WorldJobsData API

Quick start

All endpoints return JSON. No authentication, no API key, no sign-up. Fetch directly from any browser or server.

// Fetch AI job risk data for Germany
fetch('https://www.worldjobsdata.com/data/countries/de.json')
  .then(r => r.json())
  .then(d => {
    console.log(d.country_name);                  // "Germany"
    console.log(d.stats.weighted_avg_ai_exposure);         // 5.3
    console.log(d.occupations.length);            // usually 9-10 (ISCO-08 major groups); some countries (e.g. Germany) report finer sub-major-group detail, up to 42
    console.log(d.wb_economy.gdp_per_capita_usd); // World Bank GDP
    console.log(d.hdr_data.hdi);                  // UNDP HDI score
  });

Base URL

All responses are served from Vercel's global CDN with Cache-Control: public, max-age=86400. Files update when new data is published - roughly monthly.

CORS is fully open - fetch from any origin including the browser. A machine-readable OpenAPI 3.1 spec is available at /data/openapi.json, compatible with Postman, Swagger UI, and any OpenAPI tooling.

Coverage

Data tiers reflect source quality. Tier A countries (21 total, including US, UK, DE, CA, AU, and 16 others) have full ISCO-08 occupation data with wages, growth projections, and education breakdown. Tier B/C countries (19 and 166 respectively) have employment shares and AI scores but may lack wages or growth data.

Endpoints

Country list

// Response structure (abbreviated)
{
  "countries": [
    {
      "code": "US",                              // UPPERCASE alpha-2
      "name": "United States",
      "tier": "A",                               // A | B | C
      "weighted_ai_exposure": 5.1,
      "weighted_robotics_risk": 3.5,
      "total_employment_thousands": 143978.2,
      "has_wages": true,
      "has_growth": true,
      "youth_unemployment_rate": 8.9,
      "risk_velocity_label": "Disruption imminent (0-3 years)"
      // ...28 more fields
    }
    // ...205 more countries
  ],
  "tier_counts": { "A": 21, "B": 19, "C": 166 }
}

Country data

// GET /data/countries/us.json
{
  "country_code": "US",
  "country_name": "United States",
  "year": 2025,
  "tier": "A",
  "total_employment_thousands": 143978.2,
  "occupations": [
    {
      "isco_code": "4",
      "label": "Clerical support workers",
      "ai_exposure": 8.5,
      "robotics_risk": 2.5,
      "wfh_potential": 8.5,
      "employment_thousands": 16227.9,
      "employment_pct": 11.27,
      "median_annual_wage_usd": 46818.01
    }
    // ...8 more occupation groups
  ],
  "wb_economy": { /* World Bank indicators */ },
  "hdr_data": { /* UNDP HDI indicators */ }
}

Global ISCO scores

// GET /data/isco_scores.json (abbreviated - object keyed by ISCO-08 code, not an array)
{
  "4": {
    "digital_ai_exposure": 8.5,
    "robotics_risk": 2.5,
    "offshoring_risk": 7.5,
    "wfh_potential": 8.5,
    "ai_rationale": "...",
    "robotics_rationale": "..."
  }
  // ...53 more codes, keyed "0" through detailed sub-major codes like "42"
}

Schema reference

Country object

Occupation object

World Bank economy object (wb_economy)

Source: World Bank Open Data (CC BY 4.0). Most recent year per indicator.

UNDP HDR object (hdr_data)

Rate limits

For high-volume use (10k+ requests/day), cache responses on your side. The Cache-Control: max-age=86400 header means files are stable for 24 hours. Data refreshes happen monthly at most.

To be notified when data updates, watch the GitHub repository or check the year field in the country JSON.

Licence

WorldJobsData scores and analysis are published under CC BY 4.0. Attribution: "WorldJobsData (worldjobsdata.com)".

Underlying data sources:

Code examples

JavaScript - rank all countries by AI exposure

// Fetch meta.json once, sort by AI exposure
const meta = await fetch('https://www.worldjobsdata.com/data/meta.json')
  .then(r => r.json());

const ranked = meta.countries
  .filter(c => c.weighted_ai_exposure != null)
  .sort((a, b) => b.weighted_ai_exposure - a.weighted_ai_exposure);

console.log('Most AI-exposed:', ranked[0].name, ranked[0].weighted_ai_exposure);

Python - compare two countries

import requests

def get_country(code):
    url = f'https://www.worldjobsdata.com/data/countries/{code.lower()}.json'
    return requests.get(url).json()

us = get_country('us')
de = get_country('de')

# Compare avg AI exposure
print(us['country_name'], us['stats']['weighted_avg_ai_exposure'])
print(de['country_name'], de['stats']['weighted_avg_ai_exposure'])

# Compare HDI
print(us['hdr_data']['hdi'], de['hdr_data']['hdi'])

R - load all countries

library(jsonlite)

meta_url <- "https://www.worldjobsdata.com/data/meta.json"
meta <- fromJSON(meta_url)
countries_df <- meta$countries

# Top 10 by AI exposure
top10 <- head(countries_df[order(-countries_df$weighted_ai_exposure), ], 10)
print(top10[, c("name", "weighted_ai_exposure", "tier")])