Unemployment Ranking by Region

Ranking of autonomous communities by number of unemployed

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#RegionsTotal UnemployedYear-over-Year
1ANDALUCÍA595,322-7.9%
2CATALUÑA321,279-3.4%
3COMUNITAT VALENCIANA292,093-7.3%
4MADRID (COMUNIDAD DE)276,430-3.9%
5CANARIAS146,649-7.7%
6CASTILLA - LA MANCHA117,747-8.3%
7GALICIA111,572-8.4%
8PAÍS VASCO106,483-1.3%
9CASTILLA Y LEÓN101,352-4.8%
10MURCIA (REGIÓN DE)74,520-6.9%
11EXTREMADURA64,811-10.3%
12ASTURIAS (PRINCIPADO DE)50,996-6.8%
13ARAGÓN48,684-5.1%
14BALEARS (ILLES)30,219-3.4%
15NAVARRA (COMUNIDAD FORAL DE)29,187-3.2%
16CANTABRIA27,917-5.4%
17RIOJA (LA)12,078-3.6%
18CIUDAD AUTÓNOMA DE CEUTA9,128-9.2%
19CIUDAD AUTÓNOMA DE MELILLA7,987-9.8%

Unemployment analysis by autonomous communities

The unemployment ranking by autonomous communities reveals significant structural differences in the Spanish labor market. Southern regions, especially Andalusia and Extremadura, historically record the highest unemployment rates, influenced by greater dependence on seasonal sectors such as agriculture and tourism. In contrast, northern communities such as the Basque Country, Navarre, and La Rioja maintain significantly lower unemployment levels.

Year-over-year trends show a general reduction in unemployment across most communities, although the pace of improvement varies considerably. Regions with greater economic diversification — industry, advanced services, and technology — tend to show a faster and more sustained labor market recovery.

Data from SEPE (State Public Employment Service) includes monthly registered unemployment, broken down by sex, age, and economic sector. It is important to distinguish between registered unemployment (people enrolled as job seekers) and the EPA (Active Population Survey) unemployment rate, which uses a different methodology and may yield different figures.

Factors such as regional productive structure, workforce education levels, regional employment policies, and connectivity with national and international markets directly influence these figures. For a more detailed analysis, we recommend consulting the individual pages of each autonomous community.