Sizyoongo Munenge | Belgorod State National Research University (original) (raw)

Papers by Sizyoongo Munenge

Аннотация В работе данные по 16 индикаторам инновационной статистики распределены по 83 регионам ... more Аннотация В работе данные по 16 индикаторам инновационной статистики распределены по 83 регионам России в контексте их наличия или отсутствия на уровни 2005,2008,2011 и 2014 гг., что позволило сформировать четыре бинарные матрицы. На их основе построены плотные субматрицы, состоящие из единиц, что говорит о наличии данных по инновационной статистики для выделенных индикаторов и регионов. Так, для исходной бинарной матрицы размерности 83x16 (2005 г.) получена плотная субматрица размерности 36x16, которая на уровень 2014 г. имела размерность 49x16, что говорит об улучшении полноты данных инновационной статистики в разрезе всех регионов России. Как и следовало ожидать, наихудшая ситуация по полноте данных инновационной статистики наблюдалась для северных и восточных регионов, но за десятилетний период наблюдалась положительная динамика по улучшению этой полноты. Abstract. The article presents data on 16 indicators of innovation statistics distributed according to Russian regions with reference to their availability or absence on levels of 2005,2008,2011 and 2014, resulting in the formation of 4 binary matrices. On their basis a dense sub-matrices consisting of units have been constructed which indicate the availability of data on innovation statistics for selected indicators and regions. For an initial binary matrix of dimension 83x16 (2005), a dense sub-matrix of dimension 36 x16 has been obtained through a matrix dimension of 49 x 16 at a level of 2014 indicating an improvement of data completeness of innovation statistics in the framework of all Russian regions. Nevertheless, as expected the worst situation on the data completeness of innovation statistics has been observed for the Northern and Eastern regions, but for the ten-year period there has been observed a positive dynamics on the improvement of data completeness. Ключевые слова: пространственный анализ, статистические данные, инновационные индикаторы, бинарные матрицы, регионы России

The research is aimed at providing the most complete analytcal overview of all versions of the Eu... more The research is aimed at providing the most complete analytcal overview
of all versions of the European Regional Innovaton Scoreboard in view of its
great importance for adaptaton to domestc conditons. The analysis of all
versions of the European Regional Innovaton Scoreboard is made on the basis of the general report of the European Commission with a view to introducing all of its analytcal tools into the domestc scientfc circulaton. It is shown
that the Regional Innovaton Scoreboard is formed on the basis of a matrix of
partal indicators of innovaton actvity, distributed according to their classes
and regions. On the basis of partal indicators, integral indicators of innovaton actvity of regions are calculated every two years, afer which they are
ranked in order of decreasing their innovaton actvity. Such a full descripton
of the European Regional Innovaton Scoreboard is being introduced into the
domestc scientfc circulaton for the frst tme and will be very useful for all
domestc developers of analytcal instruments and methods for assessing the
regional innovatve actvity.

The article presents linear regression equations between the number of organisations of the regio... more The article presents linear regression equations between the number of organisations of the regional innovation infrastructure according to databases of the National Information and Analytical Center for monitoring innovation infrastructure of scientific and technological activities and regional innovation systems and the
Web portal of innovation and business information support ‖Innovations and entrepreneurship‖ on the one part and
the gross regional product on the other part for the years 2007 and 2014. Initially high determination coefficients (
R2 ) obtained, when searching the foregoing relationship, increased still more, when excluding data for the KhantyMansiysk Autonomous District – Yugra and the Yamalo-Nenets Autonomous District, which are statistic outliers. It
is obvious that the Russian regional innovation infrastructure is low-developed, that is why it is not still the engine
for economic growth of regions, but on the contrary, economic strength of regions, their urban infrastructure and
culture are the driver for the development of the regional innovation infrastructure. The regression linear relationships between the number of universities and regional macroeconomic indicators (Gross Regional Product, population) in the regions of Russia was also obtained, which may be used in the planning process of creation, liquidation
or merging of regional or local universities.

We took databases of the National Information and Analytical Center for monitoring innovation inf... more We took databases of the National Information and Analytical Center for monitoring innovation infrastructure of scientific and technological activities and regional innovation systems and the Web portal of innovation and business information support " Innovations and entrepreneurship " , Webometrics database according to rankings of all Russian universities, as well as the database of the Russian Federal State Statistics Service on the gross regional product for all regions of Russia as an empirical basis in order to determine the regression relationship between the number of organisations of the regional innovation and university infrastructure and the gross regional product. Data on the first two innovation databases had been collected as of the end of December 2014, and the distribution of universities according to the Russian regions was made according to Webometrics data (July, 2015) and university websites. Initially high determination coefficients R 2 obtained in the course of searching the relationship between the number of innovation infrastructure organisations and universities according to two databases for all Russian regions were sharply decreasing, when excluding the data for Moscow and Saint Petersburg. The obtained results, if compared with the gross regional product and the population of regions, allow planning the allocation of the university and innovation infrastructure according to regions of Russia. Further, the article also explores linear regression equations obtained between the above mentioned databases number of organisations of the regional innovation infrastructure on the one part and the gross regional product on the other part for the years 2007 and 2014. It is obvious that the Russian regional innovation infrastructure is low-developed, that is why it is not still the engine for economic growth of regions, but on the contrary, economic strength of regions, their urban infrastructure and culture are the driver for the development of the regional innovation infrastructure.

The article presents a matrix of pairwise correlations for 26 regions of the Central and NorthWes... more The article presents a matrix of pairwise correlations for 26 regions of the Central and NorthWest Federal Districts of Russia between ten scientific-innovation and macroeconomic indicators comprising a number of objects of the innovation infrastructure according to two databases, a number of universities, university potential which had been calculated based on Webometric rankings of universities, a number of Scopus-publications in universities of the regions during the year 2015, and, in general, the Gross Regional Product, a number of population, the Gross Regional Product per capita, population density. In all cases there were obtained high values of Pearson's Correlation Coefficient. It has been concluded that a high level of scientific-innovation development of regions is based on a high degree of social-economic development of their urbanized territories that is the Gross Regional Product and a number of population, rather than the reverse.

The Article presents the cross-correlation between scientific-innovation and macroeconomic indica... more The Article presents the cross-correlation between scientific-innovation and macroeconomic
indicators. Three absolute indicators of a number of objects of innovation and university infrastructures and one standardized ratio of the university infrastructure connected with the Webometrics Rankings of universities were taken as scientific-innovation indicators, as well as some macroeconomic indicators.
A high pair correlation has been obtained of all indicators between each other, indicating their
correct selection and analysis.

В статье университетская инфраструктура рассматривается как часть инновационной инфраструктуры, с... more В статье университетская инфраструктура рассматривается как часть инновационной инфраструктуры,
состоящей из инновационных объектов различного вида (производственно-технологические, экспертно-консалтинговые, кадровые, информационные и финансовые организации и компании). В качестве эмпирической основы для установления регрессионной взаимосвязи между количествами объектов инновационной
и университетской инфраструктуры мы взяли базы данных Национального информационно-аналитического
центра по мониторингу инновационной инфраструктуры научно-технический деятельности и региональных
инновационных систем и Портала информационной поддержки инноваций и бизнеса «Инновации и предпринимательство», а также базу данных Webometrics по рейтингам всех университетов России. Данные по
первым двум инновационным базам данных были собраны на конец декабря 2014 г., а распределение университетов по регионам России было осуществлено по данным Webometrics (июль 2015 г.) и сайтам университетов. Первоначально высокие коэффициенты детерминации (R2), полученные при поиске связей между
количествами университетов и организаций инновационной инфраструктуры по двум базам данных для всех
регионов России, резко уменьшались при исключении данных по Москве и Сакнт-Петербургу. При аналогичном регрессионном анализе взаимосвязей между количествами организаций инновационной инфраструктуры по двум базам данных такого резкого уменьшения коэффициента детерминации не наблюдалось.
Полученные результаты, в случае их сопоставления с региональным валовым продуктом и численностью
населения регионов, позволяют планировать размещение университетской и инновационной инфраструктуры по регионам России.

The correlation linear relationships between regional macroeconomic indicators (gross regional pr... more The correlation linear relationships between regional macroeconomic indicators (gross regional product, population) and the number of universities in the regions of Russia was obtained which may be used in the planning process of creation, liquidation or merging of regional or local universities

The article presents comparative analysis of publication activity level of 31 federal and nationa... more The article presents comparative analysis of publication activity level of 31 federal and national research universities of Russia based on Web of Science and Scopus databases for the period from 2006 to 2011. This analysis provided the opportunity to identify three groups of universities according to publication activity increase intervals at the period concerned; to find out annual correlation ratios between the publication activity data of the Russian leading universities received from Web of Science and Scopus databases; to carry out clustering of the Russian leading universities on the basis of the distribution of their publication activity for the year 2011 according to two databases under research; to construct the cross-correlation matrix for the Russian leading universities with the most congruent dynamics of Scopus-publications for the period from 2006 to 2012.

Research Result.ECONOMIC RESEARCH SERIES, 2015

The author discusses the Knowledge Assessment Methodology (KAM) of the World Bank and the underly... more The author discusses the Knowledge Assessment Methodology (KAM) of the World Bank and the underlying
empirical researches. On its basis, the Basic Scorecard for the aggregated and integrated indicators of three SubSaharan country-blocs (COMESA, ECOWAS, ECCAS) is built. On the basis of the scorecard and classification scale of the levels of countries’ development by weighted KAM-indicators, the matrices of the levels of development for the member countries of the regional groups on these indicators for the period from 2000 to 2012 are constructed.
The study has shown that Mauritius has benefited most from COMESA integration, followed by Egypt, Zambia,
Kingdom of Swaziland and Kenya. Among the countries of ECOWAS and ECCAS, Ghana and Burkina Faso have the best positions for the first group of countries respectively, and Rwanda and Cameroon for the second one.
A similar Basic Scorecard for the partial weighted indicators of the above mentioned Sub-Saharan African
integrations is built which allowed to build the matrix of strong and weak points of the knowledge economy of the
countries under study on nine variables belonging to the three realms of the knowledge economy. Among COMESA countries, only Mauritius and Swaziland have shown the knowledge economy strengths.
COMESA has the best aggregated and integral indicators of the regional groups under research; it’s followed
by ECOWAS and ECCAS.
The offered benchmarking tools, adapted for the comparative evaluation of the Sub-Saharan Africa countries’
knowledge economy indicators, can be used by the coordinating bodies of COMESA, ECOWAS and ECCAS countries in management of their global positioning.

The GCI-methodology, which was developed earlier through introduction of a five-level classificat... more The GCI-methodology, which was developed earlier through introduction of a five-level classification
scale for twelve aggregated indicators of global competitiveness of the countries, is used for constructing
the frequency tables of the number of cases of different global competitiveness levels, the matrices of
global competitiveness levels of the countries on twelve aggregated GCI indicators and the matrices of the
strengths and weaknesses of the global competitiveness for three groups of the countries of Sub-Saharan
Africa. It is shown that Mauritius, Rwanda and the Seychelles Islands have the best positions in the global
competitiveness. All these countries are close to reach the lower bound of the high global competitiveness
level.
The authors suggest two variants for calculating the aggregated indicator of global competitiveness
of the arbitrary group of the countries that have led to the close results of the calculations for the three
groups of countries in Sub-Saharan Africa. The calculations showed that the above mentioned aggregated
indicators for COMESA countries exceeded those for countries of ECOWAS and ECCAS, and for the last
two countries these indicators were almost the same.
Кey words: GCI methodology; twelve aggregated indicators; five-level classification scale; COMESA;
ECOWAS; ECCAS.

THE ANALYSIS OF GLOBAL COMPETITIVENESS OF SUB-SAHARAN AFRICA COUNTRIES BY MEANS OF GCI-METHODOLOGY-АНАЛИЗ ГЛОБАЛЬНОЙ КОНКУРЕНТОСПОСОБНОСТИ СТРАН СУБСАХАРCКОЙ АФРИКИ С ПОМОЩЬЮ GCI-МЕТОДОЛОГИИ. Available from: https://www.researchgate.net/publication/272162627_THE_ANALYSIS_OF_GLOBAL_COMPETITIVENESS_OF_SUB-SAHARAN_AFRICA_COUNTRIES_BY_MEANS_OF_GCI-METHODOLOGY-____C____GCI- [accessed Apr 4, 2015].

On the basis of open access sources, the methodology of comparative analysis of the national scie... more On the basis of open access sources, the methodology of comparative analysis of the national scientific and educational systems was elaborated. It has been verified for example on the countries of Sub-Saharan Africa. Based on open access data matrix (N^) with a dimension (m><n) is constructed. In which Ny represents the number of objects (Universities, Research Centers, OA repositories, OA journals, Scopus journals) of ith country, belonging ith objects type, m is the number of countries, n is the number of objects type. With the aim of clustering Sub-Saharan African countriesin terms of the degree of the development of their scientific and educational potential based on the matrix (N^), we introduced an integral indicator in form of a vector n, = (r, n;) where R = Sr p is the number of non-zero elements in the ith line of matrix (N^). Coordinate N; shows the aggregated potential of scientific and educational system in selected set of indicators and p is the degree of diversification of the system. Based on the calculation of the vector Ni, we identified 7 clusters of Sub-Saharan African countries. The first two leading clusters were South Africa, Nigeria, Kenya, Tanzania, Ghana, Sudan, Ethiopia and Uganda.

Conference Presentations by Sizyoongo Munenge

Для 26 регионов Центрального и Северо-Западного федеральных округов России построена матрица по п... more Для 26 регионов Центрального и Северо-Западного федеральных округов России построена матрица по парных корреляций между десятью
научно-инновационными и макроэкономическими индикаторами, включающими в себя количества объектов инновационной инфраструктуры
по двум базам данным, количество университетов, университетский
потенциал, рассчитанной на основе вебометрических рейтингов университетов, количество Scopus-публикации в университетов региона в
2015 г. и в целом, РВП, численность населения, РВП на душу населения и плотность населения.
Во всех случаях получены высокие значении коэффициент корреляций Пирсона. Сделано заключение, что в основе высокого уровня научно-инновационного развития регионов лежит высокая степень социально-экономического развития их урбанизированных территорий, то
есть РВП и численность населения, а не наоборот.

The paper presents comparative analysis of publication activity level of 31 federal and national... more The paper presents comparative analysis of publication activity level of 31 federal and national research universities
of Russia based on Web of Science and Scopus databases for the period from 2006 to 2011.This analysis pro
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vided the opportunity to identify three groups of universities according to publication activity increase intervals at the
period concerned; to find out annual correlation ratios between the publication activity data of the Russian federal and
national research universities received from Web of Science and Scopus databases; to carry out clustering of the Russian
federal and national research universities on the basis of the distribution of their publication activity for the year
2011 according to two databases under research.

Research Result.ECONOMIC RESEARCH SERIES, 2015