Statistical processing

Source data

The commuting statistic is compiled on RAS. Since April 2015 Labour Market Account (LMA) is the data foundation for RAS. In connection to that the commuting statistic is revised back to 2008. In the same context the dating of the statistic was changed so that its now dated according to the time of reference at the end of November. That means that the latest count is called end of November 2016, where it would previously have been called 2017. Read more about the labour market account and the register-based labour force statistic.

The data sources in LMA are various internal and external registers, e.g.:

• eIncome register
• The central business register
• The register with information about persons receiving public benefits
• The educational register
• The employment classification module
• The income register
• The population register
• The register for persons receiving maternity or sickness benefit

For the commuting statistic it is the information about address of workplace and residence for employed persons in RAS that is especially interesting, since these information are the foundation of the calculation of commuting distance.

Frequency of data collection

The statistic is published yearly.

Data collection

The data collection is done by separate processing of each source register. After that a transverse data processing is done (also called treatment of overlaps) where information in the various registers are compared, and corrected when needed. Finally data are linked to other registers to add background information and form the population.

Data validation

The data foundation for RAS has since April 2015 been The Labour Market Account (LMA). LMA is produced both with and without an hourly standardization. The non-hourly standardized longitudinal register (LMA-UN) is the data foundation for RAS and therefore the commuting statistic. Thus the data validation takes place in LMA-UN.

In connection to the production of LMA a comprehensive validation of each input data is done. The most important ones are:

1. The data source for wage earner jobs are the eIncome register. The wage earner jobs contains information about which workplace the job is at. The workplace is the foundation for the information about industry, sector and geography. In some cases the reporting from the employer are incorrect. In that case a correction of the errors are conducted. The eIncome register contains information about work function (DISCO-08) for persons employed at workplaces covered by the wage statistic. If the workplace is not covered by the wage statistic the information about work function comes from the work classification module when it's available here. In addition other errors are corrected by the eIncome register.

2. The data source for information about self-employed is the business register, the income statistic, the eIncome register and the unemployment statistic. These sources are individually validated at the formation of information about self-employed.

3. The data source for information about absence due to sickness and maternity leave is the statistic of maternity leave and sickness benefits. Data is processed a great deal compared to e.g. a temporary determination of whether the absence is from employment or unemployment.

Transverse data treatment/data validation

The purpose of the transverse treatment/validation of data is to erase, correct or create labour market conditions in cases where the various data sources do not coincide. This is done by a so called treatment of overlaps. The rules used for the treatment are complex. Here some of the most important areas are mentioned:

• Selection of jobs for self-employed on basis of a series of criteria
• Determination of whether the absence due to sickness or maternity leave is from employment or unemployment
• Harmonizing information about subsidized employment

After that data is connected to other registers etc.

Data compilation

The commuting statistic is compiled on the register-based labour force statistic (RAS). RAS compiles the primary connection to the labour market at the end of November for residence. Read more about the data production i RAS here.

Especially two variables from RAS are central for the commuting statistic - the address of residence and the workplace. Based on these information the distance between the persons residence and workplace is calculated. The calculation requires that all movements between residence and workplace is done by car; the shortest road distance is prioritized; and regimentations, turn restrictions and level crossings are taken into account. Approximately 93 pct. of all relations between residence and workplace can be calculated. The remaining 7 pct. can't be calculated due to inadequate information about address; the workplace is outside of Denmark or on a ship; or logic isolation. Logic isolation is e.g. a laboratory that has to be placed isolated and therefore is placed on an island without official ferry service.

In some cases the distance can be calculated even though the address is inadequate. If the address in RAS can't be retrieved from the address database a manual relocation to the nearest adjacent address in the database is done. Since this process is time consuming the relocation is only done for those addresses that have at least 100 persons employed or 20 persons resident.

If the reason is logic isolation an address in the port, from where there is information about ship transfer to the island, is defined. From this address a sail route to the island is specified and the length of this imaginary sail route is added to the distance between the start address and the end address.

All other non placeable addresses is gathered in municipal centers. The centers is calculated based on existing addresses of residence and workplace. On that basis the distance is calculated.

A commuting distance can thus be calculated for all employed persons in RAS except for those working abroad or on ships.

The average commuting distance is based on the commuting distance for all commuters.

Adjustment

No corrections of data besides what is described under data validation and data compilation.