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).
Quality
Attribution
World Bank, World Development Indicators (ILO modeled estimates); derived signals by Frontier Data Hub
Schema
| Column | 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 |
Sample rows
| 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 |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)API endpoint: https://datazimuts.com/v1/datasets/ilo_unemployment_signals/global_ilo_unemployment_signals
Tip: fetch /llms.txt for the full machine-readable catalog.