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This paper investigates whether new venture performance becomes easier to predict as the venture ages: does the fog lift? To address this question we primarily draw upon a theoretical framework, initially formulated in a managerial context by Levinthal (Adm Sci Q 36(3):397–420, 1991) that sees new venture sales as a random walk but survival being determined by the stock of available resources (proxied by size). We derive theoretical predictions that are tested with a 10-year cohort of 6579 UK new ventures in the UK. We observe that our ability to predict firm growth deteriorates in the years after entry—in terms of the selection environment, the ‘fog’ seems to thicken. However, our survival predictions improve with time—implying that the ‘fog’ does lift.
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Predicting new venture survival and growth:
Does the fog lift?
Alex Coad .Julian S. Frankish .
Richard G. Roberts .David J. Storey
Accepted: 10 February 2016 / Published online: 17 March 2016
ÓThe Author(s) 2016. This article is published with open access at Springerlink.com
Abstract This paper investigates whether new ven-
ture performance becomes easier to predict as the
venture ages: does the fog lift? To address this
question we primarily draw upon a theoretical frame-
work, initially formulated in a managerial context by
Levinthal (Adm Sci Q 36(3):397–420, 1991) that sees
new venture sales as a random walk but survival being
determined by the stock of available resources (prox-
ied by size). We derive theoretical predictions that are
tested with a 10-year cohort of 6579 UK new ventures
in the UK. We observe that our ability to predict firm
growth deteriorates in the years after entry—in terms
of the selection environment, the ‘fog’ seems to
thicken. However, our survival predictions improve
with time—implying that the ‘fog’ does lift.
Keywords Entrepreneurship Firm growth
Survival analysis Coefficient of determination
Selection environment Gambler’s Ruin theory
JEL classifications L26 L25
1 Introduction
Economic dynamics are characterised by a noisy
selection environment that (imperfectly) rewards
superior performance. This paper investigates whether
the selection environment becomes clearer—more
predictable—in the years after entry. The benefits of
greater predictability accrue to business owners, to
providers of finance and to governments. For business
owners, the value is the availability of a route-map to
enable them to plan ahead and check progress over
time (Dencker et al. 2009). For providers of finance,
being able to more accurately estimate the optimal date
to provide finance is valuable, because too early an
investment may be too risky, whereas delay may mean
the opportunity is seized by a rival (Cumming et al.
2015). Finally, governments are continually faced with
the choice of using taxpayer’s funds to support and
stimulate start-ups, or instead to delay support until
performance metrics become clearer (Pons Rotger
et al. 2012). An optimal combination of support at
different stages as new ventures evolve could provide
considerable social and economic returns.
A. Coad (&)D. J. Storey
University of Sussex, Jubilee Building, Falmer,
Brighton BN19SL, UK
e-mail: A.Coad@sussex.ac.uk
D. J. Storey
e-mail: D.J.Storey@sussex.ac.uk
A. Coad
JRC-IPTS, European Commission, Edificio Expo,
41092 Seville, Spain
J. S. Frankish
Barclays Bank, Poole, Dorset, UK
R. G. Roberts
University of Birmingham, Birmingham, UK
123
Small Bus Econ (2016) 47:217–241
DOI 10.1007/s11187-016-9713-1
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
This paper is motivated by a desire to explain and
then to use these explanations to predict, post-start
performance so providing the benefits of greater
predictability to all three parties. It takes two alterna-
tive measures of performance (Miller et al. 2013)—
survival and sales growth—and assesses whether, over
time, our ability to explain these two performance
variables improves. In the phrasing of this paper—
does the fog lift with time? If so, when does this
greater clarity appear? Is it after 1 year, or 10 years?
Or, does the fog lift only gradually but continually? Is
there a clear ‘step’ at a clearly-identified point in time?
Our theoretical starting point is the Levinthal
(1991) random walk model which we apply to new,
as opposed to well-established, ventures. Here, each
enterprise has an initial stock of resources which
expands or contracts depending on post-entry growth,
which is determined by stochastic shocks. Exit takes
place when the stock falls below a minimum
threshold.
1
Given these assumptions we distinguish between
new venture sales growth and survival. Following
Levinthal (1991), if the sales growth of a new venture
follows a random walk, there is no improvement in our
ability to predict growth in the years after entry—
hence the fog is thick and remains thick over time.
However, we assume new ventures have different
(financial) resource endowments, enabling those with
more resources to survive shocks that would lead to
the exit of those with fewer resources. These financial
endowments are either present at start-up, or accumu-
lated through post-entry growth. Survival rates are
therefore expected to increase and become more
predictable, in the years after entry, as surviving new
ventures acquire the financial resources that enable
them to ‘ride out’ the inevitable vicissitudes of trade
that characterise their early months and years.
These predictions for growth and survival are tested
using a cohort of 6579 new ventures in the UK, all of
which began to trade in the same quarter of 2004,
where every financial transaction is tracked over
10 years. With this unique data we show that our
ability to explain sales growth decreases as the venture
ages, because as time goes by this becomes more
random. When we focus only on firms that survive
until the end of year 10; however, for this subsample of
surviving firms, our ability to predict growth remains
constant over time. Regarding survival, our ability to
predict which firms will remain in operation increases
slightly in the years after entry. Our results are
therefore broadly consistent with our model.
Our specific contribution is then to demonstrate
that, even if the sales growth of a new venture
increasingly approximates a random walk, its survival
becomes more predictable. The growth fog becomes
thicker over time, but the survival fog becomes less
dense. Perhaps the paper most closely related to ours is
Lotti et al. (2009), who present evidence that firms
converge to a random growth model (i.e. Gibrat’s
(1931) ‘Law of Proportionate Effect’) in the years
after entry. In our paper, however, we look more
widely at our ability to explain growth and survival in
the years after entry. Another related paper is Wiklund
et al. (2010), who observe that the explanatory power
of financial indicators decreases in the years after
entry, when the task is to explain survival. In our
analysis, we include other variables (beyond financial
indicators) as explanatory variables for performance
(measured in terms of both survival and growth), and
present finer-grained evidence on the year-by-year
evolution of the model fit statistics.
The remainder of the paper is set out as follows:
Sect. 2provides the theoretical context that is used in
Sect. 3to derive hypotheses. Section 4presents our
methodology. Section 5presents the dataset, and we
test our hypotheses in Sect. 6. Section 7concludes.
2 Theory development
Conceptualising firm performance as a random walk has
a long history in economics (Gibrat 1931; Ijiri and
Simon 1964; Levinthal 1991; Denrell et al. 2015).
Random processes produce results that closely match
the outcomes of many top performing companies, to the
extent that investigating whether or not performance is
purely random remains a valid research question
(Henderson et al. 2012; Denrell et al. 2015;Storey
2011). This need not imply that managers do not put
thoughtful planning and effort into their business
decisions, because it could be that competition is so
1
Gimeno et al. (1997) extend this framework to allow for
individual-specific thresholds, according to which some indi-
viduals with attractive outside options may exit before they
reach a minimum level of resources. Furthermore, one could
conceive possible extensions in which the exit threshold is not
exogenous but endogenous and potentially time-variant.
218 A. Coad et al.
123
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fierce, and businesses are all more or less ‘neck-and-
neck’, that there may not be any easily observed
systematic factors that allow new ventures to enjoy
prolonged above-average performance in the years after
entry. ‘Chance models are, in fact, compatible with
effortful managers who carry out deliberate actions’
(Denrell et al. 2015, p. 936). Our preference for chance
models in this paper is because random walk models
offerusefulapproximationstoreal-worldphenomena
(Levinthal 1991; Henderson et al. 2012; Denrell et al.
2015), and also because random walk models can
provide simple and clear theoretical predictions that can
be developed into testable hypotheses.
Levinthal (1991) was amongst the first to formally
explore how random processes could shed light on
venture survival in a managerial context. His model
had two key assumptions. The first was that firm
growth was modelled as a random walk, and the
second was that survival depended upon access to
resources or assets that could be used to finance the
shocks experienced by the business in a random walk.
2
Levinthal emphasised that the random walk model
is compatible with variations in competence amongst
enterprises. He writes (p. 399):
While variation in competence should shift the
mean of the possible distribution of outcomes,
and perhaps the variance as well, the presence or
absence of competence does not fundamentally
alter the stochastic nature of the process.
The Levinthal (1991) model also puts forward that the
amount of the assets is determined by two factors—
past performance and initial resources. We assume
that access to more financial resources improves the
chances of survival. However, there are two key
respects in which the assets of the new venture differ
from that in an established firm. The first is that, in an
established venture, the assets primarily comprise
those accumulated over time, whereas those available
to the new venture are considerably more likely to be
those in place when the venture begins. Second, in an
established firm the accumulated assets constitute a
‘track-record’ which can help internal and external
parties assess future performance, whereas no such
record exists for a new venture. This is particularly
problematic for external suppliers of finance—banks,
trade creditors—who then seek ‘signals’ of credibility,
such as collateral (Voordeckers and Steijvers 2006).
Our model therefore assumes the returns from
venture creation are a random walk and this payoff
structure attracts individuals who are optimistic and
favour situations where, although the expected returns
may be negative (Hamilton 2000), the variance is high
and positively skewed. Survival, in turn, reflects the
availability of resources (i.e. resources available at
start-up, as well as those obtained from post-entry
performance).
More formally expressed, growth occurs through
the following random process:
xt¼xt1þet;ð1Þ
where x
t
is the logarithm of firm size at time t, and eis a
random shock (additive in logs, but multiplicative on a
linear scale) with mean land standard deviation r.
Survival is a function of the stock of accumulated
resources, so survival, S, depends on whether a firm’s
resources exceed a minimum threshold size x*:
S¼1ifxl[x;otherwise S¼0ð2Þ
where x
l
is a latent variable that corresponds to xif
x
l
[x*, but remains unobserved if x
l
Bx*. If the exit
threshold is positive, i.e. x*[0, then players will not
persist until their resources reach zero, but quit the
‘gambling table’ even when resources are positive
(Gimeno et al. 1997).
3 Hypotheses derivation
Our primary interest is in whether the selection
environment for new ventures improves—becomes
more predictable—in the years after entry. To do this
we investigate the explanatory power (or goodness-of-
fit, represented by the R
2
statistic) of models that seek
to explain the growth and survival of new ventures.
3.1 Growth
If new venture growth is a random walk, a
`la Levinthal,
the dynamics of (log) size are x
t
=x
t-1
?e
t
, the growth
rate (in log-differences; Tornqvist et al. 1985)is
2
The Levinthal (1991) model assumes that exit takes place
when the business cannot meet its financial obligations.
However, Gimeno et al (1997) shows that the ability to assemble
these resources is endogenous in the sense that they vary
according to the alternative employment options open to the
business owner(s).
Predicting new venture survival and growth 219
123
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expressed entirely in terms of a random shock: x
t
-
x
t-1
=e
t
. Growth is well approximated by a random
walk in the years after start-up, and our inability to make
systematic predictions for post-entry growth implies
that the expected R
2
from growth regressions is low and
remains low in the years after entry.
3
Hypothesis 1 The R
2
from regressions of the
determinants of new venture growth does not increase
in the years after entry.
3.2 Survival
To examine survival, firm size at time t is x
t
, with start-
up size being denoted as x
0
. In a random walk model a
`
la Levinthal (1991), firm size evolves, with x
t
=-
x
t-1
?e
t
, where e
t
is distributed with mean land
variance r
2
. When l=0, we have a pure random
walk, whereas when l[0 (following Le Mens et al.
2011) then there is a steady increase in expected
resource stock over time.
Firms are assumed to exit when their size (proxied by
their resource stock) reaches zero. The time taken until
the firm first exhausts its resources (i.e. x
l
Bx*, for the
case when x*=0) is expressed as the cumulative
distribution function of a random variable in the
following way (known as the Bachelier–Le
´vy formula):
FðTjx0;l;rÞ¼Nltþx0
rffiffit
p

þe2x0l
r2Nltx0
rffiffit
p

ð3Þ
where N() represents the cumulative density of the
standard normal distribution (Levinthal 1991; Coad
et al. 2014). Time to exit is thus a function of three
parameters: the trend in the random walk l, the
variance r
2
of the growth shocks, and start-up size x
0.
4
Even if growth is a random process, expected survival
time can be increased by increasing the size at start-up
x
0
(Levinthal 1991; Coad et al. 2014). The R
2
from
survival regressions therefore depends on both start-up
size and growth since start-up.
We now apply a simulation model to derive
implications of the Levinthal random walk model for
the evolution of the R
2
. We generate an artificial
dataset of 50,000 firms, whose start-up size is
calibrated according to the lognormal distribution
with mean 10.55 and standard deviation 1.5, in order
to closely follow the start-up size distribution
observed in our data. We then generate a distribution
of growth rates, distributed according to the Laplace or
‘symmetric exponential’ (Stanley et al. 1996; Bottazzi
and Secchi 2006), with mean l=-0.1 and standard
deviation r=0.9 (again, closely following the values
observed in our data).
5
Firm size evolves as a random
walk, x
t
=x
t-1
?e
t
, given the distributions of start-
up size and growth rates given above, for t=60
periods. The exit threshold x* is set at 7 in the baseline
case, which is deliberately chosen to be a relatively
high value that will guarantee that in each period some
firms will exit (thus avoiding a degenerate value for
the R
2
in any year’s survival regression in which all
firms survive). For each individual period up to
t=60, we estimate a probit survival regression (with
a constant term and a single explanatory variable:
lagged size) and record the Nagelkerke R
2
statistic.
Figure 1shows that the R
2
clearly increases in the
years following start-up. This is because, with the
passage of time, surviving firms overcome the liability
of newness and grow to become sufficiently large that
they have accumulated a ‘buffer’ stock of resources,
and no longer operate on the brink of the exit
threshold. Firms that start small, on the other hand,
are more likely to be quickly weeded out through a
selection effect. As these chaotic, short-lived firms are
removed, the selection environment becomes less
‘foggy’. The central point here is that the R
2
value rises
over time even when growth is a random walk.
Hypothesis 2 The R
2
from regressions of the
determinants of new venture survival increases in the
years after entry.
3
We consider it trivial that the R
2
will be low and driven by
stochastic noise, therefore we do not see the need to use a
simulation model here to demonstrate the evolution of the R
2
.
4
It was correctly pointed out to us by a Referee that this does
not capture the situation where ‘‘a wealthy entrepreneur operates
a very small firm (with a low amount of annual sales). Such a
small firm could experience negative shocks which are then
financed by the personal wealth of the entrepreneur’’. We
acknowledge that this highlights a mismatch between the
theoretical construct and the empirics. However we do not see
how this could be resolved, since even the Bank has an imperfect
idea of the wealth of its clients and would only be incentivised to
quantify that wealth in the unlikely event that this particular NV
were seeking large funding.
5
See Table 1for summary statistics on our dataset.
220 A. Coad et al.
123
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4 Testing for changes in the density of the fog
Of crucial interest for our paper is measuring what we
call ‘fog’—the coefficient of determination, or R
2
statistic. The standard R
2
statistic is expressed in terms
of how well an OLS regression model can explain the
total variation in the data:
R2SSreg
SStot ¼1SSres
SStot

where SS
reg
is the regression sum of squares (i.e. the
explained sum of squares), SS
tot
is the total sum of
squares, and SS
res
is the residual (i.e. unexplained)
sum of squares. The R
2
statistic provides meaningful
information on how well a set of variables can explain
a given outcome, or how well we can predict real-
world outcomes on the basis of our available infor-
mation (Bertrand and Schoar 2003; see also Syverson
2011, p. 340). Cox and Snell (1989) suggested that the
R
2
statistic be generalised to other regression models
(such as regression models with binary dependent
variables) where maximum likelihood is the criterion
of fit. They suggested the following R
2
statistic:
Cox-Snell R2¼1L0ðÞ=L^
b
no
2
n
where L(^
b) and L(0) denote the likelihoods of the fitted
and ‘null’ models, respectively. The Cox–Snell R
2
statistic has a number of desirable properties (e.g. it is
asymptotically independent of the sample size),
although a drawback is that it reaches a maximum
value that is lower than unity for discrete models
(Nagelkerke 1991). Therefore, it has been suggested
that the Cox–Snell R
2
be adjusted as follows, to obtain
what has become known as the Nagelkerke R
2
statistic,
after Nagelkerke (1991):
Nagelkerke R2¼Cox-Snell R2=max R2

where max R2

¼1L0
ðÞ
2
n:
Because of its desirable statistical properties we use
the Nagelkerke R
2
statistic, although we check that our
results are not sensitive to this choice of R
2
statistic.
We begin by running regressions on cross sections
corresponding to each year, where the dependent
variable is either growth rate or survival probability.
6
For each year we obtain a Nagelkerke R
2
statistic. We
then plot the evolution of the Nagelkerke R
2
over time
using line charts—one chart for growth, one for
survival.
Fig. 1 Evolution of the Nagelkerke R
2
using simulated data, for
60 periods. y-axis: Nagelkerke R
2
obtained from probit
regressions where exit depends on lagged size. x-axis: time
period. Baseline case (with exit threshold x*=7) appears as a
solid line;x*=8 for the long-dash line;x*=9 for the short-
dash line. Linear trend-line plotted for the baseline case
6
Note that, in contrast to a large body of research on survival
models, we do not investigate the determinants of a firm’s total
survival duration, but instead the chances of survival for any one
particular year.
Predicting new venture survival and growth 221
123
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5 The dataset: Barclays bank customer accounts
5.1 Start-up: definition
We exploit a rich and unique dataset drawn from non-
financial firms identified as start-ups or new ventures
that entered the business customer base of Barclays
Bank between March and May 2004. At that time
about one in four UK start-ups banked with Barclays.
The sample excludes established businesses that
switched from another Bank. We are aware that a
new business does not necessarily start trading imme-
diately upon opening an account. Indeed, for Barclays’
customers, approximately five per cent of start-ups
show no activity through their account in the subse-
quent 12 months. We addressed this by only including
firms that showed activity in the month following
entry to the customer base.
7,8
We therefore focus on a cohort of 6579 firms that
have the same start date. We consider this to be
important, because firms starting in different years
may not be readily comparable (especially if the
macroeconomic conditions at start-up have persistent
effects on firm development in subsequent years).
Focusing on a single cohort means that firms face the
same macro-economic conditions at each year of their
development and can therefore be meaningfully
compared (Ryder 1965; Anyadike-Danes et al.
2015). We then track the cohort for a maximum of
10 years, a period of time that we consider to be
sufficiently long for our purposes, given that over
80 % of the ventures will have exited in that time
(Anyadike-Danes and Hart 2014).
5.2 Start-up: data
Prior to opening the new business account, data were
collected on the founder(s) gender, age, highest level
of educational qualifications; prior business experi-
ence; previous ownership; and/or ownership amongst
immediate family members. Finally, to capture access
to non-financial resources, owners were asked about
the sources of advice and support they used prior to
start-up.
These data were then supplemented by the bank as
part of its general account opening process. This
covers the legal form of the business, the activity type
(sector/branch/market) and its location (standard
region) within the UK. Table 2in ‘Appendix’ sets
out the data definitions in full.
5.3 Ongoing data
To measure the size of the business we used credit
turnover—the value of payments into a current
account,
9
which we will refer to as ‘sales’. This serves
as a very close approximation to sales revenue
inclusive of taxes.
10
The much greater granularity of
sales compared with using measures of employee
numbers is a particular strength.
11
It is also reliable,
comprehensive and, because every financial transac-
tion is documented, the scale of volatility can be
reliably quantified. Although credit turnover was
initially observed by the bank at monthly intervals,
the data we have have been aggregated over
12 months to analyse annual values, since our focus
here is to explain long-run, rather than short-run
changes.
5.4 Exit and closure
Establishing precisely when a business has closed is
perhaps the most challenging aspect of any study of
new ventures. Even for datasets taken from near
comprehensive official sources, the date at which exit
occurs may be some time after actual closure.
12
7
We also included a small proportion of firms who did not
show activity in their first full month, but in either May or June
2004. In these cases the start month of the firm was recorded as
the month prior to activity.
8
The UK, unlike many countries in continental Europe, is not
characterised by multiple banking (Ongena and Smith 2000).
The account at a single bank is therefore likely to capture the full
trading activities of the new venture.
9
Excluding payments from related accounts, e.g. deposit
accounts held by the business.
10
Prior empirical work has measured growth in terms of
numerous metrics such as employment, sales, profits, business
valuation. We follow Zimmerman and Zeltz (2002, p. 417) who
explain that ‘‘growth in sales is especially important for new
ventures since their economies of scale are too important for
them to continue without increasing their scale of operations.’’
11
These strengths are discussed in more detail shortly.
12
For example Storey et al (1987, p. 45), in a study of the
closure of 177 Limited Companies that ‘‘failed’’, identified
seven decision-rules that were required to identify the year in
which the enterprise ceased to trade.
222 A. Coad et al.
123
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When using bank records, there are two main issues
to resolve. The first is to distinguish between those
businesses that have closed, and those that have
switched to another bank. For our dataset we used
Barclays closure-reason-codes that record why any
given account has been closed. 1.38 % of our initial
sample switched over the 10 years covered by the
dataset, i.e. they had closed their account with
Barclays, but continued to trade.
13
These were
dropped from our sample before we started the
analysis.
The second issue is judging when a given business
has actually closed. While the majority of Barclays
customers ceasing to trade clearly close at a specific
time when no more transactions take place, an
important minority become dormant, i.e. their account
remains open, but with no activity.
14
For the firms in
our sample we used a simple rule—if the business had
shown no sales in consecutive 6-month periods, then it
was deemed to have closed in the first of these
periods.
15
It is important to note that this process identifies
closures. It is not limited to business ‘failures’. By the
latter we mean those firms that cease to trade with
some external financial liability. Of course, as noted
earlier, a closing firm may, or may not, have met the
objectives of its owner(s), although closure may
equally reflect that a better opportunity has presented
itself to the business owner(s) (Headd 2003; Harada
2007). Finally, cases of entrepreneurial exit (but
business continuation) such as an initial public offer-
ing (IPO), merger or acquisition (M&A) or trade sale
will not have a confounding effect on our measure-
ment of business exit (Wennberg et al. 2010; Coad
2014), because if the firm continues operations with
the same bank account, it will be treated in our dataset
as a continuing firm, whereas if it switches its bank
account to a different bank, it will be treated in our
dataset as a ‘switcher’ and dropped prior to analysis.
Nevertheless, cases of IPOs, M&As and even trade
sales are negligible because our new ventures are both
young, small and representative of all sectors (apart
from financial services). The tech-based services in
which these outcomes are particularly characteristic
constitute only a tiny proportion of the sample.
16
5.4.1 Dependent variables
We take two dependent variables as alternative
indicators of new venture ‘performance’ (Miller
et al. 2013). Survival is a binary variable, equal to 1
if the enterprise continues to trade at end of period (=0
if the enterprise exited). The Growth Rate is measured
in terms of growth in credit turnover (or ‘sales’, the
value of payments into a current account) excluding
payments from a related account (deposit account).
17
Sales growth has many advantages over other metrics
of growth, such as employment, for new ventures. The
first is because growth in terms of employment is
‘clunky’ (Coad et al. 2015, p. 6) due to integer
constraints in terms of employee headcounts. These
are particularly important for new ventures (e.g. a solo
self-employed individual contemplating her first hire,
who can either remain static or double her size—and
nothing in between).
18
Second, the decision to take on
a new/first employee is a huge decision by a NV and
presents problems of interpretation since it reflects a,
difficult to specify, combination of past and current
performance as well as future expectations. Finally,
most new ventures, in our sample, are too small to
employ others—certainly when they start to trade.
19
13
This could be an understatement of the true number if there
were imperfections in the reporting process meaning that some
switchers were not recorded. However, our rates are broadly in
line with Fraser (2005, p. 90) for all types of UK (SME)
businesses.
14
Indeed, some of these may have switched rather than closed.
15
Some Barclays customers can show little or no activity for a
number of months before seeing turnover return to non-
negligible levels. This reflects the nature of many ‘micro’
businesses.
16
It has been pointed out to us, both by referees and others, that
this procedure of retaining the same bank account number is not
the case in other countries and contexts. We think our
classification is a clear benefit, since it also includes the tiny
proportion of new ventures that might grow by acquisition in
their early years.
17
Our indicator of growth rates is continuous and can be
positive or negative (i.e. decline). We agree with Davila et al.
(2015) that the research community has focussed dispropor-
tionately upon both growth and high growth.
18
Detailed data on fractional or part-time employees, total
wage bill, or total hours worked, are extremely unusual in
longitudinal databases on new venture performance.
19
Datasets which identify new ventures as those taking their
first employee are therefore likely to be both larger and longer-
established than those identified as making sales for the first
time.
Predicting new venture survival and growth 223
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
We calculate the annual growth rate in the usual
way (see e.g. Tornqvist et al. 1985; Coad 2009)by
taking log-differences of sales, i.e.
Sales growth i;tðÞ¼ðlog sales i;tðÞðÞ
log sales i;t1ðÞðÞð4Þ
We estimate regression equations 1 year at a time, one
cross section at a time, to obtain an R
2
statistic for each
year. A logistic regression model is applied for our
survival estimations (Jenkins 1995; Wiklund et al.
2010), which is compatible with our focus on survival/
death within a single year (rather than survival
durations over many years). Our regression equations
for the growth and survival of firm iin year tare as
follows:
Growth i;tðÞ¼a1þb1log sales i;t1ðÞ
þb2Growth i;t1ðÞ
þc1Entrepreneur i;tðÞ
þd1Business i;tðÞ
þh1Account i;tðÞþe1i;tðÞ
ð5Þ
Survival i;tðÞ¼a2þb3log sales i;t1ðÞ
þb4Growth i;t1ðÞ
þc2Entrepreneur i;tðÞ
þd2Business i;tðÞ
þh2Account i;tðÞþe2i;tðÞ
ð6Þ
where our explanatory variables can be grouped
together at the entrepreneur level (age, education,
business experience, sources of advice), the business
level (number and gender of owner(s), legal form,
industry, region), and the bank account level (volatil-
ity, overdraft behaviour).
20
5.4.2 Independent variables
The independent variables used in the analysis are
defined in Table 2in ‘Appendix’. It also sets out where
these variables have been used in previous work on
survival/growth of new/small enterprises and the
results obtained. The first group are the ‘usual
suspects’ such as Legal form (Company, Partnership,
Sole Trader); Number of owners; Gender; Age (and
Age squared); Education level categories; Sources of
advice (EABL scheme, Accountant, Solicitor, Col-
lege, SR Seminar, PYBT scheme, Family, or Other),
and a full set of dummies for industry and geograph-
ical region.
A second group is information on bank account
activity: sales volatility, availability and extent of use
of authorised overdraft facilities, and the use and
extent of use of unauthorised overdrafts. These
variables have not been explored in previous work
that seeks to explain firm growth and survival, and so
their inclusion can be considered to be a strength of
this paper.
21
Table 2in ‘Appendix’ does not point to the
omission of key variables that might cause our
regression equations to be grossly misspecified. Prior
work on firm growth generally has low values for the
R
2
statistic (usually lower than 15 %, see the survey in
Coad 2009, Table 7.1) so although there remains a risk
of specification error and omitted variable bias, there
are no clear guidelines in the literature as to which (if
any) variables or regression specifications would be
more appropriate.
5.5 Summary statistics
Table 1provides an overview of the size and growth
of new ventures in our sample which, it will be
recalled, all began trading in the second quarter of
2004. The median sales in year 1 (i.e. 2005) are
£38,712 which is far smaller than the threshold for
value-added tax (VAT) registration (set at £58,000 for
the 12 months from 1 April 2004, and rising to
£73,000 by 1 April 2011), above which firms start to
appear in UK administrative datasets. Around 50 % of
new ventures will exit within 3 years of starting to
trade, which is similar to that observed from UK
20
We do not claim to have resolved the endogeneity between
the explanatory variables and the performance outcome vari-
ables. We also do not claim to have discovered causal effects
regarding growth or survival. Instead, we are interested in
describing how the R
2
statistics change in the years after entry.
21
However, their inclusion may introduce endogeneity into the
analysis of growth and survival (e.g. although bank account
activity may affect survival and growth, future survival and
growth prospects may precede or co-evolve with bank account
activity). We therefore remind the reader that the coefficient
estimates for our variables reflect partial correlations (i.e.
associations) rather than causal effects. We also investigate how
our results change according to whether or not these bank
account variables are included.
224 A. Coad et al.
123
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administrative data on new ventures (Anyadike-Danes
and Hart 2014).
To investigate the impact of our rich coverage of
micro firms, we complement our baseline results with
those obtained from restricting our sample of new
ventures to those of above-median start-up size. This
makes our sample more similar to other work on new
ventures that has a disproportionate coverage of larger
new ventures (Yang and Aldrich 2012). The signifi-
cance of this is that, only by year 10, would the median
surviving firm from this dataset have had sales
sufficient for them to be included in official data.
A second key factto emerge from the lower section of
Table 1is that (positive) growth in sales is by no means
the ‘norm’ for new ventures. The mean growth rate is
negative in every single year, although the median
growth rate is only negative in 3 years. The term ‘sales
growth,’ when applied to NVs, is for this reason
potentially misleading if it is not understood that growth
rates can be negative (Davila et al. 2015). Indeed,
negative growth rates (i.e. decline) are very common.
Figure 2presents the growth rate distribution,
which resembles the usual Laplace or symmetric
exponential distribution found in other work (Bottazzi
and Secchi 2006; Coad and Tamvada 2012; Daunfeldt
and Halvarsson 2015). In every year, about half of the
firms will have negative growth rates, which empha-
sises further that our use of the term ‘sales growth’
does not imply that new ventures all have (positive)
growth, but that there are many cases of decline (i.e.
negative growth rates).
Summary statistics for the explanatory variables are
presented in Table 3in ‘Appendix’.
6 Testing the hypotheses
This section presents the crux of our empirical
contribution, which can be found in our plots of the
evolution of the R
2
statistic over time (see Figs. 3a, b,
4). We present the evolution of the Nagelkerke R
2
statistic for four regression specifications—in some
cases we include lagged growth as an explanatory
variable (at the cost of losing an extra year’s results),
and in some cases we focus on a subsample of
relatively large firms (i.e. those with above-median
Table 1 Summary
statistics for size and
growth rates
Note that there are 6579
firms at the start of year 1
Mean SD 10 % 25 % Median 75 % 90 % Obs.
Sales
Year 1 114,095 508,678 5475 14,687 38,712 103,658 260,652 5524
Year 2 144,319 546,146 5547 16,529 44,524 124,414 323,178 4162
Year 3 168,352 645,409 5222 17,253 47,855 138,347 373,255 3211
Year 4 183,939 542,018 5438 18,532 51,964 158,026 428,499 2593
Year 5 190,217 552,839 5945 17,996 51,168 152,264 451,445 2152
Year 6 192,157 706,588 5239 17,517 47,924 147,866 453,727 1823
Year 7 213,050 938,730 5700 18,475 53,019 161,941 512,618 1604
Year 8 253,250 1,333,538 6668 19,516 58,134 177,112 577,178 1424
Year 9 277,643 1,640,798 6533 19,274 57,258 180,390 595,597 1311
Year 10 300,699 2,046,271 6860 22,673 64,989 196,821 592,880 1208
Sales growth
Year 1 – – – –
Year 2 -0.055 0.940 -0.964 -0.270 0.053 0.356 0.753 4162
Year 3 -0.133 0.946 -1.001 -0.303 0.022 0.240 0.566 3211
Year 4 -0.110 0.864 -0.873 -0.280 0.013 0.226 0.503 2593
Year 5 -0.189 0.907 -0.991 -0.378 -0.067 0.135 0.427 2152
Year 6 -0.221 0.833 -0.864 -0.368 -0.080 0.086 0.359 1823
Year 7 -0.089 0.772 -0.696 -0.207 0.005 0.185 0.475 1604
Year 8 -0.055 0.698 -0.593 -0.198 0.000 0.184 0.458 1424
Year 9 -0.078 0.731 -0.592 -0.222 -0.022 0.147 0.436 1311
Year 10 -0.037 0.678 -0.518 -0.175 0.020 0.203 0.484 1208
Predicting new venture survival and growth 225
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
sales in year 1). Regression results tables for the
baseline specification are also presented for the sake of
completeness as Tables 4,5and 6in ‘Appendix’.
6.1 Plotting the R
2
statistics
6.1.1 Sales growth
Figure 3a shows how the Nagelkerke R
2
statistic for
sales growth regressions evolves over the first 10 years.
It starts off in year 2 at values of 28–37 % (depending on
the regression specification), which is considerably
higher than normally found in the literature on growth
rate regressions (no doubt due to our unusually rich
information on businessbehaviour).A closer look at the
regression coefficients, reported in Table 4in ‘Ap-
pendix’ for the baseline model, shows that the most
significant variables are the bank account activity
variables (volatility and overdraft behaviour).
Figure 3a shows that the R
2
decreases in the years
after entry for the four specifications shown. In year 2
it is in the range of 27–37 %, whereas by year 10 it is in
the range of 13–22 %. Year 5, which corresponds to
the deep recession of 2009, does not stand out or
interrupt the overall trend. As new ventures age, it
seems to become increasingly difficult to accurately
predict their growth. This implies the fog seems to
thicken and is in line with Lotti et al. (2009), who
observe that Gibrat’s Law appears to hold as a ‘long-
run regularity’ as time goes by, and growth becomes
harder to predict.
Another observation is that, from both Fig. 3a, b,
for nearly all years, the Nagelkerke R
2
values for the
equations that only include the larger enterprises (i.e.
the ‘large startup size’ subsample) are higher than for
the baseline sample. This implies it is harder to explain
the growth performance of smaller firms, which
exhibit greater volatility. This may explain, at least
in part, why analyses using relatively large and well-
established ‘new ventures’ (Hmieleski and Baron
2009; Dencker et al. 2009; Baum and Bird 2010) are
able to show higher explanatory power than those
included here.
Table 4in ‘Appendix’ shows this decreasing trend
in the explanatory power of our regressions is observed
for alternative indicators of goodness of fit—the
standard R
2
statistic as well as the Cox–Snell R
2
statistic—for the baseline case. Further explorations
show that this is also the case for the 3 other regression
specifications (results available upon request).
Figure 3a shows that the explanatory power of
growth rate regressions decreases over time, but it
does not explain why. Changes in the ‘fog’—our
ability to predict growth—could be due to internal
developmental factors in new firms, or they could
reflect selection effects—whereby the composition of
the sample of survivors is affected by the selective exit
of certain types of firms.
Fig. 2 Growth rate
distributions for different
years. Note the log scale on
the y-axis
226 A. Coad et al.
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Indeed, previous research has shown that many firms
will exit in the years after entry (Audretsch et al. 1999;
Santarelli and Vivarelli 2007), and it could be that
changes in our ability to explain growth are due to
changes in the sample composition over time. One way
of eliminating the role of selection effects is to restrict
the analysis to only those firms that survive the full
10-year period. Any change in the ability to explain
growth for this subsample would then be due to internal
developmental factors rather than selection effects.
The results are plotted in Fig. 3b (and the regres-
sion results for the baseline case are presented in
Table 5in ‘Appendix’). For the subsample of
surviving firms, the R
2
shows no clear trend over
time. For surviving firms, there is no clear change in
our ability to explain their growth in the years after
entry. Any deterioration in our ability to explain
growth in the years after entry (shown in Fig. 3a)
would therefore seem to be driven by the relative ease
of explaining the growth (or perhaps more precisely:
the decline) of short-lived firms.
22
Fig. 3 a OLS growth
regression Nagelkerke R
2
statistics for individual
cross sections for the first
10 years, for 4 different
growth rate regression
specifications and bOLS
growth regression
Nagelkerke R
2
statistics for
the first 10 years, for 4
different growth rate
regressions (NVs that
survive until the end of year
10). Key: Baseline: full
sample. Baseline ?lag:
full sample controlling for
lagged growth. Largest
startup size: above-median
start-up size only. Largest
startup size ?lag: above-
median start-up size
subsample, controlling for
lagged growth
22
More specifically, it appears that our ability to explain the
relative growth performance of short-lived firms is largely due
to the bank account activity variables. When these variables are
omitted, we can no longer observe that our ability to explain
growth decreases in the post-entry years. (If anything, it seems
Predicting new venture survival and growth 227
123
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Overall, therefore, the evidence in Fig. 3a, b
suggests that our ability to explain growth deteriorates
in the years after entry, with this becoming closer to
random over time. This seems to be driven by the
changing composition of the sample of surviving firms
(i.e. selection effects) rather than any internal devel-
opmental factors within firms. Focusing on a core
subsample of NVs that survive until the end of year 10
(and thus removing any sample composition effects
because we have the same number of observations in
each year), our ability to explain growth remains
roughly constant over time (Fig. 3b). Overall, this
mixed evidence is in keeping with Hypothesis 1.
6.1.2 Survival
To test Hypothesis 2 we run year-by-year regressions
(presented in detail in Table 6in ‘Appendix’ for the
baseline case) and plot the evolution of the Nagelkerke
R
2
statistics in Fig. 4. The ‘fog’ regarding survival—
i.e. our ability to explain the survival of firms—seems
to clear in the years after entry. Figure 4shows how
the Nagelkerke R
2
starts off at around 15 % in year 2
and increases to 26–36 % by year 10. This is
consistent with our simulation model and the predic-
tions of Hypothesis 2.
The key difference between the growth rate
regressions (Fig. 3a, b), on the one hand, and the
survival regressions (Fig. 4), on the other hand, is that
‘the fog clears’ in the years after entry when the task is
to explain survival, yet it remains dense when the task
is to explain growth.
6.2 Robustness analysis
Further evidence on the robustness of our findings
comes from considering alternative measures of
goodness of fit, in addition to the Nagelkerke R
2
.
These are shown at the bottom of Tables 4,5and 6in
Appendix’. For the growth regressions, the R
2
and
Cox–Snell R
2
statistics closely mirror the Nagelkerke
R
2
, with no clear trend in the R
2
statistic. For survival,
we report the Cox–Snell R
2
, as well as information on
the percentage of cases correctly classified: the latter
increase in most years after start-up, hence confirming
our earlier results using the Nagelkerke R
2
statistic.
The Cox–Snell measure provides results that are less
clear-cut, however: although it rises during the early
years, it reaches a peak in year 6.
Another way of exploring the robustness of our
results is by taking an alternative regression specifica-
tion with a different set of explanatory variables. Earlier
we commented on the fact that our database contains a
number of variables relating to bank account activity,
which constitute a rich and unique source of informa-
tion on firm behaviour, although these variables remain
little-known in the literature, and also they may raise
concerns of endogeneity (e.g. risky unauthorised over-
draft behaviour may be a cause or a consequence of
poor performance in terms of growth or survival
Fig. 4 Logit survival
regression: Nagelkerke R
2
statistics for individual
cross sections for first
10 years, for 4 different
survival regression
specifications. Key to
regression specifications:
the baseline model refers to
the full sample with or
without controlling for
lagged growth. Regressions
labelled ‘large startup size’
refer to a subsample of
firms with above-median
start-up size (i.e. above-
median values of sales in
the first year)
Footnote 22 continued
that our ability increases post-entry, when bank account activity
variables are not included.) These extra results are available
from the corresponding author upon request.
228 A. Coad et al.
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
prospects). We repeated the analysis excluding the
variables relating to bank account volatility and
obtained the following results. For the growth rate
regressions, a first observation was that the Nagelkerke
R
2
statistics were very low, in the range of 3–7 % for our
baseline specification. If anything, the Nagelkerke R
2
statistics appeared to increase slightly in the years after
entry, although this increase was not monotonic. When
the growth rate regressions were performed on the core
sub-sample of firms surviving until the end of the
10-year period, the Nagelkerke R
2
generally decreased,
if anything, in the years after entry. Our clearest results
were observed in our survival regressions, where the
Nagelkerke R
2
followed an increasing trend in the years
after entry. All in all, when we repeated the analysis
without the bank account activity variables, our results
for survival were relatively clear in showing that the
survival ‘fog’ tends to clear in the years after entry (i.e.
that the Nagelkerke R
2
generally increases in the years
after entry). Our results for growth were less clear-cut,
probably because after dropping the bank account
variables the overall explanatory power was very low
(Nagelkerke R
2
statistics of around 5 % or lower) and
hence the lower signal-to-noise ratio made it hard to
detect any clear trend.
7 Conclusion
Business owners, providers of finance, and governments
have much to gain from developing a better understand-
ing of the factors influencing the performance of new
ventures (NVs) in the years after entry. The starting point
for this paper was that the post-entry performance of new
ventures is highly diverse, the selection environment is
noisy (characterised by imperfect mechanisms of sur-
vival/growth of the fittest), and that our ability to explain
and perhaps forecast the survival/growth in new ventures
is weak. In the terminology of this paper, the fog was
thick. The challenge therefore was to examine whether,
as the new venture aged, it became easier to explain
performance: did the fog lift? Our final question was, if
the fog does lift with time, did visibility improve in steps
or stages (Phelps et al. 2007; Levie and Lichtenstein
2010) or was the process more continuous?
To address these questions we primarily drew
upon a theoretical framework that sees new venture
sales growth as a random walk (Levinthal 1991;Le
Mens et al. 2011), and survival being determined by
the stock of available resources (proxied by size),
where these resources are either present at start-up or
accumulated after entry. We used this theory to derive
testable hypotheses that our ability to explain growth
(i.e. the R
2
from growth regressions) should remain
low over time, but that our ability to explain survival
should increase in the years after entry.
We conducted our tests on 6579 new ventures which,
because they were genuinely representative of NVs,
were on balance considerably smaller than those
identified in prior work. These NVs were tracked over
the years 2004–2014, generating two key findings.
First, in the sales growth regressions, the goodness-of-
fit measure (Nagelkerke R
2
) decreases in value in the
years after entry—implying that our ability to explain
firm growth deteriorates, or that ‘the fog thickens’.
However, when we sidestep issues of ‘selection’ and
focus only on a subsample of NVs that we know will
survive until the end of the period of observation, then
our ability to explain the growth in this subsample of
survivors remains low but does not change over time.
Hence, any decrease in our ability to explain growth in
the years after entry appears to be driven by the
presence of short-lived firms, rather than being due to
internal developmental factors within surviving firms.
In any case, our ability to explain growth remains low
throughout the period investigated.
23
Second, in the
survival equations, using three performance metrics we
find that, on balance, the goodness-of-fit increases in
years since start-up. This suggests that the fog does lift
somewhat with time when the task is to predict survival.
In terms of the questions posed at the start of the
paper, we take our evidence as showing that the
growth rate fog is always thick and shows no signs of
improvement with time, in line with our theory.
Survival visibility, however, does seem to improve
with time, but not in a clear ‘step’ fashion.
Finally, we see important areas for developing this
approach. Currently our data track a cohort of new
23
Our finding that there is little predictability in sales growth is
in itself a valuable finding, with many implications for scholars
and providers of finance. Some (admittedly speculative) possi-
ble implications can be mentioned here. First, stakeholders
might be encouraged to take a broad-based approach to
investing, rather than trying to invest in just one firm suspected
of soon embarking on fast growth; and second, it hints that there
might be little to be gained for investors from investing too
heavily in collecting detailed information on firms to predict
their growth performance—because growth is so hard to predict.
Predicting new venture survival and growth 229
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ventures during an unusual period—beginning in
benign macro-economic conditions that are followed
by a deep recession. Ideally we would like to know
whether our findings hold under different macro-
conditions. However, future efforts in this direction
will face challenges of obtaining comprehensive
datasets on NVs (from year 1) that also include a rich
set of explanatory variables.
Acknowledgments We are grateful to Jose Garcia Quevedo,
Gabriele Pellegrino, Maria Savona, Karl Wennberg, and
participants at the RATIO Institute Stockholm and DRUID
2013 (ESADE, Barcelona) for many helpful comments. A.C.
gratefully acknowledges financial support from the ESRC, TSB,
BIS and NESTA on grants ES/H008705/1 and ES/J008427/1 as
part of the IRC distributed projects initiative, as well as from the
AHRC as part of the FUSE project. The views expressed are
purely those of the authors and may not in any circumstances be
regarded as stating an official position of the European
Commission. J.S.F. and R.G.R. write only in a personal
capacity and do not necessarily reflect the views of Barclays
Bank. Any remaining errors are ours alone.
Open Access This article is distributed under the terms of the
Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unre-
stricted use, distribution, and reproduction in any medium,
provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Com-
mons license, and indicate if changes were made.
Appendix
See Tables 2,3,4,5and 6.
Table 2 Variable names and definitions
Description Prior work Relationship with growth
and survival
Dependent variables
Open =1 if enterprise continues to trade at end of period
(Open =0 if the enterprise exited)
Growth rate Growth is measured as Credit turnover—value of
payments into a current account—excluding
payments from a related account (deposit account).
In our baseline estimates, the annual growth rate is
the log-differences of turnover
(log(turnover(t)) -log(turnover(t-1)), which is
then normalised with mean =0, SD =1
Independent variables
Lagged (log) turnover Credit turnover—the value of payments into a current
account, excluding payments from related accounts,
e.g. deposit accounts held by the business—is the
metric of size. It is a very close approximation to
sales revenue (turnover) inclusive of taxes
Coad et al. (2013)?with survival
Owner(s) characteristics
Age Mean age of owner(s) at start-up Persson (2004)?with survival
Age squared Square of age Persson (2004)?with survival
-with growth
Education dummies Highest level of educational attainment by owner(s):
1=none; 2 =GCSE; 3 =A-level; 4 =Degree or
higher
Dencker et al. (2009)
Parker (2009)
?with survival
Mixed with growth and survival
Business experience:
family
business experience of parents: 0 =No; 1 =Yes Gimeno et al. (1997)?with Survival
n/s with growth
Business experience: self Previous business experience of Owner: 0 =No;
1=Yes
Dencker et al. (2009)?with survival when combined
with other variables
No owners Owners in excess of minimum number for legal form:
0=No; 1 =Yes
Klotz et al. (2014) Mixed results
Male owner(s) At least one male owner: 0 =No; 1 =Yes Gilbert et al. (2006), Klotz et al.
(2014)
Dencker et al., (2009)
Mixed results with both growth
and survival
No gender effect
Sources of advice See individual sources identified below Solomon et al. (2013), Chrisman
and McMullan (2004)
?with survival and growth
?(small) with survivaland growth
Pons Rotger et al. (2012)
230 A. Coad et al.
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 2 continued
Description Prior work Relationship with growth
and survival
Enterprise agency/business
link EABL
Advice/support (prior to start-up) from enterprise
agency/business link: 0 =No; 1 =Yes
Mole et al. (2011)?with growth in some cases
Accountant Advice/support (prior to start-up) from Accountant:
0=No; 1 =Yes
Frankish et al. (2011) n/s with growth or survival
Solicitor Advice/support (prior to start-up) from Solicitor:
0=No; 1 =Yes
Frankish et al. (2011) n/s with growth or survival
College Advice/support (prior to start-up) from a College:
0=No; 1 =Yes
Frankish et al. (2011) n/s with growth or survival
Start-right seminar Participation in Barclays Start-right seminar: 0 =No;
1=Yes
Coad et al. (2016) n/s with survival and growth
?with bank loyalty
Princes Youth Business
Trust
Advice/support (prior to start-up) from Princes Youth
Business Trust : 0 =No; 1 =Yes
Greene (2009) n/s using sophisticated evaluation
methods
Family Advice/support (prior to start-up) from Family:
0=No; 1 =Yes
Frankish et al. (2011) n/s with growth or survival
Other Advice/support (prior to start-up) from any other
source: 0 =No; 1 =Yes
Frankish et al. (2011) n/s with growth or survival
Bank account activity
variables
Volatility Ratio of the standard deviation of monthly turnover to
the mean monthly turnover, summed over two six-
month periods to obtain annual volatility indicator
Coad et al. (2013)-with survival
-with growth
Overdraft excess =1 if in excess of authorised overdraft limit at any time Coad et al. (2013), Frankish et al.
(2012)
-with survival
n/s with growth
Overdraft excess duration Proportion of period in excess of authorised overdraft
limit
Coad et al. (2013), Frankish et al.
(2012)
-with survival
-with growth
Authorised overdraft use =1 if authorised overdraft used at any time Coad et al. (2013), Frankish et al.
(2012)
n/s with survival
?with growth
Extent of auth. OD use Mean proportion of authorised overdraft limit used Coad et al. (2013), Frankish et al.
(2012)
n/s with survival
-with growth
Legal form dummies
Legal form 1 =Company, 2 =Partnership, 3 =Sole Trader Storey (1994) Limited liability companies
?with growth and survival
Industry dummies Business activity: 1 =Agriculture,
2=Manufacturing, 3 =Construction, 4 =Motor
Trades, 5 =Wholesale, 6 =Retail, 7 =Hotels &
Catering, 8 =Transport, 9 =Property Services,
10 =Business Services, 11 =Health, Education
and Social Work, 12 =Other Services
Dencker et al. (2009) Dencker et al. (2009) has three
sectors. n/s with survival
Region dummies Region: 1 =East Midlands, 2 =East of England,
3=London, 4 =North East, 5 =North West,
6=South East,7 =South West, 8 =West Midlands,
9=Yorkshire and The Humber, 10 =Wales
Botham and Graves (2011)?London and South-East for
Growth
n/s for survival
Table 3 Summary statistics for the explanatory variables, for year 1
Variable Mean SD Min Max Obs.
Age 39.036 10.225 16.2 78.16 6570
Education dummies
\NVQ2 (none) 0.228 0.419 0 1 6579
NVQ2 (GCSE) 0.332 0.471 0 1 6579
NVQ3 (A-level) 0.171 0.377 0 1 6579
NVQ4 (degree or higher) 0.270 0.444 0 1 6579
Predicting new venture survival and growth 231
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 3 continued
Variable Mean SD Min Max Obs.
Family business experience 0.633 0.482 0 1 6579
Personal business experience 0.720 0.449 0 1 6579
Sources of advice
EABL 0.102 0.303 0 1 6579
Accountant 0.361 0.480 0 1 6579
Solicitor 0.049 0.215 0 1 6579
College 0.040 0.196 0 1 6579
Start-right seminar 0.007 0.086 0 1 6579
Prince’s youth business trust 0.014 0.116 0 1 6579
Family 0.300 0.458 0 1 6579
Other 0.064 0.245 0 1 6579
Bank account activity
Volatility 0.844 0.603 0 4.843 5524
Auth. OD use 0.186 0.389 0 1 5524
Extent of auth. OD use 0.041 0.118 0 0.93 5524
Unauthorised OD use 0.407 0.491 0 1 5524
Extent of unauthorised OD use 0.042 0.103 0 1 5524
Excess owners 0.156 0.363 0 1 6562
Male owners 0.963 0.631 0 5 6562
Legal form dummies
Company 0.372 0.483 0 1 6579
Partnership 0.133 0.340 0 1 6579
Sole trader 0.495 0.500 0 1 6579
Industry dummies
Agriculture 0.010 0.100 0 1 6579
Manufacturing 0.049 0.215 0 1 6579
Construction 0.147 0.354 0 1 6579
Retail 0.178 0.382 0 1 6579
Transport 0.027 0.161 0 1 6579
Accommodation 0.094 0.292 0 1 6579
Information 0.060 0.237 0 1 6579
Real estate 0.035 0.183 0 1 6579
Professional 0.072 0.258 0 1 6579
Administrative 0.150 0.357 0 1 6579
Education 0.008 0.090 0 1 6579
Health 0.016 0.125 0 1 6579
Arts 0.034 0.182 0 1 6579
Other 0.122 0.327 0 1 6579
Region dummies
East Midlands 0.073 0.261 0 1 6570
East of England 0.155 0.362 0 1 6570
London 0.223 0.416 0 1 6570
North East 0.037 0.189 0 1 6570
North West 0.067 0.251 0 1 6570
South East 0.127 0.333 0 1 6570
South West 0.098 0.298 0 1 6570
West Midlands 0.095 0.294 0 1 6570
Yorkshire and Humber 0.061 0.239 0 1 6570
Wales 0.063 0.242 0 1 6570
232 A. Coad et al.
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Table 4 Growth rate regressions for each cross section, for the full sample of firms (without controlling for lagged growth)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Lagged (log) turnover -0.180*** -0.191*** -0.169*** -0.237*** -0.150*** -0.163*** -0.133*** -0.123*** -0.101***
(0.0166) (0.0161) (0.0179) (0.0341) (0.0202) (0.0225) (0.0240) (0.0236) (0.0222)
Age -0.0214** 0.0116 0.00809 0.00726 0.00335 0.00718 -0.00312 0.00177 0.00565
(0.00964) (0.00927) (0.00995) (0.0110) (0.0109) (0.0116) (0.0122) (0.0133) (0.0137)
Age squared 0.000195* -0.000157 -9.77e-05 -0.000104 -4.99e-05 -8.55e-05 3.10e-05 -3.88e-05 -8.45e-05
(0.000110) (0.000108) (0.000115) (0.000129) (0.000127) (0.000134) (0.000141) (0.000151) (0.000156)
Education dummies
(omitted =none)
GCSE -0.0118 -0.0889** 0.00798 0.0545 0.00255 0.0245 0.0169 0.0335 -0.0574
(0.0324) (0.0377) (0.0393) (0.0445) (0.0451) (0.0481) (0.0450) (0.0500) (0.0539)
A-level -0.0264 -0.0322 0.0204 0.0543 -0.126** 0.0935 0.0585 0.0137 -0.0174
(0.0401) (0.0435) (0.0482) (0.0556) (0.0555) (0.0618) (0.0592) (0.0656) (0.0664)
Degree or higher 0.00186 -0.0496 0.00257 0.160*** 0.0155 0.132** 0.0620 0.104 0.0803
(0.0391) (0.0468) (0.0474) (0.0528) (0.0539) (0.0568) (0.0561) (0.0668) (0.0624)
Business experience
Family 0.00198 0.00955 -0.0598* 0.113*** 0.0570 0.0530 0.0409 -0.0208 0.0464
(0.0263) (0.0290) (0.0316) (0.0347) (0.0368) (0.0360) (0.0364) (0.0394) (0.0394)
Self 0.000326 0.0137 -0.00835 0.0551 0.0371 0.00632 0.00454 0.0447 -0.0225
(0.0308) (0.0328) (0.0358) (0.0422) (0.0404) (0.0409) (0.0422) (0.0478) (0.0482)
Sources of advice
Enterprise agency/business
link (EABL)
-0.0427 0.0700 -0.0324 0.00278 0.0121 -0.111* -0.0239 0.0363 0.0210
(0.0439) (0.0556) (0.0613) (0.0612) (0.0582) (0.0652) (0.0738) (0.0515) (0.0594)
Accountant -0.00961 -0.00276 -0.0476 0.0240 -0.0305 -0.0524 -0.00756 0.0269 0.0954**
(0.0272) (0.0300) (0.0330) (0.0388) (0.0375) (0.0367) (0.0379) (0.0400) (0.0401)
Solicitor 0.0180 -0.0537 0.0888 -0.0935 0.152 -0.0365 -0.0194 -0.0502 -0.0162
(0.0802) (0.0753) (0.0717) (0.0999) (0.0962) (0.0881) (0.0883) (0.107) (0.0978)
College 0.00454 0.0600 0.0680 0.0248 0.0679 -0.0595 0.0272 0.00801 -0.145
(0.0633) (0.0613) (0.0517) (0.0718) (0.0721) (0.0777) (0.110) (0.104) (0.103)
Start-right seminar -0.123 0.0806 -0.124 -0.149 0.406* 0.363* 0.337* 0.482*** 0.168
(0.177) (0.194) (0.233) (0.122) (0.233) (0.197) (0.186) (0.161) (0.116)
Princes Youth Business Trust -0.00364 -0.203 0.0349 -0.531* 0.340 -0.0896 0.335 0.0680 0.175
(0.141) (0.169) (0.135) (0.280) (0.216) (0.284) (0.304) (0.310) (0.237)
Family 0.0289 0.0450 0.00718 -0.0615 -0.104** -0.00892 0.0350 -0.0479 -0.00179
(0.0290) (0.0339) (0.0334) (0.0395) (0.0423) (0.0424) (0.0470) (0.0460) (0.0452)
Predicting new venture survival and growth 233
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Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 4 continued
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Other 0.0579 -0.0515 -0.0923 0.102 -0.122 0.146** -0.0547 -0.109 -0.0443
(0.0584) (0.0642) (0.0597) (0.0807) (0.0886) (0.0693) (0.0875) (0.112) (0.0701)
Bank account activity variables
Volatility -0.639*** -0.626*** -0.561*** -0.606*** -0.508*** -0.486*** -0.358*** -0.356*** -0.287***
(0.0374) (0.0478) (0.0521) (0.0583) (0.0736) (0.0662) (0.0692) (0.0728) (0.0713)
Overdraft excess 0.0518* 0.0106 0.0715** -0.0202 -0.0104 0.0174 0.00951 0.0268 0.0472
(0.0301) (0.0343) (0.0348) (0.0426) (0.0441) (0.0468) (0.0497) (0.0553) (0.0535)
Overdraft excess duration -0.971*** -0.985*** -1.024*** -0.739*** -1.017*** -1.189*** -0.677*** -1.385*** -0.837*
(0.146) (0.142) (0.157) (0.168) (0.242) (0.269) (0.262) (0.406) (0.440)
Authorised overdraft use 0.279*** 0.152*** 0.00449 0.0956* 0.0455 0.152*** 0.0729 0.0994* -0.0561
(0.0414) (0.0387) (0.0480) (0.0524) (0.0736) (0.0536) (0.0491) (0.0528) (0.0668)
Extent of auth. OD use -0.257*** -0.173** -0.0335 -0.0774 -0.133 -0.158 -0.0323 -0.238*** 0.113
(0.0791) (0.0754) (0.0785) (0.0906) (0.129) (0.107) (0.121) (0.0912) (0.129)
No. owners 0.101** 0.113** 0.0208 0.0335 0.0489 -0.0387 -0.0278 -0.0238 0.0471
(0.0427) (0.0464) (0.0474) (0.0536) (0.0490) (0.0492) (0.0485) (0.0499) (0.0512)
Male owner(s) 0.0957*** 0.0506 0.0768* 0.0578 -0.0577 0.0665 0.142** -0.0204 0.0426
(0.0337) (0.0428) (0.0460) (0.0505) (0.0464) (0.0579) (0.0687) (0.0588) (0.0682)
Legal form dummies
(omitted =Company)
Partnership -0.0701* -0.124*** -0.129*** -0.240*** -0.173*** -0.211*** -0.101* -0.108* -0.0233
(0.0391) (0.0461) (0.0493) (0.0647) (0.0665) (0.0641) (0.0546) (0.0619) (0.0603)
Sole trader -0.134*** -0.189*** -0.229*** -0.196*** -0.0772 -0.159*** -0.173*** -0.184*** 0.0118
(0.0359) (0.0383) (0.0434) (0.0587) (0.0499) (0.0497) (0.0485) (0.0592) (0.0512)
Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Region dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Constant 2.748*** 2.255*** 2.122*** 2.887*** 1.954*** 1.934*** 1.659*** 1.744*** 1.358***
(0.298) (0.284) (0.322) (0.470) (0.384) (0.392) (0.399) (0.412) (0.426)
Observations 4147 3201 2584 2146 1819 1600 1421 1308 1205
R-squared 0.258 0.268 0.240 0.257 0.235 0.244 0.147 0.185 0.116
LL -5013 -3861 -2928 -2513 -2005 -1634 -1394 -1312 -1169
LL(0) -5630 -4360 -3283 -2832 -2248 -1858 -1507 -1446 -1243
Cox–Snell R
2
0.258 0.268 0.240 0.257 0.235 0.244 0.147 0.185 0.116
Nagelkerke R
2
0.276 0.287 0.261 0.277 0.257 0.271 0.167 0.208 0.132
Robust standard errors in parentheses
*** p\0.01; ** p\0.05; * p\0.1
234 A. Coad et al.
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Table 5 Growth rate regressions for each cross section, for firms that survive until the end of year 10 (without controlling for lagged growth)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Lagged (log) turnover -0.117*** -0.122*** -0.117*** -0.0977*** -0.0695*** -0.105*** -0.115*** -0.124*** -0.101***
(0.0252) (0.0219) (0.0239) (0.0178) (0.0165) (0.0235) (0.0264) (0.0256) (0.0222)
Age -0.0367*** -0.00495 -0.00577 0.00343 -0.0193* 0.0123 0.000883 -0.00720 0.00565
(0.0108) (0.00994) (0.00999) (0.00957) (0.0102) (0.0117) (0.0132) (0.0137) (0.0137)
Age squared 0.000353*** 5.81e-05 2.91e-05 -4.99e-05 0.000198* -0.000141 -1.77e-05 6.45e-05 -8.45e-05
(0.000121) (0.000113) (0.000114) (0.000113) (0.000115) (0.000129) (0.000152) (0.000157) (0.000156)
Education dummies
(omitted =none)
GCSE -0.0372 0.0295 -0.00948 0.0200 -0.0106 -0.0201 0.00310 0.0267 -0.0574
(0.0470) (0.0418) (0.0373) (0.0387) (0.0421) (0.0386) (0.0443) (0.0514) (0.0539)
A-level -0.00554 0.0382 -0.0103 0.00809 -0.0912 0.0592 0.0619 0.0285 -0.0174
(0.0551) (0.0513) (0.0528) (0.0494) (0.0559) (0.0514) (0.0581) (0.0639) (0.0664)
Degree or higher -0.0490 0.0250 -0.0677 0.0631 0.00755 0.0166 0.0248 0.0954 0.0803
(0.0584) (0.0529) (0.0475) (0.0475) (0.0516) (0.0497) (0.0583) (0.0688) (0.0624)
Business experience
Family 0.0148 0.0248 -0.00885 0.0235 -0.0228 -0.00852 0.0467 -0.0115 0.0464
(0.0368) (0.0359) (0.0332) (0.0321) (0.0332) (0.0318) (0.0366) (0.0397) (0.0394)
Self 0.0233 0.00953 0.0365 0.0263 0.0475 -0.00373 0.00269 0.0402 -0.0225
(0.0457) (0.0381) (0.0406) (0.0358) (0.0377) (0.0405) (0.0457) (0.0511) (0.0482)
Sources of advice
Enterprise agency/business link
(EABL)
-0.0287 0.0638 0.00346 -0.00304 0.0660 -0.0332 -0.0332 0.00160 0.0210
(0.0719) (0.0568) (0.0457) (0.0558) (0.0578) (0.0621) (0.0813) (0.0519) (0.0594)
Accountant 0.000112 0.000980 -0.0261 -0.0345 0.00750 -0.00326 -0.00514 0.00567 0.0954**
(0.0391) (0.0355) (0.0316) (0.0337) (0.0351) (0.0343) (0.0389) (0.0396) (0.0401)
Solicitor 0.00102 -0.269** -0.0256 0.0689 0.126* -0.0654 -0.0599 -0.0893 -0.0162
(0.0720) (0.127) (0.0882) (0.0943) (0.0687) (0.0880) (0.0946) (0.111) (0.0978)
College 0.0209 0.0721 0.0125 -0.102 0.0655 -0.188** 0.0699 -0.0295 -0.145
(0.0872) (0.0617) (0.0574) (0.0651) (0.0639) (0.0835) (0.108) (0.104) (0.103)
Start-right seminar -0.320*** -0.183** 0.0203 -0.314 0.162** 0.0424 0.288 0.305*** 0.168
(0.0939) (0.0868) (0.203) (0.354) (0.0766) (0.213) (0.249) (0.101) (0.116)
Princes Youth Business Trust 0.273 0.510 -0.0217 -0.619 0.136 -0.149 0.259 -0.0146 0.175
(0.325) (0.472) (0.190) (0.482) (0.216) (0.322) (0.303) (0.330) (0.237)
Family 0.0778* 0.0331 -0.0282 -0.00653 -0.0612 -0.00967 0.0126 -0.0352 -0.00179
(0.0429) (0.0415) (0.0347) (0.0354) (0.0375) (0.0441) (0.0528) (0.0455) (0.0452)
Predicting new venture survival and growth 235
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Table 5 continued
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Other 0.204** -0.0227 -0.158** 0.0495 -0.0882 0.157** -0.0264 -0.148 -0.0443
(0.0865) (0.0836) (0.0789) (0.0755) (0.0872) (0.0704) (0.0828) (0.124) (0.0701)
Bank account activity variables
Volatility -0.140* -0.340*** -0.152** -0.197*** -0.243*** -0.206*** -0.262*** -0.317*** -0.287***
(0.0838) (0.0835) (0.0700) (0.0663) (0.0813) (0.0782) (0.0853) (0.0875) (0.0713)
Overdraft excess -0.0654 0.00928 0.110** -0.0682 -0.00233 0.0354 0.00146 0.00322 0.0472
(0.0462) (0.0396) (0.0455) (0.0420) (0.0529) (0.0462) (0.0530) (0.0575) (0.0535)
Overdraft excess duration -0.524 -0.766*** -0.831** -0.489** -1.073** -1.136*** -0.546** -0.823* -0.837*
(0.424) (0.289) (0.377) (0.236) (0.469) (0.360) (0.261) (0.468) (0.440)
Authorised overdraft use 0.181*** 0.0904** 0.0840** 0.0881* 0.0886 0.103** 0.0812* 0.120** -0.0561
(0.0523) (0.0433) (0.0419) (0.0487) (0.0591) (0.0462) (0.0477) (0.0518) (0.0668)
Extent of auth. OD use -0.00699 0.00374 -0.106 -0.105 -0.221** -0.110 -0.0297 -0.228** 0.113
(0.103) (0.0884) (0.0794) (0.0873) (0.109) (0.117) (0.128) (0.0970) (0.129)
No. owners 0.0959* 0.0538 0.0573 0.0157 0.0241 -0.0171 0.0150 0.0118 0.0471
(0.0539) (0.0508) (0.0434) (0.0440) (0.0437) (0.0428) (0.0486) (0.0507) (0.0512)
Male owner(s) -0.0363 0.0352 0.00650 0.0311 -0.0264 0.00436 0.141** -0.0357 0.0426
(0.0516) (0.0516) (0.0455) (0.0452) (0.0430) (0.0550) (0.0694) (0.0616) (0.0682)
Legal form dummies
(omitted =Company)
Partnership -0.0857 -0.150** -0.127* -0.0858* -0.0427 -0.206*** -0.0792 -0.105* -0.0233
(0.0641) (0.0586) (0.0680) (0.0517) (0.0565) (0.0569) (0.0568) (0.0609) (0.0603)
Sole trader -0.157*** -0.186*** -0.188*** -0.0825* -0.0564 -0.131*** -0.127*** -0.209*** 0.0118
(0.0466) (0.0436) (0.0477) (0.0425) (0.0475) (0.0489) (0.0479) (0.0602) (0.0512)
Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Region dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Constant 2.368*** 1.796*** 1.608*** 1.185*** 1.248*** 1.117*** 1.173*** 1.961*** 1.358***
(0.385) (0.387) (0.392) (0.330) (0.352) (0.427) (0.414) (0.439) (0.426)
Observations 1205 1205 1205 1205 1205 1205 1205 1205 1205
R-squared 0.112 0.144 0.119 0.112 0.121 0.152 0.106 0.135 0.116
LL -1091 -1033 -918.0 -920.6 -972.4 -969.1 -1110 -1163 -1169
LL(0) -1162 -1127 -994.6 -992.3 -1050 -1068 -1178 -1250 -1243
Cox–Snell R
2
0.112 0.144 0.119 0.112 0.121 0.152 0.106 0.135 0.116
Nagelkerke R
2
0.131 0.170 0.148 0.139 0.147 0.183 0.124 0.154 0.132
Robust standard errors in parentheses
*** p\0.01; ** p\0.05; * p\0.1
236 A. Coad et al.
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Table 6 Survival regressions for each cross section, for the full sample of firms (without controlling for lagged growth)
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Log turnover 0.145*** 0.0745** 0.110*** 0.150*** 0.238*** 0.189*** 0.319*** 0.124 0.264***
(0.0294) (0.0324) (0.0384) (0.0454) (0.0530) (0.0607) (0.0689) (0.0845) (0.0854)
Age 0.0415* 0.0340 0.0563* 0.0994*** 0.0760 0.115** 0.0339 0.116 0.0831
(0.0225) (0.0258) (0.0340) (0.0371) (0.0481) (0.0517) (0.0619) (0.0711) (0.0819)
Age squared -0.000391 -0.000301 -0.000537 -0.000939** -0.000897 -0.00128** -0.000247 -0.00128 -0.00110
(0.000267) (0.000303) (0.000405) (0.000443) (0.000565) (0.000586) (0.000722) (0.000795) (0.000956)
Education dummies
(omitted =none)
GCSE 0.0615 -0.0312 -0.0931 0.177 -0.0131 0.00565 0.0424 -0.0297 0.561*
(0.0934) (0.112) (0.139) (0.157) (0.186) (0.222) (0.238) (0.283) (0.312)
A-level 0.176 -0.0885 -0.0250 -0.0606 -0.0185 -0.135 0.477 0.0725 0.396
(0.112) (0.128) (0.166) (0.184) (0.231) (0.261) (0.300) (0.345) (0.369)
Degree or higher -0.153 0.0166 -0.208 0.0879 -0.152 0.298 0.322 -0.00378 0.513
(0.102) (0.126) (0.151) (0.174) (0.207) (0.258) (0.277) (0.345) (0.337)
Business experience
Family -0.00363 -0.0560 -0.0201 -0.182 0.0934 0.0312 0.248 0.130 0.172
(0.0721) (0.0867) (0.107) (0.122) (0.143) (0.174) (0.188) (0.222) (0.251)
Self -0.0768 0.152 0.0161 -0.226 -0.0494 0.0131 -0.197 -0.668** -0.0285
(0.0802) (0.0953) (0.122) (0.138) (0.168) (0.195) (0.205) (0.286) (0.297)
Sources of advice
Enterprise agency/
business link (EABL)
-0.143 0.0832 0.0555 0.248 0.244 0.434 -0.248 -0.425 0.998*
(0.111) (0.146) (0.175) (0.206) (0.251) (0.296) (0.314) (0.375) (0.591)
Accountant -0.122 0.0178 0.0441 -0.0422 0.119 -0.154 -0.164 -0.0117 0.0741
(0.0747) (0.0895) (0.110) (0.126) (0.149) (0.178) (0.187) (0.241) (0.248)
Solicitor 0.0536 -0.190 -0.222 -0.116 0.0641 -0.0252 -0.142 0.994 2.096
(0.170) (0.181) (0.228) (0.287) (0.332) (0.382) (0.445) (0.816) (1.371)
College 0.0177 0.405* 0.0642 -0.0983 0.358 0.754 0.746 1.094 0.701
(0.170) (0.236) (0.243) (0.305) (0.359) (0.594) (0.632) (0.673) (0.713)
Start-right seminar 0.00155 -0.218 1.209 -0.104 -0.215 -1.096* -0.939 -3.688**
(0.427) (0.581) (0.946) (0.711) (0.812) (0.622) (1.398) (1.737)
Princes Youth Business Trust -0.113 0.777 -0.907** 1.136 -0.233 -0.233 0.930
(0.318) (0.502) (0.386) (0.844) (0.726) (0.960) (0.927)
Predicting new venture survival and growth 237
123
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Table 6 continued
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Open
(t?1)
Year 2 Year 3 Year 4 Year 5 Year 6 Year 7 Year 8 Year 9 Year 10
Family 0.00543 0.0260 -0.110 0.314** -0.116 -0.0253 0.149 -0.348 -0.275
(0.0778) (0.0934) (0.112) (0.140) (0.160) (0.188) (0.210) (0.249) (0.272)
Other -0.0314 -0.0155 -0.328* -0.0638 0.0258 0.473 0.914* -0.668* -0.0917
(0.139) (0.167) (0.194) (0.269) (0.312) (0.415) (0.495) (0.394) (0.433)
Bank account activity variables
Volatility -0.543*** -0.725*** -0.694*** -0.612*** -0.779*** -0.794*** -0.608*** -0.894*** -0.623***
(0.0638) (0.0695) (0.0808) (0.0932) (0.109) (0.127) (0.149) (0.172) (0.174)
Overdraft excess -0.135* -0.195** -0.242** -0.0879 -0.574*** -0.276 -0.905*** -0.171 -0.716***
(0.0807) (0.0932) (0.115) (0.136) (0.156) (0.199) (0.206) (0.258) (0.273)
Overdraft excess duration -3.666*** -2.520*** -1.861*** -1.335*** -1.496*** -1.664*** 0.103 -2.210*** -1.429**
(0.412) (0.315) (0.287) (0.357) (0.407) (0.463) (0.501) (0.637) (0.692)
Authorised overdraft use 0.0710 0.225 -0.0647 0.127 -0.350 -0.234 0.362 -0.591* 0.359
(0.127) (0.143) (0.166) (0.197) (0.222) (0.276) (0.347) (0.323) (0.438)
Extent of auth. OD use 0.0299 -0.195 -0.235 0.275 -0.0538 0.175 -0.736 0.745 -0.682
(0.417) (0.315) (0.355) (0.405) (0.434) (0.538) (0.653) (0.631) (0.842)
No. owners 0.181 -0.00809 0.203 0.426** -0.0168 -0.441* -0.0962 0.279 -0.197
(0.116) (0.127) (0.158) (0.187) (0.191) (0.248) (0.257) (0.355) (0.325)
Male owner(s) 0.134 0.163 0.0222 0.413*** 0.0851 0.739*** 0.480* 0.0466 0.608*
(0.0896) (0.109) (0.136) (0.153) (0.201) (0.225) (0.253) (0.312) (0.311)
Legal form dummies
(omitted =Company)
Partnership -0.396*** -0.613*** -0.571*** -0.157 0.233 -0.0815 0.0298 -0.202 -0.0739
(0.115) (0.132) (0.163) (0.197) (0.244) (0.319) (0.319) (0.402) (0.361)
Sole trader -0.234** -0.191* -0.0942 -0.0158 0.647*** -0.0126 0.485** -0.265 0.400
(0.0942) (0.110) (0.136) (0.154) (0.188) (0.215) (0.234) (0.293) (0.298)
Industry dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Region dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes
Constant 0.147 0.423 -0.00254 -2.351** -2.194* 12.59*** -2.069 1.271 10.74***
(0.686) (0.783) (0.923) (1.078) (1.321) (1.535) (1.767) (2.167) (2.179)
Observations 5499 4147 3201 2557 2146 1819 1562 1393 1302
LL -2768 -1965 -1348 -1022 -757.5 -538.2 -460.0 -320.5 -285.3
238 A. Coad et al.
123
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