1.1. Contact organisation
Statistics Sweden
1.2. Contact organisation unit
Economic Statistics and Analysis
Innovation, Business sector production and Research
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Statistics Sweden
Att. Nils Adriansson
ESA/NUP/INF
Solna strandväg 86, Solna
SWEDEN
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
2 August 2023
2.2. Metadata last posted
2 August 2023
2.3. Metadata last update
11 April 2024
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
3.2.1. Additional classifications
| Additional classification used | Description |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | Same definitions as FM2015. Definition and the five criterias are listed as explatory notes in the questionnaire. |
| Fields of Research and Development (FORD) | No deviations. |
| Socioeconomic objective (SEO by NABS) | No deviations. |
3.3.2. Sector institutional coverage
| Government sector | No deviations in general. Some private non-profit institutions that are controlled and mainly financed by Central government are also included |
| Hospitals and clinics | Not included as separate institutional units. Hospitals and medical centres owned by regional councils (and part of local government) are included in the regional councils reporting and therefore part of GOV. |
| Inclusion of units that primarily do not belong to GOV | No. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | R&D administration and other supporting activities are part of R&D. Exclusions of indirect supporting activities are made in line with FM §2.122. |
| External R&D personnel | External R&D personnel HCs and FTEs are collected by gender and occupation. Included in total R&D personnel delivered to Eurostat |
| Clinical trials | Not specifically mentioned in the questionnaire, but if the respondents consider the activities to fulfill the five criterias in the R&D definition they should be included. No special sector inclusion criterias are used for clinical trials. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available. The GOV survey covers funding from rest of the world. |
| Payments to rest of the world by sector - availability | Available. The GOV survey covers funding to rest of the world. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes. |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | The GOV survey collect data for both extramural and intramural R&D for each sector separately. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not in general. |
3.4. Statistical concepts and definitions
The main variables of interest are R&D expenditure and R&D personnel. R&D expenditure is defined as all current and capital costs associated with R&D activities and is measured in millions in the national currency (SEK). R&D personnel is defined as all personnel performing R&D or providing direct support to R&D activities and is measured by headcount (HC) and full-time equivalents (FTE). Definitions are in accordance with FM15.
3.4.1. R&D expenditure
| Coverage of years | Calender year. |
| Source of funds | Data are collected for each source of fund as identified in Table 4.3 in FM for GOV. For the external souces of funds transfer funds are distinguished from exchange funds. |
| Type of R&D | Available according to FM guidlines. Intramural R&D expenditure are broken down by basic research, applied research and, experimental development. |
| Type of costs | The GOV survey collects a detailed breakdown of current costs and capital costs. Current costs are distinguished by labour cost; cost for external R&D personnel; and other operating expenses (excl. costs for external personnel). Capital costs are distinguished by land and buildings; machinery and equipment; capitalised computer software; and other intellectual property products. In section 4.4 of the Frascati manual it is described that Capital expenditures are the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of R&D for more than one year. We make no distinction in our questionnaire regarding the time the fixed asset has to have been used in R&D-performance. All acquisition of fixed assets (according to the companies' accounting systems) used in R&D is included, in order to make the question answerable. Otherwise in line with Frascati manual recommendations. |
| Defence R&D - method for obtaining data on R&D expenditure | R&D expenditure by socioeconomic objectives is collected. Socioeconomic objectives is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) and R&D for decence purposes (NABS14) is collected. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 31 December 2021. |
| Function | Only researchers and staff other than researchers is collected. Staff other than researchers is not broken down by technicians & equivalent staff and other supporting staff. |
| Qualification | From 2007 only one level of formal education is included, ISCED 8. |
| Age | Breakdown by age is not collected or estimated for the Government sector. |
| Citizenship | Breakdown by citizentship is not collected or estimated for the Government sector. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | 31 December 2021. |
| Function | Only researchers and staff other than researchers is collected. Staff other than researchers is not broken down by technicians & equivalent staff and other supporting staff. |
| Qualification | Breakdown by qualification is not collected or estimated for the Government sector. |
| Age | Breakdown by age is not collected or estimated for the Government sector. |
| Citizenship | Breakdown by citizenship is not collected or estimated for the Government sector. |
3.4.2.3. FTE calculation
The FTE is defined as work on R&D performed by one full-time employed person during one year. The FTE should, according to the national questionnaires, be reported with an accuracy of 0.1.
The national questionnaires request information on the number of FTE performed on R&D during the preceding 12 months’ period.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| Not available | ||
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
The statistical units are so called government units and non-profit institutions belonging to the government sector (NPIs):
• Central government - Central government authorities including social security funds,
• Secondary local government - Regional councils,
• Primary local government - Municipalities,
• Local and regional R&D units,
• NPIs controlled by government units
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | The target population consists of all government units belonging to central government and local government, including social security funds that is part of general government and NPIs thar are controlled by government units. Only 15 of many NPIs are included which are the ones that are the likely R&D performers. | |
| Estimation of the target population size | Central government agencies: 185 County councils: 20 Municipalities: 290 Public NPI: 15 Local and regional R&D units: 0 (estimated from year 2019 and onward) |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | The frame population consists of all central government agencies, municipalities, regional councils, local and regional R&D units and public NPIs that is controlled and mainly financed by central government. The frame used for determining the central government agencies and public NPIs in the population is the register from The Swedish National Financial Management Authority and Statistics Sweden's Statistical Business Register. The population of municipalities and regional councils is determined by the Statistical Business Register. Local and regional R&D units are defined as organisations mainly serving local government units in R&D, without being organisationally connected to them. As of year 2019 the local and regional R&D units are estimated, i.e. data is not collected through a survey. Local and regional R&D units are included in the data based on an ten-year average of 3 percent, i.e. 3 precent of the total sum of all other sector's extramural R&D and FTEs are added to the govenrment sector to account for the local & regional R&D units. The local and regional R&D units are not included in the populations of any of the other Frascati sectors. |
| Methods and data sources used for identifying a unit as known or supposed R&D performer | The Swedish Business Register, which is continuously updated with information from the Swedish National Tax Board, contains data of very high quality concerning the populations for central government agencies, counties and municipalities. |
| Inclusion of units that primarily do not belong to the frame population | No. |
| Systematic exclusion of units from the process of updating the target population | None. |
| Estimation of the frame population | 510 units |
3.7. Reference area
Sweden.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure in national currency in thousands, and R&D personnel by head count and full-time equivalents.
2021
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements | Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No R&D specific legislation at the national level. |
| Legal acts | All statistical data collection and production of official statistics is regulated by the Official Statistics Act (2001:99) and the Official Statistics Ordinance (2001:100). Confidentiality is regulated by the Public Access to Information and Secrecy Act (2009:400). In addition to this, Statistics Sweden has a mandate to regulate on the obligation to provide raw data and administrative data for business enterprises and government units (including higher education institutions). |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Individuals are not obligated to respond, however Statistics Sweden has a right to regulate obligations for business enterprises and local government units (including higher education institutions) to provide raw data and administrative data. Government units that are a part of central government are obliged to provide data for the GOV R&D survey according to the Official Statistics Act (2001:99). |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Microdata is available for research or statistical purposes. An application must be made in which the research project is described and the use of the microdata specified. The system for researchers to access microdata stored at Statistics Sweden is called Microdata Online Access (MONA). Access is only granted if the confidentiality of data can be ensured by the recieving party. |
| Planned changes of legislation | No planned changes of legislation. |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law: The major policy in place to ensure confidentiality and prevent unauthorised disclosure of data that identify a person or economic entity is the Public Access to Information and Secrecy Act (2009:400). There are also specific conditions concerning the confidentiality of official statistics in the Official Statistics Act (2001:99).
b) Confidentiality commitments of survey staff: The major policy in place to ensure confidentiality and prevent unauthorised disclosure of data that identify a person or economic entity is the Public Access to Information and Secrecy Act (2009:400). There are also specific conditions concerning the confidentiality of official statistics in the Official Statistics Act (2001:99).
7.2. Confidentiality - data treatment
For aggregate outputs, primary cell suppression is used as a general rule to ensure no confidential information is disclosed. To ensure that the information cannot be calculated using data in other cells, secondary cell suppression is used. These cells will be flagged as confidential. This only applies för the non-market NPIs.
Annexes:
Data protection policy
8.1. Release calendar
The release policy and the release calendar are publicly available at Statistics Sweden's website.
8.2. Release calendar access
The publication calendar is available on Statistics Sweden's website.
Annexes:
Publishing calendar
8.3. Release policy - user access
Statistics Sweden's release policy states that all statistics must be made available to all users equally and at the same time. Statistics are always released at 8.00 am on weekdays. Users are also informed of the availability of new statistics by news releases on Statistics Sweden's website. It is possible for users to subscribe to get e-mail notifications when new statistics within a certain subject area are released. Statistics are released by being made available in the statistical database. The release policy is available on Statistics Sweden's website.
Annualy.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, links, ... | |
| Regular releases | Y | The statistical database is updated and a news release is published on Statistics Sweden's website in October. This is followed by online articles on R&D in the Government sector. All content is made publicly available, free of charge. |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | The general publication on R&D in Sweden consists of statistical database tables and a news release. The variables reported in the database tables are R&D expenditure and R&D personnel (both as headcount and full-time equivalents). Intramural R&D expenditures are reported by region, type of cost, type of R&D, source of funds, while extramural R&D expenditure are reported on by recipient. Statistics on R&D personnel are broken down by sex, function, type of personnel and region. All publications are made available online only. |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
Y | Details on all publications are available on Statistics Sweden's website: Research and development in Sweden (scb.se). Brief descriptions of the contents are available in English |
1) Y – Yes, N - No
Annexes:
Press release R&D statistics 2021
Sector specific publication
10.3. Dissemination format - online database
An online statistical database is available on Statistics Sweden's website (see link in Annex). During 2022, the R&D tables in the database were accessed 3 431 times.
Annexes:
Online database for R&D statistics
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Microdata is available for research or statistical purposes. An application must be made in which the research project is described and the use of the microdata specified. The system for researchers to access microdata stored at Statistics Sweden is called Microdata Online Access (MONA). Access is only granted if the confidentiality of data can be ensured by the recieving party |
| Access cost policy | Statistics Sweden applies the principle of full cost coverage, i.e. the charge covers the actual cost of processing and producing the microdata requested. |
| Micro-data anonymisation rules | All microdata is anonymised. Statistics Sweden can use a common anonymisation key when microdata from several sources is requested at the same time. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
| Internet: main results available on the national statistical authority’s website | Y | Only aggregate figures are available on Statistics Swedens website for the Government sector. | Data are available in the online statistical database on Statistic Sweden’s website. |
| Data prepared for individual ad hoc requests | Y | Both microdata and aggregate figures. | Access to microdata is only granted for research or statistical purposes (applicable for NPIs only, otherwise it is considered a public document). All ad hoc requests are priced at full cost coverage. |
| Other | Y |
1) Y – Yes, N - No
10.6. Documentation on methodology
The main documentation on methodology is titled Statistikens framställning (translates to Statistical production) which is updated when new statistics are published. There is a common document covering all sectors for the R&D statistics in which the specific methodology for each sector is described. This documentation is only available in Swedish.
Annexes:
Methodology documentation (available in Swedish only)
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
The main documentation on quality management is titled Kvalitetsdeklaration (translates to Quality report) and is updated when new statistcs are published. There is a common document covering all sectors for the R&D statistics in which the quality for the statistics on each sector is described. This documentation is only available in Swedish.
Annexes:
Quality report (available in Swedish only)
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Statistical data is always accompanied by a quality report and a methodolgy report. These reports are available online on Statistics Sweden's website and follows a common standard for all official statistics in Sweden. Statistical database tables also contain footnotes in case there is important information about the data that users need to be aware of when using the data. |
| Request on further clarification, most problematic issues | Very few users give feedback on clarity. |
| Measure to increase clarity | Statistics Sweden works continuosly with improving the clarity in the documentation. |
| Impression of users on the clarity of the accompanying information to the data | The overall impression of users is that clarity is good. |
11.1. Quality assurance
The quality management process at Statistics Sweden is described in a Quality policy. There is also a handbook on quality in official statistics which provides guidance concerning quality management and definitions and guidance on the quality criteria. The following quality criteria for official statistics are regulated by the Official Statistics Act (2001:99) and are the same as are reported in this document:
- Relevance
- Accuracy
- Timeliness
- Punctuality
- Availability and clarity
- Comparability
- Coherence
The framework for quality assurance set out in the Quality policy is a cyclic process with four steps. First is understanding legal requirements and user needs. Second is ensured processes. The third step is evaluation and analysis followed by improvement and development as the fourth step. The first step requires a good dialog with users of the statistics. One forum for such dialog is the User Council for R&D statistics. The second step is based on standardised, efficient, and secure processes which are ensured partly by automatization and digitalisation, partly by following the standardised methods, tools and processes set up for statistical production and found in Statistikproduktionsstödet (translates to the Statistical Production Guide). The third step means that the production processes continuously need to be evaluated. One way in which this done is by a yearly survey to all producers of official statics in which they evaluate the quality of the statistics produced or published during the year. Based on the results of the evaluations, decisions are made concerning which improvement and development activities are to be prioritised over the coming period, constituting the fourth and final step before the process begins again at the first step.
11.2. Quality management - assessment
There are no major problems with the R&D methodology. Measurement error is considered the most important source of error in the statistics as a result of the relatively complex concepts involved in R&D statistics and that respondents are required to report on.
For the central government agencies the biggest problem is to determine what is R&D activities and what is not, especially in humanities and social sciences.
The biggest issue with the survey of the regional councils is the personnel resources. All regonal councils have trouble distinguishing between R&D activities and other activities, and therefore difficult to determine how many persons and FTEs are spent on R&D.
For municipalities it is the distinction between R&D activities and other activities that is difficult and they also have difficulties estimating how much financial resources are invested in R&D.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 - Institutions | Among the most important users in this class are the European Commission (through Eurostat), the Ministry of Enterprise and Innovation, the Ministry of Education, the Swedish Higher Education Authority and the Swedish Research Council. Regional and local government, as well as higher education institutions are also users of R&D statistics concerning the Higher education sector. | Comparability over time is one of the most important requirements. The Ministry of Education in particular also require a high degree of timeliness as the statistics are used when formulating the central government budget. For the European Commission, comparability between member states is a priority. Some of the most important breakdowns of the statistics required by these users are: |
| 2 - Social actor | Trade associations such as Teknikföretagen (a trade association for the Swedish industry sector) and the Swedish Association of Local Authorities and Regions are identified as some of the most important users in this class. | Comparability between groups is an important quality aspect for these users. They tend to have specific interests and want to be able to compare the development in specific breakdowns. Breakdown by region is the most requested by this group of users. |
| 3 - Media | Trade media is considered to be the most important users in this class. | Timeliness and accessibility are important aspects to this group of users. Press releases containing citations from experts on the statistics at the time of publication is one measure taken to better accommodate the needs of the news media. |
| 4 - Researchers and students | Researchers and students at higher education institutions and research institutes such as RISE and the Research Institute of Industrial Economics are the most important users in this class. | Accuracy is an important quality aspect for this user class as well as comparability both over time, between groups and with other statistics. This is also a group of users who request detailed data and often microdata. Access to microdata and the possibility to make ad-hoc requests for data on other breakdowns than those that are openly available is therefore important to this group. |
| 5 - Enterprise or businesses | No mapping has been done to identify the most important users among enterprises and businesses | |
| 6 - Other | Other important users are the public. | Clarity is among the most important aspects for the general public. This user class cannot be expected to have a detailed knowledge about the concepts and definitions used in the R&D statistics which makes clarity in the documentation and in other publications important. |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
The views and opinions from users are primarily collected through the User council for statistics on research and development which meets twice yearly. The user council consists of representatives from the Ministry of Enterprise, the Ministry of Education, the Swedish Higher Education Authority, the Swedish Research Council, Vinnova (Sweden's innovation agency), RISE, the Swedish Association of Local Authorities and Regions, the Swedish Agency for Growth Policy Analysis, the Research Institute of Industrial Economics, Lund University and Teknikföretagen (the trade association for the Swedish industry sector). Minutes from the last meeting of the user council are available in Swedish at Statistics Sweden's website.
Annexes:
User council for statistics on research and development, description and minutes
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No user satisfaction survey has been conducted. User satisfaction is mainly monitored through user councils. |
| User satisfaction survey specific for R&D statistics | No specific user satisfaction survey for R&D statistics has been conducted. There is, however, a specific user council for R&D statistics. |
| Short description of the feedback received | Overall user satisfacation is high. |
12.3. Completeness
All content requirements are regulated by the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. No additional national regulation regarding the content of R&D statistics exist. Data is available on all mandatory variables and breakdowns as well as some data that is requested on a voluntary basis. The completeness of the R&D statistics concerning the Goverment sector is therefore deemed good.
12.3.1. Data completeness - rate
109 of 109 mandatory cells
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | X | |||||
| Obligatory data on R&D expenditure | X | |||||
| Optional data on R&D expenditure | X | To not increase the response burden | ||||
| Obligatory data on R&D personnel | X | |||||
| Optional data on R&D personnel | X | To not increase the response burden | ||||
| Regional data on R&D expenditure and R&D personnel | X |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | |||
| Type of R&D | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | |||
| Type of costs | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | Intellectual property products included as separate post. | 2019 | Frascati manual 2015 implementation. |
| Socioeconomic objective | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | |||
| Region | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | Not all goverment units are asked to distribute their R&D expenditure by region in the questionnaire. Only goverment autthorities distribute their R&D expenditure by region. Since the reference year 2019 other goverment units (other than goverment authorities) R&D expenditure has been allocated to their seat county according to the business register. | 2019 | |
| FORD | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | The national nomenclature for classification according to field of research was revised to better follow the FORD classification. | 2011 | Improving international comparability. |
| Type of institution | N | Biennially. | Even years i.e. [...] 2018, 2020 etc. |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | |||
| Function | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | No separate cells for technicians or other supporting staff. | 2013 | |
| Qualification | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | Only HCs by formal qualifications in ISCED 8 are collected | ||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Biennially. | Even years i.e. [...] 2018, 2020 etc. | HC:s no longer distributed on regions in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of HCs. | 2017 | Since the distributions of expenditure, HCs and FTEs by region were very similar on the institutional unit level, this change was made to reduce the response burden. | |
| FORD | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | The national nomenclature for classification according to field of research was revised to better follow the FORD classification. | 2011 | Improving international comparability. |
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1 | Frequency of data collection | Gap years – years with missing data | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | |||
| Function | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | No separate cells for technicians. | 2013 | |
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | FTE:s no longer distributed on regions in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of FTEs. | 2017 | Since the distributions of expenditure, HCs and FTEs by region were very similar on the institutional unit level, this change was made to reduce the response burden. |
| FORD | Y | Biennially. | Even years i.e. [...] 2018, 2020 etc. | The national nomenclature for classification according to field of research was revised to better follow the FORD classification. | 2011 | Improving international comparability. |
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2 | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | - | 3 | 2 | 4 | 1 | 5 | +/- |
| Total R&D personnel in FTE | - | 3 | 2 | 4 | 1 | 5 | +/- |
| Researchers in FTE | - | 3 | 2 | 4 | 1 | 5 | +/- |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1 |
4 (Good)2 |
3 (Satisfactory)3 |
2 (Poor)4 |
1 (Very poor)5 |
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = In the event that at least one out of the three criteria described above would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
Does not apply. Census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Does not apply. |
| Government | Does not apply. |
| Higher education | Does not apply. |
| Private non-profit | Does not apply. |
| Rest of the world | Does not apply. |
| Total | Does not apply. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Does not apply. |
| Technicians | Does not apply. | |
| other support staff | Does not apply. | |
| Qualification | ISCED 8 | Does not apply. |
| ISCED 5-7 | Does not apply. | |
| ISCED 4 and below | Does not apply. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors: There is som under-coverage of non-market government controlled NPIs in the form of museums. Some of these are likely to perform R&D. Some other foundations are excluded that might perform (of fund) R&D but the amount is not considerd significant.
b) Measures taken to reduce their effect: Not applicable.
c) Share of PNP (if PNP is included in GOV): Not applicable.
13.3.1.1. Over-coverage - rate
0 %.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors: The respondents fill out an electronic questionnaire. Measurement errors are caused by the fact that the R&D definitions are complicated and that the time that respondents are willing to take to fill in the questionnaire is limited. A risk is that respondents have their own definitions of R&D (or their accounting system defiinition) in mind when answering, which may or may not correspond to the definitions provided in the questionnaire. Overall the measurment error is considered small and random.
b) Measures taken to reduce their effect: Values are compared with corresponding values from previous survey years. There are a number of flags in the survey as welll as in the internal tool used for evaluating the data, that are triggered by reported values too far from the correspondent value of the previous survey. A closer contact is kept with the largest companies, to try to make sure that they report in line with the Frascati definitions of R&D to the extent it is possible.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
-Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
-Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = 1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
| Central Government, goverment agencies including social security funds - 177 | 185 | 0.04 |
| Local government, regional councils - 20 | 20 | 0 |
| Local government, municipalities - 257 | 290 | 0.11 |
| Non-markes NPIs - 6 | 15 | 0.6 |
*Local and regional R&D units in Sweden are also included in Local government. These units have been etimated regarding reference year 2021 and are not included in the above table.
**Non-markets NPIs are not required by law to respond to the survey which is why the unit non-response rate is high.
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = 1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample) * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
| Intramural R&D expenditure | 0 | |
| Extramural R&D expenditure | 0 | |
| R&D personnel | 0 |
13.3.3.3. Measures to increase response rate
Improvements in questionnaire design and support to respondents. There are 2 written reminders and important respondents are reminded via email.
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Electronic online questionnaires applied. Direct registration by the respondent online. |
| Estimates of data entry errors | No error estimate available |
| Variables for which coding was performed | Does not apply. |
| Estimates of coding errors | |
| Editing process and method | During the data entering process, the information is checked for plausibility. If any inconsistencies are detected, data are corrected/edited in the IT-plattform that is used. Either the inconsistently is obvious and edited directly and in other cases contacting (telephone, e-mail) the unit for further information is required. There are no editing rates available. |
| Procedure used to correct errors | If errors occur during the micro/macro checking, respondents are contacted and asked to confirm provided information of give an explanation for the deviation. |
13.3.5. Model assumption error
The local and regional R&D units are assumed to contribute by 3 precent of the total R&D in the government sector, including under coverage.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period:
2021-12-31
b) Date of first release of national data:
2022-07-24
c) Lag (days):
195
14.1.2. Time lag - final result
a) End of reference period:
2021-12-31
b) Date of first release of national data:
2022-10-27
c) Lag (days):
300
14.2. Punctuality
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.
14.2.1. Punctuality - delivery and publication
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No real issues. Small divergences from FM described in other sections.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197, Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly paragraph 5.2). | No | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | Number of persons engaged in R&D at a given date (e.g. end of period) |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | In section 4.4 of the Frascati manual it is described that Capital expenditures are the annual gross amount paid for the acquisition of fixed assets that are used repeatedly or continuously in the performance of R&D for more than one year. We make no distinction in our questionnaire regarding the time the fixed asset has to have been used in R&D-performance. All acquisition of fixed assets (according to the companies' accounting systems) used in R&D is included, in order to make the question answerable. Otherwise in line with Frascati manual recommendations. | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No | University hospitals are controlled by regional councils and their contribution is reported by these units. |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No | Census |
| Survey questionnaire / data collection form | No | Electronic questionnaire |
| Cooperation with respondents | No | During the data collection period, respondents can communicate with Statistics Sweden in case they are unable to provide data at the requested date. In such cases, repondents can be allowed a deferment. Communication can also occur in case respondents need further directions on definitions or other issues on how the quetionnaire should be answered. |
| Data processing methods | No | After follow-up and contacting the units to clarify missing or unclear data, plausibility checks are carried out and missing items are imputed (few cases were units are considered as important). |
| Treatment of non-response | No | Very little non-response. Item-non-response is handeled by contacting the unit and validation in the questionnaire. If this approach is not successful then data values from the previous survey are used togehter with expert estimations |
| Variance estimation | Does not apply | Census |
| Data compilation of final and preliminary data | No | Final data for uneven reference years are results from the GOV survey. Final data for even reference years and for the main indicators are based on forecasts which the units provide in the regular GOV surveyand. Preliminary data for uneven reference years are are based on the responeses at the time, values for non-response units that are considered important is imputed using results from the previous survey. |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | 2013-2021 | 1979, 1993, 2005, 2011, 2013, 2015, 2021 | 1979: Public and private institutes serving industry, now included in the business enterprise sector, were included in the Government sector. 1993: SSH R&D was included in the Government sector, resulting in a break in series. 2005: From 2005 County councils, Municipalities and Local or regional research institutes were included in the government sector. 2011: The personnel in the county councils are included in the figures for the first time. However, this data is not yet complete. 2013: Up until 2011, data on personnel was collected according to three occupational categories: researchers, technicians and other support staff. From 2013, the data is collected by two occupational categories, researchers and other R&D personnel. 2015: No major changes in the data production process where made. A couple of more responses from counties and the restructuring of the swedish police complicates comparisons. 2021: The Swedish Institute of Space Physics is reclassified as belonging to the Government sector. 2021: Both internal and external R&D-personnel is now included in total R&D-personnel (HC:s) |
| Function | 2013-2021 | 2011, 2013 | 2011: The personnel in the county councils are included in the figures for the first time. However, this data is not yet complete. 2013: Up until 2011, data on personnel was collected according to three occupational categories: researchers, technicians and other support staff. From 2013, the data is collected by two occupational categories, researchers and other R&D personnel. In 2013 investments in computer software was added to type of costs. |
| Qualification | |||
| R&D personnel (FTE) | 2013-2021 | 1979, 1993, 2005, 2011, 2013, 2015, 2021 | 1979: Public and private institutes serving industry, now included in the business enterprise sector, were included in the Government sector. 1993: SSH R&D was included in the Government sector, resulting in a break in series. 2005: From 2005 County councils, Municipalities and Local or regional research institutes were included in the government sector. 2011: The personnel in the county councils are included in the figures for the first time. However, this data is not yet complete. 2013: Up until 2011, data on personnel was collected according to three occupational categories: researchers, technicians and other support staff. From 2013, the data is collected by two occupational categories, researchers and other R&D personnel. 2015: No major changes in the data production process where made. A couple of more responses from counties and the restructuring of the swedish police complicates comparisons. 2021: The Swedish Institute of Space Physics is reclassified as belonging to the Government sector. 2021: Both internal and external R&D-personnel is now included in total R&D-personnel (FTE:s) |
| Function | 2021-2021 | 2011, 2013 | 2011: The personnel in the county councils are included in the figures for the first time. However, this data is not yet complete. 2013: Up until 2011, data on personnel was collected according to three occupational categories: researchers, technicians and other support staff. From 2013, the data is collected by two occupational categories, researchers and other R&D personnel. |
| Qualification | |||
| R&D expenditure | 2019-2021 | 1979, 1993, 1995, 2005, 2015, 2019, 2021 | 1979: Public and private institutes serving industry, now included in the business enterprise sector, were included in the Government sector. 1993: SSH R&D was included in the Government sector, resulting in a break in series. 1995: Capital expenditure for R&D in higher education is excluded for that year; consequently all 1995 data concerning HERD, total GERD and government-financed GERD are underestimated and not comparable to corresponding data for previous and following years. 1997: Due to a change in statute, the funding of Public Research Foundations previously considered as funding from the PNP sector has been reclassified as funding from the Government sector 2005: From 2005 County councils, Municipalities and Local or regional research institutes were included in the government sector. 2015: No major changes in the data production process where made. A couple of more responses from counties and the restructuring of the swedish police complicates comparisons. 2019: Prior to reference year 2019 R&D financed by the Swedish ALF funds (the "Agreement on Medical Training and Research") were included in HES and excluded from GOV. As of 2019 R&D financed by ALF funds are included in GOV and are excluded from HES, i.e. the opposite relationship. The main reason for this change is that R&D financed by ALF funds should be represented in the sector where the funding is consumed, which is within the regional sector that is a part of GOV. The change results in a large increase in total GOVERD, but also a decrease in total HERD 2021: The Swedish Institute of Space Physics is reclassified as belonging to the Government sector. |
| Source of funds | 2019-2021 | 2019 | 2019: ALF funds are now included in SECTFUND=GOV. The main reason for this change is that R&D financed by ALF funds should be represented in the sector where the funding is consumed, which is within the regional sector that is a part of GOV. The change results in a large increase in total GOVERD, but also a decrease in total HERD |
| Type of costs | 2019-2021 | 2013 | 2013: investments in computer software was added to type of costs. |
| Type of R&D | 2019-2021 | ||
| Other | 2019-2021 |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Final data for even reference years and for the main indicators are based on forecasts which the units provide in the regular GOV survey.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Used as input to NA.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
| Intramural R&D expenditure-not applicable | |||||
| Extramural R&D expenditure-not applicable | |||||
| R&D-personnel-not applicable | |||||
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
| Preliminary data (delivered at T+10) | 8 237 662 | 5 886 | 4 176 |
| Final data (delivered T+18) | 8 237 661,744 | 5 885,687 | 4 175,358 |
| Difference (of final data) | -0,256 | 0,687 | -0,642 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 917,551 SEK thousand |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 919,514 SEK thousand |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| Comments on costs | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
| Number of Respondents (R) | 460 | |
| Average Time required to complete the questionnaire in hours (T)1 | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Statistics Sweden's revision policy covers three types of revisions: planned and reoccuring revisions, revisions due to conceptual and/or methodological changes and corrections.
1. Planned and reoccuring revisions - In order to accommodate user timeliness needs, Statistics Sweden publisch preliminary figures. These figures are then revised once or several times before final data are released. In case of planned revisions, users will be informed of the number of revisions as well as the revision dates.
2. Revisions due to conceptual and/or methodological changes - Methodological changes can have systematic effects on the statistics. Concepts, definitions or classifications can be changed in order to better capture the target variables. In case of such changes, and if deemed necessary and possible, revisions of earlier final data can be made in order to produce comparable time series. Users will be informed of revisions of this kind in advance, with an explanation of why the revision is necessary.
3. Corrections - In case of errors in published data, corrections can be made. When an error has been identified, the need for correction is evaluated without delay based on the magnitude of the error and the importance of the statistics. Corrections are always published in a clear and easily accessible manner, with information on why the correction is necessary.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | Research and development in the government sector |
| Type of survey | Census survey |
| Combination of sample survey and census data | Not applicable. Cenus survey only. |
| Combination of dedicated R&D and other survey(s) | Not applicable. The dedicated R&D surveys are not combined with other surveys. |
| Sub-population A (covered by sampling) | Not applicable, no sampling used in this survey. |
| Sub-population B (covered by census) | No sub-population used, the population is covered by the census. |
| Variables the survey contributes to | The survey contributes those variables that Sweden is obliged to answer according to the regulation. |
| Survey timetable-most recent implementation | Data collection from March 2022-August 2022. Survey results are published in October 2022. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Goverment Units; The legal unit | ||
| Stratification variables (if any - for sample surveys only) | Not applicable. | ||
| Stratification variable classes | Not applicable | ||
| Population size | 510 | ||
| Planned sample size | Census | ||
| Sample selection mechanism (for sample surveys only) | Not applicable. Census survey. | ||
| Survey frame | All goverment units that is divided into five sub-populations: Central government authorities, regional councils, Municipalities,Public non-profit organisations, Local and regional R&D units. |
||
| Sample design | Not applicable. | ||
| Sample size | Not applicable. | ||
| Survey frame quality | The quality of the frame is good. There is no known coverage error in this survey. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | National Business Register by Statistics Sweden. |
| Description of collected data / statistics | Bakground information for the statistical units; for example number of employees, institutional sector etc. |
| Reference period, in relation to the variables the survey contributes to | Calender year. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Central govenrment authorities, Regional councils, Municipalities, Non-market NPIs |
| Description of collected information | The central government authorities and the public non-profit organisations are asked to allocate their: - Extramural expenditure by receiving units, source of funds and socio-economic objective. - Intramural expediture by county (NUTS3) source of funds, socio-economic objective, type of cost, type of R&D. - Forecast for the following year, intramural expenditure. Internal R&D personnel: External R&D personnel:
The county councils are asked to allocate their: Internal R&D personnel: External R&D personnel:
The municipalities are asked to allocate their: - Extramural expenditure by receiving units, source of funds and socio-economic objective. Internal R&D personnel: External R&D personnel: |
| Data collection method | Electronic questionnaire. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Not applicable. Census survey. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Survey with electronic questionnaire. |
| Incentives used for increasing response | No incentives are used for increasing reponse. |
| Follow-up of non-respondents | Two reminders by post and one email reminder if necessary. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 90 percent |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | No non-response analysis conducted. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire (to the goverment authorities in this example) is available on page 44 in attached URL. |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Methodology is documented, see attached URL. |
Annexes:
Documentation
18.4. Data validation
Several measures are taken to ensure data validation. Data validation are done both at a micro and macro level. Micro validation measures consists of internal and external controls in the questionnaires to check for any reporting inconsistencies and missing values (item non-response). Individual examination of large R&D performers is also done. Respondents are re-contacted to verify these inconsistencies and missing values. Data validation on a macro level consists of evaluating macrodata, totals and by requested breakdowns, comparing against previous years.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
0,6 %
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | In the survey respondents are asked to make a forecast of intramural R&D expenditure and R&D-personnel (number of FTEs) for the following year. |
| Data compilation method - Preliminary data | For the years when a survey is conducted the preliminary statistics are preliminary result of the survey. In case the final result are published before the Eurostat delivery, the final data is provided. Every other year the data is based on forecasts from the respondents. See the answer above. |
18.5.3. Measurement issues
| Method of derivation of regional data | Goverment authorities and on-market NPI are asked to distribute their R&D expenditures regionally. The proportions for the distributions are then used to distribute R&D personnel by region. Remaining units are not asked this questions in the questionnaire, instead their R&D expenditures are allocated to the region of their county seat in accordance with the information in the statistical business register. |
| Coefficients used for estimation of the R&D share of more general expenditure items | No coefficients are estimated. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT and depreciation are excluded in the measurement of R&D expenditure. The exclusion is mentioned in the instructions for the questionnaire. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No additional differences. |
18.5.4. Weighting and estimation methods
| Description of weighting method | |
| Description of the estimation method | Estimation of the indicators is done by sumation of the variable values for each responding unit. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
11 April 2024
The main variables of interest are R&D expenditure and R&D personnel. R&D expenditure is defined as all current and capital costs associated with R&D activities and is measured in millions in the national currency (SEK). R&D personnel is defined as all personnel performing R&D or providing direct support to R&D activities and is measured by headcount (HC) and full-time equivalents (FTE). Definitions are in accordance with FM15.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
The statistical units are so called government units and non-profit institutions belonging to the government sector (NPIs):
• Central government - Central government authorities including social security funds,
• Secondary local government - Regional councils,
• Primary local government - Municipalities,
• Local and regional R&D units,
• NPIs controlled by government units
See below.
Sweden.
2021
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure in national currency in thousands, and R&D personnel by head count and full-time equivalents.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
Annualy.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
