1. Driving Regional Performance: Theory and Measurement in Innovation
Sources of Innovation: A Rural – Urban Divide?
University Knowledge Spillovers, Geographic Proximity and Innovation:
an analysis of patent filings across U.S. counties
Ping “Claire” Zheng, Ph.D.
Economic Research Analyst
Indiana Research Business Center
Indiana University
Timothy F. Slaper, Ph.D.
Research Director
Driving Regional Performance: Theory and Measurement in Innovation

2. Research Questions
Does knowledge creation at institutions like universities lead to innovation?
Do the innovative benefits of knowledge creation within the halls of academic building and labs spillover into the neighboring region?
Does proximity to those knowledge anchor institutions affect the relative effect of those spillovers?
Does distance matter?

3. Motivation
The extent and importance of knowledge spillovers of great interest several disciplines
public economics and economic development
higher education and public policy
Knowledge creation considered essential for
economic competitiveness
raising productivity
raising income and standards of living 
The forces of agglomeration and the externalities of industrial co-location related to knowledge diffusion

4. How Regions Prosper
Alfred Marshall in the late 19th century examined the spatial dimension of economic activities to determine the sources of rising productivity and economic growth
Externalities accrue to similar producers located in close proximity
Labor market pooling ● Economies of scale
Network effects ● Specialized supply chains
Knowledge can be associated as one force behind the economics of agglomeration

5. Agglomeration
Urbanization externalities – arise from urban size and density
Dense networks that support the creation and uptake of ideas and worker know-how
Diverse, independent, collection of entities – universities, trade associations, industry and government research labs
This force – Jacobs (1969) externalities – are seen to spur dramatic product and process innovations
Cities are where ideas go to procreate

6. Agglomeration
Location externalities – the so-called M-A-R externalities – named after Marshall (1890/1966), Arrow (1962) and Romer (1986)
Spillovers between researchers, entrepreneurs and businesses within an industry or other institutional structure
Relatively small differences in technologies
Narrow technological leaps between actors
Low barriers for knowledge spillovers
Innovations not ground-breaking but incremental and reflected in productivity gains rather than ground-breaking new products

7. Knowledge Spillovers
Jacobian and M-A-R externalities result from knowledge creation institutions and diffusion relationships, both formal and informal
Griliches (1979) pioneered research into knowledge creation
Jaffe (1986) investigated technological spillovers
Jaffe (1988) evidence that the scope of the R&D spillovers might go far beyond a firm’s related industries
Jaffe (1989) university knowledge spillovers
Other university R&D attention, Anselin et. al., 1997 and 2000; Audretsch et. al., 2005; and Boschma 2005

8. Four reasons for further research
Only one study tested the relative influence of knowledge spillovers depending on distance
Measure of spatial interdependence based on official, often arbitrary, contiguous boundaries
Not on distance from the knowledge creation source
The regional context, or enabling conditions for knowledge to diffuse, are often overlooked
Assess synergies between different types of knowledge creating institutions, namely universities and businesses

9. Modeling Strategy Two-factor Cobb-Douglas production function
Output measure for knowledge – measured by patent output
Two input measures – university and private R&D expenditures
𝐾= 𝐼 𝛽 1 𝑈 𝛽 2 𝐶
C is a drift term that accounts for everything else
Gravity approach considers spatial transaction costs measuring the intensity of knowledge influence between any two regions as it diminishes continuously with increasing distance
Decay function of distance from knowledge creation institutions: 𝐴 𝑖 = 𝑊 𝑛 𝑒 −𝛽 𝑑 𝑛 𝑖

10. Modeling Strategy Three distance thresholds: 50, 100, 250 miles
Threshold of 50 Ansenlin et. al. (1997)
75 Varga (2000)
186 Bottazzi and Peri (2003)
University R&D at county level
Industry R&D proxy at county level: high-tech establishments greater than 500 employees
Also controlled for county size, presence of VC, proprietorship (entrepreneurship proxy) and educational profile (#graduate degrees)

11. Patents
Kspl 50
Kspl 100
Kspl 250

12. Marginal effects of knowledge spillovers
High Level Results
Research anchor, simple
model
(1)
Marginal effects of knowledge spillovers, simple model
(2)
Marginal effects of knowledge spillovers
Non-research county
(3)
Research anchor
(4)
50-mile
2.9***
0.69***
0.43***
1.01***
100-mile -
0.73***
0.47***
0.62***
250-mile
0.84***
0.60***
0.13
Column (1): the mean difference in patent creation between research and non-research anchors
- Research anchors, or hot spots, on average produced 29 times more patents than the non-research counties
Column (2): marginal effects of knowledge spillovers under three distance thresholds
Columns (3) and (4): marginal effects of knowledge spillovers separated for research and non-research anchors
The Kspl effects decline with distance for research counties

13. The 50-mile cutoff produces knowledge spillover scores of 0 for many non-research (R&D free) anchor counties
Many non-anchor counties produced a significant number of patents
- See red oval along the Y axis in upper left panel
These counties shift right into the general set of all counties under the 250-mile cutoff
- See red circle in bottom left panel
Patenting but R&D free counties then contribute to the positive linear association between patents and knowledge spillovers
For research anchor counties in the right panels, there is a positive linear association between patents and knowledge spillovers under the 50-mile cutoff
The relationship dissolves under the 250-mile threshold

14. II. Research anchor counties
Dependent variable: Ln(TTLPAT)
I. All counties
II. Research anchor counties
Inputs
50 miles
100 miles
250 miles 1 2 3 4 5 6
RND_BIN
***
*** **
-0.223
-0.259
-0.312
Ln(KSPL)
0.0920***
0.0757***
0.0794***
0.3799***
0.2856***
0.1890***
-0.013
-0.015
-0.022
-0.072
-0.07
-0.069
RND_BIN x Ln(KSPL)
0.2332***
0.2117***
0.1289**
-0.062
-0.06
STEM_BIN ** ** **
-0.122
-0.123
-0.124
Ln(STEM)
0.0930***
0.0926***
0.0946***
0.0524
0.0474
0.0442
-0.033
-0.039
Ln(GRAD)
0.6463***
0.6556***
0.6515***
1.2682***
1.2848***
1.3122***
-0.055
-0.127
-0.129
HTLRG_BIN
0.5007***
0.5129***
0.5061***
0.1137
0.1159
0.1239
-0.073
-0.105
-0.108
-0.11
Ln(HTLRG)
0.2631***
0.2747***
0.2807***
0.9965***
0.8544***
0.8130***
-0.042
-0.195
-0.253
-0.28
RND x Ln(HTLRG) ** ** **
***
*** **
-0.121
-0.046
-0.052
-0.051
Ln(PRPR)
0.3362***
0.3570***
0.3679***
0.8470***
0.8715***
0.8985***
-0.065
-0.236
-0.235
-0.232
Ln(TTLEMP)
0.7712***
0.7841***
0.7970***
0.9680***
0.9879***
1.0164***
-0.023
-0.064
VC_BIN
0.4467***
0.4683***
0.4859***
-0.082
Ln(EMPBR)
***
***
***
-0.159
-0.165
-0.161
Constant
***
***
***
***
***
***
-0.321
-0.318
-0.32
-0.816
-0.817
-0.821
Observations
3,110
522
Adjusted R-squared
0.789
0.787
0.786
0.795
0.792
0.788

15. Tests for Robustness
We tested for endogeneity of Kspl scores using the DWH test for a two-stage least squares estimation
Used enrollment in the STEM majors and the total university educational expenditure as additional instruments
Found no evidence for endogeneity
Tested for spatial correlation
Evidence for spatial lags in both the dependent variable and the error term in model using R&D expenditures directly, not Kspl
No evidence for a spatial lag in the dependent variable for gravity measure for knowledge spillover scores in model

16. Conclusion
Counties with research anchors exhibited differences in patent rates compared counties without university R&D
Knowledge spillovers have positive linear association with total patents at all three geographic spans
suggests that university knowledge creation could influence innovation as far as 250 miles
Marginal effects of Kspl diminishes with distance, but not as profoundly as expected
Evidence that concentration of both R&D expenditures and STEM degree awards have positive effects on patenting rates

17. Questions?
Ping “Claire” Zheng, Ph.D. Timothy F. Slaper, Ph.D.