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Shaping the formation of university-business

research collaborations:What type of proximity does really matter?

Pablo DEste (INGENIO, CSIC-UPV)Frederick Guy (Birkbeck College, London)Simona Iammarino (London School of Economics, LSE)

5th May 2011, Barcelona

2nd

MOVE-ACC1-IESE Workshop on Innovation Series


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Aim of the study

To examine:

the conditions that explain the formation ofuniversity-business (U-B) research collaborations

the role of different types of proximity (not only

geographical) in shaping U-B collaborations


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Setting the scene (I) U-B interactions have become prominent as subject of academic

research and as policy instruments to foster innovation:

U-B interactions facilitate bi-directional flows of knowledge:

Increase awareness, among industrial practitioners, of the potentialapplications of the findings from fundamental / embryonic research at univ.

New ideas for university research that emerge from the problems faced by

businesses in their innovation-related activities

U-B interactions favour the establishment ofenduring social

networksbetween the partners involved:

build truthful relationships, conducive to the transmission of sensitiveinformation

create favourable conditions for successful learning processes (ofteninvolving complex problem-solving activities from upstream research tocommercialisation)


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Setting the scene (II) however, there are also potential downsides associated to

U-B research collaborations:

may restrict a wider or faster dissemination of academic research

findings (because business partners may be reluctant to disclose sensitiveinformation)

could shift academic research agendas towards more applied research

(as opposed to a more basic one)

might conflict with the pursuit of blue-sky, curiosity-driven research,

favouring more targeted / short term research objectives

The relative balance will largely depend on the specific characteristics

of the learning processand the actual management of the relationship


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Theoretical framework (I): on the formation of U-B researchpartnerships

Geographical proximity matters:

Wide support from the literature to the existence oflocalised knowledgespillovers from university research to industrial innovation:

Advantages obtained by social actors in accessing and using knowledge created byother co-located actors (Jaffe, 1989; Jaffe et al., 1993; Audretsch & Feldman, 1996: van Oort, 2004)

Spatial proximity is expected to favour linkages that involve exchanges of advancedtechnical and scientific expertise, which require frequent personal contacts foreffective transmission (Mansfield & Lee, 1996; Breschi & Lissoni, 2001; Storper & Venables, 2004)

U-B research collaborations constitute a prototypical example of interaction

susceptible to:

benefit from spatial proximity requires frequent face-to-face contacts for successfullearning at the interface between technical and scientific knowledge

generate localised knowledge spillovers - involve intended and un-intendedknowledge flows


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Theoretical framework (I): the formation of U-B researchpartnerships and geographical proximity Geographical proximity matters:

Wide support from the literature to the existence of localised knowledge

spillovers from university research to industrial innovation: Advantage that social actors accrue in accessing and using knowledge created by

other co-located actors

Spatial proximity is expected to favour linkages that involve exchanges of advancedtechnical and scientific expertise, which requires face-to-face contacts for effective

transmission

U-B research collaborations constitute a prototypical example of interaction

susceptible to: U-B benefit from spatial proximity

involve intended and non-intended knowledge flows localised knowledge spillovers

H1: The probability of a new research partnership between a university

and a firm increases with the level of geographical proximity betweenthose organisations


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Theoretical framework (II)

However, the effects of geographical proximity may be weakened orstrengthened by other forms of proximity(Kirat & Lung, 1999; Torre & Gilly, 2000; Boschma, 2005, Ponds et al., 2007)

[] geographical proximity per se is neither a necessary nor asufficient condition for learning to take place (Boschma, 2005, 62)

It is not a necessary condition, because other forms of proximity may

function as substitutes: ex.Institutional Proximitybetween partners may help solving problems of

coordination, regardless of spatial considerations

It is not a sufficient condition, because (complex) learning processes mayrequire other forms of proximity in addition to spatial proximity:

ex. Cognitive proximity may be needed to complement spatial proximity tofacilitate inter-organisational learning processes, as in the case ofTechnologically Related Industrial Clusters


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Theoretical framework (III)

Institutional Proximity:

Institutional Proximity refers to the extent to which partners have similarnorms, routines and incentive systems, which facilitate the coordination oflearning processes (Maskell & Malmberg, 1999; Boschma, 2005; Ponds et al., 2007)

What is tacit and difficult-to-transfer knowledge depends on the sharedcodification capabilities and established practices of the actors involved inthe partnerships (Steinmueller, 2000; Cowan et al., 2000)

Collaborative experience gained through participation in previous projects should

produce management skills, shared values and social bonds that strengtheninstitutional proximity


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Theoretical framework (III)

Institutional Proximity:

Institutional Proximity refers to the similarity of the partners norms, routines andincentive systems that facilitate the coordination of learning processes (Maskell &Malmberg, 1999; Boschma, 2005; Ponds et al., 2007)

What is tacit and difficult-to-transfer knowledge depends on the sharedcodification capabilities and established practices of the actors involved in thepartnership (Steinmueller, 2000; Cowan et al., 2000)

Collaborative experience gained through participation in previous projects should

produce management skills, shared values and social bonds that strengtheninstitutional proximity

H2.a. The probability of a new research partnership between a university and a firmincreases with the level of institutional proximity between those organisations

H2.b. The experience of partners in prior research collaborations relaxes theimportance of geographical proximity on the formation of a new partnership

Geographical and institutional proximity are likely to be substitutes (in terms of their impacton U-B partnership formation)


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Theoretical framework (IV)

Industrial Clusters:

By industrial clusters we broadly refer to spatial concentration of firms

(including firms undertaking similar and complementary industrial activities) Linkages between universities and firms could be seen as one component

of a much larger set of inter-organisational knowledge exchanges, the bulkof which is represented by inter-firm linkages

U-B partnerships are likely to be connected to industrial clusters clustered firms (particularly high-tech ones) have strong relational capabilities

and benefit from social networks that help forming ties with local universities U-B links might stimulate the emergence and growth of industrial clusters

(Saxenian, 1994; Zucker et al., 1998; Malmberg & Maskell, 2002; Casper, 2007)


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Theoretical framework (IV)

Industrial Clusters:

U-B partnerships are likely to be connected to spatial concentrations of firms

either because U-B links stimulate the growth of industrial clusters, or because clustered firms have stronger relational capabilities and benefit from

social networks that help forming ties with local universities (Saxenian, 1990; Zucker et al.,1998; Casper, 2007)

Linkages between universities and firms could be seen as one componentof a much larger set of inter-organisational knowledge exchanges, the bulkof which is represented by inter-firm linkages

H3. The positive impact of geographical proximity on the formation of a new

partnership between a university and a firm is strengthened if the firm is part of anindustrial cluster

Being part of an industrial cluster and being geographically close to a university arecomplements (in terms of favouring U-B partnership formation)


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Dataset

Our main source of data is based on:

Collaborative research projects from grants awarded by the UK

Engineering & Physical Sciences Research Council (EPSRC), for theperiod 1999-2003

The data contains 4525 instances of U-B partnerships our unit ofanalysis

There are: 2031 business units / 83 UK universities / 318 universitydepartments

A business unit is a defined by the pair: company name - location Location is given by postcode Multiple locations of a single corporation are treated as different business units


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Dataset (II)

Information that we added to the dataset

Scientific field of the academic partners, from universitydepartment details

Industry of the business units (match with ISIC codes; includingmanufacturing and services)

Geographical distances (as the-crow-flies, in Km) for eachpossible pair: Distances were calculated using the postcodes of universities and business

units University - business unit Business unit - business unit


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Characteristics of partnerships

Discipline % of partnerships

Chemical Engineering 5.9

Chemistry 9.4

Civil Engineering 11.0

Computer Science 7.4

Electrical and Electronic Engineering 14.5

General Engineering 11.6

Mathematics 2.5

Mechanical, Aero. and Manuf. Eng. 21.3

Metallurgy and Materials 9.9

Physics 6.3

Total (%) 100%

Breakdown by discipline of university departments

involved in the partnerships (no observations: 4525 partnerships)

74% of the

partnerships arewith Engineeringrelated departments


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Characteristics of partnerships

Industry % of firms

Chemicals & Chemical Related 11.8

Electrical / Electronics 9.3

Instruments 5.9

Machinery / Metals 10.4

Transport 7.7

Utilities & Construction 7.9

Manufacture n.e.c. 3.9

Computer Services 5.1

Research & Development 5.4

Consultancy and other Business Services 17.4

Services n.e.c. 15.4

Total (%) 100%

Service firmsaccount for43% ofpartnerships

Breakdown by industry (no observations: 4525 partnerships)


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Distances

Distribution features of distances (in kms)

Mean(kms)

Median(kms)

Min(kms)

Max(kms)

1stQuartile

3rdQuartile

166.5 137.2 0 840.9 64.9 240.4

25% of partnerships involve a spatial distance below 64.9 Kms

75% of partnerships involve a spatial distance below 240 Kms


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Geographical distribution

Location of firms () anduniversities () included inthe dataset


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Distribution of companies by number of partnerships

60.1

18.6

7.14.8

2 1.5 0.8 1.2 0.62.5

0.8

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 to 19 20 or

above

%

Number of partnerships with universities

Proportion of firms

60% of the firms had only onepartnership with universities


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Method

Case control approach

Pairing each instance of research collaboration in 2003 (i.e. 630

partnerships) with a critical number of U-B pairs that could have happenedbut did not

We used 82 non-occurrence cases for each of the 630 occurrences

These 82 non-occurrences correspond to all the alternative universities withwhich each business unit collaborating in 2003 could have interacted, but did not(e.g. Sorenson & Stuart, 2001; Sorenson et al, 2006)

We exploit the longitudinal dimension of the data by using the first fouryears (i.e. 1999-2002) of the dataset to build our explanatory variables


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Method (II)

Main constructs:

Dependent variable:

Dichotomous variable: takes value 1 for actual occurrences of U-Bpartnerships in the final year of our 5-year period (i.e. 2003), andtakes value zero for the paired non-occurrences

Our total number of observations amounts to 52920 (i.e. 630 x 83),of which 630 are actual U-B partnerships

Logistic regression models were used to estimate the likelihood ofthe formation of U-B research partnerships Also estimated using Rare Events Logit (King & Zeng, 1999)


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Method (III)

Independent variables:

Geographical proximity (Geoprox):

Measured as the inverse of the square root of distance between auniversity and a firm in kms (1/dij), where i refers to firm and j refersto university

Institutional proximity (PriorPartnerships):

Measured by the extent of the two partners prior engagement inresearch collaborations, over the period 1999-2002

For each firm and university, we take the number of partnerships in theearlier period. PriorPartnerships is the square root of the product of thefirms and the universitys prior experience


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Method (IV)

Clustering Index:For each business in our dataset, we compute a measure of clustering: how

much spatially close one firm is to all other firms in the dataset

Simple Clustering Index (CI):

Sum of inverse distances from firm i to all other business units in our dataset

Where i and j refer to business units, and dij

is the square root ofdistance between i an j in kms


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Method (IV) Clustering Index:For each business in our dataset, we compute a measure of clustering: how

much spatially close one firm is to all other firms in the dataset

Simple Clustering Index (CI): Sum of inverse distances from firm i to all other business units in our dataset

Weighted Clustering Index (Technological complementarity CI - TCCI): Similar to CI, but weighting spatial proximity according to the technological

complementarities of the industries (k and l) to which firms belong: Rkl

Where i and j refer to business units, and dij is the square root ofdistance between i an j in kms

Computation of Rkl comes from the extent to which twoindustries participate in the same projects more often thanwould have been expected if industries joined projectsrandomly


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Proportion of partnerships across scientific disciplines, by industry


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Method (VI)

Control variables:

Service dummy:

Dichotomous variable that takes the value 1 if the firm belongs toa service sector

University clustering index:

From the standpoint of each business unit: this is an index ofspatial proximity of each firm to the universities in our sample


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Results Logit estimates for the probability of research partnership formation


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(1)

Geographical Proximity (GeoProx) 2.471 ***

PriorPartnershipsPriorPartnerships * GeoProx

Clustering Index (CI)

Tech. Complem. Cluster. I. (TCCI)

GeoProx * CIGeoProx * TCCI

University Cluster Index (UCI)

GeoProx * UCI

ServicesGeoProx * Services

Constant -4.679 ***

Obsesrvations 52920

Pseudo R2

0.021* p< 0.05, ** p< 0.01, *** p< 0.001


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(1) (2)

Geographical Proximity (GeoProx) 2.471 *** 2.640 ***

PriorPartnerships 3.134 ***PriorPartnerships * GeoProx -1.408

Clustering Index (CI) ---

Tech. Complem. Cluster. I. (TCCI) ---

GeoProx * CI ---GeoProx * TCCI ---

University Cluster Index (UCI) -0.136 *

GeoProx * UCI 0.074

Services -0.193GeoProx * Services 0.031

Constant -4.679 *** -4.868 ***

Obsesrvations 52920 52920

Pseudo R2

0.021 0.039* p< 0.05, ** p< 0.01, *** p< 0.001


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(1) (2) (3) (4)

Geographical Proximity (GeoProx) 2.471 *** 2.640 *** 2.520 *** 3.117 ***

PriorPartnerships 3.134 *** 3.113 *** 3.592 ***PriorPartnerships * GeoProx -1.408 -1.327 -3.362

Clustering Index (CI) --- 0.065 ---

Tech. Complem. Cluster. I. (TCCI) --- --- 0.236 ***

GeoProx * CI --- -0.539 ** ---GeoProx * TCCI --- --- -0.960 ***

University Cluster Index (UCI) -0.136 * -0.161 * -0.172 **

GeoProx * UCI 0.074 0.272 0.151

Services -0.193 -0.241 * -0.301 **GeoProx * Services 0.031 0.484 0.512

Constant -4.679 *** -4.868 *** -4.849 *** -4.912 ***

Obsesrvations 52920 52920 52920 52920

Pseudo R2

0.021 0.039 0.041 0.044* p< 0.05, ** p< 0.01, *** p< 0.001


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(1) (2) (3) (4)







GeoProx * UCI 0.074 0.272 0.151


Constant -4.679 *** -4.868 *** -4.849 *** -4.912 ***

Obsesrvations 52920 52920 52920 52920

Pseudo R2

0.021 0.039 0.041 0.044* p< 0.05, ** p< 0.01, *** p< 0.001

H1.


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(1) (2) (3) (4)







GeoProx * UCI 0.074 0.272 0.151


Constant -4.679 *** -4.868 *** -4.849 *** -4.912 ***

Obsesrvations 52920 52920 52920 52920

Pseudo R2

0.021 0.039 0.041 0.044* p< 0.05, ** p< 0.01, *** p< 0.001

H2a.


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(1) (2) (3) (4)







GeoProx * UCI 0.074 0.272 0.151


Constant -4.679 *** -4.868 *** -4.849 *** -4.912 ***

Obsesrvations 52920 52920 52920 52920

Pseudo R2

0.021 0.039 0.041 0.044* p< 0.05, ** p< 0.01, *** p< 0.001

H2b.


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(1) (2) (3) (4)







GeoProx * UCI 0.074 0.272 0.151


Constant -4.679 *** -4.868 *** -4.849 *** -4.912 ***

Obsesrvations 52920 52920 52920 52920

Pseudo R2

0.021 0.039 0.041 0.044* p< 0.05, ** p< 0.01, *** p< 0.001

H3.H3.


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Results (II)

-.002

0

.002

.004

.006

MarginalEffectofFirm-UniversityProximity

0 20 40 60Spatial Clustering of Firms - Unweighted Index

Marginal Effect of Firm-University ProximityProbability of Partnership

As Clustering of Firms Varies

Figure 1a

-.002

0

.002

.004

.006

Marg

inalEffectofFirm-Un

iversityProximity

0 20 40 60Complementarity-Weighted Spatial Clustering of Firms

Marginal Effect of Firm-University ProximityProbability of Partnership

As Clustering of Firms Varies

Figure 1b


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Discussion and emerging conclusions

Measuring channels through which knowledge istransmitted:

Many empirical studies on localised knowledge spillovers have assumedco-location in geographically pre-defined spaces as a proxy forknowledge flows

Crucial to find out how knowledge is transmitted, among whom and atwhat distance: this remains a challenge in empirical studies oninnovation and economic geography (Breschi & Lissoni, 2001)

This study has addressed this by looking at one specific channel of inter-

organisational knowledge flows - research collaborations betweenuniversities and businesses:

involve frequent face-to-face contacts between participating actors

involve intended and un-intended knowledge flows between actors


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Geographical proximity matters in shaping U-Bpartnerships:

Spatial proximity increases the probability of forming aresearch partnership between firms and universities

Short distances help bringing people together, favouring

information contacts and facilitating the exchange of tacitknowledge

The strong effect of spatial proximity holds after accounting

for the impact of other forms of proximity that may act assubstitutes


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Institutional proximity alsomatters:

the stronger the actors experience in U-B partnerships, the

more likely that they establish a research collaboration The extent to which the organisations share values and

routines, and build trust and common rules, play a veryimportant role in shaping U-B research collaborations

However, we do not observe a substitution effect betweengeographical and institutional proximity:

institutional proximity does not attenuate the importance ofgeographical proximity in shaping U-B collaborations

Or to put it differently, does not alleviate the disadvantage of spatialdistance


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We find that Industrial Clustering and spatial proximity aresubstitutesin terms of shaping the formation of universityindustry partnerships

Geographical proximity decreases its importance in shaping the probabilityof partnership formation, when firms are part of an industrial cluster

Actually, in the case of the most densely clustered firms, the effect of

geographical proximity becomes almost unimportantin shaping U-Bpartnership formation

While geographical and institutional proximity have a strong impact on shapingU-B partnership formation

Concentrating public resources in universities proximate to existing industrialclusters will not substantially contribute to foster localU-B partnerships


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Why?

Presence in a dense cluster of knowledge intensive firms may contributeto the firm's ability to establish, or gain from, partnerships at a greater

distance (including partnerships with universities) (Gordon & McCann, 2000)

Clustered firms involved in distant interactions with non-localorganisations, enjoy lower risks of cognitive and social lock-in (Maskell,2001)

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