090°Données ouvertes
Jeux de données ouverts, entièrement documentés — interrogeables ici, et lisibles par n’importe quel LLM.
Les titres et les descriptions proviennent des sources de données, en anglais.
2 jeux de données
US Labor-Income Intelligence (FRED, keyless)
Monthly US wage intelligence panel, 1964-01 onward: average hourly earnings of production and nonsupervisory employees, total private (dollars per hour, seasonally adjusted; FRED AHETPI, U.S. Bureau of Labor Statistics, Current Employment Statistics, redistributed keyless via FRED fredgraph.csv). Carries 1-month and 12-month changes (levels and percents), the 3-month annualized percent change, and a contraction_flag marking months with lower nominal earnings than a year earlier. Scope note: production/nonsupervisory workers (about four-fifths of private payrolls), not all employees. Method caveats: BLS revises history with each Employment Situation vintage (kept as published, never back-filled); genuine upstream gaps are kept as honest nulls, never imputed. Who joins this: shops deflate earnings_yoy_pct against CPI for the real-paycheck signal; subscription businesses use earnings-momentum streaks as churn-risk macro features.
Monthly US labor-utilization intelligence panel, 1939-01 onward: average weekly hours of production and nonsupervisory employees in manufacturing (hours, seasonally adjusted; FRED AWHMAN, U.S. Bureau of Labor Statistics, Current Employment Statistics, redistributed keyless via FRED fredgraph.csv). Carries 1-month and 12-month changes (levels and percents), the 3-month annualized percent change, and a contraction_flag marking months with fewer hours than a year earlier. Scope note: manufacturing production workers only — pair with us_hourly_earnings_monthly (total private) with care; the scopes are not matched. Method caveats: BLS revises history with each Employment Situation vintage (kept as published, never back-filled); genuine upstream gaps are kept as honest nulls, never imputed. Who joins this: shops and subscription businesses join hours_yoy_pct on year_month + country_code as a consumer spending-power feature — hours are cut before layoffs, so this leads unemployment.
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