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Gender Wage Differentials and the Spatial Concentration of High-Technology Industries

  • Elsie Echeverri-CarrollEmail author
  • Sofía G. Ayala
Chapter
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Moretti (2004) finds that the distribution of human capital across cities in the United States became more unequal during the 1990s. He believes that one reason for the increased concentration of human capital in some metropolitan areas was the high-tech boom of that decade, since it benefited a handful of already highly skilled cities. This trend reflects the decisions of skilled workers and the skill-intensive industries that employed them to colocate in the same cities or regions (high-tech clusters). Zucker et al. (1998), for instance, find that the entry decisions of new biotechnology firms in cities depends on the stock of human capital in outstanding scientists there, as measured by the number of relevant academic publications. Colocation benefits workers (who enjoy the productivity-enhancing effects associated with local learning processes) as well as high-tech firms (which profit from highly productive and creative workers who enhance the firms’ innovation processes). The primary cooperative linkages in high-technology clusters are those related to knowledge exchange. As Fingleton et al. (2004) note, sharing knowledge is the key to the generation and maintenance of innovation flows that are particularly relevant in these clusters. A strong evidence of the learning networks-innovation relationship comes from studies showing that patents (a proxy for innovations) are more likely to emerge from the same states or metropolitan areas as the cited patents than one would expect based in the preexisting concentration of related research activity (Jaffe et al. 1993).

Keywords

Instrumental Variable Female Worker Wage Premium Location Quotient Wage Elasticity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This study is based on work supported by the National Science Foundation under Grant No. 0318174 and by the Bureau of Business Research, The University of Texas at Austin. Opinions, findings, and conclusions or recommendations are those of the authors and do not necessarily reflect the view of any of these organizations. The authors thank two anonymous reviewers and Robert A. Peterson for constructive comments.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.IC2 Institute, University of Texas at AustinAustinUSA

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