Authors: David Rigby*, UCLA
Topics: Economic Geography, Quantitative Methods, Quantitative Methods
Keywords: smart specialization, technology, regional performance
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Diplomat Room, Omni, West
Presentation File: No File Uploaded
Smart specialization was conceived as a “bottom-up” policy framework to help EU policy-makers identify new technology growth paths connected to the existing knowledge cores of regions. Operationalization of smart specialization policy requires a mapping of technologies in knowledge space. This mapping measures the “distance” between technology types and thus an index of the “cost” of moving from one technology to another. Alongside the cost of technological adjustment, smart specialization also requires a ranking of the benefits of new technologies that are captured with a measure of technological complexity. Given the costs and benefits of movement in knowledge space, regional policy-makers can examine alternative trajectories of technological development in a smart way. In this paper we map U.S. metropolitan areas in knowledge space and identify the knowledge cores of cities between 1975 and 2010. Fixed effects panel models are employed to show that cities developing knowledge clusters in a manner consistent with smart specialization enjoyed significantly stronger economic performance over the period examined. These results hold across all metropolitan areas and they are robust to analysis over smaller subsets of data such as the cities of the U.S. rustbelt. Spatial autocorrelation is used to build instrumental variables in the estimation and push analysis more strongly to uncover causation.