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Euro-area unemployment signals (rates, divergence, distance from EA average)

Monthly European unemployment signals derived from Eurostat's harmonized unemployment series (35 countries and aggregates, seasonally adjusted, % of labour force): year-on-year and 3-month changes in percentage points, 30-month annualized change volatility, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-month cross-country volatility rank, the cross-country dispersion of unemployment rates (the euro labor-market divergence gauge) and each country's distance from the EA21 euro-area average. All rows are country-normalized (ISO alpha-3, aggregates kept as stable codes) so they join cleanly with other country-keyed datasets. Raw data: Eurostat (une_rt_m); derived signals by Frontier Data Hub.

Source: Euro-Area Unemployment Signals (derived)13,373 rowsUpdated: 9/22/2026
unemploymentlaboreuro-areaeuropeeurostatdivergencevolatilitymomentumanomaly-detectionforecastingsignals

Quality

99.1

Attribution

Eurostat (European Commission; derived signals by Frontier Data Hub)

Schema

ColumnTypeDescription
datestringReference month (first day of the month, YYYY-MM-DD) of the Eurostat une_rt_m unemployment series.
countrystring
country_codestring
series_idstringEurostat SDMX series key for the une_rt_m dataset (e.g. M.SA.TOTAL.PC_ACT.T.AT): monthly, seasonally adjusted, total unemployment, percentage of population in the labour force; trailing token is the geo code.
series_labelstringEurostat series label as published: frequency, seasonal adjustment, age class, unit (percentage of population in the labour force) and country/aggregate name.
valuefloatHarmonized unemployment rate as published by Eurostat for une_rt_m: percentage of the labour force (ILO definition), monthly and seasonally adjusted; see the Eurostat labour force survey methodology notes.
yoy_change_ppfloat
momentum_3mfloat
volatility_30dfloat
anomaly_flaginteger
forecast_1mfloat
rankinteger
dispersion_stdfloat
ea_gapfloat

Sample rows

datecountrycountry_codeseries_idseries_labelvalueyoy_change_ppmomentum_3mvolatility_30danomaly_flagforecast_1mrankdispersion_stdea_gap
1983-01-01DenmarkDNKM.SA.TOTAL.PC_ACT.T.DKMonthly, Seasonally adjusted data, not calendar adjusted data, Total, Percentage of population in the labour force, Total, Denmark8.103.134798922692536
1983-01-01FranceFRAM.SA.TOTAL.PC_ACT.T.FRMonthly, Seasonally adjusted data, not calendar adjusted data, Total, Percentage of population in the labour force, Total, France7.103.134798922692536
1983-01-01IrelandIRLM.SA.TOTAL.PC_ACT.T.IEMonthly, Seasonally adjusted data, not calendar adjusted data, Total, Percentage of population in the labour force, Total, Ireland12.903.134798922692536
1983-01-01ItalyITAM.SA.TOTAL.PC_ACT.T.ITMonthly, Seasonally adjusted data, not calendar adjusted data, Total, Percentage of population in the labour force, Total, Italy6.903.134798922692536
1983-01-01LuxembourgLUXM.SA.TOTAL.PC_ACT.T.LUMonthly, Seasonally adjusted data, not calendar adjusted data, Total, Percentage of population in the labour force, Total, Luxembourg3.303.134798922692536

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/euro_unemp_signals/euro_area_unemployment_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/euro_unemp_signals/euro_area_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/euro_unemp_signals/euro_area_unemployment_signals

Tip: fetch /llms.txt for the full machine-readable catalog.