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This article reviews consumption practices concerning vintage, a fashion style based on used or retro‐style garments. Existing studies connect vintage with authenticity, nostalgia and identity. We explore how the vintage style deploys and comments on consumer culture, bypassing producers by wearing old garments to communicate ‘authentic’ identities. We argue that existing theories on consumption, fashion and subculture cannot fully explain vintage practices. Bypassing the dichotomies and one‐dimensional explanations of these theories, we show that vintage, with its ambivalent relation to both subcultural distinction practices and mainstream consumer culture, serves as a prism through which to examine and understand the complexities and subtleties of 21st century consumption practices.

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Abstract-In this paper, a vector autoregressive model is developed for a sample of ocean dry bulk freight rates. Although the series of freight rates are themselves found to be non-stationary, thus precluding the use of many modelling methodologies, evidence provided by cointegration tests points to the existence of stable long-term relationships between the series. An assessment of the forecasts derived from the model suggests that the specification of these long-term relationships does not improve the accuracy of short-or long-term forecasts. These results are interpreted as a corroboration of the efficient market hypothesis. 0 1997 Elsevier Science Ltd INTRODUCTION

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Societal challenges such as migration, poverty, and climate change can be considered 'wicked problems' for which no optimal solution exists. To address such problems, public administrations increasingly aim for data-driven policy making. Data-driven policy making aims to make optimal use of sensor data, and collaborate with citizens to co-create policy. However, few public administrations have realized this so far. Therefore, in this paper an approach for datadriven policy making is developed that can be used in the setting of a Policy Lab. A Policy Lab is an experimental environment in which stakeholders collaborate to develop and test policy. Based on literature, we first identify innovations in data-driven policy making. Subsequently, we map these innovations to the stages of the policy cycle. We found that most innovations are concerned with using new data sources in traditional statistics and that methodologies capturing the benefits of data-driven policy making are still under development. Further research should focus on policy experimentation while developing new methodologies for data-driven policy making at the same time.