Financing SMEs in Canada: Barriers Faced by Women, Youth, Aboriginal and Minority Entrepreneurs in Accessing Capital — Phase 2: Gap Analysis and Recommendations for Further Research

Framework for Future Research

As the analysis highlights, there are significant gaps in existing research on barriers to SME financing in general, and barriers faced by specific business-owner profiles in particular. Because of this, it is difficult to determine whether the Profile Groups examined in this study faced greater (or lesser) barriers to a comparable SME population in general.

In order to collect data to address this issue specifically, it is critical to have adequate baseline data on SME financing to compare against the experiences of Profile Groups. Ideally, this will be in the form of a data set that can be disaggregated by relevant criteria, in particular data related to firm characteristics (e.g., age, stage, sector, industry, geographic location); data related to type of financing sought (e.g., debt financing, equity financing); and data related to business-owner characteristics (e.g., gender, ethnicity, age).

Based on the findings of Phase 1 and 2 of this project, national-level survey-based research is recommended, sampling across randomly selected members of the existing SME population. The results can be captured in database format allowing for a variety of cross-cuts on the collected information including business owner characteristics.

A sample size of approximately 10,000 SMEs is recommended, which should be large enough to ensure that data will be collected for at least 1,000 SMEs in each business owner sub-category. The findings of this survey research will provide a comprehensive, up-to-date picture of the state of the SME financing situation, a picture that does not currently exist.

It should be noted this survey addresses only SMEs which are already in existence. While it would be desirable to collect information about 'non-participants' as well (e.g., those who were unable to secure any form of financing for an entrepreneurial venture, or, those who did not attempt to due to other considerations – eg. student loans that need to be repaid) it would also be very expensive and time consuming to broaden the sample to ensure the inclusion of a significant number of 'non-participants' in this survey.

For this survey, collection of data in three main areas is suggested: firm characteristics; type of financing sought and received; and business owner profile. Data on the following variables is recommended for inclusion:

(Note: the categorization of data within sub-categories (e.g., age ranges) is somewhat arbitrary and should be adjusted as necessary to accommodate existing data sets or categorizations.)

Firm Characteristics:

Note: If over the history of the company all have been maintained, the questionnaire will have to be designed to capture each stage.

Financing Types:

Business Owner Characteristics:

Interrogating the Database

If the above information is collected in database format, it can be sorted in a variety of ways that will not only address the questions that were the original drivers of this research project, but also extend significantly beyond the original scope of this project.

This information would provide baseline data on the accessibility of financing for SMEs across Canada, and allow researchers to test the hypotheses raised in the research literature and in this project. It emerges repeatedly that firm characteristics (e.g., size, age, industry, sector, geographic location) play a significant role in determining an entrepreneur's access to funding, and in the case of debt financing, are more important than the characteristics of the business owner. However, the state of Canadian research in this area is not comprehensive enough to prove or disprove this hypothesis.

Without adequate baseline data for the SME sector as a whole, it is very difficult to determine whether the barriers faced by Profile Groups are greater or lesser than those faced by others in the sector. To answer this question, data are required not only for the experiences of the Profile Groups, but also on the entire SME sector for comparative purposes. As an example, data collected may show that women-owned businesses receive only 10% of venture capital in Canada. Knowing that venture capitalists fund only certain types of entrepreneurial ventures (typically knowledge-based industries with a high potential for growth) this figure is only meaningful when compared to the percentage of women-owned businesses that meet the criteria to be eligible for venture capital.

With respect to particular Profile Groups, the database could be sorted and cross-cut in a variety of ways. Queries could be made to identify all SMEs owned by particular Profile Groups and these results could then be crossreferenced to other variables to the desired level of specificity. As an example, data cuts could be done to identify:

• SMEs owned by women, and of this population:
  • SMEs owned by Aboriginal women;
    • Searching for/receiving debt financing
    • Searching for/receiving equity finacing
  • SMEs owned by women under the age of 30;
  • SMEs owned by women in knowledge-based industries;
    • % receiving venture capital
  • SMEs owned by women with >5 employees;
    • In low-growth sectors
    • In high-growth sectors
  • Others cross-cuts as of interest (other suggestions below)

Based on the findings of the literature review and the gap analysis, the following areas of investigation are recommended for specific Profile Groups.

Women:

• Disaggregation of data re: expected growth of business (e.g., % starting businesses in low-growth vs. high-growth sectors);
• Extent of business experience on access to debt and equity financing;
• Disaggregation of data according to business size (e.g., micro-business, small business, medium-sized enterprise) and impact of business size on funding;
• Experiences with financial institutions (e.g. range of debt financing options sought vs. those received;
• Others?
• Other issues of interest, which would require further data, likely collected through qualitative means such as focus groups:
  • Impact of family responsibilities on female entrepreneurs
  • Attitudes towards business growth and type of financing sought.

Youth:

• Extent of business experience on access to debt and equity financing;
• Impact of lack of equity or collateral and available financing options;
• Concentration on early stage businesses and impact on financing available;
• Concentration of youth in micro-business ventures and impact on financing;
• Impact of age on SME financing options (e.g., differences between those <20 years, and those 20-30 years of age);
• Experience with financial institutions (e.g., range of debt financing options sought vs. those received);
• Other issues of interest, which would require further data, likely collected through qualitative means such as focus groups:
  • Impact of student debt loads on SME financing options;
  • Impact of economic strata/social capital on accessibility of 'love money';

Aboriginals:

• Differences between accessibility of SME financing between on-reserve and off-reserve Aboriginals;
• Impact of Aboriginal business concentration in low-growth sectors on access to SME financing;
• Sectoral breakdown of Aboriginal businesses (e.g., primary, secondary, tertiary and quaternary) and impact on financing options;
• Concentration of Aboriginal ventures in small-scale businesses, and effects on access to financing;
• Geographical differences (e.g., rural/urban; centre/periphery) and impacts on SME financing;

Visible Minorities and Language Groups:

• Similar to issues raised above, but tailored to suit specific interests (e.g., differences between official language minorities and other language groups);

A sample of other issues that may emerge and interesting for follow-up (based on comments in the research literature or in discussions for this project):

• Does it hold true that debt financing is more dependent on firm characteristics such as size and sector than on characteristics of business owners?
• Are business-owner characteristics more important for equity financing than firm characteristics?
• What role does geography play in the accessibility of SME financing (e.g., what differences appear between rural and urban areas? Between centre and periphy areas?)
• What is the impact of economic strata and social capital on access to love money (investment from friends and family?) How does this affect the Profile Groups?
• What type(s) of SMEs are most likely to successfully access financing? Which are least likely? How does this intersect with the business choices of Profile Groups?
• What impact does the choice of sector (e.g., primary vs. tertiary) have on the accessibility of SME funding? What are the clusterings of Profile Groups with respect to these sectors?