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Introduction

The following information covers the basic concepts that define the data provided in this product, the underlying methodology of the program survey, and key aspects of the data quality. It emphasizes the strengths and limitations of the data, and contributes to more efficient use and analysis of the data. This information is also useful when making comparisons with data from other surveys or sources of information, and in drawing conclusions regarding changes over time.

1. Description of the data concepts
2. Statistical methodology
3. Definitions of key financial variables

Description of the data concepts

Data are collected and compiled on the basis of the Financial Management System (FMS) classification manual (catalogue no. 68F0023-X). Moreover, the data are compiled for the entire public sector population (see the public sector chart in this section), as enumerated by Public Institutions Division. This census is made possible by utilizing audited financial statements, Public Accounts and other administrative information available from federal, provincial, territorial, and local governments and their agencies. This information is supplemented with data obtained by surveying hospitals and health authorities, which is conducted by the Canadian Institute for Health Information (CIHI), residential care facilities data collected by the Health Statistics Division (HSD) and colleges and universities data collected by the Centre for Education Statistics (CES). Data pertaining to federal, provincial, territorial, and local government business enterprises are compiled from annual reports obtained from public sources and annual and quarterly survey returns.

Description and use of the data

The data presented herein comprise financial statements typically prepared by governments and their agencies to record their financial positions.

The data include:

1. Financial asset and liability items contained in a government balance sheet
2. Revenue, expenditure, surplus (+) or deficit (-) statements of governments, and
3. Asset, liability and equity items encompassed in a balance sheet and revenue and expenses found in the income statement of government business enterprises
4. Public sector employment, wages and salaries information

These statistics are used in two broad ways. They provide a measure of the financial position by public sector component and sub-component (see public sector chart on the following page). These statistical measures are used by a wide variety of economists and industry analysts in both the private and government sectors. Secondly, these data are used as the benchmark for the annual and quarterly estimates of the government sector in the Canadian System of National Accounts (CSNA).

Coverage

The domestic economy consists of personal, business and government sectors. This publication covers the government sector as well as financial and non-financial business enterprises controlled by federal, provincial, territorial, and local governments that are engaged in commercial activities in the business sector:

The Public Sector Universe (PSU) contains all institutional units controlled and mainly financed by government. The PSU is kept up-to-date through the public accounts and web sites of federal and provincial/territorial governments in Canada. Local government data are also maintained from the administrative records of their respective provincial and territorial Departments of Local Affairs, from information contained in official Provincial and Territorial Gazettes, from municipal directories and from responses to on-going sub-annual municipal surveys.

The statistical unit

For statistical purposes, Statistics Canada defines a hierarchical structure of units for each organization. The four standard statistical units that are used are listed below, from largest to smallest:

1. Enterprise
2. Company
3. Establishment
4. Location

The statistical unit for this publication is the enterprise. Within the public sector statistical universe, institutional units are measured. These units are comparable to enterprises in the hierarchical structure listed above. The public sector contains all institutional units controlled and mainly financed by government. 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 ^{1 , 2 , 3}  . 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 public sector.

Accounting concepts, definitions and practices

The concepts and definitions for most federal, provincial, territorial, and municipal governments are based on the guidelines of the Public Sector Accounting Board (PSAB) of the Canadian Institute of Chartered Accountants (CICA). Accounting practices are in accordance with the Generally Accepted Accounting Principles (GAAP) of the Canadian Institute of Chartered Accountants.

Diagram 1

Public sector chart

Figure 1

Financial classification and presentation

Because there is no widely accepted standard classification for financial items, it was necessary to devise the Financial Management System (FMS) in order to present information in a homogeneous way for all public sector enterprises. The financial nomenclature for this publication has been condensed somewhat to allow for a generic presentation across public sector components and levels of government.

The Financial Management System (FMS) is an accounting framework designed to produce statistical series that are both consistent and compatible. It encompasses the financial transactions and employment data for all public sector statistical (enterprise) units. Direct links exist between the FMS, the Organization for Economic Co-operation and Development (OECD) Tax Classification and the Government Finance Statistics (GFS) of the International Monetary Fund (IMF) Functional Expenditure Classification. Both the FMS and GFS systems classify government expenditures according to the main purpose or function for which the expenditure is made. Similarly, FMS and GFS classify revenue according to the tax base or the source from which it originates.

Statistical methodology

Survey design

The following data sources were combined to form a census of all units in the population of interest, the public sector statistical universe:

1. Public sector employment, wages and salaries data were obtained from the central-pay administrations of the federal, provincial, and territorial governments. Most of the remaining statistical (enterprise) units, local general government and school boards, health and social services institutions, colleges and universities, special funds, and government business enterprises were accessed from the Survey of Employment, Payrolls and Hours Survey (SEPH) conducted by Labour Statistics Division of Statistics Canada. A few statistical (enterprise) units not covered by central pay administrations or SEPH were surveyed directly by Public Institutions Division.
2. Data pertaining to federal, provincial, territorial, and local government business enterprise units were obtained from annual reports from public sources and annual and quarterly survey returns.
3. Administrative data originating from the audited financial statements and Public Accounts of federal, provincial, and territorial governments and of their agencies were used in the production of financial position statistics. This administrative information is supplemented by financial details provided directly by provincial and territorial governments.
4. Preliminary estimates for local general government revenue and expenditure data are estimated using an annual representative probability sample survey of municipalities for each province/territory. Final data are derived from administrative (census) sources.

For the fiscal year 2006, the survey frame contained approximately 10,962 thousand statistical units included in our population of interest. Annual data for all public sector statistical units were obtained through publicly available administrative sources.

Collection and processing

Publicly available government accounting reports based on the organization structures and the accounting and reporting practices of individual governments are the primary administrative data sources used in compiling annual public sector statistical series. Information from available data sources is essentially presented in inconsistent formats containing different sets of variables. In order to merge the data, it is necessary to transform these data sources into a common set of variables that comprise complete financial statement information. Certain details were omitted in the process due to the unavailability of data from all sources.

Data were collected at the enterprise level for both the government and Government Business Enterprise (GBE) components of the public sector.

Edit and imputation

Several checks are performed on the data to verify internal consistency and identify extreme values. 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 at the 100% level. Imputation for non-response varies by public sector sub-component, but overall is less than 2%. Similarly, the overall impact of imputation on major financial variables is also less than 2%.

Estimation

Government Revenue and Expenditure

Data are obtained from a census of institutional units for all government levels in Canada as defined by the Public Sector Universe with the exception of the First Nations and other aboriginal governments.

Data on federal and provincial/territorial governments are entirely obtained from administrative data sources.

For local governments, preliminary data are obtained via surveys while final data are derived from administrative (census) sources. Preliminary estimates for local general government revenue and expenditure data are estimated using an annual representative probability sample of municipalities for each province/territory.

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:

1. Getting the detail:

Published public accounts and local government financial statements do not always contain the detail needed to precisely convert public accounts entries required for the FMS and CSNA classifications. Generally speaking, the greater the detail in the source data, there is greater precision in applying classification codes. The practice is to first obtain the public accounts and then to approach individual governments and solicit the additional detail required to accurately apply the classifications. Increasingly, data are obtained from governments electronically. This enhances accuracy in two ways. One, it eliminates the possibility of transcription errors inherent in using printed public accounts and the solicited supplementary financial detail on paper. Secondly, the electronic data contains far more detail than the paper products they replace and this permits the application of classifications to detailed data resulting in greater precision.

2. Quality control on processing:

Once public accounts publications are obtained and combined with supplementary information, there are many transactions required to transform these raw data into CSNA and FMS estimates. Strict quality control is maintained on all of these transactions such as historical continuity, data validation, and data confrontation. In the case of local government data, the most current years are generated using a probability-sample survey. Standard quality control techniques such as outlier detection are used during processing. Final data are obtained through a census provided by the departments of municipal affairs in each province.

3. Transfers -- matching expenditures to receipts:

Because the program covers all expenditures (including transfers to other governments) and all revenue sources (including receipts of transfers from other governments), the two are matched and disparities are addressed since these transactions must be eliminated in the consolidation process. This applies not only to general government-to-general government transfers (e.g., equalization), but also to grants and other payments to health, social service, education and similar entities, regardless if they are transfers from one level of government to another or within a given government. Transfer payments come from the records of the donor entity and transfer income comes from the records of the recipient entity. The matching of these two records enables us to detect disparities and when these disparities constitute errors, to correct them. A similar exercise is undertaken for transactions between components of government relating to the purchase of goods and services (sales of goods and services) and interest payments (interest revenue).

Assets and liabilities

Estimates are derived from the compilation of data obtained from the data sources for each institutional unit in the population of interest.

1. Getting the detail:

Published public accounts and local government financial statements do not always contain the detail needed to precisely convert public accounts entries required for the FMS and CSNA classifications. Generally speaking, the greater the detail in the source data, there is greater precision in applying classification codes. The practice is to first obtain the public accounts and then to approach individual governments and solicit the additional detail required to accurately apply the classifications. Increasingly, data are obtained from governments electronically. This enhances accuracy in two ways. One, it eliminates the possibility of transcription errors inherent in using printed public accounts and the solicited supplementary financial detail on paper. Secondly, the electronic data file often contains far more detail than the paper products they replace and this permits the application of classifications to detailed data resulting in greater precision.

2. Quality control on processing:

Once public accounts publications and financial statements are obtained and combined with supplementary information, there are many transactions required to transform these raw data into CSNA and FMS estimates. Strict quality control is maintained on all of these transactions such as historical continuity, data validation, and data confrontation.

3. Financial assets of a government component that are liabilities of another government component:

Since the program covers all financial assets of governments (including those financial assets that are liabilities of another government) and liabilities (including those liabilities that are financial assets of other governments), the two are matched and disparities are addressed since these transactions must be eliminated in the consolidation process. The matching of these two records enables us to detect disparities, and when these disparities constitute errors, to correct them.

In the production of consolidated financial assets and liabilities of the federal, provincial and territorial, and local governments, all financial assets of a government component that are liabilities of another government component are eliminated or netted to avoid double counting. A similar process is done in the production of consolidated provincial, territorial, and local government financial assets and liabilities by province.

The combined survey results were analyzed before publication. In general, this included a detailed review of the individual responses (especially for the largest enterprises), a review of general economic conditions as well as historical trends and comparisons with other data sources such as the public accounts, budgets and estimates of governments.

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 primarily based on government budget forecasts. As the reference year moves into the past, with each additional year the input data becomes more reliable. The public accounts and local government financial statements are eventually subject to audit and these audited accounts and statements form the benchmarks of historical data.

In 2003, the International Monetary Fund (IMF) reviewed the government finance statistics program from Statistics Canada according to the observance of IMF standards and codes. The IMF developed a set of 16 elements against which a specific statistical program is evaluated. In all 16 cases, except for one, the IMF gave the highest level of observance for Statistics Canada government finance statistics.

While considerable effort was made to ensure high standards throughout all collection and processing operations, the resulting estimates are inevitably subject to a certain degree of error. There are two categories of errors in statistical information - sampling errors and non-sampling errors. Non-sampling errors can arise from a variety of sources and are difficult to measure and their importance can differ according to the purpose to which the data are being put. Among non-sampling errors are gaps in the information provided by public sector bodies and errors in processing, such as data capture.

Non-sampling errors are the only type that applies to the federal, provincial, and territorial government and school board data of this program, given that there is no sampling process used to produce these data. Preliminary estimates for local general government revenue and expenditure data are derived using an annual representative probability sample of municipalities for each province/territory. The sampling design covers about 13% of the number of units in the population representing roughly 80% of the economic activity and ensures that major municipalities are part of the sample. About 480 units are surveyed from a total population of approximately 3,700 municipalities. Municipalities of all sizes are represented. The response rate is around 70%. Survey weights are derived from population counts and correspond to mid-year population estimates benchmarked to the census of population every 5 years. Final data for local general governments are based on the audited financial statements of most municipalities in Canada, obtained in summary form from the administrative records of their respective provincial and territorial Departments of Municipal Affairs.

Comparability of data and related sources

Financial Management System (FMS) aggregate statistics frequently differ from those published by the governments of the jurisdictions to which they refer. Nevertheless, the FMS uses detailed data from these jurisdictions as inputs to its own calculations. The input data to the FMS are frequently not final until several years after the reference date, and the more recent the input data the more the data are subject to revision. In the case of FMS data for the most recent two years, those of the most recent year are based primarily on budget forecasts and those of the year before that, on non-audited annual reports. These are eventually replaced by official public accounts/financial statements issued by each of the jurisdictions covered by the FMS. The subsequent availability to Statistics Canada of these revised or final data requires, in turn, that the FMS data be amended accordingly. While the more recent data are necessarily less reliable than data for several years in the past, the use of preliminary information results in major advances in timeliness. Data are now released within three months of the end of the reference period. In light of the contribution of timeliness to the relevance of the data, this trade-off is in the interests of the data users.

In addition, the annual series are continually evaluated through trend analysis, as well as through comparisons to other financial series, to assess the quality of the data and to ensure consistency. An example of this cross-check occurs in the annual benchmarking of government sector data with the Canadian System of National Accounts, Input-Output tables, and Gross Domestic Product (GDP) series.

The procedures used to classify the expenditures of provincial and territorial general governments and health and social service institutions to the functions Health and Social Services and their sub-functions changed starting in 1997/1998. Commencing in 1997/1998, additional detail was available concerning provincial and territorial government expenditures on their programs and activities and this permitted a better allocation of expenditures between the functions Health and Social Services. Therefore, the data for these functions, for years prior to 1997/1998 are only comparable when Health and Social Service functions are aggregated together.

The Financial Management System's, financial statistics experienced significant methodological revision with the 1997 Historical Revision of the Canadian System of National Accounts (CSNA). Increased harmony between the Financial Management System and the CSNA was achieved. Details of the changes to the Financial Management System statistics are included in the publication Financial Management System (FMS) (Catalogue no. 68F0023-X). The coverage of the Canadian public sector has been extended as well to provide data for new sub-components of government. As a result of these methodological improvements, the data contained in this publication is not directly comparable to the data contained in earlier FMS publications. Revised public sector statistics compiled according to the FMS classification framework are available on a consistent and comparable basis back to fiscal year 1988/1989.

Reference period

The objective of these annual series is to reflect the governments’ involvement in the production of goods and services and associated resource allocation process in the economy, for a specific reference period. Health and social service institutions and federal and provincial and territorial general government financial data that are derived from administrative sources are governed by the April to March fiscal year of governments. The same is true for the majority of government business enterprise financial statistics. Municipal governments and school boards fiscal year is the calendar-year reference period for the most part. Meanwhile the fiscal year ends of universities and colleges vary, ending either in March, June, September or December.

Confidentiality

Statistics Canada is prohibited by law from releasing any data which would divulge information obtained under the Statistics Act that relates to any identifiable person, business or organization without the prior knowledge or the consent in writing of that person, business or organization. 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.

Limitations of the data

Due to certain financial reporting constraints, balance sheet data could not be obtained for the following sub-components of the public sector: local government business enterprises, universities, colleges and health and social service institutions.

To be valid for either time-series or cross-sectional analysis, the definitions of data must be consistent within time periods or across time periods. Put differently, the differences and similarities in data must reflect only real differences and not differences in the concepts or definitions used in preparing the data.

The ability to use the data for analysis depends on the conceptual framework in which the data is being used. With this in mind, it is important to be aware that governments employ different accounting conventions. Some report on a modified cash basis, others use the accrual approach. Adjustments can bring data produced under these various conventions to a common basis, but complete conversion to a single accounting base is not possible. For example, in the Financial Management System (FMS) (modified cash basis of accounting) when a government acquires/purchases a fixed asset the expenditures related to this purchase are included in the reference period during which the expenditures are made. For governments who have moved to a full accrual basis of reporting this means an adjustment to their public accounts based data as they will have capitalized the expenditures relating to the acquisition of the fixed asset and amortized the cost over the period of its estimated useful life. The FMS reflects tax revenues on a modified cash basis while some governments present tax revenues on an accrual basis in their public accounts and therefore there will be a difference between FMS and public accounts based tax revenue statistics.

The structure of government is forever changing. For example, in any given year, a program or service may be performed by a government department and the next year it could be delivered by an arms-length agency or even contracted-out. Therefore, it is difficult to make year to year comparisons of reporting structures and financial transactions without numerous adjustments to the basic data. The Financial Management System was developed to replace the diverse formats of government financial reports by establishing statistical series that are consistent and allow valid comparisons with the various governments' financial and non-financial reports.

Complete intergovernmental comparability of the data presented by the Financial Management System is hindered by several factors. For example, intergovernmental transactions are not always reported at the same time by both parties involved, and fiscal year-ends may differ. In addition, responsibilities between levels of government are shared differently and varying levels of service is provided. No attempt is made to adjust data to account for inconsistencies in how services are delivered at any level or among levels of government. However, the consolidation convention of the FMS, which allows for the integration of two or more levels of government into a single consolidated unit, such as consolidated provincial and local governments, considerably reduces the impact of these discrepancies in service. Efforts are continuously directed toward making existing measures more useful through the development of consistent concepts, definitions, classification systems and framework.