Модус казённого имущества
The Working Paper examines the peculiarities of the Russian model of corporate governance and control in the banking sector. The study relies upon theoretical as well as applied research of corporate governance in Russian commercial banks featuring different forms of ownership. We focus on real interests of all stakeholders, namely bank and stock market regulators, bank owners, investors, top managers and other insiders. The Anglo-American concept of corporate governance, based on agency theory and implying outside investors’ control over banks through stock market, is found to bear limited relevance. We suggest some ways of overcoming the gap between formal institutions of governance and the real life.
This paper uses the banking industry case to show that the boundaries of public property in Russia are blurred. A messy state withdrawal in 1990s left publicly funded assets beyond direct reach of official state bodies. While we identify no less than 50 state-owned banks in a broad sense, the federal government and regional authorities directly control just 4 and 12 institutions, respectively. 31 banks are indirectly state-owned, and their combined share of state-owned banks’ total assets grew from 11% to over a quarter between 2001 and 2010. The state continues to bear financial responsibility for indirectly owned banks, while it does not benefit properly from their activity through dividends nor capitalization nor policy lending. Such banks tend to act as quasi private institutions with weak corporate governance. Influential insiders (top-managers, current and former civil servants) and cronies extract their rent from control over financial flows and occasional appropriation of parts of bank equity.
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.