ArticlePDF Available

Abstract and Figures

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.
This content is subject to copyright. Terms and conditions apply.
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
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
D. J. Storey
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
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
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
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
(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.
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
More formally expressed, growth occurs through
the following random process:
where x
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
is a latent variable that corresponds to xif
[x*, but remains unobserved if x
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
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
, the growth
rate (in log-differences; Tornqvist et al. 1985)is
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
expressed entirely in terms of a random shock: x
. 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
from growth regressions is low and
remains low in the years after entry.
Hypothesis 1 The R
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
, with start-
up size being denoted as x
. In a random walk model a
la Levinthal (1991), firm size evolves, with x
, where e
is distributed with mean land
variance r
. 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
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):
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
of the growth shocks, and start-up size x
Even if growth is a random process, expected survival
time can be increased by increasing the size at start-up
(Levinthal 1991; Coad et al. 2014). The R
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
. 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).
Firm size evolves as a random
walk, x
, 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
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
Figure 1shows that the R
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
value rises
over time even when growth is a random walk.
Hypothesis 2 The R
from regressions of the
determinants of new venture survival increases in the
years after entry.
We consider it trivial that the R
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
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.
See Table 1for summary statistics on our dataset.
220 A. Coad et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
statistic. The standard R
statistic is expressed in terms
of how well an OLS regression model can explain the
total variation in the data:
SStot ¼1SSres
where SS
is the regression sum of squares (i.e. the
explained sum of squares), SS
is the total sum of
squares, and SS
is the residual (i.e. unexplained)
sum of squares. The R
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
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
Cox-Snell R2¼1L0ðÞ=L^
where L(^
b) and L(0) denote the likelihoods of the fitted
and ‘null’ models, respectively. The Cox–Snell R
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
be adjusted as follows, to obtain
what has become known as the Nagelkerke R
after Nagelkerke (1991):
Nagelkerke R2¼Cox-Snell R2=max R2
where max R2
Because of its desirable statistical properties we use
the Nagelkerke R
statistic, although we check that our
results are not sensitive to this choice of R
We begin by running regressions on cross sections
corresponding to each year, where the dependent
variable is either growth rate or survival probability.
For each year we obtain a Nagelkerke R
statistic. We
then plot the evolution of the Nagelkerke R
over time
using line charts—one chart for growth, one for
Fig. 1 Evolution of the Nagelkerke R
using simulated data, for
60 periods. y-axis: Nagelkerke R
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
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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.
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
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
which we will refer to as ‘sales’. This serves
as a very close approximation to sales revenue
inclusive of taxes.
The much greater granularity of
sales compared with using measures of employee
numbers is a particular strength.
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
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.
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.
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.
Excluding payments from related accounts, e.g. deposit
accounts held by the business.
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.’’
These strengths are discussed in more detail shortly.
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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.
These were
dropped from our sample before we started the
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.
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
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.
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).
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).
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.
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)
Indeed, some of these may have switched rather than closed.
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’
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.
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.
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.
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
Predicting new venture survival and growth 223
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
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
Growth i;tðÞ¼a1þb1log sales i;t1ðÞ
þb2Growth i;t1ðÞ
þc1Entrepreneur i;tðÞ
þd1Business i;tðÞ
þh1Account i;tðÞþe1i;tðÞ
Survival i;tðÞ¼a2þb3log sales i;t1ðÞ
þb4Growth i;t1ðÞ
þc2Entrepreneur i;tðÞ
þd2Business i;tðÞ
þh2Account i;tðÞþe2i;tðÞ
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).
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.
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
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
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
statistics change in the years after entry.
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
statistic over time (see Figs. 3a, b,
4). We present the evolution of the Nagelkerke R
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.
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
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
6.1.1 Sales growth
Figure 3a shows how the Nagelkerke R
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
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
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
statistic as well as the Cox–Snell R
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.
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
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.
Fig. 3 a OLS growth
regression Nagelkerke R
statistics for individual
cross sections for the first
10 years, for 4 different
growth rate regression
specifications and bOLS
growth regression
Nagelkerke R
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
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
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
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
These are shown at the bottom of Tables 4,5and 6in
Appendix’. For the growth regressions, the R
Cox–Snell R
statistics closely mirror the Nagelkerke
, with no clear trend in the R
statistic. For survival,
we report the Cox–Snell R
, 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
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
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.
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
statistics were very low, in the range of 3–7 % for our
baseline specification. If anything, the Nagelkerke R
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
generally decreased,
if anything, in the years after entry. Our clearest results
were observed in our survival regressions, where the
Nagelkerke R
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
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
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
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
) 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.
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
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
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://, 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.
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
Dencker et al. (2009)
Parker (2009)
?with survival
Mixed with growth and survival
Business experience:
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;
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.
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.
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;
Coad et al. (2016) n/s with survival and growth
?with bank loyalty
Princes Youth Business
Advice/support (prior to start-up) from Princes Youth
Business Trust : 0 =No; 1 =Yes
Greene (2009) n/s using sophisticated evaluation
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
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.
-with survival
n/s with growth
Overdraft excess duration Proportion of period in excess of authorised overdraft
Coad et al. (2013), Frankish et al.
-with survival
-with growth
Authorised overdraft use =1 if authorised overdraft used at any time Coad et al. (2013), Frankish et al.
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.
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
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
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
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
0.258 0.268 0.240 0.257 0.235 0.244 0.147 0.185 0.116
Nagelkerke R
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
-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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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
0.112 0.144 0.119 0.112 0.121 0.152 0.106 0.135 0.116
Nagelkerke R
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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)
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
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 6 continued
Variables (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
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.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Anyadike-Danes, M., Bjuggren, C.-M., Gottschalk, S., Ho
W., Johansson, D., Maliranta, M., & Myrann, A. (2015).
An international cohort comparison of size effects on job
growth. Small Business Economics, 44, 821–844. doi:10.
Anyadike-Danes, M., & Hart, M., (2014). All grown up? The
fate after 15 years of the quarter of a million UK firms born
in 1998. Sept 14th, Mimeo.
Audretsch, D. B., Santarelli, E., & Vivarelli, M. (1999). Start-up
size and industrial dynamics: Some evidence from Italian
Manufacturing. International Journal of Industrial Orga-
nization, 17, 965–983.
Baum, J. R., & Bird, B. J. (2010). The successful intelligence of
high growth entrepreneurs: Links to new venture growth.
Organization Science, 21(2), 397–412.
Bertrand, M, & Schoar, A. (2003), Managing with style: The
effect of managers on firm policies. MIT Sloane School of
Management, Working Paper 4802-02.
Botham, R., & Graves, A. (2011). Regional variations in new
firm job creation: The contribution of high growth start-
ups. Local Economy, 26(2), 95–107.
Bottazzi, G., & Secchi, A. (2006). Explaining the distribution of
firm growth rates. Rand Journal of Economics, 37(2),
Chrisman, J. J., & McMullan, W. E. (2004). Outsider assistance
as a knowledge resource for new venture survival. Journal
of Small Business Management, 42(3), 229–244.
Coad, A. (2009). The growth of firms: A survey of theories and
empirical evidence. Cheltenham: Edward Elgar.
Coad, A. (2014). Death is not a success: Reflections on business
exit. International Small Business Journal, 32(7), 721–732.
Coad, A., Frankish, J. S., Nightingale, P., & Roberts, R. G.
(2014). Business experience and start-up size: Buying
more lottery tickets next time around? Small Business
Economics, 43(3), 529–547.
Coad, A., Frankish, J. S., Roberts, R. G, & Storey D. J. (2016
forthcoming). Why should banks provide entrepreneurship
training seminars? International Small Business Journal.
doi: 10.1177/0266242615593138.
Coad, A., Frankish, J. S., Roberts, R. G., & Storey, D. J. (2013).
Growth paths and survival chances: An application of
Gambler’s Ruin theory. Journal of Business Venturing,
28(5), 615–632.
Coad, A., Frankish, J. S., Roberts, R. G., & Storey, D. J. (2015).
Are firm growth paths random? A reply to ‘‘Firm growth
and the illusion of randomness’’. Journal of Business
Venturing Insights, 3, 5–8.
Coad, A., & Tamvada, J. P. (2012). Firm growth and barriers to
growth among small firms in India. Small Business Eco-
nomics, 39, 383–400.
Cox, D. R., & Snell, E. J. (1989). The analysis of binary data
(2nd ed.). London: Chapman and Hall.
Cumming, D. J., Ari Pandes, J., & Robinson, M. J. (2015). The
role of agents in private entrepreneurial finance. En-
trepreneurship Theory and Practice, 39(2), 345–374.
Daunfeldt, S.-O., & Halvarsson, D. (2015). Are high-growth
firms one-hit wonders? Evidence from Sweden. Small
Business Economics, 44, 361–383.
Table 6 continued
Variables (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
LL(0) -3067 -2227 -1569 -1171 -915.9 -668.9 -556.1 -392.1 -360.1
Cox–Snell R
0.103 0.119 0.129 0.110 0.137 0.134 0.116 0.0977 0.109
Nagelkerke R
0.154 0.180 0.207 0.184 0.239 0.257 0.227 0.227 0.256
% Correctly classified 76.63 78.85 82.04 83.38 85.97 88.62 89.05 92.75 92.47
Robust standard errors in parentheses
*** p\0.01; ** p\0.05; * p\0.1
Predicting new venture survival and growth 239
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Davila, A., Foster, G., He,X., & Shimizu, C. (2015). The rise and
fall of start-ups: Creation and destruction of revenue and
jobs by young companies. Australian Journal of Manage-
ment, 40(1), 6–35. doi:10.1177/0312896214525793.
Dencker, J. C., Gruber, M., & Shah, S. K. (2009). Pre-entry
knowledge, learning and the survival of new firms. Orga-
nization Science, 20(3), 516–537.
Denrell, J., Fang, C., & Liu, C. (2015). Chance explanations in
the management sciences. Organization Science, 26(3),
Frankish, J. S., Roberts, R. G., Coad, A., Spears, T. C., & Storey,
D. J. (2012). Do entrepreneurs really learn? Or do they just
tell us that they do? Industrial and Corporate Change,
22(1), 73–106.
Frankish, J. S., Roberts, R. G., & Storey, D. J. (2010). Enterprise
profiles in deprived areas: are they distinctive? Interna-
tional Journal of Entrepreneurship and Small Business,
9(2), 127–142.
Frankish, J. S., Roberts, R. G., & Storey, D. J. (2011). Enter-
prise: A route out of disadvantage and deprivation? In A.
Southern (Ed.), Enterprise, deprivation and social exclu-
sion: The role of small business in addressing social and
economic inequalities. London: Routledge.
Fraser, S. (2005). Finance for small and medium-sized enter-
prises. CSME, University of Warwick, UK.
Gibrat, R. (1931). Les ine
´conomiques: applications, aux
´s des richesses, a
`la concentration des entreprises,
aux populations des villes, aux statistiques des familles,
etc.: d’une loi nouvelle la loi de l’effet proportionnel.
Recueil Sirey, Paris.
Gilbert, B. A., McDougall, P. P., & Audretsch, D. B. (2006).
New venture growth: A review and extension. Journal of
Management, 32(6), 926–950.
Gimeno, J., Folta, T. B., Cooper, A. C., & Woo, C. Y. (1997).
Survival of the fittest? Entrepreneurial human capital and
the persistence of underperforming firms. Administrative
Science Quarterly, 42(4), 750–783.
Greene, F. J. (2009). Assessing the impact of policy interven-
tions: The influence of evaluation methodology. Environ-
ment and Planning C, 27(2), 216–229.
Hamilton, B. H. (2000). Does entrepreneurship pay? An
empirical analysis of self-employment. Journal of Political
Economy, 108(3), 604–631.
Harada, N. (2007). Which firms exit and why? An analysis of
small firm exits in Japan. Small Business Economics, 29,
Headd, B. (2003). Redefining business success: Distinguishing
between closure and failure. Small Business Economics,
21, 51–61.
Henderson, A. D., Raynor, M. E., & Ahmed, M. (2012). How
long must a firm be great to rule out chance? Benchmarking
sustained superior performance without being fooled by
randomness. Strategic Management Journal, 33, 387–406.
Hmieleski, K. M., & Baron, R. A. (2009). Entrepreneurs’ opti-
mism and new venture performance: A social cognitive
perspective. Academy of Management, 52(3), 473–488.
Ijiri, Y., & Simon, H. (1964). Business firm growth and size.
American Economic Review, 54(2), 77–89.
Jenkins, S. P. (1995). Practitioners corner: Easy estimation
methods for discrete-time duration models. Oxford Bulletin
of Economics and Statistics, 57(1), 129–138.
Klotz, A. C., Hmieleski, K., Bradley, B. H., & Busenitz, L. W.
(2014). New venture teams: A review of the literatureand
roadmap for future research. Journal of Management,
40(1), 226–255.
Le Mens, G., Hannan, M. T., & Po
´los, L. (2011). Founding
conditions, learning, and organizational life chances: Age
dependence revisited. Administrative Science Quarterly,
56(1), 95–126.
Levie, J., & Lichtenstein, B. B. (2010). A terminal assessment of
stages theory. Introducing a dynamic states approach to
entrepreneurship. Entrepreneurship Theory and Practice,
34(2), 317–350.
Levinthal, D. (1991). Random walks and organizational mor-
tality. Administrative Science Quarterly, 36(3), 397–420.
Lotti, F., Santarelli, E., & Vivarelli, M. (2009). Defending
Gibrat’s Law as a long-run regularity. Small Business
Economics, 32, 31–44.
Miller, C. C., Washburn, N. T., & Glick, W. H. (2013). The myth
of firm performance. Organization Science, 24(3),
Mole, K. F., Hart, M., Roper, S., & Saal, D. S. (2011). Broader or
deeper? Exploring the most effective intervention profile
for public small business support. Environment and Plan-
ning A, 43(1), 87–105.
Nagelkerke, N. J. D. (1991). A note on a general definition of the
coefficient of determination. Biometrika, 78(3), 691–692.
Ongena, S., & Smith, D. C. (2000). What determines the number
of bank relationships? Cross-country evidence. Journal of
Financial Intermediation, 9(1), 26–56.
Parker, S. C. (2009). The economics of entrepreneurship.
Oxford: Oxford University Press.
Persson, H. (2004). The survival and growth of new establish-
ments in Sweden, 1987–1995. Small Business Economics,
23(5), 423–440.
Phelps, R., Adams, R., & Bessant, J. (2007). Life cycles of
growing organizations: A review with implications for
knowledge and learning. International Journal of Man-
agement Reviews, 9(1), 1–30.
Pons Rotger, G., Gørtz, M., & Storey, D. J. (2012). Assessing the
effectiveness of guided preparation for new venture cre-
ation and performance: Theory and Practice. Journal of
Business Venturing, 27(4), 506–521.
Ryder, N. B. (1965). The cohort as a concept in the study of
social change. American Sociological Review, 30(6),
Santarelli, E., & Vivarelli, M. (2007). Entrepreneurship and the
process of firms’ entry, survival and growth. Industrial and
Corporate Change, 16(3), 455–488.
Solomon, G. T., Bryant, A., May, K., & Perry, V. (2013). Sur-
vival of the fittest: Technical assistance, survival and
growth of small businesses and implications for public
policy. Technovation, 33, 292–301.
Stanley, M. H. R., Amaral, L. A. N., Buldyrev, S. V., Havlin, S.,
Leschhorn, H., Maass, P., et al. (1996). Scaling behavior in
the growth of companies. Nature, 379, 804–806.
Storey, D. J. (1994). The role of legal status in influencing bank
financing and new firm growth. Applied Economics, 26(2),
Storey, D. J. (2011). Optimism and chance: The elephants in the
entrepreneurship room. International Small Business
Journal, 29(4), 303–321.
240 A. Coad et al.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Storey, D. J., Keasey, K., Watson, R., & Wynarczyk, P. (1987).
The performance of small firms: Profits, jobs and failures.
London: Croom Helm.
Syverson, C. (2011). What determines productivity? Journal of
Economic Literature, 49(2), 326–365.
Tornqvist, L., Vartia, P., & Vartia, Y. O. (1985). How should
relative changes be measured? American Statistician,
39(1), 43–46.
Voordeckers, W., & Steijvers, T. (2006). Business collateral and
personal commitments in SME lending. Journal of Bank-
ing and Finance, 30(11), 3067–3086.
Wennberg, K., Wiklund, J., Detienne, D. R., & Cardon, M. S.
(2010). Reconceptualizing entrepreneurial exit: Divergent
exit routes and their drivers. Journal of Business Venturing,
25, 361–375.
Wiklund, J., Baker, T., & Shepherd, D. (2010). The age-effect of
financial indicators as buffers against the liability of new-
ness. Journal of Business Venturing, 25(4), 423–437.
Yang, T., & Aldrich, H. E. (2012). Out of sight but not out of
mind: Why failure to account for left truncation biases
research on failure rates. Journal of Business Venturing,
27(4), 477–492.
Zimmerman, M. A., & Zeltz, G. Z. (2002). Beyond survival:
Achieving new venture growth by achieving legitimacy.
Academy of Management Review, 27(3), 414–431.
Predicting new venture survival and growth 241
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”),
for small-scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are
maintained. By accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use
(“Terms”). For these purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or
a personal subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or
a personal subscription (to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the
Creative Commons license used will apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data
internally within ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking,
analysis and reporting. We will not otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of
companies unless we have your permission as detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that
Users may not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to
circumvent access control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil
liability, or is otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by
Springer Nature in writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer
Nature journal content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates
revenue, royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain.
Springer Nature journal content cannot be used for inter-library loans and librarians may not upload Springer Nature journal
content on a large scale into their, or any other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any
information or content on this website and may remove it or features or functionality at our sole discretion, at any time with or
without notice. Springer Nature may revoke this licence to you at any time and remove access to any copies of the Springer Nature
journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express
or implied with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or
warranties imposed by law, including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be
licensed from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other
manner not expressly permitted by these Terms, please contact Springer Nature at
... Prior studies on mature firms often use R&D spending over sales to indicate R&D intensity Zhou et al., 2017). However, studies have shown that whereas new ventures regularly spend money on R&D, wages, rent, materials, etcetera, their sales are highly unstable in the early years (Coad et al., 2016;Robinson, 1999). Our data shows similar cases, where the standard deviation of R&D intensity calculated using sales revenue as the denominator is 72.4% larger than when calculated using total expense as the denominator. ...
... We controlled for several firm-level factors, starting with firm type, a dummy variable with a value of 1 if a firm's is a sole proprietorship and 0 otherwise. Sole proprietorships employ unique decision-making processes, have lower credibility and network fewer resources, and have more difficulty obtaining external finances (Coad et al., 2016). Firm size was measured as the logarithm of the number of employees, as larger firms may find it easier to survive (Pe'er et al., 2016). ...
Full-text available
Plain English Summary While capital is essential for new ventures to innovate and survive, is having more of it always good? Our research shows “No” because more money may “spoil the child” by reducing the benefits that new ventures enjoy from R&D investment. We analyzed 791 new technology ventures across six years and found evidence of a side effect of munificent financial resources, such that when ventures have high levels of financial munificence, they garner fewer survival benefits from increasing R&D. This side effect is weakened when ventures have CEOs who are more experienced, highly educated, or female. These findings extend previous research on the limitations of financial munificence by showing its negative moderating effect on the R&D–survival relationship. For entrepreneurs and venture capitalists in the industry, we advise caution regarding the role of abundant financial resources in new ventures.
... 8 An anonymous referee pointed out to us that, in some countries, formality can ensure enhanced access to the legal system which could be of value in dispute resolution. E. Laing et al. firms to offset temporal variations in cash receipts (Coad et al., 2016); customers, in turn, have also long seen formality as a proxy for reliability (Freedman & Godwin, 1994). Finally, formality-as in the choice of limited liability status-may reflect the confidence of the owner about the business. ...
Full-text available
Plain English Summary Policy-makers: Decide what type of entrepreneurship you want for your country and, only then, choose your policies, because ‘one size doesn’t fit all.’ Entrepreneurs come in all shapes and sizes, ranging from informal street market traders on the one hand to formal tech giants in majestic offices on the other. The view of most governments is that entrepreneurship is ‘good’ because it not only provides employment for the market trader and for the tech giant, but also for many others in the economy. Governments therefore spend taxpayers’ money funding entrepreneurs to start and grow their businesses, but this raises two key questions: first, should it be the formal tech giant, or the informal street trader, that receives public funds and second, how should these be provided? Our conclusion, based on evidence from more than 80 high- and low-income countries, is that effective policy not only has to take account of the formal/informal distinction and the income level of the country, but also how that policy is delivered. We show that, in low-income countries, formal entrepreneurship is more likely to be enhanced by state policies to promote education and female activity rates; it is less likely to be stimulated by the creation of more enterprises. This is because relatively few informal enterprises subsequently make the transition to formality and to significant job creation.
... Among the articles detecting inactive ventures, some track sales to check whether a venture is still active. Doing so, Coad et al. (2016) treat inactive ventures as failed while Patel et al. (2019) exclude them from the analysis. Most studies in this category classify inactive ventures as failed even though some may understand inactivity as a temporary or unclear condition (Gimmon and Levie, 2010;Rodeiro-Pazos et al., 2017) while others interpret inactivity as failure (Criaco et al., 2014;Löfsten, 2016;Rannikko et al., 2019). ...
Despite the increased attention dedicated to research on the antecedents and determinants of new venture survival in entrepreneurship, defining and capturing survival as an outcome represents a challenge in quantitative studies. This paper creates awareness for ventures being inactive while still classified as surviving based on the data available. We describe this as the 'living dead' phenomenon, arguing that it yields potential effects on the empirical results of survival studies. Based on a systematic literature review, we find that this issue of inactivity has not been sufficiently considered in previous new venture survival studies. Based on a sample of 501 New Technology-Based Firms, we empirically illustrate that the classification of living dead ventures into either survived or failed can impact the factors determining survival. On this basis, we contribute to an understanding of the issue by defining the 'living dead' phenomenon and by proposing recommendations for research practice to solve this issue in survival studies, taking the data source, the period under investigation and the sample size into account.
... Yang and Aldrich (2017) found that among the range of factors driving venture survival, realizing positive cash flow in a single month lowered the mortality rate of new ventures by 65%. As sales volatility could spell significant survival problems (Coad, Frankish, and Roberts et al. 2016), auditors may help provide necessary stability by helping develop management control systems. Though growth is the desired outcome, as argued earlier, it is a luxury for entrepreneurs when the survival of the firm is at stake. ...
Auditing in ventures may provide the necessary financial reporting bulwark, however, auditing also has direct and indirect costs that may be less efficacious in a venture with limited routines and capabilities, and could take an entrepreneur’s attention away from venture goals. We draw on a quasi-natural experiment in Sweden, where from 2011 small private firms meeting threshold criteria were exempt from audit. The law resulted in three groups of ventures – (i) those who were above the threshold criteria and continued with an audit, and among those who were exempt some chose to (ii) voluntarily audit or (iii) opted-out of the audit. Starting with ventures established in 2007 (about three years before the passage of the law), and drawing records of the Swedish Companies Registration Office, our results show that while opting out of audit slightly improves the odds of survival, it has detrimental effects when sales volatility or return on assets are high. Those voluntary auditing can realize a higher debt ratio, but also face a decline in sales and net profit. The findings have implications for entrepreneurs in particular and policymakers considering initiation or repealing of audit requirements for ventures.
Full-text available
Business and Psychology research (and the Social Sciences, in general) is heavily biased toward explaining the past. The holy grail in such explanation-oriented research is to develop causal theory, and to test this theory with historical data against a null no-effect benchmark. We seek to expand the methodological toolkit by adding a comparative and predictive research design. First, by organizing an inter-theory battle, we move away from classic null hypothesis testing. Second, by predicting the future, we add prediction as a complement to the traditional explanation of the past. By way of illustration, we select a case in the Entrepreneurship field and theorize about the ranked predictions as to the relative growth performance of a sample of Small and Medium-sized Enterprises (SMEs). For this, we adopt two widely acknowledged theories in the literatures of Business and Psychology: The Competitive Strategy theory and the Motive Disposition theory. We use Gamblers' Ruin or Random Walk theory, arguing that company growth cannot be predicted, as the null benchmark. After identifying key explanatory predictive variables of our basic pair of theories, with Gamblers' Ruin or Random Walk theory's non-predictability as our benchmark, we produce ranked predictions as to the relative growth performance of 294 Belgian entrepreneurs and their SMEs. Later in 2023, we will test the predictive accuracy of these two selected theories and their predictive variables by comparing the predictive rankings with realized growth, as well as vis-à-vis randomness.
Purpose The paper aims to provide robust evidence about the relationships between key individual characteristics of owners and managers (OMs) and small and medium enterprises (SMEs)' growth and the moderating influence of the country context on these relationships. Design/methodology/approach The authors meta-analyzed 62 studies presenting a cumulative sample of 175 effect-sizes and 174,590 SMEs. Findings The authors found that SMEs led by more experienced men with higher levels of education are more likely to grow. While the relationship between OMs' experience and SMEs' growth is significant for differing country contexts, national characteristics affect the magnitude of the influence that OMs' education and gender specifically exert on SME expansion. The authors also found that the positive impact of OMs' human capital on SMEs' growth increases when these firms are focused on technology. Research limitations/implications The study yielded small-effect sizes for the impact of OMs' human capital and gender on SMEs' growth. Researchers can assess the influence of these characteristics on SMEs' growth along with other individual dimensions. Originality/value The current study is the first meta-analytical investigation about the influence of OMs' gender on SMEs' growth. The study focuses solely on SME OMs, as SMEs are not simply larger businesses on a smaller scale. The authors employ a wide set of country-level moderators in the research going beyond most empirical examinations of the topic that have given only marginal attention to moderators.
This article discusses recent results and future research possibilities in the areas of econometrics and firm growth, drawing on Dosi and Marengo’s “10 building blocks” of evolutionary theory. These 10 building blocks are: dynamics first!, microfoundations, realism is a virtue, bounded rationality, persistent heterogeneity, novelty in the system, selection mechanisms, emergent properties at the aggregate level, emergence of organizational forms and institutions, and coevolution across levels of analysis and timescales. As it happens, many of our comments relate to the theme of “realism is a virtue.” We also suggest, in some cases, which econometric techniques might be more appropriate for research into firm growth and performance, given these 10 building blocks.
Full-text available
A significant share of new technology-based ventures exit through trade sale at an early stage of firm development. While trade sale is an important exit route for entrepreneurs and investors, and a potential source of new innovations and technology for acquiring firms, we have limited knowledge about the factors that help to effectively achieve a trade sale. We employ a unique dataset tracking the population of research-based spin-offs in Norway and conduct in-depth case studies of nine trade sales. Building on 52 interviews and other secondary data, we inductively develop propositions outlining three dimensions that lead to a successful trade sale—potential synergies, credible alternatives, and uncertainty reduction. We show that these enablers of trade sales are not only linked to the focal venture but also related to the idiosyncratic dyad with the buyer, reflecting both the potential for and likelihood of trade sale. Consequently, our study contributes to the literatures on entrepreneurial exit and academic entrepreneurship by mapping the important but under-explored area of trade sale as an exit mode. Plain English SummaryPotential synergies and credible alternatives increase the potential of a research-based spin-offs’ trade sale, but the likelihood of a trade sale depends on how uncertainty reduction is managed. A trade sale is an important exit route for entrepreneurs and investors, and a potential source of new innovations and technology for acquiring firms. Research-based spin-offs are often acquired during their early stages of development by large corporations. We track the population of Norwegian research-based spin-offs and study nine trade sales in depth. Our findings concerning the importance of synergy potential, credible alternatives, and uncertainty reduction have implications for both academic entrepreneurs and potential buyers for how they can complete an exit through trade sale. Since scientific research is critical for society, our findings have implications for policymakers in the form of interesting ideas for influencing trade sales, a potentially important route for commercialization of scientific research.
The link between the management of uncertainty and knowledge creation is the core element behind firm survival, as these two factors are critical for true innovation. This article links the survival of highly innovative firms to their knowledge creation and application in the context of two types of uncertainty management: (i) the individual firm's ability to handle uncertainty; (ii) the aggregate local “neuroticism” in facing uncertainty that characterizes the geographic location where the firm operates. The study is inspired by Audretsch and Dohse's model of firm growth and geographic location. We augment this model with George Shackle's potential surprise function for handling individual uncertainty. Additionally, we extend the model by also considering the psychological profile of localities, in particular their level of neuroticism according to the so-called Big Five taxonomy. Using data for the highly innovative Cambridge Region (UK) for the period 2010–2014, we find that, on individual level, the daring companies survive less frequently, but appear to live longer if they manage to survive. Survival also appears to be influenced by locational characteristics related to the local level of neuroticism. In particular, being located in a place with higher neuroticism is associated with lower survival rates.
Full-text available
Evaluating the attractiveness of startup employment requires an understanding of both what startups pay and the implications of these jobs for earnings trajectories. Analyzing Danish registry data, we find that employees hired by startups earn roughly 17% less over the next 10 years than those hired by large, established firms. About half of this earnings differential stems from sorting—from the fact that startup employees have less human capital. Long-term earnings also vary depending on when individuals are hired. Although the earliest employees of startups suffer an earnings penalty, those hired by already-successful startups earn a small premium. Two factors appear to account for the earnings penalties for the early employees: Startups fail at high rates, creating costly spells of unemployment for their (former) employees. Job-mobility patterns also diverge: After being employed by a small startup, individuals rarely return to the large employers that pay more.
Full-text available
The contribution of different-sized businesses to job creation continues to attract policymak-ers' attention; however, it has recently been recognised that conclusions about size were confounded with the effect of age. We probe the role of size, controlling for age, by comparing the cohorts of firms born in 1998 over their first decade of life, using variation across half a dozen northern European countries Austria, Finland, Germany, Norway, Swe-den and the UK to pin down size effects. We find that a very small proportion of the smallest firms play a crucial role in accounting for crosscountry differences in job growth. A closer analysis reveals that the initial size distribution and survival rates do not seem to explain job growth differences between countries, rather it is a small number of rapidly growing firms that are driving this result.
Full-text available
The theory of firm growth is in a rather unsatisfactory state. However, the analysis of large firm-level datasets which have become available in recent years allows us to begin building an evidence base which can, in turn, be used to underpin the development of more satisfactory theory. Here we study the 239 thousand UK private sector firms born in 1998 over their first 15 years of life. A first, and quite striking, finding is the extraordinary force of mortality. By age 15, 90% of the UK firms born in 1998 are dead, and, for those surviving to age 15, the hazard of death is still about 10% a year. The chance of death is related to the size and growth of firms in an interesting way. Whilst the hazard rate after 15 years is largely independent of size at birth, it is strongly affected by the current (age 14) size. In particular, firms with more than five employees are half as likely to die in the next year as firms with less than five employees. A second important finding is that most firms, even those which survive to age 15, do not grow very much. By age 15 more than half the 26,000 survivors still have less than five jobs. In other words, the growth paths – what we call the ‘growth trajectories’ – of most of the 26,000 survivors are pretty flat. However, of the firms that do grow, firms born smaller grow faster than those born larger. Another striking finding is that growth is heavily concentrated in the first five years. Whilst growth does continue, even up to age 15, each year after age five it involves only a relatively small proportion of firms. Finally, there are two groups of survivors which contribute importantly to job creation. Some are those born relatively large (with more than 20 jobs) although their growth rate is quite modest. More striking though, is a very small group of firms born very small with less than five jobs (about 5% of all survivors) which contribute a substantial proportion (more than one third) of the jobs added to the cohort total by age 15.
Full-text available
One of the most striking findings from tracking a birth cohort of firms is the extraordinary force of mortality: by age 15, 90% of the UK firms born in 1998 are dead; and, of those that survived to age 15, the hazard of death is still about 10% a year. The chance of death is related to the size and growth of firms in an important way. After 15 years the hazard rate is largely independent of size at birth, but is strongly affected by size at age 15 (firms with less than five employees are still twice as likely to die as firms with more than five employees). Evidently, growth is good for survival prospects. However, the age 15 survivors are still overwhelmingly small, because 90\% of the cohort at birth had less than five employees. Against this background we investigate the growth paths -- what we call the `growth trajectories' -- over 15 years of the 26,000 survivors of the 239,000 firms born into the 1998 birth cohort. Although very few of the 26 thousand 15 year survivors have grown very much, of those that have, smaller firms have grown faster than larger, and most growth is heavily concentrated in the first five years. Whilst survivors of those firms born relatively large remain large their modest growth does add importantly to job growth. More striking though, there is a very small group of those firms born very small (about 5% of all survivors) which contribute a substantial proportion (more than one third) of the jobs added to the cohort total by age 15.
Full-text available
We propose that random variation should be considered one of the most important explanatory mechanisms in the management sciences. There are good theoretical reasons to expect that chance events strongly impact organizational behavior and outcomes. We argue that models built on random variation can provide parsimonious explanations of several important empirical regularities in strategic management and organizational behavior. The reason is that random variation in a structured system can give rise to systematic patterns at the macro level. Here, we define the concept of a chance explanation; describe the theoretical mechanisms by which random variation generates patterns at the macro level; outline how key empirical regularities in management can be explained by chance models; and discuss the implications of chance models for theoretical integration, empirical testing, and management practice.
Full-text available
This article examines the theory and evidence in support of entrepreneurial learning (EL), measured in terms of whether individuals have previously owned a business, and time since start-up. Under this theory, entrepreneurial performance is argued to be enhanced by EL which itself is enhanced by business experience. However, if business performance is strongly influenced by chance then evidence of EL will be difficult to identify. We test for EL using a large scale data set comprising 6671 new firms. We choose business survival over 3 years as our performance measure and then formulate three tests for EL. None of the three tests provide compelling evidence in support of EL.
Sustained superior firm performance may arise from skillful management, but it can also be a product of luck. We benchmark how long and how well a firm must perform to be confident that its sustained superiority is not the product of a lucky random walk. We find that (a) the number of sustained superior performers in COMPUSTAT from 1965-2005 exceeds the number we would expect by chance, yet (b) lucky random walks occur often enough to fool many observers, so (c) especially stringent benchmarks are needed to insure that the ratio of exceptionally capable to exceptionally lucky firms is high enough to allow for valid study.
This article examines an apparent contradiction at the heart of the provision of management training and advice for new and small firms. Assessments using self-report data show high levels of satisfaction, implying that the training/advice is effective and appreciated. In contrast, assessments using robust statistical methods point to modest, or even zero, impact upon firm performance. Accordingly, our core research question explores whether there is an identifiable performance benefit of management training or whether impact is limited to emotional attachment to the training provider – reflected in enhanced loyalty. We test this by examining the effects of a bank seminar provided for new enterprises and find it had no significant effects on either the survival or sales growth of participants. However, those new enterprises who participated in the seminar were significantly less likely to switch to a rival bank, implying the seminars may have induced a feeling of loyalty among clients. Finally, we discuss the implications for theory, for the bank and for the providers of training for new and small firms more widely.