Enhancing SME Financing Data User Needs: A Canadian Perspective
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Explore the evolution of SME financing data, user needs, and programs in Canada, highlighting challenges and innovative solutions to improve data quality and accessibility for stakeholders.
Enhancing SME Financing Data User Needs: A Canadian Perspective
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Presentation Transcript
SME FINANCING DATAUSER 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 • 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
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
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
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
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
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
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
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
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
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
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
Highlightsof Survey Findings • In 2001: • Only 18% sought loans • 80% successful
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
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
Conclusions • Vital data program • Users satisfied • Active research program leading to more data demands • Avoid complacency; Even successful data programs are in jeopardy