Кто выиграл от принятия Закона о торговле?
The paper is aimed at assessing the regulatory impact of the new trade law (2009). A standardized data survey is used for revealing market sellers that may gain from the introduction of the trade law. 512 filled questionnaires were collected from the managers of chain stores and their suppliers in fi ve big cities of Russia in November-December 2010. Federal Anti-Trust Service statistics is used to examine the scale and dynamic of regulatory impact on the trade law enforcement. The author concludes that the initial effects of the trade law do not correspond to the declared goals. At the same time, the market sellers confront with the additional transaction costs of administrative control, prosecutions and fi nes due to the extended prerogatives of the anti-trust authorities.
The paper estimates the first consequences of the new Trade Law aimed at restoring a «balance of market power» in retailer-supplier relationships. Empirical data were collected in November-December 2010 from the grocery sector and home electronic appliances sector. 512 filled questionnaires were received from managers of retail chains and their suppliers in five Russia’s cities. The author argues that proclaimed goals of the Trade Law have not been achieved. Contract relationships have not changed for three quarters of retailers and suppliers. There are no significant effects on conditions of market entry, frequency of vertical restraints, and general estimations of the level of contract requirements.
This article is devoted to developmet of regulatory impact assessment (RIA) in Russia as part of the institutional reforms regarding legislative procedures.
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.