Конкурентоспособность национальных экономик в условиях глобализации мирового хозяйства: факторы, инструменты, модели
This research studies the relationship of cross-border mergers and acquisitions to international trade through the lens of Russian pharmaceutical market. To this aim, the study analyses the woks of foreign economists dedicated to evaluating the link between foreign direct investment and international trade, and the influence of mergers and acquisitions on countries’ export and import flows. The research also presents a correlation analysis between the volume of Russian pharmaceutical exports and imports and cross-border deals performed by foreign pharmaceutical companies in Russia. We characterize these deals and conduct a comparative analysis of the regional structure of Russian pharmaceutical exports and imports as well as of the countries of origin of buyers in cross-border mergers and acquisitions. The results of the analysis indicate a positive relationship between cross-border mergers and acquisitions and Russian pharmaceutical exports, which is reflected in the export volume growth and its geographical diversification. However, it is outlined that particular problems of the industry hinder the amelioration of Russian positions in international exports. Similarly, the relationship between cross-border deals and Russian imports is positive: the major pharmaceutical products supply flow occurs from the countries of origin of buyers in cross-border mergers and acquisitions conducted in the Russian pharmaceutical sector.
This paper examines how export and export destination stimulates innovation by Russian manufacturing firms. The discussion is guided by the theoretical models for heterogeneous firms engaged in international trade which predict that, because more productive firms generate higher profit gains, they are able to afford high entry costs, and trade liberalization encourages the use of more progressive technologies and brings higher returns from R&D investments. We will test the theory using a panel of Russian manufacturing firms surveyed in 2004 and 2009, and use export entry and export destinations to identify the causal effects on various direct measures of technologies, skill and management innovations. We find evidence on exporters’ higher R&D financing, better management and technological upgrades. Exporters, most noticeably long-time and continuous exporters, are more active in monitoring their competitors, both domestically and internationally, and more frequently employ highly qualified managers. Exporters are more active in IT implementation. When it comes to export destination, we find that non-CIS exporters are more prone to learning. However, we cannot identify that government or foreign ownership shows any impact on learning-by-exporting effects.
The review covers the WTO report “Can Blockchain revolutionize international trade?” The report studies the multifaceted effects of Blockchain on international trade and its multiple applications. Digitalization of the cross-border transactions as the key effect would be particularly beneficial for the most paper-intensive processes, including trade finance, trade facilitation, trade in services, intellectual property and public procurement. The significant positive and transformative effect of Blockchain on international trade goes without saying, but the author warns against being too enthusiastic on the prospects of the full-size trade digitalization. As this requires enhanced trust between parties of the cross-border transactions, as well as international cooperation and joint efforts to build Blockchain ecosystems, and tackle legal and policy issues.
We address the external effects on public sector efficiency measures acquired using Data Envelopment Analysis. We use the health care system in Russian regions in 2011 to evaluate modern approaches to accounting for external effects. We propose a promising method of correcting DEA efficiency measures. Despite the multiple advantages DEA offers, the usage of this approach carries with it a number of methodological difficulties. Accounting for multiple factors of efficiency calls for more complex methods, among which the most promising are DMU clustering and calculating local production possibility frontiers. Using regression models for estimate correction requires further study due to possible systematic errors during estimation. A mixture of data correction and DMU clustering together with multi-stage DEA seems most promising at the moment. Analyzing several stages of transforming society’s resources into social welfare will allow for picking out the weak points in a state agency’s work.