Canadian Government Finance Statistics

Detailed information for 2022





Record number:


The objective of this program is the publication of financial statistics concerning the federal government, the provincial, territorial, and local governments, government business enterprises, health and educational institutions and the Canada and Quebec pension plans.

Data release - November 22, 2023


Governments play an important role in Canadian society and the economy. They provide many important services such as health, education, justice and general administration. Governments also set fiscal policies that help steer the economy. Given this role, it is important to both quantify and monitor the activities of government.

In recent years, Canadian governments have moved from a modified cash-based accounting system to an accrual-based accounting system. As such the statistical system underlying government finance statistics must also change. Statistics Canada has decided to move towards reporting government finance statistics on a Government Finance Statistics (GFS) 2001 basis. The GFS 2001 is an internationally accepted accrual accounting framework for GFS. Statistics Canada has adopted this framework as the Canadian Government Finance Statistics (CGFS).

The strength of the CGFS framework lies in the fact that it is a set of interrelated statements that integrate flows and stock positions for different levels of governments and government business enterprises. GFS are useful in helping understand the management of government finances and the effectiveness of policy decisions. They are also closely linked to other macroeconomic frameworks such as the Canadian System of Macroeconomic Accounts, balance of payments and international investment position. This ensures the data are comparable to other macroeconomic indicators. CGFS allow users to analyze the financial soundness of the government and government business enterprises in the same way that financial soundness and stability are measured in the corporate or household sector. CGFS also enable users to determine whether government decisions are sustainable over the long term, and assess government liquidity constraints as well as financing needs. This framework allows users to analyze government expenditures under two classifications: the Economic Classification of Expenses (CGFS) and the Canadian Classification of Functions of Government (CCOFOG).

In March 2016, consolidated CGFS data were released for the first time.

Consolidation is a method of presenting one overarching statistic for a set of units. It involves eliminating all transactions and debtor-creditor relationships among the units being consolidated. In other words, the transaction of one unit is paired with the same transaction as recorded for the second unit and both transactions are eliminated.

The CCOFOG expenses data exclude expenses related to the acquisitions of non-financial assets and consumption of fixed capital.

Consolidated data are released for the provincial, territorial and local governments (PTLG), which include provincial and territorial governments, health and social service institutions, universities and colleges, municipalities and other local public administrations, and school boards.

PTLG data can be compared across provinces and territories because consolidation takes into account differences in administrative structure and government service delivery by removing the effects of internal public sector transactions within each jurisdiction.

Consolidated data are also released for the Canadian general government, which combines federal government data with PTLG data, but excludes data for the Canada Pension Plan and Quebec Pension Plan.

Reference period: Fiscal year ending closest to December 31.

Collection period: Quarterly (two months following the end of the quarter) and annually (starting six months after the end of the fiscal year)


  • Balance sheets
  • Economic accounts
  • Financial and wealth accounts
  • Government
  • Government financial statistics

Data sources and methodology

Target population

The target population consists of all institutional units controlled and mainly financed by governments (federal, provincial, territorial and local) in Canada including government business enterprises. The population covers all of the components of the Public Sector Universe (PSU). The government component includes all ministries, departments, agencies, non-autonomous funds and organizations, universities and colleges, health and social service institutions and school boards.

Institutional units are comparable to enterprises in the Statistics Canada hierarchical structure of business units. Institutional units are economic entities that are capable, in their own right, of owning assets, incurring liabilities, and engaging in economic activities and transactions with other entities. Control may take the form of full ownership of the institutional unit or a majority holding of the voting shares. The availability of a complete set of annual financial statements is a prerequisite in order for an entity to be classified as an institutional unit within the government component of the PSU.

Instrument design

This methodology does not apply.


No sampling is done for this statistical program.

Data sources

Data are extracted from administrative files and derived from other Statistics Canada surveys and/or other sources.

Canadian Government Finance Statistics are built primarily from publicly available sources of data and quarterly electronic accounting files (general ledgers) extracted from the administrations' accounting systems. Not only is this a timely source of data but these files contain a wealth of detail for all financial dimensions. For the vast number of jurisdictions, this information is also reported at the program level; an important component which allows us to properly align expenditures by functions of government.

Where general ledgers are not available audited financial statements published by the institutional unit are used.

View the Questionnaire(s) and reporting guide(s).

Error detection

Most of the data come from audited financial statements and accounting systems of governments; therefore, minimal error detection is required. The data goes through several automated and manual checks to verify internal consistency, balance to control totals and identify extreme values. Current year data is also compared to data from prior years to ensure consistency.


For non-response units, imputation is performed using historical information where historical information is available; otherwise, donor imputation is used. The donor imputation procedure involves using available auxiliary information to substitute the data from an entity with similar characteristics. The coverage of the public sector population is virtually complete. Imputation for non-response varies by public sector sub-component, but for all components, the imputation rate is less than 2%. Similarly, the overall impact of imputation on major financial variables is also less than 2%.


Estimates are derived from the compilation of data obtained from the data sources for each institutional unit in the population of interest. The following processes are used to optimize accuracy:

A) Getting the details:
Audited financial statements do not always contain the detail needed to precisely convert accounting to Canadian Government Finance Statistics (CGFS). Generally speaking, the greater the detail in the source data, the greater the precision in applying classification codes.

General ledgers contain more detail. Where there is still not enough detail available, the practice is to approach individual governments and solicit the additional detail required to accurately apply the classification, or to obtain further information from budget estimates and forecasts.

B) Quality control on processing:
Once general ledgers and financial statements are obtained and combined with supplementary information, there are many transactions required to transform these raw data into CGFS estimates. Strict quality control is maintained on all of these transactions such as historical continuity, data validation and data confrontation.

Quality evaluation

The data analysis that occurs before publication includes a detailed review of the individual responses (especially for the largest institutional units), a review of general economic conditions as well as historic trends and comparisons with original public accounts data before the conversion to the Canadian Government Finance Statistics (CGFS). Any anomaly is verified and resolved before data are published. An example of this cross-check occurs in the annual benchmarking of the government sector data with the Canadian System of Macroeconomic Accounts (CSMA) via the input-output tables and the gross domestic product series. The relevance of government finance statistics for the other parts of CSMA derives from the fact that governments are very large players in the economy whose financial transactions have to be included in the national accounts like those of any other large sector.

As an additional data validation measure prior to publication, select CGFS datasets are shared with the Department of Finance Canada and the respective provincial and territorial statistical focal point offices through work-in-progress agreements. Work-in-progress agreements include the sharing of datasets, analytical and information products prior to release for quality assurance purposes.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Revisions and seasonal adjustment

The input data to the Canadian Government Finance Statistics are not final until three years after the reference year, and the more recent the input data are, the more they are subject to revision.

Data for the most recent year (for example year t) are based primarily on quarterly general ledgers from the accounting systems for the federal and some provincial governments, from budget forecasts and quarterly input from provinces and territories and from data estimated from previous years for education and health institutions and local governments.

Data for the preceding year (year t-1) are primarily based on annual general ledgers and public accounts for the federal and provincial-territorial governments, as well as a mixture of previous years' estimates and financial statements for the education and health institutions and for local government.

Data for the third year (year t-2), correspond to the benchmarking and revision cycle of the Canadian System of Macroeconomic Accounts (CSMA) where all data from the CSMA are benchmarked to the input-output matrices and resulting gross domestic product.

While the more recent data are necessarily more subject to revision than data for earlier years, the use of preliminary information results in major advances in timeliness. In light of the contribution of timeliness to the relevance of the data, this trade-off is in the interests of the data users. The size of the revisions varies according to the difference between forecasts and actual data.

Data accuracy

The data produced are derived from a multitude of entities in the government component of the public sector. Statistics Canada has no control over the accuracy of the input data at the time they are received, although it does have the advantage of eventually having access to audited financial documents. We ensure that no errors are introduced through automated checks that verify internal consistency and identify extreme values, and we apply procedures that maximize the error-detection possibilities inherent in the data.

The inherent quality of the input data varies systematically through time, with the most recent data (current year) being the least reliable (and the least detailed) since they are largely based on government budget forecasts and estimation. For earlier reference years, with each additional year the input data becomes subject to smaller revisions as administrative data and audited financial statements become available.

Response rates
The coverage of the public sector population is virtually complete. Imputation for non-response varies by public sector sub-component, but for all components, the imputation rate is less than 2%.

Non-sampling error
Non-sampling errors can arise from a variety of sources. They are difficult to measure and their importance can differ according to the purpose for which the data are being used. Among non-sampling errors are gaps in the information provided by public sector bodies and errors in processing, such as data capture. Efforts have been made to minimize non-sampling errors in a number of ways including, designing survey questionnaires to reduce misinterpretation by respondents, performing edits on data during and after data capture, making efforts to reduce non-response, and maintaining ongoing communication with data suppliers.


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