STB/95/051 ASSESSING THE DISTRIBUTIONPOWER Annexes OF NATIONAL INNOVATIONSYSTEMS

PILOT STUDY: THE NETHERLANDS

Drs. Pim den HertogDr. Theo J.A. RoelandtDr. Patricia BoekholtHendrien van der GaagwithDrs. Patrick van der HofstadHenri Huisman

Apeldoorn, December 1995

Contents

ANNEX I: STATISTICAL ANNEX

ANNEX II: CANADA LIST

ANNEX III: APELDOORN LIST

ANNEX IV: MEGA-CLUSTERS

ANNEX V: PARTICIPANTS NATIONAL EXPERT MEETING

ANNEX 1: STATISTICAL ANNEX

Figure 1: Conceptual framework

* The statistical annex is structured using the conceptual framework described insection 2.2. For that reason figure 2.1, representing the conceptual model, isincluded in this annex as well. Numbers of tables and figures refer to boxes andarrows given in this figure.

I-2

ANNEX II: CANADA LIST

This list was used in the beginning of the project to review available sources on thebasis of an indicative list of indicators provided by the OECD. Canada provided thefirst overview. In the mean time most participating countries have made up such a list.The list displayed here reflects the situation for the Netherlands.

II-1

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

I.S

tock

san

dflo

ws

ofkn

owle

dge

Thi

sca

tego

ryof

(mos

tly)

’sto

ck’

indi

cato

rsis

quite

wel

les

tabl

ishe

d(R

&D

stat

istic

s,pa

tent

s,sc

ient

ome-

tric

s,et

c.)

I.1L

ea

rnin

gth

rou

gh

tra

inin

g

I.1a

form

altr

aini

ngpr

ovid

edby

empl

oyer

s(in

tens

ity,

dist

ribut

ion

byty

peof

firm

/tech

nolo

gy)

Var

ious

sour

ces

avai

labl

e:D

utch

Cen

tral

Sta

tistic

alO

ffice

(CB

S),

CP

B/M

inne

,R

OA

,in

nova

tion-

surv

ey

Som

ead

ditio

nal

case

stud

ies

avai

labl

ee.

g.fr

omS

ER

,R

OA

,B

arte

ls.

Itis

expe

cted

that

I.1co

uld

bew

ell

cove

red

usin

gav

aila

ble

data

,ty

peof

empl

oym

ent

poss

ibly

isth

em

ost

diffi

cult

part

.I.1

bsc

ienc

ean

den

gine

erin

gpe

rson

nel

(tot

alna

tiona

lst

ock

and

brea

kdow

nby

sect

oran

dty

peof

empl

oym

ent)

Var

ious

wel

les

tabl

ishe

dso

urce

sav

aila

ble:

OE

CD

/ME

RIT

,U

NE

SC

O,

CB

S,

Min

istr

yof

Edu

catio

n,C

ultu

rean

dS

cien

ces

I.2L

ea

rnin

gb

yd

oin

g

I.2a

cum

ulat

ive

R&

Dex

pend

iture

s(d

istr

ibu-

tion

byty

peof

firm

/tech

nolo

gy)

OE

CD

/ME

RIT

,in

nova

tion-

surv

ey,

CB

S,

EC

-D

GX

IIID

istr

ibut

ion

byte

chno

logy

mig

htpr

ove

tobe

diffi

cult

I.3E

mb

od

ied

kno

wle

dg

e(i/o

-an

aly

sis)

I.3a

high

tech

capi

tal

and

inte

rmed

iate

inpu

ts(d

omes

tical

lypr

oduc

ed/a

cqui

red

from

outs

ide)

CB

SB

ased

onC

BS

-mat

eria

lw

ew

illbe

able

topr

ovid

ete

chno

logy

flow

mat

rices

I.3b

softw

are

(dom

estic

ally

prod

uced

/acq

uire

dfr

omou

tsid

e)C

BS

I.4D

ise

mb

od

ied

kno

wle

dg

e(c

itatio

na

na

lysi

s)

I.4a

pate

nts

(dom

estic

ally

prod

uced

/acq

uire

dfr

omou

tsid

e)O

EC

D/M

ER

IT,

CW

TS

/EP

O,

US

Pat

ent

Offi

ce,

CP

BS

ome

addi

tiona

lca

sest

udy

mat

eria

le.

g.B

ulth

uis/

NE

I/DN

B,

SE

R

II-2

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

I.4b

scie

ntifi

cpa

pers

(dom

estic

ally

prod

u-ce

d/ac

quire

dfr

omou

tsid

e)C

WT

S/v

ario

usin

tern

atio

nal

data

base

sA

join

tef

fort

may

pay

off

here

.

II.F

orm

sof

know

ledg

esh

arin

gan

dtr

ansf

er1

-6

:un

iver

sitie

s,re

sear

chin

stitu

tes

and

indu

stry

7-

11:

betw

een

supp

liers

and

user

s

II.1

Un

ive

rsity

-in

du

stry

rese

arc

hce

ntr

es

(UIR

C’s

)C

BS

,T

echn

olog

yF

ound

atio

n(S

TW

)A

lthou

ghso

me

stat

istic

sar

eav

aila

ble,

espe

cial

lyth

eC

BS

-dat

ase

emto

bepr

omis

ing,

Thi

sis

one

ofth

em

ost

prob

lem

atic

indi

cato

rsbe

caus

eof

defin

ition

alpr

oble

ms

(wha

tar

eex

actly

UIR

C,

can

we

incl

ude

RT

Os)

and

lack

ofde

taile

dda

tae.

g.to

cove

rII.

1c/d

Obt

aini

nga

syst

emat

icov

ervi

ewof

rele

vant

orga

nisa

tions

prov

esto

bequ

itedi

fficu

lt

II.1a

num

ber

and

tech

nolo

gica

lsp

ecia

lisat

ion

ofU

IRC

’s

II.1b

mag

nitu

deof

R&

Def

fort

asso

ciat

edw

ithU

IRC

’s

II.1c

num

ber

and

type

ofun

iver

sity

facu

lty,

re-

sear

chsc

ient

ists

and

stud

ents

asso

ciat

edw

ithU

IRC

activ

ities

II.1d

impo

rtan

ceof

educ

atio

nal

func

tions

ofU

IRC

’s(n

umbe

rof

stud

ents

affil

iate

dw

ithU

IRC

’sbe

ing

hire

dby

part

icip

atin

gfir

ms)

II-3

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

II.2

Brid

gin

gin

stitu

tion

s

II.2a

Typ

ean

dec

onom

icim

port

ance

ofbr

id-

ging

inst

itutio

nsIn

nova

tion-

surv

eyA

dditi

onal

lym

ainl

yqu

alita

tive

mat

eria

lis

avai

labl

ew

hich

need

tobe

obta

ined

from

agr

eat

man

yso

urce

s.

II.3

Pe

rso

nn

el

mo

bili

ty

II.3a

Rat

esof

mob

ility

from

univ

ersi

tyan

dre

sear

chin

stitu

tes

toen

terp

rise

sect

orN

ost

atis

tics

avai

labl

eO

nly

som

ein

dica

tions

from

case

stud

ies.

Thi

sis

one

ofth

ear

eas

for

whi

chfo

rth

eN

ethe

rland

sad

ditio

nal

data

gath

erin

gw

ould

bene

eded

.

II.3b

recr

uitm

ent

and

dest

inat

ions

ofun

iver

sity

pers

onne

lw

hen

leav

ing

univ

ersi

ty

II.3c

part

ial

mob

ility

(sab

batic

al?,

cont

ract

re-

sear

ch,

mix

ed-r

esea

rch

posi

tions

)A

poss

ibili

tyw

ould

beto

surv

eyun

iver

sitie

san

dre

sear

chin

stitu

tes,

but

itis

expe

cted

that

nost

atis

tics

will

beav

aila

ble

atsu

cha

dese

greg

ated

leve

l.

II.3d

spin

offs

(firm

scr

eate

dby

acad

emic

rese

arch

ers)

II.4

Re

vea

led

ad

van

tag

ein

tra

nsf

er

an

de

ng

ine

erin

gsc

ien

ces

II.4a

shar

eof

scie

ntifi

cpu

blic

atio

nsin

tran

sfer

scie

nce

CW

TS

usin

gva

rious

(inte

r)na

tiona

lda

taba

ses

II-4

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

II.5

Me

tho

ds

of

lea

rnin

gin

un

ive

rsity

-in

du

s-tr

yre

latio

ns:

imp

ort

an

ceo

ffo

llow

ing

sou

rce

so

rm

eth

od

sfo

rle

arn

ing

ab

ou

tre

sea

rch

con

du

cte

din

un

ive

rsiti

es

for

ab

usi

ne

ssfir

m

II.5a

univ

ersi

typu

blic

atio

ns(f

orbu

sine

ssfir

ms)

Som

ein

cide

ntal

inpu

tfr

omth

ein

nova

tion-

surv

ey,

Min

istr

yof

Edu

catio

n,C

ultu

rean

dS

cien

ces,

Alg

emen

eR

eken

kam

er(G

ener

alA

ccou

ntin

gO

ffice

),un

iver

sitie

san

dR

TO

slik

eT

NO

/GT

I’sas

wel

las

indi

catio

nsof

join

tR

&D

e.g.

invo

lvem

ent

inE

urop

ean

Re-

sear

chP

rogr

amm

es

The

sein

dica

tors

coul

don

lybe

cove

red

byus

ing

vario

usle

ssw

ell

esta

blis

hed

stat

istic

san

din

cide

ntal

case

stud

ies.

Mos

tof

the

rele

vant

orga

nisa

tions

have

not

colle

cted

thes

eso

rtof

data

syst

emat

ical

ly.

Esp

ecia

llyII.

5a/c

/d/e

are

diffi

cult

toco

ver.

II.5b

conf

eren

ces

II.5c

trai

ned

staf

f

II.5d

info

rmal

cont

acts

II.5e

tem

pora

ryex

chan

ges

II.5f

cont

ract

R&

D

II.5g

join

tR

&D

II.6

Un

ive

rsity

rese

arc

ho

utp

ut,

de

live

red

insu

itab

lefo

rmfo

rin

du

stria

lu

se

II.6a

Inte

nsity

and

spec

ialis

atio

nof

univ

ersi

ties’

pate

ntin

gac

tiviti

esC

WT

S/E

PO

II.6b

%of

rese

arch

ers

from

univ

ersi

ties

havi

ngpu

blis

hed

stud

ies

(!)

and

pate

ntap

plic

atio

ns

CW

TS

usin

gva

rious

(inte

r)na

tiona

lda

taba

ses

II.6c

Am

ount

ofte

chni

cal

know

ledg

e(e

.g.

softw

are)

prod

uced

and

dist

ribut

edw

ithou

tch

arge

byun

iver

sitie

s

II.7

Pro

du

cer-

use

rin

tera

ctio

ns

II-5

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

II.8

Re

sea

rch

coo

pe

ratio

nw

ithin

an

db

etw

ee

ne

nte

rprise

s;F

irm

sre

po

rtin

gre

sea

rch

coo

pe

ratio

nb

yp

art

ne

r

II.8a

Intr

a-fir

mco

oper

atio

n(b

etw

een

busi

ness

units

)In

nova

tion-

surv

ey,

ME

RIT

data

onD

utch

part

icip

atio

nE

urop

ean

Fra

mew

ork

Pro

gram

mes

Esp

ecia

llyth

ein

nova

tion

surv

eyda

taco

ver

mos

tof

the

indi

cato

rsm

entio

ned

for

1992

.

Qui

tea

few

(cas

e)st

udie

sw

ithso

me

quan

titat

ive

indi

catio

nse.

g.B

arte

ls,

Moe

rdijk

&V

anO

oste

non

outs

ourc

ing

and

coop

erat

ion

inre

sear

ch.

II.8a

/d/e

/gm

aypr

ove

tobe

diffi

cult

toco

ver

asda

taha

veno

tbe

enid

entif

ied

yet.

II.8b

betw

een

firm

s:ho

rizon

tal

II.8c

betw

een

firm

s:ve

rtic

al(s

uppl

ier-

user

coop

erat

ion)

II.8d

cons

ulta

nts

II.8e

tech

nica

lre

sear

chin

stitu

tion

insp

ecifi

cin

dust

ries

II.8f

rela

tive

impo

rtan

ceof

inst

itutio

nal

form

sof

co-o

pera

tive

rese

arch

,lik

e:*j

oint

vent

ures

,*t

echn

olog

ical

coop

erat

ion

insp

ecifi

cin

dust

ry(e

.g.

LEI)

*lar

geco

oper

ativ

epr

ogra

mm

esin

volv

ing

mul

tidis

cipl

inar

yre

sear

ch

II.8g

impo

rtan

ceof

info

rmal

netw

orks

,as

ake

ym

echa

nism

for

shar

ing

know

ledg

e

II-6

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

II.9

imp

ort

an

ceo

fp

rop

ert

yrig

ht

syst

em

inkn

ow

led

ge

tra

nsf

er

&d

istr

ibu

tion

II.9a

%bu

sine

ssR

&D

expe

nditu

res

resu

lting

inin

telle

ctua

lpr

oper

tyrig

hts

Dat

am

ainl

ycl

ose

toII.

9a,

II.9b

/cpr

oves

tobe

diffi

cult

II.9b

econ

omic

effe

cts

ofin

telle

ctua

lpr

oper

tyrig

hts

II.9c

cros

s-lic

ensi

ngac

tiviti

es

II.10

pa

rtic

ipa

tion

inst

an

da

rdis

atio

na

ctiv

itie

sN

oda

taav

aila

ble

II.11

orie

nta

tion

of

tech

no

log

ya

nd

inn

ova

tion

po

licy

tow

ard

sd

iffu

sio

n

II.11

are

lativ

eam

ount

ofm

oney

allo

cate

dto

war

dsdi

ffusi

onat

both

natio

nal

and

regi

onal

leve

ls

Min

istr

yof

Eco

nom

icA

ffairs

,M

ER

IT,

TN

O,

Bar

tels

Mai

nly

qual

itativ

em

ater

ial

isav

aila

ble

e.g.

Hol

land

(tec

hnol

ogy

polic

ies

inth

ere

gion

).

II-7

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

III.

Effe

ctiv

enes

sof

know

ledg

esh

arin

gan

dtr

ansf

er:

1-

3:un

iver

sitie

s,re

sear

chin

stitu

tes

and

indu

stry

4-

5:am

ong

com

petit

ors

and

betw

een

supp

lier

&us

ers

(tec

hnol

ogy

diffu

sion

thro

ugh

flow

sof

embo

died

R&

Dan

dm

arke

tdi

strib

utio

nof

pate

nted

know

ledg

e)

III.1

Inte

nsi

tyo

fu

niv

ers

itykn

ow

led

ge

-ba

seb

yb

usi

ne

ssfir

ms

III.1

aim

port

ance

ofte

chni

cal

know

ledg

eob

tain

edfr

omun

iver

sitie

sto

the

inno

-va

tive

activ

ities

ofbu

sine

ssfir

ms

For

III.ib

CW

TS

/EP

O

III.1

bac

adem

icci

tatio

ns:

freq

uenc

yus

edin

corp

orat

epa

tent

sA

dditi

onal

data

gath

erin

gis

need

ed.

III.2

Co

-op

era

tive

R&

Db

etw

ee

nu

niv

ers

itya

nd

ind

ust

ry

III.2

anu

mbe

rof

form

alco

-ope

rativ

epr

ojec

tsbe

twee

nun

iver

sity

and

indu

stry

Ann

ual

repo

rts

from

indi

vidu

alun

iver

sitie

sm

aygi

vean

indi

catio

n.C

ompr

ehen

sive

over

view

isdi

fficu

ltas

not

all

proj

ects

will

bere

port

edin

the

sam

ew

ay.

Use

ofth

e(n

ew)

WB

SO

-sch

eme

(SE

NT

ER

)m

ight

give

insi

ght.

III.2

bjo

int

turn

over

ofIII

.2.a

III.2

cin

tens

ityof

co-p

aten

ting

betw

een

univ

ersi

ties

and

indu

stry

CW

TS

/EP

O

III.2

dte

chno

logi

cal

spec

ialis

atio

nof

III.2

c

III.2

ein

tens

ityof

co-p

ublic

atio

nbe

twee

nun

iver

sity

and

indu

stry

For

III.2

eC

WT

Sus

ing

vario

us(in

ter)

-na

tiona

lda

taba

ses

III2f

tech

nolo

gica

lsp

ecia

lisat

ion

ofe.

II-8

SU

GG

ES

TE

DIN

DIC

AT

OR

SA

VA

ILA

BLE

SO

UR

CE

SC

OM

ME

NT

S

III.3

Ind

ust

ria

lco

ntr

ibu

tion

tob

asi

cre

sea

rch

III.3

ash

are

ofbu

sine

ssfir

ms

into

tal

expe

nditu

reon

basi

cre

sear

chM

ER

IT/O

EC

D

III.3

bnu

mbe

rof

rele

vant

publ

icat

ions

byin

dust

rial

rese

arch

ers

CW

TS

usin

gva

rious

(inte

r)na

tiona

lda

taba

ses

III.4

Dis

trib

utio

no

fkn

ow

led

ge

am

on

gco

mp

etit

ors

an

db

etw

ee

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III.4

adi

ffusi

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teof

new

prod

ucts

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proc

esse

sC

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(Nat

iona

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nts)

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nova

tion-

surv

ey

III.4

bdi

ffusi

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teof

spec

ific

high

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hpr

oduc

ts

II-9

ANNEX III: APELDOORN LIST

* This list reviews themes and topics on which well defined indicators andstructured data are missing. In the first column those themes and topics proposedby the OECD (see annex II) that are (hardly) available for the Netherlands aregiven. The second column identifies relevant themes and topics that are notincluded in this OECD-list.

III-1

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III-4

ANNEX IV: MEGA-CLUSTERS

* A more detailed analysis is included in H. van der Gaag (1995),Clusterseconomisch en technologisch bekeken. Een verkenning van de statistischeidentificatiemogelijkheden, STB-95-039, TNO-STB, Apeldoorn.

Although there are various methods to identify clusters, two main-types can bediscerned e.g. mono-graphical case studies and more statistical studies. By combiningthe two a picture emerges of the economic structure of a country in terms of networksof industries (at various levels of aggregation) and specialisation patterns.

Based on multiple sector studies conducted by TNO-STB and quantitative methodsusing data on products and services and technology flows1 between sectors, ninemega-clusters were identified. These clusters are displayed in figure IV.1. Not onlymanufacturing industries can be placed in the cluster chart, also services with activitiesin specific industry can thus be allocated to one of the clusters. It shows how thevarious economic activities relate to each other. Of course there are overlaps betweenthe clusters and some industries can not be easily classified in one of them. Table IV.1gives a more detailed description of the economic activities included in the differentmega-clusters.

1 For the Dutch case we estimated ’embodied’ intermediate innovation flows using the followingdata sources and methods:- make- and use-tables containing detailed information on making and using intermediate

products at an aggregation level of about 650 categories of products and about 230economic sectors;

- cluster analysis, identifying networks of strongly interconnected value chains;- Dutch Innovation Survey 1992, containing detailed information on innovation performance

and on the share of new products in economic sectors’ turnover.

IV-1

Figure IV.1. Mega clusters in the Dutch economy

IV-2

Table IV.1. Clusters in the dutch economy

Cluster Value Added(million guilders)

Main economic activities

Construction 79.929 Manufacture of building materials, earthenware;Construction and installation on constructionprojects

Chemicalindustry

18.078 Chemical basic products industry, manufacture ofartificial and synthetic yarns and fibres; Chemicalfinal product industry; Rubber and plastic-processing industry; Petroleum Industry

Services 214.409 Commercial (Trade; Hotels, restaurants, etc;Business services; Cleaning services etc) and non-commercial (Education,Public Administration,Corporate Business Organs, Social Services etc)

Energy 27.012 Electricity generation, Gas distribution, Watersupply, Crude Petroleum and natural gas pro-duction and exploration.

Health 29.731 Health and veterinary services (hospitals, nursinghomes, general practitioners services, Dentist,Psychiatric institutions and other medical services;Manufacture of pharmaceutical and antisepticdressings, Manufacture of Medical instruments

Agro-food 34.788 Agriculture; Horticulture,Forestry; Fishing,;Meat processing industry; Flourprocessing; Canning, preserving and processing offruits and vegetables; Animal feed, Oils and fats,Manufacture of dairy products, Starch, Sugarindustry, Beverages and Manufacture of chocolateand confectionary.

Multi-media 13.967 Printing, publishing and related industries, Photostudios and Film & Foto-laboratories, Computerservices; Advertising and Publicity Agencies;Theatres, Cinemas; Movie Organisations.

Manufacturingindustries(mainlyincludingdiscreteproduction)

59.741 Electrotechnical industries,Automobile industry, Manufacture of transport,Basic metal industry; Manufacture of metalproducts, Machinery; Manufacture of instrumentsand optical goods; Wood and furniture in-dustry;Paper and cardboard industry; Paperwareand corrugated cardboard industry; manufacture ofglass and glass products; Textiles industry.

Transport &Ports

36.532 See and Air transport, Railways, Tram and Busservices, travel agencies, shipping and othertransport and storage

IV-3

ANNEX V: PARTICIPANTS NATIONAL EXPERTMEETING

IV-4

Ir. F.C.H.D. van de BeemtTechnology Foundation (STW)

Mr. E. BrouwerFoundation for Economic Research of the University of Amsterdam (SEO)

Mr. P. den HertogTNO Centre for Technology and Policy Studies (TNO-STB)

Prof. Dr. A. KleinknechtEconomic and Social Institute of the Free University of Amsterdam (ESI-VU)

Mr. B. MinneCentral Planning Bureau (CPB)

Mr. J. PronkNetherlands Central Bureau of Statistics (CBS)

Dr. T.J.A. RoelandtTNO Centre for Technology and Policy Studies (TNO-STB)

Mr. J. van SteenMinistry of Education, Culture and Sciences

Dr. R.J.W. TijssenCentre for Science and Technology Studies (CWTS)

Dr. B. VerspagenMaastricht Economic Research Institute on Innovation and Technology (MERIT)

Dr. R. van ZijpMinistry of Economic Affairs

V-1