Panel Paper: Informed Industry Targeting: Grow Clusters for Learning Industries at Optimal Sizes

Friday, April 7, 2017 : 2:55 PM
Founders Hall Room 311 (George Mason University Schar School of Policy)

*Names in bold indicate Presenter

Li Fang, University of Maryland, College Park
Industry targeting, despite its disgraceful name for unfairness, is practically unavoidable. Policy makers have to either prioritize and focus resources, or dilute endeavors and achieve little. Thus, the question is not whether to prioritize, but how to prioritize.

Recently, economic developers have recognized the role of clusters—geographical concentrations of industries—in elevating innovation. U.S. Departments invested enormously to encourage innovation through clusters in high-tech, biochemistry or energy-related industries, with no evidence showing the desirability of their industry choices.

Prior studies fail to sufficiently guide industry targeting. Most literature proposes picking highly concentrated or most frequently patenting industries, without acknowledging that the best performers may not benefit the most from clusters. A few studies compared clustering effects across industries, but either aggregated industries at high levels (three-digit NAICS code or above) or only compared a limited set of industries within a specific discipline. Moreover, studies to date fail to address what (geographical) cluster size fits an industry the best.

This paper is the first to compare the effects of clusters on citation-weighted patenting across a wide range of six-digit NAICS industries, and to identify industry-specific optimal cluster sizes by maximizing clustering effects over a size spectrum. The paper employs patent application data and subsequent citation data from the United States Patent and Trademark Office and establishment data from the Quarterly Census of Employment and Wages.

The expected findings are: 1) a ranking of six-digit NAICS industries that would produce the largest number of citation-weighted patent applications in clusters, and 2) the optimal cluster size associated with each industry that maximizes patent production compared to other sizes. These results can help practitioners to perform knowledgeable industry targeting and grow clusters in the most learning-prone industries at optimal geographical sizes, which will generate the greatest amount of high-quality patents.