Does Barcelona Activa fight female inequality?

Barcelona, Catalonia, Spain

By Mar Iglesias I Salvador

Seeking to analyze public programs and courses run by Barcelona Activa aimed at fighting female inequality through the social economy.

Since 2015, the Barcelona City government in Spain has made a bet to promote the social economy of the city with the main goal of promoting stable and quality employment. The social economy comprises all those organizations like co-operatives, mutual societies, special employment centres, social integration enterprises, associations, foundations, among others, which put persons and social impact before profit, are based on democratic decision-making and are committed with local development, equal opportunities for women and men, social cohesion, the integration of persons at risk of exclusion, stable and quality employment, work-life balance and sustainability.

To foster the social economy, the City government created a new department within Barcelona Activa, the local economic development agency owned by the City: the Direction for Socio-Economic Innovation (ISE). Since its creation, ISE has been in charge of providing training and mentoring programs and short courses to Barcelona’s residents interested in the social economy and organizations which belong to it. Since its creation, the main goal of ISE has become to fight the feminization of poverty in the city through the promotion of the social. Thus women have been the main target of ISE programs and courses.

However, women are not equal. Some have more privileges than others. Nationality, economic status, education level, etc intersect with gender to generate maps of privilege and vulnerability. Thus the question arises: which women are benefitting from ISE programs and courses? Are there inequalities in women’s access to these resources? Is ISE creating more inequalities among women instead of reducing them?

To try to give an answer to those questions, I started gathering data related to socioeconomic inequality in the city. I decided to focus on four variables: female migrant status rate, female unemployment rate, female tertiary education rate and median household income. The idea was to map those variables using data from BCN OpenData and Instituto Nacional de Estadística, observe the resulting spatial patterns, and compare those to the location of origin of ISE program and courses participants.

I first analysed female unemployment rates, producing a map which shows spatial clusters and outliers. The map shows the neighbourhoods in the city where high (green) or low (purple) female unemployment rates are spatially clustered, and where there are isolated places of high unemployment adjacent to areas of low unemployment (yellow) and low poverty adjacent to high unemployment (blue).

From that map, I learnt that in Barcelona there are large clusters of low female unemployment in the north (Ciutat Meridiana, Torre Baró, la Trinitat Nova) and the northwest (Vallvidrera, el Tibidabo i les Planes; Sant Genís dels Agudells; Montbau; la Vall d’Hebron; la Teixonera). On the other hand, one finds large clusters of high female unemployment in the Eixample (el Camp d’en Grassot i Gràcia Nova, la Dreta de l’Eixample, l’Antiga Esquerra de l’Eixample, la Nova Esquerra de l’Eixample, Sant Antoni). Finally, there are not many outliers: there are two neighbourhoods with low unemployment adjacent to the high unemployment cluster of the Eixample (el Barri Gòtic and la Font de la Guatlla) and another one (el Congrés i els Indians) isolated in the northern part of the city, and there is only one area with high unemployment adjacent to the low unemployment cluster: Horta. Next, I mapped female migrant status rates and tertiary education rates. When one compares it with unemployment data, no apparent spatial pattern emerges.

I also observed that there also seems to be no apparent correlation with median household income.

Finally, I mapped out program and courses participants. Most participants come from El Poble-sec, L’Eixample, La Maternitat i Sant Ramon I La Guineueta. Observing that pattern, I noticed how there seems to be a spatial overlap between high female unemployment rates and the location of origin of program and courses participants.

Therefore, in conclusion, while there seems to be no discernible pattern from the maps in relation to women’s migrant status, female tertiary education level or median household income, there seems to be a spatial correlation between female unemployment rates and program and courses participants’ location, suggesting that participants in ISE activities come from neighbourhoods with higher rates of female unemployment. More research, however, should be made to ascertain whether the higher probability that a woman coming from a neighbourhood with higher female unemployment means that participants are indeed unemployed, or just surrounded by more unemployed women.