Central Statistical Bureau of Latvia

Enterprise Structural and Financial Statistic Section (BES);

Section of Social Statistics Data Compilation and Analysis (HES, GOV)

Central Statistical Bureau of Latvia

Lāčplēša street 1, Rīga, LV  1010

Latvia

Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.

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 (FM 2015, Chapter 12), 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).

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 (Commission Implementing Regulation (EU) 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 (europa.eu)).

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.

Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.

See below.

GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).

The main variables collected in the framework of GBARD statistics are:

Government budget allocations for R&D (GBARD) are all allocations distributed to R&D in central (federal) government, regional (state) and local (municipal) government. They therefore refer to budget provisions, not to actual expenditure. Local government budget funds may not be included if their contribution is not significant or if the data cannot be collected.

BES - enterprise, HES and GOV - institutions

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.

Latvia

See point 12.3.

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.

GBARD data are available in:

XDC: Thousands of nacional currency. All financial variables are provided in thousands of euros.

a) Calendar year: 2021

b) Fiscal year:

    Start month:

    End month:

See below.

Since the beginning of 2021, GBARD statistics are based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.  GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.

Statistics Law

Cabinet regulation Nr. 782 "Official Statistics Programme for 2022–2024" (only in Latvian)

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

-          EBS Methodological Manual on R&D Statistics

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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:

• Regulation (EC) No 223/2009 of the European Parliament and of the Council on European statistics 
• Regulation (EU) 2016/679 of the European Parliament and of the Council
• Statistics Law.

 b)       Confidentiality commitments of survey staff: 

• Code of Ethics 
• Privacy Statement

Statistical data shall be considered confidential if they directly or indirectly allow for identification of the private individuals or State authorities regarding which personal statistical data have been provided (primary and secondary confidentiality are applied). 

All table cells whose values are derived from less than 4 respondents are treated as confidential. In order to ensure confidentiality, the dominance criteria shall also be useded.

The release policy and release calendar exists and they are publicly accessible. All official statistics are published according to the data release calendar, at 13.00.

Release calendar is available.

Users are informed that the data is being released by release calendar. Before the official time of publication, some officials are granted access to statistical data to ensure them time needed for data analysis, understanding and preparation of the point of view. Before provision of such information, the CSB assesses the need and benefits to the society, as well as concludes an agreement on compliance with data confidentiality. Information on the privileged access to statistical data is published on the CSB website.

GBARD statistics is conducted and disseminated every year together with R&D statistics. But the GBARD data is not published.

See below.

1) Y - Yes, N – No

See below.

1) Y – Yes, N - No 

Not available

Not available

See below.

See below.

Not available

1) Y – Yes, N - No 

Not available

Not applicable

See below.

CSB has introduced Quality Management System (QMS). The system is directed towards providing high user satisfaction and ensuring compliance with regulatory enactments. Based on the structure of Generic Statistical Business Process Model (GSBPM), QMS defines and at the level of procedures describes processes of statistical production as well as sets the persons responsible for the monitoring of processes at all stages of the statistical production. QMS defines the sequence how processes are implemented (i.e., activities to be performed (incl. verifications of processes and statistics, sequence and implementation requirements thereof, as well as persons responsible for the implementation)), procedures used in the evaluation of processes and statistics, as well as any improvements needed.
Since 2018, QMS of the CSB has been certified by the standard ISO 9001:2015 “Quality Management Systems. Requirements” (certified scope: Production of official statistics – planning, development, data acquisition, processing, analysis and dissemination).

Quality of statistics is assessed in accordance with the existing requirements of external and internal regulatory enactments and in accordance with the established quality criteria.

Regulation (EC) no 223/2009 of the European Parliament and of the Council on European statistics states that European Statistics European statistics shall be developed, produced and disseminated on the basis of uniform standards and of harmonised methods. In this respect, the following quality criteria shall apply: relevance, accuracy, timeliness, punctuality, accessibility, clarity, comparability and coherence.

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.)

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.

1)  Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.

2)  Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.

See below.

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.

2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

1) Stage: P - provisional, F - final. 

2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.

3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled

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 GBARD.

1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.  

2) If at least one out of the three criteria described above would not be fully met.

3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.

4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.

5) If all the three criteria described above are not met.

Not applicable

Not applicable

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: The institutions mentioned in the FM2015 are fully covered

b)      Measures taken to reduce their effect: 

Not applicable

Not applicable

Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.

a)       Description/assessment of measurement errors: Not applicable

b)      Measures taken to reduce their effect: Not applicable

Non response errors: occur 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.

a) Problems in obtaining data from targeted information providers: Not applicable

b) Measures taken to reduce their effect: Not applicable

c) Effect of non-response errors on the produced statistics: Not applicable

Not applicable

Not applicable

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.

a)       Data processing and editing processes: Respondents responds through electronic online questionnaires (CAWI). The programme for data input does not allow inputting erroneous for it has logical and mathematical data controls.

b)      Description of errors: 

c)       Measures taken to reduce their effect:  

Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).

Description/assessment: Not applicable

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.

Date of first release of national data: At national level GBARD data is not being published; preliminary data sent to Eurostat on 27.06.2022.

Date of first release of national data: Data sent to Eurostat on 14.12.2022

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 available

The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 2020/1197, Frascati manual and the EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.

GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.

See below.

See point 12.3.

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

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This part compares GBARD statistics from the provisional and final budget for the reference year.

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’)

Revision Policy is an important component of good governance practice addressed more and more often in the international statistical society. The objective of the Revision Policy is to lay down the order of review or revision of the prepared and published data. The first chapter of the present document explains the terms applied in the Revision Policy, the second chapter shortly characterises the CSB Revision Policy, whereas the third chapter stipulates the revision cycle of the statistical data produced by the CSB.

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a)       Provisional data: Data sources are CSB annual "Survey of the performance of research activities in the business sector" (statistical report form 2-research), "Report of the performance of scientific work by the higher education institution, the scientific institution under its supervision" (1-research) and "Report of scientific work by the public sector scientific institution" (3-research).

b)      Final data: Data sources are CSB annual "Survey of the performance of research activities in the business sector" (statistical report form 2-research), "Report of the performance of scientific work by the higher education institution, the scientific institution under its supervision" (1-research) and "Report of scientific work by the public sector scientific institution" (3-research).

c)       General University Funds (GUF): Data source is CSB annual "Report of the performance of scientific work by the higher education institution, the scientific institution under its supervision" (1-research)

See 12.3.3.

See below.

1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.

Data source is CSB annual "Report of the performance of scientific work by the higher education institution, the scientific institution under its supervision" (1-research).

Collected data has been compared with previous years.

See below.

No imputated data.

See below.

 Not applicable

 Not applicable