STOIC

.gitattributes

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README.md

@@ -6,8 +6,8 @@ tags:
 - LLM
 - Economic Impacts
 - Anthropic
-license: mit
 viewer: true
+license: mit
 configs:
 - config_name: release_2026_01_15
   data_files:
@@ -17,7 +17,6 @@ configs:
     path: "release_2025_09_15/data/intermediate/aei_raw_1p_api_2025-11-13_to_2025-11-20.csv"
 ---
 
-
 # The Anthropic Economic Index
 
 ## Overview
@@ -28,8 +27,6 @@ The Anthropic Economic Index provides insights into how AI is being incorporated
 
 This repository contains multiple data releases, each with its own documentation:
 
-- **[Labor market impacts](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/labor_market_impacts)**: Job exposure and task penetration data
-- **[2026-03-24 Release](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/release_2026_03_24)**: Updated analysis with Opus 4.5/4.6 and learning curves
 - **[2026-01-15 Release](https://huggingface.co/datasets/Anthropic/EconomicIndex/tree/main/release_2026_01_15)**: Updated analysis with economic primitives and Sonnet 4.5
 - **[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
@@ -39,13 +36,11 @@ This repository contains multiple data releases, each with its own documentation
 ## Resources
 
 - [Index Home Page](https://www.anthropic.com/economic-index)
-- [5th report](https://www.anthropic.com/research/economic-index-march-2026-report)
 - [4th report](https://www.anthropic.com/research/anthropic-economic-index-january-2026-report)
 - [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)
 
-- [Labor market impacts](https://www.anthropic.com/research/labor-market-impacts)
 
 ## License
 
@@ -57,18 +52,6 @@ For inquiries, contact econ-research@anthropic.com.
 
 ## Citation
 
-### Fifth release
-
-```
-@online{anthropic2026aeiv5,
-        author = {Maxim Massenkoff and Eva Lyubich and Peter McCrory and Ruth Appel and Ryan Heller},
-        title = {Anthropic Economic Index report: Learning curves},
-        date = {2026-03-24},
-        year = {2026},
-        url = {https://www.anthropic.com/research/economic-index-march-2026-report},
-}
-```
-
 ### Fourth release
 
 ```

release_2026_03_24/data/aei_raw_1p_api_2026-02-05_to_2026-02-12.csv

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release_2026_03_24/data/aei_raw_claude_ai_2026-02-05_to_2026-02-12.csv

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release_2026_03_24/data_documentation.md

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-# Data Documentation
-
-This document describes the data sources and variables used in the fourth Anthropic Economic Index (AEI) report.
-
-## Claude.ai Usage Data
-
-### Overview
-The core dataset contains Claude.ai usage metrics aggregated by geography and analysis dimensions (facets).
-
-**Source files**:
-- `aei_raw_claude_ai_2026-02-05_to_2026-02-12.csv` 
-
-**Note on data sources**: The AEI raw file contains raw counts and percentages.
-
-### Data Schema
-Each row represents one metric value for a specific geography and facet combination:
-
-| Column | Type | Description |
-|--------|------|-------------|
-| `geo_id` | string | Geographic identifier (ISO-3166-1 country code for countries, ISO 3166-2 region code for country-state, or "GLOBAL"). Examples: "USA", "AGO-LUA" (Angola-Luanda), "ALB-02" (Albania-Fier) (raw version uses 2- instead of 3-letter country codes) |
-| `geography` | string | Geographic level: "country", "country-state", or "global" |
-| `date_start` | date | Start of data collection period |
-| `date_end` | date | End of data collection period |
-| `platform_and_product` | string | "Claude AI (Free and Pro)" |
-| `facet` | string | Analysis dimension (see Facets below) |
-| `level` | integer | Sub-level within facet (0-2) |
-| `variable` | string | Metric name (see Variables below) |
-| `cluster_name` | string | Specific entity within facet (task, pattern, etc.). For intersections, format is "base::category" |
-| `value` | float | Numeric metric value |
-
-### Facets
-
-**Geographic Facets:**
-- **country**: Country-level aggregations
-- **country-state**: Subnational region aggregations (ISO 3166-2 regions globally)
-
-**Content Facets:**
-- **onet_task**: O*NET occupational tasks
-- **collaboration**: Human-AI collaboration patterns
-- **request**: Request complexity levels (0=highest granularity, 1=middle granularity, 2=lowest granularity)
-- **multitasking**: Whether conversation involves single or multiple tasks
-- **human_only_ability**: Whether a human could complete the task without AI assistance
-- **use_case**: Use case categories (work, coursework, personal)
-- **task_success**: Whether the task was successfully completed
-
-**Numeric Facets** (continuous variables with distribution statistics):
-- **human_only_time**: Estimated time for a human to complete the task without AI
-- **human_with_ai_time**: Estimated time for a human to complete the task with AI assistance
-- **ai_autonomy**: Degree of AI autonomy in task completion
-- **human_education_years**: Estimated years of human education required for the task
-- **ai_education_years**: Estimated equivalent years of AI "education" demonstrated
-
-**Intersection Facets:**
-- **onet_task::collaboration**: Intersection of O*NET tasks and collaboration patterns
-- **onet_task::multitasking**: Intersection of O*NET tasks and multitasking status
-- **onet_task::human_only_ability**: Intersection of O*NET tasks and human-only ability
-- **onet_task::use_case**: Intersection of O*NET tasks and use case categories
-- **onet_task::task_success**: Intersection of O*NET tasks and task success
-- **onet_task::human_only_time**: Mean human-only time per O*NET task
-- **onet_task::human_with_ai_time**: Mean human-with-AI time per O*NET task
-- **onet_task::ai_autonomy**: Mean AI autonomy per O*NET task
-- **onet_task::human_education_years**: Mean human education years per O*NET task
-- **onet_task::ai_education_years**: Mean AI education years per O*NET task
-- **request::collaboration**: Intersection of request categories and collaboration patterns
-- **request::multitasking**: Intersection of request categories and multitasking status
-- **request::human_only_ability**: Intersection of request categories and human-only ability
-- **request::use_case**: Intersection of request categories and use case categories
-- **request::task_success**: Intersection of request categories and task success
-- **request::human_only_time**: Mean human-only time per request category
-- **request::human_with_ai_time**: Mean human-with-AI time per request category
-- **request::ai_autonomy**: Mean AI autonomy per request category
-- **request::human_education_years**: Mean human education years per request category
-- **request::ai_education_years**: Mean AI education years per request category
-
-### Core Variables
-
-Variables follow the pattern `{prefix}_{suffix}` with specific meanings:
-
-**From AEI raw file**: `*_count`, `*_pct`
-
-#### Usage Metrics
-- **usage_count**: Total number of conversations/interactions in a geography
-- **usage_pct**: Percentage of total usage (relative to parent geography - global for countries, parent country for country-state regions)
-
-#### Content Facet Metrics
-**O*NET Task Metrics**:
-- **onet_task_count**: Number of conversations using this specific O*NET task
-- **onet_task_pct**: Percentage of geographic total using this task
-- **onet_task_pct_index**: Specialization index comparing task usage to baseline (global for countries, parent country for country-state regions)
-- **onet_task_collaboration_count**: Number of conversations with both this task and collaboration pattern (intersection)
-- **onet_task_collaboration_pct**: Percentage of the base task's total that has this collaboration pattern (sums to 100% within each task)
-
-#### Occupation Metrics
-- **soc_pct**: Percentage of classified O*NET tasks associated with this SOC major occupation group (e.g., Management, Computer and Mathematical)
-
-**Request Metrics**:
-- **request_count**: Number of conversations in this request category level
-- **request_pct**: Percentage of geographic total in this category
-- **request_collaboration_count**: Number of conversations with both this request category and collaboration pattern (intersection)
-- **request_collaboration_pct**: Percentage of the base request's total that has this collaboration pattern (sums to 100% within each request)
-
-**Collaboration Pattern Metrics**:
-- **collaboration_count**: Number of conversations with this collaboration pattern
-- **collaboration_pct**: Percentage of geographic total with this pattern
-
-**Multitasking Metrics**:
-- **multitasking_count**: Number of conversations with this multitasking status
-- **multitasking_pct**: Percentage of geographic total with this status
-
-**Human-Only Ability Metrics**:
-- **human_only_ability_count**: Number of conversations with this human-only ability status
-- **human_only_ability_pct**: Percentage of geographic total with this status
-
-**Use Case Metrics**:
-- **use_case_count**: Number of conversations in this use case category
-- **use_case_pct**: Percentage of geographic total in this category
-
-**Task Success Metrics**:
-- **task_success_count**: Number of conversations with this task success status
-- **task_success_pct**: Percentage of geographic total with this status
-
-#### Numeric Facet Metrics
-For numeric facets (human_only_time, human_with_ai_time, ai_autonomy, human_education_years, ai_education_years), the following distribution statistics are available:
-
-- **{facet}_mean**: Mean value across all conversations
-- **{facet}_median**: Median value across all conversations
-- **{facet}_stdev**: Standard deviation of values
-- **{facet}_mean_ci_lower**: Lower bound of 95% confidence interval for the mean
-- **{facet}_mean_ci_upper**: Upper bound of 95% confidence interval for the mean
-- **{facet}_median_ci_lower**: Lower bound of 95% confidence interval for the median
-- **{facet}_median_ci_upper**: Upper bound of 95% confidence interval for the median
-- **{facet}_count**: Total number of observations for this facet
-- **{facet}_histogram_count**: Count of observations in each histogram bin (one row per bin, bin range in cluster_name, e.g., "[1.0, 1.0)")
-- **{facet}_histogram_pct**: Percentage of observations in each histogram bin (one row per bin)
-
-For numeric intersection facets (e.g., onet_task::human_only_time), the same metrics are available per category (e.g., per O*NET task), with cluster_name containing the category identifier:
-- **{base}_{numeric}_mean**: Mean value for this category
-- **{base}_{numeric}_median**: Median value for this category
-- **{base}_{numeric}_stdev**: Standard deviation for this category
-- **{base}_{numeric}_count**: Number of observations for this category
-- **{base}_{numeric}_mean_ci_lower/upper**: 95% CI bounds for the mean
-- **{base}_{numeric}_median_ci_lower/upper**: 95% CI bounds for the median
-
-#### Special Values
-- **not_classified**: Indicates data that was filtered for privacy protection or could not be classified
-- **none**: Indicates the absence of the attribute (e.g., no collaboration, no task selected)
-
-### Data Processing Notes
-- **Minimum Observations**: 200 conversations per country, 100 per country-state region (applied in enrichment step, not raw preprocessing)
-- **not_classified**:
-  - For regular facets: Captures filtered/unclassified conversations
-  - For intersection facets: Each base cluster has its own not_classified (e.g., "task1::not_classified")
-- **Intersection Percentages**: Calculated relative to base cluster totals, ensuring each base cluster's percentages sum to 100%
-- **Country Codes**: ISO-3166-1 format for countries, three letter codes in the enriched file (e.g., "USA", "GBR", "FRA") and two letter codes in the raw file (e.g., "US", "GB", "FR"); ISO 3166-2 format for country-state regions (e.g., "AGO-LUA", "ALB-02" in enriched file, or "US-CA" in raw file)
-- **Variable Definitions**: See Core Variables section above
-
-## 1P API Usage Data
-
-### Overview
-Dataset containing first-party API usage metrics along various dimensions based on a sample of 1P API traffic and analyzed using privacy-preserving methods.
-
-**Note**: Unlike Claude.ai data, API data has **no geographic breakdowns** (no country or country-state facets). All API metrics are reported at global level only (`geography: "global"`, `geo_id: "GLOBAL"`).
-
-**Source file**: `aei_raw_1p_api_2026-02-05_to_2026-02-12.csv` 
-
-### Data Schema
-Each row represents one metric value for a specific facet combination at global level:
-
-| Column | Type | Description |
-|--------|------|-------------|
-| `geo_id` | string | Geographic identifier (always "GLOBAL" for API data) |
-| `geography` | string | Geographic level (always "global" for API data) |
-| `date_start` | date | Start of data collection period |
-| `date_end` | date | End of data collection period |
-| `platform_and_product` | string | "1P API" |
-| `facet` | string | Analysis dimension (see Facets below) |
-| `level` | integer | Sub-level within facet (0-2) |
-| `variable` | string | Metric name (see Variables below) |
-| `cluster_name` | string | Specific entity within facet. For intersections, format is "base::category" or "base::index"/"base::count" for mean value metrics |
-| `value` | float | Numeric metric value |
-
-### Facets
-
-**Content Facets:**
-- **onet_task**: O*NET occupational tasks
-- **collaboration**: Human-AI collaboration patterns
-- **request**: Request categories (hierarchical levels 0-2 from bottom-up taxonomy)
-- **multitasking**: Whether conversation involves single or multiple tasks
-- **human_only_ability**: Whether a human could complete the task without AI assistance
-- **use_case**: Use case categories (work, coursework, personal)
-- **task_success**: Whether the task was successfully completed
-
-**Numeric Facets** (continuous variables with distribution statistics):
-- **human_only_time**: Estimated time for a human to complete the task without AI
-- **human_with_ai_time**: Estimated time for a human to complete the task with AI assistance
-- **ai_autonomy**: Degree of AI autonomy in task completion
-- **human_education_years**: Estimated years of human education required for the task
-- **ai_education_years**: Estimated equivalent years of AI "education" demonstrated
-
-**Intersection Facets:**
-- **onet_task::collaboration**: Intersection of O*NET tasks and collaboration patterns
-- **onet_task::multitasking**: Intersection of O*NET tasks and multitasking status
-- **onet_task::human_only_ability**: Intersection of O*NET tasks and human-only ability
-- **onet_task::use_case**: Intersection of O*NET tasks and use case categories
-- **onet_task::task_success**: Intersection of O*NET tasks and task success
-- **onet_task::human_only_time**: Mean human-only time per O*NET task
-- **onet_task::human_with_ai_time**: Mean human-with-AI time per O*NET task
-- **onet_task::ai_autonomy**: Mean AI autonomy per O*NET task
-- **onet_task::human_education_years**: Mean human education years per O*NET task
-- **onet_task::ai_education_years**: Mean AI education years per O*NET task
-- **onet_task::cost**: Mean cost per O*NET task (indexed, 1.0 = average)
-- **onet_task::prompt_tokens**: Mean prompt tokens per O*NET task (indexed, 1.0 = average)
-- **onet_task::completion_tokens**: Mean completion tokens per O*NET task (indexed, 1.0 = average)
-- **request::collaboration**: Intersection of request categories and collaboration patterns
-- **request::multitasking**: Intersection of request categories and multitasking status
-- **request::human_only_ability**: Intersection of request categories and human-only ability
-- **request::use_case**: Intersection of request categories and use case categories
-- **request::task_success**: Intersection of request categories and task success
-- **request::human_only_time**: Mean human-only time per request category
-- **request::human_with_ai_time**: Mean human-with-AI time per request category
-- **request::ai_autonomy**: Mean AI autonomy per request category
-- **request::human_education_years**: Mean human education years per request category
-- **request::ai_education_years**: Mean AI education years per request category
-- **request::cost**: Mean cost per request category (indexed, 1.0 = average)
-- **request::prompt_tokens**: Mean prompt tokens per request category (indexed, 1.0 = average)
-- **request::completion_tokens**: Mean completion tokens per request category (indexed, 1.0 = average)
-
-### Core Variables
-
-#### Content Facet Metrics
-**O*NET Task Metrics**:
-- **onet_task_count**: Number of 1P API records using this specific O*NET task
-- **onet_task_pct**: Percentage of total using this task
-
-**Request Metrics**:
-- **request_count**: Number of 1P API records in this request category
-- **request_pct**: Percentage of total in this category
-
-**Collaboration Pattern Metrics**:
-- **collaboration_count**: Number of 1P API records with this collaboration pattern
-- **collaboration_pct**: Percentage of total with this pattern
-
-**Multitasking Metrics**:
-- **multitasking_count**: Number of records with this multitasking status
-- **multitasking_pct**: Percentage of total with this status
-
-**Human-Only Ability Metrics**:
-- **human_only_ability_count**: Number of records with this human-only ability status
-- **human_only_ability_pct**: Percentage of total with this status
-
-**Use Case Metrics**:
-- **use_case_count**: Number of records in this use case category
-- **use_case_pct**: Percentage of total in this category
-
-**Task Success Metrics**:
-- **task_success_count**: Number of records with this task success status
-- **task_success_pct**: Percentage of total with this status
-
-#### Numeric Facet Metrics
-For numeric facets (human_only_time, human_with_ai_time, ai_autonomy, human_education_years, ai_education_years), the following distribution statistics are available:
-
-- **{facet}_mean**: Mean value across all records
-- **{facet}_median**: Median value across all records
-- **{facet}_stdev**: Standard deviation of values
-- **{facet}_mean_ci_lower**: Lower bound of 95% confidence interval for the mean
-- **{facet}_mean_ci_upper**: Upper bound of 95% confidence interval for the mean
-- **{facet}_median_ci_lower**: Lower bound of 95% confidence interval for the median
-- **{facet}_median_ci_upper**: Upper bound of 95% confidence interval for the median
-- **{facet}_count**: Total number of observations for this facet
-- **{facet}_histogram_count**: Count of observations in each histogram bin (one row per bin)
-- **{facet}_histogram_pct**: Percentage of observations in each histogram bin (one row per bin)
-
-#### Indexed Facet Metrics (API-specific)
-For indexed facets (cost_index, prompt_tokens_index, completion_tokens_index), values are normalized so that 1.0 represents the average:
-
-- **{facet}_index**: Re-indexed mean value (1.0 = average across all categories)
-- **{facet}_count**: Number of records for this metric
-
-#### Intersection Metrics
-For categorical intersections (e.g., onet_task::collaboration):
-- **{base}_{secondary}_count**: Records with both this base category and secondary category
-- **{base}_{secondary}_pct**: Percentage of the base category's total with this secondary category
-
-For numeric intersections (e.g., onet_task::human_only_time):
-- **{base}_{numeric}_mean**: Mean value for this category
-- **{base}_{numeric}_median**: Median value for this category
-- **{base}_{numeric}_stdev**: Standard deviation for this category
-- **{base}_{numeric}_count**: Number of observations for this category
-- **{base}_{numeric}_mean_ci_lower/upper**: 95% CI bounds for the mean
-- **{base}_{numeric}_median_ci_lower/upper**: 95% CI bounds for the median
-
-## External Data Sources
-
-We use external data to enrich Claude usage data with external economic and demographic sources.
-
-### ISO Country Codes
-
-**ISO 3166 Country Codes**
-
-International standard codes for representing countries and territories, used for mapping IP-based geolocation data to standardized country identifiers.
-
-- **Standard**: ISO 3166-1
-- **Source**: GeoNames geographical database
-- **URL**: https://download.geonames.org/export/dump/countryInfo.txt
-- **License**: Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/)
-- **Download date**: September 2, 2025
-- **Key fields**:
-  - `iso_alpha_2`: Two-letter country code (e.g., "US", "GB", "FR")
-  - `iso_alpha_3`: Three-letter country code (e.g., "USA", "GBR", "FRA")
-  - `country_name`: Country name from GeoNames
-- **Usage**: Maps IP-based country identification to standardized ISO codes for consistent geographic aggregation
-
-### ISO Region Code Mapping
-
-Region-level geographic data uses ISO 3166-2 standard subdivision codes. Some countries were excluded from region-level analysis due to mapping issues between source data codes and ISO 3166-2 standards. Country-level data remains available for all countries.