WP 3: Diversity and SMEs in the Emerging Economy

1. What explains the outcomes for diverse enterprises, particularly in relation to finance?

2. How can research make a difference?

3. How can outcomes be changed?

sara.carter@strath.ac.uk m.ram@bham.ac.uk k.trehan@bham.ac.uksamuel.mwaura@strath.ac.uk A.N.Other@bham.ac.uk

Work Programme 1Mining for Diversity Data: Understanding drivers & barriers of entrepreneurial growth in under-represented groups (Months 4-12)

• SME Finance Monitor: modelling and testing different types of discouragement (with Samuel Mwaura, Strathclyde)

• BHPS: Relationship between household structure and entrepreneurial engagement (with Darja Reuschke, St Andrews)

• Scottish Household Survey: Relationship between self-employment, income & life-satisfaction (with Stewart Dunlop, Strathclyde)

 Output: White Paper on Mining Diversity Data, December 2013 (with a related event in January 2014 and Research Paper February 2014)

Work Programme 2Understanding the connections between households and the decision to finance & grow diverse businesses (Months 13-36)

• 30 household case studies planned, stratified across diverse social groups, by region and sector

• Factors influencing enterprise participation• Variance by household & business life-cycle• Perceptions of disadvantage and unequal treatment by resource providers• How government & resource providers can increase level of successful

enterprise in these groups

• Joint work between Strathclyde & Birmingham

Work Programme 3

Mapping & activating support for diverse business networks through the EDA – an agenda for action research (Months 4-36)

• Several EDA events already held – will extend to 5 cities

• Special EDA event held at Barclays to challenge perceptions of discrimination by banks

• Extensive policy work on women-owned businesses, mainly Scotland but reaching to Westminster

• Membership of several committees for policy & practice engagement

A TYPOLOGY OF BORROWERS: A 3D VIEW OF REAL AND POTENTIAL DEMAND FOR FINANCING

Notional demand (All firms)

Manifest Demand (Applicant

borrowers)**5%

Latent demand (Non-applicant firms)**95%

Engaged Demand (“Drafted

borrowers”)**90%

Unmet Demand (“Declined

borrowers”)**10%

Dormant Demand “Dormant

borrowers”**70%

Ripe Demand “Probable borrowers”

*Recently injected personal funds (therefore could use

credit)**30%

Disinterested Demand (Disinterested borrowers”)

*Chose to use personal funds than borrow**40%

Bottled Demand (“Discouraged borrowers”) *Felt personal funds were

the only choice**60%

Defunct Demand (“Defunct borrowers”)

*Used credit in the past but not now

Distant Demand (“Distant borrowers”)

*Residual group: never applied for or used

credit; no indication of present need

Shy (Assumed would be

declined)**24%

Twice shy (Declined in the

past)**28%

Coy (Other

reasons)**48%

3D typology of borrowers that presently desire credit (real

demand)

3D typology of potential borrowers that have no present

need for credit (potential demand)

*Pertinent question in the SME Finance monitor data **Indicative shares from the raw data