OECD Reviews of National Statistical Systems: Kazakhstan 2026

4. Assessment of the quality and comparability of official statistics

4.1. National accounts

4.1.1. Non-financial national accounts

Assessment and recommendations

This section assesses the annual and quarterly non-financial accounts compiled by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS). The assessment is based on publicly available data and reports, metadata provided by the BNS through OECD questionnaires, and discussions held during the OECD mission to Astana in November 2024.

The BNS has made significant progress in implementing the recommendations of the 2023 UNECE/Eurostat/EFTA/OECD Global Assessment of the National Statistical System of Kazakhstan (UNECE et al., 2023[1]). The national accounts programme continues to strengthen its alignment with international macroeconomic standards such as the 2008 System of National Accounts (SNA) (e.g. capitalisation of Research and Development and weapons systems). Methodological improvement projects are also supported by international organisations such as the International Monetary Fund (IMF) and the Asian Development Bank (ADB). These projects cover areas such as seasonal adjustment, recalculating Gross Domestic Product (GDP) using the three methods after the release of the Supply and Use Tables (SUTs), on non-financial assets.

The establishment of a taskforce for implementing the 2025 SNA is a positive step. Its action plan, which is under preparation, should be aligned with the global implementation strategy for the 2025 SNA and Balance of Payments and International Investment Position Manual, Seventh Edition (BPM7), adopted by the UN Statistical Commission, targeting 2029-2030 (Inter-secretariat Working Group on National Accounts, 2025[2]). The strategy should also cover the transition to updated classifications (International Standard Industrial Classification of All Economic Activities (ISIC)/Nomenclature statistique des activités économiques dans la Communauté européenne (NACE) and Central Product Classification (CPC)/Classification of Products by Activity (CPA)) in national accounts and relevant source statistics. OECD technical assistance could support the implementation of 2025 SNA.

The BNS national accounts programme is fairly comprehensive, covering a wide range of statistics and accounts, including various satellite accounts. Annual and quarterly non-financial accounts broadly align with best policies and practices in OECD Member countries in terms of timeliness and punctuality, alignment with international standards, and accessibility. However, further improvements are needed in accuracy and credibility, coverage and relevance, coherence and interpretability. The main recommendations for improvement for the BNS include:

• Update and maintain existing quality reports so that users are well informed.

• Develop a single methodological report, ideally at the time of the next benchmark revision, documenting data sources, methods and compilation practices.1

• Expand coverage of national accounts by:

  • Compiling and publishing the remaining annual and quarterly tables (further detailed below).

  • Compiling and publishing non-cumulated quarterly data in levels (as official statistics).

• Extend time series for its annual and quarterly non-financial accounts based on the NACE Revision 2 classification (or the revised NACE 2.1/ISIC Revision 5).

• Progressively introduce chain-linked estimation of accounts for volume measures.

• Strengthen metadata and user guidance, including a Readme worksheet in dissemination files. Any deviations from statistical standards, as well as data vintage, should be clearly communicated to users as part of the metadata (using for instance the naming conventions provided in 2025 SNA Chapter 21 section F).

The OECD could provide methodological support for the compilation of quarterly sector accounts as well as technical assistance for the implementation of the recommendations for improvement identified during this review and support closer alignment with OECD best policies and practices.

Context, data sources and methodology

The national accounts are compiled by about 30 BNS staff, divided in five teams: economic balances (i.e. Supply and Use Tables (SUTs) and Input-Output Tables (IOTs)); production account; accumulation and satellite accounts; structural statistics; and distribution and use of income accounts.

A combination of statistical surveys and administrative data sources are used on the production side. Gross output and intermediate consumption by industry are derived from a range of industry specific forms, while output of financial and insurance activities and public administration is based on administrative data sources such as profit and loss statements and reports of the execution of the state budget. Financial Intermediation Services Indirectly Measured are derived from reports on bank loans, deposits and other sources, including the National Bank of the Republic of Kazakhstan (NBK).

On the income side, statistical surveys are used for estimating wages for the non-financial, Non-profit Institutions Serving Households (NPISH) and household sector. For the financial sector and government sector, administrative data are used. On the expenditure side, surveys are used for estimating final consumption of household sector and NPISH, and gross capital formation. Administrative data are used to estimate government consumption, while balance of payments information is used to derive exports and imports.

The current, capital, financial accounts and parts of the balance sheet are compiled according to the statistical work plan (i.e. regularly). Accounts for other changes in volume, revaluation and comprehensive balance sheet are still compiled on an experimental basis.

SUTs and IOTs are compiled annually (in current prices). SUT compilation occurs at the level of 114 activities x 698 products (fairly detailed) with dissemination at the 72 x 125 breakdown. For IOTs, compilation occurs at 114 products x 114 products and dissemination at 68 products x 68 products. Both sets of tables are based on NACE Revision 2 and CPA 2008 (so oriented towards the EU classifications). The BNS, with the assistance of the ADB team, has formed an experimental SUT at the previous year’s price for 2017-2018. Additional human resources are required for its annual formation, since the work is very labour-intensive. The IOTs are derived from SUTs by mathematical procedure (BNS, 2019[3]), so no direct statistical observation appears to be used.

The BNS also produces a range of satellite accounts, including environmental-economic accounts, tourism, health (pilot), and regional accounts (for the capital, two other large cities and 17 regions). On the website an account for natural resources sectors (oil and gas - primary, secondary, tertiary; non-oil and gas) (BNS, 2025[4]) is also available, as well as an account (BNS, 2025[5]) that provides a disaggregation of gross value added by ownership, i.e. into private, state ownership and property of other States.

Quality dimensions and indicators

Accuracy and credibility

Annual and quarterly non-financial national accounts compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of accuracy and credibility.

The BNS has provided an extensive list of data sources, both survey and administrative data, by ISIC category that it uses for compiling the production accounts and by aggregate for the expenditure accounts. However, more information would be welcome on the specific methodology applied to compile the accounts, on the validation checks performed, and on accuracy assessments.

Regarding the validation checks for non-financial sectoral accounts, two specific consistency checks are suggested:

• For intra-table consistency, both cross-sector consistency and the additivity of subcomponents to parent transactions need to be verified. Five mutually exclusive institutional sectors should be available: non-financial corporations, financial corporations, public administration sector, the sector of non-profit institutions serving households and the households' sector. The cross-sector consistency check ensures that, for each relevant transaction (e.g. P1 for output), the sum across sectors equals the total economy (e.g. on the revenue side of output: P1RS1 = P1RS11 + P1RS12 + P1RS13 + P1RS14_S15, S14_S15 being the aggregation of the sector of non-profit institutions serving households and the households’ sector). An example of verifying that a parent transaction equals the sum of its subcomponents is: P1r = P11r + P12r + P13r (i.e. output is the sum of market output, output for own final use, and other non-market output), or D3p = D31p + D39p (i.e. subsidies receivable equal the sum of subsidies on products and other subsidies on production).

• Regarding cross-table consistency, for the general government sector, consistency with Government Finance Statistics tables should be verified. For instance, it is important to check that individual (P31) and collective (P32) final consumption expenditure of general government, as well as net lending/net borrowing (B9), as presented in the non-financial sectoral accounts (T0800), are consistent with the corresponding values in the GFS tables on main aggregates of general government (T0200) and in the expenditure of general government by Classification of the Functions of Government (COFOG) CPA (T1100).

Quality reports on Gross domestic product by final expenditure method are prepared and made available by the BNS on its website every three years, in accordance with the centralised Schedule for the Preparation of Quality Reports. The next report is expected to be released in March 2027. The available quality reports for the SUTs identify several issues, related to the inconsistent classifications of institutional units by economic sectors between the BNS and the NBK and of fiscal reporting. It is not clear whether these issues have been addressed. In any case, it would be important to update existing quality reports so that users are well informed.

The BNS has made important advances in automating the production of national accounts, including the three approaches for compiling GDP and compiling regional GDP. However, it will remain important to maintain quality control for instance to check results of automated balancing routines.

Coherence

Annual and quarterly non-financial national accounts compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of coherence.

A key issue is that results for the production, income and expenditure approaches are released sequentially rather than simultaneously, which reduces transparency and comparability for users. At the time of this review, some inconsistencies could be observed across published datasets, e.g. differences between GDP estimates in dynamic tables for 2024 and between Gross Value Added as recorded in SUTs and in main aggregates for 2023. While some variation may reflect different vintages (i.e. progressive estimates) of the same dataset and may be resolved over time, these discrepancies concern conceptually different measures which should ultimately be aligned. As the GDP by expenditure approach already includes a row ”statistical discrepancy”, it is unclear why there would be any differences between the various GDP estimates. From a user perspective it is problematic that different estimates for the same concept (e.g. GDP in 2024) can be found on the website.

Statistical discrepancies in the expenditure approach are also significant (around 2% in recent years) and consistently positive, suggesting a possible structural bias that should be investigated further. Moreover, it would be better not to publish any discrepancies that create unclarity for users but rather resolve discrepancies when balancing the accounts.

SUT balancing is a positive feature, as the framework is designed to integrate the three GDP approaches (production, expenditures, and income) and resolve inconsistencies. However, imbalances between supply and use (e.g. 7.9% in 2022) indicate underestimation of the use part. The BNS is therefore encouraged to bring forward the integration of SUTs into the annual national accounts compilation process, using them as a framework to ensure consistency across GDP approaches. This would also support reducing or eliminating statistical discrepancy over time. Since 2025, the BNS published experimental SUTs enabling to improve consistency across the three GDP approaches.

The SUT quality report notes important data gaps in key areas of SUT compilation, including trade and transport margins, Value Added Tax, intermediate consumption, and the use of imported products. The report also mentions that the BNS uses data on large and medium-sized enterprises to describe the structure of intermediate consumption by type of economic activity in the context of products,2 which introduces limitations as the resulting structure is applied at the level of the entire economy and may not reflect heterogeneity across large, medium and small enterprises as regards technological progress.

In line with good practices in OECD Member countries, the BNS is encouraged to explore alternative data sources such as e-invoices (detailing who is purchasing what product) or banking information to derive better and more comprehensive estimates of intermediate consumption. Another potential avenue would be to explore the potential for direct use of trade in goods statistics for compiling supply and use tables.

Alignment with international standards

Annual and quarterly non-financial national accounts compiled by the BNS broadly align with best policies and practices in OECD Member countries in terms of alignment with international standards.

Good progress has been made over the last couple of years by the BNS in aligning with the international macroeconomic standards (e.g. on the non-observed economy, accounting for Research and Development). Work (supported by the IMF) is also ongoing to improve consistency between the various macroeconomic statistical standards.

National Accounts are compiled according to 2008 SNA standards, with research and development and weapons systems included in gross fixed capital formation and stock estimates as of 2024. Regarding the non-observed economy, the BNS has undertaken a lot of effort to ensure exhaustiveness of the national accounts with measurement based on the N1-7 categories recommended internationally for measuring the non-observed economy, which is to be applauded. A table (with time series covering 2013‑2024) provides an overview of the size of the non-observed economy by ISIC sections disaggregated into unobserved and illegal activities. The average size of the unobserved economy is large but declining rapidly from 28% towards 17% in that same period.

The activity of embassies is included in general government indicators, with data provided by the Ministry of Finance of the Republic of Kazakhstan. Adjustments are made for the income of employees working abroad based on data from the Balance of Payments. The BNS is encouraged to make appropriate corrections to bring these estimates in line with accrual basis of recording.3

Coverage and relevance

Annual and quarterly non-financial national accounts compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of coverage and relevance.

While the BNS continues to expand the coverage of annual and quarterly non-financial accounts, a number of annual tables are not yet available or published (see Annex 4.A for a more detailed assessment). These include employment tables broken down by main activity and detailed industry activity (Tables 0111 and 0303), the household final consumption expenditure tables by durability, by classification of individual consumption according to purpose (COICOP) and by expenditures on the territory and abroad (respectively Tables 0117, 0501 and 0502 for COICOP 1999 or Table 0500 for COICOP 2018 classification), annual capital formation by type of activity (Table 0302), annual fixed assets by economy activity and by assets (Table 2000), annual balance sheets (Table 2600) and social insurance pension schemes (T2900). The BNS transitioned to COICOP 2018 starting in the first quarter of 2026 although a detailed breakdown is not available and is currently working on measuring capital stocks and consumption of capital by applying the perpetual inventory method which are welcome and important improvements. The 2009 OECD Manual on Measuring Capital (OECD, 2009[6]) provides several approaches to establishing long time series and may be worth exploring.

Several quarterly main aggregates are not available/published: disposable income, saving and net lending/borrowing (T0107), population and employment (T0110 and T0111) and household final consumption expenditure by durability (T0117). There are no quarterly sector accounts (T0801) yet. The compilation of quarterly sector accounts would require additional human resources as well as methodological support from the OECD.

The time coverage of the time series is relatively short compared to OECD standards and requirements, or not available as consistent comparable series over time (e.g. according to the same industry classification over time). Volume data refer to fixed (2005)4 constant prices, which is an old base year that would need to be updated. It is however strongly recommended to switch towards volume estimates based on chained prices. Since December 2024, an experimental estimate of quarterly Gross Domestic Product compiled using the three approaches with 2010 as the base year has been made available on the BNS website. These estimates cover the period from the first quarter of 2010 to 2026. As part of the transition from the experimental estimate to the official publication, the base year is planned to be updated from 2010 to 2022.

Official quarterly data are published on a cumulative basis, and volume is expressed in indices or year_‑on_‑year growth rates. An important recent advancement has been to move towards regular (i.e. non‑cumulative) quarterly estimates which are now published on an experimental basis. This development is very important, and the BNS is encouraged to move these experimental estimates towards official statistics when quality is deemed sufficient. The official publication of discrete quarterly Gross Domestic Product estimates compiled using the three approaches is planned for the beginning of 2027.

On the positive side, Supply and Use Tables are very detailed in terms of industry and product breakdown and cover all relevant tables: supply, use at purchasers’ prices, use at basic prices broken down between imports and domestic output, trade and transport margins matrix and taxes less subsidies matrix.

Timeliness and punctuality

Overall, annual and quarterly non-financial national accounts compiled by the BNS broadly align with best policies and practices in OECD Member countries in terms of timeliness and punctuality, except for the GDP by expenditure for which it would be recommended to publish annual data at T+9 months after the end of the reference period, and quarterly data together with production data (at T+45 and T+90 days). It would be even better if the BNS could provide a first estimate of annual GDP around T+3 months as is the case of many countries (e.g. all European countries).

To calculate GDP by final expenditure method, Balance of Payments (BOP) data are used. The NBK publishes BOP data on a T+90 days basis. Therefore, it is impossible to estimate GDP by final expenditure method at the same point in time. As a result, GDP using the final expenditure method is published on a T+105 days basis. The BNS is encouraged to explore with the NBK the possibility to reduce the lag for BOP compilation or provide early estimates based on partial information. This is in line with the recommendations made in the IMF Quarterly National Accounts Manual - 2017 Edition (IMF, 2018[7]), para. 10.12 to 10.14.

The BNS publishes an advance release calendar on its website. Dates are currently provided in accordance with the schedule for the dissemination of official statistical information for 2025‑2027 (BNS, n.d.[8]), in line with the statistical work programme covering the same period. However, the BNS may consider moving to a rolling advance release calendar that is updated regularly (e.g. on a 12‑month basis). Deviations from published release dates are rare, but users are informed in advance through relevant channels.

Accessibility

Annual and quarterly non-financial national accounts compiled by the BNS broadly align with best policies and practices in OECD Member countries in terms of accessibility.

Non-financial national accounts data can be accessed through the official BNS website, with Excel files easily downloadable (Statistics of national accounts) (BNS, n.d.[9]). Quarterly online publications highlight key results of the quarterly accounts (tables with very brief description of key developments).

The BNS website is available in multiple languages, which is very helpful to reach a wide user base.

Interpretability (availability of metadata)

Annual and quarterly non-financial national accounts compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of interpretability.

The website contains a series of 29 methodological reports (BNS, n.d.[10]) related to national accounts topics, but the latest report dates from 2021. The BNS is encouraged to compile a single methodological report for instance at the time of the next benchmark revision with a comprehensive description of data sources, methods and compilation practices (such as Gross National Income inventories common in the EU area).5

The various Excel spreadsheets often have several worksheets covering different time periods (e.g. 2008‑2009; 2010‑2015) - presumably due to breaks in series. It would be helpful to explain the differences between these time series in the metadata or in a Readme worksheet included in the files.

More generally, the BNS is encouraged to improve the links between data and metadata in the online database systems.

4.1.2. Government finance statistics and public sector debt

Assessment and recommendations

Government finance statistics and public sector debt statistics are compiled by the Ministry of Finance of the Republic of Kazakhstan (MoF) in accordance with the Government Finance Statistics Manual (GFSM) 2014 (IMF, 2014[11]). The assessment is based on a review of publicly available data and reports on the MoF website, metadata provided by the MoF via OECD questionnaires, and additional information gathered during the OECD mission to Astana in November 2025.

Government finance statistics are published on a quarterly and annual basis. The results broadly align with best policies and practices in OECD Member countries in terms of accuracy and credibility, coherence, alignment with international standards, coverage and relevance, and accessibility, although some improvements may still be possible. The quarterly results also meet the requirements in terms of timeliness and punctuality, but the annual results are published with a longer time lag. In terms of interpretability, some improvements are also possible.

No information could be found on public sector debt statistics. During the review process, the MoF has clarified that the publication of public sector debt statistics based on the results of the financial year will start with 2026.

The main recommendations include:

• Explore feasibility of compiling public sector debt statistics (to the extent that they are not currently being compiled). To this end, it will be important to develop a comprehensive list of public sector entities, established by an inter‑institutional working group, published by the BNS, and aligned with the business register maintained by the BNS and the classification of specific entities. The MoF has indicated that these activities ensuring a clear classification of institutional units by the general government and public sector categories, as well as harmonisation of the classification of institutional units to improve the reliability of statistics, should be completed by December 2027.

• Develop a detailed plan for the work of the inter‑institutional working group on the classification of government and public sector entities, a timeline for addressing the various entities in the list, and the time to implement changes in the core statistics (Government Finance Statistics, Public Sector Debt and National Accounts). It is recommended to avoid continuous updates over a longer period of time and instead cluster them at the time of a benchmark revision.

• Inform users about the upcoming methodological changes well in advance.

• Assess the feasibility of reducing the time lag for the publication of the annual GFS tables to T+9 months.

Context, data sources and methodology

Government finance statistics are currently compiled by one person in the MoF. The results are compiled according to and fully consistent with the GFSM 2014. It covers all the relevant accounts as collected by the IMF. These are set up in a different way than the relevant OECD Tables but generally cover the same information. Several tables are compiled on a quarterly and annual basis.

The government consists of: 1) the central government which comprises 21 ministries and 19 departments; 2) 1 549 budgetary units of the central government; 3) four extra-budgetary entities (the National Fund, the Compensation Fund for Victims, the Educational Infrastructure Support Fund, the Special State Fund); 4) two social security funds (the State Social Insurance Fund, and the Social Health Insurance Fund); 5) local executive bodies comprising three cities of republican significance, the capital, 17 regional centres, 231 districts and cities, and 182 local budget program administrators; and 6) 2 378 budgetary units of local government.

Quarterly outputs include a consolidated report “statement of sources and uses of cash" and itemised tables on revenues (T1), expenditures (T2), and transactions in assets and liabilities (T3). The data sources for the quarterly data concern the following: Report on the execution of the republican budget, report on the execution of the local budget, report on the receipt and use of the National Fund of the Republic of Kazakhstan, receipts and expenses of JSC "State Social Insurance Fund", and receipts and expenses of the NPO "Social Health Insurance Fund".

Annual outputs concern four summary reports (ST), i.e. statement of operations (STI); statement of sources and uses of cash (STII); consolidated statement of stocks and flows (STIII); and statement of total changes in net asset value (STIV). Furthermore, they have detailed tables (T) on the following topics: revenue (T1); expenses (T2); transactions in assets and liabilities (T3); holding gains and losses on assets and liabilities (T4); balance sheet (T6); debt liabilities at par/market value (T6A); expenses related to functions of government (T7); transactions with financial assets and liabilities by counterparty sector (T8); positions in stocks of financial assets and liabilities by counterparty sector (T8B); and total other economic flows of assets and liabilities (T9).

The data sources for the annual data concern the following: reports on the execution of the national budget, on the execution of the local budget, and on the receipt and use of the National Fund of the Republic of Kazakhstan. Furthermore, they include information on the revenues and expenses of the State Social Insurance Fund, of the non-profit organisation Social Health Insurance Fund, of the Victims' Compensation Fund, of the Education Infrastructure Support Fund, of the Special State Fund, and the revenue and expenses for Paid and Sponsored Services. Finally, they also use data on assets in the consolidated financial statements.

There is still discussion on the delineation of the government (and public) sector. There is an inter‑institutional working group that discusses the proper classification of the various entities (6 600 in total), looking at qualitative and quantitative criteria. It is expected that the work of this group will be finalised in December 2027, and it would be good to develop a work plan with a more concrete time schedule, prioritising those entities that may have the largest impact on the results. Furthermore, it would be good to develop a plan for the implementation of any changes to the list of entities in the GFS (and national accounts) results and to inform the users accordingly, well in time. In that regard, it needs to be avoided that improvements are implemented on a continuous basis as this will imply that users will be confronted with level shifts on a continuous basis. Moreover, it may lead to a lot of additional work for the compilers if they want to ensure consistent time series with each revision of the results. It would be better to save up the changes until a specific point in time and to then implement all of them at the same time in the relevant statistics, possibly together with other major changes to the core statistics. These can then all be implemented at the same time as part of a benchmark revision.

Regarding public sector debt statistics, it is not fully clear what information is currently available. During the review process, the MoF has clarified that, pursuant to a legal provision entering into force on 1 January 2026 (Article 135 of the Budget Code of the Republic of Kazakhstan), the central authorised body for budget execution prepares a report on Government Finance Statistics of the Republic of Kazakhstan, disaggregated by the general government sector, the public sector, and the corresponding subsectors, which are determined on the basis of classifiers developed in accordance with the legislation of the Republic of Kazakhstan in the field of official statistics and generally accepted international standards. The report contains analytical information on the current state of public sector liabilities, including data on repayment and servicing volumes of principal debt, amounts of interest, commissions, and other payments in accordance with loan agreements for each project implemented through government external borrowings. In this regard, the report will contain information on public sector debt statistics based on the results of the financial year, starting with 2026.

At the same time, there is no classifier of institutional units and enterprises assigned to a specific sector (public, private, and other sectors). To this end, it will be important to develop a comprehensive list of public sector entities, aligned with the statistical business register as owned by the BNS. The classification of specific entities could be discussed in the inter‑institutional working group. During the review process, the MoF has indicated that the completion of activities aimed at ensuring a clear classification of institutional units of the economy by the general government and public sector categories, as well as the harmonisation of the classification of institutional units to improve the reliability of statistics, is planned for December 2027.

Quality dimensions and indicators

Accuracy and credibility

Government finance statistics compiled by the MoF align with best policies and practices in OECD Member countries in terms of accuracy and credibility. The results are fully in line with the GFSM 2014. They have a comprehensive coverage of government entities and have good quality data sources to arrive at high-quality results.

It is noticed that there are small statistical discrepancies (between the financial and non-financial accounts) for the government sector in the national accounts. It will be important to assess what may be causing these discrepancies and to remove them to the extent possible.

Furthermore, the inter‑institutional working group is still discussing the delineation of the government and public sector, which may lead to some reclassifications affecting government finance statistics and public sector debt statistics. As mentioned before, it would be good to develop a work plan with a concrete time schedule, prioritising those entities that may have the largest impact on the results, and to develop a plan for the implementation of any changes.

It is not clear whether quality reports are available to accompany the publications. If available, quality reports should be published on the institutional website along with the publications for all users.

To the extent that public sector debt statistics are not yet published, it is recommended to look into their regular quarterly compilation, starting with an overview of the list of public sector entities and assessing possible data sources.

Coherence

Government finance statistics align with best policies and practices in OECD Member countries in terms of coherence, except for some small statistical discrepancies between the financial and non-financial accounts for the government sector.

Furthermore, it is not fully clear to what extent the GFS results align with the results as included in the national accounts. Work started in 2024 on the comparability of data provided to the IMF by the BNS under the 2008 SNA and the MoF in accordance with the GFSM 2014, which provides a good opportunity to analyse any differences. It will be important to carefully conduct this analysis and try to remove any differences in results in close collaboration between the two institutions. In that regard, it is also important to have good institutional arrangements to regularly discuss the alignment of the two statistics and to set up collaborations where relevant to address specific issues.

Alignment with international standards

Government finance statistics compiled by the MoF align with best policies and practices in OECD Member countries in terms of alignment with international standards. They are fully in accordance with the GFSM 2014 and there are no known deviations from the 2008 SNA. However, as explained above, the BNS and MoF are currently comparing the data as compiled as part of the GFS data delivery and the SNA data delivery. This may reveal potential issues that should then be resolved.

Coverage and relevance

Government finance statistics compiled by the MoF align with best policies and practices in OECD Member countries in terms of coverage and relevance. However, as mentioned, the data are compiled and published according to the IMF Government Finance Statistics tables which are set up in a different way than the relevant OECD Tables. Furthermore, there are a few conceptual differences in the recording of specific transactions, they include slightly different aggregates and apply different consolidation rules.

The OECD collection on government finance statistics concerns Table 0200 (on main aggregates of General Government), Table 0900 (on taxes and social contributions receipt) and Table 1100 (on Government expenditure by function). This information seems available in the annual datasets as published by the MoF, although often not in the same format and with slightly different results. The information as included in Table 0200 is covered in Tables T1 and T2 of the annual GFS dataset by the MoF. The information as included in Table 0900 is covered in Table T1 and Table T7 corresponds to what is collected in Table 1100 of the OECD data collection.

For a full in-depth comparison, it would be useful if the MoF could also populate the OECD templates. This may help assessing potential data gaps, analysing any inconsistencies vis-à-vis other OECD Tables, and developing recommendations to further improve specific areas if relevant. It would also make it easier to compare Kazakhstan with OECD Member countries.

The annual government finance statistics are published in line with the internationally harmonised classifications, including all relevant breakdowns. GFS tables are published by year. It was not possible to assess the length of the time series. It would be useful if this could be specified by the MoF.

It is not clear whether (quarterly) public sector debt tables are published. However, financial accounts data on the general government are available. This would need to be complemented with data on public corporations to arrive at a comprehensive overview of the public sector. The MoF is encouraged to look into the compilation of these statistics, to the extent that this is not yet the case.

Timeliness and punctuality

Government finance statistics compiled by the MoF partially align with best policies and practices in OECD Member countries in terms of timeliness and punctuality. Similarly to OECD Member countries, quarterly government finance statistics data are published around T+75 days. However, the annual data only becomes available at T+11 months which is later than requested by the OECD (i.e. T+9 months). The MoF is recommended to look into the possibility to improve the timeliness of the annual data.

It is not clear whether an annual release calendar is available. If this is not the case, it is advised to develop one, preferably on a rolling 12-month basis.

Accessibility

Government finance statistics compiled by the MoF broadly align with best policies and practices in OECD Member countries in terms of accessibility.

Although data can be accessed through the MoF official website, it requires a specific search to obtain the relevant Excel files (MoF, n.d.[12]). Regular publications are available on the different releases, but the results are not presented in a structured way (e.g. ordered chronologically). Furthermore, the links only refer to a single reference period, not allowing you to look at (or download) longer time series. It is recommended to the MoF to develop a dedicated webpage where users can find all relevant GFS tables in a structured way.

The website of the MoF is in multiple languages which is very helpful to reach a wide user base.

Interpretability (availability of metadata)

Government finance statistics compiled by the MoF do not align with best policies and practices in OECD Member countries in terms of interpretability. Metadata do not seem to be available. The only references included in the data files refer to the IMF website on the GFS data collection. The MoF is recommended to develop clear metadata files to accompany the data, providing more information on the data files as well as explanations on specific trends, corrections and revisions to the data.

4.1.3. Financial accounts and balance sheets

Assessment and recommendations

Financial accounts and balance sheets are compiled by the BNS. The assessment was based on a review of publicly available data and reports on the BNS website, metadata provided by the BNS via OECD questionnaires, and additional information gathered during the OECD mission to Astana in November 2025.

Financial accounts are currently published on an annual basis, according to the statistical work plan, providing results for the five main sectors (non-financial corporations, financial corporations, government, households, and Non‑Profit Institutions Serving Households (NPISH)) and the rest of the world. The BNS is not yet compiling quarterly results. Furthermore, annual financial balance sheets are only compiled on an experimental basis. Data on revaluations and other changes in the volume of assets are currently missing. While the financial accounts broadly align with best policies and practices in OECD Member countries in terms of alignment with international standards, accessibility, and interpretability, important gaps still exist in terms of accuracy and credibility, coherence, coverage and relevance, and timeliness and punctuality. Main recommendations for improvement for the BNS include:

• Explore feasibility of regularly compiling the full set of institutional sector accounts, assessing available data sources, main challenges with the data, current data gaps, and how some of the issues may be solved by using the strength of the framework (i.e. looking at horizontal and vertical consistency).

• Incorporate the compilation of balance sheets, revaluations and other changes in addition to the financial and non-financial accounts; in view of their role in the framework and in assisting to arrive at coherent, consistent and comprehensive results.

• Involve all relevant departments and institutions responsible for the input data on the various (sub)sectors and instruments and define clear institutional arrangements for the regular exchange of data and metadata, and to discussing specific data issues.

• Compile and, where possible, publish results at more granular levels of detail for subsectors and items. This will improve quality of results, enabling detailed plausibility checks for users.

• Explore the feasibility to obtain input data at the micro data level with the various data providers (including the central bank and the central data depository). Consider whether changes in the legislation (e.g. law on personal data, banking) are needed for the BNS to be able to properly conduct its statistical tasks.

• Assess steps and resources needed to compile the full set of institutional sector accounts (including data collection, data processing and adjustment, imputation for missing elements, and balancing all the information in the framework) starting with annual results and expanding to quarterly data. Experiences carried out in other countries may provide a useful benchmark.

• Organise an international workshop to discuss and develop a work plan for the coming years. As this process may take quite a bit of time, it is recommended to start compiling the full set of accounts on an experimental basis for a couple of years before publishing results, while at the same time continuing the current publication.

OECD technical assistance could support the implementation of the recommendations for improvement identified during this review and alignment with OECD best policies and practices.

Context, data sources and methodology

Financial accounts and balance sheets, as part of the national accounts, are currently compiled by one person in the BNS. The results are compiled according to and fully consistent with the 2008 System of National Accounts (SNA), with all transactions and balance sheets being reflected on an accrual basis.

In terms of data sources, the main data provider is the National Bank of Kazakhstan (NBK). Furthermore, the BNS receives administrative data from the central securities depository and the MoF, as well as reports from the Unified Accumulative Pension Fund, from the State Social Insurance Fund and the Social Health Insurance Fund. However, in various cases, this information is only provided in aggregated form. It would be relevant to explore whether more granular data can be obtained to have more insights into specific entities and to be able to cross-check data at a more granular level of detail. Ideally, the BNS should have access to microdata. If this cannot be arranged due to legal hurdles, it would be important to assess to what extent these hurdles can be removed (for example via the statistical law). In terms of statistical data, the BNS has information on the financial and economic activities of national companies (statistical form 1‑PF) and on the activities of a small enterprise (statistical form 2-MP).

Data from the NBK is used to populate most elements for the financial corporations’ sector. Furthermore, it provides input for the other sectors. For non-financial corporations, data from the statistical forms 1-PF and 2-MP constitute important inputs. For the government sector, budget data are used, as well as data from the State Social Insurance Fund and the Social Health Insurance Fund, in addition to the data from the central bank. For the household sector, most of the items are based on counterpart information (from the NBK and from pension and insurance funds, the latter mainly for the item AF.6) or derived as a residual (e.g. item AF.2). Data for NPISH are mainly based on data from the NBK and for some items derived by extrapolation (e.g. item F.2). In addition, a specific data source is used for two financial instruments, i.e. data from the central securities depository are used for the items debt securities (AF.3) and equity and investment funds (AF.5). However, data are only obtained at an aggregated level and has some specific caveats (see below). Finally, the information for the rest of the world is directly obtained from the balance of payments.

For the experimental estimates for the balance sheets, data are obtained from Monetary and Financial Statistics (for financial corporations), from the Government Finance Statistics (for general government), and from the International Investment Position (for the rest of the world). These data also enable reflecting results on a from-whom-to-whom (or flow-of-funds) basis. The latter is particularly useful as it may help to estimate specific missing information at a detailed level. In this regard, information seems to be missing for non-financial corporations, households and NPISH, although part of their data could possibly be populated on the basis of counterpart information. The from-whom-to-whom tables would help to provide the necessary inputs.

In terms of methodology, all the information is brought together in the framework of the national accounts. However, as there are several data gaps and as there are challenges/quality issues with some of the data sources, it is difficult to cross-check the results and to make meaningful plausibility checks.

It will be important to assess the feasibility of regularly compiling the full set of institutional sector accounts. This assessment should cover available data sources, data-quality challenges, current gaps and how some of these issues could be addressed using the strengths of the accounting framework, including horizontal and vertical consistency checks. In addition to the financial and non-financial accounts, it is recommended to also incorporate the compilation of balance sheets, and revaluations and other changes (which are currently not being compiled) in view of their role in the framework and in assisting to arrive at coherent, consistent and comprehensive results. This will significantly improve the quality of the data as well as the coverage of the statistics, in terms of topics covered (extending it to balance sheets and revaluations and other changes in the volume of assets) and in terms of (sub)sectors and instruments covered.

The statistical business register constitutes an important input in the compilation of national accounts, including the financial accounts and balance sheets. It will be important to maintain an up-to-date register that is used for the various official economic statistics, ensuring consistency across economic statistics and to avoid any duplication or missing entities in the results. To the extent possible, all relevant institutions responsible for economic statistics should make use of the same register. If they insist on maintaining their own register, it will be important to frequently align the registers and to discuss any inconsistencies or differences in view on the classification of specific entities. A dedicated working group could be established to ensure this alignment on a regular basis.

It will also be important to set up a clear process to maintain the register (within the BNS and possibly also as used by other statistical agencies) and to ensure a correct reflection of the industry classification (NACE/ISIC). As the industry classification may also be relevant for other agencies in view of administrative purposes, it will be relevant to separate the register that is used for these administrative purposes (where industry classification as listed by the company cannot be changed) from the register that is used for statistical purposes, so that compilers can adjust specific information where relevant to ensure a proper description of the domestic economy.

Quality dimensions and indicators

Accuracy and credibility

The annual financial accounts as compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of accuracy and credibility. Whereas the results are fully in line with the 2008 SNA and on an accrual basis, the number of data sources is rather limited, and some sources have quality issues, constraining the ability to cross-check data and assess the comprehensiveness and plausibility of the results.

An important issue is that transactions in debt securities and unlisted shares are not recorded at market value because the central securities depository does not have access to these values when entities record specific transactions. The BNS should explore whether this can be addressed within the depository system or through supplementary data sources. Combining transaction data with data on balance sheet (changes) and information (or assumptions) on revaluations and other changes in the volume of assets may also help derive transactions at market value.

Furthermore, no market valuation is currently available for the pension liabilities of defined contribution pension schemes, and no information is available on unfunded pension liabilities. Further analysis of Kazakhstan’s pension system is needed to assess how the various schemes should be reflected in the accounts, and how the relevant information could be obtained to arrive at the relevant estimates (both in the financial as well as in the non-financial accounts).

Due to the current data gaps, it is difficult to assess inconsistencies or missing information, and no explicit adjustments are made for non-observed transactions and/or stocks. As noted above, it will be important to start compiling the full set of institutional sector accounts, bringing in more data sources and estimations where possible, and to then assess the consistency of the data and to impute for any missing information (including non-observed transactions and/or stocks). Confronting different types of information within the framework of the national accounts would strengthen the accuracy and credibility of the results.

The financial and non-financial accounts should also be analysed in conjunction. At present, significant discrepancies exist between the net lending/borrowing from the capital account and from the financial account. For the government sector and for the rest of the world, the discrepancies are relatively small, but they are quite significant for the non-financial corporations and the household sectors, and to a lesser extent for the financial corporations. The BNS should use the SNA framework to investigate the various estimates, assess the reliability of the results, and balance accounts where necessary.

The revision policy follows the general BNS approach, but no information is available on the size of revisions. Once the BNS establishes regular compilation of full institutional sector accounts, revision analyses should be undertaken. In the meantime, the BNS is recommended to monitor the size and direction of the revisions in its main data sources.

Coherence

The financial accounts and balance sheets compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of coherence.

The BNS conducts manual checks on the data, mainly via Excel. However, as the number of data sources is rather limited, it is not feasible to cross-check the consistency of most of the results. This will be feasible once the BNS has been able to start regularly compiling full institutional sector accounts. This will also enable them to cross-check with other statistics.

At present, the data for the financial accounts (and experimental data on balance sheets) are fully consistent with data from Monetary and Financial Statistics, Government Finance Statistics, Balance of Payments and International Investment Position. Once incorporating this information in the framework of the national accounts and confronting these results with other (new) data sources, as well as checking the consistency with non-financial accounts data and with changes in balance sheets, it will be possible to better assess the quality of all of these inputs. In some cases where inconsistencies exist, this may require a deeper analysis of the source statistics (in close collaboration with the data providers) and where possible adjustments to these source statistics. In view of the latter, it would be ideal if the data from these statistics are already shared with the BNS before they are published, so that there is still room to make specific balancing adjustments while ensuring consistency in the data as published by the BNS and the source statistics (like Monetary and Financial Statistics and Balance of Payments).

Alignment with international standards

The financial accounts and experimental balance sheet data are fully in line with the 2008 SNA, although there are some issues, for example to record certain transactions at market value (see above). Furthermore, a lot of the input data also align with other macroeconomic statistical standards, such as the Monetary and Financial Statistics Manual, the Government Finance Statistics Manual and the Balance of Payments and International Investment Position Manual.

The BNS has also been closely following the work on the recent SNA update, i.e. the 2025 SNA. However, it foresees an issue with data sources for specific new recommendations (particularly in the area of crypto assets). The BNS would require technical assistance from international experts to help it move to the updated standards. As practical guidance is currently being developed, it would be relevant to keep track of this ongoing work. Furthermore, a dedicated session could be foreseen in a potential workshop to focus on some specific challenges in view of the new SNA.

Coverage and relevance

The financial accounts and balance sheets compiled by the BNS do not fully align with best policies and practices in OECD Member countries in terms of coverage and relevance.

The OECD collection comprises four data sets: financial accounts and balance sheets accounts on both a consolidated and non-consolidated basis. Data are required by institutional sector (including selected sub-sectors), and for financial assets and liabilities by eight categories of instruments, most of them split into sub-instruments. In addition, countries are asked to report balancing items for both net financial transactions and financial net worth.

The BNS publishes annual consolidated financial accounts for the five main sectors and the rest of the world. Balance sheets are compiled only on an experimental basis, quarterly estimates are not available, and no data are compiled on revaluations and other changes in the volume of assets. Time series cover 2010‑2023.

For financial accounts, it would be useful to expand the level of detail to include subsectors of financial corporations and government, as well as subitems for some of the financial instruments. The BNS has explored the possibility of extending the time series back to 1995 or 2000, but this is currently not feasible due to data limitations. The immediate priority should be the regular compilation of full institutional sector accounts and the closure of some data gaps. The feasibility of estimating earlier years can be assessed at a later stage.

As mentioned before, financial accounts, balance sheets, revaluations and other changes in the volume of assets, and non-financial accounts should be compiled in conjunction. This would improve the quality and coverage of the results, while addressing current data gaps in balance sheet information.

In the longer term, the BNS could also explore the compilation of quarterly financial accounts, which are required by the OECD. However, for the short term, efforts should focus on annual accounts, while ensuring that source data are collected at a high frequency to support future quarterly compilation.

The BNS does not yet compile several additional OECD datasets, including quarterly households assets and liabilities (collecting more detailed information on the household sector to better identify and analyse households’ wealth in OECD Member countries), quarterly institutional investors’ assets (collecting more detailed information on the assets of investment funds, insurance companies and pension funds to obtain more insights in their role in financial markets), and annual non-bank financial intermediation (collecting more detailed breakdowns of other financial corporations and some financial instruments to obtain more insights in the potential build-up of risks by non-bank financial intermediaries) statistics. These could be explored once regular institutional sector accounts are firmly established.

The BNS already compiles from-whom-to-whom tables (also known as flow-of-funds tables), although they are not currently publicly disseminated. Given their importance for institutional sector accounts, it is recommended to continue the use of these tables, possibly compiling results at a more granular level of detail where possible. This may significantly improve the results and facilitate the compilation. When results are deemed of sufficient quality, it may be considered publishing them (assessing what would be the best level of detail).

Timeliness and punctuality

The financial accounts and balance sheets compiled by the BNS do not align with best policies and practices in OECD Member countries in terms of timeliness and punctuality.

The BNS currently publishes financial accounts data 15 months after the reference year (T+15 months), which is significantly later than the OECD requirement, which asks for data after 9 months (T+9 months). Once the full institutional sector accounts are firmly established, the BNS may explore, in a dedicated workshop with data providers, the feasibility of improving the timeliness.

The BNS publishes an advance release calendar on its website. Dates are currently provided in accordance with the schedule for the dissemination of official statistical information for 2025‑2027 (BNS, n.d.[8]), in line with the statistical work programme covering the same period. However, the BNS may consider moving to a rolling advance release calendar that is updated regularly (e.g. on a 12-month basis). Deviations from published release dates are rare, but users are informed in advance through relevant channels.

Accessibility

Annual financial accounts as compiled by the BNS broadly align with best policies and practices in OECD Member countries in terms of accessibility. Financial accounts data can be accessed through the official BNS website, with Excel files easily downloadable (BNS, n.d.[13]). The BNS website is available in multiple languages which is very helpful to reach a wide user base.

Interpretability (availability of metadata)

The financial accounts and balance sheets as compiled by the BNS broadly align with best policies and practices in OECD Member countries in terms of interpretability. The BNS publishes all accompanying information to the financial accounts on its official website (BNS, n.d.[10]). The Excel file also includes a dedicated sheet with methodological notes, providing information on the definition and coverage of the various financial instruments.

4.2. Environmental-economic accounts

4.2.1. Assessment and recommendations

The Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) already disseminates various accounts of the System of Environmental-Economic Accounting (SEEA) (e.g. environmental taxes, environmental protection expenditure, and air emission accounts for non-greenhouse gas (non-GHG) pollutants) and has made substantial progress in developing Physical Energy Flow Accounts (PEFA) and Air Emission Accounts (AEA).

The main findings and recommendations for improvement for Kazakhstan’s PEFA and AEA, summarised below, were developed during an OECD-BNS workshop in Astana in June 2025.

The BNS should consider developing automated, reproducible workflows for the regular compilation, as well as developing documentation, including metadata. Peer exchange with countries with longer experience in compiling PEFA and AEA would be helpful. In this context, the presentation on PEFA compilation at the Joint UNECE/OECD Expert Meeting on Implementation of SEEA in March 2026 in Geneva was highly appreciated.

Physical Energy Flow Accounts (PEFA)

Kazakhstan has made notable progress towards compiling PEFA, supported by extensive microdata across energy production, transformation and consumption, as well as detailed Energy Balances compiled using methodological guidance from the International Energy Agency (IEA). The draft PEFA follows the SEEA Energy structures and includes detailed product classifications. Key achievements include improved transformation/end use distinction and incorporation of natural inputs and energy residuals.

Outstanding issues include territory and residence adjustments (for road, air and maritime transport), the allocation of road transport energy to industries and households, and the balancing of Supply and Use Tables through recording energy losses.

Main recommendations for improvement, in order of priority:

1. Finalise PEFA for 2023 by addressing the points below:

a. Verify Energy Balance compliance with international definitions of bunkering and transport energy use (consultation with IEA recommended).

b. Improve territory/residence adjustments, especially road transport fuel purchased by non-residents on the territory and residents abroad, by exploring different data sources including tourism statistics and toll data.

c. Refine industry allocations using microdata (secondary electricity/heat production, disaggregation of aggregated service categories).

d. Ensure the balance between Supply and Use Tables (e.g. input-output identity), particularly by recording energy losses.

2. Fill in the global PEFA questionnaire for one year and start reporting to the global data collection by United Nations Statistics Division (UNSD)/OECD.

3. Explore the possibility of extending the time series (e.g. possibly using Energy Balance in the old format).

Air Emission Accounts (AEA)

Kazakhstan has a comprehensive national greenhouse gas (GHG) inventory with long time series data, providing a solid basis for developing AEA. Important progress was made during the 2025 workshop in better understanding the conceptual framework and allocation methods for applying the inventory-first approach using a matrix linking Intergovernmental Panel on Climate Change (IPCC) categories to International Standard Industrial Classification of All Economic Activities/Nomenclature statistique des activités économiques dans la Communauté européenne (ISIC/NACE) industries.

The main technical challenges relate to implementing the residence versus territory principle for road, air, and maritime transport, with some data gaps for residents’ fuel purchases abroad and non-residents fuelling on the territory.

Main recommendations for improvement, in order of priority:

1. Finalise AEA for carbon dioxide (CO₂) for 2023 by addressing the points below:

a. Implement the inventory-first approach using a matrix linking IPCC categories to ISIC industries and households

b. Clarify the scope of road-transport emissions in Kazakhstan’s national GHG inventory.

c. Implement residence/territory adjustments. Consider collecting more granular data from airlines and ship operators. Consider using OECD estimates of CO₂ emissions from air and maritime transport if national data are missing or insufficient.

d. Improve allocation across different ISIC categories by incorporating PEFA for energy-emissions and national accounts data (e.g. Supply and Use Tables/output data) for non-energy emissions.

2. Fill in the global AEA questionnaire for CO₂ one year and start reporting to the global data collection by UNSD/OECD.

3. Explore the possibility of extending the gas coverage (e.g. to include methane (CH₄) and nitrous oxide (N₂O)) and the time series.

4.2.2. Physical Energy Flow Accounts (PEFA)

Context, data sources and methodology

As part of the review of the national statistical system of Kazakhstan, the BNS and the OECD organised a workshop on developing PEFA and AEA. The workshop took place from 23 to 27 June 2025 in Astana, supported by preparatory online meetings from March to May 2025 and building on earlier SEEA workshops held in 2018 and 2021. The objective of the workshop was to support Kazakhstan in compiling PEFA and AEA in a form that can be reported through the annual global data collection coordinated by the OECD and the UNSD.

The main source for the PEFA compilation in Kazakhstan is the Fuel and Energy Balance, produced annually by the BNS. It provides detailed information on the supply and use of energy products in physical units (terajoules) and has been developed with methodological guidance from the IEA.

PEFA compilation is supported by detailed underlying energy microdata, including extraction, production and transformation, distribution, final consumption and losses. Energy statistics also include enterprise questionnaires (coal, oil, gas, electricity and heat) and end-user and sectoral information on energy consumption.

Complementary sources that could support further improvements include household survey data on energy consumption (conducted every five years, most recently published in 2023), national accounts (Supply and Use Tables and output data), tourism statistics, and motorway toll data. Some other potential sources (e.g. balance of payments, customs/revenue data, and credit card payment information) were reviewed but deemed insufficiently detailed for PEFA adjustments.

OECD experimental estimates for air transport energy use would be helpful for territory/residence adjustments.

Energy products and flows in the energy balances are mapped to the corresponding product and sector categories in PEFA.

Complementary data sources can support improvements in territory/residence adjustments, road transport allocation, and more detailed sectoral breakdowns where needed. Possible methodological developments are included below.

Quality dimensions and indicators

Accuracy and credibility

Because Kazakhstan’s PEFA is under development, a full assessment of statistical accuracy is premature. However, the availability of detailed energy microdata and well-structured energy balances provides a strong basis for producing high-quality PEFA.

Once PEFA is finalised, validation checks should be conducted, for example, internal consistency checks across supply/use and transformation/end-use tables and external consistency checks against energy balances.

Coherence

A full coherence assessment will be more meaningful once PEFA has been finalised. However, compiling PEFA directly from the Fuel and Energy Balance and associated microdata supports coherence with the national energy statistics framework.

In preliminary compilation, imbalances between supply and use tables were observed. The input-output identity should be met for each industry and households (i.e. balancing column totals between supply and use). Counterbalancing energy use by transformation outputs and energy losses (for transformation use) and by energy losses returned to the environment (for end use) helps balance the two tables.

Alignment with international standards

Kazakhstan’s PEFA compilation is broadly aligned with the SEEA Central Framework (2012) and SEEA-Energy. It is structured as physical supply and use tables, structured by natural energy inputs, energy products, and residuals, and thus designed to support reporting through the global PEFA questionnaire.

PEFA requires consistency with the residence principle of national accounts. Territory/residence adjustments - especially for transport fuels - are therefore essential. Inbound tourism statistics and motorway toll data were identified as potentially useful sources for estimating fuel purchases by non‑residents on the territory.

Fuel purchased by residents abroad should be recorded as imports. Outbound tourism statistics were identified as the best available basis. However, the outbound tourism survey likely does not capture the road transport industry, and this limitation should be documented in metadata and questionnaire footnotes.

Energy balances are compiled in line with the International Recommendations for Energy Statistics (IRES). The scope of international bunkering for air and maritime transport should be reviewed in consultation with the IEA.

Coverage and relevance

The scope of Kazakhstan’s PEFA is comprehensive, covering natural energy inputs, detailed energy products, transformation-use of energy, final use of energy, and residuals. It has strong potential relevance, for example, for monitoring energy use by industry and households and supporting analysis of energy efficiency and energy intensity.

Correct differentiation between transformation use and end use is essential, and the workshop supported improvements in classification for several energy uses.

Confidentiality issues in specific products can be addressed using predefined confidentiality footnotes in the reporting template.

The relevance of PEFA would be strengthened by improving sectoral detail. Where feasible, aggregated categories in the energy balances (e.g. “commercial and government services”) should be disaggregated further using microdata. Microdata can also support improved allocation of transport fuels across industries and households, and more accurate treatment of secondary production of electricity and heat.

Finalising PEFA compilation for one reference year is the immediate priority. Once completed, extending the time series will improve analytical value. Energy balances in the current format cover 2021-2023.

Timeliness and punctuality

Because PEFA is not yet published, timeliness cannot be assessed in terms of dissemination schedules.

Compilation of the energy balances for 2024 was expected in August 2025. This would provide a good basis for timely PEFA production going forward. The UNSD collects PEFA data at the end of September each year. Further automation of compilation processes could help improve timeliness.

Accessibility

Since Kazakhstan’s PEFA is still under development and not yet disseminated, accessibility cannot be assessed at this stage.

Interpretability (availability of metadata)

Once PEFA is finalised and disseminated, interpretability would be strengthened by producing metadata on key definitions, scope, boundary adjustments, mapping from the energy balance to SEEA categories, treatment of transport fuel allocation, description of confidentiality treatments, and known limitations (e.g. partial coverage of residents fuelling abroad).

4.2.3. Air Emission Accounts (AEA)

Context, data sources and methodology

The development of AEA takes place in the same context as PEFA, as part of Kazakhstan’s broader efforts to implement the SEEA. Since Kazakhstan already disseminates AEA for non-GHG air pollutants, current work focuses on extending AEA to GHGs.

The principal source for GHG emissions is the national GHG emission inventory, compiled by Zhasyl Damu in accordance with Intergovernmental Panel on Climate Change (IPCC) guidelines and submitted annually to the United Nations Framework Convention on Climate Change, using the Common Reporting Table. The most recent submission was made on 15 April 2025 and covers the period 1990-2023. Compilation of the national inventory is supported by a cross-ministerial working group.

To allocate inventory emissions (classified under IPCC source categories) to AEA industries (International Standard Industrial Classification of All Economic Activities/Nomenclature statistique des activités économiques dans la Communauté européenne - ISIC/NACE) and households, several auxiliary datasets discussed during the workshop can be used, including PEFA use tables and/or underlying energy statistics, as well as national accounts output data and Supply and Use Tables.

Additional data sources considered potentially helpful for territory/residence adjustments are inbound and outbound tourism statistics and motorway toll data. In addition, OECD experimental estimates for CO₂ emissions from air and maritime transport by resident and non-resident operators can be used where national data are incomplete.

Given the detailed nature and long time series of Kazakhstan’s national inventory, the workshop recommended an inventory-first approach to compile AEA. Under this approach, inventory emissions are taken as the starting point (territory-based), system boundaries are adjusted to reflect the residence principle, and emissions are allocated to ISIC/NACE industries and households.

To operationalise this approach, a matrix integrating IPCC inventory categories (columns) with ISIC/NACE industries and households (rows) was proposed. This matrix explicitly addresses differences in accounting principles, such as territory versus residence, and differences in scope, notably international transport.

Practical compilation steps were detailed during the workshop and a subsequent draft technical report. Auxiliary data sources support the allocation of emissions from individual IPCC categories across multiple industries, while transport-related data support the estimation of bridging items needed to reconcile territory- and residence-based concepts.

The workshop recommended clarifying the scope of road transport emissions in the national inventory, in particular whether they are based on fuel sold on the territory or on vehicles registered in Kazakhstan. For road transport by residents abroad, as in the PEFA compilation, outbound tourism statistics were considered the best available source but were likely incomplete for the transport industry. For air and maritime transport, improved airline/port surveys are the most reliable option, complemented by OECD experimental estimates.

Quality dimensions and indicators

Accuracy and credibility

As Kazakhstan’s draft GHG AEA has not yet been shared with the OECD, an evaluation of accuracy and credibility is premature.

Once AEA is finalised, validation checks should be conducted, for example, internal consistency checks and plausibility across time series.

Coherence

An evaluation of coherence is premature at this stage. However, the AEA compilation using the matrix integrating inventories and accounts supports the coherence between the two datasets. Cross-consistency checks between PEFA and AEA would also be helpful.

Alignment with international standards

An evaluation of alignment with international standards is premature at this stage. However, correct implementation of territory/residence adjustments is a key requirement for alignment with the SEEA Central Framework. In addition, AEA should be developed in line with the OECD/UNSD AEA questionnaire.

Coverage and relevance

An evaluation of coverage and relevance is premature at this stage. In general, long time series and detailed sectoral breakdowns strengthen the relevance of AEAs. After finalising the compilation for CO₂, it is recommended to extend coverage to other gases (CH₄ and N₂O first, followed by the remaining GHGs).

Timeliness and punctuality

AEA for GHGs is currently under development, therefore, timeliness and punctuality cannot yet be assessed. The national GHG inventory is reported annually in April for year T-2, in line with the United Nations Framework Convention on Climate Change cycle. Once AEA compilation becomes regular, it should follow shortly after inventory updates. The UNSD collects AEA data at the end of September each year. Further automation of AEA compilation processes could help improve timeliness.

Kazakhstan is also well placed to explore the compilation of quarterly AEAs, given the availability of infra‑annual GDP data.

Accessibility

Since Kazakhstan’s AEA is still under development and not yet disseminated, accessibility cannot be assessed at this stage.

Interpretability (availability of metadata)

Once AEA is finalised and disseminated, interpretability would be strengthened by producing metadata on key definitions, scope, boundary adjustments, mapping from the IPCC categories to ISIC/NACE economic activities and households, treatment of transport emissions, and known limitations (e.g. partial coverage of residents fuelling abroad).

4.3. Consumer Price Indices and Residential Property Price Indices

4.3.1. Assessment and recommendations

The Consumer Price Indices (CPIs) and Residential Property Price Indices (RPPIs) are officially produced by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS). The assessment process consisted of the examination of the existing national and international sources for price statistics and the collection of data and metadata supplied by Kazakhstan via specific questionnaires designed by the OECD for this review. The information gathered provided the main inputs for this assessment.

Overall, CPIs and RPPIs are broadly aligned with best policies and practices in OECD Member countries in terms of accuracy and credibility, coherence, alignment with international standards, coverage and relevance, timeliness and punctuality, accessibility, and interpretability. However, the BNS is encouraged to consider the following improvements for the CPIs:

1. Continue to further investigate the possibility to include expenditure related to owner-occupied housing in the consumer price index. Although there is no consensus on the preferred approach for this inclusion, a common approach with other domains such as National Accounts is recommended.

2. Finalise the implementation of the Classification of Individual Consumption According to Purpose (COICOP) 2018, as decided in October 2025, in cooperation with the National Accounts and the Department of Statistical Registers and Classifications.

3. Consider an extension of the number of items in the representative basket, which is low compared to the ones used in OECD Member countries for the compilation of the consumer price indices. The CPI basket has been updated and will contain 537 items starting from 2026. In total, 92 new items have been added, 63 outdated items have been removed, and 42 items have been modified.

4. Extend the geographical coverage of the CPIs to the whole territory of Kazakhstan to cover rural areas.

5. Continue the experimental work on price monitoring based on tax receipts with the aim to integrate this work in the regular compilation of the consumer price indices. Continue exploring the possibility to cooperate directly with private retail chains or third-party data providers to obtain access to scanner data and to complement price information received from fiscal receipts, including on a regional basis.

6. Produce more detailed and frequent quality reports, using standard templates across all statistical domains. These quality reports should be more closely linked to data outputs.

7. Investigate improving the links between data and metadata in online database systems, bearing in mind likely future requirements of the OECD and other international organisations for automatic, machine‑to‑machine data and metadata transfers.

Further. the BNS is encouraged to consider the following improvements for the RPPIs:

1. Further investigate the possibility to obtain access to administrative data (e.g. land registers, tax authorities, or notaries) to complement information on asking prices with information on the actual transaction prices of properties.

2. Continue to develop web scraping to obtain access to online house price information and consider sharing this information internally, for example to complement statistical registers. A particular attention needs to be paid to the impact of web-scraped data on quality. A parser tool has been developed, and data are collected monthly for 41 types of consumer goods and services, as well as for the housing market price index. To ensure the quality of price calculations, hedonic regression methods are being studied (a consultation was organised in October 2025).

3. Consider compiling house price indices broken down by dwelling type (single and multi-family dwellings). The BNS will use parsing data to develop price indices by housing type: apartment sale price indices and house sale price indices.

4. Continue the development and implementation of hedonic methods in the compilation of house price indices and ensure their consistency with the methods for adjusting quality in other statistical domains, including national accounts. To date, a consultation on the use of the hedonic calculation method was organised in October 2025 enabling the future implementation of hedonic methods in the compilation of RPPIs.

4.3.2. Consumer price indices

Context, data sources and methodology

The BNS compiles CPIs in accordance with the Law on State Statistics and the methodological documentation No. 230 of 2015 and No. 135 of 2017 (BNS, n.d.[14]), both approved by the Statistics Committee of the Ministry of National Economy. CPIs are included in the BNS annual plan for statistical activities.

The BNS has been producing price statistics since the Soviet times, but over the last 20 years has been increasingly aligning with international standards and good practices. The Kazakh CPIs reflect the changes in prices of goods and services acquired, used or paid over time by private households for consumption purposes. The CPI is used to guarantee the value of contracts with index clauses (e.g. for indexing wages and salaries in collective agreements, for indexing pensions, etc.), as well as for the comparison of the price movements within a particular country between different economy sectors. CPIs also serve for deflating individual categories of national accounts data and other statistical series, as well as for analytical purposes.

The CPI calculation is based on a representative basket of 508 goods and services covering relevant components of consumption in Kazakhstan. A list of all goods and services in the representative basket is provided in the methodological documentation No. 135 of 2017. The selected items are based on domestic retail trade statistics, focusing on those with significant regional turnover and goods suited for mass consumers. Prices are collected in a sample of around 12 000 retail and service facilities, selected based on their turnover. On average, 120 000 individual prices are collected every month.

The list of goods and services in the CPI basket is updated every five years (last update in 2021), aligned with the change in the base year. The weights of goods and services are updated annually based on the Household Budget Survey and the structure of the System of National Accounts (SNA) for household expenditure on final consumption. Figure 4.1 provides an overview of the number of goods and services collected and Figure 4.2 shows their weights in the basket.

Figure 4.1. Number of goods and services in the CPI basket in 2024

Source: BNS.

Figure 4.2. Structure of the weights in the CPI basket in 2024, in %

Source: BNS.

Prices of goods and services are collected through different means:

• Prices for 460 items are collected through traditional price collection methods including:

  • by authorised price collectors through mobile application or paper method;

  • via internet for services such as passenger rail and air transport services, certain types of medical services, sanatorium; and

  • by phone for educational institutions, organizations providing funeral services and visited once a quarter.

• Prices for 41 items, including household goods, cars or mobile phones, are collected via web scraping techniques for items.

• Prices for seven items (legal services, insurance, mobile communications, telephone subscription fees) are collected via internet.

The Government of Kazakhstan mandated the BNS to collect prices of socially significant food products on a weekly basis (Government Decree No. 145 of 2010). Other basic prices are collected once a month between the 7th to 25th of the reporting month. During periods of high price volatility in the consumer market, the collection of prices for volatile goods and services is carried out more frequently.

The BNS applies the following quality adjustment methods described in Chapter 8 of the methodological documentation No. 230 of 2015:

• Implicit quality adjustment methods that indirectly estimate the value of quality change.

  • Direct price comparison: used to directly compare the price of a replacement product with the price of a replaced product if they are fully comparable in quality. Price collectors are instructed to measure the price for the same variety throughout the year and if the variety disappears permanently from the market, they should choose another one with the most similar quality.

  • Imputation methods (general average/class average): imputes the price change for a missing product by using the average price change of similar products in one group or directly comparable products.

  • Price carry-forward: for temporarily out-of-stock goods, carrying over the price of the previous period to the next period when there is information about their future receipts.

• Explicit quality adjustment methods to directly estimate the value of quality change.

  • Expert adjustment: experts estimate the value of quality changes based on their knowledge and market information. This method is used when the use of other, alternative methods is impossible.

  • Production costs: uses the additional cost of production as a proxy for the value of quality improvements.

  • Option costs: estimates the value of new features/options that were previously optional.

The methodological documentation No. 230 of 2015 notes that the choice of quality adjustment method depends on the ability to access information from external sources required for quality assessment methods, the degree of cooperation with the staff of the base facilities, the adequacy of resources and the qualifications of price collectors. As such, there are no automatic quality adjustments procedures for any product group.

The BNS plans to make greater use of alternative data sources, including web scraping and other electronic transfers, replacing direct price collection where possible. Since 2024, the BNS produces experimental statistics for price monitoring based on fiscal receipts for 20 items in three regions (Astana, Almaty and Karagandy). The main source for fiscal data on retail chains in Kazakhstan is the State Revenue Committee of the Ministry of Finance. Consumer prices based on fiscal receipts are still experimental and are not included in the compilation of official price statistics. Comprehensive documentation on the experimental price statistics based on fiscal receipts, including a user dashboard, is available online on the BNS website.

The BNS explored the possibility to cooperate directly with private retail chains to obtain access to scanner data for the compilation of its CPIs but decided to prioritise the use of tax receipts to minimise the burden on businesses and because of the time required to overcome the legal issues for accessing scanner data from retailers or third-party data providers. However, tax receipts may also cover sales made to non‑resident households whereas the CPI should be limited to resident households. They may also fail to capture goods sold on the black market, such as cigarettes. It is suggested to continue to look at the possibilities to improve data collection, including with scanner data for the compilation of the CPIs.

The BNS is actively cooperating with international price experts, including from the IMF and Statistics Norway, to improve the methodology for CPI calculation and to integrate the price collection from tax receipts into the regular CPI compilation. The BNS also envisages to implement hedonic price methods to control for changes in product quality.

Quality dimensions and indicators

Accuracy and credibility

The CPIs compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of accuracy and credibility. In particular:

• The BNS mainly uses traditional data collection approaches, but, in line with many OECD Member countries, has already implemented web scraping for some goods and services, and is actively investigating alternative sources for others.

• The accuracy of the CPIs is routinely assessed according to clear rules set out in the methodological documentation No. 230 of 2015, Chapter 9 on ensuring the quality and reliability of primary data. Price data collected are checked for validity by comparing the new data with those from the previous collection. In case of significant difference, interviewers compare prices for similar types of goods (services) in other basic facilities and nearby retail outlets that are not selected for observation.

• Regular control visits for price collectors are in place to assess compliance with the standard price registration procedures and to identify methodological difficulties such as replacements of goods.

• Retrospective controls or re-visits are carried out when significant price changes have been detected, either on the same day or shortly after the initial price registration to avoid problems of price changes. These controls aim to validate the correct price registration and compliance with the required characteristics.

• Manual and automated validation checks are applied to eliminate errors in registered price data. Validation checks include verification through comparative tables, outlier detections and variance analysis.

• Kazakhstan monitors accuracy of consumer prices through a regular analysis of the coefficient of variation to identify large errors in recording prices. The relative dispersion is also measured to detect significant difference within price data. More information is available in the CPI quality report 2021 and the methodological documentation No. 230 of 2015.

• The BNS applies various quality adjustment methods to ensure accuracy and credibility of its CPIs as detailed in Chapter 8 of the methodological documentation No. 230 of 2015.

• The BNS has plans to introduce hedonic modelling to further improve accuracy of price statistics which is in line with practices in OECD Member countries.

The BNS collects prices for a representative basket of 508 goods and services that cover relevant components of consumption in Kazakhstan. The number of items is low, compared to the representative baskets for the compilation of the consumer price indices in OECD Member countries. The BNS is invited to pay a particular attention to the composition and representativeness of the items in its representative basket and to consider an extension of the number of items.

Data accuracy is regularly assessed, and some information is given in quality reports (the most recent one in 2021), though only examples, rather than quantitative estimates of overall accuracy are provided. The BNS should consider producing more detailed and more frequent quality reports, using standard templates across all statistical domains. These quality reports should be more closely linked to data outputs.

Coherence

The CPIs compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of coherence. Cross-country comparability and consistency within datasets are achieved with the implementation of international guidelines recommended by the OECD. According to the 2021 quality report, CPI time series are comparable back to 1993, and no breaks in time series have been identified during the review. Automatic format and logic checks are in place to ensure coherence within the dataset and price data are compared to data from other domains (e.g. National Accounts) to ensure cross datasets coherence.

CPIs disseminated on the BNS website and provided to the OECD for this review have one decimal. This does not fully align with best policies and practices in OECD Member countries. The provision of CPIs with more than one decimal would further improve international comparability in the OECD databases and the BNS may consider providing users with indices with minimum three decimals.

Alignment with international standards

The CPIs compiled by the BNS align with best policies and practices in OECD Member countries in terms of alignment with international standards. The BNS methodology for price statistics is in line with the SNA 2008 and the Consumer Price Index Manual 2020 (IMF et al., 2020[15]) in most respects:

• Kazakh CPIs are classified according to the COICOP 1999, and the change to the classification COICOP 2018 is scheduled for 2026, as it is the case in many OECD Member countries.

• CPI weights are updated annually based on the results of the Household Budget Survey and the structure of household final consumption expenditure in the System of National Accounts. This is in line with the recommendations of the CPI Manual to update weights regularly and at least every five years.

• The compilation of the CPI starts with the calculation of elementary indices compiled as unweighted geometric mean (Jevons index) based on prices for goods or services. Elementary indices are compiled for each geographical region. Elementary aggregates at national level are then calculated as a weighted arithmetic mean of elementary indices on regional level using regional weights. Elementary indices are then aggregated to the CPI using a Laspeyres-type formula, in line with international recommended practice.

• The BNS employs implicit (e.g. direct price comparisons, imputation methods) and explicit (e.g. option costs, expert adjustment) methods for quality adjustments, in line with the provision in the Consumer Price Index Manual 2020.

Coverage and relevance

The CPIs compiled by the BNS partially align with best policies and practices in OECD Member countries in terms of coverage and relevance.

In terms of geographical coverage, the CPIs cover a very large part of the territory of Kazakhstan. CPIs are collected for 85 localities, including 20 cities of regional and national significance and 65 district centres. Rural areas represented 7% of total retail trade in 2024. The geographical locations are selected within each region based on the population number criterion. Prices are not collected in smaller towns and villages and the BNS is encouraged to extend the geographical coverage to the rural areas to ensure a national coverage of the consumer price indices and further improve international comparability and alignment with OECD best policies and practices. To this end, the BNS could use fiscal data.

The reference population includes all private households in the country, regardless of the type of area in which the population lives, their position in income distribution, and their ethnic and demographic characteristics. All goods and services bought by the reference population for the purpose of final consumption are included in the index, without excluding any items as non-essential or undesirable. However, CPIs do not include goods and services purchased for business purposes, expenses for acquiring valuable items, income tax payments, social security contributions, or fines.

In terms of time coverage, CPI time series are available since 1993 according to the 2021 CPI quality report. This would meet the OECD requirements in terms of time coverage. However, monthly time series in the online database Taldau are available from January 2011 and the BNS provided the OECD with CPI time series starting from January 2008 during the review.

In terms of variable coverage, CPIs by COICOP divisions/group/class are not available and the following OECD target series are missing:

• CPI Energy (composed of COICOP 04.5 - Electricity, gas, and other fuels, and COICOP 07.2.2 ‑ Fuel and lubricants for personal transport equipment)

• CPI All items less food less energy (composed of COICOP 01-12 - All items - Food and non-Alcoholic beverages, less Energy - COICOP 04.5 and COICOP 07.2.2)

• CPI for COICOP 04.2 - Imputed rentals for housing

• CPI Housing (composed of COICOP 04.1 - Actual rentals for housing, COICOP 04.2 - Imputed rentals for housing and COICOP 04.3 - Maintenance and repair of the dwelling)

• CPI Services less housing (composed CPI Services less COICOP 04.1, 04.2, and 04.3.2 Services for the maintenance and repair of the dwelling)

Some OECD Member countries compute the component series of the CPI target indicators only. In such case, the OECD can compute CPI target indicators using weights and component series provided by the national statistical office.

The scope of the national CPIs also excludes the expenditure related to owner-occupied housing, imputed rents, dwelling or land and other gross fixed capital formation, although it is recommended that owner‑occupied housing costs be included in the CPI. The BNS started to evaluate the inclusion of owner‑occupied housing costs, and it is encouraged to investigate the possibility to include expenditure related to owner‑occupied housing in the consumer price index. Although there is no consensus on the preferred approach for this inclusion, a common approach with other domains such as National Accounts is recommended.

Timeliness and punctuality

The monthly CPIs compiled by the BNS align with best policies and practices in OECD Member countries in terms of timeliness and punctuality. Monthly CPIs are released on first day after the end of the reference period (T+1 day), comparing favourably with the lag observed in OECD Member countries.

The BNS publishes the CPIs simultaneously to all users by noon, in accordance with its advance release calendar. The advance release calendar, currently covering the period 2025‑2027 (BNS, n.d.[8]), is published in accordance with the statistical work programme of the BNS covering the same period. Dates are provided per calendar year and changes to publication dates are announced on the news section of the BNS website including reasons for such changes. All users can access the CPIs as soon as they are released and there is no pre-access release procedure in place for specific users.

Accessibility

The CPIs compiled by the BNS align with best policies and practices in OECD Member countries in terms of accessibility. CPIs are available on the BNS website in Excel format (BNS, n.d.[16]), through an interactive inflation dashboard (BNS, n.d.[17]), and through an online database Taldau (BNS, n.d.[18]). Most information on CPIs accessible on this website, including in Excel spreadsheets and in Taldau, is available in English. However, some data are not yet available via the English language interfaces, and the BNS is encouraged to further increase the volume of information available in English.

CPIs are also published in monthly Statistics of prices publications including visualisations, methodological explanations and links to related publications. These publications are accessible in English on the BNS website.

Interpretability (availability of metadata)

The CPIs compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of interpretability. Methodological information is provided together with the monthly price data publications in English. In addition, the BNS website has a dedicated section with methodological information for each statistical domain such as a price statistic (BNS, n.d.[14]). The methodological documentation (No. 230 of 2015 and No. 135 of 2017) provides comprehensive information on compilation methods of Kazakh CPIs. Metadata are also attached to the data and time series disseminated through Excel spreadsheets.

While metadata are findable on the website on the BNS, metadata are less accessible in the online database systems. The BNS should investigate improving the links between data and metadata in online database systems, bearing in mind likely future requirements of the OECD and other international organisations for automatic, machine-to-machine data and metadata transfers.

The BNS publishes national quality reports for CPI and other price indices which follows the European Statistical System Standard for Quality Reports Structure. The frequency of these quality reports is unclear, and the most recent CPI quality report has been published in 2021 while the next one is planned to be released in 2026. The BNS should consider producing more frequent quality reports, using standard templates across all statistical domains, and these should be more closely linked to data outputs.

The inflation dashboard (BNS, n.d.[17]) provides an interactive introduction to the most important methodological components in Russian language contributing to strengthening the statistical literacy of all users.

4.3.3. Residential Property Price Indices and related indicators

Context, data sources and methodology

The BNS compiles residential property price indices (RPPIs), referred to as house price indices, in accordance with the Law on State Statistics, and the methodological documentation No. 41 of 2016 “Methodology for constructing a price index in the housing market” as approved by the Statistics Committee of the Ministry of National Economy (BNS, n.d.[14]). House price indices are included in the BNS annual plan of statistical activities.

The Kazakh house price indices measure the change in the sale and rental prices of residential properties. House prices are available for different regions in Kazakhstan and are broken down by prices for new dwellings, existing dwellings and housing rent.

The compilation of house prices is based on two data sources: (i) data from construction companies and real estate agencies; and (ii) information from online advertisements. A housing price survey is conducted in cities and regions with a sufficiently high number of real estate agencies and housing types. The BNS does not have access to administrative sources such as land registers, tax authorities, or notaries.

Since 2017, the BNS has been receiving data from the Real Estate Register. Experimental calculations were carried out for the period 2013-2016. These calculations showed that the register data did not allow housing to be stratified by class, type, and other characteristics, leading to a high likelihood of significant fluctuations in price indices. These experimental calculations were continued but were not included in official production. In October 2025, a consultation on the hedonic method was organised and the BNS plans to conduct additional hedonic calculations using the Real Estate Register data and, upon obtaining quality results, intends to incorporate the use of administrative data into index development.

The BNS applies stratification methods to divide the sample of residential properties into similar groups with minimal price differences. This method considers various price determining factors such as area (total and living area), location, construction year, type and material of external walls, floor level, number of rooms, or elevator availability. After dividing the sample of residential properties into distinct groups (strata), the average house price for each stratum is calculated using the geometric mean of residential property prices. The average price for Kazakhstan is based on average house prices of cities/regions and their weight components. The weighting scheme is determined by data on commissioned housing for new dwellings and by data on the number of transactions for existing dwellings.

The Jevons formula is used to calculate an individual price index to reflect the price change of a specific housing type between the reporting period and the base period. The Laspeyres formula is used to calculate an aggregate price index, which is the weighted average price change across housing types.

Quality dimensions and indicators

Accuracy and credibility

The house price indices compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of accuracy and credibility.

The BNS uses relevant and commonly used data sources for the compilation of house price indices, including data from construction companies, real estate agencies and online advertisements. However, not all advertised properties will end in transactions and the final transaction price may differ. Indices based on the asking price only can present an over-optimistic outlook when the housing market becomes depressed and an over-pessimistic outlook when the housing market is recovering. Hence, the BNS is encouraged to investigate the possibility to obtain access to administrative data (e.g. land registers, tax authorities, or notaries) to complement information on asking prices with information on the actual transaction prices of properties.

Limited information on accuracy is provided in the quality report “Prices in the housing market” and little information is available online on data validation practices. During the review process, the BNS mentioned automatic validation procedures through formal and logic controls to ensure coherence and accuracy of house price indices. However, more frequent and detailed documentation of quality would allow users to get a better understanding of the accuracy of the indices.

There are plans to further develop and apply quality adjustment methods, including hedonic methods, for the compilation of house price indices. The BNS is encouraged to continue this work and to ensure consistency with national accounts.

Since January 2023, the BNS has used automatic techniques (web scraping) to collect data from real estate websites and compile experimental price and housing market price indices. A parser tool has been developed, and data are collected monthly for 41 types of consumer goods and services, as well as for the housing market price index. To ensure the quality of price calculations, hedonic regression methods are currently under study, following a consultation organised in October 2025. Web scraping techniques enable the collection of a vast amount of information at a reasonable cost. However, not all the required information may be available online, and the information collected may be duplicated and may not accurately represent the target population. The BNS is encouraged to continue developing web scraping techniques to access online house price information, which could then be shared internally to complement statistical registers, for example. However, a particular attention must be paid to the impact of web scraped data on quality, and it is important to ensure that the collected information represents the statistical population accurately.

Coherence

The house price indices compiled by the BNS partially meet OECD requirements in terms of coherence. House price time series available online are consistent over time and show no breaks. However, some house price index time series do not seem fully consistent across the available online datasets, and further investigation is needed.

During the review, some inconsistencies were detected when comparing average selling prices of new houses in Astana and price indices in 2016 and 2017.6

Alignment with international standards

The house price indices compiled by the BNS align with best policies and practices in OECD Member countries in terms of alignment with international standards in most respects. House price indices are aligned with the 2013 edition of the Handbook on Residential Property Price Indices (hereafter RPPI Handbook) (Eurostat et al., 2013[19]), which provided the basis for the national methodological documentation (”Methodology for constructing a price index in the housing market”, No. 41) approved in 2016 by the Statistics Committee of the Ministry of National Economy.

The BNS has plans to develop and apply hedonic methods in the compilation of house price indices to further align with the compilation methods of the RPPI Handbook. Hedonic regressions are widely used by national statistics offices to adjust the property price index measurement for changes in quality of properties transacted. The BNS organised a consultation on hedonic price indices in October 2025 and is encouraged to continue this work on hedonic methods in the compilation of house price indices and to ensure their consistency with the methods for adjusting quality in other statistical domains, including national accounts.

Coverage and relevance

The house price indices compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of coverage and relevance.

In terms of variable coverage, house price indices are available by region and dwelling vintage (new and existing dwellings). However, the BNS is encouraged to consider compiling house price indices broken down by type of dwelling (i.e. single and multi-family dwellings), which are not currently available. In addition, an aggregated index is available for the new and existing dwellings. However, the BNS does not compile a total house price index for all regions and housing types (new and existing), and the development of this index is planned to start in 2027.

In terms of geographical coverage, Kazakh house price indices cover three main cities in Kazakhstan (Astana, Almaty and Shymkent) and regional centres.

In terms of time coverage, annual house price time series are available from the year 2005 for most regions in Kazakhstan, monthly house price indices are available in the online database Taldau from January 2012, and quarterly house price indices from the first quarter of 2012.

Timeliness and punctuality

The house price indices compiled by the BNS are aligned with best policies and practices in OECD Member countries in terms of timeliness and punctuality. House price indices are published on a monthly basis while most OECD Member countries compile quarterly house price indices. Monthly house price indices are in general released nine days after the end of the reference month (T+9 days). The data collection for house prices is finalised around the 20th of each month.

Accessibility

The house price indices compiled by the BNS align with best policies and practices in OECD Member countries in terms of accessibility. House price indices are available on the BNS website in Excel format (BNS, n.d.[16]) and through an online database Taldau (BNS, n.d.[20]; BNS, n.d.[21]). Most information on house prices on the BNS website including Excel spreadsheets and Taldau are accessible in English. However, some data are not yet available via English language interfaces, and the BNS is encouraged to further increase the amount of information available in English.

Interpretability (availability of metadata)

The house price indices compiled by the BNS largely align with best policies and practices in OECD Member countries in terms of interpretability. The BNS website has a dedicated section with methodological information for each statistical domain, including for statistics (BNS, n.d.[14]) and the methodological documentation (No. 41 of 2016) provides some information on the compilation of Kazakh house price indices. In addition, a brief methodological note is available in the house price indices spreadsheets disseminated in Excel (BNS, n.d.[22]).

While metadata are findable on the BNS website, they are less accessible in the online database systems. The BNS should investigate improving the links between data and metadata in online database systems, bearing in mind likely future requirements of the OECD and other international organisations for automatic, machine-to-machine data and metadata transfers.

The BNS publishes national quality reports (BNS, n.d.[23]) for house price indices and other price indices. These reports are aligned with the European Statistical System Standard for Quality Reports Structure. However, there is little information on the accuracy of house prices, and the publication date of the latest house price quality report is not indicated. The BNS should consider producing more detailed and frequent/regular quality reports, using standardised templates across all statistical domains. These quality reports should be more closely linked to data outputs.

4.4. Labour and labour compensation statistics

4.4.1. Assessment and recommendations

The Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) is the official producer of labour and labour compensation statistics in Kazakhstan. The assessment process included a review of existing national and international sources of labour and labour compensation statistics, as well as the collection of data and metadata provided by Kazakhstan through OECD questionnaires, and information gathered during an OECD mission to Astana in November 2024.

Overall, the labour and labour compensation statistics compiled by the BNS generally align with best policies and practices in OECD Member countries in terms of accuracy and reliability, coherence, alignment with international standards, coverage and relevance, timeliness and punctuality, accessibility, and interpretability. However, the BNS is encouraged to consider the following improvements:

a. Continue to explore the potential of administrative sources to supplement or replace survey data.

b. Continue the transition from paper-based to electronic data collection. Starting with April 2025, primary information on employment issues has been collected from households using tablets.

c. Examine the impact of the move to new data sources, particularly time series, and the impact of new data collection methods on quality (consistency).

d. Produce quality reports more frequently, using standard templates aligned with international practices across all statistical areas and linking them more closely to outputs. During the review process, the BNS has indicated that the structure of the quality reports is scheduled to be reviewed in 2026.

4.4.2. Context, data sources and methodology

The BNS collects statistical data on labour and labour compensation in accordance with the Law on State Statistics, and the “Rules for the submission of primary statistical data by respondents", approved by Order of the Head of the BNS (Order No. 173 of 2010), the Labour Code of the Republic of Kazakhstan (No. 414‑V SAM of 2015) and the Social Code of the Republic of Kazakhstan (No. 224-VII SAM of 2023). These statistics are included in the BNS annual statistical work plan.

The BNS has been collecting labour and labour compensation statistics since the Soviet era, but over the past 20 years it has increasingly brought these statistics in line with international standards and best practices. Data on some key indicators have been published online since 2001.

The BNS collects the following statistics on work and wages:

• Monthly Labour Force Survey: In 2023, it was sent to approximately 4.3% of households using the housing register as a sampling frame. The overlap is controlled at approximately 30% throughout the year, while the full sample is updated within 12 months. The sample is conducted in two stages based on a methodology developed jointly with experts from theFederal Statistical Office of Germany (Destatis) as part of the KAZSTAT Project. All members of the selected households over 15 years of age are included. Statistics are compiled in accordance with the standards and definitions adopted by the 19th International Conference of Labour Statisticians (ICLS), using international classifications (Nomenclature statistique des activités économiques dans la Communauté européenne (NACE Revision 2)) and the International Standard Classification of Occupations (ISCO‑08)). Information on the main indicators of the labour market is compiled and published on a quarterly basis by region on the official BNS website. Indicators of decent work, quality of employment, volunteer and informal activities are compiled and published on an annual basis.

• Labour compensation statistics are collected through quarterly and annual surveys of enterprises selected from the statistical business register. These surveys are conducted in the form of quarterly and annual labour force reports. The sample includes all enterprises with over 100 employees and a 30% sample of small enterprises. The methodology follows the standards adopted by the 12^th ICLS and uses ISCO-08. Seasonal adjustment is applied using the JDemetra+ program.

• Labour Cost Index: It is calculated annually based on labour compensation survey data.

Currently, employment data in Kazakhstan is primarily collected through the Labour Force Survey, a household survey conducted by human interviewers. In rural areas, surveys are primarily conducted on paper (paper-assisted personal interviewing (PAPI)), but a shift to computer-assisted personal interviewing (CAPI) is already underway, beginning in urban areas and expanding nationwide. Since April 2025, the labour force survey has been carried out nationally using tablets.

Labour compensation data are collected through business surveys. All large and medium-sized enterprises are covered, with a sample of smaller businesses also being surveyed.

At the same time, the BNS is exploring the possibility of using administrative data, including pension contribution data. The BNS receives and uses data on pension contributions and data from the State Revenue Committee of the Ministry of Finance on taxes as additional information for comparison and analysis in labour and labour compensation statistics.

The methodology used by the BNS for labour and labour compensation statistics complies with the standards of the International Labour Organization (ILO) and the resolutions of the 19th ICLS. The statistical classifications ISCO-08 and NACE Revision 2 are used, as well as the concepts and definitions recommended by the ILO.

In 2025, the BNS has updated the methodology in line with the resolutions of the 21st ICLS and adopted the 2018 version of the International Classification of Status in Employment. The 2026 Labour Force Survey Questionnaire (T-001) has been revised. Questions have been added to the Questionnaire to generate indicators according to the updated version of the International Classification of Status in Employment (ICSE-18). Results for ICSE-18 are planned to be compiled and published in 2026.

Quality reports are generated in accordance with the approved methodology for assessing the quality of official statistical information. When determining the frequency of quality reports, the cancelled national statistical observations are taken into account, as well as changes to the methodology and processes of data collection, processing and dissemination, taking into account the needs of the BNS industry divisions.

4.4.3. Quality dimensions and indicators

Accuracy and reliability

Labour and labour compensation statistics compiled by the BNS generally align with best policies and practices in OECD Member countries for accuracy and reliability. The methodological principles regarding accuracy and reliability are in many respects consistent with international standards and comparable to practices applied in most OECD Member countries.

• The BNS primarily uses traditional approaches to data collection, based on household and business surveys. These surveys are transitioning from paper-based to electronic data collection methods. Like many OECD Member countries, the BNS is already actively exploring the possibility of using administrative data to supplement or replace survey data. This includes experimental work comparing survey data with data from administrative sources.

• During the data collection process, format and logic control methods are used to identify and prevent obvious errors.

• The BNS has a policy for introducing amendments. The revision of published official statistical information for statistical purposes is carried out in accordance with the Law on State Statistics (paragraph 18-1) of Article 12), and the rules for the revision of published official statistical information for statistical purposes in the event of a change in statistical methodology and on the basis of updated, documented information (Order No 20 of 28 December 2028).7

• Overall, response rates are comparable to those of other OECD Member countries in urban areas and somewhat higher in rural areas.

• Quality reports are prepared every three years and published in a dedicated section of the BNS website. Quality reports are generated for all forms of labour and employment statistics. The most recent available report is for 2022. During the review process, the BNS has indicated that the structure and frequency of the quality reports are scheduled to be reviewed in 2026.

Overall, the accuracy of the data appears good. International recommendations and best practices are followed, and the relatively large sample sizes compared to some OECD Member countries should contribute to a good level of accuracy. However, it is possible to reduce the sample burden with minimal impact on accuracy by exploring improved sampling methods.

Coherence

The consistency of labour and labour compensation statistics in Kazakhstan is generally good, although further work may be needed to improve the consistency of data from various sources. Currently, this primarily concerns the consistency of household and business survey results, but consistency with administrative and other alternative data sources should also be considered. The BNS has undertaken some work in this area and is encouraged to continue these efforts and present the results in quality reports.

The main indicators of the labour market obtained as a result of the labour force survey are compared with the reports of enterprises (business surveys), individual entrepreneurs, as well as administrative data from the Ministry of Labour and Social Protection of the Population of the Republic of Kazakhstan and the Tax Committee of the Ministry of Finance. Enterprise data (business survey data) are also compared with administrative data source on pension contributions and tax deductions. This information is reflected in the quality report.

The consistency of employment data collected from households through the labour force survey and labour compensation data collected through business surveys could be examined in more detail. For example, the breakdown by industry could be examined, as the assumed industry reported by households may not match the industry calculated based on value added or its proxy indicators in business statistics. The BNS is working to reconcile data from labour force and business surveys. For example, to calculate labour productivity, the number of employees (sections of the labour force survey results) is distributed up to the fifth digit of the economic activity type, based on the structure of employees compiled from business surveys (1-T+2MP). The number of self-employed workers (sections of the labour force survey results) is distributed up to the fifth digit of the economic activity type, based on the structure of individual entrepreneurs. The employed population is then calculated as the sum of both employees and self‑employed workers.

Alignment with international standards

The BNS strictly adheres to international standards, with few deviations primarily due to the usual delays in implementing new versions of standards and classifications, as in all other countries. Where new versions of these standards and classifications have been released, there are clear plans and schedules for their implementation. For example, in 2025, the BNS has updated its labour statistics methodology in accordance with the resolutions of the 21st ICLS, with ongoing work to adopt the updated version of ICSE (ICSE-18). The labour force survey questionnaire (T-001) has been revised for 2026. Questions have been added to generate indicators according to the updated version of ICSE (ICSE-18). Results for ICSE-18 are planned to be available starting in 2026.

The BNS uses a national version of the NACE classification with some limited differences to take account of the national context, but it is aligned with NACE and there are no problems in reporting data according to NACE.

Coverage and relevance

The coverage and relevance of labour and labour compensation statistics compiled by the BNS are generally good and meet user expectations and OECD requirements.

The geographic scope of labour and labour compensation statistics covers all of Kazakhstan, compiled across all geographic levels including districts, single-industry towns, and villages. This level of detail is consistent with that of many OECD Member countries, although some countries that actively use administrative data are capable of compiling statistics for smaller geographic areas. The BNS is currently exploring ways to achieve this goal.

In terms of time coverage, most labour force data are collected and published quarterly, although core indicators are published monthly and some series, including the gender pay gap, median and modal wages, are compiled annually. The frequency of data publication is generally consistent with that of most OECD Member countries. Labour force data have been available since 2001, and labour compensation data since 2015. Many OECD Member countries have longer time series, but those provided by the BNS should be suitable for most purposes.

The units of measurement are consistent with those used by OECD Member countries, as the BNS follows ILO recommendations. Full compliance with the latest versions of international standards, as already planned by the BNS, will ensure continued alignment with best policies and practices in OECD Member countries.

There are no major gaps in coverage. The BNS also compiles data on employment in the informal sector in accordance with the ILO methodology on informal employment.

The BNS actively participates in relevant international groups and forums, regularly soliciting user feedback. This helps ensure the relevance of information.

Timeliness and punctuality

The labour and labour compensation statistics produced by the BNS meet the requirements of timeliness and punctuality and are in line with best policies and practices in OECD Member countries in this regard.

Labour force data is published as follows:

• monthly data - 10 days after the reporting period

• quarterly data - 45 days after the reporting quarter

• annual data - 90 days after the reporting year

Labour compensation data is published as follows:

• quarterly data - 32 days after the reporting quarter

• annual data - 90 days after the reporting year

These time delays are comparable with those in OECD Member countries.

The BNS publishes an advance publication calendar on its website. Currently, dates are published for the period 2025‑2027 in accordance with the statistical work programme covering the same period, but the BNS may consider switching to a rolling advance release calendar that is updated regularly (e.g. on a 12‑month basis). Deviations from published release dates are rare, but users are informed in advance through appropriate channels.

Accessibility

Labour and labour compensation data provided by the BNS are readily available and usable in user-friendly formats. The BNS publishes dynamic data on labour and labour compensation in machine-readable format through the Taldau Information-Analytical System in Application Programming Interface (API) mode, taking into account metadata, as well as on the BNS official website, taking into account XLS, JSON, and CSV formats.

The data are published in Kazakh, English, and Russian. Key labour force and labour compensation indicators are accessible with one click from the main page. Detailed tables, primarily in Excel format, are also available. Access to microdata is granted to accredited researchers upon written request. Microdata are often anonymised by removing details such as name and address but may still be considered sensitive because it can indirectly identify individuals with unusual characteristics.

Interpretability (availability of metadata)

Kazakhstan largely aligns with best policies and practices in OECD Member countries regarding the interpretability of labour and labour compensation statistics.

The necessary structural metadata is provided along with the data, along with key definitions and brief methodological notes. Contact information for the responsible statisticians is also provided. The structural metadata and summary reference metadata are published in English. The provided metadata are sufficient for understanding the data. The existing metadata are easily accessible and can be reused by humans, but this can be more difficult in the context of machine-to-machine data transfer.

Quality reports also confirm the interpretability of the data. Currently, they are released every three years. The BNS may consider releasing them more frequently.

4.5. Structural and demographic business statistics

4.5.1. Assessment and recommendations

At present, the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) does not compile or disseminate business demography statistics according to international recommendations. However, the BNS is well advanced in the investigating and its ability to compile these statistics following international recommendations, noting that business demography statistics can be based on registration data. The administrative data available in Kazakhstan could be used to produce business demography statistics, using legal units as statistical units, similarly to what many EU countries did. Moving to enterprises as statistical units will take more time and effort, and indeed some EU countries are still struggling with this.

The BNS added that within their statistical business register, the distinction between statistical units and legal units has long been implemented, and all surveys are based on statistical units. The principle of stability is ensured through the use of “frozen frames”, whereby data on economic activity types and enterprise size are fixed at the beginning of each year and may be changed only in exceptional cases, as stipulated in the methodology for the updating of the statistical business register.

The BNS noted that at present it receives taxpayer (entities) data (which could be used as a proxy in the OECD Timely Indicators of Entrepreneurship online dataset) from the State Revenue Committee of the Ministry of Finance of the Republic of Kazakhstan (MoF). The data provide information on taxpayers who are in the stage of liquidation (and could become bankrupt) and are used as an additional source for updating the statistical business register. Based on these data, monthly variables on newly registered; temporarily suspended activities, and in the process of liquidation are published. These variables could be helpful for the OECD online dataset, and more information is needed to move forward.

The BNS is recommended to follow both the 2021 European business statistics methodological manual for statistical business registers (Eurostat, 2021[24]) and the 2008 Eurostat-OECD Manual on Business Demography Statistics (OECD/Eurostat, 2008[25]) as it continues its move to compiling full business demography statistics.

Structural business statistics, which are the responsibility of the BNS National Accounts Department, are at present not compiled or disseminated. The BNS compiles most of the variables (e.g. turnover) ultimately required but does not additionally compile these variables by the required breakdowns, namely by industry (International Standard Industrial Classification of All Economic Activities (ISIC)) and by employment size-class. The BNS considers that it could compile the structural business statistics required by the OECD (for example, using its limited national employment size-class classification).

The BNS should continue to investigate its ability to produce a range of the structural business statistics required by the OECD, namely variable by industry by employment size-class.

The BNS plans to use administrative data from the MoF to replace two of the major surveys that would normally be used in the compilation of structural business statistics. The current plan is from 2025 to use accounting software systems for automatic uploading of statistical reports on the forms of structural statistics from respondents. Then from 2026 undertake integration of data on structural statistics and fixed assets with administrative sources and use information from administrative sources to generate small and medium-sized enterprises (SME) statistics. Finally, in 2027 publish structural statistics and SME statistics. In this regard, the BNS should consider applying more standard definitions of large, medium, small and micro enterprises, to improve international comparability of data.

In 2022, the BNS embarked on a “Digital Statistical Registers” project. In 2024, a list of national registers was approved by Order of the Minister of Digital Development, Innovation and Aerospace Industry, on the basis of which the digital statistical registers will be enriched. The purpose of the project is to reduce reporting burden and increase the reliability of the statistical information based on administrative data. The BNS plans to create digital statistical registers for all sectors of the economy and update them based on data from government agencies and alternative sources.

The BNS should continue the efforts to replace survey data with administrative data (e.g. tax data), however these data will most likely not be sufficient to properly measure the structure of the economy. It would be important to further investigate the most efficient way to continue observing the economic structure (e.g. by expanding sectoral statistical surveys, using alternative data sources such as banking information, etc.).

4.5.2. Context, data sources and methodology

The Law on State Statistics, and the Entrepreneurial Code (2015) provide the legal basis for the BNS to collect, compile and disseminate business demography and structural business statistics. This legal framework is complemented by regulations governing the submission of statistical data, the maintenance of the statistical business register (SBR), and the quality management of official statistics.

In addition, the following legal regulations aid the ability to compile and monitor the required statistics:

• Rules for the submission of primary statistical data by respondents, approved by Order of the Head of the BNS (Order No. 173 of 9 July 2010)

• "Methodology for updating the statistical business register and forming catalogues of surveyed units in statistical bodies", approved by the Order of the Acting Chairman of the Statistics Committee of the Ministry of National Economy of the Republic of Kazakhstan No. 96 of 24 June 2015, and registered with the Ministry of Justice of the Republic of Kazakhstan under No. 11718 on 22 July 2015

• Quality Policy, approved by the Order of the Chairman of the Statistics Committee of the Ministry of National Economy of the Republic of Kazakhstan No. 67 of 23 April 2015

Within the BNS, business demography statistics are the responsibility of the Department of Statistical Registers and Classifications, and structural business statistics are the responsibility of the Department of National Accounts - Division of Structural Statistics.

• The statistical business register, first established in its current form in 1997 following earlier initiatives launched in 1994, is the core source for both business demography and structural business statistics. It covers legal entities, branches (including foreign branches), and individual entrepreneurship (legal units); and company; local unit; unit of activity; and the local unit of the type of activity (statistical units).

• The statistical business register is maintained through administrative data sources, including information from the State Database of Legal Entities of the Ministry of Justice (electronic notifications), registration, re-registration and liquidation of legal entities, branches and representative offices, as well as individual business entities in the form of joint venture, and changes from the information system of the State Revenue Committee of the MoF registered and deregistered individual entrepreneurs from the Ministry of Labour and Social Protection (pension contributions); and from other government bodies (licences, government support measures, etc.).

• The statistical business register is further updated using information collected through a range of statistical surveys. These include surveys on economic activity (1-SR), fixed assets (Form 11), business tendencies (KO-6), employment (1-T), small enterprises (2-MP), financial performance (1-PF), trade and catering services (1-VT), and services output (2-Services). Several of these surveys also serve as key inputs for structural business statistics.

The Department of Statistical Registers and Classifications provides sample populations for 26 programmes across the BNS, including agricultural, labour, population, services, energy and national accounts statistics.

The OECD recommends a number of international standards in the compilation of business demography statistics and structural business statistics, including:

• OECD Structural and Demographic Business Statistics 2009 (OECD, 2010[26])

• Eurostat Methodological Manual on European Structural Business Statistics 2024 edition (Eurostat, 2024[27])

• Eurostat-OECD Manual on Business Demography Statistics 2008 (OECD/Eurostat, 2008[25])

• Eurostat European business statistics methodological manual for statistical business registers - 2021 edition (Eurostat, 2021[24])

4.5.3. Quality dimensions and indicators

As noted, at present the BNS does not compile or disseminate business demography statistics according to international recommendations and does not compile or disseminate structural business statistics. Therefore, the following sections mostly focus on statistical business register variables and national accounts variables (related to structural business statistics) that the BNS disseminates (see Annex 4.B and Annex 4.C).

Accuracy and credibility

According to the BNS, a quality report on the statistical business register is published every two to three years. The report assesses coverage of observations and compares results with alternative sources and industry statistics.

The quality of SBR data is supported through adherence to the Fundamental Principles of Official Statistics of the United Nations, the use of international classifications, automated validation and consistency checks, compliance with legal requirements, and the protection of respondent confidentiality.

The BNS also regularly reports quality related information to international organisations, including the OECD, the Eurasian Economic Commission and the Interstate Statistical Committee of the Commonwealth of Independent States.

Coherence

In compiling business demography statistics, the following fields from the statistical business register can be considered relevant:

• Business identification number/Individual identification number - BIN_IIN

• Type of legal unit

• Date of registration

• Date of re-registration

• Date of entering liquidation

• Main (and secondary) type of economic activity (GCEA/OKED - Kazakh General Classification of Economic Activities)

For type of activity, the main (principal) activity relates to the added value which exceeds the added value of any other type of activity carried out by an economic entity, and the secondary type of activity relates to the type of activity, in addition to the main one, which is carried out with the aim of producing products (goods and services) for third parties.

The BNS assesses the quality of the information/classifications and the internal consistency of the statistical business register. This is performed automatically based on format and logical controls. Inconsistent records are presented in the form of an error log, which is then analysed by specialists to determine corrective actions. The 2019 quality report produced by the BNS on key indicators derived from the statistical business register indicates that dynamic indicators on legal entities, branches and representative offices are comparable and continuous since 1999.

Alignment with international standards

The BNS at present does not follow international standards in the compilation of business demography statistics, in particular the 2007 Eurostat-OECD Manual on Business Demography Statistics (OECD/Eurostat, 2008[25]). For structural business statistics, while the BNS compiles most of the variables (e.g. turnover) ultimately required, it does not additionally compile these variables by the required breakdowns, namely by industry (International Standard Industrial Classification of All Economic Activities (ISIC)) and by employment size-class.

The BNS has indicated that national classifications are harmonised with their corresponding international classifications (e.g. ISIC). National classifications include:

• administrative-territorial objects (CATO/KATO)

• types of economic activity (GCEA/OKED)

• enterprise size (CDE/KRP)

• economic sectors (CSE/KSE)

• forms and types of ownership (KFS)

Coverage and relevance

The BNS indicated that business demography statistics could in the future be available quarterly (and possibly monthly), while structural business statistics could be available annually (and possibly quarterly). The BNS participates in regional and international meetings, including those organised by the Commonwealth of Independent States and UNECE covering structural and demographic business statistics.

Generally, the BNS has a good understanding of its users, including through annual online voluntary user satisfaction surveys (see the “User Questionnaire” available on the BNS website under “Surveys/User Questionnaire”). The BNS also includes good contact information with its disseminated products, including asking for suggestions and comments.

The statistical business register provides information on registered and operating legal entities, branches, subsidiaries of foreign legal entities, and individual entrepreneurs, classified by activity, ownership, region and size. Indicators on legal entities, branches and representative offices are comparable and continuous since 1999.

Enterprises are classified as small (up to 100 persons), medium (from 101 to 250 persons), or large (over 250 persons).

The BNS releases statistics on enterprises at the monthly, quarterly and annual frequency, including data on SMEs, young entrepreneurs, foreign-owned enterprises and the overall number of registered and operating entities. These statistics are utilised by government agencies, local executive bodies, individuals and legal entities, as well as international organisations, e.g. the OECD, the Eurasian Economic Commission and the Interstate Statistical Committee of the Commonwealth of Independent States. In addition, the BNS provided directly to the OECD the Excel file “T-02-06-M” which contained a range of indicators from the statistical business register (Annex 4.B).

Timeliness and punctuality

Relevant indicators for business demography and structural business statistics, such as the main indicators of the number of entities, are published monthly (see Annex 4.B for the list of the indicators published).

National accounts, which structural business statistics would be based on, release quarterly GDP on the 85th day after the reporting period (T+85 days). In addition, “Business activity of enterprises of the Republic of Kazakhstan” are released on the 15th day after the reporting period (T+15 days), with “Monitoring of small and medium-sized businesses” released on the 17th day after the reporting period (T+17 days).

The data are published and disseminated in the advance release calendar accessible from the BNS website in accordance with the schedule for the dissemination of official statistical information for 2025‑2027, approved by Order of the Head of the BNS (Order No. 232 of 31 December 2024).

Accessibility

The main BNS website contains a “Statistics” link that lists the subject “Industry” where some relevant statistics can be found (in Excel) with accompanying releases.

Access to microdata is allowed via the “Rules for the presentation of databases in deidentified form for use in scientific and scientific-technical activities” and according to the Law on State Statistics. Databases can be provided in deidentified form. Users need to submit an application, including the usual requirements, e.g. researcher details, project outline, confidentiality obligation, etc. Access to the databases may be provided remotely through the “User Account” on the BNS website or through the physical “Researcher’s Office”.

Interpretability (availability of metadata)

The BNS has provided the following publicly available methodological documents related to business demography statistics and structural business statistics:

• Methodology for maintaining and updating the Statistical Business Register information system (2021) (BNS, 2021[28])

• Methodology for calculating the indicators of small and medium-sized businesses (2017) (BNS, 2017[29])

The BNS provided directly to the OECD the Excel file “T-02-06-M” which contained metadata for the 35 indicators from the statistical business register, containing information on legal entities, branches and subsidiaries of foreign legal entities, as well as individual entrepreneurs that have been registered or re‑registered with the registration authorities.

4.6. International merchandise trade and trade by enterprise characteristics statistics

4.6.1. Assessment and Recommendations

The quality of international merchandise trade statistics compiled and disseminated by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) is aligned with best policies and practices in OECD Member countries. Three main sources are used in their compilation. First, as Kazakhstan is part of the Eurasian Economic Union (Armenia, Belarus, Kyrgyzstan and Russia - the EAEU - known as “Mutual Trade”), imports and exports of merchandise trade are collected via both a specific statistical form and invoices. For all other countries, data on imports and exports of goods is collected by the customs service (located in the Ministry of Finance of the Republic of Kazakhstan (MoF)).

Kazakhstan compiled and published annual trade by enterprise characteristics statistics for reference year 2021. The BNS continued to compile these indicators annually although the results were not publicly released until their publication for reference year 2025 in July 2026.

The BNS will replace the current form used to collect international merchandise trade statistics (mutual trade) with company invoices (referred to as SNT). In 2025, the BNS undertook an analysis of moving to using invoices for mutual trade statistics. It confirmed many advantages, but also noted some data gaps, that still need to be addressed. Additionally, some flows will need an additional collection method, notably trade via international pipelines (where agreements with suppliers will be required).

The BNS is encouraged to continue its fruitful work on addressing the challenges associated with the transition to a new source of mutual trade data, and in particular, on analysing how this change may impact various areas of its work. The BNS is also encouraged to continue exploring ways to integrate international trade through the statistical business register to compile trade by enterprise characteristics statistics.

4.6.2. Context, data sources and methodology

The BNS has indicated that foreign trade statistics are needed in the compilation of the balance of payments and the system of national accounts, and particularly at the level of the EAEU, data on mutual trade are necessary to monitor the internal market of the EAEU, the economy, and for trade negotiations.

The Law on State Statistics provides the legal basis for the BNS to compile and disseminate international merchandise trade statistics and trade by enterprise characteristics.

The law defines methodology for calculating statistical indicators of foreign trade, with some relevant points being:

• Mutual trade: Trade with members of the Eurasian Economic Union (EAEU - Armenia, Belarus, Kazakhstan, Kyrgyzstan, and Russia).

• The country of origin of the goods is the country in which the goods were completely produced or subjected to sufficient processing. In this case, the country of origin of goods means either a group of countries, customs or unions of countries, or a region or part of a country, if there is a need to separate them for the purposes of determining the country of origin of goods.

• The country of destination of the goods is the last country to which the goods will be delivered, regardless of where they were originally sent and whether or not they were, on their way to that last country, the subject of any commercial transactions or other operations that change their legal status.

• The departure country is the country from which the goods are shipped (dispatched).

• Exports and imports for Kazakhstan with all partner countries are formed on the basis of data from the State Revenue Committee (Customs) of the MoF, and data on mutual trade submitted in form 1-TS to the BNS.

• Cost of goods is expressed in United States dollars (invoice value is also collected).

In addition to the Law on State Statistics, in Kazakhstan there are a number of other legal instruments that aid the BNS in the compilation and dissemination of international merchandise trade statistics and trade by enterprise characteristics. These are:

• Methodology for maintaining statistics of mutual trade in goods of the Member States of the Eurasian Economic Union, approved by Decision of the Board of the Eurasian Economic Commission (No. 210 of 25 December 2018)

• Methodology in the field of maintaining statistics of international trade in goods, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy (Order No. 204 of 14 December 2015)

• On classifiers used to fill in customs declarations, approved by the Commission of the Customs Union (No. 378 of 20 September 2010)

• Rules for the provision of primary statistical data by respondents, approved by Order of the Head of the BNS (Order No. 173 of 9 July 2010)

• Rules for the submission of administrative data by administrative sources, approved by Order of the Acting Head of the BNS (Order No. 183 of 14 July 2010)

• Rules for the provision of deidentified databases for use in scientific and scientific-technical activities, approved by Order of the Head of the BNS (Order No. 168 of 2 July 2020)

• Statistical work plan for 2025-2027, approved by Order of the Acting Head of the BNS

• Schedule for the dissemination of official statistical information, approved by Order of the Head of the BNS (Order No. 232 of 31 December 2024)

• Rules for the free-of-charge provision of statistical information that is not provided for by the schedule for the dissemination of official statistical information and developed on the basis of primary statistical data submitted by respondents in accordance with the schedule for submitting primary statistical data by respondents, approved by Order of the Head of the BNS (Order No. 113 of 20 May 2010)

• Article 28 of the Entrepreneurial Code of the Republic of Kazakhstan dated 29 October 2015, ensures the protection of information constituting a commercial secret.

• Information security policy, approved by Order of the Head of the BNS (Order No. 252 of 31 October 2016), defines the goals, objectives, guidelines and practical techniques in the field of information security. The main goal of the Policy is to ensure the availability of official statistical information, confidentiality of information stored and processed on computer equipment under the conditions of its integrity and authenticity.

Statistics of foreign trade in goods for Kazakhstan with the rest of the world (i.e. excluding the EAEU) are formed on the basis of customs declarations of the State Revenue Committee of the Ministry of Finance of the Republic of Kazakhstan.

The statistical form 1-TS "Report on mutual trade in goods with the Member States of the Eurasian Economic Union" is submitted to the BNS by legal entities and/or their structural and separate subdivisions, individual entrepreneurs, as well as individuals who export and/or import with the Member States of the Eurasian Economic Union (with around 10 000 respondents reporting each month). The deadline for submission is until the 20th (inclusive) day after the reporting period. The deadline for providing all official data is monthly on the 15^th day of the month, 45 days after the reporting period. Data collection is carried out through the AIS "Electronic Statistics" portal in the "Online Data Collection" information system, located on the BNS website.

The BNS, as part of the transition to using administrative data, also receives, accompanying invoices for goods (SNT) for mutual trade, from the State Revenue Committee of the MoF. Every month more than 18 thousand enterprises confirm accompanying invoices for goods. Invoices are submitted, for exports, no later than the day the goods start moving, and for imports, before crossing the border of the Republic of Kazakhstan.

The BNS obtains data on international merchandise trade from a number of administrative sources, in addition to data obtained through customs authorities and directly via form 1-TS. In many cases, formal agreements have been signed to ensure data quality and coverage. Administrative data sources include:

• State Revenue Committee of the MoF (a joint order on information exchange has been signed)

• Information from tax authorities on taxpayers engaged in export-import operations with the EAEU Member States

• Ministry of Agriculture provides a list of market entities that have received veterinary certificates for the export of meat and meat products to the EAEU countries (a joint order on information exchange has been signed)

• National Bank of the Kyrgyz Republic through a Memorandum of Cooperation (data sharing on foreign trade operators engaged in export and import activities)

• Federal Customs Service of the Russian Federation (a Memorandum of Cooperation has been signed) allows the exchange of lists of participants in foreign economic activity on a monthly basis, the Russian side provides a list of Kazakh participants in foreign economic activity, the Kazakh side sends a list of Russian participants in foreign economic activity to the Federal Customs Service of the Russian Federation

Transition to invoices

With the transition towards using administrative data rather than survey data, data on foreign trade flows within the EAEU will now include date from not only form 1-TS (administrated by the BNS), but also "Commodity Accompanying Documents for Goods" (i.e. invoices) collected by the MoF (State Revenue Committee). Currently, over 10 000 respondents submit form 1-TS reports to the BNS monthly. As a result of this transition, the MoF will collect data on commodity accompanying documents directly from enterprises, which currently totals over 18 000 enterprises monthly. The State Revenue Committee has been collecting these data since April 2023. The MoF will then transmit these data to the BNS. The new approach will ensure the efficiency of statistics generation, increase the frequency of incoming data, and reduce the reporting burden on respondents.

Analysis of invoice data for use in calculating mutual trade volumes was conducted with the expectation that the new approach would ensure the efficiency of data collection, increase the frequency of data receipt, and reduce the reporting burden on respondents.

The BNS has identified some issues with the invoice-value data, including absence of country of origin, the statistical value is an optional field, the nature of the transaction code, absence of format-logical control, e.g. for consistency checks, and lack of accompanying invoices for electricity, crude oil and natural gas (namely goods transported via pipelines or wires). However, the BNS is confident that the new data source for compiling international trade statistics for the EAEU will not impact quality and could improve efficiency.

4.6.3. Quality dimensions and indicators

Accuracy and credibility

International merchandise trade statistics in Kazakhstan appear to be measured correctly from the relevant sources and correctly characterise the phenomenon they are meant to measure. The BNS uses three sources to measure international merchandise trade statistics, namely:

• mutual trade in goods with the Member States of the EAEU, form 1-TS collected by the BNS directly

• mutual trade in goods with the Member States of the EAEU, trade invoices are collected by State Revenue Committee of the MoF

• international trade with all other countries collected via Customs declarations to the State Revenue Committee of the MoF

In addition, the BNS uses alternative sources to ensure full coverage and provide a coherent picture of the situation. Solid validation practices exist to verify the accuracy of the data and main sources of potential errors are investigated and quality controlled.

The BNS has the raw international trade statistics and a functioning statistical business register and has indicated that it may be possible to compile further enterprise characteristics statistics (see Annex 4.D for current data).

The software for processing primary statistical information ensures that various format logical controls are undertaken, with data analysis carried out dynamically using variables such as average prices, additional unit of measurement, etc. These processes are automated in BNS software systems.

The BNS has indicated the following reasons for data discrepancies in EAEU statistics:

• statistical forms not submitted by foreign trade participants

• incorrect trade codes used by foreign trade participants

• the presence of a time lag between the date of shipment of goods for export in the country of departure and the date of registration of import in the receiving country (roll-over deliveries)

The BNS participates in annual meetings of the authorised bodies of the EAEU countries to conduct "mirror" comparisons of mutual trade statistics. Based on the results of the meetings, the reasons for the discrepancies in the data of the partner countries are identified and measures taken to reduce them. In this regard, and as an example, work undertaken by the BNS to reduce data discrepancies in EAEU statistics in 2017 saw a reduction in the discrepancy from 36.6% to 2.0% for the exports of Kazakhstan to Armenia compared with imports of Armenia from Kazakhstan, and a reduction from 6.1% to 2.2% for imports of Kazakhstan from Armenia compared with exports of Armenia to Kazakhstan.

Additionally, the quality and reliability of data on statistics of foreign trade are supported by generally accepted procedures:

• compliance with the basic principles of primary accounting

• use of standard international statistical classifications

• confirmation of the accuracy of primary data, the possibility of obtaining additional information from respondents is legally possible

• comparative analysis of statistical data is carried out (using cross-industry administrative data)

Coherence

International merchandise trade statistics in Kazakhstan are logically connected and mutually consistent. The BNS follows good practices during data collection and processing with well established procedures used to reconcile differences.

Statistical adjustments of unaccounted volumes of mutual trade in goods are carried out based on information submitted by the tax authorities. Data correction is carried out at the detailed level of goods subject to foreign trade codes and at the enterprise level in the context of partner countries. Additionally, an annual update of mutual trade with the EAEU countries is undertaken after making adjustments based on data from tax authorities taking into account full coverage of respondents.

Alignment with international standards

Starting around 2010 the World Bank undertook a “Project on Strengthening the National Statistical System” (KAZSTAT Project) to improve the efficiency and effectiveness of the national statistical system to provide relevant, timely and reliable data in line with internationally accepted methodology and best practices in Kazakhstan. As part of this project and working with the Federal Statistical Office of Germany (Destatis) and the European Union, methodology in the field of international merchandise trade statistics was enhanced, further aligning international trade statistics in Kazakhstan with the relevant international standards.

In addition, a “Global assessment of the statistical system of the Republic of Kazakhstan” was undertaken by the United Nations Economic Commission for Europe in 2008 and 2017. This ensures that the methodology in the field of international trade in goods statistics is constantly being reviewed by international organisations.

The BNS uses the Commodity Nomenclature of Foreign Economic Activity of the Eurasian Economic Union as a classifier of goods (which is aligned with the Harmonized Commodity Description and Coding System).

In following international practice, exports are valued at FOB (free on board) and imports are valued at CIF (cost including insurance and freight). The compilation of international merchandise trade statistics in Kazakhstan follows the General Trade System.

Coverage and relevance

Kazakh international merchandise trade statistics meet international requirements in terms of variables, detail, frequency, measurement units, geographical coverage, historical coverage, and availability. Kazakhstan has worked towards ensuring that these statistics correspond to the needs of national and international users.

The main indicators include foreign trade (exports/imports), the structure of exports and imports by main goods and exports and imports by regions, etc. In the first quarter of 2025, Kazakhstan’s exports were 16 362 USD million with imports 12 911 USD million.

Monthly data start in July 2011. For example, data released in May 2025 (covering the period from January to March 2025) included:

• Foreign trade turnover of the Republic of Kazakhstan (publication)

• Foreign trade turnover

• On foreign trade and mutual trade in goods with the member states of the EAEU

• Main indicators of mutual trade of the Republic of Kazakhstan with the EAEU countries by regions

• Main indicators of foreign trade of the Republic of Kazakhstan by country

• Structure of exports and imports of the Republic of Kazakhstan by main commodity groups

• Structure of exports and imports of the Republic of Kazakhstan by main commodity groups with the EAEU countries

• Export and import of the Republic of Kazakhstan with the EAEU countries by 4, 6, 10 signs of the EAEU TN VED in the context of "product-country"

• Export and import of the Republic of Kazakhstan with the EAEU countries by 4, 6, 10 signs of the EAEU TN VED in the context of "country-goods"

• Export and import of goods of the Republic of Kazakhstan by 4, 6, 10 signs of the TN VED EAEU

• Export and import of the Republic of Kazakhstan for meat and meat products

Kazakhstan compiled and published annual trade by enterprise characteristics statistics for the reference year 2021. The BNS continued to compile these indicators annually although the results were not publicly released until their publication for the reference year 2025 in July 2026. The BNS indicated that it may be possible to compile further enterprise characteristics statistics (see Annex 4.D).

International trade data are regularly provided to international organisations, including for example to the United Nations for the UN Comtrade database.

Foreign and mutual trade statistics are important for decision-making in Kazakhstan, both at the state level and at the level of individual enterprises. The main users of foreign trade statistics in the public sector are the Administration of the President, the Office of the Prime Minister, the NBK, the Ministry of National Economy, the MoF, the Ministry of Foreign Affairs, the Senate and government bodies.

Businesses use foreign trade statistics for market research, and the BNS has indicated that the number of users of these statistics is increasing every year. The BNS holds focus groups meeting annually, with the participation of users and respondents, representatives of interested government agencies, and at which the indicators of national statistical forms are analysed in detail for their relevance and the elimination of duplication with other departmental statistical forms and administrative data.

Timeliness and punctuality

Kazakhstan follows best policies and practices in terms of timeliness and punctuality for international merchandise trade statistics. International merchandise trade statistics are compiled by the BNS and disseminated monthly on the 15th of the month, 45 days after the end of the reporting period (T+45 days). The data are collected monthly, the deadline for submission by respondents is until the 20th (inclusive) after the reporting period. In addition, the statistical work plan and the schedule for the dissemination of official statistics (release calendar) are posted in advance on the BNS website.

Accessibility

Kazakhstan follows best practices in terms of accessibility for international merchandise trade statistics. A number of links and press releases (publications) relating to foreign trade are released each month on the BNS website. Data are released in Excel files and include a “Methodological explanations” tab. There is also a dashboard on foreign trade on the BNS website.

Interpretability (availability of metadata)

International trade metadata and methodology produced by the BNS make it easy for the user to understand and properly use the data. Metadata are published with each release and included in Excel files. The metadata and methodological notes outline the definitions and concepts, target populations, variables and terminology. The metadata are findable and accessible, and metadata and methodological information for international trade statistics can be located on the BNS website (BNS, n.d.[30]; BNS, n.d.[31]).

In addition, over the years the BNS has published several methodology papers on the statistics of foreign trade, mutual trade and commodity markets:

• On classifiers used to fill in customs declarations (20 September 2010)

• Reference the nature of the transaction (5 June 2018)

• Directory of territories of the EAEU countries (1 September 2015)

• Methodology for maintaining statistics of mutual trade in goods of the member states of the Eurasian Economic Union (25 December 2018)

• Methodology for compiling balances of resources and use of certain types of products (goods) and raw materials (12 December 2023)

• Methodology in the field of maintaining statistics of international trade in goods (14 December 2015)

The BNS recently published the quality report - On foreign trade and mutual trade in goods with the member states of the Eurasian Economic Union. In addition, the BNS produces documented information on the quality management system, including a quality manual, documented procedures, instructions, and process maps.

4.7. Balance of payments and international trade in services statistics

4.7.1. Assessment and recommendations

Balance of payments statistics, including international trade in services statistics by partner country, are compiled and disseminated by the National Bank of the Republic of Kazakhstan (NBK, Balance of Payments Department). The assessment process included a review of existing national and international sources of balance of payments and international trade in services statistics, as well as the collection of data and metadata provided by Kazakhstan through OECD questionnaires, and information gathered during an OECD mission to Astana in November 2024. These statistics are overall aligned with policies and practices in OECD Member countries in terms of accuracy and credibility, coherence, alignment with international standards, coverage and relevance, timeliness and punctuality, accessibility and interpretability. The NBK is encouraged to consider the following improvements:

• Extend the coverage of the international trade in services by partner country data.

• Disseminate a single methodology document for balance of payments.

• Start to investigate how the revision to international standards (BPM7, MSITS 2026) and classifications could be implemented in Kazakhstan.

The NBK, responsible for the compilation and dissemination of balance of payments and international trade in services statistics, has a clear legal mandate to undertake this function. The NBK uses a variety of data sources in the compilation, including internal financial statistics, direct enterprise surveys and administrative data which align well with good practice. The NBK cooperates with the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS), the Ministry of the National Economy and other central government institutions to source the required data.

Kazakh balance of payments and international trade in services statistics are compiled in accordance with the main international standards including the Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) and the Manual on Statistics of International Trade in Services 2010 (MSITS 2010).

4.7.2. Context, data sources and methodology

The NBK can undertake its responsibility to compile and disseminate balance of payments and international trade in services statistics through a number of legal texts. The most relevant of these being:

• Law of the Republic of Kazakhstan No. 2155 of 30 March 1995 “On the National Bank”. According to Article 8 of the Law, the functions of the NBK include the formation and dissemination of statistical information on the balance of payments, international investment position and external debt of the country.

• Decree of the Government of the Republic of Kazakhstan No. 71 of 29 January 1999"On the development of the balance of payments of the Republic of Kazakhstan". According to this resolution, the NBK is the state body responsible for the formation and publication of the balance of payments and is empowered to collect statistical information for the balance of payments from all respondents.

• The Law of the Republic of Kazakhstan No. 257-IV of 19 March 2010 "On State Statistics". According to this Law, primary statistical data are confidential and are used by state statistics bodies solely for the production of statistical information. Confidentiality of primary statistical data by state statistics bodies is ensured during their collection, processing and storage.

• The Code of the Republic of Kazakhstan on Administrative Offenses No. 235-V of 5 July 2014. The Code provides for penalties for the loss, sale, transfer or other illegal disclosure of primary statistical data, statistical information and/or databases that allow the identification of a respondent by an official of the state statistics bodies, a subordinate organisation of the department of the authorised body in the field of state statistics.

In addition, the NBK has formalised agreements on information exchange with a number of state bodies, such as the Ministry of Finance of the Republic of Kazakhstan (MoF) (which includes the State Revenue Committee which in turn includes Customs) and the BNS amongst others.

Like most statistical institutions and central banks that compile balance of payments, the NBK utilises various core data sources from a range of institutions. These include:

• customs and mutual trade statistics (Eurasian Economic Union or EAEU)

• statistical forms (see Annex 4.E) and administrative reports of banks, other financial and non-financial organisations, including branches of foreign companies and government agencies (that also provide administrative data for individuals)

• accounting data of the NBK, reports of external managers of financial assets/liabilities and the global custodian on the assets of the National Fund

• data provided by the BNS, the MoF, as well as other ministries and agencies

• public data sources

Going into more detail, the following outlines data sources broken down by balance of payments components:

• Goods on a balance of payments basis: The BNS and the State Revenue Committee of the MoF provide both import and export merchandise trade statistics on bilateral trade with member states of the EAEU (from the BNS) and trade with all other countries (from the State Revenue Committee of the MoF). To conform with balance of payments methodology (e.g. change of ownership) a number of adjustments are needed to the international merchandise trade statistics provided. These include:

  • Shuttle trade: Border Control of the National Security Committee provide data on the number of individuals crossing the border, by country and trip purposes allowing the NBK to evaluate the volume of export and import transactions of individuals that are not recorded in official statistics.

  • Goods in ports: Primary statistical data from transport enterprises are collected using form 8‑PB “Report on transportation services, services related to transportation, and other international operations”.

    • Starting in the first quarter of 2026, forms 8-PB “Report on transportation services, services related to transportation and other international operations” and 18-PB “Report on rail, pipeline, and electricity transmission services” consolidated and replaced forms 2‑PB, 3‑PB, 4‑PB and 5‑PB.

  • Goods under processing and Net exports of goods under merchanting. Primary statistical data for enterprises engaged in external economic transactions are collected using form 10‑PB “Report on international transactions with non-residents”.

  • Nonmonetary gold: Data are mostly sourced from internal financial reports.

  • Other adjustments: Data on transactions through customs payment orders (automobiles) comes from the data of partner countries and other information from the BNS and the State Revenue Committee of the MoF.

• Services: The NBK compiles international trade in services mostly from a combination of enterprise surveys and administrative data. Where known, data sources are the following for each sub-component:

  • Starting in the first quarter of 2026, forms 8-PB “Report on transportation services, services related to transportation, and other international operations” and 18-PB “Report on rail, pipeline, and electricity transmission services” consolidated and replaced forms 2‑PB, 3‑PB, 4‑PB and 5‑PB.

  • Manufacturing services on physical inputs owned by others; Maintenance and repair services. Forms: 8‑PB “Report on transportation services, services related to transportation, and other international operations”; 10‑PB “Report on international transactions with non-residents”; and 18‑PB “Report on rail, pipeline, and electricity transmission services”.

  • Transport-Passenger, Freight, Other, and Postal and courier services. Data are collected from transport enterprises, “Kazpost”, and courier services enterprises. Forms: 8‑PB “Report on transportation services, services related to transportation, and other international operations”; and 18‑PB “Report on rail, pipeline, and electricity transmission services”.

  • Travel. Border Control of the National Security Committee provide data on the number of individuals crossing the border, by country and trip purposes. The NBK evaluates the expenses of residents travelling overseas and of non-residents in Kazakhstan and uses administrative data on payments of individuals (residents) related to (overseas) training and treatment. Data are also collected using form 8-PB “Report on transportation services, services related to transportation, and other international operations”.

  • Construction. Forms: 7‑PB “Statement of International Operations, External Assets and Liabilities of the General Government Sector”; and 10‑PB “Report on international transactions with non-residents”. Administrative data submitted to the NBK by branches (representative offices) of foreign non-financial organisations operating in the Republic of Kazakhstan.

  • Insurance and pension services. Insurance companies engaged in external economic transactions via forms: 10‑PB “Report on international transactions with non-residents”; 11‑PB‑OS “Report on insurance (reinsurance) of non-residents and reinsurance of risks with non‑residents in the sector ‘general insurance’”; and 11‑PB‑SZH “Report on insurance (reinsurance) of non-residents and reinsurance of risks with non-residents in the sector ‘life insurance’”.

  • Financial services. Data are collected from banks, professional participants of the securities market, enterprises engaged in external economic transactions, professional participants of the securities market, MoF, and branches of foreign legal entities, via the forms: 8-PB “Report on transportation services, services related to transportation, and other international operations”; 9‑PB “Report on the status of financial claims on non-residents and liabilities to them”; 10‑PB “Report on international transactions with non-residents”; 14‑PB “Report on external government, government-guaranteed loans and loans attracted under the guarantee of the Republic of Kazakhstan”; 15‑PB “Report on international securities transactions with non-residents”; and 18-PB “Report on rail, pipeline, and electricity transmission services”. Administrative data are collected by the NBK through its role in monitoring currency transactions in the Republic of Kazakhstan.

  • Telecommunications, computer, and information services. Data are collected from communications companies, general government bodies, banks, enterprises engaged in external economic transactions, and branches of foreign legal entities, via forms: 7‑PB “Statement of International Operations, External Assets and Liabilities of the General Government Sector”; 8-PB “Report on transportation services, services related to transportation, and other international operations”; 9‑PB “Report on the status of financial claims on non-residents and liabilities to them”; 10‑PB “Report on international transactions with non-residents” and 18-PB “Report on rail, pipeline, and electricity transmission services”. Administrative data are collected via information on the NBK’s contracts signed with non‑residents.

  • Charges for the use of intellectual property not included elsewhere (n.i.e.); Other business services; and Personal, cultural, and recreational services. Data are collected from enterprises engaged in external economic transactions, general government bodies, banks, and branches of foreign legal entities, via forms: 7‑PB “Statement of International Operations, External Assets and Liabilities of the General Government Sector”; 8-PB “Report on transportation services, services related to transportation, and other international operations”; 9‑PB “Report on the status of financial claims on non-residents and liabilities to them”; 10‑PB “Report on international transactions with non-residents”; and 18-PB “Report on rail, pipeline, and electricity transmission services”.

  • Government goods and services n.i.e.: Data from general government bodies are collected via forms: 7‑PB “Statement of International Operations, External Assets and Liabilities of the General Government Sector”; and 9‑PB “Report on the status of financial claims on non-residents and liabilities to them”.

• Primary income; Secondary income; Capital account; and Financial Account: Data sources for the remaining balance of payments comments and accounts are sourced via forms: 1‑PB “Report on financial claims on and liabilities to non-residents”; 7‑PB “Statement of International Operations, External Assets and Liabilities of the General Government Sector”; 9‑PB “Report on the status of financial claims on non-residents and liabilities to them”; 11‑PB‑OS “Report on insurance (reinsurance) of non-residents and reinsurance of risks with non-residents in the sector ‘general insurance’”; 11‑PB‑SZH “Report on insurance (reinsurance) of non-residents and reinsurance of risks with non-residents in the sector ‘life insurance’”; 14‑PB “Report on external government, government-guaranteed loans and loans attracted under the guarantee of the Republic of Kazakhstan”; 15‑PB “Report on international securities transactions with non‑residents”; 17‑PB “Report on loans issued to non-residents”; and OPB‑1 “Balance of Payments Survey Questionnaire for Enterprises”. In addition, the MoF, Ministry of Labour and Social Protection of the Population and Ministry of the National Economy provide administrative data involving balance of payments transactions, including covering government loans, liabilities and assets of the public sector, grants received for technical assistance and labour migration. Internally, information on Reserve Assets and the National Fund are provided by other departments in the NBK.

Kazakh balance of payments and international trade in services statistics are compiled in accordance with the main international standards including the IMF’s Balance of Payments and International Investment Position Manual, Sixth Edition (BPM6) (IMF, 2009[32]) and the UN’s Manual on Statistics of International Trade in Services 2010 (MSITS 2010) (UN et al., 2012[33]). The NBK follows relevant guides for compilers that complement the manuals, namely: BPM6 Compilation Guide; and MSITS 2010 Compiler’s Guide.

In 2002 Kazakhstan participated in the IMF Coordinated Portfolio Investment Survey (CPIS-2001), and in 2010 Kazakhstan participated in the IMF Coordinated Direct Investment Survey (CDIS-2009). Kazakhstan subscribed to the IMF’s Special Data Dissemination Standard (SDDS) in 2006 and meets SDDS requirements (however, the metadata for the balances of payments appears out of date, for example, referring to BPM5). The IMF undertakes “Article IV Consultations” which include staff reports and statements on statistics with the last report for Kazakhstan undertaken in November 2024.

The NBK has undertaken an investigation into imports of goods purchased by individuals through foreign international Internet sites, based on bank surveys on non-cash payments using Kazakh payment cards in overseas countries. Using these survey results, transactions can be classified using the specific payment card codes (MCC - type of activity of a trade/service outlet) and the volume and type of imports of goods purchased via the Internet was estimated. In the future, and using this methodology, the NBK plans to expand the coverage of statistics on international trade in services received through internet sites (e.g. digital ordering and digital delivery of services).

4.7.3. Quality dimensions and indicators

Accuracy and credibility

Like most central banks that compile balance of payments statistics and international trade in service statistics, the NBK uses a combination of internal statistics, enterprise/government surveys, and administrative data as sources in the compilation. The main sources include international trade data from the BNS and the State Revenue Committee of the MoF and undertaking a wide range of quarterly surveys to relevant enterprises and government agencies.

The NBK follows good practices in the compilation of the balance of payments statistics, including the sourcing of relevant data, and verifying data accuracy from the sources. The accuracy and credibility of the balance of payments statistics is monitored, and data are validated through manual checks, Excel and automated processes. Cross check with payments within the framework of currency control are undertaken, and data verification with financial statements of companies. More broadly, mirror data with border countries are used to assess accuracy.

The NBK uses surveys/forms (see Annex 4.E) to source balance of payments data. In undertaking surveys, the NBK maintains a business register to specify relevant respondents which is updated quarterly (maximum 10 000 enterprises for 1‑PB “Report on financial claims on and liabilities to non-residents”, 1 000 enterprises for 8‑PB “Report on transportation services, services related to transportation, and other international operations”, 4 000 enterprises for 10‑PB “Report on international transactions with non‑residents”, and 100 enterprises for 18‑PB “Report on rail, pipeline transport and electricity transmission services”). There is an ongoing aim to reduce respondent burden. A methodology for sampling was approved by decree in 2020.

Compilers actively follow the economic and financial press and contact media when required to provide interpretation. News conferences are held for the benefit of the financial press, at which feedback is received, and more complete explanations may be provided as necessary.

Coherence

The NBK follows the main international standards, classifications, and guidelines to ensure the use of compatible concepts, definitions, classifications, and methods. The NBK produces coherent time series over time by following BPM6 and MSITS 2010. It is not certain if balance of payments statistics compiled and disseminated by the NBK are done within a quality framework.

Kazakh balance of payments data are interconnected via:

• International merchandise trade statistics which are the underlying data for "Goods on a balance of payments basis" in the current account.

• National accounts - balance of payments data are included in the System of National Accounts (SNA) Rest of the World accounts. The financial account of the balance of payments is consistent with the SNA financial account (by sector).

• International investment position - components in the financial account of the balance of payments are fully consistent with changes due to transactions in the international investment position, including data on transactions in the statistics of international reserves.

Alignment with international standards

In its statistical activity the NBK is guided by the principles of consistency and comparability of statistics with generally accepted international standards, classifications and methods, ensuring equal access of users to official statistical information. The sixth edition of the IMF “Balance of Payments and International Investment Position Manual” (BPM6) is the methodological basis for compiling the balance of payments in Kazakhstan. At present, the NBK does not have a specific date for the adoption of BPM7 but has requested IMF technical assistance to develop a plan for the transition.

Users are informed of major changes in methodology when such changes are introduced (e.g. the introduction of digital trade in 2015, and second-hand cars imported by individuals from Russia from 2015, based on insurance information to identify Russian plates registered by Kazakh citizens). Users are also informed of all other changes, e.g. clarification of the reporting or methodology, the emergence of new sources of information, etc.

These changes and revisions to the data, together with updated data, are published in the quarterly publication, available on the NBK official website.

Quarterly data first published are considered preliminary and users are notified when final balance of payments estimates will be released. Data are revised twice a year, namely in October and April with data revisions covering the two years prior to the reporting year.

Coverage and relevance

Balance of payments and international trade in services statistics compiled by the NBK broadly align with best policies and practices in OECD Member countries in terms of coverage. Balance of payments and international trade in services statistics seem complete regarding requirements in terms of variables, detail, frequency, measurement units, geographical and historical coverage. However, some international trade in services statistics by partner country components are not disseminated.

Data on the balance of payments, the international investment position, and external debt, as well as analytical tables, are published on a quarterly basis. The NBK website also provides statistics on foreign trade in services, broken down by type of service and country. The historical coverage seems to meet requirements with balance of payments data available from 2005 (1993 for BPM5) and international trade in services by partner country available from 2013. Data are provided in US dollar (USD) and transactions and stocks are measured at actual/market prices.

At present, the NBK does not publish seasonally adjusted quarterly components of the current account of the balance of payments.

For international trade in services statistics by partner country, it was noted that the following components are at present not disseminated due to the need to comply with confidentiality principles regarding the disclosure of information, as well as the use of estimated coefficients in the valuation of certain components of transportation services (freight) that do not stipulate a country-by-country breakdown:

• SA-Manufacturing services on physical inputs owned by others

• SC-Transport

• SF-Insurance and pension services

• SL-Government goods and services n.i.e.

• Travel

The NBK is encouraged to extend the international trade in services by partner country data to cover the missing components.

Timeliness and punctuality

Quarterly balance of payments statistics are published by the NBK within 90 days after the end of the reference quarter (T+90 days). There are three public advance release calendars available online on the NBK website. The first release calendar (NBK, n.d.[34]) is updated monthly, for example shows statistical releases for two months (current month plus the next one). The second advance release calendar (NBK, n.d.[35]) is part of the NBK’s “Special Data Dissemination Standard (SDDS)” and provides release dates up to six months in advance. A “Schedule of Release of statistical information” (NBK, n.d.[36]) is also available with options of choosing “Browse by category” and “One month ahead” and the ability to download the calendar in Excel format.

Balance of payments data are simultaneously released to all interested parties. Government bodies do not have access to information until the moment of its official publication. Data are published without comments from ministries or other government departments.

Accessibility

Balance of payments and international trade in services statistics can be accessed through the NBK official website, with Excel files easily downloadable (NBK, n.d.[37]). An analytical presentation of the balance of payments is published quarterly in the NBK's publication “Statistical Bulletin” (NBK, n.d.[38]).

The NBK has recently established and operates an Open Data Repository (NBK, n.d.[39]) - a specialised portal for publishing the NBK’s official statistical information in a machine-readable format. This project is being implemented as part of NBK’s Data Factory to develop interactive analytical tools. The Repository provides API (application programming interface) connectivity (a data exchange standard that enables software applications to interact with one another), which facilitates the work of developers and analysts, and enables the integration of the NBK’s data ecosystem with external platforms of government agencies and other interested organisations.

There is wide range of users of Kazakh balance of payments statistics, within the country (President’s Office, state agencies, public), international organisations (IMF, Eurasian Economic Commission-EEC) and other departments within the NBK.

Interpretability (availability of metadata)

The NBK provides a webpage entitled “Methodology” (NBK, n.d.[40]) that includes all methodology related to balance of payments. This includes:

• On improvements made in balance of payments statistics (4 April 2019)

• Guideline on valuation of unquoted shares and other equity instruments (22 June 2017)

• Methodological comment on direct investment statistics indicators (22 June 2017)

• Methodological comment to the balance of payments (22 June 2017)

• Press-release №25 "On transition to the new methodology of external sector statistics and on the balance of payments and external debt situation of the Republic of Kazakhstan over the 1st quarter of 2013" (28 June 2013)

Additionally, a separate methodology page is provided and a link with contact information for a staff member working in the Balance of Payment Department (NBK, n.d.[41]; NBK, n.d.[42]).

While this provides the user with a lot of information, it would be preferable to disseminate all information into a single methodology document for balance of payments.

4.8. Short-term economic statistics

4.8.1. Assessment and recommendations

Short-term economic statistics reviewed by the OECD for this report include industrial production indices,8 retail sales, permits issued for dwellings and passenger cars registrations compiled by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS). The assessment process included a review of existing national and international sources of short‑term economic statistics, as well as the collection of data and metadata provided by Kazakhstan through OECD questionnaires, and information gathered during an OECD mission to Astana in November 2025.

Kazakhstan compiles and disseminates most of the short‑term economic statistics required by the OECD.

• Industrial production indexes: The BNS compiles and disseminates all the required production indexes (mining, manufacturing, energy and construction) and an aggregated production index. These are compiled as part of the BNS’ “Short-Term Economic Indicator” (STEI), which in turn is a major input into the monthly Gross Domestic Product (GDP) estimate.

• Retail sales: As part of compiling the STEI, the BNS compiles and disseminates data on retail sales in volume and value.

• Building permits granted for dwellings: At present this indicator is not collected, compiled or disseminated by the BNS.

• Passenger car registrations: The BNS collects and disseminates a monthly “Availability of registered vehicles” indicator on its website.

As part of the Japanese Fund for Prosperous and Resilient Asia and the Pacific Technical Assistance Project by the Asian Development Bank, a training seminar on seasonal adjustment for industrial and construction statistics (monthly and quarterly data) was undertaken in July 2025. The BNS notes the following “ongoing and planned works” for short-term economic statistics:

• benchmarking using proportional Denton and Cholette-Dagum methods with annual overlap, implemented in XLPBM (an add-on to MS Excel)

• seasonal adjustment in JDemetra+ using X13 ARIMA SEATS, with application of 10 national calendar regressors

Based on this assessment, Kazakh short-term economic statistics follow international standards and align with OECD best policies and practices in terms of accuracy and credibility, coherence, alignment with international standards, coverage and relevance, timeliness and punctuality, accessibility and interpretability. The BNS is encouraged to collect, compile and disseminate building permits granted for dwellings, which could be sourced from the Ministry of Industry and Construction via an agreement, much like is done for vehicle data from the Ministry of Internal Affairs. Having such high‑frequency data would not only provide other government departments (e.g. the National Bank of the Republic of Kazakhstan) a leading indicator, but also provide the BNS an additional data source in assuring coverage, accuracy, credibility and coherence of its short-term economic statistics (notably construction).

In addition, the BNS is encouraged to consider the following recommendations and improvements to further align with OECD best policies and practices:

• Improve accessibility: At present, it does not seem possible to download a “full” time series for the variables covered. While the BNS provides a good dissemination page, with a wide range of short‑term economic statistics by various breakdowns, the dissemination medium is nearly always Excel, and in most cases only one year’s worth of data is provided, i.e. 12 months. Therefore, the BNS could investigate the implementation of a simple time series manager or application programming interface (API) for serious users of the data.

• Improve interpretability: At present metadata are lacking on the length of series, start dates, or simple lists of all the variables available. This information would help users a lot.

4.8.2. Industrial production indexes

Context, data sources and methodology

Industrial production indexes are part of the short-term economic statistics under review. The OECD defines five target series for industrial production indexes, measuring volume changes of production output, valued at basic prices, based on the International Standard Industrial Classification of All Economic Activities (ISIC) Revision 4 as follows:

1. Production of total industry; Industry (B-D)

2. Production in total mining; Mining and Quarrying (B)

3. Production in total manufacturing; Manufacturing (C)

4. Production of total energy; Electricity, gas, steam and air conditioning supply (D)

5. Production of total construction; Construction (F)

The BNS compiles and disseminates the five industrial production indexes required by the OECD ‑ total, mining, manufacturing, energy and construction.

Industrial production indexes are compiled in accordance with the Law on State Statistics. In addition, each index has an approved methodology; these are:

• Methodology for formation of indicators and the calculation of the gross output of industrial products (goods, services), approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 202 of 20 December 2017)

• Methodology for the formation of construction statistics indicators, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 245 of 21 October 2016)

• Methodology for calculating the index of industrial production, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 235 of 11 October 2016)

• Methodology for the formation of indicators on the statistics of commercial services, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 32 of 9 February 2016)

• Methodology for the formation of the fuel and energy balance and the calculation of individual statistical indicators characterising the energy sector, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 160 of 11 August 2016)

The BNS compiles the industrial production indexes as part of the overall compilation of the Short‑Term Economic Indicator (STEI). The monthly STEI accounts for 60% of the economy, covering 6 sectors: Agriculture, Industry, Construction, Wholesale and retail trade, Transport and storage, and Telecommunications.

The industrial indexes are mostly based on the statistical surveys: Form 1‑P "Enterprise Report on Production and Shipment of Products (Goods, Services)" (monthly, quarterly, annually); and Form 01‑IP (ind) "Report on Industrial Production (Goods, Services) by Individual Entrepreneur" (once every two years). The two industrial surveys collect data both in value and physical terms. Enterprises are surveyed using a continuous method, and according to Article 8 of the Law on State Statistics, primary statistical data are confidential and are used by state statistical bodies solely for the purpose of producing statistical information.

The monthly form 1‑P is submitted by legal entities and/or their structural and separate subdivisions with the main activity "Industry" (according to the codes of the Kazakh General Classification of Economic Activities - GCEA codes 05-33, 35-39) with a staff number of more than 100 employees (medium and large enterprises), as well as with a staff number of up to 100 employees (small enterprises) with an annual production volume of more than 1 000 million tenge.

The quarterly form 1‑P is submitted by legal entities and/or their structural and separate subdivisions with the main activity "Industry" (GCEA codes 05-33, 35-39) with a staff number of up to 100 employees (small enterprises) (excluding enterprises with an annual production volume of more than 1 000 million tenge), as well as with a secondary activity "Industry" regardless of the number of employees. The annual form 1‑P is submitted by legal entities and/or their structural and separate subdivisions, regardless of the number of employees (large, medium, and small enterprises), with the main and/or secondary activity "Industry" (GCEA codes 05-33, 35-39). Form 01‑IP (ind) statistical reporting is submitted by individual entrepreneurs with the main and/or secondary activity (GCEA codes 05-33, 35-39) and peasant or farm enterprises regardless of the type of activity engaged in industrial production.

For construction, the BNS sees the objective to compile data on the construction industry and commissioned facilities in order to meet the needs of users (the population, enterprises, government agencies, and the international community) with high-quality official statistics. The primary statistical inputs for construction include: Form 1‑KS "Report on Completed Construction Works (Services)" (about 1 000 enterprises); Form 2‑KS "Report on the commissioning of facilities“ (about 110 enterprises); Form 1‑IS "Report on the commissioning of facilities by individual developers“ (about 170 enterprises); and Form D‑004 "Quarterly Household Expenditure and Income Questionnaire".

Administrative data are also used in the compilation of the construction index. Administrative data from state bodies for architectural and construction control and local executive bodies for architecture, urban planning, and construction are sourced.

The basic flow for the survey process of short-term economic statistics is:

• submission of reports by respondents - after the reporting month until the 5th day

• processing of primary statistical data, until the 6th-7th - regional departments

• verification and generation of final data, until the 8th-9th - Information and Computing Centre and Industry departments

• formation of GDP and STEI, until the 10th-15th - Department of National Accounts

When compiling statistical information on industrial production, the BNS uses the following classifications (BNS, n.d.[43]):

• Classification of Enterprise Size by Number of Employees (CDE/KRP)

• Classification of Forms and Types of Ownership (KFS)

• Classification of Organisational and Legal Forms of Management (KOPF)

• Classification of Economic Sectors (CSE/KSE)

• Statistical Classification of Products by Activity (SCPP/SKPP)

• General Classification of Economic Activities (GCEA/OKED)

• Classification of Administrative-Territorial Objects (CATO/KATO)

Quality dimensions and indicators

Accuracy and credibility

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of accuracy and credibility. The BNS uses survey data (via forms) in the compilation of the monthly indexes while also utilising administrative data provided by a variety of institutions. The BNS has established a quality control process for the basic information it gathers using a variety of quality control processes.

The BNS notes that it makes adjustments for incomplete enterprise coverage and missing reports on industrial production - 0.1% of total volume. Production by medium, large, and small industrial enterprises (with annual output exceeding 1 billion tenge), obtained from monthly statistical observations account for 92.2% of total volume.

Coherence

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of coherence. There are “Excel automated procedures” in place and the verification of the completion of primary statistical data is carried out through format-logical and arithmetic-logical controls. According to paragraph 17 of Article 12 of the Law on State Statistics, when producing statistical information to confirm the reliability of primary statistical data, the BNS has the right to request additional information from respondents. In addition, data on oil and gas production are reconciled with the administrative data of the Ministry of Energy of the Republic of Kazakhstan monthly and annually. Another data source used within the framework is the Household Income and Expenditure Survey.

The STEI, which is also released by region, feeds directly into the monthly GDP where it provides 60% of the total economy value providing strong coherence across these macroeconomic statistics.

Alignment with international standards

The BNS uses national methodology, based on legal text, and national classifications (BNS, n.d.[43]), which have been developed in accordance with international statistical standards. For the methodologies these include:

• UN International Recommendations for Industrial Statistics 2008 (UN, 2009[44])

• UN International Recommendations for the Index of Industrial Production 2010 (UN, 2013[45])

• International Recommendations for Energy Statistics (IRES) (UN/Oslo Group on Energy Statistics/Intersecretariat Working Group on Energy Statistics, 2018[46])

• Eurostat Short-Term Business Statistics Methodological Manual (Eurostat, 2002[47])

• UN Industrial Development Organization Practical Guide to Compiling the Index of Industrial Production for Commonwealth of Independent States (CIS) Countries (UN Industrial Development Organization, 2015[48])

For classifications these include: the NACE Revision 2 - Statistical Classification of Economic Activities in the European Community, CPA 2008 - Statistical Classification of Products by Activity in the European Community, and PRODCOM 2008 - List of Products for European Industrial Statistics.

Coverage and relevance

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of coverage and relevance. The BNS compiles and disseminates the industrial production indexes required by the OECD - total, mining, manufacturing, energy and construction. These industrial production indexes meet OECD requirements in terms of variables, detail, frequency, and measurement units. The BNS is encouraged to provide more detailed information on the historical coverage of its industrial production indexes (i.e. available time series length).

In addition to the industrial production indexes required by the OECD, within this subject the BNS also disseminates the following breakdowns: by region and by type of economic activity; per capita; volume of shipped industrial products; and stocks of finished goods. It is also noted that, for construction, more than 20 main output indicators are disseminated.

Timeliness and punctuality

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of timeliness and punctuality. The BNS releases the monthly indexes to the public between the 15th and 17th of the month (i.e. 15-17 days after the reference month). The BNS releases the results according to the schedule for the dissemination of official statistical information (currently covering the period 2025‑2027 (BNS, n.d.[8])) that is publicly available on the institution’s website (BNS, n.d.[49]).

Accessibility

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of accessibility. Statistics are available on a dedicated webpage “Statistics of industrial production” (BNS, n.d.[50]), which includes links to: Publications; Spreadsheets (Excel files); and Dynamic tables. In addition, this dedicated page also includes other direct links to: Methodology; Quality Reports; Contacts; and Feedback and Suggestions. The BNS could consider the possibility of developing an API (application programming interface) where users could download data directly, full time series, without having to use Excel files.

The home page of the BNS also includes a tab for “Industry Statistics” that includes links to dedicated pages for: Statistics of industrial production; Statistics of agriculture, forestry, hunting and fisheries; Transport; Statistics of services; Energy Statistics; Statistics of tourism; Construction statistics; Investments statistics; and Structural statistics.

Interpretability (availability of metadata)

Kazakh industrial production indexes are aligned with OECD best policies and practices in terms of interpretability. The BNS noted the following metadata (BNS, n.d.[51]) covering the compilation of industrial statistics: Methodology for calculating the index of industrial production; and Methodology for the formation of indicators and the calculation of the gross output of industrial products (goods, services). The BNS provides for construction (BNS, n.d.[52]) two metadata documents and for energy (BNS, n.d.[53]) one: Methodology for determining the gross output for the industry "Construction"; Methodology for the formation of construction statistics indicators; and Methodology for the formation of the fuel and energy balance and the calculation of individual statistical indicators characterizing the energy sector.

4.8.3. Retail sales, building permits issued for dwellings and passenger car registrations

Context, data sources and methodology

Excluding industrial production indexes covered in the previous section, short-term economic statistics under review include:

• Retail sales: Total retail trade (Volume); and Total retail trade (Value)

• Building permits issued for dwellings - currently not collected, compiled or disseminated by the BNS. The BNS has indicated that “… data on permits issued for the construction of housing are not used in the compilation of construction statistics. Currently, the Ministry of Industry and Construction of the Republic of Kazakhstan is the authorised body responsible for compiling information on issued permits and submitted notifications.”

• Passenger vehicle registrations

Retail sales are compiled in accordance with the Law on State Statistics. In addition, there is approved methodology:

• Methodology for the formation of indicators for domestic trade statistics, approved by Order of the Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 209 of 15 December 2015)

• Methodology for the formation of indicators on the statistics of commercial services, approved by Order of the Acting Chairman of the Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (Order No. 32 of 9 February 2016)

Statistical information on “The number of motor vehicles in the Republic of Kazakhstan” is compiled and disseminated in accordance with the joint Order “On approval of the rules for information interaction between the Agency of the Republic of Kazakhstan on Statistics dated 7 October 2011 No. 284, and the Ministry of Internal Affairs of the Republic of Kazakhstan dated 10 October 2011 No. 537”. These data are compiled by the BNS on the basis of data provided by the Ministry of Internal Affairs of the Republic of Kazakhstan and published on a monthly and annual basis on the website.

In addition, the BNS has a statistical methodology for the formation of indicators of transport statistics, in accordance with international standards and with the Law on State Statistics.

The above-mentioned statistical information for retail sales and passenger cars is published on a monthly basis on the BNS website.

Retail sales: Total retail trade (Volume); and Total retail trade (Value)

The OECD target series are monthly/quarterly total retail trade based on deflated turnover (i.e. volume) and on turnover (i.e. value of sales) in the retail sector (both non-adjusted and seasonally adjusted series).

The BNS compiles and disseminates retail sales as part of the Short-Term Economic Indicator (STEI) which feeds into the monthly GDP estimate. Retail trade is published annually and monthly on the basis of two statistical surveys in accordance with the Statistical work plan of the BNS. Statistical survey form 1‑VT “Report on Sales of Goods and Services”: Data is collected annually, the deadline for submission by respondents is until 15 March (inclusive) after the reporting period. Form 2‑trade “Report of Sales of Goods and Services” (monthly): Data are collected monthly, the deadline for submission by respondents is until the 3rd (inclusive) after the reporting period.

In both the monthly and the annual survey, large and medium enterprises are surveyed on a census basis (the BNS call this “continuous accounting”). Small enterprises are surveyed on a sample basis (selective accounting) and make up around 10% of the monthly survey and 30% of the annual survey. On a monthly basis, data for individual entrepreneurs comes from their latest annual statistical observation (settlement), while annually these enterprises are sampled (selective accounting). Finally, on a monthly and annual basis for enterprises with a secondary type of activity "Trade", structural statistics and other relevant surveys in various areas of statistics are used in the compilation process.

Building permits issued for dwellings

The OECD collects building permits issued for dwellings based on the number of construction permits issued for new dwellings (residential buildings) on a monthly or, at least, quarterly basis.

At present, the BNS does not collect, compile or disseminate these data. The Ministry of Industry and Construction of the Republic of Kazakhstan is responsible for these data.

Passenger car registrations

The OECD collects passenger car registrations based on the number of new passenger cars registered for the first time on a monthly or, at least, quarterly, basis. These data are compiled by the BNS based on data provided by the Ministry of Internal Affairs.

Quality dimensions and indicators

The BNS does not compile the OECD target series on building permits issued for dwellings. Unless specified otherwise, the following assessment is restricted to the short-term economic statistics as they pertain to retail trade sales and passenger car registrations.

Accuracy and credibility

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of accuracy and credibility. As in other OECD Member countries, short‑term economic statistics are mostly complied in the national statistics office using national surveys with input from a range of other government institutions (administrative data).

For retail trade sales, the compilation process is that followed in general for the Short-Term Economic Indicator (STEI) as outlined above for industrial production indexes. Registrations of passenger cars are provided by the Ministry of Internal Affairs along with a wide range of other administrative data related to transport statistics.

Coherence

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of coherence. For retail trade sales the BNS notes that there are “Excel automated procedures” in place and that the verification of the completion of primary statistical data is carried out through format-logical and arithmetic-logical controls. According to paragraph 17 of Article 12 of the Law on State Statistics, when producing statistical information to confirm the reliability of primary statistical data, the BNS has the right to request additional information from respondents.

Retail trade sales are part of STEI, which is also provided by region and feeds directly into the monthly GDP where it provides 60% of the total economy value providing strong coherence across these macroeconomic statistics.

Alignment with international standards

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of alignment with international standards.

The BNS uses national methodology, based on legal text, and national classifications. It notes that its methodologies and classifications have been developed in accordance with international statistical standards. For the methodologies these include:

• International recommendations on statistics of retail and wholesale trade 2008, developed by the Statistical Committee of the United Nations (UN)

• Methodological recommendations for statistical monitoring of retail trade turnover and calculation of its physical volume index, developed by the Interstate Statistical Committee of the Commonwealth of Independent States (CIS Statistical Committee) in 2011

These methodologies use concepts and definitions as defined in national law, for example - Domestic trade statistics reflect the characteristics and activities of structural units related to section G "Wholesale and retail trade, repair of cars and motorcycles" of the General Classification of Economic Activities (GCEA), regardless of their size, legal form and forms of ownership.

Coverage and relevance

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of coverage and relevance. The BNS compiles and disseminates the retail trade statistics required by the OECD - both in value and volume - in terms of detail, frequency and units. For passenger car sales, the BNS has assured the OECD that these data are available and compiled on the basis of data provided by the Ministry of Internal Affairs. For both these statistics, the BNS also disseminates a range of breakdowns, for example by region for retail trade sales and by vehicle type and engine size for passenger car registrations.

Timeliness and punctuality

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of timeliness and punctuality. Passenger car registrations are released monthly on the 15th day of each month following the reference period (T+15 days). Domestic trade statistics (retail trade sales) are released on the 12th or 13th day each month following the reference period.

As noted in the industrial indexes section previously, the BNS has an online release calendar (BNS, n.d.[49]), which provides release dates in accordance with the schedule for the dissemination of official statistical information (currently covering the period 2025‑2027 (BNS, n.d.[8])).

Accessibility

Kazakh retail trade sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of accessibility. These statistics are available on dedicated webpages “Transport” (BNS, n.d.[54]) and “Statistics of domestic trade” (BNS, n.d.[55]), with both pages including links to: Publications; Spreadsheets (Excel files); and Dynamic tables. In addition, these pages also include other direct links to: Methodology; Quality Reports; Contacts; and Feedback and Suggestions. The BNS could consider the possibility of developing an API (application programming interface) where users could download data directly, full time series, without having to use Excel files.

Interpretability (availability of metadata)

Kazakh retail sales and passenger car registrations statistics are aligned with OECD best policies and practices in terms of interpretability. The BNS noted the following metadata covering the compilation of these statistics: Methodology for the formation of indicators for domestic trade statistics (BNS, n.d.[56]); and Methodology for the formation of indicators of transport statistics (BNS, n.d.[57]). In addition, the Excel files providing the data include a tab named “Methodological explanations” which provides definitions for the variables.

Regarding the dissemination of monthly passenger car registrations, the BNS has noted that: “The information is based on data from the Ministry of Internal Affairs of the Republic of Kazakhstan on the total number of registered vehicles. Due to technical work on updating the vehicle database on the Ministry of Internal Affairs side, detailed information on vehicle characteristics (engine size, year of manufacture and fuel type) will be published in subsequent publications.”

These metadata re also noted in the Excel file. On an annual basis a lot more detailed data are provided on vehicles in Kazakhstan, e.g. by type, region, engine size, etc.

4.9. Short-term financial statistics

4.9.1. Assessment and recommendations

Short-term financial statistics under review include financial statistics on interest rates, monetary aggregates and share price index. In Kazakhstan, short-term financial statistics on interest rates and monetary aggregates are compiled and disseminated by the National Bank of the Republic of Kazakhstan (NBK), and on the share price index by the Kazakhstan Stock Exchange (KASE) (KASE, n.d.[58]). The assessment process included a review of existing national and international sources of short-term financial statistics, as well as the collection of data and metadata provided by Kazakhstan through OECD questionnaires, and information gathered during an OECD mission to Astana in November 2025.

Kazakhstan undertakes, compiles and disseminates some of the short-term financial statistics required by the OECD although the 3-month and 10-year interest rates do not exactly match OECD requirements. Additionally, some questions exist on the openness of the purchase and sale of shares in the exchange of the share price index.

The available data are easily accessible via the NBK and KASE websites, together with methodology and metadata.

Based on this assessment, Kazakh short-term financial statistics align with OECD best policies and practices in terms of accuracy and credibility, coherence, alignment with international standards, timeliness, accessibility, and interpretability. The NBK and KASE are encouraged to consider the following recommendations and improvements to further align with OECD best policies and practices in terms of punctuality and coverage and relevance:

• Improve punctuality, by developing a 12-month-ahead advance release calendar updated on a rolling basis. At present, the NBK release calendar only appears to show one month in advance.

• Improve coverage and relevance, by considering the compilation and dissemination of seasonally adjusted monetary aggregates.

4.9.2. Context, data sources and methodology

The short-term financial statistics under review cover:

• Interest rates: Overnight interbank rate; Short-term rate (3-month interbank offer rate); and Long‑term rate (10‑year government bonds), set by the NBK.

• Monetary aggregates: Narrow Money, M1; and Broad Money, M3, compiled by the NBK.

• Share price index, compiled by the KASE.

The compilation and dissemination of the monetary statistics are governed by the terms and conditions of the Law of the Republic of Kazakhstan “On the National Bank” (Articles 7 and 8) and the Law “On Banks and Banking Activity” (Article 54), in particular identifying monetary and banking statistics as a key function for NBK, to which banks must provide any requested information about funds, deposits, loans granted, banking operations and data related to banking secrecy. The NBK compiles monetary statistics, including monetary aggregates, based on the IMF Monetary and Financial Statistics Manual and Compilation Guide (2017) (IMF, 2017[59]).

The law provides the following guiding principles for NBK:

1. consistency and comparability of official statistics with generally accepted international standards, classifications and methods

2. professional independence and autonomy in the implementation of statistical activities

3. ensuring equal user access to official statistical information

4. confidentiality and use of primary statistical data exclusively for statistical purposes

5. use of all types of information sources taking into account quality, timeliness, costs and burden on respondents

6. reliability, scientific validity, timeliness of provision and public availability of official statistical information

7. ensuring the safety and security of statistical information, primary statistics, administrative and alternative data

The data source forming the basis for the NBK monetary statistics is the monthly balance sheets of commercial banks (referred to in the law as “second-tier” banks) and NBK administrative data. Commercial banks submit monthly balance sheets and statistical reporting forms, the balance sheet accounts of the NBK and second-tier banks are grouped on the basis of residence into external and internal assets/liabilities.

Banks submit their itemised balance sheet reports electronically, which are then entered into the NBK’s Automated Information System. The data of the detailed balance sheet reports submitted by banks are subject to automated cross-checking with additional information on loans and deposits. The "Statistics" system automatically outputs the report on error with indication of any discrepancies between the data submitted by banks.

The detailed balance sheet data are subject to reconciliation with the second-tier banks' balance sheet data. The NBK receives data on itemised balance sheets and groups accounts in undertaking a monthly “Monetary Survey” of depository institutions and calculates monetary aggregates.

The KASE is a private entity that started operation on 17 November 1993. It is the only multifunctional exchange platform in the Republic of Kazakhstan for trading stocks. The KASE conducts trading in shares, corporate bonds, government securities of the Republic of Kazakhstan and government securities of foreign countries, bonds of international financial organisations, sovereign and corporate Eurobonds, depositary receipts, securities of investment funds and exchange traded funds. The KASE holds the valid license No. 4.2.3/1 to carry out activities on the securities market dated 19 July 2012 issued by the Committee for Control and Surveillance of the Financial Market and Financial Organisations of the National Bank of the Republic of Kazakhstan.

Interest rates: Overnight interbank rate; Short-term rate (3-month interbank offer rate); and Long-term rate (10-year government bonds)

Overnight interbank rate

Call money and day-to-day loans play a predominant role in interbank money dealings and between banks and money market dealers and overnight interbank rates describe the call-money rates. The key instrument of the NBK’s monetary policy is the base rate (pre-2021 known as the “official refinancing rate”). By setting the level of the base rate, the NBK determines the indicative value of the interbank short‑term rate in order to achieve the objective of ensuring price stability in the medium term. The base rate defines the centre of the interest-rate corridor, with:

• Ceiling: NBK’s overnight lending (repo) facility rate

• Floor: NBK’s overnight deposit facility rate

• The NBK’s liquidity operations aim to keep the Tenge Overnight Index Average (TONIA) close to the base rate - hence the TONIA serves as the operational target rate

The base rate and its associated metadata are published on the NBK website via the monthly Statistical Bulletin of the National Bank publication (NBK, n.d.[38]).

Short-term interest rate

The 3-month interbank offer rate is attached to loans given and taken amongst banks for any excess or shortage of liquidity over several months. The NBK has noted the following:

• There is no official 3-month interbank offer rate published by the NBK or KASE. The closest substitutes are short-term deposit auction rates, repo rates, 1-month NBK notes (previously 3‑month NBK notes were also issued), or 3-month government bond yields.

Long-term interest rate

Long-term interest rates are principally yields on the secondary market of government securities. Generally, the yield is calculated at the pre-tax level and before deductions for brokerage costs and commissions and is derived from the relationship between the present market value of the bond and that at maturity, taking into account also interest payments paid through to maturity. The NBK has noted the following:

• There is no exact corresponding series for 10-year government bonds, but the OECD can use MEUKAM for longer dated issues. The following summarises the tenors for fixed coupon government securities at the moment of issuance (series do not change during the life of the bond, series name):

  • State short–term treasury obligations - MEKKAM - Up to 12 months

  • State medium–term treasury obligations - MEOKAM - Up to 5 years

  • State long–term treasury obligations - MEUKAM - Above 5 years

The NBK outlines that at the moment government bonds (quoting their ISIN9) closest to the 10‑year mark on the market have the following years to maturity: KZKD00001269 - 9.3 (years); KZKD00001186 - 10.8 (years).

Additionally, the KASE compiles average yield-to-maturity indicators for Kazakhstan Government Bonds (KZBG), on non-indexed government securities issued by the Ministry of Finance of the Republic of Kazakhstan. The indicator “KZGB_Yl” is for bonds with a term of more than five years (calculated on the results of each trading day). The NBK has suggested that the KZGB_YI is comparable to a “long-term benchmark yield” or “10-year government bond yield” in other markets.

Monetary aggregates: Narrow Money (M1) and Broad Money (M3)

The data required for the formation of monetary statistics, including monetary aggregates, are submitted by reporting organisations in electronic form through the NBK’s Automated Information System. The reporting periodicity is monthly. Monthly reporting to the “Monetary Survey” is mandatory for respondents with the individual information obtained remaining confidential. All aggregate statistics generated by the NBK on the basis of received reports are publicly available on the website of the NBK. The use of sampling methods is not required, since all respondents submit reports on a mandatory basis (i.e. a census survey) in accordance with the legislation of the Republic of Kazakhstan.

The OECD target definitions are as follows: Narrow money (M1) covers all coins and notes plus personal money in current accounts that may be used as means of payment (currency, i.e. banknotes and coins, plus overnight deposits). It refers to monthly stock figures, expressed in current price (“nominal” terms), measured as end of period stock data. Broad money (M3) comprises intermediate money (M2) and marketable instruments issued by the monetary financial institutions sector. Certain money market instruments, in particular money market fund shares/units and repurchase agreements are included in this aggregate. A high degree of liquidity and price certainty make these instruments close substitutes for deposits. As a result of their inclusion, M3 is less affected by substitution between various liquid asset categories than narrower definitions of money and is therefore more stable.

The NBK defines monetary aggregates as follows: M0 (currency in circulation, i.e. money outside of the banking system); M1 equal to M0 plus transferable deposits of non-bank legal entities and the population in tenge; M2 equal to M1 plus other deposits in tenge and transferable deposits of non-bank legal entities and the population in foreign currency; and M3 (broad money) equal to M2 plus other deposits of non‑bank legal entities and the population in foreign currency.

Share price index

The OECD target series is for a national all-share or broad share price index (price indices and not return indices, i.e. they exclude dividend payments). The monthly indices are, where possible, averages of daily closing quotations.

The “KASE Index”, as compiled daily by the KASE, represents changes in prices of shares included in its representative list with account taken of the level of capitalisation of their issuers and the share of stocks in free float. The KASE Index is a benchmark of the Kazakhstan stock market, the value of which is determined based on the most liquid shares of the largest and most financially stable Kazakh companies that are included in the official list of the KASE.

4.9.3. Quality dimensions and indicators

Accuracy and credibility

Kazakh short-term financial statistics are aligned with OECD best policies and practices in terms of accuracy and credibility. The sources used to compile short-term financial statistics in Kazakhstan and the institutions that compile them match good practice generally used by OECD Member countries to measure these subjects.

The base rate is the main instrument of the NBK's monetary policy, which makes it possible to regulate nominal interbank interest rates in the money market. By setting the level of the base rate, the NBK determines the target value of the targetable (target) interbank short-term money market rate to achieve the goal of price stability in the medium term.

The NBK compiles monetary aggregates based on administrative data from its own departments and data collected through its Monetary Survey. The information is validated using available data sources. Revisions to monetary aggregates related to the reclassification of assets, changes in methodology and detection of discrepancies when comparing similar indicators obtained from other sources of information are published during the reporting period, or in the following reporting periods.

The KASE calculates the share price index based on trading statistics.

Coherence

Kazakh short-term financial statistics are aligned with OECD best policies and practices in terms of coherence. The NBK compiles these statistics from administrative and census surveys leading to a high level of coherence; additionally, interest rates do not get revised. The NBK notes that the reporting provided by second-tier banks is controlled and this ensures reliability. In particular, control over reporting is exercised through intra-form (the simplest example: assets = liabilities) and inter-form control (i.e. the data of one report must be comparable with data from another report).

Alignment with international standards

Kazakh short-term financial statistics are aligned with OECD best policies and practices in terms of alignment with international standards, ensuring international comparability. The NBK compiles its short‑term financial statistics within the framework of the IMF Monetary and Financial Statistics Manual and Compilation Guide, while there is no specific guideline or standard for the production of share price indexes. The IMF 2024 Article IV Consultation with the Republic of Kazakhstan gave Kazakhstan a “B” for its “Data Adequacy Assessment Rating”, namely that the data provided to the IMF have some shortcomings but are broadly adequate for surveillance. None of the shortcomings identified in the report related to interest rates or monetary aggregates.

Coverage and relevance

Kazakh short-term financial statistics are broadly aligned with OECD best policies and practices in terms of coverage and relevance. The coverage of short-term financial statistics compiled by the NBK, for the available variables, meet the OECD requirements in terms of detail, frequency, measurement units, and historical coverage, although seasonally adjusted monetary aggregates are not calculated.

It should be noted that at present Kazakhstan cannot provide the OECD with two short-term financial variables, namely: a 3-month interbank offer rate; and, a 10-year government bond rate. The government does issue government bonds around the 10-year mark, which are traded on the secondary market, but does not have a specific 10-year government bond. Additionally, it should be noted that at present there are some OECD Member countries who also do not have a 3-month interbank offer rate.

In terms of variable, frequency and historical coverage, Kazakh short-term financial statistics are available as follows:

• Interest rates:

  • Overnight interbank rate (base rate) - monthly from January 2015

• Monetary aggregates:

  • M1 monetary aggregates - monthly from January 1994

  • M3 monetary aggregates - monthly from January 1994

• Share price index: the KASE was officially founded in November 1993. The initial value of the index was fixed as of 28 September 2007.

Timeliness and punctuality

Kazakh short-term financial statistics are generally aligned with OECD best policies and practices in terms of timeliness and punctuality. The delay between the end of the reference period and the publication compares well with OECD best policies and practices, notably:

• Interest rates:

  • Overnight interbank rate (base rate) is daily.

• Monthly monetary aggregates are available on 11th workday10 after the reference month.

• Share price index is daily.

Release dates are available in an online one-month ahead advance release calendar published on the NBK website (NBK, n.d.[36]). The calendar provides the release dates of quarterly and monthly publications of the NBK. An advance release calendar is available (NBK, n.d.[35]), but the NBK could investigate its ability to develop a 12-month-ahead advance release calendar updated on a rolling basis.

Accessibility

Kazakh short-term financial statistics are aligned with OECD best policies and practices in terms of accessibility, being accessible online and allowing users the ability to collect these statistics directly from the relevant websites. For example, the NBK has a “Statistics” tab available on the main institutional webpage which allows access to the required statistics, and where also there is a link to “Special Data Dissemination Standard (SDDS)” which provides links to data on interest rates and monetary aggregates, The required statistics in most cases can be downloaded in Excel format.

Interpretability (availability of metadata)

Kazakh short-term financial statistics are broadly aligned with OECD best policies and practices in terms of interpretability and quality of metadata. The NBK has many pages dedicated to metadata both on its main website and in its statistical publications such as the monthly Statistical Bulletin. In the “Statistics” tab on the main website there is a link to “Special Data Dissemination Standard (SDDS)” which in turns leads to metadata and methodology documents on interest rates and monetary aggregates.

4.10. Business Tendency Surveys and Consumer Opinion Surveys

4.10.1. Assessment and recommendations

The National Bank of the Republic of Kazakhstan (NBK) compiles monthly Business Tendency Surveys (BTS) and commissions FusionLab (formerly GfK Kazakhstan) to undertake consumer inflation expectation surveys (with questions normally found in Consumer Opinion Surveys (COS)).

Monthly and quarterly BTS and COS (Inflation expectations) statistics are published on the NBK’s data dissemination website with limited metadata. BTS statistics are included and used in the quarterly Market Review‑Outcomes of Enterprise Surveys in the Economy (NBK, n.d.[60]) publication and the monthly Kazakhstan Business Activity Index (BAI) (NBK, n.d.[61]) publication with COS data used in the monthly Inflation Expectations (NBK, n.d.[62]) publication.

At present, it is not possible to assess whether BTS and COS compiled by the NBK (and commissioned to FusionLab using the “Population Survey”) are aligned with international standards such as the OECD Handbook on Business Tendency Surveys (OECD, 2003[63]) and with OECD best policies and practices in terms of accuracy and credibility, coherence, coverage and relevance, timeliness and punctuality, accessibility, and interpretability. The NBK is encouraged to consider the following aspects to improve alignment with international standards, coverage and relevance, coherence, accuracy and interpretability:

• Improve coverage: The NBK compiles and disseminates a very large and broad range of BTS and COS statistics, this is impressive. However, while there is clearly some overlap with the OECD Recommended Target Indicators, from a first inspection it seems that it would require a major overhaul of both the questions (and possible responses offered) and compilation methods (e.g. from diffusion to net balances) by the NBK to align their indicators with those required by the OECD.

• Improve alignment with international standards in the compilation of both BTS and COS statistics.

• Improve coherence, by specifying which classifications are used in the compilation of the statistics.

• Improve interpretability and accuracy, by providing a single “Methodological” document, with metadata and information about compilation process, for each survey/publication (BAI, Market Review, Inflation Expectations).

4.10.2. Context, data sources and methodology

Article 70 of the Law on the National Bank allows the NBK to collect information from respondents. However, the BTS compiled by the NBK is not part of the national statistical system and information collected is mostly used for internal needs.

Population surveys on inflation expectations commissioned by the NBK have been conducted on a monthly basis since January 2016 by FusionLab (formerly GfK Kazakhstan).

Business Tendency Surveys (BTS)

The NBK has monthly and quarterly BTS that feed into the monthly Kazakhstan BAI and quarterly Market Review‑Outcomes of Enterprise Surveys in the Economy. The questionnaires for these surveys are online in Excel format (only in Russian). Both publications provide a brief overview of the subject on their home pages, along with links to the actual publication (and in some cases Excel files). All data can also be found on the NBK’s data platform website (NBK, n.d.[39]) under the tab “Data > Survey Results”.

The quarterly survey, launched in 2001, covers enterprises of all sizes in all sectors (except financial and social) in all 20 regions of Kazakhstan. Around 3 450 enterprises participated in the Q1 2025 round. The questionnaire includes around 60 qualitative and quantitative indicators, with examples shown in Annex 4.F (e.g. 13 questions for construction and 14 for services). Results are published quarterly in the Market Review which presents leading and coincident indicators on the state of the business climate in the real sector. Most results are expressed as a diffusion index, calculated as the sum of positive responses plus half of “no change” responses; a value above 50 indicates a positive change and below 50 a negative change.

The monthly survey, started in 2016, is conducted primarily for compiling the BAI, which is an indicator that tracks real sector activity and business cycle development based on enterprises’ reported changes. The results are calculated as an aggregated change in the selected indicators, using diffusion indices and are seasonally adjusted using the X-12-ARIMA method with a multiplicative decomposition of the seasonality of the series.

Overall, the monthly and quarterly surveys cover key indicators such as production volume, orders, employment, salary, investment plans, price expectations, etc. and some financial report indicators. Respondents are informed of the survey’s purpose and of confidentiality guarantees in the invitation letter.

Consumer Opinion Survey (COS)

The Consumer Opinion Survey (COS), conducted by FusionLab, uses a mixed-mode approach, combining telephone and online questionnaires. The survey covers the resident adult population aged 18 and above in regional centres and cities of national significance. The sample is randomly drawn and stratified by gender, age, ethnicity, and region in line with official demographic statistics, with around 1 500 respondents per monthly wave. The questionnaire includes 30 monthly and quarterly questions (see Annex 4.G), covering: inflation expectations, consumer and saving behaviour of the population; and the economic outlook of the population.

4.10.3. Quality dimensions and indicators

Accuracy and credibility

Kazakh BTS and COS are partially aligned with OECD best policies and practices in terms of accuracy and credibility. Business tendency and consumer opinion measures are based on survey data, like in OECD Member countries. Given the limited amount of information provided by the NBK it is not possible to fully assess this quality dimension.

Representativeness of the BTS monthly and quarterly sample is assessed by the relative share of sales income of enterprises which are included in official statistics. At present the share of sales income of these enterprises in the surveys accounts for about 35%.

An automatic software logic control is performed when entering data (missing data, contradictory responses, sudden changes in dynamics). Outliers are excluded when calculating financial indicators using software and Excel. Monthly revisions are possible due to seasonal clearing and adjustments to real indicators used in the compilation.

FusionLab, which conducts the household inflation expectations survey, provides the NBK with the calculated values of the relevant indices.

Coherence

It is not possible to determine if BTS and COS statistics align with OECD best policies and practices in terms of coherence.

It is clear from the information provided and referencing the publications that the NBK breaks the information down by classifications such as industry and region; however, no mention is made of classifications used. For both the Market Review and Inflation Expectations publications, official statistics from the BNS are used in the compilation of some indicators.

Alignment with international standards

It is not possible to determine if BTS and COS statistics, as compiled by the NBK and FusionLab, adhere to international statistical standards and meet OECD best policies and practices in terms of alignment with international standards.

The main references for the compilation of BTS and COS are the OECD Handbook on Business Tendency Surveys (2003) (OECD, 2003[63]), the UN Handbook on Economic Tendency Surveys (2016) (UN, 2016[64]) as well as the methodology in the Joint Harmonised EU Programme of Business and Consumer Surveys User Guide (European Commission, 2016[65]). These are not mentioned by the NBK in the OECD questionnaire or in the methodology and metadata text available.

As part of the quarterly Market Review a “Composite Leading Indicator” is compiled and disseminated. The methodology notes that the indicator reflects a summarised evaluation of opinions of managers of the interviewed enterprises regarding the existing and anticipated situation in their enterprises and possesses the forward-looking feature of the real GDP behaviour for 1-2 quarters. The metadata note that the construction of indicator is based on the OECD methodology “OECD System of Composite Leading Indicators, Methodology Guideline” (OECD, 2012[66]). Additionally, on a monthly basis as part of the BAI, a “Business Climate Index” is compiled, which according to the metadata is calculated according to the methodology of the Ifo Institute.

Coverage and relevance

BTS and COS compiled by the NBK do not appear to meet OECD best policies and practices in terms of variable coverage and measurement units, while they do for historical and geographical coverage.

Monthly BTS time series are available from November 2016, and quarterly time series are available from 2001. Inflation expectations data (COS statistics) are available from January 2020.

In terms of variable coverage, Kazakh BTS statistics do not cover the OECD Recommended Target Indicators (OECD, 2025[67]) as required by the OECD. For BTS, the OECD requires four confidence indicators: Industry; Retail Trade; Construction; and Services. These confidence indexes correspond to the arithmetic average of the net balances for a certain number of sub-components (questions in the survey), for example for construction these are the total order books level; and employment future tendency. The questions associated with these indicators are:

• Book Orders - Level - Excluding normal seasonal changes, do you consider that the present level of your total order book or production schedules in volume terms is: [(1) Above normal, (2) normal, (3) below normal];

• employment - Future Tendency - Excluding normal seasonal changes, what changes do you expect during the next 3-4 months with regards to the number of people employed in your company: [It will (1) increase, (2) remain the same, (3) decrease].

The monthly BAI includes the following variables: BAI by economy; Production; Service; Construction; Mining; and Trade.

On a quarterly basis there is a lot more data available for a wider range of variables and coverage that could be relevant for the OECD, including: Inventories; Production volume; Demand for goods/service; and Capacity utilisation level. For most of these variables a detailed list of diffusion indexes is then available for a wide range of industry sectors. For example, for the “Production volume” variable there are 28 indexes available, covering 14 industries, with each industry (Agriculture, Mining, Manufacturing, etc.) each having two diffusion indexes - Production volume; Production volume expectations - see Annex 4.H.

For Inflation Expectations (COS), FusionLab undertakes the “Population Survey” and these results are used to compile the indicators. The full list of the questions is available in Annex 4.G. From preliminary review, some overlaps exist with the questions asked in the survey, and the possible responses. The NBK is encouraged to align the questions in the survey with the OECD Handbook on Business Tendency Surveys.11

In addition, the NBK calculates a “Consumer Sentiment Index” as the average value of private indices:

• changes in personal financial situation over the past year

• changes in personal financial situation in the coming year

• prospects for the development of the country in the coming year

• prospects for the development of the country in the next 5 years

• large purchases

In term of geographical coverage, BTS questionnaires are sent to all 20 regions of Kazakhstan, while for COS the sample is stratified by region. OECD requirements in terms of geographical coverage are therefore met.

Time series in the monthly BAI publication are seasonally adjusted.

The NBK is encouraged to investigate how the monthly and quarterly surveys could be adapted to meet OECD requirements in terms of coverage, including measurement units as the OECD requires “net balances”, the difference between positive and negative answers in percentage points of total answers. The NBK compiles results for the quarterly Market Review in the format of a “diffusion index”. Net balances can be derived from the diffusion indices.12

Timeliness and punctuality

Timeliness and punctuality of BTS and COS are not aligned with the OECD best policies and practices.

The BTS quarterly survey is conducted in the month after the reference period; results are published two weeks later (by the 15th day of the second month after the reference quarter). The BTS monthly survey is conducted before the 25th day of the reference month, and results are published in 10 days (by the 4th day after the reference month).

The NBK has a public “Schedule of Release of statistical information”, namely a public release calendar (NBK, n.d.[36]), available on its website. There are two issues with this release calendar, firstly, and as noted by the NBK in the OECD questionnaire, this online release calendar does not include information for BTS or COS (and this appears to be the case after examination). Secondly, the release calendar only includes release dates for the next month. Both points are in no way aligned with and do not meet OECD requirements.

Accessibility

Kazakh BTS and COS comply with the OECD best policies and practices in terms of accessibility.

The NBK maintains an Open Data Repository (NBK, n.d.[39]) which provides interactive tables/graphs, search functions, and individual tabs to the NBK disseminated statistics. BTS and COS data can be found in the “Survey Results” tabs - “Inflations expectations” and “Enterprise Monitoring”. There are options to download all data in various formats (CSV, PDF, JSON and XLSX) choosing variables and series lengths. Additionally, there is a “Methodology” option, however at present this states the following text “There is no data to display yet.”

For BTS statistics, both the quarterly Market Review and the monthly BAI maintain their own publication web pages where both the publication (in PDF) and data (in Excel) can be found. Both publications include many graphs, figures and tables.

For COS statistics, there is a webpage dedicated to “Inflation expectations” which includes the publication (in PDF) and an Excel file with all the data. Like with the BTS publications, the COS publication includes many graphs, figures and the final page is dedicated to methodology.

Interpretability (availability of metadata)

Kazakh BTS and COS are partially aligned with the OECD best policies and practices in terms of interpretability. BTS metadata are in the quarterly Market Review and the monthly Kazakhstan BAI publications and accompanying Excel files, although they do not cover all methodological aspects, are fragmented, and do not provide a clear and simple overview of the compilation process.

In case of COS, limited metadata and methodology are included in the publication, the accompanying Excel file, and in the document “Methodological aspects of calculating median inflation expectations” (NBK, n.d.[68]). The NBK shared two working papers related to inflation expectations:

• “Identification of key consumer categories of goods and services as factors in the formation of perceived inflation” (NBK, 2025[69])

• “Determination of the Optimal Interval Size for Calculating the Median of Inflation Expectations” (NBK, 2024[70])

4.11. Income distribution statistics

4.11.1. Assessment and recommendations

The OECD collects income distribution indicators through internationally comparable frameworks, including measures of household income, poverty, and inequality. These statistics support monitoring living standards, assessing inclusiveness of growth, and informing social and fiscal policy.

Kazakhstan has developed a relatively strong statistical foundation for measuring income distribution, supported by the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) and strengthened by recent efforts to modernise data sources and improve data quality. The system relies primarily on household budget survey data complemented by administrative and emerging alternative data sources.

The household budget survey provides nationwide coverage and collects detailed information on income, expenditures, and poverty. Approximately 12 000 households are interviewed annually using a sampling frame based on the national housing registry, ensuring coverage of urban and rural areas and enabling regional disaggregation.

The review confirms strong technical capacity and commitment to innovation, including pilot work using administrative records and bank transaction data, as well as interest in integrating digital data sources to improve accuracy and timeliness. However, further work is needed to strengthen representativeness, methodological transparency, and comparability of inequality measures.

Four priority areas for improvement for the BNS were identified:

• Strengthen integration and methodological transparency. The planned integration of survey, administrative, and bank data represents a significant step forward. Continued efforts are needed to define common methodological scripts, strengthen metadata, and develop validation frameworks to ensure consistent and comparable results across sources.

• Improve coverage and representativeness. Income from informal activities, self-employment, and high-income households remains underrepresented. Further efforts are needed to improve completeness and ensure statistical representativeness across income groups.

• Enhance coherence with national accounts. Reconciliation between survey-based income and national accounts is progressing, but clearer documentation of reconciliation methods and adjustments is needed to improve transparency and user confidence.

• Expand analytical outputs and comparability. Beyond the Gini coefficient, publishing additional inequality indicators (e.g. S20/S80 ratios, decile ratios) would enrich analytical insights and improve international comparability.

The BNS is encouraged to continue collaboration with the OECD to enhance methodological transparency, improve international comparability, and strengthen analytical outputs on income distribution. OECD technical assistance could support integration of alternative data sources and harmonisation of metadata and quality frameworks.

4.11.2. Context, data sources and methodology

Income distribution statistics in Kazakhstan are primarily produced by the BNS through the household budget survey and complementary administrative sources. The system aims to measure income distribution, poverty, and living standards across the population. Survey-based income statistics include nominal per capita income, poverty measures, and inequality indicators such as the Gini coefficient. The household budget survey is covered in more details in the chapter on statistical infrastructure.

Kazakhstan is progressively integrating alternative and administrative data sources to modernise income distribution statistics, which has a potential to be a major innovation in Kazakhstan’s income statistics. Agreements are being prepared with financial institutions to use bank data for validation, while the Digital Family Map is being expanded for statistical use, including estimation of median income and validation of poverty indicators. The Digital Family Map integrates administrative records (e.g. salaries, pensions, social benefits). The system supports social protection targeting and statistical validation.

Kazakhstan plans to integrate bank data and administrative sources into income measurement, develop mobile applications for digital data collection, reduce the implementation burden of surveys, and enhance data accuracy and timeliness. Wealth distribution measurement is not yet a priority but may become relevant as financial assets expand.

4.11.3. Quality dimensions and indicators

Accuracy and credibility

The BNS aspires to combine survey, bank and administrative data to advance income measurement. Administrative and alternative data have the potential to improve accuracy of transfer income measurement, but caution is needed with representatives of these data across different income cohorts. Accuracy is supported through multiple layers of validation and quality control.

The household budget survey uses digitalised data collection procedures. The survey covers approximately 12 000 households annually. Households maintain expenditure and income diaries, and interviewers follow a structured schedule to ensure consistent data collection. Regional supervisors and headquarters staff perform validation checks, and lessons learned are used to improve interviewer training and data quality. Each interviewer follows a fixed schedule and visits approximately 30 households, ensuring consistent panel coverage and data quality. Non-response remains a challenge, particularly in large cities; imputation is applied only for food consumption, while income inconsistencies are clarified with local authorities.

Kazakhstan is piloting the use of bank transaction data from a major national bank covering a large share of the population. These data allow estimation of income levels and structure, self-employment income, social transfers, and consumption patterns. Alternative data are used for validation and may reduce survey burden in the future. Capital income and dividends are emerging but remain relatively limited.

Coherence

The BNS aims to ensure coherence across data sources by comparing survey data with national accounts and bank transaction data. Differences across sources are analysed to improve accuracy; for example, bank data capture service expenditures more fully, while survey data better reflect food consumption patterns. Regional adjustments account for differences in economic structure and the shadow economy. Survey and bank data show broadly consistent expenditure structures, though services and self-employment income differ. A macro-level income estimates are used to complement survey-based measures. Clearer documentation of reconciliation methods and adjustments is needed to improve transparency and user confidence.

Standard scripts and metadata protocols are planned to ensure harmonised data use across institutions and continued efforts are needed to define common methodological scripts, strengthen metadata, and develop validation frameworks to ensure consistent and comparable results across sources.

Alignment with international standards

The BNS conducts the household budget survey in accordance with internationally recommended standards. Equivalence scales used for household adjustments are based on national research and benchmarked against international studies.

Kazakhstan calculates and publishes the Gini coefficient quarterly, providing regular monitoring of income inequality. Inequality measures are derived from equivalised household income using national equivalence scales benchmarked against international studies. Poverty depth and severity indicators are considered to complement inequality measures. Regular inequality measurement broadly aligns with international practice. However, a broader inequality metrics would deepen the analysis. Beyond the Gini coefficient, publishing additional inequality indicators (e.g. S20/S80 ratios, decile ratios) would enrich analytical insights and improve international comparability.

Coverage and relevance

Kazakhstan produces income distribution statistics primarily through the household budget survey, using a nationally representative sample covering all regions. The survey collects detailed information on household income, expenditures and poverty indicators. Income estimates include wages, social transfers, self-employment income and other monetary resources.

Kazakhstan publishes both nominal and real income indicators, enabling monitoring of income dynamics over time. A macro-level nominal per capita income aggregate is produced to complement survey data and ensure macroeconomic consistency. This measure integrates macroeconomic information to capture income components not fully observed in household surveys. The combined micro–macro approach has a potential to strengthen consistency and completeness of income distribution statistics. Nominal income estimates incorporate regional economic differences and shadow economy shares.

Income indicators are disaggregated by region, urban-rural areas, residence, and demographic characteristics. Regional differences reflect structural economic characteristics, including varying shares of informal activity and sectoral composition. Financial investment income is higher in major urban areas, while informal and agricultural income is more prevalent in southern regions. Regional disaggregation has the potential to support further analysis of spatial income disparities.

Informal income and self-employment earnings remain difficult to measure fully. Sectors most associated with underreported income include trade, construction, agriculture and services. Income structure is captured comprehensively, but informal and capital income have measurement challenges. Measurement improvements are underway using alternative data sources, although informal income coverage remains a challenge. Informal economy activity is higher in certain regions and sectors. Further adjustments may be needed to support more realistic regional income estimates and further efforts are needed to improve completeness and ensure statistical representativeness across income groups.

Timeliness and punctuality

The BNS reassured that income distribution statistics in Kazakhstan are produced and disseminated on a regular basis. The household budget survey is conducted continuously throughout the year. Main indicators, including the Gini coefficient, are compiled and published quarterly, which allows timely monitoring of income inequality trends.

The results of the household budget survey are released within approximately 90 days after the reference period, which is broadly aligned with international practices for survey-based income statistics. Going forward, the BNS plans to integrate administrative and bank transaction data as well as consider the development of digital data collection tools to further reduce collection and processing time as well as enable more frequent updates.

Accessibility

Income distribution statistics are disseminated through the BNS official publications and online platforms available to the public, ensuring broad access to main indicators and data. These are provided in multiple formats and languages, including English, which facilitates accessibility of information for domestic and international users.

Published data and indicators include national and regional and urban-rural breakdowns, and to some extent demographic disaggregation, which facilitates research use for policy analysis. Quarterly releases include measures of statistical precision, such as standard errors, which support informed interpretation. Going further, the BNS aims to further develop more user-friendly dissemination tools to enable more granular analysis of income disparities across population groups.

Interpretability (availability of metadata)

Kazakhstan provides methodological documentation that, for example, describes the household budget survey, its sampling design and indicator construction, which supports interpretation and use of income distribution statistics.

Ongoing efforts to integrate survey, administrative, and bank data are envisaged in order to further strengthen metadata and methodological transparency for more consistent interpretation across different sources. The development of standard methodological scripts and metadata protocols is warranted to harmonise definitions, reconcile statistical procedures with national accounts, and clarify adjustments for informal income and regional differences. Improvement of publicly available metadata and quality documentation would further strengthen international comparability of these data.

4.12. Well-being statistics

4.12.1. Assessment and recommendations

Kazakhstan has made significant progress in developing a comprehensive system of well-being statistics that integrates both objective and subjective dimensions of quality of life. The Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (BNS) and other institutional partners have established a solid representation of data increasingly aligned with international standards and informed by collaboration with international organisations, including the OECD, the Food and Agriculture Organization of the United Nations, the United Nations Children’s Fund (UNICEF) and the Oxford Poverty and Human Development Index (OPHI).

The current system combines household survey data, administrative sources, and internationally harmonised indicators to monitor income, poverty, social conditions, food security, and broader well-being outcomes. The Quality of Life Index (QLI), introduced in 2018, reflects an effort to align national measurement with the OECD Better Life Index, while the Multidimensional Poverty Index and Child Deprivation Index provide policy-relevant insights into deprivation and inequality.

The review confirms that Kazakhstan’s statistical foundations are solid and increasingly modernised, with strong institutional commitment to digitalisation, methodological transparency, and the use of innovative data sources. At the same time, further efforts are needed to strengthen international comparability, improve metadata and interpretability, and expand regional granularity. Although important progress in terms of coverage has been achieved during the review, the BNS is recommended to further ensure the alignment of the definitions and metadata of the new indicators with the OECD Well-Being programme (see Annex 4.I).

Based on this assessment, the BNS is encouraged to consider the following improvements:

• Strengthen alignment with OECD well-being measurement standards. Kazakhstan’s QLI is broadly aligned with the OECD Better Life Index; however, a methodological review would help refine weighting, scaling, and interpretation to ensure closer alignment with OECD Guidelines on Measuring Subjective Well-being and international best practices. Further collaboration with the OECD could support methodological refinement and enhance policy relevance.

• Expand regional and demographic granularity. While several indicators are disaggregated by region, gender and age, subnational breakdowns remain limited for the QLI due to data gaps. Expanding territorial granularity would improve policy targeting and reveal disparities in well-being outcomes across regions and population groups.

• Improve metadata, accessibility and interpretability. Kazakhstan publishes indicators in multiple languages and provides regular dissemination; however, comprehensive metadata in English describing definitions, survey design, question wording and methodological choices would further strengthen international comparability and user understanding. A unified public dashboard integrating subjective and objective indicators would also enhance accessibility and policy use.

• Enhance coherence and transparency across data sources. Kazakhstan’s well-being framework integrates survey, administrative and internationally comparable data. Continued efforts are needed to ensure methodological transparency, consistent normalisation procedures, and clear documentation of how data sources are combined.

• Explore new thematic areas and data innovations. Kazakhstan’s advanced digital ecosystem provides opportunities to expand well-being measurement to emerging areas such as digital well-being, mental health, and social connectedness.

The BNS expressed interest to collaborate with the OECD on further improvement of Kazakhstan’s QLI for greater international comparability and alignment with the OECD Well-being Framework methodology. It is also invited to consult Knowledge Exchange Platform on Well-being Metrics and Policy Practice, developed by the OECD Centre on Well-being, Inclusion, Sustainability and Equal Opportunity, carrying information on experiences and solutions across countries on the development and policy application of well-being frameworks and associated metrics and tools.

4.12.2. Context, data sources and methodology

Well-being statistics in Kazakhstan are coordinated primarily by the BNS (Department of Labour Statistics and Standards of Living). The system combines survey-based, administrative, and internationally harmonised indicators to capture multiple dimensions of quality of life. The well-being measurement system draws on several complementary instruments, which aim to ensure coverage of income-based, subjective, and non-income dimensions of well-being across urban and rural areas and for different demographic groups:

• QLI introduced in 2018, comprising 11 dimensions and 24 indicators broadly aligned with the OECD Better Life Index. National results are available, though subnational breakdowns remain limited

• Multidimensional Poverty Index (MPI) and Child Deprivation Index developed with OPHI, UNICEF and United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), enabling regional disaggregation and supporting national policy programmes

• Subjective poverty and deprivation surveys capturing self-assessed income sufficiency and access to goods and services

• Subjective Food Security Index developed with the FAO and implemented for eight years, measuring perceived access to adequate food and published internationally

• Administrative data sources, including environmental statistics (Kazhydromet), electoral participation (Central Election Commission), and crime statistics (General Prosecutor’s Office)

Kazakhstan’s well-being indicators have been developed in collaboration with international organisations and broadly align with international frameworks, such as, the OECD Better Life Index, SDGs, FAO food security methodology, and Alkire-Foster methodology for multidimensional poverty. The QLI was initiated by the BNS to enable international comparability and benchmarking.

Indicators are published in three languages and designed for both national and international users. The QLI applies equal weights across dimensions to ensure neutrality, while MPI indicators use standardised scoring and normalisation procedures. Results combine subjective assessments with administrative data to support interpretation in a broader policy context.

Kazakhstan integrates survey and administrative data to ensure consistency across well-being dimensions. The QLI combines household survey results with administrative indicators aligned with OECD Better Life Index dimensions, while the MPI complements monetary poverty statistics and supports national development strategies. Consistent normalisation and weighting procedures support comparability across indicators.

Data are collected through nationally representative household surveys and validated administrative sources. Accuracy is supported through multiple thematic questionnaires, normalisation procedures, and cross-validation between subjective and objective indicators. Administrative data used in composite indicators are validated by official providers.

As part of its future developments, Kazakhstan plans to:

• Complete the integration of multidimensional poverty and child deprivation measurement.

• Update and refine the QLI and explore regional disaggregation.

• Strengthen methodological cooperation with the OECD and international partners.

• Develop a unified dashboard integrating well-being and poverty indicators.

• Expand digitalisation of household surveys and modernise data collection tools.

Overall, Kazakhstan has established a robust and evolving well-being measurement system. Continued methodological refinement, improved metadata transparency, expanded territorial coverage, and the integration of missing and emerging well-being dimensions would further strengthen its alignment with OECD best practices and enhance its policy relevance.

4.12.3. The indicator-level coverage of selected well-being statistics

The OECD How’s Life? Well-being Database contains more than 80 indicators, sourced from several OECD databases and external international data collection projects. The scope of this review covers a selected set of indicators that have either been discussed at the review mission or are unique to the OECD How’s Life? Well-being Database (see Annex 4.J).

Difficulty making ends meet (financial strain)

Kazakhstan produces relevant proxy measures aligned with OECD concepts, though the specific OECD question wording is not used. These indicators are related to financial strain and poverty risk, including subjective poverty assessments, deprivation indicators, and measures based on the 60% median income threshold. Subjective poverty is measured through household surveys using self-assessment of income sufficiency and perceived deprivation. Respondents classify their income situation (e.g. low, medium, sufficient) and report access to essential goods and services. A deprivation module includes approximately 14‑16 deprivation items capturing limited access to goods, services and living conditions. These measures provide a strong proxy for financial strain and are available by region, urban/rural residence, gender and age.

Income poverty (relative poverty)

Kazakhstan produces internationally comparable relative poverty measures, while maintaining absolute poverty for national policy. The BNS publishes relative poverty indicators based on the threshold of 60% of median income for international comparability. Official policy uses an absolute poverty line based on the subsistence minimum, while relative poverty is used for international reporting and analytical purposes. The BNS is considering a gradual transition toward relative measures to better identify vulnerable groups.

Multidimensional poverty

The BNS has developed a Multidimensional Poverty Index (MPI) using the Alkire-Foster methodology in cooperation with OPHI, UN agencies and national stakeholders. The MPI is based on household surveys and integrates indicators aligned with national programmes and strategies. The MPI enables regional disaggregation and identifies population groups most affected by deprivation. A child deprivation index has been developed in collaboration with UNICEF.

Income inequality

Core inequality indicators are available, however, expanding the indicator set would improve the analytical depth. The BNS produces key inequality indicators including Gini coefficients, income decile shares and top/bottom income ratios. Inequality indicators are derived from household survey data and published with regional breakdowns. Further analytical development (e.g. additional ratios such as S20/S80) may be considered.

Subjective poverty and deprivation

Kazakhstan collects subjective deprivation data broadly aligned with OECD material deprivation concepts. Subjective poverty measures are derived from survey questions on income sufficiency and deprivation. Respondents evaluate their living conditions using a deprivation checklist and satisfaction scale (1-10). Indicators are disaggregated by region and demographic characteristics.

Subjective food security

The indicator aligns with international methodology and represents a strong dimension of material well‑being. The subjective food security indicator is based on an eight-question survey module implemented for eight years. The methodology has been validated by the FAO and the results are published internationally. The indicator measures perceived adequacy and access to food.

Health status

Health-related indicators incorporated into well-being dashboards rely on validated administrative and survey data sources. Health measures are integrated within multidimensional poverty and well-being frameworks. Health indicators are integrated into broader frameworks; however, the expansion of subjective health and mental health measures would strengthen coverage.

Environmental quality

Environmental indicators included in the QLI draw on administrative data from the national meteorological and environmental agency. Air quality and environmental measures are integrated following the OECD Better Life Index guidance. Environmental quality indicators rely on validated administrative sources and broadly align with OECD classification.

Safety and crime

Crime and safety indicators are sourced from official criminal statistics provided by the General Prosecutor’s Office. These indicators are integrated into the QLI. Safety indicators are available through administrative sources.

Civic engagement and voter participation

Political participation indicators rely on administrative data from the Central Election Commission. These data support measurement of civic engagement within the QLI. Administrative data provide some coverage of civic participation data.

Education and skills

Education indicators incorporated into the well-being framework include internationally comparable measures such as the Programme for International Student Assessment (PISA) results. Education outcomes are integrated into the QLI to support international comparison. Education indicators appear to be aligned with the OECD guidelines.

Life satisfaction

Kazakhstan collects subjective well-being data through quality-of-life survey instruments, including satisfaction scales. However, life satisfaction measured on the OECD-recommended 0-10 scale is not yet fully integrated as a standalone headline indicator. The BNS expressed interest in expanding coverage of life satisfaction and related subjective well-being dimensions. Subjective well-being data exist but would need to be strengthened with further alignment with OECD guidelines.

Mental health and psychological well-being

Mental health indicators are not systematically collected and neither integrated into Kazakhstan’s QLI framework. Development of mental health indicators represents an important future priority.

Social connections and social connectedness

Social connectedness indicators are not yet systematically measured. Expansion into social connectedness has been identified as a potential development area. Future inclusion would improve alignment with OECD well-being dimensions.

Consideration of digital well-being statistics

Kazakhstan’s consideration of emerging data sources creates opportunities to develop indicators related to digital well-being. Kazakhstan is well positioned to apply statistics on emerging technologies in measuring (digital) well-being.

References

[5] BNS (2025), Gross value added, https://stat.gov.kz/api/iblock/element/53571/file/en/.

[4] BNS (2025), Gross value added and Volume index by sectors, https://stat.gov.kz/api/iblock/element/53569/file/en/.

[28] BNS (2021), Methodology for maintaining and updating the information system Statistical Business Register, https://stat.gov.kz/upload/iblock/a86/f1zdr4ic42lj28857irgnbyao23zx4vw/3.docx.

[3] BNS (2019), Methodology for compiling the tables “Resources-Usage” and “Cost-Output”, https://stat.gov.kz/upload/iblock/bce/lmln8wwhe76g21vw1wh92xq1f3shtp0m/26.Methodology%20for%20compiling%20the%20tables%20Resources-Usage%20and%20Cost-Output.rar.

[29] BNS (2017), Methodology for calculating indicators small and medium enterprises, https://stat.gov.kz/upload/iblock/271/qhjrzgcnr2m6vtixebv4fjeazexu9asf/%E2%84%96130%20%D0%BE%D1%82%2020.09.2017%20%D0%9C%D0%A1%D0%9F_%D1%80%D1%83%D1%81%20(1).docx.

[73] BNS (n.d.), Deflator of Gross Domestic Product by method of production, https://stat.gov.kz/api/iblock/element/44200/file/en/.

[55] BNS (n.d.), Economics - Statistics of domestic trade, https://stat.gov.kz/en/industries/economy/local-market/.

[78] BNS (n.d.), Experimental Estimates - Quarterly Gross Domestic Product, https://stat.gov.kz/api/iblock/element/284977/file/en.

[72] BNS (n.d.), Gross Domestic Product volume index by method of production, https://stat.gov.kz/api/iblock/element/44204/file/en/.

[54] BNS (n.d.), Industry statistics - Transport, https://stat.gov.kz/en/industries/business-statistics/stat-transport/.

[53] BNS (n.d.), Methodology - Energy Statistics, https://stat.gov.kz/en/methodology/25830/.

[10] BNS (n.d.), Methodology - National accounts, https://stat.gov.kz/en/methodology/25849/.

[52] BNS (n.d.), Methodology - Statistics of construction, https://stat.gov.kz/en/methodology/25836/.

[56] BNS (n.d.), Methodology - Statistics of domestic trade, https://stat.gov.kz/en/methodology/25839/.

[31] BNS (n.d.), Methodology - Statistics of foreign, mutual trade and commodity markets, https://stat.gov.kz/en/methodology/25840/.

[51] BNS (n.d.), Methodology - Statistics of industrial production, https://stat.gov.kz/en/methodology/25833/.

[14] BNS (n.d.), Methodology - Statistics of prices, https://stat.gov.kz/en/methodology/25843/.

[57] BNS (n.d.), Methodology - Transport, https://stat.gov.kz/en/methodology/25841/.

[74] BNS (n.d.), National accounts of the Republic of Kazakhstan, https://stat.gov.kz/api/iblock/element/108478/file/en/.

[49] BNS (n.d.), Release calendar, https://stat.gov.kz/en/release-calendar/.

[43] BNS (n.d.), Statistical classifications, https://stat.gov.kz/en/classifiers/statistical/25795/.

[30] BNS (n.d.), Statistics of foreign, mutual trade and commodity markets, https://stat.gov.kz/en/industries/economy/foreign-market/.

[50] BNS (n.d.), Statistics of industrial production, https://stat.gov.kz/en/industries/business-statistics/stat-industrial-production/.

[9] BNS (n.d.), Statistics of national accounts, https://stat.gov.kz/en/industries/economy/national-accounts/.

[75] BNS (n.d.), Statistics of national accounts - Dynamic tables, https://stat.gov.kz/en/industries/economy/national-accounts/dynamic-tables/?period=quarter.

[13] BNS (n.d.), Statistics of national accounts - Financial account of the Republic of Kazakhstan, https://stat.gov.kz/api/iblock/element/283364/file/en/.

[16] BNS (n.d.), Statistics of prices, https://stat.gov.kz/en/industries/economy/prices/.

[17] BNS (n.d.), Statistics of prices - Interactive dashboards - Inflation dashboard, https://stat.gov.kz/en/industries/economy/prices/dashboards/28532/.

[23] BNS (n.d.), Statistics of prices - Quality reports, https://stat.gov.kz/en/industries/economy/prices/quality-reports/.

[22] BNS (n.d.), Statistics of prices - Spreadsheets, https://stat.gov.kz/en/industries/economy/prices/spreadsheets/?year=&name=37814&period=&type=.

[20] BNS (n.d.), Taldau - Housing market price indices, https://taldau.stat.gov.kz/en/NewIndex/GetIndex/703083.

[21] BNS (n.d.), Taldau - Housing market prices, https://taldau.stat.gov.kz/en/NewIndex/GetIndex/703116.

[18] BNS (n.d.), Taldau - Information-Analytical System, https://taldau.stat.gov.kz/en/Search/SearchByKeyWord.

[8] BNS (n.d.), The Schedule for the dissemination of official statistical information for 2025-2027, https://www.gov.kz/memleket/entities/stat/documents/details/888373?lang=en.

[65] European Commission (2016), The Joint Harmonised EU Programme of Business and Consumer Surveys: User Guide, https://ec.europa.eu/economy_finance/db_indicators/surveys/documents/bcs_user_guide_en.pdf.

[27] Eurostat (2024), European business statistics - Methodological manual for structural business statistics: 2024 edition, https://doi.org/10.2785/7019202.

[24] Eurostat (2021), European business statistics methodological manual for statistical business registers - 2021 edition, https://doi.org/10.2785/093371.

[47] Eurostat (2002), Methodology of short-term business statistics: Interpretation and guidelines, https://ec.europa.eu/eurostat/documents/3859598/5874981/KS-BG-02-002-EN.PDF.pdf.

[19] Eurostat et al. (2013), Handbook on Residential Property Prices Indices (RPPIs), https://doi.org/10.2785/34007.

[7] IMF (2018), Early Estimates of Quarterly GDP, https://www.imf.org/external/pubs/ft/qna/pdf/2017/chapter10.pdf.

[59] IMF (2017), Monetary and Financial Statistics Manual and Compilation Guide, https://doi.org/10.5089/9781513579191.069.

[11] IMF (2014), Government Finance Statistics Manual 2014, https://doi.org/10.5089/9781498343763.069.

[32] IMF (2009), Balance of Payments and International Investment Position Manual, Sixth Edition, https://www.imf.org/external/pubs/ft/bop/2007/pdf/bpm6.pdf.

[15] IMF et al. (2020), Consumer Price Index Manual: Concepts and Methods, https://doi.org/10.5089/9781484354841.069.

[2] Inter-secretariat Working Group on National Accounts (2025), Strategy for Implementing 2025 SNA and BPM7, United Nations Statistics Division, https://unstats.un.org/unsd/nationalaccount/docs/Implementation_Strategy_UNSC_ENDORSED.pdf.

[58] KASE (n.d.), Kazakhstan Stock Exchange, https://kase.kz/en.

[12] MoF (n.d.), Search results for “government finance statistics”, https://www.gov.kz/memleket/entities/minfin/search/1?contentType=documents&period=&searchText=government+finance+statistics&slug=minfin&lang=en.

[77] National Accounts Institute of Belgium (2022), Gross National Income methodological inventory BELGIUM, https://dashboards.nbb.be/doc/dq/e_method/gni_methodological_inventory_belgium_version_2022_publication.pdf.

[69] NBK (2025), Identification of Key Consumer Categories of Goods and Services as Factors in the Formation of Perceived Inflation, https://nationalbank.kz/file/download/115575.

[70] NBK (2024), Determination of the Optimal Interval Size for Calculating the Median of Inflation Expectations, https://nationalbank.kz/file/download/106279.

[35] NBK (n.d.), Advance Release Calendar, https://nationalbank.kz/en/page/kalendar-rasprostraneniya-dannyh.

[37] NBK (n.d.), Balance of Payments, https://nationalbank.kz/en/news/platezhnyy-balans-vn-sektora.

[41] NBK (n.d.), Balance of Payments - The Data: Coverage, Periodicity, and Timeliness, https://nationalbank.kz/en/news/metadannye/7253.

[34] NBK (n.d.), Calendar of events and publications, https://nationalbank.kz/en#tab-main-2.

[62] NBK (n.d.), Inflation expectations, https://nationalbank.kz/en/page/inflyacionnye-ozhidaniya.

[61] NBK (n.d.), Kazakhstan Business Activity Index (BAI), https://nationalbank.kz/en/page/indeks-delovoy-aktivnosti-po-kazahstanu-ida.

[76] NBK (n.d.), List of Forms of Statistical Reporting on the Balance of Payments, https://nationalbank.kz/ru/news/formy-statisticheskoy-i-administrativnoy-otchetnosti/7463.

[60] NBK (n.d.), Market Review. Outcomes of Enterprise Surveys in the Economy, https://nationalbank.kz/en/page/konyunkturnyy-obzor-rezultaty.

[68] NBK (n.d.), Methodological aspects of calculating median inflation expectations., https://nationalbank.kz/file/download/107780.

[42] NBK (n.d.), Methodological comment - Balance of Payments, https://nationalbank.kz/en/news/metodologicheskiy-kommentariy.

[40] NBK (n.d.), Methodology, https://nationalbank.kz/en/news/metodologiya-vn-sektor.

[38] NBK (n.d.), NBK Statistical Bulletin, https://nationalbank.kz/en/news/statisticheskiy-byulleten/rubrics/2596.

[39] NBK (n.d.), Open Data Repository of the National Bank of the Republic of Kazakhstan, https://data.nationalbank.kz/.

[36] NBK (n.d.), Schedule of Release of statistical information, https://nationalbank.kz/en/statisticalinformation/grafik-vypuska-statisticheskoy-informacii.

[67] OECD (2025), Business Tendency and Consumer Surveys: OECD Recommended Target indicators, https://www.oecd.org/content/dam/oecd/en/about/programmes/oecd-data-collection-programme/short-term-statistics/business-tendency-and-consumer-surveys-target-indicators.pdf.

[66] OECD (2012), OECD System of Composite Leading Indicators, https://www.oecd.org/content/dam/oecd/en/data/methods/OECD-System-of-Composite-Leading-Indicators.pdf.

[26] OECD (2010), Structural and Demographic Business Statistics 2009, OECD Publishing, Paris, https://doi.org/10.1787/9789264072886-en.

[6] OECD (2009), Measuring Capital - OECD Manual 2009: Second edition, OECD Publishing, Paris, https://doi.org/10.1787/9789264068476-en.

[79] OECD (2003), Business Tendency Surveys: A Handbook, https://doi.org/10.1787/9789264177444-en.

[63] OECD (2003), Business Tendency Surveys: A Handbook, OECD Publishing, Paris, https://doi.org/10.1787/9789264177444-en.

[71] OECD (n.d.), OECD Data Collection Programme - National Accounts, https://www.oecd.org/en/about/programmes/oecd-data-collection-programme/national-accounts.html.

[25] OECD/Eurostat (2008), Eurostat-OECD Manual on Business Demography Statistics, OECD Publishing, Paris, https://doi.org/10.1787/9789264041882-en.

[64] UN (2016), Handbook on Economic Tendency Surveys, https://doi.org/10.18356/7f81152b-en.

[45] UN (2013), International Recommendations for the Index to Industrial Production 2010, https://digitallibrary.un.org/record/1474611/files/BG-IndustrialStats.pdf.

[44] UN (2009), International Recommendations for Industrial Statistics 2008, https://digitallibrary.un.org/record/691868?ln=en&v=pdf#files.

[48] UN Industrial Development Organization (2015), Practical Guide to Compiling the Index of Industrial Production for CIS Countries, https://downloads.unido.org/ot/52/65/5265974/TODOROV_PPS_PRS_STA_RER_2015.pdf.

[33] UN et al. (2012), Manual on Statistics of International Trade in Services 2010 (MSITS 2010), https://unstats.un.org/unsd/publication/seriesm/seriesm_86rev1e.pdf.

[46] UN/Oslo Group on Energy Statistics/Intersecretariat Working Group on Energy Statistics (2018), International recommendations for energy statistics (IRES), https://digitallibrary.un.org/record/3900606?ln=en&v=pdf#files.

[1] UNECE et al. (2023), Global Assessment of the National Statistical System of Kazakhstan: Final Report, https://unece.org/sites/default/files/2023-11/GA%20Kazakhstan%202023%20Report.pdf.

Annex 4.A. Coverage compared to OECD data collection tables for non-financial accounts

This coverage assessment is based on the OECD Data Collection Programme for national accounts (OECD, n.d.[71]).

Annual data

Annual national accounts including GVA, GDP, disposable income, saving, household final consumption expenditure, population and employment, investment and pensions - Tables 0101, 0102, 0103, 0107, 0110, 0111, 0117, 0119, 0301, 0302, 0303, 0501, 0502, 0800, 2000, 2600 and 2900.

Annual GDP tables following the three approaches (T0101, T0102 and T0103) are available online, also in English. GDP by expenditure is available from 1990 onwards and GDP by production from 1998 onwards. Volume series are available in constant prices, but not yet as chain-linked estimates. The transition to chain-linking (base year 2010) is being conducted on an experimental basis, with official publication planned for 2026.

GDP by production and income (covering both annual and sub-annual data) are disaggregated by ISIC section level (about 20 activities).

A GDP Volume index by method of production (BNS, n.d.[72]) is available that expresses (for each ISIC section + economy as a whole production compared to previous year as an index) and a Deflator of GDP by the production method (BNS, n.d.[73]) (in % to the previous year). Similar tables are available also for the income approach.

Annual non-financial sectoral accounts (T0800) are available online: National accounts of the Republic of Kazakhstan (BNS, n.d.[74]) from 2010 to 2023.

Employment data broken down into employees and self-employed by main (T0111) and detailed (T0303) type of economic activity (according to ISIC Revision 4) should be available (the BNS also publishes labour productivity indicators) but no link was provided nor could information be found online.

Gross Fixed Capital Formation (GFCF) data (T0302) are available by assets but not by industrial activity. Furthermore, (net) capital stock data (T2000, T2600) is available on an experimental basis by asset and institutional sector (although it is not clear whether sector means industrial activity or institutional sector).

Household final consumption expenditure tables (T0117, T0501 and T0502) are available according to COICOP 1999 but are not published.

Furthermore, Kazakhstan has not started compiling a pension table (T2900), due to a lack of requests from users, but will consider the possibility of its implementation.

Quarterly data

Quarterly national accounts including GVA, GDP, disposable income, saving, household final consumption expenditure, population and employment) - Tables 0101, 0102, 0103, 0107, 0110, 0111, 0117.

Quarterly GDP data are available according to the three approaches. However, the official data are still compiled on a cumulative basis, although also experimental estimates of discrete (i.e. non-cumulative) and seasonally adjusted (BNS, n.d.[75]) quarterly accounts have recently started to be published.13 This is a very important development.

1. GDP by production data cover the period 2010-2025 and the activity breakdown follows NACE Revision 2 (ISIC Revision 4). There are also quarterly data back to 2007, but they are based on ISIC Revision 3. Both, the flash release (at T+45 days) and the T+90 days release, only include data on a cumulative basis (Q1; Q1+Q2; Q1+Q2+Q3; year) and non-seasonally adjusted. Current prices are available in levels, but volume data are only published as indices and year‑on‑year growth rates. Furthermore, volume data are based on fixed constant prices.

2. GDP by final expenditure method covers the period 2007-2025. It shows figures of total GDP as compiled from the production approach together with a statistical discrepancy. Data are only available on a cumulative basis, and volume data are expressed in indices, not levels. Furthermore, expenditure data are published with some delay (at around 100 days after the end of the reporting quarter) compared with the production approach (T+45 days and T+90 days).

3. GDP by income starts in 2010 and is published in regular release at T+92 days. Data are now presented for GDP and main components (for total branches) and for the main components broken down by activity (ISIC Revision 4).

A new approach for calculating quarterly GDP has been developed that brings quarterly national accounts in line with annual data through a benchmarking procedure. This is an excellent methodological innovation that is line with OECD best practices.

Quarterly sector accounts (non-financial part) - Table 0801

No quarterly sector accounts data are currently published, but quarterly dynamics of the main aggregates (Gross Disposable Income, saving, and net lending/borrowing) without a sectoral breakdown will be available on the official BNS website by the end of the year. The BNS has indicated a lack of resources to compile the full sequence of accounts on a quarterly basis as well as the need for technical support from international experts. Compilation would also require setting-up data sharing arrangements of quarterly data on the external sector and financial statistics with the NBK.

Supply and use tables

Tables 1500, 1600, 1601, 1602, 1610, 1611, 1612, 1620, 1630, 1631, 1632, 1633, 1634.

The 2022 and 2023 SUTs are available, that cover the OECD needs for industry and product coverage as well as the different tables.

For industry and products, the use of ISIC and CPA in the national SUT file is very welcome as they match perfectly with OECD needs.

Referring to different SUT tables, the OECD Secretariat also welcomes the availability of the Supply table, Use table at purchasers’ prices, Use tables at basic prices broken down between imports and domestic output as well as the two valuation matrices: Trade and transport margin matrix and taxes less subsidies matrix.

Annex 4.B. Current statistical business register variables disseminated by the BNS

Disseminated statistical business register variables (35). There are three employment class-sizes, depending on the average annual number of employees: small (up to 100 persons); medium (from 101 to 250 persons); and large (over 250 persons):

• Registered legal entities

• By type of activity and form of ownership

• By type of activity and attribute of activity

• By type of activity and organizational and legal forms

• By industry and forms of ownership

• By regions of the Republic of Kazakhstan and forms of ownership

• By regions of the Republic of Kazakhstan and activity

• By regions of the Republic of Kazakhstan and organizational and legal forms

• By regions of the Republic of Kazakhstan for the month of September

• By type of activity and regions of the Republic of Kazakhstan

• Operating legal entities

• By type of activity and form of ownership

• By type of activity, forms of ownership and gender relation

• By type of activity and organizational and legal forms

• By industry and forms of ownership

• By regions of the Republic of Kazakhstan and forms of ownership

• By regions of the Republic of Kazakhstan, forms of ownership and gender relations

• By regions of the Republic of Kazakhstan and organizational and legal forms

• By type of activity and regions of the Republic of Kazakhstan

• Registered individual entrepreneurs

• By type of activity and gender attitude

• By type of activity and regions of the Republic of Kazakhstan

• Operating subjects of individual entrepreneurship

• By type of activity and gender attitude

• By type of activity and regions of the Republic of Kazakhstan

• Liquidated legal entities

• By type of activity and organizational and legal forms

• By regions of the Republic of Kazakhstan and organizational and legal forms

• Registered branches and branches of foreign legal entities

• By type of activity and activity attribute

• By type of activity and regions of the Republic of Kazakhstan

• By regions of the Republic of Kazakhstan and forms of ownership

• Operating Branches and Branches of foreign legal entities

• By type of activity and regions of the Republic of Kazakhstan

• By regions of the Republic of Kazakhstan and forms of ownership

Annex 4.C. Structural business statistics requested by the OECD Secretariat

Structural business statistics requested by the OECD Secretariat - variable by ISIC by employment class‑size, i.e. number of persons employed (1_9; 10_19; 20_49; 50_249; 250+; 1_19; 20+; Total):

• TUTT Turnover or gross premiums written

• PROD Production at producers' prices or factor costs

• VALU Value added at basic prices or factor costs

• GOPS Gross operating surplus

• INGS Total purchases of goods and services

• GITG Gross Investment in tangible goods

• EMPN Employment, number of persons employed

• EMPE Employment, number of employees

• REMN Compensation of labour, all persons employed

• REME Compensation of labour, employees

• WASN Wages and salaries, all persons employed

• WASE Wages and salaries, employees

• ENTR Number of enterprises

• ESTB Number of establishments

• CSGS Change in stocks of goods and services

• INEN Purchases of energy products (in value)

• GFLA Investment in land

• GFBS Investment in existing buildings and structures

• GFEQ Investment in machinery and equipment

• EMPF Employment, number of females employees

• FTEE Employment, number of employees in full time equivalent units

• EHOU Hours worked by employees

• ESUP Other employers' social contributions, employees

Annex 4.D. Availability of trade by enterprise characteristics statistics

The BNS provided “Table I: Trade and Business Registers: population concerned (FTEs, ISIC Revision 4)” for the year 2022 in regard to the availability of trade by enterprise characteristic statistics. Metadata, including footnotes, were provided in the Excel file. The following variables were in the Excel file:

1. Trade Register

a. Number of trade operators (1) [Imports/Exports]

b. Trade value (1 000 USD), BOP basis, Trade in goods [Imports/Exports]

c. Trade value (1 000 USD), Customs basis Merchandise Trade [Imports/Exports]

2. All Trade operators in Trade Register linkage [IF DIFFERENT]

a. Number of trade operators (2) [Imports/Exports]

b. Trade value (1 000 USD), Customs basis Trade in Goods [Imports/Exports]

3. All Enterprises in the selected Business Register vintage

a. Number of enterprises (3) (published annual figure, if available)

4. Trade operators successfully matched with the BR

a. Number of foreign trade operators linked (1) [Imports/Exports]

b. Number of enterprises linked (3) [Imports/Exports]

c. Trade value (1 000 USD) linked [Imports/Exports]

d. Trade coverage (%) [Imports/Exports]

Metadata (from the BNS):

1. As defined by the identification number. Includes only the above exemption threshold operators.

2. Trade identified in the trade register, as a proportion of total trade. Total trade volume includes an estimate of the trade volume below the threshold.

3. As defined in the statistical business register (difference between trade operator and enterprise).

4. Trade identified in the trade register, and matched with the statistical business register, as a proportion of total trade. Total trade volume includes an estimate of the trade volume below the threshold.

Population 1 includes all trade operators above the statistical transaction threshold.

Population 2 includes all trade operators from Population 1 but certain traders may be excluded for reasons such as incomplete or wrong identification codes.

Population 3 includes all enterprises in the national business register.

Population 4 includes all trade operators from Population 2 that can be successfully matched with business register.

Annex 4.E. List of statistical reporting forms for the balance of payments

Statistical forms 1-PB, 2-PB, 3-PB, 4-PB, 5-PB, 7-PB, 9-PB, 10-PB, 11-PB-OS, 11-PB-SZH, 14-PB, 15‑PB, 17-PB, OPB-1 were approved by the Resolution of the Board of the National Bank of the Republic of Kazakhstan No. 33 of 23 June 2023 "On approval of forms of departmental statistical observations". Order No. 33 was registered with the Ministry of Justice of the Republic of Kazakhstan under No. 36409 on 8 July 2025. Starting in the first quarter of 2026, forms 8-PB and 18-PB consolidated and replaced forms 2‑PB, 3‑PB, 4‑PB and 5‑PB.

Annex Table 4.E.1. List of statistical reporting forms for the balance of payments

Form index/Name of the form	Respondents of the form	Frequency and deadline for submitting
1-PB Report on financial claims on and liabilities to non-residents	Selected legal entities	Quarterly No later than the 10th day of the second month after the reporting period
2-PB Report on transport services received from non-residents (provided to non-residents) [abolished in 2026]	Enterprises providing transportation and forwarding services by air, sea (river), automobile, pipeline transport and transmission of electricity	Quarterly No later than the 30th day after the reporting period
3-PB Report on railway transport services received from non-residents (provided to non-residents) [abolished in 2026]	JSC NC Kazakhstan Temir Zholy, JSC Passenger Transportation, railway transport enterprises	Quarterly No later than the 30th day after the reporting period
4-PB Report on transactions carried out on behalf of non-resident transport enterprises [abolished in 2026]	Representatives of non-resident transport enterprises of all types of transport, except rail	Quarterly No later than the 30th day after the reporting period
5-PB Report on services provided to non-resident transport companies [abolished in 2026]	Enterprises engaged in auxiliary and supplementary transport activities	Quarterly No later than the 30th day after the reporting period
7-PB Statement of International Operations, External Assets and Liabilities of the General Government Sector	Government bodies of Kazakhstan	Quarterly No later than the 30th day of the first month after the reporting period
8-PB Report on transportation services, services related to transportation, and other international operations [introduced in 2026]	Selected organisations	Quarterly No later than the 30th day of the first month after the reporting period
9-PB Report on the status of financial claims on non-residents and liabilities to them	Second-tier banks, branches of non-resident banks in the Republic of Kazakhstan, joint-stock company "Development Bank of Kazakhstan"	Quarterly No later than the 20th day of the first month after the reporting period
10-PB Report on international transactions with non-residents	Selected organisations	Quarterly No later than the 30th day of the first month after the reporting period
11-PB-OS Report on insurance (reinsurance) of non-residents and reinsurance of risks with non-residents in the sector "general insurance"	Insurance organizations operating on the basis of a license in the field of "general insurance"	Quarterly No later than the 20th day of the first month after the reporting period
11-PB-SZH Report on insurance (reinsurance) of non-residents and reinsurance of risks with non-residents in the sector "life insurance"	Insurance organizations operating under a license in the life insurance industry	Quarterly No later than the 20th day of the first month after the reporting period
14-PB Report on external government, government-guaranteed loans and loans attracted under the guarantee of the Republic of Kazakhstan	NBK, MoF	Quarterly No later than the 30th day of the first month after the reporting period
15-PB Report on international securities transactions with non-residents	Second-tier banks, branches of non-resident banks in Kazakhstan and the Development Bank of Kazakhstan Joint-Stock Company; brokers and/or dealers; investment portfolio management organizations; nominal holders and registrars of securities; organizations implementing investment management of pension assets; a single accumulative pension fund - for its own assets and for pension assets under the trust management of the NBK; a voluntary accumulative pension fund	Quarterly No later than the 20th day of the first month after the reporting period
17-PB Report on loans issued to non-residents	Joint Stock Company "BTA Bank"	Quarterly No later than the 25th day of the first month after the reporting period
18-PB Report on rail, pipeline and electricity transmission services [introduced in 2026]	Selected organisations	Quarterly No later than the 30th day of the first month after the reporting period
OPB-1 Balance of Payments Survey Questionnaire for Enterprises	Legal entities included in the sample. Government bodies and banks do not submit the statistical form	At the request of the territorial body of the NBK. Until the date specified in the questionnaire

Source: List of Forms of Statistical Reporting on the Balance of Payments (NBK, n.d.[76]), available in Kazakh and Russian. Note that a 404 error message is issued when accessing the English version.

Annex 4.F. BTS questions

Survey: construction industry; and service sector

13 Questions

Be aware of the changes in the business landscape for your business: Improvement; or - Same; or ‑ Deterioration.

1. How do you assess the changes in business conditions in the current month compared to the previous month?

a. How has access to finance/credit changed?

b. How has the tax burden changed?

c. How has the availability of infrastructure changed?

d. How have the issues of business protection changed (number of inspections, fines, etc.

e. How the demand for products/services has changed

2. What are your expectations regarding the business situation for the next 6 months?

How has your business changed over the past month? Rise; or - Same; or - Fall.

3. Level of business activity (investment attractiveness, financial stability, creditworthiness of the enterprise)

4. Volume of new orders for the product

5. Number of employees or working hours

6. The level of demand for labour in the next month

Duration increased; unchanged; duration decreased

7. Delivery time of raw materials and materials

8. What do you think the inflation rate will be next month? In other words, what percentage of the total price of goods and services will increase/decrease after one month compared to the end of the previous month?

9. What do you think will be the level of inflation in the next 12 months? In other words, what percentage of the total price of goods and services will increase/decrease in the price of goods and services in a year?

Survey: production sector; and for mining enterprises

14 Questions

Be aware of the changes in the business landscape for your business: Improvement; or - Same; or - Deterioration.

1. How do you assess the changes in business conditions in the current month compared to the previous month?

a. How has access to finance/credit changed?

b. How has the tax burden changed?

c. How has the availability of infrastructure changed?

d. How have the issues of business protection changed (number of inspections, fines, etc.)?

e. How the demand for products/services has changed

2. What are your expectations regarding the business situation for the next 6 months?

a. How has your business changed over the past month? Rise; or - Same; or - Fall.

3. Volume of new orders for the product

4. Number of employees or working hours

5. Volume of new product production

6. Volume of commodity and material stocks

7. The level of demand for labour in the next month

a. Duration increased; unchanged; duration decreased

8. Delivery time of raw materials and materials

9. What do you think the inflation rate will be next month? In other words, what percentage of the total price of goods and services will increase/decrease after one month compared to the end of the previous month?

10. What do you think will be the level of inflation in the next 12 months? In other words, what percentage of the total price of goods and services will increase/decrease in the price of goods and services in a year?

Annex 4.G. COS questions - inflation expectations

Question 1. In your opinion, in what way prices of goods and services changed in the last month?

Question 1а. In your opinion, what prices of goods and services increased faster in the last month?

Question 2. In your opinion, in what way prices of goods and services will change in the next month?

Question 3. In your opinion, in what way prices of goods and services were changing in the last 12 months?

Question 4a. In your opinion, in what extent prices of goods and services increased in the last 12 months?

Question 5. In your opinion, in what way prices of goods and services will be changing in the next 12 months?

Question 6. In your opinion, in what extent prices of goods and services will increase in the next 12 months?

Question 6а. Based on what information do you estimate price changes in the future? [Quarterly]

Question 6b. What factors have most influenced your opinion that prices will rise? [Quarterly]

Question 7. Please name all sources of your family income during the last month.

Question 8. Please name the group, which corresponds to the average monthly income of your family over the past month

Question 9. In what way the financial status of your family changed in the last 12 months?

Question 10. In your opinion, in what way financial status of your family will change in the next 12 months?

Question 11. Could you please tell me, whether you have any personal savings, money savings?

Question 12. Could you please tell me, in what way you are keeping your personal savings, money savings?

Question 13. Could you please tell me, in which currency you are keeping your cash savings?

Question 14. In what way your savings changed in the last 12 months?

Question 16. Could you please tell me, whether you managed to save any sum of money over the last month?

Question 17. You said you managed to save a certain sum of money over the last month. This sum of money, that you managed to save, can be described as:

Question 18. In your opinion, what sum of money can be considered as savings today? [Quarterly]

Question 19. Generally speaking, what time is it now to make savings?

Question 20. Could you please tell me, whether you are planning to buy anything on credit or to take a bank loan in the next 12 months? [Quarterly]

Question 21. Could you please tell me, do you have any credit, if so, which one? [Quarterly]

Question 22. Please specify the range of monthly payments on your credits: [Quarterly]

Question 23. Talking about economic situation in our country in general, what is your outlook for country’s development over the next 12 months?

Question 24. Talking about economic situation in our country in general, what is your outlook for country`s development over the next 5 years?

Question 25. In your opinion, is it generally good or bad time to make major purchases / expenses?

Question 25a. Please specify if you have made major purchases/expenses in the last 3 months?

Question 26. In your opinion, the prices for what types of goods and services grew faster in the past month?

Question 27. What do you think the annual inflation rate will be in 5 years?

Notes:

Starting from January 2020:

• the formulation of questions 1, 2, 3, 4, 5, and 6 is changed by using "goods and services" instead of "food, non-food goods and services"

• question 19 is asked in a new form

• question 21 is asked in a new form and, additionally, answer options have been included

• questions 23, 24, and 25, are asked in a new text

Annex 4.H. Quarterly Diffusion Indexes, Production Volume example

Annex Figure 4.H.1. Quarterly Diffusion Indexes, Production Volume example

Source: NBK Open Data Repository (NBK, n.d.[39]) under Data > Survey Results > Enterprise Monitoring > Production volume.

Annex 4.I. OECD Well-being Framework

The OECD Well-being Framework, first launched in 2011, guides the OECD’s work to assess whether life as a whole is getting better for people living in OECD Member countries (Annex Figure 4.I.1). It includes current well‑being outcomes, their distribution across the population, and the systemic resources that help to sustain outcomes over time and for future generations. This ensures a comprehensive approach to well‑being, inclusion and sustainability (spanning economic, social, relational and environmental aspects).

Current well-being data focus on living conditions at the individual, household and community levels and describe how people experience their lives “here and now”. These data are complemented by statistics on the resources that help to sustain well-being over time: specifically, via four types of capital, countries’ investments in (or depletions of) these, and risk and resilience factors that will shape future changes in well-being. Separating the reporting of current well-being and its sustainability helps to assess whether maximising the former comes at the cost of compromising the latter (or vice versa), which can inform intertemporal trade-offs in policy design and indicate the intergenerational outlook for a country’s well-being.

Annex Figure 4.I.1. The OECD Well-being Framework

Source: OECD (2024), How's Life? 2024: Well-being and Resilience in Times of Crisis, OECD Publishing, Paris, https://doi.org/10.1787/90ba854a-en; OECD (2020), How’s Life? 2020: Measuring Well-being, OECD Publishing, Paris, https://doi.org/10.1787/23089679.

Current well-being is comprised of 11 dimensions. These dimensions relate to material conditions that shape people’s economic options (income and wealth, work and job quality, housing) and quality-of-life factors that encompass how well people are (and how well they feel they are), what they know and can do, and how healthy and safe their places of living are (health, knowledge and skills, environmental quality, subjective well-being, safety). Quality of life also encompasses community relationships, or how connected and engaged people are, and how and with whom they spend their time (work-life balance, social connections, civic engagement).

As national averages often mask large inequalities in how different parts of the population are doing, the distribution of current well-being is taken into account by looking at three types of inequality: gaps between population groups (e.g. between men and women, old and young people, etc., collectively described as horizontal inequalities); gaps between those at the top and those at the bottom of the achievement scale in each dimension (e.g. the income of the richest 20% of individuals compared to that of the poorest 20%), referred to as vertical inequalities; and deprivations (i.e. the share of the population falling below a given threshold, such as a minimum level of skills or health). The systemic resources that support future well-being over time are expressed in terms of four types of capital, i.e. stocks that last over time but are also affected by decisions taken (or not taken) today. Economic capital includes both man-made and financial assets; natural capital encompasses naturally occurring assets and ecosystems, from tradable items such as minerals and timbers through to oceans and the atmosphere; human capital refers to the skills and future health of individuals; and social capital refers to the social norms, shared values and institutional arrangements that foster cooperation.

Many of these capital stocks stretch well beyond those “owned” by private agents and are, effectively, public goods: for example, an individual’s beliefs in how much others can be trusted contributes to the overall atmosphere of interpersonal trust in a country or community, while greenhouse gas emissions in one country influence the world’s overall climate. In addition to considering capital stocks and flows, the How’s Life? Well-being dashboard also includes some key risk and resilience factors that might affect the well-being value of those stocks and flows in future. For example, high levels of household debt can pose risks to future economic prospects, while the inclusiveness of decision-making in politics can be a protective factor for well-being.

Annex 4.J. The OECD How’s Life? 2024 Dashboard, aligned with the Well-being Framework

Annex Table 4.J.1. Current well-being

Dimension	Indicator	Definition	Type
Income and wealth	Household income	Household net adjusted disposable income	Average
	Household wealth	Household median net wealth	Average
	S80/S20 income share ratio	Ratio of average (equivalised) household disposable income of the top 20% of the income distribution to the average income of the bottom 20%	Inequality (vertical)
Housing	Housing affordability	Disposable income after housing costs	Average
	Overcrowding rate	Overcrowding rate	Inequality (deprivation)
Work and job quality	Employment rate	Employment rate	Average
	Gender wage gap	Gender wage gap	Inequality (horizontal)
	Long hours in paid work	Employees working very long (paid) hours	Inequality (deprivation)
Health	Life expectancy	Life expectancy at birth	Average
	Gap in life expectancy by education	Gap in life expectancy among men with low (no schooling, primary and lower secondary educational attainment) and high (tertiary) education at age 25	Inequality (horizontal)
Knowledge and skills	Student skills in science	Cognitive skills of 15-year-old students in science	Average
	Students with low skills	Share of 15-year-old students with low scores in maths, reading and science	Inequality (deprivation)
Environmental quality	Access to green space	Share of urban population with access within a ten‑minute walk	Average
	Exposure to outdoor air pollution	Population exposure to outdoor air pollution by fine particulate matter above World Health Organisation Guidelines	Inequality (deprivation)
Subjective well‑being	Life satisfaction	Mean life satisfaction with responses ranging from 0 (not at all satisfied) to 10 (completely satisfied)	Average
	Negative affect balance	Negative affect balance	Inequality (deprivation)
Safety	Homicides	Deaths due to assault	Average
	Gender gap in feeling safe	Gender gap in feeling safe at night	Inequality (horizontal)
Work-life balance	Time off	Time allocated do leisure and personal care	Average
	Gender gap in hours worked	Extra minutes of total time spent working (paid and unpaid) that women work, relative to men	Inequality (horizontal)
Social connections	Social interactions	Time spent interacting with friends and family as primary activity	Average
	Lack of social support	Share of people who report having no friends or relatives whom they can count on in times of trouble	Inequality (deprivation)
Civic engagement	Voter turnout	Voter turnout among the population registered to vote	Average
	Having no say in government	Share of people who feel they have no say in what the government does	Inequality (deprivation)

Source: For full details, including sources, please refer to OECD (2024), How's Life? 2024: Well-being and Resilience in Times of Crisis, OECD Publishing, Paris, https://doi.org/10.1787/90ba854a-en.

Annex Table 4.J.2. Resources for future well-being

Dimension	Indicator	Definition	Type
Economic capital	Produced fixed assets	Produced fixed assets	Stock
	Financial net worth of general government	Adjusted financial net worth of general government	Risk factor
	Household debt	Household debt	Risk factor
Natural capital	Greenhouse gas emissions	Total greenhouse gas emissions from domestic production, excluding those from land use, land-use change and forestry	Risk factor
	Material footprint	Used raw material extracted to meet the economy’s final demands	Flow
	Red List Index of threatened species	Combined indicator of extinction risk for birds, mammals, amphibians, cycads and corals	Risk factor
Human capital	Educational attainment among young adults	Upper secondary educational attainment among young adults	Stock
	Labour underutilisation rate	Broad labour underutilisation rate	Risk factor
	Premature mortality	Potential years of life lost due to a range of medical conditions and fatal accidents	Flow
Social capital	Trust in others	Mean interpersonal trust on a scale from 0 (you do not trust any other person) to 10 (most people can be trusted)	Stock
	Trust in government	Share of the population responding “yes” to a question about confidence in the national government	Stock
	Gender parity in politics	Share of women in national parliament	Resilience factor

Source: For full details, including sources, please refer to OECD (2024), How's Life? 2024: Well-being and Resilience in Times of Crisis, OECD Publishing, Paris, https://doi.org/10.1787/90ba854a-en.

Notes

← 1.  The Gross National Income inventories in the EU area could be used as a reference.

← 2.  “Report on financial and economic activities of the enterprise” (index 1- PF, frequency - annual).

← 3.  Generic guidance is provided in the SNA para. 7.84 and in the GFSM 2014 (IMF, 2014[11]), where there is some text on The Time of recording and measurement of Taxes and other compulsory transfers from para. 3.77 to 3.86 and in particular para. 3.80 second point “Cash amounts are recorded in the accounts, but they are time-adjusted so that they are attributed to the period when the activity took place to generate the liability” and para 3.81 “If taxes are imposed on specific transactions or events, they are recorded at the time the underlying transaction or event occurs, even though these times may not coincide with the actual payment of the tax to the government. This implies that taxes on products and imports are recorded at the time the products in question are produced, imported, or sold, depending on the basis of the taxation. Examples include sales taxes, value-added taxes, import duties, and estate and gift taxes.”

← 4.  In the experimental estimates, the BNS switched the base year to 2010, but it is less clear if this also applies to the regular release as volume indices are presented as year-over-year growth rates (BNS, n.d.[78]).

← 5.  This is for instance an example from Belgium: Gross National Income methodological inventory BELGIUM (National Accounts Institute of Belgium, 2022[77]).

← 6.  For instance, average selling prices of new housing in Astana was 338 494 tenge (KZT) per square meter in December 2016. In January 2017, the sample for new housing in Astana was updated, and the average price for December 2016, based on the updated sample, was 309 872 KZT. In December 2017, the price of new housing in Astana was 324 098 KZT per square meter, and the price index was calculated relative to the updated December 2016 sample.

← 7.  The rules have been approved by the Order of the Chairman of the Statistics Committee of the Ministry of National Economy of the Republic of Kazakhstan No. 20 of 28 December 2018 and registered with the Ministry of Justice of the Republic of Kazakhstan under No. 18136 on 29 December 2018.

← 8.  Production of total industry; Production in total mining; Production in total manufacturing; Production of total energy; and Production of total construction.

← 9.  ISIN, or International Securities Identification Number, is a 12-digit code whose sole purpose is to uniquely identify a security, such as a stock or a bond.

← 10.  In the “Analytical accounts of the Central Bank: NBK, 2025” methodology available on the website, under the heading “Timeliness”, it is stated: “The 6th business day after the reference month.” for the “monetary base”. However, referencing the release calendar, 11 working days after the month seems correct.

← 11.  For example, question 5 asks: In your opinion, in what way prices of goods and services will be changing in the next 12 months? While the OECD recommends: By comparison with the past 12 months, how do you expect the consumer prices will develop in the next 12 months? Responses to the Population Survey question are: Will be increasing faster than now; Will be increasing the same way as now; Will be increasing slower than now; Will remain at the current level/unchanged; Will decrease; Do not know. Responses for the OECD question are: increase more rapidly; increase at the same rate; remain the same; increase at a slower rate; stay about the same; fall; don't know.

← 12.  The conversion of diffusion indices to new balances is explained in the OECD Handbook on Business Tendency Surveys (OECD, 2003, p. 33[79]).

← 13.  The metadata in one of the Excel files indicates that explains that seasonal adjustment is done with the X13-ARIMA-SEATS method via JDemetra+. Furthermore, series are seasonally adjusted (SA) directly and there is no benchmarking with non-seasonally adjusted data. This is something that could be improved in the future to ensure that the sum of the quarters is consistent between SA and non-SA data. Also, the experimental estimates are currently released with some delay (120 days after the end of the reporting quarter).