445402ISB
is
bj
Small Firms
Article
Place, sorting effects and
barriers to enterprise in
deprived areas: Different
problems or different firms?
International Small Business Journal
0(0) 1­–24
© The Author(s) 2012
Reprints and permission:
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DOI: 10.1177/0266242612445402
isb.sagepub.com
Neil Lee
Lancaster University Management School, UK
Marc Cowling
University of Exeter Business School, UK
Abstract
Policies to stimulate enterprise in deprived areas typically attempt to remove the specific obstacles
faced by firms in deprived locations. Yet there is little evidence that firms in deprived areas actually
perceive different problems to those in more affluent places. Alternatively, different types of firms
may locate in deprived places. This article uses a sample of 7670 English SMEs to investigate this
issue. It asks two questions: Do firms in deprived areas perceive different barriers to success
than other firms? And is this because of their location (a ‘place’ effect) or other characteristics (a
‘firm’ effect)? We find only limited evidence that ‘place’ effects are in operation: of nine potential
obstacles only a lack of access to finance is significant, controlling for other firm characteristics.
However, this finding may be important given that firms in deprived areas are equally likely to be
growth orientated.
Keywords
entrepreneurship, deprived areas, SMEs, social exclusion
Introduction
Deprived areas tend to have lower rates of new business formation than more affluent places. In
response, stimulating enterprise in disadvantaged areas has been an important government agenda
in many countries. Both policymakers and academics, most prominently through Michael Porter’s
Corresponding author:
Neil Lee, Department of Economics, Lancaster University Management School & The Work Foundation, 21 Palmer Street,
London SW1H 0AD, UK
Email: [email protected]
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International Small Business Journal 0(0)
‘The Competitive Advantage of the Inner City’, have suggested that targeting the ‘enterprise gap’
would help address deprivation (Harrison and Glasmeier, 1997; Hartshorn and Sear, 2005; Porter,
1995, 1997). In the United Kingdom, successive governments have tried to address the gap. The
Labour government of 1997–2010 ‘enthusiastically promoted enterprise as a means of turning
around the fortunes of deprived areas’ (Drever, 2006: 5), and the subsequent Conservative–Liberal
Democrat coalition also has considered specific policy on firms in deprived areas (for example,
HM Treasury, 2011a). In order to achieve these goals, a series of policies has been suggested which
target the specific barriers to enterprise faced by firms and entrepreneurs in deprived areas: obstacles which are likely to restrict their success, such as a lack of access to capital, poor skill levels or
difficulties in recruiting.
Yet, there is little evidence on whether firms in deprived areas actually face different obstacles
to firms in more affluent places. Firms may sort into different neighbourhoods of a city, with location reflecting firm characteristics rather than vice versa. So, an alternative hypothesis is that firms
in deprived areas have different characteristics, with these being more important than location in
determining the obstacles they face. The deprived areas considered by many policies are only part
of wider labour markets; for example, deprived Lambeth is only part of the successful urban economy of London. Firms in Lambeth may value something which the borough offers, such as local
markets or relatively cheap property. Local residents are not deprived because of a lack of demand
in the economy, which entrepreneurship may address, but because price mechanisms and the housing market sort individuals with low incomes into deprived neighbourhoods (Cheshire, 2006,
2009). In short, rather than firms in deprived areas facing different problems, deprived areas may
have firms with different characteristics.
Despite the importance of this distinction for both policy and academic research, research has
yet to consider these competing hypotheses. This article addresses this gap through an analysis of
the Annual Small Business Survey 2007–2008, a survey of 7670 small and medium-sized enterprises (SMEs) in England. The article has two principal research questions:
RQ1: Do SMEs in deprived areas perceive different obstacles to success than firms that are not?
RQ2: Are these differences explained by firm characteristics (‘firm’ effects), or where they are located
(‘place’ effects)?
The article is the first to test the assumptions underpinning policy on enterprise support in deprived
areas. This has been an important research area, particularly given the popularity of Michael
Porter’s (1995, 1997) work, and subsequent policies that focus scarce resources on small geographical areas as a means of targeting deprivation. Yet, research on this subject has focused on
individual deprived neighbourhoods without considering the wider urban context (e.g. Williams
and Williams, 2011). This article also links to the literature on barriers to entrepreneurship – internal or external obstacles limiting the success of firms (see Coad and Tamvada, 2011; Lee, 2011;
Storey, 1994). In doing so, it contributes to the broader policy-focused literature on small firms,
social inclusion and economic development (see Blackburn and Smallbone, 2008).
We estimate a series of multivariate regression models which test whether, controlling for
industry, age, region, ownership and firm demographics, firms in deprived areas experience different obstacles to those outside. Testing for nine different obstacles, the results provide only limited
support for theory and policy focused on enterprise in deprived areas. While firms in deprived
areas tend to perceive greater obstacles to success, this is explained mostly by other firm
Lee and Cowling
3
characteristics such as industry and ownership, rather than location in deprived areas. However,
access to finance is perceived as a greater issue by firms in deprived areas, controlling for other
characteristics. This provides an important area for policy interventions and suggests a useful area
for future research.
The remainder of the article is structured as follows. The next section outlines the theoretical
basis for supporting small firms in deprived areas and discusses how government policy in the UK
has addressed this. Much of this policy is based on the notion that firms in deprived locations face
particular obstacles to success. The following section presents the data and shows that firms in
deprived areas tend to differ from those outside, being larger, more likely to have multiple sites and
to be led by a member of a minority ethnic group. The next section demonstrates that firms in
deprived locations – once controlling for characteristics such as sector and size – are more likely
to perceive that only access to finance is an obstacle to their success. The article concludes with the
implications for theory and policy.
Enterprise in deprived areas
A number of studies have considered the role of enterprise in addressing social exclusion (Blackburn
and Ram, 2006; Kitching, 2006). One key strand of this literature focuses upon attempts to stimulate enterprise in deprived areas (Blackburn and Ram, 2006). In a series of papers in the mid-1990s,
Michael Porter (1995, 1997) argued that deprived inner cities of the USA had considerable potential for enterprise. These areas had positive attributes in the form of proximity to central business
districts, untapped human capital, market demand from local residents and a location near existing
business clusters. Yet, inner-city firms faced distinct obstacles to growth, including higher building
costs, greater tax burdens, regulation, crime, poor infrastructure, relatively low employee skills,
worse management skills, an absence of entrepreneurial attitudes and difficulty in accessing capital
(Porter, 1995). According to Porter, if government policy could address these obstacles, inner cities
could become centres of private enterprise and residents would benefit.
The idea that enterprise could address deprivation has been important for policymakers in the
UK (Greene, 2002; Henry et al., 2003). One of the targets set by the Labour government of 1997–
2010 was as follows:
Help to build an enterprise society in which small firms of all kinds thrive and achieve their potential, with
(i) an increase in the number of people considering going into business, (ii) an improvement in the overall
productivity of small firms, and (iii) more enterprise in disadvantaged communities. (Department of Trade
and Industry, 2002, p. 2)
As well as being a drag on national productivity, the ‘enterprise gap’ between rich and poor places
was seen as a social policy issue, with enterprise having the potential to increase employment rates
and incomes among the disadvantaged (Department of Trade and Industry, 2003; HM Treasury,
2004). Government policy since then has included measures to ensure that firms in deprived areas
have access to finance (HM Treasury, 2011a). More generally, policies such as Enterprise Zones
have continued to target the barriers faced by firms in a small geographical area (HM Treasury,
2011b).
Policies which focus on enterprise in deprived areas often have common characteristics. First,
there is a tendency to target a small geographical area, such as a ward or neighbourhood, which is
only part of a wider urban economy. Second, there is an assumption that firms in this location face
different obstacles to other firms, with government policy used to address these challenges. For
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International Small Business Journal 0(0)
example, Enterprise Areas were introduced in the 2000 most deprived wards in the country to
address apparent difficulties in accessing finance, property market failure, low levels of private
sector investment, lack of suitable business premises, the need for targeted business support and
advice to raise skills and awareness of opportunities for enterprise (HM Treasury, 2004).
A second example is the Local Enterprise Growth Initiative (LEGI), a policy announced in the
2005 budget (HM Treasury, 2005). LEGI aimed to overcome a set of perceived barriers to enterprise that existed only in these places (Drever, 2006). These ranged from access to finance, lack of
experience in starting or running a business, poor skills and the need for training, institutional and
administrative barriers to enterprise, a lack of an enterprise culture or more simply a poor business
environment.
Policies aiming to support enterprise in deprived areas are open to question on at least two
fronts. The first problem is that by targeting a small geographical area that is part of a wider urban
economy, policies ignore the sorting of firms into different neighbourhoods. In this respect,
research on the sorting of firms is similar to the neighbourhood effects literature, which initially
focused on the assumption that individuals in deprived areas suffered worse outcomes in education
and the labour market partly because of the composition of those neighbourhoods (Cheshire, 2009).
According to this view, geographical concentrations of deprived individuals often suffered from
poor public services, a lack of role models and weaker social networks, and this compounded the
initial disadvantage of local residents. Independent of their personal characteristics, if an individual
lived in a deprived area, social outcomes would be worse than those outside.
However, evidence has challenged this interpretation. Research on the Moving to Opportunity
programme, where families in deprived US neighbourhoods were subsidised to move to more
affluent areas, failed to support this view (Cheshire, 2009). Long-term evidence suggested little
overall improvement in educational outcomes, health or risky behaviour (such as drug use), with
positive effects for women and negative effects for men (Kling et al., 2007). This led to an alternative interpretation: that individuals sort into poor neighbourhoods on the basis of their personal
characteristics and the housing market. Poorer neighbourhoods are likely to be cheaper, and so
attract those on low incomes without actually making them poor.
Similar firm-level processes may operate. Sorting effects will help firms move into neighbourhoods with characteristics that they value: an idea widely considered in the literature on
agglomeration economies (Baldwin and Okubo, 2006; Cheshire, 2009). Firms may benefit by
learning from nearby firms, having access to specialised suppliers, customers or labour markets.
More prosaically, deprived areas may have low land values, be close to transport infrastructure
or have derelict land suitable for development. Some firms will value these attributes more than
others, and relative valuation will sort companies into different neighbourhoods. Policies subsidising firms to locate in particular neighbourhoods such as LEGI ignore these benefits, and may
lead to sub-optimal outcomes.
In addition, survival effects may apply. Firms in neighbourhoods with characteristics for which
they are unable to pay or barriers they cannot overcome, will fail or move (Saito and Gopinath,
2009). Firms which value amenities or proximity to particular markets will benefit from adjacent
locations. For other firms, paying for these amenities makes no sense, and studies have tended to
support this view. For example, new firms located in specialised industrial clusters are likely to
survive longer as they benefit from exchanges of knowledge, skilled labour and access to specialised customers and suppliers (Wennberg and Lindqvist, 2010). Saridakis et al. (2012) show that
new firms in less affluent parts of the UK were less likely to survive a three-year period.
A further problem is that deprivation is normally measured by resident characteristics rather
than the success of the local economy. As mentioned previously, policies in the UK have tended to
focus on relatively small units, such as the ward or neighbourhood; yet measures of deprivation at
Lee and Cowling
5
the neighbourhood level may ignore the strength of wider economies, as deprived wards can result
from sorting effects and so, can exist even in highly successful urban economies (Cheshire, 2009;
Gordon, 2003). In many UK cities, deprived urban neighbourhoods may be in areas which are
actually relatively good places for business activity. The City fringe area of central London, for
example, is an area of resident deprivation which is close to the financial centre of the city. The
problem is that residents do not have the appropriate qualifications to benefit from employment
in financial services, but some categories of firm, such as financial or business service firms, do
benefit from such locations.
In places such as the City fringe, where the existing residents fail to benefit from the successful
urban economy nearby, simply creating further employment is unlikely to lead to significant
improvements in labour market outcomes. Improving enterprise in relatively small areas may create jobs that are simply taken by outsiders, (Blackburn and Ram, 2006; Drever, 2006; Lyon et al.,
2002). Efforts may be better targeted at enabling residents to access existing opportunities.
Similarly, attempts to improve demand for local firms may fail, as not all firms are embedded into
local markets, with firms developing strategies at range of scales (Clark et al., 2004a).
A further major criticism is the explicit assumption in policies such as LEGI and the work of
Michael Porter that firms in deprived areas actually face different obstacles to other firms. A
number of academic studies have considered the problems faced by firms in deprived areas (Lee
et al., 2011; Oc and Tiesdell, 1999; Rouse and Jayawarna, 2006; Williams and Williams, 2011).
These often find evidence that firms in deprived areas do face barriers; Williams and Williams
(2011) identify a lack of affordable workspace, location factors such as poor image or fear of
crime, and problems accessing growth finance. Lee et al. (2011) find that the social networks
available to groups in deprived areas limit the extent to which they can obtain capital, as do
Rouse and Jayawarna (2011). Evidence such as this suggests that firms in deprived areas do face
a number of barriers to growth.
Yet, these studies have tended to focus exclusively on deprived areas, and so lack a meaningful
control group. For example, Williams and Williams (2011) conducted interviews with 459 entrepreneurs only in the Sheffield LEGI area. Similarly, Rouse and Jayawarna (2011) only considered
firms in deprived areas. As the authors recognise, this means that they are unable to assess whether
these barriers are due to location, or firm characteristics. Of course it is possible that, independent
of firm characteristics, location in a deprived area will influence the barriers faced by firms.
Deprived areas may have more informal economic activity, with a local culture that is more forgiving of such activities (Williams, 2010). Firms may develop attitudes or behaviours simply
from co-locating with other firms operating similar practices. In addition, deprived areas may
have other, common problems which stem from their location, such as a lack of access to business
support services or mainstream banking provision. Entrepreneurs may find that their social networks are restricted, and that this limits access to certain types of funding (Rouse and Jayawarna,
2011).
Yet, if sorting and survival effects operate, firms in deprived locations will have different characteristics. There is some evidence that this is the case; new firms in deprived areas tend to start
with lower investments than those outside (Rouse and Jayawarna, 2011), be in different sectors and
have entrepreneurs with different characteristics (Frankish et al., 2010; Oc and Tiesdall 1999).
Addressing problems related to these characteristics may be a more appropriate way to target such
obstacles. For example, evidence suggests that entrepreneurs’ ability to access finance varies by
ethnic group (Smallbone et al., 2003); in addition, particular ethnic groups may be more likely to
start firms in deprived places (Philips, 1998). If the problems faced by ethnic groups relate to ethnicity rather than location, it might be more appropriate for policy to focus on addressing these
rather than helping firms in a particular place. In sum, the barriers faced by firms in deprived areas
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International Small Business Journal 0(0)
may relate to their characteristics rather than their location. In the remainder of this article we consider whether this is the case.
Method
Data collection
The data for this article was taken from the Annual Small Business Survey 2007–2008, which is a
telephone survey of SMEs (defined as having fewer than 250 employees) across the UK by the
Department for Business, Innovation and Skills. In order to ensure comparability of the measure
for deprived areas and at least reasonable comparability of policy issues, we only used English
firms. Thus, our sample for analysis was 7670 firms.
The Annual Small Business Survey data includes a variable for whether the firm is located in
one of the 15 percent most deprived Super Output Areas in England.1 Of the firms surveyed, 806
are based in deprived areas, giving a weighted percentage of 10.2 percent of the sample. The use
of Super Output Areas is something of a blunt geographical indicator, as it fails to account for
whether the firm is located in a deprived part of a larger and more successful urban area (such as
Lambeth in London), or those which do not form parts of wider urban areas. However, it does have
the strong advantage of being close to the geography at which policy is made (for example one
policy, the Phoenix Development Fund, applied to the 15 percent most deprived wards). These
units are also small enough to capture sorting effects within a particular city, and so allowed us to
test the theoretical basis of such claims.
Another issue is that the data comprises only existing firms, and so fails to account for nascent
entrepreneurship or failed ventures. Given the limitations of existing data sources, it is impossible
to address this problem in this article. Future research may wish to consider this issue.
Descriptive statistics
Table 1 gives descriptive statistics on firms in and outside of deprived areas. Firms in deprived
areas are likely to be slightly larger (F-stat = 8.96, prob. 0.003), with an average size of 9.74
employees compared to 7.96 employees outside. Firms in deprived areas are significantly more
likely to be led by a member of a minority ethnic group (F-stat = 105.53, prob. 0.00001), with
around 15.57 percent in this category compared to just 5.92 percent outside, affirming common
perceptions of a deprived areas/ethnic minority link (Oc and Tiesdell, 1999). Finally, we note that
firms in deprived areas are significantly more likely to be growth-oriented, with 58.97 percent
growth-orientated in deprived areas, but only 52.62 percent aiming for growth outside (F-stat =
11.76, prob 0.0006).
Table 1. Characteristics of firms in deprived areas
Not in deprived area
In deprived areas
Total
Average employees
(excluding zero
employee firms)
% minority-ethnic
led
% aiming for growth
7.96 (16.29)
9.74 (19.75)
8.21
5.92 (23.60)
15.57 (36.28)
6.92
52.62 (49.93)
58.97 (49.21)
53.28
Note: Figures in parenthesis are standard deviations.
0.0006
-0.0793*
0.0065
-0.0150*
-0.0297*
0.011
0.0129
0.0536*
-0.0025
-0.0236*
0.0225*
0.0253*
0.0224*
-0.0402*
0.0837*
-0.0838*
0.0337*
0.0087
-0.013
0.0259*
-0.0031
-0.0059
0.0099
0.0388*
-0.0029
0.0018
0.0244*
-0.0217*
-0.01
-0.0153
-0.0184*
0.012
-0.0091
0.0017
-0.0116
0.0505*
-0.0652*
0.0994*
0.011
0.0537*
-0.0586*
-0.011
-0.0138
-0.0228*
-0.0063
0.0047
0.0150*
-0.0107
0.0054
-0.0021
-0.0456*
0.0490*
-0.0069
-0.0234*
0.0003
-0.0269*
0.0240*
0.0320*
-0.0435*
-0.0175*
0.0236*
-0.0509*
-0.0358*
-0.0101
0.0107
-0.002
-0.0053
0.0360*
0.0077
0.0104
-0.0012
0.0606*
-0.0047
0.0065
0.0692*
-0.0432*
-0.0128
-0.0368* 1
0.0735* -0.0567* 1
-0.0222* 0.1272* -0.0313*
-0.0174* -0.0188* -0.0443*
-0.0519* -0.0352* -0.0886*
1
-0.0344*
-0.1064*
-0.0213*
-0.0203*
-0.0460*
1
0.0018
1
-0.0338* -0.003
-0.0327*
-0.0693*
-0.0255*
-0.0135
-0.0433*
1
-0.0465*
-0.0584*
-0.0088
***p<0.01, ** p<0.05, * p<0.1
The economy
Obtaining finance
Cash flow
Taxation, VAT,
etc.
Recruiting staff
Regulations
Shortage of skills
Location
Lack of customer
demand
Deprived
Ethnic-led
Degree
Production
Construction
Services
Multi-site
Age: 4–10
Age: 10+
Board size
Family firm
Female-owned
0.0177
-0.0034
0.0521*
0.0447*
0.0553*
-0.0654*
0.0296*
0.0003
0.0051
0.0445*
-0.0261*
-0.0228*
1
-0.0112
-0.0222*
0.0202*
0.0062
-0.008
-0.0253*
-0.0252*
0.0375*
-0.0237*
-0.001
0.0066
0.003
-0.0085
0.0202*
1
-0.0024
-0.0122
-0.0038
0.0265*
0.0028
-0.0002
0.0025
-0.0152
0.0089
-0.0085
-0.0067
-0.0159*
0.0058
1
1
0.0819*
-0.005
0.0445*
-0.0166
-0.0013
0.0153
‘0.0003
‘0.0146
‘0.0440*
-0.0539*
‘-0.0056
1
0.0418*
-0.0301*
-0.0497*
0.0685*
‘-0.0137
‘0.0334*
‘-0.0534*
‘-0.0473*
‘0.0267*
‘-0.0146*
1
0.0467*
-0.0646*
0.0098
0.1023*
0.0026
0.0022
0.1192*
-0.1093*
-0.0505*
The economy Obtaining Cash flow Taxation, Recruiting Regulations Shortage of Location Lack of Deprived Ethnic-led Degree
finance
VAT, etc. staff
skills
customer
demand
Table 2a. Correlation matrix (part 1)
Lee and Cowling
7
1
-0.1544*
-0.7021*
-0.0334*
-0.0500*
0.0707*
-0.0222*
-0.0019
-0.0717*
***p<0.01, ** p<0.05, * p<0.1
Production
Construction
Services
Multi-site
Age: 4–10
Age: 10+
Board-size
Family firm
Femaleowned
Production
1
-0.4907*
-0.0309*
0.0001
0.0017
-0.0429*
0.0461*
-0.0546*
Construction
Table 2b. Correlation matrix (part 1)
1
0.0546*
0.0597*
-0.0830*
‘0.0541*
-0.0604*
0.1024*
Services
1
-0.0465*
0.0733*
0.2126*
-0.0777*
-0.0553*
Multi-site
1
-0.8428*
-0.0826*
-0.0769*
0.0360*
Age: 4–10
1
0.1068*
0.0845*
-0.0583*
Age: 10+
1
-0.2318*
-0.0774*
Board-size
1
-0.0293*
Family firm
1
Female-owned
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Lee and Cowling
9
The correlation matrix (Tables 2a and 2b) highlights some interesting points. First, in relation to
barriers, we note that the highest correlation is only 0.127 between shortage of skills and problems
with recruitment. In general, the pairwise correlations between barriers are much smaller. For
example, the demand measures (lack of demand and the economy) have a pairwise correlation of
-0.0433, which suggests that they are not measuring the same thing. In relation to deprived areas,
only two correlations are significant, with cash flow barriers being negative (pairwise correlation
= -0.023) and location being positive (pairwise correlation = 0.022). We conducted some additional analysis to establish whether or not obstacles are independent or clustered to deal with the
issue of common method bias, and found that only six were positively correlated and the test scale
score was only 0.076, which does not pass standard thresholds, even for experimental work. The
average inter-item correlation was surprisingly low at 0.01. Although we expected a stronger association between barriers, the finding that barriers are independent is important in its own right.2
Testing obstacles to success in deprived areas
The model tests whether firms in deprived areas experience different barriers to growth to firms
elsewhere. A simple cross-sectional probit regression model is used. This predicts a binary response
variable (whether the firm experiences a particular barrier to growth) compared to a set of explanatory variables. It is specified as:
Yi = α + β1 Deprivedi + β2 FirmSizei + β3 Sectori + β4 DevelopmentStagei +
β5 BoardSizei + β6 FamilyFirmi + β7 FemaleLedi + β8 EthnicLedi +
β9 OwnerDegreei + β10 MultiSitei + β11 Regioni + ε
where Y is the outcome variable indicating whether firm ‘i’ perceives that a particular issue is an
obstacle to the success of the business. ‘Deprived’ is a binary variable which indicates whether a
firm is in one of the 15 percent most deprived Super Output Areas in England. We also control for
a series of other firm characteristics such as size, sector, age, ownership characteristics and region.
Barriers to success
The dependent variable is a binary with the value 1 if firms perceived a barrier, and 0 otherwise.
This is taken from the business owner’s responses to the question: ‘Which if any [of these obstacles] represent obstacles to the success of your business?’ We used data on nine potential obstacles
which are closely related to past enterprise policies and theoretical assumptions of the problems
faced by firms.
The limitation of this measure is that it reflects the subjective perceptions of firms (Baum
et al., 2011; Coad and Tamvara, 2011). Firms may blame external factors such as the economy
rather than internal issues such as their own business strategy. Moreover, perceptions may be
altered by media attention to issues such as regulation (Lee, 2011), the past experience of firms
or the management team’s own ability (Baum et al., 2011; Leonidou, 2000). These also rely on
recall from respondents who may not always be aware of the true barriers that they face to
success (Baum et al., 2011).
However, using these questions is in line with other studies addressing the obstacles faced by
firms in particular activities (Baum et al., 2011; Coad and Tamvara, 2011). The respondents to the
Annual Small Business Survey are entrepreneurs and senior managers, people who are likely to see
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the answers to these questions as important and to have considered them previously (Baum et al.,
2011). Moreover, systematic analysis of the range of barriers which may impact on firms can be
achieved only through a set of questions like this.
The obstacles we used are as follows. First, policies such as LEGI are based on the idea that
demand for goods and services will be lower in deprived areas, making local demand an issue for
firms (Drever, 2006). However, theoretical work suggests that surviving firms will have adapted
their business to serve other markets (Clark et al., 2004a). The Annual Small Business Survey does
not have a variable for the strength of the local economy, and so a variable for whether general
economic conditions are an obstacle to success was used (‘Economy’). While this does not perfectly capture local economic demand, it should reflect a large element of this effect. A second
variable captures general economy-wide demand (‘Demand’). Both of these variables would be
expected to reflect local economic conditions as well as the national economy, and so be positive
for firms in deprived areas.3 Yet the importance of either may be limited only to certain sectors, as
other firms rely on national and international networks or markets (Clark et al., 2004a, 2004b).
Second, another two barriers relate to cash flow and access to finance. Policies such as Enterprise
Areas aimed to support firms in accessing finance and helping cash flow difficulties. There is also
a wide academic literature suggesting that access to finance capital is highly important for success
(Avdeitchikova, 2009; Mason and Harrison, 2002). Two variables were included to test whether
firms located in deprived areas find this a particular problem: whether firms see obtaining longterm finance as an obstacle to success (‘Obtaining finance’), and whether short-term cash flow
problems are an obstacle to success (‘Cash flow’). Note that the survey was conducted between
November 2007 and March 2008, and so largely predated the decline in the economy that took
place in 2008 (Williams and Cowling, 2009).
Third, other policies have focused on general business support. The Phoenix Development
Fund, for example, offered advice to firms in a range of areas. One area that firms need advice on
is taxation, and so a variable was included to test whether this is a particular obstacle for firms in
deprived areas (‘Taxation, VAT, NI or Business Rates’). Firms in deprived areas are more likely to
trade ‘off the books’, and difficulties with taxation may be one reason for this (Williams, 2010). In
addition, firms may lack the social networks and access to the informal or formal business advice
which may help them overcome regulation. A variable was included to test whether firms perceive
regulation as an obstacle (‘Regulation’).
Fourth, another set of policies focus on the ability of firms to recruit and retain appropriately
skilled staff, derived from the idea that there will be skill problems in deprived areas. Variables for
whether recruitment (‘Recruiting staff’) and skills (‘Shortage of skills generally’) are obstacles
were used to test the impact of place on hether these are particular obstacles for firms. As noted
above, it is unclear whether firms in deprived areas will employ staff living in those areas. If they
do not, recruitment difficulties may be less likely while skills shortages might be more so.
Finally, we included a variable which explicitly captures the effects of firm location on firm
success (‘Location’). This variable provided us with the purest test of our hypotheses regarding
‘place’ or ‘firm’ effects. If particular facets of deprived areas do cause firms to see this as an
obstacle, this suggests that there may be a rationale for targeting deprived areas.
Independent variables
In order to assess whether results are due to ‘place’ effects, such as deprived area location or to
‘firm’ effects, we also controlled for a series of other firm-level characteristics. The first two reflect
the characteristics of the owner(s) of the firm. Human capital and skills within a firm are important
Lee and Cowling
11
determinants of whether a firm is able to access particular business support services, and more
broadly in helping predict survival and growth. The seminal works of Mincer (1958) on human
capital and wages, and Becker (1964) on human capital, define human capital in terms of education, which is general human capital, training which can be general or firm-specific, and experience which captures learning and informal human capital development. More nuanced work in the
entrepreneurship literature (and the finance literature) relating to collective human capital in teams
of entrepreneurs has used the number in the entrepreneurial team (or board size; Eisenberg et al.,
1998) as a proxy for this construct (Coeurduroy et al., 2012; Unger et al., 2011).
Ultimately, we were constrained by the nature of the survey variables available; we used two:
the first is whether the owner has a degree or not (‘Degree-educated owner’). Qualifications are
only a proxy for actual ‘human capital’, but they are the closest available in the Annual Small
Business Survey. As firm human capital also may reflect the size of the leadership team, we
included a second variable capturing the absolute size of the board of directors (‘Board Size’).
Larger boards may provide better supervision of management, a wider range of contacts and better
problem-solving. This may reduce the barriers to growth faced by firms yet, this may not always
be the case, and other studies have suggested that board size is not always linked to better firm
performance (Gabrielsson, 2007).
In addition, there has been considerable discussion of the role of minority ethnic entrepreneurs
in developing deprived locations (Oc and Tiesdell, 1999; Blackburn and Ram, 2006). The extent to
which different ethnic minorities, women and families engage in entrepreneurial activity varies,
but the evidence suggests that overall, ethnic minorities have higher rates of entrepreneurship and,
in part to avoid a legacy of discrimination, women have lower rates (Clark and Drinkwater, 2010;
Cowling and Taylor, 2001); also, family businesses constitute an absolute majority of the total
business population (Westhead and Cowling, 1998). Because these ownership issues will influence
the barriers that firms face, we controlled for whether a firm has an ethnically concentrated ownership (‘Ethnic-led firm’), female-concentrated ownership (‘Female-led firm’), or is owned by a
single family (‘Family-led firm’). These variables may be important, as they reflect alternative
groups among which policymakers may try to stimulate entrepreneurship, and they will face different obstacles as both firms and entrepreneurs (Duberley and Carrigan, 2012; Ishaq et al., 2010).
The subsequent variables reflect the characteristics of the firm: these are four broad industrylevel dummies for whether firms are in manufacturing, construction and services. As barriers will
differ for firms of different sizes, four size categories were used to account for the size of the firm
(none, 1–9, 10–49, 50–249 employees). Finally, we introduced a set of nine regional dummies for
the English government office regions. These should control for region-specific issues, such as the
wider conditions of the regional economy.
Results
Univariate analysis
Before estimating our econometric models for the nine perceived obstacles to firm success, we
consider, at the univariate level, the differences between firms in deprived areas and other areas
and the relative rankings of perceived obstacles to firm success. These barriers are important, as
they provide context on the absolute barriers that firms face in deprived areas, without controlling
for their characteristics.
As Figure 1 shows, national level factors are perceived to represent the three most important
obstacles to success, although regulatory burdens are much less of an issue for firms located in
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Location
Skills
Demand
Recruitment
Access to
finance
Cash flow
Tax
Economy
Regulations
20
18
16
14
12
10
8
6
4
2
0
Not
Deprived
Figure 1. Ranking of perceived obstacles to success by deprived area status
deprived areas (ANOVA one-way sig. = 0.0001). Financing concerns also represent a significant
concern for a minority of firms; again, this is an area of differentiation between firms in deprived
areas and others, with deprived area firms significantly more likely to perceive access to finance as
an obstacle (one-way sig. = 0.0001).
Other potential obstacles to success such as labour market issues, the general level of demand
and locational barriers are relatively minor concerns. However, it is interesting to note that locational barriers are significantly more likely to be perceived as an obstacle to success by firms in
deprived areas (one-way sig. = 0.0001), with four times as many firms in deprived areas reporting
this as an obstacle, although the proportion seeing this as a problem is still low.
Locating in deprived areas
To begin, we model the probability that a firm will locate in a deprived area. This is important, as
it may be that particular types of firms choose to locate themselves in such areas, and this in turn
may affect the nature of the obstacles that they face, or indeed their ability to overcome obstacles.
The model shows that the larger a firm, the higher the probability that they will be located in a
deprived area. Large firms have a 10.7 percent higher probability of being in a deprived area compared to a single person entity. There is also a sector difference, with manufacturing firms being the
most likely to be in a deprived area, and primary industry firms (the reference category) the least
likely. At the regional level, firms operating in the North-East have a 15 percent higher probability
of being in a deprived area, firms in Yorkshire & Humberside a 14.7 percent higher probability, and
those in the North-West an 11.9 percent higher probability. In contrast, those in the South-East have
the lowest probability of all regions. Generally, while entrepreneur and owner characteristics were
not a significant determinant of deprived location, the results show that ethnic-led firms had a 7.1
percent higher probability of locating in a deprived area.
We also conducted an initial strand of analysis to look at the issue of potential endogeneity. Here
we estimated the probability of being in a deprived area or not (1,0) as a function of the full set of
firm and owner characteristics + barriers. Of the full set of obstacles tested, only two were found
13
Lee and Cowling
Table 3. Latent propensity of locating in a deprived area on obstacles
Obstacle
Coefficient on latent propensity
Significance
Economy
Obtaining finance
Cash flow
Tax
Recruiting staff
Regulations
Skill shortages
Location
Lack of customers
-0.13
-0.12
-0.01
-0.08
0.02
-0.13
-0.07
-0.02**
-0.06
0.50
0.23
0.95
0.57
0.78
0.50
0.11
0.02
0.56
***p<0.01, ** p<0.05, * p<0.1
to be significantly associated with deprived area status. These were ‘the economy’, which was
positive (+5% higher probability) and significant but only at the 10% level, and ‘cash flow’, which
was negative (-7% lower probability) and significant at the 1 percent level. Further multivariate
probit analysis, allowing for cross-equation correlations, confirmed this general finding that barriers were not generally a driver of deprived area status.
Is there a selection effect for firms locating in deprived areas
and the obstacles they face?
As the key variable is the dummy variable on whether or not a firm is in a deprived area – and we
have noted previously the differences in terms of what types of firms locate in deprived areas – it
is an important empirical question whether there is an element of self-selection in this decision. If
so, this may bias our interpretation of the deprived area effect on the probability that firms will be
more likely to face certain obstacles and barriers (Table 3).4
First, we assume that responses to questions concerning self-reported obstacles such as obtaining finance, cash flow, recruiting staff and location can be interpreted as indicators of individual,
firm-level latent probabilities of being in these states that differ by characteristics. Therefore, by
analogy we envisage that each respondent has a latent probability of being in a deprived location
that depends on a variety of personal and firm characteristics.
Second, we explore the intrinsic simultaneity between the likelihood of being in a
deprived location, on the one hand, and reported obstacles, on the other. It is likely that both
will depend on both the current and past trajectory of location and economic circumstances,
which are strongly interlinked. In order to examine a model of latent probabilities in a
simultaneous equation structure, we utilised the method outlined by Mallar (1977) and
Maddala (1983). Following Mallar (1977), we used a two-stage estimation technique as follows. The first step estimates the reduced form equation for location in a deprived area by
probit maximum likelihood. From this we obtained the predicted values which were then
substituted into the nine individual obstacle models. The key results concerning the significance of this key, latent propensity and deprived area variable in each of the nine obstacles
models are presented in Table 4.
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Table 4. Characteristics of location in deprived areas, probit models
Firm located in deprived areas
Firm size:
Small
Medium
Large
Industry sector:
Manufacturing
Construction
Services
Stage of development:
Early-stage firm
Established firm
Firm demographic:
Board size
Family-led firm
Female-led firm
Ethnic-led firm
Degree-educated owner(s)
Multi-site firm
Regional dummies:
East of England
London
North-East
North-West
South-East
South-West
West Midlands
Yorkshire & Humber
Pseudo R2
N observations
dF/dx
P>|z|
0.057***
0.088***
0.107***
0.000
0.000
0.000
0.454***
0.178***
0.090***
0.000
0.001
0.000
-0.001
0.016
0.973
0.555
0.000
-0.012
0.023
0.071**
0.012
-0.014
0.902
0.495
0.441
0.017
0.359
0.250
0.008
0.049
0.150***
0.119***
-0.059***
0.027
0.048*
0.147***
0.16
4837
0.738
0.127
0.000
0.000
0.000
0.381
0.094
0.002
***p<0.01, ** p<0.05, * p<0.1
Note: Reference category for the regional dummies: East Midlands.
As can be observed, the only model in which the latent propensity to be located in a deprived
area impacts on the nature of obstacles faced is the locational barrier model. In this case it is significant and acts to reduce the probability that location per se is a barrier. This implies that we must
account for selection effects, but only in our locational barrier model; and that the types of firms
that are most likely to locate in deprived areas are also the least likely in general to report location
per se as a barrier.
Growth orientation of firms in deprived areas
Before we begin our econometric investigation into the determinants of the nine potential obstacles
to success, it is apposite to consider whether firms in deprived areas are more likely to aim to grow.
Lee and Cowling
15
Growth-orientated firms are likely to perceive and face different constraints to firms with more
modest ambitions (Baum et al., 2011). In order to test this, we considered their relative growth
orientation. The procedure was as above but here the growth orientation variable, which is binary
in form, is our dependent variable. Results are given in Table 5 (Model 1). Note that in our reporting of the analysis, we have elected to present the marginal effects for ease of understanding and
interpretation.
The key finding is that firms in deprived areas are no more likely to have a growth orientation
than other firms. Thus, our univariate result, which showed that firms in deprived areas are more
likely to be growth-orientated, is due to other characteristics and not their deprived area location per
se. It was also the case that minority ethnic and female-owned firms were no different from white
and male-owned firms in this respect. Human capital was found to be important, as degree-educated
owners are 9.7 percent more likely to have a growth orientation, and a positive relationship between
board size (which indicates collective human capital) and having a growth orientation. There was
also a firm size effect, with micro-firms, small firms and medium-sized firms – 16.8 percent, 27.4
percent and 31.5 percent respectively – being more likely to have a growth objective than firms with
no employees. At the regional level, the results show that firms located in Yorkshire & Humberside,
West Midlands, North-West, and South-West were least likely to be growth-orientated. Firms with
multiple sites were 10.6 percent less likely to be growth-orientated. Finally, we observed a strong
and negative relationship between having a growth orientation and the stage of a firm’s life cycle,
with established firms (more than 2 years old), and early-stage firms (1–2 years old) – 40.1 percent
and 21.5 percent respectively – less likely to be growth-orientated than start-up firms. In short, we
note that younger and larger-sized firms with higher levels of human capital are more likely to be
growth-orientated. However, there is no difference in growth orientation between firms within and
outside of deprived areas; any difference in performance are due to other factors.
Barriers to success
Tables 5 and 6 report the full (marginal effects) probit models for each of the nine barriers to success. For ease of discussion, we have grouped the results into five broad categories. Table 5 gives
the results for growth orientation along with economic barriers (including lack of demand and the
general state of the economy) and financing (including access to finance and cash flow). Table 6
gives results for the categories of government (including taxation and regulation), skills (including
recruitment and general skills shortages) and geographical barriers (specific locational barriers).
In Table 5, Models 2 and 3 report the findings relating to economic barriers. The first point of
note is that being located in a deprived area is unimportant in terms of being more or less likely
to perceive general economic barriers to success. One reason for this is simply adaptation, as
weaker firms will not survive and firms will adapt their competitive strategies in weak economies
(Clark et al., 2004a, 2004b). Few of the firms which fail to adapt will have been in our sample.
Firm owners with higher levels of formal and general human capital are more likely to perceive
that general economic conditions are a significant barrier to success than those with lower levels
of human capital. While this formal human capital effect held for lack of demand in the economy,
the general human capital effect (proxied by board size) was reversed. In addition, it was found
that start-up firms were much more likely to perceive general economic conditions as a barrier
than even very early-stage firms. The scale of the difference in probabilities was 3.8 percent compared to one to two-year-old firms, and 4.5 percent compared to firms more than two years old.
This suggests that timing of market entry is important for start-ups. While firm size appeared to
play no role in the perception of general demand as a barrier, it did in relation to general economic
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Table 5. Determinants of growth outlook and obstacles to firm success, probit models
Category
Orientation
Firm orientation/
perceived barrier
(1) Firm is growth (2) Demand
oriented
dF/dx
Deprivation status:
Deprived area
Firm size:
Small
Medium
Large
Industry sector:
Manufacturing
Construction
Services
Stage of development:
Early-stage firm
Established firm
Firm demographics:
Board size
Family-led firm
Female-led firm
Ethnic-led firm
Degree-educated
owner(s)
Multi-site firm
Regional dummies:
East of England
London
North-East
North-West
South-East
South-West
West Midlands
Yorkshire &
Humber
Pseudo R2
N observations
Economic:
Finance:
(3) The economy (4) Obtaining
finance
P>|z| dF/dx
P>|z| dF/dx
P>|z| dF/dx
0.177
0.001
0.954
0.020
0.366
0.168*** 0.000 -0.001
0.274*** 0.000 -0.001
0.315*** 0.000 -0.008
0.787
0.826
0.407
0.040*** 0.001 0.004
0.055*** 0.001 -0.001
0.061** 0.017 -0.015
0.053
(5) Cash flow
P>|z| dF/dx
0.031** 0.041 -0.003
0.566
0.893
0.307
0.019
0.011
0.002
0.891
0.113
0.504
0.942
0.084 -0.024
0.323 -0.033
0.128 -0.001
0.521
0.364
0.977
0.147 -0.038*
0.883 -0.045*
0.099 -0.021
0.122 -0.041* 0.089
0.063 -0.037** 0.019 -0.058** 0.020
0.011 0.008*
0.919 -0.006
0.626 -0.001
0.003 -0.024
0.003 0.041***
0.064 0.011***
0.714 -0.014
0.968 0.009
0.314 0.051**
0.007 0.009
0.001 -0.002
0.127 0.006
0.439 0.014
0.023 0.019
0.344 -0.010
0.822
0.678
0.456
0.510
0.499
-0.106** 0.020 -0.006
0.598 -0.003
0.898
0.005
0.748
0.624
-0.081
-0.075
-0.073
-0.119**
-0.034
-0.090*
-0.150**
-0.157**
0.071
0.008
0.099
0.000
0.001
0.005
0.130
0.068
0.080** 0.025 -0.001
0.040
0.276 -0.003
0.016
0.686 -0.020
0.024
0.450 0.000
0.026
0.443 0.003
0.052
0.116 0.002
0.054
0.189 -0.003
0.029
0.463 0.009
0.978
0.890
0.214
1.000
0.910
0.925
0.911
0.702
0.069
-0.050
0.052
0.309 0.050*
0.471 -0.015
0.390 0.022
-0.215*** 0.000 -0.016
-0.401*** 0.000 -0.002
0.045***
-0.009
-0.031
0.068
0.097***
0.10
7670
0.001 -0.010**
0.733 -0.001
0.321 0.004
0.173 -0.022***
0.000 0.022***
0.154
0.226
0.278
0.025
0.560
0.092
0.023
0.015
0.029*
0.058***
0.042*
0.062***
0.063***
0.047**
0.034
0.041*
0.07
7670
0.03
7670
0.042
0.038
0.028
0.05
7670
0.153 -0.023
0.202 -0.021
0.114 -0.073*
P>|z|
0.010
0.506
0.562
0.053
-0.036
-0.025
-0.068***
-0.034
-0.036
-0.042*
-0.027
-0.015
0.206
0.438
0.010
0.191
0.211
0.098
0.405
0.655
0.02
7670
***p<0.01, ** p<0.05, * p<0.1
Note: Reference category for the regional dummies: East Midlands.
conditions. Here, medium-sized firms were 6.1 percent more likely than firms with zero employees
to report this as a barrier, with small firms being 5.5 percent more likely, and micro firms being 4
percent more likely. Sector was not important, with the exception that production firms were 5
percent more likely to perceive a lack of demand as a barrier. Regions were important in terms of
lack of demand, but not generally in terms of wider economy barriers. This suggests that our
demand measures did not measure the same underlying construct, possibly one reflecting local
conditions more than the other.
In relation to perceived financing barriers (Models 4 and 5), a deprived area location is associated
with a 3.1 percent higher probability of reporting an access to finance barrier. This is consistent
Lee and Cowling
17
with Marshall (2004), who reports that half of those with no financial products live in one of the
50 most deprived areas of England and Wales, and concludes that financial exclusion is a product
of people and place. In contrast, deprived area status was not associated with cash flow as a barrier.
This former effect suggests that policy initiatives aimed at providing banking services in deprived
localities with no mainstream high street banking provision are important. In addition, it might
suggest that locality is an important determinant of bank lending decision criteria through a local
demand effect and/or a collateral effect (lower property values). Indeed Cowling (2010), in his
study of the UK Small Firms Loan Guarantee Scheme, strongly suggests that collateral is the key
driver of bank unwillingness to lend through normal channels, and a clear justification for loan
guarantee schemes for good entrepreneurs with little wealth which can act as collateral. Similarly,
Carter and Van Auken (2005) attribute these factors as critical determinants of small firms’ use of
bootstrap financing.
That cash flow is not significant is a challenge to this result, but might imply a difference
between short-term access to finance through High Street banking services and the wider issue of
firm finance through specialist services. Equally, it might imply that banks are concerned with ability to service loan repayment; the latter effect also might be affected by restricted social networks.
This interpretation may be supported by the only other significant effect, in terms of perceiving that
access to finance was a barrier among ethnic-owned firms who were 5.1 percent more likely to see
this issue as a barrier to success: one of the key explanations for which is that restricted social
networks may reduce the ability of some (but by no means all) ethnic groups in accessing finance
(Oc and Tiesdell, 1999). It may be that firms facing cash flow difficulties do not survive, but also
that those which cannot raise long-term finance are unable simply to grow.
On cash flow, service firms were 7.3 percent less likely to perceive this as a barrier than firms
in other sectors. Region mattered, with firms located in London and the South-East – 6.8 percent
and 4.2 percent respectively – being less likely to perceive cash flows as a barrier to success. Again
we note that start-up firms perceive more barriers to success. Here, start-ups – 5.8 percent and 4.1
percent respectively – were more likely to perceive that cash flows were a barrier to success than
established and early-stage firms.
Table 6 gives the results for government, skills and location barriers. Regarding perceived barriers derived from direct government policy, taxation and regulation (Models 1 and 2), we note that
a deprived area location has no effect on the perception of taxation as a barrier, but actually reduces
the probability that regulatory barriers are perceived to be an obstacle to success by 5.5 percent.
One explanation might be greater participation in the informal economy in deprived areas, with
firms more willing to avoid regulation (Williams, 2010). Perceived regulatory barriers were higher
for all size classes of firm, with employees with a peak in the small (10–49 employee) size class.
No effect was identified for taxation. Sector was also important but acted in different ways in
respect of taxation and regulation. On the former, all non-primary sectors were more likely to perceive taxation as a barrier. On the latter, all non-primary sectors were less likely to perceive regulation as a barrier. In contrast, regions did not appear to matter, with the notable exception that firms
located in Yorkshire & Humberside were 7.5 percent more likely to cite taxation as a barrier.
In terms of stage of development, early stage firms (one to two years old) were 4.4 percent more
likely to perceive taxation as a barrier, and 9.5 percent more likely to perceive regulatory barriers
than start-up firms. It was also the case that established firms (more than two years old) were 12
percent more likely to perceive regulatory barriers. In general, this evidence suggests that earlystage firms perceive more barriers than start-up firms. There were also some interesting gender
differences. Here, the results show that women-owned businesses were 2.8 percent less likely to
perceive taxation as a barrier, and 4.5 percent less likely to perceive regulations as a barrier. Familyowned businesses were 3.3 percent more likely to perceive regulatory barriers.
18
International Small Business Journal 0(0)
Table 6. Determinants of government, skills and geographical obstacles to firm success, probit models
Category
Government:
Skills:
Geography:
Perceived barrier
(1) Taxation,VAT, (2) Regulation
NI or business
rates
(3) Recruiting staff (4) Shortage of
skills generally
(5) Location
Deprivation status:
Deprived area
Firm size:
Small
Medium
Large
Industry sector:
Manufacturing
Construction
Services
Stage of development:
Early-stage firm
Established firm
Firm demographics:
Board-size
Family-led firm
Female-led firm
Ethnic-led firm
Degree-educated
owner(s)
Multi-site firm
Regional Dummies
East of England
London
North-East
North-West
South-East
South-West
West Midlands
Yorkshire &
Humber
Pseudo R2
N observations
dF/dx
P>|z| dF/dx
P>|z| dF/dx
P>|z|
-0.003
0.903 -0.055**
0.010 -0.006
0.540
0.016
0.014
-0.030
0.174
0.346
0.134
dF/dx
0.003
P>|z| dF/dx
P>|z|
0.691
0.005**
0.044
0.002**
0.003*
0.000
0.050
0.068
0.868
0.057*** 0.000
0.113*** 0.000
0.078** 0.013
0.021*** 0.008
0.052*** 0.000
0.054*** 0.005
0.010*
0.095
0.030*** 0.002
0.048*** 0.006
0.112* 0.065 -0.110*** 0.001
0.142** 0.028 -0.084** 0.024
0.073** 0.049 -0.181*** 0.000
0.041
0.119
0.090*** 0.007
0.023
0.129
0.029
0.138 -0.001
0.677
0.062*** 0.007 -0.003*** 0.007
0.014
0.126 0.000
0.769
0.044
0.024
0.017
0.003
0.104
0.323
0.095** 0.011
0.120*** 0.000
0.263 0.000
0.849 -0.013
0.965 -0.001
0.171 -0.001
0.402
0.584
0.965
0.308
0.002
0.336
0.558
-0.001
0.007
-0.028*
-0.029
-0.003
0.816 0.006
0.624 0.033*
0.094 -0.045**
0.218 -0.011
0.832 0.021
0.322 0.003
0.064 -0.006
0.031 0.027**
0.753 0.003
0.244 0.003
0.189 0.001
0.447 -0.003
0.015 0.001
0.834 0.012
0.715 0.015**
0.552 0.000
0.649 -0.001
0.933 0.006***
0.281 -0.001
0.015 0.001
-0.009
0.692 -0.023
0.421 -0.028*
0.084 -0.010
0.367
0.016
0.032
0.012
0.018
-0.001
0.010
0.036
0.075*
0.645
0.398
0.760
0.576
0.979
0.766
0.395
0.095
0.931
0.407
0.684
0.197
0.319
0.239
0.608
0.744
0.120
0.254
0.725
0.407
0.533
0.435
0.786
0.253
0.02
7670
0.003
-0.032
-0.017
0.048
0.040
0.043
-0.022
-0.014
0.05
7670
0.039
0.029
0.009
0.018
0.014
0.017
0.006
0.031
0.05
7670
-0.011
-0.013
-0.010
-0.014*
-0.016*
-0.012
-0.004
-0.012
0.06
7670
0.305
0.176
0.320
0.088
0.065
0.172
0.735
0.247
0.002*** 0.000
-0.002*
-0.001
0.002
0.000
-0.002
-0.002
-0.002
-0.002
0.072
0.716
0.596
0.904
0.185
0.119
0.221
0.109
0.16
7670
***p<0.01, ** p<0.05, * p<0.1
Note: Reference category for the regional dummies: East Midlands.
Next, we report on the results concerning skills and recruitment barriers (Models 3 and 4). The
first point of note is that firms located in deprived areas were no more or less likely to perceive
skills or recruitment barriers than firms in wealthier areas. There was little evidence to suggest that
region mattered, with the notable exceptions of firms located in the North-West and South-East –
1.4 percent and 1.6 percent respectively – being less likely to perceive skills shortages as a barrier.
Stage of development did not play a significant role either.
Lee and Cowling
19
Firm size did matter, with the importance of skills and recruitment constraints increasing firm
size class. At the sector level, construction firms were significantly more likely to perceive skills
and recruitment constraints than firms in any other sectors. Finally, we note that women-owned
firms were 2.7 percent more likely to perceive recruitment barriers.
Finally, we considered whether firms in deprived areas are more likely to report location as a
barrier to success (Model 5). The results from the model with no selection suggest that firms in
deprived areas are 0.5 percent more likely to perceive location as a barrier, but once selection is
taken into account, this effect disappears. Small firms, female-led firms and multi-site firms are
more likely to regard this as a problem, while this is less the case for construction firms.
Conclusion
Policymakers and academics have considered enterprise to be an important way of addressing
deprivation (Drever, 2006; Greene, 2002; Harrison and Glasmeier, 1997; Hartshorn and Sear,
2005; Porter 1995, 1997). However, the approach faces a number of criticisms; not least the lack
of evidence as to whether firms in deprived areas actually do face distinct obstacles. While firms
may suffer from being located in deprived areas (a ‘place’ effect), instead it might be the case that
particular firms are sorted into deprived areas (a ‘firm’ effect). The evidence presented here provides only limited support for the ‘place’ effects, as it appears that small firms in deprived locations
face few obstacles to success which other firms do not, regardless of whether controls for other
firm characteristics are used. Actually, they may be less likely to face regulation as an obstacle.
The only obstacle which remains is lack of access to long-term finance. This suggests that the
image of cash-poor small firms in deprived areas is true to some extent, and that there may be
some justification for policies that address these problems. This finding reflects other evidence
that access to capital tends to be geographically biased, and that the problem is often less about
lack of available capital; rather that those in certain groups and particular neighbourhoods, cities
or even regions find it hard to access this capital (Avdeitchikova, 2009; Mason and Harrison,
2002). Access to capital may reflect the impact of social networks, and other research in this area
has suggested that the limitations of social networks in deprived areas may restrict firm’s ability
to access finance (Lee et al., 2011). However, the policy implication of this is not clear, as it
might be that firms which have difficulty gaining finance locate in deprived areas which are
cheaper, or it may simply be a ‘collateral’ effect, with firms having insufficient assets to set
against borrowing.
While the broad evidence here tends to suggest that it is not location which causes problems for
these firms, it is further reinforced by our finding that location itself is not considered to be a more
significant barrier to success for firms in deprived areas. However, there may still be some practical
merit to approaches focusing support on deprived areas; it may be a good way of reaching firms
with particular characteristics, such as large numbers of minority ethnic staff, although it is
unlikely to be as effective as actually targeting these firms. Policy on SMEs in deprived locations
is not simply a matter of enterprise, but rather an attempt to meet social objectives, and so in these
terms, the policies may be more successful (Bennett, 2008).
The results also challenge other policies focusing enterprise support on small geographical
areas. In 2011 the UK government instigated a new Enterprise Zone policy, with firms located in
relatively small geographical areas, gaining inducements such as reductions in business rates. The
policy aims explicitly to tackle the ‘key barriers to local enterprise’ (Department for Communities
and Local Government, 2011: 2). The evidence from the present study suggests that at least two of
the barriers the policy cites – business rates and regulatory burdens – are not greater problems, for
20
International Small Business Journal 0(0)
firms in deprived areas at least. The present study’s results suggest that such policies are unlikely
to be effective.
The results overall suggest a number of policy interventions which may be more successful.
First, the present results do suggest that obtaining finance is seen as more of a problem in
deprived areas. Addressing this problem – whether real or perceived – is important. Second,
they suggest limitations to very local attempts at business support. Particularly in the current
climate of austerity, refocusing business support on firms with the potential and ambition to
grow, regardless of where they are located, may be more appropriate. Third, it is important to
focus efforts on the residents of deprived areas rather than the firms, in order to ensure they
have the capacity to take up opportunities where those opportunities are located. More appropriate policies may include those seeking to improve the skill levels of the unemployed or those
on low incomes. Of course, this is not an easy or original suggestion, and there are few ‘easy
wins’ in addressing deprivation. Efforts to improve enterprise skills among deprived groups,
such as courses run by The Prince’s Trust, may be more effective ways of using enterprise to
address deprivation.
Implications of the study and suggestions for future research
The results of this study have wider implications for theory in this area. As is common to a number
of similar studies, the research suggests that the impact of enterprise and SMEs in addressing social
exclusion may be limited (Blackburn and Ram, 2006; Kitching, 2006). They highlight the need to
consider deprived labour markets as part of wider economies, rather than as self-contained and
isolated. Theory which focuses exclusively on the analysis of deprived areas also needs to consider
wider economic geographical context (Cheshire, 2009). Future research on firms needs to build on
the results of this study and consider the implications of this form of entrepreneurial sorting for
enterprise policy.
The result for financing suggests that there may be more complex patterns of access to localised
networks and other place effects operating, which may require comparative case study research to
investigate more closely. Recent research focusing on the employment chances of deprived youth,
for example, has tended to highlight the independence of the interrelationship between place and
individual-level effects (White and Green, 2011). It is likely that in this case, it is the complex
relationships between the two which shape outcomes at the firm level, and this is an important area
for further study.
Limitations of the study
There are important caveats to the analysis presented here, which should be considered in
future studies on this topic. Using self-reported data on barriers to growth allows a comparison
to be made across groups, but prevented us from assessing the complicated relationships
between identity, business success and perceived barriers (Oc and Tiesdall, 1999; Parry, 2010).
Perceived barriers may prevent firms from growing, or entrepreneurs from starting firms
(Doern, 2009). A further problem of cross-sectional studies is causality and the potential issue
of endogeneity affecting the results (Robson and Bennett, 2000). Another issue is that the
dataset contained only firms which exist, and fails to account for nascent firms or firms which
have failed, in part because of the obstacles to success discussed. These issues are important
questions for future research.
Lee and Cowling
21
Acknowledgments
We would like to thank both the editors and referees for helpful suggestions. Thanks also to James Phipps,
Phill Lacey, Henry Overman and Paul Cheshire. The data was provided by the Department for Business,
Innovation and Skills.
Notes
1. Super Output Areas are standardized geographical units with a mean population of 7200. Deprivation is
based on the Indices of Multiple Deprivation (IMD): an index of 37 measures of resident deprivation
including income, employment, housing, education and crime.
2. We thank a referee for suggesting these further tests, and think it strengthens the core findings of the
analysis.
3. Additional analysis of the correlation between these barriers suggests that only six are positively correlated, with the test scale score only 0.076: this does not pass standard thresholds, even for experimental
work. The average inter-item correlation was surprisingly low at 0.01.
4. We thank the editors for raising this important issue and suggesting a way of addressing it econometrically.
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Neil Lee is Head of the Socio-Economic Research Centre at the Work Foundation and Research Fellow in
Economics in the Department of Economics, Lancaster University Management School. His research interests are entrepreneurship, innovation, cities and economic development.
Marc Cowling is Professor of Management (Entrepreneurship) at the University of Exeter Business School.
His research interest centres on the economics of small business. He has a strong interest in the econometric
evaluation of public policies towards small firms. Recently he has evaluated the UK government’s Small
Loans Guarantee Scheme as well as the hybrid venture capital programmes of the UK and Australian
governments.