knowledge flows and local innovation activity

Knowledge Flows and Local Innovation Activity:Evidence from the US

K. Drivas, C. Economidou, S. Karkalakos, and M. Tsionas

University of Piraeus and Athens University of Economics & Business

TECHNISJan. 29, 2014

Drivas et al. (2013) Knowledge Flows & Innovation in the US 1 / 26

Motivation

Motivation

Generation of new technological knowledge and exploitation of existedone is crucial for economic growth (Romer, 1986; Jones, 2005).

How does proximity shape knowledge flows and local innovationproduction?

Is embodied (in goods, researchers) knowledge any different fromdisembodied (ideas)?

How do knowledge flows contribute to (local) innovation ofproduction?

Knowledge diffusion, via different channels, has not been studied in acommon framework; branches of the spillover literature have progressedon separate avenues in analyzing knowledge flows.

Potential inter-dependencies across channels of knowledge flows (Autor,Dorn and Hanson, 2013).


Lit. Review

Lit. Review

Patent-citation literature proxies knowledge flows via citations ofpatents (Jaffe et al., 1993; Peri, 2005; Mancusi, 2008) (NBER,Patent and Citation Database).

Trade-growth literature proxies knowledge flows via:trade (imports) flows (Coe and Helpman, 1995; Keller, 2002);foreign direct investment, FDI, flows (Blomstrom and Kokko, 1998).

Recently developed strand: proxies knowledge diffusion viamobility of highly-skilled personnel (Disambiguation andco-authorship networks of the U.S. Patent Inventor Database, 2011by Lai, D’ Amour, Yu, & Fleming).

Still under exploration: knowledge diffusion via trade of patents(Serrano, 2010, 2011) (USPTO, Patent Assignment Database).


Contribution

Contribution

Different channels (four) of knowledge flows are jointly studied in acommon framework. No systematic attempt in this direction hasbeen undertaken to date.

each channel has been studied separately from different researchavenues.

Development and application of novel and appropriate econometrictechniques (to account for unobserved heterogeneity and potentialchannel inter-dependencies)

single equation estimation, as single channels are studied in theliterature.

Comprehensive study of knowledge flows across states of the US

relates to and compliments the studies of Jaffe et al. (1993),Mowery and Ziedonis (2001).


Purpose

Purpose

This paper studies the diffusion of knowledge and the effect it has on

innovation activity across the states of the US, using recently developed

data and applying appropriate econometric techniques.

Our empirical analysis is concentrated around two questions:

Is proximity important for knowledge flows?

Do knowledge flows contribute to innovation production?


Framework of Analysis

Production of Innovation

Qit = Iit(Ait)γ(Aα

it)µ (1)

where,

Q index of innovative output;I a set of institutional and policy factors specific to state i;A is R&D stock accumulated from past and current R&Dinvestments in state i;Aα is the stock of R&D accumulated in states other than i andaccessible (hence the α superscript) to state i at time t.



Production of Innovation (cond.)

If R&D in one state were completely and immediately diffusible to allother states, one could consider the external R&D stock accessible tostate i simply as:

Aαit = ∑

j 6=iAjt (2)

However, considering that diffusion of research results across states maybe less than perfect, the external accessible R&D stock in state i isgiven by:

Aαit = ∑

j 6=iφijAjt (3)

where, φij is the share of R&D stock gained by state i originated fromstate j.



Production of Innovation (cond.)

Substituting equation (3) into equation (1) and taking logs:

lnQit = lnIit + γlnAit + µln(∑j 6=i

φijAjt) (4)

where:

Ait is own R&D stock;

φijAjt is flow-weighted external accessible to a state R&D stock;

φij is a vector or four parameters, φij =[φPij , φC

ij , φNij , φG

ij

]representing the flow-weighted external R&D stock gained via tradedpatent flows, φP

ij , citation flows, φCij , inventors’ mobility flows, φN

ij , ortrade of goods, φG

ij .



Modeling Flows, φij

φijt = βij + β1StateBorderij + β2Nearby States [500miles]ij+ β3Distance [500− 1000miles]ij + β4Distance [1000− 1500miles]ij+ β5Distance [1500− 2000miles]ij + β6Distance [2000− 2500miles]ij+ β7Zijt + εijt (5)

whereφij is (one of the four types of) knowledge flows between two states i (destination) andstate j (origin); βij is origin and destination state fixed effects;

StateBorder: dummy for adjacency, takes the value of 1 if states share a commonborder, 0 otherwise;

Nearby States [500miles]: dummy for nearby States, takes the value of 1 if states arelocated within an area of 500 miles and do not share common border, 0 otherwise(selection of 500 miles: distance between two centers of the farthest apart neighboringstates, CO & OK);

Distance[ ]: dummies for distance intervals of 500 miles, 0 otherwise;

Z set of technological & structural closeness between state i; and state j; and ε is error.



Vector Z: Technological Closeness

Technological Distance: Difference in logged average real R&Dspending per scientist between two states, i and j at year t,

TechnologicalDistance =| ln R&DiScientistsi

− ln R&DjScientistsj

|

(Griffith et al. 2004, Peri, 2005)

[Range: 0 - 3]

Structural Closeness: correlation between two states’ patentportfolios with respect to technology fields at year t,

StructuralCloseness = shi′shj√

∑37s=1 sh2is ∑37

s=1 sh2js

(Jaffe, 1986; Hall et al., 2001; Peri, 2005)

[Range: 0 - 1]



Estimation Strategy

First, we estimate a tri-variate non-linear SUR for traded patentflows, citation flows and inventors’ mobility flows (all count data).

SUR Poisson (King, 1989): inability to account for over-dispersionor extra-Poisson variation of count data – limited usefulness.SUR Negative Binomial (Winkelmann, 2000): generalizes SURPoisson assumptions, but allows for over-dispersion.

We develop and apply SUR Negative Binomial, allowing forunobserved heterogeneity in the data.

Second, to account for continuous (trade of goods) and count(traded patents, citations, inventors’ mobility) data, we developand apply SUR of mixed count and continuous responses.

As benchmark, we estimate single (univariate) equations ofknowledge flows using negative binomial estimation.



Multivariate Negative Binomial SUR w/ Heterogeneity

Unlike past contributions (Winkelmann, 2000), we introduce furtherunobserved heterogeneity in the following form:

log λsm = x′sβm + εsm,m = 1, ...,M and s = 1, ....N (5)

where εs ∼ NM(0,Σ).

We Bayesian analysis, organized around Markov Chain Monte Carlo (MCMC)methods for inference.



SUR of Mixed Continuous and Count Responses

Suppose that we have now an additional R× 1 vector of responses,

ysr, r = M+ 1, ...,M+R.Equation (5) is therefore extended in the following form:

log λsm = x′sβm + εsm,m = 1, ...,Mysr = x′sβr + εsr, r = M+ 1, ...,M+R (6)

We redefine εi = [εi1, ..., εiM, εi,M+1, ..., εi,M+R]′ and assume:

εs ∼ NM+R (0,Σ)

where Σ is an (M+R)× (M+R) covariance matrix.

We Bayesian analysis, organized around Markov Chain Monte Carlo(MCMC) methods for inference.


Data

Descriptive Statistics

Sample: 50 states of the US (excl. DC) for the period, 1993-2006.

Table: Summary Statistics

variables Obs Mean St. Dev. Min Max Source

PatentTradeFlows 32256 4.71 41.63 0 3262 USPTOCitationFlows 32256 210.76 1316.56 0 85287 NBERCitationT Flows 32256 80.21 252.26 0 18003 NBERCitationNT Flows 32256 130.45 897.03 0 61205 NBER

Inventor Flows 32256 13.07 174.01 0 10669 Lai et al. (2011)TradeFlows 6227 3396.93 16467.95 0.96 535263 BTS (CFS)StateBorder 2256 0.10 0.29 0 1 MapNearby States [500miles] 2256 0.12 0.32 0 1Distance [500− 1, 000miles] 2256 0.32 0.47 0 1Distance [1, 000− 1, 500miles] 2256 0.25 0.43 0 1Distance [1, 500− 2, 000miles] 2256 0.13 0.34 0 1Distance [2, 000− 2, 500miles] 2256 0.08 0.27 0 1TechnologicalDistance 32256 0.63 0.50 0 3 constructed (NSF)StructuralCloseness 32256 0.70 0.18 0.05 1 constructed


Data

Map: Innovation Activity in the US

(27050,228780](8347,27050](3122,8347][0,3122]

California with 228,780 Patents, New York with 114,378 Patents

Number of Patents per State


Results

Is Proximity Important for Knowledge Flows?

Table: Proximity and Knowledge Flows

Tri-variate SUR Estimates of Flows Four-variate SUR Estimates of FlowsTradedPatent Citation Inventor TradedPatent Citation Inventor Goods

StateBorder -2.31*** -1.42*** -3.71*** -2.25*** -1.31*** -3.77*** -2.26***(0.002) (0.000) (0.004) (0.008) (0.000) (0.010) (0.010)

NearbyStates[500 miles] -2.58*** -1.55*** -4.07*** -2.45*** -1.48*** -4.27*** -3.23***(0.003) (0.000) (0.006) (0.009) (0.000) (0.016) (0.016)

Distance[500-1,000miles] -2.66*** -1.59*** -4.19*** -2.52*** -1.46*** -4.6*** -3.94***(0.002) (0.000) (0.004) (0.006) (0.000) (0.010) (0.010)

Distance[1,000-1,500miles] -2.74*** -1.67*** -4.26*** -2.59*** -1.52*** -4.55*** -4.57***(0.003) (0.000) (0.004) (0.010) (0.000) (0.009) (0.009)

Distance[1,500-2,000miles] -2.76*** -1.77*** -4.32*** -2.62*** -1.64*** -4.51*** -4.89***(0.003) (0.000) (0.006) (0.011) (0.000) (0.013) (0.013)

Distance[2,000-2,500miles] -2.74*** -1.45*** -4.30*** -2.61*** -1.29*** -4.30*** -4.97***(0.002) (0.000) (0.003) (0.006) (0.000) (0.008) (0.008)

TechnologicalDistance -0.25*** -0.01*** -0.33*** -0.22*** 0.01*** -0.84*** -0.13***(0.002) (0.000) (0.004) (0.008) (0.000) (0.010) (0.010)

Structural Closeness 0.78*** 0.25*** 0.11*** 0.76*** 1.02*** 0.82*** 0.05***(0.006) (0.000) (0.011) (0.024) (0.000) (0.029) (0.029)

χ2(30) 5.79 7.09Observations 32,256 32,256 32,256 6,227 6,227 6,227 6,227All regressions include time dummies and states fixed effects. Standard errors reported in parentheses; (***), (**), (*):significance at 1%, 5% and 10% level, respectively.


Results

Findings: Geographical Space

Table: Drop of Knowledge Flows w.r.t. in-State Flows

On Crossing: TradedPatents Citations Inventors′Mobility TradeBorder State 10% 24% 2.4% 10%Nearby States [500] 7.6% 21% 1.7% 4.0%Distance[500− 1000] 7.0% 20% 1.5% 2.0%Distance[1000− 1500] 6.5% 19% 1.4% 1.0%Distance[1500− 2000] 6.3% 17% 1.3% 0.8%Distance[2000− 2500] 6.5% ↑ 23% ↑ 1.4% ↑ 0.7% ↓

↓ 0.4% per 500 miles ↓ 2.5% per 500 miles ↓ 0.1% per 500 miles ↓ 0.8% per 500 miles

State border and distance are still significant hurdles for for goods and inventor flows,but also for the "weightless" ideas!Distance does not only proxy transaction/mobility costs but also informationalfrictions and, therefore, acts as "informational" barrier to interactions, networklinkages, cultural affinities and familiarities, among economic agents.


Results

Findings: In Words

(i) Geographic nearness and border are more essential to (embodiedknowledge on) goods/inventors than ideas (disembodied knowledge).

(ii) Non-market based flows are less restricted than market-based.

Specifically:

Citation (traded patent) flows are 10 to 12 (5 to 6) times lessrestricted by state border than inventors’ mobility flows and are 3(equally restricted to) than trade of goods flows.

Citation (traded patent) flows travel 16 to 18 (6) times farther inspace than inventors’ mobility flows and 14 to 15 (5) than trade ofgoods flows.


Results

Findings: Comparison with the Lit.

Compared to patent-citation literature, our citation estimates are:

close to Jaffe et al. (QJE, 1993): drop of 50% to 60% in the citationflows when they transcend a state’s border (US study)

in line with Mowery and Ziedonis (Working Paper, 2001):knowledge flows, based on citations, stretch 3 times more in spacethan those based on patent licenses (US study)

similar to Maurseth and Verspagen (Scan. J of Econ., 2002):distance reduces flows to 70% to 80% (122 european regions)

similar to Peri (RESTAT, 2005): reduction of 25% when knowledgeflows cross a region border and a 3% drop for each 1,000 kmtraveled (141 world’s regions)


Results

Findings: Technological Space

Similarity in technological effect and technological specializationenhances knowledge flows across states.

Specifically:

4% (via citations) up to 28% (via inventors’ mobility) moreknowledge exchange between states with similar technologicaleffort than dissimilar.

research-oriented regions are important to the accumulation oftalented people (Lucas, 1988).

12% (via inventors’ mobility) up to 118% (via traded patents)more knowledge exchange from a state with similar technologicalspecialization in sectors than dissimilar.

related technologies greatly benefit trade of patents (Bode, 2004;Peri, 2005).


Results

Robustness Checks

Exclude Alaska, Hawaii (most distant), and California (outlier)Due to "California effect" (significant drop of obs) and smaller sizeof estimates. Conclusions drawn remain the same.

Use only flows from (top 10) innovator states (instead flows fromall states)

Similar estimates - citations less localized.Split sample in two sub-periods: 1993-1999 & 2000-2006

Unaltered importance of geographic and technological space overtime.

Split sample in two parts: High-cited vs. Low-cited (high-citedpatents 4% of all patents). Also, cites of traded vs. cites ofnon-traded patents

High-cited patented inventions are slightly more far stretched.Traded patent cites are less far reached.

Use different definitions of dependent variablesResults are unaltered.

Overall, results do not change in any significant way.Drivas et al. (2013) Knowledge Flows & Innovation in the US 21 / 26

Results

Findings: Graphical Visualization

Decay of Knowledge Flows Due to Geographical Barriers0

1000

2000

3000

4000

Tra

ded

Pat

ents

WithinState Neighbor 0-500Miles 1000-1500Miles >2000Miles

Trade of Patents: Actual

Trade of Patents: Fitted

080

0016

000

2400

0In

vent

or F

low

s


Inventor Flows: Actual

Inventor Flows: Fitted

025

000

5000

075

000

1000

00C

itatio

ns


Citations of Patents: Actual

Citations of Patents: Fitted

010

0020

0030

00G

oods

Tra

de


Trade of Goods: Actual

Trade of Goods: Fitted


Results

Do Knowledge Flows Contribute to InnovationProduction?

Table: Elasticities of Innovation Function

Flows fromAll States Flows fromTop 10 StatesActual Fitted Actual Fitted(1) (2) (3) (4)

lnR&Down 0.400*** 0.436*** 0.190* 0.221**(0.091) (0.087) (0.109) (0.111)

lnR&Dcitations 0.418*** 0.427*** 0.501*** 0.486***(0.104) (0.104) (0.102) (0.105)

lnR&Dpatents 0.108** 0.148*** 0.113** 0.116**(0.048) (0.049) (0.048) (0.046)

lnR&Dinventors 0.028 -0.00001 0.086 0.094(0.087) (0.085) (0.086) (0.083)

lnR&Dtrade 0.156 0.124 0.174** 0.154*(0.114) (0.098) (0.087) (0.083)

Constant 1.458*** 1.329*** 1.727*** 1.707***(0.362) (0.330) (0.294) (0.303)

Observations 134 134 97 97R2 0.874 0.876 0.813 0.810All regressions include time dummies. Standard errors reported in parentheses;(***), (**), (*): significance at 1%, 5% and 10% level, respectively.


Results

Findings:

A 100% increase of state’s own R&D, is associated with anincreases in the production of innovation by, approximately, 19%up to 44%.

External accessible R&D has an effect on the innovation of statesas large as that of their own R&D stock. External accessible R&D,gained via

citation flows, increases production of innovation by 42% up to 50%(Peri, 2005; 40%-50%. Bottazzi and Peri, 2007; 55%)

patent trade flows, increases production of innovation by 11% to15%.

trade flows increases production of innovation by 12-18%.

inventors’ mobility flows has no significant effect.


Conclusion

Conclusion

1 Is Proximity Important for Knowledge Flows?Yes, it is.

Geographic nearness is essential to embodied (to inventors, goods)than disembodied (patents, citations of patents) knowledge flows.

Technological effort proximity is essential to knowledge flows basedon inventors’ mobility flows.

Structural proximity (technological similarity of sectors) is essentialto disembodied (mainly to traded patent) knowledge flows.

2 Do Knowledge Flows Contribute to Innovation Activity?Yes, they do.

Accessible R&D, gained through knowledge flows, has a strongpositive effect on a state’s innovation activity as large, for somecases, as that of state’s own R&D stock.

Significance of disembodied knowledge flows is confirmed (Grossmanand Helpman, 1991; Rivera-Batiz and Romer, 1991).


Future Research Challenges

Issue(s) deserving further inquiry

Causes of "home bias" created by formal and informal barriers.

Sectoral knowledge diffusion analysis (still unavailable data, forsome channels).


knowledge flows and local innovation activity

Education

diusion of knowledge

knowledge flows innovation

researchers knowledge

proxies knowledge diusion

proximity shape knowledge

ows blomstrom

innovation qit

local innovation production