Research and development (R&D) (rd)

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

Restricted from publication

1.4. Contact person function

Restricted from publication

1.5. Contact mail address

Statistics Sweden
Att. Elin Stendahl ESA/NUP/INF
Solna strandväg 86, Solna
SWEDEN

1.6. Contact email address

Restricted from publication

1.7. Contact phone number

Restricted from publication

1.8. Contact fax number

Not required.

28 October 2025

2.1. Metadata last certified

28 October 2025

2.2. Metadata last posted

28 October 2025

2.3. Metadata last update

18 August 2025

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). 

The guiding document to preparing the quality reports is the European Statistical System (ESS)  Handbook for Quality and Metadata Reports — re-edition 2021.

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.

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).
• The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011

3.3. Coverage - sector

See below.

3.3.1. General coverage

Definition of R&D

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

R&D definition used is in line with the Frascati Manual (FM) definition.

3.3.2. Sector institutional coverage

Government sector	Included are all units of central and local government, including non-profit institutions that are controlled by central and local government units. Regional government institutions do not exist in Sweden. Social security funds in Sweden are themselves central government agencies and therefore also included. Excluded are higher education institutions. The central bank of Sweden is also included since it is a government agency and non-market producer.
Hospitals and clinics	Public hospitals and clinics are, for the most part, not separate institutional units. Instead, they are an integrated part of the regions (part of local government). This also includes the university hospitals. Other public hospitals and clinics that are separate institutional units and part of the government sector are controlled by the regions and included in their reporting, and therefore covered.
Inclusion of units that primarily do not belong to GOV and the borderline cases.	Some borderline cases of non-profit institutions that are classified as non-profit institutions serving households by the national accounts are included. Statistics Sweden has made the assessment, based on the criteria of control, that these units belong to the Government sector.

3.3.3. R&D variable coverage

R&D administration and other support activities	R&D administration and other supporting activities are included in R&D labour costs and R&D personnel. Exclusions of indirect supporting activities are made in line with FM §2.122.
External R&D personnel	External R&D personnel (HC and FTE) are collected separately by gender and occupation. External personnel are included in total R&D personnel delivered to Eurostat.
Clinical trials: compliance with the recommendations in FM §2.61.	Not specifically mentioned in the actual questionnaire. However, in the instructions for the survey it is specified that phase 1-3 clinical trials could be included if these activities are in line with the definition of R&D, and that phase 4 clinical trials in general should be excluded. The exclusion of phase 4 clinical trials is in practice hard to verify.

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	The survey covers funding the rest of the world by the following categories: foreign business enterprises, the European commission, international organisations (excluding the EC), foreign government entities (GOV) and foreign private non-profit institutions (PNP).
Payments to rest of the world by sector - availability	The survey covers funding to the rest of the world by the following categories: foreign business enterprises (BES), the European commission, international organisations (including the EC), foreign higher education institutions (HES), foreign government entities (GOV) and foreign private non-profit institutions (PNP).

3.3.5. Extramural R&D expenditures

According to the Frascati Manual (FM), 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 method used to separate extramural R&D expenditure from intramural R&D expenditure is by having separate questions in the questionnaire which asks for intramural R&D and extramural R&D separately. The respondents are also provided with the definitions of intramural/extramural R&D in the beginning of the questionnaire as well as in relation to each question to help with the distinction.
Difficulties to distinguish intramural from extramural R&D expenditure	The distinction is made clear to the respondents and in general the there is no problem with the distinction. However, some respondents could find it difficult to distinguish extramural R&D expenditures from costs of external personnel (other current costs). There is no systematic double counting. However, there could be potential double in the case of defence agencies. Some defence agencies act as intermediaries that receive R&D funds from defence agencies and then pass through these funds to other performers (often other defence agencies or the defence industy). In the questionnaire it is highlighted that extramural R&D is funding for R&D performed and not re-allocations to other units.

3.4. Statistical concepts and definitions

See below.

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 FM §4.104-4.108 table 4.3. For external funds, transfer funds are distinguished from exchange funds.
Type of R&D	Breakdown by type of R&D is collected according to FM guidelines in section 2.5. Intramural R&D expenditure are broken down by basic research, applied research and experimental development.
Type of costs	The 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 to support R&D (excluding costs for external personnel). Capital costs are broken down 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	Defence GOVERD is obtained by collecting intramural R&D expenditure by socioeconomic objectives, based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS). Intramural R&D expenditure for defence purposes (NABS14) is used to distinguish civil R&D from defence-related R&D.

3.4.2. R&D personnel

See below.

3.4.2.1. R&D personnel – Head Counts (HC)

Coverage of years	Total number of persons on 31 December 2023.
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. Only internal R&D personnel by qualification is collected.
Age	Not available
Citizenship	Not available

3.4.2.2. R&D personnel – Full Time Equivalent (FTE)

Coverage of years	Calendar year
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	Not available
Age	Not available
Citizenship	Not available

3.4.2.3. FTE calculation

In the questionnaire information on the number of FTE performed on R&D during the reference year is requested.

The FTE of R&D personnel is defined as work on R&D performed by one full-time employed person during one year. The FTE should, according to the questionnaire, be reported with an accuracy of 0.01.

3.5. Statistical unit

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.

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	Government units of central and local government, including non-profits institutions they control and social security funds, known or presumed to perform and/or fund R&D.	Not applicable.
Estimation of the target population size	The size of the target population is 313 institutional units. These are, in part, the units which reported that they either performed or funded R&D. Unit non-response are assumed to be part of the target population and therefore included in the estimation.	Not applicable.

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 Swedish Business Register (units active in November 2023) was used to identify the institutional units classified to the Government sector, corresponding to General government (S. 13) as defined by the national accounts. Each unit in the business register is classified with a sector code according to the Standard Classification by Institutional Sector (INSEKT 2014). INSEKT 2014 is the Swedish adaption of ESA.  Public higher education institutions were excluded by not covering those classified into NACE 854. An investigation was done on borderline cases of non-profit institutions serving households (classified to sector 15) to determine if they are controlled by government units or not. The determination was based on the criteria of control highlighted in FM §8.14 Box 4.3.
Methods and data sources used for identifying a unit as known or supposed R&D performer	Most core central government units (central government agencies) of Central government are surveyed since they are assumed to at least occasionally perform or fund R&D. However, only central government agencies that have some economic activity are included. The data source used to identify those with economic activities is a central government agency register provided by The Swedish National Financial Management Authority. Social security funds in Sweden are central government agencies and are all included in the frame population.  Local government (core local government units) and government controlled non-profit institutions are partly included. Even though R&D is not very significant in the municipalities/primary local government authorities (290) they are assumed to at least occasionally perform or fund R&D and therefore all are surveyed. All regions (20) are known to perform R&D and are the biggest performers of R&D in the Swedish government sector.  Most of the Local federations (federations of local government authorities) do not engage in R&D activities and therefore most of them are excluded from the frame population. Units with less than 20 employees are excluded unless they are in NACE 72. For the remaining, the known or supposed R&D performers or funders are identified from other sources on the internet. A criterium is that the local federation has at least 1 employee. For government controlled non-profit institutions the same applies. Firms and other units with less than 5 employees are excluded unless they are in NACE 72. Foundations whose functions is to primarily fund R&D and public museums are all surveyed regardless of main economic activity and size class (at least 1 employee). For the remaining, the known or supposed R&D performers or funders are identified from other sources on the internet.
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	The frame population is equal to the target population. 558 institutional/statistical units: Central government agencies including social security funds (183) Local government; Municipalities (290), regions (20), and local federations (20) Non-profit institutions (45)

3.7. Reference area

Not requested.

3.8. Coverage - Time

Not requested, see concept 12.3.3 (Data availability).

3.9. Base period

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 is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

Calendar year 2023 is the reference period.

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. The transmission of R&D data is mandatory for Member States and EEA countries.

The 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. 

6.1.2. National legislation

Existence of R&D specific statistical legislation	No specific legislation.
Are respondents obliged by the national law to provide raw and administrative data:	Yes, respondents are obliged to answer the parts of the survey which are mandated by EU regulation referenced in section 6.1.1.

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

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.

At the level of the ESS the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).

At national level: 

• 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).
• Confidentiality commitments of survey staff: Statistics Sweden has a confidentiality policy to which all survey staff must adhere. It contains guidance on the practical application of the legal acts stated above.

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 government controlled non-profit institutions.

8.1. Release calendar

The release policy and the release calendar are publicly available at Statistics Sweden's website.

8.2. Release calendar access

For Eurostat this is: Release calendar - Eurostat (europa.eu)

For Statistics Sweden this is: Release calendar - Statistics Sweden

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.

The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.

Nationally, R&D data are disseminated yearly. Provisional statistics are published in July and final statistics in October.

10.1. Dissemination format - News release

See below.

10.1.1. Availability of the releases

	Availability (Y/N)1)	Links
Regular releases	Y	R&D activity in Sweden grows in 2023
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)	Links
General publication/article	N
Specific paper publication (e.g. sectoral provided to enterprises)	Y	Research and development in the Government sector 2023 (Only available in Swedish)

1) Y – Yes, N - No 

10.3. Dissemination format - online database

All R&D data are published in Statistics Sweden’s online database: Statistical database - Select table

10.3.1. Data tables - consultations

Not requested.

10.4. Dissemination format - microdata access

As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.

10.4.1. Provisions affecting the access

Access rights to the micro-data	Microdata of core government units is available for all users.  Microdata of government controlled non-profit institutions 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 receiving 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 in the case of non-prodit institutions. 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 of non-profit institutions is only granted for research or statistical purposes. For core government units microdata is available for all users. All ad hoc requests are priced at full cost coverage.
Other	N

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. 

Metodology report (Swedish): Statistikens framställning - Forskning och utveckling i Sverige 2023

Metodology report (English): Metodology report - Research and development in Sweden 2023

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

See below.

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, a methodology report and metadata documentation. The information is available online on Statistics Sweden's website and follows common standards 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. Quality report (Swedish): Kvalitetsdeklaration - Forskning och utveckling i Sverige 2023 Quality report (English): Quality report - Research and development in Sweden 2023 Metadata documentation (only available in Swedish): Dokumentation av mikrodata - www.scb.se

Requests on further clarification, most problematic issues

Sometimes users ask questions about reasons behind changes in the figures over time, whether it is due to a time-series break or not.

11.1. Quality assurance

At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).

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

The methodology used is based on the Frascati Manual recommendations. The quality of the statistics is assessed regularly, and the R&D statistics meet the quality requirements. Being a census survey and using a well defined frame population ensures good coverage of likely known or presumed R&D performers & funders in the government sector. With the survey being compulsory, except from some of the non-profit institutions, the response rate is high, approx. 90 percent. Given the concentration of R&D expenditure to the top performers and funders, the responses from the largest performers and funders are carefully reviewed and re-contact is made for clarification of any inconsistencies or changes in their responses.

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 to the regions is the personnel resources. All regions have trouble distinguishing between R&D activities and other activities such as healthcare and other training activities, and therefore difficult to determine how many persons that are involved in R&D activities and how many FTEs they perform. 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 Climate and 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 Government 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 state budget and other policy indicators. 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: Region (NUTS 2) Sex Employment category
2. Social actors	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, short-and longtime trends, in those industries or sectors that they represent with other industries or sectors. 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. This user group tends to use the statistics for short time changes.
4. Researcher 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. Enterprises or business	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

To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.

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. Statistics Sweden regularly arranges meetings with our primary users to take into account their suggestions for improvements.
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 satisfaction is considered high.

12.3. Completeness

See below.

12.3.1. Data completeness - rate

100 %.

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.

	Reasons for missing cells
Preliminary variables	Not applicable.
Obligatory data on R&D expenditure	Not applicable.
Optional data on R&D expenditure	GOVERD by main economic activity is in principle possible to deliver. However, Statistics Sweden has indications that some units, that are not classified in NACE 72 according to the Swedish business register, are in fact research institutes. Additionally, there are instances where units may be incorrectly classified into NACE 72. This necessitates further investigation before providing this breakdown.
Obligatory data on R&D personnel	Not applicable.
Optional data on R&D personnel	In general, optional breakdowns specified in the Commission implementing regulation are not delivered since the corresponding variables are not collected. This is to avoid increasing the response burden for the respondents of the survey. The variables are also not available in other administrative sources.
Regional data on R&D expenditure and R&D personnel	Not applicable.

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Source of funds	1981	Every other year
Type of R&D	2013	Every other year
Type of costs	1981	Every other year		Computer software and other intellectual property products included	Computer software: 2015 Other intellectual property products: 2019	Frascati manual 2015 implementation.
Socioeconomic objective	2003	Every other year		GOVERD by Socioeconomic objective is collected using Nomenclature for the. Analysis and comparison of Scientific programmes and Budgets (NABS 2007)	2019	No strong user needs for the old classification developed by Nordforsk. Using the NABS classification simplifies the transmission to Eurostat.
Region	1999	Every other year	2001	Not all units are asked to distribute their R&D expenditure by region in the  questionnaire. Only central government agencies distribute their R&D expenditure by region (NUTS3). Other government unit´s R&D expenditure have been allocated to their seat county according to the business   register.	2019	Institutional units at the local level rarely perform R&D outside of its location. Thus, the change was made to reduce the response burden.  For non-profit institutions an institutional approach is also used in order to reduce the response burden
FORD	2015	Every other year		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	2005	Every other year

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	1999	Every other year.
Function	2007	Every other year.		No separate cells for technicians and other supporting staff.	2013.	To reduce the response burden
Qualification	1999	Every other year.		Only head counts of R&D personnel with formal qualifications at the doctoral or equivalent level is collected	2007
Age	N
Citizenship	N
Region	1993	Every other year.		Head counts no longer distributed by region in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of head counts.	2019	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	2003	Every other year.		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	2007	Every other year.

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	Changes - Description	Changes - Year of introduction	Changes - Reasons
Sex	1997	Every other year.
Function	2007	Every year		No separate cells for technicians and other supporting staff.	2013	To reduce the response burden
Qualification	1981	Every other year	2007-
Age	N
Citizenship	N
Region	1993	Every other year		Full-time equivalents no longer distributed by region in the questionnaire. Instead, the R&D expenditure regional distribution is applied on the total number of full-time equivalents.	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	2003	Every other year	2005-	The national nomenclature for classification according to field of research was revised to better follow the FORD classification.
Type of institution	2009	Every other year

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
Extramural R&D expenditure.	2005	Every other year.	By sector of performance/recipients of funds		A detailed breakdown by receiving units which can be aggregated to the corresponding institutional sector.
Extramural R&D expenditure.	2005	Every other year.	By type of funds.		Exchange and transfer funds.
Extramural R&D expenditure.	2005	Every other year.	By socioeconomic objective		NABS 2007 since 2019. Before a classification developed by Nordforsk was used
Intramural R&D expenditure.	2019	Every other year.	By type of funds	Source of funds	Total external funds by exchange and transfer funds.

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

12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)

Cross-classification	Unit	Frequency
Not available.

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:

• • Coverage errors,
  • Measurement errors,
  • Non response errors and
  • 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 errors1)	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	Non-applicable.	5	1	2	3	4	+/-
Total R&D personnel in FTE	Non-applicable.	5	1	2	3	4	+/-
Researchers in FTE	Non-applicable.	5	1	2	3	4	+/-

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 (6). If errors of a particular type do not exist, the sign ‘:‘ is used.

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' = If at least one out of the three criteria described above is not fully met.

3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.

4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not 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

See below.

13.2.1.1. Variance Estimation Method

Not applicable. The survey is a census.

13.2.1.2. Confidence interval for R&D expenditure by source of funds

Source of funds	R&D expenditure
Business enterprise	Not applicable. Census.
Government	Not applicable. Census.
Higher education	Not applicable. Census.
Private non-profit	Not applicable. Census.
Rest of the world	Not applicable. Census.
Total	Not applicable. Census.

13.2.1.3. Confidence interval for R&D personnel by occupation and qualification

		R&D personnel (FTE)
Occupation	Researchers	Not applicable. Census.
	Technicians	Not applicable. Census.
	other support staff	Not applicable. Census.
Qualification	ISCED 8	Not applicable. Census.
	ISCED 5-7	Not applicable. Census.
	ISCED 4 and below	Not applicable. Census.

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.

•  Description/assessment of coverage errors : The coverage between the target population and the frame population overlaps to a very high degree. Over coverage naturally occurs since not all units in the frame population will perform or fund R&D during the reference year. This is especially the case for central government agencies and municipalities which can occasionally perform or fund R&D to fulfil some of its objectives. Over coverage can also occur if units in the frame have dissolved during the reference year but still active in the business register. The over coverage has no effect on the overall accuracy of the statistics. The potential under-coverage for local federations and non-profit institutions is highly negligible regarding the quality and comparability of the statistics.
• Measures taken to reduce their effect: Units that have dissolved during the reference year will not be in the business register and the frame population during the next collection round. In the case of local federations and non-profit institutions, measures to reduce the over-coverage are to remove those that have not reported any R&D activities from the two previous collections rounds or remove those that have explicitly stated in the questionnaire that they do not engage in R&D activities (e.g., those who only provide facilities and other infrastructure to other R&D performers.).
• Share of PNP (if PNP is included in GOV): PNP is not included in GOV. There are however, as mentioned before, some units that are part of PNP according to the national accounts, but the assessment has been made that they are part of GOV. The share of those units are 1.8 percent.

13.3.1.1. Over-coverage - rate

Not requested.

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.

• Description/assessment of measurement errors: 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 definition) in mind when answering, or that they have difficulties in separating R&D from other related activities, which may or may not correspond to the definitions provided in the questionnaire.
• Measures taken to reduce their effect: Values are compared with corresponding values from previous survey years. There are several flags in the questionnaire as well as in the internal tool used for valdating the data, which are triggered by reported values too far from the correspondent value of the previous survey, or when incorrect values are provided. A closer contact is kept with the largest units, 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)] * 100

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)
502	558	10.04 %

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 Expenditure	R&D Personnel (FTE)	Researchers (FTE)
Item non-response rate (un-weighted) (%)	0 %	0 %	0 %
Comments

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	The data collection is made with an electronic/web questionnaire. Data is then saved in an internal IT-platform and then read into a SQL-database. Throughout the questionnaire there are checks that warns the respondents if they have entered inconsistent or incorrect values.
Estimates of data entry errors	No error estimates available
Variables for which coding was performed	Not applicable.
Estimates of coding errors	Not applicable
Editing process and method	Plausibility checks are performed at both micro and macro levels. Most unit-level validations are integrated into the IT platform used during the data processing phase. When inconsistencies or potential errors are identified—either through automated checks or manual review—the data is examined in detail. If the issue cannot be resolved internally, the respondent is contacted via email or phone for clarification. Certain variables require derivation before final validation. Once all adjustments are made, consistency checks are carried out to ensure that the sum of detailed breakdowns matches the reported totals.
Procedure used to correct errors	Errors are addressed based on their impact on the statistics. In cases where significant discrepancies are detected—those that may affect the quality or interpretation of the statistical output—respondents are contacted for validation. They are asked to clarify their reporting and provide background information for any major changes. If the respondent confirms that an error has occurred, the data is corrected directly in the IT platform using the accurate information provided. These corrections are then automatically updated in the system and transferred to the SQL database. Minor errors that are clearly identifiable and do not affect the statistical outcome are corrected directly by the statistical team without contacting the respondent.

13.3.5. Model assumption error

Not requested.

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)

• End of reference period: End of 2023
• Date of first release of national data: 11 July 2024
• Lag (days): 193 days

14.1.2. Time lag - final result

• End of reference period: End of 2023
• Date of first release of national data: 31 October 2024
• Lag (days): 305 days

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

Overall, international comparability is good. Small divergences from FM are described in the following 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 (FM) 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 sub-chapter 5.2).	No deviation
Researcher	FM2015, § 5.35-5.39.	No deviation
Approach to obtaining Headcount (HC) data	FM2015, § 5.58-5.61 (in combination with  Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation	Respondents are asked to report the number of persons engaged in R&D at the end of the reference year, December 31, 2023.
Approach to obtaining FTE data	FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation	Respondents are asked to report the number of full-time equivalents on R&D during the reference year, 2023.
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25	No deviation	Total R&D personnel include both internal and external R&D personnel.
Intramural R&D expenditure	FM2015, Chapter 4 (mainly sub-chapter 4.2).	Yes	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 deviation	The statistical unit is the institutional unit.
Target population	FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation	The target population is all institutional units known or likely to perform and/or fund R&D.
Sector coverage	FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
Hospitals and clinics	FM2015, § 8.22 and 8.34	No deviation	University hospitals and other public hospitals are classified within the government sector, as they are regionally owned and managed. In most cases, these hospitals are not treated as distinct institutional units; instead, they are incorporated into the administrative structure of the regional authorities. Public hospitals that do not qualify as separate institutional units under sector S.13, but that may contribute to research and development (R&D), are nonetheless included in the regions’ reporting, as their activities are integrated within the regional administration.
Borderline research institutions	FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation	Certain institutional units that are formally classified as non-profit institutions serving households (S.15) in the national accounts are, in practice, included within the government sector (GOV). Based on established control criteria, Statistics Sweden has determined that these units should be treated as part of the general government. This classification is guided by the principles set out in the Financial Manual (FM), particularly §8.14 and Box 4.3, which outline how governance, control, and financial oversight determine sector affiliation.
Fields of research & development coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Socioeconomic objectives coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Reference period	Reg. 2020/1197 : Annex 1, Table 18	No deviation

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 (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.

Methodological issues	Reference to recommendations	Deviation from recommendations	Comments on national treatment / treatment deviations from recommendations
Data collection method	FM2015 Chapter 8 (mainly sub-chapter 8.5).	No deviation	Cenus.
Survey questionnaire / data collection form	FM2015 Chapter 8 (mainly sub-chapter 8.5).	No deviation	Electronic questionnaire.
Cooperation with respondents	FM2015 Chapter (mainly sub-chapter 8.5).	No deviation	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, respondents can be allowed a deferment. Communication can also occur in case respondents need further directions on definitions or other issues on how the questionnaire should be answered.
Data processing methods	FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6).	No deviation	After follow-ups and contacting the units to clarify missing or unclear data, plausibility checks are carried out and missing items are imputed (few cases where units are considered important). After data is edited and corrected then other variables are derived. the estimation is done by summing the variable values.
Treatment of non-response	FM2015 Chapter 8 (mainly sub-chapter 8.5).	No deviation	Very little unit non-response. Item-non-response is essentially not possible because of flags in the questionnaire. There could be cases where the respondents choose to submit a value of 0 even though it should not be possible. This is handled by contacting the unit and guide them. If this approach is not successful, then data values are imputed from the previous survey or via expert imputations.
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.7).	Not applicable.
Data compilation of final and preliminary data	Reg. 2020/1197 : Annex 1, Table 18	No deviation	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 survey. Preliminary data for uneven reference years are based on the responses 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)	2011-2019, 2021-2023	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 R&D 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: Total R&D personnel (HC) now includes internal and external R&D personnel.  In previous years total personnel only included internal R&D personnel.
Function	2013-2023	2011, 2013	2011: The R&D personnel in the county councils are included in the figures for the first time. However, this data is not yet complete. 2013: R&D personnel: Technicians and Other supporting staff were replaced with the category Staff other than researchers (e.g. technical and administrative R&D personnel)"
Qualification	1999-2005, 2021-2023
R&D personnel (FTE)	2011-2020,  2021-2023	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: Total R&D personnel (FTE) now include internal and external personnel. In previous years total R&D personnel only included internal R&D personnel.
Function	2021-2023	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	1981-2005
R&D expenditure	2019-2020, 2021-2023	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	2007-2017, 2019-2023	2019	2019: ALF funds are now included in funds from the government sector. 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	2019-2023	2013	2013: Investments in Capitalised computer software was added.
Type of R&D	2019-2023
Other

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

No data is collected during even-numbered years. To generate estimates for total intramural R&D expenditure and R&D personnel during these years, units are asked in the biennial R&D survey to provide forecasts for their R&D spending and the number of full-time equivalent R&D personnel for the relevant calendar year.

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

Data from the R&D survey is used as input to National Accounts. The R&D statistics is fully in coherence with the National Accounts as it uses the classification ESA2010.

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)	9 149 224	6 069	4 513
Final data (delivered T+18)	9 187 888	6 126,99	4 515,97
Difference (of final data)	+38 664	+57,99	+2,97

Comments:

....

15.4.2. Consistency between R&D personnel and expenditure

	Average remuneration per year (cost in national currency)	Explanations of consistency issues, if any
Consistency between FTEs of internal R&D personnel and R&D labour costs (1)	0,964 SEK million	Average remuneration per year is not available for non-profit institutions.
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	0,896 SEK million	Average remuneration per year is not available for non-profit institutions

(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)	Cost for the NSI in time use/person/day
Staff costs	Not available.
Data collection costs	Not available.
Other costs	Not available.
Total costs	Not available.

1)       The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.

Comments on costs:

16.2. Components of burden and description of how these estimates were reached

	Value	Computation method
Number of Respondents (R)	533	Calculating the count of distinctive reporting units who answered the survey. For two municipalities, the reporting unit was not the same as the institutional units.
Average Time required to complete the questionnaire in hours (T)1)	4h	The average time is calculated based on the time reported by the respondents as reported by themselves at the end of the survey.
Average hourly cost (in national currency) of a respondent (C)	831 SEK/h
Total cost	SEK 1.77 Million

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

Not requested.

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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

18.1.1. Data source – general information

The R&D statistics for the Government sector are based on a survey called “Forskning och utveckling i Sverige 2023”. Data collection is done with an electronic questionnaire which is sent out to all institutional units in the frame population. For two municipalities, the questionnaire is sent to the City District Administrations instead. The survey design is a census survey, hence all units in the frame population are surveyed. The survey collects information about the intramural and extramural R&D expenditures, R&D personnel in headcounts and full-time equivalents, as well as forecasts for R&D expenditure and R&D personnel for the even reference year (2024). The respondents are asked to allocate their extramural R&D by recipients of funds, source of funds, and their intramural R&D by type of cost, source of funds, fields of research and development (FORD), socioeconomic objectives and by region. For R&D personnel they are asked to report personnel by function and sex.

18.1.2. Sample/census survey information

Sampling unit	Institutional units.
Stratification variables (if any - for sample surveys only)	Not applicable.
Stratification variable classes	Not applicable.
Population size	558 institutional units. 597 reporting units.
Planned sample size	Not applicable.
Sample selection mechanism (for sample surveys only)	Not applicable.
Survey frame	Frozen version of the Swedish business register. Units active in November 2023.
Sample design	Not applicable.
Sample size	Not applicable.
Survey frame quality	Very good.
Variables the survey contributes to	The survey contributes to variables that Sweden is obliged to deliver to the EC according to the regulation. This includes intramural R&D expenditure, R&D personnel and researchers. The survey also collect data regarding extramural R&D expenditure.

18.1.3. Information on collection of administrative data or of pre-compiled statistics

Source	No such data collection is carried out.
Description of collected data / statistics
Reference period, in relation to the variables the administrative source contributes to
Variables the administrative source contributes to

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 government agencies, Regions, Municipalities, Local federations and non-profit institutions controlled by government
Description of collected information	1. Engagement in R&D activities (yes or no) 2. Extramural R&D expenditure (non-mandatory for non-profit institutions) a. by recipients of funds b. by type of funds (exchange or transfer funds) c. by source of funds d. by socioeconomic objectives 3. Intramural R&D expenditure a. by type of cost b. by source of funds c. by type of funds (exchange or transfer funds) d. by type of R&D (distribution, %) e. by fields of research and development f. by socioeconomic objectives g. by region (central government agencies only) h. total intramural R&D expenditures for the current year (2024) 4. Internal R&D personnel a. by function and sex (HC and FTE) b. by formal qualification (ISCED 8 and HC only) c. by function and sex in FTE for the reference year 2024 5. External R&D personnel a. by function and sex (HC and FTE) b. by function and sex in FTE for the reference year 2024
Data collection method	The data are collected directly from each unit by an electronic questionnaire on the web.
Time-use surveys for the calculation of R&D coefficients	Not applicable.
Realised sample size (per stratum)	Not applicable. Census. In total, 597 reporting units corresponding to 558 institutional units
Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.)	The R&D surveys is conducted bi-annually by an electronic questionnaire.
Incentives used for increasing response	No incentives are used for increasing response rates.
Follow-up of non-respondents	Three reminders are sent. By email to those were we have an email-address, otherwise by post.
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 at the institutional unit level. 89 percent at the reporting unit level.
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)	No non-response analysis is done.

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:	Forskning och utveckling i Sverige 2023 – Offentlig sektor (Myndigheter) Instruktioner till forskning och utveckling inom den offentliga sektorn (myndigheter)
Other relevant documentation of national methodology in English:	No documentation available in English.
Other relevant documentation of national methodology in the national language:	UF0301 Statistikens framställning 2023

Annexes:
R&D national questionnaire - Governmetn sector (government agencies and Swedish only)
Instructions for questionnaire (government agencies and swedish only)
Methodology report

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 consist of internal and external controls in the questionnaires to check for any reporting inconsistencies, and individual examination of large R&D performers reports. Respondents are re-contacted to verify or correct these inconsistencies or supplement any missing data in the questionnaire. Data validation on a macro level consists of evaluating macrodata, totals and by requested breakdowns, comparing against previous years to detect any potentials deviations.

18.5. Data compilation

See below.

18.5.1. Imputation - rate

Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)

0,79 percent

18.5.2. Data compilation methods

Data compilation method - Final data

The R&D survey is conducted biennially. In addition to gathering data on R&D performance and extramural R&D for the reference year (odd reference years), respondents are also asked to estimate or provide a forecast of their intramural R&D expenditures and R&D personnel (measured in full-time equivalents, by function and gender) for the year (even reference years) in which the survey is conducted. Final data for odd reference years, when the survey is conducted, data is compiled when micodata is fully validated and when all values have been corrected after recontacting the respondents. Final data för even reference years are compiled using the answers from the respondents’ forecasts. If the reported forecasts still have some inconsistencies after the collection round, respondents are recontacted again to clarify and correct their values. If some units were imputed when compiling final statistics for odd reference years, the same units are imputed. Intramural R&D expenditures are adjusted for inflation using the GDP deflator. Some logical adjustments are also made. If a unit in the questionnaire has stated that it will carry out intramural R&D activities during the even reference year (forecast) and has reported expenditures for the odd reference year but did not provide specific values, the missing values are imputed based on the expenditures from the odd reference year.

Data compilation method - Preliminary data

Compilation is done using the same methodology as final data, at the latest possible time before deadline to ensure as much data is available for the preliminary statistics.  For even reference years, final data and preliminary data will be the same in most cases.

18.5.3. Measurement issues

Method of derivation of regional data	Central government agencies are asked to distribute their R&D expenditures by region, corresponding to NUTS3. The proportions for the distributions of intramural R&D expenditure 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 used.
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	Deductible VAT and depreciation are excluded in the measurement of R&D expenditure. The exclusions are mentioned in the text to the questions as well as in instructions for the questionnaire.

18.5.4. Weighting and estimation methods

Description of weighting method	Not applicable. Census survey, no weights used.
Description of the estimation method	Not applicable. Census survey, no weights used

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). 

The guiding document to preparing the quality reports is the European Statistical System (ESS)  Handbook for Quality and Metadata Reports — re-edition 2021.

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.

18 August 2025

See below.

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.

See below.

Not requested.

Calendar year 2023 is the reference period.

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:

• • Coverage errors,
  • Measurement errors,
  • Non response errors and
  • Processing errors.

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

See below.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.

Nationally, R&D data are disseminated yearly. Provisional statistics are published in July and final statistics in October.

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.