WORKING PAPER NO. 84
May 2004
AN ANALYSIS OF GROWTH IN NEW FIRMS
AND THE IMPACT OF LIQUIDITY CONSTRAINTS
Graham Hay and Kevin F. Mole
Warwick Business School’s Small and Medium Sized Enterprise Centre
Working Papers are produced in order to make available to a wider public,
research results obtained by its research staff. The Director of the
CSME, Professor David Storey, is the Editor of the Series. Any enquiries
concerning the research undertaken within the Centre, should be addressed
to: The Director, CSME, Warwick Business School, University of Warwick,
Coventry CV4 7AL, Tel. 024 76 522074,
e-mail david.storey@wbs.ac.uk
ISSN 0964-9328 – CSME WORKING PAPERS
Details of papers in this series may be requested from
Publications Secretary, CSME
Sharon.west@wbs.ac.uk
2
AN ANALYSIS OF GROWTH IN NEW FIRMS AND THE IMPACT OF
LIQUIDITY CONSTRAINTS
By
GRAHAM HAY and *KEVIN F. MOLE
*Corresponding author
Centre for Small and Medium Sized Enterprises
Warwick Business School
Tel. +44 24-7652-3918
Fax. +44 24-7652-3747
e-mail: Kevin.mole@wbs.ac.uk
ABSTRACT
This paper analyses growth for a sample of 624 firms located in three English
counties. Using 52 variables, heteroskedasticity adjusted OLS is used to estimate their
role in generating growth in new firms for the period 2000 to 2001 and for 1997 to 2001.
Summary statistics show constrained founders own slightly more businesses; make greater
use of external finance and are equally like to survive. Estimation results show liquidity
constraints are significant over four years but not one. The evidence is consistent with
learning models of both the entrepreneur and bank. Growth appears deterministic but
Gibrat’s law cannot be rejected.
ACKNOWLEDGEMENTS: The authors would like to thank Mark Stewart, for his
words of advice. We would like to thank the Leverhulme Trust for their financial support
of this research.
3
1. INTRODUCTION
A considerable body of literature on new firm formation has been accumulated
over the last twenty years or so, however emphasis has been on determining what makes
an entrepreneur and on the decision to switch into self-employment. The aim of this paper
is to determine firstly, which factors are important for helping new firms to grow and
secondly, the impact of liquidity constraints on growth. New firms present a problem of
asymmetric information for banks and other financiers which may result in credit rationing
(Stiglitz and Weiss, 1981). But how much do liquidity constraints matter for the
subsequent growth of a firm over four or more years after its foundation? The persistence
of a detrimental impact may provide strong evidence of market failure, lowering economic
welfare and, therefore, provide strong support for policy intervention.
The study retrospectively analyses a cross-sectional dataset constructed from
responses to a survey of new businesses. The dataset covers 624 new firm starts in three
UK counties between 1990 and 2001. Our growth measures are: the number of jobs
added, the percentage change in the number of fulltime equivalent employees between
2000 and 2001 and a Birch index over both one year and four. Our liquidity constraint
measure is a binary variable with a value of one if the firm was constrained in its first
year.
We find that high growth founders possess greater human capital and are more
likely to be constrained in their first year. A greater proportion provide workforce training
and they are more technologically sophisticated.
Regression results show liquidity
constraints have no effect over one year but do so over four. Four years may be a better
test of growth, however, we suspect that liquidity constraints may become less
constraining over time as learning processes, including bank learning, impact on the
surviving firms.
Technological sophistication is positive but issuing share capital has a
4
negative effect. Firm age and size indicate deterministic and learning processes at work.
But using an absolute growth measure, Gibrat’s law cannot be rejected.
The paper proceeds as follows: section two is the literature and theory review. By
way of introduction there is a brief review of the transition literature before focussing on
the theory and research findings concerning growth. Section three outlines the model for
estimation.
This is accompanied by a discussion on the methodology applied to
investigate growth in new firms. Section four describes the data used; it discusses the
data’s collection, characteristics and flaws. Sections five presents, discusses and analyses
the results, beginning with the summary statistics before considering the estimation
results. Section six draws the paper to an end with a brief summary and the main
conclusions.
5
2. LITERATURE REVIEW
Blanchflower et al (2001) use data from the 1997-1998 International Social Survey
Programme to find that far more wish to be self employed than actually are. Their
conjecture is that a lack of capital holds back potential entrepreneurs. Capital markets
therefore, seem imperfect. This does not mean they are irrational. Stiglitz & Weiss
(1981) offer an asymmetric information framework to show that, acting rationally, banks
limit the number of loans, not the total value. Despite the existence of several sources of
finance , research supports a credit rationing model. Lindh & Ohlsson (1996) find a
significant, non-endogenous, positive impact of windfall gains (lottery winnings and
inheritances) on the probability of being self-employed. However, human capital controls
are very weak so it could be the lottery dummy proxies for low human capital. Using
inheritances and gifts, Blanchflower and Oswald (1998) adopt a similar approach to find
an effect of equivalent magnitude and sign. Both papers indicate the presence of liquidity
constraints, ceteris paribus.
Blanchflower’s and Oswald’s results also show lottery
windfalls do not proxy for low human capital. Black et al (1996) reveal that for 85% of
UK business loans the collateral value exceeds the loan value. Supporting evidence gives
the appearance of credit rationing, where collateral requirements price agents out and they
choose to remain employed. Their results suggest a 10% increase in the value of housing
equity would increase new VAT registrations by 5-6% each year, consistent with the
liquidity constraint hypothesis. In Evans & Jovanovic (1989) probit estimates with net
family assets (for an all male survey) generate the same qualitative results. Thus, most
entrepreneurs face binding constraints.
Accepting this as the case, how do constraints affect firm growth? In one paper,
Carpenter and Petersen (2002) indicate growth is affected by finance. They present a very
plausible model where, in the presence of credit-rationing and collateral requirements, if a
6
firm’s finance constraint is binding an extra dollar of investment increases the asset value
by more than one dollar. Their regressions of asset growth on internal finance estimate a
coefficient in excess of one. The marginal returns to investment are positive and so the
authors conclude that for most small firms growth is constrained by a lack of internal
finance.
More generally, it indicates undercapitalisation due to liquidity constraints
generated by capital market conditions and a lack of internal funds. Their results also
show a weaker growth-internal finance relationship for firms that use share capital,
implying a relaxation of the internal finance constraint. A common weakness of many
studies is that too much attention is given to firm size and age while other potentially
significant variables are omitted. Becchetti and Trovato (2002) provide a more robust
analysis with the inclusion of, for example, industry and finance variables. In their
analysis of Italian, small manufacturers, state subsidies and credit rationing are significant,
while age and size remain so. Gibrat’s law does not hold for small firms and firms’ three
year growth rates are decreasing in age. Birth delayed by one year increases the growth
rate by 0.2%. For those in receipt of a state subsidy the growth rate was 1.5% higher.
Being turned down for finance, or rationed, had a negative impact on growth rates.
Although the negative coefficient for being rationed may indicate low ability and hence
rational lending, Becchetti and Trovato (2002) interpret these as an indication of liquidity
constraints seriously affecting growth.
Brito and Mello (1995) suggest that learning by outside financiers is critically
important in determining growth, as the firm gains a track record then bankers learn about
the firm and can lend more comfortably. The new firm, with no track record is a more
uncertain proposition for an outside financier. This puts the new firm at a competitive
disadvantage with lower earnings and therefore, less to reinvest; so they struggle to fully
capitalise or are undercapitalised and less competitive, leading to suboptimal output and
7
profits. By implication, initial constraints have a persistent, stifling effect on growth,
albeit one that decays over time.
Holtz-Eakin et al (1994) analysed ‘growth’ by
estimating the impact of wealth measures on entrepreneurial earnings in one year. They
estimate a $150,000 inheritance (in 1982 or 1983) increases 1985 earnings by nearly 20%.
Their growth measure is a snapshot measure of a level of income and gives no indication
of change over time; however, it shows a persistent effect; suggesting constrained firms
under-perform. An alternative from Cressy (1996) is that a struggling performance is due
to low ability disguised as a constraint; although this is rejected by Harada’s results (2003)
which show post-entry performance declines in founder’s age and education.
It may be that constraints reduce the probability of growth, but not the rate. The
Evans and Jovanovic model (1989) implies undercapitalised firms have higher investment
returns and are likely to reinvest a greater proportion of earnings. Evans and Jovanovic
(1989) also report a negative relation between estimated ability and assets. An implication
of their paper, therefore, is that the more able are more likely to be constrained. This
would return a positive coefficient on a constraint measure where it proxies for ability, and
high ability begets high growth. Consequently, smaller (constrained) firms should grow
faster than larger (unconstrained) firms. However, for this paper we hypothesise an
adverse effect:
H1: Being liquidity constrained has a significant and negative effect on firm growth.
More generally however, we are interested in the broader range of variables that
determine growth. By the neoclassical theory, single product firms have U-shaped average
cost curves. Firms grow up to their minimum efficient scale (MES), but beyond this there
is no incentive to grow. This explains relatively faster growth in smaller firms, but it does
not explain why some older firms continue to grow. And in reality many firms are multi-
8
product. Thus, Hart (2000) rejects the neoclassical theory and emphasises the role of
institutional forces, such as Government policies designed to foster the growth of small
businesses. Under imperfect competition the firm has a downward sloping demand curve
and firm growth is limited by product demand, not cost conditions. In attaining MES,
firms operate at the same average cost and so compete on, among other things, product
differentiation and quality of product/service. Innovative or niche products are possible
avenues to growth and so firm strategy becomes important, as outlined by Storey (1994).
In suggesting growth is increasing in size, pecuniary and technical economies of
scale contradict Gibrat’s law that the rate of growth is independent of size. Where
increasing returns to scale (IRS) are observed , growth should be a virtuous cycle. Those
that expand enjoy a competitive advantage which stifles the growth of smaller, less
productive firms. Learning by doing is another potential dynamic economy of scale. By
repeating production, firms learn how to do things better and how to solve problems. The
age-growth relation is therefore predicted to be negative. In contrast, Brito and Mello
(1995) describe a model of imperfect financial markets that predicts a positive age-growth
relation. Since lenders prefer established firms to new, possibly more profitable firms, the
new firms are more likely to be constrained. But loan schedules shift out over time as
banks learn about firms: giving a positive age-growth relation, as age relaxes financial
constraints. In fact, their second derivative is negative, consistent with a learning model
and a ‘S-curve’ hypothesis. This learning framework resembles evolutionary models in
which firms respond to stimuli and retain successful responses. By suggesting serial
correlation in growth, it lends itself to a deterministic growth model. We formulate the
following hypothesis to test the age-growth relation:
H2: Younger firms will display higher growth rates.
9
In a paper that subtly provides a human capital role if efficiency is assumed to be a
function of human capital, Jovanovic (1982) presents an incomplete information model of
growth. Differences in size are due to differences in efficiency, not capital base. In the
model costs are random and different, with the firm’s mean being its ‘true’ cost. Firms
know the distribution of true costs but only after entry do they learn their randomly
assigned true cost and then update the cost distribution. Consequently, output growth is
lower for older firms: consistent with diminishing returns to learning and operation at
(near) lowest average cost. The model predicts a negative age-growth relation, and a
negative size-growth relation. These predictions hold in a US study of five-year growth
rates for 422 small firms (Variyam and Kraybill, 1992).
They also find firms in
manufacturing and construction grow by 4% to 6% more than those in retail and service
sectors.
By extension, Brito’s and Mello’s model conflicts with this size-growth
prediction. Assuming that new, constrained firms are also smaller, it predicts a positive
size-growth relationship. So, we test:
H3a: Gibrat’s law, that the rate of growth is independent of size, does not hold.
H3b: With H2a holding, growth is an inverse function of size.
Another prediction follows from Jovanovic’s assumptions: homogeneous products;
random costs; firms are price takers and output prices follow a deterministic path. Taken
together they mean there is no incentive to conduct R&D or introduce new (in the unique
sense) products. So we test the hypothesis:
H4: By Jovanovic’s model, growth should be uninfluenced by R&D and new product
introduction.
10
In a way Jovanovic’s model seems to combine stochastic and deterministic
mechanisms, where a stochastic process determines the probability of ‘winning’ (being
low cost) and the deterministic process reinforcing the winners’ positions. Beyond these
mechanisms however, firm growth will also depend on the goals of the founder. Is the
founder a ‘satisficer’ or income-substituter who rejects profit or sales maximisation for a
quiet life? This firm is unlikely to grow much. Or does the founder aim to maximise sales
instead of profits? Baumol’s proposal was that such a goal leads to higher output and
sales than under profit maximisation. This results in expansion and the generation of more
jobs. This allows an interesting proposition: assuming sales maximisers are more likely to
compete on price while profit maximisers compete on quality or ‘niche’ products, we
identify two such groups and test to see if sales maximisers grow more. Romanelli (1989)
in a sample of new firm found that those who emphasised sales through a broad product
range grow faster than those who stuck to a narrow product range so there is some
evidence for the Baumol thesis.
H5: Sales maximisers exhibit higher growth than profit maximisers.
The literature review suggests five hypotheses that liquidity constraints affect
growth, that younger firm grow more quickly, that Gibrat’s law does not hold, that R&D
and new product introduction have no influence on growth and that sales-maximisers
grow faster than profit-maximisers. In doing so, we implicitly are comparing models of
growth from Brito and Mello (1995) against Evans-Jovanovic (1989) on the impact of
liquidity constraints, and the negative age-growth relation of neoclassical economics
against Brito and Mello’s positive correlation (1995). The impact of R&D and new
products is a test of Jovanovic (1982) and the relationship between sales and profitmaximisers is a test of Baumol’s model.
11
3. DATA
The data come from a new firm survey conducted by the CSME in 2001. To
compile the dataset, researchers compared B.T. telephone directories for 2000 with those
for 1995. The selected firms started within the counties between 1990 and 2001, and were
wholly independent, non-retail businesses.
The interviewees were located in
Buckinghamshire, Shropshire or Tees Valley and were interviewed in person.
This dataset is limited by its cross-sectional nature, the absence of failed firms and
to a degree, its size (N=624). Cross-sectional data provide a snapshot of a moment in time
only and so the dynamic role of influential factors cannot be fully analysed. The survey
questions did cover several years so the dataset permits some comparative static analysis.
The availability of large, good quality datasets on start-ups is still poor. Furthermore, only
through surveys can we glean information about the founder, the firm and ambitions.
Lastly, the omission of failed firms prevents estimation of survival rates, and we can say
nothing about the characteristics of firms that fail or their growth profile. Their omission
also means parameter estimates will tend to overestimate the influence of variables. Table
1 shows the distribution of firms by age. The firms ranged in age from less than one to
eleven years old. The sample is very young with more than half our dataset under five.
All firm ages relate to when the firm started-up originally. Some firms in the sample
started up previously on another site; their age is calculated from this initial inception date.
Table 1: Age distribution(Yrs)
N=624
%
Cum.%
<1
1
2
3
4
5
6
7
8
9
10
11
10
1.6
1.6
61
9.8
11.4
99
15.9
27.2
105
16.8
44.1
75
12
56.1
91
14.6
70.7
85
13.6
84.3
30
4.8
89.1
22
3.5
92.6
23
3.7
96.3
14
2.2
98.6
9
1.4
100
12
The distribution across the three counties is uneven, with Tees Valley being home to just
over half the sample (51.3%). Buckingham-shire and Shropshire are each home to 24.4%
of firms. The size distribution of firms, by fulltime equivalent employment (FTE), is
presented in Table 2. The percentage of firms above the small classification is tiny.
99.4% of the firms can be classed as small or micro, while 41.8% actually have no more
than two employees. Looking at the tables it is safe to say the firms in the sample are
small and young.
Table 2: Size distribution by FTE 2001
N=624
% (2001)
Cum. % (2001)
0-2
2.5-9
9.5-49
50+
261
41.8
41.8
284
45.5
87.3
75
12.1
99.4
4
0.6
100
Growth for the year seems to have bypassed most of our sample. Table 3 reveals that
72%-74% of our firms show negative or zero growth. A doubling in size or greater was
enjoyed by 6% of our sample, while 4.5% added five or more jobs.
Table 3: Distribution by FTE growth rate, 2000-2001
N=603
%
Cum. %
<0
=0
>0 & ÿ 0.1
>0.1 & ÿ 0.5
> 0.5 & < 1
ÿ1
87
14.4
14.4
362
60
74.5
9
1.5
76
97
16
92
14
2.3
94.4
34
6
100
>0 & <5
145
23.3
95.5
ÿ
28
4.5
100
by jobs added, 2000-2001
N=622
%
Cum. %
ÿ
449
72.2
72.2
13
4. MODEL AND METHODOLOGY
Having selected this dataset of 624 observations, it was edited and transformed to
consist of the 53 variables given in Appendix 1. DEGREE, ALEVEL, PROFESS, NVQ
and OLEVEL were all generated from a ‘highest qualification’ variable.
OLEVEL
incorporates those with ‘other’ qualifications. PORTFOLIO was generated by interacting
a binary variable for whether the founder owns other businesses with the number of other
businesses owned.
A ‘don’t know’ with an unrecorded number of businesses was
interpreted as zero other businesses.
For legal form there were four measures for
incorporated companies and they are captured by INCCO (N=233). PTRSHP comes from
the one partnership measure (N=108). Firms are sole proprietorships or ‘other’ forms in
all other cases (N=283, other=3). Using the recorded two-digit SIC code, ten dummies, of
the form SICCODEXX, were created to control for the ten most represented sectors
(71.5% of firms). TECHSOPH scores the firm’s technological sophistication on the
degree to which it uses IT for operating (see Appendix 2). The lowest score was 0 and the
maximum was 5. SALES and PROFIT are used to identify the firms as sales maximisers
or profit maximisers (see Appendix 2), and to test if sales maximisers grow more than
profit maximisers. To test how sources of finance affect growth three variables were
created.
INTERFIN indicates the firm started up using ‘personal savings’, ‘house
mortgage’ or ‘loans from friends and family’. EXTERFIN means the firm used a loan/
overdraft from a clearing bank or a loan from a finance company. STATEFIN is the
dummy for finance from public authorities or organisations. The survey also asked the
following question, ‘During the first year while you were settling in, were there any
difficulties with any of the following, or not?........(g) Finance?’. An affirmative response
to this was taken to mean the founder faced a liquidity constraint in his/her first year, and
so this was used to create LIQCON, which equals one if the founder was constrained.
14
The model used is a simple linear model relating growth to the variables chosen, so:
g = x’β + ε
where g is the growth measure. x’β may be decomposed into distinct elements relating to
the entrepreneur, the firm, strategy and finance. So:
g = x’β1 + y’β2 + z’β3 + ε
where x, y and z represent the entrepreneur, firm and strategy & finance vectors
respectively. A full list of the vector variables is available in Appendix 1. The growth
measures used are the percentage change in fulltime equivalent employees (FTE) for the
2000 to 2001 period (JOBCHG) or the level change for the same period (JOBADD). An
average annual rate was considered but employment figures are recorded for select years
only, so average annual rates cannot be calculated for firms that started up in the
intervening years. Using the percentage growth measure, we lose 21 observations in
estimation to firms with zero employment in 2000. For the summary statistics, the fast
growers are those with growth rates of one or more or those that add at least 5 jobs. We
also use the top 10% of a Birch Index distribution. The Index is calculated as follows:
BI = (change in FTE, 2000-2001) * (FTE2001 / FTE2000)
Probit analysis was considered initially but discussions highlighted a severe
problem with heteroskedasticity. We proceeded to estimate our model using OLS. The
model is tested for heteroskedasticity using the Breush-Pagan test. If the model fails the
test, it is re-estimated using robust (White adjusted) standard errors. The estimator is
inefficient but consistent and unbiased.
Consequently, we can make appropriate
inferences based upon b without having to specify the type of heteroskedasticity. The
White covariance matrix used is scaled up by (n / n – k) to combat the suggested overoptimism of the original matrix, (k is the number of explanatory variables). Growth is
15
regressed on each vector individually to determine individual effects, before regressing on
the full set of variables. Following this a streamlined model is developed using the
estimation results along with the theory and evidence. This reduced model is then reestimated and augmented for Birch fast growers.
In extending the analysis, tables 7 and 8 show the results for Heckman 2-step
estimation where the dependent variables are the Birch scores over one and four years
respectively. The four year results reduce the responses to 286 and 307 respectively. On
the basis of the results in Table 8, the model was re-estimated using OLS with robust
(White) standard errors. This model is presented in Table 9.
16
5.RESULTS
Summary statistics
Table 4 presents key means and frequencies. Notable differences, robust across
JOBADD, JOBCHG and Birch are that high growth founders have more managerial
experience and will own 1½-2 times more businesses. Slightly more have previous
business experience. Their firms will be younger, consistent with a learning process; and
they show a strong preference for an incorporated legal form over partnership but less so
over sole proprietorship. This fits with a moral hazard and limited liability framework. A
higher proportion of high growth firms are concentrated in the business related services
sector (SICCODE74): 26/36% v 16/17%. Many more fast growers provide workforce
training (70%-80% v 51%-54%) and furthermore, their TECHSOPH average is higher:
quality of service seems important. For finance, high growth firms use more finance from
banks or finance companies but slightly less from personal resources. So EXTERFIN
seems to have a positive influence. Finally, the high growth founders show a tendency to
make greater use of professional services pre-start-up, especially banks.
The one
exception is relatively high growth firms made less use of accountants before starting.
More high growers were turned down for finance at some point, implying a signalling
failure or that high growers are more tenacious and resourceful.
Looking at LIQCON, the proportion constrained is consistently around 28%. The
exceptions are the high growers under JOBCHG and Birch. This fits with undercapitalisation and subsequent higher rates of growth. Looking at the Birch Index alone,
more fast growers are academically educated and have previous sector experience. They
are much more likely to be male (90% v 76%). It indicates a positive size-growth relation:
symptomatic of pecuniary or technical economies of scale; or bank learning.
17
Table 4: Variable Summary by growth
G
Variable
(Birch low growth summary statistics available on request)
FOUNDAGE
MALE
DEGREE
ALEVEL
PROFESS
NVQ
OLEVEL & OTHER
% with no qual.
UNEMP
BUSINESSB4
MANAGER
SAMESECTOR
SOCMARG
PRETRAIN
PORTFOLIO
Birch high
JOBADD
JOBCHG
High
growthLow
growthHigh growthLow
growthgrowth
N=28
N=594
N=34
N=569
N=62
39.76 (11.01)
39.22 (9.52)
36.24 (8.59) 39.28 (9.51) 37.48 (8.26)
0.82
0.77
0.79
0.78
.90
0.25
0.177
0.147
0.179
0.258
0.286
0.200
0.206
0.204
0.242
0.071
0.099
0.147
0.097
0.081
0.179
0.232
0.235
0.230
0.177
0.071
0.178
0.147
0.176
0.097
0.143
0.113
0.118
0.114
0.145
0.214
0.227
0.206
0.230
0.210
0.357
0.349
0.382
0.341
0.371
0.741
0.623
0.706
0617
0.721
0.852
0.661
0.676
0.670
0.738
0.464
0.490
0.5
0.492
0.435
0.107
0.093
0.088
0.093
0.081
0.357
0.226
0.324
0.227
0.435
FIRMAGE
FIRMSIZE
Location: BUCKS
SHROP
Legal form: INCCO
PTRSHP
Sector: SICCODE22
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE55
SICCODE72
SICCODE74
SICCODE92
SICCODE93
3.39 (2.01)
11.5 (12.19)
0.286
0.357
0.714
0.107
0
0
0.107
0.036
0.036
0.036
0.071
0.357
0
0
4.34 (2.45)
4.17 (5.77)
0.242
0.239
0.358
0.175
0.039
0.035
0.088
0.069
0.049
0.081
0.071
0.163
0.034
0.089
3.15 (2.41)
3 (3.83)
0.206
0.324
0.412
0.059
0.059
0.029
0.059
0.118
0.059
0
0.088
0.265
0
0.088
4.42 (2.39)
4.74 (6.54)
0.248
0.232
0.371
0.178
0.035
0.035
0.091
0.067
0.047
0.081
0.070
0.165
0.035
0.088
3.85 (2.16)
9.95 (8.53)
0.177
0.354
0.742
0.081
0.016
0.048
0.113
0.016
0.032
0.048
0.097
0.258
0.016
0.016
WORKTRAIN
TECHSOPH
PLANNING
NEWPROD
COMPET
EXPORTS
PREACCT
PREBANK
PRESOLIC
R&D
SALES
PROFIT
0.821
3.5 (1.79)
0.786
0.821
3.04 (0.344)
1.5 (4.25)
0.464
0.607
0.428
0.607
0.142
0.036
0.537
2.75 (2.12)
0.579
0.598
3.09 (0.458)
4.44 (14.77)
0.367
0.438
0.19
0.401
0.15
0.062
0.735
3.56 (2.08)
0.5
0.618
3.08 (0.471)
6.2 (15.99)
0.324
0.529
0.265
0.324
0.118
0.088
0.541
2.75 (2.11)
0.589
0.606
3.09 (0.447)
4.31 (14.61)
0.371
0.436
0.192
0.411
0.148
0.060
0.839
3.94 (1.74)
0.694
0.790
3.08 (0.525)
5.13 (14.34)
0.484
0.613
0.387
0.468
0.081
0.081
INTERFIN
EXTERFIN
STATEFIN
LIQCON
TDOWN
DISCOUR
JOBADD
JOBCHG
EQUITY
0.821
0.321
0.071
0.286
0.214
0.179
9.73 (9.22)
1.06 (1.12)
0
0.860
0.281
0.157
0.277
0.175
0.14
0.111 (1.7)
0.068 (0.34)
0.00510
0.824
0.353
0.059
0.412
0.265
0.147
3.84 (4.16)
1.40 (0.816)
0
0.861
0.285
0.162
0.269
0.172
0.141
0.148 (2.05)
0.025 (0.23)
0.0053
0.790
0.435
0.177
0.371
0.258
0.161
4.63 (3.67)
0.648 (0.539)
18
R&D seems marginally more important (47% v 41%); many more introduced a new
product (79% v 59%); and there is no preference for sales or profit maximisation.
Estimation results
Tables 5 and 6 present regression results using JOBCHG and JOBADD
respectively, with t-values in square brackets. Significance refers to the 5% level unless
indicated. All models failed the Breush-Pagan test, so were re-estimated using robust
standard errors.
By both dependent variables, the entrepreneur vector offers little
explanatory power. Reported R2’s are around 0.02. In the JOBCHG model (#1) nothing
is significant except PORTFOLIO at the 10% level. In the JOBADD model (#6) only
previous sector experience is significant. In both cases, no other work experience or
education variable is significant, undermining the importance of human capital in postentry performance. The firm vector (#2 & #7) has more power explaining the rate of
change rather than the level change. While its R2 for JOBADD is 0.0343, none of its
variables are significant in explaining the variance in JOBADD. The insignificance of
size and age characterises growth as a stochastic process. In the JOBCHG model (#2)
firm size is insignificant also but incorporated legal form (INCCO) is not and has a
positive influence. This supports the idea that the limited liability status of incorporated
firms sees them pursue riskier ventures to be rewarded with higher growth when
successful. Since the dataset only contains survivors, the relation is not surprising. The
only other significant variable is firm age. The strategy vector explains the most variance
in either growth measure (consistent with Mole et al., 2002). Significant variables robust
across both measures are TECHSOPH, PREBANK and EQUITY. Those who sought
bank advice pre-startup grew more, ceteris paribus. The significance of the PREBANK
may be related to its role as a lender but external finance is not significant.
19
Table 5: White corrected OLS estimation results, dependent variable = JOBCHG
Variable
1
2
3
4
5
INTERCEPT
FOUNDAGE
FOUNDAGE^2
MALE
DEGREE
ALEVEL
PROFESS
NVQ
OLEVEL&OTHER
UNEMP
BUSINESSB4
MANAGER
SAMESECTOR
SOCMARG
PRETRAIN
PORTFOLIO
0.038 [0.13]
0.004 [0.28]
-0.00009 [-0.52]
0.026 [0.62]
0.004 [0.06]
-0.029 [-0.53]
-0.022 [-0.34]
-0.0095 [-0.17]
0.022 [0.35]
-0.049 [-1.20]
-0.017 [-0.43]
0.053 [1.59]
0.049 [1.29]
-0.042 [-0.98]
0.024 [0.51]
0.056 [1.69]
0.384 [4.24]
0.064 [0.52]
0.308 [0.87]
0.006 [0.37]
-0.0001 [-0.58]
-0.006 [-0.14]
-0.032 [-0.48]
-0.041 [-0.66]
-0.09 [-1.22]
-0.014 [-0.22]
0.018 [0.28]
-0.04 [-0.94]
-0.016 [-0.38]
0.044 [1.17]
0.051 [1.31]
0.003 [0.07]
0.039 [0.73]
0.037 [1.21]
0.299 [3.40]
-0.119 [-2.99]
0.009 [2.98]
-0.025 [-3.24]
0.0004 [2.47]
-0.049 [-0.83]
0.01 [0.19]
0.078 [1.34]
-0.044 [-1.05]
-0.026 [-0.31]
-0.027 [-0.28]
0.03 [0.36]
-0.01 [-0.12]
-0.096 [-0.98]
-0.006 [-0.10]
-0.005 [-0.04]
0.052 [0.84]
-0.026 [-0.42]
-0.048 [-0.61]
-0.117 [-3.54]
0.003 [3.06]
-0.020 [-2.84]
0.0003 [2.28]
FIRMAGE
FIRMAGE^2
FIRMSIZE
FIRMSIZE^2
Location: BUCKS
SHROP
Legal form: INCCO
PTRSHP
Sector: SICCODE22
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE55
SICCODE72
SICCODE74
SICCODE92
SICCODE93
-0.113 [-3.67]
0.008 [3.33]
-0.0054 [-0.94]
0.00004 [0.34]
-0.020 [-0.44]
-0.015 [-0.31]
0.10 [2.02]
-0.044 [-1.30]
0.01 [0.15]
-0.031 [-0.38]
0.028 [0.39]
0.0003 [0.00]
-0.014 [-0.16]
-0.023 [-0.58]
0.052 [0.42]
0.066 [1.25]
-0.029 [-0.71]
-0.01 [-0.18]
WORKTRAIN
TECHSOPH
PLANNING
NEWPROD
COMPET
EXPORTS
PREACCT
PREBANK
PRESOLIC
R&D
SALES
PROFIT
INTERFIN
EXTERFIN
STATEFIN
LIQCON
AGELIQ
TDOWN
DISCOUR
EQUITY
N=
ÿ2 (Breush-Pagan)
F (Wald test)
R2
569
59.28 (0.00)
0.82 (0.66)
0.019
603
327.5 (0.00)
1.9 (0.014)
0.073
0.086 [1.89]
-0.056 [-1.71]
0.136 [3.73]
0.029 [2.96]
-0.011 [-0.32]
-0.003 [-0.10]
-0.019 [-0.52]
-0.0005 [-0.46]
-0.046 [-1.27]
0.092 [2.38]
0.026 [0.61]
-0.064 [-1.67]
0.013 [0.25]
0.011 [0.16]
-0.048 [-1.12]
-0.031 [-0.75]
-0.047 [-1.21]
0.27 [2.60]
-0.052 [-2.98]
0.043 [0.90]
-0.031 [-0.57]
-0.468 [-2.93]
0.187 [3.97]
0.025 [2.35]
-0.017 [-0.42]
0.017 [0.41]
-0.01 [-0.23]
-0.00002 [-0.02]
-0.064 [-1.56]
0.092 [1.98]
0.033 [0.71]
-0.054 [-1.36]
-0.0004 [-0.01]
0.013 [0.14]
-0.043 [-0.85]
-0.002 [-0.05]
-0.066 [-1.49]
0.184 [1.43]
-0.028 [-1.27]
0.028 [0.57]
-0.049 [-0.82]
-0.509 [-3.18]
0.162 [3.88]
0.019 [2.19]
562
122.56 (0.00)
2.51 (0.0003)
0.086
530
340.59 (0.00)
1.76 (0.001)
0.177
563
352.74 (0.00)
4.76 (0.00)
0.1272
0.074 [2.00]
-0.299 [-4.00]
20
The greater the firm’s technological sophistication the more it grew. It is possible this
works by lowering costs and price, thereby increasing demand, output and thus
employment. EQUITY has a negative effect on growth. Note also, in each case neither
R&D or new product introduction is significant, as predicted by Jovanovic ’s model; and
sales maximisation has no influence on performance. In the JOBADD model (#8) having
a business plan before start-up has a significant and positive effect. Workforce training
significantly increases the rate of growth (model #3). While for the level change a
liquidity constraint is not significant, it is in explaining the rate of change. It indicates a
positive effect for new firms that declines in firm age and turns negative at around five
years, ceteris paribus: there is persistence. This could be explained by undercapitalisation
and consequent higher growth, with the effect expiring around five and being overtaken
by diminishing returns to scale due to the fixity of the capital base. Alternatively, it could
represent high ability with the effect declining in age as the entrepreneur decentralises
control and delegates to managers.
For the full specification for JOBCHG (#4) the R2 is comparable to that in Evans
or Variyam et al (=0.177). For JOBADD the R2 = 0.106. These are disappointing given
the greater number of variables included.
significant.
In both, no entrepreneur variables are
With no firm variables significant either, Gibrat’s law holds and the
implication is absolute growth is derived entirely from the firm’s strategy.
21
Table 6: White corrected OLS estimation results, dependent variable = JOBADD
Variable
6
7
8
9
10
INTERCEPT
FOUNDAGE
FOUNDAGE^2
MALE
DEGREE
ALEVEL
PROFESS
NVQ
OLEVEL&OTHER
UNEMP
BUSINESSB4
MANAGER
SAMESECTOR
SOCMARG
PRETRAIN
PORTFOLIO
0.171 [0.11]
-0.004 [-0.05]
0.00005 [0.06]
0.033 [0.15]
0.247 [0.32]
-0.538 [-0.91]
-0.521 [-1.08]
-0.473 [-0.95]
-0.402 [-0.81]
-0.053 [-0.20]
-0.066 [-0.27]
0.353 [1.63]
0.509 [2.04]
0.138 [0.34]
0.273 [0.73]
0.444 [1.47]
0.655 [1.49]
0.639 [0.99]
1.25 [0.46]
-0.034 [-0.31]
0.0004 [0.32]
-0.177 [-0.59]
-0.120 [-0.17]
-0.653 [-0.96]
-0.828 [-1.45]
-0.478 [-0.88]
-0.535 [-1.02]
0.173 [0.68]
-0.072 [-0.24]
0.202 [0.97]
0.402 [1.43]
0.473 [0.83]
0.309 [0.68]
0.406 [1.23]
0.156 [0.50]
-0.011 [-0.04]
-0.007 [-0.29]
-0.03 [-0.28]
0 [0.00]
0.08 [0.20]
0.185 [0.53]
0.492 [1.30]
-0.002 [-0.01]
-0.109 [-0.22]
0.081 [0.18]
-0.018 [-0.04]
0.047 [0.12]
-0.410 [-0.88]
0.224 [0.58]
0.807 [0.66]
-0.032 [-0.06]
-0.993 [-1.68]
-0.418 [-1.21]
-0.105 [-2.52]
FIRMAGE
FIRMAGE^2
FIRMSIZE
FIRMSIZE^2
Location: BUCKS
SHROP
Legal form: INCCO
PTRSHP
Sector: SICCODE22
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE55
SICCODE72
SICCODE74
SICCODE92
SICCODE93
-0.176 [-0.86]
0.006 [0.34]
0.057 [0.59]
-0.0017 [-0.56]
0.265 [0.74]
0.094 [0.35]
0.681 [1.90]
-0.073 [-0.38]
-0.021 [-0.08]
-0.189 [-0.56]
-0.135 [-0.35]
-0.055 [-0.20]
-0.516 [-1.32]
0.162 [0.55]
0.75 [0.67]
0.184 [0.40]
-0.446 [-1.56]
-0.251 [-1.23]
WORKTRAIN
TECHSOPH
PLANNING
NEWPROD
COMPET
EXPORTS
PREACCT
PREBANK
PRESOLIC
R&D
SALES
PROFIT
INTERFIN
EXTERFIN
STATEFIN
LIQCON
AGELIQ
TDOWN
DISCOUR
EQUITY
N=
ÿ2 (Breush-Pagan)
F (Wald test)
R2
587
312.10 (0.00)
0.92 (0.546)
0.022
622
266.91 (0.00)
1.83 (0.0189)
0.0343
-0.897 [-2.64]
0.306 [0.99]
0.142 [2.13]
0.492 [2.07]
0.151 [0.70]
-0.253 [-1.41]
-0.007 [-1.33]
-0.041 [-0.17]
0.624 [2.63]
0.825 [1.61]
-0.056 [-0.18]
0.528 [0.64]
-0.127 [-0.37]
-0.458 [-1.84]
-0.258 [-0.82]
-0.788 [-1.81]
0.222 [0.46]
-0.162 [-1.58]
0.073 [0.25]
0.121 [0.35]
-1.68 [-2.11]
0.442 [1.57]
0.105 [1.53]
0.548 [2.06]
0.163 [0.58]
-0.294 [-1.24]
-0.006 [-1.09]
-0.195 [-0.71]
0.619 [2.53]
0.989 [1.35]
-0.07 [-0.22]
0.59 [0.65]
0.148 [0.34]
-0.376 [-1.27]
-0.162 [-0.54]
-0.84 [-1.65]
-0.019 [-0.03]
-0.083 [-0.65]
0.154 [0.54]
-0.22 [-0.62]
-2.57 [-3.86]
-1.55 [-3.39]
579
293.3 (0.00)
2.09 (0.0038)
0.0647
546
559.69 (0.00)
2.84 (0.00)
0.106
622
242.15 (0.00)
3.53 (0.0005)
0.054
0.115 [2.27]
0.426 [2.11]
0.554 [2.68]
0.819 [1.81]
-0.711 [-1.90]
22
All the individual results for the strategy vector hold qualitatively with the exception of
TECHSOPH, which becomes insignificant.
In the JOBCHG model, significant firm
variables are age and size. Incorporated status is now insignificant. The age relation is
nearly identical to that in model #2. It indicates a negative age-growth relation up to just
under age seven and a positive one thereafter. This suggests a learning process at work in
the early years is later superseded by Brito and Mello’s (1995) bank learning model.
Interestingly, firm size is significant with a relationship similar in shape to that of age. It
is negative for firms with under ~31 employees and positive thereafter. This implies a
deterministic process at work in smaller firms which is eclipsed by pecuniary and
technical economies of scale as firms grow. Where smaller firms are also younger, it fits
with Brito and Mello (1995). Qualitatively, having controlled for other variables, all the
strategy vector results hold except that for the liquidity constraint, which becomes
insignificant. From the summary statistics, this is likely to be due to the inclusion of
MALE, SOCMARG and PORTFOLIO.
Turning to the reduced specifications, in neither case did the omission of the
constraint measure render any entrepreneur variables significant, or vice versa. Despite
the summary statistics, no evidence was found of an effect of human capital on the
liquidity constraint. Given its more parsimonious structure of only ten variables, the
specification for the growth rate (#5) has a respectable R2 of 0.127. The legal form
variables are included on the basis of a joint significance test. Firm size and age remain
statistically significant, rejecting the stochastic models and Gibrat’s law. The effects are
near identical to those in model #4. Thus the growth rate decreases and then increases in
age and size. It also meets with Evans’ et al (1989) theory of undercapitalisation and
higher returns, where new firms are undercapitalised and smaller. The positive relation
implies increasing returns to scale or, more likely, pecuniary economies of scale. It does
23
support Brito and Mello (1995): constraints are relaxed as the bank-firm relationship ages,
releasing untapped growth potential. The insignificance of the constraint measure in #4
and #5 supports this. The legal form variables show partnerships in our sample contracted,
relative to incorporated firms and sole-proprietorships. Incorporated firms expanded. The
provisions of workforce training and technological sophistication are robust to the reduced
specification and have similar effects. Workforce training increased the growth rate by
around 16%, ceteris paribus. PREBANK still holds with a marginally reduced effect.
Despite banks’ bad reputation, firms from our sample that sought pre-startup advice from
banks grew faster. It implies bank advice is most tailored to new business needs and very
relevant. Equity remains significant and considerably negative. This is robust in all
models. There are two possibilities for this: first, the issuing of equity weakens the
constraint and allows full capitalisation, so the growth rate falls. It may be that growth
leads to the issuing of share capital and not vice versa .
For the JOBADD model (#10) it was harder to distil a model. STATEFIN is
included but it is not significant at the 5% level. Its significance at the 10% level shows
those using state finance created fewer jobs, ceteris paribus. It indicates therefore, policy
failure or adverse selection. This fits with the summary statistics, which show many less
high growth firms use state finance. For the JOBADD model (#10), the TECHSOPH and
EQUITY results also hold. Firm size is not important, upholding Gibrat’s law and age is
linearly negative: older firms add fewer workers, ceteris paribus. This indicates a learning
effect that persists rather than die out; and it does not support learning by banks. The
model indicates that firms in the cultural, recreational or sporting activities sector
contracted relative to other sectors, which may reflect low entry barriers. Like the
JOBCHG model, pre-startup advice from the bank, or solicitor in this case, aids growth.
The solicitor is included by virtue of a joint significance test. An interesting inclusion is a
24
pre-startup business plan. Having one sees a firm take on more employees, consistent
with previous work. Overall, models #5 and #10 reject growth as a stochastic process and
give support to deterministic, learning models (learning by the firm and the bank). They
highlight that in our sample, being liquidity constrained does not significantly affect firm
growth; and sales maximisers do not grow more. They show human capital has no effect
on firm growth.
Two possibilities are present, however, in the analysis so far. One is that the
selection effects are significant in the liquidity constrained firms, consistent with the
Evans-Jovanovic model where firms with greater ability are constrained. The second is
that the growth rate over one year is too lumpy and that growth over four years would be
more consistent. Caves (1998) in a review suggests that year-on-year growth can be
negatively correlated but that over three years growth is Further estimation was conducted
to verify these results and carry our analysis forward. The results are presented in tables 7,
8, and 9. Tables 7 and 8 show the results for Heckman 2-step estimation where the
dependent variables are the Birch scores over one and four years respectively. In both
cases a higher score indicates, on balance, greater growth. On the basis of these results,
the Birch score over four years was re-estimated using OLS with robust (White) standard
errors.
Turning to Table 7, the first thing to note is th at Heckman’s lambda is not
significant, so there is no selection by liquidity. Looking at the primary equation, very
few variables are significant at the 5% level and the constraint dummy is not significant at
all. Age and size however, are significant in the quadratic form. Qualitatively, age has the
same effect as earlier estimates. Growth is falling in age to around eight and increasing
thereafter, similar to some of our earlier estimates. Although significant in the quadratic
form, size is virtually linear in effect. So for the year, the growth score is increasing in
25
firm size at a near constant rate. These results are consistent with the synthesis of a
learning effect on the firm’s and bank’s behalf, and a size effect in the shape of scale
economies. SICCODE85 (health and social work) has a strong negative effect on the
growth measure.
26
Table 7: Treatment effects model -- two-step estimates. DV = Birch score over one year.
Wald ÿ2(28)
Prob > ÿ2
N
173.11
0.0000
501
Variable
Coefficient
Std. Error
z
P>|z|
95% confidence interval
-0.799
0.050
0.290
-0.002
-0.640
0.450
0.030
0.018
-0.131
0.526
1.490
-0.568
-0.442
0.379
-0.235
-0.175
-0.907
-0.371
1.418
0.018
0.813
1.469
-3.728
-0.572
-0.296
-0.256
1.924
0.281
0.025
0.039
0.0005
0.337
0.249
0.140
0.428
0.458
0.922
1.094
0.977
0.661
0.732
0.906
0.992
0.753
1.253
1.634
0.783
0.583
1.017
1.226
0.991
0.7205
0.894
0.928
-2.84
2.00
7.49
-3.28
-1.90
1.80
0.21
0.04
-0.29
0.57
1.36
-0.58
-0.67
0.52
-0.26
-0.18
-1.20
-0.30
0.87
0.02
1.39
1.45
-3.04
-0.58
-0.41
-0.29
2.07
0.004
0.046
0.000
0.001
0.058
0.072
0.830
0.966
0.776
0.568
0.173
0.561
0.504
0.605
0.796
0.860
0.229
0.767
0.386
0.981
0.163
0.148
0.002
0.564
0.681
0.775
0.038
-1.350
0.001
0.214
-0.003
-1.301
-0.040
-0.244
-0.820
-1.028
-1.280
-0.653
-2.483
-1.738
-1.056
-2.011
-2.119
-2.383
-2.826
-1.785
-1.517
-0.330
-0.523
-6.130
-2.515
-1.708
-2.009
0.106
-0.248
0.098
0.366
-0.001
0.021
0.940
0.304
0.856
0.767
2.333
3.634
1.347
0.854
1.814
1.541
1.768
0.569
2.084
4.621
1.553
1.955
3.462
-1.326
1.370
1.116
1.497
3.742
0.805
0.388
0.538
-0.759
1.163
0.387
0.486
0.788
0.179
-1.746
0.158
0.139
0.157
0.317
0.310
0.147
0.136
0.286
0.077
0.184
5.08
2.79
3.43
-2.39
3.76
2.64
3.57
2.76
2.32
-9.48
0.000
0.005
0.001
0.017
0.000
0.008
0.000
0.006
0.020
0.000
0.494
0.115
0.230
-1.381
0.556
0.099
0.219
0.228
0.028
-2.108
1.115
0.661
0.845
-0.137
1.770
0.674
0.753
1.348
0.330
-1.385
Lambda (ÿ)
0.235
0.565
0.42
0.677
-0.872
1.342
Rho (ÿ)
Sigma (ÿ)
Lambda (ÿ)
0.067
3.499
0.235
0.565
BIRCHSCORE
FIRMAGE
FIRMAGE^2
EMPLOYEES
EMPLOYEES^2
R&D
OTHERFIRM
TECHSUPP
BUCKS
SHROP
SICCODE22
SICCODE28
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE52
SICCODE55
SICCODE60
SICCODE66
SICCODE72
SICCODE74
SICCODE80
SICCODE85
SICCODE92
SICCODE93
LIQCON
INTERCEPT
LIQCON
TDOWN
SOCMARG
SHROP
SICCODE55
HIGHLAB
SHORTDMAND
BANKNOW
OTHERFIN
PROF98
INTERCEPT
HAZARD
27
Two others are significant at the 10% level: the dummy variables for whether the
firm conducts any R&D (R&D) and whether the founder is a director/owner of any other
firms (OTHERFIRM). R&D has a negative impact on the one year growth measure while
the coefficient on OTHERFIRM is positive.
The negative impact of R&D can be
explained as the consumption of valuable resources to the detriment of short term growth,
in order to improve long term growth.
All of the previous work concentrated on one year’s growth. Table 8 presents the
equivalent results for the four year Birch score.
Again Heckman’s lambda is not
significant, so there is no sample selection bias. However, the estimated model for the
Birch score is noticeably different from the one year model. Five variables are significant
at the 5% variable and crucially the constraint measure is one of them. Over four years, an
initial liquidity constraint has a significant negative effect on growth, indicating that
constrained firms grew less than unconstrained firms. This supports our hypothesis (H1)
and concurs with Holtz-Eakin et al: their estimates show a persistent and adverse effect.
In contrast to the model in Table 7, FIRMAGE is not significant in any form. Growth is
increasing in size again, but at approximately half the rate compared to the one year
measure. This would appear to reject growth as a stochastic process and give cautious
support to H3a but not H3b.
28
Table 8: Treatment effects model -- two-step estimates. DV = Birch score over four years.
Wald ÿ2(28)
Prob > ÿ2
N
70.38
0.0011
286
Coefficient
Std. Error
z
P>|z|
95% confidence interval
-1.127
0.069
0.154
-0.001
-1.750
-0.111
-1.126
-0.092
0.058
-0.264
0.4405
0.194
0.082
-0.094
0.711
0.433
0.828
0.959
0.692
1.420
0.873
0.712
1.573
-0.147
-7.947
0.120
0.840
1.006
-0.367
0.613
0.530
-4.221
0.067
0.115
-2.979
6.219
0.815
0.060
0.065
0.001
0.765
0.154
0.599
0.619
0.605
0.574
0.436
0.544
0.248
0.585
0.739
0.754
0.849
0.726
1.745
1.900
2.001
1.184
1.344
1.565
2.181
1.412
1.815
2.377
1.318
1.016
1.769
2.063
1.480
1.383
1.483
3.559
-1.38
1.15
2.39
-1.01
-2.29
-0.72
-1.88
-0.15
0.10
-0.46
1.01
0.36
0.33
-0.16
0.96
0.57
0.98
1.32
0.40
0.75
0.44
0.60
1.17
-0.09
-3.64
0.08
0.46
0.42
-0.28
0.60
0.30
-2.05
0.05
0.08
-2.01
1.75
0.167
0.251
0.017
0.312
0.022
0.470
0.060
0.882
0.923
0.646
0.313
0.722
0.739
0.872
0.336
0.565
0.329
0.186
0.692
0.455
0.662
0.547
0.242
0.925
0.000
0.932
0.643
0.672
0.781
0.546
0.764
0.041
0.964
0.934
0.045
0.081
-2.725
-0.048
0.028
-0.002
-3.250
-0.413
-2.300
-1.306
-1.127
-1.389
-0.415
-0.872
-0.403
-1.241
-0.738
-1.044
-0.836
-0.463
-2.727
-2.303
-3.048
-1.607
-1.062
-3.214
-12.223
-2.648
-2.717
-3.652
-2.950
-1.378
-2.937
-8.265
-2.834
-2.595
-5.886
-0.756
0.471
0.186
0.281
0.001
-0.250
0.190
0.047
1.121
1.243
0.862
1.296
1.259
0.568
1.052
2.161
1.910
2.493
2.382
4.111
5.143
4.794
3.032
4.208
2.921
-3.672
2.887
4.398
5.665
2.216
2.604
3.998
-0.178
2.969
2.826
-0.073
13.194
0.738
0.328
0.735
-1.117
0.954
0.595
0.645
0.725
0.530
-2.292
0.243
0.188
0.213
0.574
0.385
0.204
0.186
0.388
0.171
0.304
3.04
1.74
3.45
-1.94
2.48
2.92
3.46
1.87
3.10
-7.53
0.002
0.081
0.001
0.052
0.013
0.003
0.001
0.062
0.002
0.000
0.262
-0.040
0.317
-2.242
0.199
0.196
0.279
-0.036
0.195
-2.888
1.214
0.697
1.152
0.009
1.708
0.994
1.010
1.486
0.865
-1.695
Lambda (ÿ)
0.777
0.939
0.83
0.408
-1.064
2.618
Rho (ÿ)
Sigma (ÿ)
Lambda (ÿ)
0.179
4.343
0.777
0.939
BIRCHSCORE
FIRMAGE
FIRMAGE^2
EMPLOYEES
EMPLOYEES^2
MALE
YRSSCHOOL
MANAGER
SAMESECTOR
SOCMARG
BUSINESSB4
OTHERFIRM
R&D
TECHSOPH
WORKTRAIN
INCCO
PTRSHP
SHROP
BUCKS
SICCODE22
SICCODE28
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE52
SICCODE55
SICCODE60
SICCODE66
SICCODE72
SICCODE74
SICCODE80
SICCODE85
SICCODE92
SICCODE93
LIQCON
INTERCEPT
LIQCON
TDOWN
SOCMARG
SHROP
SICCODE55
HIGHLAB
SHORTDMAND
BANKNOW
OTHERFIN
PROF98
INTERCEPT
HAZARD
29
Nevertheless, the overall positive effect of size indicates the benevolent influence
of scale economy mechanisms. (The positive coefficient arises as a result of larger firms
adding, in absolute terms, more employees; so that the absolute measure dominates
potentially lower rates of growth to give a higher Birch score.) Moving on, the gender
effect is significant and negative: male entrepreneurs grew less, compared to female
entrepreneurs. It may be that men tend to own larger businesses in the first place and so
grow proportionately less.
Looking at sectors, SICCODE52 (retail trade) and
SICCODE85 have a significant negative effect on employee growth. Previous managerial
experience had a negative impact on four year growth in our sample. This could be a size
effect similar to that for males. As a manager the individual enjoys a higher wage.
Assume that switching into entrepreneurship requires at least the same wage, and that
income is generally related to size or the number of people managed. This implies that a
manager who switches is taking charge of a relatively large start-up and so is likely to
grow proportionately less.
Tables 7 and 8 indicate that OLS estimation is free of sample selection bias. Given the
significance of LIQCON in Table 8, we re-estimated the four year Birch score by OLS;
the results are presented in Table 9. The diagnostics however, indicated the presence of
heteroskedasticity and so OLS was employed in conjunction with robust (White) standard
errors. Only three variables are significant at the 5% level: EMPLOYEES, SICCODE85
and LIQCON. In this specification SICCODE85 has a slightly stronger effect as the
coefficient is now -5.047 compared to -4.221. LIQCON is marginally weaker at -2.502 cf
(-2.979), while EMPLOYEES is virtually unchanged at 0.156. Qualitatively though, the
effects are the same as in Table 8: growth was increasing in size while constraints exerted
a negative influence over four years. The insignificance of firm age rejects the hypothesis
of growth being a deterministic process. Younger firms do not appear to grow more than
30
older firms. In contrast, by the one year measure growth is falling in age up to around
eight, consistent with a learning framework. With regard to H2 therefore, the evidence is
not conclusive. This does not however swing the argument in favour of the stochastic
argument. The significance of size in all models with qualitatively identical effects means
we cannot reject H3a. A marked difference however is that growth is increasing in size,
rejecting H3b. This indicates scale economies do exist and facilitate growth by lowering
product costs.
We hypothesise that the non-diminishing effect arises because the
timescale is too short for producers to establish their true cost and reach their MES.
Interestingly in Table 9, new product introduction (NEWPROD) is significant at the 10%
level with a positive effect. There is no such effect in Table 8. Table 7 indicates a
negative effect of R&D over the shorter term, significant at the 10% level; but Table 8
indicates no such effect over the longer term. Overall, there is little support for H4 and
certainly, at the 5% level of significance we cannot reject H4. Table 9 confirms the
negative influence of constraints over the four year period found in Table 8. Meanwhile,
Table 7 indicates no significant effect over the shorter term (one year). It is hard therefore
to say whether H1 can be rejected or not. For the one year period it can, with a constraint
having no short term effect whatsoever. For the four year period it cannot be rejected and
this indirectly lends support to Brito and Mello (1995), where the removal of a constraint
increases growth. We could say that the results regarding H1 are inconclusive, however
we feel they indicate the influence of constraints extends over four years (consistent with a
persistent, adverse effect) but are not observable over one year due to the lumpiness of job
change (growth?). The distribution of hiring patterns is not significantly different for
constrained and unconstrained firms over one year, but is over four years.
31
Table 9: Regression with robust standard errors. DV = Birch score over four years.
F( 34, 272)
Prob > F
R-squared
Root MSE
N
1.92
0.0024
0.2159
5.1933
307
Robust
Coefficient
Robust
Std. Error
t
P>|t|
95% confidence interval
0.649
-0.086
0.156
-0.001
-1.021
-1.072
-0.653
-0.796
1.116
0.392
-0.030
-0.102
0.274
1.013
0.620
1.595
2.232
1.614
1.182
0.566
0.498
1.576
0.237
-4.536
-0.089
0.671
0.235
-0.390
-0.419
0.423
-5.047
-1.327
-0.399
-2.502
-0.357
1.163
0.105
0.065
0.001
0.955
0.616
0.657
0.605
0.579
0.626
0.018
0.075
0.444
1.026
0.621
0.935
1.536
1.028
1.177
1.132
1.027
0.976
1.247
5.521
0.831
1.092
1.224
1.299
1.105
0.996
2.053
1.034
0.963
0.971
3.669
0.56
-0.82
2.41
-1.07
-1.07
-1.74
-0.99
-1.32
1.93
0.63
-1.68
-1.36
0.62
0.99
1.00
1.70
1.45
1.57
1.00
0.50
0.49
1.62
0.19
-0.82
-0.11
0.61
0.19
-0.30
-0.38
0.42
-2.46
-1.28
-0.41
-2.58
-0.10
0.577
0.415
0.017
0.286
0.286
0.083
0.321
0.190
0.055
0.532
0.093
0.175
0.538
0.325
0.319
0.089
0.147
0.118
0.316
0.617
0.628
0.107
0.849
0.412
0.915
0.539
0.848
0.764
0.705
0.672
0.015
0.200
0.679
0.010
0.923
-1.641
-0.292
0.029
-0.002
-2.902
-2.285
-1.947
-1.987
-0.024
-0.841
-0.065
-0.249
-0.601
-1.008
-0.602
-0.247
-0.792
-0.411
-1.135
-1.662
-1.523
-0.345
-2.218
-15.405
-1.724
-1.479
-2.174
-2.947
-2.595
-1.538
-9.089
-3.362
-2.296
-4.412
-7.580
BIRCH4
FIRMAGE
FIRMAGE^2
EMPLOYEES
EMPLOYEES^2
MALE
MANAGER
SAMESECTOR
BUSINESSB4
NEWPROD
R&D
Q45cb
Q45db
Q59a
INCCO
PTRSHP
SHROP
BUCKS
SICCODE22
SICCODE28
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE52
SICCODE55
SICCODE60
SICCODE66
SICCODE72
SICCODE74
SICCODE80
SICCODE85
SICCODE92
SICCODE93
LIQCON
INTERCEPT
2.940
0.121
0.283
0.001
0.860
0.142
0.641
0.395
2.256
1.625
0.005
0.045
1.148
3.033
1.842
3.436
5.255
3.638
3.500
2.795
2.520
3.497
2.693
6.333
1.546
2.822
2.644
2.167
1.757
2.383
-1.005
0.708
1.498
-0.591
6.867
Diagnostics:
Heteroskedasticity:
ÿ2(1)
Prob > ÿ2(1)
377.28
0.000
Using Cook-Weisberg test; H0: Constant Variance
32
These results are consistent with the learning hypothesis and we cannot say younger firms
grow more/ faster. The liquidity constraint results indirectly support Brito and Mello
(1995) and so provide for some sort of learning mechanism. Furthermore, the significance
age suggests Gibrat’s law does not hold and that growth is not stochastic. Constraints do
matter and hinder growth over the longer term, in contrast to our earlier results. But,
consistent with our earlier results, R&D and new product introduction do not influence
growth.
33
6. CONCLUSION
Many theories exist on why a firm should grow, operating mainly through firm age
and size.
Dynamic economies of scale suggest both stochastic and deterministic
processes. Jovanovic’s model seems to combine these with firms learning their true but
randomly assigned cost. Stochastic models predict no effect of age or size on growth.
Regression results indicate that across our whole sample the entrepreneur’s human
capital was insignificant.
For relative growth, age and size have a decreasing then
increasing effect. We take this as evidence of learning in the early years diminishing in
effect to be replaced by Brito’s et al process. We conclude also, for the sample, legal
status matters and incorporated firms grow faster. For absolute growth, Gibrat’s law holds
but the bank learning model is not supported. A stochastic process is therefore possible.
Initial constraints are unimportant because their effect is transferred onto size and
capitalisation. Measures taken to improve productivity and delivery of service help firms
grow by maintaining a competitive advantage. Hence, more technologically firms grow
faster as do those who provide training. Banks are very important for advice due to their
vast experience and position as lender.
For future research, a natural extension is the use of follow-up data to analyse
survival and compare influences with those for growth. Furthermore, for policy a fruitful
field would be to look at the trade-off between growth and survival. This would help
policy pick firms that survive and grow, and not one or the other. With regard to
Becchetti et al it would be good to know if rationed firms meet their funding from other
sources or are constrained at birth. This could help extricate ability and its effect. For any
of these, more quantitative variables would be useful, especially with regard to financing.
34
APPENDIX 1: VARIABLE LIST
Entrepreneur variables:
MALE
DEGREE
ALEVEL
PROFESS
0/1
0/1
0/1
0/1
NVQ
0/1
OLEVEL
0/1
FOUNDAGE
UNEMP
integer
0/1
BUSINESSB4
0/1
MANAGER
SAMESECTOR
SOCMARG
0/1
0/1
0/1
PRETRAIN
0/1
PORTFOLIO
PRIORSIZE
integer
integer
founder’s gender. 1=Male.
=1 if founder’s highest qualification is a degree.
=1 if founder’s highest qualification is HNC/HND /A-level.
=1 if founder’s highest qualification is a professional
qualification.
=1 if founder’s highest qualification is a vocational
qualification.
=1 if founder’s highest qualification is O-level, or other.
founder has no qualification if all above =0.
age of founder when business started.
was founder unemployed immediately before start-up?
1=Yes.
did the founder have previous entrepreneurial experience.
1=Yes.
=1 if founder a manager in his/her last job?
=1 if founder’s last job in same sector as start-up?
social marginality measure. =1 if founder born and bred in
the county?
=1 if founder had business start-up training before starting
up?
number of other firms owned by founder.
how many people did founder’s last employer employ?
Firm variables:
FIRMAGE
FIRMSIZE
Location: BUCKS
SHROP
integer
0/1
0/1
Legal form: INCCO
PTRSHP
0/1
0/1
Sector: SICCODE22
SICCODE29
SICCODE45
SICCODE50
SICCODE51
SICCODE55
SICCODE72
SICCODE74
0/1
0/1
0/1
0/1
0/1
0/1
0/1
0/1
SICCODE92
SICCODE93
0/1
0/1
age of firm since original opening.
number of fulltime equivalent employees in 2000.
=1 if firm located in Buckinghamshire.
=1 if firm located in Shropshire.
Tees Valley when BUCKS=0 and SHROP=0.
=1 if firm incorporated company.
=1 if firm a partnership.
sole proprietor when LIMCO=0 and PTRSHP=0.
=1 if firm in publishing, printing etc
=1 if firm manufactures machinery and equipment.
=1 if firm in construction.
=1 if firm sells, maintains, repairs motor vehicles.
=1 if firm in wholesale trade & commission trade.
=1 if firm hotel or restaurant.
=1 if firm in computer and related activities.
=1 if firm in ‘other business activities’: legal, accountancy,
consulting etc.
=1 if firm in recreational, cultural & sporting activities.
=1 if firm in ‘other service activities’.
firm in other sector where all of the above =0.
35
APPENDIX 1: VARIABLE LIST (Cont)
Strategy variables:
WORKTRAIN
TECHSOPH
EXPORTS
PREACCT
PREBANK
PRESOLIC
R&D
SALES
PROFIT
CUSTCONC
HIGHLAB
0/1
integer
(0,5)
0/1
0/1
integer
(1,5)
%
0/1
0/1
0/1
0/1
0/1
0/1
%
0/1
SHORTDEMAND
0/1
PLANNING
NEWPROD
COMPET
=1 if firm provides formal training for workers.
index based score as measure of firm’s technological
sophistication. 0=lowest, 6=highest.
=1 if firm had business plan before start-up.
=1 if firm introduced new product since starting.
sector average score for founders’ perceptions of level of
competition in sector. 1=benign, 5=very hostile.
percentage of sales to outside UK.
=1 if founder used accountant before start-up.
=1 if founder used bank before start-up.
=1 if founder used solicitor before start-up.
=1 if firm conducts R & D.
=1 if firm a sales maximiser.
=1 if firm a profit maximiser.
percentage of sales to two biggest customers.
=1 if founder had a problem with high labour turnover in the
first year
=1 if founder had a problem with shortage of demand in the
first year
Finance variables:
INTERFIN
0/1
EXTERFIN
STATEFIN
LIQCON
TDOWN
DISCOUR
EQUITY
BANKNOW
OTHERFIN
PROF98
0/1
0/1
0/1
0/1
0/1
0/1
0/1
0/1
0/1
=1 if firm started up with finance from personal savings,
friends and family or house mortgage.
=1 if starts up with loan from bank or finance company.
=1 if firm started up with finance from local authorities.
=1 if firm is constrained in first year.
=1 if founder ever turned down for finance.
=1 if founder a discouraged borrower.
=1 if issued share capital in the last year.
=1 if firm has bank finance now.
=1 if firm has otherwise uncategorised finance now.
=1 if firm made a profit in 1998
Interaction terms:
AGELIQ
= FOUNDAGE*LIQCON
36
APPENDIX 2: VARIABLE DERIVATIONS
•
TECHSOPH score derived from the following two questions:
Do you:
Index score (this author’s creation)
use e-mail for communication with customers and/or0
suppliers?
1
have a website?
2
use web site for selling on-line?
3
procure inputs on-line?
4
have a fully integrated electronic business?
Subsequent definitions are considered to be technologically more sophisticated. Hence an affirmative
response scores more as we proceed down the list.
To this score an extra point was added if the firm kept its records on computer. The question asked was:
In what form are your business records kept?
Hand-written?
Computer?
Other?
So the TECHSOPH score lies between zero and five.
•
SALES and PROFIT dummies derived from the following question:
From the following list,
Understanding the customer
Lower prices
Novelty of product/ service
Low cost base
Skills of our workforce
Owners managerial skills
Better administrative organisation and procedures
Locational advantages
the founder was asked:
Which two are most important for the competitiveness of your business?
If the firm selected one and two it was considered a sales maximiser. If it chose any combination of three,
four and five it was designated a profit maximiser. A zero value for both represents an alternative
combination. Assuming the market is competitive, the firm will struggle to raise prices and so a profit
maximiser will attempt to increase its profit margin by lowering costs, or by increasing the premium
consumers pay by creating a unique or high quality product. Sales maximisers are assumed to want to sell
as many units as possible to as many customers, and are assumed to be prepared to lower prices to achieve
this. In understanding the customer, the firm is attempting to sell more units by appealing to the customers’
desires and sensibilities.
37
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