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MNEs in business statistics and implementation of NACE Rev 2.1

Sector: Government • Location: Sweden

Source: EU Funding & Tenders Portal

Project
Ended

Area 1 of the action on European profiling on the structure of the Top tier MNE groups: SCB has participated in several profiling grant projects during recent years. We have profiled, some of our largest MNEs using the European Profiling Methodology. Now when Eurostat has implemented the top tier approach and a Complexity and Statistical Impact (CSI) index with the help of the Task Force for th

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The project “MNEs in business statistics and implementation of NACE Rev 2.1” is an infrastructure initiative in the Government sector, located in Sweden. Taiyo aggregates data on it from EU Funding & Tenders Portal.

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Description

Description

Area 1 of the action on European profiling on the structure of the Top tier MNE groups: SCB has participated in several profiling grant projects during recent years. We have profiled, some of our largest MNEs using the European Profiling Methodology. Now when Eurostat has implemented the top tier approach and a Complexity and Statistical Impact (CSI) index with the help of the Task Force for the Future EGR, we have chosen to focus our work on this methodology. We plan to take further steps in this area of work by doing European profiling on the structure of the top tier MNEs focusing on updating the legal unit (LEU), the control relationships (REL) and the global enterprise groups (GEG) data. Area 4 of the action on Implementation of the NACE Rev.2.1 in the Business Registers: The implementation of NACE Rev.2.1 induces the need to re-code statistical units to the new version of the classification. A fast and smooth transition to the new classification is necessary to be able to present statistics of good quality hence it is a key variable for stratifying, reporting, and analysing. Mainly, the re-coding at Statistics Sweden will be performed by a new automatic re-coding algorithm including e.g. large language models, which may have a higher extent of uncertainty and some objects may not be suitable to be re-coded by this approach. Quality of all re-coding methods are dependent on the input data quality, but models may be more sensitive. The intention with this project is to measure and improve the re-coding quality. This may be done with the following overall processes: 1. improving input data quality through extracting more up-to-date data about the units in the business register, 2. Performing effective quality controls of the automatic re-coding algorithm.

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High

Data quality score

100%

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