WorldJobsData API

Quick start

All endpoints return JSON. No authentication, no API key, no sign-up. Fetch directly from the 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_ai_avg); // e.g. 5.3
    console.log(d.occupations.length);    // 9 or 10
    console.log(d.wb_economy.gdp_per_capita_usd); // World Bank GDP
    console.log(d.hdr_data.hdi);          // UNDP HDI score
  });

Base URL

https://www.worldjobsdata.com

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

CORS is open - you can 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 (US, UK, DE, FR, JP, etc.) have full ISCO-08 occupation data with wages, growth projections, and education breakdown. Tier B/C countries 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": 143100,
      "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": 35, "B": 61, "C": 110 }
}

Country data

// GET /data/countries/us.json
{
  "country_code": "US",
  "country_name": "United States",
  "year": 2024,
  "tier": "A",
  "total_employment_thousands": 143100,
  "occupations": [
    {
      "isco_code": "4",
      "label": "Clerical support workers",
      "ai_exposure": 8.5,
      "robotics_risk": 2.5,
      "wfh_potential": 8,
      "employment_thousands": 18800,
      "employment_pct": 13.1,
      "median_annual_wage_usd": 45760
    }
    // ...8 more occupation groups
  ],
  "wb_economy": { /* World Bank indicators */ },
  "hdr_data": { /* UNDP HDI indicators */ }
}

Global ISCO scores

// GET /data/isco_scores.json (abbreviated)
[
  {
    "isco_code": 4,
    "label": "Clerical support workers",
    "ai_exposure": 8.5,
    "robotics_risk": 2.5,
    "offshoring_risk": 5.0,
    "wfh_potential": 8
  }
  // ...9 more groups
]

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 as of the last enrichment run.

UNDP HDR object (hdr_data)

Rate limits

For high-volume use (10k+ requests/day), please cache responses on your side. The Cache-Control: max-age=86400 header tells you 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 have their own licences:

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_ai_avg'])
print(de['country_name'], de['stats']['weighted_ai_avg'])

# 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")])