J Evol Econ

DOI 10.1007/s00191-017-0502-z
R E G U L A R A RT I C L E

Did firm age, experience, and access to finance count?

SME performance after the global financial crisis

Marc Cowling 1 & Weixi Liu 2 & Ning Zhang 3

# Springer-Verlag Berlin Heidelberg 2017

Abstract This paper examines the relationships between firm age and entrepre-
neurs experience on SME performance after the 2008/09 global financial crisis.
We find that in general the crisis had a long-lasting scarring effect on the SME
sector, but there is evidence of some recovery in performance. Interestingly, the
well-established, and negative, firm age-growth relationship still holds, but
entrepreneurial experience did not have any substantive effects on small busi-
ness performance. Our findings suggest that the severity of the crisis meant that
previous entrepreneur experiences had little value in this unique and uncertain
environment. However, young firms still accounted for a disproportionately
high share of growth, especially among the fastest growing firms.

Keywords Firm age . Entrepreneurs experience . Job dynamics . Sales dynamics .

Financial crisis

JEL Classification L25 . L26

* Marc Cowling
M.Cowling2@brighton.ac.uk

Weixi Liu
w.liu@bath.ac.uk
Ning Zhang
helen.zhang@uwe.ac.uk

1
Brighton Business School, Brighton, UK
2
University of Bath School of Management, Bath, UK
3
Bristol Business School, University of the West of England, Bristol, UK
 Cowling M. et al.

1 Introduction

Small business growth is a topic of interest and relevance in many different areas of
economics and management studies given the significance of small and medium
enterprises (SMEs) in job creation, innovation and economic growth (Acs and Storey
2004; Audretsch and Keilbach 2004; Audretsch et al. 2008; Davila et al. 2003; Lerner
1999, 2002). Until recently, and following on from the seminal work of Birch (1979),
the prevailing orthodoxy was that smaller firms grew faster than larger firms and that
younger firms grew faster than older firms (Davidsson et al. 2010; Evans 1987; Geroski
and Gugler 2004; Yasuda 2005), although there were some dissenting views (largely
the early US work of Davis et al. 1996). Yet more recently this former size-growth
evidence base has been empirically challenged and the balance of evidence suggests
that, at the minimum, the (negative) age-growth relationship dominates the (negative)
size-growth relationship (Neumark et al. 2011; Haltiwanger et al. 2013; Anyadike-
Danes et al. 2014) relationship, although some researchers (e.g. Coad et al. 2013, 2014)
maintain that growth is well approximated by a random process equivalent to the ‘toss
of a coin’.
Although numerous studies have been conducted on small firm growth, our under-
standing about the phenomenon is far from complete (Davidsson et al. 2010). One
under-researched, albeit important and relevant area concerns the growth performance
of SMEs in periods of economic disequilibrium, as well as the actions taken to weather
the economic downturn and the effectiveness of such actions. In particular, do
well-established relationships such as that between young firms and superior
growth, typically observed in stable macroeconomic conditions, hold in such
unique economic circumstances or are they over-turned? Equally, does entre-
preneurial experience become increasingly important when uncertainty and risk
characterise the external environment?
The global financial and economic crisis (GFC), which began in September
2007, has posed major challenges to larger and smaller firms alike. The UK is
amongst the countries that have been hit the most deeply by the crisis,
contributing to a fall of 6.4% in GDP in the subsequent six quarters that
constituted the official recession (December 2007 to June 2009). This equates
to around three years of post-war trend level economic growth for the UK
economy (Cowling et al. 2012). On the one hand, due to large contractions in
the general demand for goods and services, the recession poses a greater threat
to smaller firms given larger firms’ competitive advantage derived from scales
of economy and scope (Dass 2000; Porter 1980). On the other hand, it is
generally believed that the small business sector of the economy is more
dynamic and opportunistic than the large firm sector, and that periods of
disequilibrium and economic instability are precisely the times when the best
entrepreneurs are able to take advantage of new opportunities as large firms and
the public sector withdraw from markets (Acs and Storey 2004; Grilli 2010).
This is an entrepreneurial quality effect, in effect separating the wheat from the
chaff (Kitson 1995).
However, even five years after the onset of the GFC, UK GDP is 3.31% lower than
its pre-recession figure. Whilst avoiding further contractions since 2012, the UK
economy is still facing serious challenges and the economic forecast has continued to
Did firm age, experience, and access to finance count?

be dampened, especially in the presence of Eurozone’s debt crisis (BCC, 2012). Yet
even for the more resilient firms, achieving sustainable growth over the long run when
market demands continue to contract and the economy continues to be mired in a low-
growth period, can be problematic. This is especially true if such within-recession
resilience was a result of using higher pre-recession profit to fund investment and/or
absorb the loss during the recession (Wickham 2010). Obviously, the longer the period
of low growth and instability lasts, the more unlikely retained profit will be able to
support future business growth. More importantly, in order to achieve a desirable
growth whilst maintaining the capability to adapt when the upswing comes and
realising opportunities for long-term value creation, entrepreneurs have to incur higher
short-term costs and adopt a more progressive strategy, which will also impair growth
performance or even business survival if such practice is not companied by any
improvement of macro-economic conditions.
Thus it is the intention of this paper test whether two well-established empirical facts
relating to SMEs and growth retain their validity in a prolonged period of macroeco-
nomic instability and low growth. We use a longitudinal data set for the UK, which
spans the period from December 2010 to June 2012 the 2nd and 3rd years of recession
since the 2008/09 financial crisis, to address 2 key questions:

& Does the negative firm age – growth relationship still hold after the GFC?
& Does entrepreneurial experience help sustain superior growth after the GFC?

In doing so, we hope to add to our general understanding of what really happens to
the SME sector when the economy is recovering extremely slowly from the GFC and a
severe economic downturn. This context is particularly interesting and unique (Fig. 1)
as economic recessions in the UK do not normally last this long (NIESR 2012). This
will enable us to consider the growth performance dynamics of the SME sector and also
identify areas government actions that might be appropriate.
The rest of the paper is organised as follows. In the next section we review some of
the key literature relating to SME growth. Section 3 presents out data and discusses key
variables to be used in our analysis. Section 4 presents the results of our empirical

Fig. 1 UK economic recessions: How recessions compare. Source: National Institute for Economic and
Social Research (2013)
 Cowling M. et al.

analyses. Section 5 explores the significance and relevance of the results of our study
and draw out the implications for policy-makers and practitioners. The last section
concludes the paper.

2 Literature review

The growth of small firms has been the focus of numerous theoretical and empirical
studies in entrepreneurship research (McKelvie and Wiklund 2010; Coad 2009).
Despite this substantial research volume, theoretical development remains
fragmented and slow (Davidsson and Wiklund 2000; Wiklund et al. 2009)
and empirical evidence highly inconsistent (Storey 1994; Shepherd and
Wiklund 2009).1 In this study, we particularly focus on the effect of firm age
and entrepreneurial experience on small business performance as the economy
steps out of one of the most severe recessions in decades.

2.1 Age and business performance

It is a general convention by politicians and small business advocates that SMEs

contribute, sometimes disproportionately, to job creation and this perception is sup-
ported by a number of empirical studies (Birch 1979, 1987; Neumark et al. 2011). Firm
age is usually ignored or treated as a variable closely related to firm size by early
empirical work (Coad et al. 2013). However, more recent studies (Coad et al. 2013;
Davis et al. 1996; Haltiwanger et al. 2013; Haltiwanger and Krizan 1999) have
challenged this approach and content that firm age should be included as a separate
variable when examining firm performance.
Literature on small business survival suggests that younger businesses in their
formative years are more likely to be concerned with survival than growth if they do
not fail within the first few years of starting up (Cowling 2006). Therefore, growth
should be observed in more matured businesses which have passed the ‘survival mode’
(Audretsch and Mahmood 1994; Watson 2012). On the other hand, older firms may
also suffer from ‘liabilities of age’, such as the owners’ lower commitment and
involvement compared to young firms (Churchill and Lewis 1983), and a firm’s
performance is usually found to be diminishing as the firm ages (Chandler and
Hanks 1993, 1994; Durand and Coeurderoy 2001; Nunes et al. 2013; Yasuda
2005). From a learning perspective, however, business performance is likely to
improve as both the firm and entrepreneurs become more aged and thus
experienced (Vassilakis 2008).
The seminal work by Haltiwanger et al. (2013) attempts to reconcile the conflicting
empirical findings. They argue that whilst both size and age should be considered, it is
age that is playing a more critical role in employment growth dynamics. They found
that once firm age is controlled for, the negative relationship between size and growth
would disappear. Their findings essentially counter traditional theoretical and empirical

1
The nature of empirical evidence has been found to be dependent on the inclusion or exclusion of exiting
firms in the estimation sample, and whether sample firms were below or above the minimum efficient scale
(Teruel-Carrizosa 2010).
Did firm age, experience, and access to finance count?

work that focuses only on size by assuming that being small is the key to job creation,
and attribute the debate on size and growth to potential omitted variables bias that can
occur by not controlling for firm age. They highlight the importance of business start-
ups, in the sense that young firms are essential sources of growth, which just happened
to be small firms in most cases. Therefore, with potential measurement issues resolved,
firm growth should be negatively associated with age.
However, less has been said about the effect of firm age and cyclical
performance of small businesses during an economic downturn, where a priori
expectation is that resource availability plays a more vital role in small business
performance. One recent exception is Fort et al. (2013), who advanced the
work of Haltiwanger et al. (2013) by differentiating between small businesses
of varying ages. They find that young and small firms are more sensitive to
cyclical shocks than larger firms, and have experienced considerably more
severe decline in employment during the latest recession.

2.2 Entrepreneur experience and business performance

A survey of recent literature on small business performance has shown that

human capital is in general positively linked to success (Unger et al. 2011).
Cowling (2006) divided entrepreneurial human capital (EHC) into two catego-
ries: formal and informal. The former is commonly proxied by the entrepre-
neur’s education level, and the latter usually by variables such as entrepreneur
age, health, family, and prior experience. In terms of formal human capital,
there is fairly strong empirical support, across a number of empirical studies,
for the notion that businesses with more educated entrepreneurs experience
faster early stage growth (Cowling 2002; Dimov and Shepherd 2005; Rauch
et al. 2009). However, empirical evidence on the impact of informal human
capital is far less conclusive (Cowling 2006). This is probably due to the
fragmented measures of informal human capital used in the previous literature.
For example, whilst there is virtually no evidence found between performance
and the age of entrepreneur, some studies have found a positive relationship
between experience and small business performance (Burke et al. 2000; Honig
1998; Watson et al. 2003; Westhead et al. 2005; Zarutskie 2010).
Economic recession represents a typical environmental shock faced by small
business owners. Unlike large firms, small firms respond differently when
confronted by the challenges of an economic downturn (Latham 2009). On
the one hand, compared to larger firms, recession poses a greater threat to
smaller firms given larger firms’ competitive advantage derived from scales of
economy and scope (Dass 2000; Porter 1980). Therefore, survival is the
primary concern for small businesses during the recession (at least during the
short-run) and to achieve this, SMEs have to undergo cost-cutting but at the
risk of reducing their capacity and thus the ability to adapt adequately when
recovery comes (Kitching et al. 2011). On the other hand, the structural and
organisational flexibility of small firms (Dean et al. 1998; Reid 2007;
Smallbone et al. 2012) may provide leverage to SMEs during the economic
turmoil against larger firms. To this end, periods of disequilibrium create new
opportunities for entrepreneurs (Parker et al. 2012; Schumpeter 1942) to pursue
 Cowling M. et al.

growth-oriented strategies and they are more likely to adopt a revenue-

generating strategy (Latham 2009) by incurring higher costs in the short-run
so as to retain the capability to adapt when the upswing comes and realise
opportunities for long-term value creation (Kitching et al. 2011).

3 Method

This section describes the data source for this study and the survey method from which
the data is derived, followed by a discussion on both the dependent and independent
variables in the analysis.

3.1 Sample

This study is intended to analyse existing data from two previous survey sources which
cover information of small businesses in immediately after, and over the next two years
that followed the 2008/09 financial crisis.
The benchmark data is derived from the UK Small Business Survey (SBS) in
2010, commissioned by the Department for Business, Innovation and Skills
(BIS). The SBS survey has been conducted on a biannual basis 2 since 2010
and as a follow up to the 2007/8 Annual Small Businesses Survey (ASBS), the
SBS 2010 involved a large-scale telephone survey conducted between July and
September 2010, exactly one year after the official recession ended. The main
purpose of the survey is to “monitor key enterprise indicators and how these
have changed in comparison to previous surveys” and “to gauge SME inten-
tions, needs, concerns and the obstacles to fulfilling their potential” (BIS,
2010). A total of 4580 SMEs (businesses with fewer than 250 employees)
were interviewed using a stratified random sample selection method evenly
across thirteen regions in the UK and the samples were randomly drawn across
all commercial sectors of the economy to ensure that the sample is representa-
tive of the UK small business population. Amongst this sample of SMEs, 33%
are micro enterprises (1 to 9 employees), 33% are small enterprises (10 to 49
employees), 17% are medium enterprises (50 to 249 employees) and the rest
17% have no employees.
Conducted by the UK Department for Business, Innovation & Skills, a
sample of employer (i.e. firms with at least one employee) SMEs entering the
SBS 2010 were re-contacted in a series of ‘Business Barometer’ surveys to
determine how well or badly they have performed in the previous year, and to
assess their levels of business confidence going forward. In each of the six
‘Business Barometer waves3’, starting from December 2010 to June 2012 with
intervals of two to three months, an average of 500 (non-repeating) SMEs were
re-surveyed using questions similar to the SBS 2010. The ‘matching’ of the
SBS 2010 and ‘Business Barometer’ surveys yield a cross-sectional data set of

2
Before 2008, the equivalent Annual Small Business Survey was conducted annually.
3
The six waves are: December 2010, February 2011, August 2011, November 2011, February 2012 and
June 2012. No firms were re-surveyed in more than one Barometer wave.
Did firm age, experience, and access to finance count?

3167 SMEs. The composition of SMEs is fairly similar to the benchmark SBS
sample, with 42% being micro enterprises, 37% small enterprises and 21%
medium enterprises. By their setting, both data sets only include surviving
businesses.

3.2 Dependent variables

The growth literature has put too little emphasis on the measurement of growth
(Delmar 1997) and measures such as sales, employment, profit, asset and so on
are used extensively throughout the literature (Weinzimmer et al. 1998). Only
recently has growth started to be treated as a multidimensional, heterogeneous
and complex construct (Achtenhagen et al. 2010; Leitch and Neergaard 2010).
We follow the suggestions by Delmar (1997) to use multiple growth measures,
namely the annual percentage changes in employment (EGROWTH) and sales
(SGROWTH), to ensure some extent of comparability with previous studies, as
well as to reflect the theoretical consideration of growth from different per-
spectives. Moreover, we choose sales and employment as our measures of
growth because they are the primary channels through which small businesses
contribute to the economy (Acs and Storey 2004; Audretsch et al. 2008;
Cowling 2006), making them two natural candidates and mostly used variables
for growth measures (Achtenhagen et al. 2010; Delmar 1997; Unger et al.
2011; Weinzimmer et al. 1998). In both cases, the performance variables are
winsorised at the 1% level to remove the effect of outliers.

3.3 Explanatory variables

Independent variables in this study can be classified into business- and entrepreneur-level
characteristics, as well as indicators on SME access to finance. We also include six time
indicators (WAVE1 to WAVE6) to match the timing of the six ‘Business Barometer’ surveys.
The main business characteristics include firm size, age, sector, region, corporate
structure, and so on. Firm size is measured by employee numbers (EMP). Business age
is reported in the dataset as banded variables (up to 3 years, 4 to 10 years and more than
10 years, labelled as AGE_3LESS, AGE_4TO10 and AGE_10MORE, respectively).
Variables on corporate structure include whether or not a business is family owned
(FAMOWN) or incorporated (CORP).
Owner/entrepreneur characteristics measure the firm’s human capital and consist of
owner age (OAGE), gender (whether or not the business is women led, WLED), race
(whether or not the business is minority group led, MLED), prior experiences and level
of education. An experienced employer (EXP) is defined as having previously set up a
business, charity or been self-employed. The level of education (DEGREE) is measured
by whether or not the owner has a university degree or above (postgraduate or doctoral
degrees). Although a very rough proxy, owner age can also be reflective of entrepre-
neurial experience, both contextual and non-contextual. The availability of financial
resources is an important facilitator of small business growth (Beck and Demirguc-
Kunt 2006), and depending on the outcome of finance seeking, a firm can be fully
funded from either external or internal sources (FULLACCESS), partially constrained
(PARTACCESS) or fully constrained (NOACCESS).
 Cowling M. et al.

3.4 Empirical methodologies

The primary objective of this study is to investigate the growth performance of the small
business sector, and the determinants of growth outcomes. Since both growth measures
(percentage changes in employment and sales) are by construction continuous variables,
an OLS model with adjustments made for robustness of the standard errors4 is adopted.
In order to address the possible endogeneity of the regressors, lagged firm size (EMP)
and performance 5 are used where applicable as additional regressors in the models.
Further, to address the possible heteroscedasticity of growth rate distributions, quantile
regression models are used to test the effect of age and experience on different growth
rate quantiles for the Barometer sample.

4 Results

This section first reports sample descriptive statistics for the variables and then the
empirical results from multivariate regression analyses.

4.1 Descriptive statistics

Table 1 reports the descriptive statistics of dependent and independent variables for
both the whole sample and by three firm age bands. In order to be consistent with the
barometer survey data, companies with no employees are excluded from our analysis,
resulting in a sample of 2991 firm-level observations for our benchmark, SBS 2010,
analysis, whereas the sample size from business barometers is 2830. Since most of the
variables are dummies variables, it is worth noting that the mean of each dummy is
equivalent to the percentage of observations where the variable takes a value of one.
From the SBS 2010 data, the average employment and sales growth are 1.3 and
−1.1%, respectively. However, in the next two years that followed, employment growth
has dropped by 1 percentage point whilst sales performance increases to 0.1%, but still
significantly below the pre-recession level of 5.2% (Cowling et al. 2015). The opposite
growth patterns in employment and sales show that entrepreneurs view employment as
an investment rather than performance measure, in the sense that they have
viewed the recession as an opportunity to expand their business to take
advantage of a return to economic growth when the market demand rises.
Moreover, it is obvious that whilst the growth of older firms is modest or
even negative, it is the youngest firms (less than 3 years old) that have
achieved exceptional growth over the post-recession period. A standard mean-
comparison test shows that the average performance of young firms is signif-
icantly higher for both employment and sales. Also the difference in the
distribution of sales performance measures using both data sets is statistically
significant according to the Fligner-Policello test. It can be seen that younger
firms are on average smaller with younger owners, more growth-oriented, less

4
We also try to allow for cluster effects (by sector) in our analyses as robustness checks but that does not
change our findings substantially.
5
Lagged performance is only available when using the Barometer data set.
Table 1 Variable definition and sample descriptive statistics

Variable Definition (1) SBS 2010 (2) Business Barometers

All Age: less than Age: 4 to 10 Age: more than All Age: less than Age: 4 to 10 Age: more than
(N = 2991) 3 (N = 144) (N = 616) 10 (N = 2231) (N = 2830) 3 (N = 132) (N = 522) 10 (N = 2176)

Mean S t d . Mean Mean Mean Mean S t d . Mean Mean Mean

Dev. Dev.

Small business performance

EGROWTH % Change in employee 1.27 26.31 14.25 5.82 -0.49 0.19 29.25 8.60 3.54 -1.13
numbers over the past
12 months
SGROWTH % Change in sales over the past -1.14 19.88 7.21** 0.57 -1.94 0.06 17.48 4.16* 0.80 -0.37
12 months
Did firm age, experience, and access to finance count?

Business characteristics
EMPt – 1 Number of employees 29.76 41.01 10.94 17.41 33.87 31.16 40.29 14.73 17.93 35.20
12 months ago
AGE_3LESS Firm less than 3 years old (0, 1) 0.05 0.21 – – – 0.05 0.21 – – –
AGE_4TO10 Firm between 4 and 10 years 0.21 0.40 – – – 0.18 0.39 – – –
old (0, 1)
Firm more than 10 years old (0, 0.74 0.43 – – – 0.77 0.42 – – –
AGE_10M- 1)
ORE
CORP Firm is incorporated (0, 1) 0.85 0.36 0.74 0.83 0.86 0.87 0.33 0.78 0.85 0.88
FAMOWN Firm is family owned (0, 1) 0.56 0.50 0.51 0.53 0.57 0.56 0.50 0.61 0.51 0.57
EXPORTER Firm exports (0, 1) 0.26 0.44 0.15 0.24 0.27 0.28 0.45 0.16 0.25 0.30
ENG Firm located in England (0, 1) 0.76 0.43 0.71 0.77 0.76 0.81 0.39 0.73 0.84 0.81
SCOT Firm located in Scotland (0, 1) 0.06 0.24 0.10 0.07 0.06 0.06 0.25 0.08 0.08 0.06
Table 1 (continued)

Variable Definition (1) SBS 2010 (2) Business Barometers

All Age: less than Age: 4 to 10 Age: more than All Age: less than Age: 4 to 10 Age: more than
(N = 2991) 3 (N = 144) (N = 616) 10 (N = 2231) (N = 2830) 3 (N = 132) (N = 522) 10 (N = 2176)

Mean S t d . Mean Mean Mean Mean S t d . Mean Mean Mean

Dev. Dev.

WALES Firm located in Wales (0, 1) 0.02 0.14 0.03 0.02 0.02 0.02 0.13 0.01 0.02 0.02
NI Firm located in Northern 0.16 0.37 0.16 0.15 0.17 0.11 0.31 0.18 0.06 0.11
Ireland (0, 1)
Primary & manufactory sectors 0.15 0.36 0.06 0.12 0.19 0.21 0.41 0.10 0.14 0.25
PRIM&MA- (0, 1)
NU
CONSTRU Construction sector (0, 1) 0.09 0.28 0.11 0.10 0.08 0.11 0.31 0.22 0.13 0.10
TR&D Transport, retail & distribution 0.31 0.46 0.41 0.33 0.30 0.26 0.44 0.39 0.29 0.25
sectors (0, 1)
SERVICES Business and other services 0.43 0.50 0.41 0.46 0.43 0.40 0.49 0.29 0.44 0.40
sectors (0, 1)
Owner/Entrepreneur characteristics
OAGE Owner’s age 50.15 10.74 42.85 46.40 51.47 52.21 10.35 46.80 48.74 53.41
WLED Women-led business (0, 1) 0.14 0.35 0.21 0.17 0.13 0.13 0.33 0.11 0.15 0.12
MLED Ethnic minority-led business (0, 0.07 0.25 0.16 0.10 0.05 0.03 0.18 0.10 0.05 0.03
1)
EXP Employer with prior experience 0.26 0.44 0.30 0.29 0.25 0.28 0.45 0.27 0.36 0.26
(0, 1)
DEGREE Employer with college degree 0.50 0.50 0.45 0.52 0.50 0.49 0.50 0.49 0.48 0.50
or above (0, 1)
0.77 0.42 0.90 0.82 0.75 0.74 0.44 0.76 0.77 0.73
Cowling M. et al.
Table 1 (continued)

Variable Definition (1) SBS 2010 (2) Business Barometers

All Age: less than Age: 4 to 10 Age: more than All Age: less than Age: 4 to 10 Age: more than
(N = 2991) 3 (N = 144) (N = 616) 10 (N = 2231) (N = 2830) 3 (N = 132) (N = 522) 10 (N = 2176)

Mean S t d . Mean Mean Mean Mean S t d . Mean Mean Mean

Dev. Dev.

Firm aiming to grow in the next

ORIENTA- 2–3 years (0, 1)
TION
Access to external finance
Firm acquired all the finance 0.88 0.32 0.79 0.83 0.90 0.91 0.28 0.85 0.84 0.93
FULLACCE- sought (or no need) (0, 1)
Did firm age, experience, and access to finance count?

SS
Only acquired part of the 0.02 0.15 0.03 0.02 0.02 0.02 0.13 0.01 0.02 0.02
PARTACCE- finance sought (0,1)
SS
NOACCESS Acquired no finance sought (0, 0.10 0.30 0.18 0.15 0.08 0.07 0.26 0.14 0.14 0.06
1)

* p < .05; ** p < .01 for one-tailed Fligner-Policello robust rank order test for difference in growth performance distributions between firms less than 3 years old and firms more than
10 years old. The 2010 SBS covers UK SME performance up till September 2010 and the Business Barometers cover the period between December 2010 and June 2012
 Cowling M. et al.

35%

30%

25%

20%

15%

10%

5%

0%

-5%

-10%

-15%
Mid 2010 Dec-10 Feb-11 Aug-11 Nov-11 Feb-12 Jun-12
(SBS10)

Less than 3 yrs 4 to 10 yrs More than 10 yrs

Fig. 2 Employment growth during and immediately after GFC for different age groups. *Source: Authors
own calculation based on SBS 2010 and Business Barometer surveys

likely to export and more financially constrained. For both SBS and Barometer
samples, the correlation coefficients between size, firm and owner age are small
enough (results not reported but available upon request) to rule out the possi-
bility of collinearity between these variables.

10%

8%

6%

4%

2%

0%

-2%

-4%
Mid 2010 Dec-10 Feb-11 Aug-11 Nov-11 Feb-12 Jun-12
(SBS10)

Less than 3 yrs 4 to 10 yrs More than 10 yrs

Fig. 3 Sales growth during and immediately after GFC for different age groups. *Source: Authors own
calculation based on SBS 2010 and Business Barometer surveys
Did firm age, experience, and access to finance count?

Figures 2 and 3 illustrate how the employment and sales growth have evolved after
the GFC, for firms of different age groups. Consistent with prior conjectures, younger
firms in general have better, but more variable, performance in terms of both employ-
ment and sales growth. However, there is significant time variance regarding the
performance differentials for different age groups. It is found that the youngest firms
have the best relative performance during the short-term after the recession officially
ended (as depicted by 2010 SBS and the first few waves of Barometer surveys). As the
economy moves further from the recession, the comparative advantage of younger
firms becomes less prominent, and even transposed.

4.2 Multivariate regression results

The starting point was to econometrically model the dynamics of business sales
and employment growth during the post-recession periods. Regressions using
the SBS 2010 data are used as benchmarks and those using the barometer data
enable a closer look at growth dynamics and the effect of age and experience
on post-recession performance.
Table 2 reports the coefficient estimates for both sales and employment growth
equations. Model 1 is the benchmark employment growth model using data derived
from SBS 2010. It can be seen that, business age has no effect on employment
performance immediately after the recession. We use the logarithm of employee
numbers in the regressions to pick up the possible non-linearity between business size
and performance. The coefficient estimate (β = −2.92, p < 0.01) suggests that firm size
is negatively related to employment growth, but at a decreasing marginal rate. We
include lagged sales growth (employment growth in sales growth equation) as a control
variable and find it significantly and positively correlated with employment change
(β = 0.46, p < 0.01). Except for entrepreneurial growth orientation (β = 3.08, p < 0.01),
neither human capital nor access to finance variables significantly influence SME
employment growth immediately after the recession.
Employment growth two years after the GFC (Model 2) has some notable differ-
ences especially with respect to business age. On average, the oldest firms have a lower
rate of growth in employment (β = −9.23, p < 0.05), indicating that as the economic
environment improves, the sensitivity of firm age on employment growth becomes
stronger and supports the liability of age argument. The availability of financial
resources is more important for employment growth during the economic recovery
and firms that failed to secure any external finance required create significantly fewer
(β = −5.58, p < 0.05) jobs than other firms.
Specifications 3 and 4 of Table 2 report the coefficient estimates for small business
sales performance immediately after (Model 3) and over the next two years after the
2008/09 crisis (Model 4). For both specifications larger firms are likely to have higher
sales growth, but with decreasing marginal effect. Unlike employment, there is an
explicit sector effect in sales performance, where firms in construction recovered most
slowly from the recession (β = −3.75, p < 0.05).
Regarding sales growth in the following two years, it is found that businesses that
export outside the UK outperform those who do not export by more than 2 percentage
points in terms of sales growth. Firms with better access to external finance also
performed better. The positive coefficient estimates on both firm size and access to
 Cowling M. et al.

Table 2 Post-recession employment and sales growth

Employment Growth Sales Growth

Model 1 Model 2 Model 3 Model 4

Business Characteristics
ln(EMPt – 1) -2.921*** -0.587 1.015*** 0.538**
(0.408) (0.468) (0.276) (0.249)
AGE_4TO10 -3.704 -5.982 -4.792* -2.529
(4.478) (4.760) (2.680) (2.208)
AGE_10MORE -6.417 -9.229** -6.310** -3.873*
(4.355) (4.581) (2.576) (2.086)
SGROWTHt – 1 0.464*** 0.302***
(0.035) (0.039)
EGROWTH t – 1 0.269*** 0.096***
(0.020) (0.018)
CORP 0.528 1.468 0.559 -1.621*
(1.428) (2.030) (0.915) (0.926)
FAMOWN -0.006 -0.475 -1.084 -1.768**
(0.944) (1.162) (0.766) (0.715)
EXPORTER -1.057 1.234 1.371 2.297***
(1.009) (1.230) (0.853) (0.750)
SCOT 3.131 1.832 -0.408 0.985
(2.004) (2.267) (1.421) (1.236)
WALES -3.799 -0.831 -0.091 -1.006
(3.236) (2.701) (2.219) (1.659)
NI 0.594 3.378* -0.391 -1.471
(1.158) (1.918) (0.930) (1.060)
CONSTRU -2.388 -2.816 -3.753** -2.427
(2.190) (2.298) (1.736) (1.621)
TR&D -2.549* -1.621 -0.309 -1.827**
(1.363) (1.456) (0.989) (0.887)
SERVICES -1.100 0.599 0.256 -2.879***
(1.324) (1.521) (1.046) (0.884)
Owner/Entrepreneur characteristics
OAGE -0.075* -0.058 -0.028 -0.033
(0.044) (0.056) (0.033) (0.034)
WLED -1.160 -1.142 0.507 -1.134
(1.424) (1.826) (0.992) (0.869)
MLED 0.157 -4.240 -0.611 -1.746
(2.239) (2.749) (1.614) (2.457)
EXP 1.774* -0.985 0.259 -0.789
(1.070) (1.179) (0.808) (0.702)
DEGREE 0.357 0.522 -0.509 -0.448
(0.949) (1.174) (0.730) (0.694)
Did firm age, experience, and access to finance count?

Table 2 (continued)

Employment Growth Sales Growth

Model 1 Model 2 Model 3 Model 4

ORIENTATION 3.079*** 6.101*** 3.110*** 4.980***

(0.982) (1.268) (0.778) (0.754)
Access to finance -0.028
PARTACCESS 0.462 -2.040 0.845 2.645
(3.625) (4.709) (3.110) (3.443)
NOACCESS -2.205 -5.577** -3.894*** -3.381**
(1.865) (2.289) (1.276) (1.438)
Time indicators
WAVE2 -0.973 0.678
(2.053) (1.228)
WAVE3 -0.430 1.045
(1.837) (1.113)
WAVE4 -2.379 1.519
(1.989) (1.169)
WAVE5 -2.295 1.661
(1.719) (1.082)
WAVE6 -0.812 1.842*
(1.881) (1.108)
N 2991 2830 2991 2830
R2 0.167 0.073 0.156 0.071
F statistics 24.09*** 5.56*** 13.65*** 7.14***

Asymptotic robust standard errors reported in the parentheses. The 2010 SBS (Models 1 & 3) covers UK SME
performance up till September 2010 and the Business Barometers (Models 2 & 4) cover the period between
December 2010 and June 2012. PRIM&MANU is the reference group for the sector dummies. The lagged
growth variables (SGROWTHt – 1 and EGROWTHt – 1) are not available in the SBS data (Models 1 &3) and
the current growth rates are used instead
* p < .10; ** p < .05; *** p < .01

finance suggests that resource availability is a very important driver of sales growth as
the economy gradually recovers. Similarly though, younger and exporting firms tend to
have better sales performance, but it is firms in transport, retail & distribution and
services that suffered the most when economic recovery remained slow and unclear.
Similar to the employment performance, growth orientation is the only important
owner/entrepreneur characteristic that is significantly and positively associated with
sales growth in both within- and post-recession periods.
OLS models only capture the average effect of the explanatory variables on the
conditional mean of the outcome variable. However, there is extensive evidence that
growth rates can be extremely volatile and its distribution (variance) appears to be
heteroskedastic (Bottazzi and Secchi 2013; Reichstein et al. 2010), so growth can be
inappropriately characterised by OLS without accounting for the full conditional
distributional properties of the growth process (Coad et al. 2014). As a robustness
Table 3 Quantile regressions: employment and sales growth two years after the crisis

Employment Growth Sales Growth

10% Quantile 25% Quantile 50% Quantile 75% Quantile 90% Quantile 10% Quantile 25% Quantile 50% Quantile 75% Quantile 90% Quantile

Business Characteristics
ln(EMPt – 1) 4.073*** 0.539 -0.010 -0.037 -2.180*** 2.864*** 1.212*** 0.089 0.187 -0.830*
(0.733) (0.425) (0.045) (0.250) (0.624) (0.551) (0.327) (0.091) (0.195) (0.487)
AGE_4TO10 2.846 -1.942 -0.134 -6.443 -3.448 -2.799 1.030 0.075 -3.444 -9.606
(4.101) (4.580) (0.726) (5.849) (3.323) (5.190) (2.396) (0.655) (3.398) (6.481)
AGE_10MORE 3.000 -1.132 -0.191 -9.833* -12.549*** -1.661 0.671 -0.223 -5.777* -12.807**
(3.919) (4.345) (0.729) (5.694) (3.296) (4.968) (2.227) (0.595) (3.265) (6.147)
SGROWTHt – 1 0.225*** 0.248*** 0.310*** 0.143*** 0.192***
(0.042) (0.031) (0.068) (0.024) (0.037)
EGROWTHt – 1 0.121*** 0.091*** 0.037** 0.098*** 0.135***
(0.026) (0.015) (0.016) (0.017) (0.039)
CORP 2.083 -0.786 -0.020 1.582** 3.906* -6.797*** -2.093 -0.135 0.213 0.654
(2.608) (1.840) (0.165) (0.747) (2.295) (1.849) (1.300) (0.243) (0.504) (1.541)
FAMOWN 0.178 -1.264 0.024 -0.789 0.425 -3.049** -1.899** -0.092 -0.267 -0.572
(1.833) (0.993) (0.137) (0.630) (1.357) (1.512) (0.845) (0.203) (0.565) (1.156)
EXPORTER 2.677 1.664* 0.067 1.713** 1.798 1.804 1.708* 0.500* 3.728*** 5.862***
(1.936) (1.006) (0.173) (0.791) (1.573) (1.480) (0.894) (0.296) (0.762) (1.480)
Owner/Entrepreneur Characteristics
OAGE 0.063 0.122** -0.000 -0.080** -0.231*** 0.029 0.008 0.003 0.006 -0.119*
(0.092) (0.048) (0.005) (0.032) (0.069) (0.071) (0.040) (0.007) (0.027) (0.069)
WLED -1.021 0.070 0.023 0.394 1.738 -1.320 0.147 -0.046 -0.273 -1.852
(3.020) (1.770) (0.157) (0.818) (2.587) (2.208) (1.153) (0.198) (0.547) (1.487)
Cowling M. et al.
Table 3 (continued)

Employment Growth Sales Growth

10% Quantile 25% Quantile 50% Quantile 75% Quantile 90% Quantile 10% Quantile 25% Quantile 50% Quantile 75% Quantile 90% Quantile

MLED 0.341 0.529 0.091 -0.248 -2.638 -8.110 -3.612 0.060 0.138 2.627
(5.276) (3.103) (0.437) (1.535) (3.847) (7.278) (3.991) (0.533) (1.547) (6.600)
EXP -1.284 -0.527 0.003 0.615 0.575 -0.038 0.079 -0.042 -0.188 -2.128*
(1.979) (1.163) (0.125) (0.695) (1.450) (1.742) (0.870) (0.193) (0.519) (1.103)
DEGREE 4.403** 1.448 0.019 0.277 1.026 -1.247 -0.711 -0.056 0.139 1.262
(1.950) (1.104) (0.123) (0.658) (1.442) (1.558) (0.794) (0.183) (0.495) (1.210)
ORIENTATION 3.513* 4.613*** 0.120 3.009*** 9.247*** 5.001*** 4.171*** 0.440 4.500*** 6.726***
(1.917) (1.315) (0.165) (0.609) (1.667) (1.544) (1.207) (0.309) (0.522) (1.147)
Access to finance
Did firm age, experience, and access to finance count?

PARTACCESS -1.703 -6.253* -1.630 -4.472*** 2.450 -3.666 -3.888 -0.765 -1.244 32.563
(6.648) (3.603) (1.855) (1.525) (10.454) (3.923) (2.712) (1.757) (2.659) (20.970)
NOACCESS -6.894** -8.868*** -1.092 -1.390 -3.869* -5.326 -6.700*** -0.630 -1.428 -3.281
(3.091) (2.116) (1.338) (1.052) (2.200) (3.263) (1.712) (0.871) (1.012) (2.475)
Sector Effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Region Effect Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 2991 2991 2991 2991 2991 2830 2830 2830 2830 2830
Pseudo R2 0.130 0.084 0.017 0.060 0.142 0.097 0.064 0.028 0.065 0.096

Standard errors (and hence t statistics) are bootstrapped, using 250 replications. The analyses use Business Barometers data that covers the period between December 2010 and
June 2012
* p < .10; ** p < .05; *** p < .01
 Cowling M. et al.

check, we use quantile regressions to further test the effect of business age and owner
experience across different parts of the growth rate distribution. Table 3 reports the
coefficient estimates of the same variables in Table 2 for SME employment and sales
performance two years after the GFC using the Business Barometer Survey data across
five quantiles of the growth distribution. Note that the 50% quantile, or the median of
both sales and employment growth in our data set is dominated by zeroes (stable
performance), regression results at this particular quantile should thus be viewed and
interpreted with caution.
For both employment and sales growth, the effect of firm age differs considerably,
where the strong negative effect identified in the OLS models is only found in higher
quantiles of the performance distributions, and the difference is statistically significant.
The OLS effect of firm age deviates substantially from the median estimates, suggest-
ing that young SMEs are contributing disproportionally to the performance of high-
growth firms. In sharp contrast, resource availability proxied by employee numbers and
access to external finance has a strong influence towards lower growth rate quantiles.
This indicates that size and age may influence small business performance through
difference channels. It could also imply that for poorer performing businesses where
survival is a more imminent concern, the availability of both internal (labour) and
external (finance) resources outweighs the potential benefits of a young firm.
Consistent with the OLS results, entrepreneurial experience has no effect on growth
but growth orientation is an important growth determinant for all but one quantile.

5 Discussion

We focused our growth analysis on firm age and entrepreneurial experience and their
potential effects on small business performance during a unique period when the
economy continued to be characterised by low growth and uncertainty five years after
the onset of the deepest UK recession since the ‘Great Depression’ in the 1930s.
Generally speaking and in line with the growth of UK GDP, the decreasing trend in
sales growth for UK SMEs ceased from the beginning of 2011. However, this im-
provement in sales performance is extremely limited and the average growth of 0.1% is
much lower than the pre-recession level of 5% reported in Cowling et al. (2015).
Although on average higher than both the within- and pre-recession figures, small
business performance measured by employment growth appears much more volatile
and hard to predict, giving rise to the conclusion that employment and sales may have
different relevance to entrepreneurs as measures of small business performance. The
high growth rate of employment immediately after the recession and a declining trend
in the subsequent years as compared to sales growth is a possible sign of labour
hoarding amongst the small business sector, where increasing employee numbers
during an economic turmoil is seen by entrepreneurs from a strategic perspective as
an investment in preparation for the demand rise in the upcoming recovery, rather than
a pure measure of business performance.
In terms of the question as to how many firms are still capable of achieving growth
after the recession (Figs. 2 and 3), we note that in general and despite the lack of
finance and other resources (as depicted by smaller firm size and higher likelihood of
financial constraints) younger firms appear to be more resilient and have picked up
Did firm age, experience, and access to finance count?

growth momentum more quickly than their older counterparts, particularly those aged
10 years or above. This is consistent with the ‘liability of age’ hypothesis. As the
economy recovers, such comparative advantage in growth reduces or even disappears.
This is especially true for growth performance in employment. Our analysis shows that
on average firms over ten years old underperformed other firms by approximately 10
percentage points on employment and 5 percentage points on sales. This is due to the
exceptionally high growth rate of young firms especially those aged 3 years or less, as
show in Figs. 2 and 3, indicating that it is the younger, business start-ups that contribute
the most to the economic growth during a pro-longed recession. We find that whilst the
crisis is likely to have a profound negative impact on the small business sector for a
relatively long time, the well-established, and negative, firm age-growth relationship
still holds. This finding is particularly interesting given that the literature suggests that
younger and smaller firms are found to be more sensitive to cyclical shocks. In other
words, these firms are expected to suffer more during periods of economic crisis. Our
results might suggest that the ‘liability of age’ dominates the financial frictions that are
more profound for young firms, particularly in periods of crisis. This would be in line
with bureaucratic firm decision-making processes acting as a constraint of larger SME
compared to the relative agility and speed of entrepreneurial decision-making in
response to unanticipated shocks. Never the less, one should interpret the above
findings with caution because our sample by nature (survey data) does not include
non-surviving businesses.
Moreover, consistent with Haltiwanger et al. (2013), when business age is controlled for,
firm size is no longer a significant determinant of SME growth performance, except for
sales growth several years after the GFC (Specification 4, Table 2), when resource
availability proxied by firm size plays a vital role as the economy was recovering slower
than expected. This positive size-performance relation is found in a number of empirical
studies, for example, Sapienza and Grimm (1997) and Zhao et al. (2011). Further analyses
over different quantiles of the growth performance distribution show that there is a trade-off
between size and age. We find that younger firms contribute disproportionally to business
performance particularly at higher growth quantiles, but this negative age-performance
sensitivity is offset and then dominated by the positive effect of firm size at lower growth
quantiles. This could be the case when better performing firms have already achieved the
optimal firm size and growth of the firm is more than enough to satisfy the firm’s resource
requirement, whilst poorer performing firms tend to rely more heavily on the availability of
both labour and capital resources to support their growth.
In terms of business and entrepreneur characteristics, we find business characteris-
tics more important in determining small business performance. We also that there is a
positive synergy between sales and employment growth. This positive relationship is
generally irrespective of macroeconomic environment. What this does suggest is that
any policy levers that stimulate either job growth or sales growth will be likely to create
a positive economic multiplier. Unlike periods of economic growth when industry
sector plays a very minor role in the determination of employment and sales growth
(Cowling et al. 2015), we provide further support to recent studies (Bank of England
2010; ONS 2011) that SMEs in certain sectors are more resilient whilst some others are
more prone to the economic downturn. We note that firms in construction continued to
experience significant declines in sales even after the official end of the recession.
 Cowling M. et al.

However, when recovery emerged it is firms in transport, retail & distribution, and
services that were found to be the slowest in picking up the growth momentum.
Entrepreneur characteristics play a very minor role in SME growth performance
over the sample period, except for the strongly significant effect of entrepreneurial
growth orientation. Entrepreneurial experience has little impact on SME post-recession
performance. The result suggests that entrepreneurs were struggling to draw any
inferences from previous experiences and apply them to a business context with little
resemblance to the past. This finding is also in line with the conjecture that small
business growth may be nothing more than a random walk (Coad et al. 2014). This
general finding poses some important policy questions. On the one hand, it could
suggest that because the average quality of the entrepreneur is low, then they do not
have the ability to undertake actions to ameliorate the impacts of the crisis. Certainly,
the literature on entrepreneurial learning and speed of adjustment suggests that learning,
if it occurs at all, is slow. Equally, it could simply be that in the face of an economic
crisis macroeconomic forces overwhelm the relatively marginal contribution of the
entrepreneur, even if they were talented. A third option is that entrepreneurial orienta-
tion captures important, but unobservable to us, characteristics of entrepreneurs which
are associated with firm growth. For policy-makers, each potential explanation has
different implications. If the poor quality entrepreneur argument holds, then the obvious
policy action is to seek to increase the human capital of the entrepreneurial population.
If the EO argument holds, then there is no obvious policy remedy, at least until we
unravel what lies behind EO and separates out those entrepreneurs with growth
aspirations and those without. If the overwhelming macroeconomic forces argument
holds, then it is the overall government policy response to an economic crisis that
matters for the entrepreneurial population.
Our empirical results add further evidence regarding the debate over the difference
between small business growth measures, as reflected in the different employment and
sales performance sensitivity to firm and owner characteristics. SME sales performance
appears to be influenced by a wider set of factors (legal form, sectors, export, etc.), but
no such pattern was found for employment growth. To sum up, firms appear to have
based their employment decisions purely on entrepreneurial growth orientation, which
is closely and negatively associated with firm age in many cases (Cowling et al. 2015).
Again, it suggests that entrepreneurs tend to treat employment as a form of resource
(Penrose 1959) and sales as a measure of performance. Therefore, employment growth
is more likely a result of strategic considerations independent of factors associated with
business and macroeconomic conditions, whilst sales growth is a more objective
measure of performance affected by various exogenous factors.

6 Conclusion

This study draws on previous research on small business growth and undertakes a ‘big
picture’ analysis on the growth performance of SMEs after one of the greatest financial
crises of the global economy. We use the percentage change in employment and sales as
two alternative measures of small business growth and estimate a set of regressions that
explain small business performance immediately after the 2008/09 financial crisis
(using UK SBS data collected in mid-2010) and over an extended period up till mid-
Did firm age, experience, and access to finance count?

2012 (using data from six ‘business barometer’ surveys), respectively. We find clear
evidence that younger firms grew faster on average than older firms, but entrepreneurial
experience had no identifiable growth effect.
Our overall findings suggest in general, the recession has a long-lasting negative
effect on the small business sector. There is evidence of recovery in small business
performance the further we are away from the crisis, however the progress is shown to
be extremely slow and limited especially in terms of sales performance. Unlike periods
in a more stable, and growing macroeconomic environment, higher growth is primarily
and consistently found in growth-oriented, young firms with no financial constraints,
irrespective of either size or entrepreneurial human capital. Further, the fact that the
growth performance of SMEs is unevenly distributed across sectors indicates that
certain types of SMEs are indeed more resilient than others in a recession. Beyond
this, the slow recovery of the small business sector in a volatile economic environment
calls for a comprehensive package of post-recession policy support. Future entrepre-
neurship policies should provide sustainable motivation for small business growth,
since successfully achieving growth as a young firm may reinforce entrepreneurs’
intention to grow, leading to further favourable performance.
Potentially interesting future research topics could aim at addressing the limitations
of this study given the use of secondary, multi-wave cross-sectional survey data. The
current study excludes non-surviving or exiting firms and sole proprietors (firms with
no employees), so our results could be subject to selection bias. A natural extension of
the present research is to look at the effects of the same variables on business survival,
or alternative performance measures, such as productivity. Moreover, the cross-
sectional data does not allow us to draw meaningful and robust inference on the
dynamics of firm growth over time, so a re-investigation of the research questions
using data in a longitudinal and panel setting would further advance our understanding
on the interaction between business age and performance.

Acknowledgements Initial funding for this study was provided by the UK Department for Business,
Innovation and Skills.

Compliance with ethical standards

Conflicts of interest The authors declare that they have no conflict of interest.

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