The purpose of this paper is to introduce a series of articles on the linksbetween innovation, the evolution of industry and employment. These relationsprovide the building blocks of a new industrial policy. The articles areincluded in Innovation, Industry Evolution and Employment published by CambridgeUniversity Press in 1999. The continued rising unemployment coupled withmoderate growth in Europe has triggered a plea by policy makers for rethinkingthe policy approach that brought about European prosperity during the post-warera. The resulting policy debate has been cast in terms of an inevitabletradeoff between greater employment but at the sacrifice of lower wages on theone hand, versus the maintenance of wages and living standards but at the costof less employment on the other. In terms of this debate, the Anglo-Americansolution has been the former, while the continental Europeans have chosen thelatter. The purpose the thirteen articles of Innovation, Industry Evolution andEmployment is to suggest that this policy debate has been miscast. There is analternative. This alternative involves structural change, and in particularshifting economic activity out of traditional moderate-technology industries andinto new emerging knowledge-based industries. In other words, it involvesinnovation, industry evolution and their consequences for employmentdevelopment. To shed light on the links between innovation, industry evolutionand employment generation, the Tinbergen Institute of Erasmus UniversityRotterdam, the School of Policy Studies at Georgia State University, and EIMSmall- and Medium Sized Business Research and Consultancy in Zoetermeer, theNetherlands hosted a two day conference on the subject in Rotterdam andZoetermeer, 29-30 August, 1997. The thirteen articles included in Innovation,Industry Evolution and Employment reflect a carefully edited and revisedselection of the papers_new of this conference. What the articles have in common isthat they link some measure of economic performance to technology andinnovation, and they do it using a dynamic framework based on a longitudinaldatabase that tracks micro-observations over time.