There is consensus among scholars and policy makers that knowledge is one of the key drivers of long-run economic growth. It is also clear from the literature that not all knowledge has the same value. However, too often in economic geography and cognate fields we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. In this paper we measure the complexity of knowledge across patent classes and we map the distribution and the evolution of knowledge complexity across U.S. cities from 1975 to 2004. We build on the 2-mode structural network analysis proposed by Hidalgo and Hausmann (2009) to develop a knowledge complexity index (KCI) for Metropolitan Statistical Areas (MSAs). The KCI is based on more than 2 million patent records from the USPTO, and combines information on the technological structure of 366 MSAs with the 2-mode network that connects cities to the 438 primary (USPTO) technology classes in which they have Relative Technological Advantage (RTA). The complexity of the knowledge structure of cities is based on the range and ubiquity of the technologies they develop. The KCI indicates whether the knowledge generated in a given city can be produced in many other places, or if it is so sophisticated that it can be produced only in a few select locations. We find that knowledge complexity is unevenly distributed across the U.S. and that cities with the most complex technological structures are not necessarily those that produce most patents.
