Survey on Financing of Small and Medium Enterprises
Detailed information for 2004
The survey is designed to find out what kinds of financing small and medium enterprises are using, and to collect information on recent attempts to obtain new financing.
Data release - December 1, 2005 (Part 1- qualitative data); February 21, 2006 (Part 1-quantitive data and Part 2)
The objective of this survey is to collect general characteristics on small businesses and their financing intiative. It collected information on the types of debt, lease and equity financing that small and medium enterprises (SMEs) rely on. Furthermore,it collected information on any recent attempts to obtain new financing.
Statistics Canada conducts this survey on behalf of Industry Canada and Finance Canada, as part of a larger program of research on small- and medium-sized firms which recently started up. The survey is part of an ongoing study on the availability of financing to SMEs. The data obtained from this survey will be used by both the public and private sectors. Industry Canada will use this information to study the availability of financing to SMEs and to recommend policy changes to assist businesses. Other government departments will use this information to develop national and regional programs and for policy planning. Businesses will use this information for market analysis or to compare the performance of the firm with the performance of firms of a similar size within the same industry. Industry associations will use the information for industry performance measurement and for market development, and suppliers of financing for SMEs will use the information to determine gaps in their services.
- Business performance and ownership
- Small and medium-sized businesses
Data sources and methodology
The target population includes all enterprises that are on the Business Register (BR) Universe file. The frame is supplemented by adding enterprises that are added to the BR after the creation of the initial frame. The following enterprises are excluded from the population:
1- Enterprises with 500 or more employees
2- Enterprises with over $50 million in gross revenue
3- Enterprises coded as being non-profit (schools, hospitals, charities, ..)
5- Joint ventures
6- Municipal/Federal Government
7- Enterprises in specific industries, identified by the North American Industry Classification System. They include utilities (22), finance and insurance (52), management of companies and enterprises (55), educational services (61), public administration (91), automotive equipment rental and leasing (5321), commercial and industrial machinery and equipment rental and leasing (5324), out-patient care centres (6214), medical and diagnostic laboratories (6215), other ambulatory health care services (6219), general medical and surgical hospitals (6221), psychiatric and substance abuse hospitals (6222), specialty (except psychiatric and substance abuse) hospitals (6223), and community food and housing, and emergency and other relief services (6242).
The survey questionnaire is designed by Statistics Canada in collaboration with Industry Canada. Statistics Canada formats the questionnaire and ensures the questionnaire complies with Statistics Canada policy.
This is a sample survey with a cross-sectional design.
Statistics Canada's Business Register is used as the survey frame for the target population of all private sector, for-profit enterprises with fewer than 500 employees and gross revenues less than $50 million in Canada. The sampling frame contains 1,939,780 enterprises.
The initial stratification is by region, industry type, size and age of business, and participation in the Canadian Small Business Financing Act guarantee program. The number of employees in the enterprise is used to define the size of a business and the age of the business is estimated using the date that the business was entered onto the Business Register. A sample of enterprises, based on the stratification is randomly selected from the BR.
The sample size for was for 34,509 businesses.
Data collection for this reference period: Part 1 : September 2004 - February 2005 to Part 2 : November 2004 - March 2005
Responding to this survey is voluntary.
Data are collected directly from survey respondents.
Collection is done in two parts. In Part 1, most qualitative type questions concerning the businesses' latest financing requests are collected using a Computer Assisted Telephone Instrument (CATI). For Part 2, a mail out/back questionnaire is used to collect detailed financial information on liabilities on the Balance Sheet from all businesses that responded to Part 1. Telephone follow-up is used in Part 2 to increase response rates.
View the Questionnaire(s) and reporting guide(s).
On-line consistency edits and validity edits are built into the CATI application to detect errors. Other errors and outliers are detected during data processing. All outliers are resolved manually.
For both Parts 1 and 2, a nearest neighbour imputation system is implemented. This method involves locating a donor and a recipient of similar size and characteristics. Data values for missing or incomplete variables in the recipient's record are imputed from the donor.
Parameters of interest are estimated with Statistics Canada's Generalized Estimation System (GES).
Initial sample weights are adjusted to account for refusals and other non-response. The weights were also adjusted to account for units that were known to be alive but were Unable to Contact or Unable to Locate. Estimates are produced for over 100 defined domains of interest based on stratification variables (e.g. region, industry) as well as questionnaire variables (e.g. number of employees, type of creditor).
A post-stratified estimator is used to calibrate to a known total number of enterprises in each of the number of employee categories. These known counts are obtained from the BR, taking into account out-of-scope rates.
Industry Canada analyzes the results of the survey to assess data quality.
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.
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.
Since all the Survey on Financing of Small and Medium Enterprises estimates are based on sample results, they are subject to sampling error. This error can be expressed as a standard error. Each tabulated statistic includes a measure of the sampling error associated with it. For example, the proportion of firms in the target population that would respond YES to a given question is estimated to be 50%, with a standard error of 0.04. In repeated sampling, the estimate would be expected to fall between 46% and 54%, nineteen times out of 20. The following rules based on the standard error are used to assign a measure of quality to all of the estimates of percentages.
Standard error: Quality code
0 - 0.025: A - Excellent
0.025 - 0.05: B - Good
0.05 - 0.075: C - Average
0.075 - 0.10: D - Mediocre
0.10 - 0.125: E - Poor, use with caution
>0.125: F - Very poor, use with high caution (or not disseminated)
Overall, most estimates in this survey have a quality code of "good" to "excellent".
Survey estimates may also contain non-sampling error. Non-sampling errors are not related to sampling and may occur for many reasons. Population coverage errors, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are examples of non-sampling errors. Non-response is an important source of non-sampling error. While the impact of non-sampling errors is difficult to evaluate, measures such as response rates and imputation rates can be used as indicators of the potential level of non-sampling error. The survey had a response rate of 47%.