ВТО: торговые переговоры между шахом и матом?
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.
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.