We compared salaries, t for 98 occupations using the International Standard Classification of Occupation ISCO-08, calculated minum costs of living, income taxation and housing costs for Finland, Sweden, Norway and Denmark to decide how much money professional people would have to spare in the different countries. We used all the data to create a easy to use and fun calculator that calculated where you would have the most money left in you pocket after all necessery expences were payed. We also used the data t The project was a cooperation between Svenska Yle, SR in Sweden ans Yle, initiated by
Within the frame of the project we published stories about financial politics within the nordic countries, salary levels, taxation and migration. The outlets were Svenska Yle, Sveriges radio and Yle. The stories were published online, on the radio and on TV. Our findings generated wider discussions about salary levels in Finland and differences between the labour market within the Nordic countries. Later our findings have been used by trade unions in Finland as a mean to improve vages. The project generated new knowledge about the actual economic impact emigration/immigration to another Nordic country would have for different professionals. The project reveled serious differencies in salaries for the same occupation and in a finnish perspective it was an eyeopener that the finns earn less in almost every occupation. Almost no one would benefit from moving to Finland from another nordic country for work.
The application was published in four different language versions and on three different publication platforms; In Swedish, finnish spoken in Sweden, finnish and swedish spoken in Finland. The plattforms were Svenska Yle, Yle and Sveriges Radio. This was the first ever joint datajournlistic venture by two national public service companies (three organisations). We shared data, knowledge, analytic tools and publication plattforms.
We used R and Pandas to analyze the data, vue.js and Highcharts to visualise, Trello and Slack to coordinate.
What was the hardest part of this project?
One big obstacle was to re-calculate all the differencies in the datasets so that they were comparable. In order to do that we had to do an in-depth analysis of the origin of the data so that we would know that we are comparing the samt thing (full time salaries without overtime compensation or other forms of compensations). We also had to make sure the costs of living and taxation analysis were comparable by braking the factors down and rebuild them in the same way.
Another struggle was to coordinate and project manage two so very different teams with different work culture and to see that our data generated as myúch impact as possible.
What can others learn from this project?
That it is fruitful to compare regional data but the more the alike the countries are the better. This kind of comparison would be very hard to do on a european level.
This was a project where the data generated a lot of stories apart from the main publication.
Cooperation pays off but it demands a lot of work.