How enterprising?!Do we all have to be (social)

entrepreneurs now?

Mike GordonDepartment of Geography

University of Sheffieldm.j.gordon@shef.ac.uk

4th Annual Social Enterprise Research ConferenceLSBU, London, 4th-5th July 2007

Does social enterprise do what it says on the label?

• “A social enterprise is a business with primarily social objectives whose surpluses are principally reinvested for that purpose in the business or in the community, rather than being driven by the need to maximise profit for shareholders and owners.” (DTI, 2002)

• How enterprising are social enterprises? Where are they? Are they the product of a new culture of enterprise, or of something else? Are they really businesses? What evidence is there that the sector is moving towards financial sustainability? What trading approaches are being adopted? Do we all have to be (social) entrepreneurs now?

The location of social enterprises: some alternative theories

• Social enterprises are just like any other enterprise, so they will be more common in more entrepreneurial areas than in less entrepreneurial ones.

• Social enterprises are more likely to occur where other opportunities are few, so we would expect to find them in more deprived communities.

• Social enterprises are more likely to occur in local authority areas which have received EU Structural Funds, because the EU has long supported the social economy and these local authorities will be more geared towards the Third Sector.

• Social enterprises are more liable to be found in local authority areas which have received certain types of UK funding, because the Government has supported community and social enterprise for some years

• Social enterprises will be more common in regions with pro-active Regional Development Agencies than in regions where the RDA is less active.

What data could we use to test these theories?• CIC registrations from August 2005 to end November 2006 – proxy for social

enterprise (599 CICs, less 44 for Wales, Scotland and the CoL = 555)• VAT registration data 2005 – indicator of SME start-ups and proxy for general

entrepreneurialism• Five of the six measures of deprivation in the IMD 2004 (two converted to ratios) –

to show the extent of deprivation in each local authority area• Average house prices in county/unitary areas in March 2006 (calculated from Land

Registry data for March 2007) and/or percentage of home ownership in each district (derived from the Census 2001) – general indicators of wealth and likely ability to raise or access business finance

• District eligible for EU Objective 1 or Objective 2 Structural Funds, 2000-6• District in receipt of SRB4 or SRB5 or SRB6 or Phoenix Development Fund, or

eligible for NRF 2000-6• Regional dummy variables – to measure for RDA effects• Note that this analysis of CICs is rough and ready; it uses reasonably accessible

publicly available data and creates proxies to represent some of the concepts being explored. The aim is to obtain a broad picture of what is happening, but the analysis is not exact or comprehensive and has to be treated with some caution.

Constructing a regression model: playing with the variables

• Omitted Wales and Scotland from the analysis – some data absent, incomplete or organised on a different basis

• Excluded City of London because the model confirmed it as unusual – a very extreme leverage case

• Used principal components analysis to carry out an exploratory factor analysis, because of the evident multicollinearity of the five deprivation variables – all five loaded very strongly on the same component and the resulting factor score was saved as a new, composite deprivation variable

• Calculated the mean number of CICs per 100,000 adult population by region to help decide which of the regional dummy variables to leave out of the final model – NE region had highest mean and was chosen, so it serves as the comparator for the other regions

• Correlation of all potential predictor (X) variables, except the regional dummy variables, indicated a certain degree of significant multicollinearity between variables, so it was decided to drop SRB4, SRB5, NRF anf home ownership from the final analysis

Modelling start-up rates of CICs in English LAs and excluding the City of London: table of coefficients

B Sig.

(Constant) .470 .422

O1 funding 2000-2006 1.240 .010**

O2 funding 2000-2006 .593 .016**

SRB6 funding .278 .151

Area with local Phoenix Development Fund project -.299 .154

Average house price (£) for LA area as at March 2006 5.47E-.006 .081

SME per 100K population .001 .331

Deprivation factor score .453 .000**

NW dummy -1.295 .002**

YH dummy -.999 .036**

EM dummy -.937 .028**

WM dummy -1.413 .001**

EE dummy -.567 .198

London dummy -1.289 .032**

SE dummy -.567 .217

SW dummy -.002 .996

R² .186

Adjusted R² .149

F .000**

Durbin-Watson 1.832

Dependent Variable: CICs per 100K population * or ** significant at p<0.05

Key conclusions from the regression analysis 1

• There is a significant relationship between CICs and deprivation; all else being equal, the more deprived a local authority area is, the more CICs per 100,000 adult population it will have (or, conversely, the more affluent the local authority area, the fewer CICs per 100,000 adult population). One implication of this is that Government policy towards social enterprise and enterprise in deprived areas may be having some effect. However, this has to be treated with caution and needs further exploration.

• Given that deprivation indicators in essence measure underlying poverty in an area, it is not surprising that – with a significant relationship between CIC formation rates and deprivation – there is no significant relationship between CIC start-up rates and house prices. The latter were used as a proxy for cost of capital, because they represent wealth and the likelihood that people in areas with higher house prices should be able to raise or otherwise access business funding.

Key conclusions from the regression analysis 2• There is no significant relationship between the formation of CICs and

SMEs, so the rate of CIC start-ups appears to have nothing to do with enterprise or a general entrepreneurial culture. The model implies that CIC start-ups, and thus social enterprise more generally, are not a product of local entrepreneurialism, nor are they a substitute for a lack of local entrepreneurialism: the two things are unrelated.

• There is a significant relationship between the rate of CIC start-ups and both EU Objective 1 and EU Objective 2 funding. The latter are dummy variables, and the comparators are local authorities without the relevant funding, so, for instance, the Objective 1 coefficient tells us that, other things being equal, local authorities with Objective 1 funding had, on average, 1.2 more CIC start-ups per 100,000 adult population than local authorities without such funding. Taken with the previous point, this suggests that CIC formation may have more to do with the availability of certain types of funding, and the policies which accompany that funding, than with enterprise culture or activity.

Key conclusions from the regression analysis 3• Five of the eight regional dummy variables have a significant relationship

with the rates of formation of CICs. In all these cases, the relationship is a reverse (negative) one. Remembering that the interpretation of these dummy variable relationships has to be seen with respect to the omitted dummy variable for the North East, we can say that, all other things being equal, a local authority in the North West (for instance) can expect there to be 1.3 fewer start-ups than an equivalent local authority in the North East. In turn, a local authority in Yorkshire and the Humber can expect there to be 1 fewer start-ups than an equivalent local authority in the North East, and so on. However, there is no significant relationship between the start-up rate of CICs and the regional variables for either the East of England, the South East, or the South West regions. The variations in rates between those regional variables which show a significant relationship between themselves and CIC formation rates may suggest differences in policy between those regions, but this would also require further explanation.

Regression residuals for the South Yorkshire local authorities

South Yorkshire

district

No. of

CICs

CICs/100K

pop

Standardized

residuals

Barnsley 0 0 -1.29

Doncaster 4 1.7 -0.05

Rotherham 0 0 -1.08

Sheffield 4 0.9 -0.33

The residuals are the difference between the actual and predicted scores from the regression; the negative scores show that all four South Yorkshire districts underperformed slightly against expectations when compared to the results for England as a whole. That said, none of the South Yorkshire local authorities were significantly underpredicted, and the model came close to predicting the number of CICs in Sheffield and (especially) Doncaster pretty accurately. Both authorities had roughly the same number of CICs present as national trends would lead us to expect.

Distribution across South Yorkshire local authorities of social enterprises responding to questionnaire survey

Although 40% of the county’s resident population live in Sheffield, 64% of South Yorkshire’s social enterprises are located there. But Doncaster and Rotherham have only 58% and 54% respectively as many social enterprises as their share of the county population might lead one to expect. Nor is this simply a reflection of different rates of entrepreneurialism between the authorities: the proportion of the county’s VAT registration rates for 2005 in each local authority is roughly the same as its share of the population. The level of social enterprise activity in Sheffield is doubly unusual, therefore.

Local authority Numbers of social

enterprises

Resident population

mid-1991

VAT registrations

2005

Barnsley 15 (12.2%) 223,200 (17.3%) 425 (16.0%)

Doncaster 16 (13.0%) 291,700 (22.6%) 575 (21.6%)

Rotherham 13 (10.6%) 253,700 (19.7%) 495 (18.6%)

Sheffield 79 (64.2%) 520,100 (40.4%) 1165 (43.8%)

Total: 123 enterprises

South Yorkshire social enterprises: trading income

Approximate percentage of total income from trading

20 16.3 16.3

23 18.7 35.0

23 18.7 53.7

17 13.8 67.5

28 22.8 90.2

12 9.8 100.0

123 100.0

100%

75-99%

50-74%

25-49%

1-24%

0%

Total

ValidFrequency Percent

CumulativePercent

South Yorkshire social enterprises: financial sustainability

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid already 100%

financially self-sufficient30 24.4 25.2 25.2

aiming for complete

financial self-sufficiency26 21.1 21.8 47.1

aiming for 75-99% self-

sufficiency30 24.4 25.2 72.3

aiming for 50-74% self-

sufficiency15 12.2 12.6 84.9

aiming for <50% self-

sufficiency18 14.6 15.1 100.0

Total 119 96.7 100.0

Missing non-response 4 3.3

Total 123 100.0

Trading income and financial self-sufficiency

• The DTI’s 2005 survey of UK social enterprises found that 82% (£14.8 billion) of the nearly £18 billion turnover of CLGs and IPSs was from trading revenues, and that 88% of those surveyed generated 50% or more of their income through trading (IFF, 2005, p.3, 15), but the UK survey said nothing about the actual financial sustainability of the enterprises.

• In South Yorkshire, just 16% of questionnaire respondents derived 100% of their income from trading, whilst 54% obtained more than 50%. Just 25% said their social enterprise was already 100% financially self-sufficient, with a further 22% aiming for that.

South Yorkshire social enterprises: trading approaches

Trading approach

Intend to use**

(No. and %)

Ranked #1

(No. and %)

Income from selling goods and/or services in

the open market

95 (84%) 56 (51%)

Asset-based development, deriving rental

income from land and/or buildings

33 (29%) 12 (11%)

Income from public procurement/public

sector contracts/service level agreements

74 (66%) 38 (35%)

Other trading income

22 (20%) 7 (6%)

Total responding to question 113 (100%) 110 (100%)

** N.B. These categories are not mutually exclusive

Social enterprise: prospects as businesses?• For social enterprises, income from open market sales or from procurement are

both in competition with the private sector, but with the added disadvantage for social enterprises of having to expend resources to meet social aims.

• There are any number of suggested processes and methodologies for assessing the added value of social enterprises, but apparently relatively little in the way of formal requirements, commitment, acceptance, or resources to implement these.

• Recent reports (e.g. NAO, 2007x2, DSC, 2007) do not seem to be optimistic about progress with procurement; there are problems with the size and capacity of voluntary sector organisations, the size of contracts, full cost recovery, profit margins, and other barriers in dealing with public bodies.

• The potential of asset-based development is recognised (Quirk, 2007), but much more needs to be done; there can also be problems, such as heavy repair and maintenance costs, and marginal returns.

• Do social enterprises really have good prospects as businesses, or should they stick to being ‘businesslike’?

• If social enterprises are businesses with a social purpose, what are the real distinctions between them and companies with a strong commitment to Corporate Social Responsibility?