compétitivité urbaine dans le contexte de la nouvelle économie. Québec: Presses de

To understand the processes of growth and change within regional economies researchers periodically engage in the evaluation and categorization of those regions. The resulting typologies serve to shape perceptions regarding key industries (e.g. biotechnology, IT) and successful regions (e.g. Silicon Valley, Boston). However, these discourses of knowledge production and localized innovation rarely connect to the underlying narratives of regional growth and decline either in theory or in practice. Since 2007, there is a renewed interest in mapping the long-term economic trends in US regions motivated by questions about the origins and effects of the global recession. To merge the discussions of the spatial distribution of innovation and production, I turn a theoretical framework provided by the emerging discussion of “evolutionary economic geography ” (EEG). EEG provides an analytical approach to regional economies which balances innovation against job creation rather than privileging technology over production. First, I begin by tracing six regions through a set of historical analyses of regional economies used to develop influential typologies. I then trace those regions through the “typology of innovation districts ” project to ascertain their current position as innovative regions relative to other US regions. Finally, I analyze these six regions using recent employment data. The findings indicate that the geographies of innovation and production may be diverging rather than converging in the US presenting a challenge for regional development policy.

The role of talent and creativity in economic development has been a subject of growing interest to social scientists. Human capital is observed both to be an important contributor to growth and to be unevenly distributed geographically. While there is consensus on the importance of human capital to economic development, debate takes shape around two central issues. First, there is the question of how best to measure human capital. The conventional measure of human capital is based on educational attainment (share of population with a bachelorís degree and above). But more recent research suggests that it is more important to measure what people do than what they study, and thus occupationally based measures, associated principally with creative class occupations, have been introduced. Second, there is debate over the factors that yield the geographic distribution of human capital in the first place. Three alternative factors have been found to play a role: universities; amenities (measured here as diversity of service industries); and openness

The relationships between talent, technology and regional development have been widely examined in the advanced economies. While there is a general consensus as to the important role talent plays in regional development, debate has emerged on two key issues. The first involves the efficacy of educational (i.e. human capital) versus occupational (i.e. the creative class) measures of talent; the second involves the factors affecting the distribution of talent. In this study, we have used structural equation models and path analysis. We employed both educational and occupational measures of talent to examine the relationships between talent, technology and regional economic performance in China, and to isolate the effects of tolerance, differing levels of consumer service amenities, and the location of universities on the distribution of talent. Contrary to the findings of empirical studies on the developed economies, we found the relationships between the distribution of talent and technology and between the distribution of talent and regional economic performance in China to be weak. We found the presence of universities – a factor highly influenced by government policy – and the actual stock of talent to be strongly related. We also found that tolerance, as measured by the “Hukou index, ” plays an important role in the distribution of talent and technology in China.

This draft presents work in progress, and may not be quoted, copied, cited, or distributed without written permission of the authors. Comments and suggestions, however, are most welcome. The opinions, findings, conclusions, and recommendations expressed in this material are those of the authors, and do not necessarily represent the views of the Public Policy Institute of California or the National Science Foundation. To request additional copies, or for a listing of other PPIC Working Papers, please call 415.291.4495 or go to www.ppic.org. Our thanks to colleagues at PPIC, including Mike Teitz, Hans Johnson, and Deborah Reed, and to Greg Schrock, Pingkang Yu and Michael Leary at the University of Minnesota, and staff at the California