Quarterly Survey of Financial Statistics for Enterprises

Detailed information for first quarter 2004





Record number:


Information collected as part of the Quarterly Survey of Financial Statistics for Enterprises provides data used to measure the financial position and performance of incorporated businesses by industry aggregations.

Data release - June 3, 2004


The data collected by the Quarterly Survey of Financial Statistics for Enterprises (QFS) comprise financial statements prepared by incorporated businesses to record their financial position and performance. The data include asset, liability and equity items encompassed in a quarterly balance sheet and revenue and expense items as reported on a quarterly income statement, along with additional supplementary items.

Information collected by the Quarterly Survey of Financial Statistics for Enterprises serves two broad objectives. The first measures the financial position and performance of incorporated businesses by industry aggregations. The statistics are used by a wide variety of economists and industry analysts, including federal and provincial regulatory bodies that monitor financial and other institutions in Canada. This information is also a critical input into the measure of corporate profits and capital consumption allowances in the Canadian System of National Accounts (CSNA).

The second broad objective is to provide information on financial holdings and transactions in the CSNA sector accounts. The accounts comprise the National Balance Sheet Accounts and the Financial Flow Accounts. Within the CSNA, the Canadian economy is composed of the incorporated business sector, including non-financial and financial businesses, the government sector, and the persons and unincorporated business sector, which includes non-profit institutions serving households. QFS data are combined with additional information for the business and other sectors in order to produce complete economy-wide accounts which show the creation and distribution of wealth as well as the financing of economic activity. This is made possible by presenting considerable detail on financial institutions within the sector accounts framework.

In addition, the flow of funds and outstanding positions between Canadian residents and non-residents is measured in Canada's Balance of International Payments and in Canada's International Investment Position, respectively. Both of these releases make use of the quarterly survey data in measuring corporate financial activity with non-residents.

Reference period: Quarter


  • Business performance and ownership
  • Financial statements and performance

Data sources and methodology

Target population

The Canadian economy consists of the incorporated business sector, the government sector, and the persons and unincorporated business sector, which includes non-profit institutions serving households. This publication covers incorporated financial and non-financial business enterprises. Business enterprises controlled by governments, surveyed by the Public Institutions Division of Statistics Canada, and non-profit enterprises, are excluded from the Quarterly Survey of Financial Statistics for Enterprises.

The statistical unit used in this survey is the enterprise.

Instrument design

The survey questionnaires comprise financial statements typically prepared by incorporated businesses. Corporate activities across the economy are extremely diverse, resulting in the utilization of a variety of unique financial reporting variables. To accommodate the diversity in financial reporting across industries, fifteen different questionnaires are used. The majority of items on the questionnaires have remained unchanged for several years. However, periodically situations arise necessitating the modification of questionnaires. These changes are proposed through a review committee and are field tested with respondents and data users to ensure that the changes are reasonable and sustainable. The final versions of the revised questionnaires are prepared by Data Dissemination Division and the revision date of the form is updated.


This is a sample survey with a cross-sectional design.

The frame used for sampling purposes is the Statistics Canada Business Register (BR). A stratified random sample is used. There are two size strata for each of the Level III (67 categories) aggregations (take-all and take-some). All units in the first stratum (take-all) are selected. For the lower stratum (take-some), sampling rates vary by aggregation, but average to about one unit selected in seven. The total sample size is approximately 5,500 enterprises.

The stratum boundaries for the take-all, take-some and take-none strata vary by industry aggregation. The boundaries are available upon request.

Data sources

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

The survey is collected on a quarterly basis. Survey questionnaires are sent to the sampled enterprises approximately one week prior to the quarter-end. The survey is directed to the financial reporting department and is to be completed by the chief financial officer or a senior reporting officer.

The respondent has 30 days after their quarter-end to complete and return the questionnaire to Statistics Canada. A reminder is sent to non-reporters around day 15 of the follow-up period. A telephone contact is made with non-reporting enterprises during a two week period around the 30 day cut-off to discuss reporting delinquency and possible special arrangements. A second reminder is sent to persistent non-reporters later in the month subsequent to the 30 day cut-off date.

Respondents can reply to the quarterly survey by mail, Fax or e-mail. Due to timing constraints, information may be transmitted by telephone to Statistics Canada, but subsequent confirmation is required either by mail, Fax or e-mail.

In exceptional cases an enterprise may not be able to comply with the legal reporting deadlines and special reporting arrangements are determined.

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

Error detection

Most reporting and data entry errors are corrected through the application of data editing procedures. This is the application of checks to detect missing, invalid or inconsistent entries or to point to data records that are potentially in error. Some of these edits involve the adherence to basic accounting principles. In addition, collected data are also checked against historical data, at the aggregate level, as well as against other sources of related data and information.


Units which do not respond in the current period are imputed (their characteristics are estimated). Units are imputed by applying a growth factor to previously reported data when available. The growth factor is estimated using the survey responses for the units that are most similar to the unit being imputed.

When partial survey data covering three key variables (total assets, operating revenue, operating profit) are received, the imputation factors are calculated at the unit level using these partial data. For records without historical information, a donor imputation system is used. Information on the size of the non-respondent is obtained and a similar sized respondent is found. The size information consists of the three key variables. If this information is not available, the Business Register revenue and asset values are used. In the former situation, the donor record is used to calculate the distribution of the detailed values around the three key variables. In the latter case the donor's values are directly copied over to the non-respondent. In the case where donor imputation is required for two or more consecutive quarters, a new donor is not reselected. Rather, the imputation factor is applied to the previously imputed data.

The response values for sampled units are multiplied by a sampling weight in order to estimate for the entire surveyed population. The sampling weight is calculated using a number of factors, including the probability of the unit being selected in the sample.

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. In other words, 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 are being used.

These data are consistent with the Generally Accepted Accounting Principles (GAAP) of the Canadian Institute of Chartered Accountants. As such, they do not necessarily agree with the concepts used within the Canadian System of National Accounts.

While the GAAP concepts are appropriate for the application of the data, there may still be some problems of consistency (between units or over time) for items where GAAP does not prescribe a particular treatment or allows some latitude. One of the general problems with GAAP for some uses is that it prescribes a historical cost treatment of assets (i.e., their cost at the time of acquisition). As a result, caution should be used when comparing balance sheet data and ratios over time and across industries.


The overall estimates are derived from two different components: a sampled portion and a non-sampled (take-none) portion. A sample survey is conducted for larger businesses above a prescribed size using a mailed questionnaire. Sample results are multiplied by a weighting factor to represent the universe from which the sample was drawn. The sampling weight is based on the probability of the unit being selected in the sample. For businesses below the sampling threshold, the (take-none) estimate is derived by applying the quarter to quarter movement of sample responses to annual data compiled from Canada Revenue Agency financial statements representing the non-sampled portion of the business population. The model projects the value of the take-none portion of the population by the 67 categories of the Level III aggregation using estimates from the surveyed population and other parameters. The proportion of each of the two components of the final estimate (survey and take-none model) varies significantly across industry groups. The proportion represented by the surveyed component ranges from 5% to 100% of the population for both revenue and assets at the Level III aggregation.

Sampling and the calculation of weights pertains only to that part of the population of enterprises which are in scope for survey purposes. To understand both the sampling strategy and the calculation of weights, it is necessary to divide the population of enterprises into two parts - those that were in the universe at the time of selection and those that have been added since the original sample selection. In this strategy the weights for those in the sample at the time of selection would be calculated using the inverse of the probability of selection for the stratum to which the unit belongs, and would not change except for adjustments due to restructuring of units. New units in the universe (births) are selected with certainty, i.e., they have a weight of one.

Quality evaluation

A process of annual benchmarking is used to ensure that the quarterly series is consistent with the Unified Enterprise Survey (UES) annual levels; the latter are based upon a census of information from surveyed and administrative sources. Every year, with the release of the QFS first quarter, the quarterly series is benchmarked to the most recently available UES Part 1 Annual data. The benchmarking that occurred with the first quarter of 2004 data encompassed revisions of the 2001 quarterly series to the 2001 UES annual data. In addition, the subsequent eight quarters (in this case, the eight quarters of 2002 and 2003) were updated as part of the benchmarking process to incorporate the revisions arising from the benchmarking, as well as to allow for any corrections to the captured quarterly data.

In addition, the quarterly series is 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 insurance industry, where Insurance Bureau of Canada statistics are used as a tool in the generation of the QFS financial series.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which 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.

Revisions and seasonal adjustment

The seasonal adjustment method used is a computerized ratio-to-moving-average method in widespread use at Statistics Canada. It is based on the U.S. Bureau of the Census Method II, but has some additional features. Beginning with the first quarter of 1999, the Quarterly Financial Statistics series uses "end-point" seasonal adjustment, which recalculates seasonal factors each quarter as more recent data becomes available. For a more complete description of these features, refer to "X11ARIMA v. 2000 -Seasonal Adjustment Method Foundations and User's Manual."

Series containing no significant seasonality have not been seasonally adjusted. In these cases, the unadjusted series are used in the place of seasonally adjusted data.

Beginning with the first quarter of 2004 publication, the quarterly financial series switched to the North American Industry Classification System (NAICS Canada 2002) basis of industrial classification from the previously used NAICS Canada 1997 basis. The previous set of industrial aggregations consisting of 157, 58 and 24 groups has been replaced by a new set of 67, 48 and 22 industry groups, and a group of 10 financial instruments. Historical data on a NAICS Canada 2002 basis were created using a concordance that converted the old NAICS Canada 1997 codes to the new NAICS Canada 2002 codes.

Data accuracy

Sample surveys are designed to provide the highest sampling efficiency (the smallest sample that will produce a sampling error of a given size). This optimization is usually performed for only a few variables, limited by the data items that are available at the time of sample design and selection, the resources available, and the complexity introduced by trying to optimize for many variables at one time. The sample used for these statistics was designed to produce a reasonable level of accuracy for assets and revenue by industry group. Consequently, other items may be less accurately estimated.

A measure of the sampling error is the standard error. This measurement is based upon the idea of selecting several samples, although in reality only one sample is drawn. Sampling variability can also be expressed relative to the estimate itself. The standard error as a percentage of the estimate is called the coefficient of variation (cv), or the relative standard error. Small cv's are desirable, since the smaller the cv, the smaller the sampling variability relative to the estimate.

The sample for the Quarterly Survey of Financial Statements was drawn such that the cv at the the 67 industry level should be no more than 10% for operating revenue or total assets.

The estimate for small businesses (take-none portion) is prepared by applying a statistical model to predict the value of the take-none portion of the population at the 67 industry level using the estimates from the surveyed population and other parameters. The error introduced by this method depends on several factors, including the contribution of these strata to the overall estimate and the error in estimating the movement of the strata using sampled units and other external factors.

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