To obtain a copy of any of the following documentation, contact Client Services (613-951-1746; fax: 613-951-0792; hd-ds@statcan.gc.ca).

• CCHS Annual Component – Content Plan 2007-2014
• Canadian community health survey content overview (2011-2012)
• Optional content selection, 2011-2012
• CCHS 2012 and 2011-2012 Microdata User Guide
• CCHS 2011-2012 Derived Variables Documentation
• CCHS 2011-2012 Alphabetic Index
• CCHS 2011-2012 Topical Index
• CCHS 2011-2012 Data Dictionary (rounded frequencies)
• CCHS 2011-2012 Record Layout
• Household Weights
• CCHS 2011-2012 Share File Approximate Sampling Variability Tables
• Canadian Community Health Survey – Errata (updated June 2013)
• Income Imputation for the Canadian Community Health Survey
• Interpreting Estimates from the Redesigned CCHS
• Mode Study
• Health Surveys - Aspects that may explain differences in the estimates obtained from two different survey occasions

Industry Accounts Division
April, 2013

Introduction

The 2012 historical revision to the Canadian System of National Accounts (CSNA) has resulted in substantial impacts on the Input-Output tables published by Statistics Canada. Beginning with reference year 2009, the Input-Output (IO) tables will incorporate new classifications meant to enhance their relevance to contemporary issues, conceptual revisions required to better align them with the latest international standard, the System of National Accounts 2008 (SNA 2008)^{Note 1}, and some time series breaks from previously published estimates due to improvements in estimation methods and to revisions in source data that could not be integrated during normal production cycles.

Since classification changes and other improvements are introduced with 2009 and prior estimates are not revised, the new IO tables are not fully comparable to previously published estimates for the period 1961 to 2008. While 2009 and future estimates will be fully integrated with the quarterly Canadian Economic Accounts and other CSNA products, prior estimates are no longer aligned.

This paper explains the modifications to the structure of the IO tables brought about by the conceptual, methodological, and classification changes. However, it does not attempt to empirically quantify the impact of these changes nor those of the statistical improvements. An overview of the aggregate statistical changes in the IO tables introduced in 2009 can be gleaned from other CSNA publications that have provided analysis of revisions to the GDP aggregates introduced by the historical revision^{Note 2}. Section I will discuss the conceptual and methodological changes while Section II will provide an overview of the main classification changes.

I. Conceptual and methodological revisions

There are four conceptual revisions and one major methodological^{Note 3} revision that affect the IO tables. The first three conceptual revisions relate to the capitalization of expenditures on research and development (R&D), military weapons systems, and exploration services. The fourth revision is in the treatment of the personal expenditures of non-residents on education and medical services. The methodological change affects the treatment of inter-provincial payments of taxes on products.

The first two changes capitalize expenditures on R&D and military weapons systems that were previously treated as intermediate consumption, and are new conceptual revisions introduced in SNA 2008. The third change removes the routing of exploration services through the non-residential construction industries. A simplification that is due to the introduction of a new fixed capital formation category for intellectual property products in SNA 2008. The fourth change expands the coverage of the personal expenditure travel categories to include medical and education services. This change in treatment has no impact on the total level of personal expenditures; it only implies a shift of values between the non-travel and travel categories. This latter change is not due to the new international standard but is rather designed to bring the CSNA closer to pre-existing international definitions. Finally, the refinement to the treatment of taxes on products paid by non-residents of a province or territory improves the coherence of the valuations and therefore the quality of the provincial supply-use framework.

i. Research and development

Research and development is defined by SNA 2008 as “creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and use of this stock of knowledge to devise new applications” (SNA 2008, par. 10.103). Since the economic benefits of such activities accrue over a period of time that exceeds the standard one year demarcation, R&D is treated as capital formation. In cases where the expenditures do not entail any economic benefits, they are treated as intermediate consumption (SNA 2008, par. 6.230). While measurement difficulties had previously prevented the SNA from treating R&D as an asset, recent progress in compilation methods have made it possible to opt for this change in treatment.

The output of R&D is measured in the usual manner based on receipts from sales for market producers and sum of costs for non-market producers. However, most R&D by market producers is produced on own account and capitalizing these activities requires imputing an explicit output. A consistent valuation of own-account output of R&D by market producers would require valuing it as if it were sold on the market. In practice, though, it is valued on the basis of the total production costs including the costs of fixed assets used in production. That is, no attempt is made to estimate a net return on capital for own-account production of R&D.

Finally, the international standard recognizes that research and development “is not an ancillary activity, and a separate establishment should be distinguished for it when possible” (SNA 2008, par. 6.207). Thus, where feasible, R&D output does not appear as a secondary output of industries but is instead classified to the R&D industry (IO industry code BS541700)^{Note 4}.

The capitalization of R&D in the business sector raises the GDP level of the industries that make those expenditures by the amount of market purchases and own-account output of R&D. On the expenditure side, GDP increases by an equal amount of investments on research and development in the Intellectual Property Products (IPP) categories of the fixed capital formation categories.

For the government and Non Profit Institutions Serving Households (NPISH) sectors, other operating surplus and consequently income-based GDP are raised by the amount of capital consumption of the stock of R&D. Expenditure-based GDP increases by an equivalent amount. The final consumption expenditures of government decrease by the sum of purchases and own-account production of R&D and increase by the level of consumption of fixed capital of R&D. Government and NPISH sector investments in own-account and purchased R&D appear in the IPP categories.

ii. Capitalization of military weapons systems

SNA 2008 recommends treating all expenditures on military equipment as fixed capital formation and on durables such as munitions and bombs as inventory additions, to be withdrawn from inventories and recorded as intermediate consumption when used (SNA 2008, par. 6.232). The new standard also recommends the separate classification of weapons systems and military inventories, at least for the capital account (SNA 2008, chapter 10).

Previously, in accordance with SNA 1993, the CSNA treated both weapons systems and munitions as intermediate expenditures. Weapons systems have been reclassified from intermediate inputs to capital expenditures. The treatment of military inventories, however, has not changed to comply with the new standard; the small values involved were not deemed to be worth the increased compilation costs.

In the IO tables, weapons systems are now included with the values of other capital expenditures on Machinery and Equipment (M&E) of the defence industry. The income-expenditure accounts of the CSNA show an estimate of the value of total weapons purchases.

The overall impact of the capitalization of weapons systems as compared to the previous treatment is to increase GDP levels. In the defence services industry, the reduction of intermediate inputs is accompanied by an increase in other operating surplus due to the additional consumption of fixed capital associated with the augmented capital stock. The impact on the output of the industry is the result of the net impact of decreasing intermediate inputs and increasing other operating surplus. In final demand, government final consumption expenditure on defence services replicates the changes to output while M&E increases by an equivalent amount to the decreased intermediate inputs of weapons.

Other operating surplus and therefore income-based GDP increase by the additional amount of consumption of fixed capital associated with the increased stock of M&E. Expenditure-based GDP increases by an equivalent amount through the net change of increased M&E minus the decrease in government current expenditures.

iii. Capitalization of exploration services

The new framework defines a separate category for IPPs under Fixed Capital Formation (FCF). The new IPP category covers investments in software, R&D, and mineral exploration. Previously in the IO tables, exploration activities appeared as construction investment, embedded in the ‘Gas and oil facility construction’ commodity for oil and gas and ‘Other engineering construction’ for mining.

The production of oil and gas exploration was and remains classified to the oil and gas extraction industry and similarly the production of mining exploration to the services incidental industry. Previously, the production was rerouted through the construction industries. An imputation was made to show exploration services as consumption of intermediate inputs and as outputs of the construction industries. This did not affect value-added by industry but did create a double count of gross transactions. A reclassification of the exploration activities from the construction to the IPP categories in final demand has removed the need to route transactions through the construction industries.

iv. Travel expenditures on education and medical services

In the previous vintage of the IO tables, the expenditures of residents on education and medical services were included in the household final consumption expenditure categories of education and medical services of the geography of their permanent residence regardless of whether these occurred within or outside their geography of residence. In the modernized framework, these services are now treated similarly to all other expenditures and appear instead in the household consumption categories of the geography where they actually occur. Concomitantly, the travel categories now also show the expenditures on education and medical services of residents abroad and of non-residents, similarly to all other expenditures. These changes do not affect total household final consumption expenditures but only their distribution across the travel and non-travel categories.

v. Interprovincial trade and taxes on products

The IO accounts now include flows of taxes on products in inter-provincial exports and imports at basic prices; an element that did not appear in previous vintages of the IO tables. Previously, the IO tables would only show taxes collected by a province or territory as applicable to expenditures within the province or territory. Methodological changes have been implemented to show taxes paid by the purchaser that are remitted to jurisdictions outside their geography of residence. Tax margin files have been expanded to include for each province, the taxes paid to other provinces. In final demand at basic prices, taxes paid by residents of a province or territory to other provinces or territories are shown under interprovincial or territorial imports; conversely, taxes paid by non-residents are shown under interprovincial exports.

II. Classification changes

In comparison to the previous version, the new tables have more services and fewer goods for both industries and commodities, and a redefinition of the content of the fictive commodities and industries. They now also incorporate a complete sector for NPISH, a separate industry for the better sectoring of the activities of aboriginal government, the creation of a new category in FCF for IPP and an accompanying redefinition of the coverage of M&E and construction, and the elimination of a separate construction category for transfer costs on non-residential construction made redundant by the allocation of these costs to the relevant construction industries.

i. Industries

The industry classification structure is organized according to three broad sectors of the economy: the business, government, and NPISHs sectors. The business sector is disaggregated by industry according to the North American Industrial Classification System (NAICS), which classifies establishments into industries on the basis of the similarity of their production processes. The NPISH sector is similarly disaggregated by NAICS industry but, unlike the business sector, most activities are concentrated in a few industries. The government sector is not disaggregated by NAICS industry, but instead by broad functions, such as education, health, recreation, administration, etc. Government business enterprises that behave essentially like private enterprises by deriving most of their income from market sales are classified to the business sector industries and not the government sector.

In general the IO tables show the secondary outputs of industries, however, an exception is made in the case of the secondary output of construction activities. The latter are allocated to the construction industries. Thus, the construction industries include all construction activities including contract and own-account construction by establishments not classified to the construction industry. Coverage of the construction industries remains activity based, however, as mentioned in section I (iii), exploration services are no longer included in construction output.

In addition to the industrial classification system, the IO accounts have established fictive industries as a routing mechanism. A number of goods and services originating in different industries, whose use is related to a common activity and for which there is limited statistical information on consumption, are grouped into fictive industries. Estimates are made of the commodity inputs into the fictive industries but no primary inputs are assigned to them, so their output is equal to their intermediate inputs. Some of the fictive industries are redefined to realign them with more current data sources. The composition of the fictive aggregations can be seen in the inputs of the fictive industry.

As with the previous vintage, the IO industry classification is still based on the NAICS 2007. However, to enhance its relevance to current economic structures, the new IO classification generally provides less detail in the goods-producing industries and greater detail in the services industries. Overall, the number of industries is reduced from 298 to 235. For example, while the number of food, textile, and chemical manufacturing industries is reduced, further detail is provided by separating oil and gas extraction into conventional and non-conventional extraction, the wholesale industry into 9 wholesaling industries, and the retail industry into 12 retailing industries. The aboriginal government services industry, previously embedded in the non-profit sector is now classified to the government sector.

The IO industry codes indicate the sectoring. Business sector industries begin with the letters ‘BS’ (211 industries), government sector industries with ‘GS’ (11), NPISH with ‘NP’ (7), and the fictive industries with ‘FC’ (6).

Concordances between NAICS 2007 and the Input-Output industrial classification system are available from the Industry Accounts Division.

ii. Commodities

The new commodity classification is based on a new standard, the North American Product Classification System (NAPCS). The new standard is more aligned with contemporary economic structures and provides more rigorous and detailed definitions of the IO commodities. The introduction of this system is also part of a broader harmonization of commodity classifications at Statistics Canada used, for example, in the compilation of manufacturing, services, and trade and industrial prices data. The total number of commodities is decreased from 727 to 481 in comparison to the old classification. In general, the number of goods commodities is reduced while the number of services commodities is increased. And obviously, the same comments apply to the fictive commodities as to the fictive industries.

A disadvantage of such a major overhaul of the classification is the loss of time series continuity with previously published IO tables. The many-to-many relationships between the old and the new classifications preclude the possibility of creating a concordance from the old to new commodities.

iii. Final demand categories

The final demand table shows expenditures on commodities by distinct final expenditure categories. The categories show each of the final consumption expenditures of households, NPISH, and government, fixed capital formation expenditures, inventory additions and withdrawals, and exports and imports. Most of the final demand categories embed further details. The final consumption expenditures of households are disaggregated by type of expenditure, the final consumption expenditures of government are disaggregated by level of government and broad function, while fixed capital formation is disaggregated by industry.

a. Household final consumption expenditure categories

The new household final consumption expenditure categories are based on the international classification standard, the Classification of Individual Consumption According to Purpose (COICOP). The new categories no longer distinguish imputed expenditures on own-output or income-in-kind, with the exception of the imputation for owner-occupied dwellings.

The old personal expenditure categories combined the expenditures of the household sector with the collective consumption expenditures of the NPISH and aboriginal government. The new categories exclude the two latter activities.

The four travel categories remain unchanged. However, as previously mentioned, trade in education and medical services by the personal sector are now included in travel expenditures. As a consequence, the related non-travel PE categories decrease by an amount equivalent to the value of these new travel imports and increase by an amount equivalent to the value of these travel exports.

At their most detailed level, the new PE categories are now the same between the IO tables and the Income and Expenditure Accounts. This will allow users a more seamless transition between the timelier IEA product and the less timely but enhanced commodity detail available from the IO tables.

b. NPISH categories

The NPISH final consumption expenditure categories, previously embedded with the personal expenditure categories are now shown as a separate category in final demand. Previously they were included in the ‘Operating expenditures of non-profit institutions serving households’ as well as other personal expenditure categories covering medical care, education, culture, and recreation among others.

c. Government Final Consumption Expenditure categories

The Government Final Consumption Expenditure category now include a separate category for the expenditures of Aboriginal government, which were previously included in the personal expenditures categories.

d. Fixed capital formation industries

Previously, the FCF category showed the construction and M&E expenditures of industries. In the new classifications, the FCF category shows expenditures on construction, M&E, and IPP. The new IPP category covers investments in software, R&D, and mineral exploration. In the previous vintages of the IO tables, software investments were included in M&E, spending on mineral exploration in the construction category, and expenditures on R&D in the intermediate consumption of industries. FCF industries are realigned with the new industries in the input and output tables. One of the main differences is the explicit classification of the NPISH sector industries, which were previously combined with the business sector industries. Transfer costs on non-residential construction are no longer shown as an aggregate for the total business sector but are distinguished separately for each construction industry.

ii. Margins

There are three important changes that affect the margins. The pipeline margin has been split into two separate crude oil and natural gas pipeline margins. There are no changes to the tax margins at the national level but the provincial tax margins now articulate the province of origin and the province of destination of the tax payments. Finally, the wholesale margin no longer embeds the value of the non-margin wholesaling commissions.

iii. The aggregation structure of the classifications

New aggregations have been designed to accompany the new classifications. The main criteria used for determining the IO aggregations were: analytical usefulness, economic significance, and the protection of confidential information. It is worth reemphasizing that regardless of the level of aggregation the figures in the 2009 tables will not be directly comparable to figures from tables for any reference years prior to 2009.

Table 1 shows the number of industries, commodities, and final demand categories for the Detailed confidential level (DC), Detailed (D), and Summary (S) aggregation levels of the tables. The aggregations are hierarchical in nature. The DC level IO tables are not released publically due to confidentiality restrictions. The national IO tables are published at the D and S levels while the provincial tables are only published at the S level due to confidentiality restrictions. The D aggregation is released nationally with some data suppression for confidentiality, that is, the tables are not completely additive due to missing confidential values. While suppressed values are set to zero, all published values and totals are correct values. At the provincial level, GDP and output by industry will also be published at the D level, with some data suppressions for confidentiality.

Currently, the IO tables in basic prices are published on CANSIM while the margin and purchaser price tables are available on request from Industry Accounts Division.

In general, total output and GDP components by industry contain no suppressions for confidentiality. In final demand, category totals as well as household final consumption expenditures by commodity are free of suppressions. And finally, the S level national IO tables are free of any suppressions for confidentiality.

The Summary aggregation was designed to provide the maximum amount of information at the provincial level given confidentiality constraints. They provide about 10 more industries and 15 more commodities than the previous provincial IO tables. The final demand categories now include the new Non-Profit Institutions Serving Households sector and Intellectual Property Product categories.

Notes

1. United Nations. 2009. System of National Accounts 2008. New York.
2. Statistics Canada. 2012. Revisions analysis – Canadian System of National Accounts 2012. Latest Developments in the Canadian Economic Accounts. Catalogue no. 13-605-X. Ottawa: Statistics Canada.
3. The conceptual framework provides the theoretical definition of what is being measured while the methodologies define the real-world methods used to measure it.
4. In general, market revenues for R&D services are used as an indicator of firms that are of sufficient size and autonomy to qualify as separate establishments.

Objectives, uses and users
Concepts, variables and classifications
Coverage and frames
Sampling
Questionnaire design
Response and nonresponse
Data collection and capture operations
Editing
Imputation
Estimation
Revisions and seasonal adjustment
Data quality evaluation
Disclosure control

1. Objectives, uses and users

1.1. Objective

The Monthly Retail Trade Survey (MRTS) provides information on the performance of the retail trade sector on a monthly basis, and when combined with other statistics, represents an important indicator of the state of the Canadian economy.

1.2. Uses

The estimates provide a measure of the health and performance of the retail trade sector. Information collected is used to estimate level and monthly trend for retail sales. At the end of each year, the estimates provide a preliminary look at annual retail sales and performance.

1.3. Users

A variety of organizations, sector associations, and levels of government make use of the information. Retailers rely on the survey results to compare their performance against similar types of businesses, as well as for marketing purposes. Retail associations are able to monitor industry performance and promote their retail industries. Investors can monitor industry growth, which can result in better access to investment capital by retailers. Governments are able to understand the role of retailers in the economy, which aids in the development of policies and tax incentives. As an important industry in the Canadian economy, governments are able to better determine the overall health of the economy through the use of the estimates in the calculation of the nation’s Gross Domestic Product (GDP).

2. Concepts, variables and classifications

2.1. Concepts

The retail trade sector comprises establishments primarily engaged in retailing merchandise, generally without transformation, and rendering services incidental to the sale of merchandise.

The retailing process is the final step in the distribution of merchandise; retailers are therefore organized to sell merchandise in small quantities to the general public. This sector comprises two main types of retailers, that is, store and non-store retailers. The MRTS covers only store retailers. Their main characteristics are described below. Store retailers operate fixed point-of-sale locations, located and designed to attract a high volume of walk-in customers. In general, retail stores have extensive displays of merchandise and use mass-media advertising to attract customers. They typically sell merchandise to the general public for personal or household consumption, but some also serve business and institutional clients. These include establishments such as office supplies stores, computer and software stores, gasoline stations, building material dealers, plumbing supplies stores and electrical supplies stores.

In addition to selling merchandise, some types of store retailers are also engaged in the provision of after-sales services, such as repair and installation. For example, new automobile dealers, electronic and appliance stores and musical instrument and supplies stores often provide repair services, while floor covering stores and window treatment stores often provide installation services. As a general rule, establishments engaged in retailing merchandise and providing after sales services are classified in this sector. Catalogue sales showrooms, gasoline service stations, and mobile home dealers are treated as store retailers.

2.2. Variables

Sales are defined as the sales of all goods purchased for resale, net of returns and discounts. This includes commission revenue and fees earned from selling goods and services on account of others, such as selling lottery tickets, bus tickets, and phone cards. It also includes parts and labour revenue from repair and maintenance; revenue from rental and leasing of goods and equipment; revenues from services, including food services; sales of goods manufactured as a secondary activity; and the proprietor’s withdrawals, at retail, of goods for personal use. Other revenue from rental of real estate, placement fees, operating subsidies, grants, royalties and franchise fees are excluded.

Trading Location is the physical location(s) in which business activity is conducted in each province and territory, and for which sales are credited or recognized in the financial records of the company. For retailers, this would normally be a store.

Constant Dollars: The value of retail trade is measured in two ways; including the effects of price change on sales and net of the effects of price change. The first measure is referred to as retail trade in current dollars and the latter as retail trade in constant dollars. The method of calculating the current dollar estimate is to aggregate the weighted value of sales for all retail outlets. The method of calculating the constant dollar estimate is to first adjust the sales values to a base year, using the Consumer Price Index, and then sum up the resulting values.

2.3. Classification

The Monthly Retail Trade Survey is based on the definition of retail trade under the NAICS (North American Industry Classification System). NAICS is the agreed upon common framework for the production of comparable statistics by the statistical agencies of Canada, Mexico and the United States. The agreement defines the boundaries of twenty sectors. NAICS is based on a production-oriented, or supply based conceptual framework in that establishments are groups into industries according to similarity in production processes used to produce goods and services.

Estimates appear for 21 industries based on special aggregations of the 2012 North American Industry Classification System (NAICS) industries. The 21 industries are further aggregated to 11 sub-sectors.

Geographically, sales estimates are produced for Canada and each province and territory.

3. Coverage and frames

Statistics Canada’s Business Register ( BR) provides the frame for the Monthly Retail Trade Survey. The BR is a structured list of businesses engaged in the production of goods and services in Canada. It is a centrally maintained database containing detailed descriptions of most business entities operating within Canada. The BR includes all incorporated businesses, with or without employees. For unincorporated businesses, the BR includes all employers with businesses, and businesses with no employees with annual sales that have a Goods and Services Tax (GST) or annual revenue that declares individual taxes.  annual sales greater than $30,000 that have a Goods and Services Tax (GST) account (the BR does not include unincorporated businesses with no employees and with annual sales less than $30,000).

The businesses on the BR are represented by a hierarchical structure with four levels, with the statistical enterprise at the top, followed by the statistical company, the statistical establishment and the statistical location. An enterprise can be linked to one or more statistical companies, a statistical company can be linked to one or more statistical establishments, and a statistical establishment to one or more statistical locations.

The target population for the MRTS consists of all statistical establishments on the BR that are classified to the retail sector using the North American Industry Classification System (NAICS) (approximately 200,000 establishments). The NAICS code range for the retail sector is 441100 to 453999. A statistical establishment is the production entity or the smallest grouping of production entities which: produces a homogeneous set of goods or services; does not cross provincial boundaries; and provides data on the value of output, together with the cost of principal intermediate inputs used, along with the cost and quantity of labour used to produce the output. The production entity is the physical unit where the business operations are carried out. It must have a civic address and dedicated labour.

The exclusions to the target population are ancillary establishments (producers of services in support of the activity of producing goods and services for the market of more than one establishment within the enterprise, and serves as a cost centre or a discretionary expense centre for which data on all its costs including labour and depreciation can be reported by the business), future establishments, establishments with a missing or a zero gross business income (GBI) value on the BR and establishments in the following non-covered NAICS:

• 4541 (electronic shopping and mail-order houses)
• 4542 (vending machine operators)
• 45431 (fuel dealers)
• 45439 (other direct selling establishments)

4. Sampling

The MRTS sample consists of 10,000 groups of establishments (clusters) classified to the Retail Trade sector selected from the Statistics Canada Business Register. A cluster of establishments is defined as all establishments belonging to a statistical enterprise that are in the same industrial group and geographical region. The MRTS uses a stratified design with simple random sample selection in each stratum. The stratification is done by industry groups (the mainly, but not only four digit level NAICS), and the geographical regions consisting of the provinces and territories, as well as three provincial sub-regions. We further stratify the population by size.

The size measure is created using a combination of independent survey data and three administrative variables: the annual profiled revenue, the GST sales expressed on an annual basis, and the declared tax revenue (T1 or T2). The size strata consist of one take-all (census), at most, two take-some (partially sampled) strata, and one take-none (non-sampled) stratum. Take-none strata serve to reduce respondent burden by excluding the smaller businesses from the surveyed population. These businesses should represent at most ten percent of total sales. Instead of sending questionnaires to these businesses, the estimates are produced through the use of administrative data.

The sample was allocated optimally in order to reach target coefficients of variation at the national, provincial/territorial, industrial, and industrial groups by province/territory levels. The sample was also inflated to compensate for dead, non-responding, and misclassified units.

MRTS is a repeated survey with maximisation of monthly sample overlap. The sample is kept month after month, and every month new units are added (births) to the sample.  MRTS births, i.e., new clusters of establishment(s), are identified every month via the BR’s latest universe. They are stratified according to the same criteria as the initial population. A sample of these births is selected according to the sampling fraction of the stratum to which they belong and is added to the monthly sample. Deaths occur on a monthly basis. A death can be a cluster of establishment(s) that have ceased their activities (out-of-business) or whose major activities are no longer in retail trade (out-of-scope). The status of these businesses is updated on the BR using administrative sources and survey feedback, including feedback from the MRTS. Methods to treat dead units and misclassified units are part of the sample and population update procedures.

5. Questionnaire design

The Monthly Retail Trade Survey incorporates the following sub-surveys:

Monthly Retail Trade Survey - R8

Monthly Retail Trade Survey (with inventories) – R8

Survey of Sales and Inventories of Alcoholic Beverages

The questionnaires collect monthly data on retail sales and the number of trading locations by province or territory and inventories of goods owned and intended for resale from a sample of retailers. The items on the questionnaires have remained unchanged for several years. For the 2004 redesign, the general questionnaires were subject to cosmetic changes only. The questionnaire for Sales and Inventories of Alcoholic Beverages underwent more extensive changes. The modifications were discussed with stakeholders and the respondents were given an opportunity to comment before the new questionnaire was finalized. If further changes are needed to any of the questionnaires, proposed changes would go through a review committee and a field test with respondents and data users to ensure its relevancy.

6. Response and nonresponse

6.1. Response and non-response

Despite the best efforts of survey managers and operations staff to maximize response in the MRTS, some non-response will occur. For statistical establishments to be classified as responding, the degree of partial response (where an accurate response is obtained for only some of the questions asked a respondent) must meet a minimum threshold level below which the response would be rejected and considered a unit nonresponse.  In such an instance, the business is classified as not having responded at all.

Non-response has two effects on data: first it introduces bias in estimates when nonrespondents differ from respondents in the characteristics measured; and second, it contributes to an increase in the sampling variance of estimates because the effective sample size is reduced from that originally sought.

The degree to which efforts are made to get a response from a non-respondent is based on budget and time constraints, its impact on the overall quality and the risk of nonresponse bias.

The main method to reduce the impact of non-response at sampling is to inflate the sample size through the use of over-sampling rates that have been determined from similar surveys.

Besides the methods to reduce the impact of non-response at sampling and collection, the non-responses to the survey that do occur are treated through imputation. In order to measure the amount of non-response that occurs each month, various response rates are calculated. For a given reference month, the estimation process is run at least twice (a preliminary and a revised run). Between each run, respondent data can be identified as unusable and imputed values can be corrected through respondent data. As a consequence, response rates are computed following each run of the estimation process.

For the MRTS, two types of rates are calculated (un-weighted and weighted). In order to assess the efficiency of the collection process, un-weighted response rates are calculated. Weighted rates, using the estimation weight and the value for the variable of interest, assess the quality of estimation. Within each of these types of rates, there are distinct rates for units that are surveyed and for units that are only modeled from administrative data that has been extracted from GST files.

To get a better picture of the success of the collection process, two un-weighted rates called the ‘collection results rate’ and the ‘extraction results rate’ are computed. They are computed by dividing the number of respondents by the number of units that we tried to contact or tried to receive extracted data for them. Non-monthly reporters (respondents with special reporting arrangements where they do not report every month but for whom actual data is available in subsequent revisions) are excluded from both the numerator and denominator for the months where no contact is performed.

In summary, the various response rates are calculated as follows:

Weighted rates:

Survey Response rate (estimation) =
Sum of weighted sales of units with response status i / Sum of survey weighted sales

where i = units that have either reported data that will be used in estimation or are converted refusals, or have reported data that has not yet been resolved for estimation.

Admin Response rate (estimation) =
Sum of weighted sales of units with response status ii / Sum of administrative weighted sales

where ii = units that have data that was extracted from administrative files and are usable for estimation.

Total Response rate (estimation) =
Sum of weighted sales of units with response status i or response status ii / Sum of all weighted sales

Un-weighted rates:

Survey Response rate (collection) =
Number of questionnaires with response status iii/ Number of questionnaires with response status iv

where iii = units that have either reported data (unresolved, used or not used for estimation) or are converted refusals.

where iv = all of the above plus units that have refused to respond, units that were not contacted and other types of non-respondent units.

Admin Response rate (extraction) =
Number of questionnaires with response status vi/ Number of questionnaires with response status vii

where vi = in-scope units that have data (either usable or non-usable) that was extracted from administrative files

where vii = all of the above plus units that have refused to report to the administrative data source, units that were not contacted and other types of non-respondent units.

(% of questionnaire collected over all in-scope questionnaires)

Collection Results Rate =
Number of questionnaires with response status iii / Number of questionnaires with response status viii

where iii = same as iii defined above

where viii = same as iv except for the exclusion of units that were contacted because their response is unavailable for a particular month since they are non-monthly reporters.

Extraction Results Rate =
Number of questionnaires with response status ix / Number of questionnaires with response status vii

where ix = same as vi with the addition of extracted units that have been imputed or were out of scope

where vii = same as vii defined above

(% of questionnaires collected over all questionnaire in-scope we tried to collect)

All the above weighted and un-weighted rates are provided at the industrial group, geography and size group level or for any combination of these levels.

Use of Administrative Data

Managing response burden is an ongoing challenge for Statistics Canada. In an attempt to alleviate response burden and survey costs, especially for smaller businesses, the MRTS has reduced the number of simple establishments in the sample that are surveyed directly and instead derives sales data for these establishments from Goods and Service Tax (GST) files using a statistical model. The model accounts for differences between sales and revenue (reported for GST purposes) as well as for the time lag between the survey reference period and the reference period of the GST file.

For more information on the methodology used for modeling sales from administrative data sources, refer to ‘Monthly Retail Trade Survey: Use of Administrative Data’ under ‘Documentation’ of the IMDB.

Table 1 contains the weighted response rates for all industry groups as well as for total retail trade for each province and territory. For more detailed weighted response rates, please contact the Marketing and Dissemination Section at (613) 951-3549, toll free: 1-877-421-3067 or by e-mail at retailinfo@statcan.

6.2. Methods used to reduce non-response at collection

Significant effort is spent trying to minimize non-response during collection. Methods used, among others, are interviewer techniques such as probing and persuasion, repeated re-scheduling and call-backs to obtain the information, and procedures dealing with how to handle non-compliant (refusal) respondents.

If data are unavailable at the time of collection, a respondent's best estimates are also accepted, and are subsequently revised once the actual data become available.

To minimize total non-response for all variables, partial responses are accepted. In addition, questionnaires are customized for the collection of certain variables, such as inventory, so that collection is timed for those months when the data are available.

Finally, to build trust and rapport between the interviewers and respondents, cases are generally assigned to the same interviewer each month. This action establishes a personal relationship between interviewer and respondent, and builds respondent trust.

7. Data collection and capture operations

Collection of the data is performed by Statistics Canada’s Regional Offices.

Weighted Response Rates

Respondents are sent a questionnaire or are contacted by telephone to obtain their sales and inventory values, as well as to confirm the opening or closing of business trading locations. Collection of the data begins approximately 7 working days after the end of the reference month and continues for the duration of that month.

New entrants to the survey are introduced to the survey via an introductory letter that informs the respondent that a representative of Statistics Canada will be calling. This call is to introduce the respondent to the survey, confirm the respondent's business activity, establish and begin data collection, as well as to answer any questions that the respondent may have.

8. Editing

Data editing is the application of checks to detect missing, invalid or inconsistent entries or to point to data records that are potentially in error. In the survey process for the MRTS, data editing is done at two different time periods.

First of all, editing is done during data collection. Once data are collected via the telephone, or via the receipt of completed mail-in questionnaires, the data are captured using customized data capture applications. All data are subjected to data editing. Edits during data collection are referred to as field edits and generally consist of validity and some simple consistency edits. They are used to detect mistakes made during the interview by the respondent or the interviewer and to identify missing information during collection in order to reduce the need for follow-up later on. Another purpose of the field edits is to clean up responses. In the MRTS, the current month’s responses are edited against the respondent’s previous month’s responses and/or the previous year’s responses for the current month. Field edits are also used to identify problems with data collection procedures and the design of the questionnaire, as well as the need for more interviewer training.

Follow-up with respondents occurs to validate potential erroneous data following any failed preliminary edit check of the data. Once validated, the collected data is regularly transmitted to the head office in Ottawa.

Secondly, editing known as statistical editing is also done after data collection and this is more empirical in nature. Statistical editing is run prior to imputation in order to identify the data that will be used as a basis to impute non-respondents. Large outliers that could disrupt a monthly trend are excluded from trend calculations by the statistical edits. It should be noted that adjustments are not made at this stage to correct the reported outliers.

The first step in the statistical editing is to identify which responses will be subjected to the statistical edit rules. Reported data for the current reference month will go through various edit checks.

The first set of edit checks is based on the Hidiriglou-Berthelot method whereby a ratio of the respondent’s current month data over historical (last month, same month last year) or auxiliary data is analyzed. When the respondent’s ratio differs significantly from ratios of respondents who are similar in terms of industry and/or geography group, the response is deemed an outlier.

The second set of edits consists of an edit known as the share of market edit. With this method, one is able to edit all respondents, even those where historical and auxiliary data is unavailable. The method relies on current month data only. Therefore, within a group of respondents, that are similar in terms of industrial group and/or geography, if the weighted contribution of a respondent to the group’s total is too large, it will be flagged as an outlier.

For edit checks based on the Hidiriglou-Berthelot method, data that are flagged as an outlier will not be included in the imputation models (those based on ratios). Also, data that are flagged as outliers in the share of market edit will not be included in the imputation models where means and medians are calculated to impute for responses that have no historical responses.

In conjunction with the statistical editing after data collection of reported data, there is also error detection done on the extracted GST data. Modeled data based on the GST are also subject to an extensive series of processing steps which thoroughly verify each record that is the basis for the model as well as the record being modeled. Edits are performed at a more aggregate level (industry by geography level) to detect records which deviate from the expected range, either by exhibiting large month-to-month change, or differing significantly from the remaining units. All data which fail these edits are subject to manual inspection and possible corrective action.

9. Imputation

Imputation in the MRTS is the process used to assign replacement values for missing data. This is done by assigning values when they are missing on the record being edited to ensure that estimates are of high quality and that a plausible, internal consistency is created. Due to concerns of response burden, cost and timeliness, it is generally impossible to do all follow-ups with the respondents in order to resolve missing responses. Since it is desirable to produce a complete and consistent microdata file, imputation is used to handle the remaining missing cases.

In the MRTS, imputation is based on historical data or administrative data (GST sales). The appropriate method is selected according to a strategy that is based on whether historical data is available, auxiliary data is available and/or which reference month is being processed.

There are three types of historical imputation methods. The first type is a general trend that uses one historical data source (previous month, data from next month or data from same month previous year). The second type is a regression model where data from previous month and same month previous year are used simultaneously. The third type uses the historical data as a direct replacement value for a non-respondent. Depending upon the particular reference month, there is an order of preference that exists so that top quality imputation can result. The historical imputation method that was labelled as the third type above is always the last option in the order for each reference month.

The imputation methods using administrative data are automatically selected when historical information is unavailable for a non-respondent. The administrative data source (annual GST sales) is the basis of these methods. The annual GST sales are used for two types of methods. One is a general trend that will be used for simple structure, e.g. enterprises with only one establishment, and a second type is called median-average that is used for units with a more complex structure.

10. Estimation

Estimation is a process that approximates unknown population parameters using only part of the population that is included in a sample. Inferences about these unknown parameters are then made, using the sample data and associated survey design. This stage uses Statistics Canada's Generalized Estimation System (GES).

For retail sales, the population is divided into a survey portion (take-all and take-some strata) and a non-survey portion (take-none stratum). From the sample that is drawn from the survey portion, an estimate for the population is determined through the use of a Horvitz-Thompson estimator where responses for sales are weighted by using the inverses of the inclusion probabilities of the sampled units. Such weights (called sampling weights) can be interpreted as the number of times that each sampled unit should be replicated to represent the entire population. The calculated weighted sales values are summed by domain, to produce the total sales estimates by each industrial group / geographic area combination. A domain is defined as the most recent classification values available from the BR for the unit and the survey reference period. These domains may differ from the original sampling strata because units may have changed size, industry or location. Changes in classification are reflected immediately in the estimates and do not accumulate over time. For the non-survey portion, the sales are estimated with statistical models using monthly GST sales.

For more information on the methodology for modeling sales from administrative data sources which also contributes to the estimates of the survey portion, refer to ‘Monthly Retail Survey: Use of Administrative Data’ under ‘Documentation’ of the IMDB.

The measure of precision used for the MRTS to evaluate the quality of a population parameter estimate and to obtain valid inferences is the variance. The variance from the survey portion is derived directly from a stratified simple random sample without replacement.

Sample estimates may differ from the expected value of the estimates. However, since the estimate is based on a probability sample, the variability of the sample estimate with respect to its expected value can be measured. The variance of an estimate is a measure of the precision of the sample estimate and is defined as the average, over all possible samples, of the squared difference of the estimate from its expected value.

11. Revisions and seasonal adjustment

Revisions in the raw data are required to correct known non-sampling errors. These normally include replacing imputed data with reported data, corrections to previously reported data, and estimates for new births that were not known at the time of the original estimates. Raw data are revised, on a monthly basis, for the month immediately prior to the current reference month being published. That is, when data for December are being published for the first time, there will also be revisions, if necessary, to the raw data for November. In addition, revisions are made once a year, with the initial release of the February data, for all months in the previous year. The purpose is to correct any significant problems that have been found that apply for an extended period. The actual period of revision depends on the nature of the problem identified, but rarely exceeds three years. Time series contain the elements essential to the description, explanation and forecasting of the behaviour of an economic phenomenon: "They are statistical records of the evolution of economic processes through time."¹ Economic time series such as the Monthly Retail Trade Survey can be broken down into five main components: the trend-cycle, seasonality, the trading-day effect, the Easter holiday effect and the irregular component.

The trend represents the long-term change in the series, whereas the cycle represents a smooth, quasi-periodical movement about the trend, showing a succession of growth and decline phases (e.g., the business cycle). These two components—the trend and the cycle—are estimated together, and the trend-cycle reflects the fundamental evolution of the series. The other components reflect short-term transient movements.

The seasonal component represents sub-annual, monthly or quarterly fluctuations that recur more or less regularly from one year to the next. Seasonal variations are caused by the direct and indirect effects of the climatic seasons and institutional factors (attributable to social conventions or administrative rules; e.g., Christmas).

The trading-day component originates from the fact that the relative importance of the days varies systematically within the week and that the number of each day of the week in a given month varies from year to year. This effect is present when activity varies with the day of the week. For instance, Sunday is typically less active than the other days, and the number of Sundays, Mondays, etc., in a given month changes from year to year.

The Easter holiday effect is the variation due to the shift of part of April’s activity to March when Easter falls in March rather than April.

Lastly, the irregular component includes all other more or less erratic fluctuations not taken into account in the preceding components. It is a residual that includes errors of measurement on the 1. A Note on the Seasonal adjustment of Economic Time Series», Canadian Statistical Review, August 1974.  A variable itself as well as unusual events (e.g., strikes, drought, floods, major power blackout or other unexpected events causing variations in respondents’ activities).

Thus, the latter four components—seasonal, irregular, trading-day and Easter holiday effect—all conceal the fundamental trend-cycle component of the series. Seasonal adjustment (correction of seasonal variation) consists in removing the seasonal, trading-day and Easter holiday effect components from the series, and it thus helps reveal the trend-cycle. While seasonal adjustment permits a better understanding of the underlying trend-cycle of a series, the seasonally adjusted series still contains an irregular component. Slight month-to-month variations in the seasonally adjusted series may be simple irregular movements. To get a better idea of the underlying trend, users should examine several months of the seasonally adjusted series.

Since April 2008, Monthly Retail Trade Survey data are seasonally adjusted using the X-12- ARIMA2 software. The technique that is used essentially consists of first correcting the initial series for all sorts of undesirable effects, such as the trading-day and the Easter holiday effects, by a module called regARIMA. These effects are estimated using regression models with ARIMA errors (auto-regressive integrated moving average models). The series can also be extrapolated for at least one year by using the model. Subsequently, the raw series—pre-adjusted and extrapolated if applicable— is seasonally adjusted by the X-11 method.

The X-11 method is used for analysing monthly and quarterly series. It is based on an iterative principle applied in estimating the different components, with estimation being done at each stage using adequate moving averages3. The moving averages used to estimate the main components—the trend and seasonality—are primarily smoothing tools designed to eliminate an undesirable component from the series. Since moving averages react poorly to the presence of atypical values, the X-11 method includes a tool for detecting and correcting atypical points. This tool is used to clean up the series during the seasonal adjustment. Outlying data points can also be detected and corrected in advance, within the regARIMA module.

Lastly, the annual totals of the seasonally adjusted series are forced to the annual totals of the original series.

Unfortunately, seasonal adjustment removes the sub-annual additivity of a system of series; small discrepancies can be observed between the sum of seasonally adjusted series and the direct seasonal adjustment of their total. To insure or restore additivity in a system of series, a reconciliation process is applied or indirect seasonal adjustment is used, i.e. the seasonal adjustment of a total is derived by the summation of the individually seasonally adjusted series.

12. Data quality evaluation

The methodology of this survey has been designed to control errors and to reduce their potential effects on estimates. However, the survey results remain subject to errors, of which sampling error is only one component of the total survey error. Sampling error results when observations are made only on a sample and not on the entire population. All other errors arising from the various phases of a survey are referred to as nonsampling errors. For example, these types of errors can occur when a respondent provides incorrect information or does not answer certain questions; when a unit in the target population is omitted or covered more than once; when GST data for records being modeled for a particular month are not representative of the actual record for various reasons; when a unit that is out of scope for the survey is included by mistake or when errors occur in data processing, such as coding or capture errors.

Prior to publication, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for large businesses), general economic conditions and historical trends.

A common measure of data quality for surveys is the coefficient of variation (CV). The coefficient of variation, defined as the standard error divided by the sample estimate, is a measure of precision in relative terms. Since the coefficient of variation is calculated from responses of individual units, it also measures some non-sampling errors.

The formula used to calculate coefficients of variation (CV) as percentages is:

CV (X) = S(X) * 100% / X
where X denotes the estimate and S(X) denotes the standard error of X.

Confidence intervals can be constructed around the estimates using the estimate and the CV. Thus, for our sample, it is possible to state with a given level of confidence that the expected value will fall within the confidence interval constructed around the estimate. For example, if an estimate of $12,000,000 has a CV of 2%, the standard error will be $240,000 (the estimate multiplied by the CV). It can be stated with 68% confidence that the expected values will fall within the interval whose length equals the standard deviation about the estimate, i.e. between $11,760,000 and $12,240,000.

Alternatively, it can be stated with 95% confidence that the expected value will fall within the interval whose length equals two standard deviations about the estimate, i.e. between $11,520,000 and $12,480,000.

Finally, due to the small contribution of the non-survey portion to the total estimates, bias in the non-survey portion has a negligible impact on the CVs. Therefore, the CV from the survey portion is used for the total estimate that is the summation of estimates from the surveyed and non-surveyed portions.

13. Disclosure control

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.

Confidentiality analysis includes the detection of possible "direct disclosure", which occurs when the value in a tabulation cell is composed of a few respondents or when the cell is dominated by a few companies.

To obtain a copy of any of the following documentation, contact Client Services (613-951-1746; fax: 613-951-0792; hd-ds@statcan.gc.ca).

• CCHS Annual Component – Content Plan 2007-2014
• Canadian community health survey content overview (2011-2012)
• Optional content selection, 2011-2012
• CCHS 2012 and 2011-2012 Microdata User Guide
• CCHS 2011-2012 Derived Variables Documentation
• CCHS 2011-2012 Alphabetic Index
• CCHS 2011-2012 Topical Index
• CCHS 2011-2012 Data Dictionary (rounded frequencies)
• CCHS 2011-2012 Record Layout
• Household Weights
• CCHS 2011-2012 Share File Approximate Sampling Variability Tables
• Canadian Community Health Survey – Errata (updated June 2013)
• Income Imputation for the Canadian Community Health Survey
• Interpreting Estimates from the Redesigned CCHS
• Mode Study
• Health Surveys - Aspects that may explain differences in the estimates obtained from two different survey occasions

Introduction

Statistics Canada launched an interactive crowdsourcing tool, called the Innovation Channel, on its Intranet site as part of an initiative to develop a corporate Innovation Framework. The purpose of this web portal is to engage Statistics Canada employees and give them an opportunity to share, exchange and collectively discuss innovative and creative ideas to enhance Statistics Canada’s programs and services, as well as the workplace.

Objective

A privacy impact assessment for Statistics Canada’s Innovation Channel was conducted to determine if there were any privacy, confidentiality and security issues associated with the portal, and if so, to make recommendations for their resolution or mitigation.

Description

The Innovation Channel is an internal collaboration tool aimed at gathering and sharing ideas. The tool, which is accessible from Statistics Canada’s Intranet site, is available only to Statistics Canada employees.

Employees may access the Innovation Channel to browse through ideas or comments already posted by their peers or to publish their own. They can also vote for ideas and rank them.

All content posted onto the channel must comply with Statistics Canada’s rules of engagement. All ideas and comments are monitored following an established moderation protocol.

Conclusion

This assessment of the Innovation Channel did not identify any privacy risks that cannot be managed using existing safeguards.