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It is essential to understand how the countries with the highest number of tourist arrivals have managed to recover or not based on the competitiveness of the tourism industry during the pandemic stage. It is necessary to evaluate the policies implemented by each government to maintain the competitive performance of their industries. This chapter proposes a comprehensive review of the policies implemented in the 10 most visited countries according to UNWTO data. Most of these policies are geared toward economic and financial flexibility strategies for companies and individuals in the industry under study. The effectiveness of these policies is evaluated with statistical information extracted from a unified UNWTO database to reduce biases in the effectiveness analysis. Finally, concluding remarks are offered on the effectiveness of the policies and their contribution to the sector's recovery.

In a pandemic framework (COVID-19), this chapter explores the impact of the global economy and socio-cultures concerning three axes: recreational, tourism, and hospitality. Although we slowly see an economic revival, it is well known that this sector of study is very susceptible to being affected by the context of nations. Following restrictions and measures taken by governments around the world to reduce the number of cases of coronavirus infections, many nations closed their borders, affecting international travel and by 2020 tourism had been reduced to the near cessation of operations due to the imminent fear of this poorly studied disease, and the service sector was negatively affected. It should be added that, according to the World Tourism Organization's projections, a decrease of between 20 and 30% is forecast for 2020 compared to the previous year.

Copula modeling enables the analysis of multivariate count data that has previously required imposition of potentially undesirable correlation restrictions or has limited attention to models with only a few outcomes. This article presents a method for analyzing correlated counts that is appealing because it retains well-known marginal distributions for each response while simultaneously allowing for flexible correlations among the outcomes. The proposed framework extends the applicability of the method to settings with high-dimensional outcomes and provides an efficient simulation method to generate the correlation matrix in a single step. Another open problem that is tackled is that of model comparison. In particular, the article presents techniques for estimating marginal likelihoods and Bayes factors in copula models. The methodology is implemented in a study of the joint behavior of four categories of US technology patents. The results reveal that patent counts exhibit high levels of correlation among categories and that joint modeling is crucial for eliciting the interactions among these variables.

Corporate governance has attracted enormous attention both in the area of law and in the area of financial economics. In comparative corporate governance studies, many people have devoted their energy to find a best corporate governance model. I argue that a functional analysis does not support the view that there is a single best corporate governance model in the world. I further use the transplantation of an English style takeover law into China to show that the importation of foreign law is not always based on careful analysis whether the imported foreign law is the best in the world. Furthermore, I use the subsequent adjustment of the transplanted English takeover law in China to show that the imported foreign law is subject to local political and economic conditions. If there is no best corporate govern model and the transplantation of foreign law into other countries with different social and political background does not achieve similar objectives, the search for a best corporate governance model is misguided. Just as tort law or constitutional law regimes may have diversified models, so do corporate governance regimes in countries with different historical, social and political backgrounds.