Determinants of Firm Start-Up Size

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



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.



5/13

214

HolgerGorg

t

al.

Figure

1

Distribution f

firm

tart-up

ize in Ireland.

1

-

.8

~


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


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



7/13

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



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


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



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



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


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-(ovorô>^-cotoh-cy)^-(Oior.o)'r-cou5rÔ)T-cooh-0)T-foiorô)i

rrT-(\jt\jNNv|nnnon^^^ti;iooioio)(p(pp(p


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*


13/13

222

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t al.

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M. E. and R. E.

Hamilton, 984,

Small Firms'

SeedbedRole and he

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Boeri,

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Post-Entry

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nternational

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Cable,

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Richard

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Documents

Determinants of Firm Start-Up Size