Unemployment inequality in Spain: how many people like you are out of work?

Country/area: Spain

Organisation: elDiario.es

Organisation size: Big

Publication date: 19/12/2021

Credit: Victòria Oliveres


Victòria Oliveres: Data journalist member of elDiario.es data team. She is specialized in data gathering, analysis and visualization and focused in education, health, environment and gender stories.

Project description:

Using data from the Labor Force Survey (EPA) published each quarter by the Spanish National Institute of Statistics (INE), this project analyses the unemployment rate evolution for specific population groups. Considering the gender, age, educational level, region of the country and quarter of more than ten million records, we built the unemployment evolution for more than 900 demographic profiles. Then, with the voice of experts and testimonials we explain the differences between groups and with interactive visualizations the audience can compare themselves with the rest of the population.

Impact reached:

Although every quarter most media report on the unemployment rate published by the EPA, the focus is usually on its evolution in comparison to previous quarters or years. Sometimes youth unemployment or the gender gap also make it to headlines, but rarely all the variables are crossed to have a more detailed picture. In this article we published for the first time the unemployment rates of very specific groups of the population.
The article was widely commented on social networks by experts and readers, who also specifically shared the graphs with the unemployment rate of their group and drew their own conclusions. In addition, its publication coincided with the end of the negotiation of the labour reform in Spain, which made it a very topical issue. Also, for elDiario.es it meant the acquisition of new subscribers.

Techniques/technologies used:

To obtain, merge, analyze and reorganize the data we used R and RStudio. Some visualizations were developed using the third-party tool Datawrapper and JavaScript, others only using HTML, CSS and typeface of people silhouettes WeePeople. The search tool that needed more interactivity was programmed with D3.js. For the scrollytelling we used Flourish and the JavaScript library Scrollama. 

What was the hardest part of this project?

To prepare this article there were two processes that required a lot of effort. On the one hand, the collection, order, and analysis of more than ten million records included in the microdata from quarterly surveys for 17 years. In an R script we joined and restructured the data so that it could be visualized in a complex but comprehensive way.
On the other, the creation of visualizations to make them more understandable and useful for the audience. For each one we chose different elements and used various tools, although later we unified their aesthetics. The group-specific unemployment rate finder, with its multiple menus that work at the same time, was the hardest part.

What can others learn from this project?

With this project it is possible to see the advantages and possibilities of using microdata from surveys or other statistics offered by official institutes rather than just talk about the tables that summarize the results, which are often the ones that are reported. This methodology could be imitated to cover other topics where dividing the population into small groups would give a very different photo than giving a single rate for the entire country. In other countries, the process could be reproduced to see if there are the same differences in the unemployment rate.

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