{
    "proposals": [
        {
            "join_class": "currency",
            "column": "AI_Usage_and_Impact_on_Students_and_Professionals.Monthly_Income",
            "kind": "external_candidate",
            "entry_id": null,
            "name": "World Bank PPP conversion factor, private consumption (national currency per international dollar)",
            "probable_url": "https://data.worldbank.org/indicator/PA.NUS.PRVT.PP",
            "license_expectation": "CC BY 4.0",
            "join_story": "Joining on country would put incomes and monthly AI spend from different countries on a comparable purchasing-power footing. The share of income that goes to AI tools could then be compared fairly across countries.",
            "teaches": "Whether a tool looks expensive because of the price or because of the local cost of living."
        },
        {
            "join_class": "category",
            "column": "AI_Usage_and_Impact_on_Students_and_Professionals.Profession",
            "kind": "external_candidate",
            "entry_id": null,
            "name": "O*NET occupation taxonomy (O*NET-SOC titles and occupational groups)",
            "probable_url": "https://www.onetcenter.org/database.html",
            "license_expectation": "CC BY 4.0",
            "join_story": "The many free-text profession labels could be mapped to broad occupational groups. Productivity and time saved could then be compared by kind of work rather than by individual job title.",
            "teaches": "Which kinds of work gain the most from AI, once the long tail of job titles is rolled up."
        }
    ]
}