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

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?

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

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

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

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

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

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

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

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)

Patents Kspl 50 Kspl 100 Kspl 250

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

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

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 -0.8626*** -0.8939*** -0.6570** -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 -0.3025** -0.3064** -0.3190** -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) -0.2535** -0.2752** -0.2501** -0.2056*** -0.1436*** -0.1184** -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) -0.6924*** -0.6572*** -0.6999*** -0.159 -0.165 -0.161 Constant -9.3010*** -9.5718*** -9.8195*** -14.2996*** -14.5418*** -14.6701*** -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

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

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

Questions? Ping “Claire” Zheng, Ph.D. pzheng@indiana.edu Timothy F. Slaper, Ph.D. tslaper@Indiana.edu