Part C - Quality of GFS data

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Australian System of Government Finance Statistics: Concepts, Sources and Methods
Reference period


As stated in Part B above, the ABS Data Quality Framework (DQF) is used to assess the quality of ABS statistical collections and products, including administrative data by evaluating these against the seven dimensions of quality listed in paragraphs 16.4 to 16.12 of this manual. The quality of GFS is influenced by the nature of the source data available during the different phases of the GFS statistical cycle. The factors affecting quality of data at each stage of the statistical cycle are discussed in the following paragraphs.

GFS output products


GFS output products are published on a quarterly and annual basis. These publications are freely available on the ABS website ( More detailed or customised data requests, not available from the published data, may also be available from the ABS on request.


The publication Government Finance Statistics, Australia (ABS Cat. no. 5512.0) contains detailed explanatory notes and a glossary that provides information on the data sources, terminology, classifications and other technical aspects associated with GFS statistics. Additionally, detailed information on the concepts, sources and methods used in compiling GFS can be found in this manual, the AGFS15.



The main influence on the accuracy of GFS data is non–sampling errors. Non–sampling errors arise from inaccuracies in collecting, recording and processing the data. The most significant of these errors are misreporting of data, and processing errors. Every effort is made to minimise error by working closely with data providers, training of processing staff and efficient data processing procedures.


Where the economic activity of some units is relatively insignificant, undercoverage can arise. These few units are either omitted or some of their activities are not covered by the collection methodology.

Interstate comparisons


The GFS statistics are compiled using standard definitions, classifications and treatment of government financial transactions to facilitate comparisons between levels of government, and between states within a level of government. However, the statistics also reflect real differences between the administrative and accounting arrangements of the various governments and these differences need to be taken into account when making interstate comparisons. For example, only a state level of government exists in the ACT and a number of functions performed by it are undertaken by local government authorities in other jurisdictions.


Interstate comparisons of data may also be significantly affected by differences in the mix of operations undertaken by state / territory governments and local governments. For example:

  • Water and sewerage undertakings maybe operated by state / territory government, local government or a combination of both; or
  • Government transport undertakings are operated exclusively by state / territory authorities in all states except Queensland where bus transport is operated by the local government sector.


Each ABS GFS publication details a DQF statement called a Quality Declaration, detailing an assessment of the GFS output across the seven dimensions of the DQF.

Quarterly data


As revisions can be made to quarterly GFS data as a result of new and updated information available from jurisdictions and the use of a degree of sampling in compilation, and because the time frame for quality assurance is shorter, the quarterly estimates are the most timely output of GFS data. However, the accuracy and reliability of these statistics can be affected.

Data sources


The quarterly statistics are based on information provided in, or underlying, the published accounting statements and reports of governments and their authorities. For the general government sector for the Commonwealth Government and all state / territory governments, the primary quarterly data sources are public accounts and budget management systems of state / territory treasuries and the Commonwealth Department of Finance. For the public non–financial corporation sector, GFS are collected from a survey of the largest corporations in several jurisdictions where the relevant treasury does not provide that data as part of its accounting reporting. For local government, the main data source is a quarterly GFS survey of local governments from all jurisdictions. There are no local government bodies in the ACT.



Quarterly GFS data is sourced from Commonwealth and state / territory accounts that are not finalised and which are subject to revision. For this reason summing the four quarters of a financial year will not equal the final annual data.

Final data


Final data are the complete audited data for any jurisdiction for any given year. These data generally satisfy the level of detail required. However, some dissections required for national accounting purposes are not normally available in financial statements and audited accounts and these have to be estimated. For example, State-level estimates of Commonwealth Government final consumption expenditure, personal benefit payments and gross fixed capital formation are derived for publication in Australian National Accounts: State Accounts (ABS Cat. no. 5220.0).

Data sources


The annual statistics are based on information provided in, or underlying, the published accounting statements and reports of governments and their authorities. For the Commonwealth and state / territory governments the primary data sources are:

  • Public accounts and budget management systems of state / territory treasuries and the Commonwealth Department of Finance;
  • Annual reports of departments and authorities;
  • Budget papers; and
  • Reports of the Auditors-General.


For local government, the main data sources are annual statements of accounts completed by local authorities. There are no local government bodies in the ACT.



Annual GFS data are revised on an annual basis. For this reason differences can occur between equivalent aggregates published in earlier issues of this publication.

Data collection timetables


Timetables for the collection and processing of GFS quarterly and annual data are necessarily very tight because users (who also include the providers of data) require the data as input to their own timeconstrained programs. Quarterly production target dates are set mainly to meet the quarterly national accounts timetable, which requires the supply of quarterly GFS data six weeks after the end of the reference period. These deadlines affect the accuracy and reliability of GFS through their impact on the:

  • Quality of data supplied by data providers;
  • Amount of data analysis that can be done;
  • Quality of data classification;
  • Checking and editing of input and output data;
  • Amount of estimation and imputation required;
  • Number of revisions processed; and
  • Verification of output.


While some of these processes can be carried out concurrently, only a limited amount of time can be allocated in total to all the tasks involved in order to meet fixed deadlines, so trade-offs between accuracy and timeliness have to be made.


Timeliness of GFS output differs for the different streams of data. Quarterly estimates are the most timely. The final data are usually released within nine to 12 months of the reference period.

Data coverage


Not all in-scope enterprises are individually covered in GFS because the cost of collecting data from small units outweighs gains in accuracy and reliability. The way in which individual units are covered in GFS dictates the level of data estimation, which affects the quality of GFS. Most units are ‘directly’ covered while other units are ‘indirectly’ covered. A directly covered unit is one for which data from the unit’s accounts are included in GFS. An indirectly covered unit is one for which economic flows and stocks are deduced from data recorded by the directly covered units with which the indirectly covered unit undertakes transactions.


Indirect coverage of units is employed where the data of individual units are not readily available, are not available in sufficient time or are of insufficient statistical significance to warrant the cost of direct coverage. The most common example of units which are indirectly covered are public hospitals. Most of the data for the public hospitals in each state and territory can be deduced from data in the records of the relevant jurisdiction’s health department.


While the detrimental impact of the indirect (partial) coverage of in-scope units on the accuracy and reliability of GFS has not been quantified, the amount of information missed by use of the procedure is considered to be small.


A small number of in-scope units are deliberately excluded from coverage because the cost of their inclusion outweighs the marginal increase in the accuracy of GFS. No statistical expansion is made to account for this under-coverage.

Estimation errors


The quarterly data are compiled using a mix of full enumeration of larger units and some sampling of smaller units. Stratified random sampling of local government units is used to produce quarterly estimates for the local government sector. As well, some dissections of quarterly data for other levels of government are estimated using previously recorded ratios. Overall, the use of sampling in Australia’s GFS is relatively minor.


Estimation errors for individual levels of government arising from the adjustments made for undercoverage built into the quarterly collection cannot be quantified readily. The estimation techniques involve assuming that the relationships between the collected and uncollected data that existed in the last annual benchmark census remain the same in the current quarter. The estimates made represent only a very small proportion of the value recorded for the data items concerned.

Data processing errors


The ABS GFS processing system has been designed to incorporate a series of data checks and edits with the purpose of minimising or eliminating data processing errors. However, data processing errors can go undetected either because there is insufficient time to undertake all the checks and edits, or because there is not a check or edit covering a particular error. Such occurrences affect the accuracy and reliability of GFS output. Undetected errors arising from incomplete editing are part of the trade off between accuracy and timeliness. The errors in question are usually small and are usually detected when more complete editing can be undertaken. Errors that are not detected by input editing may be detected in output editing, which is an essential complement to the input editing process.


Errors may occur when a data provider either provides an incorrect figure or has to provide an estimate for data that are not readily available from accounting records. Errors can also occur because analysts may misclassify transactions in such a way that the errors are not detected in the editing process.


It is impossible to quantify the effect of undetected data processing errors. However, the effect of such errors that go undetected for a time but are eventually detected is reflected in revisions, which are quantifiable.



Inaccuracies and imbalances may arise during the process of consolidating data. Inaccuracies can arise because accounting records do not enable identification of intra-sector flows and stocks, or because errors and omissions are made in the allocation of source and destination codes. Such errors will usually give rise to imbalances that will be detected in the consolidation process. Every effort is made to resolve such imbalances that are material. When imbalances cannot be resolved in time for publication, the data are forced into a balance by adopting a convention (e.g. the record of the ‘higher’ level of government prevails) or making a judgement as to which of the two values should be accepted. Forced balancing does not necessarily give the ‘right’ answer. However, because the data to which forced balancing is applied should not be material, errors arising from this source should not be significant.

Data revisions


Revisions are amendments made to previously released data. They can occur for a number of reasons. As previously discussed, a major reason for revisions in GFS is the replacement of data over the processing cycle. Revisions are also required because errors are detected in data after their initial release. Conceptual and methodological changes also give rise to revisions.


Revisions are made to the quarterly GFS data as required as a result of new and updated information available from jurisdictions. Annual GFS data are revised on an annual basis. For this reason differences can occur between equivalent aggregates published in earlier issues of the publication.


Revisions to GFS data are not applied immediately, but are applied at specified times that coincide with the release of publications. This means that, at any point in time, the data may include estimates that will not be updated until revisions are applied. However, restriction of the application of revisions to particular times is preferable to having a data set that is continually subject to change.


The times of the application of revisions to GFS data are currently dictated by the revisions policy for the Australian System of National Accounts. The policy allows revisions to be applied in the releases for various quarters as required by National Accounts Branch.

Verification of data


Prior to the publication of GFS data, data is sent to each respective Commonwealth, state and territory jurisdiction by the ABS for verification. This process serves as a form of output editing by suppliers of GFS data. The verification process also allows the ABS to assess coherence and identify differences between source data compiled under accounting frameworks compared with GFS data compiled by the ABS.

Quality Assurance


The ABS has in place, and continues to maintain, a system of quality assurance to assess and manage risks associated with data quality. This system is designed to ensure GFS data published by the ABS is fit for purpose. For more information on how data quality is defined or applied to the GFS data outputs see the ABS website: or the Data Quality Declaration in each of the ABS GFS publications.

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