Survey of Regulatory Compliance Cost
Detailed information for 2016
This survey provides data on the current cost of regulatory compliance for small and medium-sized businesses in meeting key information obligations imposed by various levels of government.
Data release - March 19, 2019
This survey is being conducted in order to measure regulatory compliance costs for small and medium-sized businesses in meeting key regulatory requirements that are the responsibility of various levels of government. The survey results are intended to help determine whether efficiency measures introduced by government are reducing the compliance burden facing businesses.
The survey is a key component of the Paperwork Burden Reduction Initiative (PBRI). The PBRI was established in response to the Budget 2004 commitment to create a working group of government officials and small business representatives that would make measurable reductions in paperwork burden.
The survey consists of two components: the main component and the service provider component. The main survey collects detailed information on the time spent and the salaries of the people within the business who are involved in preparing and submitting information relating to individual regulations. As well, it collects a list of outsourced activities (including non-regulatory) and the total cost for the activities being outsourced to external service providers.
The service provider component of the survey collects information from service providers about their business clients and the services provided to them. Data are collected for three industry groups (legal services ; accounting, tax preparation, bookkeeping and payroll services ; and architectural, engineering and related services ).
The information from service providers is essential in order to be able to allocate the total dollar costs of all outsourced services reported in the main survey across the 11 regulatory requirements of interest. Without this information, a very significant portion of regulatory costs would be missing from the main survey estimates.
The 11 regulatory requirements in-scope for this survey are:
- T4s, T4As and T5018s (including RL-1 forms in Quebec)
- Federal and provincial business income tax return filings
- Corporate income tax instalments
- Federal and provincial sales tax remittances (GST/HST or PST)
- Payroll remittances
- Record of Employment (ROE)
- Workers' compensation remittances
- Workers' compensation claims
- Corporate registration (annual filing or business status change) forms
- Municipal and other provincial government regulatory requirements
- Other mandatory federal government regulatory requirements
Reference period: The calendar year
Collection period: October 2017 through March 2018
- Business performance and ownership
Data sources and methodology
The target population includes all establishments that are on the Business Register (BR) that meet the following conditions:
1- All establishments in the North American Industry Classification System (NAICS), with the exception of 22, 55, 61, 6214, 6215, 6219, 6221, 6222, 6223, 6242, 814110 and 91.
2- Establishments with between 1 and 499 employees.
3- Establishments with revenue greater than $30,000.
4- Enterprises with more than three establishments are excluded.
5- Establishments located in the territories are excluded.
The establishment was selected as the sampling unit so that the resulting data set would capture the costs associated with regulations which differ from one province to another.
Service providers component
The target population includes all establishments that are on the BR that meet the following conditions:
1- Establishments belonging to the following NAICS industry groups:
- Legal services (5411)
- Accounting, tax preparation, bookkeeping and payroll services (5412)
- Architectural, engineering and related services (5413).
2- Establishments with revenue greater than $30,000.
3- Establishments located in the territories are excluded.
An initial draft of the survey questionnaire was jointly designed by Statistics Canada and Industry Canada (now Innovation, Science and Economic Development Canada), in 2004, after consultation with various stakeholders in both the private and public sectors. Early in 2005, Industry Canada commissioned Phoenix Strategic Perspectives, a private sector consulting firm, to conduct focus group tests of the questionnaire in various Canadian cities in both official languages. The results of the focus groups were then incorporated into the questionnaire.
Subsequently, Statistics Canada conducted a pre-test of the revised questionnaire. This type of qualitative testing is a necessary evaluation of the survey collection instrument that is carried out for complex questionnaires. Unlike focus group testing, pre-test respondents are required to complete the questionnaire and then discuss the process in one-on-one interviews. The pre-test was conducted in May 2005 and results of the test indicated a need to revise the questionnaire further and conduct another pre-test in July 2005. The questionnaire that evolved from the second pre-test was used in the national survey in the fall of 2005.
Prior to the 2008 iteration of the survey, minor changes were made to the main survey questionnaire based on suggestions from Industry Canada and other stakeholders.
For 2011, the paper questionnaires were adapted to electronic questionnaires and were retested. While much of the content of the 2011 questionnaires was similar to the 2008 versions, the structure and flow of the questions were changed. The new questionnaires were easier to fill out and had automatic edits included.
For 2016, both questionnaires remain in electronic format, and both were retested. In the main questionnaire, the respondent is now asked to declare total time spent working on various regulatory compliance measures instead of average time spent "per document." All other changes corresponded to improvements made to how questions were worded. Most of the modifications were made to improve the service provider questionnaire. Additional improvements included expanding the scope to NAICS groups 5411 and 5413, adding a distribution of business clients by size, and including the average hourly rate per service offered.
This is a sample survey with a cross-sectional design.
Statistics Canada's Business Register was used as the survey frame for the target population of all private sector, for-profit establishments with 1 to 499 employees (excluding those in the territories and those belonging to an enterprise with more than three establishments). The sampling frame contained 904,062 establishments.
The sample size consists of 23,463 establishments and was stratified by employment size (1 to 4, 5 to 19, and 20 to 499 employees) and industry.
Service provider component
For service providers, the sampling frame contained 113,351 establishments and the sample size for the survey is 5,000 establishments. The sample for the service provider component was stratified by region (Atlantic Canada, Quebec, Ontario, Prairies and British Columbia), by industry (5411-legal services; 5412-accounting, tax preparation, bookkeeping and payroll services; and 5413-Architectural, engineering and related services) and revenue. A take-all stratum of high-revenue units and a take-some stratum were created for each region and industry cell.
Data collection for this reference period: 2017-10-23 to 2018-03-12
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Data were collected via electronic questionnaires.
A pre-contact was done initially to confirm the correct contact at all sampled establishments, to obtain an e-mail address for those contacts, and to confirm that individual respondents were in-scope. Subsequently, all in-scope respondents were e-mailed an invitation to participate in the survey, with links to the electronic questionnaire application and instructions on how to access the application. At least two phone follow-up attempts and four e-mail follow-up attempts are made to all respondents in order to convince them to return their questionnaires.
View the Questionnaire(s) and reporting guide(s).
Error detection is an integral part of both collection and data processing activities. Automated edits were applied to data records during collection to identify capture and reporting errors. Respondents were asked to validate their reported data when these collection edits failed.
Prior to imputation, a series of edits were applied to the collected data to identify errors and inconsistencies. Outlier detection was also performed on select variables to identify improbable or influential values. All outliers were further verified and those deemed to be outliers were imputed along with incoherent and missing values.
Errors and inconsistencies in the data were reviewed and resolved by referring to data for similar units in the survey, data from previous iterations of the survey and information from external sources. If a record could not be resolved, it was flagged for imputation.
Finally, edit rules were incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data were consistent.
After microdata verification, a variable was created for each of the survey variables to identify those that had either failed the verification rules or had missing values. Two classes of units were created: total non-response cases and partial non-response cases. Total non-response units were treated through weighting, as the weights of responding units in the same homogenous class with respect to the propensity to respond were adjusted to represent the non-response units as well. These adjusted weights were calibrated to the strata population counts. Partial non-response units were completed using imputation.
Partial non-response imputation was performed using methods like ratio imputation, mean imputation and donor imputation. We also indirectly imputed sub-totals by redistributing values from imputed totals.
Imputation was performed within groups of units called imputation classes. For most variables, these imputation classes were formed of similar size units (employment), in the same geography and in the same industry.
A minimum number of units was required within each imputation class. When imputation classes were too small, larger classes were created by combining several classes together.
To ensure internal consistency (coherence between variables of the same record), missing or incoherent variable values were imputed in the sequence in which they appeared on the questionnaire. This means that an imputed question at one point in the questionnaire may have been used as a matching variable for a question located further along in the questionnaire.
Most imputation of survey variables was performed in an automated way using BANFF, a generalized system designed by Statistics Canada.
A complete file of weighted micro data was created for all sampled establishments in the survey population for which data were reported or imputed. Weights were adjusted to account for total non-response so that the final estimates would be representative of the entire survey population. Weighted estimates were produced using the Generalized Estimation System (G-Est).
Where possible, estimates were compared with similar data from previous iterations of the survey to ensure that the survey results were consistent with historical findings. In addition, subject matter experts from outside Statistics Canada were given an opportunity to review the survey microdata and estimates, as well as provide feedback on their quality prior to their official release.
Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential. If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data.
In order to prevent any data disclosure, confidentiality checks were performed for direct disclosure as well as for the secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises. Residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.
Revisions and seasonal adjustment
This methodology does not apply to this survey.
Since all estimates for the Survey of Regulatory Compliance Costs are based on sample results, they are subject to sampling error. This error can be expressed as a coefficient of variation. The following rules based on the coefficient of variation are used to assign a measure of quality to all of the estimates.
Quality code A - Excellent (Coefficient of variation - Up to 5%).
Quality code B - Good (Coefficient of variation - 5% up to 10%).
Quality code C - Average (Coefficient of variation - 10% up to 15%).
Quality code D - Mediocre (Coefficient of variation - 15% up to 25%).
Quality code E - Poor, use with caution (Coefficient of variation - 25% up to 35%).
Quality code F - Too unreliable to be published (Coefficient of variation - 35% or higher).
The survey response rate was calculated as the number of respondents divided by the number of estimated in-scope units. The number of in-scope units includes all respondents, in-scope seasonal or part-time operations and an estimate of the number of in-scope units included among non-respondents. For the main component, the response rate was computed as 54%.