We analyze political economy of Russian Vertical-of-Power in federal-regional aspects. In the year 2008 in Russia new president (the formal head of Russian government) was elected, in the next elections to come in the 2012 most probably that Putin is going to return his formal presidential chair. In this paper we test if governor appointed by new president followed by extra federal money (extra payments from federal budget to regional budget). There are two hypotheses: if new governor appointed by new president leads to significant increase in payments from federal budget to regional budget or there were no changes in payments from federal budget to regional budget. The data analysis confirms the first hypothesis.
This work looks at a model of spatial election competition with two candidates who can spend effort in order to increase their popularity through advertisement. It is shown that under certain condition the political programs of the candidates will be different. The work derives the comparative statics of equilibrium policy platform and campaign spending with respect the distribution of voter policy preferences and the proportionality of the electoral system. In particular, it is whown that the equilibrium does not exist if the policy preferences are distributed over too narrow an interval.
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.