Knowledge about non-R and D expenditure on innovation activities such as patenting and licensing, design, trial production, tooling-up, manpower training,market research and investment in fixed assets, is still extremely sparse. Questions about the latter were very poorly answered in the recent CommunityInnovation Survey (CIS). With the aid of information regarding the quality of replies, we estimate missing values and then reach a national estimate of1992 innovation expenditure in the manufacturing and service industries of the Netherlands. For policy discussions as well as for modelling, it isinteresting to note that expenditure on product-related R and D (6,24 billion guilders) represents about 26% of total product (and service) innovationexpenditure, the latter including 125 billion guilders for investment in fixed assets

Brouwer, E.

Kleinknecht, A.

VU Research Portal

VU Research PortalMeasuring the unmeasurable: a country's non-R and D expenditure on product andservice innovationBrouwer, E.; Kleinknecht, A.1995document versionPublisher's PDF, also known as Version of recordLink to publication in VU Research Portalcitation for published version (APA)Brouwer, E., & Kleinknecht, A. (1995). Measuring the unmeasurable: a country's non-R and D expenditure onproduct and service innovation. (Research Memorandum; No. 1995-9). Faculty of Economics and BusinessAdministration, Vrije Universiteit Amsterdam.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            • Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            • You may not further distribute the material or use it for any profit-making activity or commercial gain            • You may freely distribute the URL identifying the publication in the public portal ?Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.E-mail address:vuresearchportal.ub@vu.nlDownload date: 22. May. 2021ETFaculte i t  der  Economische Wetenschappen en Econometr ie053481~5  Serie Research Memorandaicu5! Measuring the unmeasurable:A country%  non-R&D expenditure on product and service innovationErik BrouwerAlfred  KleinknechtResearch Memorandum 1995-9February 1995vrije Universiteit amsterdam.Measuring the unmeasurable:A country%  non-R&D expenditure on product and serviceinnovationbYErik Brouwer and AlFred  KleinknechtAbstract:Knowledge about non-R&D expenditure on innovation activities such  as patenting and licen-sing, design, trial production, tooling-up, manpower training, market research and invest-ment in fixed assets,  is stil1 extremely sparse. Questions about the latter were very poorlyanswered in the recent Community Innovation Survey (CIS). With the aid of information re-garding the quality of replies, we estimate missing values and then reach  a national estimateof 1992 innovation expenditure in the manufacturing and service industries of the Nether-lands. For policy discussions as wel1  as for modelling, it is interesting to note that expendi-ture on product-related R&D  (6,24  billion guilders) represents about 26% of total product(and service) innovation expenditure, the latter including 125 billion guilders for investmentin fixed assets.Acknowledgement:In this paper we draw  from the database “Innovation in the Dutch manufacturing and serviceindustries (1992)“,  built by the Foundation for Economie  Research of the University ofAmsterdam (SEO) with financial support from the Netherlands Ministry of Economie  Affairsand DG 13 of the European Commission. An earlier version of this paper was presented atthe meeting of National Experts on Science, Technology and Zndzutry  (NESTI),  Paris:OECD, 8-9 December 1994. We thank Bart Verspagen, Bert Minne and Hans van Ophemfor helpful remarks on our estimates. The usual disclaimers apply.Correspondence  to:Alfred Kleinknecht,ESI-VU,De Boelelaan 1105,NL-1081 HV AmsterdamIn the past, innovation measurement tended to be confined to R&D.  This is frequently unsa-tisfactory since the innovation process  also  requires a number of non-R&D  activities such  asthe acquisition of patents and licences, design, trial production and tooling up, training ofpersonnel, market research and, last but not least, investment in new production capacity.While such non-R&D expenditure may be of considerable  quantitative importante,  innova-tion policy as wel1 as theorizing and modelling stil1 have to rely on R&D  statistics as themajor source of information systematically collected  over time and across  al1  OECD coun-tries. In many of these countries, information about non-R&D expenditure on innovation isvirtually non-existent .The recent pilot imrovation survey organized by the European Commission and Eurostat wasa first  attempt  at capturing non-R&D innovation expenditure on a European scale.  Smal1sample pre-testing of the harmonized European innovation survey questionnaire in five cotm-tries suggested that roughly half of the sample firms were unable to answer to the questionabout innovation expenditures adequately (see Kleinknecht 1993). Obviously, firms are notyet accustomed to collect  such information and to report it in postal  surveys. As we expectedfirms  to have difftculties  in answering, we included the following additional question in thesurvey in the Netherlands (in brackets:  percentages of answers):Your answers  to the above questions  (on innovation expenditure) consisted of:0 fairly accurate figures  (manufacturing: 23, &%, services: 20,O  %)0 rough estimates (manufacturing: 46,6%,  services: 32,1%)0 you were unuble  to answer  (manufacturing: 29,6%,  services: 47,8%).Given our stratified (net) sample of 7.784 firrns from al1  sectors of the manufacturing andservice industries of the Netherlands and given a response rate  of 52%,  our database is inprinciple  representative for firms with 10 and more employees on a national scale.  Nonethe-less, it is obvious that traditional methods of extrapolation are not feasible in this case. Notonly the very high rates  of missing values and ‘rough estimates’ are disturbing. There is alsoa bias problem. Our logit analysis of properties of firms revealed that those that had a mis-sing value (or which gave a “rough estimate”) can  generally be characterized as somewhat‘weaker’  irmovators (Brouwer & Kleinknecht, 1994: 121-125). The implication is that asimple extrapolation of data (assuming that firrns with missing values do not essentially dif-fer from those that answered) would lead to a substantial over-estimation of national product(and service) innovation expenditure.We have therefore used an altemative methodology. The basic  idea behind this is that theremust be a fairly close relationship between a firm’s product innovation expenditure on the2one hand, and, on the other hand, its product-related R&D  expenditure (plus other firm cha-racteristics such as branch,  size, etc.). If this is valid, we could use R&D  and other firmcharacteristics (which, in general, are quite wel1  reported in other parts of the questionnaire)in order to simulate what should have been reported by those firms that were “tmable  to ans-wer”  or which gave only a “rough estimate” to the question on innovation expenditure.We start from firms that indicated that they gave a “fairly accurate” answer. By means  ofOLS regressions, we try to explain a firm’s innovation expenditure on product (and service)innovation as a function of its expenditure on product- (and service-) related R&D  and someother factors.  When  using log specifications we obtained fairly good regression estimates:the equations are homoscedastic, tests on the functional form are positive, residues  are nor-mally distributed, and the R-squares look satisfactory.In conclusion, the estimated OLS models provide  a fairly good predictor of a firrn’s innova-tion expenditure. In Table 1 we document the OLS model which explains the log of a firm’sexpenditure on product (and service) innovations, acluding investments in fured  assets  rela-ted to product innovations. Table 2 documents  a similar estimate of imrovation expenditures,including  investments in fixed assets  related to product innovation.Table 1 Factors which explain a firm’s (log of) expenditure on productand service innovation, excluding investments in fixed assets  rela-ted to product or service innovationsexogenous variables co@.  : t-values:constant termcontinuous variables:log of product-related RsrD  expenditure(incl.  R&D  COUtraCted  OUt)firm size (log of number of employees)dummy variables:R&D  iS  a permanent (nOt  OCCaSiOXUl)  activityfirm acquired external technological knowledgesector dummies:chemical industryconstruction or installation industry-2,31 - 7,os0.57 11,370,55 7,490,31 2,320,42 3,300,34 2,Ol-1,20 - 2,41Notes: * n = 292 firms (i.e. firms that gave a “fairly accurate” answer; ndeviates from the n in Table 2, because of missing values);* * R-square: 0,71* * * al1  variables relate  to the year 1992.Table 2 Factors that explain a firm’s (log of) expenditure on product andservice innovation, including  investments in fixed assets  related toproduct or service innovationsexogenous  variables CO@.  : t-values:constant termcontinuous variables:log of product-related RsrD  expenditure( i d .  R&D  COUtraCkd  OUt)fïrm size (log of number of employees)dummy variables:R8.D  is a permanent (not occasional) activityfkn acquired extemal technological knowledgesector dummies:chemical industry-1,32 -3,170.45 7,700,61 6,850,02 0,130,27 1,600,34 1,22Notes: * n = 215 firms (i.e. firms that gave a “fairly accurate” answer; ndeviates from the n in Table 1, because of missing values);* * R-square: 0,62* * * al1  variables relate  to the year 1992.4Product-related R&D  expenditure is an important explanatory variable of product innovationexpenditure. The positive coefficients for firm size reveal that larger firms, with given R&Dexpenditures, have higher  non-R&D innovation expenditure. The same holds  for chemicalfirms and for those that bought extemal technological knowledge and, in the case of Table1, for firms that consider R&D  as a permanent (other than an occasional) activity.Below, the coefftcients from Tables  1 and 2 are used for a simulation of expenditure on pro-duct and service innovation of those firms that had a missing value or which gave only a“rough estimate” . With respect to the exogenous variables in the two tables,  there are fewproblems with missing values. It should be noted that we make one crucial assumption inour simulation: The relationship between R&D  and innovation expenditure measured amongfirms that gave “quite  an accurate answer” also  holds  for firms that gave a “rough estimate”or no answer at all. Clearly, the realism of our simulation stands or falls with the realism ofthis assumption. As mentioned above, our logit analysis of properties of firms shows thatweaker  innovators are more likely to give a “rough estimate” or no answer (Brouwer &Kleinknecht 1994: 121-126). On the other hand, we see no apriori reason why the relation-ship of R&D  to non-R&D innovation expenditure should differ systematically among stron-ger or weaker  innovators.Our simulation of (R&D  and non-R&D) expenditure on product and service innovation ofDutch manufacturing and service firms in 1992 is given in Table 3 (split by size classes) andin Table 4 (split by branches). Innovation expenditure excZzdìng  investments in futed assetsis estimated at 11,l billion guilders. The 95 % confidence interval of this estimate is +/-1,47  billion guilders. ’ A comparison between the first and the second  cohunn in Tables  3and 4 suggests that investments in fixed assets  related to product and service innovation haveconsiderable  weight: innovation expenditure including investments is estimated to amount to23.687 billion guilders. The 95 % confidence interval is +/-  2,296 billion guilders.Our estimate of R&D  expenditure related to product and service innovation in manufacturingand service industries in 1992 amounts to 6,24  billion guilders (3,93  billion in rnanufactu-ring and 2,31  billion in services).2  From this it follows that the mean  share of product-rela-1 The limits  of 80% and 90% confidence intervals are: +/-  0,963 billion and 1,236billion guilders respectively; see Cramer (1986: 31-33) for the method of estimatingthese confidence intervals .2  Insiders wil1  note that our estimates of R&D  expenditure (just  as our estimates ofR&D  man years or of numbers of firms engaging in R&D)  are considerably higherthan comparable figures by the Dutch Centra1 Statistical Office. This has to, do withour capturing of small-scale and often informal R&D-activities in smaller enterprises5ted R&D  in total product and service innovation expenditures (exduding  investments in fixedassets  related to product and service innovation) is about 56% (57% in manufacturing and54% in services). The share of product-related R&D  in total product and service innovationexpenditure (including investments in fixed assets)  is 26% (34 % in manufacturing and 19%in services). It is remarkable that investment in fixed assets  has more weight in services thanin manufacturing .which tend to be under-counted in official surveys. For a detailed argument see Klein-knecht (1987) and Kleinknecht & Reijnen (1991).6Table 3 Expenditure on product and service innovation in Dutch manu-facturing and services@ split by size classessizeclasses(numbers ofemployees)manufacturing :10-1920-4950-99100-199200-499500 and moretotal manufacturingservices :10-1920-4950-99100-199200-499500 and moretotal servicestotal manuf.  and servi-cesinnov . expenditure, innov. expenditu-excl. investment re incl. invest-(million guilders)# ment (millionguilders)’n1 3 4 545 2353 8 1 1.162 451273 6 8 0 594372 800 3 9 1478 1.201 2735.255 7.232 1 3 86.893 ll.529 2.082491 1.8319 4 1 3.576663 1.802604 1.508411 8 5 11.143 2.5904.253 12.15811.146 23.6874 8 84 7 93252 5 12 7 01 9 72.0124.094@ the values in this table were obtained by simulating missing vahres  and ‘roughestimates’ by means  of the equations given in Tables  1 and 2, adding the figu-res from firms that gave ‘quite  an accurate answer’. The fígures have beenraised to national totals.# product-related innovation expenditure include expenditure such  as: RAI,  pat-ents and licences, design, trial production, tooling-up, manpower training,market research (not market introduction), but nat  investments in fmed assets.* this column includes the same categories  as the previous col-,  plus  expendi-ture on investments in fíxed assets  related to product (and service) irmovation.7Table 4 Expenditure on product and service innovation in Dutch manufacturing andservices split by branchesbranches:food and beveragestextiles and leatherwood and building  materialspaper, printing & publishg.chemicals, plasticsbasic  metal and metal goodsother industriesbranch  unknowntotal of manufacturingservices :public utility (gas, water)construction & installationtradehotels, restaurants, repairtransport, communicationbanks, insuranceother commercial servicesother non-comm. servicestotal services:total manuf.  and servicesinnovation expen- innovation expen-diture including diture exchdinginvestment (in investment (inmillion guilders) million guilders)704 1.4127 4 2251 3 7 432288 7042.113 2.9683.286 5.0661 0 6 3861 8 6 3346.894 ll.5291 0 5 228 6 71 8 2 1.849 2481.136 3.478 4 1 71 4 0 4 6 9 1 4 4477 1.293 273408 1.101 1 7 21.082 2.724 5 1 6724 1.015 1 7 54.254 12.157 2.01211.148 23.686 4.094n2281 3 52 1 92722518898 172.082Notes: see Table 3Finally, Table 5 provides  an indication of the structure of product and service innovation ex-penditure. Other than the figures in Tables  3 and 4, percentages in Table 5 are a based onsimulations but on (weighted) arithmetic means  of the answers given by 322 firms that indi-8cated that they gave a ‘fairly accurate answer’. Table 5 shows the share of R&D  in innova-tion expenditure exchding  investment in fixed assets.  As has already  been mentioned, thelatter group is biased towards ‘strong’ innovators. This explains why the share of R&D  inimrovation expenditure is higher  in this group than according to Tables  3 and 4.Table 5 Percentage shares of various types of product innovation expendituretype of innovation expenditure:product-related wpatents and licencesdesigntrial production, tooling-up, manpower trainingmarket research (excluding market introduction)other expendituretotalsmanufg .64%2%3%17%3%11%100%services: total:75% 69%5% 4%1% 2%9% 13%2% 3%7% 9%100% 100%Note: The figures are weighted arithmetic means, based on 322 firms  that gave a ‘fairly accurateanswer’. They are biased in favour of ‘strong’ innovators and canmt be interpreted as nationaltotals.When  investment in fixed assets  is excluded from product innovation expenditure, product-related R&D  takes the lion’s share of product innovation expenditure. Although the resultsin Table 5 are biased towards ‘strong’ innovators, they can  stil1 be interpreted as showingthat factors  such as patenting, licensing, design and market research form only a minor frac-tion.  The major part of innovation expenditure is due to trial production, tooling-up and trai-ning, as wel1 as ‘other costs’  . It should be noted that our estimates not only exclude processinnovation expenditures, but also  under-count product (and service) innovation expendituresto the extent that expenditures on advertising related to new product introduction are notcovered.  They have been deliberately omitted from the CIS questionnaire by pragmaticreasons: in earlier trial surveys, marketeers were usually unable to separate routine adver-tisements from new product advertisements.In spite of the unavoidable caveats mentioned, our estimates on the amount and structure ofirmovation expenditure do shed some light on an hitherto sparsely explored field. The out-comes  may be of interest to policy makers as wel1  to model builders who,  sooner or later,wil1  have to integrate product innovation into their macro-models.9References:Brouwer, E. & A. Kleinknecht (with the assistance of L. Harrewijn, W. Smits and J. Wes-terling, 1994): Znnovution in Dutch munufacturing and service industries, 1992 (in Dutch),Report to the Dutch Ministry of Economie  Affairs, The Hague: BTE 27, September 1994.Cramer,  J.S. (1986): Econometrie  applications  of maximum likelihood methods, New York:Cambridge University Press.Kleinknecht, A. & J.O.N. Reijnen (1991): ‘More evidente  on the undercounting of smal1firm R&D’,  in: Research Policy, Vol. 20: 579-587.Kleinknecht, A. (1993): ‘Testing innovation indicators for postal  surveys: Results from afive-country project’, in: A. Kleinknecht & D. Bain (eds.): New concepts  in innovation out-put measurement, London: Macmillan, New York: St Martin’s Press, p. 153-188.1 0

English

Measuring the unmeasurable: a country's non-R and D expenditure on product and service innovation

DSpace at VU

ET
Faculte i t  der  Economische Wetenschappen en Econometr ie
05348
1~5  Serie Research Memoranda
i
cu5
! Measuring the unmeasurable:
A country%  non-R&D expenditure on product and service innovation
Erik Brouwer
Alfred  Kleinknecht
Research Memorandum 1995-9
February 1995
vrije Universiteit amsterdam
.Measuring the unmeasurable:
A country%  non-R&D expenditure on product and service
innovation
bY
Erik Brouwer and AlFred  Kleinknecht
Abstract:
Knowledge about non-R&D expenditure on innovation activities such  as patenting and licen-
sing, design, trial production, tooling-up, manpower training, market research and invest-
ment in fixed assets,  is stil1 extremely sparse. Questions about the latter were very poorly
answered in the recent Community Innovation Survey (CIS). With the aid of information re-
garding the quality of replies, we estimate missing values and then reach  a national estimate
of 1992 innovation expenditure in the manufacturing and service industries of the Nether-
lands. For policy discussions as wel1  as for modelling, it is interesting to note that expendi-
ture on product-related R&D  (6,24  billion guilders) represents about 26% of total product
(and service) innovation expenditure, the latter including 125 billion guilders for investment
in fixed assets.
Acknowledgement:
In this paper we draw  from the database “Innovation in the Dutch manufacturing and service
industries (1992)“,  built by the Foundation for Economie  Research of the University of
Amsterdam (SEO) with financial support from the Netherlands Ministry of Economie  Affairs
and DG 13 of the European Commission. An earlier version of this paper was presented at
the meeting of National Experts on Science, Technology and Zndzutry  (NESTI),  Paris:
OECD, 8-9 December 1994. We thank Bart Verspagen, Bert Minne and Hans van Ophem
for helpful remarks on our estimates. The usual disclaimers apply.
Correspondence  to:
Alfred Kleinknecht,
ESI-VU,
De Boelelaan 1105,
NL-1081 HV Amsterdam
In the past, innovation measurement tended to be confined to R&D.  This is frequently unsa-
tisfactory since the innovation process  also  requires a number of non-R&D  activities such  as
the acquisition of patents and licences, design, trial production and tooling up, training of
personnel, market research and, last but not least, investment in new production capacity.
While such non-R&D expenditure may be of considerable  quantitative importante,  innova-
tion policy as wel1 as theorizing and modelling stil1 have to rely on R&D  statistics as the
major source of information systematically collected  over time and across  al1  OECD coun-
tries. In many of these countries, information about non-R&D expenditure on innovation is
virtually non-existent .
The recent pilot imrovation survey organized by the European Commission and Eurostat was
a first  attempt  at capturing non-R&D innovation expenditure on a European scale.  Smal1
sample pre-testing of the harmonized European innovation survey questionnaire in five cotm-
tries suggested that roughly half of the sample firms were unable to answer to the question
about innovation expenditures adequately (see Kleinknecht 1993). Obviously, firms are not
yet accustomed to collect  such information and to report it in postal  surveys. As we expected
firms  to have difftculties  in answering, we included the following additional question in the
survey in the Netherlands (in brackets:  percentages of answers):
Your answers  to the above questions  (on innovation expenditure) consisted of:
0 fairly accurate figures  (manufacturing: 23, &%, services: 20,O  %)
0 rough estimates (manufacturing: 46,6%,  services: 32,1%)
0 you were unuble  to answer  (manufacturing: 29,6%,  services: 47,8%).
Given our stratified (net) sample of 7.784 firrns from al1  sectors of the manufacturing and
service industries of the Netherlands and given a response rate  of 52%,  our database is in
principle  representative for firms with 10 and more employees on a national scale.  Nonethe-
less, it is obvious that traditional methods of extrapolation are not feasible in this case. Not
only the very high rates  of missing values and ‘rough estimates’ are disturbing. There is also
a bias problem. Our logit analysis of properties of firms revealed that those that had a mis-
sing value (or which gave a “rough estimate”) can  generally be characterized as somewhat
‘weaker’  irmovators (Brouwer & Kleinknecht, 1994: 121-125). The implication is that a
simple extrapolation of data (assuming that firrns with missing values do not essentially dif-
fer from those that answered) would lead to a substantial over-estimation of national product
(and service) innovation expenditure.
We have therefore used an altemative methodology. The basic  idea behind this is that there
must be a fairly close relationship between a firm’s product innovation expenditure on the
2
one hand, and, on the other hand, its product-related R&D  expenditure (plus other firm cha-
racteristics such as branch,  size, etc.). If this is valid, we could use R&D  and other firm
characteristics (which, in general, are quite wel1  reported in other parts of the questionnaire)
in order to simulate what should have been reported by those firms that were “tmable  to ans-
wer”  or which gave only a “rough estimate” to the question on innovation expenditure.
We start from firms that indicated that they gave a “fairly accurate” answer. By means  of
OLS regressions, we try to explain a firm’s innovation expenditure on product (and service)
innovation as a function of its expenditure on product- (and service-) related R&D  and some
other factors.  When  using log specifications we obtained fairly good regression estimates:
the equations are homoscedastic, tests on the functional form are positive, residues  are nor-
mally distributed, and the R-squares look satisfactory.
In conclusion, the estimated OLS models provide  a fairly good predictor of a firrn’s innova-
tion expenditure. In Table 1 we document the OLS model which explains the log of a firm’s
expenditure on product (and service) innovations, acluding investments in fured  assets  rela-
ted to product innovations. Table 2 documents  a similar estimate of imrovation expenditures,
including  investments in fixed assets  related to product innovation.
Table 1 Factors which explain a firm’s (log of) expenditure on product
and service innovation, excluding investments in fixed assets  rela-
ted to product or service innovations
exogenous variables co@.  : t-values:
constant term
continuous variables:
log of product-related RsrD  expenditure
(incl.  R&D  COUtraCted  OUt)
firm size (log of number of employees)
dummy variables:
R&D  iS  a permanent (nOt  OCCaSiOXUl)  activity
firm acquired external technological knowledge
sector dummies:
chemical industry
construction or installation industry
-2,31 - 7,os
0.57 11,37
0,55 7,49
0,31 2,32
0,42 3,30
0,34 2,Ol
-1,20 - 2,41
Notes: * n = 292 firms (i.e. firms that gave a “fairly accurate” answer; n
deviates from the n in Table 2, because of missing values);
* * R-square: 0,71
* * * al1  variables relate  to the year 1992.
Table 2 Factors that explain a firm’s (log of) expenditure on product and
service innovation, including  investments in fixed assets  related to
product or service innovations
exogenous  variables CO@.  : t-values:
constant term
continuous variables:
log of product-related RsrD  expenditure
( i d .  R&D  COUtraCkd  OUt)
fïrm size (log of number of employees)
dummy variables:
R8.D  is a permanent (not occasional) activity
fkn acquired extemal technological knowledge
sector dummies:
chemical industry
-1,32 -3,17
0.45 7,70
0,61 6,85
0,02 0,13
0,27 1,60
0,34 1,22
Notes: * n = 215 firms (i.e. firms that gave a “fairly accurate” answer; n
deviates from the n in Table 1, because of missing values);
* * R-square: 0,62
* * * al1  variables relate  to the year 1992.
4
Product-related R&D  expenditure is an important explanatory variable of product innovation
expenditure. The positive coefficients for firm size reveal that larger firms, with given R&D
expenditures, have higher  non-R&D innovation expenditure. The same holds  for chemical
firms and for those that bought extemal technological knowledge and, in the case of Table
1, for firms that consider R&D  as a permanent (other than an occasional) activity.
Below, the coefftcients from Tables  1 and 2 are used for a simulation of expenditure on pro-
duct and service innovation of those firms that had a missing value or which gave only a
“rough estimate” . With respect to the exogenous variables in the two tables,  there are few
problems with missing values. It should be noted that we make one crucial assumption in
our simulation: The relationship between R&D  and innovation expenditure measured among
firms that gave “quite  an accurate answer” also  holds  for firms that gave a “rough estimate”
or no answer at all. Clearly, the realism of our simulation stands or falls with the realism of
this assumption. As mentioned above, our logit analysis of properties of firms shows that
weaker  innovators are more likely to give a “rough estimate” or no answer (Brouwer &
Kleinknecht 1994: 121-126). On the other hand, we see no apriori reason why the relation-
ship of R&D  to non-R&D innovation expenditure should differ systematically among stron-
ger or weaker  innovators.
Our simulation of (R&D  and non-R&D) expenditure on product and service innovation of
Dutch manufacturing and service firms in 1992 is given in Table 3 (split by size classes) and
in Table 4 (split by branches). Innovation expenditure excZzdìng  investments in futed assets
is estimated at 11,l billion guilders. The 95 % confidence interval of this estimate is +/-
1,47  billion guilders. ’ A comparison between the first and the second  cohunn in Tables  3
and 4 suggests that investments in fixed assets  related to product and service innovation have
considerable  weight: innovation expenditure including investments is estimated to amount to
23.687 billion guilders. The 95 % confidence interval is +/-  2,296 billion guilders.
Our estimate of R&D  expenditure related to product and service innovation in manufacturing
and service industries in 1992 amounts to 6,24  billion guilders (3,93  billion in rnanufactu-
ring and 2,31  billion in services).2  From this it follows that the mean  share of product-rela-
1 The limits  of 80% and 90% confidence intervals are: +/-  0,963 billion and 1,236
billion guilders respectively; see Cramer (1986: 31-33) for the method of estimating
these confidence intervals .
2  Insiders wil1  note that our estimates of R&D  expenditure (just  as our estimates of
R&D  man years or of numbers of firms engaging in R&D)  are considerably higher
than comparable figures by the Dutch Centra1 Statistical Office. This has to, do with
our capturing of small-scale and often informal R&D-activities in smaller enterprises
5
ted R&D  in total product and service innovation expenditures (exduding  investments in fixed
assets  related to product and service innovation) is about 56% (57% in manufacturing and
54% in services). The share of product-related R&D  in total product and service innovation
expenditure (including investments in fixed assets)  is 26% (34 % in manufacturing and 19%
in services). It is remarkable that investment in fixed assets  has more weight in services than
in manufacturing .
which tend to be under-counted in official surveys. For a detailed argument see Klein-
knecht (1987) and Kleinknecht & Reijnen (1991).
6
Table 3 Expenditure on product and service innovation in Dutch manu-
facturing and services@ split by size classes
size
classes
(numbers of
employees)
manufacturing :
10-19
20-49
50-99
100-199
200-499
500 and more
total manufacturing
services :
10-19
20-49
50-99
100-199
200-499
500 and more
total services
total manuf.  and servi-
ces
innov . expenditure, innov. expenditu-
excl. investment re incl. invest-
(million guilders)# ment (million
guilders)’
n
1 3 4 545 235
3 8 1 1.162 451
273 6 8 0 594
372 800 3 9 1
478 1.201 273
5.255 7.232 1 3 8
6.893 ll.529 2.082
491 1.831
9 4 1 3.576
663 1.802
604 1.508
411 8 5 1
1.143 2.590
4.253 12.158
11.146 23.687
4 8 8
4 7 9
325
2 5 1
2 7 0
1 9 7
2.012
4.094
@ the values in this table were obtained by simulating missing vahres  and ‘rough
estimates’ by means  of the equations given in Tables  1 and 2, adding the figu-
res from firms that gave ‘quite  an accurate answer’. The fígures have been
raised to national totals.
# product-related innovation expenditure include expenditure such  as: RAI,  pat-
ents and licences, design, trial production, tooling-up, manpower training,
market research (not market introduction), but nat  investments in fmed assets.
* this column includes the same categories  as the previous col-,  plus  expendi-
ture on investments in fíxed assets  related to product (and service) irmovation.
7
Table 4 Expenditure on product and service innovation in Dutch manufacturing and
services split by branches
branches:
food and beverages
textiles and leather
wood and building  materials
paper, printing & publishg.
chemicals, plastics
basic  metal and metal goods
other industries
branch  unknown
total of manufacturing
services :
public utility (gas, water)
construction & installation
trade
hotels, restaurants, repair
transport, communication
banks, insurance
other commercial services
other non-comm. services
total services:
total manuf.  and services
innovation expen- innovation expen-
diture including diture exchding
investment (in investment (in
million guilders) million guilders)
704 1.412
7 4 225
1 3 7 432
288 704
2.113 2.968
3.286 5.066
1 0 6 386
1 8 6 334
6.894 ll.529
1 0 5 228 6 7
1 8 2 1.849 248
1.136 3.478 4 1 7
1 4 0 4 6 9 1 4 4
477 1.293 273
408 1.101 1 7 2
1.082 2.724 5 1 6
724 1.015 1 7 5
4.254 12.157 2.012
11.148 23.686 4.094
n
228
1 3 5
2 1 9
272
251
889
8 1
7
2.082
Notes: see Table 3
Finally, Table 5 provides  an indication of the structure of product and service innovation ex-
penditure. Other than the figures in Tables  3 and 4, percentages in Table 5 are a based on
simulations but on (weighted) arithmetic means  of the answers given by 322 firms that indi-
8
cated that they gave a ‘fairly accurate answer’. Table 5 shows the share of R&D  in innova-
tion expenditure exchding  investment in fixed assets.  As has already  been mentioned, the
latter group is biased towards ‘strong’ innovators. This explains why the share of R&D  in
imrovation expenditure is higher  in this group than according to Tables  3 and 4.
Table 5 Percentage shares of various types of product innovation expenditure
type of innovation expenditure:
product-related w
patents and licences
design
trial production, tooling-up, manpower training
market research (excluding market introduction)
other expenditure
totals
manufg .
64%
2%
3%
17%
3%
11%
100%
services: total:
75% 69%
5% 4%
1% 2%
9% 13%
2% 3%
7% 9%
100% 100%
Note: The figures are weighted arithmetic means, based on 322 firms  that gave a ‘fairly accurate
answer’. They are biased in favour of ‘strong’ innovators and canmt be interpreted as national
totals.
When  investment in fixed assets  is excluded from product innovation expenditure, product-
related R&D  takes the lion’s share of product innovation expenditure. Although the results
in Table 5 are biased towards ‘strong’ innovators, they can  stil1 be interpreted as showing
that factors  such as patenting, licensing, design and market research form only a minor frac-
tion.  The major part of innovation expenditure is due to trial production, tooling-up and trai-
ning, as wel1 as ‘other costs’  . It should be noted that our estimates not only exclude process
innovation expenditures, but also  under-count product (and service) innovation expenditures
to the extent that expenditures on advertising related to new product introduction are not
covered.  They have been deliberately omitted from the CIS questionnaire by pragmatic
reasons: in earlier trial surveys, marketeers were usually unable to separate routine adver-
tisements from new product advertisements.
In spite of the unavoidable caveats mentioned, our estimates on the amount and structure of
irmovation expenditure do shed some light on an hitherto sparsely explored field. The out-
comes  may be of interest to policy makers as wel1  to model builders who,  sooner or later,
wil1  have to integrate product innovation into their macro-models.
9
References:
Brouwer, E. & A. Kleinknecht (with the assistance of L. Harrewijn, W. Smits and J. Wes-
terling, 1994): Znnovution in Dutch munufacturing and service industries, 1992 (in Dutch),
Report to the Dutch Ministry of Economie  Affairs, The Hague: BTE 27, September 1994.
Cramer,  J.S. (1986): Econometrie  applications  of maximum likelihood methods, New York:
Cambridge University Press.
Kleinknecht, A. & J.O.N. Reijnen (1991): ‘More evidente  on the undercounting of smal1
firm R&D’,  in: Research Policy, Vol. 20: 579-587.
Kleinknecht, A. (1993): ‘Testing innovation indicators for postal  surveys: Results from a
five-country project’, in: A. Kleinknecht & D. Bain (eds.): New concepts  in innovation out-
put measurement, London: Macmillan, New York: St Martin’s Press, p. 153-188.
1 0


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Measuring the unmeasurable: a country's non-R and D expenditure on product and service innovation

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