During an ordinary week, people don’t usually work for more than 40 hours. But that’s if you’re not counting the cooking, cleaning, washing, running of errands, taking care of children, and other activities that most households have to do on the side. In Costa Rica women continue to bear the burden of unpaid domestic work, which consumes twice as much time for women when compared to men, according to a data analysis over a national survey. Although there are still people who consider domestic work “is not a job” its value equals a quarter of GDP in 2020
The project revealed, for the first time in the country, disaggregated data for different profiles of women who carry the burden of unpaid domestic work. It also demonstrated the enormous amount of money with which household chores subsidize the national economy.
The report allowed to open spaces for debate in news programs specialized in gender. For example, Contravía (https://www.youtube.com/watch?v=vOer0aCujDY)
It was also used in workshops on masculinity of the organization Voces Vitales, to exemplify gender gaps (https://twitter.com/egeax/status/1375864785875705872)
R: To convert the SPSS file of the National Survey of Time Use, data wrangling, analysis, and data visualization
Excel to understand the data tables of Unpaid Domestic Work Account of the Central Bank, related them and create women´s profiles of economic contribution, according to several variables and indicators.
Tableau to create more complex datavizes
What was the hardest part of this project?
Understand the meaning of data in the Time Use Survey, according to the dictionary of definitions used by the National Institute of Statistics. The variables in the database were coded like this: A1, T2, TDNR_H, for example. Before drawing any conclusions, it was necessary to understand very well what each variable meant.
Understand the 21 data tables in which the Central Bank separates the national account of unpaid domestic work. Then, based on these tables, create a precise indicator to estimate the relative weight of each variable in the grand total of the value of women’s unpaid domestic work. The variables used by the Bank are: activity, sex, area, age, level of education. This calculation was the one that allowed to create the database that feeds the visualization called: The great economic value of women´s unpaid domestic work and to make visible the contributions of these women, according to different profiles.
Finally, defining which visualizations and what types of graphics best told the story and helped better represent the gender gap.
What can others learn from this project?
Always go beyond the general data provided by official institutions. Analyzing data on your own allows you to disaggregate them and see fresh approaches of news, going beyond the obvious one. In this case, that helps to better visualize gender gaps.
Directly consult the people in charge of the databases. I did it with the statistician in charge of the Time Use survey of the National Institute of Statistics and Censuses and she helped me a lot to verify information and most importantly: discard assumptions.
Data visualization does not always have to be fancy to be effective