measuring the efficiency of china's regional innovation...

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Measuring the Efficiency of China's RegionalInnovation Systems: Application of Network DataEnvelopment Analysis (DEA)Kaihua Chen a & Jiancheng Guan ba School of Management, Beijing University of Aeronautics & Astronautics, 100191,Beijing, Chinab School of Management, Fudan University, 200433, Shanghai, China E-mail:

Available online: 26 Aug 2011

To cite this article: Kaihua Chen & Jiancheng Guan (2012): Measuring the Efficiency of China's Regional InnovationSystems: Application of Network Data Envelopment Analysis (DEA), Regional Studies, 46:3, 355-377

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Measuring the Efficiency of China’s RegionalInnovation Systems: Application of Network Data

Envelopment Analysis (DEA)

KAIHUA CHEN∗ and JIANCHENG GUAN†∗School of Management, Beijing University of Aeronautics & Astronautics, 100191 Beijing, China

†School of Management, Fudan University, 200433 Shanghai, China. Emails: [email protected],[email protected] and [email protected]

(Received May 2008: in revised form April 2010)

CHEN K. and GUAN J. Measuring the efficiency of China’s regional innovation systems: application of network data envelopment

analysis (DEA), Regional Studies. This study applies a relational network data analysis envelopment to the systematic evaluation of

the innovation efficiency of China’s regional innovation systems by decomposing the innovation process into the two connecting

sub-processes, technological development and subsequent technological commercialization. The results show that only one-fifth

of China’s regional innovation systems are operating on the empirical best-practice frontier during the whole process

from technological development to commercialization. Furthermore, it is found that substantial inconsistencies exist between

technological development capacity and commercialization capacity in most regional innovation systems, and that downstream

commercialization capacity plays a more important role in the innovation performance of regional innovation systems.

Regional innovation systems Innovation process Technical efficiency Network data envelopment analysis (DEA)

Bootstrap approach China

CHEN K. et GUAN J. Evaluer l’efficacite des systemes d’innovation regionaux en Chine: l’application de la DEA en reseau, Regional

Studies. Cet article cherche a appliquer une analyse DEA (data envelopment analysis) relationnelle en reseau a l’evaluation systema-

tique de l’efficacite innovatrice des systemes d’innovation regionaux en Chine a partir d’une decomposition du processus d’inno-

vation en deux sous-processus, a savoir le developpement technologique et la commercialisation technologique qui en decoule.

Les resultats laissent voir que seulement un cinquieme des systemes d’innovation regionaux en Chine fonctionnent a la frontiere

empirique des pratiques d’excellence tout au long du processus qui va du developpement technologique a la commercialisation.

Qui plus est, il s’avere que d’importantes contradictions s’imposent entre la capacite de developpement technologique et la capa-

cite de commercialisation dans la plupart des systemes d’innovation regionaux, et que la capacite de commercialisation en aval joue

un role plus important quant a la performance innovatrice des systemes d’innovation regionaux.

Systemes d’innovation regionaux Processus d’innovation Efficacite technologique Analyse DEA en reseau Amorcage

Chine

CHEN K. und GUAN J. Messung der Effizienz der regionalen Innovationssysteme von China: eine Anwendung der Netzwerk-

Dateneinhullanalyse, Regional Studies. In dieser Studie wird eine relationale Netzwerk-Dateneinhullanalyse auf die systema-

tische Beurteilung der Innovationseffizienz der regionalen Innovationssysteme von China angewandt. Hierfur wird der

Innovationsprozess in die beiden zusammenhangenden Subprozesse aufgegliedert: technologische Entwicklung und die

anschließende technologische Kommerzialisierung. Aus den Ergebnissen geht hervor, dass nur ein Funftel der regionalen Inno-

vationssysteme von China wahrend des gesamten Prozesses von der technologischen Entwicklung zur Kommerzialisierung an

der Grenze der besten empirischen Praxis arbeiten. Daruber hinaus stellen wir fest, dass zwischen der Kapazitat fur techno-

logische Entwicklung und der Kapazitat fur Kommerzialisierung erhebliche Unregelmaßigkeiten bestehen und dass die

Regional Studies, Vol. 46.3, pp. 355–377, March 2012

0034-3404 print/1360-0591 online/12/030355-23 # 2012 Regional Studies Association http://dx.doi.org/10.1080/00343404.2010.497479http://www.regional-studies-assoc.ac.uk

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user

高亮

user

高亮

Kapazitat der prozessabwarts angelagerten Kommerzialisierung fur die Innovationsleistung von regionalen Innovationssystemen

eine wichtigere Rolle spielt.

Regionale Innovationssysteme Innovationsprozess Technische Effizienz Netzwerk-Dateneinhullanalyse Bootstrap-

Ansatz China

CHEN K. y GUAN J. Medicion de la eficiencia de los sistemas de innovacion regional de China: aplicacion de un analisis envolvente

de datos de redes, Regional Studies. En este estudio aplicamos un analisis envolvente de datos relacionales de redes para la evaluacion

sistematica de la eficiencia de la innovacion de los sistemas de innovacion regional de China descomponiendo el proceso de inno-

vacion en los dos subprocesos conectados: el desarrollo tecnologico y la posterior comercializacion tecnologica. Los resultados

muestran que solamente una quinta parte de los sistemas de innovacion regional operan en la frontera empırica de las mejores

practicas durante todo el proceso desde el desarrollo tecnologico hasta la comercializacion. Asimismo se observa que existen con-

siderables incoherencias entre la capacidad de desarrollo tecnologico y la capacidad de comercializacion en la mayorıa de sistemas

de innovacion regional y que la capacidad de comercializacion desempena un papel mas importante en el rendimiento de innova-

cion de los sistemas de innovacion regional.

Sistemas de innovacion regional Proceso de innovacion Eficacia tecnica Analisis envolvente de datos de redes (DEA)

Enfoque Bootstrap China

JEL classifications: O11, O18, O32, O47

INTRODUCTION

Science and technology (S&T) production withinregional innovation systems (RISs) is based on a multi-input, multi-output transformation relation, so it canbe assumed that the aggregate performance of the RIScan be characterized by the efficiency of the input–output relation based on a consideration of all relevantinputs and outputs (ZABALA-ITURRIAGAGOITIA et al.,2007; FRITSCH and SLAVTCHEV, 2006, 2007;BROEKEL and BRENNER, 2007; BROEKEL, 2008;GUAN and CHEN, 2010a). It is an important issue toinvestigate the operational quality related to the trans-formation process of limited innovation resources forimproving regional innovative outputs. Furthermore,China’s national innovation system is too large andcomplex to be summarized with a single model, so theregional dimension should not be overlooked (ORGAN-

ISATION FOR ECONOMIC CO-OPERATION AND

DEVELOPMENT (OECD) and MINISTRY OF SCIENCE

AND TECHNOLOGY (MOST), 2007).China’s regions have played and will continue to play a

key role in the advancement of S&T in China (OECDand MOST, 2007). However, for historic reasons thereare significant interregional inequalities in the develop-ment of S&T and the economy among the regions inChina (LEE, 2000; GUAN and LIU, 2005). These inequal-ities have – to some extent – impeded the harmonicdevelopment of the total economy of China (GUAN

and LIU, 2005). Among the critical issues facing policy-makers of regional governments, as well as of thecentral government, are how to construct and improvethe lagging regional innovation systems, enhance the har-monic development of regional innovative capacities andfurther impel sustainable economic development.

With the transformation of a planned economy to amarket one, China has experienced significant changes

in its economic and ownership structure. This trans-formation has also had a large impact on S&Tand econ-omic management (BROCKHOFF and GUAN, 1996;YAM et al., 2004). Each province is gradually becomingrelatively independent from the central authorities.Regional governments all around the country aremaking great efforts to improve the economic andsocial development as harmoniously as possible. There-fore, a province-level empirical analysis seems to be anappropriate approach for investigating China’s nationalinnovation systems (for example, LI, 2009; and GUAN

and CHEN, 2010a), through which the cross-regionalvariation in innovation efficiency performance can beinvestigated and analysed. Besides, both the mobiliz-ation of labour forces and the operation of the wholeinnovation process happen more often from within,rather than between, provinces. This also provides afeasible basis (for more advantages, see LI, 2009) forthe cross-province empirical analysis.

The technical efficiency of a region to a largedegree reflects its capacity of transforming innovativeinvestment into innovative outputs and economicprofits. Therefore, the innovation performancerelated to the technical efficiency of a region is thekey for this region in order to acquire competenceadvantage. Moreover, measuring and comparing tech-nical efficiencies of peer RISs from the S&T input–output transformation perspective, and then identify-ing the interregional disparities that are of great impor-tance to build a sound national innovation system.However, the innovation processes related to theRISs have not been sufficiently understood in virtueof empirical investigations, except for limited studiesrelated to European regions (for example, FRITSCH,2002; FRITSCH and SLAVTCHEV, 2006, 2007;BROEKEL and BRENNER, 2007; BROEKEL, 2008;

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and ZABALA-ITURRIAGAGOITIA et al., 2007). Withrespect to China’s case, some work has beenimplemented (for example, GUAN and LIU, 2005;GUAN and CHEN, 2010a; and LI, 2009). However,these studies only partially characterize the regionalinnovation process.

A typical technological innovation process in theeconomic sense is accomplished only with the firstcommercial transaction (FREEMAN and SOETE, 1997),which is focused on two consecutive but interactivecomponent processes: a technological developmentprocess and a technological commercialization process(PAKES and GRILECHES, 1980; FURMAN et al., 2002;MOON and LEE, 2005). From the perspective ofregional innovation policy-makers, the upstreamdevelopment process is concerned with developmentefficiency; the downstream commercialization processis concerned with commercialization efficiency; andthe whole technological innovation process is con-cerned with overall innovation efficiency. The existingliterature related to the technical efficiency of theRISs mainly focused on the upstream technologicaldevelopment efficiency, and neglected the downstreamcommercialization efficiency and the overall innovationefficiency. Some studies attempted to measure theRISs’ overall innovation efficiency, but usually cameacross the difficulty: are the patents innovative outputsor economic inputs? (for example, ZABALA-ITURRIA-

GAGOITIA et al., 2007).The purpose of the present study is to construct a

complete measurement framework characterizing theRISs’ production framework from original S&T invest-ment to final commercial outputs, and measure theRISs’ process-oriented technical efficiency, which isimplemented in China’s context. It is hoped thatthis study will benefit China’s regional innovation

policy-making, and the conceptual and methodologicalframework can be extended to other regional, nationalor sectoral innovation systems.

The paper is organized as follows. The secondsection introduces a network two-stage conceptual fra-mework for analysing the transformation procedurefrom regional original S&T inputs to intermediate tech-nological outputs and then final economic profits of theinnovation process. The third section describes themethods used and explains how both network DEAand super-efficiency DEA are applied to the proposedtwo-stage network framework for the overall efficiencyof the whole RIS as well as the component efficiency ofthe two internal sub-stages. Moreover, a bootstrapapproach is employed to investigate the influencesfrom outliers (or extremes) and random elements onthe RISs’ efficiency measures. The fourth section pro-vides important empirical findings for evaluatingChinese RISs’ technological development and com-mercialization efficiency. A concluding section providespolicy suggestions for improving the whole regionalinnovation performance in China.

CONCEPTUAL FRAMEWORK AND

MEASURING INDICATORS

An innovation process is essentially a knowledge-generation and -implementation process in which aninnovation production unit utilizes S&T resources fromvarious channels, creates new knowledge and increaseseconomic wealth. Just as DVIR and PASHER (2004)defined, an innovation is the process of convertingknowledge and ideas into a benefit value. Fig. 1 describesthe consecutive innovation process composed of techno-logical development and commercialization processes

Fig. 1. Two-stage conceptual transformation framework of an innovation processNote: For acronyms, see Table 1

Efficiency of China’s Regional Innovation Systems: Application of Network DEA 357

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through which the original innovative investments aretransformed into the final market profits. As depicted,the conceptual framework is focused on thetransformation process of innovative elements, that is, atypical innovation process in terms of a productionpoint of view. Specifically, the upstream technologicaldevelopment process from the original S&T investmentsto the incremental S&T knowledge creation (that is,intermediate technological innovation product) is thefirst stage. It is linked with the second stage, that is, thedownstream technological commercialization processfrom the incremental technological knowledge to themarket profits depending on the non-innovativeintermediate inputs, by the incremental technologicalknowledge. Note that the two sub-processes are rela-tional and not independent since they are connected bythe technological innovation product. This means thatthe intermediate technological innovation product hasa ‘double identity’, which is the output in the first sub-process and the input in the second sub-process interms of its role. Moreover, the implementation of thedownstream commercialization process needs the inter-mediate inputs of non-research and development (R&D)labour force and capital. In this sense, the technologicalinnovation process embedded in an RIS is a networktwo-stage production framework. The integrated con-ceptual framework not only leads one to assign primaryimportance to the upstream technological developmentperformance, but also reminds one of attachingparticular importance to the downstream technologicalcommercialization performance in the economic sense.

Note that the conceptual framework does not takeinto account the moderations from the region-specificstructural or contextual factors1 as defined inFURMAN et al. (2002), BROEKEL and BRENNER

(2007), and BRENNER and BROEKEL (2009). As afore-mentioned, the purpose of the present study is focusedon characterizing the transformation process of physicalS&T innovation resources in each regional unit. Thetwo-stage analytical and measurement perspective ofphysical innovative resources transformation in theinnovation process has been frequently embodied inthe extant study at various levels, such innovationprocess analytical frameworks as the ‘R&D projectdiagram’ (GEISLER, 1995), the ‘R&D processdiagram’ based on a typical R&D laboratory (BROWN

and SVENSON, 1998), the ‘National S&Tactivity frame-work’ (MOON and LEE, 2005), the ‘Innovation pro-duction process framework’ based on the China’shigh-technology industries’ innovation activities(GUAN and CHEN, 2010b), as well as the ‘Knowledgeproduction function diagram’ based on the technologi-cal R&D activities (GRILICHES, 1990). Of course, onehas to recognize that the transformation quality deter-mining the output level for the two individual sub-pro-cesses within a region is affected by some non-physicalregion-specific structural or contextual factors(FURMAN et al., 2002; BROEKEL and BRENNER,2007; BRENNER and BROEKEL, 2009) by interactionswith innovation processes. Among them, the industrialstructure related to the regional economic environmentshould receive special attention.2

Even so, to characterize the network two-stage pro-duction framework, a multiple input–output indicatorssystem is necessary (BROWN and SVENSON, 1998;FURMAN et al., 2002; MOON and LEE, 2005; GUAN

and LIU, 2005; ZABALA-ITURRIAGAGOITIA et al.,2007; GUAN and CHEN, 2010a, 2010b). It is importantto measure system performance as a whole ratherthan quantifying particular measures or key indicators(ZABALA-ITURRIAGAGOITIA et al., 2007). Therefore,the overall hypothesis in this paper advocates that theefficiency of the RIS is jointly and interactively affectedby multiple input–output factors, rather than by a single(combined) factor expressed as an index. In the existingstudy related to the efficiency measures of China’s RISs,the Research Group on Development and Strategy ofScience and Technology of China (RGDSST) and theMinistry of Science and Technology (MOST) ofChina have implemented a basic study by employingthirteen input–output indictors related respectively toregional innovation activities. Since the year 2000, thefindings based on each single performance measurehave been reported in the Annual Report of RegionalInnovation Capability of China 2005–2006 (RGDSST,2006). However, the information on a single measureonly presents a partial comparison across China’s RISsin terms of input or output orientation. One possiblereason for this approach is the fact that the role

Table 1. Indicators in the two-stage concept model

Acronym Full name Unit

Original S&T inputs

E(S&T) Expenditure on science and

technology

RMB10 000

P(S&T) Number of science and technol-

ogy personnel

Persons

FDI Foreign direct investment US$10 000

EIT Expenditure on the import of

technology

RMB10 000

EPDT Expenditure on the purchase of

domestic technology

RMB10 000

VCIDTM Value of contractual inflows in

domestic technical markets

RMB10 000

Intermediate technology outputs

INV Invention Item

UM Utility model Item

DES External design Item

Intermediate physical inputs

CAP Capital stock RMB100 million

LAB Labour 10 000 persons

Final commercialization outputs

GDP Gross domestic products RMB100 million

SNP Sale of new products RMB10 000

VOE Value of export US$10 000

AIUR Annual income in urban residents

per capita

RMB per person


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differences in heterogeneous input–output measuresrelated to both S&T and economic activities duringinnovation processes as well as complex interactionsamong them are still insufficiently understood. There-fore, it is one of the authors’ motivations to balanceand integrate the incommensurable information ofthese heterogeneous factors for systematically and com-prehensively measuring and comparing China’s RISs.Table 1 gives an explanation of the indicators used inthe model. The two-stage transformation process isfirstly defined/based on the thirteen measures used bythe Annual Report of Regional Innovation Capability ofChina in the evaluation of Chinese RISs’ efficiency. Indoing so, the aggregate performance of an RIS simul-taneously determined by all chosen indicators as wellas the interaction between S&Tand economic activitiesduring the innovation process is emphasized.

The first stage of Fig. 1 concentrates on the techno-logical development efficiency of the upstream activitiesin the regional technological innovation process. Thisstage deals with the efficiency issue of original S&Tinputs from various channels and intermediate techno-logical outputs for an RIS. The input factors at theregional level include:

. Expenditure on science and technology (E(S&T)).

. Number of science and technology personnel(P(S&T)).3

. Foreign direct investment (FDI) in monetary terms.

. Expenditure on the import of technology (EIT).

. Expenditure on the purchase of domestic technology(EPDT).4

. Value of contractual inflows in domestic technicalmarkets in monetary terms (VCIDTM).

With respect to the output of stage 1, that is, theintermediate technological products of an innovationprocess, the patents (applied or granted counts) maybe the most appropriate proxy (for example,FRITSCH, 2002; ACS et al., 2002; GUAN and LIU,2005; FRITSCH and SLAVTCHEV, 2006, 2007;BROEKEL and BRENNER, 2005, 2007; BROEKEL,2008; ZABALA-ITURRIAGAGOITIA et al., 2007; GUAN

and CHEN, 2010a, 2010b; and LI, 2009), althoughGRILICHES (1990) pointed out:

not all inventions are patentable, not all inventions are

patented, and the inventions that are patented differ

greatly in quality.

(p. 1669)

The present study employed the counts of appliedpatents as an indicator for the intermediate innovativeproducts of China’s regions. In China, patents are classi-fied into three categories by the National Patent Officeof China: inventions, utility models and external designs(SUN, 2000). Inventions (INV) are ‘new technical pro-posals for products, methods, or both’.5 Compared withinventions, utility models (UM) and external designs(DES) are more incremental. They can be described

as minor innovations which are industry oriented.UM are described as ‘new technical proposals onshape, structure of a product or the combination ofboth’, while DES refer to ‘aesthetics and industry-appli-cable new designs for shape, design or color of aproduct, or their combination’ (SUN, 2003). All threetypes of patents recognized by the Chinese PatentOffice are employed to describe outputs of the S&Tactivities in stage 1 for an RIS. In this sense, the per-formance in the first stage should be viewed as techno-logical development efficiency.

According to MOON and LEE (2005), patent numbersare not only an R&D output indicator, but also play animportant role in economic activities. However,patents cannot describe the performance of economicactivities of a region, and can only be used as a proxyfor the use of new technology (ACS and AUDRETSCH,1993; ACS et al., 2002). The economic indicators thatare available in the China Statistical Yearbooks and usedby the Annual Report of Regional Innovation Capability ofChina are employed as the economic outputs from thesecond stage. These economic indicators include grossdomestic products (GDP), the sale of new products(SNP), the value of export in monetary terms (VOE),as well as the annual income of urban residents(AIUR). Amongst the outputs, GDP, SNP and VOEindices are measured at the level of a complete region,whereas the AIUR indicator is the annual income perurban resident. Therefore, the performance in thesecond stage may be viewed as technological commer-cialization efficiency.

Besides, the implementation of downstream com-mercialization stage is inevitably involved by the partici-pation of labour force (LAB) and capital (CAP) engagedin non-innovative activities (as shown in Fig. 1). There-fore, the two indicators LAB and CAP were additionallyincluded to incorporate those required non-innovativeelements participating downstream economic activities.To account fully for the accumulative effects of the CAPinvestment series on innovation commercialization,regional capital stocks were constructed from theregional investment series by using the perpetual inven-tory method (PIM) (KUO and YANG, 2008; ENFLO andHJERTSTRAND, 2009). The basic idea with PIM is thatthe capital stock K(t+1) in year t + 1 can be estimated as:

K(t+1) = I(t) + (1 − d)K(t)

where I(t) is the gross investment; and d is the averagedepreciation rate in the current period. The initialstock (K(0)) is calculated as:

K(0) =I(0)

g + d

where g is the growth rate for the capital investmentseries. To simplify the calculation, it was assumed that


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investment grows at a constant annual growth rate. Thegrowth rate was measured by the average growth rate ofthe capital investment series for all provinces in theevaluation during the sample period (KUO and YANG,2008). Furthermore, following LI (2003), the averagedepreciation rate was assumed to be 6% (also YOUNG,2003).

Note that when applying DEA, the input and outputvariables in empirical models are thought of as beingdiscretionary and physical indicators, which are usuallymeasured by statistical and observable aggregatevalues. In the essential view, they are those factors thatcan be considered as part of a production process and,therefore, should be considered in the definition ofthe production possibilities set (PPS). Therefore, allindicators in Table 1 are measured by statistical andobservable aggregate values.

METHODS

As mentioned above, the RISs’ S&T production isbased on a multi-input and multi-output relation, inwhich, differently from standard production activity,both inputs and outputs are not only quantitatively het-erogeneous, but also sometimes truly incommensurablein terms of units of measurement (Table 1). Therefore,the model development of reconciling diverse measuressimultaneously oriented to multiple inputs and multipleoutputs is considered above all. DEA, as discussed in thefollowing, is a method particularly suitable for organiz-ing and analysing these complex data measuringinnovation production systems (GUAN et al., 2006a;GUAN and CHEN, 2010a, 2010b).

DEA is non-parametric and deterministic in thesense that all deviations from the frontier are assumedto be the result of technical inefficiency. It has certainadvantage in the analysis of public-sector activities andsemi-public activities such as the RIS (ZABALA-ITURRIAGAGOITIA et al., 2007). It is an effective wayby which one can simultaneously utilize multipleoutputs and multiple inputs with each being stated indifferent units of measurement (CHARNES et al.,1994). As alternative approaches, FRITSCH (2002) andFRITSCH and SLAVTCHEV (2006, 2007) have appliedparametric approaches for estimating regional inno-vation efficiency. Unfortunately, the parametricapproaches are subject to the non-linear form of pro-duction function as well as the multiple outputs,although they can take into account statistical noiseand random elements. The inherent problems withparametric models have led many researchers to applynon-parametric methods. Moreover, the simulationresults given by BANKER and NATARAJAN (2008) indi-cate that traditional DEA-based procedures performbetter than parametric methods in the efficiency esti-mation of individual decision-making units (DMUs).Therefore, it is not surprising that the existing literature

has preferred to employ the DEA-based approaches toinvestigate regional or national innovation systems (forexample, ROUSSEAU and ROUSSEAU, 1997, 1998;GUAN and LIU, 2005; ZABALA-ITURRIAGAGOITIA

et al., 2007; BROEKEL and BRENNER, 2007;BROEKEL, 2008; SHARMA and THOMAS, 2008; andGUAN and CHEN, 2010a).

With particular respect to modelling the conceptualframework shown in Fig. 1, where the whole RIS trans-formation process is composed of two interactive com-ponent processes, first of all it is required that anintegrated methodological framework simultaneouslyincorporates the two sub-technologies defined by tech-nological development and commercialization activitiessets. In the extant study, the overall efficiency measure-ment of the whole RIS treats reference technologies asblack box (GUAN et al., 2006a; ZABALA-ITURRIAGA-

GOITIA et al., 2007; LI, 2009; GUAN and CHEN,2010a), which neglects the operation of the interactingsub-processes within it. The relational network pathdiagram of the RIS given in Fig. 1 shows that the tech-nological development process is not independent ofthe technological commercialization process. Thismeans that the two sub-technologies are connected byintermediate products into a hybrid network pro-duction technology (FARE and GROSSKOPF, 1996a,1996b). Clearly, traditional standard DEA models arenot appropriate for the overall efficiency measure ofthe network two-stage production system since theyneglect the operation of internal interacting sub-processes (KAO, 2009).

The activity analysis model developed by FARE andGROSSKOPF (1996a, 1996b), which explicitly takesinto account intermediate inputs or products, is suitedto the network two-stage framework. Unfortunately,the interdependent relationship between componentprocesses was not incorporated into the envelopmentmeasurement framework for overall efficiency (KAO,2009). So did COOK et al.’s (2000) multi-componentratio model. In fact, there is a condition for embodyingthe interdependent relationship that the intensity vari-ables (for envelopment form) or the multipliers (forratio form) on the intermediate measures participatingin the two sub-processes must be equal. Moreover,these authors are not explicitly interested in obtainingmeasures of efficiency at each stage. To obtain theperfect modelling for overall efficiency and the twocomponent efficiencies with the consideration of aninterdependent relationship between the two sub-pro-cesses, the present study employs KAO’s (2009) relationalnetwork DEA model, which is an extended model ofFARE and GROSSKOPF’s (1996a, 1996b). This modelcan help to measure the technical efficiency of thewhole RIS and the two sub-processes in an integratedanalytical framework in the sense that the componentand overall efficiencies are estimated simultaneously.

More importantly, KAO’s (2009) relational networkDEA model presents discriminative efficiencies


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measures due to including more constraints on theinternal operations in contrast to the traditional one-stage Charnes, Cooper & Rhoades (CCR) DEAmodel (CHARNES et al., 1978). This is an attractiveproperty in the special case of a small sample size. Inempirical studies, a difficulty frequently encounteredin real-world applications of DEA is that the numberof DMUs that are available is usually limited. A smallsample size often produces results that are misleading.Moreover, it produces a relatively large proportion ofefficient DMUs, which makes the subsequent rankingsdifficult. This is also possible even when satisfying therules of thumb that are used to constrain the numericalrelationship between the number of observations andthe sum or product of numbers of inputs and outputs(COOPER et al., 2004; DYSON et al., 2001). In theemployed network DEA, the calculated efficiencyresults are more discriminative and robust (KAO,2009). This will be further confirmed in the empiricalstudy in the fourth section.

It is desirable that the employed network DEA modelmakes the component efficiency measures connected invirtue of constructing a network production frontierdetermined by all heterogeneous component sub-technologies, and it presents cooperative and interde-pendent efficiencies measures. However, the traditionalindependent efficiency measures of individual sub-stages are attractive due to them depending on theirown homogeneous technologies (SEIFORD and ZHU,1999). In this paper, the super-efficiency formulations(ANDERSEN and PETERSEN, 1993) of CCR DEAmodel are further employed to calculate super-efficien-cies, which can produce completely comparableranking results for individual efficiency scores sets.

Despite its popularity, deterministic DEA estimatorshave some obvious drawbacks.6 First, it has long beenrecognized that DEA estimates of inefficiency aresensitive to outliers in the data. Second, DEA estimatorsalso suffer from inherent bias and stochastic elements(SIMAR and WILSON, 1998; ENFLO and HJERT-

STRAND, 2009). Moreover, in many applications,including that used in this paper, there are simply toofew observations available to obtain meaningfulestimates of inefficiency using deterministic DEA esti-mators. This study followed SIMAR and WILSON’s(1998) bootstrap methods to estimate the confidenceefficiency intervals in the context of the out-orientedCCR DEA model, which is used to investigate theinfluences from outliers and random elements.

EMPIRICAL ANALYSIS

Sample data

All the data used in this paper were derived from theChina Statistical Yearbook and China Statistical Yearbookon Science and Technology from 1995 to 2007 publishedby National Bureau of Statistics and Ministry of

Science and Technology between 1996 and 2008.Thirty7 province-level regions were chosen to be usedas the DMUs. Moreover, all the economic data weredeflated by the Chinese retail price index by taking1995 as the base year.

The output in S&T production is lagged, but with alag structure which is not fixed (BONACCORSI andDARAIO, 2003). In the extant work, a two-year lagfrom innovative investment to patent application inChina’s RISs was considered (for example, GUAN andLIU, 2005; and GUAN and CHEN, 2010a). In thisstudy, following their methods, two years’ delaybetween inputs and outputs in each sub-processduring the whole innovation process was taken withinthe RISs. This means that the delay time in the wholeinnovation process, that is, the production period,amounts to four years. In order not to overload thispaper, but providing dynamic trends of each RIS’s inno-vation performance, only three consecutive productionperiods – 1995–1999, 1999–2003 and 2003–2007 –were chosen. In this sense, the original input years are1995, 1999 and 2003, and the corresponding inter-mediate output years are 1997, 2001 and 2005 duethe two years’ lag which are accompanied by the inter-mediate input years. Accordingly, the final output yearsare 1999, 2003 and 2007, respectively, due to anothertwo years’ lag.

Note that the deepening of China’s S&T reform thatstarted in 1995 went through the whole period of years1995–2007 (OECD and MOST, 2007). Therefore, theempirical results at the average level over the threeobserved periods can shed light on the dynamic per-formance of China’s innovation policies. Moreover,the year 2001 when China joined the World TradeOrganization (WTO) belongs to the second period ofyears 1999–2003. In other words, the empiricalresults can additionally provide some evidence of com-paring the change of innovation performance beforeand after China joined the WTO.

Interdependent efficiency using network DEA

It is certain that the whole RIS is technically efficientonly when the two component stages (or processes)are technically efficient since the whole network pro-duction technology is composed of the two connectedproduction sub-technologies controlling two stages.Theoretically, most RISs could be expected to beclose to the network technology frontier and behaveas efficient units, but the results reported in Table 2show that there is only a small percentage being techni-cally efficient. Specifically, six regions (20%) – Fujian,Guangdong, Hainan, Qinghai, Ningxia and Xinjiang– were technically efficient for the whole RIS duringthe first production period across years 1995–1999(column 2); nine regions (30%) – Shanxi, Neimenggu,Shanghai, Fujian, Guangdong, Hainan, Gansu, Qinghaiand Xinjiang – were technically efficient during the


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second production period across years 1999–2003(column 5); and six regions (20%) – Tianjin, Shanghai,Guangdong, Hainan, Qinghai and Xinjiang – per-formed efficiently in the latest production periodacross years 2003–2007 (column 8). Among them,four regions (13%) – Guangdong, Hainan, Qinghaiand Xinjiang – were stably efficient during the threeperiods across years 1995–2007. Beijing, as thebiggest receipt of S&T inputs and with most univer-sity/public research institutes, was on the best-practicefrontier for technological development performance;however, its overall system performs inefficiently dueto its inefficiency in technological commercializationperformance. This is inseparable from its specific inno-vative environment where there is the lion’s share ofbasic research in public institutes but not an industrialbase able to commercialize the innovative results(OECD and MOST, 2007). Therefore, the primarytask for it is to promote the transformation of techno-logical products. Besides, three regions – Hebei, Zhe-jiang and Guizhou – displayed the same performance.The reverse is true for Shanghai, where an activebusiness sector is to some extent deprived of a strong,

application-oriented basic research infrastructure(OECD and MOST, 2007). Therefore, its innovationactivities perform efficiently at the various stagesduring the two later observed periods.

Fig. 2 displays the trend of the average (that is,nation-level) innovation performance of individual effi-ciency scores sets over the three production periods.The change trend of the average efficiency score forthe overall stage as well as stage 2 focused on techno-logical commercialization first decreased during thetransition period 1999–2003 and later increasedduring the nearer period 2003–2007, while thechange trend of the average efficiency score for stage1 focused on technological development behavedoppositely, that is, first increased and later decreased.There are many complex environmental determinantsof the trend changes, such as economic environmentvariations, which are not topic issues in this study. It isargue here that the same trend of the overall stage asstage 2 in terms of average efficiency score indicatesthat stage 2 to a great extent determines the perform-ance change of the overall stage, that is, it plays amore important role in the regional overall innovation

Table 2. Each regional innovation system’s network efficiency in each stage during the three individual production periods

Region

Period 1: 1995–1999 Period 2: 1999–2003 Period 3: 2003–2007

Overall stage Stage 1 Stage 2 Overall stage Stage 1 Stage 2 Overall stage Stage 1 Stage 2

1. Beijing 0.9112 0.6413 0.9915 0.8094 1.0000 0.8094 0.9978 1.0000 0.9978

2. Tianjin 0.9619 0.3994 0.9908 0.9866 0.9029 1.0000 1.0000 1.0000 1.0000

3. Hebei 0.8698 1.0000 0.8698 0.7747 1.0000 0.7747 0.9539 1.0000 0.9539

4. Shanxi 0.9953 0.8087 1.0000 1.0000 1.0000 1.0000 0.9117 0.5202 1.0000

5. Neimenggu 0.9897 1.0000 0.9897 1.0000 1.0000 1.0000 0.9551 0.5792 1.0000

6. Liaoning 0.9473 0.6010 1.0000 0.8924 0.3878 1.0000 0.9089 1.0000 0.9089

7. Jilin 0.9100 0.3780 1.0000 0.9137 1.0000 0.9137 0.9723 0.7513 1.0000

8. Heilongjiang 0.9051 0.8388 0.9467 0.9756 0.8868 0.9811 0.9777 0.7263 1.0000

9. Shanghai 0.8934 0.2395 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

10. Jiangsu 0.7897 0.2850 0.9462 0.7179 0.3762 1.0000 0.7624 0.4787 1.0000

11. Zhejiang 0.8585 1.0000 0.8585 0.9938 1.0000 0.9938 0.9174 1.0000 0.9174

12. Anhui 0.9499 0.9047 1.0000 0.8460 0.4219 0.8657 0.8478 0.4179 0.8864

13. Fujian 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9300 0.7827 1.0000

14. Jiangxi 0.9485 0.9991 0.9489 0.9425 0.9777 0.9430 0.7872 0.5594 0.8692

15. Shandong 0.7606 0.7229 0.8172 0.7037 0.7258 0.8122 0.7525 0.5103 0.9133

16. Henan 0.7754 0.6786 0.8258 0.7057 0.4735 0.7561 0.8395 0.6918 0.8773

17. Hubei 0.6527 0.4566 0.7630 0.6251 0.6861 0.6595 0.7737 0.6215 0.8374

18. Hunan 0.8894 0.8892 0.9069 0.8451 0.9888 0.8461 0.9309 0.7278 0.9536

19. Guangdong 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

20. Guangxi 0.9479 0.9072 0.9768 0.9927 1.0000 0.9927 0.9855 0.6317 1.0000

21. Hainan 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

22. Chongqing 0.8858 0.4394 0.9235 0.8440 0.5604 0.8965 0.9677 0.9593 1.0000

23. Sichuan 0.7933 0.4685 0.9018 0.7891 0.7483 0.8220 0.8624 1.0000 0.8624

24. Guizhou 0.9367 1.0000 0.9367 0.7414 1.0000 0.7414 0.7955 1.0000 0.7955

25. Yunnan 0.9357 0.9179 0.9793 0.7823 0.7457 0.7913 0.7248 0.6446 0.7618

26. Shannxi 0.8598 0.3447 0.8994 0.6928 0.7030 0.7197 0.7004 0.3624 0.7485

27. Gansu 0.9011 0.2558 0.9213 1.0000 1.0000 1.0000 0.9004 1.0000 0.9004

28. Qinghai 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

29. Ningxia 1.0000 1.0000 1.0000 0.9958 1.0000 0.9958 0.9411 0.5142 0.9454

30. Xinjiang 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Mean 0.9090 0.7392 0.9465 0.8857 0.8528 0.9105 0.9032 0.7826 0.9376

Standard deviation (SD) 0.0861 0.2779 0.0656 0.1229 0.2143 0.1085 0.0949 0.2226 0.0778

Source: Authors’ calculations.


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performance in contrast to stage 1. The correlation ana-lyses among individual stages shown in Table 3 furtherconfirm this judgement.

Since the three efficiencies are estimated in an inte-grated framework constrained by the single networkproduction technology, it is appropriate to comparetheir efficiency scores directly. Fig. 2 shows that thecurve of efficiency scores for stage 2 is above thecurve for stage 1. This means that Chinese RISs’ capa-bility of transforming technology into economicalprofits is relative better, which is related to China’sreforming performance in enhancing firm’s innovationcapabilities and the commercialization of pubicresearch. Of course, the superior performance of stage2 over stage 1 is also connected with accelerated learn-ing from international good practices fostered by WTOmembership and observership in the OECD’s Commit-tee on Science and Technological Policy (CSTP) underfast economic growth as well as pressure from technol-ogy-based competition in domestic and internationalmarkets (OECD and MOST, 2007). With respect tooverall performance, it is natural that the average per-formance curve is between them since it is determinedby them simultaneously.

Since the overall efficiency scores are simultaneouslydetermined by the sub-technologies of both stages 1 and

2, it is judged that there is a relational relationshipbetween rank results based on the overall efficiencyscores and the two component efficiency scores. Spear-man’s rank and Kendall’s tau_b correlation coefficientsshown in Table 3 confirm the judgement. However,the rank correlation coefficients between the overallstage and stage 2 have more significant values whetherfor Spearman’s rank or Kendall’s tau_b method, whichfurther confirms that technological commercializationsignificantly influences the innovation performance ofan RIS. Besides, a series of non-significant rank coeffi-cients comparing stages 1 and 2 shows that there is noinevitable relational linkage between component per-formances, although they are physically connectedinto the context of the RISs’ integrated productionframework. The result also reflects the unmatchedrelationship between upstream and downstream com-ponent performances, which is related to the disparitybetween research and economic environments in mostregions.

Fig. 3 illustrates the distribution of collected effi-ciency scores sets (n ¼ 90) obtained from the networkfrontier technology for the three individual stages,where Fig. 3(a)–(c) are, respectively, based on thenetwork efficiency scores of the overall stage, com-ponent stage 1 and component stage 2 during thethree different production periods across years 1995–2007. Some important findings are as follows.

Firstly, the distribution of the three efficiency scoressets is not normal during the whole period, but exhibitsa desirable asymptotic property which is identical to thetrue distribution of efficiency. Therefore, it is reasonableto expect that the problems associated with using effi-ciency estimators will be less acute using a DEA ratherthan a parametric approach (BANKER and NATARAJAN,2008). As hypothesized by ZABALA-ITURRIAGAGOITIA

et al. (2007), since the non-parametric frontier-basedmethodology looks for best-practice observations andtakes them as a benchmark, one can expect a relativelyhigh number of observations to be 100% efficient.Theoretically, most observations could be expected tobe close to the frontier and to behave as efficientunits, but the histograms in Fig. 3 show there is widevariance across China’s RISs in the efficiency perform-ance of each stage. In contrast to the system and

Fig. 2. Comparison between average network efficiency scoresin the three individual production periods

Source: Authors’ calculations

Table 3. Correlation coefficients between three stages in terms of the calculated network efficiency scores during the three individualproduction periods

Method Stage


Overall stage Stage 1 Overall stage Stage 1 Overall stage Stage 1

Spearman’s rho 1 0.578∗∗ 0.681∗∗ 0.585∗∗

2 0.846∗∗ 0.238 0.848∗∗ 0.361 0.786∗∗ 0.211

Kendall’s tau_b 1 0.435∗∗ 0.539∗∗ 0.442∗∗

2 0.711∗∗ 0.182 0.795∗∗ 0.308∗ 0.689∗∗ 0.163

Note: ∗∗Correlation is significant at the 0.01 level (two-tailed); and ∗correlation is significant at the 0.05 level (two-tailed).



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commercialization efficiency scores, the histogramrelated to stage 1 in Fig. 3(b) shows there is wider var-iance in the technological development efficiencyscores. In terms of efficiency score ranges, the systemand commercialization efficiency scores are between0.6 and 1.0, while the development efficiency scoresare between 0.2 and 1.0. Moreover, the distributionof the efficiency scores of the technological develop-ment between 0.20 and 0.95 did not display anobvious fluctuation. Both the bigger standard deviation(SD) of 0.2418 and the smaller mean of 0.7916 confirmthe visual judgement.

As demonstrated by ZABALA-ITURRIAGAGOITIA

et al. (2007), who are concerned about comparingEuropean regional innovation performance, underlyingthe research related to regional efficiency-based inno-vation performance is the fact that although theamounts of resources within an RIS are important, itis not evident that those regions with larger amountsof resources are the most efficient ones. The presentstudy, which is based on China’s regional data, alsomade the same conclusion. In the case of China, onemajor reason is that the stock of intellectual capitalfalls behind the capital-intensive investment devotedto building the ‘hardware’ of the innovation system inChina, which creates the over-capacity in the use ofsome research infrastructures (OECD and MOST,2007). A series of negative correlation coefficientsbetween the overall efficiency and the share of S&Tresources (E(S&T) and P(S&T)) at the regional levelreported in Table 4 confirm the conclusion. Besides,the diminishing trend of coefficients (from statisticallysignificant to insignificant) related to both E(S&T)and P(S&T) from 1995 to 2007 shows the gradualimprovement of China’s innovation policy in the tran-sition from a centrally planned innovation model to amarket-based innovation model.

It is noted that in contrast to the calculated efficiencyresults from the one-stage CCR DEA model(CHARNES et al., 1978), the present network efficiencyresults are more discriminative, which can improve thelow discrimination power caused by the limited numberof observations in the one-stage CCR measure. Com-paring the calculated efficiency results respectivelyfrom two models (see Table A1 in the Appendix andTable 2), it is easy to find that the efficiency scoresfrom the network DEA model in this study are nothigher than those from the CCR model. Furthermore,in the efficiency classification, the proportion of effi-cient DMUs in the network DEA measure is relativelysmaller, which is especially true for the overall systemefficiency measures. The summary statistics show thatthe number of efficient DMUs is less than one-thirdin the network DEA measure, while that is more thantwo-thirds in the one-stage CCR measure. Moreimportantly, the employed network DEA measurepresents a desirable logic. In this context, the wholeregional innovation production is efficient only when

Fig. 3. Distribution of united regional innovation systems’(RISs) network efficiency scores during three production periods

for the three individual stagesSource: Authors’ calculations


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the two component processes are efficient (Table 2).The efficiency results from the CCR measure do notobey the logic (see Table A1 in the Appendix). Forexample, Tianjin is efficient in the whole regional inno-vation process during period 1 (years 1995–1999),while it is inefficient in stage 1 (the technologicaldevelopment process) during the same period. Clearly,this is not reasonable intuitively when overall systemefficiency is considered.

Independent component super-efficiency

DEA efficiency scores display relative performancesamong peer DMUs, so it is more appropriate tocompare the ranks rather than the numerical scoresunder different samples, that is, in the context of differ-ent technology production frontiers (BROCKETT andGOLANY, 1996). To obtain a complete ranking of allDMUs, there has to be a full degree of freedom forDEA models. The number of degrees of freedom willincrease with the number of DMUs and decrease withthe number of inputs and outputs (COOPER et al.,2004). In the present case, most regions are CCR effi-cient (see Table A1 in the Appendix) for two individualcomponent stages in each period, although it can satisfythe popular rule of thumb, that is:

n ≥ max{m × s, 3(m + s)}

where n is the number of DMUs; m is the number ofinputs; and s is the number of outputs (COOPER et al.,2004).

In order to discriminate efficient regions fully, thesuper-efficiency formulation of CCR model isemployed here to calculate the super-technical effi-ciency scores of the two sub-processes. The rankingsof the super-efficiency for stages 1 and 2 are related tox- and y-axes, respectively, that is, the x-axis refers toa region’s position for technological development effi-ciency performance among thirty regions, while they-axis refers to a region’s position for technologicalcommercialization efficiency performance amongthem. Three rectangular coordinate systems are pre-sented (Fig. 4(a)–(c)) to depict the respective relativeperformance of each region during the three

consecutive periods: years 1995–1999, years 1999–2003 and years 2003–2007.

Five key innovative regions – Beijing, Guangdong,Shanghai, Jiangsu and Zhejiang – showed different per-formance in stages 1 and 2 during the first period acrossyears 1995–1999. As shown in Fig. 4(a), Zhejiang andShanghai displayed opposite status in stages 1 and2. Zhejiang (number (6, 28)) had a relatively bettertechnological development efficiency and a relativelyworse technological commercialization efficiency,while Shanghai (number (25, 1)) shows the opposite.Guangdong (number (7, 2)) performed better in bothcomponent efficiencies. Beijing (number (3, 13)) hasobtained a better technological development efficiencyperformance, while its commercialization efficiencywas in the middle level. Jiangsu (number (26, 9)) paidincreasing attention to business R&D (OECD andMOST, 2007), so it has obtained a relatively better com-mercialization efficiency in contrast to its developmentefficiency. In the case of Guangdong and Shanghai, it isenough to remember that regional industries are crucialto commercial transformation in stage 2. During thesubsequent production period across years 1999–2003, Zhejiang’s commercialization efficiency increased(number (6, 15)), and decreased during the final pro-duction period: years 2003–2007 (number (8, 21)). Inupstream development efficiency, Shanghai hasbecome better during the middle production periodacross years 1999–2003 (number (14, 6)), and hasobtained additional progress in ranking during thefinal production period (number (15, 4)).

With respect to the largest difference, it is Gansuduring the first production period across years 1995–1999 with a ranking difference of 25. The secondlargest difference occurs at Shanghai with a rankingdifference of 24. During both the later two productionperiods across years 1999–2007, it is Guizhou that hasobtained a largest ranking difference of 26. The secondlargest difference occurs for Jiangsu with a ranking differ-ence of 23 during the middle period, and Neimengguwith a ranking difference of 22 during the latestperiod. These considerable gaps between stages 1 and2 in terms of technical efficiency show that there is anunmatched relationship between component efficien-cies that does harm to the whole RISs’ technicalefficiency. Generally speaking in terms of all regions, if

Table 4. Correlation coefficients between the overall network efficiency scores and the share of science and technology (S&T) resources(E(S&T) and P(S&T)) during the three individual production periods

Method


E(S&T) P(S&T) E(S&T) P(S&T) E(S&T) P(S&T)

Pearson –0.625∗∗ –0.414∗ –0.612∗∗ –0.313 –0.259 –0.018

Spearman’s rho –0.692∗∗ –0.651∗∗ –0.597∗∗ –0.443∗ –0.253 –0.165

Kendall’s tau_b –0.501∗∗ –0.510∗∗ –0.454∗∗ –0.334∗ –0.192 –0.131

Note: ∗∗Correlation is significant at the 0.01 level (two-tailed); and ∗correlation is significant at the 0.05 level (two-tailed).



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Fig. 4. Ranking comparisons of regional innovation systems’ (RISs) performance according to the calculated super-efficiency scores ofstage 1 (technological production process) and stage 2 (technological commercialization process) in the three individual production

periodsNote: The first and second numbers in parentheses are the ranking numbers of the super-efficiency scores for stages 1

and 2, respectively. Source: Authors’ calculations


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the two component indicators coincided, one wouldexpect the majority of points to be along a 45% line.But this is not the case for three samples. The R2-values are below 0.1. Besides, rank correlation coeffi-cients were calculated in order to check this evidenceempirically. Spearman’s rank correlation coefficientsfor three scenarios through years 1995–1999, years1999–2003 and years 2003–2007 are 0.006, 0.238 and0.115, respectively, which are not statistically significanteven at the 10% level. This indicates that most of China’sregions are in an uncoordinated state from the upstreamtechnological development efficiency to the down-stream technological commercialization efficiency.This result further confirms the above finding.

Influences analysis from outliers

Note that the above empirical analyses are based on thedeterministic DEA models. They do not take intoaccount the outliers,8 inherent bias and noise in the stat-istical sense (SIMAR and WILSON, 2008). As highlightedby BROEKEL and BRENNER (2007) and BROEKEL

(2008), it is key for regional innovation efficiencymeasures since innovation processes, even at a systemlevel, are to some extent non-deterministic in nature.If not considered, there is usually a bias in efficiencyestimates. To provide more information to potentialusers or policy-makers, the influences from outliers inthe sample data are estimated herein.

To shed light on the influences from outliers and stat-istical noise in the regional estimates, SIMAR and

WILSON’s (1998) bootstrap method was followed to esti-mate bias-corrected efficiency scores aswell as confidenceintervals of out-oriented constant returns to scale (CRS)efficiency scores during three periods for each region (seeTables A2(a) and (b) in the Appendix), which can be usedto base policy activities. The results indicate substantialbias. Since the bias estimates are large relative to the stan-dard error estimates, the bias-corrected efficiency esti-mates are preferred to the original estimates. With thebias correction, none of the resulting efficiency estimatesequals 1. Despite the fact that the sample size is rathersmall, the confidence intervals are of moderate length.In this sense, the innovation performance measure forChina’s province-level regions in this study is only an esti-mate, and it therefore has some limitations.

CONCLUSIONS AND POLICY

IMPLICATIONS

The purpose of this study is to measure China’s regionalinnovation performance associated with non-parametricproduction frontier-based measurement modelsoriented to the two interdependent component pro-cesses (stages) as well as multiple heterogeneous andincommensurable inputs/outputs. This motivation notonly makes it possible to integrate the various indicatorscharacterizing component processes for the aggregateperformance comparisons in the whole innovationprocess as well as the individual component processesof peer regions, but also can help in revealing internal

Fig. 4. Continued


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inefficiencies embedded in internal processes as well asthe physical relationships between the whole processand the individual two sub-processes, and between thetwo sub-processes.

For this purpose, this study employed a relationalnetwork data envelopment analysis (DEA) model aswell as the super-efficiency formulation of theCharnes, Cooper & Rhoades (CCR) model to estimatethe innovation performance of thirty of China’s pro-vince-level regions based on three consecutive scenarios(that is, complete production periods) starting in theyear 1995 and ending in the year 2007 with fixedtime lags. In addition, statistical inference was providedfor the out-oriented CCR efficiencies in order to diag-nose the influences from outliers or random elementsfor additional information.

Findings and conclusions

The evidence shows that most of China’s regionsperform inefficiently in the innovation performance.Only one-fifth of the studied regions perform effi-ciently in the whole innovation process and the down-stream technological commercialization process. Thepercentage of the regions performing efficiently in theupstream technological development process becomeslarger, but does not exceed one-third. As OECD andMOST (2007) argued, the inefficiencies in China’sinnovation system are inherent in the position ofChinese actors on the learning curve of best practices.

The authors did not find a significant cooperativerelationship between component processes whichtogether determine the innovation performance of thewhole regional innovation system (RIS). This findingreveals that there are some serious inconsistenciesbetween technology capability and economic perform-ance in most Chinese RISs. This means that theircapability to transform technological development effi-ciency to commercialization efficiency is relativelypoor. The empirical result is consistent with China’sactual situation (GUAN et al., 2006b). One key reasonis that there is a contradiction between upstreampublic research organization-centred research processesand downstream firm-centred commercializationprocesses in China. Moreover, it is revealed that thedownstream commercialization process plays a moreimportant role in the whole regional innovationprocesses in contrast to the upstream developmentprocesses. This fully supports the decision of promotinga firm-centred innovation system by China’s govern-ment in the 2006 national science and technology(S&T) Strategic Plan. Finally, by correlation analysisbetween the regional distribution of S&T-related invest-ments in expenditure and personnel and the overalltechnical efficiency scores, the authors have found thatheavy investment in S&T, although necessary for catch-ing up with highly developed countries, does notnecessarily bring high efficiency for the RIS and

cannot guarantee success in innovation. This confirmsthe OECD and MOST’s (2007) conclusion: theimpressive investment in resources has contributed sig-nificantly to the rapid socio-economic progress regis-tered in China in the last decade, but it has not yettranslated into a proportionate increase in innovationperformance. However, the correlations have obtainedgradual improvement from 1995 to 2007.

Policy implications and recommendations

The findings have important policy implications. First,governments at all levels should change their emphasisfrom current sharply increasing S&T investment to har-monizing tradeoffs between multidimensional innova-tive resources. Only in this way will the investment bereturned in an efficient manner in the economic sense.Second, the Chinese central government allocatingS&T resources should take into account marketdemand and shake off dependence on the planned inno-vation model. The confirmed crucial role of the techno-logical commercialization in the regional innovationperformance provides sufficient evidence. Of course, itis also necessary that government policy on pubicresearch should seek to strike a better balance betweenmission-oriented research and research driven bymarket demand (OECD and MOST, 2007). Moreover,the unmatched relationship between upstream techno-logical development and downstream commercializationfurther promotes the transformation of China’s inno-vation system into a more market-based system fromthe planned economic system. The unmatched result isalso inseparable from the top-down government policyefforts, where government policies have focused on thesupply of innovation inputs and support instruments,and on the development of formal public institutions(for example, publicly funded R&D centres, technologytransfers, education and training, and marketing inforeign markets). However, they usually neglect firminnovation orientations and strategies, absorptive capa-bilities, and specific demands in the region (GUAN

et al., 2009). It is especially true for China in the transitionfrom a reliance on technological import to one of indi-genous innovation.

From the funding instruments, it is advisable toimplement an improved mix of instruments to supportmore efficiently both market-led and mission-orientedS&T development and innovation. However, some fra-mework conditions are insufficiently conducive tomarket-led innovation in China, especially those relat-ing to corporate governance, financing of R&D andtechnology-based entrepreneurship, and enforcementof intellectual property rights (OECD and MOST,2007). Their improvement could create the necessaryconditions for the operation of an open innovationmodel in which indigenous innovation capabilitiesand R&D-intensive foreign investment could bemutually reinforcing.


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Limitations and future studies

It is wise to point out several caveats to this empiricalstudy, which motivates future studies. One limitation,which is common for the studies of cross-country orcross-region S&T performance comparisons, is thatone cannot obtain completely independent obser-vations (that is, regions in this study) in terms of S&Tinput–output correspondence. One must recognizethat there would be collaborations and communicationsin S&T activities among those chosen regions. In thissituation, the efficiency measurements would beimproved by collecting more detailed measures infuture work. The second limitation is that this studyonly included various physical and discretionary(controllable) resource-based innovation inputs, andit neglected the exogenous effects of region-specificnon-discretionary (uncontrollable) structural or contex-tual factors related to innovation infrastructure, policysetting as well as economic environment (for example,industrial structure) in efficiency estimations. Onekind of potential study in this context is to incorporateimportant non-discretionary structural or contextualfactors (for example, BROEKEL and BRENNER, 2007;and BRENNER and BROEKEL, 2009) into the presentnetwork DEA model from the systems perspective.However, uncontrollable factors influencing the per-formance of a production unit are out of the controlof the management. The direct method includinguncontrollable environmental variables in the DEAmodels together with physical innovation inputs andoutputs requires a prior understanding of the influencedirection of an uncontrollable variable (YANG andPOLLITT, 2009). Furthermore, in this situation, someinefficient decision-making units (DMUs) mightbecome efficient as the number of the uncontrollablevariables included increases (YANG and POLLITT,2009). Therefore, the direct method is not advisablein the context of DEA-based measurement. Based onthe extant study, there are two motivations worthy ofinvestigation. One is to investigate the effects of struc-tural or contextual factors on the efficiencies of the

whole regional innovation process and the two individ-ual component process variations by a second-stepregression (FRITSCH and SLAVTCHEV, 2010). There isa valuable subsequent study for the regional innovationpolicy development to determine cross-regional effi-ciency variation for the three individual processes.The other motivation is to follow FRIED et al.’s (1999,2002) measurement technique for pure regional inno-vation efficiency removing the exogenous effects ofthe operating environment determined by structuralor contextual factors (WANG and HUANG, 2007).Although the latter will face a challenge in thecontext of the network DEA measurement, it wouldprovide an unbiased efficiency estimation of regionalinnovation processes. The third limitation is attributableto the deterministic property of the network DEAmodel. As indicated by the diagnostic analysis for theinfluences of outliers and statistical noise on innovationefficiency estimations, the efficiency measurement inthe context of deterministic DEA models is biased.Thus, it is necessary in a future study to employ somerobust non-parametric approaches (for example,SIMAR and WILSON’s, 2008, order-m frontier analysis)as the estimation techniques for the current networkDEA model, although such a study would also facemore challenges in the context of the network DEAmeasurement.

Acknowledgements – This research was funded by the

National Social Science Foundation of China (Project

Number 08BJY031), the National Natural Science Foun-

dation of China (Project Number 70773006), and the Shang-

hai Leading Academic Discipline Project (Project Number

B210). The authors are very grateful for the valuable com-

ments and suggestions from the anonymous reviewers and

the Editors, which significantly improved the article. The

authors also thank Professor Ronald Rousseau, KHBO,

Industrial Sciences and Technology, Belgium, for his

English corrections to this paper. The authors’ names are

ordered alphabetically and they contributed equally to the

writing of this paper.


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APPENDIX

Table A1. Each regional innovation system’s Charnes, Cooper & Rhoades (CCR) efficiency in each stage during the three indi-vidual production periods

Region


Overall stage Stage 1 Stage 2 Overall stage Stage 1 Stage 2 Overall stage Stage 1 Stage 2

1. Beijing 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

2. Tianjin 1.0000 0.5104 1.0000 1.0000 0.9623 1.0000 1.0000 1.0000 1.0000

3. Hebei 1.0000 1.0000 0.8785 0.9214 1.0000 0.8385 1.0000 1.0000 1.0000

4. Shanxi 1.0000 0.9019 1.0000 1.0000 1.0000 1.0000 1.0000 0.5397 1.0000

5. Neimenggu 1.0000 1.0000 0.9965 1.0000 1.0000 1.0000 1.0000 0.5936 1.0000

6. Liaoning 1.0000 0.7603 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9674

7. Jilin 1.0000 0.6789 1.0000 1.0000 1.0000 0.9340 1.0000 0.8136 1.0000

8. Heilongjiang 1.0000 0.9749 1.0000 1.0000 1.0000 1.0000 1.0000 0.9381 1.0000

9. Shanghai 1.0000 0.5767 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

10. Jiangsu 0.9422 0.5631 1.0000 0.8894 0.5844 1.0000 1.0000 0.5588 1.0000

11. Zhejiang 1.0000 1.0000 0.8878 1.0000 1.0000 1.0000 1.0000 1.0000 0.9664

12. Anhui 1.0000 0.9429 1.0000 0.8907 0.6976 1.0000 0.9694 0.5259 0.9505

13. Fujian 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.7932 1.0000

14. Jiangxi 1.0000 1.0000 0.9696 1.0000 1.0000 0.9460 0.8640 0.5677 0.8803

15. Shandong 1.0000 0.8463 0.8282 0.9755 0.8396 0.8705 1.0000 0.9728 1.0000

16. Henan 0.9683 0.8395 0.9744 0.7801 0.7407 0.9536 1.0000 0.9077 0.9504

17. Hubei 0.7685 0.5337 1.0000 0.8908 0.7214 0.7124 0.8825 0.7052 0.9046

18. Hunan 1.0000 1.0000 0.9090 0.9720 1.0000 0.8797 1.0000 0.9247 0.9779

19. Guangdong 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

20. Guangxi 1.0000 0.9726 1.0000 1.0000 1.0000 1.0000 1.0000 0.6962 1.0000

21. Hainan 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

22. Chongqing 1.0000 0.4985 1.0000 1.0000 0.8351 1.0000 1.0000 1.0000 1.0000

23. Sichuan 0.9800 0.5954 0.9190 0.9180 1.0000 0.8354 1.0000 1.0000 0.8876

24. Guizhou 1.0000 1.0000 0.9391 1.0000 1.0000 0.7433 1.0000 1.0000 0.8007

25. Yunnan 1.0000 1.0000 0.9981 0.9617 1.0000 0.8296 1.0000 0.9252 0.8328

26. Shannxi 0.9215 0.5904 0.9009 0.8372 0.9969 0.7274 0.8032 0.5157 0.8192

27. Gansu 0.9268 0.4823 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9451

28. Qinghai 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

29. Ningxia 1.0000 1.0000 1.0000 1.0000 1.0000 0.9980 1.0000 1.0000 1.0000

30. Xinjiang 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000

Mean 0.9836 0.8423 0.9734 0.9679 0.9459 0.9423 0.9840 0.8659 0.9628



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Table A2(a). Out-oriented constant returns to scale (CRS) efficiencies and confidence intervals for the stage 1 (technological development process) by using the SIMAR and WILSON (1998)bootstrap method

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

95% confidence

intervals

95% confidence

intervals

Period 1: 1995–1999

1 1.0000 1.2108 0.0255 –0.2100 1.0070 1.5186 2 1.9592 2.1823 0.0119 –0.2230 1.9739 2.3771

3 1.0000 1.1868 0.0156 –0.1860 1.0069 1.4057 4 1.1087 1.3273 0.0222 –0.2180 1.1172 1.6125

5 1.0000 1.2118 0.0250 –0.2110 1.0117 1.5209 6 1.3152 1.5902 0.0407 –0.2750 1.3274 1.9777

7 1.4729 1.6282 0.0066 –0.1550 1.4879 1.8033 8 1.0257 1.1665 0.0065 –0.1400 1.0329 1.3276

9 1.7339 2.0087 0.0331 –0.2740 1.7498 2.4275 10 1.7758 2.0675 0.0333 –0.2910 1.7903 2.4387

11 1.0000 1.2075 0.0252 –0.2070 1.0101 1.5175 12 1.0605 1.2182 0.0078 –0.1570 1.0686 1.3735

13 1.0000 1.2149 0.0246 –0.2140 1.0088 1.5255 14 1.0000 1.1624 0.0094 –0.1620 1.0079 1.3429

15 1.1816 1.4276 0.0340 –0.2460 1.1911 1.7845 16 1.1911 1.3881 0.0141 –0.1970 1.2009 1.6112

17 1.8735 2.1134 0.0157 –0.2390 1.8926 2.3491 18 1.0000 1.1753 0.0131 –0.1750 1.0084 1.4253

19 1.0000 1.2143 0.0255 –0.2140 1.0068 1.5254 20 1.0281 1.1391 0.0031 –0.1110 1.0377 1.2535

21 1.0000 1.2078 0.0250 –0.2070 1.0071 1.5095 22 2.0059 2.3373 0.0391 –0.3310 2.0223 2.6903

23 1.6794 2.0331 0.0650 –0.3530 1.6933 2.5020 24 1.0000 1.2117 0.0235 –0.2110 1.0081 1.4902

25 1.0000 1.1835 0.0143 –0.1830 1.0076 1.4169 26 1.6938 2.0014 0.0382 –0.3070 1.7072 2.3370

27 2.0734 2.4064 0.0409 –0.3330 2.0916 2.8337 28 1.0000 1.2094 0.0246 –0.2090 1.0093 1.5222

29 1.0000 1.2015 0.0218 –0.2010 1.0077 1.4803 30 1.0000 1.1852 0.0133 –0.1850 1.0084 1.3851

Period 2: 1999–2003

1 1.0000 1.0998 0.0077 –0.0990 1.0032 1.3540 2 1.0391 1.1241 0.0031 –0.0850 1.0429 1.2401

3 1.0000 1.0987 0.0066 –0.0980 1.0034 1.3036 4 1.0000 1.1047 0.0083 –0.1040 1.0043 1.3622

5 1.0000 1.1038 0.0081 –0.1030 1.0040 1.3618 6 1.0000 1.1006 0.0076 –0.1000 1.0041 1.3542

7 1.0000 1.0964 0.0053 –0.0960 1.0039 1.2598 8 1.0000 1.1032 0.0086 –0.1030 1.0031 1.3718

9 1.0000 1.1019 0.0080 –0.1010 1.0037 1.3631 10 1.7110 1.8471 0.0072 –0.1360 1.7160 2.0223

11 1.0000 1.0984 0.0077 –0.0980 1.0041 1.3389 12 1.4335 1.5461 0.0052 –0.1120 1.4388 1.6996

13 1.0000 1.0869 0.0035 –0.0860 1.0046 1.2202 14 1.0000 1.0930 0.0048 –0.0930 1.0032 1.2406

15 1.1910 1.3129 0.0115 –0.1210 1.1954 1.6196 16 1.3500 1.4652 0.0061 –0.1150 1.3556 1.6351

17 1.3861 1.5073 0.0073 –0.1210 1.3916 1.6863 18 1.0000 1.0995 0.0071 –0.0990 1.0040 1.3215

19 1.0000 1.1018 0.0081 –0.1010 1.0035 1.3639 20 1.0000 1.1015 0.0074 –0.1010 1.0036 1.3227

21 1.0000 1.1015 0.0079 –0.1010 1.0038 1.3539 22 1.1975 1.2729 0.0023 –0.0750 1.2015 1.3904

23 1.0000 1.1033 0.0087 –0.1030 1.0039 1.3750 24 1.0000 1.1022 0.0084 –0.1020 1.0029 1.3721

25 1.0000 1.0878 0.0037 –0.0870 1.0045 1.2160 26 1.0031 1.0686 0.0018 –0.0650 1.0077 1.1752

27 1.0000 1.0990 0.0075 –0.0990 1.0037 1.3497 28 1.0000 1.0990 0.0074 –0.0990 1.0043 1.3461

29 1.0000 1.1038 0.0083 –0.1030 1.0043 1.3599 30 1.0000 1.1084 0.0087 –0.1080 1.0033 1.3743

(Continued )

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Table A2(a). Continued

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

95% confidence

intervals

95% confidence

intervals

Period 3: 2003–2007

1 1.0000 1.1802 0.0214 –0.1800 1.0068 1.4861 2 1.0000 1.1708 0.0178 –0.1700 1.0065 1.4371

3 1.0000 1.1778 0.0213 –0.1770 1.0072 1.4843 4 1.8530 2.0477 0.0288 –0.1940 1.8621 2.5677

5 1.6845 1.9049 0.0200 –0.2200 1.6945 2.2053 6 1.0000 1.1838 0.0223 –0.1830 1.0057 1.4915

7 1.2291 1.4258 0.0185 –0.1960 1.2390 1.6899 8 1.0659 1.2153 0.0109 –0.1490 1.0718 1.4911

9 1.0000 1.1662 0.0155 –0.1660 1.0056 1.4316 10 1.7895 1.9729 0.0150 –0.1830 1.8030 2.2790

11 1.0000 1.1799 0.0213 –0.1790 1.0075 1.4758 12 1.9015 2.0814 0.0115 –0.1790 1.9165 2.3342

13 1.2607 1.4033 0.0111 –0.1420 1.2697 1.7232 14 1.7615 1.9334 0.0121 –0.1710 1.7722 2.1927

15 1.0279 1.1724 0.0098 –0.1440 1.0357 1.4250 16 1.1017 1.2995 0.0276 –0.1970 1.1086 1.6427

17 1.4181 1.5759 0.0089 –0.1570 1.4274 1.7915 18 1.0814 1.2195 0.0071 –0.1380 1.0907 1.3981

19 1.0000 1.1804 0.0215 –0.1800 1.0062 1.4859 20 1.4363 1.5775 0.0106 –0.1410 1.4449 1.8569

21 1.0000 1.1789 0.0218 –0.1780 1.0057 1.4892 22 1.0000 1.1734 0.0186 –0.1730 1.0059 1.4437

23 1.0000 1.1820 0.0223 –0.1820 1.0070 1.4904 24 1.0000 1.1790 0.0215 –0.1790 1.0054 1.4842

25 1.0808 1.2125 0.0071 –0.1310 1.0885 1.4203 26 1.9391 2.3028 0.0819 –0.3630 1.9519 2.8576

27 1.0000 1.1872 0.0223 –0.1870 1.0058 1.4769 28 1.0000 1.1871 0.0229 –0.1870 1.0074 1.4872

29 1.0000 1.1767 0.0213 –0.1760 1.0050 1.4848 30 1.0000 1.1812 0.0228 –0.1810 1.0061 1.4898


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Table A2(b). Out-oriented constant returns to scale (CRS) efficiencies and confidence intervals for the stage 2 (technological commercialization process) by using the SIMAR and WILSON

(1998) bootstrap method

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

95% confidence

intervals

95% confidence

intervals

Period 1: 1995–1999

1 1.0000 1.0393 0.0014 –0.0390 1.0010 1.1476 2 1.0000 1.0402 0.0014 –0.0400 1.0011 1.1373

3 1.1381 1.1806 0.0012 –0.0420 1.1397 1.2594 4 1.0000 1.0401 0.0013 –0.0400 1.0011 1.1396

5 1.0034 1.0264 0.0003 –0.0230 1.0048 1.0702 6 1.0000 1.0422 0.0014 –0.0420 1.0015 1.1446

7 1.0000 1.0349 0.0007 –0.0340 1.0014 1.0897 8 1.0000 1.0351 0.0008 –0.0350 1.0014 1.0976

9 1.0000 1.0406 0.0014 –0.0400 1.0012 1.1378 10 1.0000 1.0404 0.0014 –0.0400 1.0010 1.1469

11 1.1263 1.1665 0.0010 –0.0400 1.1280 1.2346 12 1.0000 1.0413 0.0015 –0.0410 1.0011 1.1426

13 1.0000 1.0416 0.0015 –0.0410 1.0010 1.1480 14 1.0312 1.0535 0.0002 –0.0220 1.0325 1.0832

15 1.2073 1.2438 0.0007 –0.0360 1.2090 1.3098 16 1.0261 1.0673 0.0014 –0.0410 1.0271 1.1736

17 1.0000 1.0405 0.0013 –0.0400 1.0015 1.1270 18 1.0997 1.1432 0.0017 –0.0430 1.1013 1.2545

19 1.0000 1.0399 0.0014 –0.0390 1.0010 1.1430 20 1.0000 1.0367 0.0008 –0.0360 1.0013 1.1011

21 1.0000 1.0396 0.0014 –0.0390 1.0011 1.1461 22 1.0000 1.0404 0.0013 –0.0400 1.0010 1.1355

23 1.0878 1.1218 0.0006 –0.0340 1.0892 1.1661 24 1.0650 1.0997 0.0006 –0.0340 1.0664 1.1502

25 1.0017 1.0395 0.0011 –0.0370 1.0027 1.1234 26 1.1102 1.1358 0.0003 –0.0250 1.1118 1.1719

27 1.0000 1.0407 0.0014 –0.0400 1.0008 1.1433 28 1.0000 1.0393 0.0014 –0.0390 1.0013 1.1434

29 1.0000 1.0395 0.0014 –0.0390 1.0012 1.1412 30 1.0000 1.0398 0.0013 –0.0390 1.0011 1.1451

Period 2: 1999–2003

1 1.0000 1.0804 0.0049 –0.0800 1.0024 1.2675 2 1.0000 1.0833 0.0050 –0.0830 1.0031 1.2693

3 1.1928 1.2901 0.0064 –0.0970 1.1959 1.4781 4 1.0000 1.0762 0.0030 –0.0760 1.0033 1.1959

5 1.0000 1.0861 0.0052 –0.0860 1.0034 1.2636 6 1.0000 1.0822 0.0043 –0.0820 1.0026 1.2319

7 1.0710 1.1231 0.0009 –0.0520 1.0750 1.1836 8 1.0000 1.0855 0.0053 –0.0850 1.0031 1.2794

9 1.0000 1.0856 0.0054 –0.0850 1.0026 1.2759 10 1.0000 1.0848 0.0053 –0.0840 1.0029 1.2680

11 1.0000 1.0598 0.0013 –0.0590 1.0027 1.1343 12 1.0000 1.0860 0.0052 –0.0860 1.0035 1.2718

13 1.0000 1.0849 0.0052 –0.0840 1.0031 1.2714 14 1.0573 1.1250 0.0017 –0.0670 1.0605 1.2029

15 1.1487 1.2352 0.0042 –0.0860 1.1518 1.3895 16 1.0486 1.1381 0.0061 –0.0890 1.0514 1.3384

17 1.4039 1.5204 0.0078 –0.1160 1.4087 1.7158 18 1.1367 1.2115 0.0023 –0.0740 1.1390 1.3190

19 1.0000 1.0860 0.0052 –0.0860 1.0039 1.2733 20 1.0000 1.0548 0.0011 –0.0540 1.0027 1.1272

21 1.0000 1.0836 0.0052 –0.0830 1.0026 1.2715 22 1.0000 1.0778 0.0030 –0.0770 1.0029 1.1910

23 1.1971 1.2795 0.0029 –0.0820 1.2009 1.3772 24 1.3459 1.4214 0.0023 –0.0750 1.3493 1.5286

25 1.2058 1.2973 0.0037 –0.0910 1.2095 1.4295 26 1.3746 1.4363 0.0017 –0.0610 1.3788 1.5313

27 1.0000 1.0865 0.0053 –0.0860 1.0036 1.2717 28 1.0000 1.0849 0.0053 –0.0840 1.0034 1.2778

29 1.0046 1.0691 0.0019 –0.0640 1.0082 1.1790 30 1.0000 1.0756 0.0029 –0.0750 1.0032 1.1938

(Continued )

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Table A2(b). Continued

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

Regions

Out-oriented

efficiency

scores (CRS)

Efficiency bias

corrected

Standard error

estimates

Bias

estimates

Lower

bound

Upper

bound

95% confidence

intervals

95% confidence

intervals

Period 3: year 2003–2007

1 1.0000 1.0500 0.0025 –0.0500 1.0013 1.1880 2 1.0000 1.0499 0.0025 –0.0490 1.0020 1.1879

3 1.0000 1.0515 0.0027 –0.0510 1.0014 1.1884 4 1.0000 1.0504 0.0026 –0.0500 1.0016 1.1888

5 1.0000 1.0507 0.0024 –0.0500 1.0012 1.1848 6 1.0338 1.0670 0.0004 –0.0330 1.0353 1.1047

7 1.0000 1.0460 0.0017 –0.0460 1.0014 1.1472 8 1.0000 1.0483 0.0020 –0.0480 1.0016 1.1627

9 1.0000 1.0497 0.0025 –0.0490 1.0012 1.1833 10 1.0000 1.0526 0.0027 –0.0520 1.0013 1.1855

11 1.0348 1.0889 0.0027 –0.0540 1.0362 1.2273 12 1.0520 1.0976 0.0015 –0.0450 1.0536 1.1853

13 1.0000 1.0517 0.0025 –0.0510 1.0017 1.1838 14 1.1357 1.1646 0.0004 –0.0280 1.1379 1.2103

15 1.0000 1.0513 0.0025 –0.0510 1.0014 1.1882 16 1.0521 1.1019 0.0019 –0.0490 1.0535 1.2052

17 1.1054 1.1350 0.0003 –0.0290 1.1074 1.1693 18 1.0224 1.0617 0.0008 –0.0390 1.0242 1.1223

19 1.0000 1.0517 0.0025 –0.0510 1.0013 1.1827 20 1.0000 1.0524 0.0025 –0.0520 1.0017 1.1838

21 1.0000 1.0509 0.0026 –0.0500 1.0013 1.1859 22 1.0000 1.0538 0.0026 –0.0530 1.0018 1.1846

23 1.1265 1.1727 0.0013 –0.0460 1.1278 1.2505 24 1.2486 1.2859 0.0006 –0.0370 1.2501 1.3468

25 1.2012 1.2528 0.0016 –0.0510 1.2029 1.3374 26 1.2207 1.2549 0.0005 –0.0340 1.2227 1.3131

27 1.0579 1.0927 0.0006 –0.0340 1.0597 1.1531 28 1.0000 1.0490 0.0023 –0.0490 1.0015 1.1826

29 1.0000 1.0511 0.0025 –0.0510 1.0014 1.1919 30 1.0000 1.0498 0.0025 –0.0490 1.0013 1.1921


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NOTES

1. BROEKEL and BRENNER (2009) have constructed a

general and attractive analytical framework for the innova-

tiveness measure of RISs based on the innovation systems

approach (for more information, see BROEKEL and

BRENNER, 2009). In their framework, the consideration

of the inputs is systematic, which is associated with

R&D activity, economic activity (for example, industrial

diversity, industrial specificity and technology proximity),

policy setting, unit characteristics, and so on. This means

that their systematic inputs not only include the indispen-

sable participations of physical S&T innovation resources,

but also take into account the dispensable moderations

from the region-specific structural or contextual elements.

Based on LI’s (2009) factors classification on the basis of

their relationship with the knowledge production

process, the physical S&T innovation resources are used

as ‘inputs factors’, and the specific-region structural or

contextual factors are used as ‘efficiency factors’. Clearly,

the ‘inputs factors’ are associated with the efficiency

measurement, while the ‘efficiency factors’ are associated

with the determinants explorations of the cross-unit effi-

ciency variation (also FRITSCH, 2002; FRITSCH and

SLAVTCHEV, 2007, 2009).

2. As reminded by an anonymous reviewer, it is advisable to

include the effects of the regional industrial specialization

(reflecting the degree of some technology in a region)

related to some industries with intensive patenting activi-

ties (for example, pharmaceutical or electronic/electric

industries) in measuring regional innovation performance

(also FURMAN et al., 2002; BROEKEL and BRENNER,

2007; BRENNER and BROEKEL, 2009; FRITSCH, 2002;

and FRITSCH and SLAVTCHEV, 2007, 2009). However, a

series of correlation analyses show that there is not a sig-

nificant correlation between the regional industry special-

ization (reflected by R&D employment/expenditure

shares of those industries with intensive patenting activi-

ties) and the regional innovation performance (efficiency

scores) in the context of China. The unexpected result is

not accidental because it is connected with the current

characteristics of China’s innovation system and economic

structure. In contrast to the developed industrial countries

(for example, Germany) which are more firm-centred and

marked-oriented innovation systems, the effects of indus-

trial specialization related to economic environment

theoretically would decrease in the context of China

which is a more public research organization-centred

and plan-oriented innovation system. In this situation

where innovation activities are mainly implemented by

public research organization, the industrial environment

would not have significant effects on the regional inno-

vation process.

3. It may be more rational in terms of input–output corre-

spondences if R&D funds and the number of R&D per-

sonnel are used as original innovative inputs in the first

stage for intermediate innovative outputs (GUAN and

CHEN, 2010a). However, the paucity of available statistical

data for the considered time span has restricted the choice

available. The data of R&D expenditures before 1997 are

not available at the provincial level. Instead, E(S&T) and

P(S&T), which are accessible for a relatively long-term,

are often used to evaluate innovation efficiency at the

regional level in the context of China. In terms of the

development stage of China’s innovation systems mainly

depending on technological import and learning, it is

appropriate (RGDSST, 2006; CHEUNG and LIN, 2004).

Just as SUN (2000) indicated, the R&D investment does

not explain the distribution of innovative outputs in

China, which was argued in OECD and MOST (2007).

4. It refers to the deals in domestic technological markets,

and is used as a proxy of ‘domestic technology transfer’,

which is an important source for regional innovation

development (GUO, 2008).

5. Evidence (China Statistical Yearbooks on Science and

Technology, 2000-2004; CHEUNG and LIN, 2004) shows

that invention patents of China have been primarily

applied by foreigners, whilst domestic applicants have con-

tributed mostly to utility model and external design. In

this study, the patent applications from foreigners are

excluded in order to avoid overestimating the innovative

capabilities of the China’s RISs.

6. To deal with those problems, some DEA-based robust esti-

mators associated with statistical approaches are introduced

(SIMAR and WILSON, 1998, 2008; DARAIO and SIMAR,

2007). With respect to applications to innovation

measures, some tentative studies were performed by

BROEKEL and BRENNER (2007) as well as BROEKEL

(2008), which introduced the order-m frontier approach

(CAZALS et al., 2002) to construct a robust and partial

non-parametric frontier for efficiency measure by remov-

ing outliers.

7. Tibet, Hong Kang and Macao were not included due to

limited data.

8. For example, the exceptionally well-performaning regions

serve as outliers in the statistical sense (BROEKEL and

BRENNER, 2007), which dominate all comparisons

(CAZALS et al., 2002).

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measuring the efficiency of china's regional innovation...

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