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Finance for Small and Medium-Sized Enterprises:

Comparisons of Ethnic Minority and White Owned

Businesses

A Report on the 2005 UK Survey of SME Finances Ethnic Minority

Booster Survey

Dr Stuart Fraser

Centre for Small and Medium-Sized Enterprises

Warwick Business School

University of Warwick

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Acknowledgements

Thanks to Mike Young (Small Business Investment Taskforce), Helene Keller (Small

Business Service) and other members of the Small Business Service whose comments

on an earlier draft have significantly improved this report. However, I remain entirely

responsible for all remaining omissions and errors.

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Contents

Acknowledgements

Glossary 5

Executive summary 7

1. Introduction 14

1.1 Context 14

1.2 Statistical analysis 19

1.3 Structure of the report 20

2. Background 22

2.1 General issues regarding SME finance 22

2.2 Ethnic Minority Businesses 24

2.3 Ethnic Minority Business finances 27

2.4 Key business and owner characteristics 31

2.5 Summary of other business characteristics 57

Summary 65

3. Business problems 69

3.1 Main reason for starting in business 70

3.2 Main problem at start-up 74

3.3 Extent of current business problems 79

3.4 Coping with business problems at start-up 80

3.5 Coping with current business problems 85

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Summary 89

4. Finance 91

4.1 Use of external finance 92

4.2 Types of financial products used 94

4.3 Use of friends and family finance 99

4.4 Use of business and personal credit cards 101

4.5 Use of Small Firms Loan Guarantee 107

4.6 The demand and supply of new finance 108

4.6.1 Incidences of demand for new finance and the types of finance

sought

109

4.6.2 Amount of new finance sought, amount supplied and finance gaps 1184.7 Financial rejections and discouragement 137

4.8 Self-reported consequences of financial rejection 142

Summary 143

5. Financial relationships 145

5.1 Market shares of the main finance providers 146

5.2 Number of finance providers 1505.3 Length of relationship with main finance provider 154

5.4 Satisfaction with main provider of finance, bank charges and methods

of communication

156

5.5 Switching 165

5.6 Financial delinquency and loan margins 166

Summary 170

6. Econometric analysis of finance outcomes 172

6.1 Rejection 178

6.2 Financial discouragement 181

6.3 Finance gaps 184

6.4 Loan margins 191

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Summary 197

7. Conclusions 201

References 204

Appendix A (Tables) i

Tables relating to Chapter Two i

Tables relating to Chapter Three xv

Tables relating to Chapter Four xxx

Tables relating to Chapter Five xliv

Appendix B (Technical report by Fiona McAndrew, IFF

Research Ltd.)

lii

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Glossary

EMB: Ethnic Minority Business

An EMB is defined as a business in which the owner or the majority of partners or

shareholders in the business are from a particular (non-White) ethnic minority group.

There are five ethnic minority groups which are considered in this report. These ethnic

minority groups are: Indian; Pakistani; Bangladesh; Black Caribbean; and Black African.

WB: White Business

A WB is defined as a business in which the owner or the majority of partners or

shareholders in the business belong to a White ethnic group.

SME: Small and Medium Sized Enterprises

Following the definition used by the Department of Trade and Industry (DTI) an SME is

defined as a business which has less than 250 employees.

UKSMEF: United Kingdom Survey of Small and Medium Sized

Enterprise Finances

This was the first comprehensive survey of SME finances and financial relationships in

the UK which was conducted in the late summer of 2004. The EMB Finance Survey is a

follow up booster survey to UKSMEF which uses the same methodology and survey

instrument as the original survey. The UKSMEF report, data and survey instrument are

available for download from the UK Data Archive (University of Essex): www.esds.ac.uk

(SN 5326).

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Statistical significance

The report frequently refers to differences in means/proportions between ethnic groups

which are statistically significant. This means that the hypothesis that the population

means/proportions for the groups are identical has been tested and statistically rejected

(implying that one group has a higher/lower population mean/proportion than the other

group). These tests are conducted by comparing 95% confidence intervals (see below)

for the estimated means/proportions across ethnic groups. A test based on: i)

comparing the confidence intervals for different ethnic groups; and ii) inferring

significance in instances where confidence intervals do not overlap, provides a

conservative test of differences in means/proportions (full details from the author on

request). Unless stated otherwise, the word significant is used synonymously with the

phrase statistically significant.

95% Confidence interval

These intervals provide a range for estimates of population means/proportions which

contains the true population mean/proportion with 95% probability. A confidence interval

which crosses zero leads to an inference that the corresponding population

mean/proportion is zero. Narrower confidence intervals are associated with moreaccurate estimates. The width of the interval reflects the size of the sub-sample involved

in the estimate (the larger the sample the narrower the interval) and the amount of

variation, pertaining to the variable, in the population (more variation implying a wider

interval).

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Executive Summary

Ethnic Minority Businesses (EMBs) are making an increasingly important contribution to

the UK economy. EMBs, like any other business, require sources of finance to fund the

enterprise at start-up and, later, to fuel expansion. Previous research has suggested

that EMBs experience greater problems in raising external finance than White

businesses (WBs). This appears to be especially the case for Black owned businesses.

However it is not immediately clear whether these problems are due to ethnic

discrimination by finance providers or due to differences in lending risk between EMBs

and WBs.

Objectives of the study

EMBs are a highly heterogeneous group so that aggregate comparisons between EMBs

and WBs are apt to be highly misleading. In view of this, the objectives of this report are

to use data from: i) the EMB Finance Survey (2005); and ii) the UK Survey of SME

Finances (UKSMEF, 2004) (pertaining to EMBs and WBs respectively) to conduct:

Disaggregated analysis of finance outcomes for six ethnic groups:

o Indian

o Pakistani

o Bangladeshi

o Black Caribbean

o Black African

o White

Analysis of variations in risk factors (e.g., sectoral concentrations, track records

and collateral) and financial relationships across the six ethnic groups.

Econometric analysis of the extent to which differences in finance outcomes

amongst EMBs can be explained by differences in risk factors and financial

relationships.

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Econometric analysis of the extent to which there is a residual element of ethnic

based differences (unexplained ethnicity variations) in finance outcomes after

controlling for risk factors and financial relationships.

On this last point, unexplained ethnicity variations may be due to ethnic discrimination byfinance providers. However, it is important to point out that these variations could have

alternative explanations which are based on non-ethnic factors.

The phrase significantly different is used in the report to describe differences in survey

estimates between ethnic groups which are statistically significant: that is, the difference

is unlikely to be due to chance.

General findings

Bangladeshi and Black owned businesses have the fewest financial assets and

tend to be located in the most economically and socially deprived areas.

Bangladeshi and Black owned businesses report experiencing the greatest

problems with finance (in terms of both access and cost) of all ethnic groups and

have the lowest self-confidence in dealing with finances.

However Black African owner managers are the most qualified in terms of

academic and financial qualifications and are the most likely to engage in

business planning at start-up.

Regarding the supply of banking services the supply to EMBs is more

concentrated in the Big Four banks compared with the supply to WBs

suggesting a relative lack of competition in the supply to EMBs.

Also EMBs, in particular Black owned businesses, are less satisfied with the level

of service from their finance provider and pay higher banking charges than WBs.

Black owned businesses have higher rates of financial delinquency (missed debt

repayments and unauthorized overdraft borrowing) than other ethnic groups.

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Key conclusions

The reports key conclusions relate to the following finance outcomes:

Financial rejections (denial of finance by finance providers)

Feelings of discouragement from applying for finance

Finance gaps (excess of amounts demanded over those supplied)

The cost of borrowing (term loan and overdraft margins over Bank of England

base rate)

Regarding financial rejection and discouragement (Chapter Four):

Black African owned businesses have a 37.4% likelihood of outright rejection.

This is significantly higher compared to Indian (5.8%), Pakistani (13.2%) and

White owned businesses (10.4%).

Black Caribbean owned businesses have a 28.1% likelihood of outright rejection.

Again this is significantly higher compared to Indian, Pakistani and White owned

businesses.

Partial rejection rates vary between 18% (Bangladeshi businesses) and 30.2%

(Black African businesses).

However there are no significant differences in partial rejection rates across

ethnic groups.

Black African businesses are the most likely to feel discouraged from applying for

finance 45.9% of businesses in this group which needed new finance felt

discouraged.

The corresponding figure for Black Caribbean businesses is 40.6%.

Both groups of Black owned businesses are significantly more likely to feel

discouraged from applying for finance than Indian (11.6%), Pakistani (22.9%)

and White owned (7.1%) businesses.

However, after removing differences in risk levels and financial relationships between

EMBs, the report finds that (see Chapter Six):

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Amongst EMBs ethnicity plays no residual role in explaining differences in

rejection rates.

Amongst EMBs ethnicity plays no residual role for ethnicity in explaining

discouragement having controlled for risk and financial relationships.

Results for finance gaps (i.e., the difference between the amount of finance demanded

and the amount supplied) show that (see Chapter Four):

There are significant finance gaps (i.e., significantly greater than zero), in

absolute terms, amongst all ethnic groups apart from Indian owned businesses.

The highest finance gaps are amongst Black African (14,102) and Pakistani

owned businesses (13,518).

WBs have lower finance gaps (5,435) if not the lowest (Bangladeshi: 3,970).

However, none of these differences in finance gaps are statistically significant.

On the other hand, looking at finance gaps relative to asset bases, Black owned

businesses experienced significantly higher finance gaps (0.4-0.6: 40-60% of

asset base) than Indian owned businesses (

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This gap is almost 15 percentage points amongst Black African owned

businesses (significant at the 10% level).

Relative to the amount of finance sought:

Finance gaps are 11 percentage points larger for Pakistani owned businesses

relative to Indian owned businesses which sought the same amount of finance

(significant at the 5% level).

This gap is almost 17 percentage points amongst Black African owned

businesses (significant at the 5% level).

Regarding term loan margins (see Chapter Five):

WBs paid the lowest margins on average (2.3 points over base).

Black African businesses paid the highest margins on average (3.7 points over

base).

These differences are not, however, statistically significant.

Results for overdraft margins indicate that (see Chapter Five):

WBs paid the lowest margins on overdrafts (2.0 points over base).

Pakistani owned businesses paid the highest overdraft margins (3.5 points over

base) which figure is significantly higher than amongst WBs.

After removing the effects of differences in risk levels and financial relationships

amongst EMBs the report finds that (see Chapter Six):

Bangladeshi owned businesses paid 1.5 percentage points more on their term

loans than Indian owned businesses (significant at the 5% level).

There is no role for ethnicity in explaining overdraft margins having controlled for

standard risk factors and financial relationships.

In general these results suggest that risk factors and financial relationships are able to

explain differences in finance outcomes across EMBs. Whilst there is some evidence of

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unexplained variations in the financial outcomes of Black African, Pakistani and

Bangladeshi businesses, which could be due to ethnic discrimination, it is also possible

that these variations are caused by non-ethnic factors. For example about a third of

Black African and Pakistani businesses are high-growth/high-risk firms for which equity

finance may be more suitable than debt finance.

Next steps

Whilst non-ethnic risk factors are able to explain most of the wide variations in financial

outcomes amongst EMBs, these variations remain a cause for concern in their own right

since, in some cases, they are very large (financial rejections and discouragement

particularly) and could lead to the perception of ethnic discrimination. In this regardbetter communications between finance providers and EMBs may help to remove these

perceptions and draw attention to the actual criteria by which loans are priced and

allocated. However, in conjunction with better communications, improved financial

support and advice, particularly for Bangladeshi and Black owned businesses, may be

required to tackle head-on the underlying causes of poorer financial outcomes.

Methodology

The survey was conducted among 860 small and medium sized ethnic minority

businesses (defined as firms with up to 250 employees) in the private sector in the UK.

An ethnic minority business was defined as one where the owner or the majority of

partners or shareholders in the business were from an ethnic minority. Public sector and

not for profit organisations were excluded.

The survey fieldwork was conducted by telephone by IFF Research, an independent

market research company, at IFFs CATI centre between 5 September and 18November 2005. In addition to the 860 completed interviews, a further 50 ethnic

minority businesses identified in the data for the earlier UKSMEF (2004) survey were

added to this new dataset in order to bolster base sizes. This brought the total to 910

enterprises. The breakdown of sample sizes by ethnic group is as follows:

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Indian: 202

Pakistani: 202

Bangladeshi: 103

Black Caribbean: 203

Black African 200

The data on WBs was collected in UKSMEF (2004) using a similar methodology (see

Fraser, 2005). This sample consists of 2,373 businesses.

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1. Introduction

1.1 Context

About 6% of the SME population are ethnic minority businesses (EMBs); this amounts to

around 218,000 businesses (Fraser, 2005). EMBs employ almost a million people

(about 7% of the total employment by SMEs) and generated revenues of over 58 billion

for the UK economy in 2004 (source: UKSMEF). In this regard, EMBs constitute an

important part of the SME sector and the UK economy as a whole. The economic

importance of EMBs looks set to increase in the future since the ethnic minority

population in the UK is growing faster than the White population. 1

EMBs, like any other business, require sources of finance to fund the enterprise at start-

up and, later, to fuel expansion. Unless the business owner is very wealthy or the

business is mature enough to have generated its own capital, at least some of this

finance will have to be accessed from external sources. On this issue, Fraser (2005)

indicated that most SMEs are currently enjoying favourable access to external finance.

However, the report also hinted at poorer access amongst EMBs as a whole relative to

otherwise similar white-owned businesses (WBs). However due to the small sample of

EMBs more definitive conclusions were not possible. Previous research has also

suggested that EMBs experience greater problems in raising external finance than WBs

(Jones et al, 1994) and that Black owned businesses experience the greatest problems

with external finance (Curran and Blackburn, 1993). The question of whether these

different financial outcomes are due to ethnic discrimination or whether they reflect

differences in lending risk across ethnic groups was not addressed in these reports.

A review of the evidence on ethnic minority finances, reported in Bank of England

(1999), found no evidence of actual discrimination against EMBs by finance providers.

However, the report recognised a perception amongst EMBs of unfair treatment by

1 The ethnic minority population in Great Britain grew by 53% between 1991 and 2001 (Source: Census, April 2001,Office for National Statistics). In contrast average population growth over the decade 1994-2004 was only 3.3%.

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finance providers. The report gave the following possible explanations for these

perceptions:

Lender risk aversion the implication here is that EMBs are riskier than WBs and

so receive loans on terms (higher interest rates/shorter maturity) which reflectthese risk differentials.

Sectoral concentrations because EMBs are concentrated in sectors with high

failure rates (retail, catering and transport) they are less attractive to (risk-averse)

lenders regardless of the business owners ethnicity.

Business planning and experience in this regard it would be expected that

higher rates of business planning would improve access to finance. However the

report notes that whereas black owned businesses are more likely to write

business plans than either Asian or White-owned businesses they are less

successful in obtaining bank finance. The report also suggested that Asian

entrepreneurs have high levels of experience which should improve their access

to finance.

Availability of collateral the report highlights collateral shortages (home

ownership) amongst Caribbean and Bangladeshi entrepreneurs as a possible

explanation for poorer access to finance amongst these ethnic groups.

Information issues/financial relationships the report identifies poor information

flows between lenders and EMBs as an issue. Mutually poor financial

relationships are likely to make EMBs appear riskier to lenders (objectively

worsening access to finance) and worsen perceptions amongst EMBs that they

are being discriminated against.

Discrimination finally, the report left open the possibility that, even after taking

into account the above issues, some of the EMB finance issues are due to a

residual element of discrimination amongst finance providers. This

discrimination could either be direct based on irrational prejudices even to the

detriment of the finance providers profits or statistical, in which case lenders

use ethnicity as a proxy for unobservable risk factors. In the latter case a good

EMB may find it harder to get a loan, than an otherwise similar White-owned

business, simply because their ethnicity is associated with higher risk types on

average.

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The paucity of hard data at the time, however, limited the conclusions that the Bank of

England report was able to reach. In fact the report highlighted the desirability of

improved information and further research in the field of EMB finances.

More recently Smallbone et al (2003) presented evidence from a large scale survey ofaccess to finance and business support amongst EMBs. Their findings indicated that

African-Caribbean businesses were at a particular disadvantage in accessing bank loans

relative to Asian and White-owned businesses. They regarded this finding as a matter

for concern not least because the African-Caribbean businesses in their sample had

above average levels of human capital (management qualifications and training).

An important finding in Smallbone et al (2003) was to show that there is greater variation

in finance outcomes between ethnic minority groups than between EMBs in aggregateand WBs. Indeed, in contrast to African Caribbean businesses, there was convergence

regarding (favourable) access to bank loans at start-up between Asian and White-owned

businesses. This led the authors to conclude:

For policy makers, this raises the question of whether or not it is useful and/or

appropriate to treat EMBs as a category from a finance and business support standpoint.

One of the implications for public policy makers is to recognize that access to finance

issues are greater in some ethnic minority communities than in others (Smallbone etal, 2003, p. 308/9)

EMBs form a relatively small proportion of the SME population so that finding them for a

survey is problematic. Combined with the general unwillingness of businesses to

divulge financial information the implication is that obtaining large samples of EMBs for

the purposes of a finance survey is no small task. Also ensuring the samples of EMBs

are representative of the different EMB populations is hampered by the lack of a

generally accepted sampling frame (i.e., a source of information about the populations ofEMBs). In these regards this EMB finance survey differs from previous surveys on this

issue (with the possible exception of Smallbone et al, 2003) in that it is both:

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Large scale (there are 910 EMBs in the sample)

Representative: in the analysis, the data are weighted to the population using an

authoritative source of population figures compiled by the Small Business

Service (see Appendix B).

The objectives of this report are to use these data to conduct:

Disaggregated analysis of finance outcomes for six ethnic groups:

o Indian

o Pakistani

o Bangladeshi

o Black Caribbean

o Black Africano White

Analysis of variations in risk factors (e.g., sectoral concentrations, track records

and collateral) and financial relationships across the six ethnic groups.

Analysis of the extent to which differences in finance outcomes across EMBs can

be explained by differences in risk factors and financial relationships.

Analysis of the extent to which there is a residual element of ethnic discrimination

after controlling for risk factors and financial relationships.

In some cases, for example the analysis of EMB start-ups, the samples are very small

(less than 50 businesses) so that caution is required in interpreting the analysis due to a

potential lack of statistical significance. However, given the novelty of many of the

results best estimates are reported even when the sub-samples involved are very small.

In these cases health warnings are attached to the analysis and, in any case, the

reader is informed (as with most of the results) as to whether or not the results are

statistically significant.

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Some of the key findings which emerge from the analysis of the two sets of survey data

are as follows:

Bangladeshi and Black owned businesses have the fewest financial assets and

tend to be located in the most economically and socially deprived areas. Bangladeshi and Black owned businesses report experiencing the greatest

problems with finance (in terms of both access and cost) of all ethnic groups and

have the lowest self-confidence in dealing with finances.

However Black African owner managers are the most qualified in terms of

academic and financial qualifications and are the most likely to engage in

business planning at start-up.

Black owned businesses have larger finance gaps (the difference between the

demand for new finance and supply) relative to asset bases than other ethnic

groups (40-60 pence per 1 of business assets).

Regarding the supply of banking services the supply to EMBs is more

concentrated in the Big Four banks compared with the supply to WBs

suggesting a relative lack of competition in the supply to EMBs.

Also EMBs, in particular Black owned businesses, are less satisfied with the level

of service from their finance provider and pay higher banking charges than WBs

which is possibly due to the lower level of competition for EMB bank accounts.

Black owned businesses have higher rates of financial delinquency (missed debt

repayments and unauthorized overdraft borrowing) than other ethnic groups).

However, after removing the effects of differences in risk factors and financial

relationships, analysis of finance outcomes amongst EMBs reveals that:

o Ethnicity is not a determinant of financial rejections or discouragement.

o However, Black African owned businesses have an average finance gap

which is 7,824 bigger than the corresponding gap for an Indian owned

business of the same level of risk.

o Also, Pakistani owned businesses have an average finance gap which is

4,280 bigger than the corresponding gap for an Indian owned business

of the same level of risk.

o Bangladeshi owned businesses pay a risk premium of 1.5 percentage

points on term loans relative to the margins paid by Indian owned

businesses of the same level of risk.

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In general these results suggest that risk factors and financial relationships are able to

explain differences in finance outcomes across EMBs. Whilst there is some evidence of

unexplained variations in the financial outcomes of Black African, Pakistani and

Bangladeshi businesses, which could be due to ethnic discrimination, it is also possiblethat these variations are caused by non-ethnic factors. For example about a third of

Black African and Pakistani businesses are high-growth/high-risk firms for which equity

finance may be more suitable than debt finance.

1.2 Statistical analysis

The analysis in this interim report is conducted using two separate data-sets:

The original UKSMEF carried out in 2004 which is used for the analysis of

WBs.

The EMB Finance Survey carried out in 2005 which is used for the analysis of

EMBs.

The analysis in this report is weighted so that the results are representative of the

respective populations of ethnic groups. The weights used for the main survey arepopulation weights calculated using universe data on the absolute number of businesses

in each cell (size within sector and by region: see Fraser, 2005, Appendix 1). However,

the weights for the EMB Finance Survey were calculated on the basis of population

percentages of businesses in the (sub-) population. 2

Whereas this difference in the weights, between the two data-sets, has no impact on the

ability to compare means and proportions of EMB and white-owned businesses

respectively, it does preclude analysis of population totals for EMBs (e.g., total lending,total deposits etc.). Also, the absence of an integrated data-set, with an appropriate

2 In effect this changes the scale of the weights between the two surveys: for the main survey the weights sum to thenumber of businesses in the corresponding universe; whereas in the EMB survey the weights sum to the sample size.Also, due to the paucity of EMB universe data, the EMB weights are only able to adjust for sampling rates by firm size(in contrast the main survey adjusted for firm size within sector and by region).

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weighting measure, precludes regression analysis involving samples of both EMBs and

WBs (see Chapter Six). 3

1.3 Structure of the report

The remainder of the report is structured as follows. Chapter Two sets out some

background issues regarding variations in entrepreneurial activity and access to finance

across ethnic groups. This chapter also looks at ethnic variations in sectoral

concentrations, human and financial capital, track records, deprivation and other risk

factors with a view to giving some preliminary explanations for differences in access to

finance across ethnic groups. Chapter Three looks at the motivations for starting in

business and business owners assessments of the extent of problems encountered atstart-up, and currently, in running the business. This chapter also looks at the use of

business planning, financial management and external advice in the context of strategies

for overcoming business problems.

Chapter Four presents hard evidence on the finances of EMBs and WBs. In particular

this chapter reports: the use of different types of financial products; the demand and

supply of new finance; financial rejections; and incidences where the business owner felt

discouraged from applying for new finance because they believed they would berejected. This analysis allows the extent of finance gaps (difference between demand

and supply) as well as the incidence of financial constraints (financial rejection and

discouragement) to be quantified.

Chapter Five looks at issues related to the supply of financial services and financial

relationships. This analysis includes variations in the levels of competition in the supply

of financial services, the lengths of financial relationships, levels of customer satisfaction

with the service provided and banking charges. In addition this chapter presents

3 Regression analysis (and other statistical testing requiring observations on individual firms rather than just means or proportions of groups), involving both EMBs and white-owned businesses, would require the two data-sets to beintegrated into a single data-set. To achieve this integration successfully, the combined data-set would require theconstruction of an appropriate set of weights that took into account both: i) the over-sampling of EMBs relative to white-owned businesses; and, ii) over/under-sampling by other business characteristics.

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evidence on differences in financial delinquency (missed debt re-payments and/or

unauthorized borrowing on an overdraft) and loan margins.

The analysis culminates in Chapter Six with an econometric analysis of finance

outcomes (rejection, discouragement, finance gaps and loan margins) amongst EMBs.This analysis examines whether there is a residual role of ethnicity in explaining finance

outcomes having controlled for risk factors and financial relationships. Chapter Seven

presents conclusions and recommendations for further study.

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2. Background

2.1 General issues regarding SME finances

Financial markets rely on the flow of information between finance providers and

borrowers to work efficiently. Finance providers require information on the

creditworthiness of borrowers so they can decide whether to supply funding and, if they

do, on what terms. An absence of this information will stem the supply of finance. This

would clearly be to the detriment of borrowers but may also be harmful to society as a

whole for example, where a business with job-creation potential is unable to finance its

growth plans. Equally, borrowers require information on finance providers so they can

find the best deal and switch providers if necessary. Without this information borrowers

may become vulnerable to higher charges and poorer service from their current finance

provider.

In the context of SMEs, policy makers have had long standing concerns that some

smaller firms with viable business propositions are unable to access any or sufficient orappropriate finance. The absence of a track record, amongst start-ups in particular, may

make it impossible for the entrepreneur to convince the finance provider that the

business is worth investing in. Finance providers may also require loans to be secured

on tangible assets, belonging to the business or its owner, so that funds can be

recouped in the event that the business goes into bankruptcy. Again, smaller and

younger firms are at a relative disadvantage due to a paucity of available assets to use

as security. These issues form the basis for public intervention in the form of the Small

Firms Loan Guarantee (SFLG). This intervention has been refocused to assist youngfirms with high growth prospects but which lack sufficient tangible assets to secure a

commercial loan (Graham, 2004).

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More recently, public concerns have been expressed about the lack of competition in the

supply of financial services to SMEs (Competition Commission, 2002). This has led to

the introduction of measures to make it easier for businesses to:

Compare the prices of banking services. Purchase these services from different suppliers.

Switch between finance providers.

In relation to the twin issues of access to finance and competition in the supply of

finance UKSMEF 2004 made the following key findings:

Access to Finance

For most SMEs issues with finance are near the bottom of the pecking order of

business problems (coping with red-tape is at the top of the list of problems)

44% of SMEs (1.6 million businesses) sought new finance in the last 3 years.

The average amount of new finance sought was just under 82,000.

Amongst businesses needing new finance, 11% experienced outright rejection

(180,000 businesses).

However EMBs are over 2 percentage points more likely to experience rejection

than WBs.

Supply of banking services

The Big Four banks account for almost 80% of the market for SME banking

services.

SME relationships with their main providers are long (15 years on average) and

monogamous (60% have only one provider for all their finances).

The average monthly bank charge is about 50.

Almost 1 in 3 SMEs report some dissatisfaction with these bank charges.

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However, each year, only 1 in 40 SMEs switch banks.

The majority of these SMEs switch because they are dissatisfied with service

rather than bank charges.

The original survey was unable to report detailed findings for the supply of banking

services to EMBs due to the small number of these businesses in the sample. Also the

results on access to finance could not be disaggregated by ethnic group due to the small

sample. In this context, the EMB booster survey provides an opportunity to make the

requisite disaggregated comparisons of EMB finances against those of WBs.

2.2 Ethnic Minority Businesses

There is wide variation in entrepreneurial propensities over ethnic groups. The following

graph of self-employment 4 rates by ethnic group shows that in 2004:

21% of Pakistanis in employment were self-employed.

About 12% of Indian, Bangladeshi and White British people in employment were

self-employed.

Black African and Black Caribbean groups have the lowest rates of self-

employment (around 6%).

4 Self-employment is an imperfect measure of entrepreneurship in that it tends to capture more marginal forms of thisactivity. Business ownership may be a truer measure of entrepreneurial activity but these data tend to be less wellrecorded in particular amongst ethnic groups.

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Figure 2.2.1 Self-employment as a percentage of all in employment by ethnic group in

2004

Source: Annual Population Survey, January 2004 to December 2004, Office for National Statistics

These differences in self-employment will reflect variations in a complex array of

interacting factors across ethnic groups. These factors include:

Access to resources: Groups which have greater access to human and financial

capital will have a greater capacity to set up in business. This access will depend on the

education, skills and financial wealth of the group. In this regard Bangladeshi and Black

Caribbean communities have been reported to have fewer financial assets than other

ethnic groups (Bank of England, 1999). This shortfall in assets may reduce the capacity

of individuals in these communities to take up business opportunities. In addition

discrimination by suppliers of resources, including finance, against EMBs would

potentially curtail the entrepreneurial capacities of individuals from minority communities.

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Social networks: The capacity to mobilize human and financial capital through social

networks is also likely to influence variations in business ownership. Ethnic groups with

strong social networks will have an advantage in this regard. For example, strong family

ties amongst Asian communities may give Asian businesses access to labour andfinancial capital which may not be so readily available to businesses in other ethnic

groups.

Access to markets: A business opportunity consists of both an idea and a market (i.e.,

customers). To the extent that ethnic groups have specialised tastes that need

satisfying, there are potential markets which can only be accessed by entrepreneurs

who understand these tastes intimately. These entrepreneurs are most likely to come

from the same community as their customers.

Whilst ethnic markets place a potential constraint on growth, the business may be able

to expand into non-ethnic markets. An example of this is Indian food retailing which

started out serving the specific needs of the Indian community but with changing

consumer tastes now serves all ethnic groups. 5

On the other hand, a business from one ethnic community may find its access to

markets limited where consumers from other ethnic groups discriminate against thebusiness on the grounds of ethnicity. 6 This will limit the business opportunities available

to individuals in the community which experiences discrimination. As a result business

take-up and performance (survival and growth) may be low in that community.

Deprivation: An absence of opportunities in the labour market, due to low education or

training, may push individuals into running marginal businesses out of economic

5 A stellar case study of this is the Indian entrepreneur Geeta Samtani. Samtani founded Geeta Foods Ltd in 1990 in

response to the popularity of her homemade mango chutney amongst friends and family. Her range of Indian foodproducts is now sold nationally through supermarkets outlets and there are plans to expand the business into exportmarkets.6 This discrimination could be direct in which case the consumer chooses not to purchase the product from thebusiness because of irrational ethnic prejudices against the business owner. The consumer may pay for this prejudiceby buying the product at a premium from a business owner who belongs to the same ethnic community as theconsumer (the consumers prejudice effectively gives this latter business owner monopoly power over consumers fromtheir ethnic group). On the other hand the discrimination could be statistical. In that case the quality of the productproduced by the ethnic business is on average of inferior quality. Accordingly the consumer may choose not tobuy the product from any individuals from the ethnic community rather than risk buying an inferior quality product.

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necessity. 7 This may raise the quantity of self-employed individuals within a community.

However, these businesses are borne out of necessity rather than opportunity and are

likely to be deficient in skills and financial capital. Business performance, in terms of

survival and growth, is therefore likely to be low. Due to these poor prospects

businesses in deprived communities may find it hard to raise external finance.Consequently they may become trapped at the margins of economic viability - unable to

raise performance for want of capital but unable to raise capital for want of better

performance. This is a classic instance where public intervention may be required to

help free the business from a deprivation trap so that its potential can be realized.

Deprivation remains a particular issue for Bangladeshi and Black Caribbean groups.

Other ethnic groups, in particular the Indian community, appear to have been more

successful in scaling the economic and social ladder (see section 2.4 below). Theextent to which deprivation is a barrier to finance which is independent of ethnicity will be

examined in this report.

Attitudes to entrepreneurship: The enterprise culture within an ethnic group is also

likely to influence ethnic variations in levels of entrepreneurship. For example, some

Middle Eastern communities have a strong historical and cultural tradition of trading

which may predispose individuals from these communities to running a business.

Cultural variations in attitudes to risk (in particular, the stigma attached to bankruptcy)and talents for spotting business opportunities may also contribute to ethnic differences

in entrepreneurships.

2.3 Ethnic Minority Business finances

Previous research has indicated that EMBs face above average problems regarding

access to finance. Jones et al (1994) conducted a study of 403 small businesses in 15localities of which 178 were Asian owned, 54 were African-Caribbean and 171 were

White. This study found that 60% of the Asian businesses had sought a bank loan

versus around 40% of the African-Caribbean and White owned businesses. Around

7 Econometric analyses of the determinants of self-employment have frequently found that unemployment is asignificant push factor which increases the likelihood of self-employment.

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40% of the African-Caribbean loan applicants reported encountering problems in

obtaining the loan (either rejection or loan conditions which the applicant felt were

unreasonable). About a third of the Asian applicants reported similar problems. Only

20% of the White applicants reported problems with obtaining loans. African-Caribbean

businesses were also more likely than Asian or White owned businesses to rely on non-market sources of finance at start-up (50% versus 30-40%).

Curran and Blackburn (1993) interviewed 76 EMBs from the Greek-Cypriot, Bangladeshi

and African-Caribbean communities. They also found that African-Caribbean start-ups

were more likely than other ethnic groups to rely on non-market sources of finance:

almost 70% relied on personal savings whereas the figure amongst Greek-Cypriots and

Bangladeshi start-ups was between 50% and 60%. These authors also found that 1 in 2

of the African-Caribbean businesses said they found it very difficult to raise finance forexpansion. Only 1 in 10 of the Greek-Cypriot and Bangladeshi businesses reported

similar levels of difficulty.

Smallbone et al (2003) conducted a large scale telephone survey of EMBs (856

businesses from the African-Caribbean, Indian, Pakistani, Bangladeshi and Chinese

communities) supplemented with a sample of 1,350 WBs. The survey found that

African-Caribbean business owners were the most likely to have formal management

training or qualifications. However African-Caribbean businesses had the lowest rate ofaccess to bank finance at start-up (21% versus 49% of Chinese owned start-ups).

African-Caribbean businesses also had the least success in obtaining external finance in

the 12 months prior to interview (62% versus 88% of Bangladeshi owned businesses).

Most recently Fraser (2005) based on a sample of 2,500 SMEs which included 102

EMBs, found that EMBs are 2 percentage points more likely than WBs to experience

financial rejection controlling for financial relationships, business and sector risk.

However this study was unable to report results disaggregated by ethnic group as it wasdesigned to look at SME finances in general and not EMBs in particular.

A review of the evidence on ethnic minority finances, reported in Bank of England

(1999), found no evidence of actual discrimination against EMBs by finance providers.

However, the report recognised a perception amongst EMBs of unfair treatment by

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finance providers. The report gave the following possible explanations for these

perceptions:

Risk aversion: Finance providers make lending decisions on the basis of an

assessment of the risk that the borrower will be unable to repay the loan. Increased riskof non-payment is associated with a higher cost of borrowing, shorter loan maturities, the

offering of smaller loans than requested or, if the risk is too high, the outright denial of

credit. To the extent that EMBs are riskier than WBs then this will be reflected in loan

amounts and conditions which appear more favourable to WBs but which reflect risk

differentials not ethnicity.

Sectoral distributions: An important factor which may explain risk differentials between

ethnic groups relates to the sectors in which groups are concentrated. To the extent thatEMBs are concentrated in sectors with high failure rates (retail, catering and transport)

they are less attractive to (risk-averse) lenders regardless of the business owners

ethnicity.

Business planning and experience : In this regard it would be expected that higher

rates of business planning would improve access to finance. However the Bank of

England report notes that whereas black owned businesses are more likely to write

business plans than either Asian or white-owned businesses they are less successful inobtaining bank finance. The report also suggested that Asian entrepreneurs have high

levels of experience which should improve their access to finance.

Location in deprived areas and availability of collateral: The Bank of England report

highlights collateral shortages (home ownership) amongst Caribbean and Bangladeshi

entrepreneurs as a possible explanation for poorer access to finance amongst these

ethnic groups. Also EMBs are more likely to be located in deprived inner city areas.

This may lead to a shortage of key resources including skills and financial capital asdiscussed in section 2.2.

Information issues: The Bank of England report identifies the problem of poor

information flows between lenders and EMBs. This is a problem which is exacerbated

by a lack of data on EMBs. Cultural and language barriers are further obstacles to the

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free flow of information. Two-way information deficiencies are likely to make EMBs

appear riskier to lenders (objectively worsening access to finance) and worsen

perceptions amongst EMBs that they are being discriminated against.

Discrimination: The Bank of England report left open the possibility that, even aftertaking into account risk and information issues, there may exist a residual element of

ethnic discrimination amongst finance providers. This discrimination could either be:

Direct discrimination this is discrimination against individuals based on the

irrational prejudices of the finance provider. This discrimination would result in

viable EMBs being systematically denied finance. Consequently the finance

provider would pay for its prejudice by losing out on the profits from lending to the

viable EMBs. It follows that the less competitive is the market for financialservices the greater the scope for finance providers to indulge irrational

prejudices.

Statistical discrimination Lenders use sector, business characteristics, and

information on the businesss behaviour in relation to its current account, to

predict the risk that the business will default on a loan. This information helps

lenders to decide whether to allocate credit, and if so, on what terms (risk-based

discrimination). However it is likely that the lenders risk model will be an

incomplete description of risk. In this context ethnicity may act as a proxy forunobserved risk factors. This means that lenders decisions, which are directed

at individuals within an ethnic group, will involve an assessment of the average

risk of the group leading to statistical discrimination. It is possible therefore that

a good EMB may find it harder to get a loan, than an otherwise similar white-

owned business, because their ethnic group is associated with higher risk types

on average. However the lender is behaving rationally (i.e., maximizing profits)

assuming the finance providers assessment of group risk is correct.

The issue of competition in the supply of financial services was highlighted alongside

access to finance in section 2.1. As far as this author is aware, there is no hard

evidence regarding competition in the supply of financial services to EMBs. However,

this issue is of equal importance to, and indeed interacts with, the issue of access to

finance. In general an absence of competition will lead to higher prices and poorer

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service. EMBs may be particularly disadvantaged by a lack of competition if the supply

of services to them is more concentrated than for WBs, and hence subjected to higher

tariffs and poorer service. The issue of competition interacts with EMB access to finance

in that (direct) ethnic discrimination is less likely in a highly competitive market for EMB

financial services since financiers could less afford to indulge their prejudices. The issueof competition in the market for EMB financial services is discussed in Chapter Five. It

is however out-with the scope of this report to analyze empirically the relationship

between competition in financial markets and ethnic discrimination.

2.4 Key business and owner characteristics

The context for this analysis is to offer a preliminary view of some key risk factors whichmay account for ethnic variations in access to finance. In view of previous discussions

the principal risk factors which are examined here are:

Firm size

Sectoral concentrations.

Experience and other human capital (academic and financial qualifications).

Deprivation and availability of collateral.

Analyses of other business characteristics (other risk factors) are summarized later in

this section. Information issues, which were highlighted by the Bank of England as a

cause of perceptions of ethnic discrimination in financial markets, are discussed under

financial relationships in Chapter Five. The issue of whether there is a residual

component of ethnic discrimination in access to finance, after controlling for financial

relationships and other risk factors, is examined in Chapter Six.

Firm size

Tables A2.1 (employment size; see Appendix A) shows that:

EMBs are generally more likely to have employees than WBs.

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In particular 69% of WBs have no employees, compared with only 5% of

Bangladeshi businesses (this difference being statistically significant).

Indeed WBs are significantly more likely to have no employees than all types of

EMB.

At least regarding comparisons between WBs and Asian businesses, these employee

size differences may reflect concentrations of Asian owned businesses in Wholesale and

Retail. Also Asian owned businesses may have greater access to labour through strong

social networks.

Table A2.2 (turnover) indicates that:

Indian owned businesses made almost 688,003 in sales in 2004-5. This is significantly higher than amongst Black Caribbean (164,650) and Black

African (200,076) businesses.

WBs averaged a turnover of 434,505 which is significantly higher than amongst

Black owned businesses.

Smaller firms are usually viewed by finance providers as being more risky than larger

firms. One reason for this is that smaller firms tend to keep fewer financial records

making risk assessments more difficult for finance providers; and they may be more

susceptible to shocks through concentration on a single product or service or reliance on

one or two key individuals. Also smaller firms tend to be younger (implying a shorter

track record) and have fewer financial assets (implying less available security), factors

which increase lending risk: differences in age and assets are considered below. A

further issue is that high lending fixed costs, specifically borrower screening and

monitoring costs, can potentially make lending relatively small amounts unattractive to

finance providers. However developments in credit scoring techniques have reduced

the fixed costs of small business lending considerably in the last decade.

For these various reasons Indian owned businesses may be more attractive to finance

providers than Black owned businesses due to size. Differences in turnover may also

explain better access to finance amongst WBs relative to Black owned businesses.

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Figure 2.4.1: Firm size (employees)

12.2%10.0%

5.1%

27.4%

12.2%

61.6%

82.2%

87.6%90.2%

69.4%

84.6%

32.1%

4.6%2.2%

4.7%2.9% 2.6%

5.8%

1.0% 0.3% 0.3% 0.6% 0.6%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Indian Pakistani Bangladeshi Black Caribbean Black African W hite

01--910--4950--249

Base=All businesses by ethnic groupIndian=202Pakistani=202Bangladeshi=103Black Caribbean=203Black African=200White=2,373

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Figure 2.4.2: Firm size (average turnover)

Turnover ()

688,003

628,658

342,641

164,650

200,076

434,505

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Indian Pakistani Bangladeshi Black Caribbean Black Af rican W hite

Turnover ()

Base: All businesses by ethnic group:Indian=202

Pakistani=202Bangladeshi=103Black Caribbean=203Black African=200White=2,373

Sectoral concentrations

Table A2.3 reports sectoral concentrations for EMBs and WBs. This table shows that:

Asian businesses are most heavily concentrated in Wholesale and Retail (Indian:

54.8%; Pakistani: 47.8%; Bangladeshi: 32.5%). These concentrations are

significantly higher than amongst WBs (15.4%)

Bangladeshi businesses are also highly concentrated in Hotels and Restaurants

(25.9%).

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Black and White owned businesses are most heavily concentrated in Real

Estate, Renting and Business Services (Black Caribbean: 25.7%; Black African:

41.6%; WBs: 36.1%).

Indeed Black African and White owned businesses are significantly more likely to

operate in this sector than Asian businesses.

These results support previous evidence on the typical activities of Asian businesses.

Also the sectoral distributions of Black and White owned businesses are broadly similar

with these businesses clustering in a high value added sector of the economy. On this

evidence sectoral concentrations may offer an explanation for differential access to

finance between Asian owned businesses on the one hand and Black and White owned

businesses on the other. However, sectoral concentrations would appear to be an

unlikely explanation for differences in access to finance between Black and White ownedbusinesses.

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Figure 2.4.3: Business Sectors

47.8%

32.5%

19.9%

26.1%25.7%

41.6%

36.1%

54.8%

15.4%

19.7%20.3%20.8%

0%

10%

20%

30%

40%

50%

60%

Indian Pakistani Bangladeshi BlackCaribbean

Black African White

Manufacturing

Construction

Wholesale/Retail

Hotels and Restaurants

Transport, Storage and Communications

Real Estate, Renting and Business Services

Health and Social Work

Other Community, Social and PersonalServicesEducation


Notes: Comparison of sector distributions are based on common sector classifications across the main and EMB surveys respectively. Thistherefore precludes comparisons of the following sectors: agriculture (main survey only); education (EMB survey only); and financial intermediation(EMB survey only).

The following tables report the distributions of business assets across sectors. It would

be expected that businesses operating in sectors which generate small levels of fixed

assets would be financially disadvantaged relative to businesses, with similar financing

requirements, in sectors with higher asset levels. The reason for this is that lenders

often require there to be some security on a loan and businesses with higher fixed asset

levels are more able to comply with this requirement. Also, lenders prefer fixed to

intangible assets (such as intellectual property) since fixed assets can be more readily

valued and sold in the event of default.

In this regard the following table reports the distributions of assets across sectors for

EMBs and WBs. Wholesale/Retail and Real Estate, Renting and Business Services are

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highlighted as these are the sectors with the heaviest concentrations of Asian and Black

and White owned businesses respectively.

Table 2.4.1 indicates that:

The sectors in which EMBs and WBs are most heavily concentrated

(Wholesale/Retail and Real Estate, Renting and Business Services) have similar

levels of business assets around 200,000 worth on average (there being no

statistically significant differences in the average asset levels of these sectors).

However the difference in average business assets of EMBs in construction

(37,751) compared with the corresponding figure amongst WBs (146,652) is

statistically significant.

Also EMBs in Transport, Storage and Communications have significantly lower

asset levels than amongst WBs (55,340 versus 168,389).

At the level of business assets it would seem that there is a level playing field in terms of

the principal EMB and WB sectors. This suggests, at the sectoral level, that differences

in business assets are an unlikely principal explanation for ethnic variations in access to

finance. Nonetheless this evidence on ethnic variations in business assets is only

indirect being intermediated by sector. Therefore direct evidence on ethnic variations in

business assets are presented later in this section. Also, small businesses are oftenheavily reliant on the personal assets of the owner. In this regard variations in personal

assets could play a further role in explaining ethnic differences in access to finance.

Direct comparisons of personal assets by ethnic group are therefore also carried out

later in this section.

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Table 2.4.1: Average business assets by sector

All EMBs

Sector Average businessassets ()

Std. Err. [95% Conf. Interval]

Manufacturing 173,495.7 32,330.7 109,255.3 237,736.0Construction 37,751.1 13,871.1 8,984.2 66,518.0Wholesale/Retail 213,243.2 30,702.2 152,781.0 273,705.3Hotels andRestaurants

229,893.2 62,100.4 105,539.4 354,247.1

Transport, Storageand Communications

55,340.2 18,810.2 173,23.3 93,357.0

Real Estate,Renting andBusiness Services

200,151.0 75,905.7 50,463.7 349,838.2

Health and SocialWork

337,426.2 137,929.0 56,807.5 618,044.9

Other Community,Social and PersonalServices

139,075.1 53,919.3 28,957.2 249,193.1

Education 16,452.9 6,866.5 1,725.7 31,180.1FinancialIntermediation

38,377.3 24,008.6 -14,463.0 91,218.6

Base: All EMBs by sector:Manufacturing=108Construction=34Wholesale/Retail=310Hotels andRestaurants=60Transport, Storage andCommunications=53Real Estate, Renting andBusiness Services=235Health and SocialWork=42Other Community, Socialand Personal Services=37Education=19FinancialIntermediation=12

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White

Sector Average businessassets ()


Agriculture 439,273.1 51,157.2 338,283.6 540,262.6Manufacturing 320,019.6 46,827.1 227,651.8 412,387.5Construction 146,652.1 35,892.3 76,036.5 217,266.8Wholesale/Retail 653,465.9 260,302.7 141,326.6 1,165,605.0Hotels andRestaurants

406,294.8 58,877.9 290,027.6 522,561.9


168,388.6 27,402.0 114,307.7 222,469.4


182,530.5 32,315.3 118,997.1 246,063.9


132,177.3 19,295.8 94,030.8 170,323.7


236,232.0 80,795.7 76,974.8 395,488.2

Base: All Whitebusinesses by sector:Agriculture=191Manufacturing=204Construction=371Wholesale/Retail=365Hotels andRestaurants=184Transport, Storage andCommunications=201Real Estate, Renting andBusiness Services=440Health and SocialWork=167Other Community, Social

and PersonalServices=250

Another reason why sector is important for lenders risk assessments is that sunk costs

vary over sectors. These are irrecoverable costs, associated with investment in plant

and equipment which cannot be put to uses other than in the current venture. In sectors

with high sunk costs, such as the manufacture of specialised goods, business assets

cannot be easily transferred to another use. The entrepreneur is therefore more likely to

be committed to making the current business a success since alternative uses of the

firms assets are limited. From the lenders perspective there is less risk that theentrepreneur will be inclined to give up on the current venture. However in sectors with

low sunk costs there is more risk to the lender since the entrepreneur can give up the

current business with little financial penalty: taxi driving and running a market stall are

good examples of this.

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A related issue is that sectors with low sunk costs are likely to be highly competitive

since the barriers to entry are low. Profitability, and the chances of survival, in these

sectors is therefore likely to be low. In other words the potential returns in these sectors

are low and risk is high making investment unattractive to outside investors.

In this context the following table looks at the return on assets (profits divided by assets)

across EMB and WB sectors. Wholesale and Retail and Real Estate, Renting and

Business Services are again highlighted. Table 2.4.2 shows that:

EMBs in Wholesale and Retail (most likely Asian owned businesses) earn on

average 80 pence per pound of total assets (which is the same as amongst

WBs).

EMBs in Real Estate, Renting and Business Services (most likely Black ownedbusinesses) earn 1.70 per pound of total assets.

WBs, in the same sector, earn about 2.80 per pound of total assets. However,

this profitability is not significantly higher than amongst EMBs.

The indication from these results is that Black and White owned businesses tend to

operate in a sector which is highly profitable. Asian owned businesses, on the other

hand, are most commonly found in a sector which is significantly less profitable.

Businesses in Real Estate, Renting and Business Services may therefore be more likelyto attract external investors than businesses in Wholesale and Retail. The entry barriers

(sunk costs) which help to maintain the higher profitability of businesses in Real Estate,

Renting and Business Services may include specialised training and qualifications

required to run businesses in these sectors.

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Table 2.4.2 Profitability (average return on assets) by sector

All EMBs

Sector Average return on

assets


Manufacturing 0.5 0.1 0.2 0.8Construction 1.2 0.6 -0.2 2.5Wholesale/Retail 0.8 0.1 0.5 1.0Hotels andRestaurants

0.5 0.3 -0.1 1.1


1.4 0.5 0.3 2.4


1.7 0.3 1.2 2.2


1.7 0.7 0.2 3.1


1.4 0.5 0.4 2.3

Education 1.3 0.4 0.3 2.3FinancialIntermediation

1.5 0.4 0.2 2.7

Base: All EMBs by sector:Manufacturing=108Construction=34Wholesale/Retail=310Hotels andRestaurants=60Transport, Storage andCommunications=53Real Estate, Renting andBusiness Services=235Health and SocialWork=42Other Community, Socialand Personal Services=37Education=19FinancialIntermediation=12

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White

Sector Average return onassets


Agriculture 0.6 0.2 0.3 0.9Manufacturing 1.1 0.2 0.7 1.5Construction 2.1 0.3 1.6 2.7Wholesale/Retail 0.8 0.1 0.6 1.0Hotels andRestaurants

0.7 0.2 0.3 1.1


0.8 0.1 0.5 1.1


2.8 0.5 1.8 3.8


2.3 0.5 1.3 3.2


1.8 0.3 1.2 2.5

Base: All Whitebusinesses by sector:Agriculture=191Manufacturing=204Construction=371Wholesale/Retail=365Hotels andRestaurants=184Transport, Storage andCommunications=201Real Estate, Renting andBusiness Services=440Health and SocialWork=167Other Community, Social

and PersonalServices=250

Human capital

A variety of academic qualifications and business experience are examined under the

heading of human capital. Whether an academic or vocational qualification, or indeed

business experience, is a more relevant measure of the skills required to run a business

will depend on the nature of the business. A PhD in a relevant field may be important forrunning a high technology business whilst a vocational qualification or experience may

be more relevant in other cases.

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Table A2.4 reports owners highest academic qualifications. This table shows that:

About 20% of Indian owner-managers have an undergraduate degree (but not

significantly higher than WBs amongst which about 12% of owners have an

undergraduate degree). 22% of Pakistani owner managers have an undergraduate degree (significantly

higher than amongst WBs).

28% of Bangladeshi owner managers have an undergraduate degree

(significantly higher than amongst WBs).

However the highest qualification of Black Caribbean owner managers tends to

be O-levels (18%).

Black African owner managers are the most highly educated group with 37%

having a postgraduate degree and 24% having an undergraduate degree (both

significantly higher than amongst WBs).

Indeed 15% of White owner managers have no qualifications (a significantly

higher figure than amongst Black African businesses).

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Figure 2.4.4: Highest academic qualification of owner-manager

Academic Qualifications (Indian Owned Businesses)

8%

11%

14%

5%

5%

16%

20%

15%

1% 4%

1%0%

0%

No academic qual.

O-levels

A-levels

HND/HNC

City and Guilds/NVQ

Professional qual.

Undergraduate degree

Postgraduate degree

Diploma/Certificate

Apprenticeship/tradequal.Teaching Qualification

Other

Don't know

Academic Qualifications (Pakistani Owned Businesses)

9%

11%

8%

6%

5%

10%

22%

19%

4%0%0%

3%2%

No academic qual.

O-levels

A-levels

HND/HNC

City and Guilds/NVQ

Professional qual.


Postgraduate degree

Diploma/Certificate

Apprenticeship/trade

qual.Teaching Qualification

Other

Don't know

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Academic Qualifications (Bangladeshi Owned Businesses)

Academic Qualifications (Black Caribbean Owned Businesses)

11%

18%

8%

10%15%

7%

12%

12%

2%3% 1%

1% 0%

No academic qual.

O-levels

A-levels

HND/HNC

City and Guilds/NVQ

Professional qual.


Postgraduate degree

Diploma/Certificate


Other

Don't know

12%

15%

9%

4%

3%

6%

28%

16%

0%0%3% 3%

2%

No academic qual.

O-levels

-levels AHND/HNC

City and Guilds/NVQ

Professional qual.


Postgraduate degree

Diploma/Certificate


Other

Don't know

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Academic Qualifications (Black African Owned Businesses)

Academic Qualifications (White Owned Businesses)

15%

14%

9%

6%

14%

13%

12%

9%

1%0%

4%2%1%

No academic qual.

O-levels

A-levels

HND/HNC

City and Guilds/NVQ

Professional qual.


Postgraduate degree

Diploma/Certificate


Other

Don't know


4%4%

2%

6%

4%

16%

24%

37%

3% 0%0%0%2%

No academic qual.

O-levels

-levels AHND/HNC

City and Guilds/NVQ

Professional qual.


Postgraduate degree

Diploma/Certificate


Other

Don't know

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The following table of years of business experience shows that:

This experience appears to compensate for the lack of academic qualifications

White owner managers have the highest average levels of business experience

(20.6 years). This is significantly higher than amongst Pakistani (13.8 years),Bangladeshi (11.5 years), Black Caribbean (10.8 years) and Black African (10.5

years) businesses.

amongst WBs.

In contrast, Black African owner managers, which have the highest academic

qualifications, have the lowest levels of business experience (significantly lower

than amongst Indian, Pakistani and White owned businesses).

These results indicate there is a trade off between accumulating academic qualifications

versus gaining business experience. In effect time spent in education is time not spent

gaining business experience. To the extent that finance providers value practical

business experience more than academic qualifications White owned businesses may

be more successful than Black African businesses in accessing finance despite the high

levels of human capital amongst Black African entrepreneurs.

Table 2.4.3: Average years of business experience Average years of

experienceStd. Err. [95% Conf. Interval]

Indian 17.6 0.8 16.0 19.2PakistaniBanglades

13.8 0.8 12.3 15.3hi

an

1businesses by

23

03

11.5 0.9 9.7 13.4Black Caribbe 10.8 0.6 9.6 12.1Black African 10.5 0.6 9.4 11.6White 20.6 0.5 9.6 21.5Base: Allethnic groupIndian=202Pakistani=20Bangladeshi=10Black Caribbean=2Black African=200White=2,373

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Figure 2.4.5: Average years of business experience

Business Experience

17.6

13.8

11.510.8 10.5

20.6

0

5

10

15

20

25

Indian Pakistani Bangladeshi Black Car ibbean Black African Whi te

Average Years of Experience

Base: All businesses by ethnic groupIndian=202Pakistani=202Bangladeshi=103Black Caribbean=203Black African=200White=2,373

In summary Black African owners are the most highly formally educated group. Other

things being equal, this human capital should raise the performance of the business

relative to the less educated group of White owner managers and increase access to

finance. However to the extent that finance providers value business experience over

academic qualifications then WBs could have an important advantage over EMBs.

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Availability of collateral and deprivation

The section begins with direct comparisons of business and personal assets by ethnic

group. The following table of business assets shows that:

Indian and Pakistani businesses have the highest level of business assets

(589,786 and 306,644 respectively).

In this regard Pakistani businesses have significantly higher asset levels than

Black Caribbean (65,156) and Black African (79,091) businesses.

Indeed Black owned businesses also have significantly lower asset levels than

WBs (268,640).

Table 2.4.4: Average business assets

Average assets () Std. Err. [95% Conf. Interval]

Indian 588,786.1 266,618.3 62,563.8 1,115,008.0Pakistani 306,644.4 82,764.2 143,293.4 469,995.3Bangladeshi 117,107.2 24,387.3 68,664.9 165,549.5Black Caribbean 65,156.5 10,081.6 45,242.5 85,070.5Black African 79,091.0 16,027.4 47,441.4 110,740.6White 268,640.0 41,891.5 186,487.2 350,792.8Base: All businessesreporting assets:Indian=175Pakistani=175Bangladeshi=92Black Caribbean=157Black African=163White=2,121

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Figure 2.4.6: Average business assets

Bases:

All businesses reporting current turnover:Indian=182Pakistani=144

Bangladeshi=62Black Caribbean=129Black African=129White=2,136

All businesses reporting assets:Indian=175Pakistani=175Bangladeshi=92Black Caribbean=157Black African=163White=2,121

688,003

628,658

342,641

164,650

200,076

434,505

588,786

306,644

117,107

65,15679,091

268,640

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

Indian Pakistani Bangladeshi Black Caribbean Black African White

Turnover ()Assets ()

Regarding personal assets the following table indicates that:

Pakistani owner-managers have the most personal assets on average

(439,180) which is significantly higher than amongst Bangladeshis (201,239),

Black Caribbean (159,594) and Black Africans (218,767)

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Indeed Bangladeshi and Black Caribbeans also have significantly lower personal

assets than amongst Indians (385,932).

Further, Black Caribbeans have significantly lower personal assets than amongst

WBs (323,995)

Table 2.4.5: Owner-manager net worth (average personal assets)

Average net wor th()


Indian 385,931.8 47,121.4 292,764.4 479,099.2Pakis tani 439,180.2 102,327.5 236,707.8 641,652.6Bangladeshi 201,238.9 37,923.4 125,754.3 276,723.5Black Caribbean 159,594.4 24,264.8 111,644.1 207,544.7Black African 218,766.7 47,886.6 124,092.3 313,441.1White 323,995.3 24,801.4 275,349.3 372,641.3Base: All businessesreporting owner-managers net worth:Indian=140Pakistani=129Bangladeshi=80Black Caribbean=149Black African=141White=1,629

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Figure 2.4.8: Average net worth of owner-manager

Average Net Worth ()

385,931

439,180

201,239

159,594

218,767

323,995

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

Indian Pakistani Bangladeshi Black Caribbean Black African White

Average Net Worth ()

Base: All businesses reporting owner-managers net worthIndian=140Pakistani=129Bangladeshi=80Black Caribbean=149Black African=141White=1,629

These results suggest that variations in business and personal assets may play an

important role in explaining ethnic variations in access to finance. In particular higher

asset levels amongst Indian and Pakistani businesses may point to greater success in

accessing finance compared with Black owned businesses.

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The above evidence suggests that some ethnic groups are more deprived of financial

resources than others. However an absence of financial resources is only one aspect of

deprivation. An absence of, for example, jobs, skills, good health and decent housing

will also limit the opportunities available to individuals. In this context the Office of the

Deputy Prime Minister has published an index of multiple deprivation for England. Thisindex is formed as a weighted aggregate of 7 domains of deprivation:

Income deprivation

Employment deprivation

Health deprivation and disability

Education, skills and training deprivation

Barriers to housing and services

Living environment deprivation

Crime

Each domain is comprised of a number of indicators of deprivation which are relevant to

the domain. From these indicators a deprivation score is obtained where larger scores

denote greater levels of deprivation. A deprivation rank is obtained from these scores

which assigns a rank of unity to the most deprived area and a rank of 32,482 (which

equals the number of Super Output Areas in England) to the least deprived area. It is

the deprivation rank which is used in the following analysis and in the remainder of this

report.

The following table reports the mean deprivation ranks of ethnic groups. This shows

that:

WBs tend to be situated in areas which are less deprived than the areas in which

EMBs are situated.

In particular Bangladeshi businesses tend to be located in the most deprived

areas.

Black Caribbean businesses tend to be located in the second most deprived

areas.

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These results show that businesses with the fewest financial assets (i.e., Bangladeshi

and Black Caribbeans) tend also to be located in the most deprived areas. This

suggests that Bangladeshi and Black Caribbean owned businesses may be severally

disadvantaged by an absence of financial, economic and social resources relative to

other ethnic groups.

Table 2.4.6: Deprivation rank (England only)

Mean rank Std. Err. [95% Conf. Interval]

Indian 9,972 706 8,580 11,364Pakistani 6,896 473 5,963 7,829Bangladeshi 5,684 664 4,366 7,003Black Caribbean 6,824 534 5,770 7,878Black African 7,205 453 6,311 8,099White 18,306 412 17,499 19,114Base: All businesses inEngland by ethnic group:Indian=182Pakistani=194Bangladeshi=94Black Caribbean=187Black African=195White=1,814

Businesses in deprived areas are more likely to have been set up as a response to

economic necessity and will have fewer resources available to them. These businesses

may therefore tend to be marginal in character and their performance may accordinglytend to be poorer than businesses situated in wealthier areas. To examine this

hypothesis the following table looks at the performance of businesses located in different

quartiles of the distribution of deprived areas. The performance measure reported in the

following table is the return on assets (profits divided by business assets).

The results in the following table show that:

The return on assets in the most deprived areas is 80 pence per 1 of businessassets (amongst EMBs).

The return on assets in the wealthiest areas is 2.20 per 1 of business assets

(amongst EMBs).

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In other words, amongst EMBs, business performance appears to increase as the level

of deprivation decreases (although the difference in return on assets between the lowest

and highest deprivation quartile is not statistically significant). Indeed, a similar pattern

of performance and deprivation is also noted amongst WBs. Overall, this offers some

support to the idea that deprivation is associated with poorer business performance.

Table 2.4.7: Average return on assets by deprivation quartile

All EMBs

Average return onassets

Std. Err. [95% Conf. Interval]Deprivationquartile

First quartile (0-25%) 0.8 0.1 0.7 1.0Second quartile(26%-50%)

1.2 0.2 0.8 1.5

Third quartile (51%-75%)

1.3 0.4 0.4 2.2

Fourth quartile (76%-100%)

2.2 0.9 0.3 4.1

Base: All EMBs inEngland by deprivationquartile:First quartile=561Second quartile=199Third-quartile=61Fourth quartile=31

WBs Average return on

assetsStd. Err. [95% Conf. Interval]Deprivation

quartile

First quartile (0-25%) 1.1 0.2 0.7 1.5Second quartile(26%-50%)

1.3 0.2 0.8 1.8

Third quartile (51%-75%)

1.5 0.2 1.0 1.9

Fourth quartile (76%-100%)

1.8 0.2 1.4 2.2

Base: All WBs in Englandby deprivation quartile:

First quartile=481Second quartile=482Third quartile=481Fourth quartile=482

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Figure 2.4.8: Average return on assets by deprivation quartile

0.8

1.2

1.3

2.2

1.1

1.3

1.5

1.8

0.0

0.5

1.0

1.5

2.0

2.5

Bottom 25% Mid-bottom 25% Mid-top 25% Top 25%

Return on assets: EMBsReturn on assets: WOBs

Base: All businesses in England by EMB/WOB and deprivation quartile.EMBs:First quartile=561Second quartile=199Third-quartile=61Fourth quartile=31WBs:First quartile=481Second quartile=482Third quartile=481Fourth quartile=482

These results on wealth and deprivation confirm previous studies in showing that

Bangladeshi and Black Caribbean businesses have the fewest financial resources and

are located in the most deprived areas. This deprivation is also shown to compromise

performance which will limit these businesses ability to access external finance.

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2.5 Summary of other business characteristics

This chapter concludes by looking at ethnic variations in other business characteristics

which could influence access to finance. These characteristics are tabulated in full in

Appendix A.

Business age

Table A2.5 shows that:

The average age of WBs is about 18.5 years (significantly older than all ethnic

groups apart from Indians).

Indian owned businesses average around 16 years of age (significantly older

than all ethnic groups apart from WBs).

There is a large drop to the next oldest group, Pakistani owned businesses,

which have an average age of just over 8 years

Bangladeshi and Black African businesses are the youngest averaging around 7

and 6 years of age respectively.

Also Figure 2.5.1 shows that:

Indian and White business groups have the lowest proportion of start-ups:

around 1 in every 20 businesses in these groups is aged less than 2 years. This

is significantly lower than the percentage of start-ups amongst all other ethnic

groups.

In this regard more than 1 in 3 Bangladeshi businesses are aged less than 2

years.

Entrepreneurship is often regarded as a learning process. 8 Initially there may be large

uncertainty about the talents of the entrepreneur and the demand for the firms product

(markets). With the passage of time these uncertainties become resolved with the less

talented or fortunate exiting the market leaving behind the most talented (and luckiest)

8 This view has been expressed by writers in the Austrian school of entrepreneurship theory such as von Hayek andKirzner.

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