I. Yu. Yegorov, Yu. O. Ryzhkova

System for Monitoring Operation of Industrial Parks as an Instrument to Secure Budgetary Support for Innovation and Investment Activities

Ukraine urgently needs to address the issues related with building the innovative economy as a source of sustainable growth, smoothing out regional disparities, eliminating the depressed condition of the industry.

The purpose of this article is to substantiate the rationale for developing a system for monitoring of operation of industrial parks in Ukraine, with outlining the nomenclature of relevant indicators and monitoring procedure.

Monitoring of operation of industrial parks in Ukraine is supposed as a source for analytical information for local and central administrative offices responsible for making t legal decisions pertaining to innovation and investment, as well as other public administrations and stake holders (businesses, research organizations etc.).

A database of industrial parks in Ukraine is built by the authors, containing detailed information on the existing projects of industrial parks in Ukraine and the proposals to create new ones.

The nomenclature of indicators for monitoring consists of seven modules including absolute and relative indicators. The indicators can be used to derive generalized assessments of the efficiency of regional investment and innovation capacities, their impact on competitiveness and sustainable development in an area, which is essential for forecasting and building investment and innovation strategies in Ukrainian regions. Also, a system of score indicators is proposed, which uses relative change in the values of parameters, thus allowing for monitoring developments in industrial parks over time and mitigating, in a way, negative effects. For taking management decisions to support an industrial park, by-phase procedure of monitoring is proposed. Criteria for ranking of industrial parks are given, to check correlations between legally fixed and really pursued objectives of a park.

Key words: industrial park, system of indicators, monitoring, assessment criteria.


О. B. Khotetovska

Peculiarities of Existing Methods for Ranking of Banks by Competitiveness Level

Pressing methodological issues of estimating bank competitiveness are considered, values of competitive edges of a bank at bank services market are investigated. Ranking of competitive position of a group of Ukrainian financial institutions is made by level of dynamic competitive edge.

Ranks of Ukrainian financial institutions are given by level of the integral index of their dynamic competitive edges during 2003–2008. As follows from the analysis, the higher is the estimated index of dynamic competitive edge of a bank, the higher is the level of its competitive dynamic edge in relation to its competitors and, accordingly, its rank.

The dynamics of competitive position of selected Ukrainian banks is analyzed by index of growth rate. The Ukrainian “PRIVATBANK” is found to take the top rank among 178 Ukrainian financial institutions by most part of indicators.

Analysis of the core indicators of the Ukrainian bank sector for 2011–2013, made by rating agencies, allows for concluding that the performance of a major part of the Ukrainian banks was capable to secure stability of the national financial system. Statistics of rating groups covering the sample of active banks enable to compare them in various periods of time. Therefore, studies of competitive edge of a bank, estimation of the competitive position of a group of banks offer a valuable tool for rating agencies, investors, bank clients and the bank sector as a whole.

Key words: competitive position of a bank, estimation of the dynamic competitive edge of bank, ranking scale, ranking of banks by level of integral criterion of dynamic competitive edge.


G. V. Holubova

Statistical Analysis of Main Socio-Economic Indicators of Ukraine in 2007–2013

The purpose of this work is analysis of the main socio-economic indicators of Ukraine in 2007–2013.

A statistical analysis of selected socio-economic indicators shows the following trends: 2009 vs. 2008, GDP declined by 3.7%, industrial output decreased by 17.7%, construction output – by 41.1%, freight turnover decreased by 23%, passenger turnover – by 12%, exports decreased by 42%, imports – by 49%; wage arrears grew by 31% in 2013 as compared with 2012.

GDP grew in 2013 by only 2.3% compared with 2012, and by 52.3% compared with 2008. 2013 vs. 2012, construction output reduced by 5.5%, industrial output dropped by 0.85%, passenger turnover reduced by 3%, and freight turnover – by 4%. Commodity exports and imports declined by 8.9%. The only growing sectors in 2013 vs. 2012 were agriculture (with 20-percent growth in output) and retail trade (10-percent growth in turnover); wage arrears increased by 14%.

Comparative analysis of per capita GDP in Ukraine and selected European countries is made, showing that its measure in Ukraine is several times lower than in the EU.

According to estimations of the purchasing power of Ukrainian hryvnia, its purchasing power fell by only 0.5% in 2013 relative to 2012.

The rate of growth (reduction) of nominal and real wages per worker is estimated. The lead coefficient shows a slightly lower growth in real wages relative to nominal wages in 2013.

To analyze financial dependence and investment attractiveness of Ukraine, per capita debt in Ukraine, factors of growth (reduction) in financial dependence and lead coefficient are estimated.

The analysis confirms effects of the crisis of 2008. The growth rate of per capita external debt was significantly higher in 2008–2009 than the growth rate of per capital direct foreign investments. Yet, in 2013 the investment attractiveness of Ukraine increased, with the growth rate of per capital foreign direct investment being 5,2% higher than the growth rate of per capita external debt.

The key problem for Ukraine still lies in poor economic development, poverty, social insecurity, lack of programs for young family support etc. The situation is aggravated by the huge debt being significantly higher than the investment attractiveness, imbalance of foreign trade with the resulting shortage of foreign currency badly needed as domestic reserves or for repayment of external debt.

Keywords: GDP, consumer price index, average nominal and real wages, external debt, foreign direct investment.


V. S. Mikhailov, L. L. Poltavets

Approaches to Statistical Forecasting of Energy Resources Consumption

Main methods and models for statistical forecasting of final energy consumption are constructed. Use of the linear model in the forecasting is substantiated. Estimates for energy resources consumption in Ukraine in 2013–2016 are given.

It is argued that energy resources supply to meet the needs of domestic customers constitutes a challenge for Ukraine. Facing this challenge requires thorough studies of energy resources use, with making correct and theoretically sound forecast of the amounts of their supply, final consumption, and stocks in future.

The study aims to build a model that would allow for estimating expected amount of final consumption of energy resources by type and main consumer groups, to use it for estimating energy consumption. This objective involves solution of the problems: (i) constructing a model for final consumption of energy resources on the basis of data from energy balance; (ii) statistical forecasting of the amounts of final energy consumption on the basis of the constructed model.

The linear regression model built on the basis of the analysis can be regarded as robust. It allows for conclusion about weak, although significant, negative linear correlation, which shows declining average annual final consumption of energy resources in Ukraine. The regression model is used to estimate expected final energy consumption in Ukraine for the next time span, 2013–2016.

The results of forecasting final consumption of energy resources in Ukraine allow for the assumption (with 95-percent probability) that the short-term period (2013–2016) will feature the declining amounts of energy consumption by household sector, in parallel with the increasing use of energy resources by transport. Consumption of coal and natural gas will be shrinking, in parallel with the growing consumption of bio-fuel and wastes. Expected consumption of crude oil and oil refinery products, electric and thermal energy will be virtually unchanged. This lays a solid ground for elaboration of relevant administrative decisions.

Keywords: statistical prediction, the final consumption of energy, linear model, the short-term, the energy balance.


G.  M. Yurchyk, N. M. Samolyuk

Analysis and Mechanisms for Poverty Elimination in Ukraine

Poverty as a social and economic phenomenon is inherent in all countries. The concepts of absolute, relative and subjective poverty being used globally, its official definition in Ukraine is based on the relative poverty concept.

Objective and subjective criteria are used to the existing poverty concepts. The most common objective criteria of poverty are considered to be the poverty line. Today, the two main approaches to its definition are used, absolute (baseline) and relative (statistical). In Ukraine, absolute and relative edge of poverty is approved in accordance with the living standard and 75% of the median per capita income. In addition to the poverty edge, the absolute daily consumption (less than $ 5 U. S. purchasing power parity), nutrition value of the daily diet (less than 2100 kcal) and the share of spending on food (more than 60% of the total spending) are included in the  absolute criteria. However, subjective poverty measure is based on the results of the citizens’ own judgment of the consumption capacity of their incomes.

Statistical analysis of poverty in Ukraine by the objective criteria shows the highest poverty when measured by the share of expenditures on food (41.5% in 2011), and the lowest poverty when measured by daily consumption by the UN method (4.0% in 2011). The most vulnerable categories from the viewpoint of poverty risk are households with two or more children. However, the subjective poverty is significantly higher (more than 60% in 2011.). Taking this into account, the policy-making on poverty elimination should rely upon the objective criteria for its identification.

The mechanism for poverty elimination in Ukraine is composite because it involves both passive and active policy tools. Inactive (passive) programs for poverty elimination are focused on fighting poverty by targeted financial support for the poor while active programs are meant to eliminate the poverty by promoting employment and entrepreneurship.

The study of conditions and procedures for targeted social support in Ukraine shows that its eligibility is based on criteria such as a ratio of income to the living wage (to help low income families) and the share of spending on housing services (subsidy). There is a divergence in Ukraine between the criteria of poverty derived by its comprehensive evaluation method, and eligibility criteria for targeted social support. It results in low efficiency of the existing targeted social support programs, because eligibility criteria in Ukraine are significantly reduced comparing to the defined objective criteria of poverty. Effective elimination of poverty in Ukraine is not possible without stimulating economic activity by active policy instruments. Unfortunately, current economic tendencies in Ukraine are not quite optimistic. Therefore, on poverty elimination effort in Ukraine needs to be backed by comprehensive targeted programs encompassing fiscal, investment, monetary and credit spheres, and by fighting the shadow economy.

Keywords: poverty, poverty, poverty assessment, poverty threshold, living wage, absolute poverty and relative poverty.


E. V. Chekotovsky

Relative Statistical Indexes: History and Theory.

Part III. Evolution of Types of Relative Statistical Indexes and Their Classifications

The paper deals with historical aspects of initialization and development of relative statistical indexes theory on the basis of literature review.

The most important function of relative indexes is to provide comparison of statistical data. Comparison as a means of cognition has the especially important role in statistics, as no generalization or analysis can be made without statistical data comparisons. Comparison in statistics is based on calculations of relative indexes.

It is emphasized that although relative indexes were widely used in the earliest statistics research, their theoretical substantiation as a particular statistical category arose much later.

The first attempt to investigate theoretical background of relative statistic indexes was made by German professor O. Etingen, who in his work “Moral Statistics and Christian Ethics”, published in 1868, for the first time showed the necessity and the meaning of the so called relative (proportional) numbers; he was the first to describe calculation methodology for four types of relative indexes. He considered such important theoretical issues as choice of bases for comparison, their expression forms? and made first steps towards the classification of relative indexes.

Nowadays there is no single approach to definition of the main methodological notions of relative statistical indexes. There has been no single or conventional definition of this statistical category; various interpretations and even names of it are given. The two main approaches to its interpretation are shown: (i) relative index is defined as the result of correlation (division) of two values; (ii)  relative index is a numerical measure of comparison of two statistical  indexes. A revised definition of relative indexes is proposed on the basis critical analysis of these interpretations.

Evolution of expression forms and types of relative indexes is shown, with reference to main conditions and principles for construction and applications of relative statistical indexes and changes and additions in the expression forms and types of relative indexes.

Following the analysis of advantages and disadvantages of relative statistical indexes classification given by various authors, the classification is proposed by the following criteria: content of compared absolute indicators, algorithm, time feature, additivity, functional purpose.

Key words: relative statistical indexes, comparison, relation, correlation, calculation method, expression forms, types of relative indexes, principle for construction and applications of relative statistical indexes, classification.