The inequality of elections in Madrid: how right-wing parties always win only with the votes of the richest neighborhoods
Organisation size: Big
Publication date: 04/05/2021
Credit: Raúl Sánchez, Victòria Oliveres, Ana Ordaz
Raúl Sánchez: Spanish data and investigative journalist covering stories of inequality, gender, corruption and (now) Covid at elDiario.es. He coordinates elDiario.es data team.
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.
Ana Ordaz: Data journalist member of elDiario.es data team. Supporting with data and visualization stories at elDiario.es and focused on covering Covid pandemic in the last year.
Right-wing parties have been winning elections in Madrid, the Spanish capital, for more than 25 years. An analysis of the electoral data block by block by elDiario.es revealed the gap in participation and exclusion between the poor and rich areas of the Community of Madrid, is what allows conservative candidates to win the elections only being the most voted the richest 30% neighborhoods. The investigation, published in the electoral campaign of the regional elections in Madrid, also analyzed the demography behind the victory of the new center-right candidate in the capital.
For this project we crossed the electoral results with demographic and income data in more than 4,000 census sections (blocks) of the entire Community of Madrid. The results demonstrated the gap of political exclusion of the most impoverished communities of the capital that allow the conservative candidates to chain victories in the legislative elections being the most voted only in the richest areas. The story was published with an interactive scroll that served to explain as clearly as possible the key demographics of the Madrid elections.
The piece of analysis was one of the most read of the year in elDiario.es and also one of the ones that brought the most subscribers to the newspaper. In addition, the point of the article was one of the main themes of the campaign – the electoral mobilization of the poor areas of Madrid – and the candidates of the main parties in the contest shared the publication on their social media accounts. Precisely, we analyzed this electoral gap between the poor and rich areas during the election day of May 4, when we updated minute by minute a piece with the electoral demography in which readers could explore the social groups where each had more strength.
What was the hardest part of this project?
The hardest part of the project was to obtain and cross historical electoral data and demographic data and income data for each of the 4,000 census blocks analyzed. Each of the variables had to be obtained from different sources with a different format so we have to normalize these data before crossing it with an R script. Also the process to create the narrative structure of the piece was really hard to be as clear as possible because Election data can be difficult to understand. We not only wanted to reveal that gap of political exclusion to the readers, but also visualize it in the clearest way possible and also do a pedagogical job of not only teaching but also explaining the graphics that we show them.
With this purpose, we proposed a first sketch of the structure of the piece that went from the simplest idea to the most complex. We began by explaining how conservatives were voted more in the richest areas until we revealed the reasons behind the high level of political exclusion in the poorest neighborhoods of Madrid. The end result is an animated two-line graph summarizing hundreds of thousands of voting and income records across the region.
In addition, we programmed a script to live updating during election night the graphs that indicate the gaps in the vote for each bloc and party according to age, income, educational level or place of birth
What can others learn from this project?
This project can inspire other journalists to analyze demographic voting patterns in other countries to check how levels of social and political exclusion affect electoral results in different regions or countries of the world, which is not usually used in the electoral process outside the United States. This project demonstrates that you can try to explain and understand complex data and graphs (like the one in the 4M demographic piece) with a lot of pedagogy and detailed explanations.