Abushahmanova, J.

Chistyakova, Natalia

Shabaldina, Natalia

Spitsin, Vladislav V.

English

Tomsk State University Repository

Informatics, Networking and Intelligent Computing – Zhang (Ed.)© 2015 Taylor & Francis Group, London, ISBN: 978-1-138-02678-0Trends in the development of databases on statisticsin the OECD, the EU and RussiaN. Chistyakova, V. Spitsin & J. AbushahmanovaNational Research Tomsk Polytechnic University, Tomsk, RussiaN. ShabaldinaNational Research Tomsk State University, Tomsk, RussiaABSTRACT: The problems of information provision of socio-economic processes at the national and regionallevels are described in this article. The role of statistical information in the management of socio-economic pro-cesses is shown. The main sources of information for analysis and making managerial decisions and advantagesof interactive statistical database in comparison with other forms of information are offered for discussion. Fordetailed study three databases were selected: OECD statistics system, the system of Eurostat, and the Russiansystem EMISS.1 INTRODUCTIONThe aim of this article is to analyze and improve thestatistical tools to assess the result of post-industrialeconomy development (Scott, 2013). This goalinvolves the following tasks:– Classification of Russian and foreign systems ofstatistical indicators and identify their strengths andweaknesses;– Comparative analysis of international and Russianon-line databases;– Identification of the main trends in the post-industrial economy development and the prob-lems of their reflection in the system of statisticalindicators;– Justification of the process approach to the for-mation of statistical performance in the face ofpost-industrial economy;– Development of recommendations for improvingthe system of statistical indicators.The subject of the study is to develop statisti-cal information systems in terms of post-industrialeconomy. Object of this paper are the open-accessinternational and Russian systems of statistical indi-cators that can be used to analyze the impact of thepost-industrial economy development.2 CLASSIFICATION OF INTERNATIONALAND RUSSIAN SYSTEMS OF STATISTICALINDICATORSHierarchy of statistical indicators presented in Table 1.Considering the system of statistical indicatorspresented high-above,only three systems are interac-tive databases (OECD.Stat Extracts, Eurostat, EMISS)which helps to create custom queries and make unload-ing in Excel file. Given the current level of informa-tion technology development, all Russian statisticaldatabase system should be the same. At this moment,Russian statistical database which are not allowed togenerate user requests are generally not available inthe public domain and inconvenient for informationprocessing (especially statistical database “Rosstat”,statistical database High Economic school). Theseonline databases should be placed on the Internet inthe open or paid access.3 COMPARATIVE ANALYSIS OFINTERNATIONAL AND RUSSIAN ON-LINESTATISTICAL DATABASESComparison of interactive databases OECD.StatExtracts (OECD, 2012), Eurostat (Eurostat 2012),EMISS (Fed. State statistics service 2013) allowsmaking the following conclusions.3.1 Grouping informationIn OECD.Stat Extracts and Eurostat data is gener-ated in the context of individual indicators, and inthe context of objects or sets of objects of social andeconomic system. In particular, OECD.Stat Extractsand Eurostat, choosing one or another branch, youcan get information on a number of core indicators:employment, the volume of products shipped, fixedinvestment, exports and imports of goods, etc. In Euro-stat (Eurostat 2012) made the transition to a set ofobjects and allocated an aggregate object: high-techindustries and knowledge-intensive services. Theseare available as this aggregate object and on itscomponents. It means that in the analysis not only135Table 1. Hierarchy of statistical indicators, their advantagesand disadvantages.Level ofStatisticalInformationSystem ofStatisticalIndicatorsDescription of the StatisticalIndicators System and TheirAdvantages and DisadvantagesInternationallevelOECD.StatExtracts,Eurostat,Statisticaldatabase ofUnitedNationsEconomicCommissionfor EuropeInteractive statistics databaseUnited States, the EU and othercountries: South Korea, Japan,etc. Individual indicators dataare available in Russia. Allowsto make a comparisons betweencountries. Contain a wide rangeof indicators. There areindicators by sectors ofeconomy, including thefollowing groups: high, mediumand low-tech-industryperformance science, smallbusiness, etc. However, noindicators characterizinginnovation enterprises.Statelevel ofRussianstatisticalinformationEMISS,statistical-database“Rosstat”,statistical-databaseHighEconomicschoolEMISS – interactive database ofRussian statistics. Contains anumber of forms, dedicated tothe innovation statistics andLCM, but detailing these formsin the context of FEA andregions being formed.Database Rosstat gradually loseits relevance as largely duplicatedata available in EMISS.Database High Economic school –contains detailed informationon innovation activities(including in the context ofhigh-tech, low-tech and mediumtechnology industries), Science,Education, etc. However, in theuncomfortable and poorlyrepresented regional detail data.DepartmentalstatisticsRussiaStatistics ofUniversitiesandR&DorganizationIssued separate statistical reportat Russia or regions. No way toget the data through the Internet.Uncomfortable at work.included industry, but also associated with industriesof different types of services.EMISS here is far behind. Currently, data gener-ation is a cross-sectional indicators (individual char-acteristics) of socio-economic system of Russia. Thetransition to the formation of cross-sectional dataobjects or their combination is not performed.3.2 Hierarchy and aggregation objectsIn OECD.Stat Extracts (OECD, 2012) multilevel hier-archical system of objects were designed (at the levelof economic activity). It is very easy to navigate andreport data, as each level can be opened in a separatelist. Upper level – the whole economy. Second level:manufacturing, services, etc. The third and fourthlevels – their detail FEA. In addition, there are specialaggregations of:– High, medium-low technology industries and theeconomy;– Collar business sector, etc.Ensure the formation of tables with different param-eters on a user-defined set of these objects. It is veryconvenient for the study of structural proportions ofthe economy and their changes over time (UNESCO1984a,b).In EMISS this hierarchy cannot be viewed. Lists ofindicators grouped concerning the types of economicactivities, set of indicators of each list may differ, andunloading a single table containing data on agricul-ture, manufacturing and services sector is not possible.Moreover, in detailing the manufacturing industry,a list of 845 types of economical activities withoutany hierarchy is very inconvenient. Also there is noaggregation of high, medium and low-tech-industries.3.3 Length of comparable time series data and thereflection of recent yearsEMISS allows analyzing comparable data on indus-try in Russia since 2005. Currently available datafor 2011–2012 is their operational update. InEurostat the classification of foreign economic activ-ity has changed in 2008, which led to the rupture andincompatibility of data (data for 1995–2007 , and from2008 till present time). Many indicators from Euro-stat data are available only to 2009 or 2010. In theOECD.Stat Extracts the classification of foreign eco-nomic activity also has changed in 2008, but thereare comparable data for certain indicators over theperiod from 1970 to 2012. However, we must take intoaccount the dynamics of exchange rates, as well asthe integration process, in particular the integration ofwestern and eastern Germany in 1990, the formationof the EU, etc.4 JUSTIFICATION OF PROCESS APPROACHUSING TO THE FORMATION OFSTATISTICAL INDICATORS IN TERMS OFPOSTINDUSTRIAL ECONOMYTraditionally, information in statistical reports isgrouped by sectional indicators (individual charac-teristics) of socio-economic systems. Submission ofinformation on paper was forced to act in this way.The transition to online databases allows applying dif-ferent forms of grouping and presenting information,such as:Grouping sectional indicators (individual characteris-tics) of socio-economic systems;Grouping of objects in the context (i.e. type of eco-nomic activity), social and economic systems;Grouping together in the context of related objects(i.e. science and high-tech industry) socio-economicsystems;Grouping by a major processes taking place in socio-economic systems.136If there is a choice, then what should be the form ofgrouping and presentation of information?They must be consistent with the management ofsocio-economic development of the country or in aparticular territory. Objects of management are notindividual objects of socio-economic system but pro-cesses, including objects and communication systems,as well as interaction with the environment.Therefore, the main direction of improvement ofmodern post-industrial economy statistics should bea gradual transition from the reflection characteris-tics of individual objects to the formation of complexinformation in the context of major socio-economicprocesses.In international statistics, this transition has alreadybegun. OECD.Stat Extracts and Eurostat data arealready formed in the context of object or collectionof objects of social and economic system. At the levelof international statistical classifications of economicactivities are the following aggregated of economicactivities:The information economy;Information and communication technologies;Energy;And others.For example, the aggregate sector, “Informationand Communication Technologies” includes: manu-facture of electronic components and computers, theirsales, software development, telecommunicationsservices [19].In transition to the formation EMISS sectionaldata objects or aggregate have not been implemented.At the same time adding to the data collections EMISS“Indicators of innovation”, “Science Indicators” anddata on investment in the context of economic activi-ties and regions would make a significant step towardssolving this problem and movingon to forming cross-sectional data objects and their combination.The next stage isthe transition to the formationof cross-sectional data in socio-economic processes.In particular, we recommend the following selectionprocess:Production of fuel and energy resources: mining, oilproduction, exports and imports, internal communi-cation and consumption;Agro-industry: agriculture, food processing, exportand import, internal communication and consump-tion;Manufacture of high-tech products: high-tech indus-tries, the corresponding field of science relatedservice industries, exports and imports, internalcommunication and consumption;Manufacture of electrical equipment, vehicles,machinery and equipment for medium technologyindustry high level, the corresponding field of sci-ence, exports and imports, internal communicationand consumption;And other processes.For these processes must be available a wide rangeof indicators to measure effectiveness and capacitydevelopment, including:The volume of products shipped;Gross value added;Number of employees;The wage fund;Innovation indicators;Export/import;Investment in fixed assets.The ability to generate data on all the above param-eters in the context of forms of ownership, includ-ing the identification of: Russian, foreign and jointRussian and foreign ownership, as well as publicproperty; and other indicators.5 CONCLUSIONWe propose the following areas to improve the systemof statistical indicators:1. Provide the ability to analyze processes in theeconomy and aggregation analysis objects,2. Expand the time range of comparable indicators.3. Discuss the expert community to use the processapproach to the formation of a system of statisticalindicators in terms of post-industrial economy.ACKNOWLEDGEMENTThis work was funded within the framework of realiza-tion of Strategic Program on National ResearchTomskPolytechnic University Competitiveness Enhancementin the Group of Top Level World Research andAcademic Institutions.REFERENCESPeter Scott. 2013. Statistical Analysis in the Database.UNESCO 1984a. “Manual for Statistics on Scientific andTechnologicalActivities”, ST-84/WS/12, UNESCO, Paris.UNESCO 1984b. “Guide to Statistics on Science and Tech-nology”, ST.84/WS/19, UNESCO, Paris.OECD 2002. Frascati Manual 6th Edition: Proposed StandardPractice for Surveys on Research and Experimental Devel-opment, Paris: Organisation for Economic Co-operationand Development.UNESCO 2006a, ISCED 1997: International Standard Clas-sification of Education, UNESCO, Paris.OECD.Stat Extracts – URL: http://stats.oecd.org/#Eurostat 2012 – URL.Eurostat methodology 2012 URL.United Nations Economic Commission for Europe statisticaldatabase.Unified Interdepartmental Statistical Information System(UISIS).Federal State Statistics Service.HSE Data Books.Available Alternate Structures/United Nations StatisticsDivision.137

Trends in the development of databases on statistics in the OECD, the EU and Russia

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