Global ILO unemployment signals (labor-market breadth)
Global unemployment signals from the ILO modeled estimates via World Bank WDI (~200 economies, annual 1991 ->): 10-year change, OLS trend slope, 3-sigma shock flags, 5-year linear-extrapolation forecasts, cross-country ranks, the female-minus-male gender gap, crisis/recovery flags and a 10-year z-score. The breadth companion to the OECD 20-country monthly set: every country's unemployment story on one harmonized grid. World Bank WDI (non-commercial terms).
Qualité
Attribution
World Bank, World Development Indicators (ILO modeled estimates); derived signals by Frontier Data Hub
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Reference year (January 1 of the year; WDI annual). |
| country | string | Country name (ISO economies only; World Bank regional/income aggregates excluded). |
| country_code | string | ISO 3166-1 alpha-3 country code. |
| series_id | string | WDI indicator code: SL.UEM.TOTL.ZS (total), SL.UEM.TOTL.MA.ZS (male), SL.UEM.TOTL.FE.ZS (female). |
| series_label | string | Official WDI indicator name as published in the indicator metadata. |
| value | float | Unemployment rate in percent: the share of the labor force without work but available for and seeking employment (modeled ILO estimate; harmonized across countries); World Bank WDI. |
| change_10y_pp | float | |
| yoy_change_pp | float | |
| trend_slope_10y | float | |
| unemp_z_10y | float | |
| anomaly_flag | integer | |
| forecast_5y | float | |
| rank | integer | |
| gender_gap_pp | float | |
| crisis_flag | integer | |
| recovery_flag | integer | |
| high_unemployment_flag | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | change_10y_pp | yoy_change_pp | trend_slope_10y | unemp_z_10y | anomaly_flag | forecast_5y | rank | gender_gap_pp | crisis_flag | recovery_flag | high_unemployment_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1991-01-01 | Afghanistan | AFG | SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) | 10.47 | — | — | — | — | 0 | 33.68684848484848 | 161 | 3.0100000000000007 | 0 | 0 | 0 |
| 1992-01-01 | Afghanistan | AFG | SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) | 10.456 | — | -0.014000000000001123 | — | — | 0 | 33.68684848484848 | 168 | 3.007999999999999 | 0 | 0 | 0 |
| 1993-01-01 | Afghanistan | AFG | SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) | 10.403 | — | -0.05299999999999905 | — | — | 0 | 33.68684848484848 | 187 | 3.0010000000000003 | 0 | 0 | 0 |
| 1994-01-01 | Afghanistan | AFG | SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) | 10.38 | — | -0.022999999999999687 | — | — | 0 | 33.68684848484848 | 182 | 2.998000000000001 | 0 | 0 | 0 |
| 1995-01-01 | Afghanistan | AFG | SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate) | 10.369 | — | -0.011000000000001009 | -0.02780000000000032 | — | 0 | 33.68684848484848 | 177 | 2.9979999999999993 | 0 | 0 | 0 |
Télécharger un échantillon
Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).
Utiliser avec un LLM
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/ilo_unemployment_signals/global_ilo_unemployment_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/ilo_unemployment_signals/global_ilo_unemployment_signals").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API: https://datazimuts.com/v1/datasets/ilo_unemployment_signals/global_ilo_unemployment_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.