Механизмы рекрутирования глав региональных законодательных собраний (на примере регионов ЦФО)
Legislators are entrusted with key parliamentary functions and are important figures in the decision-making process. Their behaviour as political elites is as much responsible for the failures and successes of the new democracies as their institutional designs and constitutional reforms.
This book provides a comparative examination of representative elites and their role in democratic development in post-communist Central and Eastern Europe (CEE). It argues that as the drivers of the transformation process in CEE, individual and collective parliamentary actors matter. The authors provide an in-depth analysis of representatives from eleven national parliaments and explore country-specific features of recruitment and representation. They draw on an integrated dataset of parliamentary elites for individual, party family, and parliamentary variables over the 20 years following the collapse of Communism and develop a common framework for the analysis of variations in democratisation and political professionalisation between parliaments and political parties/party families across CEE.
This unique volume will be of interest to students and scholars of comparative politics, elite research, post-communist politics, democratisation, legislative studies, and parliamentary representation.
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.