Brexit: initial reflections

Even though the opinion polling before the British referendum on membership of the European Union showed a narrow gap between the two sides, the actual result—a vote to leave—on the morning of 24 June 2016 came as a surprise to many. Yet in truth both the referendum and its outcome had deep roots in British politics. In this article we cast an eye over the history of Britain's relationship with the EU, which has long been marked by a mixture of awkwardness and successful influence. We trace the origins of the referendum in long‐run tensions between, and within, the political parties, and in the lukewarm public support for European integration. We also examine more contingent, short‐term factors relating to the referendum campaign itself. We conclude by commenting on the divisions exposed by the vote along lines of geography, education, class and wealth, and suggest that reconciling these with the continuing tensions in the party landscape make a clean and speedy exit from the EU unlikely.     

GOVERNMENT QUALITY AND THE ECONOMIC RETURNS OF TRANSPORT INFRASTRUCTURE INVESTMENT IN EUROPEAN REGIONS

Transport infrastructure investment is a cornerstone of growth‐promoting strategies. However, the link between infrastructure investment and economic performance remains unclear. This may be a consequence of overlooking the role of government institutions. This paper assesses the connection between regional quality of government and the returns of different types of road infrastructure in the regions of the European Union. The results unveil the influence of regional quality of government on the economic returns of transport infrastructure. In weak institutional contexts, investment in motorways—the preferred option by governments—yields significantly lower returns than the more humble secondary road. Government institutions also affect the returns of transport maintenance investment.     

REGIONAL GROWTH,INNOVATION, AND LATENT NONLINEAR EFFECTS

This paper studies the link between knowledge, innovation, and growth in European regions using nonparametric methods. Our findings suggest that knowledge inputs and the share of innovative firms have a heterogeneous and nonlinear relationship with growth. This evidence has been exploited to examine the consequences of alternative policies using a counterfactual estimation setup, the results of which imply that increasing the formal knowledge base may be optimal in most regions. Less knowledge and innovation intensive regions will also benefit from a higher innovation potential and from a trustworthy and entrepreneurial economic environment.     

The New Washington Consensus: Millennial Philanthropy and the Making of Global Market Subjects

This paper outlines the emergence of a New Washington Consensus associated with leading philanthropies of the new millennium. This emergent development paradigm by no means represents a historic break with the market rationalities of neoliberalism, nor does it represent a radical departure from older models of early 20th century philanthropy. Rather, it is new in its global ambition to foster resilient market subjects for a globalized world; and new in its employment of micro‐market transformations to compensate for macro‐market failures. Focusing on reforms pioneered by the new philanthropic partnerships in education and global health, the paper indicates how the targets of intervention are identified as communities that have been failed by both governments and markets. The resulting interventions are commonly justified in terms of “return on investment”. But the problems they target keep returning because the underlying causes of failure are left unaddressed.     

Applying the technique of image classification to climate science: the case of Andalusia (Spain)

This paper proposes an empirical climate classification method based on the application of multivariate statistics. In this method, the technique of supervised and unsupervised image classification is used to classify the data and define climatic units. envi software is used to perform the image classification, specifically, the Iterative Self‐Organizing Data Analysis Technique algorithm. Supervised classification is also applied based on reference variables, fundamental parameters and a classifier. The obtained results display greater objectivity, reliability, operability, accessibility and reproducibility than previous climate classifications devised for the region of Andalusia (Spain), taking into account that these previous classifications were not based on quantitative criteria.