Volume index of production in construction

The volume index of production in construction characterises changes in the construction output during the reference period, in comparison with the base period. Index is important to monitor and evaluate development of the construction sector and is widely used in the analysis of business cycles. It provides approximation to the trend of the value added volume over a given reference period.

Construction output consists of the value of own-account construction works completed by the construction enterprises during the reference period. 

Data are calculated and published in compliance with Statistical Classification of Economic Activities in the European Community (NACE Rev. 2).

The indicator covers all economically active enterprises whose main or secondary activity according to NACE Rev. 2. is construction (Section F, Classes 41.20 to 43.99).

Surveyed units - kind of activity units.

Responding units - enterprises.

The target population covers all economically active enterprises the main or secondary activity of which in compliance with the NACE Rev. 2 is construction (Section F, classes 41.20–43.99).

Construction companies registered in Latvia that perform works in Latvia and abroad.

Since 2000.

Base year used to calculate the volume index of production in construction is 2021.

• Index.
• Percentage change on previous period, seasonally adjusted data.
• Percentage change on the corresponding period of the previous year, calendar adjusted data.

Quarterly and annual data.

• Council Regulation (EC) No 1165/98 of 19 May 1998 concerning short-term statistics
• Regulation (EU) 2019/2152 of the European Parliament and of the Council of 27 November 2019 on European business statistics, repealing 10 legal acts in the field of business statistics

Data are sent to Eurostat and the OECD

Confidentiality of the information provided is protected by Statistics Law: 

• Section 7, Paragraph two, Clause 8, which sets out the obligation of the statistical institution to ensure statistical confidentiality;
• Section 17, which defines the procedures for data processing and the requirements for data protection;
• Section 19, Paragraph one, which stipulates that official statistics must be disseminated in a way that does not allow either directly or indirectly identify a private individual or a State institution;
• Section 19, Paragraph two, which stipulates that the statistical institution shall publish the official statistics which have been produced within the framework of the Official Statistics Programme in a publicly available form and by a predetermined deadline on the portal of official statistics. Until the moment of publication of official statistics this statistics shall not be published

Confidential cells in business statistics are defined by using minimum frequency rule (n) and dominance criterion (n, k).

A cell in a table is considered safe if there are at least four contributors (respondents, n=4) and the share of the largest contributor in the total cell value does not exceed 80 % (1.80) or the share of two largest contributors in the total cell value does not exceed 90 % (2.90).

All official statistics are released according to the data release calendar at 13:00

Data release calendar

Statistical release dates and times are pre-announced in the data dissemination calendar.

Quarterly.

Press release is published quarterly. 

• Statistical Yearbook of Latvia; 
• Latvia. Key statistics (only in Latvian).

Statistics database

Not available.

• COMMISSION IMPLEMENTING REGULATION (EU) 2020/1197
• European Business Statistics Manual (2021)
• Methodological manuals, guides, working group reports, metadata, etc. information is available from Eurostat

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To achieve high user satisfaction and ensure compliance with regulatory requirements, the CSB has introduced a Quality Management System (QMS). The system defines and, at the procedural level, describes processes of statistical production and identifies the persons responsible for their monitoring throughout all production stages. Its structure follows the principles of the Generic Statistical Business Process Model (GSBPM).

The QMS sets out the sequence in which processes are implemented – that is, the activities to be performed, including verifications of processes and produced statistics, the order and implementation requirements of these activities, and the persons responsible for their execution. It also defines the approach to evaluating production processes and their outcomes, and to implementing necessary improvements.

The CSB quality management system is certified to the ISO 9001:2015 standard Quality management systems — Requirements since 2018 (scope of certification: development, production and dissemination of official statistics). The original certification audit was performed by BM Trada Latvija SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.

The CSB information security management system is certified to the ISO/IEC 27001:2022 standard Information security, cybersecurity and privacy protection — Information security management systems — Requirements since 2017 (scope of certification: collection, processing and storage of information and data for functions of the Central Statistical Bureau of Latvia. Provision of statistical information for inland and foreign users). The original certification audit was performed by BM Certification SIA and a recertification audit, in 2024, was performed by Bureau Veritas Latvia SIA.

The quality of statistics is assessed in accordance with the existing requirements of both external and internal regulatory enactments, as well as the established quality criteria.

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

As the national statistical institute and the principal authority of the national statistical system, the CSB has set common general institutional-level quality requirements for authorities responsible for producing or providing national statistics. These requirements are based on the European Statistics Code of Practice, which comprises 16 principles.

The overall assessment of data quality is good.

The necessity, methodology, as well as the detail and timeliness of the calculation of the construction output index are specified in the binding regulations and recommendations. The inclusion of any additional indicators or cells to be filled in the form increases the workload of the respondents and may affect the quality of the data, as the information needed to complete the report appears with a certain delay, which in some cases is very long-lasting.

The interest in the volume of construction of buildings in Latvia and abroad separately, as well as the breakdown of these values ​​according to the classification of buildings for the reasons mentioned above is not summarized. The ability of the relevant indicators to significantly influence the development of the overall index should also be considered, which would justify the calculation of additional indicators.

The task of the CSB is to produce reliable statistics to support the analysis of socio-economic processes and to inform future decision-making.

Send feedback on data quality to pasts@csp.gov.lv

Accuracy is achieved by eliminating non-sampling errors as much as possible, as well as by analyzing data revisions.

Although measures are being taken to update the sample, the main sources of error are the level of non-response and overcoverage. Given the deadlines set for the collection and processing of the report, the non-response rate to the rapid assessment tends to be high. However, the report forms are still active after the submission deadline. This provides an opportunity to submit or update the data, which reduces the level of non-response.

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IPC measurement error occurs in cases where the respondent misunderstands the required information or submits inaccurate information that does not comply with the reporting methodology. Such errors are corrected through automatic validations, while other identified errors are corrected by contacting the respondent. When filling in the e-report, the respondent has the opportunity to get acquainted with detailed instructions for filling in the report, which are regularly updated based on the identified shortcomings in filling in the report. In case the respondent refuses to make the correction, an imputation is made based on other available information, including information found in the administrative data.

When resolving cases of non-response by units, automatic reminders are sent to respondents about delays in submitting data. If the respondent does not provide data, an attempt is made to contact him by telephone. In cases where the respondent categorically refuses to submit data, the data is imputed.

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Errors of this nature in the preparation of construction indicators are rare and are identified in a timely manner. Processing errors can occur in the process of data aggregation if the appropriate field attributes are not set for individual units, fulfilling the necessary conditions for algorithm operation. A small part of the data is imputed and the imputations are calculated based on the time series of the respondent's historical data, administrative data, data submitted in other reports or data of similar companies. The calculated imputation values ​​are evaluated by an expert and a decision is made to correct them if necessary. Thus, errors can also occur in the imputation coding process when assigning an inappropriate imputation code, as well as in entering the imputations themselves when there is a significant difference between the fast and final estimate values. Some processing errors may also occur during the processing of microdata for the sizing of a third part of the population, in cases where there are enterprises that have historically been included in construction but no longer carry out actual construction work. When identifying such companies, the administrative data are adjusted. In some cases, there are often respondents who adjust or refine the values ​​submitted to the final estimate, which creates a difference between the final estimate and the quick estimate. In cases where such actions have a significant impact on the value of the index, the respondent is contacted to find out the reason for the change.

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Data are published on the 40th day after the end of the reference period.

EU data on Eurostat website Section: Construction, building and civil engineering.

Data on the construction output index are comparable since 2000. Construction output at current prices by industry is available from 2015, but by type of construction from 2018.

Number of comparable time periods

As there are no breaks in the time series, the indicator is equal to the number of time points in the time series.

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In short-term business statistics (STS), construction output takes into account only the volume of own-account construction work, while structural business statistics (SBS) also include subcontractors.

The STS does not cover NACE 4110, which is related to real estate development. STS and SBS have different sample sizes and surveyed units (STS - KAU, SBS - enterprise).

Construction output includes only activities carried out according to NACE Rev. 2 division, i.e. activities such as:

• architectural and engineering services (design, drawing), construction supervision, author supervision, expertise,
• sale of real estate,
• sale of materials,
• the portion of administration costs that cannot be allocated to the asset object,
• purchase of land, etc.,

are not included in construction output.

In the national accounts, short-term construction output data are used to calculate GDP, and there is mostly consistency between the data, with small differences in the calculation method.

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One of the CSB priorities, in line with the strategic directions of the European Statistics System and current approaches to producing statistics, is to collect data through broader use of administrative sources together with regular CSB surveys, while proportionately reducing response burden.

In cooperation with administrative data holders and within the competences set out in the Statistics Law, CSB is addressing the issues related to the use of administrative data to ensure that the sources used are as complete and reliable as possible, helping to reduce the administrative burden on businesses and households.

CSB measures to improve the use of administrative data and reduce response burden (2024) – available in Latvian only.

Revision policy is an important component of good governance practice. The aim of the CSB Revision Policy is to define how the statistics produced and published by the CSB are reviewed and revised. The first chapter explains the main terms used in the document, the second chapter provides a brief description of the CSB Revision Policy, and the third chapter outlined the revision cycle for the statistics produced by the CSB.

Revision policy guidelines (8 September 2022, PDF, 597 KB)

Data revisions are made, if respondent specifies, supplements or corrects data on some of the previous periods or if methodology is specified.

Data revisions are performed if the respondent specifies, supplements or corrects data about any of the previous periods or if methodological changes are made.

In the revision analysis process, it is possible to identify that initial data is inaccurate or that aggregation methods are ineffective. If it is possible to find that the revisions are significantly different from zero (consistently positive or negative), then the initial estimates are unreliable. Such information on audits can be used to improve aggregation methods and prevent systemic anomalies.

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The data are compiled based on the CSB quarterly questionnaire on construction (1-construction).

Framework source: Statistical Business Register (SUR)

Type of sample: single - stage sample

Sampling coordination: independent sampling

Stratification variables: target population, industry, size class by number of employees

Allocation: optimal proportional

Sampling: A stratified simple random sample

Target population: In 2025, economically active statistical units whose main activity is within NACE Rev. 2 Section F, excluding Division 4110 and statistical units with ISIC starting with S13. For sample design, the indicator of employment is the average number of employees over the last six available months. Binding legal basis: Regulation (EU) 2019/2152 of the European Parliament and of the Council of 27 November 2019 on European business statistics.

The sample includes all enterprises with at least 20 employees, as well as enterprises in NACE Rev. 2 classes F4213 and F4212 with at least 5 employees. Units with fewer employees are surveyed using a stratified simple random sample. Statistical units with fewer than 5 employees (except those in NACE Rev. 2 classes F4322, F4332, F4334, F4339 with fewer than 20 employees, and F4333, F4399 with fewer than 7 employees), as well as general partnerships (PS), farm enterprises (ZS) and sole proprietors (IK), are estimated using administrative data (VAT turnover).

The number of units in each stratum varies. Adjustments are made by including newly active enterprises (meeting the above criteria) in the survey frame, refining the classification of enterprises by economic activity, obtaining additional or corrected information from respondents, as well as using updated administrative data sources.

Quarterly

Data on construction output are collected using Form 1-Construction "Report on self-construction by enterprises". Most data is submitted in the form of an e-report, other forms of submission (mail, e-mail, telephone) are less common.

When the form or automatic validation controls change, training for data collectors (statisticians) is provided, as well as instructions are prepared for both respondents and statisticians.

Respondents receive notifications and reminders about the need to submit a report if the submission deadline is approaching. If the respondents do not submit the report in time, they will be contacted by statisticians. Each report (respondent) not submitted on time is coded with an appropriate non-response code with an explanation attached to it.

When connecting to the e-reporting system and selecting the appropriate report, the information about the applicant is filled in automatically and only the information needs to be checked and corrected (in case of changes).

The CSB uses special software for data entry and processing with automatic report management and data validation tools - ISDAVS (Integrated Statistical Data Processing Management System).

Validation algorithms are generally continuously improved based on the reports, comments and recommendations provided.

Calculation of the volume index of production in construction (henceforth – the IPC) is based on the chain-linked index method. Within the framework of it, the average volume of construction work done (at constant prices) in previous year is used as a calculation basis, and the value added of construction enterprises produced two years ago by NACE chapters is used as weights. IPC is calculated by recalculating construction production value indicator at constant prices with the help of corresponding construction cost indices.

Weights are changed every year, thus changes in the structure of construction are taken into account.

*Submitted (previously imputed) values that significantly deviate from the unit’s historical trend and are inconsistent with the situation/changes reflected in administrative data (VAT and number of persons). Such unpredictable values may have a substantial impact on the index estimate.

Time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof. Seasonality and calendar effects are present in a large number of economic time series.

Time series is a sequence of observations collected at regular time intervals, for example, a monthly time series. It characterises indicator changes or development thereof. Seasonality and calendar effects are present in a large number of economic time series.

Seasonality or seasonal fluctuations of time series mean those movements, which recur with similar intensity in the same season each year. For example, each year Christmas shopping time can be observed in time series reflecting retail sales statistics. Change of seasons, social habits and influence of institutional factors are among the main causes of seasonality.

The calendar effects cover influence of calendar on time series. It is impact left by differing number of working days (or Mondays, Tuesdays and other days of the week) in months on changes of indicator. For example, number of working days differing among the months may affect goods produced time series.

When the time series are influenced by seasonality or calendar effects, it may be difficult to get clear understanding on indicator changes over the time. Seasonal adjustment is made to eliminate seasonal fluctuations and calendar effects in time series.

As a result seasonally adjusted time series, from which seasonality and calendar effects have been removed, are produced. It means that seasonally adjusted time series provide an estimate for what is “new” in the series, for example turning points in trends, business cycle or irregular component. Moreover seasonal adjustment results in calendar adjusted time series, in which calendar effects or varying number of working days in months has been eliminated. Specifics of seasonally adjusted statistics allows improving data comparability over time:

• Seasonally adjusted time series do not contain seasonal fluctuations and calendar effects, thus it is possible to compare, for example, data on the current month with the previous month's data;
• Calendar adjusted time series are not influenced by calendar effects and are used to compare, for example, statistics on current month with the data on corresponding month of the previous year.

The seasonal adjustment is made taking into account seasonal adjustment guidelines developed by the European Statistical System.