Innovation capacities in advanced economies: Relative performance of small open economies

Research in International Business and Finance 27 (2013) 106– 123

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Research in International Businessand Finance

journal homepage: www.elsevier.com/locate/r ibaf

Innovation capacities in advanced economies: Relativeperformance of small open economies

Eleanor Doylea, Fergal O’Connorb,∗

a Institute for Business Development and Competitiveness, School of Economics, University College Cork, Irelandb Lancashire Business School, University of Central Lancashire, UK

a r t i c l e i n f o

Article history:Received 26 July 2011Received in revised form 1 August 2012Accepted 8 August 2012

Available online 18 August 2012

Keywords:InnovationPatentsResearch and DevelopmentSmall open economy

a b s t r a c t

This paper offers an empirical examination of the determinantsof a nation’s ability to produce commercially viable innovations,measured as Patents Granted across a sample of 23 advancedeconomies. The approach employed is based on estimating NationalInnovative Capacity that focuses on the long-run ability ofeconomies to produce and/or commercialise innovative technolo-gies, in the spirit of Furman et al. (2002). The time period of ouranalysis covers 1993 to 2005 and employs panel estimation.

Motivated by differences in the rate of innovation betweeneconomies with different economic structures we examine theSmall Open Economies (SOEs) in our country sample to assesswhether there is a significant difference between the determi-nants of Innovative Capacity in SOEs and the other larger developedeconomies.

We find that advanced SOEs and larger economies do not dif-fer substantially in their determinants of producing innovativetechnologies and, notwithstanding the limitations of Patents asmeasures of innovative activity, we conclude that policy choice andvariation plays a key role in determining the productivity of R&D,when measured as patenting activity.

© 2012 Elsevier B.V. All rights reserved.

1. Introduction

Innovative capacity lies at the heart of factors affecting every nation’s future competitivenessparticularly for advanced modern economies, since under a Solow (1956) type growth frameworksuch economies are likely to have exhausted their ability to generate increased output from further

∗ Corresponding author. Tel.: +44 1772 201 201.E-mail address: [email protected] (F. O’Connor).

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E. Doyle, F. O’Connor / Research in International Business and Finance 27 (2013) 106– 123 107

Fig. 1. Patents Granted per Million Population (2008), Ranked by 2008 GDP ($).Source: IMF WEO, USPTO.

investments in capital. In this paper we assess whether the factors that drive this vital innovation inSmall Open Economies (SOEs) are significantly different to that of larger economies.

We use Patents Granted by the United States Patents and Trademarks Office (USPTO) as a measureof national innovative output. As with any economic definition of success, innovative success requireselaboration and explanation. Undoubtedly Patents Granted are an imperfect proxy of the innovativecapacity of an economy, yet they represent the only directly observable and comparative measureof innovative output over time suitable for the analysis conducted here for the sample of countriesconsidered over the time period selected for consideration. Their suitability and a fuller definition ofthe measure are discussed in more detail in Section 4.1.

Our method employs the National Innovative Capacity framework developed by Furman et al.(2002) which uses (i) a country’s infrastructure, (ii) the prevalence of industrial clusters and (iii) thequality of links between the two to examine determinants of innovative capacity. This provides amodel of how a country can produce commercially valuable innovation over the long term, drawingtogether earlier work by Romer (1990), Porter (1990) and Nelson (1993) to inform the three constituentelements.

The variation in the ability of countries to produce new-to-world technologies is striking. Somecountries consistently outperform others by a large margin. For example, Canada, the US, Finland,Switzerland and Japan produced well over 100 patents per year per million of population in 2008,while most other advanced economies average approximately 60 patents per million and still otherssuch as Spain, Portugal, New Zealand and Italy all may be considered to ‘underperform’ with less than25 patents per million.

Such variation in patenting outcomes is not explained by larger economies performing better orsmaller, more nimble economies generating better results. There is, nevertheless, a strong patentingbias in those countries which have a history of patenting such as the US and Switzerland (due topath dependency and the importance of the history of resource commitments). However, other ‘new’innovative countries’ rates of growth in patents per million have been nothing short of phenomenal:Singapore, for example, has an average annual patent growth rate of 30% between 1981 and 2008,going from just over 1 patent per million in 1981 to 84 in 2008 (Fig. 1).

Such performance begs analysis and raises the question for us in this paper as to whether smallereconomies are supported or hindered by their relatively low scale, or low critical mass in economicterms, in achieving innovative success. We also examine whether an SOE’s innovative capacity isoptimised by an emphasis on certain factors or if the same basic mix of factors is found to be effectiveoffering findings of relevance from a policy perspective.

108 E. Doyle, F. O’Connor / Research in International Business and Finance 27 (2013) 106– 123

The issues considered in this paper focus, firstly, on whether the mix of drivers of InnovativeCapacity varies across advanced economies when categorised by their SOE status. Thus, this paperaddresses possible heterogeneities that may exist in relation to different economy structures. Weexamine the extent to which a set of factors drive a nation’s Innovative Capacity, as previously found inthe literature, and question whether or not the mix of policy choices, in terms of the factors mentionedabove, for an SOE are significantly different from other economies.

While some limited literature on innovative capacity examines specific SOEs, such as considerationof New Zealand in Marsh (2000), it tends to concentrate on an individual industry rather than adopt-ing a broader international perspective which is the chosen perspective offered here. This question,therefore, addresses a gap in the literature by targeting our assessment on the relative performanceof SOEs.

We set out the background to the National Innovative Capacity (NIC) approach and underlyingmodel in Section 2, and potentially relevant measures that determine (NIC). In Section 3 possiblereasons for the SOE heterogeneity relative to larger economies are considered. In Section 4 the dataselected for analysis is described. Empirical results for a number of various specifications are examinedin Section 5 with Section 6 providing our conclusions.

2. National Innovative Capacity framework

The National Innovative Capacity framework from Furman et al. (2002) integrates three perspec-tives on the sources of innovation:

• ideas-driven growth theory as outlined in Romer (1990) 1;• microeconomics-based models of national competitive advantage and industrial clusters developed

by, e.g. Porter (1990); and• research on national innovation systems as proposed in Nelson (1993).

Both the characteristics of the direct producers of patents are relevant in this context, as are theoutcomes generated by previous capital investments, policies and supports for innovation-based activ-ities. This framework concentrates on tangible measures to explain innovation, in a complementarymanner to Veechi and Brennan (2009) where the focus is on national culture as the driver of innovationat a national level.

The view we take here is that Innovative Capacity should be viewed differently to pure science andtechnology advances, as we are interested specifically in economically viable applications. The discoveryof a new technology (or significant facts/information) is considered to be independent of its benefit toan economy unless it can be harnessed domestically through having both the structures and resourcesavailable to exploit its value before the knowledge becomes diffused and may be exploited elsewhere.With limited data availability and suitability for identification of economically viable applications ofscientific advances, however, we limit ourselves to a proxy for such activity in the form of patents.

This approach facilitates the identification of a set of economic factors that drive patenting activ-ity/intensity and also allows for a policy-centred focus on how best to consider the long-term choicesthat impinge on innovation capacity. This policy-centred focus applies as easily to business develop-ment policy, on the one hand, as to business strategy on the other, given the microeconomic basis ofthe cluster concept outlined in Porter (1990).

2.1. Common innovative infrastructure

This element of the Innovative Capacity framework as outlined in Furman et al. (2002) relatesto features of an economy’s innovation infrastructure that confer no particular advantage on any

1 In Romer’s (1990) model of endogenous technological change, productivity growth is driven by a constant allocation ofresources to an ideas-producing sector, hence the use in related literature, including this paper, of the ‘Ideas Production Function’,e.g. in Eq. (1) below.


sector or cluster yet provide support for innovation activities generally across the economy. It usesendogenous growth theory to identify the two main determinants of the quality of the CommonInnovative Infrastructure as:

• the aggregate level of technological sophistication or its accumulated stock of knowledge and• the range of the talent pool of workers appropriate for the generation of new knowledge in an

economy.

In addition to these to determinants Furman et al. (2002) add other universal factors that aidinnovation, such as the proportion of Graduates from Higher Education, the extent of Property RightsProtection, and the incentives for innovation in the form of the availability of R&D Tax Credits.

2.2. Cluster specific environment

This aspect of the Innovative Capacity framework makes reference to Porter’s microeconomics ofcompetitiveness approach, specifically the fact that while wider policy-related issues facilitate inno-vation it is ultimately the firms that create new technologies. This firm-level impact on nationalinnovative capacity depends upon the microeconomic environment present within and across anation’s clusters. Porter observed that

“A nation’s successful industries are usually linked through vertical (buyer/supplier) or hori-zontal (common customers, technology, channels, etc.) relationships . . . The phenomenon ofindustry clustering is so pervasive that it appears to be a central feature of advanced nationaleconomies” (Porter, 1990: 149).

Studies such as Keupp and Gassmann (2009) point out that, although firms do try to use the knowl-edge of foreign subsidiaries to drive innovation at firm level, this rarely has an impact as geographyseems to remain important to this process.

A variety of cluster-specific circumstances, investments, and policies impact on the extent to whicha country’s industrial clusters compete on the basis of technological innovation. Innovation in partic-ular pairs of clusters may also be complementary to one another, both due to knowledge spilloversand other interrelationships – be they vertical or horizontal.

This is a particularly difficult feature to include when estimating an econometric model as thereare few national or international statistics pertaining directly to the extent of cluster activity thatare available for the period of the analysis conducted here (for more on issues in the challenges ofapplying a cluster approach, see Doyle and Fanning, 2007). Instead a number of proxies are identifiedand estimated for our purposes in this paper.

2.3. Quality of linkages

The quality of the two previous factors is reinforced by the way in which they are linked togethersystemically. For instance even firms within a well-developed cluster will be better able to produceeconomically viable new-to-world technologies if they have access to a pool of scientists and engineersand access to basic research and, in some cases, perhaps access to advice and support from localuniversities.

2.4. Modelling innovative capacity

The basis of the model specified by Furman et al. (2002) uses the findings of prior research into thegeographic impact of knowledge spillovers, the differences in access to human capital and ways thatregional differences are driven by public policies. Ideas-driven endogenous growth models form thebasis of the model that is extended to incorporate additional and more nuanced factors from industrialorganisation previously not used, the composition of funding (public versus private), public policiesand the degree of technological specialisation. For example, while Public R&D spending adds to the


innovative process by reinforcing the Common Innovation Infrastructure, Private R&D spending andthe Specialisation of a country’s technological outputs also reflects the nation’s cluster innovationenvironment.

To estimate the relationship between the production of international Patents Granted and observ-able contributors to National Innovative Capacity, we use the Ideas Production Function of endogenousgrowth theory as a baseline (Eq. (1)):

Aj,t = ıHAj,tA

ϕj,t

(1)

where Aj,t is the flow of new-to-the-world technologies from country j in year t, HAj,t

the total level of

capital and labour resources or inputs devoted to the ideas sector of the economy, and Aϕj,t

is the totalstock of knowledge held by an economy at a given point in time relevant to drive production of ideasin the future.

As the National Innovative Capacity framework suggests that a broader set of influences determineinnovative performance a production function for new-to-the-world technologies is extended fromEq. (1) generating Eq. (2):

Aj,t = ıj,t(XINFj,t , YCLUS

j,t , ZLINKj,t )HA

j,tAϕj,t

(2)

The additional variable XINF refers to the level of general resource commitments and policy choicesthat constitute the common innovation infrastructure; YCLUS refers to the particular environment forinnovation in a country’s industrial clusters; and ZLINK captures the strength of linkages between thecommon infrastructure and the nation’s various clusters. Under Eq. (2), we assume that the variouselements of National Innovative Capacity are complementary in the sense that the marginal boost toideas production from increasing one factor is increasing in the level of all of the other factors.

The parameters associated with Eq. (2) are estimated using a panel dataset of 23 OECD countriesplus Singapore over 13 years. These estimates can, therefore, depend on cross-sectional variation,time-series variation, or both. While comparisons across countries can easily lead to problems ofunobserved heterogeneity, cross-sectional variation provides the direct inter-country comparisonsthat can reveal the importance of specific determinants of national innovative capacity. Time-seriesvariation may be subject to its own sources of endogeneity (e.g. shifts in a country’s fundamentalsmay reflect idiosyncratic circumstances in its environment), yet time-series variation provides insightinto how a country’s choices manifest themselves in terms of observed innovative output.

Recognizing the issues surrounding panel estimations our analysis explicitly compares how esti-mates vary depending on the source of identification. In most of our analysis, we include year dummiesin order to account for the evolving differences across years in the overall levels of innovative outputand a dummy for the US to account for differences in the definition of the dependant variable for thateconomy which is explained in Section 4.1.

The analysis is organized around a log–log specification, except for qualitative variables andvariables expressed in percentages. The estimates, thus, have a natural interpretation in terms ofelasticities, are less sensitive to outliers, and are consistent with the majority of prior work in this areaincluding Jones (1995a), Furman et al. (2002), Gans and Stern (2003), Gans and Hayes (2008).

Finally, with regard to the sources of error, we assume that the observed difference from the pre-dicted value given by Eq. (2) (i.e. the disturbance) arises from an idiosyncratic country/year-specifictechnology shock unrelated to the fundamental determinants of national innovative capacity. Inte-grating these choices and letting L denote the natural logarithm, our main specification takes thefollowing form of Eq. (3):

LAj,t = ̨ + ıINFLX INFj,t + ıCLUSLYCLUS

j,t + ıLINKLZLINKj,t + � LHA

j,t + ϕLAϕj,t

j + εj,t (3)

This equation indicates that, conditional on a given level of R&D inputs (HA), variation in theproduction of innovation (Aϕ) reflects R&D productivity differences across countries or over time.For example, a positive coefficient on elements of ıINF, ıCLUS or ıLINK suggests that the productivityof R&D investment is increasing in the quality of the common innovation infrastructure, the inno-vation environment in the nation’s industrial clusters, and the quality of linkages between these.


Source: Authors’ calculations based on data from UPSTO data .

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Pate

nts p

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Patents Granted Per Milli on Pop-Large Count ries

Canada

Germany

France

Italy

Japan

Korea

Spain

Fig. 2. Patents Granted Per Million Pop – Large Countries.Source: Authors’ calculations based on data from UPSTO data.

As Aj,t – measured by the level of international Patents Granted – is only observed with delay, ourempirical work imposes a 3-year lag between the measures of innovative capacity and the observedrealization of innovation output. This follows Furman et al. (2002), but differs from Gans and Stern(2003) and Gans and Hayes (2008), who impose no lag and a two year lag, respectively. We find thatusing alternative lag structures does not significantly alter our results.

3. Small open economies

3.1. Defining an SOE

There are 30 economies in the OECD and this study also includes Singapore as an example of an SOEfor which patenting activity has become particularly notable. However, Portugal, Turkey, Iceland andGreece were not used in our sample as the required data was unavailable for the Specialisation vari-able (detailed in Section 4.3). In addition, Luxembourg was excluded given its idiosyncratic economiccharacter. Mexico and Poland were also excluded due to the extremely low and unchanging level ofPatents Granted (as illustrated in Figs. 2 and 3). Therefore, our country sample includes 23 countries.

In the literature there appears to be no quantitative method to define an SOE despite the fact thatthe term is frequently used as highlighted in, for example, Mendoza (1991) and Berrill (2010). Theconceptual definition of an SOE is an economy that participates in international trade, but is smallenough compared to its trading partners that its policies or trading practices do not alter world prices,interest rates, or incomes.

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nts p

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p

Patents Granted Per Milli on Popula�on-Small Open Economi es

Aust raliaAust riaBelgium

Denmark

Finland

Hungary

Ireland

Netherl ands

Fig. 3. Patents Granted Per Million Population – Small Open Economies.


Table 1Selection of small open economies and others.

SOE Size: % of aggregate GDP Openness (%) Large Size: % of aggregate GDP Openness (%)

Australia 1.36 46 USA 37.81 26Austria 0.74 101 UK 5.70 58Belgium 0.90 169 Japan 17.98 20Czech Republic 0.22 147 Germany 7.34 67Denmark 0.62 80 France 5.14 56Finland 0.47 76 Italy 4.24 56Hungary 0.18 127 Canada 2.79 87Ireland 0.37 176 Spain 2.24 62Netherlands 1.49 130 South Korea 2.06 87New Zealand 0.20 72Norway 0.65 77Singapore 0.36 342Sweden 0.95 89Switzerland 0.96 88

Source: Authors’ calculations based on data from Penn World Tables.

We considered for each of our 23 countries whether they could be characterised as an SOE or noton the basis of:

1) Import/Export Openness of the economy, calculated as exports plus imports relative to GDP sourcedfrom the Penn World Tables.

2) Size of the economy, calculated as the relative GDP weighting of each country in our overall sample.The GDPs of the 23 countries were aggregated and the proportion each accounted for was calculated.

For the purposes of this paper an SOE is defined as one whose GDP makes up less than 2% of the 23countries aggregate GDP and its exports plus imports expressed as a proportion of GDP is equal to orgreater than 70%, which is within half a standard deviation of the mean of 100%.

Table 1 provides a full list of the countries included in our sample and their status as an SOE or‘other’, i.e. non-SOE. This process was somewhat ad hoc and for some countries it was unclear howthey should best be categorised. Canada, for instance, is studied as an SOE by Appelbaum and Kohli(1979), but its GDP was nearly 3% of the aggregate, while its international trade openness was 87% ofGDP.

3.2. Possible reasons for SOE heterogeneity

There are a number of reasons that SOEs might have a different set of explanatory factors in termsof their innovative capacity or that some factors may be of greater importance in maximising theirpotential for ideas production. Merely due to more limited resource availability, smaller economiestend to be more open and much research indicates that openness and labour productivity are related:for example Doyle and Martinez-Zarzoso (2011) find that in the long-run a 1% increase in opennessleads to an increase of 0.8% in labour productivity. Although not focusing specifically on the SOEquestion, they also find that while trade has a positive effect on labour productivity, this effect decreasesfor more populated countries.

3.2.1. Scale effectsThe idea that in larger agglomerations of people new ideas and innovations will be more readily

available is an old one. William Petty in 1682 commenting on the reconstruction of London after theGreat Fire of 1666 wrote:

“it is more likely that one ingenious curious man may rather be found amongst 4 millions thanamong 400 persons.” (Simon, 1998:34).


This scale effect has also been incorporated in neo-classical growth theories such as Romer (1990)which forms a part of the basis for the National Innovation Capacity framework used in this paper. InRomer’s (1990) model, the growth rate of an economy is proportional to the total amount of researchundertaken in it. Any increase in the population of an economy will generally lead to an increase inthe proportion of the workforce undertaking R&D, thereby increasing the growth rate of per capitaincome.

Accordingly, a small open economy’s ability to produce new-to-world technologies may be affecteddetrimentally due to the lower probability of its having the ‘ingenious curious’ persons that WilliamPetty identifies as well as lacking the critical mass of researchers to maximise growth as in Romer’stheory.

In studies testing scale effects in economic growth, however, there is no clear evidence that largereconomies grow faster. Jones (1995b) studied time series evidence and concluded against scale effectsin economic growth. However on a more concentrated level Backus et al. (1990) found scale effectswere evident in the manufacturing output in a variety of models used. More recently, Frankel andRomer (1999, see p. 380) explain that the size or scale of a country impacts not only its propensityto trade externally but also internally and both types of trade are valid determinants of productivitygrowth.

3.2.2. Knowledge spilloversStudies such as Bye et al. (2008) emphasise the importance of knowledge spillovers for SOEs. Cohen

and Levinthal (1989) point out that R&D has “two faces” in its interaction with an economy. Not onlydoes it produce new innovations directly but it also allows for the easier absorption and understandingof new technologies, generated both domestically and internationally.

Due to the lack of scale in SOEs mentioned above, the importance to SOEs of absorbing all knowledgefrom international sources in order to be able to act at the technological frontier in producing new-to-world technologies means that a different policy emphasis may well be required by them. Faehn (2008)discusses how national policy can enhance the exploitation of the international knowledge stock andfinds that subsidising R&D is important for domestic innovation as it is effective in generating theseknowledge spillovers from abroad. Faehn (2008) also finds that exports play an important role forSOEs in encouraging knowledge spillovers.

4. Data

4.1. Patents granted as a proxy for innovative output

Our analysis requires an observable country-specific indicator of the level of commercially valuableinnovative output in a given year and over a period of time. We follow previous research and employas the dependant variable the number of international patents (PATENTS) Granted, defined as “thenumber of patents granted to inventors from a particular country other than United States by theUSPTO in a given year. For the United States, PATENTS is equal to the number of patents granted tocorporate or government establishments (this excludes individual inventors)” (Furman et al., 2002:909).

Following Eaton and Kortum (1996), Kortum and Lerner (1999), Griliches (1984) and Furman et al.(2002) we recognise a number of difficulties in relation to using Patents Granted as a measure ofinnovation at a national level, such as

• not all inventions are patentable,• not all inventions of economic value are patented,• not all patented innovations have the same quality or value to an economy,• there are varying degrees of willingness to patent across countries and sectors.

However, there are also major benefits from assessing innovative capacity through Patents Granted.As Trajtenberg (1990:183) argues, they are the only objective and observable measure available with a“well-grounded claim for universality”. While there are survey questions and responses that attempt


to measure a country’s level of innovative capacity such as and its technological readiness (e.g. theExecutive Opinion Survey of the annual Global Competitiveness Reports) these are not available overthe time-frame required for our analysis. In addition, there is always the issue with Survey Instrumentsthat their design can impinge on the answers provided by respondents, however, Survey Instrumentsthat have been honed over several years are not likely to be liable to such concerns.

The way in which Patents Granted are counted for the purposes of this study means that onlyeconomically valuable inventions are relevant for inclusion. This is due to the high cost for an inventorfrom outside the United States to register a patent with the USPTO. This should act as a barrier topatenting an invention unless there is a strong belief that it will produce a sufficient economic return.

However, a patent application from the USA does not face the same high costs to register with theUSPTO. Therefore in this study a patent registered by a US resident is counted only if it was eitherregistered by a firm or the government, again reducing the number of patents registered that mightlack economic value, being patented by an individual inventor for reasons other than economic profit.Any asymmetry this may cause between US and non-US patents does not affect our results as weinclude a US dummy variable in all panel regressions.

In order to provide support for our selection of Patents Granted as our dependent variable, despitethe issues identified relating to its use as a proxy for Innovative Capacity, we examine a number ofcorrelations of interest offering quantitative measures of the link between Patents Granted and realeconomic activity. We provide a number of correlations between a country’s patent stock (for 5 yearsand 17 years) and its annual Value Added in knowledge and technology-intensive industries, as wellas its annual exports of high-technology goods, with all results provided in Appendix B. These providemeasures of real activity that should be positively affected by economically valuable innovation as weposit for Patents Granted.

All correlations between Patents Granted and Value Added for knowledge and technology-intensiveindustries are positive, except for Switzerland. The average correlation for the sample is 68% whenwe use patent stock measured over the previous 5 years and 85% when we include patent stockaccumulated over the previous 17 years. Data is not available for all sampled countries for the exportsmeasure but it provides a similar picture with positive correlations and a sample average of 67% and81% for 5 and 17 years of lagged patent stock, respectively. All four calculated average correlations arestatistically significantly greater than zero.

We acknowledge, however, that we must still interpret our findings carefully, noting that ourdependant variable is an imperfect proxy relating to innovations that are economically viable applica-tions and cognisant that the ‘true’ rate of innovation is unobservable.

4.2. Independent variables

Following previous literature this paper uses proxies for measures of Common InnovativeInfrastructure, Cluster Specific Environment and the Quality of Linkages in order to estimate thedeterminants of National Innovative Capacity. These are detailed in Table 2 which includes variabledefinitions and data sources. Table 3 details means and standard deviations for each variable includedin our analysis.

4.3. Specialisation

While most variables for our analysis were readily available, SPECIALISATION was estimated basedon a methodology developed by Ellison and Glaeser (1997). Since individual clusters will tend to beassociated with technologies from specific technological areas, this is a measure of the degree of tech-nological focus in an economy and acts as a proxy for the intensity of innovation-based competitionin a nation’s clusters. SPECIALISATION is a ‘relative’ concentration index based on the degree to whicha given country’s USPTO-granted patents are concentrated across the three broad technology classesinto which all patents fall (chemical, electronics, and mechanical as defined by the USPTO). While themeasure of specialization is too general to identify specific clusters and the role of the mix of clustersin shaping R&D productivity, SPECIALISATION was designed by Furman et al. (2002) as a noisy but


Table 2Variable descriptions and sources.

Full variable name Definition Source

Dependent variablePatentsj,t International Patents

Granted by Year ofApplication

Non-US countries: Patentsgranted by the USPTO. US:Patents granted by theUSPTO to corporations orgovernment.

USPTO Patent Database

Independent variablesQuality of the common innovation infrastructure

R&D PPLj,t Aggregate PersonnelEmployed in R&D

Full time equivalent R&Dpersonnel in all sectors

OECD Science &Technology Indicators

R&D $j,t Aggregate Expenditure onR&D

Total R&D expenditures inMill of US$ (base 2000)


Property Rights Protectionj,t Legal Structure andSecurity of Property Rights

Average survey responseby executives on a 1–10scale regarding relativestrength of Legal Structureand Security of PropertyRights

Economic Freedom of theWorld Index

ED SHAREj,t Share of GDP Spent onSecondary and TertiaryEducation

Public spending onsecondary + tertiary educ.as share of GDP

World Bank: Edstats

OPENNESSj,t Freedom to TradeInternationally

Average survey responseby executives on a 1–10scale regarding relativestrength of freedom totrade internationally

Economic Freedom of theWorld Index

GDP/POPj,t GDP Per Capita Gross Domestic Productper capita, constant price,chain series, US$

IMF: World EconomicOutlook

GDPj,t GDP Gross Domestic Productconstant price, chain series,US$, Billions.

IMF: World EconomicOutlook

Cluster-specific innovation environmentPRIVATE R&Dj,t Percentage of R&D Funded

by Private IndustryR&D expenditures fundedby industry divided bytotal R&D expenditures


SPECIALISATIONj,t Ellison and Glaeser (1997)concentration index,excluding the United States

Relative concentration ofinnovative output inchemical, electrical andmechanical USPTO patentclasses

Computation from USPTOdata using formulae fromFurman et al. (2002)detailed below

Quality of linkagesUNIV R&Dj,t Percentage of R&D

Performed by UniversitiesR&D expenditures ofuniversities divided bytotal national R&Dexpenditures


unbiased measure capturing an important consequence of cluster dynamics, i.e. the relative special-ization of national economies in specific technologies fields.

Specifically, traditional measures of specialization, when based on the Herfindahl Index, ignore twoissues important for cross-country comparisons: technology classes differ in terms of their averageshare across all countries and some countries have only a small number of patents overall. While theEllison and Glaeser index was developed and applied to measuring the specialization of industriesacross geographic regions, Furman et al. (2002) applied it as a measure of the degree of specialisationof research output following previous authors such as Lim (2000). In the present context, the Ellisonand Glaeser formula adjusts the country-observed shares for each technology class to account for the


Table 3Variable means and standard deviations.

Variable N Mean Standard deviation

Patentsj,t 293 4607 10,081

Quality of the common innovation infrastructureR&D PPLj,t 293 193,276 300,649R&D $j,t 293 24,687 49,779Property Rights Protectionj,t 293 8.21 1.21ED SHAREj,t 293 3.4 0.94OPENNESSj,t 293 8.1 0.63GDP/POPj,t 293 25,619 9480GDPj,t 293 1120 2059

Cluster-specific innovation environmentPRIVATE R&Dj,t 293 61 11SPECIALISATIONj,t 293 0.53 0.64

Quality of linkagesUNIV R&Dj,t 293 22 6

average share for that technology group across the sample; and for the total number of patents in each‘country-year’ observation, as follows:

Specalisationi,j,t =∑

j

(Patentsi,j,t

Patentsi,j,t−1

) (∑i(si,j,t − xi,t)

2

1 −∑

x2i,t

− 1Patentsi,j,t

)(4)

where Patentsi,j,t = Patents of country i in year t across each technology class j, si,j,t = share of class jpatents in total country patents in year t, xi,t = average share of patent class j over all i in any t.

Fig. 4 offers a sample of results of the Specialisation measure. The falling value of this measureover time points to limitations in its specification. If a country has a very well developed cluster inchemicals, for example, and no patents from the other two classifications, it achieves a high score.However, if a country then begins to develop a new cluster in electronics, its specialisation score falls.This makes the variable difficult to interpret as at the point of development of new clusters it moves inthe opposite direction than would be expected. It is included in order to make this study comparableto the findings of similar studies such as Furman et al. (2002) and Gans and Hayes (2008).

Source: Autho rs’ calcula tions based on data from UPSTO data .

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0.6

0.7

0.8

0.9

1

GERMA NY

FRANCE

CANADA

BELGIUM

AUSTRIA

Austr alia

Fig. 4. Specialisation Index.Source: Authors’ calculations based on data from UPSTO data.


5. Empirical approach and results

This section outlines the results from our empirical analyses of the drivers of National InnovativeCapacity across our sample of 23 countries and with specific focus on the results for the sample ofSOEs, as defined earlier (see Appendix A for comparison of country sample and time frame betweenthe current paper and that of Furman et al., 2002). We first test all models for parameter stability. Wethen included slope and level dummies into our regressions to assess if there are specific differencesin the way that SOEs produce new-to-world technologies when compared to other larger economies.

The panel regression method selected is the Random Effects Method. Each regression is testedusing the Hausman test of Fixed versus Random Effects to assess its appropriateness: all regressionsare found to be suitable for Random Effects estimation.

5.1. Chow tests for parameter stability

All three models outlined in Section 2.4 were tested for parameter stability using a Chow test. Thistest assesses if there is a difference in the structure of the relationship between the dependant variableand the independent variables, when estimated for the group of SOEs or other larger economies.

This is done by estimating Eqs. (1)–(3) (see Section 2.4) three times: first with all countries included,then solely for SOE countries and finally for the non-SOE countries. Including all countries in theregression assumes that the intercept as well as the slope coefficients remains the same for botheconomy types; that is, there is no structural difference in how the two economy types produce PatentsGranted. Dividing the observations into the two groups allows us to assess if there is a structuraldifference between the economy types.

To carry out the Chow test we run all three regressions to find the residual sum of squares (RSS).From the first regression we find the restricted RSS (RSSr) as we force the coefficients to have the samevalue for both economy types. We then assume the other two regressions to be independent and addtheir RSSs to get the RSSur. If there is no structural difference, then the RSSr and RSSur should not bestatistically different, and we use the F-stat calculated as in Eq. (5) below to make our assessment:

F = (RSSr − RSSur)/k

RSSur/(n2 + n3 − 2k)∼F[k,(n1+n2−2k)] (5)

where k = no. of parameters estimated and n2 + n3 = number of observations in regressions 2 and 3,respectively

We do not reject the null hypothesis of parameter stability (i.e. of no structural difference) if thecomputed F-value in an application does not exceed the critical F-value found in the F tables at a givenlevel of significance.

All models estimated and tested for parameter stability showed that there was no change in thestructure of the relationship between the dependant variable and the independent variables, whenestimated for SOEs or larger economies. This means that based on these findings innovative capacityof our sample of SOEs is driven by the same set of factors as for the other non-SOE economies. Resultsof these regressions are provided in Appendix C.

Section 5.2 investigates if there are specific factors that have statistically significantly differenteffect on innovative output, i.e. Patents Granted, for small open economies.

5.2. Determinants of innovative capacity

In each of the three equations presented in Section 2.4 we included an SOE dummy variable for eachindependent variable, as well as for the constant in order to assess if SOEs constitute a heterogonousgroup relative to non-SOEs, i.e. exhibiting different drivers of innovative capacity. Results for all of ourmodel specifications are shown in Tables 4 and 5. Each variable is reported with its respective dummyvariable provided beneath it. In all tables, statistical significance at 1%, 5% and 10% levels are denotedby ***, ** and *, respectively. The dummy variables take on a value of 1 for an SOE and 0 for non-SOEeconomies.


Table 4Determinants of new-to-world technologies – GDP/POP as knowledge stock.

Ideas Production Function Common InnovativeInfrastructure Model

NationalInnovativeCapacity Model

Eq. (1.1) Eq. (1.2) Eq. (2.1) Eq. (2.2) Eq. (3.1)

Constant −2.6044** 1.2125 −6.3061*** −6.0268*** −8.2346***Constant, SOE Dummy −1.6688 −6.1050*** −1.1061** −3.9321* −1.7041L GDP −0.0026 0.0674L GDP, SOE Dummy −0.0885 0.2552L GDP PER CAPITA 0.03111 0.0504 0.2682*L GDP PER CAPITA, SOE Dummy 0.01014 0.2608 0.0168L POP 1.6762*** 0.4300**L POP, SOE Dummy −2.120*** −0.6694**L R&D PPL 0.2696*** 0.4919*** −0.0588 −0.0779 0.0151L R&D PPL, SOE Dummy 0.6972*** 0.3668*** 0.9280 0.1631 0.0523L R&D $ 1.0101*** 1.1898*** 1.2692***L R&D $, SOE Dummy −0.1767 −0.3597* −0.4024*ED SHARE 0.0050 −0.0189 −0.0016ED SHARE, SOE Dummy 0.0782 0.1050 0.0701Property Rights 0.1790*** 0.1904*** 0.1648***Property Rights, SOE Dummy 0.0378 0.0375 0.0679Openness 0.1379* 0.1246* 0.2401***Openness, SOE Dummy 0.1890** 0.2028*** 0.0793Private R&D −0.0293***Private R&D, SOE Dummy 0.0283***Specialisation 0.3730Specialisation, SOE Dummy −0.7489*University R&D −0.0381***University R&D, SOE Dummy 0.0510***R2 0.6673 0.5954 0.9380 0.9487 0.9517

Note: Statistical significance at 1%, 5% and 10% levels are denoted by ***, ** and *, respectively.

Table 5Determinants of new-to-world technologies – patent stock as knowledge stock.

Common Innovative Infrastructure Model National Innovative Capacity Model

Eq. (2.3) Eq. (3.2)

Constant −5.7483*** −4.0317*Constant, SOE Dummy 0.8037 −0.7616L Patent Stock 0.3586*** 0.4138***L Patent Stock, SOE Dummy −0.1770* −0.2075*L R&D PPL −0.048 0.0461L R&D PPL, SOE Dummy −0.2499 −0.2405L R&D $ 0.6445*** 0.5803**L R&D $, SOE Dummy 0.2499 0.2832ED SHARE −0.0484 −0.0689ED SHARE, SOE Dummy 0.0921 0.0864Property Rights 0.1092** 0.1055**Property Rights, SOE Dummy 0.1089 0.0770Openness 0.0878 0.1465**Openness, SOE Dummy 0.1376 0.0800Private R&D −0.0210**Private R&D, SOE Dummy 0.0189*Specialisation 0.0859Specialisation, SOE Dummy −0.7476*University R&D −0.0333***University R&D, SOE Dummy 0.0345***L GDP 1993 0.3553** 0.1861*L GDP 1993, SOE Dummy −0.1901 −0.0865R2 0.949 0.953

Note: Statistical significance at 1%, 5% and 10% levels are denoted by ***, ** and *, respectively.


The estimations are presented into two separate tables, Tables 4 and 5 across specifications for (i)the Ideas Production Function, (ii) The Common Innovative Infrastructure and (iii) National InnovativeCapacity.

Our most basic model, the Ideas Production Function, is shown in Table 4 (Eqs. (1.1) and (1.2)). TheCommon Innovative Infrastructure model is shown in Eqs. (2.1)–(2.3) in Tables 4 and 5. Results for thefully expanded National Innovative Capacity model are shown in Eqs. (3.1) and (3.2) in Tables 4 and 5,respectively.

Equations in Table 4 use GDP and Population, or GDP per capita as proxies for a country’s knowledgestock. Estimations in Table 5 use the stock of patents built up by the country as a proxy for its knowledgestock, with each country’s GDP per capita from the beginning of the sample period, i.e. 1993, includedto control for its level of economic development.

5.2.1. Knowledge stock variablesWhen GDP and GDP per capita are included in estimations without reference to patent stock –

in Table 4 – the results were not statistically significant at the 5% level but generally had a positiverelationship with patent output, measured as Patents Granted. This indicates the estimated relation-ship measured whether changes in the level of economic development resulted in changes in PatentsGranted. But as all the countries sampled are relatively well developed it is possible that furtherimprovements in the already advanced economies had no statistically significant effect in either SOEsor larger non-SOE economies.

As indicated above, a country’s Patent Stock has been shown to be an important factor in deter-mining its current and future patent output in past research. Our analysis (see Table 5) also finds thatit plays a statistically significant role with a 10% increase in Patent Stock resulting in a 3–4% increasein Patents Granted in larger economies. The SOE dummy variable is significant at 10% but shows thatthe Patent Stock effect is negative in SOEs relative to other economies.

This may point to the fact that Patent Stock not only captures the accumulated knowledge stockof the country but also the fact that a country with a large stock of patents exhibits a more fullydeveloped innovative infrastructure with capacity for adding further to its stock. Our sample of SOEsalso tends to contain the less-developed of our sample countries although with notable exceptionssuch as Switzerland.

When Patent Stock was included for estimation in Table 5, rather than test for changes in the levelof development from year to year only the level of development in 1993 (the beginning of the sample)was used to provide a base level of development for each country, following previous literature. Forthe larger non-SOE economies we find that a 10% difference in a country’s level of development in1993 would have resulted in between a 1.8% and 3.5% increase in Patents Granted. Since the dummyvariables are not statistically significantly different from 0, we can conclude that SOEs are affected ina similar way to larger economies experiencing path dependency in Patents Granted relative to theirlevel of development.

5.2.2. R&D variablesIn the Ideas Production Function results provided in Table 4 (Eqs. (1.1) and (1.2)), R&D personnel is

statistically significant and has a large coefficient on Patents Granted of between 2.6% and 4.9% for a 10%increase for all economies and between 3.6% and 6.9% for SOEs. We observe with the inclusion of R&Dexpenditure in all subsequent equations that R&D personnel becomes insignificant for all economytypes. R&D expenditure is large, positive and statistically significant for all models with a 10% increasein R&D spending increasing Patents Granted considerably by between 6% and 12% across the variousspecifications outlined in Tables 4 and 5. Estimations in Table 5 indicate that SOEs are affected nodifferently than the non-SOEs as the dummy variables are not significant at the 5% level.

This differs to the findings of Furman et al. (2002), whose results indicated that both R&D personneland expenditure were positive and significant. They also noted that the sum of the coefficients on R&Dspending and Full-time equivalent personnel (FTE PPL) from their regressions is quite similar to thesingle coefficient on FTE PPL in their Ideas Production Function results. They interpret this to mean thatthe total impact of R&D inputs devoted to innovation is similar whether focusing on a single variableor including both measures.


Our finding requires further study as it may imply that our more recent data – 1993–2005 – com-pared to that used in Furman et al. (2002) – 1975–1995 – may be revealing that Patents Grantedbecame increasingly dependent on the amount of R&D expenditure used to produce economicallyviable innovations rather than on the numbers of researchers engaged in R&D activity.

The percentage of R&D undertaken in universities has a significant and negative relationship withPatents Granted in large economies but the coefficient is quite small with a 10% increase in UniversityR&D causing a 0.3% fall in patent output (Table 5). This may indicate that those R&D projects withwhich universities are involved are those of greatest complexity, hence industries’ desire to engagewith them, and hence the greater difficulty in converting such projects into Patents Granted. For SOEsthe effect is significant, positive and of a similar magnitude indicating a positive, while modest, rolefor private R&D in generating Patents Granted.

5.2.3. Additional findingsPopulation has a statistically significant relationship with Patents Granted for both economy types

in Eq. (1.1) (Table 4) while in the more detailed model for Eq. (2.1) (Table 4) the dummy variableon Population for SOEs remains significant. However, the signs are in opposite directions. For largeeconomy types an increase in the level of population increases the number of Patents Granted while anegative relationship is exhibited in SOEs. For non-SOEs this implies a benefit from scale – measuredas population – on their level of innovative capacity. This is also evident when GDP per capita is usedin Eq. (3.1) in Table 4 with an estimated impact of 2.6% on Patents Granted for a 10% increase inGDP per capita. No such advantage is conferred to SOEs given their limited scale indicating an innatedisadvantage to SOEs capacity to generate innovative output. This corresponds to the finding fromTable 5 that knowledge stock confers advantages to non-SOE countries in our sample.

Property Rights Protection is also a significant determinant of Patents Granted but there is nostatistically significant difference between its importance for SOEs and other countries (Table 4). A10% increase in Property Rights Protection causes an increase of between 1% and 1.9% increase ininnovative output. From Table 5 we see that the impact is estimated at approximately 1%. We findthat Specialisation is never statistically significant at the 5% level in any of our model specifications.

Patents Granted is positively related to Openness of the economy. The extent of the impact variesfrom 1.2% to 2.4% for non SOEs in Table 4 and by between 0.7% and 2% for SOEs. With Patent Stockincluded as an explanatory variable in Table 5 we find that Openness remains statistically significantin Eq. (3.2) only, the National Innovative Capacity model, with an impact of 1.5% on Patents Grantedfor a 10% change in the case of both SOEs and non-SOEs.

The statistically significant results for the constant term in the regressions are all negative. Thisimplies that some work must be undertaken on a nation’s Innovative Capacity before it begins toproduce economically viable applications of technology. The SOE constant dummies in Eqs. (1.2), (2.1)and (2.2) are significant, and both are negative implying that SOEs in the sample start at a disadvantageto larger economy types. This reiterates the point made in earlier studies, such as Gans and Stern (2003),that patience is a requirement to achieve an increased level of Innovative Capacity.

6. Conclusions

This paper examines the drivers of National Innovative Capacity using a new dataset of a shortertime frame but with an increased cross-sectional element relative to other earlier similar studies topermit focus on whether and to what extent, if any, SOEs exhibit drivers of innovative capacity in linewith those of larger economies. While a series of more basic models were estimated, the extendedNational Innovative Capacity models (Eqs. (3.1) and (3.2) in Tables 4 and 5, respectively) provide mostexplanatory power, as measured by R2. While many of our findings reinforce those of earlier papers,a number of differences are notable.

Our results indicate that the coefficient Aggregate Personnel Employed in R&D was consistentlyinsignificant and reduced in magnitude once the R&D Expenditure variable was included. This con-trasts with the findings of Furman et al. (2002) where R&D human capital and R&D personnel were bothfound to be significant in the production of Patents Granted. A number of explanations are possible.


One is the more recent data used in this study. This could mean that there has been a changeor structural break in the way Patents Granted have been produced in the more recent time-frame,requiring more sophisticated capital and making the number of researchers relatively less impor-tant in generating Patents Granted. A possible support for this argument is that as time goes on, theyear fixed-effects decline in the estimates, implying that it becomes increasingly difficult to generatePatents Granted. This gap may well be bridged more easily by more advanced technology as higherend researchers are in short and inelastic supply. Another possibility is that the variable is simply toobroadly defined. If it only included researchers and engineers it may again become significant. This isan area that requires further study.

The hypothesis that larger economy types benefit from scale effects is supported by the positiverelationship between population and Patents Granted for non-SOEs. The negative impact of scale forSOEs, exhibited in terms of Population in Table 4 and in terms of Patent Stock in Table 5, points toinherent barriers to SOEs engaging in growing their innovative capacity as their scale impacts on,for example, their capacity to benefit from knowledge spillovers one which their openness does notcounter. Further investigations around the definition of SOE and steps such as using an extended dataset to include a broader range of countries can consider the extent to which this finding is repeatedacross other country samples.

Where SOEs in the sample have addressed their scale weaknesses, it was through a mixture of theshare of R&D expenditure by private companies and through the share of R&D conducted by Universi-ties. The identification of the mechanisms through which this was achieved and how it was achievedrequires further research. It is in relation to these variables that SOEs managed to enhance their innova-tive capacity, measured here as Patents Granted, over and above and statistically significantly relativeto the measured determinants of innovative capacity for all countries in the sample.

Acknowledgements

The authors are grateful for funding from the Department of Trade, Enterprise and Investment,Northern Ireland that supported the research on which this paper is based. The authors are particularlygrateful to Adrian Kuah, Philip Shapira and Damian Ward for their comments. Any remaining errorsare solely those of the authors.

Appendix A. Comparison of Countries and Time-Frame with Furman et al. (2002).

Furman et al. (2002),Sample countries: (1973–1995) Current Paper,Sample Countries: (1993–2005)Australia AustraliaAustria AustriaCanada Belgium*Denmark CanadaFinland Czech Republic*France DenmarkGermany FinlandItaly FranceJapan GermanyNetherlands Hungry*New Zealand Ireland*Norway ItalySpain JapanSweden NetherlandsSwitzerland New ZealandUK NorwayUnited States Singapore*

South Korea*SpainSwedenSwitzerlandUKUnited States

Note: *denotes the countries included in the current country sample in addition to those covered in Furman et al. (2002).


Appendix B. Correlations between countries’ rolling patent stock and real economic activitymeasures

Value added of knowledge- andtechnology-intensive industries

Exports of high-technology goods

Country 5 Years Lagged Patents 17 Years Lagged Patents 5 Years Lagged Patents 17 Years Lagged Patents

Simple Average 68% 85% 67% 81%USA 89% 99% 62% 88%Japan 79% 84% 41% 38%Germany 54% 85% 71% 94%United Kingdom 71% 94% 43% 49%France 27% 81% 28% 88%Canada 78% 92% 80% 93%South Korea 91% 94% 92% 95%Switzerland −33% −38% – –Italy 41% 81% – –Sweden 50% 92% – –Netherlands 75% 93% – –Australia 95% 96% 95% 96%Belgium 50% 89% – –Finland 89% 95% – –Austria 89% 95% – –Singapore 87% 93% 86% 85%Ireland 95% 97% – –New Zealand 81% 95% – –

Source: Science and engineering indicators 2012, National Science Foundation, USA.

Appendix C. Regressions without dummies for Chow tests

Determinants of New-to-World Technologies (Patent Stock as Knowledge Stock)

Common Innovative Infrastructure National Innovative Capacity

(C2.1) Full Sample (C2.2) SOE (C3.1) Full Sample (C3.2) SOE

Quality of Common Infrastructure:L PATENT STOCK .1892 .2055 .2191 .2457

(0.000) (0.000) (0.000) (0.000)L R&D PPL 0.0872 −.0147 .0975 .1284

(0.278) (0.928) (0.220) (0.408)L R&D $ .551 .7537 .4849 .6728

(0.000) (0.000) (0.000) (0.000)ED SHARE −.0265 −.0319 .0303 −.0192

(0.380) (0.702) (0.313) (0.553)Property Rights Protection .0972 .3231 .0851 .3400

(0.013) (0.000) (0.029) (0.000)Openness .1445 .2158 .1359 .2322

(0.000) (0.000) (0.001) (0.000)

Cluster Specific Innovation Environment:Private R&D .0001 .0058

(0.0769) (0.721)Specialisation −.0265 −.0745

(0.279) (0.012)

Quality of Linkages:UNI R&D −.0098 .0050

(0.0143) (0.578)

Controls:L GDP 1993 .3413 .0820 .3343 −.0861

(0.000) (0.471) (0.004) (0.340)


Determinants of New-to-World Technologies (Patent Stock as Knowledge Stock)

Common Innovative Infrastructure National Innovative Capacity

(C2.1) Full Sample (C2.2) SOE (C3.1) Full Sample (C3.2) SOE

US Dummy −.3590 NA −.4105 NA(0.349) (0.317)

Constant −4.7931 −6.731 −4.225 −7.881(0.000) (0.000) (0.000) (0.000)

R2 0.9200 0.9392 0.9273 0.9508Chow Test No significant difference No significant difference

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Innovation capacities in advanced economies: Relative performance of small open economies