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
Restricted from publication
2.1. Metadata last certified
27 October 2025
2.2. Metadata last posted
27 October 2025
2.3. Metadata last update
27 October 2025
3.1. Data description
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit 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.
The 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 Eurostat’s 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 distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- 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 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
| Private non-profit sector | All organizations that are deemed likely of conducting their own R&D from the private non-profit sector are included in the sample. To identify these likely R&D performers, information about the organization's purpose, industry, and funding for research and innovation from research funding agencies is used. The organization must also have at least one employee to be included in the framework. |
|---|---|
| Inclusion of units that primarily do not belong to PNP and the borderline cases | No units that do not primarily belong to the PNP sector are included. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | R&D administration and other support activities are included. |
|---|---|
| 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 Frascati Manual §2.61. | Clinical trials funded by the Private non-profit sector are included |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | The PNP-sector covers R&D funding from the rest of the world by the following breakdowns:
|
|---|---|
| Payments to rest of the world by sector - availability | R&D funding to the rest of the world from the Private non-profit sector is not available. |
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 PNP survey collects data for both extramural and intramural R&D. The distinction between the two are made by having 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 survey as well as in relation to each question to help with the distinction. |
| Difficulties to distinguish intramural from extramural R&D expenditure | The questionnaire makes clear the distinction between intramural and extramural R&D by having two separate questions as well as clear definitions of the types of R&D expenditure the respondent is asked to report. However, there could be some difficulties for some respondents to distinguish between purchase of a service used for R&D (intramural R&D, other current costs) and extramural R&D. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar years. |
|---|---|
| Source of funds | Data are collected for each source of fund as identified in FM §4.104-4.108 table 4.3. |
| Type of R&D | All three types of R&D are available |
| Type of costs | Total current cost and total capital costs are available. No further breakdowns are available. |
| Defence R&D - method for obtaining data on R&D expenditure | No method is available to identify defence R&D in the Private non-profit sector. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar years. |
|---|---|
| Function | R&D personnel in head counts are only broken down into researchers and supporting staff. No breakdown off the supporting staff into technicians and other supporting staff is available. For the available functions there are no deviations from FM2015. |
| Qualification | No breakdowns of qualifications are available. |
| Age | No breakdowns of age are available. |
| Citizenship | No breakdowns by citizenship are available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar years. |
|---|---|
| Function | R&D personnel in head counts are only broken down into researchers and supporting staff. No breakdown off the supporting staff into technicians and other supporting staff is available. For the available functions there are no deviations from FM15. |
| Qualification | No breakdowns of qualifications are available. |
| Age | No breakdowns of age are available. |
| Citizenship | No breakdowns by citizenship are 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 PNP 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 is all private non-profit institutions thought to perform R&D. | Not applicable. |
| Estimation of the target population size | 30 institutional units. | Not applicable. |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See concept 12.3.2. (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.
2023.
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 the 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.
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 R&D specific statistical legislation exists. |
|---|---|
| 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
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:
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:
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
Confidentiality is ensured by implementing the EZS-method. With this method, a random noise to the data at the micro level. The method is implemented in such a way that the noise is dependent on the risk of disclosure in the data. A higher risk of disclosure will mean a higher noise is added and a smaller risk means a smaller noise.
8.1. Release calendar
The release policy and the release calendar are publicly available at Statistics Sweden's website.
For Statistics Sweden: Publishing calendar – Statistics Sweden
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
For Statistics Sweden: Publishing 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 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) | N |
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 | Micro data are 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 micro data are 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 | Aggregate figures are available | Aggregated results are available in the online statistical database on Statistics Sweden’s website. Micro-data is not available due to confidentiality issues |
| Data prepared for individual ad hoc requests | Y | Both micro data and aggregate figures. | Access to micro data is only granted for research or statistical purposes. 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.
Methodology report (Swedish): Statistikens framställning - Forskning och utveckling i Sverige 2023
Methodology 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 i Sweden 2023 Metadata documentation (only available in Swedish): Dokumentation av mikrodata - www.scb.se |
|---|---|
| Requests on further clarification, most problematic issues | Few users provide feedback on clarity. |
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 quality criteria for official statistics are regulated by the Official Statistics Act (2001:99).
The framework for quality assurance set out in the Quality policy is a cyclic process with four steps: (i) understanding legal requirements and user needs, (ii) standardised, efficient and secure processes, (iii) analysis and evaluation, and (iv) improvement and development activities.
11.2. Quality management - assessment
The guiding document to preparing the quality reports is the European Statistical System (ESS) for Quality and Metadata Reports — re-edition 2021.
The quality of the statistics is assessed regularly, and the R&D statistics meet the quality requirements. 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. Yet, the quality is considered appropriate in relation to such legal requirements and user needs as have been identified.
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 Research Council. | Eurostat is the primary data user of these statistics. At the national level the need is limited. |
| 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. |
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. 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 satisfaction is high. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100 %
12.3.2. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0.2
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | PNP is not collected in another sector |
|---|---|
| Reasons for not producing separate R&D statistics for the PNP sector | Not applicable. |
| Share of PNP expenditure in the total expenditure of the other sector | Not applicable. |
| Share of PNP R&D Personnel in the respective figure of the other sector | Not applicable. |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | R&D data is collected for the PNP sector. |
|---|---|
| PNP R&D expenditure/ GERD*100) | |
| Share of PNP R&D Personnel in the respective figure of the total national economy |
12.3.2.3. Data availability on more detail level
| 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
12.3.2.4. 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:
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.
13.2. 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
Confidence interval for Total R&D expenditure: Not applicable. Census.
Confidence interval for Total R&D personnel (FTE): 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.
a) Extent of non-sampling errors: The risk from non-sampling errors mainly stems from the risk of under-coverage if organisations with R&D activities are erroneously omitted in the frame population. The extent of this error is deemed small.
b) Measures taken to reduce the extent of non-sampling errors: : No direct measures are taken to reduce the extent of the non-sampling errors.
c) Methods used in order to correct/adjust for such errors: No direct methods are user in order to correct/ adjust for these errors.
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.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Not requested.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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)
a) End of reference period: End of 2023
b) Date of first release of national data: 11 July 2024
c) Lag (days): 193 days
14.1.2. Time lag - final result
a) End of reference period: End of 2023
b) Date of first release of national data: 31 October 2024
c) 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 or Frascati manual (FM) paragraphs and the EBS Methodological Manual on R&D Statistics 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 deviations. | |
| Researcher | FM2015, § 5.35-5.39. | No deviations. | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | Respondents are asked to report the number of persons engaged in R&D at the end of the reference period, December 31, 2023. |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviations. | Total personnel include internal and external personnel. |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly sub-chapter 4.2). | No deviations. | |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | The target population is limited to non-profit associations and foundations that are likely to perform or fund R&D during the reference year. | Households and private individuals are excluded from the population |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviations. | |
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | No deviations. | |
| Reference period for all data | Reg. 2020/1197: Annex 1, Table 18 | No deviations. |
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 | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviations. | |
| Survey questionnaire / data collection form | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviations. | Electronic questionnaire. |
| Cooperation with respondents | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviations. | Respondents can contact us directly, by email and phone, for any questions regarding the survey. |
| Data processing methods | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviations. | |
| Treatment of non-response | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No deviations. | |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No deviations. |
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) | 2011-2017, 2019-2021, 2023 | 1997, 1995, 2009, 2011, 2013, 2017,2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire. 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2009: New sample method, sharp decrease in R&D personnel. 2011: New sample method. 2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff. 2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| Function | 2011-2017, 2019-2021, 2023 | 1997, 1995, 2009, 2011, 2013, 2017,2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire. 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2009: New sample method, sharp decrease in R&D personnel. 2011: New sample method. 2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff. 2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| Qualification | 2011-2017, 2019-2021, 2023 | 1997, 1995, 2009, 2011, 2013, 2017,2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire. 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2009: New sample method, sharp decrease in R&D personnel. 2011: New sample method. 2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff. 2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| R&D personnel (FTE) | 2011-2017, 2019-2021, 2023 | 997, 1995, 2009, 2011, 2013, 2017,2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire. 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2009: New sample method, sharp decrease in R&D personnel. 2011: New sample method. 2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff. 2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| Function | 2011-2017, 2019-2021, 2023 | 1997, 1995, 2009, 2011, 2013, 2017, 2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire. 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 2009: New sample method, sharp decrease in R&D personnel. 2011: New sample method. 2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff. 2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| Qualification | 2011-2017, 2019-2021, 2023 | 1997, 1995, 2009, 2011, 2013, 2017,2023 | 1997: The sharp decrease in R&D personnel in the PNP sector is partially due to modification of the national questionnaire.
1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector.
2009: New sample method, sharp decrease in R&D personnel.
2011: New sample method.
2013: Institutions are asked to report on head counts according to two, not three occupations: researchers and other staff. Other staff include technicians and equivalent staff and other supporting staff.
2017: New variable estimation method resulting in this variable no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average.
2019: Direct collection of data is stopped, and 2017 results are used to compute results. 2023: Direct collection of data is resumed. |
| R&D expenditure | 2017-2021,2023 | 1995, 1993, 2009, 2011, 2017, 2023 | 1995: A number of institutions in the PNP sector are reclassified mainly in the business enterprise sector. 1993: SSH R&D was included in the PNP sector, resulting in a break in series. 2009: New sample method, sharp decrease in R&D expenditure. 2011: New sample method. 2017: New variable estimation method. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2023: Direct collection of data is resumed. |
| Source of funds | 2011-2017, 2019-2021, 2023 | 1997, 2009, 2011, 2017 | 1997: Due to a change in statute, the funding from Public Research Foundations previously considered as funding from the PNP sector has been reclassified as funding from the Government sector. Consequently, 1997 data on R&D funding from Government and PNP sectors are not comparable to those for previous years. 2009: New sample method. 2011: New sample method. 2017: New variable estimation method resulting in this breakdown no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2023: Direct collection of data is resumed. |
| Type of costs |
2011-2017, 2019-2021, 2023 |
2009, 2011, 2017, 2023 | 2009: New sample method. 2011: New sample method. 2017: New variable estimation method resulting in this breakdown no longer being estimated. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2023: direct collection of data is resumed. |
| Type of R&D | 2011-2017, 2019-2021, 2023 | 2013, 2017, 2023 | 2013: New question. 2017: New variable estimation method. Direct data collection is no longer performed, estimations are instead calculated by using a simple moving average. 2023: Direct collection of data is resumed. |
| 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 the even years. To produce estimates for total intramural R&D expenditure and R&D personnel for even years, units are asked in the R&D survey to forecast their R&D expenditure and number of R&D personnel in full-time equivalents.
15.3. Coherence - cross domain
See below.
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 PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 393 881 | 309 | 221 |
| Final data (delivered T+18) | 428 896 | 362 | 264 |
| Difference (of final data) | +35 015 | +53 | +43 |
Comments:
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any |
|
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | Not applicable, no breakdown by type of cost is available for the Private non-profit sector. | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not applicable, no breakdown by type of cost or data on external R&D personnel are available for the Private non-profit sector. |
(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 for the PNP survey. | |
| Data collection costs | Not available for the PNP survey. | |
| Other costs | Not available for the PNP survey. | |
| Total costs | Not available for the PNP survey. |
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) | 85 | |
| Average Time required to complete the questionnaire in hours (T)1) | 0.57 h | 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) | 1 031 SEK | |
| Total cost | 50 000 SEK |
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 private non-profit sector is based web-questionnaire which is sent out to the organisations which are thought to be likely R&D performers within the frame population. The survey collects information about the units R&D expenditure, R&D personnel as well as forecasts for R&D expenditure and personnel for the even reference year (2024). The respondents are asked to allocate their extramural R&D by recipient and their intramural R&D, source of funds, type of R&D and FORD. For R&D personnel they are asked to report personnel by function and gender.
18.1.2. Sample/census survey information
| Sampling unit | Institutional unit |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable. Census survey. |
| Stratification variable classes | Not applicable. Census survey. |
| Population size | 94 institutional units. |
| Planned sample size | Not applicable. Census survey. |
| Sample selection mechanism (for sample surveys only) | Not applicable. Census survey. |
| Survey frame | All organizations that are deemed likely of conducting their own R&D from the private non-profit sector are included in the sample. The organization must also have at least one employee. |
| Sample design | Not applicable. Census survey. |
| Sample size | Not applicable. Census survey. |
| Survey frame quality | The survey frame quality is deemed to be good. The frame is limited to organizations belonging to the private non-profit sector. The frame is amended to include organizations deemed likely to perform their own R&D by using administrative data on Swedish research projects and foundations |
| Variables the survey contributes to | Intramural R&D expenditure and R&D personnel. |
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 12.3.2.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Respondents through the web survey |
|---|---|
| Description of collected information | Data on funding for R&D and R&D personnel are collected for each institution. |
| Data collection method | The primary data collection method is an online questionnaire, implemented in Statistics Sweden's online survey tool, SIV. This questionnaire contains automated checks that ensure that the respondent cannot answer in a way that is logically inconsistent. It is also tested by an expert in survey design to ensure that the order of the questions is logical, that instructions are clear and relevant, and that questions are worded in a way to avoid misunderstanding. As a measure to avoid a systematically skewed non-response it is also possible to respond to the survey on paper. The paper questionnaire is sent out by mail to all those who have not yet responded by the second remainder. In total, four reminders are sent out by mail. |
| Time-use surveys for the calculation of R&D coefficients | No such survey is carried out. |
| Realised sample size (per stratum) | Not applicable. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online questionnaire and postal survey |
| Incentives used for increasing response | No incentives are used to increase response. |
| Follow-up of non-respondents | Reminders to fill in the survey are sent out to non-respondents. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No replacement of non-respondents. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 90 %. |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | No non-response analysis has been 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 R&D in non-profit organisations 2023 |
| Other relevant documentation of national methodology in English: | Instructions for non-profit organisations |
| Other relevant documentation of national methodology in the national language: |
Annexes:
Questionnaire R&D survey non-profit organisations
Instructions for R&D questionnaire to non-profit organisations.
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 external controls in the questionnaires to check for any reporting inconsistencies and controls in the IT-toll when data is processed. If inconsisticies or error are detected, 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)
Not applicable.
18.5.2. Data compilation methods
| Data compilation method - Final data | Data are compiled annually for the Private non-profit sector. To produce the data to be reported annually prognosis data from the R&D survey for even reference years are used. |
|---|---|
| Data compilation method - Preliminary data | Estimation 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. |
18.5.3. Measurement issues
| Method of derivation of regional data | Not applicable, no breakdown by region is available for the Private non-profit sector. |
|---|---|
| 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 | Neither VAT nor depreciations are included in intramural R&D. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable, weighting is not used. |
|---|---|
| Description of the estimation method | No statistical estimation method is used. The collected data is summed. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit 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.
The 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 Eurostat’s 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.
27 October 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. R&D statistics cover national and regional data.
2023.
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 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.
