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8/10/2019 Determinants of Firm Start-Up Size
1/13
Determinants of Firm Start-up Size: An Application of Quantile Regression for IrelandAuthor(s): Holger Grg, Eric Strobl and Frances RuaneSource: Small Business Economics, Vol. 14, No. 3 (May, 2000), pp. 211-222Published by: SpringerStable URL: http://www.jstor.org/stable/40229076.
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2/13
Determinantsf
Firm
tart-Up
Size:
An
Application
f
Quantile
egression
or reland
Holger
Gorg
Eric
Strobl
Frances
Ruane
ABSTRACT.
In
this
paper
we
provide empirical
evidence
on
the determinants f
firm
tart-up
ize
using
data
for the
manufacturing
ector
n
Ireland,
nd
compare
ur resultswith
recent
indings
or
Portuguese
manufacturing
ndustries
Mata
and
Machado,
1996).
To allow
for
irm
eterogeneity
etween
firm ntrants
we use
quantile
regression
echniques
for our
empirical
stimation.
We find hat
he determinantsf
start-
up
size differ
n their
mportance
or small
and
large-scale
entrants.
n
particular,
ndustry
ize and
ndustry rowth
eem
to affectarge-scale ntrantsnly.
1. Introduction
The
entry
fnew
firms
ntomarketsas
attracted
considerable
nterest
n the heoretical
nd
empir-
ical literature
n industrial
conomics.
ot east
since
chumpeter's
1934)
work ave
conomists
recognised
he
mportance
f
new firms
or he
constant
volution
nd
renewal f
ndustries.
n
recent
ears,
large
body
f
empirical
iterature
has
appeared,
nalysing
ainly
he
determinants
of
entry
Acs
and
Audretsch,
989a, 1989b;
Audretschnd
Acs, 1994;
Cable and
Schwalbach,
1991; Mata,
1993;
Wagner,
1994a)
and the
subsequent erformance
nd life
duration
f
new entrants
Audretsch,
991;
Audretschnd
Mahmood, 995;
Boeri
nd
Bellmann, 995;
Mata
and
Portugal,
994;
Wagner,
992,
1994b;Weiss,
1998).1
n ssue hat
asreceivedmuch
essatten-
tion s thestart-upize of firms,venthough
studies f the ifedurationf firms
cknowledge
that he ize offirmst
entry
s an
mportant
eter-
minantf
a
firm's
robability
f
survival.
Therehas beenone recent
xception,
amely,
the
study y
Mata and
Machado
1996)
which
examines
hedeterminantsf firm
tart-up
ize
using empirical
ata for
Portugal.
heir data
source
s an annual
survey
onducted
y
the
Portuguese
inistry
f
Employment
hich overs
all
manufacturing
irms
mploying
or more
employees.
he
sample
used consists f
1,079
manufacturingirmsorwhich atawere vailable
for
1984.
Mata and Machado use
regression
quantile
RQ)
estimation
echniques
o
nalyse
he
determinants
ffirm
tart-up
ize.
They
rgue
nd
provide
evidence
that the
RQ
estimator an
provide
more ccurate
nformationn thedeter-
minants
f
start-up
ize
than he
ommonly
sed
OLS
regression
odels,
which
nly
stimates
single
measure f
the entral
endency
fthe
ize
distribution.
In
this
aper
we extend
he
pproach eveloped
by
Mata ndMachado
1996)
to obtain
dditional
empiricalvidence nfirmtart-upizeusing ata
for he
manufacturing
ector
n
Ireland,
nother
small
pen
conomy
t the
periphery
f theEU.2
Our
paper
dditionally
erves
s an extensionf
previous
ork
Gorg
nd
Strobl,
999)
where
we
analyse
he
determinantsf firm
ntry
nto rish
Final version
ccepted
on
February
,
2000
Holger
Gorg
School
of
Public
Policy,
Economics
& Law
University
f
Ulster
t Jordanstown
Newtownabbey
T37
OQB
Northern
reland
Eric Strobl
Department
f
Economics
University
f
the
West
ndies
St.
Augustine
Republic
of
Trinidad
nd
Tobago
and
Frances
Ruane
Department
f
Economics
Trinity ollege
Dublin
2
Republic
of
reland
M|
Small
Business
Economics
14:
21
1-222,
2000.
rT
2000 Kluwer
Academic
Publishers.
Printed
n the Netherlands.
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3/13
212
HolgerGorg
t al.
manufacturing
ndustries
here
ntry
s defined
n
termsf
firm
umbers
nly.
erewe also take nto
consideration
he
ize of
new
entrants
hich,
s
pointed
ut
bove,
s
recognised
o have
mplica-
tions or irmerformancendfirmurvival.
The
paper
s
structured
s follows. ection
discusses
hedeterminantsf firm
tart-up
ize.
Section
presents
he conometric
ethodology
used to estimate he
mpirical
model,
iscussing
the
dvantages
f
RQ
estimation,
hile ection
introduceshe data for he rish
manufacturing
sector. ection
presents
he conometric
esults
and
compares
he
findings
or
reland
with hose
obtained
y
Mata and Machado
1996)
for he
Portuguese anufacturing
ector.
inally,
ection
6 summarisesur esults nd
presentsoncluding
comments.
2.
Determinants
f firm
tart-up
ize
The
determinantsf a firm's
tart-up
ize have
beendiscussed
xtensivelyy
Mata ndMachado
(1996),
who
suggest
number f
ndustry
har-
acteristicshat
may
mpact pon
firm's hoice
of nitial
ize.
Following
heir
nalysis,
e
postu-
late the
following mpirical
model
of the rela-
tionship
etween he
start-up
ize of firm
/,
Sin
measuredn
terms f
employment
ize,
and
severalndustryharacteristics:3
Sit
=
p0
+
^MES,
+
feUBj,
+
VJNDJt
flJURj, $5GROjt
(36A
+
et,
(1)
where
MESjt
represents
he minimum
fficient
scale n
ndustry
attime
,
UBjt
s the
ercentage
of
employment
mployed
n
firms ith ess than
MES
(i.e.,
operating
t
suboptimal
cale),
NDjt
s
the
og
of the
ndustry
ize,
TURjt
enotes urbu-
lence n
ndustry
and
GROjt
enotes he
growth
rate f
ndustry
. Dt
s a time
ummy
o
control
for
ime-specific
ffects,
uchas
changes
n
the
macroeconomicnvironmentver ime,ndejt s
the
emaining
hite oise
rror erm.
MESjt
is
measured
s the
log
of
average
employment
ize.4As
Mata nd
Machado
uggest,
it
seems
reasonable
o
assume
that,
he
higher
MES in
an
ndustry,
he
arger,
n
average,
ill
be
new
tart-ups
n
order o
be able
to
compete
ffec-
tively
n
the
market.
We
would,
herefore,
xpect
a
positive
elationship
etween
he ize
of
ntrants
and
the
MES.
SUBjt
s
a measure
f
he
roportion
f
mploy-
ment
n firms
perating
t ess
than
minimum
ffi-
cient
cale, .e.,
at
suboptimal
cale.
As
such,
t
provides
n
indirect
measure f
thecost
disad-
vantage uchfirms aveto face n the ndustry.
All other
hings qual,
the
arger
he
proportion
of
firms
perating
t
suboptimal
cale,
he ower
seems
o be the
ost
disadvantage
o such
firms
and,
hence,
he ower
may
be
the
tart-up
ize a
new
ntrant
illchoose.
The size of the
ndustry,
NDjt
s
measured
s
the
og
of total
mployment
n the
ndustry.
he
rationale
or
ncluding
hisvariable s
that,
he
larger
he
ndustry
for
given
MES),
the
arger
will
be
the ize of
new
ntrants,
s the
robability
of
retaliation
rom
ncumbents
s
ikely
obe
ower
in a large hann a smallmarket. lso,a large
marketllows
he ntrant
o
set
relativelyarger
scale of
output
han
n a small
market,
epre-
senting higher
market
otential
o
the ntrant.
TURjt
s measured
s the
product
f
employ-
ment hares
n firms hat
nter r
exist
ndustry
j.5
Turbulence
rovides
us
with an
indirect
measure f
sunk
osts,
s
a
high
ate
f
simulta-
neous
ntry
nd exit
n an
industry
an be
taken
as evidence
f low
sunkcosts.
Assuming
hat
entrantsrerisk
verse,
ne
may xpect
hat,
he
lower re
sunk
osts,
he
higher
ill
be
the tart-
up size of new entrantss the osses associated
with
possible
ailure
re ower.
The
growth
ate
f the
ndustry,
ROjt
s
cal-
culated s the
difference,
n
natural
ogs,
between
the evelof
employment
n the
ndustry
n
subse-
quent
years.
n
a fast
rowingndustry,
he
prob-
ability
f firm
urviving
s
higher
han
n a slow
growing
or
declining)
ndustry
s incumbents
may
be less
likely
o retaliate
n
a fast
growing
market. his
implies
hat
irms
may
choose
to
enter t a
larger
ize
n
fast
rowing
arkets,
ue
to the
higher robability
f success.
3.
Econometric
methodology
We estimate heabove model
usingRegression
Quantile
RQ)
estimation
echniques.6
ata and
Machado
1996)
point
o a numberf
advantages
in
using
he
RQ
estimatornstead
f
tandardeast
square egression
odels n
examining
hedeter-
minantsf
tart-up
ize.For
one,
he
RQ
estimator
allows one to
investigate
ifferentonditional
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4/13
Determinants
f
Firm
tart-Up
ize
213
distributionsather han
focusing
n a
single
tendency
easure,
uch s
themean
n
the east
square egression
odels,
nd
thus
may
provide
further
nformationn the distribution
f firm
start-upize.Secondly,talso allowsone to take
account f
possible
eterogeneity
cross
irm
izes
thats not
aptured
y
ndustry
evel covariates.
If,
for
example,
tart-up
ize reflects o some
degree
ccess o
funds,
hen he ffectf heMES
in a
particular
ndustry
ay
e different
or mall
relative
o
arge
irms.
n
a similar
ote,
he vail-
able
industrial reakdown
may
not be detailed
enough
o allow
thedistinction
etween
nterme-
diate
uppliers
nd direct
ompetitors
ithin n
industry;
he ffect f
the ovariates
n
start-up
size
s
likely
o
differt
east omewhat
or hese
twogroups ffirms.
Thirdly,
he
authors lso
point
out that he
least
quare
stimators
an
be sensitive
o even
modest
eviations
f he
esiduals
rom
ormality,
whereas
he
RQ
is
robust o
such.
Finally,
nder
the
ssumption
hat he
distribution
f
firm
ize
was
approximately
ognormal,
standard
ractise
in
the
iteraturen
firm ize
has beento
use the
logarithmic
ransformation
f
the
dependent
variable.
f,
however,
hedistribution
s
actually
not
ognormal,
hen
heOLS
estimator
ay
not e
optimal
iven
hat
t s
only quivariant
o
inear
transformationsfthedependentariablenesti-
mation.
n
contrast,
he
RQ
estimator
s
equi-
variant
o
both
monotonic
inear nd
non-linear
transformations
f the
dependent
ariable.
4.
Data
Our
data source
s
an annual
mployment
anel
survey
arried
ut for
the
rish
manufacturing
sector
ince 1973
by
Forfas,
he
policy
and
advisory
oard
for industrial
evelopment
n
Ireland.
t covers
ll known
ctive
manufacturing
companies. heresponse ate othis urvey as
on
average
een
xtremelyigh,
enerally
ver 9
per
ent.
heunit f
observation
s the
ndividual
plant,
or
which
he
umber
f
permanent
ull-time
employees
s
reported.
ach
plant
s,
amongst
other
hings,
dentified
y unique
lant
umber,
year
f
start-up
nd
ts 4-to-5
igit
NACE
code
sector
of
location.
These
identifiers
re
only
changed
f here
s an actual
hange
f
ownership.
In
order
o
make ur
ample
omparable
o the
sample
or
ortugal
sed
by
Mata
and
Machado
(1996)
we exclude
firmswith
less than
5
employees.
his eaves s with
,603
observations
on firm ntrantsn the rish
manufacturing
ector
for he eriod 973-1996. hesummarytatistics
presented
n
Table show hatmean irm
tart-up
size for ur
ample
s at
roughly
9
employees,
although
he
high
tandardeviation
mplies
hat
there
s
a
large pread
f sizes
around hismean.
This
average
s
slightly igher
han he mean
for the
Portuguese ample
used
by
Mata and
Machado,
where hemean
tart-up
ize stands t
approximately
7
employees.
he
coefficient
or
skewnessn Table indicateshat he
distribution
of
firm
tart-up
izes s
highly ight
kewedwhich
is
also shown
by
the
result hat
he median f
10 is far ess than he arithmetic) ean ize of
19
employees.
his can be
compared
with
he
Portuguese
ata,
which how a
coefficientf
skewness
f 6.55 and
a median
ize of
10, .e.,
they
re
very
imilar
o the
figures
ound
or he
Irishdata.
The maximum
irm
ize, however,is
higher
or he
rish
557)
than or he
Portuguese
sample
335)
of
Mataand Machado.7
The distributionf
firm
tart-up
ize
n
reland
is illustrated
n
Figure
,
which hows he
high
clustering
f sizes
n the
ow
size
classes,
round
5-7
employees.
s
pointed
ut
above,
standard
OLS estimationechniquesan be sensitive o
evenmodest
eviations
fthe esiduals rom or-
mality,
hereas
he
RQ
is robust o
such
devia-
tions.
igure suggests
hat
he
irm
tart-up
ize
distribution
oes not onform
o a normal istri-
bution,
nd a formal
est or
normality
ased on
TABLE
I
Summary
tatistics
or
irm
tart-up
ize
Irish
ample Portugueseample3
Observations 4,603 1,079
Mean
ize
19.13
17.21
Standard eviation
30.89
25.59
Minimum
5
5
Median
10 10
Maximum
557
335
Skewness
6.88
6.55
Kurtosis
75.32
61.13
a
Data for
Portuguese
ample reprinted
rom
Mata and
Machado
1996),
Table
I,
with
permission
rom lsevier
Science.
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5/13
214
HolgerGorg
t
al.
Figure
1
Distribution f
firm
tart-up
ize in Ireland.
1
-
.8
~
8/10/2019 Determinants of Firm Start-Up Size
6/13
Determinants
f
Firm tart-
p
Size
215
TABLE II
Quantile
egression
esults or rish
ample3
Quantiles
OLS
0.15
0.25
0.5
0.75
0.9
MES 9.122 0.354 0.621 2.163 6.656 19.373
(12.659)***
(6.839)***
(6.296)*** (11.529)***
(12.445)***
(15.581)***
Suboptimal
cale
-81.072 -4.442
-7.905
-25.795
-65.013
-141.217
(-7.119)*** (-5.495)***
(-5.054)***
(-8.706)***
(-7.456)***
(-6.786)***
Industry
ize
-0.203 0.042
-0.005
-0.135
-0.280
-1.827
(-0.369)
(1.027)
(-0.072)
(-0.943)
(-0.699)
(-2.126)***
Turbulence
1528.410
50.736 110.344
470.144
1834.417
4512.668
(7.904)*** (3.211)***
(3.904)*** (9.315)***
(13.749)***
(16.540)***
Industryrowth
37.110 0.444
0.908
2.179
14.568
32.412
(7.472)*** (1.205)
(1.360)
(1.686)*
(3.710)***
(3.442)***
Constant 2.596 4.680
5.875
8.082
13.875
20.179
(0.486) (11.882)*** (7.998)***
(5.803)***
(3.597)***
(2.441)**
Location stimates 19.128 5.586 6.533 10.455 20.098 40.944
R2
0.06 0.01 0.01
0.02
0.05
0.10
F(H0:p,
=
0)
12.28
7.09 5.63
12.33
14.05
18.72
a
t-statistics
n
parentheses. egressions
nclude
ime
dummies. sterisks enote
tatistical
ignificance
t 1
per
cent***,
5
percent**,
0
percent*
evel.
tendency
f the
data,
the
regression uantile
results
ive
a more
recise icture
f the
mpor-
tance f
he
xplanatory
ariables or he ifferent
quantiles. omparing
he esultsn
Table
I
for he
different
uantiles
hows hat he
magnitude
f he
coefficientshanges s we move longthe ize
distributionffirms.he oefficientsorMES
and
turbulence
re
higher
or he
higheruantiles
han
for ower
nes which
uggests
hat
hey
ecome
more
mportant
ariablesfor
arger tart-ups.
Suboptimal
cale
lso ncreases
n
economic)
ig-
nificance
n the
negative
irection,
s we move
o
higheruantiles.
n
other
ords,
uboptimal
cale
seems o be more f
a
negative
actor or
arger
than or maller
irms.hese esultsre lso
found
by
Mata
and
Machado,
s
Table II shows.
Industry
izedoesnot eem o
exert
ny mpact
onfirmtart-upize n relandn the stimations
of the 0.15-0.75
quantiles,
s the
statistically
insignificant
oefficients
ndicate.
nly
n
the
.9
quantile
do
we find a
statisticallyignificant
negative
ffectf
ndustry
ize,
a result
which s
contrary
o our
xpectations
s formulated
bove.
We would
have
expected positive
ffect f
industry
ize
on
start-up
ize,
as the
probability
of retaliation
f ncumbents
n a
larger
markets
likely
o be
lower han
n
a small
market.
The
growth
f the
ndustry
s,
forthe
rish
sample,
statistically
nsignificant
xplanatory
variable
n
the
0.15
and
0.25
quantiles
ut t is
statisticallyignificant
n
the
higher
uantiles.
he
magnitude
f he
oefficientsor his
ariable
lso
increasesnthehigheruantiles. ence, he tart-
up
size of
arge
ntrants
ppears
o be
positively
influenced
y growing
ndustry,
ut
his oes
not
seem to be the case for mall
sized
start-up
n
Ireland. his
may uggest
hat
articularly
arge
firms hoose to enter t a
larger
ize in
fast
growing
arkets
han
hey
would
therwise
ave,
due to the
higher
robability
f success n
a fast
growing
arket.
ontrary
oour
esult or
reland,
Mata and Machado
only
find
statistically
ig-
nificantffectf
ndustryrowth
n
firm
tart-up
size for he .15 and0.25
quantiles.
hus,
ndustry
growtheems o be a determinantffirmtart-up
only
or mall irms
n
Portuguese
anufacturing.
While
glance
t theresults
eported
or he
Irish
ample
hus
ar eems o show hat he izes
of thecoefficientsiffer etween
uantiles,
we
need otest hismore
igorously.
able
V
presents
the esults f
nterquantileange egressions,
.e.,
regressions
f thedifference
n
quantiles,
hich
allow us to examinewhetherhe
effects f the
variables re he ame tthe
espective
uantiles.9
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216
HolgerGorg
t
al.
TABLE
III
Quantile
egression
esults
or
ortuguese
ample*
Quantiles
OLS
0.15
0.25
0.5
0.75
0.9
MES 5.406 0.507 0.607 1.935 4.567 13.858
(4.978)***
(3.703)***
(2.926)***
(4.011)***
(4.260)***
(4.571)***
Suboptimal
cale
-16.353
-1.529
-2.205
-6.322
-14.448
-41.785
(-3.486)***
(-2.010)**
(-2.498)**
(-3.123)***
(-4.026)***
(-5.033)***
Industry
ize
1.201
0.244
0.150
0.366
0.577
1.014
(1.545)
(3.252)***
(1.138)
(1.367)
(0.979)
(0.926)
Turbulence
445.960
59.873
99.316
217.944
488.754
889.744
(3.253)***
(2.894)***
(2.660)***
(3.159)***
(3.799)***
(4.636)***
Industryrowth
12.901
2.713
3.985
5.592
9.234
7.710
(1.857)*
(3.133)***
(1.851)*
(1.381)
(1.238)
(0.460)
Constant
-12.938
1.579
2.905
-0.093
-3.585
-20.807
(-1.673)
(1.908)*
(2.220)**
(-0.036)
(-0.611)
(-2.054)**
Location stimates 17.205 5.855 6.674 10.294 18.164 34.097
a
t-statistics
n
parentheses.
sterisksenote
tatistical
ignificance
t
1
per
ent***,
percent**,
0
percent*
evel.
b
Reprinted
romMata
ndMachado
1996),
Table
I,
with
ermission
rom
lsevier cience.
TABLE IV
Interquantileange
egressions
or
omparison
f
quantiles3
0.25-0.15
0.5-0.25
0.75-0.5
0.9-0.75
MES
0.213
1.616
5.042
12.278
(3.133)***
(7.005)***
(10.245)***
(8.399)***
Suboptimal
cale
-3.595
-20.392
-42.203
-86.985
(-3.935)*** (-10.429)*** (-8.727)*** (-4.764)***
Industry
ize 0.013
-0.181
-0.623
-2.150
(0.250)
(-1.217)
(-1.628)
(-1.973)**
Turbulence 41.324
399.487
1484.488
3261.540
(1.336)
(4.510***
(7.126)***
(3.731)***
Industry
rowth
0.220
2.167
13.599
18.759
(0.329)
(1.646)*
(4.061)***
(2.654)***
a
t-statistics
n
parentheses.
sterisksenote tatistical
ignificance
t
1
per
ent***,
percent**,
0
percent*
evel.
The
results how hat he ffectsf theMES and
suboptimal
cale variables re different
n
the
compared uantiles. he effect fMES increases
while he ffect
f
suboptimal
ize also increases
in the
negative
irections we move
along
the
start-up
ize
distribution.he effectfturbulence
doesnot
ppear
obe differentn
the .15 and0.25
quantiles.
or
ndustry
ize and
growth
e
only
find
differentffectsn
comparisons
f
higher
quantiles.
We
also tested or
he
quality
f
coefficients
in
the
regressions
or he rish
ample
Table I)
and the
coefficients
or
the
Portuguese
ata
obtained
y
Mata and
Machado
Table II)
using
a t-test.10s TableV shows,we cannot eject he
null
hypothesis
f
equal
coefficients
n the rish
and
Portuguese
egressions
or he
MES
variable
in
the0.15
and 0.25
quantile,
hile
he tandard
t-test
s
only ignificant
t
the 10
per
cent
evel
of confidence
n themedian
egression.
he test
statisticsor ll
other
ariables,
owever,
llow
us
to
reject
he
null
hypothesis
or
he
respective
coefficients.
The differences
n the oefficients
btained
or
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8/13
Determinants
f
Firm tart-
p
Size
217
TABLE
V
Testfor
quality
etween
oefficients
n
regressions
or
reland nd
Portugal
0.15
0.25
0.5
0.75
0.9
MES -1.3592 0.1536 1.8653 16.0745 39.2947
Suboptimal
cale
-46.7265 -71.7314
-186.1268
-284.7227
-290.0135
Industry
ize -2.0442
-4.4835
-12.0260
-28.2085
-91.9272
Turbulence -36.6951 25.9882
336.2292
683.0881
54.4685
Industryrowth
-23.7468 -53.7934
-73.9580
77.2291
177.0546
the rish
ample
or he ifferent
uantilesuggest
thatt seems
rudent
o
analyse
ow ensitivehe
coefficients
re to the choice of the
respective
quantile.
o
nvestigate
his ssuewe estimatedhe
regressions
t each
quantile
etween .15 and
0.9
and
plotted
he oefficients
or hedifferent
ari-
ables nFigures -6. It s obvious hat he oeffi-
cients
o not
eemto be
overly
ensitive o the
choice
of
quantile,
s
the oefficients
re
gener-
ally ncreasing
in
absolute
alues)
ver
he
uan-
tiles
or ll
variables.
here re
light
luctuations,
however,
or
he
ndustry
ize
and
ndustryrowth
variables ut
hese o
not
ppear
o be so
grave
as to
cause
anymajor
oncerns
or
he stimation
results.
t is
noteworthy
hat
hese wovariables
are
he nesfor
whichwe
got
esults
hatwere ot
as
clear
ut s the
results or
heother ariables
in the stimation.
As pointed utabove, hedata usedbyMata
and
Machado
1996)
relate o
firmswith
five
or more
mployees
nd
we also excluded
irms
smaller
han
ive
mployees
rom
he rish
ample
to
compare
ur results
with hosefor
Portugal.
However,
inceour
data set
for reland
ncludes
all firms
ith ize
one or
morewe
are also able
to
nvestigate
he
eterminants
f
tart-up
ize for
all
firms,
ncluding
hose
with ess than
five
employees.
ur
data
set ncludes
,816
observa-
tions n
firms
ith
tart-up
ize
smaller
han ive
employees.
We
re-estimated
quation
1)
using
data for ll firmsn thedataset,theresults f
which
re
reported
n Table
VI.
The
most
triking
ifference
o theresults
n
Table
I is
that,
or he0.15
and 0.25
quantile,
the
coefficients
f all
explanatory
ariables
re
very
lose to
zero.
Thus,
even
though
he test
statistics
ndicate
hat he
oefficients
ppear
obe
statisticallyignificant
nthe stimations
or hese
quantiles,
t seems
to be
reasonable
o
say
that
they
re
economicallynsignificant
ue to their
extremely
mall ize. These
esults
may
e
due to
thefact hat
he
majority
f
small
ntrants
i.e.,
entrants ith ize
of ess than ive
mployees),
namely
,583,
enter t a size of
less than
hree
employees,
hile
,620
firms ad
a
start-up
ize
of one. This s also reflectedn
the
ocation sti-
mates,which ake n valuesof ess than wofor
the 0.15 and 0.25
quantiles.
hese
very
mall
firmsre
ikely
o be
self-employed
rofessionals
or
family
usinesses,
here he
hoice f
tart-up
size
may espond ifferently
o market
onditions
than he hoice
n
arger
irms.
n
particular,
hese
very
mall irms
ay
ot
espond
o
ndustry
har-
acteristics,
uch s the nes
aptured
n
our
mpir-
ical
model,
othe ame xtents
large
ntrantso.
The
coefficientsf
the
explanatory
ariables
in the
higher uantiles
re similar o the
results
in Table II in terms f statistical
ignificance,
althoughheyre smaller han he orresponding
results for firms
excluding
ess than five
employees.
his s not
urprising
s the
uantiles
take n lower aluesfor
he
ample ncluding
ll
firmshan or he
ample
xcluding
irms ithess
than ive
mployees,
s indicated
y
comparison
of ocation
stimates
n
Tables
VI
and
I.
6.
Summary
nd conclusions
The
tart-up
ize
or
nitial
ize)
of
firm
asbeen
found o
be an
important
eterminantf
a firm's
subsequenterformancendprospectsf urvival.
However,
hedeterminants
f
firm
tart-up
ize
have,
o thebestof our
knowledge,
ot ttracted
much
nterest
n
the
iterature,
ith he
xception
of a recent
aper
by
Mata and Machado
1996)
which
nalyses
ata
for he
Portuguese
anufac-
turing
ector.
n this
paper,
we
present
urther
empirical
videncento he
determinantsf start-
up
size,
using
data for
manufacturing
irms
n
Ireland ver
he
period
973-1996.
We
compare
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9/13
218
Holger
Gorg
t al.
Figure
2.
Coefficients
or
minimum
fficient cale.
I
i
I
20
-
|
15 /
|
10 /
|
5
^^^S
I
o
-L
-
,
, , , ,
-
,
,
,
, , ,
I
ddoooddddoooddodbdoodddo'cidoofdodo'oddo'oo
quantile
Figure
3.
Coefficients
or
uboptimal
cale.
o
I
, ,
, , , , i i
i , , ,
,
ft
r^
o>
_
*-
ninsoiromsoirnioso^ninNOirnioNOroiosoit-ninNO)
r>-
-
~-
t>->iN|
\|
^tNi
(Ortrtwnt^^^TtwiqiqinintpipiDipipsssNNOoooino);
d
o
o
o o
d^cr~~o-~L^o_cJ
ciddciocicidcicicicicicicicicicicicicidcicicicicii
-20
^^^v^^
-60
V^^^
j
|
-80
\
|
-100
V>^^
I
-120 \
I
-140
\
-160
-^
~
^
quantile
This content downloaded from 196.12.232.107 on Thu, 25 Dec 2014 06:55:09 AMAll use subject to JSTOR Terms and Conditions
http://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsphttp://www.jstor.org/page/info/about/policies/terms.jsp8/10/2019 Determinants of Firm Start-Up Size
10/13
Determinants
f
Firm tart-
p
Size
219
Figure
. Coefficients
or
ndustry
ize.
U.O "I
;
0
"^TT^V.s^r. , ^i^^7>si , ,/T>^^q. .., iry, y^ , ,..,,., j
r-^-^-cNCvicsjOicNcocococofO^^r'sr
>f^
N^^flr^iJTxo
iq
yb
ic
8/10/2019 Determinants of Firm Start-Up Size
11/13
220
Holger
Gorg
t al.
Figure
6.
Coefficients or
ndustry rowth.
30 /
|
25 /
I
20 I
\
1
15 /
|
10-
J
[
5 ./
|
or>-o>^cou^h*o)T-(ovor^o>^-cotoh-cy)^-(Oior.o)'r-cou5r^O)T-cooh-0)T-foior^o)i
rrT-(\jt\jNNv|nnnon^^^ti;iooioio)(p(pp(p
8/10/2019 Determinants of Firm Start-Up Size
12/13
Determinants
f
Firm
Start-
p
Size
221
our esults ith he
indings
btained
y
Mata nd
Machado
1996).
In
our
empirical nalysis
we take ccount f
firm
eterogeneity
etween
ntrants,.e.,
differ-
ences n the hoice f tart-upize,using uantile
regression
echniques.
e find hat hedetermi-
nants f
start-up
izefor he rish
manufacturing
firms
iffer
n
their
mportance
or small and
large-scale
ntrants.
he
size of
the mallest ew
entrants
oes not
appear
to be influenced
y
industry
ize and
ndustryrowth,
.e.,
factorshat
give
nformationbout
he ctual
ize and
growth
of
themarket
ntowhich he
firm nters.
lso,
while he ffects
f
minimumfficient
cale and
sunk
osts
are
significant
or mall
firm,
heir
impact
s
quite
mall
ompared
ith
arger
irms.
For he argestntrants,hemodel f he eter-
minantsf
tart-up
ize
s much
more onclusive.
Large
irms o
not
ppear
o enter
markets here
minimumfficient
s
low, .e.,
where conomies
of
cale re
negligible,
r
where unk osts
renot
important.
lso,
market
onditions,
iz.,
ndustry
size
and
growth
re
mportant
eterminants
aken
into onsiderations
y
arge
ntrants.
hileour
results
re
fairly
imilar
o the esults btained
y
Mata
nd
Machado
1996)
for
ortugal,
here re
significant
ifferences
articularly
ith
egard
o
the ffect
f
ndustry
ize
and
ndustryrowth
n
the hoice fstart-upize.
A natural
xtension
f he
resent
nalysis
s to
study
he
ffects
f
firm
tart-up
ize
on
firm
er-
formance
nd
firmurvival.
s
pointed
ut
n
the
Introduction,
heres
a
large
ody
f
iterature
hat
has studied
hese
elationships
sing
ata
for if-
ferentountries
ut,
o
the est
f
our
knowledge,
no
analysis
has been
undertaken
or
the rish
economy
hus
far.
While
such
an
analysis
s
beyond
he
scope
of the
present
aper,
t is an
issue
we
hope
o take
up
n future
esearch.
Acknowledgements
Part f
this
aper
was
written
hile
Holger
Gorg
was a
Visiting
esearcher
t
The
Policy
nstitute,
Trinity
ollege
Dublin.
We are
grateful
o
an
anonymous
eferee
or
helpful
omments.
Notes
1
See Geroski
(1991,
1995)
and
Caves
(1998)
for
concise
reviews of the
iterature
n firm
ntry.
2
A
comparison
of
Ireland and
Portugal
is of
particular
interestas both are designated objective 1 regions and
cohesion countrieswithin
he
European
Union.
3
Since
we want o
compare
our results
with he
findings
or
Portugal
we confine
ourselves to
using
the same
empirical
model as Mata and Machado
(1996).
4
Lyons
(1980)
suggests
an
alterantive
measure of
MES,
namely,
ne half of the
average
number f
workers
n
a firm
that,
n
average, perate
1
5
plants.
We
do nothave data avail-
able to calculate
such
a measure.
5
Even
though Beesley
and Hamilton
(1984)
originally
proposed measuring
urbulence s the sum of
entry
nd exit
in an
industry,
ata and Machado
(1996)
suggestmeasuring
it as the
product
f
entry
nd exit because the
product
will
only
attain
high
values if
entry
nd exit are both
mportant"
(p. 1311).6
The
RQ
estimatorwas
suggestedby
Koenker and Bassett
(1978),
Bassett and Koenker
1982).
7
This
may
be due to the fact that a
large proportion
f
manufacturing
irms
n Ireland re
foreign-owned
irms. ne
may expect
that
foreign-owned
irms,
which are
likely
to be
subsidiaries f
multinational
ompanies,
re
arger
han rish-
owned
firms
see
Ruane and
Gorg,
1996).
8
All
estimations
were
performed
n
Stata 6.0. The
regres-
sions
includetime
dummies,
he coefficients f which re not
reported
ut can
be obtainedfrom he authors
pon request.
9
For
example,
consider
he 0.15 and 0.25
quantile:
fio.25
=
fl0.25
^0.25*
8/10/2019 Determinants of Firm Start-Up Size
13/13
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