## 1. A technical assistance (TA) mission on source data for national accounts was

## Source details

**Canonical URL:** [1. A technical assistance (TA) mission on source data for national accounts was](https://www.imf.org/-/media/files/publications/cr/2023/english/1uzbea2023001.pdf)

## Other formats

- [Markdown version](/-/media/files/publications/cr/2023/english/1uzbea2023001.pdf.md)
- [Structured JSON version](/-/media/files/publications/cr/2023/english/1uzbea2023001.pdf.json)

---

### Mission overview
- Conducted by the Caucasus, Central Asia, and Mongolia Regional Capacity Development Center (CCAMTAC) during May 22–26, 2023.
- Purpose: assist the Statistics Agency (SA) of Uzbekistan in improving source data for compiling annual and quarterly GDP; review business statistics data collection systems (annual, quarterly, and monthly).
- Context: SA has started compiling discrete estimates of quarterly GDP required for IMF Special Data Dissemination Standard (SDDS) subscription; time series of quarterly GDP by activity since 2019 are available on the SA website.

### Progress and main findings
- National accounts team has made very good progress compiling and disseminating quarterly national accounts aggregates on a discrete basis.
- Time series of quarterly GDP by activity since 2019 available; other business statistics units still publish indicators on cumulative basis only.
- All data collection forms have been modified to collect data on both discrete and cumulative bases, but monthly and quarterly short-term indicators are still compiled from cumulative data.
- Survey questionnaires are comprehensive and available in electronic form; some survey design simplifications recommended (details in Section B).
- SA is highly motivated to achieve SDDS subscription; senior management agreed to implement suggested improvements and to cooperate with tax authorities to increase coverage of small/micro units and individual producers.

### Priority recommendations and action plan (selected milestones)
- January 2024: Compile and publish all short-term business statistics as time series (i.e., from discrete source data). — Responsible: SA
- January 2024: Redesign some survey forms, and review data collection methods for business statistics to free resources and improve data quality. — Responsible: SA
- December 2024: Work with tax authorities on improving data coverage for small and micro units as well as individual producers. — Responsible: SA

### Main issues with source data for discrete quarterly GDP
- Current questionnaire practice: request data for (i) current discrete period (month, quarter), (ii) current cumulative period, and (iii) cumulative period of the previous year.
- International best practice: request data for current discrete period (month, quarter) and the corresponding month/quarter of the previous year to allow better data validation.
- Mixed respondent feedback on accuracy of discrete versus cumulative data; mission’s view: enterprise bookkeeping in Uzbekistan can provide good quality discrete data since taxes and social contributions are levied on a discrete monthly/quarterly basis.
- Current practice: compilation and dissemination of short-term business statistics indicators are done from cumulative source data; output data on a discrete basis provided to NAD are often derived as residuals between cumulative values (“de-cumulated”).
- Risks: “de-cumulated” source data can limit GDP estimates’ ability to reflect turning points.

Recommended actions (from Section A)
- Establish validation procedures for checking the accuracy of discrete monthly and quarterly source data.
- After discussion with respondents, review survey forms to replace “cumulative period of the previous year” with “month/quarter of the previous year.”
- Abandon practice of compiling monthly/quarterly output and other indicators from cumulative data; compile indicators for cumulative periods by aggregating discrete period estimates.
- Start compiling and disseminating all short-term indicators as time series (from discrete data).

### Data collection issues for GDP by activities — Cross-cutting
- Enterprise classification: (i) large; (ii) small and micro units; and (iii) individual producers; allocation criteria include activity, employment, and other indicators.
- Main harmonized forms for non-financial sector: (i) annual report for large enterprises (1 ks korxona); (ii) monthly report for large enterprises (12 ks korxona); (iii) annual report for small and micro units (1 kb).
- Data collection: electronic forms available on SA website; main source for GDP compilation.
- Respondent deadlines: annual reports — end of February (or 27 March for enterprises with foreign capital) or beginning of March (respectively April for foreign enterprises); monthly deadline — on the 5th day of the next month.
- Concern: tight monthly/quarterly deadlines may trade off accuracy for timeliness; mission suggests meeting respondents for feedback and possibly lengthening deadlines to avoid backward revisions.
- Annual report for small and micro units: 22 pages of tables and 55 pages of instructions (in Uzbek and Russian); first section (employment) extremely detailed — mission’s view: needs significant streamlining.

Recommended actions (cross-cutting)
- Discuss with respondents the accuracy of their monthly data bearing in mind trade-offs between timeliness and accuracy.
- Review monthly form for large enterprises to consider replacing “cumulative period of the previous year” with “month/quarter of the previous year.”
- Develop sound validation techniques for discrete data in monthly report for large enterprises.
- Streamline annual report for small and micro units to make it more concise.
- Work with tax authorities on improving data coverage for small and micro units as well as individual producers.

### Industrial statistics
- Large industrial units: surveyed monthly and annually; small and micro units: surveyed quarterly and annually.
- Industrial statistics division surveys quarterly individual producers via a sample survey of around 10,000 persons interviewed by telephone.
- Industrial Production Index (IPI): compilation broadly in line with international standards but some method simplifications suggested; monthly and quarterly IPI should be compiled from discrete source data for full consistency with national accounts.

Recommendations (industrial)
- Ensure all short-term industrial indicators are compiled from discrete source data for large, small and micro units and not as residuals from cumulative data.
- Start disseminating monthly and quarterly indicators as time series; publish the IPI in time series format to meet SDDS requirements.
- Compile and disseminate indicators for cumulative periods via aggregation of discrete periods.
- Review respondents’ deadlines considering timeliness-quality trade-offs.
- Derive the IPI from discrete source data to ensure consistency with quarterly GDP time series.

### Construction and investment statistics
- SA collects monthly, quarterly, and annual reports on construction work from all enterprises; sample surveys conducted for individual producers; informal construction data collected through household budget survey.
- Data on gross fixed capital formation collected quarterly and annually; forms are comprehensive and detailed enough for national accounts.
- Respondents face the same tight deadlines (5th day of the following month and quarter).
- Construction division has implemented thorough procedures validating discrete data; accuracy of discrete data is satisfactory.
- Inconsistency: volume index of output in construction estimated by construction division not entirely consistent with NAD’s estimate; would be resolved if both compiled directly from discrete source data.

Recommendations (construction)
- Continue established validation procedures for discrete monthly and quarterly data.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Review respondents’ deadlines considering timeliness-quality trade-offs.
- Derive volume indices of output and other aggregates from discrete source data to ensure consistency with quarterly GDP time series.

### Trade statistics
- Trade statistics unit collects detailed turnover data (monthly, quarterly, annual) by commodities for retail and wholesale trade; data on quantities and values by detailed product groups are collected.
- Survey forms request turnover but not trade margins; trade margins collected by a different SA division (Statistics on Services) — mission view: turnover and trade margin data should be collected in one survey to allow better validation.
- Informal sector: over 30 percent of trade turnover accounted for by individual producers; individual producers surveyed quarterly, but exhaustiveness concerns remain.

Recommendations (trade)
- Ensure all short-term indicators compiled from discrete source data; establish sound validation procedures.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Incorporate a column on trade margins in trade data collection forms to improve consistency between turnover and trade margins.
- Work with the tax administration to improve coverage of individual trade producers.
- Establish procedures for estimating the informal sector, as agreed with CCAMTAC missions on national accounts.

### Statistics on services
- Services division collects/publishes data on market services (excluding construction and trade turnover); all units surveyed annually, larger ones monthly, small/micro and individual producers quarterly; tax authority data used to estimate services output.
- Legal requirement: SA obliged to publish monthly data on provision of market services to households with detailed service breakdown and allocation by urban/rural and region.
- Concerns: collecting such detailed data from enterprises may lead to huge bias (no bookkeeping obligation for detailed requests); tight deadlines; for some services (e.g., financial services), estimates from commercial banks are much higher than national accounts estimates.
- Mission view: SA should review trade-offs between accuracy, timeliness, and detail and propose feasible changes.

Recommendations (services)
- Ensure all short-term indicators compiled from discrete source data; establish sound validation procedures.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Transfer collection of trade margins data to SA unit in charge of trade.
- Review accuracy and feasibility of monthly data on financial services provided to households, noting Central Bank of Uzbekistan already collects such data with extended deadlines.

### GDP compilation issues
- NAD has made good progress compiling and publishing discrete quarterly GDP; time series since 2019 are available on SA website in MS Excel format.
- Review of NAD compilation spreadsheets for construction: compilation steps follow international best practices and formulae look correct.
- Inconsistency persists between quarterly GDP aggregates (NAD) and aggregates/indices published by other divisions because other departments still use cumulative data for output volume indices.
- Mission view: other departments should follow NAD compilation steps; transitions can be smooth as NAD has been training other divisions’ staff.
- NAD uses 2019 as fixed base year for estimating GDP at constant prices; plan to move to 2024 as new base year.
- Mission view on base-year practice: NAD could continue compiling constant price aggregates at fixed base year prices, but it should be updated at least every five years. Priority remains transition to discrete data and SDDS subscription; moving to volume measures (compiling aggregates at average prices of the previous year) would require significant training and TA and resources currently committed to transition to time series format.
- Taxes and trade margins at constant prices: method for taxes follows best practices; method for trade margins should be improved — trade margins at constant prices should be estimated by extrapolation of the base year margin by the volume index of sales.
- Annual turnover at constant prices: should be the sum of the quarterly deflated values; avoid deflating total annual turnover with the annual consumer price index because that method does not account for shares of turnover by quarters.
- Mission developed a spreadsheet with recommendations for NAD to use when revising the method.

### Price statistics and ongoing work
- The mission met with the price statistics department and was informed broadly on the compilation procedures for the consumer price index and the producer price index.
- The SA receives TA in price statistics from the IMF (headquarters as well as CCAMTAC).
- Work is underway on developing a hybrid imports/exports price index, which will improve the accuracy of the GDP by expenditure aggregates.

### Mission recommendations for national accounts compilation (summary)
- Work with all other SA departments on implementing the compilation steps developed for quarterly GDP into the practice for estimating output by activity at constant prices (to ensure consistency between national accounts and business statistics).
- Update the base year for national accounts at least every five years.
- Improve the method for compiling trade margins at constant prices by using the extrapolation of base year turnover with the volume index of sales.

### Detailed technical assessment and recommended priority actions (milestones and target completion dates)
- Outcome: Source data are adequate for the compilation of the national accounts
  - H Establish validation procedures for checking the accuracy of discrete monthly and quarterly data — December 2023
  - M After discussion with respondents, review survey forms to replace “cumulative period of the previous year” with “month/quarter of the previous year” — December 2023
  - H Abandon the practice of compiling monthly/quarterly output and other indicators from cumulative data; compile indicators for cumulative periods by aggregating discrete period estimates — January 2024
  - H Start compiling and disseminating all short-term indicators as time series (from discrete data) — January 2024
  - H Streamline the annual report for small and micro units (1 kb) to make it more concise — December 2023
  - M Review respondents’ deadlines bearing in mind trade-offs between timeliness and quality — December 2023
  - H Incorporate a column on trade margins in the trade data collection forms, to allow better consistency between turnover and trade margins — December 2023
  - H Review the accuracy and make a decision on the feasibility of monthly data on financial services provided to households — December 2023
  - H Work with the tax authorities on improving data coverage for small and micro units as well as individual producers — December 2023

- Outcome: Data are compiled and disseminated using the concepts and definitions of the latest manual/guide
  - H Derive volume indices of output and other aggregates from discrete source data in order to ensure full consistency between business statistics and quarterly GDP time series — January 2024
  - H Update the base year for national accounts at least every five years — June 2025
  - H Improve the method for compiling trade margins at constant prices by using the extrapolation of base year turnover with the volume index of sales — March 2024

- Priority legend:
  - H – High; M - Medium

### Officials met during the mission (from SA)
- Mr. Agzam Ikramov — Deputy Director of SA
- Mr. Abrorali Mamadjanov — Head of Macroeconomic Indicators and National Accounts
- Mr. Odiljon Mamadaliyev — Head of Department International Cooperation and Data Exchange
- Mr. Mukhtor Umarov — Head of Price Statistics Department
- Mr. Muzaffar Jamolov — Head of Investment and Construction Statistics Department
- Mr. Mamurjon Satimov — Head of Department of Business Environment Surveys, Business Development Statistics
- Mr. Sardor Juraev — Head of Industry Statistics Department
- Mr. Abdugafir Mirzahidov — Head of Services Statistics Department
- Mr. Akrom Sultanov — Head of Department of monitoring and coordination of the open data portal
- Mr. Javlan Saipov — Deputy Head of Investment and Construction Statistics Department
- Mr. Umarjon Obidov — Deputy Head of Macroeconomic Indicators and National Accounts
- Mr. Ulugbek Noipov — Deputy Head of Department International Cooperation and Data Exchange
- Mr. Sardor Begmatov — Deputy Head of Department of Foreign Economic Activity and Trade Statistics
- Mr. Sherzod Muminov — Head of Division for Preparing Information on Foreign Economic Activity and Trade Statistics
- Ms. Komila Maksumova — Head of Protocol Division
- Mr. Zayniddin Egamsukurov — Head of Producers Price Statistics Division
- Mr. Bahodir Jabbarov — Head of Division for Complex Statistical Observations of Investments in Non-Financial Assets
- Mr. Elyor Babaxanov — Head of Energy Sector of Energy Statistics, Renewable Energy Sources and Energy Efficiency Calculations

*Source: 1uzbea2023001 - 1. A technical assistance (TA) mission on source data for national accounts was*

### 1. A technical assistance (TA) mission on source data for national accounts was

### 1. A technical assistance (TA) mission on source data for national accounts was

### Mission overview
- Conducted by the Caucasus, Central Asia, and Mongolia Regional Capacity Development Center (CCAMTAC) during May 22–26, 2023.
- Purpose: assist the Statistics Agency (SA) of Uzbekistan in improving source data for compiling annual and quarterly GDP; review business statistics data collection systems (annual, quarterly, and monthly).
- Context: SA has started compiling discrete estimates of quarterly GDP required for IMF Special Data Dissemination Standard (SDDS) subscription; time series of quarterly GDP by activity since 2019 are available on the SA website.

### Progress and main findings
- National accounts team has made very good progress compiling and disseminating quarterly national accounts aggregates on a discrete basis.
- Time series of quarterly GDP by activity since 2019 available; other business statistics units still publish indicators on cumulative basis only.
- All data collection forms have been modified to collect data on both discrete and cumulative bases, but monthly and quarterly short-term indicators are still compiled from cumulative data.
- Survey questionnaires are comprehensive and available in electronic form; some survey design simplifications recommended (details in Section B).
- SA is highly motivated to achieve SDDS subscription; senior management agreed to implement suggested improvements and to cooperate with tax authorities to increase coverage of small/micro units and individual producers.

### Priority recommendations and action plan (Table 1)
- January 2024: Compile and publish all short-term business statistics as time series (i.e., from discrete source data). — Responsible: SA
- January 2024: Redesign some survey forms, and review data collection methods for business statistics to free resources and improve data quality. — Responsible: SA
- December 2024: Work with tax authorities on improving data coverage for small and micro units as well as individual producers. — Responsible: SA

### Main issues with source data for discrete quarterly GDP
- Current questionnaire practice: request data for (i) current discrete period (month, quarter), (ii) current cumulative period, and (iii) cumulative period of the previous year.
- International best practice: request data for current discrete period (month, quarter) and the corresponding month/quarter of the previous year to allow better data validation.
- Mixed respondent feedback on accuracy of discrete versus cumulative data; mission’s view: enterprise bookkeeping in Uzbekistan can provide good quality discrete data since taxes and social contributions are levied on a discrete monthly/quarterly basis.
- Current practice: compilation and dissemination of short-term business statistics indicators are done from cumulative source data; output data on a discrete basis provided to NAD are often derived as residuals between cumulative values (“de-cumulated”).
- Risks: “de-cumulated” source data can limit GDP estimates’ ability to reflect turning points.

Recommended actions (from Section A):
- Establish validation procedures for checking the accuracy of discrete monthly and quarterly source data.
- After discussion with respondents, review survey forms to replace “cumulative period of the previous year” with “month/quarter of the previous year.”
- Abandon practice of compiling monthly/quarterly output and other indicators from cumulative data; compile indicators for cumulative periods by aggregating discrete period estimates.
- Start compiling and disseminating all short-term indicators as time series (from discrete data).

### Data collection issues for GDP by activities — Cross-cutting
- Enterprise classification: (i) large; (ii) small and micro units; and (iii) individual producers; allocation criteria include activity, employment, and other indicators.
- Main harmonized forms for non-financial sector: (i) annual report for large enterprises (1 ks korxona); (ii) monthly report for large enterprises (12 ks korxona); (iii) annual report for small and micro units (1 kb).
- Data collection: electronic forms available on SA website; main source for GDP compilation.
- Respondent deadlines: annual reports — end of February (or 27 March for enterprises with foreign capital) or beginning of March (respectively April for foreign enterprises); monthly deadline — on the 5th day of the next month.
- Concern: tight monthly/quarterly deadlines may trade off accuracy for timeliness; mission suggests meeting respondents for feedback and possibly lengthening deadlines to avoid backward revisions.
- Annual report for small and micro units: 22 pages of tables and 55 pages of instructions (in Uzbek and Russian); first section (employment) extremely detailed — mission’s view: needs significant streamlining.

Recommended actions (cross-cutting):
- Discuss with respondents the accuracy of their monthly data bearing in mind trade-offs between timeliness and accuracy.
- Review monthly form for large enterprises to consider replacing “cumulative period of the previous year” with “month/quarter of the previous year.”
- Develop sound validation techniques for discrete data in monthly report for large enterprises.
- Streamline annual report for small and micro units to make it more concise.
- Work with tax authorities on improving data coverage for small and micro units as well as individual producers.

### Industrial statistics
- Large industrial units: surveyed monthly and annually; small and micro units: surveyed quarterly and annually.
- Industrial statistics division surveys quarterly individual producers via a sample survey of around 10,000 persons interviewed by telephone.
- Industrial Production Index (IPI): compilation broadly in line with international standards but some method simplifications suggested; monthly and quarterly IPI should be compiled from discrete source data for full consistency with national accounts.

Recommendations (industrial):
- Ensure all short-term industrial indicators are compiled from discrete source data for large, small and micro units and not as residuals from cumulative data.
- Start disseminating monthly and quarterly indicators as time series; publish the IPI in time series format to meet SDDS requirements.
- Compile and disseminate indicators for cumulative periods via aggregation of discrete periods.
- Review respondents’ deadlines considering timeliness-quality trade-offs.
- Derive the IPI from discrete source data to ensure consistency with quarterly GDP time series.

### Construction and investment statistics
- SA collects monthly, quarterly, and annual reports on construction work from all enterprises; sample surveys conducted for individual producers; informal construction data collected through household budget survey.
- Data on gross fixed capital formation collected quarterly and annually; forms are comprehensive and detailed enough for national accounts.
- Respondents face the same tight deadlines (5th day of the following month and quarter).
- Construction division has implemented thorough procedures validating discrete data; accuracy of discrete data is satisfactory.
- Inconsistency: volume index of output in construction estimated by construction division not entirely consistent with NAD’s estimate; would be resolved if both compiled directly from discrete source data.

Recommendations (construction):
- Continue established validation procedures for discrete monthly and quarterly data.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Review respondents’ deadlines considering timeliness-quality trade-offs.
- Derive volume indices of output and other aggregates from discrete source data to ensure consistency with quarterly GDP time series.

### Trade statistics
- Trade statistics unit collects detailed turnover data (monthly, quarterly, annual) by commodities for retail and wholesale trade; data on quantities and values by detailed product groups are collected.
- Survey forms request turnover but not trade margins; trade margins collected by a different SA division (Statistics on Services) — mission view: turnover and trade margin data should be collected in one survey to allow better validation.
- Informal sector: over 30 percent of trade turnover accounted for by individual producers; individual producers surveyed quarterly, but exhaustiveness concerns remain.

Recommendations (trade):
- Ensure all short-term indicators compiled from discrete source data; establish sound validation procedures.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Incorporate a column on trade margins in trade data collection forms to improve consistency between turnover and trade margins.
- Work with the tax administration to improve coverage of individual trade producers.
- Establish procedures for estimating the informal sector, as agreed with CCAMTAC missions on national accounts.

### Statistics on services
- Services division collects/publishes data on market services (excluding construction and trade turnover); all units surveyed annually, larger ones monthly, small/micro and individual producers quarterly; tax authority data used to estimate services output.
- Legal requirement: SA obliged to publish monthly data on provision of market services to households with detailed service breakdown and allocation by urban/rural and region.
- Concerns: collecting such detailed data from enterprises may lead to huge bias (no bookkeeping obligation for detailed requests); tight deadlines; for some services (e.g., financial services), estimates from commercial banks are much higher than national accounts estimates.
- Mission view: SA should review trade-offs between accuracy, timeliness, and detail and propose feasible changes.

Recommendations (services):
- Ensure all short-term indicators compiled from discrete source data; establish sound validation procedures.
- Start disseminating monthly and quarterly indicators as time series; compile cumulative indicators by aggregation.
- Transfer collection of trade margins data to SA unit in charge of trade.
- Review accuracy and feasibility of monthly data on financial services provided to households, noting Central Bank of Uzbekistan already collects such data with extended deadlines.

### GDP compilation issues
- NAD has made good progress compiling and publishing discrete quarterly GDP; time series since 2019 are available on SA website in MS Excel format.
- Review of NAD compilation spreadsheets for construction: compilation steps follow international best practices and formulae look correct.
- Inconsistency persists between quarterly GDP aggregates (NAD) and aggregates/indices published by other divisions because other departments still use cumulative data for output volume indices.
- Mission view: other departments should follow NAD compilation steps; transitions can be smooth as NAD has been training other divisions’ staff.
- NAD uses 2019 as fixed base year for estimating GDP at constant prices; plan to move to 2024 as new base year.
- Mission view on base-year practice: NAD could continue compiling constant price aggregates at fixed base year prices, but it should be updated at least every five years. Priority remains transition to discrete data and SDDS subscription; moving to volume measures (compiling aggregates at average prices of the previous year) would require significant training and TA and resources currently committed to transition to time series format.
- Taxes and trade margins at constant prices: method for taxes follows best practices; method for trade margins should be improved — trade margins at constant prices should be estimated by extrapolation of the base year margin by the volume index of sales.
- Annual turnover at constant prices: should be the sum of the quarterly deflated values; avoid deflating total annual turnover with the annual consumer price index because that method does not account for shares of turnover by quarters.
- Mission developed a spreadsheet with recommendations for NAD to use when revising the method.

*Source: 1uzbea2023001 - 1. A technical assistance (TA) mission on source data for national accounts was*

### 41. The mission met with the price statistics department and was informed broadly on the

### 41. The mission met with the price statistics department and was informed broadly on the compilation procedures for the consumer price index and the producer price index

### Price statistics and ongoing work
- The mission met with the price statistics department and was informed broadly on the compilation procedures for the consumer price index and the producer price index.  
- The SA receives TA in price statistics from the IMF (headquarters as well as CCAMTAC).  
- Work is underway on developing a hybrid imports/exports price index, which will improve the accuracy of the GDP by expenditure aggregates.

### Mission recommendations for national accounts compilation
- Work with all other SA departments on implementing the compilation steps developed for quarterly GDP into the practice for estimating output by activity at constant prices (to ensure consistency between national accounts and business statistics).
- Update the base year for national accounts at least every five years.
- Improve the method for compiling trade margins at constant prices by using the extrapolation of base year turnover with the volume index of sales.

### Detailed technical assessment and recommended priority actions (milestones and target completion dates)
- Outcome: Source data are adequate for the compilation of the national accounts
  - H Establish validation procedures for checking the accuracy of discrete monthly and quarterly data — December 2023
  - M After discussion with respondents, review survey forms to replace “cumulative period of the previous year” with “month/quarter of the previous year” — December 2023
  - H Abandon the practice of compiling monthly/quarterly output and other indicators from cumulative data; compile indicators for cumulative periods by aggregating discrete period estimates — January 2024
  - H Start compiling and disseminating all short-term indicators as time series (from discrete data) — January 2024
  - H Streamline the annual report for small and micro units (1 kb) to make it more concise — December 2023
  - M Review respondents’ deadlines bearing in mind trade-offs between timeliness and quality — December 2023
  - H Incorporate a column on trade margins in the trade data collection forms, to allow better consistency between turnover and trade margins — December 2023
  - H Review the accuracy and make a decision on the feasibility of monthly data on financial services provided to households — December 2023
  - H Work with the tax authorities on improving data coverage for small and micro units as well as individual producers — December 2023

- Outcome: Data are compiled and disseminated using the concepts and definitions of the latest manual/guide
  - H Derive volume indices of output and other aggregates from discrete source data in order to ensure full consistency between business statistics and quarterly GDP time series — January 2024
  - H Update the base year for national accounts at least every five years — June 2025
  - H Improve the method for compiling trade margins at constant prices by using the extrapolation of base year turnover with the volume index of sales — March 2024

- Priority legend:
  - H – High; M - Medium

### Officials met during the mission (from SA)
- Mr. Agzam Ikramov — Deputy Director of SA
- Mr. Abrorali Mamadjanov — Head of Macroeconomic Indicators and National Accounts
- Mr. Odiljon Mamadaliyev — Head of Department International Cooperation and Data Exchange
- Mr. Mukhtor Umarov — Head of Price Statistics Department
- Mr. Muzaffar Jamolov — Head of Investment and Construction Statistics Department
- Mr. Mamurjon Satimov — Head of Department of Business Environment Surveys, Business Development Statistics
- Mr. Sardor Juraev — Head of Industry Statistics Department
- Mr. Abdugafir Mirzahidov — Head of Services Statistics Department
- Mr. Akrom Sultanov — Head of Department of monitoring and coordination of the open data portal
- Mr. Javlan Saipov — Deputy Head of Investment and Construction Statistics Department
- Mr. Umarjon Obidov — Deputy Head of Macroeconomic Indicators and National Accounts
- Mr. Ulugbek Noipov — Deputy Head of Department International Cooperation and Data Exchange
- Mr. Sardor Begmatov — Deputy Head of Department of Foreign Economic Activity and Trade Statistics
- Mr. Sherzod Muminov — Head of Division for Preparing Information on Foreign Economic Activity and Trade Statistics
- Ms. Komila Maksumova — Head of Protocol Division
- Mr. Zayniddin Egamsukurov — Head of Producers Price Statistics Division
- Mr. Bahodir Jabbarov — Head of Division for Complex Statistical Observations of Investments in Non-Financial Assets
- Mr. Elyor Babaxanov — Head of Energy Sector of Energy Statistics, Renewable Energy Sources and Energy Efficiency Calculations

*Content derived from: 1uzbea2023001 - 41. The mission met with the price statistics department and was informed broadly on the*

---


_Source: https://www.imf.org/-/media/files/publications/cr/2023/english/1uzbea2023001.pdf_