Борьба с курением как элемент здорового образа жизни
The paper discuses challenges for antismoking policy in Russia, paying special attention to youth smoking problems. We present data on tobacco consumption and factors favoring individual decision to smoke. We justify government intervention by giving theoretical and empirical evidence for externalities, produced by passive smoking, information asymmetry and addiction effects. We use RLMS data and Students survey results to discuss the need and attitude towards such measures as tobacco tax increase, restrictions on smoking in public places, total ban on tobacco advertising and sponsorship etc.
The chapter provides a review of contemporary life style policies in Russia highlighting main issues and suggesting some improvements in governmental interventions.
This paper is devoted to analysis of factors that influence a healthy lifestyle of Russians. Along with such factors as age, marital status, size of a household, income, educational level, self-rated health, etc. we consider a rate of time preferences. This rate shows individual ability to postpone utility from consumption to the future. The study is based on the survey conducted by the Yuri Levada Analytical Center in 2011. We construct a representative sample of the adult population of Russia. The basic model for econometric analysis is an ordered probit model. Findings suggest that the rate of time preferences positively influences a healthy lifestyle of Russians.
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.