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SME FINANCING DATA USER NEEDS: CANADIAN PERSPECTIVE

SME FINANCING DATA USER NEEDS: CANADIAN PERSPECTIVE. OECD Workshop On SME Statistics September 17-19, 2003 Tim Davis Agriculture, Technology and Transportation Branch Statistics Canada. Overview. Recognizing the role of SMEs SME Finance Data – What We Had

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SME FINANCING DATA USER NEEDS: CANADIAN PERSPECTIVE

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  1. SME FINANCING DATAUSER NEEDS: CANADIAN PERSPECTIVE OECD Workshop On SME Statistics September 17-19, 2003 Tim Davis Agriculture, Technology and Transportation Branch Statistics Canada

  2. Overview • Recognizing the role of SMEs • SME Finance Data – What We Had • Review of the Financial Services Sector • The Canadian SME-FDI • SME Financing data: Users and Needs • SME-FDI Data Collection Programs • Highlights of Findings • User Reactions and Outstanding Needs

  3. Evolution:SME Sector Gains Respect • Prior to 1980s-Little attention to SMEs • 1980s: Political awareness but little Policy interest • Low-job recovery from 90s recession and SMEs were “discovered” • SMEs seen as engine for job creation and growth

  4. Evolution:SME Sector Gains Respect • SMEs lobbied for more substantial support • “Financing” was a frequent complaint • 1966: Government mandated a Financial Services TF to address SME financing • The “MacKay TF” eventually led to data…. And more

  5. SME Finance Data:The Way We Were • Several ad hoc “proprietary” data sets • Bankers, Venture Capitalist, SME Groups all cited data to support their positions • No comparability; Perceptions of bias • Data generated debates, but • Much confusion; Little direction

  6. Review of the Financial Services Sector • Government explicitly requested recommendations on SME financing • MacKay TF: Unable to make recommendations • Insufficient data for credible analysis • Hence: Improve data first! • The SME Finance Data Initiative was borne • Innovative partnership of policy Departments ands statistical agency

  7. The Canadian SME-FDI • Partnership of Statistics Canada, Industry Canada, Finance Canada • Comprehensive and objective data sets • Both demand for and supply of financing • Standard concepts and definitions • Analysis supports policy and drives data design • New base funding

  8. SME Financing data: Users and Needs • Three User Groups SMEs and Their Associations • Objective data to substantiate their case • Drive policy changes; improve access Financial Services Sector • Also to substantiate case • Understand potential market • Learn to serve the SME client

  9. SME Financing data: Users and Needs Researchers and Policy-makers • Need is greatest here • Guide beneficial interventions • Avoid market distortions • Identify real gaps • Isolate market bias against specific target groups

  10. A Summary of User Needs • Type of financing used by SMEs • Financing requests, by amount, by source • Financing “success” by size and characteristics • Financing supply by business size • Impact of financing decisions on SMEs • Geographic and characteristic detail if possible

  11. SME-FDI Data Collection Programs • Supply-side survey by Statistics Canada • By authorisation level not size of business • Demand-side survey by Statistics Canada • Characteristics of firms and their financing experience • Needs and attitudes of SME owners re: financing

  12. Highlightsof Survey Findings • In 2000: • 82% of SMEs seeking loans, obtained them • Only 23% sought loans • Larger SMEs -> more requests • Larger SMEs -> more successful • Banks accounted for 2/3 of requests made

  13. Highlightsof Survey Findings • In 2001: • Only 18% sought loans • 80% successful

  14. Small and medium-sized enterprises financing in Canada 2000

  15. Outstanding Data Needs Demand Side • Detailed firm characteristics • Age, Stage of development • Location, Industry • Firm owner characteristics • Education, Experience, • Age, Gender, Ethnicity • Financing impacts on unsuccessful SMEs

  16. Outstanding Data Needs • Supply Side • Financing by size of firm, not authorisation • Detail by: Size, Location, Industry, Sector • Angel financing • Longitudinal studies of financing and impacts • Continued development of concepts and definitions

  17. Conclusions • Vital data program • Users satisfied • Active research program leading to more data demands • Avoid complacency; Even successful data programs are in jeopardy

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