Реформы и регионы: системный анализ процессов реформирования региональной экономики, становления федерализма и местного самоуправления
On the basis of in-depth case studies of four Russian regions, Kirov and Voronezh oblasts and Krasnoyarsk and Perm' krais, the trade-offs among social and economic policy at the regional level in Russia are examined. All four regional governments seek to develop entrepreneurship while preserving social welfare obligations and improving compensation in the public sector. Richer regions have a greater ability to reconcile social commitments with the promotion of business. Regions differ in their development strategies, some placing greater emphasis on indigenous business development and others seeking to attract federal or foreign investment. Governors have considerable discretion in choosing their strategy so long as they meet basic performance demands set by the federal government such as ensuring good results for the United Russia party. In all four regions, governments consult actively with local business associations whereas organized labor is weak. However, the absence of effective institutions to enforce commitments undertaken by government and its social partners undermines regional capacity to use social policy as a basis for long-term economic development.
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.