1.0 Description

The Canadian Perspectives Survey Series (CPSS) is a set of short, online surveys beginning in March 2020 that will be used to collect information on the knowledge and behaviours of residents of the 10 Canadian provinces. All surveys in the series will be asked of Statistics Canada's probability panel. The probability panel for the CPSS is a new pilot project initiated in 2019. An important goal of the CPSS is to directly collect data from Canadians in a timely manner in order to inform policy makers and be responsive to emerging data needs. The CPSS is designed to produce data at a national level (excluding the territories).

The survey program is sponsored by Statistics Canada. Each survey in the CPSS is cross sectional. Participating in the probability panel and the subsequent surveys of the CPSS is voluntary.

The third survey of the CPSS is CPSS3 – Resuming Economic and Social Activities During COVID-19. It was administered from June 15, 2020 until June 21, 2020.

Any question about the survey, the survey series, the data or its use should be directed to:

Statistics Canada

Client Services
Centre for Social Data Integration and Development
Telephone: 613-951-3321 or call toll-free 1-800-461-9050
Fax: 613-951-4527
E-mail: statcan.csdidclientservice-ciddsservicealaclientele.statcan@statcan.gc.ca

2.0 Survey methodology

Target and survey population

The target population for the Canadian Perspectives Survey Series (CPSS) is residents of the 10 Canadian provinces 15 years of age or older.

The frame for surveys of the CPSS is Statistics Canada's pilot probability panel. The probability panel was created by randomly selecting a subset of the Labour Force Survey (LFS) respondents. Therefore the survey population is that of the LFS, with the exception that full-time members of the Canadian Armed Forces are included. Excluded from the survey's coverage are: persons living on reserves and other Aboriginal settlements in the provinces; the institutionalized population, and households in extremely remote areas with very low population density. These groups together represent an exclusion of less than 2% of the Canadian population aged 15 and over.

The LFS sample is drawn from an area frame and is based on a stratified, multi-stage design that uses probability sampling. The LFS uses a rotating panel sample design. In the provinces, selected dwellings remain in the LFS sample for six consecutive months. Each month about one-sixth of the LFS sampled dwellings are in their first month of the survey, one-sixth are in their second month of the survey, and so on. These six independent samples are called rotation groups.

For the probability panel used for the CPSS, four rotation groups from the LFS were used from the provinces: the rotation groups answering the LFS for the last time in April, May, June and July of 2019. From these households, one person aged 15+ was selected at random to participate in the CPSS - Sign-Up. These individuals were invited to Sign-Up for the CPSS. Those agreeing to join the CPSS were asked to provide an email address. Participants from the Sign-Up that provided valid email addresses formed the probability panel. The participation rate to the panel was approximately 23%. The survey population for all surveys of the CPSS is the probability panel participants. Participants of the panel are 15 years or older as of July 31, 2019.

Sample Design and Size

The sample design for surveys of the CPSS is based on the sample design of the CPSS – Sign-Up, the method used to create the pilot probability panel. The raw sample for the CPSS – Sign-Up had 31,896 randomly selected people aged 15+ from responding LFS households completing their last interview of the LFS in April to July of 2019. Of these people, 31,626 were in-scope at the time of collection for the CPSS - Sign-Up in January to March 2020. Of people agreeing to participate in the CPSS, that is, those joining the panel, 7,242 had a valid email address. All panel participants are invited to complete the surveys of the CPSS.

Sample Design and Size

Stages of the Sample	n
Raw sample for the CPSS – Sign-Up	31,896
In-scope Units from the CPSS – Sign-Up	31,628
Panelists for the CPSS (with valid email addresses)	7,242
Raw sample for surveys of the CPSS	7,242

3.0 Data collection

CPSS – Sign-Up

The CPSS- Sign-Up survey used to create Statistics Canada's probability panel was conducted from January 15th, 2020 until March 15th, 2020. Initial contact was made through a mailed letter to the selected sample. The letter explained the purpose of the CPSS and invited respondents to go online, using their Secure Access Code to complete the Sign-Up form. Respondents opting out of joining the panel were asked their main reason for not participating. Those joining the panel were asked to verify basic demographic information and to provide a valid email address. Nonresponse follow-up for the CPSS-Sign-Up had a mixed mode approach. Additional mailed reminders were sent to encourage sampled people to respond. As well, email reminders (where an email address was available) and Computer Assisted Telephone Interview (CATI) nonresponse follow-up was conducted.

The application included a standard set of response codes to identify all possible outcomes. The application was tested prior to use to ensure that only valid question responses could be entered and that all question flows would be correctly followed. These measures ensured that the response data were already "clean" at the end of the collection process.

Interviewers followed a standard approach used for many StatCan surveys in order to introduce the agency. Selected persons were told that their participation in the survey was voluntary, and that their information would remain strictly confidential.

CPSS3 – Resuming Economic and Social Activities During COVID-19

All participants to the pilot panel for the CPSS, minus those who opted out after previous iterations of CPSS, were sent an email invitation with a link to the CPSS3 and a Secure Access Code to complete the survey online. Collection for the survey began on June 15th, 2020. Reminder emails were sent on June 16th, June 18th and June 20th. The application remained open until June 21st, 2020.

3.1 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.

4.0 Data quality

Survey errors come from a variety of different sources. They can be classified into two main categories: non-sampling errors and sampling errors.

4.1 Non-sampling errors

Non-sampling errors can be defined as errors arising during the course of virtually all survey activities, apart from sampling. They are present in both sample surveys and censuses (unlike sampling error, which is only present in sample surveys). Non-sampling errors arise primarily from the following sources: nonresponse, coverage, measurement and processing.

4.1.1 Nonresponse

Nonresponse errors result from a failure to collect complete information on all units in the selected sample.

Nonresponse produces errors in the survey estimates in two ways. Firstly, non-respondents often have different characteristics from respondents, which can result in biased survey estimates if nonresponse bias is not fully corrected through weighting. Secondly, it reduces the effective size of the sample, since fewer units than expected answered the survey. As a result, the sampling variance increases and the precision of the estimate decreases. The response rate is calculated as follows:

[ Responding units / (Selected units – out-of-scope units) ] x 100%

The following table summarizes the response rates experienced for the CPSS3 – Resuming Economic and Social Activities During COVID-19. Response rates are broken down into two stages. Table 4.1.1a shows the take-up rates to the panel in the CPSS- Sign-Up and Table 4.1.1b shows the collection response rates for the survey CPSS3 – Resuming Economic and Social Activities During COVID-19.

Table 4.1.1a Participation to the Pilot Probability Panel for the CPSS – Sign-Up

	Stages of the Sample for the CPSS – Sign-Up
	Raw sample for the CPSS – Sign-Up	In-scope Units from the CPSS – Sign-Up	Panelists for the CPSS (with valid email addresses)	Participation Rate to the Panel for CPSS
n	31,896	31,628	7,242	22.9%

Table 4.1.1b Response Rates to the CPSS3 – Resuming Economic and Social Activities During COVID-19

	Stages of the Sample for the CPSS3 – Resuming Economic and Social Activities During COVID-19
	Panelists for the CPSS (with valid email addresses)	Respondents to CPSS3 – Resuming Economic and Social Activities During COVID-19	Collection Response Rate to CPSS3 – Resuming Economic and Social Activities During COVID-19	Cumulative Response Rate
n	7,242	4,209	58.1%	13.3%

As shown in Table 4.1.1b, the collection response rate for the CPSS3 – Resuming Economic and Social Activities During COVID-19 is 58.1%. However, when nonparticipation in the panel is factored in, the cumulative response rate to the survey is 13.3%. This cumulative response rate is lower than the typical response rates observed in social surveys conducted at Statistics Canada. This is due to the two stages of nonresponse (or participation) and other factors such as the single mode used for surveys of the CPSS (emailed survey invitations with a link to the survey for online self-completion), respondent fatigue from prior LFS response, the inability of the offline population to participate, etc.,.

Given the additional nonresponse experienced in the CPSS3 – Resuming Economic and Social Activities During COVID-19 there is an increased risk of bias due to respondents being different than nonrespondents. For this reason, a small bias study was conducted. Please see Section 6.0 for the results of this validation.

4.1.2 Coverage errors

Coverage errors consist of omissions, erroneous inclusions, duplications and misclassifications of units in the survey frame. Since they affect every estimate produced by the survey, they are one of the most important type of error; in the case of a census they may be the main source of error. Coverage errors may cause a bias in the estimates and the effect can vary for different sub-groups of the population. This is a very difficult error to measure or quantify accurately.

For the CPSS, the population covered are those aged 15+ as of July 31, 2019. Since collection of the CPSS3 – Resuming Economic and Social Activities During COVID-19 was conducted from June 15th-21st, 2020, there is an undercoverage of residents of the 10 provinces that turned 15 since July 31, 2019. There is also undercoverage of those without internet access. This undercoverage is greater amongst those age 65 years and older.

4.1.3 Measurement errors

Measurement errors (or sometime referred to as response errors) occur when the response provided differs from the real value; such errors may be attributable to the respondent, the questionnaire, the collection method or the respondent's record-keeping system. Such errors may be random or they may result in a systematic bias if they are not random. It is very costly to accurately measure the level of response error and very few surveys conduct a post-survey evaluation.

4.1.4 Processing errors

Processing error is the error associated with activities conducted once survey responses have been received. It includes all data handling activities after collection and prior to estimation. Like all other errors, they can be random in nature, and inflate the variance of the survey's estimates, or systematic, and introduce bias. It is difficult to obtain direct measures of processing errors and their impact on data quality especially since they are mixed in with other types of errors (nonresponse, measurement and coverage).

4.2 Sampling errors

Sampling error is defined as the error that results from estimating a population characteristic by measuring a portion of the population rather than the entire population. For probability sample surveys, methods exist to calculate sampling error. These methods derive directly from the sample design and method of estimation used by the survey.

The most commonly used measure to quantify sampling error is sampling variance. Sampling variance measures the extent to which the estimate of a characteristic from different possible samples of the same size and the same design differ from one another. For sample designs that use probability sampling, the magnitude of an estimate's sampling variance can be estimated.

Factors affecting the magnitude of the sampling variance for a given sample size include:

1. The variability of the characteristic of interest in the population: the more variable the characteristic in the population, the larger the sampling variance.
2. The size of the population: in general, the size of the population only has an impact on the sampling variance for small to moderate sized populations.
3. The response rate: the sampling variance increases as the sample size decreases. Since non-respondents effectively decrease the size of the sample, nonresponse increases the sampling variance.
4. The sample design and method of estimation: some sample designs are more efficient than others in the sense that, for the same sample size and method of estimation, one design can lead to smaller sampling variance than another.

The standard error of an estimator is the square root of its sampling variance. This measure is easier to interpret since it provides an indication of sampling error using the same scale as the estimate whereas the variance is based on squared differences.

The coefficient of variation (CV) is a relative measure of the sampling error. It is defined as the estimate of the standard error divided by the estimate itself, usually expressed as a percentage (10% instead of 0.1). It is very useful for measuring and comparing the sampling error of quantitative variables with large positive values. However, it is not recommended for estimates such as proportions, estimates of change or differences, and variables that can have negative values.

It is considered a best practice at Statistics Canada to report the sampling error of an estimate through its 95% confidence interval. The 95% confidence interval of an estimate means that if the survey were repeated over and over again, then 95% of the time (or 19 times out of 20), the confidence interval would cover the true population value.

5.0 Weighting

The principle behind estimation in a probability sample such as those of the CPSS, is that each person in the sample "represents", besides himself or herself, several other persons not in the sample. For example, in a simple random 2% sample of the population, each person in the sample represents 50 persons in the population. In the terminology used here, it can be said that each person has a weight of 50.

The weighting phase is a step that calculates, for each person, his or her associated sampling weight. This weight appears on the microdata file, and must be used to derive estimates representative of the target population from the survey. For example, if the number of individuals who smoke daily is to be estimated, it is done by selecting the records referring to those individuals in the sample having that characteristic and summing the weights entered on those records. The weighting phase is a step which calculates, for each record, what this number is. This section provides the details of the method used to calculate sampling weights for the CPSS3 – Resuming Economic and Social Activities During COVID-19.

The weighting of the sample for the CPSS3 – Resuming Economic and Social Activities During COVID-19 has multiple stages to reflect the stages of sampling, participation and response to get the final set of respondents. The following sections cover the weighting steps to first create the panel weights, then the weighting steps to create the survey weights for CPSS3 – Resuming Economic and Social Activities During COVID-19.

5.1 Creating the Panel Weights

Four consecutive rotate-out samples of households from the LFS were the starting point to form the panel sample of the CPSS. Since households selected from the LFS samples are the starting point, the household weights from the LFS are the first step to calculating the panel weights.

5.1.1 Household weights

Calculation of the Household Design Weights – HHLD_W0, HHLD_W1

The initial panel weights are the LFS subweights (SUBWT). These are the LFS design weights adjusted for nonresponse but not yet calibrated to population control totals. These weights form the household design weight for the panel survey (HHLD_W0).

Since only four rotate-outs were used, instead of the six used in a complete LFS sample, these weights were adjusted by a factor of 6/4 to be representative. The weights after this adjustment were called HHLD_W1.

Calibration of the Household Weights – HHLD_W2

Calibration is a step to ensure that the sum of weights within a certain domain match projected demographic totals. The SUBWT from the LFS are not calibrated, thus HHLD_W1 are also not calibrated. The next step is to make sure the household weights add up to the control totals by household size. Calibration was performed on HHLD_W1 to match control totals by province and household size using the size groupings of 1, 2, or 3+.

5.1.2 Person Panel weights

Calculate Person Design Weights – PERS_W0

One person aged 15 or older per household was selected for the CPSS – Sign-Up, the survey used to create the probability panel. The design person weight is obtained by multiplying HHLD_W2 by the number of eligible people in the dwelling (i.e. number of people aged 15 years and over).

Removal of Out of Scope Units – PERS_W1

Some units were identified as being out-of-scope during the CPSS – Sign-Up. These units were given a weight of PERS_W1 = 0. For all other units, PERS_W1 = PERS_W0. Persons with a weight of 0 are subsequently removed from future weight adjustments.

Nonresponse/Nonparticipation Adjustment – PERS_W2

During collection of the CPSS – Sign-Up, a certain proportion of sampled units inevitably resulted in nonresponse or nonparticipation in the panel. Weights of the nonresponding/nonparticipating units were redistributed to participating units. Units that did not participate in the panel had their weights redistributed to the participating units with similar characteristics within response homogeneity groups (RHGs).

Many variables from the LFS were available to build the RHG (such as employment status, education level, household composition) as well as information from the LFS collection process itself. The model was specified by province, as the variables chosen in the model could differ from one province to the other.

The following variables were kept in the final logistic regression model: education_lvl (education level variable with 10 categories), nameissueflag (a flag created to identify respondents not providing a valid name), elg_hhldsize (number of eligible people for selection in the household), age_grp (age group of the selected person), sex, kidsinhhld (an indicator to flag whether or not children are present in the household), marstat (marital status with 6 categories), cntrybth (an indicator if the respondent was born in Canada or not), lfsstat (labour force status of respondent with 3 categories), nocs1 (the first digit of National Occupational Classification code of the respondent if employed, with 10 categories), and dwelrent (an indicator of whether the respondent dwelling is owned or rented). RHGs were formed within provinces. An adjustment factor was calculated within each response group as follows:

$\frac{\text{Sum of weights of respondents and nonrespondents}}{\text{Sum of weights of respondents}}$

The weights of the respondents were multiplied by this factor to produce the PERS_W2 weights, adjusted for panel nonparticipation. The nonparticipating units were dropped from the panel.

5.2 Creating the CPSS3 weights

Surveys of the CPSS start with the sample created from the panel participants. The panel is comprised of 7,242 individuals, each with the nonresponse adjusted weight of PERS_W2.

Calculation of the Design Weights – WT_DSGN

The design weight is the person weight adjusted for nonresponse calculated for the panel participants (PERS_W2). No out-of-scope units were identified during the survey collection of CPSS3 – Resuming Economic and Social Activities During COVID-19. Since all units were in-scope, WT_DSGN =PERS_W2 and no units were dropped.

Nonresponse Adjustment – WT_NRA

Given that the sample for CPSS was formed by people having agreed to participate in a web panel, the response rates to the survey were relatively high. Additionally, the panel was designed to produce estimates at a national level, so sample sizes by province were not overly large. As a result, nonresponse was fairly uniform in many provinces. This resulted in having only one response homogeneity group (RHG) in Prince Edward Island. For the other provinces, the RHGs were formed by some combination of age group, sex, education level, rental status, LFS status, whether or not children are present in the household, eligible household size, and the first digit of the National Occupational Classification (NOC) code for respondents who are employed. An adjustment factor was calculated within each response group as follows:

$\frac{\text{Sum of weights of respondents and nonrespondents}}{\text{Sum of weights of respondents}}$

The weights of the respondents were multiplied by this factor to produce the WT_NRA weights, adjusted for survey response. The nonresponding units were dropped from the survey.

Calibration of Person-Level Weights – WT_FINL

Control totals were computed using LFS demography projection data. During calibration, an adjustment factor is calculated and applied to the survey weights. This adjustment is made such that the weighted sums match the control totals. Most social surveys calibrate the person level weights to control totals by sex, age group and province. For CPSS3, calibration by province was not possible, since there were very few respondents in some categories in the Atlantic and Prairie Provinces. In addition, there were very small counts for male respondents aged 15 to 24 in the Atlantic Provinces. For this reason, the control totals used for CPSS3 – Resuming Economic and Social Activities During COVID-19 were by age group and sex by geographic region, where the youngest age group for males in the Atlantic region was collapsed with the second youngest. The next section will include recommendations for analysis by geographic region and age group.

5.3 Bootstrap Weights

Bootstrap weights were created for the panel and the CPSS3 – Resuming Economic and Social Activities During COVID-19 survey respondents. The LFS bootstrap weights were the initial weights and all weight adjustments applied to the survey weights were also applied to the bootstrap weights.

6.0 Quality of the CPSS and Survey Verifications

The probability panel created for the CPSS is a pilot project started in 2019 by Statistics Canada. While the panel offers the ability to collect data quickly, by leveraging a set of respondents that have previously agreed to participate in multiple short online surveys, and for whom an email address is available to expedite survey collection, some aspects of the CPSS design put the resulting data at a greater risk of bias. The participation rate to the panel is lower than typically experienced in social surveys conducted by Statistics Canada which increases the potential nonresponse bias. Furthermore, since the surveys of the CPSS are all self-complete online surveys, people without internet access do not have the means to participate in the CPSS and therefore are not covered.

When the unweighted panel was compared to the original sample targeted to join the panel, in particular there was an underrepresentation of those aged 15-24, those aged 65 and older, and those with less than a high school degree. These differences were expected due to the nature of the panel and the experience of international examples of probability panels. Using LFS responding households as the frame for the panel was by design in order to leverage the available LFS information to correct for the underrepresentation and overrepresentation experienced in the panel. The nonresponse adjustments performed in the weighting adjustments of the panel and the survey respondents utilised the available information to ensure the weights of nonresponding/nonparticipating units went to similar responding units. Furthermore, calibration to age and sex totals helped to adjust for the underrepresentation by age group.

Table 6.1 shows the slippage rates by certain domains post-calibration of CPSS3 – Resuming Economic and Social Activities During COVID-19. The slippage rate is calculated by comparing the sum of weights in the domain to that of the control total based off of demographic projections. A positive slippage rate means the sample has an over-count for that domain. A negative slippage rate means the survey has an under-count for that domain. Based on the results shown in Tables 6.1 and 6.2, it is recommended to only use the data at the geographical levels and age groups where there is 0 slippage. That is nationally, by geographic region (Maritime Provinces, Quebec, Ontario, Prairie Provinces, and British Columbia), and by the four oldest age groups.

Table 6.1 Slippage rates by geographic region

Area	Domain	n	Slippage Rate
Geography	CanadaTable 6.1 Footnote 1	4,209	0%
	Prince Edward Island	98	12.2%
	Newfoundland and Labrador	119	-7.1%
	Nova Scotia	231	3.4%
	New Brunswick	194	-1.9%
	Quebec	693	0%
	Ontario	1,232	0%
	Manitoba	342	-2.1%
	Saskatchewan	290	6.5%
	Alberta	459	-1.0%
	British Columbia	551	0%
Footnote 1 Based on the 10 provinces; the territories are excluded Return to table 6.1 footnote 1 referrer

Table 6.2 Slippage rates by age group

Area	Domain	n	Slippage Rate
Age group	15 to 24 years	236	3.2%
	25 to 34 years	510	-2.7%
	35 to 44 years	711	0%
	45 to 54 years	678	0%
	55 to 64 years	924	0%
	65 years and older	1,150	0%

After the collection of CPSS3 – Resuming Economic and Social Activities During COVID-19, a small study was conducted to assess the potential bias due to the lower response rates and the undercoverage of the population not online. The LFS data was used to produce weighted estimates for the in-scope sample targeted to join the probability panel (using the weights and sample from PERS_W1). The same data was used to produce weighted estimates based on the set of respondents from the CPSS3 survey and the weights WT_FINL. The two set of estimates were compared and are shown in Table 6.3. The significant differences are highlighted.

Table 6.3 Changes in estimates due to nonparticipation in the CPSS and the COVID-19 survey

Subject	Recoded variables from 2019 LFS	Estimate for in-scope population (n=31,628)	Estimate for W3 of CPSS (n=4,209)	% Point Difference
Education	Less than High School	15.5%	13.7%	-1.7%
	High School no higher certification	25.9%	25.5%	-0.4%
	Post-secondary certification	58.6%	60.8%	2.2%
Labour Force Status	Employed	61.2%	61.6%	0.5%
	Unemployed	3.4%	3.3%	-0.1%
	Not in Labour Force	35.3%	35.0%	-0.3%
Country of Birth	CanadaTable 6.3 Footnote 1	71.7%	75.0%	3.3%
Marital Status	Married/Common-lawTable 6.3 Footnote 1	60.4%	61.1%	0.7%
	Divorced, separated, widowedTable 6.3 Footnote 1	12.8%	11.6%	-1.2%
	Single, never married	26.9%	27.3%	0.5%
Kids	Presence of childrenTable 6.3 Footnote 1	31.7%	34.6%	2.9%
Household Size	Single person	14.4%	13.9%	-0.6%
	Two person HH	34.8%	37.3%	2.5%
	Three or more people	18.4%	18.7%	0.4%
Eligible people for panel	One eligible person aged 15+	15.9%	15.5%	-0.4%
	Two eligible peopleTable 6.3 Footnote 1	49.3%	53.2%	3.9%
	Three or more eligible people	34.8%	31.4%	-3.5%
Dwelling	Apartment	12.1%	11.8%	-0.3%
	RentedTable 6.3 Footnote 1	24.8%	25.0%	0.2%
Occupational Code	Management occupations (NOC0)	6.0%	5.9%	-0.1%
	Business Finance and Administration (NOC1)	10.7%	10.9%	0.2%
	Natural and Applied Sciences and related occupations (NOC2)	5.2%	6.1%	0.8%
	Health Occupations (NOC3)	4.7%	4.5%	-0.2%
	Occupations in education, law and social, community and government services (NOC4)	7.6%	8.2%	0.6%
	Occupations in art, culture, recreation and sports (NOC5)	2.5%	2.8%	0.3%
	Sales and service occupations (NOC6)	16.6%	16.8%	0.2%
	Trades, transport and equipment operators and related occupations (NOC7)	9.6%	10.0%	0.4%
	Natural resources, agriculture and related production occupations (NOC8)	1.6%	1.8%	0.2%
	Occupations in manufacturing and utilities (NOC9)	2.9%	2.3%	-0.6%
Footnote 1 Estimates that are significantly different at α= 5%. Return to first table 6.3 footnote 1 referrer

While many estimates do not show significant change, the significant differences show that some bias remains in the CPSS3 – Resuming Economic and Social Activities During COVID-19. There is an underrepresentation of those where there were three or more eligible participants for the panel. And there is an overrepresentation of people born in Canada, households with two persons in total, and those where there were two eligible participants for the panel. These small differences should be kept in mind when using the CPSS3 – Resuming Economic and Social Activities During COVID-19 survey data. Investigation about differences in estimates is ongoing, and as evidence of differences are identified, strategies are being tested to improve the methodology from one wave of the survey to the next.

Under the authority of the Statistics Act, Statistics Canada is hereby requesting the following information, which will be used solely for statistical and research purposes and will be protected in accordance with the provisions of the Statistics Act and any other applicable law. This is a mandatory request for data.

• Employment insurance, social assistance, and other transfers
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  • Data on Canada Recovery Benefit (CRB), Canada Recovery Caregiving Benefit (CRCB), Canada Recovery Sickness Benefit (CRSB) and Canada Worker Lockdown Benefit (CWLB) recipients
• Job vacancies, labour mobility and layoffs
  • Job Postings
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  • Request for Public Service Employee Survey (PSES) data from 2018 to 2020 inclusively

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Data Integration Infrastructure Division

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• Data quality and limitations
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Business Register Coverage

The Business Register is a repository of information reflecting the Canadian business population and exists primarily to supply frames for all economic surveys in Statistics Canada. It provides a means of coordinating the coverage of business surveys and of achieving consistent classification of statistical reporting units.

Included in Business Register data are all Canadian businesses which meet at least one of the three following criteria:

1. Have an employee workforce for which they submit payroll remittances to CRA;
2. Have at least $30,000 in annual revenue;
3. Are incorporated under a federal or provincial act and have filed a federal corporate income tax form within the past three years.

Available Data

Canadian Business Counts (formerly Canadian Business Patterns)

Location counts with employees by province/Canada, NAICS and employment size ranges.

Location counts without employees by province/Canada and NAICS.

Location counts with employees by census metropolitan areas/census subdivisions, NAICS and employment size ranges.

Custom Aggregate Data Tables:

Employment Size Range

• Units: Location, establishment or enterprise counts
• Geography: All geography
• Industry: All levels of NAICS
• Employment Size Ranges: Standard 9 ranges or custom 13 or 21 ranges
• Confidentiality measures: None

Revenue Ranges

• Units: Location, establishment or enterprise counts
• Geography: Province and CA/CMA
• Industry: NAICS-2, 3
• Confidentiality measures: Rounding

Profit/Non-Profit Data (December only)

• Units: Establishment counts
• Geography: Province
• Industry: NAICS-2
• Confidentiality measures: Suppression

Business Type and Public/Private Data (December only)

• Units: Enterprise counts
• Geography: Province and CMA (14)
• Industry: NAICS-2
• Confidentiality measures: Suppression

Data variations due to methodological changes, by year

• In December 2000, and June 2005, the number of smaller businesses declined. The Business Register has analyzed new administrative sources to detect more rapidly and accurately business closures. This has resulted in the use of new signals that are now part of the processes to update the Business Register.
• The June 2006 reference period shows an increase in the number of businesses because of a methodological change. There is a new way of identifying newcomers on the Business Register. The following sectors have been affected: NAICS 48–49 (Transportation and Warehousing), NAICS 53 (Real Estate and Rental and Leasing) and NAICS 54 (Professional, Scientific and Technical Services).
• The December 2007 reference period is based on the redesigned Business Register. The statistical structure (including establishments) has been simplified to better reflect the operating structure of the business. The decrease in the number of establishments is the result of our continuous efforts to detect inactive businesses as early as possible.
• The December 2008 reference period introduced the use of "statistical location" counts, besides the usual establishment counts. The use of location counts provides a better measurement of business units. Definitions of the statistical establishment and location are provided later in this document under the "Statistical Establishment" and "Statistical Location" sections.
• The December 2008 and June 2009 reference periods show a decrease in the number of businesses. This can be attributed to the introduction of new "inactivation rules" that expanded the ability to identify units that aren't reporting any economic activity.
• For the first time, the December 2010 reference period includes all unincorporated (T1) businesses with sales of at least $30,000. This integration of T1 businesses is intended to create a more comprehensive representation of the business population on our register. Specifically, this change has mainly affected the following sectors: NAICS 53 (Real Estate and Rental and Leasing), NAICS 44–45 (Retail Trade) and NAICS 62 (Health Care and Social Assistance). The introduction of these units hasn't had a significant impact on total business counts and represents 1.6% of all locations in December 2010.
• A large increase in the June 2013 reference period is due toincorporated businesses which are now required to auto-code a NAICS to record their tax form information with the Canada Revenue Agency. The increase represents an accumulation of about two years of auto-coding. This change affected almost every sector and accounts for most of the growth in the data between December 2012 and June 2013.
• A small portion of the increase in businesses in December 2013 is due to new rules regarding the acceptance of auto-coded NAICS which resulted in these businesses being included in the data. The impact wasn't as widespread as the initial NAICS auto-code increase in June 2013 but mostly affected non-employers and the majority of sectors.
• There are two industrial classification categories introduced in 2014; unclassified which is a new category for businesses which haven't received a NAICS code and classified for businesses which have received a NAICS code. The impact of adding the unclassified category is an additional 78,718 locations with employees and 313,107 locations without employees. These counts can be easily identifiable because they're in a separate category.
• In December 2014, a revision of the employer status on all units of the Business Register resulted in approximately 70,000 businesses with employees to shift to the businesses without employees' category. This is mostly noticeable in the smaller employment size ranges. Business counts in NAICS 72—Accommodation and food services, 62—Health care and social assistance, 31–33—Manufacturing and 44–45—Retail trade see the largest decreases.
• Starting in December 2014, businesses without employees now cover all enterprises which meet one of the following criteria: is incorporated or shows at least $30,000 in revenue (nontaxable or taxable). This change affects businesses that didn't have $30,000 in taxable revenue in previous years but did have at least $30,000 in (nontaxable and taxable) revenue. These businesses will now be included and represent approximately 600,000 units. Business counts in NAICS 53—Real estate and rental and leasing and 62—Health care and social assistance have the largest increases.
• The December 2019 counts reflect a downward correction to the number of businesses, especially those without employees, due to new criteria for identifying businesses that had become inactive. Approximately 140,000 units were affected by this correction.
• The June 2020 counts cannot be used to measure the impacts of the COVID-19 pandemic. These figures continue to include most businesses that closed in the months since the crisis began. Those that close permanently will eventually cease to be included, once business wind-down and closeout procedures are completed and confirmed, which can take several months.

Data quality and limitations

The Business Register is largely based on the Business Number (BN) registration source as collected by the Canada Revenue Agency (CRA).

Time Series

Changes to the Business Register's methodology or to business industrial classification strategies can cause increases or decreases in the number of active businesses. As a result, the data do not represent changes in the business population over time. Statistics Canada recommends that users not use the data as a time series.

Creations

Generally, a location creation on the Business Register occurs shortly after a BN is created for each business registrant by CRA. The BN registrations are used to update the Business Register database weekly. Sometimes, the business is contacted to obtain the necessary information for the creation of a location record.

Inactivation

Businesses are assigned an inactive status on the Business Register when neither a tax payment nor payroll remittance has been made by these businesses for some time.

Geography

The Business Register adopted the Standard Geographical Classification, 2016 version. The link between a business and its geographical code is made using the postal code. Since the postal code is designed by Canada Post and targets the efficient delivery of the mail, there are many situations where one postal code doesn't align exactly to the boundaries of a single SGC geographic unit. The smaller and rural geographic units are more subject to this possibility.

North American Industrial Classification System

For newly created businesses, the primary industrial coding is initially processed using automated coding software. This software evaluates the activity description indicated by the business and assigns the appropriate industry classification coding (about 50% of new business records). Activity descriptions lacking precision are subjected to a manual coding process.

Key definitions found in Business Register Data

Statistical Entities

Statistical Enterprise

An enterprise is the legal operating entity at the top of the operating structure. There is only one enterprise per operating structure. It's associated with a complete set of financial statements.

Statistical Establishment

A statistical establishment is the production entity or the smallest grouping of production entities which:

1. Produces a homogeneous set of goods or services;
2. Doesn't cross provincial boundaries; and
3. Provides data on the value of output together with the cost of principal intermediate inputs used along with the cost and quantity of labour resources used to produce the output.

Statistical Location

The location is an operating entity, specifically a production entity which:

1. Conducts economic activity at or from a single physical location or group of locations;
2. Resides within the smallest standardized geographical area;
3. Is able to provide employment data at a minimum.

Employment

Source

Employment is based on both corporations' payroll remittance and profiling/survey data. These data are at first edited and imputed before being used as input for other processes.

For simple units, attached to only one legal entity, the employment is derived from payroll deductions using the 2nd maximum input within the last 12 months of data. For the complex units, aggregated employment, obtained from profiling, is first determined at the enterprise level. This value is afterward distributed at the establishment and location levels based on the profiled employment distribution from the Business Register.

Employment Size Ranges

The following are the employment size ranges available in the Business Register:

• 1 to 4
• 5 to 9
• 10 to 19
• 20 to 49
• 50 to 99
• 100 to 199
• 200 to 499
• 500+

Locations without employees include the self-employed, i.e., those who don't maintain an employee payroll, but may have a workforce which consists of contracted workers, family members or business owners. These also include employers who didn't have employees in the last 12 months.

This data should not be used in any manner to compile industry employment estimates.

Geography

The Standard Geographical Classification (SGC) is Statistics Canada's official classification for the geographical areas in Canada. It was developed to facilitate the analysis of statistical data using a uniform geographical area definition. It produces a range of geographical areas that are useful for analysis, convenient for data collection and compilation on this basis. It is intended primarily for the classification of statistical units such as locations.

Structure of the Standard Geographical Classification

Each of the three sets of areas covers all of Canada. They are hierarchical: a census subdivision aggregates to a census division, which in turn aggregates to a province or territory.

(1) Province and Territory

"Province" and "territory" refer to the major political units of Canada. From a statistical point of view, province and territory are basic areas for which data are tabulated. Canada is divided into 10 provinces and 3 territories.

(2) Census Division

Census division (CD) is the general term for provincially legislated areas (such as county and regional district) or their equivalents. Census divisions are intermediate geographic areas between the province/territory level and the municipality (census subdivision).

Usually they are groups of neighbouring municipalities joined together for the purposes of regional planning and managing common services (such as police or ambulance services). These groupings are established under laws in certain provinces of Canada.

(3) Census Subdivision

Census subdivision (CSD) is the general term for municipalities (as determined by provincial/territorial legislation) or areas treated as municipal equivalents for statistical purposes (e.g., Indian reserves, Indian settlements and unorganized territories).

Please take note, when using the CSD, of the volatility of the counts between the different reference periods. Units move from one CSD to another, not due to actual changes in physical location, but due to changes in linkages between a specific CSD and postal code.

Census Metropolitan Area and Census Agglomeration

A census metropolitan area (CMA) or a census agglomeration (CA) is formed by one or more adjacent municipalities centred on a population centre (known as the core). A CMA must have a total population of at least 100,000 of which 50,000 or more must live in the core. A CA must have a core population of at least 10,000. To be included in the CMA or CA, other adjacent municipalities must have a high degree of integration with the core as measured by commuting flows derived from previous census place of work data.

If the population of the core of a CA declines below 10,000, the CA is retired. However, once an area becomes a CMA, it is retained as a CMA even if its total population declines below 100,000 or the population of its core falls below 50,000. All areas inside the CMA or CA, that aren't population centres, are rural areas.

Other Geographies

Economic Region

An economic region (ER) is a grouping of complete census divisions (CDs) (with one exception in Ontario) created as a standard geographic unit for analysis of regional economic activity.

Census Tract

Area that is small and relatively stable. Census tracts usually have a population of 2,500 to 8,000. They are in large urban centres that must have an urban core population of 50,000 or more.

Federal Electoral District

Area represented by a Member of Parliament (MP) elected to the House of Commons.

Dissemination Area

Small area composed of one or more neighbouring blocks, with a population of 400 to 700 persons. All of Canada is divided into dissemination areas.

Forward Sortation Area

Area composed of the first three digits of the postal code which is a six-character code defined and maintained by Canada Post Corporation for the purpose of sorting and delivering mail.

"000" Residue

Please note that codes have been created for residues. They consist of the province/territory code followed by zeroes. This residual category reflects statistical units in Canada where there is insufficient information to precisely locate the locations within a census division/census subdivision as determined by the 2016 Standard Geographical Classification.

Industry Codes—North American Industry Classification System

The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, Mexico and the United States. Created, it's designed to provide common definitions of the industrial structure of the 3 countries and a common statistical framework to facilitate the analysis of the 3 economies. NAICS is based on supply or production-oriented principles, to ensure that industrial data, classified to NAICS, are suitable for the analysis of production-related issues such as industrial performance.

NAICS is a system encompassing all economic activities. It has a hierarchical structure.

NAICS Structure

Sectors

2 digits

Sub-sectors

3 digits

Industry Groups

4 digits

Industries

5 digits

National Industries

6 digits

Revenue

These revenues are derived mostly from administrative files from C.R.A. (Canada Revenue Agency). They're based on both corporations' income tax revenues and GST sales remittances. These data are at first edited and imputed before being used as input for other processes. For simple units, attached to only one legal entity, the revenue is derived from a regression model using the GST sales as independent variable, the income tax revenue being the dependent variable. For the complex units, aggregated revenue is first determined at the enterprise level. This value is afterward distributed at the establishment and location levels based on the profiled revenue distribution from the Business Register.

Contact us

Business Register Dissemination Unit
Data Integration Infrastructure Division
Statistics Canada
Tunney's Pasture
Ottawa, Ontario
K1A 0T6
statcan.statisticalregisters-registresstatistiques.statcan@statcan.gc.ca

Release date: August 13, 2020 (Previous notice)

• Introduction
• How we reached out and whom we heard from
• Summary of what we heard
  • CRDC open online consultation
• Next steps
  • Timeline

Introduction

Accountability and transparency—which are of the utmost importance for research funding organizations—are becoming increasingly critical for demonstrating how funds are deployed. Research stakeholders, the government and the public are seeking information about which areas of research are receiving support and the levels of investment in each of these areas. Furthermore, since research efforts are global, the ability to combine and compare information about funded research with other organizations is necessary to improve collaboration, improve support for research and development (R&D), and benchmark investments and performance both nationally and internationally.

Since December 2017, the Canada Foundation for Innovation, the Canadian Institutes of Health Research, the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada (SSHRC), and Statistics Canada^{Footnote 1} have been jointly developing the Canadian Research and Development Classification (CRDC). This new classification has been designed to include all research sectors and represent the current research landscape in Canada while also contributing to greater alignment with international standards. It is also comprehensive enough to support a wide range of needs within the R&D ecosystem. Furthermore, it has been developed to facilitate the peer review process and the reporting of investments by federal research funding agencies and the Government of Canada. The CRDC will help ensure the consistent compatibility and comparability of statistics across research funding agencies both in Canada and internationally while balancing the needs of different users and highlighting specific areas of Canadian research strength. The CRDC is the product of months of reviews, consultations, analysis and negotiations among the sponsors and the Canadian research community in an effort to make research classification consistent in Canada.

The CRDC is a set of three interrelated classifications:

• Type of activity (TOA): This is categorization by type of research being undertaken, e.g., fundamental, applied, experimental development.
• Field of research (FOR): This is categorization by field of research; it is the methodology used in R&D that is being considered. The categories within this classification include major fields of research based on knowledge source, subject of interest, and methods and techniques used.
  • There are four hierarchical levels: divisions are the broadest level, and groups, classes and subclasses represent increasingly detailed dissections of these categories. This resulted in a comprehensive list of fields of research—nearly 1,800 in total—to help reflect Canada's current research landscape.
• Socioeconomic objectives (SEO): This is categorization by R&D purpose or outcome.
  • There are two hierarchical levels: divisions are the broadest level, followed by groups. There are approximately 85 groups.

While Statistics Canada will use the CRDC to report on Canada's R&D activities at the national and international levels, the federal research granting agencies have been involved from the beginning of the project, as they see great benefits in having a common research classification. Adopting a common approach for classifying research and expertise across the federal research granting agencies aims to

• provide a common language for discussing research in the higher education sector, in the public sector and within government, enabling better evidence-based decision making within the research ecosystem
• make it possible to identify expertise and research areas in a truly multidisciplinary classification
• improve the identification of emerging research fields
• help identify potential collaboration opportunities to optimize research efforts and improve outcomes
• improve the identification of research funding gaps and opportunities
• provide the research community with harmonized and integrated R&D classification
• improve reporting on the agencies' combined contributions to research and science in Canada
• help the agencies streamline their operational processes for peer review, recruitment and reviewer selection.

How we reached out and whom we heard from

• Over 100 research funding agency employees
• Over 300 subject-matter experts across all sectors
• 18 webinars hosted by project sponsors
• Over 860 responses from the online consultations
• Over 1,700 notices of interest about the CRDC received through the pilot program
• Over 1,000 suggestions proposed by subject-matter experts and the research community as a whole

The project sponsors sought to engage and consult as wide of an audience as possible to collect evidence-based recommendations to help develop the CRDC. The consultation process started in February 2018 and ended in September 2019. Those consulted include

• the Australian Bureau of Statistics, Statistics New Zealand and the Australian Research Council, as they have been using a similar model for 10 years and could share their expertise and experience
• internal staff at each Canadian federal research granting agency to ensure that the CRDC supports the full range of uses of a research classification for program delivery, monitoring and reporting
• subject-matter experts in the research community to inform and validate the terminology used in and the scope of specific fields of research
• targeted stakeholders, such as federal science-based departments and agencies, provincial funding agencies, and provincial statistical agencies, to obtain feedback on the general structure and principles of the classification.

An open online consultation process ran from February 11 to March 22, 2019, to give a wider audience the chance to provide feedback on the proposed categories and terminology. The New Frontiers in Research Fund at SSHRC used a pilot version of the classification.

Summary of what we heard

In the open online consultation, participants and subject-matter experts were asked to review proposed categories and suggest any changes to specific categories—including adding, removing, combining, splitting and renaming—to represent the current Canadian research landscape, and to ensure that the classification would meet the needs of different stakeholders across the Canadian research ecosystem. The objective of the consultation process was to obtain feedback on fields of research and socioeconomic objectives, not on type of activity.

CRDC open online consultation

• 817 responses received
• 313 responses with comments on field of research
• 5% of respondents identified their field of research as "other"

Most frequent comments and suggestions provided for consideration on fields of research

Field of research	Most frequent comments and suggestions provided for consideration
General	The CRDC FOR codes are well mapped to existing categories in different research classifications. Several comments recommended updates to different categories. Cross-sector categories are not always easy to find. Some categories are not well defined and do not represent the evolution of some of the fields in the Canadian research landscape. The level of granularity in each category seems to be adequate for supporting each granting agency's needs—such as the peer review process—by allowing peer reviewers to be selected and review committee members to be identified based on common disciplines. The ability to aggregate different levels of data seems to be adequate for supporting reporting on investments, research activities in specific fields, and R&D objectives at the organizational, national and international levels. Some categories seem to be more granular than others. The delineation between categories is not always evident, and the definitions provided are not always helpful.
Category specific (examples)	Electronic and electrical engineering need to be reviewed to reflect the current research landscape. Neurosciences should be subdivided by how it pertains to each sector. Geosciences is spread across all relevant fields; however, some important categories are missing. Literature fields are neither well categorized nor representative, and it would be difficult to classify current research within the proposed categories. Industrial engineering categories will need to be updated to reflect current progress being made in Canada. Examples of specific categories that were identified as missing include rhetoric studies, disciplinary education and genetic epidemiology.

Comments and suggestions for consideration on socioeconomic objectives

Socioeconomic objectives	Comments and suggestions for consideration
General	Depending on the time frame considered when identifying the outcome of the research, the socioeconomic objectives could be different. Examples for each group category would help delineate each category or group. Interdisciplinary research spans disciplines and does not fit neatly into these objectives.
Category specific (examples)	Split arts and leisure into two categories. There is uncertainty about which category changes in health-related policy would fit into. Categories for social justice topics are missing. Well-being and mental health need to be listed under five-digit levels under health. The understanding of past societies is included, but the understanding of current societies is missing. The education categories need to be better defined, as the category title is not intuitive and creates confusion. The lists are very comprehensive, but lack interdisciplinary studies across the natural and social sciences, e.g., socioecological systems.

Comments and suggestions for consideration overall

Overall

Comments and suggestions for consideration

General

The way the codes are displayed needs to be more user friendly and intuitive to make it easier for the user to identify their area of research or expertise. Some of the definitions provided were very poor. The categories will need to be reviewed regularly to ensure that areas that are developing past "emerging" are captured in the future. The granularity and structure of the CRDC are flexible enough to meet the needs of the research community.

Next steps

The consultations provided insights to help improve the proposed CRDC and its categories to better reflect the current Canadian research landscape. Participants and subject-matter experts identified many areas and categories for improvement. Based on the consultation results, the CRDC was revised, and the suggested fields of research, socioeconomic objectives and other proposed changes were taken into account. Opportunities to minimize the burden of identifying and selecting fields of research and socioeconomic objectives are being studied to improve usability and findability.

Timeline

• Pre-consultation period
  • March to December 2017
• Consultations
  • February 2018 to September 2019
• Release of the What We Heard report
  • August 2020
• Release of the new Canadian Research and Development Classification
  • Fall 2020
• Implementation within each federal research granting agency
  • Ongoing