Data quality, concepts and methodology - Accounting concepts, definitions and practices

• Scope
• Industrial classification
• Publication level of aggregation
• Sample design for the survey component
• Seasonal adjustment
• Data quality
• Limitations of the data

The concepts and definitions for most industries are based on the guidelines of the Canadian Institute of Chartered Accountants. Regulated industries may follow practices and definitions determined by the regulators. However, these practices are usually similar to the Generally Accepted Accounting Principles (GAAP) of the Canadian Institute of Chartered Accountants.

Scope

The financial items presented in this publication are those included in the following accounts or statements:

• Balance Sheet
• Income Statement
• The Statement of Changes in Financial Position

Although they are not included in this publication, additional financial items are available for some industries upon request.

Financial classification and presentation

There is no widely accepted standard classification of financial items. Accordingly, it was necessary to devise one in order to present information in a homogeneous way for all enterprises. The financial item presentation in this publication has been condensed somewhat to allow for a homogeneous presentation across industries. Additional financial detail is available upon request.

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Industrial classification

Commencing with the first quarter of 1999, the Quarterly Survey of Financial Statistics for Enterprises is based on the North American Industry Classification System (NAICS Canada 1997). Prior to the first quarter of 1999, the Standard Industrial Classification for Companies and Enterprises (SIC-C 1980) was used.

NAICS was developed by the statistical agencies of Canada, Mexico and the United States to provide a consistent framework for the collection, analysis and dissemination of industrial statistics used by government policy analysts, by academics and researchers, by the business community, and by the public.

NAICS is a product-oriented industry classification that standardizes the way businesses are classified across Canada, Mexico and the United States. NAICS was primarily designed to classify economic production performed at the establishment level; the activity which contributes the most value-added determines the NAICS code for the establishment.

To determine which NAICS Canada 1997 code should be assigned to a statistical enterprise, the NAICS Canada 1997 code is determined for each establishment belonging to the statistical enterprise. The NAICS Canada 1997 code representing the largest value-added is then assigned to the statistical enterprise. The NAICS Canada 1997 classification system (unlike the 1980 SIC-C) does not provide classifications for integrated activities. For example, a petroleum enterprise may be involved in exploration, mining, refining, shipping and retailing of petroleum products. Whereas the 1980 SIC-C provided a classification code for integrated petroleum activities, under NAICS Canada 1997 such an enterprise is classified to the individual NAICS Canada 1997 code that relates to the activity that provides the most value-added.

NAICS Canada 1997 is a detailed, hierarchical classification with over 900 individual 6-digit industries. These are aggregated into 20 sectors of activity, such as mining, manufacturing, wholesale trade, retail trade and construction.

The quarterly financial statistics are available at three levels of industrial aggregation of NAICS Canada 1997. The most aggregate level, which is used in this publication, covers 24 categories (see Text Table 2), 17 of which represent NAICS Canada 1997 2-digit sectors while seven are at a more disaggregated level. The second level of aggregation covers 58 categories and is available upon request. The most detailed level of aggregation covers 157 categories in 1999 and 164 categories in 2000 and beyond and is also available upon request. The industrial classification system is hierarchical in nature so that the more detailed levels of data easily aggregate to the higher levels.

Example of industry classification:

An automobile dealership that sells new cars would be assigned to the NAICS Canada 1997 Industry Code 44111, "new car dealers." For purposes of the Quarterly Survey of Financial Statistics for Enterprises, this enterprise would appear in the following aggregations:

Level III (164 categories): Group 4411 "Automobile Dealers"

Level II (58 categories): Sub-Sector 441 "Retail Motor Vehicles and Accessories"

Level I (24 categories): Sector 44-45 "Retail Trade"

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Deriving the estimates

The overall estimates are derived from two different components. A sample survey is conducted for larger businesses above a prescribed size threshold using the mailed questionnaire. Sample results are multiplied by a weighting factor to represent the universe from which the sample was drawn. For businesses below the size threshold, the (take-none, see Text table 4 and Text table 5) estimate is derived by applying a regression technique to annual data compiled from Revenue Canada financial statements. The model projects the value of the take-none portion of the population by the 164 categories of the Level III aggregation (separately by country of control) 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 between industry aggregations. 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.

(See Tables 4 and 5 showing the percentage of assets and operating revenue represented by the take-none component for each of the Level 1 (24 industry) groups.)

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Sample design for the survey component

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 (164 categories) aggregations (take-all and take-some). As well, each aggregation is stratified according to country-of-control (Canada, United States or Other Foreign). 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.

Stratum boundaries

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

Sample weights and imputation for incomplete responses or non-response

Although significant effort and resources are used to maximize response rates, there is always an element of non-response in a statistical survey. 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.

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Seasonal adjustment

The seasonal component of a time series reflects sub-annual movements caused by characteristic weather changes, holidays or other factors which tend to recur every year at approximately the same time. The seasonal adjustment process attempts to quantify the seasonal component in a time series and to remove its effect from observed data.

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 1999 publication, the quarterly financial series switched to a NAICS Canada 1997 basis of industrial classification from the previously used SIC-C basis. Historical data on a NAICS Canada 1997 basis were created using a concordance that converted the old SIC-C codes to the new NAICS Canada 1997 codes. No reliable NAICS Canada 1997 coding of businesses was available prior to 1998. Users are warned that the quality of the backcasted series, and the resulting seasonal adjustment, may not be as reliable as that of statistics derived from actual survey results conducted on a NAICS Canada 1997 basis commencing with the first quarter of 1999.

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Data quality

There are two categories of errors in statistical information - sampling errors and non-sampling errors. Sampling errors are errors that arise because estimates are being prepared based on a sample of the universe rather than collecting information from all units in the universe. These errors can be measured.

Non-sampling errors can arise from a variety of sources and are much more difficult to measure. Non-sampling errors include errors in the information provided by respondents, data capture errors and other processing errors.

Sampling errors

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 country of control. 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 CVs are desirable, since the smaller the CV, the smaller the sampling variability relative to the estimate.

The sample for the Quarterly Survey of Financial Statistics for Enterprises was drawn such that the CV at the Level III (164 categories) aggregation, by country of control, should be no more than 10% for operating revenue or total assets.

Estimation errors in the non-sampled strata

The estimate for small businesses (take-none portion) is prepared by applying a statistical regression model to predict the value of the take-none portion of the population by Level III (164 categories) aggregation 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. It is difficult to calculate a coefficient of variation for the non-sampled strata, as the estimates are calculated at the Level III aggregation level only. Therefore, the CV displayed in the publication reflects only the survey portion of the estimate. When the estimate reflects only the take-none portion, the CV will be blank.

Other non-sampling errors

There are no objective measures of other non-sampling errors applied to these statistics. However, most reporting and data entry errors are corrected as a result of the intricate computer capture and edit procedures applied to the data. This is particularly effective for financial data where accounting relationships are established and balancing is required. However, most financial data collected are derived from audited financial statements resulting in minimal errors and inconsistencies. As well, the Quarterly survey of financial statistics for enterprises utilizes trained accounting staff to capture and analyze reported data to minimize the frequency of non-sampling errors.

One source of non-sampling error is the non-response error. There are several measures that can help the user evaluate this type of error, including the response rate and the data response rate.

The response rate ( Text table 3) is a measure of the proportion of those sample units which have responded in time for inclusion in the estimate. To calculate the response rate, one should simply divide the number of actual responses by the total number of sampled units. For example, a sample with 20 active units for which 10 respond for a particular quarter would have a response rate of 50%.

Response rate =

The data response rate is the proportion of the estimate which is based upon actual reported data. The data response rate can be calculated by dividing the design assets or revenue (or whatever variable is being analyzed) represented by the responding units by the corresponding value for the entire sample. In the previous example, if the 10 responding units have a design asset value totalling $15 billion out of a total sample asset value of $20 billion, the data response rate for assets would be 75%.

(Where the design value is a frame value for the record which is derived from administrative sources and is available for the entire population.)

Data response rate for assets =

(Where asset values are the design values.)

Weighted data response rates consider that units in sample represent more than themselves through weighting factors. Some units contribute more to the estimates than other units when weights are applied. The weighted data response rate can be calculated by dividing the respondents' weighted frame value by the weighted sample for assets or revenue (or whatever variable is being analyzed). In the previous example, if the weighted asset value of the responding 10 units is $40 billion out of a total sample weighted asset value of $50 billion, the weighted asset response rate would be 80%.

Weighted data response rate for assets =

Total weighted assets from completed responses
+ total weighted assets from partial responses     
Total weighted assets from completed responses
+ total weighted assets from partial responses
+ total weighted assets from others

(Where the weighted assets for a respondent is defined as the design weight multiplied by the design assets value.)

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Limitations of the data

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.

Beginning with the first quarter of 1999, the Quarterly Survey of Financial Statistics for Enterprises implemented the Statistics Canada Business Register as the survey frame. There was also a significant sample rotation that resulted in the introduction of a large number of new enterprises to the quarterly survey and the exclusion of many previously sampled enterprises from the sampled portion of the survey. Furthermore, the quarterly survey switched to the North American Industry Classification System (NAICS Canada 1997) effective the first quarter of 1999. These changes have improved the quality and reliability of the statistics. They nonetheless affect the user's ability to relate and compare the data to those previously produced on the SIC-C basis under the old methodology.

Confidentiality

The confidentiality of the reported statistics to the quarterly survey is protected under the provisions of the Statistics Act. Accordingly, statistics are released in aggregate only, with no potential identification of individually reported information. The confidentiality provisions of the Statistics Act override the provisions of the Access to Information Act to guarantee the confidentiality of reported data of individual respondents.

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