Instituto Nacional de Estadística (INE)

Science and Technology Unit

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.

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.

Statistics on science, technology and innovation were collected based on Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.

• 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).

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).

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FTE is calculated according to Frascati Manual, using the concept person/year.
All postgraduate students working on R&D are included in R&D personnel and their salaries/scholarship are included in the R&D expenditure.

Statistical units are universities, foundations serving higher education, technological institutes, other research centers and high schools teaching higher education, all of them with legal entity, both public and private ownership centers. There is a directory of organizations and centers that performed R&D in previous years or are potentially R&D performers (DIRID).

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. 

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Not requested. See point 5.

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Indicators are available according to 4 units of measure:

Whole number for number of units or number of R&D personnel in headcount.

Number with a decimal place for number of R&D personnel in full-time equivalent.

Thousands of euros for all financial variables, i.e. Turnover or R&D expenditure.

Percentage, the ratio between the selected combinations of indicators.

All questions and indicators refer to the calendar year

See below.

OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities

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:

 In Spain, the main national legal regulations applicable to the protection of statistical data are: 
-“Ley Orgánica 15/1999 de Protección de Datos de Carácter Personal” 
-“Ley 12/1989 de la Función Estadística Pública” 
-“Real Decreto 428/1993, de 26 de marzo, por el que se aprueba el Estatuto de la Agencia de Protección de Datos”. 
-“Real Decreto 994/1999, de 11 de junio, por el que se aprueba el Reglamento de medidas de seguridad de los ficheros automatizados que contengan datos de carácter personal”.

b)       Confidentiality commitments of survey staff:

Survey staff must sign a legal contract, ensuring the acknowledge of the confidentiality issues and data protection law, and therefore they also have legal commitments.

R&D data deliveries to Eurostat are checked in order to avoid primary and secondary confidentiality. This is done by checking any cell with less than 3 population units, and properly  modifying the table to avoid also secondary disclosure.

The advance release calendar that shows the precise release dates for the coming year is disseminated in the last quarter of each year.

The calendar is disseminated on the INEs Internet website (Publications Calendar)

The data are released simultaneously according to the advance release calendar to all interested parties by issuing the press release. At the same time, the data are posted on the INE's Internet website (www.ine.es/en) almost immediately after the press release is issued. Also some predefined tailor-made requests are sent to registered users. Some users could receive partial information under embargo as it is publicly described in the European Statistics Code of Practice

It is disseminated yearly.

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1) Y - Yes, N – No

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1) Y – Yes, N - No 

INEbase is the system the INE uses to store statistical information on the Internet. It contains all the information the INE produces in electronic formats. The primary organisation of the information follows the theme-based classification of the Inventory of Statistical Operations of the State General Administration . The basic unit of INEbase is the statistical operation, defined as the set of activities that lead to obtaining statistical results on a determined sector or topic using data collected individually

Tables and time series may be viewed in INEbase, within the "Science and technology" section at www.ine.es

https://ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736176754&menu=metodologia&idp=1254735576669

Not requested.

See below.

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1) Y – Yes, N - No 

https://ine.es/en/daco/daco43/metoi+d21_en.pdf

Not requested.

See below.

Quality assurance framework for the INE statistics is based on the ESSCoP, the European Statistics Code of Practice made by EUROSTAT.

 - Actions for increasing the rate of response in surveys:

     - We use the helping approach: a strategy of specifically requesting help as a way to compel participation.

     - We try to conduct a well-designed, attractive survey in order to be easier to complete it.

     - The use of multiple contacts with members of the sample. We contact non-respondents using combination of messages and surveys.

- Quality management in data processing: A check list of the different ways a data set is validated (internal consistency checks, non-zero values, number of records in is equal to number of records out) combined with responses with various outcomes (weak error and strong error)

- Annual mandatory survey with high response rate.

- Time series available.

- Methodology of the survey in line with the Frascati Manual.

- Full compliance of the Commission Regulation No 995/2012.

- Overall quality of data deemed to be very good.

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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.)

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

See below.

not available

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)

Statistics on R&D Activities are a census operation, so there are no sampling errors.

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:

 There are units that are misclassified in other sectors

b)      Measures taken to reduce their effect:

 On the one hand, we ask the Frascati tree in every questionnaire in order to detect units that are misclassified. On the other hand, we ask for information about R&D grants to national organisms/public enterprises that offer this type of grants.

1,03%

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:

R&D concepts are very complex, so measurement errors are usual.

b)      Measures taken to reduce their effect:

 Explanatory notes are included in the questionnaire throughout and there is a glossary with a detail description of concepts as well as some examples according to the sector. The field works are conducted by the S&T unit, therefore the staff involved in the collection of data is well-trained and experienced in the theme. To this end, manuals and training documents are also available. Besides, there are different checking and validation routines to detect any measurement error during the whole data collection.

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

There are two written official reminders before the organization is fined, as the completion of the survey is mandatory. Nevertheless, the organization can be contacted by phone, fax or e-mail during the process of data collection.

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:31/12/2021

b) Date of first release of national data:31/10/2022

c) Lag (days):305

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

b) Date of first release of national data:30/06/2023

c) Lag (days):547

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.

No deviations from recommendations

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.

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See below.

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

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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.

Not requested.

On the one hand, the classification used for R&D data collection activities is compatible with the SNA institutional classification, with the exception of the higher education sector, which is identified as a separate sector because of its prominence in R&D activities.

On the other hand, R&D data in the SNA calculations allows, apart from translating R&D expenditure data into a SNA compatible format, computing R&D capital stock and its appropriate deflators.

See below.

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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’)

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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.

The responses rate are checked.
Statistics are compared both over time and between regions.
A micro and macro editing is performed in order to capture inconsistencies using CSPRO and SAS programs.

See below.

R&D Expenditure 3,59% 

R%D Personnel 3,59%

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Not requested.