Story #2
Everyone's Busy – But not Equally
Time expenditure and income inequality through high-value datasets
Published on 14 February 2025
This story, which is part of a series, presents an example of how high-value income-inequality datasets can be presented in relation to other variables to build light-hearted and engaging interactive experiences.
Have you ever asked yourself what are the activities that occupy most of your day? Have you wondered how your daily schedule compares to everyone else's in your country, or in other countries?
Every decade, the Harmonised European Time Use Survey asks a large sample of people to keep a diary on how they spend their days. (1) In this story, we show the association between time expenditure patterns at the country level and several high-value datasets.
Income inequality is measured through the Gini coefficient. The higher a country scores in this index, the higher the inequality in that country is: 0 means perfect equality, and 1 would mean that a single individual has all the country's wealth and everyone else has nothing. (2)
Let's see how these variables relate at the country level.
The charts below are called scatter plots. You can read more about them – and about how they can mislead us – in this story's visualisation notes. Each scatter plot represents the association between the income inequality of a country and the number of minutes per day that people in that country say that they spend on each activity. (3)
Trend line
Each flag (
) is a country. The farther to the right → a country is, the higher its income inequality. The higher up ↑ a flag is, the longer people in that country engage in that activity on average.
Some activities, such as dish washing, cleaning and household and family care, are positively associated with income inequality; this means that, in general, the higher the Gini coefficient of a country is (the higher its inequality), the more time people in that country spend in that activity on average.
Notice that this association is not perfect in every case. Italians, for instance, say that they spend nearly 46 minutes per day cleaning their dwellings, and Italy's income inequality isn't that high.
Other activities, such as leisure travel, entertainment, and culture, are negatively associated with inequality. This means that the more unequal a country is, the less people in that country engage in that activity, on average.
Once again, note that there are exceptions to the pattern of negative association between the variables. For example, 
Serbia's inequality is relatively high, but Serbians spend a substantial amount of time on visiting and feasts.
On the other hand, 
Hungary has relatively low inequality, but Hungarians don't use much of their time for visiting and feasts.
Use the following menu to see how many other activities relate to income inequality. You'll see that, in several cases, the association between inequality and time spent on the activity you selected is weak or even non-existent.
Trend line
Finally, would you like to compare yourself to the average of your country or of other countries?
First, make an estimate of the minutes you spend on each of these activities every day by changing the length of the bars below.
When doing so, remember that we are counting both weekdays and weekends, so if you have a job or are studying, think over the whole week when assigning time to those activities – this would lead to smaller part of the days, unless you also work or study on weekends, of course.
After that, choose a country.
Want to know who spends the most time on a particular activity? Look it up!
- The latest available data for the Harmonised European Time Use Survey is from 2010. A new round of data should be available in the near future. Time expenditure patterns may have shifted due to events such as the COVID pandemic.
- The latest economic inequality data is from 2014.
- The Harmonised European Time Use Survey asks people about their daily schedules both on a weekday and a weekend day. That's one of the reasons you may notice that people on average seem to spend not that much time working or studying. Another reason for patterns like this is that these minutes are based on averages of a large sample that includes people of different ages and living conditions, so some of them may have jobs or may be studying full-time, but others don't.
Datasets used in this story
- High-value dataset: Gini coefficient of equivalised disposable income
- Time spent, participation time and participation rate in the main activity by sex and age group
- Activities grouped based on the Activity coding list for harmonised European time use surveys, 2018 (ACL 2018) taxonomy
Visualisation Notes