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The existence of parallel imports (PI) raises a number of interesting policy and strategic questions, which are the subject of this survey article. For example, parallel trade is essentially arbitrage within policy-integrated markets of IPR-protected goods, which may have different prices across countries. Thus, we analyze fully two types of price differences that give rise to such arbitrage. First is simple retail-level trade in horizontal markets because consumer prices may differ. Second is the deeper, and more strategic, issue of vertical pricing within the common distribution organization of an original manufacturer selling its goods through wholesale distributors in different markets. This vertical price control problem presents the IPR-holding firm a menu of strategic choices regarding how to compete with PI. Another strategic question is how the existence of PI might affect incentives of IPR holders to invest in research and development (R&D). The global research-based pharmaceutical firms, for example, strongly oppose any relaxation of restrictions against PI of drugs into the United States, arguing that the potential reduction in profits would diminish their ability to innovate. There is a close linkage here with price controls for medicines, which are a key component of national health policies but can give rise to arbitrage through PI. We also discuss the complex economic relationships between PI and other forms of competition policy, or attempts to limit the abuse of market power offered by patents and copyrights. Finally, we review the emerging literature on how policies governing PI may affect international trade agreements.

Cross-docking is a supply chain distribution and logistics strategy for which less-than-truckload shipments are consolidated into full-truckload shipments. Goods are stored up to a maximum of 24 hours in a cross-docking terminal. In this chapter, we build on the literature review by Ladier and Alpan (2016), who reviewed cross-docking research and conducted interviews with cross-docking managers to find research gaps and provide recommendations for future research. We conduct a systematic literature review, following the framework by Ladier and Alpan (2016), on cross-docking literature from 2015 up to 2020. We focus on papers that consider the intersection of research and industry, e.g., case studies or studies presenting real-world data. We investigate whether the research has changed according to the recommendations of Ladier and Alpan (2016). Additionally, we examine the adoption of Industry 4.0 practices in cross-docking research, e.g., related to features of the physical internet, the Internet of Things and cyber-physical systems in cross-docking methodologies or case studies. We conclude that only small adaptations have been done based on the recommendations of Ladier and Alpan (2016), but we see growing attention for Industry 4.0 concepts in cross-docking, especially for physical internet hubs.

This chapter advances a panel vector autoregressive/vector error correction model (PVAR/PVECM) framework for purposes of examining the sources and determinants of cross-country variations in macroeconomic performance using large cross-country data sets. Besides capturing the simultaneity of the potential determinants of cross-country variations in macroeconomic performance and carefully separating short- from long-run dynamics, the PVAR/PVECM framework advanced allows to capture a variety of other features typically present in cross-country macroeconomic data, including model heterogeneity and cross-sectional dependence. We use the PVAR/PVECM framework we advance to reexamine the dynamic interrelation between investment in physical capital and output growth. The empirical findings for an unbalanced panel of 90 countries over the time period from at most 1950 to 2000 suggest for most regions of the world surprisingly strong support for a long-run relationship between output and investment in physical capital that is in line with neoclassical growth theory. At the same time, the notion that there would be even a long-run (let alone short-run) causal relation between investment in physical capital and output (or vice versa) is strongly refuted. However, the size of the feedback from output growth to investment growth is estimated to strongly dominate the size of the feedback from investment growth to output growth.

This paper develops methods of Bayesian inference in a cointegrating panel data model. This model involves each cross-sectional unit having a vector error correction representation. It is flexible in the sense that different cross-sectional units can have different cointegration ranks and cointegration spaces. Furthermore, the parameters that characterize short-run dynamics and deterministic components are allowed to vary over cross-sectional units. In addition to a noninformative prior, we introduce an informative prior which allows for information about the likely location of the cointegration space and about the degree of similarity in coefficients in different cross-sectional units. A collapsed Gibbs sampling algorithm is developed which allows for efficient posterior inference. Our methods are illustrated using real and artificial data.

We compare environmental impacts associated with incoming foreign direct investment versus domestic capital in China. We use aggregate data on Chinese provinces’ economic and pollution indicators to explore the effects of the financial origin of fixed capital. Our simultaneous models consider three prime channels through which these effects work: economic scale, sectoral composition, and pollution intensity. Results show that emissions associated with foreign financed capital are lower than with domestically financed capital for some but not all of the considered types of pollution.

Both authors have been involved as lay members in research and other activities for a number of years, ensuring they represent the views of members of the public. This chapter identifies what is, and what is not, patient and public involvement as well as highlighting the importance of involving members of the public in all aspects of the research process. Best practice is explored as identified in the UK Standards for Public Involvement 2019 and the UK Policy Framework for Health and Social Care Research 2020. The implications of the Mental Capacity Act and its wording on research matters are also considered. Case studies have been incorporated to highlight the impact of involving patients and members of the public in all aspects of the research process. These include the lessons learnt by researchers and lay members of the team. The aspiration is to move towards more collaboration between members of the public and researchers; therefore, we discuss co-production of research or community-based participatory research (CBPR). We highlight the need for a better partnership between researchers and members of the public. The benefits of this are explored along with the consequences for all involved.