This article addresses the framework for constructing and analysing National Transfer Accounts. NTA  is a very useful analysis tool to study changes in the age structure of the population and income distribution processes between generations, and their economic impact on GDP. In some countries, NTA  is actively used in long-term macroeconomic forecasting (on 40-years period and more). Main NTA indicators calculate with using of national accounts database (SNA).  A vital element of the research is determining economic lifecycle results for all age-groups and identify its primary funding sources.

The  article considers comprehensive methodological and  practical approaches to the compiling the three aggregate transfer accounts - life cycle account, public reallocation account and private reallocation account - and using them as a basis for the analysis of socio-demographic processes in Russia. The macro analysis presented in the article is based on statistics of national accounts and other information sources for the years 2003-2017.

The conducted analysis resulted in compiling the system of aggregate transfer accounts for Russia for 2003-2017 and in identifying quantitative parameters of Russian economy life cycle balance and in examining qualitative changes in its funding sources. The authors own analysis demonstrated that while in 2003-2010 economic life circle balance was positive (except for the crisis year of 2009),  in 2011-2016 it became negative. The  State has been playing an increasing role in financing the economic life cycle deficit.

Both international and Russian experience of constructing regional accounts demonstrate the failure to address theoretical and practical issues related to calculating industry-specific gross value added (GVA) at a regional level. The most common approach for this type of calculations is using direct and indirect indicators. They allow for the GVA volume to be distributed across the regions if there is a lack of relevant information for its direct counting based on primary data from local production units.

The paper systematizes GVA regionalization methods proposed in international recommendations, among which the production functions method is recognized as relevant and valid to measure GVA industry volumes at a regional level. The authors tested production functions on a set of mathematical and statistical methods to make GVA regionalization indicators for the Russian industries. The paper considers if there is room for elaborating industrial production index on the GVA structure determined by a statistically significant functional relationship with production factors localized on the territory of the respective regions.

The paper explores the potential application of results of business climate monitoring of services in Russia to analyze development levels of the sector under review and Russian economy in general. Source data - are the 2012 Q1 - 2018 Q4 results. These data are summarized in the traditional composite index of business confidence, and alternative business climate indicator calculated using principal component analysis. To examine the reaction of GDP to impulses in the business climate indicator the Vector Autoregression Model was used.

The results of services surveys provide reliable information on the economic sentiment that is essential to measure recession and recovery development of the sector. Since 2013, the survey’s results demonstrate a stable negative trend in the indicators dynamics. The slight increase in entrepreneurial optimism in 2016-2018 did not result in moving confidence to a positive zone. Aggregate entrepreneurial  estimates show weak and unstable demand on services driven by a long-term decline in household real disposable income. A more extended observation period needs to conclude the BCI cyclic properties; however, it can be used now to analyze the development of the Russian services sector.

Calculations of the business climate index in the service sector showcase that it reflects changes in the growth rate of the GDP physical volume recorded by official statistics more sufficiently than the traditional index of entrepreneurial confidence (taking into account the simultaneous correlation). The insufficient length of the time-series of survey results so far limits the ability to extract and analyze the cyclic profile of a composite indicator. The authors, however, proposed using the principal component analysis to construct an alternative composite index to analyze the economic development of the services sector.