Technology Sourcing by MNEs – a Complex Process of Local Interaction

J.A. Cantwell* and C.A. Noonan**

Paper presented at the Academy of Management Conference, Seattle, August 2003

*Professor John Cantwell ** Dr. Camilla Noonan

Rutgers Business School Dept. of Business Administration

111 Washington Street University College Dublin

Newark NJ 07102-3027 Belfield, Dublin 4

USA Ireland

Tel: +1 973 353 5050 Tel: +353 1 7164739

Fax: +1 973 353 1664 Fax: +353 1 7164762

Email: Cantwell@rbsmail.rutgers.edu Email: camilla.noonan@ucd.ie

Technology Sourcing by MNEs – a Complex Process of Local Interaction

Abstract

In this paper, we seek to explain the key determinants of highly localized knowledge

exchange between foreign-owned subsidiarie s and the host environment. By drawing

upon the literature on the multinational enterprise (MNE) and the geographic

dynamics of technological activity, we develop and test a set of hypotheses that

account for the characteristics of highly localized (or regionally bounded) technology

sourcing by these subsidiaries in Germany. The hypotheses are tested by analysing the

locational and institutional origins of citations associated with over 12,000 patents

granted by the USPTO to the foreign-owned subsidiaries of large firms for their

research activity in Germany. We find that, amongst others, sourcing tends to be more

localized for technologies that can be categorized as ‘sticky’, for those that are

science-based, for those that have been more recently developed, and when drawing

upon knowledge created by non-corporate institutions. Of these, the classification of

technologies as sticky or as science-based implies the most strictly localized

knowledge sourcing, in the sense of relying on sources within the same region, and

not from other regions within Germany.

Keywords

Multinational enterprise; technology sourcing; foreign subsidiary; geography of

innovation

INTRODUCTION

Until the late 1980s, the accepted rationale for the MNE was explained in terms of

transaction costs and the desire to internalize (potential) cross-border markets for the

so-called ownership advantages of the firm. In this context, subsidiaries tended to be

viewed as mere recipients of the technologies developed by the parent firm, and their

primary technological role was to adapt this knowledge to suit the idiosyncratic tastes

of the local market. While some authors drew attention to the possibility of

subsidiary activity evolving to become more independently creative through time

(Dunning, 1958; Ronstadt 1977, 1978; Fusfeld, 1986; Chesnais, 1988), evidence

presented in support of this thesis was greeted with skepticism and seen as being

against the dominant momentum that centralized high value-added or technological

activities within the parent firm (see Pearce, 1989 for discussion).

In the late 1980s and early 1990s, paralleling developments in the literature on

technological change and the theory on firm activity more generally, a new point of

departure was heralded in the International Bus iness and Strategy literatures. The

new approach has drawn heavily on the evolutionary view of the firm and industry

(Nelson and Winter, 1982) and re-assesses the rationale for the MNE and the precise

role played by the subsidiary. More recent investigations of the MNE adopt a broader

definition of technology, viewing it as the outcome of a path-dependent, corporate

learning process, and see the MNE as a superior way of organizing technology

generation across its dispersed but interconnected international network (Cantwell,

1989). Following this, the asset-exploiting thesis was firmly supplemented with one

that emphasized the possibility of asset-augmentation activities, and overseas

locations have come to be viewed as important sources of new knowledge.

Scholars therefore increasingly focused upon the supply side in explaining the

decentralization of the R&D function and presented what is by now, a growing body

of empirical evidence that is consistent with the asset-augmenting thesis (Cantwell,

1989, 1992; Kogut and Chang, 1991; Dunning 1993, 1996; Florida, 1997; Kummerle,

1997, 1998; Dunning and Lundan, 1998; Zander, 1998, 1999; Serapio and Dalton,

1999; Cantwell and Janne, 1999; Cantwell et al. 2002).1 Allied to this, research

highlighted the centripetal characteristics of particular locations (Cantwell et al. 2001;

Cantwell and Piscitello, 2002) and the dynamics of foreign-owned firm interaction

(often referred to as spillovers) with host infrastructures (Almeida, 1996; Jaffe and

Trajtenberg, 1996; Frost 2001). Although the empirical testing of spillovers has been

underway in the literature for quite some time, the increased availability of patent

citations in machine-readable format has enabled more micro-based examinations of

the issue in recent years. In particular, a growing body of empirical research has

emerged to examine the extent to which ‘knowledge spillovers’ (and knowledge

sourcing activities) might be classified as localized. To date, citation-based studies of

this phenomenon have been largely U.S. (and only more recently, European) –based

and they lend convincing support to the theses that MNEs engage in asset-augmenting

activities abroad (Criscuolo et al., 2001; Frost, 2001), that knowledge spillovers are

indeed localized (Jaffe et al. 1993; Jaffe and Trajtenberg, 1998; Almeida and Kogut,

1999; Verspagen and Schoenmakers, 2000; Maurseth and Verspagen, 2001), that

1 Frost (2001, p. 103) notes that much of this evidence is ‘fragmented and contradictory’. While most case studies confirm this evolution in subsidiary activity, he suggests that larger scale studies are ‘less convincing’ i.e. while the technological activities of subsidiaries coincide with fields of host country specialization in some instances, the results are inconclusive. It should be noted that while most authors conclude that their results are consistent with the asset-seeking thesis (eg. Kogut and Chang, 1991, p. 409), one might suggest that a potential shortcoming of the studies is their failure to account for the increasingly complex relationships that exist between technologies and the commensurate necessity for firms to co-develop formerly unrelated technologies alongside one another. As evidenced in this study, this means that although firms may be seen to be specialized in one specific field of technology at a particular location, they might well source other (different but related) fields of technology from the host economy and then use these in combination with one another. Clearly, this should be taken account when undertaking such analyses and might well explain why the evidence regarding asset augmentation is inconclusive.

public research bodies play an important role in such processes (Jaffe and

Trajtenberg, 1996; Jaffe et al. 1998), and that a key motivation for overseas R&D is to

tap into host areas of technological strength (Almeida, 1996; Frost, 2001; Criscuolo et

al., 2001).

This paper contributes to this literature in a number of ways. First, it examines the

technology sourcing activities of foreign-owned subsidiaries based in Germany and

tests the localization thesis. To this end, a new citations dataset that spans the 1975-

95 period has been created. Germany provides a unique testing bed for this issue

since foreign-owned firms located in this country are amongst the leading

international technology creators and the country’s research infrastructure renders it

one of Europe’s key locations for science and technology development.2 Despite its

prominence and its distinguished history of technological leadership, Germany has

received comparatively little attention from analysts of the MNE. Furthermore, in

contrast to the approach taken in many of the empirical studies to date, this location

also allows us to examine a very rich and varied array of corporate technological

activity. Rather than recording technology specialization in just one or two fields of

technology (which one might observe in some of the US regions for example), the

research activities of foreign-owned firms located in Germany are a lot more varied

(for a survey see Cantwell and Noonan 2002a).

In this paper, we specifically wish to test the determinants of technology localization

at the sub-national level within Germany. Once again, Germany is a particularly

suitable location since allied to the diversity of activity, technology policy is devised

2 Most indicators (R&D expenditure as a share of GDP; numbers employed in R&D; international trade in R&D-intensive goods) suggest that Germany ranks third in the world in terms of total R&D activity. The US and Japan are the only countries that eclipse Germany. In terms of patents, approximately 40% of all USPTO patents granted to European-based inventors are attributed to research undertaken in Germany.

at both federal and regional levels. German technology policy has had a long

tradition of emphasising the science-technology interface and this has resulted in the

creation of a world-renowned scientific and technological infrastructure that aims to

promote linkages between firms and the research infrastructure at local level. The

growing importance of regional governments in devising technology policy has also

been particularly striking in recent decades. While the Federal government has been

the traditional initiator of technology policy in Germany, regional (or Länder)

governments have been steadily increasing their presence in this policy area (Meyer-

Kramer, 1990). The administrative powers of the Länder (especially in respect of

their capacity for innovation support) make them excellent candidates for the analysis

of sub national technological activity.

The paper is structured as follows. First, we present a brief overview of the literature

on subsidiary activity and the geography of innovation. Drawing upon the key

contributions of these literatures, we highlight the need for further research on the

nature of technology sourcing activities of foreign-owned firms in host economies and

further investigation into the characteristics of sticky (or highly localized) knowledge

flows at a local level. Following this, we discuss the data and methodology used in

this study and we then proceed to develop a set of hypotheses, which are used to test

the key determinants of regionally bounded technology flows within this country. We

present the findings from this investigation and conclude with a discussion of their

implications and the potential routes for further advancing this research.

TECHNOLOGY SOURCING AND THE IMPORTANCE OF GEOGRAPHIC

PROXIMITY

As noted in the introduction to this paper, the theory of the MNE has long viewed

value creation through the exploitation of technology as central to the process through

which international firms create value. However, the definition of technology adopted

was very restrictive. It was narrowly defined as the output from an R&D process that

could be articulated, codified and easily transferred across space. Cantwell, (1995, p.

22) explains how this rather restricted definition was readily imported from the

mainstream economics literature - what was observed to be internationally diffused

between firms and within MNCs was principally scientific and engineering

knowledge (all of which could be codified and public) and it was therefore natural for

scholars to focus upon this and to explain the existence of the MNE as a response to

the difficulties of contracting costs across space (Buckley and Casson, 1976).

Viewing technological activity ins tead as synonymous with innovation, i.e. as a path

dependent and highly tacit collective learning process in and around corporate

problem solving, called for a reappraisal of the theory of MNEs. Drawing on (and

indeed contribution to) the evolutionary approach to firm activity, international

business/strategy scholars now adopt this much broader view of technology.

Technology is no longer seen merely as a public good and consequently, the rationale

for MNEs is no longer tied exclusively to explanations pertaining to market failure.

Technological capabilities are found to be difficult to transfer because they are

composed of tacit as well as codifiable elements, and while some part of the new

knowledge may be articulated and codified (in the form of a new patent, for example),

it is strictly complementary to a non-codifiable element, which renders imitation and

transfer across space exceedingly difficult. This in turn represents the true basis of

sustainable competitive advantage for the firm (Nonaka and Takeuchi, 1995).

Allied to these changes in the definition of technology, scholars have reported an

equally important empirical development - industrial countries are becoming more

specialized in their technological endeavors (Archibugi and Pianta, 1992). This has

important strategic implications for firms since against the backdrop of increased

technological convergence at an industry level, large firms are forced to accumulate

and maintain competences across a much broader range of technologies (resulting in

the arrival of the Multi-Technology Corporation (MTC) (Granstrand and Sjölander,

1990; Granstrand et al. 1997)). If one accepts that technological development is a

complex, cumulative, tacit, highly context-specific activity that requires socially

organized learning processes, it is clear that geographic proximity and face-to-face

contact become highly important considerations when developing new technology (or

novel technology combinations). Therefore, these competences must be developed

within facilities that are based selectively in the most appropriate location for a given

activity from amongst centers in the growing number of countries that have become

reputable players in the science and technology arena (Lee and Proctor, 1991). As a

result, one expects to observe technological clustering effects at a local level. The

logic of such clusters may be obvious if for example, co-location is determined by the

desire to develop similar lines of technological development alongside local agents.

Equally (and more likely in an era of technological convergence) one might observe

more complex types of co- location whose logic is not immediately apparent (for some

evidence in the case of Germany, see Cantwell and Noonan, 2002a).

Ascertaining the interactions between foreign-owned subsidiaries and the host

economy has therefore become an important area of investigation. As noted earlier,

evidence confirming that subsidiaries source from their overseas locations has only

started to emerge and much work need to be done to clarify the characteristics of the

highly stick (or localized technology flows) that occur in the various international

centers of technological excellence – from whom do foreign-owned subsidiaries

source knowledge? Does sourcing reflect the technological specialization of

indigenous firms within the host economy? What are the determinants of highly sticky

sourcing at a local level? As noted above, this paper seeks to address some of these

issues in the context of Germany and to progress our understanding of innovative

subsidiary activity in overseas locations.

METHODOLOGY AND DATA

While patent citations constitute an increasingly popular source of information on

corporate technological activity, the reliability of using these data as an indicator of

knowledge flows has nonetheless been questioned. In addition to the citations that the

inventor is obliged to reference on the patent application, additional citations may be

included for a number of different reasons.3 While this has led some scholars to

question the legitimacy of using this data in empirical studies of knowledge

localization, others conclude that patent citations should be seen as a valid but noisy

3 These include: (i) legal concerns. To avoid infringement, a risk-averse patent lawyer may include additional citations that might not necessarily be considered ‘prior art’ by the inventor but are considered vital for staving off potential legal battles; (ii) citations may be included that are referred to as ‘after-the-fact cites’. In such instances, knowledge of ‘relevant prior art’ may be discovered by the patentee ex post but then added to the list of citations; (iii) ‘teaching cites’. These include inventions, which while not directly drawn upon by the inventor in the process of exploration are nonetheless viewed as basic to this process. Therefore, they are also included in the list of prior art and finally (iv) the patent examiner may add any number of additional citations that he deems relevant to the invention.

measure of spillovers (for further discussion, see Jaffe et al. 1998 and Jaffe et al.

2000). Criscuolo et al. (2001, p. 9) argue that in the context of large multinational

firms, it is reasonable to assume that a large proportion of citations will already have

been listed by the inventor. Since patents are in the public domain and readily

accessible, the authors suggest that it is highly probable that professional R&D

laboratories would have identified all existing patents in their area of technological

search. Consequently, the degree of noise is minimized in the data.

The position taken here is that regardless of who actually adds the citations, all

references to prior art are important in the investigation of spatial knowledge flows.

Since additional citations represent all influences (conscious or otherwise) on

contemporary invention, they add objectivity to the analysis of spatial knowledge

flows. Their inclusion therefore protects against any bias that might emerge in favour

of the ‘localization’ of knowledge flows if these are restricted to those for which an

inventor is able to attribute the original source, as opposed to all those on which the

inventor relied but learned of only indirectly and so was unable to attribute. Jaffe et

al. (1993, p. 596) suggest that when one's objective is to study the overall spatial

characteristics of technological development, the exact assignation of subsequent (and

therefore prior) invention may be considered inconsequential so long as it occurs at a

certain location.

Many of the studies to date have taken particular groups of frequently ‘cited’ patents

(usually within a particular technology family) and analysed the citation patterns to

these inventions (Jaffe et al, 1993; Jaffe and Trajtenberg, 1996, 1998). In adopting

this forward-looking approach, analysis runs up against what is referred to as a

truncation bias. This relates to difficulties encountered when deciding upon the

appropriate cut off points for the citation window. Stated simply, in undertaking such

analyses, the researcher is confronted with the problem of trying to ascertain an

adequate time frame within which inventions receive a number of citations roughly

proportional to the total number of citations likely to be ultimately obtained. Consider

Coases’s 1937 article as an example. This was almost never cited before 1975 but

then cited massively after that date. If one was to fix the citation window at twenty

years, one might be tempted to conclude that this seminal piece of work really had

little impact upon the academic work that followed. Of course, we know that this was

not the case – it just took the academic world a little longer to recognize the

significance of this contribution.

In terms of invention, identifying the window within which the period of most intense

citation activity is likely to occur is extremely challenging. It is virtually impossible

to be totally confident that what may be perceived to be relatively unimportant

inventions today (i.e. as evidenced by low citation activity) will not become hugely

important in the future. Hall et al. (1998) highlight the skewed nature of the

distribution of patent citations. Examining the citations made to the inventions of

4,800 publicly traded manufacturing firms 1975-1995, the authors draw attention to

the fact that citations frequently continue more than 10 years after the original patent

is granted.

In contrast to the aforementioned methodology, this study adopts a distinctly different

approach. The analysis here begins from the ‘citing’ rather than the ‘cited’ patent and

so adopts a backward looking or his torical approach. This is useful because it means

that the number of citations is fixed and definitive at the point of issue rather than

being forward-looking and open-ended as was the case in most earlier studies. As can

be seen from Figure 1, the distribution of these ‘citing’ patents is much less skewed

than the distribution of ‘cited’ patents (evidenced in Hall et al. 1998). The modal

values are 3 and 4, which is in marked contrast to the equivalent for cited patents (for

which the modal value is zero).

FIGURE 1 HERE

The Dataset

We use a sample of 12,721 patents granted by the United States Patent and Trademark

Office (USPTO) to the research facilities of large foreign-owned firms located in

Germany between 1975 and 1995.4 All references to prior art was extracted from

these patents and used as a proxy for (potential) technological influences upon these

firms from various categories of prior inventors that resided both within and outside

Germany. This data set contains 67,142 citations. Each patent (original and cited)

were coded according to the following criteria:

(i) Technology. Under the USPTO system, each patent is classified under one of

401 patent classes. In this study, these patent classes have been further

allocated into one of 56 groups of common activity (see Table A1 in the

appendix for this breakdown).

(ii) Location. Each patent citation is coded according to the residence of the first

named inventor (or the location of the research facility responsible). To

facilitate a sub-national analysis of citation activity German level, a NUTS

4 We included the 784 corporate groups which have accounted for the highest level of US patenting since 1969. Births, deaths, mergers and acquisitions as well as movement of firms between corporate groups (sometimes associated with historical changes in ownership) have been allowed for in the database.

code was attributed to each citation. 5 In the cases of non-German-based

inventors, we differentiate between:

(a) Inventors that are located in the home country of the parent firm and

(b) Inventors that are located in another foreign country.

(iii) Institution (or assignee). In addition to the technology and location code, each

patent is also classified according its assignee (or owner). Here, we

distinguish cases in which:

(a) The assignor is the same firm as that of the citing patent (i.e. self cites)6

(b) The assignor is another large firm in the same industry7

(c) The assignor is another large firm in a different industry

(d) The assignor is a ‘smaller firm’ i.e. a firm not listed in the large firm data

set.

Patents that result from collaborative research activity are attributed to the first named

assignor. Categorizing the data in this fashion enables us to differentiate between

intra- versus inter-firm citation activity. For cited patents that are the result of local

German-based technological activity, we further differentiate cases in which:

(a) The assignor is a research institution

(b) The assignor is the inventor (independent of a company)

(c) The assignor is some other individual

5 The NUTS nomenclature was created and developed in accordance with a number of principles (i) NUTS favor institutional breakdowns (ii) NUTS favor regional units of general character i.e. it excludes specific territorial units in favor of a geographical breakdown based on common areas of activity (e.g. mining regions) (Eurostat, 1995). 6 The firm refers here to the corporate group and not to any individual affiliate in isolation. 7 Adopting an approach similar to Scherer (1965), each corporate group has been allocated to one of 20 industries on the basis of its primary field of production.

HYPOTHESIS DEVELOPMENT

Characteristics of the Citing Technology

The first set of hypotheses seeks to capture the role of specific technology

characteristics in determining regionally bounded technology sourcing. Owing to the

cumulative nature of technological search, a high proportion of patent citations refer

to prior inventions that occurred within the same technological field. In their

examination of international knowledge flows, Jaffe and Trajtenberg (1998) report

that firms are more likely to cite within the same technological sector than outside it.

We refer to this type of citation as intra-sector. Since such activity involves the

absorption of similar (albeit differentiated) knowledge from other firms, one might

describe the firms as following broadly similar heuristic search processes.

Consequently, one might anticipate that very close geographical proximity (i.e. within

the same region) is not such an important consideration in such cases. Evidence to

date suggests that this is indeed the case - intra-sector cites display only a slight

tendency to geographical localization (Jaffe and Trajtenberg, 1998). Since we found

that the degree of intra-sector citation varies across technologies in our data (see

description of independent variables below), we test the following hypothesis:

Hypothesis 1: Technologies characterized by high levels of intra-sector citation are less likely to engage in regionally bounded technology sourcing.

The spatial characteristics of development in these technological sectors may be

further examined by comparing the extent to which citation to the same technology

sector of the citing patent occurs at a local and a global level. Technologies may be

partitioned into those with a high level of intra-sector citation for which similar

heuristic processes operate across space, and those for which this is less likely. Figure

2 illustrates this.

FIGURE 2 HERE

By comparing the degree of intra-sector citation at a global versus a local level, one

can distinguish ‘sticky’ and ‘slippery’ technologies. The former represent

technologies that are characterized by a relatively low degree of intra-sector citation at

a global level but a relatively high degree of intra-sector citation at a local level.

Conversely, the latter technologies are characterized by a relatively high degree of

intra-sector citation at a global level but a relatively low degree of intra-sector citation

at a local level.

This gives rise to the following hypotheses:

Hypothesis 2a: ‘Sticky’ sectors will exhibit a higher propensity to engage in intra-regional citation. Hypothesis 2b: ‘Sticky’ sectors will exhibit a lower propensity to engage in inter-regional citation. Hypothesis 3: ‘Slippery’ sectors will exhibit a lower propensity to engage in either inter- or intra-regional citation.

These two tests taken together provide us with a means of understanding the effects of

the citing technology on the propensity to source knowledge locally. As such, they

permit a more granular analysis of any variation in the degree of intra-sector citation.

Adaptation of Parent Technology

As discussed above, many studies suggest that the most frequent motivation for

locating technological activity overseas is to facilitate the customization or adaptation

of existing products and technologies to local market needs. Although the adaptive

process is stimulated by host country considerations, the technologies embodied in the

products and processes being adapted are likely to have had their intellectual roots

elsewhere, most immediately in the parent organization or in the network surrounding

the parent firm in the home base.

In contrast, more contemporary contributions to this literature draw attention to the

fact that increased technological convergence coupled with the growing relevance of

scientific exploration for technological development have forced large firms to

broadened their technological search domains (Granstrand et al., 1997; Cantwell and

Noonan 2001). Since technological expertise is internationally differentiated,

successful absorption of knowledge from various internationa l centers necessitates the

physical presence of the multinational’s R&D activities at these sites. Overseas

location of the R&D function may have such considerations explicitly in mind or

alternatively as suggested by Ronstadt (1978), the technological orientation of the

subsidiary may have evolved through time. Either way, technologies being developed

at these locations are likely to draw from the local knowledge base. By tracing the

origin of the subsidiary’s knowledge, one can deduce whether local activity is of the

home base adapting or home base augmenting type.8 In this model, we test whether

8 Prior investigation found that foreign-owned firms sourced 29% of knowledge from the home country of the parent firm, approximately 52% from other foreign countries and 19% of their knowledge from local sources. Considering that the total share of US patents granted to large firms located in Germany was 8.5% between 1975-1995 (and 7.8% between 1963-1995), this is an interesting finding. It demonstrates that the propensity of foreign-owned firms to use local sourcing is far greater than what one might expect if they drew randomly across space on technological inputs.

regional proximity to the local knowledge sources is an important element in these

home base augmenting activities:

Hypothesis 4: If a foreign-owned subsidiary’s technological activity is home base adaptive in nature, it will be less likely to draw upon prior invention of the host country.

Characteristics of the Cited Technology

The next hypothesis tests the possibility that knowledge sourcing that involves

science-based technologies will be more regionally bounded. This is likely to reflect

the higher tacit components associated with such technologies, which renders the

spatial transfer of the associated knowledge more difficult. Mindful of this issue, we

test:

Hypothesis 5: Citations to the science-based technologies are more likely to be intra-regional in character.

Immediacy

There is substantial support in the literature for the notion that time is an important

consideration in the analysis of knowledge localization. Since newer inventions are

characterised by greater amounts of tacit knowledge, rapid diffusion across space is

severely curtailed. However, through time and once codification takes place, this

knowledge can be more easily exchanged or diffused. Although earlier investigation

demonstrated that local sourcing in Germany was indeed concentrated on relatively

newer technologies (see Cantwell and Noonan 2002b), in this model we test whether

this effect is found at the sub-national level. In other words, the following hypothesis

tests whether ‘age’ is an important determinant of regionally bound citation.

Hypothesis 6: Regionally bounded technology sourcing by foreign-owned firms is characterized by more recent technologies.

Regional Institutional Characteristics

The diversity of state- funded research institutions is an important characteristic of the

national system of innovation in Germany. Technology policy in Germany has a

distinctive regional dimension and throughout the decades, these policies have

focused upon creating an attractive infrastructure through inter alia, research

institutions. Such policies presuppose that international research can be attracted to a

location to tap into the research efforts of the local research institutions. A potential

test of this policy is:

Hypothesis 7: Regionally bounded technology sourcing by foreign-owned firms will include sourcing from local research institutions.

Regional Technological Specialization

As noted in footnote 8, a preliminary analysis suggested that foreign-owned

subsidiaries sourced a relatively high proportion of their knowledge from local

sources. This is consistent with contemporary literature in this field, which argues that

the technological expertise of the host economy acts as a centripetal force in such

home-base augmenting activities. Many studies use the technological advantage of

indigenous firms as a proxy for the technological strength of the host location.

Considering this issue at regional level, it implies that the indigenous technological

expertise embedded in each region should act as a magnet for foreign-owned firm

location within the region. Prior investigation of this issue in the German context

suggested that regionally bound knowledge exchange is less likely to occur across the

same technology combinations but rather across more complex (but related)

technological combinations (for discussion, see Cantwell and Noonan 2002a). Here,

we wish to test whether the technological relationship between the citing and cited

patent has any bearing on the nature of sourcing from within the region. One might

expect that more complex forms of knowledge exchange necessitate co- location

between the knowledge generator and recipient at regional level. To this end, the

following hypothesis is examined:

Hypothesis 8: Regionally bounded knowledge sourcing occurs within areas of regionally embedded technological expertise.

MODEL SPECIFICATION

A logistic regression is used to test these eight hypotheses. The first dependent

variable INTRAREG is the probability of knowledge being sourced within the same

region by a foreign-owned firm rather than being sourced outside that region. The

second dependent variable, INTERREG is the probability that knowledge will be

sourced from outside the region of location, but within one of the other five German

regions under study. In what follows, the unit of analysis is not the firm but each

citation. The two models may be expressed formally as:

INTRAREG = f (X, C) (1)

INTERREG = f (X, C) (2)

Where X is a vector of independent variables, and C is a vector of control variables.

The following sections discuss the nature of the dependent and control variables.

Dependent Variables

As discussed above, patent citations (67,142) are used to test these hypotheses. Of

these approximately 12% were self-citations to German-based activity (ie. relate to

internal subsidiary-specific development on site) and so were removed from the data

set. A further 107 citations had to be omitted due to the absence of information. This

resulted in a sample of 58,792 citations.

In what follows, we focus attention upon the six German regions that record the

highest concentration of patenting activity. 9 The dependent variables were coded in

the following way. For each of the 58,792 citations, we created the variables

INTRAREG and INTERREG. INTRAREG is coded as 1 if the first named inventor

of the citing and the cited patent were both located in the same region and that region

is one of the six regions of interest. In all other cases, INTRAREG is coded as zero.

INTERREG is coded as 1 if the first named inventor of the citing and cited patents

were both from one of the six regions, but the citing and cited regions are different.

In all other cases, INTERREG is coded as zero.

Control Variables

Following the seminal work of Jaffe et al. (1993), researchers have attempted to

control for citation frequency by creating a sample of control citations (Frost, 2001 is

a recent example). Unfortunately, this type of control was not possible with this data

set as insufficient data was available on the original cited patents. Therefore, a fixed

9 Both foreign-owned and indigenous firms concentrate activities that lead to patenting in these regions. They are Nordrhein-Westfalen (which records 27% of total patenting activity over the 1969-95 period); Bavaria (23%); Baden Württemberg (19%) and Hessen (13%). While indigenous firms concentrate most of their activity in Nordrhein-Westfalen (29%), Baden Württemberg is the preferred region for foreign-owned firms (31% of their total patenting activity emanates from research undertaken there).

effects model was used to control for variations in citation patterns across technology

sectors and time. Dummies were created for each of the 56 sectors and each of the

years.

Independent Variables

In what follows, we describe each of the independent variables that were used in this

analysis. A summary of each of the variables is shown in Table 1.

TABLE 1 HERE

(i) Characteristics of the Citing Technology

In order to characterize the extent to which the path of knowledge creation tends to be

sector-specific (ie. builds mainly on knowledge from within the same technological

sector), the percentage of intra-sector citation for each of the 56 technologies is

calculated. For example, certain technologies e.g. mining equipment (23) and

illumination devices (37) have a high rate of intra-sector citation associated with them

– 73% of citations made were within the same technology sector as the citing

technology. On the other hand, other electrical communication systems (34) and

explosives, compositions and charges (55) have relatively low levels of intra-sector

citation – just 40% of the citations made were within the same sector as the citing

patent. We characterize this quality of the citing technology as the independent

variable DEPTH, and it could range in value from 0 to 1 (0% to 100%). The actual

range of this variable lies between 13% (disinfectants and preservatives [8]) and 93%

(nuclear reactors [32]).

To cast further light on whether sector-specific knowledge building within a region

occurs less than might be expected in otherwise high DEPTH activities, or more than

might be expected in otherwise low DEPTH activities, the DEPTH variable was

recalculated for all intra- versus inter-regional citations. By comparing the levels of

DEPTH for intra- versus inter-regional citations, we hope to cast some light on the

concept of ‘Slippery’ and ‘Sticky’ technologies. For example, Mining Equipment

(23) has a high (above median) level of DEPTH in general as well as for intra-

regional citation activity. On the other hand, illumination devices (37) has the same

degree of DEPTH in general but a relatively low rate of intra-sectoral accumulation

for intra-regional citation. Arising from this, we characterize a technology such as

illumination devices as SLIPPERY and code it as 1. All technologies that have a

DEPTH value which exceeds the median level (60%) overall but which have a below

median level of DEPTH when considering only intra-regional citation are

characterized as slippery. In all, 7 of the 56 technologies were classified as slippery.

We also attempted to identify ‘sticky’ technologies in a similar manner, as having a

below average value of DEPTH in general, but an above-average value of DEPTH for

intra-regional citations. These are technologies with an unexpectedly high level of

sector-specific accumulation in the case of intra-regional knowledge sourcing. For

example, the majority of the citations associated with the electrical communications

(34) and explosives (55) technologies do not occur within the technologies of these

citing patents – 40% compared with a median level of 60%. However, concentrating

upon intra-regional citations only, it is clear that almost 100% of the citations

associated with explosives (55) patents are made to prior invention within this

technology sector (i.e. 55). A contrasting pattern is observed in the case of electrical

communications (34). In the case of this technology, a below the median level of

intra-sector citation is observed at the intra-regional level as well as in general. This

would lead to a characterization of explosives (55) as a STICKY technology. In these

fields technological search in the more immediate spatial vicinity tends to be more

highly sector-specific than when searching in more distant locations. In all, 5 of the

56 technologies were characterized as ‘sticky’ and a value of 1 was assigned to the

STICKY variable for these 5 technologies.

(ii) Adaptation of Parent Technology

Following Frost (2001), all citing patents were examined for intra-firm citations to

invention in the parent country. PARADAPT is the independent variable that is set

equal to 1 if the citing patent includes a cite to the parent company in the home base.

(iii) Characteristics of the Cited Technology

Science-based technologies were identified as all technological sectors associated

with the Chemical and Electronic macro-sectors. The dichotomous variable

SCIENCE was set equal to 1 for all citations that were classified under either of these

two macro sectors, and assumed a value of zero otherwise.

(iv) Immediacy

The immediacy of knowledge inputs is described by the time lag between the issue

date of a citing patent and the citations contained therein. The variable IMMED is the

natural log of the number of years between the patent’s issue date and the issue date

of the prior invention cited therein.

(v) Regional Institutional Characteristics

Citations were classified according to the institutional character of the assignee. Two

variables are used to capture the nature of this relationship. Firstly, the dichotomous

variable RES is set to 1 for all citations that were assigned to government funded

research institutes, and zero otherwise. Secondly, the dichotomous variable INV was

set to 1 for all citations that were assigned to individual inventors, and zero otherwise.

Given that it was impossible to identify patents that were assigned directly to

Universities, this variable may be seen as a noisy proxy for patents that originated in

Universities, but which were assigned to individual professors in the German case.

(vi) Regional Technological Specialization

A group of independent binary variables is used to capture the importance of the

technological specialization of foreign-owned and indigenous firms at regional level.

This is proxied by the Revealed Technological Advantage (RTA) index, which is the

share of patenting in a given technological field held by some group of firms relative

to that group's share of patenting in all fields, so values greater than unity denote

specialization in a sector (for a further discussion of the properties of this index, see

Soete, 1987; Cantwell 1989, 1993 and for the actual RTA indices for the German

regions, see Cantwell and Noonan, 2002a).

Prior examination of these data has noted the strong relationship between regionally

bounded technology sourcing by foreign-owned firms and the technological

specialization of the same foreign-owned group (Cantwell and Noonan, 2002b). This

was interesting because the literature has tended to concentrate attention upon the

equivalent indigenous sector as the most relevant centripetal force at a regional or

local level. In this model we examine the hypothesis that the extant technological

activities of foreign-owned firms represents an important dimension in each region’s

knowledge infrastructure. The RTA indices of both foreign-owned (RTAf) and

indigenous firms (RTAg) are therefore included.

In addition, we are interested to test whether the technological relationship between

the citing and cited patent has any bearing on the decision to locate within the region.

As noted above, the general assumption is that more complex patterns of citation (i.e.

when the citing patent is in technology i and the cited patent is in technology j) are

more likely to require geographical proximity. To incorporate this into our model, we

draw on results generated in a previous paper, where we calculated a technology

relatedness index using the methodology of Teece et al (1994).10 This index is used

here to capture the relevance of the more complex inter-(technological) sector

exchange for knowledge localization. It is captured in the model by the variables

TRTAg (in the case of indigenous firms) and TRTAf (in the case of foreign-owned

firms). The technological characteristics of the citing and cited patent are extracted

from the data and using the technology relatedness index (reported in Cantwell and

Noonan, 2001) the relatedness measure assumes a value of 1 if the specific

combination is considered ‘related’ and 0 otherwise. This value is then multiplied by

1, if its technological field reflects local RTA (related knowledge sourcing from

locally specialized expertise), and 0 otherwise. Finally, citations that reveal

knowledge sourcing in an unrelated field of local advantage are captured by the

ORTAf and ORTAg variables. The differences in these variables can be more easily

understood by considering the following table:

10 Technology relatedness was calculated by examining the patenting activities undertaken by a large group of MNEs over the 1969-95 period. By extracting the actual patterns of patenting activity in each corporate group within or between technological sectors, firms’ shared perception of complementarity between these technologies was detected and these sectors were deemed to be ‘related’ (see Cantwell and Noonan 2001).

The TRTA variable captures instances where more complex forms of technology

exchange occur between different but related fields of activity. This is shown by

combination B in the table, when a local regional advantage is recorded in the

technology sector of the cited patent. For combination B TRTA assumes a value of 1.

The SRTA measure isolates cases that meet two conditions. Both the citing and cited

patents must belong to the same technology sector (combinations C and D in the table

are examples of exchange within some generic knowledge base) and in addition, there

must be a revealed technological advantage recorded within this technology at a

regional level (combination C only). Instead, in cases for which the citing/cited

combination neither shares the same technology field nor are related to one another,

if the region records an advantage in the sector of the cited patent, these are classified

as ORTA (combination E in the table). Thus, T represents technologically related, S

the same sector, and O other fields of local specialization.

Correlation matrices for the dependent and independent variables are reported in

Tables A2 and A3 in the appendix to this paper.

Related (Teece) exchange > 2.5

Generic (intra- sector) exchange RTA TRTA SRTA ORTA

A 0 0 0 0 0 0 B 1 0 1 1 0 0 C 0 1 1 0 1 0 D 0 1 0 0 0 0 E 0 0 1 0 0 1

Underlying Condition Resulting value

RESULTS

In this section, we summarize the results of the analysis. These are dealt with in four

sections. In the first section, we examine the model that focuses upon the

determinants of intra-regional citation, while the inter-regional citation model is

discussed directly thereafter. We then compare the results of the two models and

highlight some of the key differences between the intra-regional and inter-regional

citation behaviour of foreign-owned firms. Finally, we briefly analyse the economic

as distinct from the statistical significance of the results. Throughout this discussion,

all reported significance levels are for two-tailed tests in the interests of consistency.

Therefore, the significance levels for any hypotheses that are constructed in a single

tail form are understated.

Intra-Regional Citation

A logistic model of intra-regional citation reveals that all of the variables are

significant (see Table 2), that each block of variables is significant and that the

coefficient signs and magnitudes are stable as new blocks of variables are introduced.

TABLE 2 HERE

Focusing first on the characteristics of the citing patent (DEPTH), it is clear that the

results reject hypothesis 1. The proportion of intra-sector citation is positively (rather

than negatively) associated with INTRAREG. One possible explanation for the

failure to accept the hypothesis one is the inclusion of the variables for slippery and

sticky technologies. These variables contain part of the information that is contained

in DEPTH. To ensure that the results were not contaminated by these va riables, we

re-estimated Equation 1 and omitted the SLIPPERY and STICKY variables. We

found that the coefficient on DEPTH continues to be positive and significant.11 The

coefficient was somewhat lower (0.0029 vs. 0.008) but significant at 0.001. This

would suggest that the Jaffe and Trajtenberg (1998) finding (that citations to the same

technology sector as the citing patent are less localized) may be specific to the US and

that the degree of stickiness associated with intra-sector citation may warrant further

investigation.

Hypotheses 2a and 3 are supported (Table 2) and these results suggest that the

classification of technologies as slippery or sticky has some merit. Slippery

technologies have a lower propensity to engage in intra-regional citation while sticky

technologies have a higher propensity to engage in this type of geographically

bounded citation behaviour. Once again, a potential limitation of this result is that the

variables were constructed using the same data set – i.e. by construction, it is possible

that this result might be observed, as the pattern of intra-regional citation across

technologies was used to create the two variables. Nevertheless, the variables are

intuitively plausible and the result is one that is amenable to further empirical

analysis.

Patents that adapt parent company technology are captured by the variable

PARADAPT. We accept hypothesis 4 and find that the patents that adapt parent

11 Regarding the correlation between Depth and Slippery/Sticky: to establish whether these variables should be included, we re-estimated the regressions with and without Slippery and Sticky. The Chi-square for the inclusion of these two variables was: Inter-regional citation: Chi Square (2df) of 9.062, significance at 0.0108; Intra-regional citation: Chi Square (2df) of 20.836, significance at 0.0000.

company technology are less likely to engage in intra-regional citation. This result is

an interesting one as Frost (2001), did not find evidence of this in the US context.

The variable SCIENCE captures the nature of the cited technology. Hypothesis 5

states that citations to science-based technologies are more likely to be intra-regional

in character. The results support this hypothesis. The result is significant at the 10%

level (actual significance of 7.9%), which suggests that further refinement of this

variable in subsequent work should be considered.

The time lag between the grant date of a patent and its subsequent appearance as a

citation is captured by IMMED. Hypothesis 6 is accepted and we find that the

immediacy of a patent increases the probability of intra-regional citation. The sign on

the coefficient is negative as higher values of IMMED involve longer time lags

between the grant date and subsequent citation.

The institutional characteristics of the regional environment are captured by the

variables RES and INVENT. Both of these variables are significant and positively

associated with INTRAREG. This result is consistent with local

infrastructure/institutions being a significant source of technology for foreign-owned

firms in Germany. This becomes more apparent when we examine the role of local

regional technological advantage (RTA). All of these variables are positive and

significant. This is prima-facie evidence that regional RTAs are an important source

of technology for foreign-owned firms. This result is further discussed below and the

relative magnitudes of these variables are more fully examined.

In summary, evidence from the analysis of intra-regional citation suggests that all of

the hypotheses (with the exception of hypothesis 1) should be accepted. Hypothesis 1

was not accepted because we found that the sign on the DEPTH variable was positive

rather than negative unlike in previous work in this area. This prompted us to suggest

that prior results reported in the literature may be specific to the US and that this issue

therefore warrants further investigation.

Inter-Regional Citation

A logistic model of inter-regional citation reveals that almost all of the variables are

significant (see Table 3), that each block of variables is significant and that the

coefficient signs and magnitudes are reasonably stable as new blocks of variables are

introduced.

TABLE 3 HERE

Focusing first on the characteristics of the citing technology, it is clear that hypothesis

1 is accepted. The proportion of intra-sector citation is indeed positively associated

with INTEREG. As discussed above, one possible explanation for accepting this

hypothesis is associated with the inclusion of the variables for slippery and sticky

technologies. These variables contain part of the information that is contained in

DEPTH. To ensure once aga in that the results were not contaminated by these

variables, Equation 2 was re-estimated and the SLIPPERY and STICKY variables

omitted. We found that the coefficient on DEPTH continues to be positive and

significant. This is further evidence that prior results may be specific to the US and

that the issue of intra-sector citation may warrant further investigation.

Hypotheses 2b and 3 are accepted. This is further evidence that the classification of

technologies as slippery or sticky may be helpful. Slippery technologies are not

related to inter-regional citation and sticky technologies lower the propensity to

engage in inter-regional citation.

Patents that adapt parent company technology are captured by the variable

PARADAPT. Hypothesis 4 is rejected and in doing so, we find that patents that adapt

parent company technology are more likely to engage in inter-regional citation.

The variable SCIENCE captures the nature of the cited technology. Hypothesis 5

states that science-based technologies are more likely to be intra-regional in character.

Results from this analysis are consistent with this hypothesis. We find that if the cited

technology is in a science-based sector, it lowers the probability of inter-regional

citation. This is further confirmation of the result reported from the intra-regional

examination.

The time lag between the grant date of a patent and its subsequent appearance as a

citation is captured by IMMED. Hypothesis 6 is accepted but since results are similar

for both intra- and inter-regional citation one can conclude that this variable is an

important determinant of local knowledge sourcing for foreign-owned firms

regardless of their location within the host country. The sign on the coefficient is

negative as higher values of IMMED involve longer time lags between the grant date

and subsequent citation.

The institutional characteristics of the regional environment are captured by the

variables RES and INVENT. Both of these variables are significant and positively

associated with INTERREG. This result is again consistent with local institutions

being a significant source of technological know-how for foreign-owned firms

regardless of their location within Germany. Evidence that technological

specialization is an important determinant of inter-regional exchange is also found in

the INTERREG model. As mentioned already, a comparison of the relative

significance of regional RTA for intra- and inter-regional flows is discussed in the

following section.

In summary, these results are consistent with each of the hypotheses, other than

hypothesis 4. The probability of foreign-owned firms drawing from inter-regional

sources of technology is lowered if these technologies are science-based or if they fall

under the ‘slippery’ classification. We found that the sign on PARAD was positive

which suggests that in adapting technologies to the local market, foreign-owned firms

draw upon indigenous knowledge sources that are embedded across the German

regions. These issues are discussed more fully in the following section.

Comparison of Intra-Regional and Inter-Regional Citations

Comparing the results in Tables 2 and 3 it is apparent that most of the results are

similar with respect to the sign and significance of each of the variables. These

results have been summarized in Table 4 for ease of comparison.

TABLE 4 HERE

Overall, the results are consistent with an important spatial dimension to technology

sourcing. Consistently strong results were obtained for regional technological

advantage and the influence of local institutional factors. The evidence is also

consistent with the notion that there are important variations in these spatial

dimensions across technological sectors. We believe that the results are quite

convincing since sign changes are observed between equations 1 and 2 for the

variables SCIENCE and STICKY. This suggests that across the five specific

technologies that we have classified as STICKY and more generally, for the various

technologies that are housed under the chemical and electronic macro classifications,

especially close co- location is an important prerequisite to inter- firm knowledge

exchange. This is quite an important observation and suggests that further

investigation and research into the nature of such technological flows may prove

fruitful.

Two somewhat confusing results emerge from the analysis (though the first is perhaps

less so). The first somewhat confusing finding is the sign change on the adaptation of

parent technology. This is negative for intra-regional citation and positive for inter-

regional citation. This result suggests that the adaptation of parent technology does

not rely on highly local (i.e. regionally embedded) knowledge sources to any great

extent and consequently, that geographical proximity to such sources is not an

important consideration. Nevertheless, these results are consistent with those reported

in Cantwell and Noonan (2002b) in which a regression analysis demonstrated how the

regionally bound sourcing by foreign-owned firms seemed to reflect the technological

specialization of other foreign-owned firms at a regional level. In contrast, inter-

regional sources of technology seemed to draw from the knowledge infrastructures of

indigenous firms. Drawing the two sets of results together, this may suggest that if

foreign-owned firms are interested in adapting parent technology to the local German

(or European) market, then they rely upon or draw from indigenous sources of

knowledge from across the German regions. In contrast, when these firms are

engaged in home base augmenting type activity that involves more tacit exchange

with agents that are positioned at the relevant technological frontier, they source intra-

regionally – and in their case in Germany, this expertise is found within the foreign-

owned sector itself. This warrants further investigation particularly since Frost (2001)

also obtains somewhat mixed results on this issue.

The second and perhaps more challenging result is that associated with the DEPTH

variable. Results suggested that sectors characterized by a high level of intra-sector

citation are as likely to engage in intra- as in inter-regional knowledge sourcing. This

again warrants further investigation.

Significance of the Variables

The above analysis examines the propensity to engage in inter- and intra-regional

citation. Almost all of the variables examined are significant and report a sign that

one might expect to see ex ante. However, the results reported in Tables 2 and 3 offer

little opportunity to reflect on the relative importance of each of these variables in

understanding citation behaviour. In order to gain a better understanding of the

magnitude of each of these variables, odds ratios were computed for each of the

variables in equations 1 and 2. Odds ratios provide an estimate (with a confidence

interval) for the relationship between two binary variables and enable an examination

of the effects of other variables on that relationship, using logistic regression. As

such, it could be treated as a type of elasticity measure. Since it enables a direct

comparison of the relative impact of each of the variables used in the models, it sheds

further light on the relative impact of each of the independent variables in inter-

versus intra-regional citation. To give a sense of relative magnitudes and preserve the

signs of the independent variables, the log of the odds ratio is reported in Table 5.

TABLE 5 HERE

The first point to note from Table 5 is the strong influence exerted by regional

institutional characteristics and regional technological advantages (RTAs). Second,

there is a marked difference in the role of RTAs for inter-regional and intra-regional

citation that is once again entirely consistent with prior research.

Indigenous firm RTA in either the same sector or a related technological sector ranks

sixth and seventh (respectively) as a determinant of intra-regional citation. Leaving

aside the RTAs of foreign-owned firms, the most important local sources are the

presence of research institutions and domestic RTAs in entirely unrelated areas. This

would suggest that regional aspects of technology sourcing in Germany are linked to

the most tacit forms of local knowledge – knowledge produced in research institutes

and RTAs in entirely unrelated sectors where an in-depth understanding of the

context of discovery might be most important.12 It is also useful to note that the

single largest factor that decreases the odds of intra-regional citation is the presence of

‘slippery’ technologies.

An examination of the odds ratios for inter-regional citation is also insightful.

Consistent with the results reported Cantwell and Noonan 2002a, 2002b), the most

striking feature of inter-regional knowledge is the dominant role played by the

12 It is important to note that the ORTALOC variable captures inter-firm flows that, based on our technology relatedness index, are deemed unrelated. Recalling that relatedness was measured by examining the co-development of pairwise technology combinations by the world’s largest MNEs, this variable therefore captures rather idiosyncratic pairwise combinations of technologies that were not typically observed within the leading firms. In other words, the co-development of these particular technology combinations is not representative of large firm technological search.

indigenous sector. Indigenous RTAs in related sectors, the same sectors and other

(unrelated) sectors are the top three determinants of inter-regional citation. Individual

inventors and research institutes located across the regions follow these as the fourth

and fifth most important determinants of inter-regional citation. The greatest

disincentive to inter-regional citation is the presence of sticky technologies –

technologies that are regionally bound by their very nature.

CONCLUSIONS

This study used a logistic regression model of citation behaviour as a means of

synthesising the key determinants of regionally bound knowledge localization in

Germany. This analysis of foreign-owned firm citations reveals that technology

sourcing in Germany takes place on both a within-regional and inter-regional basis.

The determinants of regionally bound technology sourcing appear to be driven by the

nature of individual technological sectors. Rather than viewing technology sourcing

by foreign-owned firms as a generalized phenomenon therefore, it would appear that

technology ‘travels’ more easily in some sectors than others. The presence of such

‘sticky’ sectors and the complexity of science-based technologies appear to be quite

important. In terms of RTAs, most regionally bound citation occurs to other foreign-

owned firms that actively research in different but related sectors of technology,

while citation to the indigenous group of firms occurs across technology combinations

that are categorized as unrelated sectors of technological search.

At the inter-regional level, the RTAs of domestic German-owned firms are the most

important determinant of citation. This is consistent with the idea that the six German

regions examined in this study may be best viewed as a group rather than individual

regions, once one controls for the degree of mobility of knowledge transmission in

individual technological sectors. Although intra-regional sourcing communicates the

importance of the indigenous base relative to the foreign-owned knowledge base, the

substant ial degree of inter-regional citation that occurs is driven by the desire to tap

into indigenous lines of technological expertise. This is perhaps consistent with the

suggestion that foreign-owned firms tap into indigenous technologies when seeking to

adapt their knowledge to local market conditions and immediate geographic proximity

is not a necessary precondition for this. Nonetheless, it is clear that the German-

owned knowledge pool is an important factor for foreign-owned multinational

enterprises located in this country.

TABLES AND FIGURES

Figure 1 Distribution of Patent Citations

Figure 2 Spatial variation in search processes.

Citation freqFrequencuency

108.00

52.00

44.00

35.00

31.00

28.00

25.00

22.00

19.00

16.00

13.00

10.00

7.00

4.00

1.00

Fre

quen

cy

2000

1000

0

Citation freqFrequencuency

108.00

52.00

44.00

35.00

31.00

28.00

25.00

22.00

19.00

16.00

13.00

10.00

7.00

4.00

1.00

Fre

quen

cy

2000

1000

0

G < Median G > Median

L < Median

L > Median

1. Local citation 3. ‘Slippery’ reflects global

norm sourcing

at local level

2. ‘Sticky’ 4. Local citation sourcing at local reflects

global norm level

DEPTH AT GLOBAL LEVEL

DEPTH AT LOCAL LEVEL

Table 1 Definition of the Variables

Variable Operational Definition

Exp. Sign

Hypoth. No.

Dependent variable: INTERREG Host country citation (Inter regional)

Equals 1 if the citing and cited patents occur in one of the 6 regions but citing and cited regions are different; 0 otherwise

INTRAREG Host region citation (Intra regional)

Equals 1 if the citing and cited patents occur in the same region; 0 otherwise

Independent variables: I. Parent Adapting Technology PARADAP

1 if the citing patent includes a cite to to the parent firm; 0 otherwise

[-] 4 II. Indigenous RTA which is differentiated according to: (i) RTAg 1 if cited patent is in the same

technology as the citing and RTAg >1 (intra-technology sector); 0 otherwise [+] 8

(ii) TRTAg 1 if cited patent is in a 'related' technology and RTAg >1 (inter- technology sector); 0 otherwise [+] 8

(iii) ORTAg 1 if cited patent is in technology where neither (i) nor (ii) apply. [+] 8

III. Foreign RTA which is differentiated according to: (i) SRTAf

1 if cited patent is in the same technology as the citing and RTAf >1 (intra-technology sector); 0 otherwise [+] 8

(ii) TRTAf 1 if cited patent is in a 'related' technology and RTAf >1 (inter- technology sector); 0 otherwise [+] 8

(iii) ORTAf 1 if cited patent is in technology where neither (i) nor (ii) apply [+] 8

Table 1 (continued)

VII. Assignee Characteristics

RES - Research Institutions [+] 7

INV - Individual Inventors [+] 7

IV. Immediacy IMMED

Log of the time lag (years) between the citing and cited patent [-] 6

V. Citing Technology Characteristics DEPTH 1 if proportion of intra-technology

sector citation > 60% [-] 1

SLIPPERY 1 if relatively high level of intra- sector citation in general but not if regionally bound; 0 otherwise [-] 3

STICKY 1 if relatively low level of intra-sector citation in general but a high level when regionally bound; 0 otherwise [+] 2

VI. Cited Technology Characteristics SCIENCE 1 if citations are to the chemical or

electronic technologies; 0 otherwise [+]

5

Table 2: Logistic Regression Model of Intra-Regional Citation

INTRAREG SRTAFOR 3.1611 ***

(0.1217)SRTALOC 2.4644 ***

(0.0891)TRTAFOR 3.4987 ***

(0.2641)TRTALOC 2.3571 ***

(0.1788)ORTAFOR 2.5036 ***

(0.2595)ORTALOC 2.7066 ***

(0.1427)INVENT 1.5096 ***

(0.3643)RES 2.6081 ***

(0.1212)IMMED -0.4671 *** -0.4063 ***

(0.0283) (0.0327)PARADAPT -0.3586 *** -0.3654 **

(0.0892) (0.0973)SCIENCE 0.1496 * 0.1817 *

(0.0925) (0.1034)STICKY 0.5775 ** 0.622 ** 0.6454 **

(0.2176) (0.2232) (0.0197)SLIPPERY -0.6666 *** -0.6776 *** -0.741 ***

(0.1134) (0.1164) (0.1375)DEPTH 0.0087 ** 0.0095 ** 0.008 *

(0.0033) (0.0034) (0.0037)Date Dummies n.s. n.s. * *Sector Dummies *** *** *** ***Constant -4.17 *** -3.78 *** -4.3341 ***

(0.47) (0.5098) (0.5521)

ChiSq 340 *** 385 *** 682 *** 3552 ***Change ChiSq 45 *** 297 *** 2870 **** p <0.1; ** p<0.01; *** p<0.001 - all significance levels are two tail tests of H(O): x = 0

Table 3: Logistic Regression Model of Inter-Regional Citation Behaviour

INTERREG SRTAFOR 1.5697 ***

(0.1378)SRTALOC 4.4744 ***

(0.0726)TRTAFOR 2.6502 ***

(0.2973)TRTALOC 4.769 ***

(0.1536)ORTAFOR 0.814 **

(0.2803)ORTALOC 4.274 ***

(0.1246)INVENT 4.0953 ***

(0.3008)RES 2.7055 ***

(0.1127)IMMED -0.4199 *** -0.3898 ***

(0.0166) (0.0205)PARADAPT 0.0721 * 0.104 **

(0.0444) (0.0548)SCIENCE -0.0429 -0.1954 **

(0.0548) (0.0699)STICKY -0.4843 * -0.4688 * -0.6359 **

(0.206) (0.2088) (-0.0107)SLIPPERY -0.048 -0.0935 -0.0219

(0.0594) (0.0613) (0.074)DEPTH 0.0053 *** 0.0057 ** 0.0053 **

(0.002) (0.002) (0.0025)Date Dummies *** *** *** ***Sector Dummies *** *** *** ***Constant -2.6698 *** -2.1823 *** -2.9235 ***

(.2361) (0.2665) (0.3386)

ChiSq 876.874 *** 891.727 *** 1540.21 *** 10397.8 ***Change ChiSq 14.853 ** 648.487 *** 8857.58 **** p <0.1; ** p<0.01; *** p<0.001 - all significance levels are two tail tests of H(O): x = 0

Table 4: Summary of Regression Results

Intra InterSRTAFOR + +SRTALOC + +TRTAFOR + +TRTALOC + +ORTAFOR + +ORTALOC + +INVENT + +RES + +IMMED - -PARADAPT - +SCIENCE + -STICKY + -SLIPPERY - - (n.s.)DEPTH + +

Table 5: Odds Ratios

INTRA INTERSRTAFOR 3.16 1.57SRTALOC 2.46 4.47TRTAFOR 3.50 2.65TRTALOC 2.36 4.77ORTAFOR 2.50 0.81ORTALOC 2.71 4.27INVENT 1.51 4.10RES 2.61 2.71IMMED -0.41 -0.39PARADAPT -0.37 0.10SCIENCE 0.18 -0.20STICKY 0.65 -0.64SLIPPERY -0.74 -0.02DEPTH 0.01 0.01

APPENDIX

Table A1 Breakdown of 56 macro technology groups

TECHNOLOGY SECTOR

2 Distillation Processes3 Inorganic Chemicals4 Agricultural Chemicals5 Chemical Processes6 Photographic chemistry7 Cleaning agents & other compositions CHEMICAL 8 Disinfectants & Preservatives (13 sectors)9 Synthetic resins and fibres10 Bleaching and Dyeing11 Other organic compounds12 Pharmaceuticals and Biotechnology51 Coal and Petroleum products55 Explosives, Compositions and Charges

1 Food and Tobacco Products13 Metallurgical Processes14 Miscellanous Metal Products15 Food Drink and Tobacco Equipment16 Chemical and Allied Equipment17 Metal Working Equipment18 Paper Making Apparatus19 Building Material Processing Equipment20 Assembly and Material Handling Equipment21 Agricultural Equipment22 Other Construction and Excavating Equipment MECHANICAL23 Mining Equipment (21 sectors)24 Electrical Lamp Manufacturing25 Textile and Clothing Machinery26 Printing and Publishing Machinery27 Woodworking Tools and Machinery28 Other Specialised Machinery29 Other General Industrial Equipment31 Power Plants50 Non-metallic Mineral Products53 Other Instruments and Controls

30 Mechanical Calculators and Typewriters33 Telecommunications34 Other Electrical Communication Systems35 Special Radio System36 Image and Sound Equipment37 Illumination Devices ELECTRONIC 38 Electrical Devices and Systems (11 sectors)39 Other General Electrical Equipment40 Semiconductors41 Office Equipment52 Photographic Equipment

42 Internal Combustion Engines43 Motor Vehicles44 Aircraft45 Ships and Marine Propulsion TRANSPORT46 Railways and Railway Equipment (7 sectors)47 Other Transport Equipment49 Rubber and Plastic Products

32 Nuclear Reactors48 Textile, Clothing and Leather OTHER 54 Wood products (4 sectors)56 Other Manufacturing and Non-Industrial

MACRO GROUP

Table A2 Pearson Correlation dependent and independent variables

A 3 Spearman Correlation between dependent and independent variables.

INTER INTRA PARADAPT SRTALOC TRTALOC ORTALOC SRTAFORTRTAFORORTAFOR IMMED DEPTH STICKY SLIPPERY SCIENCE RES INV

INTERINTRA -0.083PARADAPT -0.026 -0.038SRTALOC 0.354 0.251 -0.013TRTALOC 0.167 0.084 0.002 -0.015ORTALOC 0.178 0.132 -0.006 -0.019 -0.008SRTAFOR 0.116 0.289 -0.020 0.292 -0.010 -0.012TRTAFOR 0.046 0.118 -0.007 -0.010 0.216 -0.005 -0.006ORTAFOR 0.060 0.104 -0.009 -0.010 -0.004 0.334 -0.006 -0.003IMMED -0.102 -0.184 -0.037 -0.087 -0.027 -0.029 -0.081 -0.030 -0.024DEPTH 0.037 0.016 -0.026 0.003 -0.039 -0.035 0.044 -0.009 -0.004 -0.026STICKY -0.020 0.006 0.010 -0.014 -0.006 0.010 -0.011 -0.006 0.004 0.012 -0.141SLIPPERY -0.006 -0.021 0.023 0.007 -0.017 -0.028 0.054 0.011 -0.005 -0.089 0.301 -0.061SCIENCE -0.029 -0.007 0.071 0.034 0.036 0.001 0.004 0.025 -0.017 -0.123 -0.119 -0.059 -0.119RES 0.108 0.011 -0.012 0.020 0.016 0.012 0.011 0.006 -0.002 -0.017 0.036 -0.005 -0.001 -0.015

INV 0.172 0.105 -0.017 0.080 0.033 0.051 0.083 0.009 0.025 0.001 0.009 -0.003 0.001 -0.034 -0.004

Note: Boldface Indicates 2 tail significance at 0.01

INTER INTRA PARADAPT SRTALOC TRTALOC ORTALOC SRTAFORTRTAFORORTAFOR IMMED DEPTH STICKY SLIPPERY SCIENCE RES INV

INTERINTRA -0.082PARADAPT -0.026 -0.038SRTALOC 0.354 0.250 -0.013TRTALOC 0.167 0.084 0.001 -0.016ORTALOC 0.178 0.133 -0.006 -0.019 -0.007SRTAFOR 0.116 0.289 -0.021 0.292 -0.010 -0.012TRTAFOR 0.047 0.118 -0.007 -0.011 0.217 -0.006 -0.006ORTAFOR 0.062 0.103 -0.009 -0.009 -0.003 0.334 -0.006 -0.003IMMED -0.105 -0.196 -0.024 -0.092 -0.028 -0.030 -0.086 -0.033 -0.024DEPTH* 0.029 0.008 -0.013 -0.008 -0.036 -0.039 0.038 -0.003 -0.003 -0.038STICKY -0.019 0.006 0.010 -0.015 -0.007 0.011 -0.011 -0.005 0.005 0.015 -0.138SLIPPERY -0.006 -0.022 0.023 0.007 -0.017 -0.028 0.055 0.010 -0.007 -0.098 0.384 -0.061SCIENCE -0.029 -0.006 0.071 0.034 0.035 0.001 0.003 0.027 -0.016 -0.141 -0.134 -0.059 -0.119RES 0.109 0.012 -0.010 0.018 0.016 0.012 0.011 0.006 -0.002 -0.019 0.026 -0.004 -0.001 -0.015

INV 0.172 0.104 -0.018 0.080 0.033 0.051 0.084 0.008 0.024 0.005 0.009 -0.002 0.003 -0.033 -0.004

Note: Boldface Indicates 2 tail significance at 0.01

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