Knowledge spillovers or markets? A sentimental journey, 1998-2006 F. Lissoni (Università di Brescia and CESPRI-Bocconi Univ.) DIMETIC Doctoral European

Knowledge spillovers or markets? A sentimental journey, 1998-2006

F. Lissoni (Università di Brescia and CESPRI-Bocconi Univ.)

DIMETIC Doctoral European Summer School - Session 2Pecs, July 2-13 , 2007

DIMETIC in Pecs - Flix on LKS 2

The journey• 1998(-2000): when you grow up, they tell you tacit knowledge

does not exist (well… it does, but it has to be handled with care) the TIPIK project and my “second PhD” on Brescia mechanical cluster

• 1999(-2001): learning to say “no”; Stefano and I dare stopping our “mindless data mining” and pause to think the ESSY project and the “LKS critical survey”

• 2002-2003: mindful data mining patent citations and networks of inventors

• 2006: back to LKS: social network analysis of knowledge flows in the US


1998: TIPIK and the Brescia mechanical cluster

1. Tacit knowledge and the geography of innovation

Literature on Industrial Districts (IDs):

• “Tacit” knowledge explains what kind of innovation IDs are good at producing, and why

• Intertwining between “tacitness” and “tradition”

Literature on New Industrial Geography (Scott, Storper…):

• Application of ID literature to High-Tech industries

• “Tacitness” as an intrinsic property of High-Tech knowledge


This local know-how is passed on by doing things and seeing how other people do things, through informal chit-chat. […] Above all, this form of knowledge is necessarily rooted in a specific area in which people are linked by the bonds of a shared history or values, where specific institutions work to the benefit of people and where codes of behaviour, lifestyles, employment patterns and expectations are inextricably implicated in productive activity.” (Brusco, 1996; pp. 149-150 - Italics are mine)

“[In] each local system an integration between “codified knowledge” and “contextual knowledge” is realized. [...]“[The] coding and decoding of knowledge often involves a set of skills which cannot be set out in a simple standardized code. Rather, it is a matter of complex and often indefinite, and not rarely “indescribable” skills which can be acquired only by direct experience, by repeated practice, or by the process of “seeing at work”” (Becattini and Rullani, 1996; p.162-164 - Italics are mine)


2. “Tacitness” vs. “codification”: a few after-thoughts

TIPIK Project on “…the impact of the tendency towards codification of knowledge” Special Issue of Industrial and Corporate Change

Cowan R., David P.A., Foray D. (2000), “The Explicit Economics of Knowledge Codification and Tacitness” (CDF)

Steinmueller E. (2000), “Does information and communication technology facilitate ‘codification’ of knowledge?” (ED)

Tacitness is not an intrinsic property of knowledge. Technical and scientific knowledge can always be codified by developing an appropriate codebook

“Epistemic communities” as units of analysis:

“ […] small working groups that work on a mutually recognized subset of knowledge issues, and who at the very least accept some commonly understood procedural authority as essential to the success of their collective knowledge-building activities […].”


However, messages that transport knowledge can be tacit in the sense that a small portion of the codebook is referred to explicitlyTacitness can be a key exclusionary mean (incentives and norms for

sharing knowledge within restricted groups of firms/scientists)Tacit messages can be sent over long distancesCo-localization may be important to create the language and

codebooks, but “epistemic communities” may well survive the end of co-localization of their members

CDF’s definition of codebook goes very near to that of “language”: no access to the codebook no understanding

I realized that quotations such as those we started with were guilty of mis-interpreting the original meaning of “tacitness” (Polany, ‘62; Nelson-Winter, ’82) they attributed to “tacit” knowledge some intrinsic properties and a merely residual nature (what cannot be codified) they turned tacitness into a new “black box”


3. “Codebook” & “Epistemic Community” as research targets: research on Brescia mechanical cluster

I decided that my empirical research for TIPIK should:

• distinguish “non-codifiable” knowledge from the case of “codified knowledge cum displaced codebook”;

• distinguish further between /not- of an “epistemic community”

• check the boundaries of the communit(ies): the firm or the district? What else?

Methodology:

field research

workflow of knowledge activities

network detection


The Brescia mechanical cluster: hosiery machinery / metal moulding & thermoplastics presses (SMEs / Historical links with local users / Export-oriented)

- Core competencies: Mechanical vs. Electronic knowledge- Incremental nature of mechanical innovations: recombination and the need for system memory - Mechanical engineers as the core “epistemic community”:

low mobility and vertical relationships- The role of “Test Customers” KEY ROLE OF FIRMS AS KNOWLEDGE BOUNDARIES


Engineer.Dept.

Test Room

Lead Users

Test Customers

Managem

ent Prototyp

e

CAD DBase

Y/NProduction Dept.

Component Suppliers

Local mechanical engineers: - low inter-firm mobility after specialization- few horizontal knowledge exchanges- need for system memory: CAD db is key resource epistemic communities are firm-based


KEY FINDINGS of the BRESCIA project

Geographical constraints are defined by communication costs (“culture” and “trust” are endogenous)

Test Customers’ co-localisation matters more for the largest (mass) producer in the cluster, than for its local SME rivals

Key Suppliers’ co-localisation matters only as far joint design activities matter: it’s not a matter of Hi-Tech vs. Low-Tech

Key/Frequent knowledge inputs from non co-localised Lead Users

Knowledge that resists more clearly to codification efforts is actually of the “know-who” kind (suppliers and “test customers” or, better, of individual technicians therein).


1999: ESSY and the “LKS Critical Survey”

Localized Knowledge Spillovers are defined in the literature as …

Knowledge externalities – positive effects of scientific or technical discoveries on the productivity of firms, which neither made the discovery themselves, nor licensed their use from the holder of the intellectual property right

…bounded in space – firms located nearby the sources of knowledge externalities benefit more than firms located elsewhere, thus introducing innovations at a faster rate

LKS as a “local public good”

Widespread use in econometric litrature on geography of innovation, based upon the “knowledge production function”

How odd… a die-hard neoclassical tool combined with a concept made popular by evolutionary economics!!!!


“The theory of knowledge spillovers, derived from the knowledge production function, suggests that the propensity for innovative activity to cluster spatially will be the greatest in industries where tacit knowledge plays an important role. (…) it is tacit knowledge, as opposed to information, which can only be transmitted informally, and typically demands direct and repeated contacts” (Audretsch , 998, p.23 - Italics are mine)


1. LKS as a by-product of the knowledge production function approach to innovation

aiii KKXY 1

Knowledge production function (Griliches, 1992)

Output of firm i depends not only on own research efforts, but also on the pool of publicly available knowledge (wij captures the fraction of knowledge in j borrowed by i)

jija KwK


Theoretical explanations for the recurrent positive findings

• Spatial proximity and knowledge tacitness

– Knowledge produced by firms and universities (partly) spills over

– Knowledge that spills over is mainly ‘tacit’- i.e. highly contextual, difficult to codify-, more easily transferred through face-to-face contacts

– Tacit knowledge is a pure public good, but a local one, i.e. most readily available to firms located nearby the sources of knowledge

• Spatial proximity raises the likelihood of establishing a contact among agents (epidemic logic)


1bis. Searching for academic spillovers: the geographic knowledge production function [Jaffe (AER, 1989); Audretsch-Feldman (AER, 1996); Acs et al. (RESTAT, 1994)]

sisisisi URIRDI 21.

I = region s / industry i innovative output IRD = Private firms R&D expenditures UR = University R&D expenditures

Methodological problems: Spatial unit of observation (i.e. state, county, city) Spatial econometric issues (i.e. spatial autocorrelation) Localisation of production (i.e. correlation with other variables)


Key problems with these explanations:

1. The odd role of knowledge: tacit but public !!!

2. No enquiry on knowledge transmission means just examples, and most of them wrong:

- local labour markets for scientists & engineers- networks of scientists social or commercial?

3. Time lag between academic R&D and innovations

Further problems:1. Industry boundaries

2. Geographical units (co-localisation issue)

Key danger with the k.p.f. – LKS approach: - conceptual confusion between externalities and markets- it grants LKS the status of “stylized fact”


Labour market for scientists & engineers (Almeida-Kogut, MS 1999)

We come back to it later (some technicalities need first to be explained)

“knowledge spillovers” significantly more localised in Silicon Valley (SV) than in other US regions

Intra-regional mobility in SV is much higher than in other regions

Inventors’ intra-regional mobility has a significant and positive effect on the probability to observe knowledge spillovers


• Positive relationship between biotech firms’ innovative performance and scientific strength of local Universities, as proxied by number of scientific papers authored by ‘star’ scientists

• HOWEVER, when ‘stars’ are divided into firm scientists (linked) and purely academic scientists (untied), the explanatory power of the latter vanishes

Knowledge embodied in scientists has high degrees of natural excludability limited access to codebook

Only way to build upon that knowledge is to enter into contractual arrangements or start-up a new firm

Scientists that do so retain their affiliation and get jobs within commuting distance

Commercial networks (markets for technology)(Zucker et al, EI 1998)


1. The Silver Bullet? JTH’s use of patent citations

“Knowledge flows leave a paper trail in the form of citations in patents ….to the extent that regional localization of spillovers is important, citations should come disproportionately from the same state/area as the originating patent” (Jaffe, Trajtenberg and Henderson, QJE 1993: 578)

“Knowledge flows (…) are invisible; they leave no paper trail by which they may be measured and tracked, and there is nothing to prevent the theorist from assuming anything about them she likes” (Krugman, 1991: 53)

Most influential attempt to measure LKS directly (no KPF). Reaction to trade theorists’ skepticism

2002-2003: mindful data mining patent citations and networks of inventors


The meaning of patent citations

PatentApplication (Y)

Citation ofprevious patents (X)

Searchreport

Citations added bythe examiner

A citation of Patent X by Patent Y means that X represents a piece of previously existing knowledge upon which Y builds paper trail of knowledge flow


JTH’s experiment with patent citations

Sample of citedpatents

(i.e. originators ofknowledge flows)

All subsequentpatents citing them

(i.e. benefit fromKnowledge flows)

For each pair, citing-cited, compare geographic address of inventors: if they come from the same region, then co-location=1, else co-location=0

Compute the fraction of pairs, citing-cited, for which co-location=1 if fraction is high then knowledge flows are highly “localised”

Compared to “what”? What is the “expected” fraction?

Step 1)

Step 2)


The “expected” fraction of localised citations

Sample of citedpatents

(i.e. originators ofknowledge flows)

All subsequentpatents citing them

(i.e. benefit fromKnowledge flows)

For each citing patent a “control” patent with same technology class, but not citing the same patent (mimics the existing spatial distribution of innovative activities)

Repeat steps 1) and 2) as above, i.e. compute the fraction of control-cited pairs for which co-location=1 this fraction provides the “expected” value


Actual vs. expected co-location of citations

Geographical matching with cited patents (%)

Citing Controls t-statistic

Geographical level

Country 69.3 58.5 7.2

State 10.5 4.1 7.9

Metropolitan area 6.9 1.1 9.6

Evidence that citations (spillovers) are more geographically localised than expected

So

urce

: JTH


2. Replicas of JTH experiments: market-based diffusion channels

MARKETS FOR TECHNOLOGY (Mowery-Ziedonis, NBER 2001)

Transfer through licensing goes along with localized transfer of tacit knowledge Evidence that spillovers (i.e. citations without licensing) are less localised than knowledge flows mediated by licenses

LABOUR MARKETS (Almeida-Kogut, MS 1999) Patent citations are significantly more localised in Silicon

Valley (SV) than in other areas Intra-regional mobility in SV is much higher than in other

regions Inventors’ intra-regional mobility has a significant and

positive effect on the probability that a patent will build upon a major patent from the same region




• Revisitation of JTH experiment , but controlling for “market-induced” social ties among inventors

• Key intuition: JTH is just a “localization test” → Absent direct measures of knowledge exchanges, spatial proximity captures ALL the mechanisms through which knowledge is transmitted: what’s left of spatial proximity when we control for social proximity?

• How to control for social proximity? NETWORK OF INVENTORS!

3. CESPRI replica of JTH experiments: telling spatial and social proximity apart


cross-firm inventors

Network of inventors: co-invention & mobility


Data

SOURCE: • EPO-CESPRI database All patent applications to the European

Patent Office (EPO), by company & inventor

SAMPLE:• All patents of US applicants with at least one US inventor in:

– Biotechnology (IPC C12M to -S)– Drugs (IPC A61K)– Organic chemistry (IPC C07, excl C07B)– (63188 inventors; 66349 patents)

• Location of patents by inventors’ MSAs and States• Time coverage 1978-2002 → co-invention network built assuming

a life span of 5-years for collaboration ties


Network of US inventors: quite a small world!

# of Inventors

% of all inv.(no isolates)

Av. path length

1991* (n. of components=2525)

Largest component 3664 23,39 17,21

2nd largest " 287 1,83 -



2nd largest " 250 1,18 -



2nd largest " 277 0,90 -

*last year of 5-years time window, e.g. 1999 refers to collaborations from 1995 to 1999


Mobility of inventors across firms and geographic areas… not that much!

8,39,263188 (100)All inventors

25,728,416730 (26,48)>1

2,02,346458 (73,52)1

% of inventors active in >1 State

% of inventors active in >1 MSA

N. of inventors (% of all inv.)

N. of assignee joined


Average spatial distance between inventors located at social distance d (largest component, 1999)

0

500

1000

1500

2000

2500

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Social distance (d)

Sp

atia

l dis

tan

ce (

Km

)


Social connection between patents

• Given a pair of patents (e.g. citing-cited/control-cited)

– They are socially connected if at least two inventors from respective teams are part of the same network component

– The geodesic distance between them is equal to the minimum geodesic distance between inventors in the respective teams

• Distance 0: same inventor in both teams

• Distance 1: at least two inventors have previously co-invented

• Distance 2: at least two inventors have a common co-inventor

• etc.

• Distance is infinite if inventors belong to disconnected components (i.e. they are not mutually reachable)


Sample of cited, citing and control patents

N. of patents

Patent pairs

% of “socially” connected patent pairs

Total Personal connection

(Distance 0)

Social connection

(Distance >0, but finite)

Cited 2014 - - - -

Citing 3012 3700 30,85 4,95 25,9

Control 3419 3700 19,76 0,16 19,6


Socially connected citing (control) patents: geodesic distances from the cited patent

Geodesic distance Citing (%) Control (%)

1 5,22 1,1

2-5 17,13 13,4

6-10 46,28 32,6

11-20 28,3 40,47

>20 3,02 4,29

Mean 9,17 10,52

Median 8,5 9,5

std deviation 10,5 11,7


Geographical matching at MSA and State level: % frequency and test of proportions

1,243,0 (0,0)13,015,53217No patent pairs at distance ≤5

1,303,9 (0,0)13,817,23512No patent pairs at distance 0

1,627,8 (0,0)14,020,83700All inventors (JTH test)

State level

1,222,5 (0,0)9,911,83217No patent pairs at distance ≤5

1,293,5 (0,0)10,813,53512No patent pairs at distance 0

1,738,1 (0,0)10,817,43700All inventors (JTH test)

MSA level

Odds Ratio

z-statistic (P>z)

Control: % match

Citing: % match

n. of obs


Interpretation

• Cross-firms mobility of workers and socially linkages among inventors (partly created by mobile individuals) drive the diffusion of knowledge

• Cross-firm inventors are relatively immobile in space…

• …networks of collaboration tend to grow locally

Spatial proximity per se

- does not have much influence in mediating knowledge flows

- it is a mere proxy of social ties

Once controlling for personal and social connection its role fades away


Other controls for social ties:

PREVIOUS WORK EXPERIENCES:Agrawal A.K., Cockburn I.M., McHale J. (2007), “Gone But Not Forgotten: Labor Flows, Knowledge Spillovers, and Enduring Social Capital”, Journal of Economic Geography (forthcoming)

ETHNIC TIESAgrawal A.K., Kapur D., McHale J. (2004), “Defying Distance: Examining the Influence of the Diaspora on Scientific Knowledge Flows”, mimeo (NBER WP?)


A flaw in JTH methodology…?

• Thompson and Fox-Kean (AER, 2005): JTH method of selecting control patents may induce spurious evidence of localised spillovers

– Control and citing patents may be unrelated and thereby control patents fail to control for pre-existing spatial concentration of innovations

• 3-digit USPC main classes too broad

• Matching should cover also secondary classes

• JTH results vanish when narrower than 3-digit classes are selected and citing-controls match also at the secondary class level


Geographical matching: % frequency and test of proportions-Control selection increasing technological proximity (TFK experiment)

n. of obs

Citing: % match

Control: %

matchOdds Ratio

MSA level

12-digit main class 3168 17,4 13,7 1,33

12-digit main & 4-digit secondary 2458 17,5 15,0 1,20


State level

12-digit main class 3168 20,8 18,4 1,16



1,73

10,817,437004 digit main class 1,62

14,020,837004 digit main class


… or just one more hint of the role of social ties?

• Increase technological proximity between citing and control patents Select patents produced by inventors belonging to the same technological community, which are possibly

– linked by strong social connections…

– … and (therefore) spatially co-located

• What is flawed is not the JTH methodology, but the interpretation of localised patent citations as evidence of pure “Marshallian” externalities (spillovers)

• Only citations mediated by market mechanisms or collaborative networks are likely to be spatially localised. Unmediated citations do not show any localisation pattern.


Summary and conclusions

• Geography matters because inventors are mobile across firms, but not in space, and the resulting social networks are also localised

• Mobility of workers has a key role as driver of knowledge diffusion direct: spreads/sell knowledge across firms indirect: connects teams and reduces social distance

• Results also confirm that imposing a narrower technological focus, one just deals with a socially connected and co-localised community of scientists no difference in the co-localisation pattern between citing and control


Research agenda: What does remain of the ‘spillovers’ story?

• What do you mean by “mobile”? Causes and consequences of labour mobility

• History of industrial clusters, and the role of locational inertia (Klepper’s work)

• The economics of science and academics’ participation to the markets for technologies

Documents

Knowledge spillovers or markets? A sentimental journey, 1998-2006 F. Lissoni (Università di Brescia and CESPRI-Bocconi Univ.) DIMETIC Doctoral European