Republic of Lithuania: Report on the Observance of Standards and Codes (ROSC)—Data Module
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International Monetary Fund
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Publication Date:
04 Dec 2002
eISBN:
9781451824094
Language:
English
Keywords:
ISCR; CR; balance of payments; state; government; sc; time series; government operation; operations statistic; statistics definition; government operations statistic; Special Data Dissemination Standard (SDDS); Data dissemination; Treasury Department of the MOF; government data
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This report on the Observance of Standards and Codes data module provides a review of Lithuania’s data dissemination practices against the IMF’s Special Data Dissemination Standard, complemented by an in-depth assessment of the quality of the national accounts, consumer price index, producer price index, government finance, monetary, and balance-of-payments statistics, using the IMF’s Data Quality Assessment Framework. The quality of Lithuania’s macroeconomic statistics has improved significantly. The authorities have also established a good track record of implementing recommendations of the past IMF technical assistance missions in the area of statistics.

Abstract

This report on the Observance of Standards and Codes data module provides a review of Lithuania’s data dissemination practices against the IMF’s Special Data Dissemination Standard, complemented by an in-depth assessment of the quality of the national accounts, consumer price index, producer price index, government finance, monetary, and balance-of-payments statistics, using the IMF’s Data Quality Assessment Framework. The quality of Lithuania’s macroeconomic statistics has improved significantly. The authorities have also established a good track record of implementing recommendations of the past IMF technical assistance missions in the area of statistics.

I. Introduction

1. The data dissemination module of this Report on the Observance of Standards and Codes (ROSC) provides a summary of Lithuania’s practices on the coverage, periodicity, and timeliness of the data categories. It is complemented by a detailed assessment of the quality of national accounts, consumer and producer price indices, and government finance, monetary, and balance of payments statistics using the Data Quality Assessment Framework (DQAF) developed by the IMF’s Statistics Department. This report is based on information provided prior to and during a staff mission from May 8–22, 2002,1 as well as publicly available information.

2. Section II includes an overview of the Special Data Dissemination Standard (SDDS) and an assessment of Lithuania’s data dissemination practices against this standard. Section III presents a summary assessment of the quality of the principal macroeconomic datasets, following the dataset-specific assessment frameworks. Finally, Section IV sets out recommendations to achieve further improvements in Lithuania’s statistics.

II. Data Dissemination Practices and the Special Data Dissemination Standard

Overview of the SDDS and current dissemination practices

3. The standard against which Lithuania’s data dissemination practices are assessed is the IMF’s SDDS.2 The SDDS is a “best practice” standard. It covers four sectors of the economy, (real, fiscal, financial, and external), as well as population, and identifies four dimensions (data, access, integrity, and quality) of data dissemination. For each of these dimensions, the SDDS prescribes two-to-four monitorable elements, or good practices, that can be observed, or monitored, by users of statistics.

4. Lithuania has subscribed to the SDDS since May 30, 1996 and started posting its metadata on the IMF’s Dissemination Standards Bulletin Board (DSBB) on April 7, 1997. Lithuania is in observance of the SDDS specifications for the coverage, periodicity, and timeliness of the data, and for the dissemination of advance release calendars, since July 12, 1999 (see Table 1). The National Summary Data Page and the Data Template on International Reserves and Foreign Currency Liquidity page were hyperlinked to the IMF’s DSBB on March 9, 2000 and June 16, 2000 respectively.

Table 1.

Lithuania: Overview of Current Practices Regarding Coverage, Periodicity, and Timeliness of Data Compared to the Special Data Dissemination Standard (SDDS)

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Note: Periodicity and timeliness: (D) daily or days; (WD) working days; (W) weekly or with a lag of no more than one week from the reference data or the closing of the reference week; (M) monthly or with a lag of no more than one month; (Q) quarterly or with a lag of no more than one quarter; (A) annually; and (…) not applicable.

5. The institutions responsible for the compilation and dissemination of the SDDS data categories are the Department of Statistics (DOS), the Ministry of Finance (MOF), and the Bank of Lithuania (BOL). The DOS compiles and disseminates the national accounts, and consumer price and producer price indices. The MOF compiles and disseminates data on central and general government operations and central government debt. The BOL has responsibility for the compilation and dissemination of the analytical accounts of the central bank, analytical accounts of the banking sector, and the balance of payments statistics. These three agencies are also responsible for the compilation of the other SDDS categories.

6. Lithuania provides access to these data through a variety of publications and the following Internet websites.

Data dimension: coverage, periodicity, and timeliness

7. Table 1 compares Lithuania’s data dissemination practices with the data dimension (coverage, periodicity, and timeliness) of the SDDS. Lithuania meets or exceeds the specifications of the data dimensions for all data categories.

Access dimension

8. Advance release calendars, giving at least one-quarter ahead notice of approximate release dates and at least one-week ahead notice of the precise release dates, are disseminated on the IMF’s DSBB, as well as on the websites of the BOL, MOF, and DOS. The public are informed of this in relevant publications of the BOL and the DOS.

9. The data are released simultaneously to all interested parties through press releases and/or on the websites of the relevant agencies and on Lithuania’s National Summary Data Page.

Monitoring of data and access dimensions

10. In accordance with the IMF Executive Board’s Third Review of the SDDS, the IMF staff began monitoring subscribers’ performance under the SDDS in July 2000. Monitoring is limited to the coverage, periodicity, and timeliness of the data and to the dissemination of advance release calendars.3 During the quarters July 2000–March 2002, Lithuania’s dissemination practices have been in observance with the SDDS requirements, and the dissemination of data on Lithuania’s National Summary Data Page has been timely.

III. Summary Assessment of Data Quality

11. The IMF complements the SDDS elements of the ROSC data module with an assessment of data quality based on the IMF’s Data Quality Assessment Framework (DQAF). The DQAF comprises a generic framework,4 and a set of dataset-specific frameworks. The frameworks cover five dimensions of data quality—integrity, methodological soundness, accuracy and reliability, serviceability, and accessibility—and a set of prerequisites.5

12. An assessment of six macroeconomic datasets (national accounts, consumer price index, producer price index, government finance, monetary, and the balance of payments statistics) was conducted using the frame of reference provided by the dataset-specific DQAF. The information resulting from the application of this framework to the Lithuanian statistical system is presented below, following the structure of the DQAF. Conclusions are also presented in the form of standardized summary tables in which the assessment of data practices is made on a qualitative basis, using a four-part scale (Table 2). In formulating its assessment, the mission took a “snapshot” of practices in place at the time of the mission. Some future plans are cited in the text, but these were not assessed.

Table 2.

Lithuania: Data Quality Assessment Framework:—Summary Presentation of Results

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Key to symbols: NA = Not Applicable; O = Practice Observed; LO = Practice Largely Observed; LNO=Practice Largely Not Observed; NO = Practice Not Observed

13. Lithuania’s macroeconomic statistics are adequate for effective surveillance, even though IMF staff identified shortcomings in some statistical practices that may detract from policy analysis and formulation.

Prerequisites of quality

This category in the DQAF identifies conditions within the agency in charge of producing statistics that have an impact on data quality. The elements within the category refer to the legal and institutional environment, resources, and quality awareness.

14. The DOS prepares the national accounts statistics, consumer price index (CPI), and the PPI. The legal and institutional environment is well defined, giving the DOS the legal authority, under the Law on Statistics, last updated in December 1999, to produce these data. The Law on Statistics establishes the confidentiality of official statistical data. However, the exception in the law for data characterizing environmental pollution creates ambiguity about the application of this principle. In terms of resources, the staff of the Price Statistics Division (PSD) and National Accounts Division (NAD) of the DOS are well qualified and highly experienced. Quality awareness is evidenced by an active strife for quality management, including by inviting international (EU) reviews of the DOS products and processes.

15. The MOF compiles government finance statistics under the legal and institutional environment set out in the “Work Program of Official Statistics” and under the terms of the Government Resolution No. 537/1997 and Government Resolution No. 1549/2001. The MOF has assigned the responsibility for compilation and dissemination of government operations statistics to its Financial Planning and Analysis Division (FPAD) and of the central government debt to its State Debt Management Department (SDMD). The FPAD has a strong legal and institutional environment that ensures timely delivery of source data covering all units of government. However, technical coordination with the BOL and the DOS needs to be strengthened. Computing resources are satisfactory. Staff allocated are highly qualified and experienced but insufficient in number to ensure a smooth transition to the Government Finance Statistics Manual 2001 (GFSM 2001), linked with the adoption of the ESA 95, and other improvements necessary in terms of data availability and accessibility. Quality awareness is evidenced by a one-year action plan, internal monitoring, processes across departments aimed at quality control, and an internal audit.

16. The BOL compiles the monetary statistics and the balance of payments statistics. Regarding the legal and institutional environment, responsibility and authority to collect data for the monetary and balance of payments statistics are clearly specified in the Law on the Bank of Lithuania (BOL Law) as amended in March 2001. Some weaknesses in data sharing between the BOL departments has resulted in inadequacies in the reporting forms for statistical purposes. The BOL does not have a regular exchange with the MOF or the DOS on monetary statistics. In the case of the balance of payments statistics, the system of data sharing among the compiling agencies for the balance of payments statistics is generally adequate. However, although the Resolution on the Compilation of the Balance of Payments of the Republic of Lithuania (Balance of Payments Resolution) of 1997 specifies the establishment of an inter-agency group to coordinate the work, this group now meets only on an irregular basis, and there have been instances when the BOL has not been kept informed of improvements in the data sources available from other agencies. Data confidentiality is guaranteed by the BOL Law and the Law on Statistics, and internal procedures are in place at the BOL to prevent the disclosure of individual data. Resources are adequate. The staff of the compiling units are experienced and well trained. Although the number of staff is broadly adequate for data compilation, work on improvements in the data sources and analysis of the data may well require an increase in staff. Three-year strategic plans and an annual review process of tasks, priorities, and resources, together with quarterly reports on the implementation of the plans, ensure the efficient use of resources. Quality awareness is high and processes are in place to monitor the quality of the collection, processing, and dissemination of the data, including through close collaboration with the ECB.

Integrity

Integrity identifies features that support firm adherence to objectivity in the collection, compilation, and dissemination of statistics so as to maintain users’ confidence. Elements refer to the professionalism and ethical standards that should guide policies and practices, which should be reinforced by their transparency.

17. As will become apparent in the assessments, Lithuania’s candidacy for accession to the European Union shapes Lithuania’s official statistics. The statistical agencies work closely with Eurostat and the ECB in the implementation of harmonized statistical methodology and practices based on EU guidelines. Some of the guidelines are quite specific in detailing the common practices that countries should follow, however others provide some flexibility for the adoption of country practices.

18. Practices are in place at the DOS to ensure professionalism in statistical policies and practices, transparency, and ethical standards. The organization is independent of the rest of government in decisions relating to the compilation of statistics. The NAD and PSD give advance notification of any major changes in methodology, source data, and statistical techniques. The DOS compiles statistics on an impartial basis and staff are free to choose the most appropriate data sources. The organization scrutinizes all media comment on their statistics and follows up on any misrepresentation. The Law on Statistics is available to the public, including on the DOS website. The DOS does not provide pre-release access to other government users and this policy is stated on the DSBB and in the Lithuania methodology.

19. The MOF staff is committed to professionalism, objectivity, political independence and ethical standards as stated in the Law on Statistics and the Law on Public Service. A strong legal basis ensures that staff remains independent from political and other influences in choosing the most appropriate sources and methods. Methodological choices are informed by reference to international standards. Regarding transparency, legal references and other terms and conditions of government finance statistics compilation and dissemination are available on the website of the MOF or on the DSBB, which notes that no officials outside the MOF have access to the data prior to their release to the public. However, the MOF does not inform the public of major changes in methodology, nor are its products clearly identified on the DOS webpage.

20. The BOL follows practices that ensure professionalism in the compilation of the monetary and balance of payments statistics. The independence of the BOL in compiling the monetary and balance of payments statistics is clearly established in the BOL Law, and the Balance of Payments Resolution. The data are compiled on an impartial basis and staff are free to choose the most appropriate data sources and statistical techniques. The BOL has the right to comment on erroneous interpretation and misuse of statistics, although such comment is not common. Transparency is generally adequate. The terms and conditions under which statistics are collected, processed, and disseminated are available to the public on the website of the BOL and on the IMF’s DSBB. No public officials outside the BOL have access to the data prior to their release, and the public are informed of this in the metadata posted on the DSBB and linked to BOL’s website. However, in the case of the balance of payments statistics, the data tables do not clearly indicate the name of the compiling division and agency, and the public are not generally informed of major changes in methodology, source data and statistical techniques, even at the time these changes are introduced. Guidelines for staff behavior and ethical standards are specified in the BOL Law and the internal rules of the bank, and are well known to staff.

Methodological soundness

Methodological soundness refers to the application of international standards, guidelines, and agreed practices. Application of such standards, which are specific to the dataset, is indicative of the soundness of the data and fosters international comparability. Elements refer to the basic building blocks of concepts and definitions, scope, classification and sectorization, and basis for recording.

21. National accounts data are compiled in accordance with the concepts and definitions of ESA 95, which is based on, and is consistent with the System of National Accounts 1993 (1993 SNA). The scope of the accounts is broad, including all the accounts and tables determined as the minimum requirements for the implementation of the 1993 SNA by the Inter-Secretariat Working Group on National Accounts, with the exception of the financial account.6 Classification systems of the accounts are generally in accordance with international guidelines. Regarding the basis for recording, transactions and plans are recorded on an accrual basis, with the exception of government accounts, which are recorded on a cash basis.

22. The CPI and PPI concepts and definitions are generally consistent with the ESA 95 and the European Union regulations, and are in line with best international practices. Standard international classifications are used for both indexes. The weights for each index are updated every year. The scope is the whole country, including some rural areas for the CPI. The prices collected represent actual transactions. The basis of recording is market prices on an accruals basis.

23. For government finance statistics, the concepts and definitions are consistent with the A Manual on Government Finance Statistics 1986 (GFSM 1986). The scope of the statistics is broad, covering all units of central government operations (including budgetary central government [the State Budget], social security, and extra-budgetary funds) on a monthly and quarterly basis, and on general government operations on an annual basis, and on central government debt on a monthly basis. There are, however, some gaps in the coverage, including foreign grants. The classification of government operations is closely in line with the GFSM 1986, but the data on central government includes the whole amount of the borrowing from the IMF instead of just the portion on-lent from the BOL, and this is classified as foreign rather than domestic debt. It also uses a currency criterion for defining foreign debt transactions. In addition, some transactions of the Guarantee Fund are booked as lending minus repayments instead of transfers/subsidies. The basis for recording is mainly on a cash basis, the debt is mainly at face value, and the government finance statistics data are presented on a gross consolidated basis, as is appropriate. Although the MOF has an action plan to review the government sector delineation and to adapt the budget classification to that of the accrual Government Finance Statistics Manual 2001 (GFSM 2001), no migration path to GFSM 2001 has yet been mapped.

24. The concepts and definitions for the monetary statistics are broadly consistent with the IMF’s Monetary and Financial Statistics Manual (MFSM). The scope of the analytical accounts of the banking sector is incomplete because it does not include banks in liquidation. Sectorization of sole proprietorships, nonprofit institutions serving households, and nonfinancial public enterprises is not consistent with MFSM. Concerning the basis for recording, securities held to maturity by credit institutions are recorded at historical values, even though they are traded in the secondary market. Loan portfolios are valued with the outstanding principal amount only, excluding any accrued interest.

25. The concepts and definitions for the balance of payments statistics are broadly in line with the fifth edition of the Balance of Payments Manual (BPM5). For scope, in principle, all resident-nonresident transactions are covered in the statistics, and no major components are excluded. Although there are some gaps in the coverage of the data, these are insignificant. The classification system used in the national presentation is consistent with the structure of the BPM5, with the expectation of the classification of the data on elements of merchandise trade, insurance, transfers (grants) from the EU, and capital transfers. Concerning the basis for recording, market prices are used to value most flows and stocks, and all foreign currency transactions are converted to the unit of account using the exchange rate prevailing on the day of the transaction. Transactions are recorded on an accruals basis as recommended by BPM5, with the exception of the data on interest payments for government debt, which is recorded on a cash basis. As recommended in BPM5, current account transactions are recorded on a gross basis, and financial account transactions are recorded on a net basis, separately for the individual asset and liability components.

Accuracy and reliability

Accuracy and reliability identifies features that contribute to the goal that data portray reality. Elements refer to identified features of the source data, statistical techniques, and supporting assessments and validation.

26. In the national accounts data, source data are collected from a combination of annual and quarterly surveys of enterprises, administrative data sources, and a continuous Household Budget Survey (HBS). The statistical techniques are generally sound, with single indicator methods being used to derive constant price estimates by type of activity. However, the perpetual inventory method is not used for estimating consumption of fixed capital, and these estimates are the aggregate value of depreciation derived from the accounting records of enterprises. Concerning assessment and validation of statistical source data, the data from the surveys are generally adjusted to account for undercoverage using data from administrative sources. However, the response rates for the surveys are not routinely assessed and where assessments of response errors or sampling errors are conducted, the results are not normally made public. On validation of statistical outputs, the statistical discrepancy between the estimates of GDP by type of economic activity and GDP by component of expenditure is not routinely investigated and it is assumed to be part of expenditure on GDP. Revisions studies are routinely carried out.

27. The CPI and PPI source data used to construct the weights and prices are obtained from a comprehensive data collection program. The HBS, used to derive the weights for the CPI, is based on a multistage probability sample design. Statistical techniques follow sound procedures and methods. Owner-occupied dwelling services and informal activities are not included in the CPI, as is common in many countries. On the other hand, adjustments are made to the CPI for known weaknesses, such as alcohol, tobacco, new cars, education, and health. The weights are changed every year, but for the PPI they are not adjusted for price changes between the weighting period and the price reference period. As to assessment and validation of source and intermediate data, appropriate measures are taken to validate data, including checks to ensure consistency with other related data sources, but the sampling technique used in both the CPI and PPI means that sampling errors are not available. Revision studies are not undertaken for the CPI. The PPI can be revised in the month following its first publication, but the CPI is never revised unless a significant error is found.

28. For government finance statistics, source data are available in a timely manner. For the State Budget, accounting sources—on a cash basis—are available at the Accounting Division of the State Treasury Department. The FPAD has arranged for the reporting on a regular basis of government finance statistics formatted source data by other units of central government—on a cash basis. It can exploit the detailed and up-to-date budget execution data of both central and local governments that the MOF tracks (revenue monthly, and expenditure quarterly) as the Municipalities Budget is part of the National Budget presented to parliament. The SDMD compiles data related to the central government debt from its operational database on an issue by issue basis. Statistical techniques employed are sound and data sources are close to government finance statistics definitions, minimizing the need for adjustment. The government finance statistics data do not include any estimates, and accordingly data are revised only exceptionally, generally following methodological changes and after revision studies. Assessment and validation of source data is generally adequate but could be more systematic against other relevant datasets, including monetary statistics and balance of payments statistics.

29. For monetary statistics, source data are derived from accounting records of the BOL and from credit institutions and permit the timely compilation of monetary statistics. The statistical techniques are adequate; for monthly data for credit unions, data from the most recent quarter are carried forward in the absence of monthly reporting by credit unions. The BOL does not calculate seasonally adjusted monetary data; however, it is examining methods to do so recommended by the European Central Bank (ECB) and Eurostat. The balance sheets of the BOL and of the credit institutions contain sufficient information for generating the monetary statistics. Assessment and validation is hampered because efforts of the compiling department to make the sectorization of source data consistent with monetary statistics are not adequately met by the reporting department concerning public nonfinancial corporations. The BOL checks the accuracy of the balance sheets submitted by credit institutions against secondary data sources, and investigates statistical discrepancies and large fluctuations. Revisions of monetary statistics are rare, and therefore, revision studies are undertaken irregularly.

30. For the balance of payments statistics, primary source data are broadly sufficient and timely, and approximate the BPM5 definitions, scope, classifications, valuation, and time of recording in most instances. For statistical techniques, where necessary, adjustments are made to improve the coverage and classifications. These are broadly adequate, but need to be further refined. Although data compilation procedures are generally sound, the data received by the BOL are manually input into the compilation tables in the computerized database, with the risk of errors in the data entry process. Adequate assessment and validation procedures are in place and have led to improvements in the data sources. The data obtained from the surveys are validated against other independent data sources whenever appropriate, and the behavior of data series is cross-checked with related series. Statistical discrepancies and other potential indicators of problems in statistical outputs are investigated. Although no formal revision studies are conducted, the reasons for significant revisions to the data are investigated and documented.

Serviceability

Serviceability focuses on practical aspects of how well a dataset meet users’ needs. Elements refer to the extent to which data are relevant, produced, and disseminated in timely fashion with appropriate periodicity, are consistent internally and with other datasets, and follow a predictable revisions policy.

31. The relevance of the national accounts statistics is monitored by the Statistical Council, which includes some data users. However, the DOS does not conduct regular user surveys. Timeliness and periodicity is observed; quarterly data are disseminated in line with the prescriptions of the SDDS. There is consistency within datasets and over a reasonable period of time, although not in all cases with other datasets. Revisions policy and practice are in place. The revisions cycle of the national accounts estimates is published; preliminary and revised data are clearly identified; revisions are measured, but not always explained.

32. The relevance of the CPI and the PPI statistics is monitored by the Statistical Council, which includes some data users. However, the DOS does not conduct regular user surveys. Timeliness and periodicity is observed; both indices are published monthly, respectively, on the sixth and fifth working day after the end of the reference month, exceeding SDDS requirements. The indexes are consistent for at least five years and are also checked against each other. A revision policy is in place, however, this is not published.

33. The government finance statistics are relevant for important users and the MOF receives feedback from the Statistical Council to help monitor the relevance of the data. However, government finance statistics is not used in the budget process and the practical utility of the data is hampered by the lack of detail in the disseminated data. The timeliness and periodicity on central government data meet the SDDS requirements. Concerning consistency, monthly and quarterly data on government operations are not reconcilable. Deficit estimates are not consistent over time and are not consistent with other statistical datasets including government operations and government debt data, balance of payments, monetary statistics, national accounts. The revision policy is explained on the DSBB, hyperlinked to the MOF website.

34. The monetary statistics are relevant, and there is a Statistical Council to help monitor the relevance of the data. However, the BOL does not fully meet users needs because statistics on the monetary authorities are not disseminated; only data on central bank accounts are published. The analytical accounts of the central bank and of the banking sector meet the timeliness and periodicity requirements of the SDDS. The monetary statistics are consistent with the balance of payments statistics, but not with the balance sheet data of other financial institutions, nor with the government finance statistics. Historical series of the analytical accounts of the central bank and/or the analytical accounts of the banking sector are reconstructed as far back as possible when significant changes in methodologies are introduced. The monetary data are generally final when first released. However, in accordance with the principles and guidelines on revision policy set out by the ECB, the last month’s data are defined as preliminary data. Preliminary data are clearly identified with an asterisk in disseminated formats and become final in the following month. This revision policy is well established, but it is not available in published format.

35. The balance of payments statistics data are relevant for users, and a Statistical Council helps to monitor this. The BOL consults regularly with the DOS to ensure that the balance of payments data meet the needs of the compilers of the data on the external sector of the national accounts. The BOL balance of payments staff also regularly participate in international statistical meetings and seminars organized by international and regional organizations on compilation issues. The timeliness and periodicity of the data meet the requirements of the SDDS. The balance of payments statistics are consistent within the dataset, and the concepts, definitions, and classifications for compiling the quarterly and annual statistics are the same. However, although extensive time series data are available, the statistics are not reconcilable over a reasonable period of time, as not all revisions and major changes in data sources, coverage and adjustments are identified in the data. In addition, it is not always possible to revise the historical data to reflect the changes in data sources, etc. The balance of payments statistics are not always consistent with those in other statistical frameworks. Although the data are consistent with the national accounts, and are broadly consistent with the monetary statistics, the data differ from the government finance statistics. Although the revision policy is predetermined and is described in the metadata posted on the DSBB, the preliminary and revised status of the data is not clearly identified in the data tables. Furthermore, the size of, and the reasons for, the revisions are not generally made public.

Accessibility

Accessibility deals with the availability of information to users. Elements refer to the extent to which data and metadata are clear and easily available and to which assistance to the user is adequate to help them find and use the data.

36. The national accounts data are generally accessible and are disseminated through press releases, on the website of the DOS, and in annual and quarterly hardcopy publications. However, neither the data activity tables nor the methodological notes contained in the various media make it clear to users that the statistical discrepancy between the estimates of GDP by the type of activity and components of expenditure is included in the estimate of changes in inventories. The data are released to all users simultaneously, according to the pre-release calendar posted on the website of the BOL. Metadata accessibility is adequate. The publications contain information on concepts, definitions, and methodology used in the compilation of the statistics and a summary of the methodology used in national accounts statistics is posted on the DSBB. Assistance to users is provided upon request, a catalogue and contact persons are available.

37. Data for CPI and PPI are generally accessible and data are first published through a press release and on the DOS website. The DOS publishes an advance release calendar specifying not just the date, but also the time of release. Additional, nonpublished nonconfidential breakdowns are supplied on request. Metadata are available for both indexes and in sufficient detail to meet the needs of all users. Assistance to users is provided through specific contact persons who are publicized for each index.

38. For government finance statistics, accessibility for government operations data is poor: (i) the detail disseminated is insufficient for user’s needs; (ii) annual data or time series are available only upon request; (iii) availability of the additional detail is not indicated on the website; (iv) no analysis accompanies the government finance statistics data table; (v) there is no publication covering government operations; and (vi) breaks in time series are not signaled. In contrast, detailed information and analysis is available on the state debt on the website but no annual publication is available. The advance release calendar is disseminated on the IMF’s DSBB, which is hyperlinked to the MOF website. Concerning metadata, methodological notes are on the DSBB. Assistance to users is provided via the internet and prompt and knowledgeable service is available upon request.

39. The monetary statistics are accessible to users. The data are disseminated in hardcopy and in electronic format. However, although some descriptions and explanations of developments of monetary data are provided in the monthly and quarterly bulletins, as well as in the annual report, these descriptions and analyses are not sufficient for proper interpretation and meaningful comparisons. An advance release calendar is disseminated on the BOL’s website, as well as on the DSBB, and a regular notice to this effect is published in the BOL’s Monthly Bulletin. The data are released simultaneously to all interested parties. The Monetary Statistics and Analysis Division produces time series containing detailed subaggregates for internal use. Users’ specific requests for detailed information that do not violate data confidentiality rules are normally satisfied. Metadata accessibility is provided as summary methodology statements for all monetary data categories which are posted on the DSBB and hyperlinked to the BOL’s website. Brief explanatory notes on the methodology are published also in the BOL’s Quarterly Bulletin. Assistance to users is provided and contact persons for monetary statistics in the BOL are disseminated on the BOL’s website and in publications. Publication catalogues are available on the BOL’s website.

40. For balance of payments statistics accessibility is adequate. Although a great deal of detailed data is disseminated, aspects of the format and layout of the tables hinder the interpretation and meaningful comparison of the data by users. Problems include the lack of rolling time series, the failure to identify the preliminary or revised status of the data, the lack of footnotes to explain breaks in the data or reasons for major revisions, etc., and the lack of commentary on the reasons for significant movements or trends in the data. The dissemination media and formats are adequate for user needs, and the data are released on a pre-announced schedule. The data are released simultaneously to all interested parties. Additional nonpublished and nonconfidential data are available on request. Metadata accessibility is adequate. Metadata that provide documentation on the statistics are disseminated in the summary of methodology statement posted on the DSBB and in the relevant publications. Assistance to users is prompt and knowledgeable, and the contact person is identified in the national publication and on the DSBB. The BOL website lists all publications and documents, including if applicable, information on how to subscribe.

IV. Staff’s Recommendations

41. Based on the results of the data quality assessment, discussions with the Lithuanian authorities in the statistics-compiling agencies, and responses to a survey and discussions with data users, the following measures are proposed to increase further Lithuania’s adherence to international statistical standards.

Executive Level Recommendations

  • Improve inter-agency coordination and consistency by re-establishing the regular quarterly meetings of the statistical agencies (and of the units within the agencies).

  • Conduct regular exercises to solicit the views of users (including the domestic public sector and the domestic and international private sectors).

  • Review and make consistent across agencies (the DOS, the MOF, and the BOL) the classifications, particularly the delimitation of the general government sector, foreign grants, hospitals, and the Guarantee Fund.

  • Strengthen government finance statistics, improve their accessibility, and increase their relevance for the budget process.

  • Remove clause on environmental protection in the Law of Statistics.

National Accounts

High priority

  • Develop a medium-term personnel plan, so that new staff may be recruited and trained in order to ensure some continuity in the compilation process considering the level of staff turnover expected within the next five years.

  • Specify in the notes accompanying the national accounts publications that the estimate of changes in inventory is estimated as a residual and not independently.

Other key recommendations

  • Estimate the consumption of fixed capital using perpetual inventory methods rather than using accounting values of depreciation as the estimate.

Producer Price Index

Other key recommendations

  • Adjust the PPI weights for price movements between the weights reference period and that for the index. This adjustment should also be applied to previous years to produce a consistent times series.

Government Finance Statistics

High priority

  • Create a planning and coordination structure such as a government finance statistics committee including all relevant departments and agencies. Draw up a comprehensive action plan, with a clear implementation calendar and detailing additional resources, spelling out a strategy for the migration to GFSM 2001, including the linkages with the ESA 95 transmission program, and delineating compilation and dissemination responsibilities.

  • Disseminate—on the website for instance—more detailed annual government finance statistics data, descriptions of methodology and procedure, indications of breaks in time series, as well as non-consolidated statistics (by subsectors).

Other key recommendations

  • Implement the analytic framework and classification of the GFSM 2001. Complete the ongoing review on the budget classification.

  • As a step toward raising the relevance of government finance statistics for the budget process, compile quarterly general government data, to be kept in-house for an observation period. Review the statistics of the central government debt and the link between monthly and quarterly government finance statistics.

  • Design a sufficiently comprehensive annual publication (either hard copy or electronic); improve the presentation of the website, incorporating time series easily downloaded with charts and commentaries.

Monetary Statistics

High priority

  • Enhance the cooperation between the Monetary Policy Department and the Credit Institutions Supervision Department for preparing data on banks in liquidation and improving the sectorization in the report forms of credit institutions, in line with the guidelines of the MFSM.

  • Publish the data on financial assets and liabilities of the monetary authorities, including the IMF accounts of the State Treasury Department of the MOF, as well as those of the BOL.

Other key recommendations

  • Provide more commentary and explanations of monetary statistics in the relevant publication.

Balance of Payments Statistics

High priority

  • Inform the public of all major changes in data sources, adjustment techniques, classification, and methodology.

  • Revise the sectoral breakdown of nonresident transfers into general government and other sectors to ensure consistency with other macroeconomic datasets.

  • Amend the format and layout of the data tables to enhance transparency and improve the interpretation of the data.

Other key recommendations

  • Enhance explanations provided along with the published balance of payments data.

  • Establish an ongoing review of possible improvements in the data sources and adjustments to the data.

  • Develop estimates of the insurance element of the c.i.f. adjustment and make revisions to the historical data on freight and insurance.

1

The mission team was headed by Ms. Claudia Dziobek and included Messrs. Thomas Alexander, Philippe de Rougemont, Satoru Hagino, and Ms. Marie Montanjees (all Statistics Department), Mr. David Hughes (expert) and Ms. Maria Delia M. Araneta (STA—Staff Assistant). Mr. Charles Enoch joined the mission for the last several days.

2

A detailed description of the SDDS can be found on the IMF’s website on the Internet at http://dsbb.imf.org.

3

Monitoring is carried out against the release dates stated in the advance release calendars and the metadata, i.e., to verify not only that the data are released according to the calendar but also that the data disseminated correspond to the metadata posted on the DSBB. Other elements of the SDDS are on a self-disclosure basis by subscribers, that is, the subscribers are asked to confirm on a quarterly basis that their descriptions of their practices are accurate.

4

Information on data quality can be found at the IMF website on the “Data Quality Reference Site” (http://dsbb.imf.org/dqrsindex.htm).

5

See also the Generic Framework set out in Appendix II of the accompanying detailed assessments volume to this report.

6

A set of financial accounts was published in June 2002, subsequent to the mission.

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Cover IMF Staff Country Reports
Republic of Lithuania: Report on the Observance of Standards and Codes (ROSC)—Data Module
Author:
International Monetary Fund
Volume/Issue:
Volume 2002: Issue 262
Publisher:
International Monetary Fund
ISBN:
9781451824094
ISSN:
1934-7685
Pages:
156
DOI:
https://doi.org/10.5089/9781451824094.002