Экономические условия и механизмы профессионального роста школьных кадров
The Programme for International Student Assessment (PISA) is a worldwide study by the Organisation for Economic Co-operation and Development (OECD) in member and non-member nations of 15-year-old school pupils' scholastic performance on mathematics, science, and reading. It was first performed in 2000 and then repeated every three years. It is done with view to improving education policies and outcomes. The data has increasingly been used both to assess the impact of education quality on incomes and growth and for understanding what causes differences in achievement across nations.
The book Khroniki obrazovatelnoi politiki: 1991—2011 [Chronicles of the Educational Policy; 1991—2011] by Boris Startsev is a chronicle of modernization of the Russian secondary and higher education system in the period between 1991 and 2011 from the economic point of view. The events are seen through the eyes of a journalist and presented as a sequence of decisions by decision-makers. The reform is described using the names of ministers and top-ranking officials, and only rarely does one come across the opinions and names of teachers. The value of the book is in the integrity of the story about the process that has been modified more than once.
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.