Indonesia: Report on the Observance of Standards and Codes—Data Module

This report on the Observance of Standards and Codes on Data Module for Indonesia highlights that the Indonesian statistical system is undergoing fundamental transition. The statistical agencies are dealing with important challenges. They are at various stages of adopting and implementing internationally recognized best practice methodologies for each major macroeconomic dataset. They are also seeking to adapt the statistical system to measure ongoing structural change in the economy, including increasing global integration.

Abstract

This report on the Observance of Standards and Codes on Data Module for Indonesia highlights that the Indonesian statistical system is undergoing fundamental transition. The statistical agencies are dealing with important challenges. They are at various stages of adopting and implementing internationally recognized best practice methodologies for each major macroeconomic dataset. They are also seeking to adapt the statistical system to measure ongoing structural change in the economy, including increasing global integration.

I. Overall Assessment

1. Indonesia subscribed to the Special Data Dissemination Standard (SDDS) on September 24, 1996; posted metadata on the Dissemination Standards Bulletin Board (DSBB) on May 21, 1997; and met the SDDS specifications for the coverage, periodicity, and timeliness of the data, as well as for the dissemination of advance release calendars on June 2, 2000. Since then, Indonesia has been in observance of the SDDS, regularly updating its metadata and maintaining its advance release calendar for all data categories. Indonesia currently uses the flexibility option for timeliness and periodicity of the labor market and general government operations data. Appendix I provides an overview of Indonesia’s dissemination practices compared to the SDDS.

2. The Report on the Observance of Standards and Codes (ROSC)—Data Module contains the following main observations. The Indonesian statistical system is undergoing fundamental transition. The statistical agencies are dealing with important challenges: they are at various stages of adopting and implementing internationally recognized best practice methodologies for each major macroeconomic dataset; they are seeking to adapt the statistical system to measure ongoing structural change in the economy, including increasing global integration; and they are reforming the statistical system to conform to the new reporting and information requirements of major institutional change, including the decentralization of governmental authority. Indonesia’s macroeconomic statistics and statistical base are broadly adequate to conduct effective surveillance at this time. The system is characterized by a strong legal environment that encourages objectivity and professionalism on the part of the statistical agencies and that underpins the overall integrity of the statistical process. The main needs are to widen the scope of the statistical framework to capture insufficiently measured economic activities and structural change, strengthen and expand the collection and analysis of the basic source data that underlie the aggregate macroeconomic statistics, improve and formalize cooperation among the major statistics-producing agencies to foster greater consistency of data among the major datasets, and accelerate the full implementation of best practice methodologies. As the quality of the statistical managers is high, and suitable levels of resources are being allocated to support the system and effect change, prospects are good that the system will adapt successfully to the changing economic environment.

3. In applying the IMF’s Data Quality Assessment Framework (DQAF), July 2003, the remainder of this section presents the mission’s main conclusions. The presentation is done at the level of the DQAF’s quality dimensions, by agency for the first two dimensions and across datasets for the remaining four.

4. With regard to prerequisites of quality, various laws and regulations ensure that the Badan Pusat Statistik (BPS), Bank Indonesia (BI), and the Ministry of Finance (MoF) have both responsibility and suitable authority to collect, compile, and disseminate the relevant statistics. Data-sharing arrangements are generally adequate between the primary data-compiling agencies and other data-producing agencies, although local government data can still be supplied under differing account formats, which greatly complicates data processing of government finance statistics (GFS). Resources are broadly commensurate with the needs of the statistical program. BPS and BI devote considerable attention to monitoring the overall quality of the statistical program and ensure that statistics remain relevant to users’ needs through regular contacts with users. In the case of the MoF, and no doubt due to the more nascent state of development of its statistical program, it would be desirable to develop a more explicit focus on the needs of data users in their operations. As to assurances of integrity, formal safeguards of the independence of statistical compilers are provided in the Statistics Law of Indonesia No. 16 of 1997 (1997 Law) and the Republic of Indonesia Act No. 23. While no similar formal arrangements are afforded to compilers of GFS, no evidence of interference exists. Statistical agencies are free to choose methodologies and appropriate data sources. Staff are well-trained, exhibiting a high degree of professionalism in their work. The terms and conditions under which statistics are compiled are generally readily available to the public. The government does not have access to statistics prior to their release, except in the case of GFS. Staff of the statistical agencies are held to a high ethical standard in the conduct of their work.

5. Concepts and definitions, in general, are methodologically sound, broadly conforming to internationally accepted standards. Monetary statistics follow the Monetary and Financial Statistics Manual (MFSM). Balance of payments statistics broadly conform to the Balance of Payments Manual, fifth edition (BPM5). Data on quarterly and annual GDP generally conform to the System of National Accounts 1968 (1968 SNA), with some changes conforming to the System of National Accounts 1993 (1993 SNA). GFS is in the process of transition to the Government Finance Statistics Manual 2001 (GFSM 2001), but local government statistics are at present compiled on a nonstandard basis. Some deficiencies appear in the scope of the datasets. Monetary statistics exclude data on mutual funds that issue deposit-like liabilities; the balance of payments statistics exclude some resident/nonresident transactions; GFS are restricted to the budgetary transactions of the general government. The basis for recording transactions, with the exception of GFS, mostly conforms with internationally accepted methodologies.

6. The accuracy and reliability of the macroeconomic statistics, while generally sound, are adversely affected in some instances by weaknesses in source data collection activities. For instance, comprehensive source data for the national accounts are collected from production industries at the expense of the nonfinancial service industries, while more effective use could be made of the International Transactions Reporting System (ITRS) in compiling balance of payments statistics. Procedures for assessing source data are generally adequate. Assessment and validation procedures for intermediate data and statistical outputs are broadly adequate. Statistical techniques include the extensive use of historical benchmarks in the case of the national accounts. Revision studies are mostly undertaken on an ad hoc basis.

7. As to serviceability, the periodicity and timeliness of the statistics meet or exceed SDDS standards, with the exception of the timeliness of general government operations. Datasets are generally internally consistent; however, the errors and omissions component of the balance of payments has been persistently large. Inconsistencies arise between merchandise trade data in the national accounts and the balance of payments, between GFS domestic financing data and monetary statistics, and between GFS and the national accounts. None are reconciled. Preliminary and revised data are clearly identified, but revision studies are only infrequently made public.

8. Accessibility of data is good, primarily through the websites of the data-producing agencies and in regular publications. Data for the most part are presented clearly with appropriate detail. Statistical publications and websites identify suitable contact points for user assistance. The lack of detailed metadata, for users of balance of payments and GFS, is a serious shortcoming.

9. Section II provides a summary assessment by agency and dataset based on a four-part scale. This is followed by staff recommendations in Section III. Practices compared to the SDDS are summarized in Appendix I. The authorities’ response to this report and a volume of detailed assessments are presented in separate documents.

II. Assessment by Agency and Dataset

10. Assessment of the quality of four macroeconomic datasets—national accounts and government finance, monetary, and balance of payments statistics—was conducted using the DQAF, July 2003. In this section, the results are presented at the level of the DQAF elements and using a four-point rating scale (Table 1). Assessments of the prerequisites of data quality and the assurances of integrity (Dimensions “0” and “1” of the DQAF) are presented in Tables 2ac. For each dataset, the assessment of methodological soundness, accuracy and reliability, serviceability, and accessibility (Dimensions “2” to “5” of the DQAF) are shown in Tables 3ad.

Table 1.

Indonesia: Data Quality Assessment Framework, July 2003—Summary Results

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Practice observed: current practices generally meet or achieve the objectives of DQAF internationally accepted statistical practices without any significant deficiencies. Practice largely observed: some departures, but these are not seen as sufficient to raise doubts about the authorities’ ability to observe the DQAF practices. Practice largely not observed: significant departures and the authorities will need to take significant action to achieve observance. Practice not observed: most DQAF practices are not met. Not applicable: used only exceptionally when statistical practices do not apply to a country’s circumstances.
Table 2a.

Indonesia: Assessment of Data Quality—Dimensions 0 and 1—Badan Pusat Statistik

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Table 2b.

Indonesia: Assessment of Data Quality—Dimensions 0 and 1—Ministry of Finance

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Table 2c.

Indonesia: Assessment of Data Quality—Dimensions 0 and 1—Bank Indonesia

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Table 3a.

Indonesia: Assessment of Data Quality—Dimensions 2 to 5—National Accounts

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Table 3b.

Indonesia: Assessment of Data Quality—Dimensions 2 to 5—Government Finance Statistics

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Table 3c.

Indonesia: Assessment of Data Quality—Dimensions 2 to 5—Monetary Statistics

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Table 3d.

Indonesia: Assessment of Data Quality—Dimensions 2 to 5—Balance of Payments Statistics

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III. Staff’s Recommendations

11. Based on the review of Indonesia’s statistical practices, discussions with the staff of data-producing agencies, and responses from data users, the mission has developed a set of recommendations. These recommendations—designed to improve Indonesia’s adherence to the internationally accepted statistical practices—would enhance the analytical usefulness of the statistics in question. Some additional technical suggestions are included in the Detailed Assessments volume.

Cross-Cutting Recommendations

  • Harmonize macroeconomic statistics across all sectors, in particular, by addressing intersectoral discrepancies between monetary statistics and GFS, as well as those between government finance and balance of payments statistics in balance of payments and national accounts.

National Accounts

  • Update census lists of enterprises continuously with registration of new enterprises and exclusion of nonoperating expenses.

  • Introduce comprehensive annual establishment surveys for nonfinancial services industries.

  • Publish annual GDP estimates with a lengthy time series (e.g., 20 years).

  • Develop a set of annual SUTS starting from 2000.

  • Expedite the conversion to the 1993 SNA.

  • Publish quarterly seasonally adjusted GDP data.

Government Finance Statistics

  • Implement new Government Accounting Standards.

  • Strengthen the current management system to track effectively changes in government cash balances.

  • Move gradually to an accrual accounting system.

  • Set up a register of all central government public sector units, classified by institutional sector.

  • Amend accounting regulations to ensure that general government units report all transactions and balances over which they exert control.

  • Compile and disseminate GFS for the general government sector and its subsectors, within six months after the end of the reference period.

  • Set up arrangements to obtain timely preliminary data for local governments.

  • Document compilation procedures for central and local government statistics.

  • Develop GFSM 2001 operating statement, statement of sources and uses of cash, and (partial) balance sheets, to the extent possible with available data, and publish these statements on the MoF websites.

Monetary Statistics

  • Collect source data on mutual funds in a format that meets statistical requirements.

  • Expand the coverage of the monetary statistics to include mutual funds.

  • Harmonize reported interbank positions between BI and commercial banks.

Balance of Payments Statistics

  • Continue the study of “Errors and Omissions,” in cooperation with BPS and Customs.

  • Publish the Balance of Payments Division’s documentation on the methodologies used to solve various problems in the balance of payments statistics.

  • Regularize the present information campaign to improve the response rates to various surveys.

  • Prepare a study of “shuttle trade.”

  • Publish more detailed metadata.

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

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Indonesia is taking flexibility options for the periodicity and timeliness of employment and unemployment data.

Indonesia is taking a flexibility option for the timeliness of the wages and earnings data.

A flexibility option is being taken for the timeliness of the data on general government operations.

Indonesia makes use of the option to publish the IIP with a time lag of nine months while publishing external debt figures on a quarterly basis.