Knowledge, Innovation and Space

Knowledge, Innovation and Space

New Horizons in Regional Science series

Edited by Charlie Karlsson, Börje Johansson, Kiyoshi Kobayashi and Roger R. Stough

The contributions in this volume extend our understanding about the different ways distance impacts the knowledge conversion process. Knowledge itself is a raw input into the innovation process which can then transform it into an economically useful output such as prototypes, patents, licences and new companies. New knowledge is often tacit and thus tends to be highly localized, as indeed is the conversion process. Consequently, as the book demonstrates, space or distance matter significantly in the transformation of raw knowledge into beneficial knowledge.

Chapter 10: Returns to higher education: a regional perspective

Mikaela Backman and Lina Bjerke

Subjects: economics and finance, economics of innovation, regional economics, innovation and technology, economics of innovation, urban and regional studies, regional economics


Regional economic growth is influenced by knowledge and knowledge spillovers that are constrained in space (Andersson and Karlsson, 2007). The knowledge embodied in individuals is increased by education and the level of education decided by the individual is mainly set by the monetary gains from the investment. Becker (1964) was one of the first to acknowledge that education is an investment in an individual’s human capital and that it can generate large differences in earnings, but also that the earnings of less educated individuals tend to be sensitive to economic fluctuations. There are different levels in the return to education: individual, social and regional levels. This chapter investigates the returns to higher education in natural science, engineering and medicine in Sweden, using a regional approach. This approach enables the comparison of the return to different regional classifications whereby it adds new knowledge to the existing literature. The empirical analysis is based on individual data for the year 2000, enabling the extraction of individual information on location, educational level, work experience and income. In order to incorporate from all sources of income, we use income instead of wage. As a result, we are dealing with pecuniary income, consisting of or measured in money.

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