar_chart.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def create_completion_vs_input_tokens_scatter(df, output_dir):
+    """
+    Create scatter plot of ln(completion tokens) vs ln(input tokens) by occupational category.
+
+    Args:
+        df: Preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Use preprocessed intersection data
+    task_metrics = extract_token_metrics_from_intersections(df)
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    # Transform to natural log
+    ln_input = np.log(task_metrics["avg_prompt_tokens"])
+    ln_output = np.log(task_metrics["avg_completion_tokens"])
+
+    # Load ONET mappings for occupational categories
+    task_statements, soc_structure = load_onet_mappings()
+
+    # Add occupational categories
+    # Standardize task descriptions for matching
+    task_statements["task_standardized"] = (
+        task_statements["Task"].str.strip().str.lower()
+    )
+    task_metrics["cluster_name_standardized"] = (
+        task_metrics.index.str.strip().str.lower()
+    )
+
+    # Create mapping from standardized task to major group
+    task_to_major_group = {}
+    for _, row in task_statements.iterrows():
+        if pd.notna(row["Task"]) and pd.notna(row["soc_major_group"]):
+            std_task = row["task_standardized"]
+            major_group = str(int(row["soc_major_group"]))[:2]
+            task_to_major_group[std_task] = major_group
+
+    # Map cluster names to major groups
+    task_metrics["soc_major"] = task_metrics["cluster_name_standardized"].map(
+        task_to_major_group
+    )
+
+    # Get major occupational groups from SOC structure
+    major_groups = soc_structure[soc_structure["Major Group"].notna()].copy()
+    major_groups["soc_major"] = major_groups["Major Group"].astype(str).str[:2]
+    major_groups["title"] = major_groups["SOC or O*NET-SOC 2019 Title"]
+
+    # Create mapping from major group code to title
+    major_group_mapping = (
+        major_groups[["soc_major", "title"]]
+        .drop_duplicates()
+        .set_index("soc_major")["title"]
+        .to_dict()
+    )
+
+    # Map major group codes to titles
+    task_metrics["occupational_category"] = task_metrics["soc_major"].map(
+        major_group_mapping
+    )
+
+    # Remove unmapped tasks
+    task_metrics = task_metrics[task_metrics["occupational_category"].notna()].copy()
+
+    # Find top 6 categories by total API calls and group others as "All Other"
+    category_usage = (
+        task_metrics.groupby("occupational_category")["api_records"]
+        .sum()
+        .sort_values(ascending=False)
+    )
+    top_6_categories = list(category_usage.head(6).index)
+
+    # Group smaller categories as "All Other"
+    task_metrics["occupational_category"] = task_metrics["occupational_category"].apply(
+        lambda x: x if x in top_6_categories else "All Other"
+    )
+
+    # Transform to natural log
+    ln_input = np.log(task_metrics["avg_prompt_tokens"])
+    ln_output = np.log(task_metrics["avg_completion_tokens"])
+
+    # Create scatter plot with same color scheme as bar chart
+    # Use exact same logic as token output bar chart for consistent colors
+    category_stats = (
+        task_metrics.groupby("occupational_category")
+        .agg(
+            {
+                "avg_completion_tokens": "mean",
+                "api_records": "sum",
+            }
+        )
+        .reset_index()
+    )
+
+    # Find top 6 categories by total API calls
+    top_6_categories = category_stats.nlargest(6, "api_records")[
+        "occupational_category"
+    ].tolist()
+
+    # Group smaller categories as "All Other"
+    def categorize(cat):
+        return cat if cat in top_6_categories else "All Other"
+
+    task_metrics["category_group"] = task_metrics["occupational_category"].apply(
+        categorize
+    )
+
+    # Recalculate final stats with grouped categories
+    final_stats = (
+        task_metrics.groupby("category_group")
+        .agg({"avg_completion_tokens": "mean"})
+        .reset_index()
+        .sort_values("avg_completion_tokens", ascending=True)
+    )
+
+    # Use exact same color assignment as bar chart
+    categories_ordered = final_stats["category_group"].tolist()
+    category_colors = {}
+    for i, category in enumerate(categories_ordered):
+        category_colors[category] = COLOR_CYCLE[i % len(COLOR_CYCLE)]
+
+    for category in categories_ordered:
+        category_data = task_metrics[task_metrics["category_group"] == category]
+        if not category_data.empty:
+            ln_input_cat = np.log(category_data["avg_prompt_tokens"])
+            ln_output_cat = np.log(category_data["avg_completion_tokens"])
+            bubble_sizes_cat = np.sqrt(category_data["api_records"]) * 2
+
+            # Clean up category name for legend
+            clean_name = category.replace(" Occupations", "").replace(", and ", " & ")
+
+            ax.scatter(
+                ln_input_cat,
+                ln_output_cat,
+                s=bubble_sizes_cat,
+                alpha=0.8,
+                c=category_colors[category],
+                edgecolors="black",
+                linewidth=0.2,
+            )
+
+    # Create uniform legend entries
+    legend_elements = []
+    for category in categories_ordered:
+        clean_name = category.replace(" Occupations", "").replace(", and ", " & ")
+        # Get count for this category
+        category_count = len(task_metrics[task_metrics["category_group"] == category])
+        legend_elements.append(
+            plt.scatter(
+                [],
+                [],
+                s=100,
+                alpha=0.8,
+                c=category_colors[category],
+                edgecolors="black",
+                linewidth=0.2,
+                label=f"{clean_name} (N={category_count})",
+            )
+        )
+
+    # Add legend for occupational categories with uniform sizes
+    ax.legend(
+        bbox_to_anchor=(1.05, 1), loc="upper left", frameon=True, facecolor="white"
+    )
+
+    # Add line of best fit
+    model = sm.OLS(ln_output, sm.add_constant(ln_input)).fit()
+    slope = model.params.iloc[1]
+    intercept = model.params.iloc[0]
+    r_squared = model.rsquared
+
+    line_x = np.linspace(ln_input.min(), ln_input.max(), 100)
+    line_y = slope * line_x + intercept
+    ax.plot(
+        line_x,
+        line_y,
+        "k--",
+        alpha=0.7,
+        linewidth=2,
+        label=f"Best fit (R² = {r_squared:.3f}, $\\beta$ = {slope:.3f})",
+    )
+    ax.legend()
+
+    # Customize plot
+    ax.set_xlabel("ln(Input Token Index)", fontsize=12)
+    ax.set_ylabel("ln(Output Token Index)", fontsize=12)
+    ax.set_title(
+        "Output Token Index vs Input Token Index across tasks",
+        fontsize=14,
+        fontweight="bold",
+        pad=20,
+    )
+    ax.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "completion_vs_input_tokens_scatter.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def create_occupational_usage_cost_scatter(df, output_dir):
+    """
+    Create aggregated scatter plot of usage share vs average cost per API call by occupational category.
+
+    Args:
+        df: Preprocessed data DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Load ONET mappings for occupational categories
+    task_statements, soc_structure = load_onet_mappings()
+
+    # Use preprocessed intersection data
+    task_metrics = extract_token_metrics_from_intersections(df)
+
+    # Add occupational categories without grouping into "All Other"
+    # Standardize task descriptions for matching
+    task_statements["task_standardized"] = (
+        task_statements["Task"].str.strip().str.lower()
+    )
+    task_metrics["cluster_name_standardized"] = (
+        task_metrics["cluster_name"].str.strip().str.lower()
+    )
+
+    # Create mapping from standardized task to major group
+    task_to_major_group = {}
+    for _, row in task_statements.iterrows():
+        if pd.notna(row["Task"]) and pd.notna(row["soc_major_group"]):
+            std_task = row["task_standardized"]
+            major_group = str(int(row["soc_major_group"]))
+            task_to_major_group[std_task] = major_group
+
+    # Map cluster names to major groups
+    task_metrics["soc_major"] = task_metrics["cluster_name_standardized"].map(
+        task_to_major_group
+    )
+
+    # Get major occupational groups from SOC structure
+    major_groups = soc_structure[soc_structure["Major Group"].notna()].copy()
+    major_groups["soc_major"] = major_groups["Major Group"].astype(str).str[:2]
+    major_groups["title"] = major_groups["SOC or O*NET-SOC 2019 Title"]
+
+    # Create a clean mapping from major group code to title
+    major_group_mapping = (
+        major_groups[["soc_major", "title"]]
+        .drop_duplicates()
+        .set_index("soc_major")["title"]
+        .to_dict()
+    )
+
+    # Map major group codes to titles
+    task_metrics["occupational_category"] = task_metrics["soc_major"].map(
+        major_group_mapping
+    )
+
+    # Remove unmapped/not classified tasks from analysis
+    task_metrics = task_metrics[task_metrics["occupational_category"].notna()].copy()
+
+    # Aggregate by occupational category using pre-calculated percentages
+    category_aggregates = (
+        task_metrics.groupby("occupational_category")
+        .agg(
+            {
+                "usage_pct": "sum",  # Sum of pre-calculated task percentages within category
+                "cost_per_record": "mean",  # Average cost per API call for this category
+            }
+        )
+        .reset_index()
+    )
+
+    # Usage share is already calculated from preprocessing
+    category_aggregates["usage_share"] = category_aggregates["usage_pct"]
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(12, 8))
+
+    # Transform variables to natural log
+    ln_cost = np.log(category_aggregates["cost_per_record"])
+    ln_usage = np.log(category_aggregates["usage_share"])
+
+    # Get colors for each category - use same logic as token output bar chart
+    # Sort by a metric to ensure consistent ordering (using usage_share descending)
+    category_aggregates_sorted = category_aggregates.sort_values(
+        "usage_share", ascending=False
+    )
+
+    category_colors = {}
+    for i, category in enumerate(category_aggregates_sorted["occupational_category"]):
+        category_colors[category] = COLOR_CYCLE[i % len(COLOR_CYCLE)]
+
+    # Create invisible scatter plot to maintain axis limits
+    ax.scatter(
+        ln_cost,
+        ln_usage,
+        s=0,  # Invisible markers
+        alpha=0,
+    )
+
+    # Add line of best fit
+    model = sm.OLS(ln_usage, sm.add_constant(ln_cost)).fit()
+    slope = model.params.iloc[1]
+    intercept = model.params.iloc[0]
+    r_squared = model.rsquared
+
+    # Generate line points
+    x_line = np.linspace(ln_cost.min(), ln_cost.max(), 50)
+    y_line = slope * x_line + intercept
+
+    # Plot the line of best fit
+    ax.plot(
+        x_line,
+        y_line,
+        "--",
+        color="black",
+        linewidth=2,
+        alpha=0.8,
+        label=f"Best fit (R² = {r_squared:.3f}, $\\beta$ = {slope:.3f})",
+    )
+
+    # Add legend
+    legend = ax.legend(loc="best", frameon=True, facecolor="white")
+    legend.get_frame().set_alpha(0.9)
+
+    # Add category labels centered at data points with text wrapping
+    for i, row in category_aggregates.iterrows():
+        # Clean up and wrap category names
+        clean_name = (
+            row["occupational_category"]
+            .replace(" Occupations", "")
+            .replace(", and ", " & ")
+        )
+        # Wrap long category names to multiple lines
+        wrapped_name = "\n".join(wrap(clean_name, 20))
+
+        ax.text(
+            ln_cost.iloc[i],
+            ln_usage.iloc[i],
+            wrapped_name,
+            ha="center",
+            va="center",
+            fontsize=8,
+            alpha=0.9,
+        )
+
+    # Set labels and title
+    ax.set_xlabel("ln(Average API Cost Index across tasks)", fontsize=12)
+    ax.set_ylabel("ln(Usage share (%))", fontsize=12)
+    ax.set_title(
+        "Usage share and average API cost index by occupational category",
+        fontsize=14,
+        fontweight="bold",
+        pad=20,
+    )
+
+    # Add grid
+    ax.grid(True, alpha=0.3)
+
+    # Adjust layout and save
+    plt.tight_layout()
+
+    output_path = Path(output_dir) / "occupational_usage_cost_scatter.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)
+
+
+def get_merged_api_claude_task_data(api_df, cai_df):
+    """
+    Create merged dataset with API cost/usage data and Claude.ai collaboration modes.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+
+    Returns:
+        DataFrame with API cost data + Claude.ai collaboration patterns for common tasks
+    """
+    # Extract API token metrics
+    api_metrics = extract_token_metrics_from_intersections(api_df)
+
+    # Get Claude.ai collaboration shares
+    claude_collab_shares = get_collaboration_shares(cai_df)
+
+    # Find common tasks between both platforms
+    api_tasks = set(api_metrics.index)
+    claude_tasks = set(claude_collab_shares.keys())
+    common_tasks = api_tasks.intersection(claude_tasks)
+
+    # Create merged dataset
+    merged_data = []
+
+    for task_name in common_tasks:
+        # Get API metrics for this task
+        api_row = api_metrics.loc[task_name]
+
+        # Get Claude.ai collaboration for this task
+        claude_collab = claude_collab_shares[task_name]
+
+        # Create merged row
+        merged_row = {
+            "cluster_name": task_name,
+            "cost_per_record": api_row["cost_per_record"],
+            "avg_prompt_tokens": api_row["avg_prompt_tokens"],
+            "avg_completion_tokens": api_row["avg_completion_tokens"],
+            "api_records": api_row["api_records"],
+            "output_input_ratio": api_row["output_input_ratio"],
+            "total_tokens": api_row["total_tokens"],
+            # Claude.ai collaboration modes
+            "collab_directive": claude_collab.get("directive", 0),
+            "collab_feedback_loop": claude_collab.get("feedback loop", 0),
+            "collab_learning": claude_collab.get("learning", 0),
+            "collab_task_iteration": claude_collab.get("task iteration", 0),
+            "collab_validation": claude_collab.get("validation", 0),
+        }
+        merged_data.append(merged_row)
+
+    merged_df = pd.DataFrame(merged_data)
+    merged_df.set_index("cluster_name", inplace=True)
+
+    return merged_df
+
+
+def reg_build_df(api_df, cai_df):
+    """
+    Build complete regression dataset for partial regression and full regression analysis.
+    Each row is an ONET task with all variables needed for figures and regression.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+
+    Returns:
+        DataFrame with complete regression dataset
+    """
+    # Load ONET mappings
+    task_statements, soc_structure = load_onet_mappings()
+
+    # Use merged dataset with API metrics + Claude.ai collaboration
+    task_metrics = get_merged_api_claude_task_data(api_df, cai_df)
+
+    # Add occupational categories (includes "All Other" grouping)
+    task_metrics_with_names = task_metrics.reset_index()
+    task_metrics_with_names = add_occupational_categories_to_metrics(
+        task_metrics_with_names, task_statements, soc_structure
+    )
+    task_metrics = task_metrics_with_names.set_index("cluster_name")
+
+    # Add collaboration missing dummies
+    collaboration_modes = [
+        "directive",
+        "feedback_loop",
+        "learning",
+        "task_iteration",
+        "validation",
+    ]
+
+    for mode in collaboration_modes:
+        collab_col = f"collab_{mode}"
+        missing_col = f"collab_{mode}_missing"
+        if collab_col in task_metrics.columns:
+            task_metrics[missing_col] = (task_metrics[collab_col] == 0).astype(int)
+        else:
+            task_metrics[missing_col] = 1
+
+    # Calculate usage variables
+    total_api_records = task_metrics["api_records"].sum()
+    task_metrics["usage_share"] = (
+        task_metrics["api_records"] / total_api_records
+    ) * 100
+    task_metrics["ln_usage_share"] = np.log(task_metrics["usage_share"])
+    task_metrics["ln_cost_per_task"] = np.log(task_metrics["cost_per_record"])
+
+    # Use all data
+    valid_data = task_metrics
+
+    # Create occupational category dummies while preserving original column
+    valid_data = pd.get_dummies(
+        valid_data, columns=["occupational_category"], prefix="occ"
+    )
+
+    # Restore the original occupational_category column for grouping operations
+    # Extract category name from the dummy columns that are 1
+    occ_cols = [col for col in valid_data.columns if col.startswith("occ_")]
+    valid_data["occupational_category"] = ""
+    for col in occ_cols:
+        category_name = col.replace("occ_", "")
+        mask = valid_data[col] == 1
+        valid_data.loc[mask, "occupational_category"] = category_name
+
+    return valid_data
+
+
+def create_partial_regression_plot(api_df, cai_df, output_dir):
+    """
+    Create partial regression scatter plot of usage share vs cost, controlling for occupational categories.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save the figure
+
+    Returns:
+        Tuple of (output_path, regression_results_dict)
+    """
+    # Use centralized data preparation (includes occupational dummies)
+    valid_metrics = reg_build_df(api_df, cai_df)
+
+    # Extract occupational dummies and collaboration variables
+    occ_cols = [col for col in valid_metrics.columns if col.startswith("occ_")]
+    collab_vars = [
+        "collab_directive",
+        "collab_feedback_loop",
+        "collab_learning",
+        "collab_task_iteration",
+        "collab_validation",
+    ]
+    collab_missing_vars = [
+        "collab_directive_missing",
+        "collab_feedback_loop_missing",
+        "collab_learning_missing",
+        "collab_task_iteration_missing",
+        "collab_validation_missing",
+    ]
+
+    # Control variables (all occupational dummies + collaboration modes)
+    control_vars = valid_metrics[occ_cols + collab_vars + collab_missing_vars].astype(
+        float
+    )
+
+    # Ensure dependent variables are float
+    y_usage = valid_metrics["ln_usage_share"].astype(float)
+    y_cost = valid_metrics["ln_cost_per_task"].astype(float)
+
+    # Step 1: Regress ln(usage_share) on controls (no constant)
+    usage_model = sm.OLS(y_usage, control_vars).fit()
+    usage_residuals = usage_model.resid
+
+    # Step 2: Regress ln(cost) on controls (no constant)
+    cost_model = sm.OLS(y_cost, control_vars).fit()
+    cost_residuals = cost_model.resid
+
+    # Find top 6 categories by usage share for coloring
+    category_usage = (
+        valid_metrics.groupby("occupational_category")["api_records"]
+        .sum()
+        .sort_values(ascending=False)
+    )
+    top_6_categories = list(category_usage.head(6).index)
+
+    # Create category grouping for coloring
+    valid_metrics["category_group"] = valid_metrics["occupational_category"].apply(
+        lambda x: x if x in top_6_categories else "All Other"
+    )
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 10))
+
+    # Create color mapping for top 6 + "All Other"
+    unique_groups = valid_metrics["category_group"].unique()
+    group_colors = {}
+    color_idx = 0
+
+    # Assign colors to top 6 categories first
+    for cat in top_6_categories:
+        if cat in unique_groups:
+            group_colors[cat] = COLOR_CYCLE[color_idx % len(COLOR_CYCLE)]
+            color_idx += 1
+
+    # Assign color to "All Other"
+    if "All Other" in unique_groups:
+        group_colors["All Other"] = "#999999"  # Gray for all other
+
+    # Create single scatter plot (no color by group)
+    ax.scatter(
+        cost_residuals,
+        usage_residuals,
+        s=100,
+        alpha=0.8,
+        color=COLOR_CYCLE[0],
+        edgecolors="black",
+        linewidth=0.2,
+    )
+
+    # Add overall trend line for residuals
+    model = sm.OLS(usage_residuals, sm.add_constant(cost_residuals)).fit()
+    slope = model.params.iloc[1]
+    intercept = model.params.iloc[0]
+    r_squared = model.rsquared
+
+    line_x = np.linspace(cost_residuals.min(), cost_residuals.max(), 100)
+    line_y = slope * line_x + intercept
+    ax.plot(
+        line_x,
+        line_y,
+        "k--",
+        alpha=0.8,
+        linewidth=2,
+        label=f"Partial relationship (R² = {r_squared:.3f})",
+    )
+
+    # Customize plot
+    ax.set_xlabel("Residual ln(API Cost Index)")
+    ax.set_ylabel("Residual ln(Usage share (%))")
+    ax.set_title(
+        "Task usage share vs API Cost Index \n(partial regression after controlling for task characteristics)",
+        fontsize=16,
+        fontweight="bold",
+        pad=20,
+    )
+    ax.grid(True, alpha=0.3)
+
+    # Simple legend with just the trend line
+    ax.legend(loc="best", frameon=True, facecolor="white", framealpha=0.9, fontsize=11)
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "partial_regression_plot.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+
+    # Save regression results
+    regression_results = {
+        "partial_correlation": np.sqrt(r_squared),
+        "partial_r_squared": r_squared,
+        "slope": slope,
+        "intercept": intercept,
+        "n_observations": len(valid_metrics),
+        "usage_model_summary": str(usage_model.summary()),
+        "cost_model_summary": str(cost_model.summary()),
+    }
+
+    # Print regression results instead of saving to file
+    print("Partial Regression Analysis Results")
+    print("=" * 50)
+    print(f"Partial correlation: {np.sqrt(r_squared):.4f}")
+    print(f"Partial R-squared: {r_squared:.4f}")
+    print(f"Slope: {slope:.4f}")
+    print(f"Intercept: {intercept:.4f}")
+    print(f"Number of observations: {len(valid_metrics)}")
+    print("\nUsage Model Summary:")
+    print("-" * 30)
+    print(usage_model.summary())
+    print("\nCost Model Summary:")
+    print("-" * 30)
+    print(cost_model.summary())
+
+    return str(output_path), regression_results
+
+
+def perform_usage_share_regression_unweighted(api_df, cai_df, output_dir):
+    """
+    Perform unweighted usage share regression analysis using Claude.ai collaboration modes.
+
+    Args:
+        api_df: API preprocessed data DataFrame
+        cai_df: Claude.ai preprocessed data DataFrame
+        output_dir: Directory to save regression results
+
+    Returns:
+        OLS model results
+    """
+    # Use centralized data preparation (includes all dummies)
+    valid_data = reg_build_df(api_df, cai_df)
+
+    # Extract all regression variables
+    X_cols = ["ln_cost_per_task"]
+    X_cols.extend(
+        [
+            f"collab_{mode}"
+            for mode in [
+                "directive",
+                "feedback_loop",
+                "learning",
+                "task_iteration",
+                "validation",
+            ]
+        ]
+    )
+    X_cols.extend(
+        [
+            f"collab_{mode}_missing"
+            for mode in [
+                "directive",
+                "feedback_loop",
+                "learning",
+                "task_iteration",
+                "validation",
+            ]
+        ]
+    )
+    X_cols.extend([col for col in valid_data.columns if col.startswith("occ_")])
+
+    # Ensure all columns are numeric
+    X = valid_data[X_cols].astype(float)
+    y = valid_data["ln_usage_share"].astype(float)
+
+    # Run unweighted OLS without constant (to include all occupational dummies)
+    model = sm.OLS(y, X).fit()
+
+    # Get heteroskedasticity-robust standard errors (HC1)
+    model_robust = model.get_robustcov_results(cov_type="HC1")
+
+    return model_robust
+
+
+def create_btos_ai_adoption_chart(btos_df, ref_dates_df, output_dir):
+    """
+    Create BTOS AI adoption time series chart.
+
+    Args:
+        btos_df: BTOS response estimates DataFrame
+        ref_dates_df: Collection and reference dates DataFrame
+        output_dir: Directory to save the figure
+    """
+    # Filter for Question ID 7, Answer ID 1 (Yes response to AI usage)
+    btos_filtered = btos_df[(btos_df["Question ID"] == 7) & (btos_df["Answer ID"] == 1)]
+
+    # Get date columns (string columns that look like YYYYWW)
+    date_columns = [
+        col for col in btos_df.columns[4:] if str(col).isdigit() and len(str(col)) == 6
+    ]
+
+    # Extract time series
+    btos_ts = btos_filtered[date_columns].T
+    btos_ts.columns = ["percentage"]
+
+    # Map to reference end dates
+    ref_dates_df["Ref End"] = pd.to_datetime(ref_dates_df["Ref End"])
+    btos_ts = btos_ts.reset_index()
+    btos_ts["smpdt"] = btos_ts["index"].astype(int)
+    btos_ts = btos_ts.merge(
+        ref_dates_df[["Smpdt", "Ref End"]],
+        left_on="smpdt",
+        right_on="Smpdt",
+        how="left",
+    )
+    btos_ts = btos_ts.set_index("Ref End")[["percentage"]]
+
+    # Convert percentage strings to numeric
+    btos_ts["percentage"] = btos_ts["percentage"].str.rstrip("%").astype(float)
+    btos_ts = btos_ts.sort_index().dropna()
+
+    # Calculate 3-period moving average
+    btos_ts["moving_avg"] = btos_ts["percentage"].rolling(window=3).mean()
+
+    # Create figure
+    fig, ax = plt.subplots(figsize=(14, 8))
+
+    # Plot main line
+    ax.plot(
+        btos_ts.index,
+        btos_ts["percentage"],
+        linewidth=3,
+        marker="o",
+        markersize=6,
+        label="AI Adoption Rate Among US Businesses",
+        zorder=3,
+    )
+
+    # Plot moving average
+    ax.plot(
+        btos_ts.index,
+        btos_ts["moving_avg"],
+        linewidth=2,
+        linestyle="--",
+        alpha=0.8,
+        label="3-Period Moving Average",
+        zorder=2,
+    )
+
+    # Styling
+    ax.set_xlabel("Date", fontsize=14)
+    ax.set_ylabel("AI adoption rate (%)", fontsize=14)
+    ax.set_title(
+        "Census reported AI adoption rates among US businesses from the Business Trends and Outlook Survey",
+        fontsize=16,
+        fontweight="bold",
+        pad=20,
+    )
+
+    # Format y-axis as percentage
+    ax.set_ylim(0, max(btos_ts["percentage"]) * 1.1)
+
+    # Rotate x-axis labels
+    ax.tick_params(axis="x", rotation=45)
+
+    # Grid and styling
+    ax.grid(True, alpha=0.3, linestyle="--")
+    ax.set_axisbelow(True)
+    ax.spines["top"].set_visible(False)
+    ax.spines["right"].set_visible(False)
+
+    # Legend
+    ax.legend(loc="upper left", fontsize=11, frameon=True, facecolor="white")
+
+    plt.tight_layout()
+
+    # Save plot
+    output_path = Path(output_dir) / "btos_ai_adoption_chart.png"
+    plt.savefig(output_path, dpi=300, bbox_inches="tight")
+    plt.show()
+    return str(output_path)

release_2025_09_15/code/aei_analysis_functions_claude_ai.py

@@ -0,0 +1,2926 @@
+#!/usr/bin/env python3
+"""
+Analysis functions for AEI Report v3 Claude.ai chapter
+"""
+
+import textwrap
+
+import geopandas as gpd
+import matplotlib.colors as mcolors
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import statsmodels.api as sm
+from matplotlib.colors import LinearSegmentedColormap, Normalize, TwoSlopeNorm
+from matplotlib.lines import Line2D
+from matplotlib.patches import FancyBboxPatch, Patch
+from mpl_toolkits.axes_grid1 import make_axes_locatable
+
+# global list of excluded countries (ISO-3 codes)
+EXCLUDED_COUNTRIES = [
+    "AFG",
+    "BLR",
+    "COD",
+    "CAF",
+    "CHN",
+    "CUB",
+    "ERI",
+    "ETH",
+    "HKG",
+    "IRN",
+    "PRK",
+    "LBY",
+    "MLI",
+    "MMR",
+    "MAC",
+    "NIC",
+    "RUS",
+    "SDN",
+    "SOM",
+    "SSD",
+    "SYR",
+    "VEN",
+    "YEM",
+]
+
+# Minimum observation thresholds
+MIN_OBSERVATIONS_COUNTRY = 200  # Threshold for countries
+MIN_OBSERVATIONS_US_STATE = 100  # Threshold for US states
+
+# Define the tier colors
+TIER_COLORS_LIST = ["#E6DBD0", "#E5C5AB", "#E4AF86", "#E39961", "#D97757"]
+
+# Anthropic brand color for borders
+ANTHROPIC_OAT = "#E3DACC"
+AUGMENTATION_COLOR = "#00A078"
+AUTOMATION_COLOR = "#FF9940"
+
+# Standard tier color mapping used throughout
+TIER_COLORS_DICT = {
+    "Minimal": TIER_COLORS_LIST[0],  # Lightest
+    "Emerging (bottom 25%)": TIER_COLORS_LIST[1],
+    "Lower middle (25-50%)": TIER_COLORS_LIST[2],
+    "Upper middle (50-75%)": TIER_COLORS_LIST[3],
+    "Leading (top 25%)": TIER_COLORS_LIST[4],  # Darkest
+}
+
+# Standard tier ordering
+TIER_ORDER = [
+    "Leading (top 25%)",
+    "Upper middle (50-75%)",
+    "Lower middle (25-50%)",
+    "Emerging (bottom 25%)",
+    "Minimal",
+]
+
+# Numeric tier color mapping (for tier values 0-4)
+TIER_COLORS_NUMERIC = {i: color for i, color in enumerate(TIER_COLORS_LIST)}
+
+# Numeric tier name mapping (for tier values 1-4 in actual data)
+TIER_NAMES_NUMERIC = {
+    1: "Emerging (bottom 25%)",
+    2: "Lower middle (25-50%)",
+    3: "Upper middle (50-75%)",
+    4: "Leading (top 25%)",
+}
+
+# Create a custom colormap that can be used for continuous variables
+CUSTOM_CMAP = LinearSegmentedColormap.from_list("custom_tier", TIER_COLORS_LIST, N=256)
+
+# Map layout constants
+MAP_PADDING_X = 0.25  # Horizontal padding for legend space
+MAP_PADDING_Y = 0.05  # Vertical padding
+ALASKA_INSET_BOUNDS = [0.26, 0.18, 0.15, 0.15]  # [left, bottom, width, height]
+HAWAII_INSET_BOUNDS = [0.40, 0.18, 0.11, 0.11]  # [left, bottom, width, height]
+
+
+# Figure style and setup
+def setup_plot_style():
+    """Configure matplotlib."""
+    plt.style.use("default")
+    plt.rcParams.update(
+        {
+            "figure.dpi": 150,
+            "savefig.dpi": 150,
+            "font.size": 10,
+            "axes.labelsize": 11,
+            "axes.titlesize": 12,
+            "xtick.labelsize": 9,
+            "ytick.labelsize": 9,
+            "legend.fontsize": 9,
+            "figure.facecolor": "white",
+            "axes.facecolor": "white",
+            "savefig.facecolor": "white",
+            "axes.edgecolor": "#333333",
+            "axes.linewidth": 0.8,
+            "axes.grid": True,
+            "grid.alpha": 0.3,
+            "grid.linestyle": "-",
+            "grid.linewidth": 0.5,
+            "axes.axisbelow": True,
+        }
+    )
+
+
+def create_figure(figsize=(12, 8), tight_layout=True, nrows=1, ncols=1):
+    """Create a figure with consistent settings.
+
+    Args:
+        figsize: Figure size tuple
+        tight_layout: Whether to use tight layout
+        nrows: Number of subplot rows
+        ncols: Number of subplot columns
+
+    Returns:
+        fig, ax or fig, axes depending on subplot configuration
+    """
+    fig, ax = plt.subplots(nrows, ncols, figsize=figsize)
+    if tight_layout:
+        fig.tight_layout()
+    else:
+        # Explicitly disable the layout engine to prevent warnings
+        fig.set_layout_engine(layout="none")
+    return fig, ax
+
+
+def format_axis(
+    ax,
+    xlabel=None,
+    ylabel=None,
+    title=None,
+    xlabel_size=11,
+    ylabel_size=11,
+    title_size=13,
+    grid=True,
+    grid_alpha=0.3,
+):
+    """Apply consistent axis formatting."""
+    if xlabel:
+        ax.set_xlabel(xlabel, fontsize=xlabel_size)
+    if ylabel:
+        ax.set_ylabel(ylabel, fontsize=ylabel_size)
+    if title:
+        ax.set_title(title, fontsize=title_size, fontweight="bold", pad=15)
+    if grid:
+        ax.grid(True, alpha=grid_alpha)
+    return ax
+
+
+def get_color_normalizer(values, center_at_one=False, vmin=None, vmax=None):
+    """Create appropriate color normalizer for data."""
+    if center_at_one:
+        # Use TwoSlopeNorm for diverging around 1.0
+        if vmin is None:
+            vmin = min(values.min(), 0.1)
+        if vmax is None:
+            vmax = max(values.max(), 2.0)
+        return TwoSlopeNorm(vmin=vmin, vcenter=1.0, vmax=vmax)
+    else:
+        # Use regular normalization
+        if vmin is None:
+            vmin = values.min()
+        if vmax is None:
+            vmax = values.max()
+        return Normalize(vmin=vmin, vmax=vmax)
+
+
+def create_tier_legend(
+    ax,
+    tier_colors,
+    tiers_in_data,
+    excluded_countries=False,
+    no_data=False,
+    loc="lower left",
+    title="Anthropic AI Usage Index tier",
+):
+    """Create a consistent tier legend for maps."""
+    legend_elements = []
+    for tier in TIER_ORDER:
+        if tier in tiers_in_data:
+            legend_elements.append(
+                mpatches.Patch(
+                    facecolor=tier_colors[tier], edgecolor="none", label=tier
+                )
+            )
+
+    if excluded_countries:
+        legend_elements.append(
+            mpatches.Patch(
+                facecolor="#c0c0c0", edgecolor="white", label="Claude not available"
+            )
+        )
+
+    if no_data:
+        legend_elements.append(
+            mpatches.Patch(facecolor="#f0f0f0", edgecolor="white", label="No data")
+        )
+
+    if legend_elements:
+        ax.legend(
+            handles=legend_elements,
+            loc=loc,
+            fontsize=10,
+            bbox_to_anchor=(0, 0) if loc == "lower left" else None,
+            title=title,
+            title_fontsize=11,
+            frameon=True,
+            fancybox=True,
+            shadow=True,
+        )
+
+    return ax
+
+
+# Data wrangling helpers
+
+
+def filter_df(df, **kwargs):
+    """Universal filter helper for dataframes.
+
+    Args:
+        df: DataFrame to filter
+        **kwargs: Column-value pairs to filter on
+                  Lists are handled with .isin()
+
+    Returns:
+        Filtered DataFrame
+    """
+    mask = pd.Series([True] * len(df), index=df.index)
+
+    for key, value in kwargs.items():
+        if value is None:
+            continue  # Skip None values
+        if key in df.columns:
+            if isinstance(value, list):
+                mask = mask & df[key].isin(value)
+            else:
+                mask = mask & (df[key] == value)
+
+    return df[mask]
+
+
+def get_filtered_geographies(df, min_obs_country=None, min_obs_state=None):
+    """
+    Get lists of countries and states that meet MIN_OBSERVATIONS thresholds.
+
+    This function does NOT filter the dataframe - it only identifies which
+    geographies meet the thresholds. The full dataframe is preserved
+    so we can still report statistics for all geographies.
+
+    Args:
+        df: Input dataframe
+        min_obs_country: Minimum observations for countries (default: MIN_OBSERVATIONS_COUNTRY)
+        min_obs_state: Minimum observations for states (default: MIN_OBSERVATIONS_US_STATE)
+
+    Returns:
+        Tuple of (filtered_countries list, filtered_states list)
+    """
+    # Use defaults if not specified
+    if min_obs_country is None:
+        min_obs_country = MIN_OBSERVATIONS_COUNTRY
+    if min_obs_state is None:
+        min_obs_state = MIN_OBSERVATIONS_US_STATE
+
+    # Get country usage counts
+    country_usage = filter_df(df, facet="country", variable="usage_count").set_index(
+        "geo_id"
+    )["value"]
+
+    # Get state usage counts
+    state_usage = filter_df(df, facet="state_us", variable="usage_count").set_index(
+        "geo_id"
+    )["value"]
+
+    # Get countries that meet threshold (excluding not_classified)
+    filtered_countries = country_usage[country_usage >= min_obs_country].index.tolist()
+    filtered_countries = [c for c in filtered_countries if c != "not_classified"]
+
+    # Get states that meet threshold (excluding not_classified)
+    filtered_states = state_usage[state_usage >= min_obs_state].index.tolist()
+    filtered_states = [s for s in filtered_states if s != "not_classified"]
+
+    return filtered_countries, filtered_states
+
+
+def filter_requests_by_threshold(df, geography, geo_id, level=1, threshold=1.0):
+    """
+    Filter requests to only include requests at a specific level that meet threshold requirements.
+
+    Args:
+        df: Long format dataframe with request data
+        geography: Current geography level ('country' or 'state_us')
+        geo_id: Current geography ID (e.g., 'USA', 'CA')
+        level: Request level to filter (default=1 for middle aggregated)
+        threshold: Minimum percentage threshold (default=1.0%)
+
+    Returns:
+        List of valid cluster_names that:
+        1. Are at the specified level (default level 1)
+        2. Have >= threshold % in the current geography
+        3. Have >= threshold % in the parent geography (USA for states, GLOBAL for countries)
+    """
+    # Determine parent geography
+    if geography == "state_us":
+        parent_geo = "USA"
+        parent_geography = "country"
+    elif geography == "country":
+        parent_geo = "GLOBAL"
+        parent_geography = "global"
+    else:  # global
+        # For global, no parent filtering needed
+        df_local = filter_df(
+            df,
+            geography=geography,
+            geo_id=geo_id,
+            facet="request",
+            level=level,
+            variable="request_pct",
+        )
+        return df_local[df_local["value"] >= threshold]["cluster_name"].tolist()
+
+    # Get local request percentages at specified level
+    df_local = filter_df(
+        df,
+        geography=geography,
+        geo_id=geo_id,
+        facet="request",
+        level=level,
+        variable="request_pct",
+    )
+
+    # Get parent request percentages at same level
+    df_parent = filter_df(
+        df,
+        geography=parent_geography,
+        geo_id=parent_geo,
+        facet="request",
+        level=level,
+        variable="request_pct",
+    )
+
+    # Filter by local threshold
+    local_valid = set(df_local[df_local["value"] >= threshold]["cluster_name"])
+
+    # Filter by parent threshold
+    parent_valid = set(df_parent[df_parent["value"] >= threshold]["cluster_name"])
+
+    # Return intersection (must meet both thresholds)
+    return list(local_valid & parent_valid)
+
+
+# Data loading
+
+
+def load_world_shapefile():
+    """Load and prepare world shapefile for mapping."""
+    url = "https://naciscdn.org/naturalearth/10m/cultural/ne_10m_admin_0_countries_iso.zip"
+    world = gpd.read_file(url)
+
+    # Remove Antarctica from the dataset entirely
+    world = world[world["ISO_A3_EH"] != "ATA"]
+
+    # Use Robinson projection for better world map appearance
+    world = world.to_crs("+proj=robin")
+
+    # Mark excluded countries using global EXCLUDED_COUNTRIES
+    world["is_excluded"] = world["ISO_A3_EH"].isin(EXCLUDED_COUNTRIES)
+
+    return world
+
+
+def load_us_states_shapefile():
+    """Load and prepare US states shapefile for mapping."""
+    import ssl
+
+    # Create unverified SSL context to handle Census Bureau cert issues
+    ssl._create_default_https_context = ssl._create_unverified_context
+
+    states_url = (
+        "https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_state_20m.zip"
+    )
+    states = gpd.read_file(states_url)
+
+    # Filter out territories but keep all 50 states and DC
+    states = states[~states["STUSPS"].isin(["PR", "VI", "MP", "GU", "AS"])]
+
+    return states
+
+
+def merge_geo_data(shapefile, df_data, geo_column, columns_to_merge, is_tier=False):
+    """Merge data with geographic shapefile.
+
+    Args:
+        shapefile: GeoDataFrame (world or states)
+        df_data: DataFrame with data to merge
+        geo_column: Column in shapefile to join on (e.g., 'ISO_A3_EH', 'STUSPS')
+        columns_to_merge: List of columns to merge from df_data
+        is_tier: Whether this is tier data (includes cluster_name)
+
+    Returns:
+        Merged GeoDataFrame
+    """
+    if is_tier and "cluster_name" not in columns_to_merge:
+        columns_to_merge = columns_to_merge + ["cluster_name"]
+
+    return shapefile.merge(
+        df_data[columns_to_merge], left_on=geo_column, right_on="geo_id", how="left"
+    )
+
+
+def prepare_map_data(
+    geo_df,
+    value_column="value",
+    center_at_one=False,
+    excluded_mask=None,
+):
+    """Prepare data and normalization for map plotting.
+
+    Args:
+        geo_df: GeoDataFrame with geographic data and values to plot
+        value_column: Name of column containing values to plot (default: "value")
+        center_at_one: If True, center color scale at 1.0 for diverging colormap (default: False)
+        excluded_mask: Boolean Series indicating which rows to exclude from normalization
+                       (e.g., countries where service isn't available). If None, no exclusions.
+
+    Returns:
+        tuple: (plot_column_name, norm) where norm is the matplotlib Normalize object
+    """
+    if excluded_mask is None:
+        excluded_mask = pd.Series([False] * len(geo_df), index=geo_df.index)
+
+    valid_data = geo_df[geo_df[value_column].notna() & ~excluded_mask][value_column]
+
+    vmin = valid_data.min() if len(valid_data) > 0 else 0
+    vmax = valid_data.max() if len(valid_data) > 0 else 1
+    norm = get_color_normalizer(
+        valid_data, center_at_one=center_at_one, vmin=vmin, vmax=vmax
+    )
+
+    return value_column, norm
+
+
+# Main visualization functions
+
+
+def plot_world_map(
+    ax, world, data_column="value", tier_colors=None, cmap=None, norm=None
+):
+    """Plot world map with data.
+
+    Args:
+        ax: matplotlib axis
+        world: GeoDataFrame with world data (already merged with values)
+        data_column: column name containing data to plot
+        tier_colors: dict mapping tier names to colors (for categorical)
+        cmap: colormap (for continuous)
+        norm: normalization (for continuous)
+    """
+    if tier_colors:
+        # Plot each tier with its color
+        for tier, color in tier_colors.items():
+            tier_countries = world[
+                (world["cluster_name"] == tier) & (~world["is_excluded"])
+            ]
+            tier_countries.plot(ax=ax, color=color, edgecolor="white", linewidth=0.5)
+    else:
+        # Plot continuous data
+        world_with_data = world[
+            world[data_column].notna() & (world["is_excluded"] == False)
+        ]
+        world_with_data.plot(
+            column=data_column, ax=ax, cmap=cmap, norm=norm, legend=False
+        )
+
+    # Plot excluded countries
+    excluded = world[world["is_excluded"] == True]
+    if not excluded.empty:
+        excluded.plot(ax=ax, color="#c0c0c0", edgecolor="white", linewidth=0.5)
+
+    # Plot no-data countries
+    no_data = world[
+        (world[data_column if not tier_colors else "cluster_name"].isna())
+        & (~world["is_excluded"])
+    ]
+    if not no_data.empty:
+        no_data.plot(ax=ax, color="#f0f0f0", edgecolor="white", linewidth=0.5)
+
+    # Set appropriate bounds for Robinson projection
+    ax.set_xlim(-17000000, 17000000)
+    ax.set_ylim(-8500000, 8500000)
+
+
+def plot_us_states_map(
+    fig, ax, states, data_column="value", tier_colors=None, cmap=None, norm=None
+):
+    """Plot US states map with Alaska and Hawaii insets.
+
+    Args:
+        fig: matplotlib figure
+        ax: main axis for continental US
+        states: GeoDataFrame with state data (already merged with values)
+        data_column: column name containing data to plot
+        tier_colors: dict mapping tier names to colors (for categorical)
+        cmap: colormap (for continuous)
+        norm: normalization (for continuous)
+    """
+    # Project to EPSG:2163 for US Albers Equal Area
+    states = states.to_crs("EPSG:2163")
+
+    # Plot continental US (everything except AK and HI)
+    continental = states[~states["STUSPS"].isin(["AK", "HI"])]
+
+    # First plot all continental states as no-data background
+    continental.plot(ax=ax, color="#f0f0f0", edgecolor="white", linewidth=0.5)
+
+    # Plot continental states with data
+    if tier_colors:
+        # Plot each tier with its color
+        for tier, color in tier_colors.items():
+            tier_states = continental[continental["cluster_name"] == tier]
+            if not tier_states.empty:
+                tier_states.plot(ax=ax, color=color, edgecolor="white", linewidth=0.5)
+    else:
+        # Plot continuous data
+        continental_with_data = continental[continental[data_column].notna()]
+        if not continental_with_data.empty:
+            continental_with_data.plot(
+                column=data_column, ax=ax, cmap=cmap, norm=norm, legend=False
+            )
+
+    # Set axis limits with padding for legend
+    xlim = ax.get_xlim()
+    ylim = ax.get_ylim()
+    x_padding = (xlim[1] - xlim[0]) * MAP_PADDING_X
+    y_padding = (ylim[1] - ylim[0]) * MAP_PADDING_Y
+    ax.set_xlim(xlim[0] - x_padding, xlim[1] + x_padding)
+    ax.set_ylim(ylim[0] - y_padding, ylim[1] + y_padding)
+
+    # Add Alaska inset
+    akax = fig.add_axes(ALASKA_INSET_BOUNDS)
+    akax.axis("off")
+
+    alaska = states[states["STUSPS"] == "AK"]
+    if not alaska.empty:
+        alaska.plot(ax=akax, color="#f0f0f0", edgecolor="white", linewidth=0.5)
+
+        if tier_colors and alaska["cluster_name"].notna().any():
+            tier_name = alaska["cluster_name"].iloc[0]
+            if tier_name in tier_colors:
+                alaska.plot(
+                    ax=akax,
+                    color=tier_colors[tier_name],
+                    edgecolor="white",
+                    linewidth=0.5,
+                )
+        elif not tier_colors and alaska[data_column].notna().any():
+            alaska.plot(column=data_column, ax=akax, cmap=cmap, norm=norm, legend=False)
+
+    # Add Hawaii inset
+    hiax = fig.add_axes(HAWAII_INSET_BOUNDS)
+    hiax.axis("off")
+
+    hawaii = states[states["STUSPS"] == "HI"]
+    if not hawaii.empty:
+        hawaii.plot(ax=hiax, color="#f0f0f0", edgecolor="white", linewidth=0.5)
+
+        if tier_colors and hawaii["cluster_name"].notna().any():
+            tier_name = hawaii["cluster_name"].iloc[0]
+            if tier_name in tier_colors:
+                hawaii.plot(
+                    ax=hiax,
+                    color=tier_colors[tier_name],
+                    edgecolor="white",
+                    linewidth=0.5,
+                )
+        elif not tier_colors and hawaii[data_column].notna().any():
+            hawaii.plot(column=data_column, ax=hiax, cmap=cmap, norm=norm, legend=False)
+
+
+def plot_usage_index_bars(
+    df,
+    geography="country",
+    top_n=None,
+    figsize=(12, 8),
+    title=None,
+    filtered_entities=None,
+    show_usage_counts=True,
+    cmap=CUSTOM_CMAP,
+):
+    """
+    Create horizontal bar chart of Anthropic AI Usage Index.
+
+    Args:
+        df: Long format dataframe
+        geography: 'country' or 'state_us'
+        top_n: Number of top entities to show (None for all)
+        figsize: Figure size
+        title: Chart title
+        filtered_entities: List of geo_id values to include (if None, include all)
+        show_usage_counts: If True, show usage counts in labels (default: True)
+    """
+    # Get data
+    df_metric = filter_df(
+        df, geography=geography, facet=geography, variable="usage_per_capita_index"
+    )
+
+    # Apply entity filtering if provided
+    if filtered_entities is not None:
+        df_metric = df_metric[df_metric["geo_id"].isin(filtered_entities)]
+
+    # Get usage counts for display if requested
+    if show_usage_counts:
+        df_usage = filter_df(
+            df, geography=geography, facet=geography, variable="usage_count"
+        )
+        # Merge to get usage counts
+        df_metric = df_metric.merge(
+            df_usage[["geo_id", "value"]],
+            on="geo_id",
+            suffixes=("", "_usage"),
+            how="left",
+        )
+
+    # Select entities to display
+    if top_n is None or top_n >= len(df_metric):
+        # Show all entities, sorted by lowest value first (will appear at bottom of chart)
+        df_top = df_metric.sort_values("value", ascending=True)
+        # Adjust figure height for many entities
+        if len(df_top) > 20:
+            figsize = (figsize[0], max(10, len(df_top) * 0.3))
+    else:
+        # Select top N entities, then sort ascending so highest values appear at top
+        df_top = df_metric.nlargest(top_n, "value")
+        df_top = df_top.sort_values("value", ascending=True)
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Get colormap and create diverging colors centered at 1
+    values = df_top["value"].values
+    min_val = values.min()
+    max_val = values.max()
+
+    # Determine the range for symmetric color scaling around 1
+    max_distance = max(abs(min_val - 1), abs(max_val - 1))
+
+    # Normalize values for color mapping
+    if max_distance > 0:
+        # Normalize to 0-1 centered at 0.5 for value 1
+        normalized = 0.5 + (values - 1) / (2 * max_distance)
+        # Truncate colormap to avoid too light colors
+        truncate_low = 0.2
+        truncate_high = 0.8
+        normalized = truncate_low + normalized * (truncate_high - truncate_low)
+        normalized = np.clip(normalized, truncate_low, truncate_high)
+    else:
+        normalized = np.ones_like(values) * 0.5
+
+    colors = cmap(normalized)
+
+    # Create horizontal bars
+    y_positions = range(len(df_top))
+    bars = ax.barh(y_positions, values, color=colors, height=0.7)
+
+    # Set y-tick labels
+    ax.set_yticks(y_positions)
+    ax.set_yticklabels(df_top["geo_name"].values)
+
+    # Set y-axis limits to reduce white space
+    ax.set_ylim(-0.5, len(df_top) - 0.5)
+
+    # Add baseline reference line at 1.0
+    ax.axvline(x=1.0, color="black", linestyle="--", alpha=0.5, linewidth=1)
+
+    # Calculate and set x-axis limits with extra space for labels
+    if max_val > 2:
+        ax.set_xlim(0, max_val * 1.25)
+    else:
+        ax.set_xlim(0, max_val * 1.2)
+
+    # Add value labels and usage counts
+    for i, bar in enumerate(bars):
+        width = bar.get_width()
+        # Always use 2 decimal places for consistency
+        label = f"{width:.2f}"
+
+        # Get usage count
+        usage_count = df_top.iloc[i]["value_usage"]
+        if usage_count >= 1000:
+            usage_str = f"{usage_count / 1000:.1f}k"
+        else:
+            usage_str = f"{int(usage_count)}"
+
+        # For top_n > 20, combine label with usage count to avoid overlap
+        if not top_n or top_n > 20:
+            combined_label = f"{label} (N={usage_str})"
+            ax.text(
+                width + 0.03,
+                bar.get_y() + bar.get_height() / 2.0,
+                combined_label,
+                ha="left",
+                va="center",
+                fontsize=8,
+            )
+        else:
+            # Add value label to the right of the bar
+            ax.text(
+                width + 0.03,
+                bar.get_y() + bar.get_height() / 2.0,
+                label,
+                ha="left",
+                va="center",
+                fontsize=9,
+            )
+
+            # Add usage count inside the bar
+            usage_str_full = f"N = {usage_str}"
+            ax.text(
+                0.05,
+                bar.get_y() + bar.get_height() / 2.0,
+                usage_str_full,
+                ha="left",
+                va="center",
+                fontsize=8,
+                color="white",
+            )
+
+    # Set labels and title
+    if top_n:
+        default_title = f"Top {top_n} {'countries' if geography == 'country' else 'US states'} by Anthropic AI Usage Index"
+    else:
+        default_title = f"Anthropic AI Usage Index by {'country' if geography == 'country' else 'US state'}"
+
+    format_axis(
+        ax,
+        xlabel="Anthropic AI Usage Index (usage % / working-age population %)",
+        title=title or default_title,
+        grid=True,
+        grid_alpha=0.3,
+    )
+
+    return fig
+
+
+def plot_variable_bars(
+    df,
+    variable,
+    facet,
+    geography="country",
+    geo_id=None,
+    top_n=None,
+    figsize=(12, 8),
+    title=None,
+    xlabel=None,
+    filtered_entities=None,
+    cmap=CUSTOM_CMAP,
+    normalize=False,
+    exclude_not_classified=False,
+):
+    """
+    Create horizontal bar chart for any variable.
+
+    Args:
+        df: Long format dataframe
+        variable: Variable name to plot (e.g., 'soc_pct', 'gdp_per_capita')
+        facet: Facet to use
+        geography: 'country' or 'state_us'
+        geo_id: Optional specific geo_id to filter (e.g., 'USA' for SOC data)
+        top_n: Number of top entities to show (None for all)
+        figsize: Figure size
+        title: Chart title
+        xlabel: x-axis label
+        filtered_entities: List of cluster_name or geo_id values to include
+        cmap: Colormap to use
+        normalize: If True, rescale values to sum to 100% (useful for percentages)
+        exclude_not_classified: If True, exclude 'not_classified' entries before normalizing
+    """
+    # Get data
+    df_metric = filter_df(
+        df, geography=geography, facet=facet, variable=variable, geo_id=geo_id
+    )
+
+    # Exclude not_classified if requested (before normalization)
+    if exclude_not_classified:
+        # Check both cluster_name and geo_id columns
+        if "cluster_name" in df_metric.columns:
+            df_metric = df_metric[
+                ~df_metric["cluster_name"].isin(["not_classified", "none"])
+            ]
+        if "geo_id" in df_metric.columns:
+            df_metric = df_metric[~df_metric["geo_id"].isin(["not_classified", "none"])]
+
+    # Normalize if requested (after filtering not_classified)
+    if normalize:
+        total_sum = df_metric["value"].sum()
+        if total_sum > 0:
+            df_metric["value"] = (df_metric["value"] / total_sum) * 100
+
+    # Apply entity filtering if provided
+    if filtered_entities is not None:
+        # Check if we're filtering by cluster_name or geo_id
+        if "cluster_name" in df_metric.columns:
+            df_metric = df_metric[df_metric["cluster_name"].isin(filtered_entities)]
+        else:
+            df_metric = df_metric[df_metric["geo_id"].isin(filtered_entities)]
+
+    # Select entities to display
+    if top_n is None or top_n >= len(df_metric):
+        # Show all entities, sorted by lowest value first
+        df_top = df_metric.sort_values("value", ascending=True)
+        # Adjust figure height for many entities
+        if len(df_top) > 20:
+            figsize = (figsize[0], max(10, len(df_top) * 0.3))
+    else:
+        # Select top N entities
+        df_top = df_metric.nlargest(top_n, "value")
+        df_top = df_top.sort_values("value", ascending=True)
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Get colormap and colors
+    values = df_top["value"].values
+    min_val = values.min()
+    max_val = values.max()
+
+    # Linear color mapping
+    if max_val > min_val:
+        normalized = (values - min_val) / (max_val - min_val)
+        # Truncate to avoid extremes
+        normalized = 0.2 + normalized * 0.6
+    else:
+        normalized = np.ones_like(values) * 0.5
+
+    colors = cmap(normalized)
+
+    # Create horizontal bars
+    y_positions = range(len(df_top))
+    bars = ax.barh(y_positions, values, color=colors, height=0.7)
+
+    # Set y-tick labels
+    ax.set_yticks(y_positions)
+    # Use cluster_name or geo_name depending on what's available
+    if "cluster_name" in df_top.columns:
+        labels = df_top["cluster_name"].values
+    elif "geo_name" in df_top.columns:
+        labels = df_top["geo_name"].values
+    else:
+        labels = df_top["geo_id"].values
+    ax.set_yticklabels(labels)
+
+    # Set y-axis limits to reduce white space
+    ax.set_ylim(-0.5, len(df_top) - 0.5)
+
+    # Calculate and set x-axis limits
+    x_range = max_val - min_val
+    if min_val < 0:
+        # Include negative values with some padding
+        ax.set_xlim(min_val - x_range * 0.1, max_val + x_range * 0.2)
+    else:
+        # Positive values only
+        ax.set_xlim(0, max_val * 1.2)
+
+    # Add value labels
+    for _, bar in enumerate(bars):
+        width = bar.get_width()
+        # Format based on value magnitude
+        if abs(width) >= 1000:
+            label = f"{width:.0f}"
+        elif abs(width) >= 10:
+            label = f"{width:.1f}"
+        else:
+            label = f"{width:.2f}"
+
+        # Position label
+        if width < 0:
+            ha = "right"
+            x_offset = -0.01 * (max_val - min_val)
+        else:
+            ha = "left"
+            x_offset = 0.01 * (max_val - min_val)
+
+        ax.text(
+            width + x_offset,
+            bar.get_y() + bar.get_height() / 2.0,
+            label,
+            ha=ha,
+            va="center",
+            fontsize=8 if len(df_top) > 20 else 9,
+        )
+
+    # Set labels and title
+    if not title:
+        if top_n:
+            title = f"Top {top_n} by {variable}"
+        else:
+            title = f"{variable} distribution"
+
+    format_axis(
+        ax,
+        xlabel=xlabel or variable,
+        title=title,
+        grid=True,
+        grid_alpha=0.3,
+    )
+
+    return fig
+
+
+def plot_usage_share_bars(
+    df,
+    geography="country",
+    top_n=20,
+    figsize=(12, 8),
+    title=None,
+    filtered_entities=None,
+    cmap=CUSTOM_CMAP,
+):
+    """
+    Create bar chart showing share of global usage.
+
+    Args:
+        df: Long format dataframe
+        geography: Geographic level
+        top_n: Number of top entities
+        figsize: Figure size
+        title: Chart title
+        filtered_entities: List of geo_id values to include (if None, include all)
+
+    """
+    # Get data
+    df_metric = filter_df(
+        df, geography=geography, facet=geography, variable="usage_pct"
+    )
+
+    # Exclude "not_classified" from the data
+    df_metric = df_metric[df_metric["geo_id"] != "not_classified"]
+
+    # Apply entity filtering if provided
+    if filtered_entities is not None:
+        df_metric = df_metric[df_metric["geo_id"].isin(filtered_entities)]
+
+    # Get top n
+    df_top = df_metric.nlargest(top_n, "value")
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Create bars
+    positions = range(len(df_top))
+    values = df_top["value"].values
+    names = df_top["geo_name"].values
+
+    # Use custom colormap
+    norm = get_color_normalizer(values, center_at_one=False)
+    colors = [cmap(norm(val)) for val in values]
+
+    bars = ax.bar(positions, values, color=colors, alpha=0.8)
+
+    # Customize
+    ax.set_xticks(positions)
+    ax.set_xticklabels(names, rotation=45, ha="right")
+    # Reduce horizontal margins to bring bars closer to plot borders
+    ax.margins(x=0.01)
+
+    default_title = f"Top {top_n} {'countries' if geography == 'country' else 'US states'} by share of global Claude usage"
+    format_axis(
+        ax, ylabel="Share of global usage (%)", title=title or default_title, grid=False
+    )
+
+    # Add value labels
+    for bar, value in zip(bars, values, strict=True):
+        label = f"{value:.1f}%"
+
+        # Add value label above the bar
+        ax.text(
+            bar.get_x() + bar.get_width() / 2,
+            value + 0.1,
+            label,
+            ha="center",
+            fontsize=8,
+        )
+
+    # Grid
+    ax.grid(True, axis="y", alpha=0.3)
+
+    return fig
+
+
+def plot_usage_index_histogram(
+    df, geography="country", bins=30, figsize=(10, 6), title=None, cmap=CUSTOM_CMAP
+):
+    """
+    Create histogram of Anthropic AI Usage Index distribution.
+
+    Args:
+        df: Long format dataframe
+        geography: Geographic level
+        bins: Number of histogram bins
+        figsize: Figure size
+        title: Chart title
+    """
+    # Get data
+    df_metric = filter_df(
+        df, geography=geography, facet=geography, variable="usage_per_capita_index"
+    )
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Create histogram
+    values = df_metric["value"].values
+    _, bins_edges, patches = ax.hist(
+        values, bins=bins, edgecolor="white", linewidth=0.5
+    )
+
+    # Color bars with custom gradient based on value
+    norm = get_color_normalizer(
+        values,
+        center_at_one=False,
+        vmin=min(bins_edges[0], 0),
+        vmax=max(bins_edges[-1], 2),
+    )
+
+    for patch, left_edge, right_edge in zip(
+        patches, bins_edges[:-1], bins_edges[1:], strict=True
+    ):
+        # Use the midpoint of the bin for color
+        mid_val = (left_edge + right_edge) / 2
+        color = cmap(norm(mid_val))
+        patch.set_facecolor(color)
+
+    # Add vertical line at 1.0 (where usage and population shares match)
+    ax.axvline(x=1.0, color="black", linestyle="--", alpha=0.5, linewidth=1)
+
+    # Add statistics
+    mean_val = values.mean()
+    median_val = np.median(values)
+
+    stats_text = f"Mean: {mean_val:.2f}\nMedian: {median_val:.2f}\nN = {len(values)}"
+    ax.text(
+        0.98,
+        0.97,
+        stats_text,
+        transform=ax.transAxes,
+        ha="right",
+        va="top",
+        fontsize=9,
+        bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+    )
+
+    # Customize
+    geo_label = "countries" if geography == "country" else "US states"
+    default_title = f"Distribution of Anthropic AI Usage Index ({geo_label})"
+
+    format_axis(
+        ax,
+        xlabel="Anthropic AI Usage Index (usage % / working-age population %)",
+        ylabel=f"Number of {geo_label}",
+        title=title or default_title,
+    )
+
+    return fig
+
+
+def plot_gdp_scatter(
+    df,
+    geography="country",
+    figsize=(10, 8),
+    title=None,
+    cmap=CUSTOM_CMAP,
+    filtered_entities=None,
+):
+    """
+    Create log-log scatter plot of GDP vs Anthropic AI Usage Index.
+
+    Args:
+        df: Long format dataframe
+        geography: Geographic level
+        figsize: Figure size
+        title: Chart title
+        cmap: Colormap to use
+        filtered_entities: List of geo_id values that meet MIN_OBSERVATIONS threshold (optional)
+    """
+    # Get usage data
+    df_usage = filter_df(
+        df, geography=geography, facet=geography, variable="usage_per_capita_index"
+    )
+
+    # Apply filtering if provided
+    if filtered_entities is not None:
+        df_usage = df_usage[df_usage["geo_id"].isin(filtered_entities)]
+
+    df_usage = df_usage[["geo_id", "cluster_name", "value"]].rename(
+        columns={"value": "usage_index"}
+    )
+
+    # Get GDP data
+    df_gdp = filter_df(
+        df, geography=geography, facet=geography, variable="gdp_per_working_age_capita"
+    )
+
+    # Apply same filtering to GDP data
+    if filtered_entities is not None:
+        df_gdp = df_gdp[df_gdp["geo_id"].isin(filtered_entities)]
+
+    df_gdp = df_gdp[["geo_id", "value"]].rename(columns={"value": "gdp_per_capita"})
+
+    # Merge
+    df_plot = df_usage.merge(df_gdp, on="geo_id", how="inner")
+
+    # Filter out zeros and negative values for log scale
+    # Explicitly check both GDP and usage are positive (will be true for filtered geos)
+    mask = (df_plot["gdp_per_capita"] > 0) & (df_plot["usage_index"] > 0)
+    df_plot = df_plot[mask]
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Create scatter plot with geo_id values as labels
+    x = df_plot["gdp_per_capita"].values
+    y = df_plot["usage_index"].values
+
+    # Transform to log space for plotting
+    log_x = np.log(x)
+    log_y = np.log(y)
+
+    # Create norm for colorbar (using natural log)
+    norm = plt.Normalize(vmin=log_y.min(), vmax=log_y.max())
+
+    # First, plot invisible points to ensure matplotlib's autoscaling includes all data points
+    ax.scatter(log_x, log_y, s=0, alpha=0)  # Size 0, invisible points for autoscaling
+
+    # Plot the geo_id values as text at the exact data points in log space
+    for ln_x, ln_y, geo_id in zip(log_x, log_y, df_plot["geo_id"].values, strict=True):
+        # Get color from colormap based on ln(usage_index)
+        color_val = norm(ln_y)
+        text_color = cmap(color_val)
+
+        ax.text(
+            ln_x,
+            ln_y,
+            geo_id,
+            fontsize=7,
+            ha="center",
+            va="center",
+            color=text_color,
+            alpha=0.9,
+            weight="bold",
+        )
+
+    # Add constant for intercept
+    X_with_const = sm.add_constant(log_x)
+
+    # Fit OLS regression in log space
+    model = sm.OLS(log_y, X_with_const)
+    results = model.fit()
+
+    # Extract statistics
+    intercept = results.params[0]
+    slope = results.params[1]
+    r_squared = results.rsquared
+    p_value = results.pvalues[1]  # p-value for slope
+
+    # Create fit line (we're already in log space)
+    x_fit = np.linspace(log_x.min(), log_x.max(), 100)
+    y_fit = intercept + slope * x_fit
+    ax.plot(
+        x_fit,
+        y_fit,
+        "gray",
+        linestyle="--",
+        alpha=0.7,
+        linewidth=2,
+        label=f"Power law: AUI ~ GDP^{slope:.2f}",
+    )
+
+    # Add regression statistics
+    # Format p-value display
+    if p_value < 0.001:
+        p_str = "p < 0.001"
+    else:
+        p_str = f"p = {p_value:.3f}"
+
+    ax.text(
+        0.05,
+        0.95,
+        f"$\\beta = {slope:.3f}\\ ({p_str})$\n$R^2 = {r_squared:.3f}$",
+        transform=ax.transAxes,
+        fontsize=10,
+        bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+        verticalalignment="top",
+    )
+
+    # Customize labels for log-transformed values
+    xlabel = "ln(GDP per working-age capita in USD)"
+    ylabel = "ln(Anthropic AI Usage Index)"
+    default_title = f"Income and Anthropic AI Usage Index by {'country' if geography == 'country' else 'US state'}"
+
+    format_axis(
+        ax, xlabel=xlabel, ylabel=ylabel, title=title or default_title, grid=False
+    )
+
+    # Grid for log scale
+    ax.grid(True, alpha=0.3, which="both", linestyle="-", linewidth=0.5)
+
+    # Add legend
+    ax.legend(loc="best")
+
+    # Create colorbar using ScalarMappable
+    scalar_mappable = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    scalar_mappable.set_array([])
+    cbar = plt.colorbar(scalar_mappable, ax=ax)
+    cbar.set_label(
+        "ln(Anthropic AI Usage Index)", fontsize=9, rotation=270, labelpad=15
+    )
+
+    return fig
+
+
+def plot_request_comparison_cards(
+    df,
+    geo_ids,
+    title,
+    geography,
+    top_n=5,
+    figsize=(10, 6),
+    exclude_not_classified=True,
+    request_level=1,
+    request_threshold=1.0,
+):
+    """
+    Create a condensed card visualization showing top overrepresented request categories
+    for multiple geographies (countries or states).
+
+    Args:
+        df: Long format dataframe
+        geo_ids: List of geography IDs to compare (e.g., ['USA', 'BRA', 'VNM', 'IND'])
+        title: Title for the figure (required)
+        geography: Geographic level ('country' or 'state_us')
+        top_n: Number of top requests to show per geography (default 5)
+        figsize: Figure size as tuple
+        exclude_not_classified: Whether to exclude "not_classified" entries
+        request_level: Request hierarchy level to use (default 1)
+        request_threshold: Minimum percentage threshold for requests (default 1.0%)
+    """
+    # Get data for specified geography
+    data_subset = filter_df(df, facet="request", geo_id=geo_ids, geography=geography)
+
+    # Filter for request_pct_index variable and specified level
+    data_subset = filter_df(
+        data_subset, variable="request_pct_index", level=request_level
+    )
+
+    # Exclude not_classified if requested
+    if exclude_not_classified:
+        data_subset = data_subset[
+            ~data_subset["cluster_name"].str.contains("not_classified", na=False)
+        ]
+
+    # Get tier and geo_name information
+    geo_info = filter_df(
+        df, geography=geography, variable="usage_tier", geo_id=geo_ids
+    )[["geo_id", "geo_name", "value"]].drop_duplicates()
+    tier_map = dict(zip(geo_info["geo_id"], geo_info["value"], strict=True))
+    name_map = dict(zip(geo_info["geo_id"], geo_info["geo_name"], strict=True))
+
+    # Set up figure with 2x2 grid for 4 geographies
+    n_rows, n_cols = 2, 2
+    fig, axes = create_figure(figsize=figsize, nrows=n_rows, ncols=n_cols)
+    axes = axes.flatten()
+
+    # Use global tier colors
+    tier_colors = TIER_COLORS_NUMERIC
+
+    # Process each geography
+    for idx, geo_id in enumerate(geo_ids):
+        ax = axes[idx]
+
+        # Apply request threshold filtering to get valid requests for this geography
+        valid_requests = filter_requests_by_threshold(
+            df, geography, geo_id, level=request_level, threshold=request_threshold
+        )
+
+        # Get data for this geography, filtered by valid requests
+        geo_data = data_subset[
+            (data_subset["geo_id"] == geo_id)
+            & (data_subset["cluster_name"].isin(valid_requests))
+            & (data_subset["value"] > 1.0)  # Only show overrepresented requests
+        ].copy()
+
+        # Get top n from the filtered requests
+        geo_data = geo_data.nlargest(top_n, "value")
+
+        # Get tier color
+        tier = tier_map[geo_id]
+        base_color = tier_colors[tier]
+
+        # Create a lighter version of the tier color for the card background
+        rgb = mcolors.to_rgb(base_color)
+        # Mix with white (85% white, 15% color for very subtle background)
+        pastel_rgb = tuple(0.85 + 0.15 * c for c in rgb)
+        card_bg_color = mcolors.to_hex(pastel_rgb)
+
+        # Fill entire axis with background color
+        ax.set_facecolor(card_bg_color)
+
+        # Create card with requests
+        card_height = 0.9  # Fixed height for all cards
+        card_bottom = 0.965 - card_height  # Consistent positioning
+
+        card_rect = FancyBboxPatch(
+            (0.10, card_bottom),
+            0.80,
+            card_height,
+            transform=ax.transAxes,
+            boxstyle="round,pad=0.02,rounding_size=0.035",
+            facecolor=card_bg_color,
+            edgecolor="none",
+            linewidth=2,
+            clip_on=False,
+        )
+        ax.add_patch(card_rect)
+
+        # Header bar
+        header_top = 0.965 - 0.10
+        header_rect = FancyBboxPatch(
+            (0.14, header_top),
+            0.72,
+            0.08,
+            transform=ax.transAxes,
+            boxstyle="round,pad=0.01,rounding_size=0.03",
+            facecolor=base_color,
+            edgecolor="none",
+            alpha=0.7,
+            clip_on=False,
+        )
+        ax.add_patch(header_rect)
+
+        # Add geography name
+        geo_name = name_map[geo_id]
+
+        ax.text(
+            0.5,
+            header_top + 0.04,
+            geo_name,
+            transform=ax.transAxes,
+            ha="center",
+            va="center",
+            fontsize=12,
+            fontweight="bold",
+            color="#1C1C1C",
+        )
+
+        # Adjust start position below header upwards
+        y_pos = header_top - 0.05
+
+        for _, row in geo_data.iterrows():
+            request = row["cluster_name"]
+            value = row["value"]
+
+            # Format ratio
+            if value >= 10:
+                ratio_str = f"{value:.0f}x"
+            elif value >= 2:
+                ratio_str = f"{value:.1f}x"
+            else:
+                ratio_str = f"{value:.2f}x"
+
+            # Wrap text
+            wrapped_text = textwrap.fill(request, width=46, break_long_words=False)
+            lines = wrapped_text.split("\n")
+
+            # Display text lines with sufficient line spacing
+            line_spacing = 0.045
+            for j, line in enumerate(lines):
+                ax.text(
+                    0.13,  # Adjust text position for wider card
+                    y_pos - j * line_spacing,
+                    line,
+                    transform=ax.transAxes,
+                    ha="left",
+                    va="top",
+                    fontsize=9,
+                    color="#1C1C1C",
+                    rasterized=False,
+                )
+
+            # Position ratio with adjusted margin for wide card
+            text_height = len(lines) * line_spacing
+            ax.text(
+                0.85,
+                y_pos - (text_height - line_spacing) / 2,
+                ratio_str,
+                transform=ax.transAxes,
+                ha="right",
+                va="center",
+                fontsize=10,
+                fontweight="bold",
+                color="#B85450",
+                rasterized=False,
+            )
+
+            # Add space between different requests
+            y_pos -= text_height + 0.05
+
+        # Remove axes
+        ax.axis("off")
+
+    # Add title
+    fig.suptitle(title, fontsize=14, fontweight="bold", y=0.98)
+
+    plt.tight_layout()
+    plt.subplots_adjust(
+        top=0.92, bottom=0.02, left=0.01, right=0.99, hspace=0.02, wspace=0.02
+    )
+
+    return fig
+
+
+def plot_dc_task_request_cards(
+    df,
+    title,
+    figsize=(10, 5),
+):
+    """
+    Create professional card visualizations showing top overrepresented O*NET tasks and requests for Washington, DC.
+
+    Args:
+        df: Long format dataframe
+        figsize: Figure size as tuple
+        title: Optional title for the figure
+    """
+    # Fixed parameters for DC
+    geo_id = "DC"
+    geography = "state_us"
+    top_n = 5
+
+    # Get tier for color
+    tier_data = filter_df(
+        df, geography=geography, variable="usage_tier", geo_id=[geo_id]
+    )
+    tier = tier_data["value"].iloc[0]
+
+    # Use tier color
+    tier_colors = TIER_COLORS_NUMERIC
+    base_color = tier_colors[tier]
+
+    # Create lighter version for card background
+    rgb = mcolors.to_rgb(base_color)
+    pastel_rgb = tuple(0.85 + 0.15 * c for c in rgb)
+    card_bg_color = mcolors.to_hex(pastel_rgb)
+
+    # Create figure with 2 subplots (cards)
+    fig, axes = create_figure(figsize=figsize, ncols=2)
+
+    # Card 1: Top O*NET Tasks
+    ax1 = axes[0]
+    ax1.set_facecolor(card_bg_color)
+
+    # Get O*NET task data
+    df_tasks = filter_df(
+        df,
+        geography=geography,
+        geo_id=[geo_id],
+        facet="onet_task",
+        variable="onet_task_pct_index",
+    )
+
+    # Exclude not_classified and none
+    df_tasks = df_tasks[~df_tasks["cluster_name"].isin(["not_classified", "none"])]
+
+    # Get top n overrepresented tasks
+    df_tasks = df_tasks[df_tasks["value"] > 1.0].nlargest(top_n, "value")
+
+    # Use fixed card heights
+    card_height_tasks = 0.955
+    card_bottom_tasks = 0.965 - card_height_tasks
+
+    # Draw card for O*NET tasks
+    card_rect1 = FancyBboxPatch(
+        (0.10, card_bottom_tasks),
+        0.80,
+        card_height_tasks,
+        transform=ax1.transAxes,
+        boxstyle="round,pad=0.02,rounding_size=0.035",
+        facecolor=card_bg_color,
+        edgecolor="none",
+        linewidth=2,
+        clip_on=False,
+    )
+    ax1.add_patch(card_rect1)
+
+    # Header for O*NET tasks
+    header_top = 0.965 - 0.10
+    header_rect1 = FancyBboxPatch(
+        (0.12, header_top),
+        0.76,
+        0.08,
+        transform=ax1.transAxes,
+        boxstyle="round,pad=0.01,rounding_size=0.03",
+        facecolor=base_color,
+        edgecolor="none",
+        alpha=0.7,
+        clip_on=False,
+    )
+    ax1.add_patch(header_rect1)
+
+    ax1.text(
+        0.5,
+        header_top + 0.04,
+        "Top 5 overrepresented O*NET tasks in DC",
+        transform=ax1.transAxes,
+        ha="center",
+        va="center",
+        fontsize=11,
+        fontweight="bold",
+        color="#1C1C1C",
+    )
+
+    # Add task items
+    y_pos = header_top - 0.05
+
+    for _, row in df_tasks.iterrows():
+        task = row["cluster_name"]
+        value = row["value"]
+
+        # Convert to sentence case and remove trailing period
+        task = task[0].upper() + task[1:].lower() if task else task
+        task = task.rstrip(".")  # Remove trailing period
+
+        # Format ratio - always with 2 decimal places
+        ratio_str = f"{value:.2f}x"
+
+        # Wrap text
+        wrapped_text = textwrap.fill(task, width=46, break_long_words=False)
+        lines = wrapped_text.split("\n")
+
+        # Display text lines
+        line_spacing = 0.045
+        for j, line in enumerate(lines):
+            ax1.text(
+                0.13,
+                y_pos - j * line_spacing,
+                line,
+                transform=ax1.transAxes,
+                ha="left",
+                va="top",
+                fontsize=9,
+                color="#1C1C1C",
+                rasterized=False,
+            )
+
+        # Add ratio at the right with consistent color
+        ax1.text(
+            0.87,
+            y_pos - (len(lines) - 1) * line_spacing / 2,
+            ratio_str,
+            transform=ax1.transAxes,
+            ha="right",
+            va="center",
+            fontsize=10,
+            color="#B85450",
+            fontweight="bold",
+        )
+
+        # Move to next item position
+        y_pos -= len(lines) * line_spacing + 0.025
+
+    ax1.axis("off")
+
+    # Card 2: Top Requests
+    ax2 = axes[1]
+    ax2.set_facecolor(card_bg_color)
+
+    # Get valid requests using threshold
+    valid_requests = filter_requests_by_threshold(
+        df, geography, geo_id, level=1, threshold=1.0
+    )
+
+    # Get request data
+    df_requests = filter_df(
+        df,
+        geography=geography,
+        geo_id=[geo_id],
+        facet="request",
+        variable="request_pct_index",
+        level=1,
+    )
+
+    # Filter by valid requests and overrepresented
+    df_requests = df_requests[
+        (df_requests["cluster_name"].isin(valid_requests))
+        & (df_requests["value"] > 1.0)
+        & (~df_requests["cluster_name"].str.contains("not_classified", na=False))
+    ]
+
+    # Get top n
+    df_requests = df_requests.nlargest(top_n, "value")
+
+    # Draw card for requests with fixed height
+    card_height_requests = 0.72
+    card_bottom_requests = 0.965 - card_height_requests
+
+    card_rect2 = FancyBboxPatch(
+        (0.10, card_bottom_requests),
+        0.80,
+        card_height_requests,
+        transform=ax2.transAxes,
+        boxstyle="round,pad=0.02,rounding_size=0.035",
+        facecolor=card_bg_color,
+        edgecolor="none",
+        linewidth=2,
+        clip_on=False,
+    )
+    ax2.add_patch(card_rect2)
+
+    # Header for requests
+    header_rect2 = FancyBboxPatch(
+        (0.12, header_top),
+        0.76,
+        0.08,
+        transform=ax2.transAxes,
+        boxstyle="round,pad=0.01,rounding_size=0.03",
+        facecolor=base_color,
+        edgecolor="none",
+        alpha=0.7,
+        clip_on=False,
+    )
+    ax2.add_patch(header_rect2)
+
+    ax2.text(
+        0.5,
+        header_top + 0.04,
+        "Top 5 overrepresented request clusters in DC",
+        transform=ax2.transAxes,
+        ha="center",
+        va="center",
+        fontsize=11,
+        fontweight="bold",
+        color="#1C1C1C",
+    )
+
+    # Add request items
+    y_pos = header_top - 0.05
+
+    for _, row in df_requests.iterrows():
+        request = row["cluster_name"]
+        value = row["value"]
+
+        # Format ratio always with 2 decimal places
+        ratio_str = f"{value:.2f}x"
+
+        # Wrap text
+        wrapped_text = textwrap.fill(request, width=46, break_long_words=False)
+        lines = wrapped_text.split("\n")
+
+        # Display text lines
+        line_spacing = 0.045
+        for j, line in enumerate(lines):
+            ax2.text(
+                0.13,
+                y_pos - j * line_spacing,
+                line,
+                transform=ax2.transAxes,
+                ha="left",
+                va="top",
+                fontsize=9,
+                color="#1C1C1C",
+                rasterized=False,
+            )
+
+        # Add ratio at the right with consistent color
+        ax2.text(
+            0.87,
+            y_pos - (len(lines) - 1) * line_spacing / 2,
+            ratio_str,
+            transform=ax2.transAxes,
+            ha="right",
+            va="center",
+            fontsize=10,
+            color="#B85450",
+            fontweight="bold",
+        )
+
+        # Move to next item position
+        y_pos -= len(lines) * line_spacing + 0.025
+
+    ax2.axis("off")
+
+    # Add subtle title if provided
+    fig.suptitle(title, fontsize=13, fontweight="bold", y=0.98)
+
+    plt.tight_layout()
+    return fig
+
+
+# Summary statistics function
+def plot_tier_summary_table(df, geography="country", figsize=(12, 6)):
+    """
+    Create a visual table showing entities per tier and example members.
+
+    Args:
+        df: Long format dataframe
+        geography: 'country' or 'state_us'
+        figsize: Figure size
+    """
+    # Get tier data
+    df_tier = filter_df(df, geography=geography, variable="usage_tier")
+
+    # Exclude US territories that appear as countries (may be confusing to readers)
+    if geography == "country":
+        us_territories_as_countries = [
+            "PRI",
+            "VIR",
+            "GUM",
+            "ASM",
+            "MNP",
+        ]  # Puerto Rico, Virgin Islands, Guam, American Samoa, Northern Mariana Islands
+        df_tier = df_tier[~df_tier["geo_id"].isin(us_territories_as_countries)]
+
+    # Get usage per capita index for sorting entities within tiers
+    df_usage_index = filter_df(
+        df, geography=geography, variable="usage_per_capita_index"
+    )
+
+    # Apply same territory filter to usage index data
+    if geography == "country":
+        df_usage_index = df_usage_index[
+            ~df_usage_index["geo_id"].isin(us_territories_as_countries)
+        ]
+
+    # Merge tier with usage index
+    df_tier_full = df_tier[["geo_id", "geo_name", "cluster_name"]].merge(
+        df_usage_index[["geo_id", "value"]],
+        on="geo_id",
+        how="left",
+        suffixes=("", "_index"),
+    )
+
+    # Use global tier colors
+    tier_colors = TIER_COLORS_DICT
+
+    # Calculate appropriate figure height based on number of tiers
+    n_tiers = sum(
+        1 for tier in TIER_ORDER if tier in df_tier_full["cluster_name"].values
+    )
+    # Adjust height: minimal padding for compact display
+    fig_height = 0.5 + n_tiers * 0.3  # Much more compact
+
+    # Create figure with calculated size
+    fig, ax = create_figure(figsize=(figsize[0], fig_height))
+    ax.axis("tight")
+    ax.axis("off")
+
+    # Make background transparent
+    fig.patch.set_alpha(0.0)
+    ax.patch.set_alpha(0.0)
+
+    # Prepare table data
+    table_data = []
+    entity_type = "countries" if geography == "country" else "states"
+    col_labels = [
+        "Tier",
+        "AUI range",
+        f"# of {entity_type}",
+        f"Example {entity_type}",
+    ]
+
+    for tier in TIER_ORDER:
+        if tier in df_tier_full["cluster_name"].values:
+            # Get entities in this tier
+            tier_entities = filter_df(df_tier_full, cluster_name=tier)
+            count = len(tier_entities)
+
+            # Calculate usage index range for this tier
+            min_index = tier_entities["value"].min()
+            max_index = tier_entities["value"].max()
+            index_range = f"{min_index:.2f} - {max_index:.2f}"
+
+            # For Minimal tier where all have 0 index, pick shortest names
+            if tier == "Minimal" and tier_entities["value"].max() == 0:
+                tier_entities = tier_entities.copy()
+                tier_entities["name_length"] = tier_entities["geo_name"].str.len()
+                top_entities = tier_entities.nsmallest(5, "name_length")[
+                    "geo_name"
+                ].tolist()
+            else:
+                # Get top 5 entities by usage index in this tier
+                top_entities = tier_entities.nlargest(5, "value")["geo_name"].tolist()
+
+            # Format the example entities as a comma-separated string
+            examples = ", ".join(top_entities[:5])
+
+            table_data.append([tier, index_range, str(count), examples])
+
+    # Create table with better column widths
+    table = ax.table(
+        cellText=table_data,
+        colLabels=col_labels,
+        cellLoc="left",
+        loc="center",
+        colWidths=[0.20, 0.18, 0.12, 0.50],
+        colColours=[ANTHROPIC_OAT] * 4,
+    )
+
+    # Style the table
+    table.auto_set_font_size(False)
+    table.set_fontsize(11)
+    table.scale(1, 2.2)
+
+    # Set all cell edges to Anthropic oat color
+    for _, cell in table.get_celld().items():
+        cell.set_edgecolor(ANTHROPIC_OAT)
+        cell.set_linewidth(1.5)
+
+    # Color code the rows with consistent black text
+    for i, row_data in enumerate(table_data):
+        tier_name = row_data[0]
+        if tier_name in tier_colors:
+            # Color the tier name cell with full opacity
+            table[(i + 1, 0)].set_facecolor(tier_colors[tier_name])
+            table[(i + 1, 0)].set_text_props(color="black", weight="bold")
+
+            # Light background for usage index range column
+            table[(i + 1, 1)].set_facecolor(tier_colors[tier_name])
+            table[(i + 1, 1)].set_alpha(0.3)
+            table[(i + 1, 1)].set_text_props(ha="center", color="black")
+
+            # Light background for count column
+            table[(i + 1, 2)].set_facecolor(tier_colors[tier_name])
+            table[(i + 1, 2)].set_alpha(0.2)
+            table[(i + 1, 2)].set_text_props(ha="center", color="black")
+
+            # Even lighter background for examples column
+            table[(i + 1, 3)].set_facecolor(tier_colors[tier_name])
+            table[(i + 1, 3)].set_alpha(0.1)
+            table[(i + 1, 3)].set_text_props(color="black")
+
+    # Style header row with Anthropic oat and black text
+    for j in range(4):
+        table[(0, j)].set_facecolor(ANTHROPIC_OAT)
+        table[(0, j)].set_text_props(color="black", weight="bold")
+
+    # Center the count column
+    for i in range(len(table_data)):
+        table[(i + 1, 1)].set_text_props(ha="center")
+
+    return fig
+
+
+def plot_tier_map(
+    df,
+    title,
+    geography,
+    figsize=(16, 10),
+    show_labels=True,
+):
+    """
+    Create a map showing per Anthropic AI Usage Tiers.
+
+    Args:
+        df: Long format dataframe with usage_tier variable
+        geography: 'country' or 'state_us'
+        figsize: Figure size
+        title: Map title
+        show_labels: whether to show title and legend (False for clean export)
+    """
+    # Filter for tier data
+    df_tier = filter_df(df, geography=geography, variable="usage_tier").copy()
+
+    # Use global tier colors definition
+    tier_colors = TIER_COLORS_DICT
+
+    # Map tiers to colors
+    df_tier["color"] = df_tier["cluster_name"].map(tier_colors)
+
+    # Set up figure
+    # Create figure with tight_layout disabled
+    fig, ax = create_figure(figsize=figsize, tight_layout=False)
+
+    if geography == "country":
+        # Load world shapefile function
+        world = load_world_shapefile()
+
+        # Merge with world data using geo_id (which contains ISO-3 codes)
+        # Use ISO_A3_EH for merging as it's complete (ISO_A3 has -99 for France)
+        world = merge_geo_data(
+            world,
+            df_tier,
+            "ISO_A3_EH",
+            ["geo_id", "color", "cluster_name"],
+            is_tier=True,
+        )
+
+        # Plot world map
+        plot_world_map(ax, world, data_column="cluster_name", tier_colors=tier_colors)
+
+    else:  # state_us
+        # Load US states shapefile function
+        states = load_us_states_shapefile()
+
+        # Merge with tier data BEFORE projection
+        states = merge_geo_data(
+            states, df_tier, "STUSPS", ["geo_id", "color", "cluster_name"], is_tier=True
+        )
+
+        # Pot states with insets
+        plot_us_states_map(
+            fig, ax, states, data_column="cluster_name", tier_colors=tier_colors
+        )
+
+    # Remove axes
+    ax.set_axis_off()
+
+    # Add title only if show_labels=True
+    if show_labels:
+        format_axis(ax, title=title, title_size=14, grid=False)
+
+    # Check which tiers actually appear in the data
+    tiers_in_data = df_tier["cluster_name"].unique()
+
+    # Add legend only if show_labels=True
+    if show_labels:
+        # Check for excluded countries and no data
+        excluded = False
+        no_data = False
+        if geography == "country":
+            if "world" in locals() and "is_excluded" in world.columns:
+                excluded = world["is_excluded"].any()
+            if "world" in locals():
+                no_data = world["cluster_name"].isna().any()
+        else:  # state_us
+            if "states" in locals():
+                no_data = states["cluster_name"].isna().any()
+
+        create_tier_legend(
+            ax, tier_colors, tiers_in_data, excluded_countries=excluded, no_data=no_data
+        )
+
+    return fig
+
+
+def plot_variable_map(
+    df,
+    variable,
+    geography="country",
+    figsize=(16, 10),
+    title=None,
+    cmap=CUSTOM_CMAP,
+    center_at_one=None,
+):
+    """
+    Create static map for any variable.
+
+    Args:
+        df: Long format dataframe
+        variable: Variable to plot (e.g., 'usage_pct')
+        geography: 'country' or 'state_us'
+        figsize: Figure size (width, height) in inches
+        title: Map title
+        cmap: Matplotlib colormap or name (default uses custom colormap)
+        center_at_one: Whether to center the color scale at 1.0 (default True for usage_per_capita_index)
+    """
+    # Get data for the specified variable
+    df_data = filter_df(df, geography=geography, facet=geography, variable=variable)
+
+    # Create figure
+    fig = plt.figure(figsize=figsize, dpi=150)
+    fig.set_layout_engine(layout="none")  # Disable layout engine for custom axes
+    ax = fig.add_subplot(111)
+
+    if geography == "country":
+        # Load world shapefile function (automatically marks excluded countries)
+        world = load_world_shapefile()
+
+        # Merge using geo_id (which contains ISO-3 codes)
+        world = merge_geo_data(
+            world, df_data, "ISO_A3_EH", ["geo_id", "value"], is_tier=False
+        )
+
+        # Prepare data and normalization
+        plot_column, norm = prepare_map_data(
+            world, "value", center_at_one, world["is_excluded"]
+        )
+
+        # Plot world map
+        plot_world_map(ax, world, data_column=plot_column, cmap=cmap, norm=norm)
+
+    else:  # state_us
+        # Load US states shapefile function
+        states = load_us_states_shapefile()
+
+        # Merge our data with the states shapefile
+        states = merge_geo_data(
+            states, df_data, "STUSPS", ["geo_id", "value"], is_tier=False
+        )
+
+        # Prepare data and normalization
+        plot_column, norm = prepare_map_data(states, "value", center_at_one)
+
+        # Plot states with insets
+        plot_us_states_map(
+            fig, ax, states, data_column=plot_column, cmap=cmap, norm=norm
+        )
+
+    # Remove axes
+    ax.set_axis_off()
+
+    # Add colorbar with proper size and positioning
+    divider = make_axes_locatable(ax)
+    cax = divider.append_axes("right", size="3%", pad=0.1)
+
+    # Create colorbar
+    scalar_mappable = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
+    scalar_mappable.set_array([])
+    cbar = plt.colorbar(scalar_mappable, cax=cax)
+
+    # Set colorbar label based on variable
+    if variable == "usage_pct":
+        cbar.set_label("Usage share (%)", fontsize=10, rotation=270, labelpad=15)
+    elif variable == "usage_per_capita_index":
+        cbar.set_label(
+            "Anthropic AI Usage Index", fontsize=10, rotation=270, labelpad=15
+        )
+    else:
+        cbar.set_label(variable, fontsize=10, rotation=270, labelpad=15)
+
+    # Set title
+    if variable == "usage_pct":
+        default_title = "Share of Claude usage by " + (
+            "country" if geography == "country" else "US state"
+        )
+    else:
+        default_title = f"{variable} by " + (
+            "country" if geography == "country" else "US state"
+        )
+
+    format_axis(ax, title=title or default_title, title_size=14, grid=False)
+
+    # Add legend for excluded countries and no data
+    legend_elements = []
+
+    # Check if we have excluded countries or no data regions
+    if geography == "country":
+        # Check for excluded countries (world['is_excluded'] == True)
+        if "is_excluded" in world.columns:
+            excluded_countries = world[world["is_excluded"] == True]
+            if not excluded_countries.empty:
+                legend_elements.append(
+                    Patch(
+                        facecolor="#c0c0c0",
+                        edgecolor="white",
+                        label="Claude not available",
+                    )
+                )
+
+            # Check for countries with no data
+            no_data_countries = world[
+                (world["value"].isna()) & (world["is_excluded"] != True)
+            ]
+            if not no_data_countries.empty:
+                legend_elements.append(
+                    Patch(facecolor="#f0f0f0", edgecolor="white", label="No data")
+                )
+
+    if legend_elements:
+        ax.legend(
+            handles=legend_elements,
+            loc="lower left",
+            fontsize=9,
+            frameon=True,
+            fancybox=True,
+            shadow=True,
+            bbox_to_anchor=(0, 0),
+        )
+
+    return fig
+
+
+def plot_soc_usage_scatter(
+    df,
+    geography,
+    filtered_entities=None,
+):
+    """
+    Create faceted scatterplot of SOC percentages vs Anthropic AI Usage Index.
+    Always creates a 2x2 grid of square subplots showing the top 4 SOC groups.
+
+    Args:
+        df: Long format dataframe with enriched data
+        geography: 'country' or 'state_us'
+        filtered_entities: List of geo_id values that meet MIN_OBSERVATIONS threshold
+    """
+    # Fixed configuration for 2x2 grid
+    n_cols = 2
+    n_rows = 2
+    n_top_groups = 4
+
+    # Apply MIN_OBSERVATIONS filtering if not provided
+    if filtered_entities is None:
+        filtered_countries, filtered_states = get_filtered_geographies(df)
+        filtered_entities = (
+            filtered_countries if geography == "country" else filtered_states
+        )
+
+    # Get Anthropic AI Usage Index data
+    df_usage_index = filter_df(
+        df,
+        geography=geography,
+        variable="usage_per_capita_index",
+        geo_id=filtered_entities,
+    )[["geo_id", "value"]].rename(columns={"value": "ai_usage_index"})
+
+    # Get usage counts for bubble sizes
+    df_usage = filter_df(
+        df, geography=geography, variable="usage_count", geo_id=filtered_entities
+    )[["geo_id", "value"]].rename(columns={"value": "usage_count"})
+
+    # Get tier data for colors
+    df_tier = filter_df(
+        df, geography=geography, variable="usage_tier", geo_id=filtered_entities
+    )[["geo_id", "cluster_name", "value"]].rename(
+        columns={"cluster_name": "tier_name", "value": "tier_value"}
+    )
+
+    # Get SOC percentages
+    df_soc = filter_df(
+        df,
+        geography=geography,
+        facet="soc_occupation",
+        variable="soc_pct",
+        geo_id=filtered_entities,
+    )[["geo_id", "cluster_name", "value"]].rename(
+        columns={"cluster_name": "soc_group", "value": "soc_pct"}
+    )
+
+    # Merge all data
+    df_plot = df_soc.merge(
+        df_usage_index, on="geo_id", how="inner"
+    )  # inner join because some geographies don't have data for all SOC groups
+    df_plot = df_plot.merge(df_usage, on="geo_id", how="left")
+    df_plot = df_plot.merge(
+        df_tier[["geo_id", "tier_name", "tier_value"]], on="geo_id", how="left"
+    )
+
+    # Use parent geography reference for consistent SOC selection
+    if geography == "country":
+        # Use global reference for countries
+        reference_soc = filter_df(
+            df,
+            geography="global",
+            geo_id="GLOBAL",
+            facet="soc_occupation",
+            variable="soc_pct",
+        )
+    else:  # state_us
+        # Use US reference for states
+        reference_soc = filter_df(
+            df,
+            geography="country",
+            geo_id="USA",
+            facet="soc_occupation",
+            variable="soc_pct",
+        )
+
+    # Get top SOC groups from reference (excluding not_classified)
+    reference_filtered = reference_soc[
+        ~reference_soc["cluster_name"].str.contains("not_classified", na=False)
+    ]
+    plot_soc_groups = reference_filtered.nlargest(n_top_groups, "value")[
+        "cluster_name"
+    ].tolist()
+
+    # Filter to selected SOC groups
+    df_plot = df_plot[df_plot["soc_group"].isin(plot_soc_groups)]
+
+    tier_colors = TIER_COLORS_DICT
+
+    # Fixed square subplot size for 2x2 grid
+    subplot_size = 6  # Each subplot is 6x6 inches
+    figsize = (subplot_size * n_cols, subplot_size * n_rows)
+
+    # Create figure
+    fig, axes = create_figure(figsize=figsize, nrows=n_rows, ncols=n_cols)
+    fig.suptitle(
+        "Occupation group shares vs Anthropic AI Usage Index",
+        fontsize=16,
+        fontweight="bold",
+        y=0.98,
+    )
+
+    # Flatten axes for easier iteration (always 2x2 grid)
+    axes_flat = axes.flatten()
+
+    # Plot each SOC group
+    for idx, soc_group in enumerate(plot_soc_groups):
+        ax = axes_flat[idx]
+
+        # Get data for this SOC group
+        soc_data = filter_df(df_plot, soc_group=soc_group)
+
+        # Create scatter plot for each tier
+        for tier_name in tier_colors.keys():
+            tier_data = filter_df(soc_data, tier_name=tier_name)
+
+            # Scale bubble sizes using sqrt for better visibility
+            sizes = np.sqrt(tier_data["usage_count"]) * 2
+
+            ax.scatter(
+                tier_data["ai_usage_index"],
+                tier_data["soc_pct"],
+                s=sizes,
+                c=tier_colors[tier_name],
+                alpha=0.6,
+                edgecolors="black",
+                linewidth=0.5,
+                label=tier_name,
+            )
+
+        # Add trend line and regression statistics
+        X = sm.add_constant(soc_data["ai_usage_index"].values)
+        y = soc_data["soc_pct"].values
+
+        model = sm.OLS(y, X)
+        results = model.fit()
+
+        intercept = results.params[0]
+        slope = results.params[1]
+        r_squared = results.rsquared
+        p_value = results.pvalues[1]  # p-value for slope
+
+        # Plot trend line
+        x_line = np.linspace(
+            soc_data["ai_usage_index"].min(), soc_data["ai_usage_index"].max(), 100
+        )
+        y_line = intercept + slope * x_line
+        ax.plot(x_line, y_line, "--", color="gray", alpha=0.5, linewidth=1)
+
+        # Format p-value display
+        if p_value < 0.001:
+            p_str = "p < 0.001"
+        else:
+            p_str = f"p = {p_value:.3f}"
+
+        # Add regression statistics
+        ax.text(
+            0.95,
+            0.95,
+            f"$\\beta = {slope:.3f}\\ ({p_str})$\n$R^2 = {r_squared:.3f}$",
+            transform=ax.transAxes,
+            ha="right",
+            va="top",
+            fontsize=9,
+            bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+        )
+
+        # Format axes
+        format_axis(
+            ax,
+            xlabel="Anthropic AI Usage Index (usage % / working-age population %)",
+            ylabel="Occupation group share (%)",
+            title=soc_group,
+            xlabel_size=10,
+            ylabel_size=10,
+            grid=False,
+        )
+        ax.grid(True, alpha=0.3)
+
+    # Add legend
+    handles, labels = axes_flat[0].get_legend_handles_labels()
+    if handles:
+        # Create new handles with consistent size for legend only
+        # This doesn't modify the actual plot markers
+        legend_handles = []
+        for handle in handles:
+            # Get the color from the original handle
+            color = (
+                handle.get_facecolor()[0]
+                if hasattr(handle, "get_facecolor")
+                else "gray"
+            )
+            # Create a Line2D object with circle marker for legend
+            new_handle = Line2D(
+                [0],
+                [0],
+                marker="o",
+                color="w",
+                markerfacecolor=color,
+                markersize=8,
+                markeredgecolor="black",
+                markeredgewidth=0.5,
+                alpha=0.6,
+            )
+            legend_handles.append(new_handle)
+
+        # Position tier legend centered under the left column with vertical layout
+        fig.legend(
+            legend_handles,
+            labels,
+            title="Anthropic AI Usage Index tier",
+            loc="upper center",
+            bbox_to_anchor=(0.25, -0.03),
+            frameon=True,
+            fancybox=True,
+            shadow=True,
+            ncol=2,
+            borderpad=0.6,
+        )
+
+    # Add size legend using actual scatter points for perfect matching
+    reference_counts = [100, 1000, 10000]
+
+    # Create invisible scatter points with the exact same size formula as the plot
+    size_legend_elements = []
+    for count in reference_counts:
+        # Use exact same formula as in the plot
+        size = np.sqrt(count) * 2
+        # Create scatter on first axis (will be invisible) just for legend
+        scatter = axes_flat[0].scatter(
+            [],
+            [],  # Empty data
+            s=size,
+            c="gray",
+            alpha=0.6,
+            edgecolors="black",
+            linewidth=0.5,
+            label=f"{count:,}",
+        )
+        size_legend_elements.append(scatter)
+
+    # Add size legend centered under the right column with vertical layout
+    fig.legend(
+        handles=size_legend_elements,
+        title="Claude usage count",
+        loc="upper center",
+        bbox_to_anchor=(0.75, -0.03),
+        frameon=True,
+        fancybox=True,
+        shadow=True,
+        ncol=1,
+        borderpad=0.6,
+    )
+
+    plt.tight_layout(rect=[0, -0.03, 1, 0.98])
+    return fig
+
+
+def collaboration_task_regression(df, geography="country"):
+    """
+    Analyze automation vs augmentation patterns controlling for task mix for
+    geographies that meet the minimum observation threshold.
+
+    Uses global task weights to calculate expected automation for each geography,
+    then compares actual vs expected automation.
+
+    Note: Includes "none" tasks in calculations since they have automation/augmentation
+    patterns in the data. Excludes "not_classified" tasks which lack collaboration data.
+
+    Args:
+        df: Input dataframe
+        geography: "country" or "state_us"
+    """
+    # Filter to geographies that meet min observation threshold
+    filtered_countries, filtered_states = get_filtered_geographies(df)
+    filtered_geos = filtered_countries if geography == "country" else filtered_states
+
+    # Get collaboration automation data
+    df_automation = filter_df(
+        df,
+        facet="collaboration_automation_augmentation",
+        geography=geography,
+        variable="automation_pct",
+        geo_id=filtered_geos,
+    )[["geo_id", "value"]].rename(columns={"value": "automation_pct"})
+
+    # Get Anthropic AI Usage Index data
+    df_usage = filter_df(
+        df,
+        geography=geography,
+        facet=geography,
+        variable="usage_per_capita_index",
+        geo_id=filtered_geos,
+    )[["geo_id", "geo_name", "value"]].copy()
+    df_usage.rename(columns={"value": "usage_per_capita_index"}, inplace=True)
+
+    # Get geography-specific task weights (percentages)
+    df_geo_tasks = filter_df(
+        df,
+        facet="onet_task",
+        geography=geography,
+        variable="onet_task_pct",
+        geo_id=filtered_geos,
+    ).copy()
+
+    # Exclude not_classified and none tasks
+    df_geo_tasks = df_geo_tasks[
+        ~df_geo_tasks["cluster_name"].isin(["not_classified", "none"])
+    ]
+
+    # Get global task-specific collaboration patterns (only available at global level)
+    df_task_collab = filter_df(
+        df,
+        facet="onet_task::collaboration",
+        geography="global",
+        geo_id="GLOBAL",
+        variable="onet_task_collaboration_pct",
+    ).copy()
+
+    # Parse task name and collaboration type from cluster_name
+    df_task_collab["task_name"] = df_task_collab["cluster_name"].str.split("::").str[0]
+    df_task_collab["collab_type"] = (
+        df_task_collab["cluster_name"].str.split("::").str[1]
+    )
+
+    # Map collaboration types to automation/augmentation
+    # Automation: directive, feedback loop
+    # Augmentation: validation, task iteration, learning
+    # Excluded: none, not_classified
+    def is_automation(collab_type):
+        if collab_type in ["directive", "feedback loop"]:
+            return True
+        elif collab_type in [
+            "validation",
+            "task iteration",
+            "learning",
+        ]:
+            return False
+        else:  # none, not_classified
+            return None
+
+    df_task_collab["is_automation"] = df_task_collab["collab_type"].apply(is_automation)
+
+    # Exclude not_classified tasks upfront
+    df_task_collab_valid = df_task_collab[
+        df_task_collab["task_name"] != "not_classified"
+    ]
+
+    # Calculate automation percentage for each task
+    task_automation_rates = {}
+    for task_name in df_task_collab_valid["task_name"].unique():
+        task_data = df_task_collab_valid[
+            (df_task_collab_valid["task_name"] == task_name)
+            & (df_task_collab_valid["is_automation"].notna())
+        ]
+
+        # Skip tasks that only have "not_classified" collaboration types
+        if task_data.empty or task_data["value"].sum() == 0:
+            continue
+
+        automation_sum = task_data[task_data["is_automation"]]["value"].sum()
+        total_sum = task_data["value"].sum()
+        task_automation_rates[task_name] = (automation_sum / total_sum) * 100
+
+    # Calculate expected automation for each country using its own task weights
+    expected_automation = []
+    geo_ids = []
+
+    for geo_id in filtered_geos:
+        # Get this geography's task distribution (excluding not_classified)
+        geo_tasks = df_geo_tasks[
+            (df_geo_tasks["geo_id"] == geo_id)
+            & (df_geo_tasks["cluster_name"] != "not_classified")
+        ]
+
+        # Skip geographies with no task data
+        if geo_tasks.empty:
+            continue
+
+        # Calculate weighted automation using geography's task weights
+        weighted_auto = 0.0
+        total_weight = 0.0
+
+        for _, row in geo_tasks.iterrows():
+            task = row["cluster_name"]
+            weight = row["value"]  # Already in percentage
+
+            # Get automation rate for this task (from global data)
+            if task in task_automation_rates:
+                auto_rate = task_automation_rates[task]
+                weighted_auto += weight * auto_rate
+                total_weight += weight
+
+        # Calculate expected automation
+        expected_auto = weighted_auto / total_weight
+        expected_automation.append(expected_auto)
+        geo_ids.append(geo_id)
+
+    # Create dataframe with expected automation
+    df_expected = pd.DataFrame(
+        {"geo_id": geo_ids, "expected_automation_pct": expected_automation}
+    )
+
+    # Merge all data
+    df_regression = df_automation.merge(df_expected, on="geo_id", how="inner")
+    df_regression = df_regression.merge(df_usage, on="geo_id", how="inner")
+
+    # Count unique tasks for reporting
+    n_tasks = len(task_automation_rates)
+
+    # Calculate residuals from regressions for proper partial correlation
+    # For automation, regress actual on expected to get residuals
+    X_expected = sm.add_constant(df_regression["expected_automation_pct"])
+    model_automation = sm.OLS(df_regression["automation_pct"], X_expected)
+    results_automation = model_automation.fit()
+    df_regression["automation_residuals"] = results_automation.resid
+
+    # For usage, regress on expected automation to get residuals
+    model_usage = sm.OLS(df_regression["usage_per_capita_index"], X_expected)
+    results_usage = model_usage.fit()
+    df_regression["usage_residuals"] = results_usage.resid
+
+    # Partial regression is regression of residuals
+    # We want usage (X) to explain automation (Y)
+    X_partial = sm.add_constant(df_regression["usage_residuals"])
+    model_partial = sm.OLS(df_regression["automation_residuals"], X_partial)
+    results_partial = model_partial.fit()
+    partial_slope = results_partial.params.iloc[1]
+    partial_r2 = results_partial.rsquared
+    partial_p = results_partial.pvalues.iloc[1]
+
+    # Create visualization - only show partial correlation
+    fig, ax = create_figure(figsize=(10, 8))
+
+    # Define colormap for automation residuals
+    colors_automation = [AUGMENTATION_COLOR, AUTOMATION_COLOR]
+    cmap_automation = LinearSegmentedColormap.from_list(
+        "automation", colors_automation, N=100
+    )
+
+    # Plot partial correlation
+    # Create colormap normalization for automation residuals
+    norm = plt.Normalize(
+        vmin=df_regression["automation_residuals"].min(),
+        vmax=df_regression["automation_residuals"].max(),
+    )
+
+    # Plot invisible points to ensure matplotlib's autoscaling includes all data points
+    ax.scatter(
+        df_regression["usage_residuals"],
+        df_regression["automation_residuals"],
+        s=0,  # invisible points for autoscaling
+        alpha=0,
+    )
+
+    # Plot country geo_id values as text instead of scatter points
+    for _, row in df_regression.iterrows():
+        color_val = norm(row["automation_residuals"])
+        text_color = cmap_automation(color_val)
+
+        ax.text(
+            row["usage_residuals"],
+            row["automation_residuals"],
+            row["geo_id"],
+            fontsize=7,
+            ha="center",
+            va="center",
+            color=text_color,
+            alpha=0.9,
+            weight="bold",
+        )
+
+    # Create a ScalarMappable for the colorbar
+    scalar_mappable = plt.cm.ScalarMappable(cmap=cmap_automation, norm=norm)
+    scalar_mappable.set_array([])
+
+    # Add regression line using actual regression results
+    # OLS model: automation_residuals = intercept + slope * usage_residuals
+    x_resid_line = np.linspace(
+        df_regression["usage_residuals"].min(),
+        df_regression["usage_residuals"].max(),
+        100,
+    )
+    intercept = results_partial.params.iloc[0]
+    y_resid_line = intercept + partial_slope * x_resid_line
+    ax.plot(
+        x_resid_line,
+        y_resid_line,
+        "grey",
+        linestyle="--",
+        linewidth=2,
+        alpha=0.7,
+    )
+
+    # Set axis labels and title
+    format_axis(
+        ax,
+        xlabel="Anthropic AI Usage Index residuals\n(per capita usage not explained by task mix)",
+        ylabel="Automation % residuals\n(automation not explained by task mix)",
+        title="Relationship between Anthropic AI Usage Index and automation",
+        grid=False,
+    )
+
+    # Add correlation info inside the plot
+    if partial_p < 0.001:
+        p_str = "p < 0.001"
+    else:
+        p_str = f"p = {partial_p:.3f}"
+
+    ax.text(
+        0.08,
+        0.975,
+        f"Partial regression (controlling for task mix): $\\beta = {partial_slope:.3f}, R^2 = {partial_r2:.3f}\\ ({p_str})$",
+        transform=ax.transAxes,
+        fontsize=10,
+        bbox=dict(boxstyle="round", facecolor="white", alpha=0.8),
+        verticalalignment="top",
+    )
+
+    ax.axhline(y=0, color="gray", linestyle=":", linewidth=1, alpha=0.3)
+    ax.axvline(x=0, color="gray", linestyle=":", linewidth=1, alpha=0.3)
+    ax.grid(True, alpha=0.3, linestyle="--")
+
+    # Add colorbar
+    fig.subplots_adjust(right=0.92)
+    cbar_ax = fig.add_axes([0.94, 0.2, 0.02, 0.6])
+    cbar = plt.colorbar(scalar_mappable, cax=cbar_ax)
+    cbar.set_label("Automation % residuals", fontsize=10, rotation=270, labelpad=15)
+
+    # Adjust plot to make room for titles and ensure all data is visible
+    plt.subplots_adjust(top=0.92, right=0.92, left=0.12, bottom=0.12)
+
+    # Return results
+    return {
+        "figure": fig,
+        "partial_slope": partial_slope,
+        "partial_r2": partial_r2,
+        "partial_pvalue": partial_p,
+        "n_countries": len(df_regression),
+        "n_tasks": n_tasks,
+        "df_residuals": df_regression,
+    }
+
+
+def plot_automation_preference_residuals(df, geography="country", figsize=(14, 12)):
+    """Plot automation vs augmentation preference after controlling for task mix.
+
+    For geographies meeting minimum observation threshold only.
+
+    Args:
+        df: Input dataframe
+        geography: "country" or "state_us"
+        figsize: Figure size
+    """
+    # First run the collaboration analysis to get residuals
+    results = collaboration_task_regression(df, geography=geography)
+
+    # Suppress figure created by collaboration_task_regression
+    plt.close(results["figure"])
+
+    # Get the dataframe with residuals
+    df_residuals = results["df_residuals"]
+
+    # Sort by automation residuals (most augmentation to most automation)
+    df_plot = df_residuals.sort_values("automation_residuals", ascending=True)
+
+    # Adjust figure size based on number of geographies
+    n_geos = len(df_plot)
+    fig_height = max(8, n_geos * 0.25)
+    fig, ax = create_figure(figsize=(figsize[0], fig_height))
+
+    # Create color map
+    colors = [
+        AUGMENTATION_COLOR if x < 0 else AUTOMATION_COLOR
+        for x in df_plot["automation_residuals"]
+    ]
+
+    # Create horizontal bar chart
+    ax.barh(
+        range(len(df_plot)),
+        df_plot["automation_residuals"].values,
+        color=colors,
+        alpha=0.8,
+    )
+
+    # Set y-axis labels with geography names only
+    y_labels = [row["geo_name"] for _, row in df_plot.iterrows()]
+    ax.set_yticks(range(len(df_plot)))
+    ax.set_yticklabels(y_labels, fontsize=7)
+
+    # Reduce white space at top and bottom
+    ax.set_ylim(-0.5, len(df_plot) - 0.5)
+
+    # Add vertical line at zero
+    ax.axvline(x=0, color="black", linestyle="-", linewidth=1, alpha=0.7)
+
+    # Labels and title
+    geo_label = "Countries'" if geography == "country" else "States'"
+    format_axis(
+        ax,
+        xlabel="Automation % residual (after controlling for task mix)",
+        ylabel="",
+        title=f"{geo_label} automation vs augmentation preference\n(after controlling for task composition)",
+        grid=False,
+    )
+
+    # Add grid
+    ax.grid(True, axis="x", alpha=0.3, linestyle="--")
+
+    # Add value labels on the bars
+    for i, (_, row) in enumerate(df_plot.iterrows()):
+        value = row["automation_residuals"]
+        x_offset = 0.2 if abs(value) < 5 else 0.3
+        x_pos = value + (x_offset if value > 0 else -x_offset)
+        ax.text(
+            x_pos,
+            i,
+            f"{value:.1f}",
+            ha="left" if value > 0 else "right",
+            va="center",
+            fontsize=8,
+        )
+
+    # Add annotations
+    y_range = ax.get_ylim()
+    annotation_y = y_range[1] * 0.85
+
+    # Left annotation for augmentation
+    ax.text(
+        ax.get_xlim()[0] * 0.7,
+        annotation_y,
+        "Prefer augmentation",
+        fontsize=9,
+        color=AUGMENTATION_COLOR,
+        fontweight="bold",
+        ha="left",
+        va="center",
+    )
+
+    # Right annotation for automation
+    ax.text(
+        ax.get_xlim()[1] * 0.7,
+        annotation_y,
+        "Prefer automation",
+        fontsize=9,
+        color=AUTOMATION_COLOR,
+        fontweight="bold",
+        ha="right",
+        va="center",
+    )
+
+    plt.tight_layout()
+
+    return fig
+
+
+def plot_soc_distribution(
+    df, geo_list, geography, figsize=(14, 10), title=None, exclude_not_classified=True
+):
+    """
+    Plot SOC occupation distribution for multiple geographies (countries or states) with horizontal bars, colored by tier.
+
+    Args:
+        df: Long format dataframe
+        geo_list: List of geo_id values to compare (e.g., ['USA', 'BRA'] for countries or ['CA', 'TX'] for states)
+        geography: Geographic level ('country' or 'state_us')
+        figsize: Figure size
+        title: Chart title
+        exclude_not_classified: If True, excludes 'not_classified' from the chart
+    """
+    # Use global tier colors and names
+    tier_colors = TIER_COLORS_NUMERIC
+    tier_names = TIER_NAMES_NUMERIC
+
+    # Get usage tier and geo_name for each geography
+    tier_data = filter_df(
+        df, geography=geography, variable="usage_tier", facet=geography, geo_id=geo_list
+    )[["geo_id", "geo_name", "value"]].rename(columns={"value": "tier"})
+
+    # Collect SOC data for all geographies first to determine consistent ordering
+    all_soc_data = []
+    for geo_id in geo_list:
+        geo_soc = filter_df(
+            df,
+            geography=geography,
+            geo_id=geo_id,
+            facet="soc_occupation",
+            variable="soc_pct",
+        ).copy()
+
+        if not geo_soc.empty:
+            # Optionally filter out not_classified
+            if exclude_not_classified:
+                geo_soc = geo_soc[geo_soc["cluster_name"] != "not_classified"].copy()
+
+            geo_soc["geo"] = geo_id
+            all_soc_data.append(geo_soc)
+
+    combined_data = pd.concat(all_soc_data)
+
+    # Use global SOC distribution for countries, USA distribution for states
+    if geography == "country":
+        reference_data = filter_df(
+            df,
+            geography="global",
+            geo_id="GLOBAL",
+            facet="soc_occupation",
+            variable="soc_pct",
+        )
+    else:  # state_us
+        reference_data = filter_df(
+            df,
+            geography="country",
+            geo_id="USA",
+            facet="soc_occupation",
+            variable="soc_pct",
+        )
+
+    # Filter out not_classified from reference data if needed
+    if exclude_not_classified:
+        reference_data = reference_data[
+            reference_data["cluster_name"] != "not_classified"
+        ]
+
+    # Sort by reference values ascending so highest appears at top when plotted
+    soc_order = reference_data.sort_values("value", ascending=True)[
+        "cluster_name"
+    ].tolist()
+
+    # Create figure
+    fig, ax = create_figure(figsize=figsize)
+
+    # Width of bars and positions
+    n_geos = len(geo_list)
+    bar_width = 0.95 / n_geos  # Wider bars, less spacing within groups
+    y_positions = (
+        np.arange(len(soc_order)) * 1.05
+    )  # Reduce spacing between SOC groups to 5%
+
+    # Sort geo_list to ensure highest tier appears at top within each group
+    # Reverse the order so tier 4 is plotted first and appears on top
+    geo_tier_map = dict(zip(tier_data["geo_id"], tier_data["tier"], strict=True))
+    geo_list_sorted = sorted(geo_list, key=lambda x: geo_tier_map[x])
+
+    # Plot bars for each geography
+    for i, geo_id in enumerate(geo_list_sorted):
+        geo_data = filter_df(combined_data, geo=geo_id)
+        geo_name = filter_df(tier_data, geo_id=geo_id)["geo_name"].iloc[0]
+        geo_tier = filter_df(tier_data, geo_id=geo_id)["tier"].iloc[0]
+
+        # Get values in the right order
+        values = []
+        for soc in soc_order:
+            val_data = filter_df(geo_data, cluster_name=soc)["value"]
+            # Use NaN for missing data
+            values.append(val_data.iloc[0] if not val_data.empty else float("nan"))
+
+        # Determine color based on tier
+        color = tier_colors[int(geo_tier)]
+
+        # Create bars with offset for multiple geographies
+        # Reverse the offset calculation so first geo (lowest tier) goes to bottom
+        offset = ((n_geos - 1 - i) - n_geos / 2 + 0.5) * bar_width
+
+        # Get tier name for label
+        tier_label = tier_names[int(geo_tier)]
+        label_text = f"{geo_name} ({tier_label})"
+
+        bars = ax.barh(
+            y_positions + offset,
+            values,
+            bar_width,
+            label=label_text,
+            color=color,
+            alpha=0.8,
+        )
+
+        # Add value labels for bars with data
+        for bar, value in zip(bars, values, strict=True):
+            if not pd.isna(value):
+                ax.text(
+                    value + 0.1,
+                    bar.get_y() + bar.get_height() / 2,
+                    f"{value:.1f}%",
+                    va="center",
+                    fontsize=5,
+                )
+
+    # Set y-axis labels - position them at the center of each SOC group
+    ax.set_yticks(y_positions)
+    ax.set_yticklabels(soc_order, fontsize=9, va="center")
+
+    # Reduce white space at top and bottom
+    ax.set_ylim(y_positions[0] - 0.5, y_positions[-1] + 0.5)
+
+    # Customize plot
+    format_axis(
+        ax,
+        xlabel="Share of Claude task usage (%)",
+        ylabel="Standard Occupation Classification group",
+        grid=False,
+    )
+
+    if title is None:
+        title = "Claude task usage by occupation: Comparison by AI usage tier"
+    format_axis(ax, title=title, title_size=14, grid=False)
+
+    # Add legend
+    ax.legend(loc="lower right", fontsize=10, framealpha=0.95)
+
+    # Grid
+    ax.grid(True, axis="x", alpha=0.3, linestyle="--")
+    ax.set_xlim(0, max(combined_data["value"]) * 1.15)
+
+    plt.tight_layout()
+    return fig

release_2025_09_15/code/aei_report_v3_analysis_1p_api.ipynb

@@ -0,0 +1,315 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AEI Report v3 API Analysis\n",
+    "This notebook produces the streamlined analysis for the AEI report API chapter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Import the analysis functions\n",
+    "from aei_analysis_functions_1p_api import (\n",
+    "    setup_plot_style,\n",
+    "    load_preprocessed_data,\n",
+    "    create_top_requests_bar_chart,\n",
+    "    create_platform_occupational_comparison,\n",
+    "    create_platform_lorenz_curves,\n",
+    "    create_collaboration_alluvial,\n",
+    "    create_automation_augmentation_panel,\n",
+    "    create_token_output_bar_chart,\n",
+    "    create_completion_vs_input_tokens_scatter,\n",
+    "    create_occupational_usage_cost_scatter,\n",
+    "    create_partial_regression_plot,\n",
+    "    perform_usage_share_regression_unweighted,\n",
+    "    create_btos_ai_adoption_chart,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set matplotlib to use the correct backend and style\n",
+    "setup_plot_style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set up output directory for saving figures\n",
+    "output_dir = Path(\"../data/output/figures/\")\n",
+    "btos_data_path = Path(\"../data/input/BTOS_National.xlsx\")\n",
+    "api_data_path = Path(\"../data/intermediate/aei_raw_1p_api_2025-08-04_to_2025-08-11.csv\")\n",
+    "cai_data_path = Path(\n",
+    "    \"../data/intermediate/aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv\"\n",
+    ")\n",
+    "\n",
+    "# Create output directory\n",
+    "output_dir.mkdir(parents=True, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Load BTOS Data\n",
+    "print(\"Loading BTOS data...\")\n",
+    "btos_df = pd.read_excel(btos_data_path, sheet_name=\"Response Estimates\")\n",
+    "btos_df_ref_dates_df = pd.read_excel(\n",
+    "    btos_data_path, sheet_name=\"Collection and Reference Dates\"\n",
+    ")\n",
+    "\n",
+    "# Load the API data\n",
+    "print(\"Loading API data...\")\n",
+    "api_df = load_preprocessed_data(api_data_path)\n",
+    "\n",
+    "# Load the Claude.ai data\n",
+    "print(\"Loading Claude.ai data...\")\n",
+    "cai_df = load_preprocessed_data(cai_data_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "create_btos_ai_adoption_chart(btos_df, btos_df_ref_dates_df, output_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the top requests bar chart\n",
+    "print(\"Creating top requests bar chart...\")\n",
+    "top_requests_chart = create_top_requests_bar_chart(api_df, output_dir)\n",
+    "print(f\"Chart saved to: {top_requests_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the platform occupational comparison chart\n",
+    "print(\"Creating platform occupational comparison chart...\")\n",
+    "occupational_comparison_chart = create_platform_occupational_comparison(\n",
+    "    api_df, cai_df, output_dir\n",
+    ")\n",
+    "print(f\"Chart saved to: {occupational_comparison_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the platform Lorenz curves\n",
+    "print(\"Creating platform Lorenz curves...\")\n",
+    "lorenz_curves_chart = create_platform_lorenz_curves(api_df, cai_df, output_dir)\n",
+    "print(f\"Chart saved to: {lorenz_curves_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the collaboration alluvial diagram\n",
+    "print(\"Creating collaboration alluvial diagram...\")\n",
+    "alluvial_chart = create_collaboration_alluvial(api_df, cai_df, output_dir)\n",
+    "print(f\"Chart saved to: {alluvial_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the automation vs augmentation panel\n",
+    "print(\"Creating automation vs augmentation panel...\")\n",
+    "automation_panel_chart = create_automation_augmentation_panel(\n",
+    "    api_df, cai_df, output_dir\n",
+    ")\n",
+    "print(f\"Chart saved to: {automation_panel_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the token output bar chart\n",
+    "print(\"Creating token output bar chart...\")\n",
+    "token_output_chart = create_token_output_bar_chart(api_df, output_dir)\n",
+    "print(f\"Chart saved to: {token_output_chart}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the completion vs input tokens scatter plot\n",
+    "print(\"Creating completion vs input tokens scatter plot...\")\n",
+    "completion_input_scatter = create_completion_vs_input_tokens_scatter(api_df, output_dir)\n",
+    "print(f\"Chart saved to: {completion_input_scatter}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the occupational usage vs cost scatter plot\n",
+    "print(\"Creating occupational usage vs cost scatter plot...\")\n",
+    "usage_cost_scatter = create_occupational_usage_cost_scatter(api_df, output_dir)\n",
+    "print(f\"Chart saved to: {usage_cost_scatter}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create the partial regression plot\n",
+    "print(\"Creating partial regression plot...\")\n",
+    "partial_plot, regression_results = create_partial_regression_plot(\n",
+    "    api_df, cai_df, output_dir\n",
+    ")\n",
+    "print(f\"Chart saved to: {partial_plot}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Perform the unweighted usage share regression analysis\n",
+    "print(\"Performing unweighted usage share regression analysis...\")\n",
+    "regression_model = perform_usage_share_regression_unweighted(api_df, cai_df, output_dir)\n",
+    "regression_model.summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Coconut",
+   "language": "coconut",
+   "name": "coconut"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "python",
+    "version": 3
+   },
+   "file_extension": ".coco",
+   "mimetype": "text/x-python3",
+   "name": "coconut",
+   "pygments_lexer": "coconut",
+   "version": "3.0.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

release_2025_09_15/code/aei_report_v3_analysis_claude_ai.ipynb

@@ -0,0 +1,868 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AEI Report v3 Claude.ai Analysis\n",
+    "\n",
+    "This notebook performs statistical analysis and creates visualizations from enriched Clio data.\n",
+    "It works directly with long format data from the preprocessing pipeline.\n",
+    "\n",
+    "**Input**: `aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv`\n",
+    "\n",
+    "**Output**: Visualizations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Setup and Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Import all analysis functions\n",
+    "from aei_analysis_functions_claude_ai import (\n",
+    "    setup_plot_style,\n",
+    "    get_filtered_geographies,\n",
+    "    plot_usage_index_bars,\n",
+    "    plot_tier_map,\n",
+    "    plot_usage_share_bars,\n",
+    "    plot_tier_summary_table,\n",
+    "    plot_gdp_scatter,\n",
+    "    plot_request_comparison_cards,\n",
+    "    plot_soc_usage_scatter,\n",
+    "    plot_dc_task_request_cards,\n",
+    "    collaboration_task_regression,\n",
+    "    plot_usage_index_histogram,\n",
+    "    plot_variable_map,\n",
+    "    plot_soc_distribution,\n",
+    "    plot_automation_preference_residuals,\n",
+    "    plot_variable_bars,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set matplotlib to use the correct backend and style\n",
+    "setup_plot_style()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Set up output directory for saving figures\n",
+    "output_dir = Path(\"../data/output/figures/\")\n",
+    "output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "output_dir_app = Path(\"../data/output/figures/appendix/\")\n",
+    "output_dir_app.mkdir(parents=True, exist_ok=True)\n",
+    "\n",
+    "# Load enriched data\n",
+    "data_path = \"../data/output/aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv\"\n",
+    "\n",
+    "# Load the data - use keep_default_na=False to preserve \"NA\" (Namibia) as string\n",
+    "df = pd.read_csv(data_path, keep_default_na=False, na_values=[\"\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Filter countries to those with at least 200 observations\n",
+    "# Filter US states to those with at least 100 observations\n",
+    "filtered_countries, filtered_states = get_filtered_geographies(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 Global"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Top countries by share of global usage\n",
+    "plot_usage_share_bars(\n",
+    "    df,\n",
+    "    geography=\"country\",\n",
+    "    top_n=30,\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"usage_pct_bar_country_top30.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create world map showing usage tiers\n",
+    "plot_tier_map(\n",
+    "    df,\n",
+    "    geography=\"country\",\n",
+    "    title=\"Anthropic AI Usage Index tiers by country\",\n",
+    "    figsize=(16, 10),\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_tier_map_country_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create tier summary table for countries\n",
+    "plot_tier_summary_table(df, geography=\"country\")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"tier_summary_table_country.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    "    transparent=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Top countries by usage per capita\n",
+    "plot_usage_index_bars(\n",
+    "    df, geography=\"country\", top_n=20, filtered_entities=filtered_countries\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_bar_country_top20.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# GDP vs usage regression for countries\n",
+    "plot_gdp_scatter(df, geography=\"country\", filtered_entities=filtered_countries)\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_gdp_reg_country_min_obs.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# GDP vs usage regression for countries\n",
+    "plot_gdp_scatter(\n",
+    "    df, geography=\"country\", filtered_entities=filtered_countries, figsize=(13.2, 8.25)\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_gdp_reg_country_min_obs_wide.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create SOC diffusion scatter plot with top 4 classified SOC groups (2x2 grid)\n",
+    "plot_soc_usage_scatter(df, geography=\"country\")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"soc_usage_scatter_top4_country_min.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the highest usage country in each tier (1-4)\n",
+    "\n",
+    "# Get usage tier and usage count data for all countries\n",
+    "tier_data = df[\n",
+    "    (df[\"geography\"] == \"country\")\n",
+    "    & (df[\"variable\"] == \"usage_tier\")\n",
+    "    & (df[\"facet\"] == \"country\")\n",
+    "][[\"geo_id\", \"value\"]].rename(columns={\"value\": \"tier\"})\n",
+    "\n",
+    "usage_data = df[\n",
+    "    (df[\"geography\"] == \"country\")\n",
+    "    & (df[\"variable\"] == \"usage_count\")\n",
+    "    & (df[\"facet\"] == \"country\")\n",
+    "][[\"geo_id\", \"geo_name\", \"value\"]].rename(columns={\"value\": \"usage_count\"})\n",
+    "\n",
+    "# Merge tier and usage data\n",
+    "country_data = usage_data.merge(tier_data, on=\"geo_id\")\n",
+    "\n",
+    "selected_countries = [\n",
+    "    country_data[country_data[\"tier\"] == tier]\n",
+    "    .sort_values(\"usage_count\", ascending=False)\n",
+    "    .iloc[0][\"geo_id\"]\n",
+    "    for tier in [4, 3, 2, 1]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Compare top overrepresented requests for 4 highest usage countries in each tier\n",
+    "plot_request_comparison_cards(\n",
+    "    df,\n",
+    "    geo_ids=selected_countries,\n",
+    "    top_n=5,\n",
+    "    title=\"Top overrepresented requests for the United States, Brazil, Vietnam and India\",\n",
+    "    geography=\"country\",\n",
+    ")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    output_dir / \"request_comparison_cards_by_tier_country_selected4.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. United States"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# State tier map\n",
+    "plot_tier_map(\n",
+    "    df, geography=\"state_us\", title=\"Anthropic AI Usage Index tier by US state\"\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_tier_map_state_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Top 20 US states\n",
+    "plot_usage_index_bars(\n",
+    "    df,\n",
+    "    geography=\"state_us\",\n",
+    "    top_n=20,\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"ai_usage_index_bar_state_top20.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create tier summary table for US states\n",
+    "plot_tier_summary_table(df, geography=\"state_us\")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"tier_summary_table_state.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    "    transparent=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Find the highest usage US state in each tier (1-4)\n",
+    "\n",
+    "# Get usage tier and usage count data for US states\n",
+    "tier_data_states = df[\n",
+    "    (df[\"geography\"] == \"state_us\")\n",
+    "    & (df[\"variable\"] == \"usage_tier\")\n",
+    "    & (df[\"facet\"] == \"state_us\")\n",
+    "][[\"geo_id\", \"value\"]].rename(columns={\"value\": \"tier\"})\n",
+    "\n",
+    "usage_data_states = df[\n",
+    "    (df[\"geography\"] == \"state_us\")\n",
+    "    & (df[\"variable\"] == \"usage_count\")\n",
+    "    & (df[\"facet\"] == \"state_us\")\n",
+    "][[\"geo_id\", \"geo_name\", \"value\"]].rename(columns={\"value\": \"usage_count\"})\n",
+    "\n",
+    "# Merge tier and usage data\n",
+    "state_data = usage_data_states.merge(tier_data_states, on=\"geo_id\")\n",
+    "\n",
+    "# Find the highest usage state in each tier\n",
+    "selected_states = [\n",
+    "    state_data[state_data[\"tier\"] == tier]\n",
+    "    .sort_values(\"usage_count\", ascending=False)\n",
+    "    .iloc[0][\"geo_id\"]\n",
+    "    for tier in [4, 3, 2, 1]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Compare top overrepresented requests for US states representing each tier\n",
+    "# CA (Tier 4), TX (Tier 3), FL (Tier 2), SC (Tier 1)\n",
+    "states_to_compare = [\"CA\", \"TX\", \"FL\", \"SC\"]\n",
+    "\n",
+    "plot_request_comparison_cards(\n",
+    "    df,\n",
+    "    geo_ids=states_to_compare,\n",
+    "    top_n=5,\n",
+    "    title=\"Top overrepresented high-level requests for California, Texas, Florida and South Carolina\",\n",
+    "    geography=\"state_us\",\n",
+    ")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    output_dir / \"request_comparison_cards_by_tier_state_selected4.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create card-style visualization for Washington DC\n",
+    "# Shows top O*NET tasks and top request categories\n",
+    "plot_dc_task_request_cards(\n",
+    "    df, title=\"Washington, DC: Highest Anthropic AI Usage Index in the US\"\n",
+    ")\n",
+    "\n",
+    "plt.savefig(\n",
+    "    output_dir / \"task_request_comparison_state_dc.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Collaboration pattern analysis with task mix control\n",
+    "# This analysis determines whether the relationship between AUI\n",
+    "# and automation preference persists after controlling for task composition\n",
+    "collaboration_task_regression(df, geography=\"country\")\n",
+    "plt.savefig(\n",
+    "    output_dir / \"collaboration_task_control_partial_corr_country.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appendix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Global"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Distribution histogram\n",
+    "plot_usage_index_histogram(\n",
+    "    df, geography=\"country\", title=\"Distribution of Anthropic AI Usage Index\"\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_histogram_country_all.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create map showing share of usage\n",
+    "plot_variable_map(\n",
+    "    df,\n",
+    "    variable=\"usage_pct\",\n",
+    "    geography=\"country\",\n",
+    "    title=\"Share of global Claude usage by country\",\n",
+    "    figsize=(14, 8),\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"usage_pct_map_country_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create world map showing usage per capita\n",
+    "plot_variable_map(\n",
+    "    df,\n",
+    "    variable=\"usage_per_capita_index\",\n",
+    "    geography=\"country\",\n",
+    "    title=\"Anthropic AI Usage Index by country\",\n",
+    "    center_at_one=True,\n",
+    "    figsize=(14, 8),\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_map_country_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# AUI for all countries\n",
+    "plot_usage_index_bars(\n",
+    "    df,\n",
+    "    geography=\"country\",\n",
+    "    filtered_entities=filtered_countries,\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_country_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# SOC distribution comparison for countries by usage tier\n",
+    "plot_soc_distribution(\n",
+    "    df,\n",
+    "    selected_countries,\n",
+    "    \"country\",\n",
+    "    title=\"Occupation groups by Claude task usage in the United States, Brazil, Vietnam and India\",\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"soc_distribution_by_tier_country_selected4.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Plot automation preference residuals after controlling for task mix\n",
+    "# This shows which countries prefer more automation vs augmentation\n",
+    "# than would be expected given their task composition\n",
+    "plot_automation_preference_residuals(df)\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"automation_preference_residuals.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## United States"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Top countries by share of global usage\n",
+    "plot_usage_share_bars(\n",
+    "    df,\n",
+    "    geography=\"state_us\",\n",
+    "    top_n=30,\n",
+    "    title=\"Top 30 US states by share of US Claude usage\",\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"usage_pct_bar_state_top30.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Distribution histogram\n",
+    "plot_usage_index_histogram(\n",
+    "    df, geography=\"state_us\", title=\"Distribution of Anthropic AI Usage Index\"\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_histogram_state_all.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create map showing share of usage\n",
+    "plot_variable_map(\n",
+    "    df,\n",
+    "    variable=\"usage_pct\",\n",
+    "    geography=\"state_us\",\n",
+    "    title=\"Share of global Claude usage by US state\",\n",
+    "    figsize=(14, 8),\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"usage_pct_map_state_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create map showing per capita usage\n",
+    "plot_variable_map(\n",
+    "    df,\n",
+    "    variable=\"usage_per_capita_index\",\n",
+    "    geography=\"state_us\",\n",
+    "    title=\"Anthropic AI Usage Index by US state\",\n",
+    "    center_at_one=True,\n",
+    "    figsize=(14, 8),\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_map_state_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plot_usage_index_bars(\n",
+    "    df,\n",
+    "    geography=\"state_us\",\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_bar_state_all.png\", dpi=300, bbox_inches=\"tight\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# GDP vs usage regression for US states\n",
+    "plot_gdp_scatter(df, geography=\"state_us\", filtered_entities=filtered_states)\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"ai_usage_index_gdp_reg_state_min_obs.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# SOC distribution comparison for US states by usage tier\n",
+    "plot_soc_distribution(\n",
+    "    df,\n",
+    "    selected_states,\n",
+    "    \"state_us\",\n",
+    "    title=\"Occupation groups by Claude task usage in California, Texas, Florida and South Carolina\",\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"soc_distribution_by_tier_state_selected4.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Top SOC chart\n",
+    "plot_variable_bars(\n",
+    "    df,\n",
+    "    variable=\"soc_pct\",\n",
+    "    geography=\"country\",\n",
+    "    facet=\"soc_occupation\",\n",
+    "    geo_id=\"USA\",\n",
+    "    title=\"Occupation groups in the US by Claude use for associated tasks\",\n",
+    "    xlabel=\"Share of total usage (%)\",\n",
+    "    exclude_not_classified=True,\n",
+    ")\n",
+    "\n",
+    "# Save the figure\n",
+    "plt.savefig(output_dir_app / \"soc_bar_country_us.png\", dpi=300, bbox_inches=\"tight\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Create SOC diffusion scatter plot with top 4 classified SOC groups\n",
+    "plot_soc_usage_scatter(\n",
+    "    df,\n",
+    "    geography=\"state_us\",\n",
+    ")\n",
+    "plt.savefig(\n",
+    "    output_dir_app / \"soc_usage_scatter_top4_state_min.png\",\n",
+    "    dpi=300,\n",
+    "    bbox_inches=\"tight\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Coconut",
+   "language": "coconut",
+   "name": "coconut"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "python",
+    "version": 3
+   },
+   "file_extension": ".coco",
+   "mimetype": "text/x-python3",
+   "name": "coconut",
+   "pygments_lexer": "coconut",
+   "version": "3.0.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

release_2025_09_15/code/aei_report_v3_change_over_time_claude_ai.py

@@ -0,0 +1,564 @@
+#!/usr/bin/env python3
+"""
+Clean Economic Analysis Figure Generator
+======================================
+Generates three key figures for V1→V2→V3 economic analysis:
+1. Usage Share Trends Across Economic Index Reports
+2. Notable Task Changes (Growing/Declining Tasks)
+3. Automation vs Augmentation Evolution
+
+ASSUMPTIONS:
+- V1/V2/V3 use same task taxonomy
+- GLOBAL geo_id is representative
+- Missing values = 0% usage
+- Percentages don't need renormalization
+"""
+
+import os
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+# Use default matplotlib styling
+plt.style.use("default")
+
+# Configuration
+FILES = {
+    "v1_tasks": "../data/input/task_pct_v1.csv",
+    "v2_tasks": "../data/input/task_pct_v2.csv",
+    "v3_data": "../data/intermediate/aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv",
+    "v1_auto": "../data/input/automation_vs_augmentation_v1.csv",
+    "v2_auto": "../data/input/automation_vs_augmentation_v2.csv",
+    "onet": "../data/intermediate/onet_task_statements.csv",
+    "soc": "../data/intermediate/soc_structure.csv",
+}
+
+AUTOMATION_TYPES = ["directive", "feedback_loop"]
+AUGMENTATION_TYPES = ["validation", "task_iteration", "learning"]
+MIN_THRESHOLD = 1.0
+COLORS = {
+    "increase": "#2E8B57",
+    "decrease": "#CD5C5C",
+    "automation": "#FF6B6B",
+    "augmentation": "#4ECDC4",
+}
+
+# ============================================================================
+# DATA LOADING
+# ============================================================================
+
+
+def load_task_data(filepath, version_name):
+    """Load and validate task percentage data for any version."""
+    if not Path(filepath).exists():
+        raise FileNotFoundError(f"Missing {version_name} data: {filepath}")
+
+    df = pd.read_csv(filepath)
+
+    if version_name == "V3":
+        # Filter V3 data for global onet tasks
+        df = df[
+            (df["geo_id"] == "GLOBAL")
+            & (df["facet"] == "onet_task")
+            & (df["variable"] == "onet_task_pct")
+        ].copy()
+        df = df.rename(columns={"cluster_name": "task_name", "value": "pct"})
+
+        # Remove "not_classified" from V3 for fair comparison with V1/V2
+        # Keep "none" as it represents legitimate unclassifiable tasks across all versions
+        not_classified_pct = df[df["task_name"] == "not_classified"]["pct"].sum()
+        df = df[df["task_name"] != "not_classified"].copy()
+
+        # Renormalize V3 to 100% after removing not_classified
+        if not_classified_pct > 0:
+            remaining_total = df["pct"].sum()
+            normalization_factor = 100 / remaining_total
+            df["pct"] = df["pct"] * normalization_factor
+            print(
+                f"  → Removed {not_classified_pct:.1f}% not_classified, renormalized by {normalization_factor:.3f}x"
+            )
+
+    # Validate structure
+    if "task_name" not in df.columns or "pct" not in df.columns:
+        raise ValueError(f"{version_name} data missing required columns")
+
+    # Normalize task names and validate totals
+    df["task_name"] = df["task_name"].str.lower().str.strip()
+    total = df["pct"].sum()
+
+    if not (80 <= total <= 120):
+        warnings.warn(
+            f"{version_name} percentages sum to {total:.1f}% (expected ~100%)",
+            stacklevel=2,
+        )
+
+    print(f"✓ {version_name}: {len(df)} tasks, {total:.1f}% coverage")
+    return df[["task_name", "pct"]]
+
+
+def load_automation_data():
+    """Load automation/collaboration data for all versions."""
+    result = {}
+
+    # V1 and V2 - always renormalize to 100%
+    for version in ["v1", "v2"]:
+        df = pd.read_csv(FILES[f"{version}_auto"])
+
+        # Always renormalize to 100%
+        total = df["pct"].sum()
+        normalization_factor = 100 / total
+        df["pct"] = df["pct"] * normalization_factor
+        print(
+            f"  → {version.upper()} automation: renormalized from {total:.1f}% to 100.0%"
+        )
+
+        result[version] = df
+
+    # V3 from processed data
+    df = pd.read_csv(FILES["v3_data"])
+    v3_collab = df[
+        (df["geo_id"] == "GLOBAL")
+        & (df["facet"] == "collaboration")
+        & (df["level"] == 0)
+        & (df["variable"] == "collaboration_pct")
+    ].copy()
+    v3_collab = v3_collab.rename(
+        columns={"cluster_name": "interaction_type", "value": "pct"}
+    )
+
+    # Remove "not_classified" from V3 collaboration data for fair comparison
+    not_classified_pct = v3_collab[v3_collab["interaction_type"] == "not_classified"][
+        "pct"
+    ].sum()
+    v3_collab = v3_collab[v3_collab["interaction_type"] != "not_classified"].copy()
+
+    # Renormalize V3 collaboration to 100% after removing not_classified
+    if not_classified_pct > 0:
+        remaining_total = v3_collab["pct"].sum()
+        normalization_factor = 100 / remaining_total
+        v3_collab["pct"] = v3_collab["pct"] * normalization_factor
+        print(
+            f"  → V3 collaboration: removed {not_classified_pct:.1f}% not_classified, renormalized by {normalization_factor:.3f}x"
+        )
+
+    result["v3"] = v3_collab[["interaction_type", "pct"]]
+
+    print(f"✓ Automation data loaded for all versions")
+    return result
+
+
+def load_occupational_mapping():
+    """Load O*NET to SOC mapping data."""
+    onet_df = pd.read_csv(FILES["onet"])
+    soc_df = pd.read_csv(FILES["soc"]).dropna(subset=["Major Group"])
+
+    onet_df["soc_major_group"] = onet_df["O*NET-SOC Code"].str[:2]
+    soc_df["soc_major_group"] = soc_df["Major Group"].str[:2]
+
+    merged = onet_df.merge(
+        soc_df[["soc_major_group", "SOC or O*NET-SOC 2019 Title"]], on="soc_major_group"
+    )
+    merged["task_normalized"] = merged["Task"].str.lower().str.strip()
+
+    print(f"✓ Occupational mapping: {merged['soc_major_group'].nunique()} SOC groups")
+    return merged
+
+
+# ============================================================================
+# ANALYSIS
+# ============================================================================
+
+
+def analyze_occupational_trends(task_data, onet_soc_data):
+    """Analyze occupational category trends across versions."""
+
+    def aggregate_by_occupation(df):
+        merged = df.merge(
+            onet_soc_data[
+                ["task_normalized", "SOC or O*NET-SOC 2019 Title"]
+            ].drop_duplicates(),
+            left_on="task_name",
+            right_on="task_normalized",
+            how="left",
+        )
+
+        unmapped = merged[merged["SOC or O*NET-SOC 2019 Title"].isna()]
+        # Only warn if there are real unmapped tasks (not just "none" and "not_classified")
+        real_unmapped = unmapped[
+            ~unmapped["task_name"].isin(["none", "not_classified"])
+        ]
+        if len(real_unmapped) > 0:
+            real_unmapped_pct = real_unmapped["pct"].sum()
+            warnings.warn(
+                f"{real_unmapped_pct:.1f}% of tasks unmapped to occupational categories",
+                stacklevel=2,
+            )
+
+        return merged.groupby("SOC or O*NET-SOC 2019 Title")["pct"].sum()
+
+    # Aggregate all versions
+    comparison_df = pd.DataFrame(
+        {
+            "v1": aggregate_by_occupation(task_data["v1"]),
+            "v2": aggregate_by_occupation(task_data["v2"]),
+            "v3": aggregate_by_occupation(task_data["v3"]),
+        }
+    ).fillna(0)
+
+    # Calculate changes and filter economically significant categories
+    comparison_df["v3_v1_diff"] = comparison_df["v3"] - comparison_df["v1"]
+    significant = comparison_df[
+        (comparison_df[["v1", "v2", "v3"]] >= MIN_THRESHOLD).any(axis=1)
+    ]
+
+    print(
+        f"✓ Occupational analysis: {len(significant)} economically significant categories"
+    )
+    return significant.sort_values("v1", ascending=False)
+
+
+def analyze_task_changes(task_data, onet_soc_data, top_n=12):
+    """Identify most notable task changes V1→V3."""
+    v1, v3 = task_data["v1"], task_data["v3"]
+
+    # Compare changes
+    comparison = (
+        v1[["task_name", "pct"]]
+        .rename(columns={"pct": "v1_pct"})
+        .merge(
+            v3[["task_name", "pct"]].rename(columns={"pct": "v3_pct"}),
+            on="task_name",
+            how="outer",
+        )
+        .fillna(0)
+    )
+
+    comparison["change"] = comparison["v3_pct"] - comparison["v1_pct"]
+    comparison["rel_change"] = np.where(
+        comparison["v1_pct"] > 0,
+        (comparison["v3_pct"] - comparison["v1_pct"]) / comparison["v1_pct"] * 100,
+        np.inf,
+    )
+
+    # Add SOC context
+    with_context = comparison.merge(
+        onet_soc_data[
+            ["task_normalized", "SOC or O*NET-SOC 2019 Title"]
+        ].drop_duplicates(),
+        left_on="task_name",
+        right_on="task_normalized",
+        how="left",
+    )
+
+    # Get all tasks with economically significant changes (>= 0.2pp)
+    significant_changes = with_context[abs(with_context["change"]) >= 0.2].copy()
+
+    # Create category column with formatted relative percentage change
+    def format_rel_change(row):
+        if row["v1_pct"] > 0:
+            rel_change = (row["v3_pct"] - row["v1_pct"]) / row["v1_pct"] * 100
+            return f"{rel_change:+.0f}%"
+        else:
+            return "new"
+
+    significant_changes["category"] = significant_changes.apply(
+        format_rel_change, axis=1
+    )
+
+    # Rename column and sort by change descending
+    significant_changes = significant_changes.rename(
+        columns={"SOC or O*NET-SOC 2019 Title": "soc_group"}
+    )
+    significant_changes = significant_changes.sort_values("change", ascending=False)
+
+    # Round to 3 decimals
+    significant_changes[["v1_pct", "v3_pct", "change"]] = significant_changes[
+        ["v1_pct", "v3_pct", "change"]
+    ].round(3)
+
+    print(f"✓ Task changes: {len(significant_changes)} notable changes identified")
+    return significant_changes
+
+
+def analyze_automation_trends(automation_data):
+    """Analyze automation vs augmentation trends across versions."""
+    # Standardize interaction names
+    for df in automation_data.values():
+        df["interaction_type"] = df["interaction_type"].replace(
+            {"task iteration": "task_iteration", "feedback loop": "feedback_loop"}
+        )
+
+    results = {}
+    for version, data in automation_data.items():
+        auto_total = data[data["interaction_type"].isin(AUTOMATION_TYPES)]["pct"].sum()
+        aug_total = data[data["interaction_type"].isin(AUGMENTATION_TYPES)]["pct"].sum()
+
+        interaction_dict = dict(zip(data["interaction_type"], data["pct"], strict=True))
+        results[version] = {
+            "automation_total": auto_total,
+            "augmentation_total": aug_total,
+            "directive": interaction_dict["directive"],
+            "feedback_loop": interaction_dict["feedback_loop"],
+            "validation": interaction_dict["validation"],
+            "task_iteration": interaction_dict["task_iteration"],
+            "learning": interaction_dict["learning"],
+        }
+
+    print("✓ Automation trends analysis complete")
+    return results
+
+
+# ============================================================================
+# VISUALIZATION
+# ============================================================================
+
+
+def setup_plot_style():
+    """Configure consistent plot styling."""
+    plt.rcParams.update({"font.size": 12, "axes.titlesize": 16, "axes.labelsize": 14})
+    sns.set_context("notebook", font_scale=1.1)
+
+
+def create_usage_trends_figure(comparison_df):
+    """Create Usage Share Trends subplot figure."""
+    setup_plot_style()
+
+    # Get top categories
+    top_cats = comparison_df[
+        (comparison_df[["v1", "v2", "v3"]] >= MIN_THRESHOLD).any(axis=1)
+    ].head(8)
+    top_cats.index = top_cats.index.str.replace(" Occupations", "")
+
+    fig, axes = plt.subplots(2, 4, figsize=(20, 15))
+    axes = axes.flatten()
+
+    line_color = "#FF8E53"
+    fill_color = "#DEB887"
+
+    # Simplified date labels (actual periods: Dec 2024-Jan 2025, Feb-Mar 2025, Aug 2025)
+    versions, version_labels = [1, 2, 3], ["Jan 2025", "Mar 2025", "Aug 2025"]
+
+    for i, (category, data) in enumerate(top_cats.iterrows()):
+        if i >= len(axes):
+            break
+        ax = axes[i]
+        values = [data["v1"], data["v2"], data["v3"]]
+
+        ax.plot(
+            versions,
+            values,
+            "o-",
+            color=line_color,
+            linewidth=3,
+            markersize=8,
+            markerfacecolor=line_color,
+            markeredgecolor="white",
+            markeredgewidth=2,
+        )
+        ax.fill_between(versions, values, alpha=0.3, color=fill_color)
+
+        # Add value labels
+        for x, y in zip(versions, values, strict=True):
+            ax.text(
+                x,
+                y + max(values) * 0.02,
+                f"{y:.1f}%",
+                ha="center",
+                va="bottom",
+                fontsize=12,
+                fontweight="bold",
+            )
+
+        ax.set_title(category, fontsize=14, fontweight="bold", pad=10)
+        ax.set_xticks(versions)
+        ax.set_xticklabels(version_labels)
+        ax.set_ylabel("Percentage", fontsize=12)
+        ax.set_ylim(0, max(values) * 1.15)
+        ax.grid(True, alpha=0.3)
+        ax.spines["top"].set_visible(False)
+        ax.spines["right"].set_visible(False)
+
+    fig.suptitle(
+        "Usage share trends across economic index reports (V1 to V3)",
+        fontsize=18,
+        fontweight="bold",
+        y=0.98,
+    )
+    plt.tight_layout()
+    plt.subplots_adjust(top=0.88)
+    return fig
+
+
+def create_automation_figure(trends):
+    """Create Automation vs Augmentation Evolution figure."""
+    setup_plot_style()
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
+
+    # Simplified date labels (actual periods: Dec 2024-Jan 2025, Feb-Mar 2025, Aug 2025)
+    version_labels = ["Jan 2025", "Mar 2025", "Aug 2025"]
+    x_pos = [1, 2, 3]
+
+    # Left: Overall trends (no fill)
+    auto_vals = [trends[v]["automation_total"] for v in ["v1", "v2", "v3"]]
+    aug_vals = [trends[v]["augmentation_total"] for v in ["v1", "v2", "v3"]]
+
+    ax1.plot(
+        x_pos,
+        auto_vals,
+        "o-",
+        color=COLORS["automation"],
+        linewidth=3,
+        markersize=8,
+        label="Automation",
+        markeredgecolor="white",
+        markeredgewidth=2,
+    )
+    ax1.plot(
+        x_pos,
+        aug_vals,
+        "o-",
+        color=COLORS["augmentation"],
+        linewidth=3,
+        markersize=8,
+        label="Augmentation",
+        markeredgecolor="white",
+        markeredgewidth=2,
+    )
+
+    # Value labels with automation above and augmentation below dots
+    y_max = max(max(auto_vals), max(aug_vals))
+    for i, (auto, aug) in enumerate(zip(auto_vals, aug_vals, strict=True)):
+        # Red (automation) always above the dot
+        ax1.text(
+            x_pos[i],
+            auto + 1.2,
+            f"{auto:.1f}%",
+            ha="center",
+            va="bottom",
+            fontweight="bold",
+            color=COLORS["automation"],
+        )
+        # Blue (augmentation) always below the dot
+        ax1.text(
+            x_pos[i],
+            aug - 1.5,
+            f"{aug:.1f}%",
+            ha="center",
+            va="top",
+            fontweight="bold",
+            color=COLORS["augmentation"],
+        )
+
+    ax1.set_xticks(x_pos)
+    ax1.set_xticklabels(version_labels)
+    ax1.set_ylabel("Percentage")
+    ax1.set_title("Automation vs augmentation trends")
+    ax1.legend()
+    ax1.grid(True, alpha=0.3)
+    ax1.spines[["top", "right"]].set_visible(False)
+    ax1.set_ylim(0, y_max * 1.15)
+
+    # Right: Individual interaction types with color-coded groups
+    interactions = [
+        "directive",
+        "feedback_loop",
+        "validation",
+        "task_iteration",
+        "learning",
+    ]
+    # Automation = red shades, Augmentation = cool shades
+    colors_individual = ["#DC143C", "#FF6B6B", "#4682B4", "#5F9EA0", "#4169E1"]
+
+    for interaction, color in zip(interactions, colors_individual, strict=True):
+        values = [trends[v][interaction] for v in ["v1", "v2", "v3"]]
+        ax2.plot(
+            x_pos,
+            values,
+            "o-",
+            color=color,
+            linewidth=2.5,
+            markersize=6,
+            label=interaction.replace("_", " ").title(),
+            alpha=0.8,
+        )
+
+    ax2.set_xticks(x_pos)
+    ax2.set_xticklabels(version_labels)
+    ax2.set_ylabel("Percentage")
+    ax2.set_title("Individual interaction types")
+    ax2.legend(bbox_to_anchor=(1.05, 1), loc="upper left")
+    ax2.grid(True, alpha=0.3)
+    ax2.spines[["top", "right"]].set_visible(False)
+
+    plt.suptitle(
+        "Automation vs augmentation evolution (V1 to V3)",
+        fontsize=16,
+        fontweight="bold",
+    )
+    plt.tight_layout()
+    return fig
+
+
+# ============================================================================
+# MAIN
+# ============================================================================
+
+
+def main():
+    """Generate all three economic analysis figures."""
+    print("=" * 80)
+    print("ECONOMIC ANALYSIS FIGURE GENERATION")
+    print("=" * 80)
+
+    # Use consistent output directory for all economic research scripts
+    output_dir = "../data/output/figures"
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Load all data
+    print("\nLoading data...")
+    task_data = {
+        "v1": load_task_data(FILES["v1_tasks"], "V1"),
+        "v2": load_task_data(FILES["v2_tasks"], "V2"),
+        "v3": load_task_data(FILES["v3_data"], "V3"),
+    }
+    automation_data = load_automation_data()
+    onet_soc_data = load_occupational_mapping()
+
+    # Analysis
+    print("\nAnalyzing trends...")
+    occupational_trends = analyze_occupational_trends(task_data, onet_soc_data)
+    task_changes = analyze_task_changes(task_data, onet_soc_data)
+    automation_trends = analyze_automation_trends(automation_data)
+
+    # Generate figures
+    print("\nGenerating figures...")
+
+    fig1 = create_usage_trends_figure(occupational_trends)
+    fig1.savefig(
+        f"{output_dir}/main_occupational_categories.png",
+        dpi=300,
+        bbox_inches="tight",
+        facecolor="white",
+    )
+    print("✓ Saved: main_occupational_categories.png")
+
+    fig3 = create_automation_figure(automation_trends)
+    fig3.savefig(
+        f"{output_dir}/automation_trends_v1_v2_v3.png",
+        dpi=300,
+        bbox_inches="tight",
+        facecolor="white",
+    )
+    print("✓ Saved: automation_trends_v1_v2_v3.png")
+
+    print(f"\n✅ All figures generated successfully!")
+    return occupational_trends, task_changes, automation_trends
+
+
+if __name__ == "__main__":
+    results = main()

release_2025_09_15/code/aei_report_v3_preprocessing_claude_ai.ipynb

@@ -0,0 +1,1840 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AEI Report v3 Claude.ai Preprocessing\n",
+    "\n",
+    "This notebook takes processed Clio data and enriches it with external sources:\n",
+    "1. Merges with population data for per capita calculations\n",
+    "2. Merges with GDP data for economic analysis\n",
+    "3. Merges with SOC/O*NET data for occupational analysis\n",
+    "4. Applies MIN_OBSERVATIONS filtering\n",
+    "5. Calculates derived metrics (per capita, indices, tiers)\n",
+    "6. Categorizes collaboration patterns\n",
+    "\n",
+    "**Input**: `aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv`\n",
+    "\n",
+    "**Output**: `aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Configuration and Setup"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Year for external data\n",
+    "YEAR = 2024\n",
+    "\n",
+    "# Data paths - using local directories\n",
+    "DATA_INPUT_DIR = \"../data/input\"  # Raw external data\n",
+    "DATA_INTERMEDIATE_DIR = (\n",
+    "    \"../data/intermediate\"  # Processed external data and Clio output\n",
+    ")\n",
+    "DATA_OUTPUT_DIR = \"../data/output\"  # Final enriched data\n",
+    "\n",
+    "# Minimum observation thresholds\n",
+    "MIN_OBSERVATIONS_COUNTRY = 200  # Threshold for countries\n",
+    "MIN_OBSERVATIONS_US_STATE = 100  # Threshold for US states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Countries where Claude doesn't operate (23 countries)\n",
+    "EXCLUDED_COUNTRIES = [\n",
+    "    \"AF\",  # Afghanistan\n",
+    "    \"BY\",  # Belarus\n",
+    "    \"CD\",  # Democratic Republic of the Congo\n",
+    "    \"CF\",  # Central African Republic\n",
+    "    \"CN\",  # China\n",
+    "    \"CU\",  # Cuba\n",
+    "    \"ER\",  # Eritrea\n",
+    "    \"ET\",  # Ethiopia\n",
+    "    \"HK\",  # Hong Kong\n",
+    "    \"IR\",  # Iran\n",
+    "    \"KP\",  # North Korea\n",
+    "    \"LY\",  # Libya\n",
+    "    \"ML\",  # Mali\n",
+    "    \"MM\",  # Myanmar\n",
+    "    \"MO\",  # Macau\n",
+    "    \"NI\",  # Nicaragua\n",
+    "    \"RU\",  # Russia\n",
+    "    \"SD\",  # Sudan\n",
+    "    \"SO\",  # Somalia\n",
+    "    \"SS\",  # South Sudan\n",
+    "    \"SY\",  # Syria\n",
+    "    \"VE\",  # Venezuela\n",
+    "    \"YE\",  # Yemen\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Loading Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_population_data():\n",
+    "    \"\"\"\n",
+    "    Load population data for countries and US states.\n",
+    "\n",
+    "    Args:\n",
+    "        verbose: Whether to print progress\n",
+    "\n",
+    "    Returns:\n",
+    "        Dict with country and state_us population dataframes\n",
+    "    \"\"\"\n",
+    "    pop_country_path = (\n",
+    "        Path(DATA_INTERMEDIATE_DIR) / f\"working_age_pop_{YEAR}_country.csv\"\n",
+    "    )\n",
+    "    pop_state_path = (\n",
+    "        Path(DATA_INTERMEDIATE_DIR) / f\"working_age_pop_{YEAR}_us_state.csv\"\n",
+    "    )\n",
+    "\n",
+    "    if not pop_country_path.exists() or not pop_state_path.exists():\n",
+    "        raise FileNotFoundError(\n",
+    "            f\"Population data is required but not found.\\n\"\n",
+    "            f\"  Expected files:\\n\"\n",
+    "            f\"    - {pop_country_path}\\n\"\n",
+    "            f\"    - {pop_state_path}\\n\"\n",
+    "            f\"  Run preprocess_population.py first to generate these files.\"\n",
+    "        )\n",
+    "\n",
+    "    # Use keep_default_na=False to preserve any \"NA\" values as strings\n",
+    "    df_pop_country = pd.read_csv(\n",
+    "        pop_country_path, keep_default_na=False, na_values=[\"\"]\n",
+    "    )\n",
+    "    df_pop_state = pd.read_csv(pop_state_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "    return {\"country\": df_pop_country, \"state_us\": df_pop_state}\n",
+    "\n",
+    "\n",
+    "def load_gdp_data():\n",
+    "    \"\"\"\n",
+    "    Load GDP data for countries and US states.\n",
+    "\n",
+    "    Returns:\n",
+    "        Dict with country and state_us GDP dataframes\n",
+    "    \"\"\"\n",
+    "    gdp_country_path = Path(DATA_INTERMEDIATE_DIR) / f\"gdp_{YEAR}_country.csv\"\n",
+    "    gdp_state_path = Path(DATA_INTERMEDIATE_DIR) / f\"gdp_{YEAR}_us_state.csv\"\n",
+    "\n",
+    "    if not gdp_country_path.exists() or not gdp_state_path.exists():\n",
+    "        raise FileNotFoundError(\n",
+    "            f\"GDP data is required but not found.\\n\"\n",
+    "            f\"  Expected files:\\n\"\n",
+    "            f\"    - {gdp_country_path}\\n\"\n",
+    "            f\"    - {gdp_state_path}\\n\"\n",
+    "            f\"  Run preprocess_gdp.py first to generate these files.\"\n",
+    "        )\n",
+    "\n",
+    "    # Use keep_default_na=False to preserve any \"NA\" values as strings\n",
+    "    df_gdp_country = pd.read_csv(\n",
+    "        gdp_country_path, keep_default_na=False, na_values=[\"\"]\n",
+    "    )\n",
+    "    df_gdp_state = pd.read_csv(gdp_state_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "    return {\"country\": df_gdp_country, \"state_us\": df_gdp_state}\n",
+    "\n",
+    "\n",
+    "def load_task_data():\n",
+    "    \"\"\"\n",
+    "    Load O*NET task statements with SOC codes.\n",
+    "\n",
+    "    Returns:\n",
+    "        DataFrame with O*NET tasks and SOC major groups\n",
+    "    \"\"\"\n",
+    "    onet_path = Path(DATA_INTERMEDIATE_DIR) / \"onet_task_statements.csv\"\n",
+    "\n",
+    "    if not onet_path.exists():\n",
+    "        raise FileNotFoundError(\n",
+    "            f\"O*NET data is required but not found.\\n\"\n",
+    "            f\"  Expected file:\\n\"\n",
+    "            f\"    - {onet_path}\\n\"\n",
+    "            f\"  Run preprocess_onet.py first to generate this file.\"\n",
+    "        )\n",
+    "\n",
+    "    # Use keep_default_na=False to preserve any \"NA\" values as strings\n",
+    "    df_onet = pd.read_csv(onet_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "    # Normalize task names for matching with Clio data\n",
+    "    df_onet[\"task_normalized\"] = df_onet[\"Task\"].str.lower().str.strip()\n",
+    "\n",
+    "    return df_onet\n",
+    "\n",
+    "\n",
+    "def load_soc_data():\n",
+    "    \"\"\"\n",
+    "    Load SOC structure data for occupation names.\n",
+    "\n",
+    "    Returns:\n",
+    "        DataFrame with SOC major groups and their titles\n",
+    "    \"\"\"\n",
+    "    soc_path = Path(DATA_INTERMEDIATE_DIR) / \"soc_structure.csv\"\n",
+    "\n",
+    "    if not soc_path.exists():\n",
+    "        raise FileNotFoundError(\n",
+    "            f\"SOC structure data is required but not found.\\n\"\n",
+    "            f\"  Expected file:\\n\"\n",
+    "            f\"    - {soc_path}\\n\"\n",
+    "            f\"  Run preprocess_onet.py first to generate this file.\"\n",
+    "        )\n",
+    "\n",
+    "    # Use keep_default_na=False to preserve any \"NA\" values as strings\n",
+    "    df_soc = pd.read_csv(soc_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "    # Get unique major groups with their titles for SOC name mapping\n",
+    "    df_major_groups = df_soc[df_soc[\"soc_major_group\"].notna()][\n",
+    "        [\"soc_major_group\", \"SOC or O*NET-SOC 2019 Title\"]\n",
+    "    ].drop_duplicates(subset=[\"soc_major_group\"])\n",
+    "\n",
+    "    return df_major_groups\n",
+    "\n",
+    "\n",
+    "def load_external_data():\n",
+    "    \"\"\"\n",
+    "    Load all external data sources from local files.\n",
+    "\n",
+    "    Returns:\n",
+    "        Dict with population, gdp, task_statements, and soc_structure dataframes\n",
+    "    \"\"\"\n",
+    "\n",
+    "    external_data = {}\n",
+    "\n",
+    "    # Load each data source with its specific function\n",
+    "    external_data[\"population\"] = load_population_data()\n",
+    "    external_data[\"gdp\"] = load_gdp_data()\n",
+    "    external_data[\"task_statements\"] = load_task_data()\n",
+    "    external_data[\"soc_structure\"] = load_soc_data()\n",
+    "\n",
+    "    return external_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filtering Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_filtered_geographies(df):\n",
+    "    \"\"\"\n",
+    "    Get lists of countries and states that meet MIN_OBSERVATIONS thresholds.\n",
+    "\n",
+    "    This function does NOT filter the dataframe - it only identifies which\n",
+    "    geographies meet the thresholds. The full dataframe is preserved\n",
+    "    so we can still report statistics for all geographies.\n",
+    "\n",
+    "    Args:\n",
+    "        df: Input dataframe\n",
+    "\n",
+    "    Returns:\n",
+    "        Tuple of (filtered_countries list, filtered_states list)\n",
+    "    \"\"\"\n",
+    "    # Get country usage counts\n",
+    "    country_usage = df[\n",
+    "        (df[\"facet\"] == \"country\") & (df[\"variable\"] == \"usage_count\")\n",
+    "    ].set_index(\"geo_id\")[\"value\"]\n",
+    "\n",
+    "    # Get state usage counts\n",
+    "    state_usage = df[\n",
+    "        (df[\"facet\"] == \"state_us\") & (df[\"variable\"] == \"usage_count\")\n",
+    "    ].set_index(\"geo_id\")[\"value\"]\n",
+    "\n",
+    "    # Get countries that meet MIN_OBSERVATIONS threshold\n",
+    "    filtered_countries = country_usage[\n",
+    "        country_usage >= MIN_OBSERVATIONS_COUNTRY\n",
+    "    ].index.tolist()\n",
+    "\n",
+    "    # Get states that meet MIN_OBSERVATIONS threshold\n",
+    "    filtered_states = state_usage[\n",
+    "        state_usage >= MIN_OBSERVATIONS_US_STATE\n",
+    "    ].index.tolist()\n",
+    "\n",
+    "    return filtered_countries, filtered_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Merge Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def merge_population_data(df, population_data):\n",
+    "    \"\"\"\n",
+    "    Merge population data in long format.\n",
+    "\n",
+    "    This function:\n",
+    "    1. Adds countries/states that have population but no usage (with 0 usage values)\n",
+    "    2. Adds population as new rows with variable=\"working_age_pop\"\n",
+    "\n",
+    "    Args:\n",
+    "        df: Input dataframe in long format\n",
+    "        population_data: Dict with country and state_us population dataframes\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with all geographies and population added as rows\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "    new_rows = []\n",
+    "\n",
+    "    # Get unique date/platform combinations to replicate for new data\n",
+    "    date_platform_combos = df_result[\n",
+    "        [\"date_start\", \"date_end\", \"platform_and_product\"]\n",
+    "    ].drop_duplicates()\n",
+    "\n",
+    "    # Process countries\n",
+    "    if \"country\" in population_data and not population_data[\"country\"].empty:\n",
+    "        pop_country = population_data[\"country\"]\n",
+    "\n",
+    "        # Get existing countries in our data\n",
+    "        existing_countries = df_result[\n",
+    "            (df_result[\"geography\"] == \"country\")\n",
+    "            & (df_result[\"variable\"] == \"usage_count\")\n",
+    "        ][\"geo_id\"].unique()\n",
+    "\n",
+    "        # Add missing countries with 0 usage (excluding excluded countries)\n",
+    "        missing_countries = (\n",
+    "            set(pop_country[\"country_code\"])\n",
+    "            - set(existing_countries)\n",
+    "            - set(EXCLUDED_COUNTRIES)\n",
+    "        )\n",
+    "\n",
+    "        for _, combo in date_platform_combos.iterrows():\n",
+    "            # Add missing countries with 0 usage (both count and percentage)\n",
+    "            for country_code in missing_countries:\n",
+    "                # Add usage_count = 0\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": country_code,\n",
+    "                        \"geography\": \"country\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"country\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"usage_count\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": 0.0,\n",
+    "                    }\n",
+    "                )\n",
+    "                # Add usage_pct = 0\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": country_code,\n",
+    "                        \"geography\": \"country\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"country\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"usage_pct\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": 0.0,\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "            # Add population data for all countries (that are not excluded)\n",
+    "            for _, pop_row in pop_country.iterrows():\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": pop_row[\"country_code\"],\n",
+    "                        \"geography\": \"country\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"country\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"working_age_pop\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": float(pop_row[\"working_age_pop\"]),\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "    # Process US states\n",
+    "    if \"state_us\" in population_data and not population_data[\"state_us\"].empty:\n",
+    "        pop_state = population_data[\"state_us\"]\n",
+    "\n",
+    "        # Get existing states in our data\n",
+    "        existing_states = df_result[\n",
+    "            (df_result[\"geography\"] == \"state_us\")\n",
+    "            & (df_result[\"variable\"] == \"usage_count\")\n",
+    "        ][\"geo_id\"].unique()\n",
+    "\n",
+    "        # Add missing states with 0 usage\n",
+    "        missing_states = set(pop_state[\"state_code\"]) - set(existing_states)\n",
+    "\n",
+    "        for _, combo in date_platform_combos.iterrows():\n",
+    "            # Add missing states with 0 usage (both count and percentage)\n",
+    "            for state_code in missing_states:\n",
+    "                # Add usage_count = 0\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": state_code,\n",
+    "                        \"geography\": \"state_us\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"state_us\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"usage_count\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": 0.0,\n",
+    "                    }\n",
+    "                )\n",
+    "                # Add usage_pct = 0\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": state_code,\n",
+    "                        \"geography\": \"state_us\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"state_us\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"usage_pct\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": 0.0,\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "            # Add population data for all states\n",
+    "            for _, pop_row in pop_state.iterrows():\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": pop_row[\"state_code\"],\n",
+    "                        \"geography\": \"state_us\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"state_us\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"working_age_pop\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": float(pop_row[\"working_age_pop\"]),\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "    # Add all new rows to the dataframe\n",
+    "    if new_rows:\n",
+    "        df_new = pd.DataFrame(new_rows)\n",
+    "        df_result = pd.concat([df_result, df_new], ignore_index=True)\n",
+    "\n",
+    "    return df_result\n",
+    "\n",
+    "\n",
+    "def merge_gdp_data(df, gdp_data, population_data):\n",
+    "    \"\"\"\n",
+    "    Merge GDP data and calculate GDP per working age capita.\n",
+    "\n",
+    "    Since we have total GDP in actual dollars, we divide by population to get per capita.\n",
+    "\n",
+    "    Args:\n",
+    "        df: Input dataframe in long format\n",
+    "        gdp_data: Dict with country and state_us GDP dataframes (total GDP in dollars)\n",
+    "        population_data: Dict with country and state_us population dataframes\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with GDP per capita data added as rows\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "    new_rows = []\n",
+    "\n",
+    "    # Get unique date/platform combinations\n",
+    "    date_platform_combos = df_result[\n",
+    "        [\"date_start\", \"date_end\", \"platform_and_product\"]\n",
+    "    ].drop_duplicates()\n",
+    "\n",
+    "    # Process country GDP\n",
+    "    if \"country\" in gdp_data and \"country\" in population_data:\n",
+    "        gdp_country = gdp_data[\"country\"]\n",
+    "        pop_country = population_data[\"country\"]\n",
+    "\n",
+    "        # Merge GDP with population to calculate per capita\n",
+    "        gdp_pop = gdp_country.merge(pop_country, on=\"iso_alpha_3\", how=\"inner\")\n",
+    "\n",
+    "        # Calculate GDP per working age capita\n",
+    "        gdp_pop[\"gdp_per_working_age_capita\"] = (\n",
+    "            gdp_pop[\"gdp_total\"] / gdp_pop[\"working_age_pop\"]\n",
+    "        )\n",
+    "\n",
+    "        for _, combo in date_platform_combos.iterrows():\n",
+    "            for _, gdp_row in gdp_pop.iterrows():\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": gdp_row[\"country_code\"],  # Use 2-letter code\n",
+    "                        \"geography\": \"country\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"country\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"gdp_per_working_age_capita\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": float(gdp_row[\"gdp_per_working_age_capita\"]),\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "    # Process state GDP\n",
+    "    if \"state_us\" in gdp_data and \"state_us\" in population_data:\n",
+    "        gdp_state = gdp_data[\"state_us\"]\n",
+    "        pop_state = population_data[\"state_us\"]\n",
+    "\n",
+    "        # Merge GDP with population\n",
+    "        # Column names from preprocess_gdp.py: state_code, gdp_total (in actual dollars)\n",
+    "        gdp_pop = gdp_state.merge(pop_state, on=\"state_code\", how=\"inner\")\n",
+    "\n",
+    "        # Calculate GDP per working age capita\n",
+    "        gdp_pop[\"gdp_per_working_age_capita\"] = (\n",
+    "            gdp_pop[\"gdp_total\"] / gdp_pop[\"working_age_pop\"]\n",
+    "        )\n",
+    "\n",
+    "        for _, combo in date_platform_combos.iterrows():\n",
+    "            for _, gdp_row in gdp_pop.iterrows():\n",
+    "                new_rows.append(\n",
+    "                    {\n",
+    "                        \"geo_id\": gdp_row[\"state_code\"],\n",
+    "                        \"geography\": \"state_us\",\n",
+    "                        \"date_start\": combo[\"date_start\"],\n",
+    "                        \"date_end\": combo[\"date_end\"],\n",
+    "                        \"platform_and_product\": combo[\"platform_and_product\"],\n",
+    "                        \"facet\": \"state_us\",\n",
+    "                        \"level\": 0,\n",
+    "                        \"variable\": \"gdp_per_working_age_capita\",\n",
+    "                        \"cluster_name\": \"\",\n",
+    "                        \"value\": float(gdp_row[\"gdp_per_working_age_capita\"]),\n",
+    "                    }\n",
+    "                )\n",
+    "\n",
+    "    # Add all new rows to the dataframe\n",
+    "    if new_rows:\n",
+    "        df_new = pd.DataFrame(new_rows)\n",
+    "        df_result = pd.concat([df_result, df_new], ignore_index=True)\n",
+    "\n",
+    "    return df_result\n",
+    "\n",
+    "\n",
+    "def calculate_soc_distribution(\n",
+    "    df, df_onet, df_soc_structure, filtered_countries=None, filtered_states=None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate SOC occupation distribution from O*NET task usage.\n",
+    "\n",
+    "    This uses the following approach:\n",
+    "    1. Map tasks directly to SOC major groups (with minimal double counting)\n",
+    "    2. Combine \"none\" and \"not_classified\" tasks into a single \"not_classified\" SOC group\n",
+    "    3. Sum percentages by SOC group\n",
+    "    4. Normalize to 100% for each geography\n",
+    "    5. Calculate for countries, US states, and global that meet MIN_OBSERVATIONS threshold\n",
+    "\n",
+    "    NOTE: For US states, only ~449 O*NET tasks have state-level data (those with sufficient\n",
+    "    observations), but these tasks still map to SOC groups the same way as for countries.\n",
+    "\n",
+    "    Args:\n",
+    "        df: DataFrame with O*NET task percentages\n",
+    "        df_onet: O*NET task data with SOC codes\n",
+    "        df_soc_structure: SOC structure with major group names\n",
+    "        filtered_countries: List of countries that meet MIN_OBSERVATIONS (optional)\n",
+    "        filtered_states: List of states that meet MIN_OBSERVATIONS (optional)\n",
+    "\n",
+    "    Returns:\n",
+    "        DataFrame with SOC distribution rows added\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "    soc_rows = []\n",
+    "\n",
+    "    # Get all O*NET task percentage data (including not_classified and \"none\")\n",
+    "    df_task_pct_all = df_result[\n",
+    "        (df_result[\"facet\"] == \"onet_task\") & (df_result[\"variable\"] == \"onet_task_pct\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    if df_task_pct_all.empty:\n",
+    "        return df_result\n",
+    "\n",
+    "    # Build masks for each geography type\n",
+    "    # Always include global\n",
+    "    global_mask = df_task_pct_all[\"geography\"] == \"global\"\n",
+    "\n",
+    "    # Apply filtering for countries\n",
+    "    if filtered_countries is not None:\n",
+    "        country_mask = (df_task_pct_all[\"geography\"] == \"country\") & (\n",
+    "            df_task_pct_all[\"geo_id\"].isin(filtered_countries)\n",
+    "        )\n",
+    "    else:\n",
+    "        # If no filter, keep all countries\n",
+    "        country_mask = df_task_pct_all[\"geography\"] == \"country\"\n",
+    "\n",
+    "    # Apply filtering for states\n",
+    "    if filtered_states is not None:\n",
+    "        state_mask = (df_task_pct_all[\"geography\"] == \"state_us\") & (\n",
+    "            df_task_pct_all[\"geo_id\"].isin(filtered_states)\n",
+    "        )\n",
+    "    else:\n",
+    "        # If no filter, keep all states\n",
+    "        state_mask = df_task_pct_all[\"geography\"] == \"state_us\"\n",
+    "\n",
+    "    # Combine masks to keep relevant geographies\n",
+    "    combined_mask = global_mask | country_mask | state_mask\n",
+    "    df_task_pct_all = df_task_pct_all[combined_mask].copy()\n",
+    "\n",
+    "    if df_task_pct_all.empty:\n",
+    "        return df_result\n",
+    "\n",
+    "    # Separate not_classified and none tasks from real O*NET tasks\n",
+    "    df_not_classified = df_task_pct_all[\n",
+    "        (df_task_pct_all[\"cluster_name\"].str.contains(\"not_classified\", na=False))\n",
+    "        | (df_task_pct_all[\"cluster_name\"] == \"none\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    # Get real O*NET tasks (excluding not_classified and none)\n",
+    "    df_task_pct = df_task_pct_all[\n",
+    "        (~df_task_pct_all[\"cluster_name\"].str.contains(\"not_classified\", na=False))\n",
+    "        & (df_task_pct_all[\"cluster_name\"] != \"none\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    # Normalize task names for matching\n",
+    "    df_task_pct[\"task_normalized\"] = df_task_pct[\"cluster_name\"].str.lower().str.strip()\n",
+    "\n",
+    "    # Get unique task-SOC pairs from O*NET data\n",
+    "    # This keeps tasks that map to multiple SOC groups (different rows)\n",
+    "    df_task_soc = df_onet[[\"task_normalized\", \"soc_major_group\"]].drop_duplicates()\n",
+    "\n",
+    "    # Merge tasks with their SOC codes\n",
+    "    df_with_soc = df_task_pct.merge(df_task_soc, on=\"task_normalized\", how=\"left\")\n",
+    "\n",
+    "    # Check for unmapped tasks and raise error if found (same as for countries)\n",
+    "    unmapped_tasks = df_with_soc[df_with_soc[\"soc_major_group\"].isna()]\n",
+    "    if not unmapped_tasks.empty:\n",
+    "        unmapped_list = unmapped_tasks[\"cluster_name\"].unique()[:10]  # Show first 10\n",
+    "        n_unmapped = len(unmapped_tasks[\"cluster_name\"].unique())\n",
+    "\n",
+    "        # Check which geographies have unmapped tasks\n",
+    "        unmapped_geos = unmapped_tasks[\"geography\"].unique()\n",
+    "\n",
+    "        raise ValueError(\n",
+    "            f\"Found {n_unmapped} O*NET tasks that could not be mapped to SOC codes.\\n\"\n",
+    "            f\"Geographies with unmapped tasks: {unmapped_geos.tolist()}\\n\"\n",
+    "            f\"First 10 unmapped tasks:\\n\"\n",
+    "            + \"\\n\".join(f\"  - {task}\" for task in unmapped_list)\n",
+    "            + f\"\\n\\nThis likely means the O*NET data is out of sync with the Clio task data.\\n\"\n",
+    "            f\"Please verify that preprocess_onet.py has been run with the correct O*NET version.\"\n",
+    "        )\n",
+    "\n",
+    "    # Create SOC name mapping if SOC structure is available\n",
+    "    soc_names = {}\n",
+    "    if not df_soc_structure.empty:\n",
+    "        for _, row in df_soc_structure.iterrows():\n",
+    "            soc_code = row[\"soc_major_group\"]\n",
+    "            title = row[\"SOC or O*NET-SOC 2019 Title\"]\n",
+    "            # Clean up title (remove \"Occupations\" suffix)\n",
+    "            clean_title = title.replace(\" Occupations\", \"\").replace(\" Occupation\", \"\")\n",
+    "            soc_names[soc_code] = clean_title\n",
+    "\n",
+    "    # Group by geography and process each group\n",
+    "    geo_groups = df_with_soc.groupby(\n",
+    "        [\"geo_id\", \"geography\", \"date_start\", \"date_end\", \"platform_and_product\"]\n",
+    "    )\n",
+    "\n",
+    "    # Also group not_classified data by geography\n",
+    "    not_classified_groups = df_not_classified.groupby(\n",
+    "        [\"geo_id\", \"geography\", \"date_start\", \"date_end\", \"platform_and_product\"]\n",
+    "    )\n",
+    "\n",
+    "    # Track statistics\n",
+    "    states_with_soc = set()\n",
+    "    countries_with_soc = set()\n",
+    "\n",
+    "    # Process all geographies\n",
+    "    all_geos = set()\n",
+    "    for (geo_id, geography, date_start, date_end, platform), _ in geo_groups:\n",
+    "        all_geos.add((geo_id, geography, date_start, date_end, platform))\n",
+    "    for (geo_id, geography, date_start, date_end, platform), _ in not_classified_groups:\n",
+    "        all_geos.add((geo_id, geography, date_start, date_end, platform))\n",
+    "\n",
+    "    for geo_id, geography, date_start, date_end, platform in all_geos:\n",
+    "        # Get mapped SOC data for this geography\n",
+    "        try:\n",
+    "            geo_data = geo_groups.get_group(\n",
+    "                (geo_id, geography, date_start, date_end, platform)\n",
+    "            )\n",
+    "            # Sum percentages by SOC major group\n",
+    "            # If a task maps to multiple SOC groups, its percentage is added to each\n",
+    "            soc_totals = geo_data.groupby(\"soc_major_group\")[\"value\"].sum()\n",
+    "        except KeyError:\n",
+    "            # No mapped tasks for this geography\n",
+    "            soc_totals = pd.Series(dtype=float)\n",
+    "\n",
+    "        # Get not_classified/none data for this geography\n",
+    "        try:\n",
+    "            not_classified_data = not_classified_groups.get_group(\n",
+    "                (geo_id, geography, date_start, date_end, platform)\n",
+    "            )\n",
+    "            # Sum all not_classified and none percentages\n",
+    "            not_classified_total = not_classified_data[\"value\"].sum()\n",
+    "        except KeyError:\n",
+    "            # No not_classified/none for this geography\n",
+    "            not_classified_total = 0\n",
+    "\n",
+    "        # Combine and normalize to 100%\n",
+    "        total_pct = soc_totals.sum() + not_classified_total\n",
+    "\n",
+    "        if total_pct > 0:\n",
+    "            # Normalize mapped SOC groups\n",
+    "            if len(soc_totals) > 0:\n",
+    "                soc_normalized = (soc_totals / total_pct) * 100\n",
+    "            else:\n",
+    "                soc_normalized = pd.Series(dtype=float)\n",
+    "\n",
+    "            # Calculate normalized not_classified percentage\n",
+    "            not_classified_normalized = (not_classified_total / total_pct) * 100\n",
+    "\n",
+    "            # Track geographies that have SOC data\n",
+    "            if geography == \"state_us\":\n",
+    "                states_with_soc.add(geo_id)\n",
+    "            elif geography == \"country\":\n",
+    "                countries_with_soc.add(geo_id)\n",
+    "\n",
+    "            # Create rows for each SOC group\n",
+    "            for soc_group, pct_value in soc_normalized.items():\n",
+    "                # Get SOC name if available, otherwise use code\n",
+    "                soc_name = soc_names.get(soc_group, f\"SOC {soc_group}\")\n",
+    "\n",
+    "                soc_row = {\n",
+    "                    \"geo_id\": geo_id,\n",
+    "                    \"geography\": geography,\n",
+    "                    \"date_start\": date_start,\n",
+    "                    \"date_end\": date_end,\n",
+    "                    \"platform_and_product\": platform,\n",
+    "                    \"facet\": \"soc_occupation\",\n",
+    "                    \"level\": 0,\n",
+    "                    \"variable\": \"soc_pct\",\n",
+    "                    \"cluster_name\": soc_name,\n",
+    "                    \"value\": pct_value,\n",
+    "                }\n",
+    "                soc_rows.append(soc_row)\n",
+    "\n",
+    "            # Add not_classified SOC row if there's any not_classified/none percentage\n",
+    "            if not_classified_normalized > 0:\n",
+    "                soc_row = {\n",
+    "                    \"geo_id\": geo_id,\n",
+    "                    \"geography\": geography,\n",
+    "                    \"date_start\": date_start,\n",
+    "                    \"date_end\": date_end,\n",
+    "                    \"platform_and_product\": platform,\n",
+    "                    \"facet\": \"soc_occupation\",\n",
+    "                    \"level\": 0,\n",
+    "                    \"variable\": \"soc_pct\",\n",
+    "                    \"cluster_name\": \"not_classified\",\n",
+    "                    \"value\": not_classified_normalized,\n",
+    "                }\n",
+    "                soc_rows.append(soc_row)\n",
+    "\n",
+    "    # Print summary\n",
+    "    if countries_with_soc:\n",
+    "        print(\n",
+    "            f\"Calculated SOC distributions for {len(countries_with_soc)} countries + global\"\n",
+    "        )\n",
+    "    if states_with_soc:\n",
+    "        print(f\"Calculated SOC distributions for {len(states_with_soc)} US states\")\n",
+    "\n",
+    "    # Add all SOC rows to result\n",
+    "    if soc_rows:\n",
+    "        df_soc = pd.DataFrame(soc_rows)\n",
+    "        df_result = pd.concat([df_result, df_soc], ignore_index=True)\n",
+    "\n",
+    "    return df_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Metric Calculation Functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_per_capita_metrics(df):\n",
+    "    \"\"\"\n",
+    "    Calculate per capita metrics by joining usage and population data.\n",
+    "\n",
+    "    Since data is in long format, this function:\n",
+    "    1. Extracts usage count rows\n",
+    "    2. Extracts population rows\n",
+    "    3. Joins them and calculates per capita\n",
+    "    4. Adds results as new rows\n",
+    "\n",
+    "    Args:\n",
+    "        df: Dataframe in long format with usage and population as rows\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with per capita metrics added as new rows\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    # Define which metrics should have per capita calculations\n",
+    "    count_metrics = [\"usage_count\"]\n",
+    "\n",
+    "    # Get population data\n",
+    "    df_pop = df_result[df_result[\"variable\"] == \"working_age_pop\"][\n",
+    "        [\n",
+    "            \"geo_id\",\n",
+    "            \"geography\",\n",
+    "            \"date_start\",\n",
+    "            \"date_end\",\n",
+    "            \"platform_and_product\",\n",
+    "            \"value\",\n",
+    "        ]\n",
+    "    ].rename(columns={\"value\": \"population\"})\n",
+    "\n",
+    "    # Calculate per capita for each count metric\n",
+    "    per_capita_rows = []\n",
+    "\n",
+    "    for metric in count_metrics:\n",
+    "        # Get the count data for this metric\n",
+    "        df_metric = df_result[df_result[\"variable\"] == metric].copy()\n",
+    "\n",
+    "        # Join with population data\n",
+    "        df_joined = df_metric.merge(\n",
+    "            df_pop,\n",
+    "            on=[\n",
+    "                \"geo_id\",\n",
+    "                \"geography\",\n",
+    "                \"date_start\",\n",
+    "                \"date_end\",\n",
+    "                \"platform_and_product\",\n",
+    "            ],\n",
+    "            how=\"left\",\n",
+    "        )\n",
+    "\n",
+    "        # Calculate per capita where population exists and is > 0\n",
+    "        df_joined = df_joined[\n",
+    "            df_joined[\"population\"].notna() & (df_joined[\"population\"] > 0)\n",
+    "        ]\n",
+    "\n",
+    "        if not df_joined.empty:\n",
+    "            # Create per capita rows\n",
+    "            for _, row in df_joined.iterrows():\n",
+    "                per_capita_row = {\n",
+    "                    \"geo_id\": row[\"geo_id\"],\n",
+    "                    \"geography\": row[\"geography\"],\n",
+    "                    \"date_start\": row[\"date_start\"],\n",
+    "                    \"date_end\": row[\"date_end\"],\n",
+    "                    \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                    \"facet\": row[\"facet\"],\n",
+    "                    \"level\": row[\"level\"],\n",
+    "                    \"variable\": metric.replace(\"_count\", \"_per_capita\"),\n",
+    "                    \"cluster_name\": row[\"cluster_name\"],\n",
+    "                    \"value\": row[\"value\"] / row[\"population\"],\n",
+    "                }\n",
+    "                per_capita_rows.append(per_capita_row)\n",
+    "\n",
+    "    # Add all per capita rows to the result\n",
+    "    if per_capita_rows:\n",
+    "        df_per_capita = pd.DataFrame(per_capita_rows)\n",
+    "        df_result = pd.concat([df_result, df_per_capita], ignore_index=True)\n",
+    "\n",
+    "    return df_result\n",
+    "\n",
+    "\n",
+    "def calculate_usage_per_capita_index(df, filtered_countries=None, filtered_states=None):\n",
+    "    \"\"\"\n",
+    "    Calculate usage concentration index: (% of usage) / (% of population).\n",
+    "\n",
+    "    This shows whether a geography has more or less usage than expected based on its population.\n",
+    "    - Index = 1.0: Usage proportional to population\n",
+    "    - Index > 1.0: Over-representation (more usage than expected)\n",
+    "    - Index < 1.0: Under-representation (less usage than expected)\n",
+    "    - Index = 0.0: No usage at all\n",
+    "\n",
+    "    The function calculates the index for all countries/states that have usage data.\n",
+    "    Excluded countries don't have usage data, so they're automatically excluded.\n",
+    "    Countries with zero usage get index=0 naturally from the calculation.\n",
+    "\n",
+    "    Args:\n",
+    "        df: Dataframe with usage and population data\n",
+    "        filtered_countries: List of countries that meet MIN_OBSERVATIONS threshold (used for baseline calculation)\n",
+    "        filtered_states: List of states that meet MIN_OBSERVATIONS threshold (used for baseline calculation)\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with usage concentration index added as new rows\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    index_rows = []\n",
+    "\n",
+    "    # Process countries\n",
+    "    # Get all countries with usage data (excluded countries won't be here)\n",
+    "    df_usage_country = df_result[\n",
+    "        (df_result[\"geography\"] == \"country\") & (df_result[\"variable\"] == \"usage_count\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    # Get population data for the same countries\n",
+    "    df_pop_country = df_result[\n",
+    "        (df_result[\"geography\"] == \"country\")\n",
+    "        & (df_result[\"variable\"] == \"working_age_pop\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    if not df_usage_country.empty and not df_pop_country.empty:\n",
+    "        # For baseline calculation, use filtered countries if provided, otherwise use all\n",
+    "        if filtered_countries is not None:\n",
+    "            # Calculate totals using only filtered countries for the baseline\n",
+    "            usage_for_baseline = df_usage_country[\n",
+    "                df_usage_country[\"geo_id\"].isin(filtered_countries)\n",
+    "            ]\n",
+    "            pop_for_baseline = df_pop_country[\n",
+    "                df_pop_country[\"geo_id\"].isin(filtered_countries)\n",
+    "            ]\n",
+    "            total_usage = usage_for_baseline[\"value\"].sum()\n",
+    "            total_pop = pop_for_baseline[\"value\"].sum()\n",
+    "        else:\n",
+    "            # Use all countries for baseline\n",
+    "            total_usage = df_usage_country[\"value\"].sum()\n",
+    "            total_pop = df_pop_country[\"value\"].sum()\n",
+    "\n",
+    "        if total_usage > 0 and total_pop > 0:\n",
+    "            # Calculate index for all countries (not just filtered)\n",
+    "            for _, usage_row in df_usage_country.iterrows():\n",
+    "                # Find corresponding population\n",
+    "                pop_value = df_pop_country[\n",
+    "                    df_pop_country[\"geo_id\"] == usage_row[\"geo_id\"]\n",
+    "                ][\"value\"].values\n",
+    "\n",
+    "                if len(pop_value) > 0 and pop_value[0] > 0:\n",
+    "                    # Calculate shares\n",
+    "                    usage_share = (\n",
+    "                        usage_row[\"value\"] / total_usage\n",
+    "                        if usage_row[\"value\"] > 0\n",
+    "                        else 0\n",
+    "                    )\n",
+    "                    pop_share = pop_value[0] / total_pop\n",
+    "\n",
+    "                    # Calculate index (will be 0 if usage is 0)\n",
+    "                    index_value = usage_share / pop_share if pop_share > 0 else 0\n",
+    "\n",
+    "                    index_row = {\n",
+    "                        \"geo_id\": usage_row[\"geo_id\"],\n",
+    "                        \"geography\": usage_row[\"geography\"],\n",
+    "                        \"date_start\": usage_row[\"date_start\"],\n",
+    "                        \"date_end\": usage_row[\"date_end\"],\n",
+    "                        \"platform_and_product\": usage_row[\"platform_and_product\"],\n",
+    "                        \"facet\": usage_row[\"facet\"],\n",
+    "                        \"level\": usage_row[\"level\"],\n",
+    "                        \"variable\": \"usage_per_capita_index\",\n",
+    "                        \"cluster_name\": usage_row[\"cluster_name\"],\n",
+    "                        \"value\": index_value,\n",
+    "                    }\n",
+    "                    index_rows.append(index_row)\n",
+    "\n",
+    "    # Process states\n",
+    "    # Get all states with usage data\n",
+    "    df_usage_state = df_result[\n",
+    "        (df_result[\"geography\"] == \"state_us\")\n",
+    "        & (df_result[\"variable\"] == \"usage_count\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    # Get population data for the same states\n",
+    "    df_pop_state = df_result[\n",
+    "        (df_result[\"geography\"] == \"state_us\")\n",
+    "        & (df_result[\"variable\"] == \"working_age_pop\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    if not df_usage_state.empty and not df_pop_state.empty:\n",
+    "        # For baseline calculation, use filtered states if provided, otherwise use all\n",
+    "        if filtered_states is not None:\n",
+    "            # Calculate totals using only filtered states for the baseline\n",
+    "            usage_for_baseline = df_usage_state[\n",
+    "                df_usage_state[\"geo_id\"].isin(filtered_states)\n",
+    "            ]\n",
+    "            pop_for_baseline = df_pop_state[\n",
+    "                df_pop_state[\"geo_id\"].isin(filtered_states)\n",
+    "            ]\n",
+    "            total_usage = usage_for_baseline[\"value\"].sum()\n",
+    "            total_pop = pop_for_baseline[\"value\"].sum()\n",
+    "        else:\n",
+    "            # Use all states for baseline\n",
+    "            total_usage = df_usage_state[\"value\"].sum()\n",
+    "            total_pop = df_pop_state[\"value\"].sum()\n",
+    "\n",
+    "        if total_usage > 0 and total_pop > 0:\n",
+    "            # Calculate index for all states (not just filtered)\n",
+    "            for _, usage_row in df_usage_state.iterrows():\n",
+    "                # Find corresponding population\n",
+    "                pop_value = df_pop_state[df_pop_state[\"geo_id\"] == usage_row[\"geo_id\"]][\n",
+    "                    \"value\"\n",
+    "                ].values\n",
+    "\n",
+    "                if len(pop_value) > 0 and pop_value[0] > 0:\n",
+    "                    # Calculate shares\n",
+    "                    usage_share = (\n",
+    "                        usage_row[\"value\"] / total_usage\n",
+    "                        if usage_row[\"value\"] > 0\n",
+    "                        else 0\n",
+    "                    )\n",
+    "                    pop_share = pop_value[0] / total_pop\n",
+    "\n",
+    "                    # Calculate index (will be 0 if usage is 0)\n",
+    "                    index_value = usage_share / pop_share if pop_share > 0 else 0\n",
+    "\n",
+    "                    index_row = {\n",
+    "                        \"geo_id\": usage_row[\"geo_id\"],\n",
+    "                        \"geography\": usage_row[\"geography\"],\n",
+    "                        \"date_start\": usage_row[\"date_start\"],\n",
+    "                        \"date_end\": usage_row[\"date_end\"],\n",
+    "                        \"platform_and_product\": usage_row[\"platform_and_product\"],\n",
+    "                        \"facet\": usage_row[\"facet\"],\n",
+    "                        \"level\": usage_row[\"level\"],\n",
+    "                        \"variable\": \"usage_per_capita_index\",\n",
+    "                        \"cluster_name\": usage_row[\"cluster_name\"],\n",
+    "                        \"value\": index_value,\n",
+    "                    }\n",
+    "                    index_rows.append(index_row)\n",
+    "\n",
+    "    # Add all index rows to result\n",
+    "    if index_rows:\n",
+    "        df_index = pd.DataFrame(index_rows)\n",
+    "        df_result = pd.concat([df_result, df_index], ignore_index=True)\n",
+    "\n",
+    "    return df_result\n",
+    "\n",
+    "\n",
+    "def calculate_category_percentage_index(\n",
+    "    df, filtered_countries=None, filtered_states=None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate category percentage index for facet specialization.\n",
+    "\n",
+    "    For countries: Compare to global percentage for that cluster\n",
+    "    For US states: Compare to US country percentage for that cluster\n",
+    "\n",
+    "    Only calculates for countries/states that meet MIN_OBSERVATIONS.\n",
+    "    Excludes \"not_classified\" and \"none\" categories as these are catch-alls.\n",
+    "\n",
+    "    Args:\n",
+    "        df: Dataframe with percentage metrics as rows\n",
+    "        filtered_countries: List of countries that meet MIN_OBSERVATIONS threshold\n",
+    "        filtered_states: List of states that meet MIN_OBSERVATIONS threshold\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with category percentage index added as new rows (only for filtered geographies)\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    # Process percentage metrics for content facets\n",
+    "    pct_vars = [\"onet_task_pct\", \"collaboration_pct\", \"request_pct\"]\n",
+    "\n",
+    "    index_rows = []\n",
+    "\n",
+    "    for pct_var in pct_vars:\n",
+    "        # Get the base facet name\n",
+    "        facet_name = pct_var.replace(\"_pct\", \"\")\n",
+    "\n",
+    "        # Get percentage data for this variable\n",
+    "        df_pct = df_result[\n",
+    "            (df_result[\"variable\"] == pct_var) & (df_result[\"facet\"] == facet_name)\n",
+    "        ].copy()\n",
+    "\n",
+    "        # Exclude not_classified and none categories from index calculation\n",
+    "        # These are catch-all/no-pattern categories that don't provide meaningful comparisons\n",
+    "        df_pct = df_pct[~df_pct[\"cluster_name\"].isin([\"not_classified\", \"none\"])]\n",
+    "\n",
+    "        if not df_pct.empty and \"cluster_name\" in df_pct.columns:\n",
+    "            # Check if this facet has levels (like request)\n",
+    "            has_levels = df_pct[\"level\"].notna().any() and (df_pct[\"level\"] != 0).any()\n",
+    "\n",
+    "            if has_levels:\n",
+    "                # Process each level separately\n",
+    "                levels = df_pct[\"level\"].dropna().unique()\n",
+    "\n",
+    "                for level in levels:\n",
+    "                    df_level = df_pct[df_pct[\"level\"] == level].copy()\n",
+    "\n",
+    "                    # Get global baselines for this level\n",
+    "                    global_baselines = (\n",
+    "                        df_level[\n",
+    "                            (df_level[\"geography\"] == \"global\")\n",
+    "                            & (df_level[\"geo_id\"] == \"GLOBAL\")\n",
+    "                        ]\n",
+    "                        .set_index(\"cluster_name\")[\"value\"]\n",
+    "                        .to_dict()\n",
+    "                    )\n",
+    "\n",
+    "                    # Get US baselines for this level\n",
+    "                    us_baselines = (\n",
+    "                        df_level[\n",
+    "                            (df_level[\"geography\"] == \"country\")\n",
+    "                            & (df_level[\"geo_id\"] == \"US\")\n",
+    "                        ]\n",
+    "                        .set_index(\"cluster_name\")[\"value\"]\n",
+    "                        .to_dict()\n",
+    "                    )\n",
+    "\n",
+    "                    # Process countries for this level\n",
+    "                    if filtered_countries is not None and global_baselines:\n",
+    "                        df_countries = df_level[\n",
+    "                            (df_level[\"geography\"] == \"country\")\n",
+    "                            & (df_level[\"geo_id\"].isin(filtered_countries))\n",
+    "                        ].copy()\n",
+    "\n",
+    "                        for _, row in df_countries.iterrows():\n",
+    "                            baseline = global_baselines.get(row[\"cluster_name\"])\n",
+    "\n",
+    "                            if baseline and baseline > 0:\n",
+    "                                index_row = {\n",
+    "                                    \"geo_id\": row[\"geo_id\"],\n",
+    "                                    \"geography\": row[\"geography\"],\n",
+    "                                    \"date_start\": row[\"date_start\"],\n",
+    "                                    \"date_end\": row[\"date_end\"],\n",
+    "                                    \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                    \"facet\": row[\"facet\"],\n",
+    "                                    \"level\": row[\"level\"],\n",
+    "                                    \"variable\": f\"{facet_name}_pct_index\",\n",
+    "                                    \"cluster_name\": row[\"cluster_name\"],\n",
+    "                                    \"value\": row[\"value\"] / baseline,\n",
+    "                                }\n",
+    "                                index_rows.append(index_row)\n",
+    "\n",
+    "                    # Process states for this level\n",
+    "                    if filtered_states is not None and us_baselines:\n",
+    "                        df_states = df_level[\n",
+    "                            (df_level[\"geography\"] == \"state_us\")\n",
+    "                            & (df_level[\"geo_id\"].isin(filtered_states))\n",
+    "                        ].copy()\n",
+    "\n",
+    "                        for _, row in df_states.iterrows():\n",
+    "                            baseline = us_baselines.get(row[\"cluster_name\"])\n",
+    "\n",
+    "                            if baseline and baseline > 0:\n",
+    "                                index_row = {\n",
+    "                                    \"geo_id\": row[\"geo_id\"],\n",
+    "                                    \"geography\": row[\"geography\"],\n",
+    "                                    \"date_start\": row[\"date_start\"],\n",
+    "                                    \"date_end\": row[\"date_end\"],\n",
+    "                                    \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                    \"facet\": row[\"facet\"],\n",
+    "                                    \"level\": row[\"level\"],\n",
+    "                                    \"variable\": f\"{facet_name}_pct_index\",\n",
+    "                                    \"cluster_name\": row[\"cluster_name\"],\n",
+    "                                    \"value\": row[\"value\"] / baseline,\n",
+    "                                }\n",
+    "                                index_rows.append(index_row)\n",
+    "            else:\n",
+    "                # No levels (onet_task, collaboration)\n",
+    "                # Get global baselines\n",
+    "                global_baselines = (\n",
+    "                    df_pct[\n",
+    "                        (df_pct[\"geography\"] == \"global\")\n",
+    "                        & (df_pct[\"geo_id\"] == \"GLOBAL\")\n",
+    "                    ]\n",
+    "                    .set_index(\"cluster_name\")[\"value\"]\n",
+    "                    .to_dict()\n",
+    "                )\n",
+    "\n",
+    "                # Get US baselines\n",
+    "                us_baselines = (\n",
+    "                    df_pct[\n",
+    "                        (df_pct[\"geography\"] == \"country\") & (df_pct[\"geo_id\"] == \"US\")\n",
+    "                    ]\n",
+    "                    .set_index(\"cluster_name\")[\"value\"]\n",
+    "                    .to_dict()\n",
+    "                )\n",
+    "\n",
+    "                # Process countries\n",
+    "                if filtered_countries is not None and global_baselines:\n",
+    "                    df_countries = df_pct[\n",
+    "                        (df_pct[\"geography\"] == \"country\")\n",
+    "                        & (df_pct[\"geo_id\"].isin(filtered_countries))\n",
+    "                    ].copy()\n",
+    "\n",
+    "                    for _, row in df_countries.iterrows():\n",
+    "                        baseline = global_baselines.get(row[\"cluster_name\"])\n",
+    "\n",
+    "                        if baseline and baseline > 0:\n",
+    "                            index_row = {\n",
+    "                                \"geo_id\": row[\"geo_id\"],\n",
+    "                                \"geography\": row[\"geography\"],\n",
+    "                                \"date_start\": row[\"date_start\"],\n",
+    "                                \"date_end\": row[\"date_end\"],\n",
+    "                                \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                \"facet\": row[\"facet\"],\n",
+    "                                \"level\": row[\"level\"],\n",
+    "                                \"variable\": f\"{facet_name}_pct_index\",\n",
+    "                                \"cluster_name\": row[\"cluster_name\"],\n",
+    "                                \"value\": row[\"value\"] / baseline,\n",
+    "                            }\n",
+    "                            index_rows.append(index_row)\n",
+    "\n",
+    "                # Process states\n",
+    "                if filtered_states is not None and us_baselines:\n",
+    "                    df_states = df_pct[\n",
+    "                        (df_pct[\"geography\"] == \"state_us\")\n",
+    "                        & (df_pct[\"geo_id\"].isin(filtered_states))\n",
+    "                    ].copy()\n",
+    "\n",
+    "                    for _, row in df_states.iterrows():\n",
+    "                        baseline = us_baselines.get(row[\"cluster_name\"])\n",
+    "\n",
+    "                        if baseline and baseline > 0:\n",
+    "                            index_row = {\n",
+    "                                \"geo_id\": row[\"geo_id\"],\n",
+    "                                \"geography\": row[\"geography\"],\n",
+    "                                \"date_start\": row[\"date_start\"],\n",
+    "                                \"date_end\": row[\"date_end\"],\n",
+    "                                \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                \"facet\": row[\"facet\"],\n",
+    "                                \"level\": row[\"level\"],\n",
+    "                                \"variable\": f\"{facet_name}_pct_index\",\n",
+    "                                \"cluster_name\": row[\"cluster_name\"],\n",
+    "                                \"value\": row[\"value\"] / baseline,\n",
+    "                            }\n",
+    "                            index_rows.append(index_row)\n",
+    "\n",
+    "    # Add all index rows to result\n",
+    "    if index_rows:\n",
+    "        df_index = pd.DataFrame(index_rows)\n",
+    "        df_result = pd.concat([df_result, df_index], ignore_index=True)\n",
+    "\n",
+    "    return df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_usage_tiers(df, n_tiers=4, filtered_countries=None, filtered_states=None):\n",
+    "    \"\"\"\n",
+    "    Calculate usage tiers based on indexed per capita usage.\n",
+    "    - Tier 0: Zero adoption (index = 0)\n",
+    "    - Tiers 1-4: Quartiles based on thresholds from filtered countries/states\n",
+    "\n",
+    "    Quartile thresholds are calculated using only countries/states with ≥MIN_OBSERVATIONS,\n",
+    "    but applied to all countries/states to ensure complete visualization.\n",
+    "\n",
+    "    Note: Tier assignments for countries/states with <MIN_OBSERVATIONS should be\n",
+    "    interpreted with caution due to sample size limitations.\n",
+    "\n",
+    "    Args:\n",
+    "        df: Input dataframe\n",
+    "        n_tiers: Number of quartiles to create for non-zero usage (default 4)\n",
+    "        filtered_countries: List of countries that meet MIN_OBSERVATIONS threshold\n",
+    "        filtered_states: List of states that meet MIN_OBSERVATIONS threshold\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with usage tier rows added\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    # Calculate tiers for indexed per capita metrics\n",
+    "    if \"variable\" in df_result.columns and \"value\" in df_result.columns:\n",
+    "        index_vars = [\"usage_per_capita_index\"]\n",
+    "\n",
+    "        quartile_labels = [\n",
+    "            \"Emerging (bottom 25%)\",\n",
+    "            \"Lower middle (25-50%)\",\n",
+    "            \"Upper middle (50-75%)\",\n",
+    "            \"Leading (top 25%)\",\n",
+    "        ]\n",
+    "\n",
+    "        tier_rows = []\n",
+    "\n",
+    "        for var in index_vars:\n",
+    "            # Process countries\n",
+    "            # Get all countries with the index variable\n",
+    "            all_country_data = df_result[\n",
+    "                (df_result[\"variable\"] == var) & (df_result[\"geography\"] == \"country\")\n",
+    "            ].copy()\n",
+    "\n",
+    "            if not all_country_data.empty:\n",
+    "                # Separate zero and non-zero usage\n",
+    "                zero_usage = all_country_data[all_country_data[\"value\"] == 0].copy()\n",
+    "                nonzero_usage = all_country_data[all_country_data[\"value\"] > 0].copy()\n",
+    "\n",
+    "                # Calculate quartile thresholds using ONLY filtered countries\n",
+    "                if filtered_countries is not None and not nonzero_usage.empty:\n",
+    "                    # Get only filtered countries for quartile calculation\n",
+    "                    filtered_for_quartiles = nonzero_usage[\n",
+    "                        nonzero_usage[\"geo_id\"].isin(filtered_countries)\n",
+    "                    ].copy()\n",
+    "\n",
+    "                    if not filtered_for_quartiles.empty:\n",
+    "                        # Calculate quartile thresholds from filtered countries\n",
+    "                        quartiles = (\n",
+    "                            filtered_for_quartiles[\"value\"]\n",
+    "                            .quantile([0.25, 0.5, 0.75])\n",
+    "                            .values\n",
+    "                        )\n",
+    "\n",
+    "                        # Apply thresholds to all non-zero countries\n",
+    "                        for _, row in nonzero_usage.iterrows():\n",
+    "                            value = row[\"value\"]\n",
+    "\n",
+    "                            # Assign tier based on thresholds\n",
+    "                            if value <= quartiles[0]:\n",
+    "                                tier_label = quartile_labels[0]  # Bottom 25%\n",
+    "                                tier_value = 1\n",
+    "                            elif value <= quartiles[1]:\n",
+    "                                tier_label = quartile_labels[1]  # 25-50%\n",
+    "                                tier_value = 2\n",
+    "                            elif value <= quartiles[2]:\n",
+    "                                tier_label = quartile_labels[2]  # 50-75%\n",
+    "                                tier_value = 3\n",
+    "                            else:\n",
+    "                                tier_label = quartile_labels[3]  # Top 25%\n",
+    "                                tier_value = 4\n",
+    "\n",
+    "                            tier_row = {\n",
+    "                                \"geo_id\": row[\"geo_id\"],\n",
+    "                                \"geography\": row[\"geography\"],\n",
+    "                                \"date_start\": row[\"date_start\"],\n",
+    "                                \"date_end\": row[\"date_end\"],\n",
+    "                                \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                \"facet\": row[\"facet\"],\n",
+    "                                \"level\": row[\"level\"],\n",
+    "                                \"variable\": \"usage_tier\",\n",
+    "                                \"cluster_name\": tier_label,\n",
+    "                                \"value\": tier_value,\n",
+    "                            }\n",
+    "                            tier_rows.append(tier_row)\n",
+    "\n",
+    "                # Add tier 0 for all zero usage countries\n",
+    "                for _, row in zero_usage.iterrows():\n",
+    "                    tier_row = {\n",
+    "                        \"geo_id\": row[\"geo_id\"],\n",
+    "                        \"geography\": row[\"geography\"],\n",
+    "                        \"date_start\": row[\"date_start\"],\n",
+    "                        \"date_end\": row[\"date_end\"],\n",
+    "                        \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                        \"facet\": row[\"facet\"],\n",
+    "                        \"level\": row[\"level\"],\n",
+    "                        \"variable\": \"usage_tier\",\n",
+    "                        \"cluster_name\": \"Minimal\",\n",
+    "                        \"value\": 0,\n",
+    "                    }\n",
+    "                    tier_rows.append(tier_row)\n",
+    "\n",
+    "            # Process states\n",
+    "            # Get all states with the index variable\n",
+    "            all_state_data = df_result[\n",
+    "                (df_result[\"variable\"] == var) & (df_result[\"geography\"] == \"state_us\")\n",
+    "            ].copy()\n",
+    "\n",
+    "            if not all_state_data.empty:\n",
+    "                # Separate zero and non-zero usage\n",
+    "                zero_usage = all_state_data[all_state_data[\"value\"] == 0].copy()\n",
+    "                nonzero_usage = all_state_data[all_state_data[\"value\"] > 0].copy()\n",
+    "\n",
+    "                # Calculate quartile thresholds using ONLY filtered states\n",
+    "                if filtered_states is not None and not nonzero_usage.empty:\n",
+    "                    # Get only filtered states for quartile calculation\n",
+    "                    filtered_for_quartiles = nonzero_usage[\n",
+    "                        nonzero_usage[\"geo_id\"].isin(filtered_states)\n",
+    "                    ].copy()\n",
+    "\n",
+    "                    if not filtered_for_quartiles.empty:\n",
+    "                        # Calculate quartile thresholds from filtered states\n",
+    "                        quartiles = (\n",
+    "                            filtered_for_quartiles[\"value\"]\n",
+    "                            .quantile([0.25, 0.5, 0.75])\n",
+    "                            .values\n",
+    "                        )\n",
+    "\n",
+    "                        # Apply thresholds to all non-zero states\n",
+    "                        for _, row in nonzero_usage.iterrows():\n",
+    "                            value = row[\"value\"]\n",
+    "\n",
+    "                            # Assign tier based on thresholds\n",
+    "                            if value <= quartiles[0]:\n",
+    "                                tier_label = quartile_labels[0]  # Bottom 25%\n",
+    "                                tier_value = 1\n",
+    "                            elif value <= quartiles[1]:\n",
+    "                                tier_label = quartile_labels[1]  # 25-50%\n",
+    "                                tier_value = 2\n",
+    "                            elif value <= quartiles[2]:\n",
+    "                                tier_label = quartile_labels[2]  # 50-75%\n",
+    "                                tier_value = 3\n",
+    "                            else:\n",
+    "                                tier_label = quartile_labels[3]  # Top 25%\n",
+    "                                tier_value = 4\n",
+    "\n",
+    "                            tier_row = {\n",
+    "                                \"geo_id\": row[\"geo_id\"],\n",
+    "                                \"geography\": row[\"geography\"],\n",
+    "                                \"date_start\": row[\"date_start\"],\n",
+    "                                \"date_end\": row[\"date_end\"],\n",
+    "                                \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                                \"facet\": row[\"facet\"],\n",
+    "                                \"level\": row[\"level\"],\n",
+    "                                \"variable\": \"usage_tier\",\n",
+    "                                \"cluster_name\": tier_label,\n",
+    "                                \"value\": tier_value,\n",
+    "                            }\n",
+    "                            tier_rows.append(tier_row)\n",
+    "\n",
+    "                # Add tier 0 for all zero usage states\n",
+    "                for _, row in zero_usage.iterrows():\n",
+    "                    tier_row = {\n",
+    "                        \"geo_id\": row[\"geo_id\"],\n",
+    "                        \"geography\": row[\"geography\"],\n",
+    "                        \"date_start\": row[\"date_start\"],\n",
+    "                        \"date_end\": row[\"date_end\"],\n",
+    "                        \"platform_and_product\": row[\"platform_and_product\"],\n",
+    "                        \"facet\": row[\"facet\"],\n",
+    "                        \"level\": row[\"level\"],\n",
+    "                        \"variable\": \"usage_tier\",\n",
+    "                        \"cluster_name\": \"Minimal\",\n",
+    "                        \"value\": 0,\n",
+    "                    }\n",
+    "                    tier_rows.append(tier_row)\n",
+    "\n",
+    "        if tier_rows:\n",
+    "            df_result = pd.concat(\n",
+    "                [df_result, pd.DataFrame(tier_rows)], ignore_index=True\n",
+    "            )\n",
+    "\n",
+    "    return df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_automation_augmentation_metrics(\n",
+    "    df, filtered_countries=None, filtered_states=None\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Calculate automation vs augmentation percentages for collaboration patterns.\n",
+    "\n",
+    "    This function:\n",
+    "    1. Categorizes collaboration patterns as automation or augmentation\n",
+    "    2. Calculates percentages excluding 'none' and 'not_classified'\n",
+    "    3. Only calculates for filtered geographies at country/state level\n",
+    "\n",
+    "    Categorization:\n",
+    "    - Automation: directive, feedback loop (AI-centric patterns)\n",
+    "    - Augmentation: validation, task iteration, learning (human-centric patterns)\n",
+    "    - Excluded: none (no collaboration), not_classified (unknown)\n",
+    "\n",
+    "    Args:\n",
+    "        df: Dataframe with collaboration data\n",
+    "        filtered_countries: List of countries that meet MIN_OBSERVATIONS\n",
+    "        filtered_states: List of states that meet MIN_OBSERVATIONS\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with automation/augmentation percentage rows added\n",
+    "    \"\"\"\n",
+    "    if \"facet\" not in df.columns or \"cluster_name\" not in df.columns:\n",
+    "        return df\n",
+    "\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    # Get collaboration data\n",
+    "    collab_data = df_result[\n",
+    "        (df_result[\"facet\"] == \"collaboration\")\n",
+    "        & (df_result[\"variable\"] == \"collaboration_count\")\n",
+    "    ].copy()\n",
+    "\n",
+    "    if collab_data.empty:\n",
+    "        return df_result\n",
+    "\n",
+    "    # Define pattern categorization\n",
+    "    def categorize_pattern(pattern_name):\n",
+    "        if pd.isna(pattern_name):\n",
+    "            return None\n",
+    "\n",
+    "        pattern_clean = pattern_name.lower().replace(\"_\", \" \").replace(\"-\", \" \")\n",
+    "\n",
+    "        # Augmentation patterns (human-centric)\n",
+    "        if \"validation\" in pattern_clean:\n",
+    "            return \"augmentation\"\n",
+    "        elif \"task iteration\" in pattern_clean or \"task_iteration\" in pattern_clean:\n",
+    "            return \"augmentation\"\n",
+    "        elif \"learning\" in pattern_clean:\n",
+    "            return \"augmentation\"\n",
+    "        # Automation patterns (AI-centric)\n",
+    "        elif \"directive\" in pattern_clean:\n",
+    "            return \"automation\"\n",
+    "        elif \"feedback loop\" in pattern_clean or \"feedback_loop\" in pattern_clean:\n",
+    "            return \"automation\"\n",
+    "        # Excluded patterns - return None to exclude from calculations\n",
+    "        elif \"none\" in pattern_clean or \"not_classified\" in pattern_clean:\n",
+    "            return None\n",
+    "        else:\n",
+    "            return None  # Unknown patterns also excluded\n",
+    "\n",
+    "    # Add category column\n",
+    "    collab_data[\"category\"] = collab_data[\"cluster_name\"].apply(categorize_pattern)\n",
+    "\n",
+    "    # Filter to only patterns that have a category (excludes none, not_classified, etc.)\n",
+    "    collab_categorized = collab_data[collab_data[\"category\"].notna()].copy()\n",
+    "\n",
+    "    if collab_categorized.empty:\n",
+    "        return df_result\n",
+    "\n",
+    "    # Process by geography\n",
+    "    new_rows = []\n",
+    "\n",
+    "    # Group by geography and geo_id\n",
+    "    for (geography, geo_id), geo_data in collab_categorized.groupby(\n",
+    "        [\"geography\", \"geo_id\"]\n",
+    "    ):\n",
+    "        # Apply filtering based on geography level\n",
+    "        if geography == \"country\" and filtered_countries is not None:\n",
+    "            if geo_id not in filtered_countries:\n",
+    "                continue  # Skip countries that don't meet threshold\n",
+    "        elif geography == \"state_us\" and filtered_states is not None:\n",
+    "            if geo_id not in filtered_states:\n",
+    "                continue  # Skip states that don't meet threshold\n",
+    "        # global is always included (no filtering)\n",
+    "\n",
+    "        # Calculate totals by category\n",
+    "        automation_total = geo_data[geo_data[\"category\"] == \"automation\"][\"value\"].sum()\n",
+    "        augmentation_total = geo_data[geo_data[\"category\"] == \"augmentation\"][\n",
+    "            \"value\"\n",
+    "        ].sum()\n",
+    "\n",
+    "        # Total of categorized patterns (excluding none and not_classified)\n",
+    "        total_categorized = automation_total + augmentation_total\n",
+    "\n",
+    "        if total_categorized > 0:\n",
+    "            # Get a sample row for metadata\n",
+    "            sample_row = geo_data.iloc[0]\n",
+    "\n",
+    "            # Create automation percentage row\n",
+    "            automation_row = {\n",
+    "                \"geo_id\": geo_id,\n",
+    "                \"geography\": geography,\n",
+    "                \"date_start\": sample_row[\"date_start\"],\n",
+    "                \"date_end\": sample_row[\"date_end\"],\n",
+    "                \"platform_and_product\": sample_row[\"platform_and_product\"],\n",
+    "                \"facet\": \"collaboration_automation_augmentation\",\n",
+    "                \"level\": 0,\n",
+    "                \"variable\": \"automation_pct\",\n",
+    "                \"cluster_name\": \"automation\",\n",
+    "                \"value\": (automation_total / total_categorized) * 100,\n",
+    "            }\n",
+    "            new_rows.append(automation_row)\n",
+    "\n",
+    "            # Create augmentation percentage row\n",
+    "            augmentation_row = {\n",
+    "                \"geo_id\": geo_id,\n",
+    "                \"geography\": geography,\n",
+    "                \"date_start\": sample_row[\"date_start\"],\n",
+    "                \"date_end\": sample_row[\"date_end\"],\n",
+    "                \"platform_and_product\": sample_row[\"platform_and_product\"],\n",
+    "                \"facet\": \"collaboration_automation_augmentation\",\n",
+    "                \"level\": 0,\n",
+    "                \"variable\": \"augmentation_pct\",\n",
+    "                \"cluster_name\": \"augmentation\",\n",
+    "                \"value\": (augmentation_total / total_categorized) * 100,\n",
+    "            }\n",
+    "            new_rows.append(augmentation_row)\n",
+    "\n",
+    "    # Add all new rows to result\n",
+    "    if new_rows:\n",
+    "        df_new = pd.DataFrame(new_rows)\n",
+    "        df_result = pd.concat([df_result, df_new], ignore_index=True)\n",
+    "\n",
+    "    return df_result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_iso3_and_names(df):\n",
+    "    \"\"\"\n",
+    "    Replace ISO-2 codes with ISO-3 codes and add geographic names.\n",
+    "\n",
+    "    This function:\n",
+    "    1. Replaces geo_id from ISO-2 to ISO-3 for countries\n",
+    "    2. Adds geo_name column with human-readable names for all geographies\n",
+    "    3. Preserves special geo_ids (like 'not_classified') that aren't in ISO mapping\n",
+    "\n",
+    "    Args:\n",
+    "        df: Enriched dataframe with geo_id (ISO-2 for countries, state codes for US states)\n",
+    "\n",
+    "    Returns:\n",
+    "        Dataframe with ISO-3 codes in geo_id and geo_name column added\n",
+    "    \"\"\"\n",
+    "    df_result = df.copy()\n",
+    "\n",
+    "    # Initialize geo_name column\n",
+    "    df_result[\"geo_name\"] = \"\"\n",
+    "\n",
+    "    # Load ISO mapping data for countries\n",
+    "    iso_path = Path(DATA_INTERMEDIATE_DIR) / \"iso_country_codes.csv\"\n",
+    "    if iso_path.exists():\n",
+    "        df_iso = pd.read_csv(iso_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "        # Create ISO-2 to ISO-3 mapping\n",
+    "        iso2_to_iso3 = dict(zip(df_iso[\"iso_alpha_2\"], df_iso[\"iso_alpha_3\"]))\n",
+    "\n",
+    "        # Create ISO-2 to country name mapping\n",
+    "        iso2_to_name = dict(zip(df_iso[\"iso_alpha_2\"], df_iso[\"country_name\"]))\n",
+    "\n",
+    "        # For all rows where geography is 'country', add country names and convert codes\n",
+    "        # This includes content facets that are broken down by country\n",
+    "        country_mask = df_result[\"geography\"] == \"country\"\n",
+    "\n",
+    "        # First, identify which geo_ids don't have ISO mappings\n",
+    "        country_geo_ids = df_result.loc[country_mask, \"geo_id\"].unique()\n",
+    "        unmapped_geo_ids = [\n",
+    "            g for g in country_geo_ids if g not in iso2_to_iso3 and pd.notna(g)\n",
+    "        ]\n",
+    "\n",
+    "        if unmapped_geo_ids:\n",
+    "            print(\n",
+    "                f\"\\nWarning: The following geo_ids are not in ISO-2 mapping and will be kept as-is:\"\n",
+    "            )\n",
+    "            for geo_id in unmapped_geo_ids:\n",
+    "                # Count rows and usage for this geo_id\n",
+    "                geo_mask = (df_result[\"geography\"] == \"country\") & (\n",
+    "                    df_result[\"geo_id\"] == geo_id\n",
+    "                )\n",
+    "                row_count = geo_mask.sum()\n",
+    "                usage_mask = geo_mask & (df_result[\"variable\"] == \"usage_count\")\n",
+    "                usage_sum = (\n",
+    "                    df_result.loc[usage_mask, \"value\"].sum() if usage_mask.any() else 0\n",
+    "                )\n",
+    "                print(f\"  - '{geo_id}': {row_count} rows, {usage_sum:,.0f} usage count\")\n",
+    "\n",
+    "        # Check for geo_ids without country names\n",
+    "        unmapped_names = [g for g in unmapped_geo_ids if g not in iso2_to_name]\n",
+    "        if unmapped_names:\n",
+    "            print(\n",
+    "                f\"\\nWarning: The following geo_ids don't have country names and will use geo_id as name:\"\n",
+    "            )\n",
+    "            for geo_id in unmapped_names:\n",
+    "                print(f\"  - '{geo_id}'\")\n",
+    "\n",
+    "        # Apply country names BEFORE converting ISO-2 to ISO-3\n",
+    "        # The iso2_to_name dictionary uses ISO-2 codes as keys\n",
+    "        df_result.loc[country_mask, \"geo_name\"] = (\n",
+    "            df_result.loc[country_mask, \"geo_id\"]\n",
+    "            .map(iso2_to_name)\n",
+    "            .fillna(df_result.loc[country_mask, \"geo_id\"])\n",
+    "        )\n",
+    "\n",
+    "        # Convert ISO-2 to ISO-3 codes\n",
+    "        df_result.loc[country_mask, \"geo_id\"] = (\n",
+    "            df_result.loc[country_mask, \"geo_id\"]\n",
+    "            .map(iso2_to_iso3)\n",
+    "            .fillna(df_result.loc[country_mask, \"geo_id\"])\n",
+    "        )\n",
+    "    else:\n",
+    "        print(f\"Warning: ISO mapping file not found at {iso_path}\")\n",
+    "\n",
+    "    # Load state names from census data\n",
+    "    state_codes_path = Path(DATA_INPUT_DIR) / \"census_state_codes.txt\"\n",
+    "    if state_codes_path.exists():\n",
+    "        df_state_codes = pd.read_csv(state_codes_path, sep=\"|\")\n",
+    "        # Create state code to name mapping (STUSAB is the 2-letter code, STATE_NAME is the full name)\n",
+    "        state_code_to_name = dict(\n",
+    "            zip(df_state_codes[\"STUSAB\"], df_state_codes[\"STATE_NAME\"])\n",
+    "        )\n",
+    "\n",
+    "        # For all rows where geography is 'state_us', add state names\n",
+    "        state_mask = df_result[\"geography\"] == \"state_us\"\n",
+    "        df_result.loc[state_mask, \"geo_name\"] = df_result.loc[state_mask, \"geo_id\"].map(\n",
+    "            state_code_to_name\n",
+    "        )\n",
+    "    else:\n",
+    "        print(f\"Warning: State census file not found at {state_codes_path}\")\n",
+    "\n",
+    "    # For global entries\n",
+    "    global_mask = df_result[\"geography\"] == \"global\"\n",
+    "    df_result.loc[global_mask, \"geo_name\"] = \"global\"\n",
+    "\n",
+    "    # Fill any missing geo_names with geo_id as fallback\n",
+    "    df_result.loc[df_result[\"geo_name\"] == \"\", \"geo_name\"] = df_result.loc[\n",
+    "        df_result[\"geo_name\"] == \"\", \"geo_id\"\n",
+    "    ]\n",
+    "    df_result[\"geo_name\"] = df_result[\"geo_name\"].fillna(df_result[\"geo_id\"])\n",
+    "\n",
+    "    return df_result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Main Processing Function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def enrich_clio_data(input_path, output_path, external_data=None):\n",
+    "    \"\"\"\n",
+    "    Enrich processed Clio data with external sources.\n",
+    "\n",
+    "    Args:\n",
+    "        input_path: Path to processed Clio data\n",
+    "        output_path: Path for enriched CSV output\n",
+    "        external_data: Pre-loaded external data (optional)\n",
+    "\n",
+    "    Returns:\n",
+    "        Path to enriched data file\n",
+    "    \"\"\"\n",
+    "    # Load processed Clio data - use keep_default_na=False to preserve \"NA\" (Namibia)\n",
+    "    df = pd.read_csv(input_path, keep_default_na=False, na_values=[\"\"])\n",
+    "\n",
+    "    # Load external data if not provided\n",
+    "    if external_data is None:\n",
+    "        external_data = load_external_data()\n",
+    "\n",
+    "    # Get filtered geographies (but keep all data in the dataframe)\n",
+    "    filtered_countries, filtered_states = get_filtered_geographies(df)\n",
+    "\n",
+    "    # Merge with population data\n",
+    "    df = merge_population_data(df, external_data[\"population\"])\n",
+    "\n",
+    "    # Merge with GDP data (pass population data for per capita calculation)\n",
+    "    df = merge_gdp_data(df, external_data[\"gdp\"], external_data[\"population\"])\n",
+    "\n",
+    "    # Calculate SOC occupation distribution from O*NET tasks\n",
+    "    # Only for geographies that meet MIN_OBSERVATIONS threshold\n",
+    "    df = calculate_soc_distribution(\n",
+    "        df,\n",
+    "        external_data[\"task_statements\"],\n",
+    "        external_data[\"soc_structure\"],\n",
+    "        filtered_countries=filtered_countries,\n",
+    "        filtered_states=filtered_states,\n",
+    "    )\n",
+    "\n",
+    "    # Calculate per capita metrics\n",
+    "    df = calculate_per_capita_metrics(df)\n",
+    "\n",
+    "    # Calculate usage index - pass filtered countries/states to only use them for baseline\n",
+    "    df = calculate_usage_per_capita_index(\n",
+    "        df, filtered_countries=filtered_countries, filtered_states=filtered_states\n",
+    "    )\n",
+    "\n",
+    "    # Calculate category percentage index - pass filtered countries/states\n",
+    "    df = calculate_category_percentage_index(\n",
+    "        df, filtered_countries=filtered_countries, filtered_states=filtered_states\n",
+    "    )\n",
+    "\n",
+    "    # Calculate usage tiers - pass filtered countries/states to only use them\n",
+    "    df = calculate_usage_tiers(\n",
+    "        df, filtered_countries=filtered_countries, filtered_states=filtered_states\n",
+    "    )\n",
+    "\n",
+    "    # Add collaboration categorization\n",
+    "    df = calculate_automation_augmentation_metrics(df)\n",
+    "\n",
+    "    # Add ISO-3 codes and geographic names\n",
+    "    df = add_iso3_and_names(df)\n",
+    "\n",
+    "    # Sort for consistent output ordering\n",
+    "    df = df.sort_values(\n",
+    "        [\"geography\", \"geo_id\", \"facet\", \"level\", \"cluster_name\", \"variable\"]\n",
+    "    )\n",
+    "\n",
+    "    # Save enriched data as CSV\n",
+    "    df.to_csv(output_path, index=False)\n",
+    "\n",
+    "    return str(output_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Merge External Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input_path = \"../data/intermediate/aei_raw_claude_ai_2025-08-04_to_2025-08-11.csv\"\n",
+    "output_path = \"../data/output/aei_enriched_claude_ai_2025-08-04_to_2025-08-11.csv\"\n",
+    "\n",
+    "enrich_clio_data(input_path, output_path)\n",
+    "print(f\"\\n✅ Enrichment complete! Output: {output_path}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py311",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

release_2025_09_15/code/preprocess_gdp.py

@@ -0,0 +1,364 @@
+"""
+Preprocess GDP data for economic analysis.
+
+This script downloads and processes GDP data from:
+1. IMF API for country-level GDP data
+2. BEA (Bureau of Economic Analysis) for US state-level GDP data
+
+Output files:
+- gdp_YYYY_country.csv (e.g., gdp_2024_country.csv): Country-level total GDP
+- gdp_YYYY_us_state.csv (e.g., gdp_2024_us_state.csv): US state-level total GDP
+"""
+
+import io
+import json
+import warnings
+from pathlib import Path
+
+import httpx
+import pandas as pd
+
+# Global configuration
+YEAR = 2024
+DATA_INPUT_DIR = Path("../data/input")
+DATA_INTERMEDIATE_DIR = Path("../data/intermediate")
+
+
+# Countries where Claude AI service is not available
+# These will be excluded from all GDP data
+EXCLUDED_COUNTRIES = [
+    "AFG",
+    "BLR",
+    "COD",
+    "CAF",
+    "CHN",
+    "CUB",
+    "ERI",
+    "ETH",
+    "HKG",
+    "IRN",
+    "PRK",
+    "LBY",
+    "MLI",
+    "MMR",
+    "MAC",
+    "NIC",
+    "RUS",
+    "SDN",
+    "SOM",
+    "SSD",
+    "SYR",
+    "VEN",
+    "YEM",
+]
+
+
+def check_existing_files():
+    """Check if processed GDP files already exist."""
+    gdp_country_path = DATA_INTERMEDIATE_DIR / f"gdp_{YEAR}_country.csv"
+    gdp_state_path = DATA_INTERMEDIATE_DIR / f"gdp_{YEAR}_us_state.csv"
+
+    if gdp_country_path.exists() and gdp_state_path.exists():
+        print("✅ GDP files already exist:")
+        print(f"  - {gdp_country_path}")
+        print(f"  - {gdp_state_path}")
+        print("Skipping GDP preprocessing. Delete these files if you want to re-run.")
+        return True
+    return False
+
+
+def load_country_gdp_data():
+    """
+    Load country-level GDP data from cache or IMF API.
+
+    Returns:
+        dict: Raw GDP data from IMF API, or None if fetch fails
+    """
+    # Check if raw data already exists
+    raw_gdp_path = DATA_INPUT_DIR / f"imf_gdp_raw_{YEAR}.json"
+    if raw_gdp_path.exists():
+        print("Loading cached IMF GDP data...")
+        with open(raw_gdp_path) as f:
+            return json.load(f)
+
+    # Download if not cached
+    imf_total_gdp_url = "https://www.imf.org/external/datamapper/api/v1/NGDPD"  # IMF returns GDP in billions USD
+
+    print("Fetching GDP data from IMF API...")
+    try:
+        with httpx.Client() as client:
+            response = client.get(imf_total_gdp_url, timeout=30)
+            response.raise_for_status()
+            gdp_data = response.json()
+            print("✓ Successfully fetched total GDP data from IMF API")
+
+            # Save raw data for future use
+            with open(raw_gdp_path, "w") as f:
+                json.dump(gdp_data, f, indent=2)
+            print(f"✓ Saved raw GDP data to {raw_gdp_path}")
+
+            return gdp_data
+    except Exception as e:
+        raise ConnectionError(f"Failed to fetch data from IMF API: {e}") from e
+
+
+def process_country_gdp_data(gdp_data):
+    """
+    Process IMF GDP data into standardized format.
+
+    Args:
+        gdp_data: Raw IMF API response
+
+    Returns:
+        pd.DataFrame: Processed country GDP data (excluding countries where service is not available)
+    """
+    # Extract GDP data for target year
+    # Structure: {"values": {"NGDPD": {"countryiso3code": {"year": value}}}}
+    gdp_values = gdp_data.get("values", {}).get("NGDPD", {})
+
+    # Build records for target year data only
+    gdp_records = []
+    target_year = str(YEAR)
+    missing_countries = []
+
+    for countryiso3code, years_data in gdp_values.items():
+        if isinstance(years_data, dict):
+            if target_year in years_data and years_data[target_year]:
+                gdp_value = years_data[target_year]
+                # Convert from billions to actual dollars
+                gdp_records.append(
+                    {
+                        "iso_alpha_3": countryiso3code,
+                        "gdp_total": float(gdp_value)
+                        * 1e9,  # Convert billions to dollars
+                        "year": YEAR,
+                    }
+                )
+            else:
+                missing_countries.append(countryiso3code)
+
+    if missing_countries:
+        warnings.warn(
+            f"{len(missing_countries)} countries missing {YEAR} GDP data. "
+            f"Examples: {missing_countries[:5]}",
+            UserWarning,
+            stacklevel=2,
+        )
+
+    df_gdp = pd.DataFrame(gdp_records)
+
+    if df_gdp.empty:
+        raise ValueError(f"No GDP data available for year {YEAR}")
+
+    # Apply country code mappings for mismatches between IMF and ISO3
+    country_code_mappings = {
+        "UVK": "XKX",  # Kosovo
+        # Add more mappings as needed
+    }
+
+    for imf_code, iso3_code in country_code_mappings.items():
+        df_gdp.loc[df_gdp["iso_alpha_3"] == imf_code, "iso_alpha_3"] = iso3_code
+
+    # Filter to only keep countries with valid ISO-3 codes
+    # This removes regional aggregates like ADVEC, AFQ, etc.
+    iso_codes_path = DATA_INTERMEDIATE_DIR / "iso_country_codes.csv"
+    df_iso = pd.read_csv(iso_codes_path, keep_default_na=False, na_values=[""])
+    valid_iso3_codes = set(df_iso["iso_alpha_3"].unique())
+
+    initial_aggregate_count = len(df_gdp)
+    df_gdp = df_gdp[df_gdp["iso_alpha_3"].isin(valid_iso3_codes)]
+    filtered_aggregates = initial_aggregate_count - len(df_gdp)
+
+    if filtered_aggregates > 0:
+        print(
+            f"  Filtered out {filtered_aggregates} non-country codes (regional aggregates)"
+        )
+
+    # Filter out excluded countries (now using 3-letter codes directly)
+    initial_count = len(df_gdp)
+    df_gdp = df_gdp[~df_gdp["iso_alpha_3"].isin(EXCLUDED_COUNTRIES)]
+    excluded_count = initial_count - len(df_gdp)
+
+    if excluded_count > 0:
+        print(f"  Excluded {excluded_count} countries where service is not available")
+
+    # Save processed GDP data
+    processed_gdp_path = DATA_INTERMEDIATE_DIR / f"gdp_{YEAR}_country.csv"
+    df_gdp.to_csv(processed_gdp_path, index=False)
+
+    print(f"✓ Saved processed GDP data to {processed_gdp_path}")
+    print(f"  Countries with {YEAR} GDP data: {len(df_gdp)}")
+    print(f"  Countries excluded (service not available): {len(EXCLUDED_COUNTRIES)}")
+    print(f"  Total global GDP: ${df_gdp['gdp_total'].sum() / 1e12:.2f} trillion")
+
+    return df_gdp
+
+
+def load_state_gdp_data():
+    """
+    Load US state GDP data from BEA file.
+
+    Returns:
+        pd.DataFrame: Raw state GDP data, or None if file not found
+    """
+    state_gdp_raw_path = DATA_INPUT_DIR / f"bea_us_state_gdp_{YEAR}.csv"
+
+    if not state_gdp_raw_path.exists():
+        error_msg = f"""
+State GDP data not found at: {state_gdp_raw_path}
+
+To obtain this data:
+1. Go to: https://apps.bea.gov/itable/?ReqID=70&step=1
+2. Select: SASUMMARY State annual summary statistics (area = "United States", statistic = Gross domestic product (GDP), unit of measure = "Levels")
+3. Download the CSV file for year {YEAR}
+4. Save it as: bea_us_state_gdp_{YEAR}.csv
+5. Place it in your data input directory
+"""
+        raise FileNotFoundError(error_msg)
+
+    print("Loading US state GDP data...")
+    # Parse CSV skipping the first 3 rows (BEA metadata)
+    df_state_gdp_raw = pd.read_csv(state_gdp_raw_path, skiprows=3)
+    df_state_gdp_raw.columns = ["GeoFips", "State", f"gdp_{YEAR}_millions"]
+
+    return df_state_gdp_raw
+
+
+def process_state_gdp_data(df_state_gdp_raw):
+    """
+    Process BEA state GDP data into standardized format.
+
+    Args:
+        df_state_gdp_raw: Raw BEA data
+
+    Returns:
+        pd.DataFrame: Processed state GDP data
+    """
+
+    # Remove the US total row (GeoFips = "00000")
+    df_state_gdp = df_state_gdp_raw[df_state_gdp_raw["GeoFips"] != "00000"].copy()
+
+    # Remove all rows starting from empty line before "Legend/Footnotes" marker
+    # BEA files have footer information after the data, with an empty line before
+    legend_index = (
+        df_state_gdp[
+            df_state_gdp["GeoFips"].str.contains("Legend", case=False, na=False)
+        ].index[0]
+        - 1
+    )
+    df_state_gdp = df_state_gdp.iloc[:legend_index].copy()
+    print(f"  Removed footer rows starting from 'Legend/Footnotes'")
+
+    # Convert GDP from millions to actual dollars
+    df_state_gdp["gdp_total"] = df_state_gdp[f"gdp_{YEAR}_millions"] * 1e6
+
+    # Clean state names
+    df_state_gdp["State"] = df_state_gdp["State"].str.strip()
+
+    # Get state codes
+    state_code_dict = get_state_codes()
+    df_state_gdp["state_code"] = df_state_gdp["State"].map(state_code_dict)
+
+    # Check for missing state codes
+    missing_codes = df_state_gdp[df_state_gdp["state_code"].isna()]
+    if not missing_codes.empty:
+        raise ValueError(
+            f"Could not find state codes for: {missing_codes['State'].tolist()}\n"
+            f"All BEA state names should match Census state codes after filtering."
+        )
+
+    # Select and rename columns
+    df_state_gdp_final = df_state_gdp[
+        ["state_code", "State", "gdp_total", f"gdp_{YEAR}_millions"]
+    ].copy()
+    df_state_gdp_final.columns = [
+        "state_code",
+        "state_name",
+        "gdp_total",
+        "gdp_millions",
+    ]
+    df_state_gdp_final["year"] = YEAR
+
+    # Save processed state GDP data
+    processed_state_gdp_path = DATA_INTERMEDIATE_DIR / f"gdp_{YEAR}_us_state.csv"
+    df_state_gdp_final.to_csv(processed_state_gdp_path, index=False)
+
+    print(
+        f"✓ Processed state GDP data for {len(df_state_gdp_final)} states/territories"
+    )
+    print(
+        f"  Total US GDP: ${df_state_gdp_final['gdp_total'].sum() / 1e12:.2f} trillion"
+    )
+    print(f"✓ Saved to {processed_state_gdp_path}")
+
+    return df_state_gdp_final
+
+
+def get_state_codes():
+    """
+    Get US state codes from Census Bureau.
+
+    Returns:
+        dict: Mapping of state names to abbreviations
+    """
+    state_codes_path = DATA_INPUT_DIR / "census_state_codes.txt"
+
+    if state_codes_path.exists():
+        print("  Loading cached state codes...")
+        df_state_codes = pd.read_csv(state_codes_path, sep="|")
+    else:
+        print("  Downloading state codes from Census Bureau...")
+        response = httpx.get("https://www2.census.gov/geo/docs/reference/state.txt")
+        response.raise_for_status()
+
+        # Save for future use
+        with open(state_codes_path, "w") as f:
+            f.write(response.text)
+        print(f"  Cached state codes to {state_codes_path}")
+
+        df_state_codes = pd.read_csv(io.StringIO(response.text), sep="|")
+
+    # Create mapping dictionary
+    state_code_dict = dict(
+        zip(df_state_codes["STATE_NAME"], df_state_codes["STUSAB"], strict=True)
+    )
+
+    return state_code_dict
+
+
+def main():
+    """Main function to run GDP preprocessing."""
+    # Check if files already exist
+    if check_existing_files():
+        return
+
+    print("=" * 60)
+    print(f"PROCESSING {YEAR} GDP DATA")
+    print("=" * 60)
+
+    # Process country-level GDP from IMF
+    print(f"\n=== Country-Level GDP (IMF) - Year {YEAR} ===")
+    gdp_data = load_country_gdp_data()
+    df_gdp_country = process_country_gdp_data(gdp_data)
+
+    # Process US state-level GDP from BEA
+    print(f"\n=== US State-Level GDP (BEA) - Year {YEAR} ===")
+    df_state_gdp_raw = load_state_gdp_data()
+    df_gdp_state = process_state_gdp_data(df_state_gdp_raw)
+
+    # Final status
+    print(f"\n✅ {YEAR} GDP data preprocessing complete!")
+    print("\n=== Summary Statistics ===")
+    if df_gdp_country is not None:
+        print(f"Countries processed: {len(df_gdp_country)}")
+        print(f"Countries excluded (service not available): {len(EXCLUDED_COUNTRIES)}")
+        print(
+            f"Total global GDP: ${df_gdp_country['gdp_total'].sum() / 1e12:.2f} trillion"
+        )
+    if df_gdp_state is not None:
+        print(f"US states processed: {len(df_gdp_state)}")
+        print(f"Total US GDP: ${df_gdp_state['gdp_total'].sum() / 1e12:.2f} trillion")
+
+
+if __name__ == "__main__":
+    main()

release_2025_09_15/code/preprocess_iso_codes.py

@@ -0,0 +1,111 @@
+"""
+Fetch ISO country code mappings from GeoNames.
+
+This script fetches comprehensive country data from GeoNames countryInfo.txt
+and saves it as a CSV file for use in data preprocessing pipelines.
+"""
+
+import io
+from pathlib import Path
+
+import httpx
+import pandas as pd
+
+
+def fetch_country_mappings(save_raw=True):
+    """
+    Fetch country code mappings from GeoNames.
+
+    Args:
+        save_raw: Whether to save raw data file to data/input
+
+    Returns:
+        pd.DataFrame: DataFrame with country information from GeoNames
+    """
+    # Fetch countryInfo.txt from GeoNames
+    geonames_url = "https://download.geonames.org/export/dump/countryInfo.txt"
+
+    with httpx.Client() as client:
+        response = client.get(geonames_url)
+        response.raise_for_status()
+        content = response.text
+
+    # Save raw file to data/input for reference
+    if save_raw:
+        input_dir = Path("../data/input")
+        input_dir.mkdir(parents=True, exist_ok=True)
+
+        raw_path = input_dir / "geonames_countryInfo.txt"
+        with open(raw_path, "w", encoding="utf-8") as f:
+            f.write(content)
+
+    # Extract column names from the last comment line
+    lines = content.split("\n")
+    header_line = [line for line in lines if line.startswith("#")][-1]
+    column_names = header_line[1:].split("\t")  # Remove # and split by tab
+
+    # Parse the tab-separated file
+    # keep_default_na=False to prevent "NA" (Namibia) from becoming NaN
+    df = pd.read_csv(
+        io.StringIO(content),
+        sep="\t",
+        comment="#",
+        header=None,  # No header row in the data
+        keep_default_na=False,  # Don't interpret "NA" as NaN (needed for Namibia)
+        na_values=[""],  # Only treat empty strings as NaN
+        names=column_names,  # Use the column names from the comment
+    )
+
+    # Rename columns to our standard format
+    df = df.rename(
+        columns={"ISO": "iso_alpha_2", "ISO3": "iso_alpha_3", "Country": "country_name"}
+    )
+
+    return df
+
+
+def create_country_dataframe(geonames_df):
+    """
+    Create a cleaned DataFrame with country codes and names.
+
+    Args:
+        geonames_df: DataFrame from GeoNames with all country information
+
+    Returns:
+        pd.DataFrame: DataFrame with columns [iso_alpha_2, iso_alpha_3, country_name]
+    """
+    # Select only the columns we need
+    df = geonames_df[["iso_alpha_2", "iso_alpha_3", "country_name"]].copy()
+
+    # Sort by country name for consistency
+    df = df.sort_values("country_name").reset_index(drop=True)
+
+    return df
+
+
+def save_country_codes(output_path="../data/intermediate/iso_country_codes.csv"):
+    """
+    Fetch country codes from GeoNames and save to CSV.
+
+    Args:
+        output_path: Path to save the CSV file
+    """
+    # Fetch full GeoNames data
+    geonames_df = fetch_country_mappings()
+
+    # Create cleaned DataFrame with just the columns we need
+    df = create_country_dataframe(geonames_df)
+
+    # Ensure output directory exists
+    output_file = Path(output_path)
+    output_file.parent.mkdir(parents=True, exist_ok=True)
+
+    # Save to CSV
+    df.to_csv(output_file, index=False)
+
+    return df
+
+
+if __name__ == "__main__":
+    # Fetch and save country codes
+    df = save_country_codes()

release_2025_09_15/code/preprocess_onet.py

@@ -0,0 +1,179 @@
+"""
+Preprocess O*NET and SOC data for economic analysis.
+
+This script downloads and processes occupational data from:
+1. O*NET Resource Center for task statements
+2. O*NET Resource Center for SOC structure
+
+Output files:
+- onet_task_statements.csv: O*NET task statements with SOC major groups
+- soc_structure.csv: SOC occupational classification structure
+"""
+
+import io
+import os
+import tempfile
+from pathlib import Path
+
+import httpx
+import pandas as pd
+
+# Global configuration
+DATA_INPUT_DIR = Path("../data/input")
+DATA_INTERMEDIATE_DIR = Path("../data/intermediate")
+
+
+def check_existing_files():
+    """Check if processed O*NET/SOC files already exist."""
+    onet_task_statements_path = DATA_INTERMEDIATE_DIR / "onet_task_statements.csv"
+    soc_structure_path = DATA_INTERMEDIATE_DIR / "soc_structure.csv"
+
+    if onet_task_statements_path.exists() and soc_structure_path.exists():
+        print("✅ SOC/O*NET files already exist:")
+        print(f"  - {onet_task_statements_path}")
+        print(f"  - {soc_structure_path}")
+        print("Skipping SOC preprocessing. Delete these files if you want to re-run.")
+        return True
+    return False
+
+
+def load_task_data():
+    """
+    Load O*NET Task Statements from cache or O*NET Resource Center.
+
+    Returns:
+        pd.DataFrame: O*NET task statements data
+    """
+    # Check if raw data already exists
+    raw_onet_path = DATA_INPUT_DIR / "onet_task_statements_raw.xlsx"
+    if raw_onet_path.exists():
+        df_onet = pd.read_excel(raw_onet_path)
+        return df_onet
+
+    # Download if not cached
+    # O*NET Database version 20.1
+    onet_url = "https://www.onetcenter.org/dl_files/database/db_20_1_excel/Task%20Statements.xlsx"
+
+    print("Downloading O*NET task statements...")
+    try:
+        with httpx.Client(follow_redirects=True) as client:
+            response = client.get(onet_url, timeout=60)
+            response.raise_for_status()
+            excel_content = response.content
+            # Save raw data for future use
+            with open(raw_onet_path, "wb") as f:
+                f.write(excel_content)
+
+            # Save to temporary file for pandas to read
+            with tempfile.NamedTemporaryFile(suffix=".xlsx", delete=False) as tmp_file:
+                tmp_file.write(excel_content)
+                tmp_path = tmp_file.name
+
+            try:
+                df_onet = pd.read_excel(tmp_path)
+                return df_onet
+            finally:
+                os.unlink(tmp_path)
+
+    except Exception as e:
+        raise ConnectionError(f"Failed to download O*NET data: {e}") from e
+
+
+def process_task_data(df_tasks):
+    """
+    Process task statements data.
+
+    Args:
+        df_tasks: Raw task data
+
+    Returns:
+        pd.DataFrame: Processed O*NET data with SOC major groups
+    """
+    # Extract SOC major group from O*NET-SOC Code (first 2 digits)
+    df_tasks["soc_major_group"] = df_tasks["O*NET-SOC Code"].str[:2]
+
+    # Save processed task data
+    processed_tasks_path = DATA_INTERMEDIATE_DIR / "onet_task_statements.csv"
+    df_tasks.to_csv(processed_tasks_path, index=False)
+
+    print(
+        f"✓ Processed {len(df_tasks):,} task statements from {df_tasks['O*NET-SOC Code'].nunique()} occupations"
+    )
+
+    return df_tasks
+
+
+def load_soc_data():
+    """
+    Load SOC Structure from cache or O*NET Resource Center.
+
+    Returns:
+        pd.DataFrame: SOC structure data
+    """
+    # Check if raw data already exists
+    raw_soc_path = DATA_INPUT_DIR / "soc_structure_raw.csv"
+    if raw_soc_path.exists():
+        return pd.read_csv(raw_soc_path)
+
+    # Download if not cached
+    soc_url = "https://www.onetcenter.org/taxonomy/2019/structure/?fmt=csv"
+
+    print("Downloading SOC structure...")
+    try:
+        with httpx.Client(follow_redirects=True) as client:
+            response = client.get(soc_url, timeout=30)
+            response.raise_for_status()
+            soc_content = response.text
+            # Save raw data for future use
+            with open(raw_soc_path, "w") as f:
+                f.write(soc_content)
+
+            # Parse the CSV
+            df_soc = pd.read_csv(io.StringIO(soc_content))
+            return df_soc
+
+    except Exception as e:
+        raise ConnectionError(f"Failed to download SOC structure: {e}") from e
+
+
+def process_soc_data(df_soc):
+    """
+    Process SOC structure data.
+
+    Args:
+        df_soc: Raw SOC structure data
+
+    Returns:
+        pd.DataFrame: Processed SOC structure
+    """
+    # Extract the 2-digit code from Major Group (e.g., "11-0000" -> "11")
+    df_soc["soc_major_group"] = df_soc["Major Group"].str[:2]
+
+    # Save processed SOC structure
+    processed_soc_path = DATA_INTERMEDIATE_DIR / "soc_structure.csv"
+    df_soc.to_csv(processed_soc_path, index=False)
+
+    print(f"✓ Processed {len(df_soc):,} SOC entries")
+
+    return df_soc
+
+
+def main():
+    """Main function to run O*NET/SOC preprocessing."""
+    # Check if files already exist
+    if check_existing_files():
+        return
+
+    # Process Task Statements
+    df_tasks_raw = load_task_data()
+    process_task_data(df_tasks_raw)
+
+    # Process SOC Structure
+    df_soc_raw = load_soc_data()
+    process_soc_data(df_soc_raw)
+
+    print("\n✅ O*NET/SOC data preprocessing complete!")
+
+
+if __name__ == "__main__":
+    main()

release_2025_09_15/code/preprocess_population.py

@@ -0,0 +1,407 @@
+"""
+Preprocess population data for economic analysis.
+
+This script downloads and processes working-age population data (ages 15-64) from:
+1. World Bank API for country-level data
+2. Taiwan National Development Council for Taiwan data (not in World Bank)
+3. US Census Bureau for US state-level data
+
+Output files:
+- working_age_pop_YYYY_country.csv (e.g., working_age_pop_2024_country.csv): Country-level working age population
+- working_age_pop_YYYY_us_state.csv (e.g., working_age_pop_2024_us_state.csv): US state-level working age population
+"""
+
+import io
+import warnings
+from pathlib import Path
+
+import httpx
+import pandas as pd
+
+# Global configuration
+YEAR = 2024
+DATA_INPUT_DIR = Path("../data/input")
+DATA_INTERMEDIATE_DIR = Path("../data/intermediate")
+
+# Countries where Claude AI service is not available
+# These will be excluded from all population data
+EXCLUDED_COUNTRIES = [
+    "AF",  # Afghanistan
+    "BY",  # Belarus
+    "CD",  # Democratic Republic of the Congo
+    "CF",  # Central African Republic
+    "CN",  # China
+    "CU",  # Cuba
+    "ER",  # Eritrea
+    "ET",  # Ethiopia
+    "HK",  # Hong Kong
+    "IR",  # Iran
+    "KP",  # North Korea
+    "LY",  # Libya
+    "ML",  # Mali
+    "MM",  # Myanmar
+    "MO",  # Macau
+    "NI",  # Nicaragua
+    "RU",  # Russia
+    "SD",  # Sudan
+    "SO",  # Somalia
+    "SS",  # South Sudan
+    "SY",  # Syria
+    "VE",  # Venezuela
+    "YE",  # Yemen
+]
+
+
+def check_existing_files():
+    """Check if processed population files already exist."""
+    processed_country_pop_path = (
+        DATA_INTERMEDIATE_DIR / f"working_age_pop_{YEAR}_country.csv"
+    )
+    processed_state_pop_path = (
+        DATA_INTERMEDIATE_DIR / f"working_age_pop_{YEAR}_us_state.csv"
+    )
+
+    if processed_country_pop_path.exists() and processed_state_pop_path.exists():
+        print("✅ Population files already exist:")
+        print(f"  - {processed_country_pop_path}")
+        print(f"  - {processed_state_pop_path}")
+        print(
+            "Skipping population preprocessing. Delete these files if you want to re-run."
+        )
+        return True
+    return False
+
+
+def load_world_bank_population_data():
+    """
+    Load country-level working age population data from cache or World Bank API.
+
+    Returns:
+        pd.DataFrame: Raw population data from World Bank
+    """
+    # Check if raw data already exists
+    raw_country_pop_path = DATA_INPUT_DIR / f"working_age_pop_{YEAR}_country_raw.csv"
+    if raw_country_pop_path.exists():
+        print("Loading cached country population data...")
+        return pd.read_csv(raw_country_pop_path, keep_default_na=False, na_values=[""])
+
+    # Download if not cached
+    url = "https://api.worldbank.org/v2/country/all/indicator/SP.POP.1564.TO"
+    params = {"format": "json", "date": str(YEAR), "per_page": "1000"}
+
+    print("Downloading country population data from World Bank API...")
+    response = httpx.get(url, params=params)
+    response.raise_for_status()
+
+    # World Bank API returns [metadata, data] structure
+    data = response.json()[1]
+    df_raw = pd.json_normalize(data)
+
+    return df_raw
+
+
+def filter_to_country_level_data(df_raw):
+    """
+    Filter World Bank data to exclude regional aggregates and keep only countries.
+
+    The World Bank data starts with regional aggregates (Arab World, Caribbean small states, etc.)
+    followed by actual countries starting with Afghanistan (AFG).
+
+    Args:
+        df_raw: Raw World Bank data
+
+    Returns:
+        pd.DataFrame: Filtered data with only country-level records
+    """
+    # Find Afghanistan (AFG) - the first real country after aggregates
+    afg_index = df_raw[df_raw["countryiso3code"] == "AFG"].index[0]
+
+    # Keep everything from AFG onwards
+    df_filtered = df_raw.iloc[afg_index:].copy()
+    print(f"Filtered to {len(df_filtered)} countries (excluding regional aggregates)")
+
+    return df_filtered
+
+
+def process_country_population_data(df_raw):
+    """
+    Process raw World Bank population data.
+
+    Args:
+        df_raw: Raw data from World Bank API
+
+    Returns:
+        pd.DataFrame: Processed country population data (excluding countries where service is not available)
+    """
+    # Filter to country level only
+    df_country = filter_to_country_level_data(df_raw)
+
+    # Select and rename columns
+    df_processed = df_country[
+        ["countryiso3code", "date", "value", "country.id", "country.value"]
+    ].copy()
+
+    df_processed.columns = [
+        "iso_alpha_3",
+        "year",
+        "working_age_pop",
+        "country_code",
+        "country_name",
+    ]
+
+    # Convert year to int
+    df_processed["year"] = pd.to_numeric(df_processed["year"])
+    df_processed = df_processed.dropna(subset=["working_age_pop"])
+
+    # Remove Channel Islands entry with invalid JG code
+    channel_islands_mask = df_processed["country_code"] == "JG"
+    if channel_islands_mask.any():
+        print(f"Removing Channel Islands entry with invalid code 'JG'")
+        df_processed = df_processed[~channel_islands_mask].copy()
+
+    # Exclude countries where service is not available
+    initial_count = len(df_processed)
+    df_processed = df_processed[~df_processed["country_code"].isin(EXCLUDED_COUNTRIES)]
+    excluded_count = initial_count - len(df_processed)
+
+    if excluded_count > 0:
+        print(f"Excluded {excluded_count} countries where service is not available")
+
+    return df_processed
+
+
+def add_taiwan_population(df_country):
+    """
+    Add Taiwan population data from National Development Council.
+
+    The World Bank API excludes Taiwan, so we use data directly from Taiwan's NDC.
+    Source: https://pop-proj.ndc.gov.tw/main_en/Custom_Detail_Statistics_Search.aspx
+
+    Args:
+        df_country: Country population dataframe
+
+    Returns:
+        pd.DataFrame: Country data with Taiwan added
+    """
+    taiwan_file = DATA_INPUT_DIR / "Population by single age _20250903072924.csv"
+
+    if not taiwan_file.exists():
+        error_msg = f"""
+Taiwan population data not found at: {taiwan_file}
+
+To obtain this data:
+1. Go to: https://pop-proj.ndc.gov.tw/main_en/Custom_Detail_Statistics_Search.aspx?n=175&_Query=258170a1-1394-49fe-8d21-dc80562b72fb&page=1&PageSize=10&ToggleType=
+2. The following options should have been selected:
+   - Estimate type: Medium variant
+   - Gender: Total
+   - Year: {YEAR}
+   - Age: Single age (ages 15-64)
+   - Data attribute: data value
+3. Download the CSV file
+4. Save it as: "Population by single age _20250903072924.csv"
+5. Place it in your data input directory
+
+Note: Taiwan data is not available from World Bank API and must be obtained separately.
+"""
+        raise FileNotFoundError(error_msg)
+
+    print("Adding Taiwan population data from NDC...")
+
+    # Load the NDC data (skip metadata rows)
+    df_taiwan = pd.read_csv(taiwan_file, skiprows=10)
+
+    # Clean the age column and sum population
+    df_taiwan["Age"] = df_taiwan["Age"].str.replace("'", "")
+    df_taiwan["Age"] = pd.to_numeric(df_taiwan["Age"])
+
+    # The data is pre-filtered to ages 15-64, so sum all values
+    taiwan_working_age_pop = df_taiwan["Data value (persons)"].sum()
+
+    # Create Taiwan row
+    taiwan_row = pd.DataFrame(
+        {
+            "iso_alpha_3": ["TWN"],
+            "year": [YEAR],
+            "working_age_pop": [taiwan_working_age_pop],
+            "country_code": ["TW"],
+            "country_name": ["Taiwan"],
+        }
+    )
+
+    # Add Taiwan to the country data
+    df_with_taiwan = pd.concat([df_country, taiwan_row], ignore_index=True)
+    print(f"Added Taiwan: {taiwan_working_age_pop:,.0f} working age population")
+
+    return df_with_taiwan
+
+
+def load_us_state_population_data():
+    """
+    Load US state population data from cache or Census Bureau.
+
+    Returns:
+        pd.DataFrame: Raw US state population data
+    """
+    # Check if raw data already exists
+    raw_state_pop_path = DATA_INPUT_DIR / f"sc-est{YEAR}-agesex-civ.csv"
+    if raw_state_pop_path.exists():
+        print("Loading cached state population data...")
+        return pd.read_csv(raw_state_pop_path)
+
+    # Download if not cached
+    url = f"https://www2.census.gov/programs-surveys/popest/datasets/2020-{YEAR}/state/asrh/sc-est{YEAR}-agesex-civ.csv"
+
+    print("Downloading US state population data from Census Bureau...")
+    response = httpx.get(url)
+    response.raise_for_status()
+
+    df_raw = pd.read_csv(io.StringIO(response.text))
+    return df_raw
+
+
+def process_state_population_data(df_raw):
+    """
+    Process US state population data to get working age population.
+
+    Args:
+        df_raw: Raw Census Bureau data
+
+    Returns:
+        pd.DataFrame: Processed state population data with state codes
+    """
+    # Filter for working age (15-64) and sum by state
+    # SEX=0 means "Both sexes" to avoid double counting
+    df_working_age = df_raw[
+        (df_raw["AGE"] >= 15) & (df_raw["AGE"] <= 64) & (df_raw["SEX"] == 0)
+    ]
+
+    # Sum by state
+    working_age_by_state = (
+        df_working_age.groupby("NAME")[f"POPEST{YEAR}_CIV"].sum().reset_index()
+    )
+    working_age_by_state.columns = ["state", "working_age_pop"]
+
+    # Get state codes
+    state_code_dict = get_state_codes()
+
+    # Filter out "United States" row (national total, not a state)
+    working_age_by_state = working_age_by_state[
+        working_age_by_state["state"] != "United States"
+    ]
+
+    # Map state names to abbreviations
+    working_age_by_state["state_code"] = working_age_by_state["state"].map(
+        state_code_dict
+    )
+
+    # Check for missing state codes (should be none after filtering United States)
+    missing_codes = working_age_by_state[working_age_by_state["state_code"].isna()]
+    if not missing_codes.empty:
+        warnings.warn(
+            f"Could not find state codes for: {missing_codes['state'].tolist()}",
+            UserWarning,
+            stacklevel=2,
+        )
+
+    return working_age_by_state
+
+
+def get_state_codes():
+    """
+    Get US state codes from Census Bureau.
+
+    Returns:
+        dict: Mapping of state names to abbreviations
+    """
+    state_codes_path = DATA_INPUT_DIR / "census_state_codes.txt"
+
+    if state_codes_path.exists():
+        print("Loading cached state codes...")
+        df_state_codes = pd.read_csv(state_codes_path, sep="|")
+    else:
+        print("Downloading state codes from Census Bureau...")
+        response = httpx.get("https://www2.census.gov/geo/docs/reference/state.txt")
+        response.raise_for_status()
+
+        # Save for future use
+        with open(state_codes_path, "w") as f:
+            f.write(response.text)
+        print(f"Cached state codes to {state_codes_path}")
+
+        df_state_codes = pd.read_csv(io.StringIO(response.text), sep="|")
+
+    # Create mapping dictionary
+    state_code_dict = dict(
+        zip(df_state_codes["STATE_NAME"], df_state_codes["STUSAB"], strict=True)
+    )
+
+    return state_code_dict
+
+
+def save_data(df_country, df_state, df_world_bank_raw, df_state_raw):
+    """
+    Save raw and processed population data.
+
+    Args:
+        df_country: Processed country population data
+        df_state: Processed state population data
+        df_world_bank_raw: Raw World Bank data
+        df_state_raw: Raw Census Bureau data
+    """
+    # Save raw data (only if doesn't exist)
+    raw_country_pop_path = DATA_INPUT_DIR / f"working_age_pop_{YEAR}_country_raw.csv"
+    if not raw_country_pop_path.exists():
+        df_world_bank_raw.to_csv(raw_country_pop_path, index=False)
+        print(f"Saved raw country data to {raw_country_pop_path}")
+
+    raw_state_pop_path = DATA_INPUT_DIR / f"sc-est{YEAR}-agesex-civ.csv"
+    if not raw_state_pop_path.exists():
+        df_state_raw.to_csv(raw_state_pop_path, index=False)
+        print(f"Saved raw state data to {raw_state_pop_path}")
+
+    # Save processed data
+    country_output_path = DATA_INTERMEDIATE_DIR / f"working_age_pop_{YEAR}_country.csv"
+    df_country.to_csv(country_output_path, index=False)
+    print(f"Saved processed country population data to {country_output_path}")
+
+    state_output_path = DATA_INTERMEDIATE_DIR / f"working_age_pop_{YEAR}_us_state.csv"
+    df_state.to_csv(state_output_path, index=False)
+    print(f"Saved processed US state population data to {state_output_path}")
+
+
+def main():
+    """Main function to run population preprocessing."""
+    # Check if files already exist
+    if check_existing_files():
+        return
+
+    # Process country-level data
+    print("\n=== Processing Country-Level Population Data ===")
+    df_world_bank_raw = load_world_bank_population_data()
+    df_country = process_country_population_data(df_world_bank_raw)
+    df_country = add_taiwan_population(df_country)
+
+    # Process US state-level data
+    print("\n=== Processing US State-Level Population Data ===")
+    df_state_raw = load_us_state_population_data()
+    df_state = process_state_population_data(df_state_raw)
+
+    # Save all data (raw and processed)
+    print("\n=== Saving Data ===")
+    save_data(df_country, df_state, df_world_bank_raw, df_state_raw)
+
+    print("\n✅ Population data preprocessing complete!")
+
+    # Print summary statistics
+    print("\n=== Summary Statistics ===")
+    print(f"Countries processed: {len(df_country)}")
+    print(f"Countries excluded (service not available): {len(EXCLUDED_COUNTRIES)}")
+    print(
+        f"Total global working age population: {df_country['working_age_pop'].sum():,.0f}"
+    )
+    print(f"US states processed: {len(df_state)}")
+    print(f"Total US working age population: {df_state['working_age_pop'].sum():,.0f}")
+
+
+if __name__ == "__main__":
+    main()

release_2025_09_15/data/input/BTOS_National.xlsx

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release_2025_09_15/data/input/Population by single age _20250903072924.csv

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release_2025_09_15/data/input/bea_us_state_gdp_2024.csv

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release_2025_09_15/data/input/census_state_codes.txt

@@ -0,0 +1,58 @@
+STATE|STUSAB|STATE_NAME|STATENS
+01|AL|Alabama|01779775
+02|AK|Alaska|01785533
+04|AZ|Arizona|01779777
+05|AR|Arkansas|00068085
+06|CA|California|01779778
+08|CO|Colorado|01779779
+09|CT|Connecticut|01779780
+10|DE|Delaware|01779781
+11|DC|District of Columbia|01702382
+12|FL|Florida|00294478
+13|GA|Georgia|01705317
+15|HI|Hawaii|01779782
+16|ID|Idaho|01779783
+17|IL|Illinois|01779784
+18|IN|Indiana|00448508
+19|IA|Iowa|01779785
+20|KS|Kansas|00481813
+21|KY|Kentucky|01779786
+22|LA|Louisiana|01629543
+23|ME|Maine|01779787
+24|MD|Maryland|01714934
+25|MA|Massachusetts|00606926
+26|MI|Michigan|01779789
+27|MN|Minnesota|00662849
+28|MS|Mississippi|01779790
+29|MO|Missouri|01779791
+30|MT|Montana|00767982
+31|NE|Nebraska|01779792
+32|NV|Nevada|01779793
+33|NH|New Hampshire|01779794
+34|NJ|New Jersey|01779795
+35|NM|New Mexico|00897535
+36|NY|New York|01779796
+37|NC|North Carolina|01027616
+38|ND|North Dakota|01779797
+39|OH|Ohio|01085497
+40|OK|Oklahoma|01102857
+41|OR|Oregon|01155107
+42|PA|Pennsylvania|01779798
+44|RI|Rhode Island|01219835
+45|SC|South Carolina|01779799
+46|SD|South Dakota|01785534
+47|TN|Tennessee|01325873
+48|TX|Texas|01779801
+49|UT|Utah|01455989
+50|VT|Vermont|01779802
+51|VA|Virginia|01779803
+53|WA|Washington|01779804
+54|WV|West Virginia|01779805
+55|WI|Wisconsin|01779806
+56|WY|Wyoming|01779807
+60|AS|American Samoa|01802701
+66|GU|Guam|01802705
+69|MP|Northern Mariana Islands|01779809
+72|PR|Puerto Rico|01779808
+74|UM|U.S. Minor Outlying Islands|01878752
+78|VI|U.S. Virgin Islands|01802710