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
Global Household-Debt Signals (derived)
Annual household-leverage signals derived from the IMF DataMapper (total household debt, all instruments, % of GDP, 44 economies, 1950 ->): 1-year and 3-year changes, 30-year change volatility, 3-sigma anomaly flags vs a trailing-10-year baseline, naive-drift 1-year forecasts, per-year cross-country volatility ranks, 10-year debt z-scores, 5-year leverage build-up, and high-debt (>60% of GDP), rapid-rise, and deleveraging flags. The global private-leverage companion to the sovereign-debt signals. Country codes normalized to verified ISO alpha-3. Raw data: International Monetary Fund (DataMapper, keyless API).
Federal Reserve Bank of New York
US state household-debt stress intelligence from the NY Fed Consumer Credit Panel / Equifax state-level workbook (keyless, redistributable under the NY Fed Terms of Use): 52 geographies x Q4 year, 2003-2025. Core measures: per-capita debt balances for auto, credit card, mortgage, and student loans, plus the percent of each balance 90+ days delinquent. Method: the ten data sheets are parsed from their fixed grid layout and pivoted to a long state x year panel; rows with a NULL total balance are dropped as unverifiable (never fabricated); the allUS national row is integrity-checked then excluded. Derived signals per state-year: a documented 0-100 stress_score (mean of full-panel min-max normalized delinquency rates, equal weights), within-year stress rank and quintile tier, debt burden rank, YoY percent change of total debt per capita, YoY change in credit-card 90+ delinquency (pp), and a severe-stress flag (score >= 75). Use as prediction features: join to customers/orders/leads by region_code + date for demand, churn, and lead-scoring models — household balance-sheet stress leads changes in discretionary spending, subscription churn, and B2B close rates. Caveats: Q4-only annual grain (forward-fill within the year for higher-frequency models); balances are nominal, not inflation-adjusted; delinquency is a stock measure (use the YoY flow signals for deterioration); Puerto Rico is published only through 2016; student-loan sheets use a thinner 1% panel.
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