Best practices: cross-border commuter statistics in Denmark, Sweden and Norway

Producing reliable cross-border statistics remains a challenge in many European regions, but some best practices already exist. One such example is the Nordic cross-border commuting statistics, which have been produced since the early 2000s. Cross-border commuting between Sweden and Denmark has been monitored most consistently. Region Skåne has been financing the statistics on a permanent basis, resulting in a long time series covering 1997–2015 and again from 2019 onwards. The statistics were temporarily suspended after 2015 because it was no longer possible to exchange data between Sweden and Denmark.

On a more ad hoc basis, the Nordic Council of Ministers has funded various studies in which Norway and Finland have also participated. In the 2000s, this took place within the framework of the Nordic Commuting Map. In the 2010s, the Nordic Mobility I project — which also included Iceland — was carried out.

Concerning the dissemination of Nordic cross-border commuting statistics, Nordregio maintains the Nordic Statistics database, which publishes indicators across 19 thematic areas, including demography, labour markets, education, income, and health. Among these, data on commuting between Sweden, Norway, Denmark, Finland and Iceland are available for the period 2015–2018, based on results from the former Nordic Mobility I project. Cross-border commuting between Denmark and Sweden is published in the Ørestat database, and since 2019, this has been done through regular quarterly publications.

In 2024, the Nordic Mobility II project was launched, with Denmark, Norway, and Sweden participating. The project is funded by the Nordic Council of Ministers/Nordregio, and Statistics Denmark acts as the project owner. The results of the Nordic Mobility II project, covering the period 2019–2024, will be included in the Nordic Statistics database.

During the workshop held in Brussels as part of the Cross-Border Data Collection Project, Statistics Denmark presented the methodology used in Denmark, Sweden, and Norway. 

These countries use monthly statistical information based on administrative registers to identify cross-border commuters. Each country uses nationally collected income declarations from employers, population registers, information about the country code (indicating the country where the employee lives), citizenship data, emigration data and other relevant information to estimate the number of incoming cross-border workers. The nationally collected income declarations from employers also form the basis for the national employment statistics. For example, in the Danish cross-border commuting statistics, the selection of the “basic” population of cross-border commuters is carried out as a simple selection based on the data used for national short-term employment statistics. Of note, from 2019 onward, countries do not exchange or link microdata across borders. The data can be broken down by sex, age, citizenship, industry, sector, municipality of work, salary, and—for Denmark, and potentially Norway—hours worked.

Methodological notes

• Workplace location is captured through national employment statistics, where each employee is linked to a workplace with a unique identifier, which in turn is associated with a specific address. These links are typically already established in national statistics. For example, the Danish short-term employment statistics already include workplace-related information (municipality, industry, sector), so no further linking is necessary.
• Denmark and Sweden determine the country of residence through a combination of country codes, citizenship, emigration records, and partial tax liabilities abroad. Norway currently determines the country of residence on the basis of citizenship, which limits precision.

Looking ahead
Efforts are underway to explore the possibility of creating a Nordic Data Hub to link cross-country data, improve background variables (e.g., municipality of residence), and enhance accuracy — particularly in Norway. In the meantime, development continues at the national level using existing sources.

This Nordic example shows that administrative data can provide detailed and policy-relevant cross-border labour statistics, both with and without the exchange of microdata between countries. It serves as a valuable reference for, for example, regions seeking to monitor developments in the area.