Datasets:

Anthropic
/

EconomicIndex

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.