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This article examines the extent to which national institutional quality affects bilateral sectoral trade flows, as well as whether the conditioning role of institutions for trade has been waxing or waning with time. Based on a new trade theory framework, we derive a sectoral gravity equation, including novel variables corresponding to the exporter's labour competitiveness levels, along with importer's price indices and sectoral incomes, and analyse industry specific bilateral trade flows of 186 countries for the period 1996-2012. We address potential endogeneity and econometric drawbacks by means of Poisson Pseudo-Maximum Likelihood estimation methods. The results indicate that both the institutional conditions at destination and the institutional distance between exporting and importing countries are relevant factors for bilateral trade. Moreover, the effect associated toinstitutional conditions at destination moderately increases over time. This is a robust outcome across economic sectors, with higher values for agriculture and raw materials than for manufacturing and services.

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The European Union (EU) annually publishes an Innovation Union Scoreboard (IUS) as a tool to measure the innovation performance of EU Member States by means of a composite index, called the Summary Innovation Index (SII). The SII is constituted by an average of 25 indicators. The SII is claimed to rank Member States according to their innovation performance. This means that the higher the average value of the 25 indicators, the better the innovation performance is said to be. The first purpose of this article is to assess whether the SII constitutes a meaningful measure of innovation performance. Our conclusion is that it does not. Our second purpose is to develop alternative, productivity or efficiency-based, measures of innovation system performance based on a simple index number, and complement it with advanced and robust nonparametric Data Envelopment Analysis techniques. By doing so, the article offers a critical review of the SII, and proposes to put more emphasis on the identification of and relation between input and output innovation indicators. The data provided by the 2014 and 2015 editions of the IUS are here used to analyze the innovation performance of all 28 EU national innovation systems. A theoretical background and reasons for selecting the indicators used are presented, and our new ranking of the innovation performance using bias-corrected efficiency scores of all EU countries is calculated. We find that the results differ substantially between the SII and the ranking based on our method, with significant consequences for the design of innovation policies.

This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.

Although a very rich list of classes of space-time covariance functions exists, specific tools for selecting the appropriate class for a given data set are needed. Thus, the main topic of this paper is to present the new R package, covatest, which can be used for testing some characteristics of a covariance function, such as symmetry, separability and type of non-separability, as well as for testing the adequacy of some classes of space-time covariance models. These last aspects can be relevant for choosing a suitable class of covariance models. The proposed results have been applied to an environmental case study.

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