Statistics Sweden

Economic Statistics and Analysis

Statistics Sweden
Att. Nils Adriansson
ESA/NUP/INF
Solna strandväg 86, Solna
SWEDEN

Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are 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).

No additional classifications used.

See below.

According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).

See below.

See below.

R&D full-time equivalents are estimated based on the time-use survey. They are calculated by multiplying the percentage of time spent on R&D activities with the extent of employment as a percentage of full-time employment. The extent of employment is based on administrative data provided by the higher education institutions.

The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.

See below.

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 HES Sector should consist of all R&D performing institutional 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. 

Not requested. R&D statistics cover national and regional data.

Not requested. See point 3.4.

Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.

National currency in thousands, head count and full-time equivalents in number.

2021.

See below.

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

- European Business Statistics Methodological Manual on R&D Statistics

Not requested.

Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

a)       Confidentiality protection required by law: The major policy in place to ensure confidentiality and prevent unauthorised disclosure of data that identify a person or economic entity is the Public Access to Information and Secrecy Act (2009:400). There are also specific conditions concerning the confidentiality of official statistics in the Official Statistics Act (2001:99). 

b)       Confidentiality commitments of survey staff: 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. 

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. For R&D statistics concerning the Higher education sector this applies to all R&D personnel statistics. For statistics on R&D expenditure, microdata is made publicly available. This is because data on government agencies should be publicly available as per the Public Access to Information and Secrecy Act (2009:400). Private higher education institutions must give Statistics Sweden their permission before microdata can be disclosed.

Any disclosure of microdata from the time-use survey must be tried. It can be disclosed only for research or statistical purposes and only to such entities that are deemed able to ensure confidentiality protection of the data. 

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

The publication calendar is available on Statistics Sweden's website.

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.

Annually.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

An online statistical database is available on Statistics Sweden's website (see link in Annex). During 2022, the R&D tables in the database were accessed 3 431 times. 

Not requested.

See below.

See below.

Not requested.

1) Y – Yes, N - No 

The main documentation on methodology is titled Statistikens framställning (translates to Statistical production) which is updated when new statistics are published. There is a common document covering all sectors for the R&D statistics in which the specific methodology for each sector is described. This documentation is only available in Swedish.

Not requested.

See below.

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.

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. For the time-use survey, object non-response is also a mounting challange. Statistics Sweden has faced declining response rates for several years which has a negative effect on the quality of the statistics. For the survey on R&D expenditure however, there are no non-response issues. As it is a census there are neither any issues concerning sampling or coverage. Results are presented in more detail in sections 12 to 15 below.

See below.

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.)

The views and opinions from users are primarily collected through the User council for statistics on research and development which meets twice yearly. The user council consists of representatives from the Ministry of Enterprise, the Ministry of Education, the Swedish Higher Education Authority, the Swedish Research Council, Vinnova (Sweden's innovation agency), RISE, the Swedish Association of Local Authorities and Regions, the Swedish Agency for Growth Policy Analysis, the Research Institute of Industrial Economics, Lund University and Teknikföretagen (the trade association for the Swedish industry sector). Minutes from the last meeting of the user council are available in Swedish at Statistics Sweden's website.

See below.

62 percent (100 percent for mandatory variables and 42 percent for voluntary variables).

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.

Criteria:

A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.

B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Y-start year, N – data not available

1) Y-start year, N – data not available

1) Y-start year, N – data not available

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

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.

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (5). In the event that errors of a particular type do not exist, is used the sign ‘-‘.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = In the event that at least one out of the three criteria above described would not be fully met.

3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would not be met.

5) 'Very Poor' = If all the three criteria are not met.

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.

The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)

The variance is estimated using Horvitz-Thompson estimation as follows:

Where;

t_z is an estimated variable,

h is an index for strata (h = 1, 2, 3,..., H),

k is an index for observations (k = 1, 2, 3,..., K),

z_k is the observed value for the observation k,

N_h is the number of objects in stratum h,

m_h is the number of responses in stratum h in the sample, and

r_h is the number of responses in stratum h

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.

Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

a)       Description/assessment of coverage errors:

R&D expenditure survey: There are no coverage errors in this survey. The population consists of all Swedish universities and colleges providing tertiary level education. All higher education institutions that receive funding for research and doctoral education are included in the census survey on R&D expenditure.

Time-use survey: External R&D personnel is not included in the frame for the time use survey. The register used to create the frame only covers persons employed by a Swedish higher education institution which means external personnel is not included. Over-coverage also occurs due to difficulties in excluding non R&D personnel from the frame.

b)      Measures taken to reduce their effect:

The issue of under-coverage in the time-use survey is amended by estimating R&D performed by external personnel using coefficients from the time-use survey and administrative data on doctoral students that are not employed by the higher education institution where they perform their research. 

To mitigate the issues of over-coverage a small survey conducted among the higher education institutions where they are asked to identify which professions among those belonging to the technical or administrative staff and where information on field of research and development is missing for a majority of the individuals in the register are relevant to include in the time-use survey.

R&D expenditure survey: 0 percent.

Time-use survey: 3 percent.

Not requested.

Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.

a)       Description/assessment of measurement errors:

Measurement errors are deemed to be the most important source of error for the Higher education sector. R&D is a complex concept which may lead to misinterpretation by respondents. Lack of information on the allocation of funds within the organisation can also cause errors of this kind.

b)      Measures taken to reduce their effect:

The questionnaire is tested at regular intervals by an expert on questionnaire construction and the instructions to fill in the questionnaire are reviewed before each survey year. The online questionnaire also includes checks to make sure that responses are logically consistent. For the survey on R&D expenditure, checks are also conducted manually when the units have responded to the survey. If any suspect values are found, the respondent is contacted to either confirm or revise their response.

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. 

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) 

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

R&D expenditure survey: Two reminders were sent out via email. 

Time-use survey: Four reminders were sent out by mail. The second reminder also enclosed the survey on paper in order to increase response rates among respondents that do not wish to respond online.

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.

Not requested.

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.

Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)

a) End of reference period: 2021-12-31

b) Date of first release of national data: 2022-07-14

c) Lag (days): 195

a) End of reference period: 2021-12-31

b) Date of first release of national data: 2022-10-27

c) Lag (days): 300

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.

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release)

See below.

Not requested.

International comparability is generelly deemed to be good. One issue to be noted concerns external personnel. Due to issues with measurement error and under-coverage error, international comparability is low. For the Higher education sector, the approach to obtaining headcount and full-time equivalent data diverges from the Frascati Manual on the issue of excluding individuals spending less than 0.1 FTE on R&D. This also affects international comparability. However, the contribution of these individuals to the total estimates on HC and FTE is small and thus its effect on international comparability is limited. 

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 and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.

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.

See below.

See below.

1)       Breaks years are years for which data are not fully comparable to the previous period.

For even reference years data is not collected using surveys. Funding for R&D in the Higher education sector is collected from the Swedish Higher Education Authority and R&D expenditure is estimated using R&D coefficients for the previous odd reference year. To estimate R&D personnel, data from the Registry of the Employees in Higher Education is used to update the population. This information, combined with data from the previous years survey, is used to estimate R&D personnel.

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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.

Not requested.

Not available, no analysis of coherence has been conducted.

The main difference between R&D statistics and education statistics is that the population differs. R&D statistics include research institutes whose R&D is controlled by HEIs while the education statistics do not. R&D expenditure reported in the R&D statistics and the education statistics may, therefore, vary even though they are based on the same data. 

See below.

This part compares key R&D variables as preliminary and final data.

(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. 

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

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’)

Not requested.

Not requested.

Not requested.

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.

See 12.3.3.

See below.

R&D expenditure survey: Data are validated both at micro and macro level. The validation at micro level consists of comparing the allocation of funds by FORD for each higher education insitution with the data provided two years previous. In case of major differences in the reporting, the respondent is contacted to confirm or revise their answer. At the macro level the data validation focuses on numeric consistency; e.g. making sure that corresponding sums in different statistical tables are the same and that there is consistency between sums and their respecitve parts. This validation helps ensure that no processing errors have occurred.

Time-use survey: Data validation at the micro level for this survey focuses on ensuring logical consistency in the data by editing data where such inconsistencies occur. Pre-established rules are used to edit micro data for logical inconsistencies such as if the respondent has responded both online and on paper and the responses do not match. The online questionnaire contains checks that ensure that a respondent cannot answer in a way that is logically inconsistent. However, in the paper questionnaire this is not possible. For these respondents it is therefore sometimes necessary to edit micro data to make it logically consistent. As with the census on R&D expenditure, the data validation at the macro level focuses on numeric consistency to make sure no processing errors have occurred.

See below.

Not applicable, imputation is not used for the Higher education sector.

Not requested.

Not requested.