Statement outlining results, risks and significant changes in operations, personnel and program

A) Introduction

Statistics Canada's mandate

Statistics Canada is a member of the Innovation, Science and Economic Development portfolio.

Statistics Canada’s role is to ensure that Canadians have access to a trusted source of statistics on Canada that meets their highest priority needs.

The Agency’s mandate derives primarily from the Statistics Act. The Act requires that the Agency collects, compiles, analyzes and publishes statistical information on the economic, social, and general conditions of the country and its people. It also requires that Statistics Canada conduct the census of population and the census of agriculture every fifth year, and protects the confidentiality of the information with which it is entrusted.

Statistics Canada also has a mandate to co-ordinate and lead the national statistical system. The Agency is considered a leader, among statistical agencies around the world, in co‑ordinating statistical activities to reduce duplication and reporting burden.

More information on Statistics Canada’s mandate, roles, responsibilities and programs can be found in the 2015–2016 Main Estimates and in the Statistics Canada 2015–2016 Report on Plans and Priorities.

The quarterly financial report

• should be read in conjunction with the 2015–2016 Main Estimates, the 2015-2016 Supplementary Estimates , the Quarterly Financial Report for the quarter ended June 30, 2015 and the Quarterly Financial Report for the quarter ended September 30, 2015;
• has been prepared by management, as required by Section 65.1 of the Financial Administration Act, and in the form and manner prescribed by Treasury Board;
• has not been subject to an external audit or review.

Statistics Canada has the authority to collect and spend revenue from other government departments and agencies, as well as from external clients, for statistical services and products.

Basis of presentation

This quarterly report has been prepared by management using an expenditure basis of accounting. The accompanying Statement of Authorities includes the Agency’s spending authorities granted by Parliament and those used by the Agency consistent with the Main Estimates for the 2015–2016 fiscal year. This quarterly report has been prepared using a special purpose financial reporting framework designed to meet financial information needs with respect to the use of spending authorities.

The authority of Parliament is required before moneys can be spent by the Government. Approvals are given in the form of annually approved limits through appropriation acts or through legislation in the form of statutory spending authority for specific purposes.

The Agency uses the full accrual method of accounting to prepare and present its annual departmental financial statements that are part of the departmental performance reporting process. However, the spending authorities voted by Parliament remain on an expenditure basis.

B) Highlights of fiscal quarter and fiscal year-to-date results

This section highlights the significant items that contributed to the net increase in resources available for the year, as well as actual expenditures for the quarter ended December 31.

Chart 1 outlines the gross budgetary authorities, which represent the resources available for use for the year as of December 31.

Significant changes to authorities

During the third quarter, Statistics Canada authorities increased by $4.5 million compared with the second quarter of 2015-2016. The increase is related to funding received for the arbitral decision awarded to Statistical Survey Operations’ interviewers workforce.

Total authorities available for 2015–2016 have increased by $80.1 million, or 14%, from the previous year, from $575.8 million to $655.9 million (Chart 1). This net increase was mostly the result of the following:

• increase for the Census of Population Program ($92 million), as well as for the Census of Agriculture ($7.2 million)
• increase in funding received for collective agreements mainly for the interviewers ($4.6 million)

This increase is offset by the following:

• decrease in the value of the carry-forward ($16.5 million)
• no reimbursement requested from Treasury Board Secretariat for specific salary expenditures in 2015-16 ($5.7 million)

In addition to the appropriations allocated to the Agency through the Main Estimates, Statistics Canada also has vote net authority within Vote 105, which entitles the Agency to spend revenues collected from other government departments, agencies, and external clients to provide statistical services. Vote netting authority is stable at $120 million in each of the fiscal years 2014–2015 and 2015–2016.

Significant changes to expenditures

Year-to-date net expenditures recorded to the end of the third quarter increased by $12.9 million, or 3.5% from the previous year, from $365.8 million to $378.7 million. (See Table A: Variation in Departmental Expenditures by Standard Object.)

Statistics Canada spent approximately 71% of its authorities by the end of the third quarter, compared with 80% in the same quarter of 2014–2015.

Table A: Variation in Departmental Expenditures by Standard Object (unaudited)
This table displays the variance of departmental expenditures by standard object between fiscal 2014-2015 and 2015-2016. The variance is calculated for year to date expenditures as at the end of the second quarter. The row headers provide information by standard object. The column headers provide information in thousands of dollars and percentage variance for the year to date variation.

Departmental Expenditures Variation by Standard Object	Q3 year-to-date variation between fiscal year 2014-2015 and 2015-2016
	$'000	%
Note: Explanations are provided for variances of more than $1 million.
(01) Personnel	31,104	9.0
(02) Transportation and communications	2,040	23.8
(03) Information	4,163	450.0
(04) Professional and special services	(3,693)	(23.5)
(05) Rentals	4,541	61.3
(06) Repair and maintenance	(311)	(28.9)
(07) Utilities, materials and supplies	48	3.4
(08) Acquisition of land, buildings and works	0	0
(09) Acquisition of machinery and equipment	2,671	416.7
(10) Transfer payments	0	0
(12) Other subsidies and payments	(13,556)	(98.8)
Total gross budgetary expenditures	27,007	6.8
Less revenues netted against expenditures
Revenues	14,134	46.8
Total net budgetary expenditures	12,873	3.5

Description for Table A: Departmental expenditures by Standard Object (unaudited) This table displays the variance of departmental expenditures by standard object between fiscal 2014-2015 and 2015-2016. The variance is calculated for year to date expenditures as at the end of the third quarter. The row headers provide information by standard object. The column headers provide information in thousands of dollars and percentage variance for the year to date variation.

01) Personnel: The increase was mainly the result of the arbitration award including the severance liquidation payment for interviewers and increased salary expenditures due to the cyclical nature of the Census.

02) Transportation and Communications: The increase was the result of timing differences between years for the postage purchase and travel for cost recovery projects as well as an increase in collection activities due to the cyclical nature of some surveys.

03) Information: The increase was the result of printing expenses for the Census as well as the coding review of the standard object definitions and inclusions (e.g., data purchases).

04) Professional and special services: The decrease was the result of the coding review of the standard object definitions and inclusions (e.g., data purchases) and a reduction in spending on informatics services.

05) Rentals: The increase was the result of the cyclical nature of the Census (e.g., rental of building space) and additional software licenses fees related to new infrastructure and due to changes in contract payment schedules.

09) Acquisition of machinery and equipment: The increase was the result of timing differences between years for the acquisition of computer equipment, as well as additional acquisitions for the Census. Also, there was an increase in the purchase of office equipment attributable to our space optimization plan.

12) Other subsidies and payments: The decrease is a result of the one-time transition payment for implementing salary payment in arrears made in the first quarter of 2014–2015 by the Government of Canada.

Revenues: The increase is primarily the result of timing differences in the receipt of funds for scheduled key deliverables and a new significant cost recovery survey contract.

C) Risks and uncertainties

In 2015–2016, Statistics Canada plans to continue to monitor financial pressures due to the continuation of the federal operating budget freeze with the following actions and mitigation strategies:

• additional analysis, monitoring and validation of financial and human resources information through a monthly financial review by budget holders;
• review of monthly project dashboards in place across the Agency to monitor project issues, risks and alignment with approved budgets;
• continued realignment and reprioritization of work.

In addition, while Statistics Canada continues to work collaboratively with its service providers to ensure affordable and timely delivery of its key statistical programs, the Agency has experienced issues and challenges with its information technology services during the third quarter.

Statistics Canada uses risk management and a risk-based decision-making process to prioritize and conduct its business.  In order to effectively do so the Agency identifies its key risks and develops corresponding mitigation strategies in its Corporate Risk Profile. 

D) Significant changes to operations, personnel and programs

There have been significant changes to operations, personnel and programs over the last quarter due to the hiring of close to 600 employees for regional operations related to the 2016 Census of Population Program. The increase in activities related to this program will perpetuate over the coming quarters.

Approval by senior officials

The original version was signed by
Wayne R. Smith, Chief Statistician
Stéphane Dufour, Chief Financial Officer
Date signed February 22, 2016

Departmental budgetary expenditures by Standard Object (unaudited) - Fiscal year 2015-2016
This table displays the departmental expenditures by standard object for the fiscal year 2015-2016. The row headers provide information by standard object for expenditures and revenues. The column headers provide information in thousands of dollars for planned expenditures for the year ending March 31; expended during the quarter ended December 31; and year to date used at quarter-end 2015-2016.

	Fiscal year 2015-2016
	Planned expenditures for the year ending March 31, 2016	Expended during the quarter ended December 31, 2015	Year-to-date used at quarter-end
	in thousands of dollars
Expenditures
(01) Personnel	484,748	131,624	377,643
(02) Transportation and communications	38,602	4,354	10,607
(03) Information	17,340	2,876	5,088
(04) Professional and special services	56,557	5,163	12,053
(05) Rentals	25,410	1,042	11,955
(06) Repair and maintenance	7,559	289	766
(07) Utilities, materials and supplies	11,104	735	1,450
(08) Acquisition of land, buildings and works	0	0	0
(09) Acquisition of machinery and equipment	14,437	827	3,312
(10) Transfer payments	100	0	0
(12) Other subsidies and payments	84	20	160
Total gross budgetary expenditures	655,941	146,930	423,034
Less revenues netted against expenditures
Revenues	120,000	24,651	44,348
Total revenues netted against expenditures	120,000	24,651	44,348
Total net budgetary expenditures	535,941	122,279	378,686

Departmental budgetary expenditures by Standard Object (unaudited) (continued)
This table displays the departmental expenditures by standard object for the fiscal year 2014-2015. The row headers provide information by standard object for expenditures and revenues. The column headers provide information in thousands of dollars for planned expenditures for the year ending March 31; expended during the quarter ended December 31; and year to date used at quarter-end 2014-2015.

	Fiscal year 2014-2015
	Planned expenditures for the year ending March 31, 2015	Expended during the quarter ended December 31, 2014	Year-to-date used at quarter-end
	in thousands of dollars
Expenditures
(01) Personnel	446,827	118,124	346,539
(02) Transportation and communications	33,810	3,514	8,567
(03) Information	3,286	519	925
(04) Professional and special services	46,744	7,503	15,746
(05) Rentals	17,233	979	7,414
(06) Repair and maintenance	9,228	413	1,077
(07) Utilities, materials and supplies	17,368	558	1,402
(08) Acquisition of land, buildings and works	0	0	0
(09) Acquisition of machinery and equipment	1,080	173	641
(10) Transfer payments	0	0	0
(12) Other subsidies and payments	227	122	13,716
Total gross budgetary expenditures	575,803	131,905	396,027
Less revenues netted against expenditures
Revenues	120,000	11,553	30,214
Total revenues netted against expenditures	120,000	11,553	30,214
Total net budgetary expenditures	455,803	120,352	365,813

Description for Appendix A: Departmental expenditures by Standard Object (unaudited) Table 1:
This table displays the departmental expenditures by standard object for the fiscal year 2015-2016. The row headers provide information by standard object for expenditures and revenues. The column headers provide information in thousands of dollars for planned expenditures for the year ending March 31; expended during the quarter ended December 31; and year to date used at quarter-end 2015-2016.

Table 2:
This table displays the departmental expenditures by standard object for the fiscal year 2014-2015. The row headers provide information by standard object for expenditures and revenues. The column headers provide information in thousands of dollars for planned expenditures for the year ending March 31; expended during the quarter ended December 31; and year to date used at quarter-end 2014-2015.

Statement of Authorities (unaudited) - Fiscal year 2015-2016
This table displays the departmental authorities for the fiscal year 2015-2016. The row headers provide information by type of authority, Vote 105 – Net operating expenditures, Statutory authority and Total Budgetary authorities. The column headers provide information in thousands of dollars for Total available for use for the year ending March 31; used during the quarter ended December 31; and year to date used at quarter-end for 2015-2016.

	Fiscal year 2015-2016
	Total available for use for the year ending March 31, 2016*	Used during the quarter ended December 31, 2015	Year to date used at quarter-end
	in thousands of dollars
* Includes only authorities available for use and granted by Parliament at quarter-end.
Vote 105 — Net operating expenditures	466,863	105,009	326,877
Statutory authority — Contribution to employee benefit plans	69,078	17,270	51,809
Total budgetary authorities	535,941	122,279	378,686

>Statement of Authorities (unaudited) - Fiscal year 2014-2015
This table displays the departmental authorities for the fiscal year 2014-2015. The row headers provide information by type of authority, Vote 105 – Net operating expenditures, Statutory authority and Total Budgetary authorities. The column headers provide information in thousands of dollars for Total available for use for the year ending March 31; Used during the quarter ended December 31; and year to date used at quarter-end for 2014-2015.

	Fiscal year 2014-2015
	Total available for use for the year ended March 31, 2015*	Used during the quarter ended December 31, 2014	Year to date used at quarter-end
	in thousands of dollars
* Includes only authorities available for use and granted by Parliament at quarter-end.
Vote 105 — Net operating expenditures	392,421	106,149	323,204
Statutory authority — Contribution to employee benefit plans	63,382	14,203	42,609
Total budgetary authorities	455,803	120,352	365,813

Description for Appendix B: Statement of authorities (unaudited)
Table 1:
This table displays the departmental authorities for the fiscal year 2015-2016. The row headers provide information by type of authority, Vote 105 – Net operating expenditures, Statutory authority and Total Budgetary authorities. The column headers provide information in thousands of dollars for Total available for use for the year ending March 31; used during the quarter ended December 31; and year to date used at quarter-end for 2015-2016.

Table 2:
This table displays the departmental authorities for the fiscal year 2014-2015. The row headers provide information by type of authority, Vote 105 – Net operating expenditures, Statutory authority and Total Budgetary authorities. The column headers provide information in thousands of dollars for Total available for use for the year ending March 31; used during the quarter ended December 31; and year to date used at quarter-end for 2014-2015.

• Who should fill in the Census of Agriculture questionnaire?
• What is an agricultural operation?
• How long will it take?
• What records will be useful in filling out your Census of Agriculture questionnaire?
• The law protects what you tell us
• Do you have to fill in the Census of Agriculture questionnaire?
• Need help?

Who should fill in the Census of Agriculture questionnaire?

The person(s) responsible for, or knowledgeable about, the management decisions of an agricultural operation.

What is an agricultural operation?

An agricultural operation produces at least one of the following products intended for sale. (It is not necessary to have had sales in the past 12 months.)

• Crops (hay, field crops, tree fruits or nuts, berries or grapes, vegetables, seed)
• Livestock (cattle, pigs, sheep, horses, game animals, other livestock)
• Poultry (hens, chickens, turkeys, chicks, game birds, other poultry)
• Animal products (milk or cream, eggs, wool, furs, meat)
• Other agricultural products (Christmas trees, sod, greenhouse or nursery products, mushrooms, honey or bees, maple syrup products)

How long will it take?

It may take about 40 minutes to complete your questionnaire, but the time will depend on the size and type of your operation. The questionnaire contains 36 steps, but most operators only have to complete about a third of them.

Statistics Canada takes all possible measures to reduce response burden such as providing a help line, giving the option of completing the questionnaire on paper, by telephone or on the Internet, etc.

What records will be useful in filling out your Census of Agriculture questionnaire?

The following records may help save you time:

• property tax statements
• 2015 income tax forms
• crop management and herd management records
• account books or computerized farm accounts
• financial statements prepared for lending institutions
• crop insurance statements

The law protects what you tell us

The confidentiality of your Census of Agriculture form is protected by law. Only Statistics Canada employees who work with census data and have taken an oath of secrecy see your form.

You can ask to see the information you provided on your 2016 Census of Agriculture after November 2016. To do so, write to the Privacy Co-ordinator, Statistics Canada, 25th Floor, R.H. Coats Building, Ottawa, Ontario K1A 0T6.

Do you have to fill in the Census of Agriculture questionnaire?

Yes. Under the Statistics Act, agricultural operators are required to complete a Census of Agriculture form.

Need help?

The Census Help Line operates Monday to Friday between 8 a.m. and 8 p.m. and Saturday and Sunday between 8:30 a.m. and 4:30 p.m. from May 2 to July 31. If you have any questions, need assistance in completing your questionnaire, or require extra forms, call 1-855-859-6273.

Preliminary Estimates, T1 Family File

User's Guide

Table of contents

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Data Source
Data Frequency
Data Quality
Confidentiality and Rounding
RRSP Contributors (product #17C0006)
RRSP Contribution Limits (ROOM) (product # 17C0011)
Canadian Savers (product #17C0009)
Canadian Investors (product #17C0007)
Canadian Investment Income (product #17C0008)
Canadian Taxfilers (product #17C0010)
Charitable Donations (product #13C0014)
Canadian Capital Gains (product #17C0012)
Statistical Tables - Footnotes and Historical Availability
Glossary of Terms
Geography

• Postal Geography
• Census Geography
• User-defined areas:

Geographic Levels – Postal Geography

• Adding Postal Areas Without Duplication
• City identification number (CityID)
• Hierarchy of postal geography

Geographic Levels – Census Geography
Geographic Levels – Special Geography
We Invite Your Comments
List of available data products

Income Statistics Division
Statistics Canada
STATCAN.income-revenu.STATCAN@statcan.gc.ca
February 2016

Text begins

Data Source

The financial and donors databanks are derived from income tax returns. For the most part, tax returns were filed in the spring of the year following the reference year. For example, for the 2014 tax year, most income tax returns were filed by April 30, 2015.

Demographic characteristics such as age are given as of December 31 of the tax year. Income information is for the calendar year under review.

The data for the products associated with this release are derived from an early version of a file that Statistics Canada receives from Canada Revenue Agency (CRA). The file benefits from timeliness, but loses some accuracy because of it. This earlier tax file, often referred to as the T1 preliminary file, contains about 97% of the records on the CRA file received four to five months later.

Data Frequency

Data are updated on an annual basis.

Data Quality

i) Number of Canadian taxfilers

The data used are direct counts from T1 preliminary tax file. For the 2014 tax year, 25.9 million Canadians or 72.5% filed tax returns.

ii) Elderly population

Some elderly Canadians receiving only Old Age Security and Guaranteed Income Supplement do not file because they have low or no taxable income. However, with the introduction of the Federal Sales Tax (FST) Credit in 1986 and the Goods and Services Tax (GST) Credit in 1990, the percentage of the elderly population filing tax returns has increased.

iii) Low Income

Persons below a certain level of income with low income have no tax liability and are not required to file tax returns. However, with the introduction of the Child Tax Credit in 1978, the Federal Sales Tax (FST) Credit in 1986, the Goods and Services Tax (GST) Credit in 1990, and the Child Tax Benefits in 1993, persons with low income are still likely to file tax returns in order to apply for these credits.

Confidentiality and Rounding

Over the years since its creation, the T1 Family File (T1FF) has become known as a reliable, annual source for income and demographic estimates. To protect the confidentiality of Canadians, all data are subject to the confidentiality procedures of rounding and suppression.

All counts are rounded. Rounding may increase, decrease, or cause no change to counts. Rounding can affect the results obtained from calculations. For example, when calculating percentages from rounded data, results may be distorted as both the numerator and denominator have been rounded. The distortion can be greatest with small numbers.

Starting with the 2007 data, all aggregate amounts are rounded to the nearest $5,000 dollars. Also as of 2007, median incomes in the data tables are rounded to the nearest ten dollars (prior to 2007 they were rounded to the nearest hundred dollars).

Since 1990, data cells represent counts of 15 or greater, and are rounded to a base of 10. For example, a cell count of 15 would be rounded to 20 and a cell count of 24 would be rounded to 20.

Note: Counts represent the number of persons. Reported amounts are aggregate dollar amounts reported.

In the data tables:
Medians, Percentiles and Average amount are rounded to the nearest ten dollars.
Percentages are published with no decimal and calculated on rounded data; therefore, the sum of percentages might not equal 100% in the case of small counts.

Suppressed Data

To maintain confidentiality, data cells have been suppressed whenever:

• areas comprise less than 100 taxfilers;
• cells represent less than 15 taxfilers;
• cells were dominated by a single filer;

Suppressed data may occur:

i) Within one area:

• when one of the income categories is suppressed, a second category must also be suppressed to avoid disclosure of confidential data by subtraction (called residual disclosure);
• when one of the gender categories is suppressed, the other gender category must also be suppressed to avoid residual disclosure;
• when one age group category is suppressed, another age group must also be suppressed to avoid residual disclosure.

ii) Between areas:

• when a variable amount in one area is suppressed, that variable amount is also suppressed in another area to prevent disclosure by subtraction.

RRSP Contributors (product #17C0006)

This databank provides information on taxfilers who contributed to a Registered Retirement Savings Plan (RRSP) during the tax year under review.

The content of the databank is as follows:

Table 1: Summary
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Number of RRSP contributors
Column 8 – Average age of RRSP contributors
Column 9 – Median employment income of RRSP contributors
Column 10 – 75th percentile of employment income of RRSP contributors
Column 11 – Amount of RRSP dollars reported (in thousands of dollars)
Column 12 – Median RRSP contribution

Table 2: Age groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of RRSP contributors
Column 8 – Percent of contributors 0 to 24 years of age
Column 9 – Percent of contributors 25 to 34 years of age
Column 10 – Percent of contributors 35 to 44 years of age
Column 11 – Percent of contributors 45 to 54 years of age
Column 12 – Percent of contributors 55 to 64 years of age
Column 13 – Percent of contributors 65+ years of age
Column 14 – Total amount of RRSP dollars reported (in thousands of dollars)
Column 15 – Percent of contributions reported by age group 0 to 24
Column 16 – Percent of contributions reported by age group 25 to 34
Column 17 – Percent of contributions reported by age group 35 to 44
Column 18 – Percent of contributions reported by age group 45 to 54
Column 19 – Percent of contributions reported by age group 55 to 64
Column 20 – Percent of contributions reported by age group 65+

Table 3: Sex
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Percent of taxfilers who are male
Column 8 – Percent of taxfilers who are female
Column 9 – Total number of RRSP Contributors
Column 10 – Percent of contributors who are male
Column 11 – Percent of contributors who are female
Column 12 – Total amount of RRSP dollars reported (in thousands of dollars)
Column 13 – Percent of contributions reported by males
Column 14 – Percent of contributions reported by females
Column 15 – Median RRSP contribution of all contributors
Column 16 – Median RRSP contribution of males
Column 17 – Median RRSP contribution of females

Table 4: Income groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of RRSP contributors
Column 8 – Percent of RRSP contributors with total income less than $20,000
Column 9 – Percent of RRSP contributors with total income between $20,000 and $39,999
Column 10 – Percent of RRSP contributors with total income between $40,000 and $59,999
Column 11 – Percent of RRSP contributors with total income between $60,000 and $79,999
Column 12 – Percent of RRSP contributors with total income $80,000+
Column 13 – Total amount of RRSP dollars reported (in thousands of dollars)
Column 14 – Percent of total RRSP amount reported by contributors with total income less than $20,000
Column 15 – Percent of total RRSP amount reported by contributors with total income between $20,000 and $39,999
Column 16 – Percent of total RRSP amount reported by contributors with total income between $40,000 and $59,999
Column 17 – Percent of total RRSP amount reported by contributors with total income between $60,000 and $79,999
Column 18 – Percent of total RRSP amount reported by contributors with total income $80,000+

RRSP Contribution Limits (ROOM) (product # 17C0011)

The Registered Retirement Savings Plan (RRSP) Room databank was created to provide information on the RRSP contribution limit (RRSP Room) available. This product can be used in conjunction with the RRSP databank which concentrates on the RRSP contributors.

In 1989, the legislation dictated that contribution limits for persons not contributing to a registered pension plan (RPP) or a Deferred Profit Sharing Plan (DPSP) was 20% of earned income to a maximum of $7,500. The limit for RPP and DPSP members was 20% of earned income to a maximum of $3,500 less the amount contributed by the employee to the RPP or DPSP.

Further amendments to the Income Tax Act relative to RRSPs, taking effect January 1, 1991, were intended to make RRSP contribution limits more equitable. The RRSP contribution limit was set at 18% of earned income for the previous tax year, to a set maximum minus the Pension Adjustment (PA). The PA represents the calculated value of the pension accrued through an RPP or a DPSP in the previous tax year.

Total RRSP Room represents the deduction limit that Canadians can claim with respect to contributions made to RRSPs. It does not include income eligible for transfers, such as retiring allowances and severance pay that may be rolled over into RRSPs. The sum of the deduction limit and rollovers represents the maximum amount that can be claimed as a deduction on line 208 of the income tax return.

Calculation of contribution limits

For 1990, maximum contributions are:

• for non-participants in RPPs and DPSPs, the lesser of 20% of earned income and $7,500
• for participants in RPPs and DPSPs, 20% of earned income to a maximum of $3,500; the maximum is reduced according to employee contributions to RPPs/DPSPs.

For 1991 to 2014:

New room = 18% of earned income - PA - PSPA

Percentage of earned income to a maximum of

• $11,500 for 1991
• $12,500 for 1992 and 1993
• $13,500 for 1994
• $14,500 for 1995
• $13,500 for 1996
• $13,500 for 1997
• $13,500 for 1998
• $13,500 for 1999
• $13,500 for 2000
• $13,500 for 2001
• $13,500 for 2002
• $14,500 for 2003
• $15,500 for 2004
• $16,500 for 2005
• $18,000 for 2006
• $19,000 for 2007
• $20,000 for 2008
• $21,000 for 2009
• $22,000 for 2010
• $22,450 for 2011
• $22,970 for 2012
• $23,820 for 2013
• $24,270 for 2014

Where PA = Pension Adjustment, and PSPA = Past Service Pension Adjustment

Prior to tax year 2000 (Room 2001):

Total Room (for tax year+1) = Unused Room (from 1991 forward) + New Room

For tax years 2000 to 2014 (Room 2001 to Room 2015):

Total Room (for tax year+1) = Unused Room accumulated since 1991 + (18% of earned income – Pension adjustment) – Current tax year contributions excluding rollovers

Data source for RRSP Room

Prior to the release of data for tax year 2000, the RRSP ROOM data were derived from a file received annually from the Canada Revenue Agency (CRA, formerly Canada Customs and Revenue Agency). CRA generated the data from an administrative system designed in response to changes to the Income Tax Act with respect to Registered Retirement Savings Plans, changes that took effect January 1, 1991.

The system records information for each taxfiler with "earned income" (income used to determine the RRSP deduction limit). The information includes each year's earned income, new room amounts and unused room amounts carried forward.

Starting with the 2001 ROOM (2000 tax data), the amount of RRSP Room is calculated from other variables on the preliminary file, variables which were previously unavailable.

This year's release of the RRSP Room data is based on 2014 income tax returns. Contributions towards these limits can be made up to February 2016, to be reported on the 2015 tax returns. The mailing address at the time of filing is the basis for the geographic information in the tables.

The content of the databank is as follows:

Table 1: Persons with room
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk number (no longer available)
Column 4 – Level of geography (see Geography section)
Column 5 – Place name
Column 6 – Amount of Room dollars reported (in thousands of $)
Column 7 – Amount of Unused Room dollars reported (in thousands of $)
Column 8 – Amount of New Room dollars reported (in thousands of $)
Column 9 – Number of taxfilers with Room
Column 10 – Number of taxfilers with Unused Room
Column 11 – Number of taxfilers with New Room

Table 2: Characteristics of persons with new room
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk number (no longer available)
Column 4 – Level of geography (see Geography section)
Column 5 – Place name
Column 6 – Number of taxfilers with New Room
Column 7 – Average Age of taxfilers with New Room
Column 8 – Percentage Female taxfilers with New Room
Column 9 – Median Earned Income of taxfilers with New Room
Column 10 – Average New Room
Column 11 – Percentage of taxfilers with New Room between $500 and $2,399
Column 12 – Percentage of taxfilers with New Room between $2,400 and $4,699
Column 13 – Percentage of taxfilers with New Room between $4,700 and $7,799
Column 14 – Percentage of taxfilers with New Room between $7,800 and $12,999
Column 15 – Percentage of taxfilers with New Room greater than $13,000

Additional notes for Table 2:

Column 11: The first value represents the 25th percentile and is recalculated periodically.
Column 12: The first value represents the 50th percentile and is recalculated periodically.
Column 13: The first value represents the 75th percentile and is recalculated periodically.
Column 14: The first value represents the 90th percentile and is recalculated periodically.
Column 15: The value represents the 97th percentile and is recalculated periodically.

Canadian Savers (product #17C0009)

This databank provides information on taxfilers who have been classified as savers.

Savers are defined as taxfilers who reported interest and investment income on line 121, but no dividend income on line 120 of the personal income tax return.

Interest and investment income sources would include interest from Canada Savings bonds, bank accounts, treasury bills, investment certificates, term deposits, earnings on life insurance policies as well as foreign interest and dividend income.

Dividend income would include dividends from taxable Canadian corporations (as stocks or mutual funds), but not dividends from foreign investments.

Taxfilers reporting Canadian dividend income would not be counted as savers, but would be classified as investors.

The content of the databank is as follows:

Table 1: Summary
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Number of savers
Column 8 – Average age of savers
Column 9 – Median total income of savers
Column 10 – Total amount of interest dollars reported (in thousands of dollars)
Column 11 – Median of interest dollars

Table 2: Age groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of savers
Column 8 – Percent of savers 0-24 years of age
Column 9 – Percent of savers 25-34 years of age
Column 10 – Percent of savers 35-44 years of age
Column 11 – Percent of savers 45-54 years of age
Column 12 – Percent of savers 55-64 years of age
Column 13 – Percent of savers 65+ years of age
Column 14 – Total amount of interest income dollars reported (in thousands of dollars)
Column 15 – Percent of interest income reported by age group 0-24
Column 16 – Percent of interest income reported by age group 25-34
Column 17 – Percent of interest income reported by age group 35-44
Column 18 – Percent of interest income reported by age group 45-54
Column 19 – Percent of interest income reported by age group 55-64
Column 20 – Percent of interest income reported by age group 65+

Table 3: Sex
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Percent of taxfilers who are male
Column 8 – Percent of taxfilers who are female
Column 9 – Total number of savers
Column 10 – Percent of savers who are male
Column 11 – Percent of savers who are female
Column 12 – Total amount of interest income reported (in thousands of dollars)
Column 13 – Percent of interest income reported by males
Column 14 – Percent of interest income reported by females
Column 15 – Median interest income of all savers
Column 16 – Median interest income of all male savers
Column 17 – Median interest income of all female savers

Table 4: Income groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of savers
Column 8 – Percent of savers with total income less than $20,000
Column 9 – Percent of savers with total income between $20,000 and $39,999
Column 10 – Percent of savers with total income between $40,000 and $59,999
Column 11 – Percent of savers with total income between $60,000 and $79,999
Column 12 – Percent of savers with total income $80,000+
Column 13 – Total amount of interest income reported (in thousands of dollars)
Column 14 – Percent of interest income reported by savers with total income less than $20,000
Column 15 – Percent of interest income reported by savers with total income between $20,000 and $39,999
Column 16 – Percent of interest income reported by savers with total income between $40,000 and $59,999
Column 17 – Percent of interest income reported by savers with total income between $60,000 and $79,999
Column 18 – Percent of interest income reported by savers with total income $80,000+

Canadian Investors (product #17C0007)

This databank provides information on taxfilers classified as investors.

Investors include taxfilers who reported dividend income on line 120 of their personal tax return. They may or may not have also reported interest and other investment income on line 121. When income is also reported on line 121, that amount is added to the amount of dividend income received, and the sum becomes the investment income of the investor.

The content of the databank is as follows:

Table 1: Summary
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Number of investors
Column 8 – Average age of investors
Column 9 – Median total income of investors
Column 10 – Amount of investment dollars (in thousands of dollars)
Column 11 – Percentage of the investment income derived from dividends
Column 12 – Median investment income

Table 2: Age groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of investors
Column 8 – Percent of investors 0-24 years of age
Column 9 – Percent of investors 25-34 years of age
Column 10 – Percent of investors 35-44 years of age
Column 11 – Percent of investors 45-54 years of age
Column 12 – Percent of investors 55-64 years of age
Column 13 – Percent of investors 65+ years of age
Column 14 – Total amount of investment income dollars reported (in thousands of dollars)
Column 15 – Percent of investment income reported by age group 0-24
Column 16 – Percent of investment income reported by age group 25-34
Column 17 – Percent of investment income reported by age group 35-44
Column 18 – Percent of investment income reported by age group 45-54
Column 19 – Percent of investment income reported by age group 55-64
Column 20 – Percent of investment income reported by age group 65+

Table 3: Sex
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Percent of taxfilers who are male
Column 8 – Percent of taxfilers who are female
Column 9 – Total number of investors
Column 10 – Percent of investors who are male
Column 11 – Percent of investors who are female
Column 12 – Total amount of investment income reported (in thousands of dollars)
Column 13 – Percent of investment income reported by males
Column 14 – Percent of investment income reported by females
Column 15 – Median investment income of all investors
Column 16 – Median investment income of all male investors
Column 17 – Median investment income of all female investors

Table 4: Income groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of investors
Column 8 – Percent of investors with total income less than $20,000
Column 9 – Percent of investors with total income between $20,000 and $39,999
Column 10 – Percent of investors with total income between $40,000 and $59,999
Column 11 – Percent of investors with total income between $60,000 and $79,999
Column 12 – Percent of investors with total income $80,000+
Column 13 – Total amount of investment income reported (in thousands of dollars)
Column 14 – Percent of investment income reported by investors with total income less than $20,000
Column 15 – Percent of investment income reported by investors with total income between $20,000 and $39,999
Column 16 – Percent of investment income reported by investors with total income between $40,000 and $59,999
Column 17 – Percent of investment income reported by investors with total income
between $60,000 and $79,999
Column 18 – Percent of investment income reported by investors with total income
$80,000+

Canadian Investment Income (product #17C0008)

This databank provides information on taxfilers who reported dividend income on line 120 of the tax return, or interest and other investment income on line 121, or both. These taxfilers include those designated as savers and those designated as investors in two other databanks available: Canadian Savers and Canadian Investors. In this databank, investment income includes both interest and dividends.

Dividend income includes dividends from taxable Canadian corporations (as stocks or mutual funds).

Interest and other investment income sources include interest from Canada Savings bonds, bank accounts, treasury bills, investment certificates, term deposits, earnings on life insurance policies as well as foreign interest and dividend income.

The content of the databank is as follows:

Table 1: Summary
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Number of taxfilers with investment income
Column 8 – Average age of taxfilers with investment income
Column 9 – Median total income of taxfilers with investment income
Column 10 – Reported investment income dollars for all taxfilers with investment income (in thousands of dollars)
Column 11 – Median investment income for all taxfilers with investment income

Table 2: Age groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of receivers of investment income
Column 8 – Percent of receivers of investment income 0-24 years of age
Column 9 – Percent of receivers of investment income 25-34 years of age
Column 10 – Percent of receivers of investment income 35-44 years of age
Column 11 – Percent of receivers of investment income 45-54 years of age
Column 12 – Percent of receivers of investment income 55-64 years of age
Column 13 – Percent of receivers of investment income 65+ years of age
Column 14 - Total amount of investment income dollars reported (in thousands of dollars)
Column 15 – Percent of investment income reported by age group 0-24
Column 16 – Percent of investment income reported by age group 25-34
Column 17 – Percent of investment income reported by age group 35-44
Column 18 – Percent of investment income reported by age group 45-54
Column 19 – Percent of investment income reported by age group 55-64
Column 20 – Percent of investment income reported by age group 65+

Table 3: Sex
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Percent of taxfilers who are male
Column 8 – Percent of taxfilers who are female
Column 9 – Total number of receivers of investment income
Column 10 – Percent of receivers of investment income who are male
Column 11 – Percent of receivers of investment income who are female
Column 12 – Total amount of investment income reported (in thousands of dollars)
Column 13 – Percent of investment income reported by males
Column 14 – Percent of investment income reported by females
Column 15 – Median investment income of all receivers of investment income
Column 16 – Median investment income of all male receivers of investment income
Column 17 – Median investment income of all female receivers of investment income

Table 4: Income groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Total number of receivers of investment income
Column 8 – Percent of receivers of investment income with total income less than $20,000
Column 9 – Percent of receivers of investment income with total income between $20,000 and $39,999
Column 10 – Percent of receivers of investment income with total income between $40,000 and $59,999
Column 11 – Percent of receivers of investment income with total income between $60,000 and $79,999
Column 12 – Percent of receivers of investment income with total income $80,000+
Column 13 – Total amount of investment income reported (in thousands of dollars)
Column 14 – Percent of investment income reported by receivers of investment income with total income less than $20,000
Column 15 – Percent of investment income reported by receivers of investment income with total income between $20,000 and $39,999
Column 16 – Percent of investment income reported by receivers of investment income with total income between $40,000 and $59,999
Column 17 – Percent of investment income reported by receivers of investment income with total income between $60,000 and $79,999
Column 18 – Percent of investment income reported by receivers of investment income with total income $80,000+

Canadian Capital Gains (product #17C0012)

This databank provides information on taxfilers who reported capital gains during the tax year under review.

Line 127 of the T1 income tax return contains the amount of taxable capital gains reported by Canadians; this value is half the actual capital gains received. The information in this databank reflects the total capital gains received; amounts reported have been grossed up to reflect this total.

The content of the databank is as follows:

Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Total number of taxfilers
Column 7 – Number of taxfilers reporting capital gains
Column 8 – Percent of taxfilers reporting capital gains who had a total income under $20,000
Column 9 – Percent of taxfilers reporting capital gains who had a total income between $20,000 and $39,999
Column 10 – Percent of taxfilers reporting capital gains who had a total income between $40,000 and $59,999
Column 11 – Percent of taxfilers reporting capital gains who had a total income between $60,000 and $79,999
Column 12 – Percent of taxfilers reporting capital gains who had a total income of $80,000+
Column 13 – Total value of capital gains (in thousands of dollars)
Column 14 – Percent of capital gains reported by taxfilers with a total income under $20,000
Column 15 – Percent of capital gains reported by taxfilers with a total income between $20,000 and $39,999
Column 16 – Percent of capital gains reported by taxfilers with a total income between $40,000 and $59,999
Column 17 – Percent of capital gains reported by taxfilers with a total income between $60,000 and $79,999
Column 18 – Percent of capital gains reported by taxfilers with a total income of $80,000+

Canadian Taxfilers (product #17C0010)

This databank provides a demographic and income profile of Canadians who filed a personal tax return in the reference year, according to the T1 preliminary file.

The content of the databank is as follows:

Table 1: Summary
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Number of taxfilers
Column 7 – Percent of taxfilers 0-24 years of age
Column 8 – Percent of taxfilers 25-34 years of age
Column 9 – Percent of taxfilers 35-44 years of age
Column 10 – Percent of taxfilers 45-54 years of age
Column 11 – Percent of taxfilers 55-64 years of age
Column 12 – Percent of taxfilers 65+ years of age
Column 13 – Average age of taxfilers
Column 14 – Median total income of taxfilers
Column 15 – 75th percentile of total income of taxfilers
Column 16 – 85th percentile of total income of taxfilers
Column 17 – 95th percentile of total income of taxfilers
Column 18 – Median employment income of taxfilers
Column 19 – 75th percentile of employment income of taxfilers

Table 2: Income Groups
Column 1 – City identification number
Column 2 – Postal area
Column 3 – Postal walk (no longer available)
Column 4 – Level of geography (see geography section)
Column 5 – Place name
Column 6 – Number of taxfilers
Column 7 – Percent of taxfilers with total income less than $20,000
Column 8 – Percent of taxfilers with total income between $20,000 and $39,999
Column 9 – Percent of taxfilers with total income between $40,000 and $59,999
Column 10 – Percent of taxfilers with total income between $60,000 and $79,999
Column 11 – Percent of taxfilers with total income $80,000+
Column 12 – Value of total income (in thousands of dollars)
Column 13 – Percent of total income reported by taxfilers with total income less than $20,000
Column 14 – Percent of total income reported by taxfilers with total income between $20,000 and $39,999
Column 15 – Percent of total income reported by taxfilers with total income between $40,000 and $59,999
Column 16 – Percent of total income reported by taxfilers with total income between $60,000 and $79,999
Column 17 – Percent of total income reported by taxfilers with total income $80,000+

Charitable Donations (product #13C0014)

This databank provides information on taxfilers classified as charitable donors. Charitable donors are defined as taxfilers reporting donations on line 340 of the tax return.

Canadians contribute in many ways to charitable organizations. The databank on charitable donors provides information on taxfilers who claimed a tax credit for charitable donations on their income tax return in the reference year. These data may include donations that might be denied by the Canada Revenue Agency (CRA) after an audit. To find out more about why donations might be denied (i.e. tax shelter gifting arrangements, false receipting) please go to the Canada Revenue Agency website.

Persons making charitable donations, but not reporting them on their personal tax return are not included in this databank. These include donations for which no receipt was provided and donations for which the receipt was lost. No estimate of such donations is included in these data.

Only donations made to approved organizations are allowable as deductions in the tax system. Donations are eligible if made to Canadian registered charities and Canadian amateur athletic associations. They are also eligible if made to: prescribed universities outside Canada; certain tax exempt housing organizations in Canada; Canadian municipalities; the United Nations; and certain charities outside Canada to which the Government of Canada has made a gift.

It is possible to carry donations forward for up to five years after the year in which they were made. In the reference year, it is possible to claim donations made in any of the previous five years, as long as they were not already claimed in a prior year. The donations made in the reference year could be claimed the same year, or could be carried forward to any of the next five years. According to tax laws, taxfilers are permitted to claim both their donations and those made by their spouses to get better tax benefits. Consequently, the number of persons who made charitable donations may be higher than the number who claimed tax credits.

A change in tax regulations was introduced in 2007. Taxfilers contributing to a recognized charity (as outlined above) were eligible to claim a tax credit of 15% of their donations on the first $200, and 29% on the rest. In 2006, the tax credit was 15.25% on the first $200 and 29% on the rest. In 2005, the tax credit was 15% on the first $200 and 29% on the rest. From 2001 to 2004, the tax credit was 16% on the first $200 and 29% on the rest. From 1992 to 2000, the tax credit was 17% on the first $200 and 29% on the rest. Previously, taxfilers had to give $250 to charity before the 29% credit was available.

The content of the databank is as follows:

Table 1: Summary
Column 1 - City identification number
Column 2 - Postal area
Column 3 - Postal walk (no longer available)
Column 4 - Level of geography (see Geography section)
Column 5 - Place name
Column 6 - Total Number of taxfilers
Column 7 - Number of charitable donors
Column 8 - Average age of donors
Column 9 - Average donation for age group 0-24
Column 10 - Average donation for age group 25-34
Column 11 - Average donation for age group 35-44
Column 12 - Average donation for age group 45-54
Column 13 - Average donation for age group 55-64
Column 14 - Average donation for age group 65+
Column 15 - Total amount of charitable donations (thousands of $)
Column 16 - Median donation
Column 17 - Median total income of donors
Column 18 - 75th percentile of donors' total income

Table 2: Age and Sex (new beginning with 1995)
Column 1 - City identification number
Column 2 - Postal area
Column 3 - Postal walk (no longer available)
Column 4 - Level of geography (see Geography section)
Column 5 - Place name
Column 6 - Total Number of taxfilers
Column 7 - Percent of taxfilers who are male
Column 8 - Percent of taxfilers who are female
Column 9 - Number of charitable donors
Column 10 - Percent of charitable donors who are male
Column 11 - Percent of charitable donors who are female
Column 12 - Percent of donors 0 - 24 years of age
Column 13 - Percent of donors 25 - 34 years of age
Column 14 - Percent of donors 35 - 44 years of age
Column 15 - Percent of donors 45 - 54 years of age
Column 16 - Percent of donors 55 - 64 years of age
Column 17 - Percent of donors 65+ years of age
Column 18 - Total median donation
Column 19 - Median donation of males
Column 20 - Median donation of females
Column 21 - Total amount of charitable donations (thousands of $)
Column 22 - Total amount of charitable donations for males (thousands of $)
Column 23 - Total amount of charitable donations for females (thousands of $)

Table 3: Income Groups (new beginning with 1997)
Column 1 - City identification number
Column 2 - Postal area
Column 3 - Postal walk (no longer available)
Column 4 - Level of geography (see Geography section)
Column 5 - Place name
Column 6 - Total number of taxfilers
Column 7 - Total number of charitable donors
Column 8 - Percent of charitable donors with total income less than $20,000
Column 9 - Percent of charitable donors with total income between $20,000 and $39,999
Column 10 - Percent of charitable donors with total income between $40,000 and $59,999
Column 11 - Percent of charitable donors with total income between $60,000 and $79,999
Column 12 - Percent of charitable donors with total income $80,000+
Column 13 - Total value of charitable donations (in thousands of dollars)
Column 14 - Percent of donations reported by donors with total income under $20,000
Column 15 - Percent of donations reported by donors with total income between $20,000 and $39,999
Column 16 - Percent of donations reported by donors with total income between $40,000 and $59,999
Column 17 - Percent of donations reported by donors with total income between $60,000 and $79,999
Column 18 - Percent of donations reported by donors with total income $80,000+

Statistical Tables - Footnotes and Historical Availability

RRSP Contributors

1. Table 1 is available in its current format starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1994 data and federal electoral districts (FEDs) with the 1997 data.
2. Table 2 (age groups) and table 3 (sex) are available in their current formats starting with the 1994 data, for postal areas, CMAs, CDs and FEDs (since 1997). Some changes were made to the age groupings over the years.
3. Table 4 (income groups) is available in its current format starting with the 2007 data, for postal areas, CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

RRSP Contribution Limits (Room)

1. Both tables are available in their current format starting with the 1993 data.
2. Data are available for all levels of the postal geography starting with the 1993 tax year.
3. Data for census metropolitan areas (CMAs) are available starting with the 1993 tax year (1994 room), census divisions (CDs) with the 1994 tax year (1995 room) and federal electoral districts (FEDs) with the 1997 tax year (1998 room).
4. The figures in Table 2 ($500, $2,400, $4,700, $7,800 and $13,000) represent the 25th, 50th, 75th, 90th and 97th percentiles of new room and are recalculated periodically.

Canadian Savers

1. Table 1 is available in its current format starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1995 data and federal electoral districts (FEDs) with the 1997 data.
2. Table 2 (age groups) and table 3 (sex) are available in their current formats starting with the 1995 data, for postal areas, CMAs, CDs and FEDs (since 1997). Some changes were made to the age groupings over the years.
3. Table 4 (income groups) is available in its current format starting with the 2007 data, for postal areas, CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

Canadian Investors

1. Table 1 is available in its current format starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1995 data and federal electoral districts (FEDs) with the 1997 data.
2. The proportion of investment income from dividends is available starting with the 1996 data (Table 1).
3. Table 2 (age groups) and table 3 (sex) are available in their current formats starting with the 1995 data, for postal areas, CMAs, CDs and FEDs (since 1997). Some changes were made to the age groupings over the years.
4. Table 4 (income groups) is available in its current format starting with the 2007 data, for postal areas, CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

Canadian Investment Income

1. Table 1 is available in its current format starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1995 data and federal electoral districts (FEDs) with the 1997 data.
2. Table 2 (age groups) and table 3 (sex) are available in their current formats starting with the 1995 data, for postal areas, CMAs, CDs and FEDs (since 1997). Some changes were made to the age groupings over the years.
3. Table 4 (income groups) is available in its current format starting with the 2007 data, for postal areas, CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

Canadian Capital Gains

1. The standard table on capital gains by income group is available in its current format starting with the 2007 data. From 1998 data up to 2006, the income groups were cumulative.
2. All levels of geography are available since the 1998 data, including census divisions, census metropolitan areas, federal electoral districts and all levels of the postal geography.

Canadian Taxfilers

1. Table 1 is available in its current format starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1995 data and federal electoral districts (FEDs) with the 1997 data.
2. Starting with the 2007 data, the column on the "% reporting in French" in table 1 has been suppressed.
3. Table 2 (income groups) is available in its current format starting with the 2007 data, for postal areas, CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

Charitable Donations

1. Table 1 (summary) is available starting with the 1990 data, according to the postal geography. Census metropolitan areas (CMAs) are available starting with the 1993 data, census divisions (CDs) with the 1995 data and federal electoral districts (FEDs) with the 1997 data.
2. Changes were made to the age groups in table 1 in 1991 and in 1997.
3. Table 2 (age groups) is available starting with the 1995 data, for the postal geography and for CMAs. CDs are available starting with the 1995 data and FEDs with the 1997 data.
4. Changes were made to the age groups in table 2 in 1997.
5. Table 3 (Income groups) is available in its current format starting with the 2007 data, for the postal geography, for CMAs, CDs and FEDs. From 1997 to 2006, the income groups were cumulative.

Glossary of Terms

75th percentile

Total income values are ranked from highest to lowest and the value reported as being the 75th percentile indicates that 25% of the taxfilers report an income equal or above that amount and 75% fall below. Percentiles are calculated for each geographical level.

For example, if the 75th percentile of total income is shown as $60,000 this means that 25% of the population under review has a total income greater than or equal to $60,000 and 75% of the population has a total income less than or equal to $60,000.

85th percentile

Starting with the 2007 data, the dollar value of the 85th percentile appears in the tables instead of the percentage above the 85th percentile like it was in previous years. Total income values are ranked from highest to lowest and the value reported as being the 85th percentile indicates that 15% of the taxfilers report an income equal or above that amount and 85% fall below. Percentiles are calculated for each geographical level.

For example, if the 85th percentile of total income is shown as $65,000 this means that 15% of the taxfilers has a total income greater than or equal to $65,000

95th percentile

Starting with the 2007 data, the dollar value of the 95th percentile appears in the tables instead of the percentage above the 95th percentile like it was in previous years. Total income values are ranked from highest to lowest and the value reported as being the 95th percentile indicates that 5% of the taxfilers report an income equal or above that amount and 95% fall below. Percentiles are calculated for each geographical level.

For example, if the 95th percentile of total income is shown as $90,000 this means that 5% of the population under review has a total income greater than or equal to $90,000

Age

Calculated as of December 31 of the reference year (i.e., tax year minus year of birth).

Capital Gains

Line 127 of the T1 income tax return shows "taxable capital gains" or half of the capital gains actually received. The information in this databank has been grossed up to represent the total capital gains received.

Charitable donation

Is the allowable portion of total donations, as reported on the income tax return. Canadians contribute in many ways to charitable organizations. These data include only amounts given to charities and approved organizations for which official tax receipts were provided and claimed on tax returns. It is possible to carry donations forward for up to five years after the year in which they were made. Therefore, donations reported for the 2012 taxation year could include donations that were made in any of the five previous years. According to tax laws, taxfilers are permitted to claim both their donations and those made by their spouses to receive better tax benefits. Consequently, the number of people who made charitable donations may be higher than the number who claimed tax credits.

Charitable donor

Is defined as a taxfiler reporting a charitable donation amount on line 340 of the personal income tax form.

CityID

Since municipality names can be, in some cases, quite long and cumbersome for handling in electronic files, municipalities are given a "city identification number". Starting in 2007, the CityID is a five digits alpha-numeric component. It is created with the first letter of Postal Code^OM followed by "9" and a four digits number. Each first letter of Postal Code is allocated a range of number from 1 to 9999 (more explanation in geography section).

Deferred profit sharing plan (DPSP)

An employer-sponsored savings plan registered by the Canada Revenue Agency. Contributions to these plans by the employer (employees cannot contribute) are based on profits. The amount accumulated in these plans can be paid out as a lump sum at retirement or termination of employment, transferred to an RRSP, received in instalments over a period not to exceed ten years, or used to purchase an annuity.

Dividend income

Includes taxable amount of dividends (eligible and other than eligible) received from taxable Canadian corporations (as stocks or mutual funds) as reported on line 120 of the personal income tax return, and then grossed down to the actual amounts received; dividend income does not include dividends received from foreign investments (which are included in interest income and reported on line 121).

Earned income

The income used to determine the RRSP deduction limit. It includes such items as employment income (less union dues and expenses), net business and rental income, disability payments and alimony received. Alimony payments, current year business and rental losses are deducted from this amount. Most investment income (other than rents) is not considered earned income. In calculating the RRSP deduction limit, earned income from the previous year is used.

Employment income

The total reported employment income. Employment income includes wages and salaries, commissions from employment, training allowances, tips and gratuities, and self-employment income (net income from business, profession, farming, fishing and commissions) and Tax Exempted Indian Employment Income (new in 1999 for wages and salaries, commissions, and in 2010 for self-employment income).

Interest income

Refers to the amount Canadians claimed on line 121 of the personal income tax return. This amount includes interest generated from bank deposits, Canada Savings Bonds, corporate bonds, treasury bills, investment certificates, term deposits, annuities, mutual funds, earnings on life insurance policies and all foreign interest and foreign dividend incomes.

Investment income

Includes both interest income and dividend income.

Investors

Taxfilers who reported dividend income on line 120 of their personal tax return. They may or may not have also reported interest and other investment income on line 121. When such income is reported on line 121, this amount is added to the amount of dividend income received, and the sum becomes the investment income of the investor.

Level of geography

Is a code designating the type of geographic area to which the information in the table applies. See the section on Geography for further information.

Median

The middle number in a group of numbers. Where a median income, for example, is given as $26,000, it means that exactly half of the incomes reported are greater than or equal to $26,000, and that the other half is less than or equal to the median amount. Zero values are not included in the calculation of medians for individuals.

New room

For 2014 this amount is calculated as 18% of 2013 earned income (from definition above) to a maximum of $24,270 minus pension adjustment (PA) minus past service pension adjustment (PSPA). Since the focus of these data is for net new room for potential RRSP contributions, PA and/or PSPA details will be omitted.

Past service pension adjustment (PSPA)

Applies only to members of defined benefit RPPs. A PSPA occurs when the pension benefit is upgraded, or additional credits purchased, for service in past years. In the first case, it is called an exempt (from certification) PSPA; in the second case, a certifiable PSPA. Only service after 1989 is considered.

Pension adjustment (PA)

Calculated value of the pension accrued in the year in an RPP or a DPSP. The PA decreases the RRSP deduction limit. To calculate this limit, the PA from the previous year is used.

Registered disability savings plan income (RDSP)

Beginning in 2008, the RDSP is for individuals for whom a valid disability certificate has been filed. Contributions can be made by the beneficiary or by qualified persons legally authorized to act for the beneficiary. The contributions are not deductible but the income earned is not taxable as long as it remains into the plan. Contributions are subject to a lifetime limit of $200,000; they will be matched in some degree by government contributions.

Registered pension plan (RPP)

An employer-sponsored plan registered with the Canada Revenue Agency and most commonly also with one of the pension regulatory authorities. The purpose of such plans is to provide employees with a regular income at retirement. The two main types of RPPs are called defined benefit (where the benefit formula is specified) and defined contribution (where only the contribution formula is defined).

Registered retirement savings plan (RRSP)

An individual retirement savings plan that is registered by the Canada Revenue Agency. It permits limited contributions, and income earned in the RRSP is exempt from tax until payments are received from the plan.

Savers

Taxfilers who reported interest and investment income on line 121 of the personal income tax return, but no dividend income on line 120.

Taxfiler

Individual who filed a personal income tax return for the reference year.

Total income

Note: this variable was revised over the years, as reflected in the comments below; data users who plan to compare current data to data from previous years should bear in mind these changes. Also, it should be noted that all income amounts are gross, with the exception of net rental income, net limited partnership income and all forms of net self-employment income.

Income reported by tax filers from any of the following sources:

• Employment income
  • Wages/Salaries/Commissions;
  • Other Employment Income as reported on line 104 of the tax form (tips, gratuities, royalties, etc.);
  • Net Self-Employment;
  • Tax Exempted Employment Income for Indians (Wages/Salaries/Commissions) for Indians (new in 1999);
  • Tax Exempted Self-Employment Income for Indians (since 2010).
• Investments
  • Interest and other investment income;
  • Dividend income.
• Government Transfers
  • Employment Insurance
    • Unemployment Insurance/Employment Insurance since 1982;
    • Quebec Parental Insurance Plan since 2006.
  • Pension Income
    • Old Age Security since 1982;
    • Net Federal Supplements (previously included in other income, shown separately since 1992)
      • Guaranteed Income Supplement created in 1967 and Spousal Allowance created in 1975, available since 1992;
      • Spousal Allowance (included in Net Federal Supplements since 1992; previously included in non-taxable income;
    • Canada and Quebec Pension plans benefits, since 1982.
  • Child Benefit
  • Family Allowance program up to 1992
  • Child Tax Credit up to 1992
  • Canada Child Tax Benefit (starting with 1993)
  • Universal Child Care Benefit since 2006
  • Federal Tax, Goods and Services Tax, Harmonized Sales Tax
    • Federal Sales Tax Credit (from 1988 to 1990);
    • Goods and Services Tax (GST) credit from 1990 to 1996;
    • Harmonized Sales Tax (HST) credit since 1997.
  • Workers' Compensation (included in other income prior to 1992 and shown separately since 1992)
  • Social Assistance (included in other income prior to 1992 and shown separately since 1992)
  • Provincial Refundable Tax Credits/Family Benefits – for a complete list please see the "Provincial Refundable Tax Credits/Family Benefits" section
  • Other Government Transfers
    • Working Income Tax Benefit (starting in 2007 depending on the province or territory; included since 2010 in the statistical tables).
• Private Pensions
• Registered Retirement Savings Plan Income (since 1994; previously in "other income"; since 1999, only for tax filers 65+)
• Other Income
  • Included as 'other income' prior to 1990
    • Net limited partnership income;
    • Alimony;
    • Net rental income;
    • Income for non-filing spouses (since 1989; included in "other income");
  • Other incomes as reported on line 130 of the tax form (fellowships, bursaries, etc.);
  • Registered Disability Savings Plan (RDSP) Income as reported on line 125 of the tax form (introduced in 2008).

Monies not included in income above are: veterans' disability and dependent pensioners' payments, war veterans' allowances, lottery winnings and capital gains.

Total room (in thousands of dollars)

The sum of "new room" and "unused room" as defined here.

Total room (number of persons)

The number of taxfilers who have either unused room from previous years, new room based on their earned income, or both. The number of persons with total room does not correspond to the sum of persons with new room and unused room, as an individual can be included in both categories.

Universal Child Care Benefit

Beginning in July 2006, the Universal Child Care Benefit (UCCB) is a taxable amount of $100 paid monthly for each child under 6 years of age. Included in Canada Child Tax Benefits in the statistical tables.

Unused RRSP contribution room (unused room)

The amount of the RRSP deduction limit that is not claimed by the taxfiler, or the amount remaining after subtracting actual contributions claimed on the tax return from that year's contribution room. Unused room can be carried forward indefinitely. The first year of unused room is from 1991 (see table B).

Geography

The data are available for the following geographic areas. See "Statistical Tables - Footnotes and Historical Availability" for further details. The mailing address at the time of filing is the basis for the geographic information in the tables.

Standard areas

Canada
Provinces and territories

Postal Geography:

• City Totals
• Urban Forward Sortation Areas (excludes Rural Routes and Suburban Services, and Other Urban Areas within City)
• Suburban Services*
• Rural Routes (Within City)*
• Rural Postal Code Areas (Within City)
• Other Urban Areas (Non-residential within city)
• Rural Communities (not in City)
• Other Provincial Totals

*These postal geography levels were available in the past but are no longer available for this data.

Some postal geography levels such as Postal Walks are no longer available for this data.

Census Geography

• Economic Regions
• Census Divisions
• Census Metropolitan Areas
• Census Agglomerations
• Census Tracts
• Federal Electoral Districts (2003 Representation Order)

User-defined areas:

For cost recovery tabulations, users may select specific areas of interest which do not correspond to standard areas. To obtain aggregated data for such areas, users can provide a list of lower level postal or census geography (Postal Codes, forward sortation areas, census tracts, census subdivisions, etc.) grouped according to their defined areas. These areas must satisfy our confidentiality requirements. See the "Special Geography" section for further information.

Geographic Levels – Postal Geography

The various data compiled from the taxfile are available for different levels of the postal geography. Coded geographic indicators appearing on the data tables are shown below with a brief description.

Adding Postal Areas Without Duplication

Data files according to the postal geography will often contain subtotals and totals. Many data users need to add certain geographies in order to come up with a total for their particular area of interest. However, including subtotals during this process results in double-counting some populations, and this leads to an erroneous total. The following is a summary of which postal areas are aggregations in the standard postal geography.

Urban FSAs (LOG 3), Rural Routes (LOG 4), suburban services (LOG 5), Rural Postal Code Areas within a city (LOG 6) and Other Urban Areas (LOG 7) add up to City Totals (LOG 8).

City Totals (LOG 8), Rural Communities not in a city (LOG 9) and Other Provincial Totals (LOG 10) add up to provincial/territorial totals (LOG 11).

Provincial/territorial totals (LOG 11) add up to the Canada total (LOG 12).

Thus, using the Level of geography codes:
3 + 4 + 5 + 6 + 7 = 8
8 + 9 + 10 = 11

City identification number (CityID)

The CityID is created for postal cities. This concept of cities does not correspond to the official boundaries of municipalities.

As of 2007, CityID has been modified.

Previous to 2007:

1. CityID was a 4 digits number
2. Each postal city had a unique number between 1 and 9999
3. Almost every number was allocated to a postal city. Few numbers remained available for future new postal cities.

Starting with 2007 data:
To create more possibilities without changing the CityID length in our systems:

1. CityID number is now combined with 1st letter of Postal Code
2. Each 1st letter of Postal Code has a possibility of numbers, ranged from 1 to 9999 (Table D)
3. Old numbers have been kept for existing postal cities and 1st letters of Postal Code have been added to them (Table C)
4. New postal cities have been assigned a new CityID number in new format (Table C)

Therefore, it is now essential to identify a postal city by adding the Postal Code 1st letter to the number in order to get the proper postal city in the proper province (Table E):

Hierarchy of postal geography

Description for chart of Hierarchy of postal geography

• Canada (12)
  • Provinces/ Territories (11)
    • City Totals (08)
      • Urban Forward Sortation Areas (03)
      • Rural Routes (04)
      • Sub-urban Services (05)
      • Rural Postal Code Areas (06)
      • Other Urban Areas (07)
    • Rural Communities (09)
    • Other Provincial Totals (10)

Geographic Levels – Census Geography

Data are also available for the following levels of the Census geography; the following table shows the coded designators for these geographies, as well as a brief description of each.

Geographic Levels – Special Geography

Clients may select geographical areas of their own definition; areas that are not part of the standard areas listed here (for example, bank service areas, retail store catchment areas). For this, clients must submit a list of lower level geographies such as Postal Codes or census tracts that make up their user defined areas. We will then aggregate the micro data to correspond to that area of interest. If there is more than one level of geography within the areas submitted by the client, this must be clearly indicated. A list of low level geographies which rollup into user defined areas is commonly referred to as a conversion file and is usually supplied to us in an Excel format.

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We are always working on ways to improve our products. The comments we receive concerning quality and presentation are essential to meet this objective. If you have any suggestions in this regard, we encourage you, the user, to provide us with your comments.

How to obtain more information

Inquiries about these data and related statistics or services should be directed to:

Client Services, Income Statistics Division

Telephone: Toll Free 1-888-297-7355 or 613-951-7355
Statistics Canada, Jean Talon Building, 5th Floor
Ottawa, Ontario K1A 0T6
Online requests: STATCAN.income-revenu.STATCAN@statcan.gc.ca

Statistics Canada's National Contact Centre provides a wide range of services: identification of your needs, establishing sources or availability of data, consolidation and integration of data coming from different sources, and general support for the use of Statistics Canada concepts and the use of statistical data.

Statistics Canada's National Contact Centre
Telephone: Toll Free 1-800-263-1136 or 613-951-8116
Telecommunications device for the hearing impaired (TTY): 1-800-363-7629

Online requests: infostats@statcan.gc.ca

You can also visit us on the web: Statistics Canada.

Standards of service to the public

Statistics Canada is committed to serving its clients in a prompt, reliable and courteous manner and in the official language of their choice. To this end, the agency has developed standards of service which its employees observe in serving its clients. To obtain a copy of these service standards, please contact your nearest Statistics Canada Regional Reference Centre.

Copyright

Published by authority of the Minister responsible for Statistics Canada.

© Minister of Industry, 2016

All rights reserved. Use of this publication is governed by the Statistics Canada Open Licence Agreement.

© This data includes information copied with permission from Canada Post Corporation

List of available data products

The Income Statistics Division of Statistics Canada tabulates statistical data derived from administrative records - most notably, the taxfile. The resulting demographic and socio-economic databanks available are listed in the table below, along with their identifying product number and the usual release dates.

Objectives, uses and users
Concepts, variables and classifications
Coverage and frames
Sampling
Questionnaire design
Response and non-response
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 withstakeholders 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 non-response

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 non-response.  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 non-respondents 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 non-response 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 method using administrative data is automatically selected when historical information is unavailable for a non-respondent. Trends are then applied to the administrative data source (monthly size) depending on whether the structure is simple, e.g. enterprises with only one establishment, or the unit has 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 years. 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. The revision period can be extended when historical revisions or restratitfication are done.

Retail trade data are seasonally adjusted using the X12-ARIMA method. This consists of extrapolating a year's worth of raw data with the ARIMA model (auto-regressive integrated moving average model), and of seasonally adjusting the raw time series. Finally, the annual totals of the seasonally adjusted series are forced to the annual totals of the original series.

The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions identified for the raw data. Also, the seasonally adjusted estimates are calculated using X-12-ARIMA, and are sensitive to the most recent values reported in the raw data. For this reason, with the release of each month of new data, the seasonally adjusted values for the previous three months are revised.  A seasonally adjusted time series is a time series that has been modified to eliminate the effect of seasonal and calendar influences. For this reason, the seasonally adjusted data allows for more meaningful comparisons of economic conditions from month to month.

Once a year, seasonal adjustments options are reviewed to take into account the most recent data. Revised seasonally adjusted estimates for each month in the previous years are released at the same time as the annual revision to the raw data. The actual period of revision depends on the number years the raw data was revised.

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.

Objectives, uses and users
Concepts, variables and classifications
Coverage and frames
Sampling
Questionnaire design
Response and non-response
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 withstakeholders 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 non-response

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 non-response.  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 non-respondents 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 non-response 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 method using administrative data is automatically selected when historical information is unavailable for a non-respondent. Trends are then applied to the administrative data source (monthly size) depending on whether the structure is simple, e.g. enterprises with only one establishment, or the unit has 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 years. 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. The revision period can be extended when historical revisions or restratitfication are done.

Retail trade data are seasonally adjusted using the X12-ARIMA method. This consists of extrapolating a year's worth of raw data with the ARIMA model (auto-regressive integrated moving average model), and of seasonally adjusting the raw time series. Finally, the annual totals of the seasonally adjusted series are forced to the annual totals of the original series.

The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions identified for the raw data. Also, the seasonally adjusted estimates are calculated using X-12-ARIMA, and are sensitive to the most recent values reported in the raw data. For this reason, with the release of each month of new data, the seasonally adjusted values for the previous three months are revised.  A seasonally adjusted time series is a time series that has been modified to eliminate the effect of seasonal and calendar influences. For this reason, the seasonally adjusted data allows for more meaningful comparisons of economic conditions from month to month.

Once a year, seasonal adjustments options are reviewed to take into account the most recent data. Revised seasonally adjusted estimates for each month in the previous years are released at the same time as the annual revision to the raw data. The actual period of revision depends on the number years the raw data was revised.

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.

Objectives, uses and users
Concepts, variables and classifications
Coverage and frames
Sampling
Questionnaire design
Response and non-response
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 withstakeholders 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 non-response

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 non-response.  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 non-respondents 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 non-response 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 method using administrative data is automatically selected when historical information is unavailable for a non-respondent. Trends are then applied to the administrative data source (monthly size) depending on whether the structure is simple, e.g. enterprises with only one establishment, or the unit has 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 years. 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. The revision period can be extended when historical revisions or restratitfication are done.

Retail trade data are seasonally adjusted using the X12-ARIMA method. This consists of extrapolating a year's worth of raw data with the ARIMA model (auto-regressive integrated moving average model), and of seasonally adjusting the raw time series. Finally, the annual totals of the seasonally adjusted series are forced to the annual totals of the original series.

The seasonally adjusted data also need to be revised. In part, they need to reflect the revisions identified for the raw data. Also, the seasonally adjusted estimates are calculated using X-12-ARIMA, and are sensitive to the most recent values reported in the raw data. For this reason, with the release of each month of new data, the seasonally adjusted values for the previous three months are revised.  A seasonally adjusted time series is a time series that has been modified to eliminate the effect of seasonal and calendar influences. For this reason, the seasonally adjusted data allows for more meaningful comparisons of economic conditions from month to month.

Once a year, seasonal adjustments options are reviewed to take into account the most recent data. Revised seasonally adjusted estimates for each month in the previous years are released at the same time as the annual revision to the raw data. The actual period of revision depends on the number years the raw data was revised.

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.

This module provides a concise summary of selected Canadian economic events, as well as international and financial market developments by calendar month. It is intended to provide contextual information only to support users of the economic data published by Statistics Canada. In identifying major events or developments, Statistics Canada is not suggesting that these have a material impact on the published economic data in a particular reference month.

All information presented here is obtained from publicly available news and information sources, and does not reflect any protected information provided to Statistics Canada by survey respondents.

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The 2021 Census counted 36,991,981 people in Canada during the national enumeration with reference date May 11, 2021.  This count is lower than the adjusted census population of 38,192,700 people calculated for the same reference date and which is used as a basis for population estimates. The difference between the two figures is not unexpected and is similar to that which was experienced for previous censuses. This note outlines why there are differences between census counts and population estimates.

The objective of a census is to provide detailed information on the population at a single point in time. In this respect, one of its goals is to enumerate the entire population. Inevitably, however, some people are not counted, either because their household did not receive a census questionnaire (for example, if a structurally separated dwelling is not easily identifiable) or because they were not included in the questionnaire completed for the household (for example, the omission of a boarder or a lodger). Some people may also be missed because they have no usual residence and did not spend census night in any dwelling. In contrast, a small number of people may also be counted more than once (for example, students living away from home may have been enumerated by their parents and by themselves at their student address).

To determine how many individuals were missed or counted more than once, Statistics Canada conducts postcensal coverage studies of a representative sample of individuals. Results of these studies in combination with the census counts are used to produce population estimates which take into account net undercoverage.

For the 2021 Census, final coverage studies have been released on September 27, 2023. In turn, these have been used to revise and update the population estimates based on the 2021 Census results. Consequently, a series of revised population estimates for the period 2016 to 2023 has been released on September 27, 2023.

One of the advantages of the census is to provide counts for small regions for which demographic estimates are not available or are less precise. On the other hand, population estimates provide more frequent measures of population counts for more aggregated levels of geography. In addition, estimates are used to measure the evolution of the population between censuses and provide explanations behind the population growth. The demographic estimates are available on a quarterly and annual basis at the national, provincial and territorial levels as well as for some subprovincial levels.