aeiv4

.gitattributes

@@ -60,3 +60,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 release_2025_09_15/**/*.csv filter=lfs diff=lfs merge=lfs -text
 release_2025_09_15/**/*.xlsx filter=lfs diff=lfs merge=lfs -text
 release_2025_09_15/**/*.json filter=lfs diff=lfs merge=lfs -text
+release_2026_01_15/**/*.csv filter=lfs diff=lfs merge=lfs -text
+release_2026_01_15/**/*.xlsx filter=lfs diff=lfs merge=lfs -text
+release_2026_01_15/**/*.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -9,14 +9,12 @@ tags:
 viewer: true
 license: mit
 configs:
-- config_name: release_2025_09_15
+- config_name: release_2026_01_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"
+    path: "release_2026_01_15/data/intermediate/aei_raw_claude_ai_2025-11-13_to_2025-11-20.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"
+    path: "release_2025_09_15/data/intermediate/aei_raw_1p_api_2025-11-13_to_2025-11-20.csv"
 ---
 
 # The Anthropic Economic Index
@@ -29,6 +27,7 @@ The Anthropic Economic Index provides insights into how AI is being incorporated
 
 This repository contains multiple data releases, each with its own documentation:
 
+- **[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
 - **[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
@@ -37,6 +36,7 @@ This repository contains multiple data releases, each with its own documentation
 ## Resources
 
 - [Index Home Page](https://www.anthropic.com/economic-index)
+- [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)
@@ -44,7 +44,7 @@ This repository contains multiple data releases, each with its own documentation
 
 ## License
 
-Data released under CC-BY, code released under MIT License
+Data released under CC-BY, code released under MIT License.
 
 ## Contact
 
@@ -52,6 +52,18 @@ For inquiries, contact econ-research@anthropic.com.
 
 ## Citation
 
+### Fourth release
+
+```
+@online{anthropic2026aeiv4,
+        author = {Ruth Appel and Maxim Massenkoff and Peter McCrory and Miles McCain and Ryan Heller and Tyler Neylon and Alex Tamkin},
+        title = {Anthropic Economic Index report: economic primitives},
+        date = {2026-01-15},
+        year = {2026},
+        url = {https://www.anthropic.com/research/anthropic-economic-index-january-2026-report},
+}
+```
+
 ### Third release
 
 ```

release_2026_01_15/data/intermediate/aei_raw_1p_api_2025-11-13_to_2025-11-20.csv

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release_2026_01_15/data/intermediate/aei_raw_claude_ai_2025-11-13_to_2025-11-20.csv

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release_2026_01_15/data_documentation.md

@@ -0,0 +1,319 @@
+# 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_2025-11-13_to_2025-11-20.csv` (pre-enrichment data in data/intermediate/)
+
+**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_2025-11-13_to_2025-11-20.csv` (in data/intermediate/)
+
+### 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/)
+- **Attribution note**: Data in the data/intermediate and data/output folders have been processed and modified from original source; modifications to data in data/intermediate include extracting only tabular data, selecting a subset of columns, and renaming columns; modifications to data in data/output include transforming data to long format
+- **Download date**: September 2, 2025
+- **Output files**:
+  - `geonames_countryInfo.txt` (raw GeoNames data in data/input/)
+  - `iso_country_codes.csv` (processed country codes in data/intermediate/)
+- **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.