This article reviews three groups of questions: objectives placed before the Russian official statistics by the Digital Economy program; basic characteristics of the Big Data in terms of data  collection theory and design; concept of smart statistics. The article  addresses key issue of understanding Big Data as «organic data»  that are primary digital records kept in real time by software and  technology without human intervention. The use of Big Data in  official statistics leads to development of the new type of continuous statistical observation. First and foremost, digital nature  of Big Data and its basic characteristics, including as microdata,  provide new possibilities for measuring phenomenon and processes,  as well as determine the specific nature of observation errors that  occur when Big Data are generated. In general, the organization and process of generating Big Data are viewed by the author as the main type of statistical observation in the future. In the heart of the  concept of smart statistics lays direct incorporation of statistical  observation into the system of primary digital records followed by  the straight through automatic data processing up to compiling  statistical aggregates. The approaches discussed in this article are  linked to objectives placed before the Russian official statistics by the Digital Economy program.

This article presents research of factors of investment attractiveness of the Kaluga region - a regional leader in formation of appealing investment climate for foreign investors and setting up  industrial technological parks. The indicators of the National  Investment Climate Ranking that has been used since 2016 are  discussed with regard to their content and structure. The author  argues that there is a need for extending the content-related  components of the Ranking by adding questions of the influence of  investment policy of the region on its socioeconomic development to  the major focus areas. The author has developed a methodology for  calculating an integrated investment climate indicator that  implements not only the basic idea of the need for additional  reviewing factors of investment climate, namely, development  potential of the region, performance of state administrative bodies and businesses, but also the basic requirements for monitoring - comparability of quantitative estimates over time and interregional  comparability of the indicators in question. Analysis of the statistical  information supports the author’s conclusion that effectiveness of  investment policy should be assessed not only by the results  achieved by the investors, but also by the socio-economic  development of the studied constituent entity.

This article reviews the research of interregional differences in the level and quality of live in the regions of the Privolzhsky (Volga) Federal District (PFD). The regions of the district were ranked based  on the integral estimation of quality of life of the population, leaders  and outsiders were designated. Analysis of skewness coefficients of  selected components of integral index revealed the multifaceted  nature of disparities in well-being and quality of life within the  regions of the PFD. Measurements of this indicator over time showed that there has been a common trend to slow down the growth of the  well-being of the population, and in many territories the situation is  characterized by stagnation and decline. In general, in the next  years these trends may, in the authors’ opinion, continue. In this regard, estimating quantity characteristics of changes in some components of the well-being and quality of life in every region,  reasons for the observed shifts are important in order to come up  with definite solutions on unwinding of imbalances in socio-economic development of selected territories.

This article focuses on comparative analysis of child benefits paid in Russia at the regional level. The research is based on methods widely used by Alfred Kahn, Sheila Kamerman, John Ditch, Jonathan  Bradshaw et al. for comparative analysis of child benefit systems in  different countries. Although method of model families is not free  from some weaknesses (because it gives information on benefits  that families should receive rather than information on benefits that  they actually get) still it provides recent data on characteristics,  similarities and differences of child benefit systems of Russian  regions. Previously child benefit packages in Russia were analyzed  by several researches - Lilia Ovcharova, Daria Popova, etc. The  author contributed through conducting statistical study of сhild  benefit packages paid at the regional level. These packages include  all transfers targeted at families with children (except of lump-sum benefits which do not significantly affect family welfare). It is stated that model families differ in earnings level and demographic  structure (couples or single mothers, with different number of  children). The article comments on quantitative characteristics of  child benefit packages for a set of model families from 37 regions.  Comparative analysis of the level of child benefits (in absolute terms  as well as to the minimum subsistence level and average wage)  allows to determine which types of families are first to gain from  regional child benefits systems and which remain vulnerable to the  risk of poverty.

This article analyzes key provisions of international recommendations «SERIEE. Environmental Protection Expenditure Accounts - Compilation Guide (Methods and nomenclatures)»  (hereinafter referred to as the SERIEE manual) concerning modern  requirements to statistics of natural resources and environmental  management. This document was prepared by the European  Commission and Eurostat in 2002; SERIEE fully stands for European  system for the collection of economic information on the  environment (the abbreviation SERIEE is based on the name in  French: le Systeme europeen pour le rassemblement d informations  economiques sur l’environnement).The 2002 SERIEE manual is based on the initial versions of the integrated System of  Environmental and Economic Accounting, SEEA, which, in turn, is a  set of the SNA (SNA-SEEA) satellite accounts. The authors believe that despite the fact that in 2012 the UN Statistical Commission adopted international standard in the form of the SEEA Central  Framework (CF SEEA-2012), the 2002 SERIEE manual without doubt continues to be an interesting international guideline both  methodologically and organizationally. It allows to significantly  improve the process of conducting summary valuations and long- term planning of environmental measures based on macroeconomic  aggregates. The authors reviewed and assessed the system of five tables of consolidated accounts, designated as B, B1, A, C and C1, presented in the manual. This is done within the general system of  environmental protection expenditure accounts (EPEA) that provides  consistent, coherent, comprehensive and detailed reflection of  environmental costs at the macrostatistical level - from production  and consumption to financing. In other words, the characteristic of  the different operations with the use of the main aggregates of the  SNA-SEEA and EPEA, such as output, intermediate consumption, value added, income, final consumption, savings, export, import, etc. The paper reveals the authors’ interpretation of the fundamental structural unit of SNS-SEEA/EPEA - total national costs of  environment protection. The paper considers not only principles and  methods that are common for the national accounting, but also the  specific features related to recording environmental protection by production and consumption of the relevant goods (products) and services. In particular, the authors examined the macro level  characteristics of the so-called connected and adapted products  (eco-oriented and eco-adaptive goods). The article analyses specific  features related to recording general taxation and subsidies, and the  so-called earmarked (related) taxes, fees and charges, aimed at  improving environmental protection and streamlining of  environmental management. It is noted that all of the  abovementioned operations are recorded not only by institutional  sectors of economy and kinds of activity, but also by specialist,  secondary and ancillary producers, which is a conceptual and indispensable requirement for implementation of the SNA-SEEA.
At the end of the article there are brief conclusions and recommendations concerning prompt elimination of the existing defects and possible adapted use of best practices in this sphere.

The article presents the authors’ views on using «Big Data» to gain new information on population and to study various social and economic phenomena and processes on its basis. As the foreign  experience clearly demonstrates, with the development of  information industry and the ubiquitous mobile communications  penetration, one of the most promising sources of «Big Data» in  terms of population coverage (using population as an object of  statistical observation) and efficiency in obtaining information on it,  is data from mobile operators. The paper also notes Russian  experience in this field, especially since 2014, when Russia managed to implement the «Geoanalysis» project on using data from mobile  operators in managerial activities of the Moscow Government. The  authors outline the history and development of the new digital data  source for population statistics, which is based on technical data of cellular networks. The paper covers baselines to Russian innovation methodological developments and algorithms for converting radio frequency events from base stations of mobile operators into  statistical indicators of number, density and dynamics of population  movements with full coverage of study area and high level of space- time specification. This article pays particular attention to issues  concerning protection of subscribers’ personal data, legality of collecting and processing the information received from mobile operators in accordance with the legislation of the Russian  Federation. The authors thoroughly examined directions for applying  statistical indicators, based on data from mobile operators, in the  field of economy, trade, culture, transport modeling, urban planning and management.