Дискриминация мигрантов в социоэкономической сфере: роль межгрупповых установок принимающего населения
This chapter addresses changes in immigration trends and their psychosocial effects in post-Soviet Russia. Russia is currently the world’s second most populous country (after the USA) in terms of its immigrant population, with most coming from the Central Asian States (Uzbekistan, Tajikistan, and Kyrgyzstan) and China. The chapter begins with an examination of the social issues that immigrants must face. The research focuses on Moscow as the most attractive destination for immigrant workers. The chapter presents the findings of an empirical study conducted on the reciprocal acculturation between immigrants and the host society in Moscow. The study examines the correlations between the immigrants’ acculturation attitudes and the host society’s acculturation expectations and perceptions of the immigrants. More specifically, the study focuses on how measures of integral security (including physical, cultural and economic security) influence the host society’s attitudes towards immigrants.
This article is talking about state management and cultural policy, their nature and content in term of the new tendency - development of postindustrial society. It mentioned here, that at the moment cultural policy is the base of regional political activity and that regions can get strong competitive advantage if they are able to implement cultural policy successfully. All these trends can produce elements of new 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.