Competitiveness and Public-Private Partnerships: Towards a More Decentralised Policy
In this contribution, we analyse the pattern of the so-called PIP (Partnerships and Public Initiatives) that have been approved between 2000 and mid-2003 in the POE1 framework. In particular, we will evaluate the extent of decentralisation that this new instrument has generated in competitiveness policy. Partnership approaches are a relatively recent phenomenon, but partnerships have received widespread attention and support from economic and political agents, including policy makers at national, regional and local levels. In fact, the term “public-private partnership” covers a wide range of concepts and practices. In our contribution, we will focus on partnerships in a competitiveness policy framework. In a first section, we discuss briefly the meaning and the extent of what we call competitiveness policy. Then, in a second section, we focus our attention in public-private partnerships as a specific instrument for policy. In particular, we make a first assessment on the distinctive principles that differentiate public-private partnerships from more traditional instruments such as direct investment in public agencies or direct subventions to firms. We follow the perspective that these principles, mainly decentralization of policy, may contribute to a greater effectiveness of policy, because a more decentralised policy is supposed to increase focus and accountability and to involve agencies with specialized skills and a more narrow range of objectives. But, also, we will refer that some inefficiencies and some lack of equity may arise from the use of private-public partnerships instrument. Finally, in the main section of this contribution, we will analyse the above-mentioned questions considering the case of the 131 PIP projects approved and financed by the POE between 2000 and mid-2003. As the major part of the variables used are nominal, and in order to define the decentralization pattern induced by this new instrument, we will use multivariate data analysis techniques in order to establish associations between several variables linked to decentralisation criteria and, also, to identify clusters of projects.
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