Теория и практика регуляторной политики в России : монография
This article is devoted to developmet of regulatory impact assessment (RIA) in Russia as part of the institutional reforms regarding legislative procedures.
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 regulatory policy report is the latest in a series written in cooperation with the Higher School of Economics and expert and business communities during the work on a comprehensive strategy to modernize the public administration system in Russia. For CSR, changing the regulatory policy along with introducing modern managerial approaches to public administration, personnel policy, and large-scale digital transformation, is a priority for successful structural reforms.
The ideas and suggestions on the regulatory policy presented by CSR were of great interest to the Russian business community. CSR received dozens of conceptual proposals from experts, businessmen, and public officials from all over Russia. We worked on promising regulatory policy tools and a comprehensive strategy for two years and a major part of our deliverables can be found in Chapter 3 of this report. Many of these proposals were also included in the Development Strategy for 2018–2024 presented by CSR at the request of the Russian President.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.
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.