Assessment of the reforms and programs results of Ukraine’s economy sustainable development by means of neural networks
-
DOIhttp://dx.doi.org/10.21511/ppm.18(3).2020.07
-
Article InfoVolume 18 2020, Issue #3, pp. 81-92
- Cited by
- 889 Views
-
218 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
It is necessary to choose proper methodology and indicators for assessing sustainable economic development as the information becomes a tool for decision-making support of sustainable development policies and implementation of programs. In Ukraine, evaluating the results of implementation of different programs for development is essential as an analytical basis for making a strategy for the next period and a prerequisite for further progress.
Certain shortcomings of linear models for evaluating the results appeared during the design and implementation of the strategy to manage sustainable economic development. The potential for establishing erroneous targets increases in the formation of strategic objectives for the next forecast period. There is a special need to choose adequate indicators to comprehensively approximate the factors of economic development and evaluation methods that allow more sensitively measuring the results of management decisions in the implementation of the strategy.
The article evaluates the results of the Sustainable Development Strategy “Ukraine – 2020”, employing the potential of the neural network method for a flexible combination of a large number of factors in constructing nonlinear models of impact on the resulting indicator. As a result of applying the neural network model with one hidden layer for evaluation, based on 16 indicators identifying economic, social, and institutional aspects of sustainable development of Ukraine, it was found that institutional transformations contribute most to achieving sustainable development. Reforms in terms of deregulation and support of entrepreneurship, property rights protection, and competitive environment have the most significant positive impact. On the other hand, low efficiency of capital market reforms, implementation of the energy efficiency program, and reform in the field of public procurement determine the need to revise the program of their fulfilment.
- Keywords
-
JEL Classification (Paper profile tab)C45, O11
-
References41
-
Tables3
-
Figures3
-
- Figure 1. Trends in the development of Ukraine’s economy in 2006–2019
- Figure 2. The structure of the model with a neural component with one source to determine the relationship between performance indicators of reforms and sustainable development programs and the volume of GDP per capita in Ukraine
- Figure 3. Graphs of the neural network of communication of indicators of reforms and programs of sustainable development effectiveness and the volume of GDP per capita of Ukraine for 2015–2019
-
- Table 1. Key indicators for assessing the implementation of the Sustainable Development Strategy “Ukraine – 2020”
- Table 2. The value of international indices as an indicator of assessing the implementation of reforms and programs of sustainable economic development of Ukraine in 2015–2019
- Table 3. Gradation of the effectiveness of reforms and programs for sustainable development of the national economy of Ukraine for 2015–2019
-
- Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a Fundamental Cause of Long-Run Growth. In Handbook of Economic Growth 1A (pp. 386-472).
- Andrews, M. (2013). The limits of institutional reform in development: changing rules for realistic solutions. International Review of Administrative Sciences, 79(3), 584-586.
- Bartlett, W., Čučković, N., imir Jurlin, K., Nojkoviǎ, A., & Popovski, V. (2013). Institutional Quality and Growth in EU Neighbourhood Countries (WP5/11 SEARCH WORKING PAPER).
- Bassanini, A., Scarpetta, S., & Hemmings, P. (2001). Economic growth: The role of policies and institutions: panel data. Evidence from OECD Countries (OECD Economics Department Working Papers, No. 283, OECD Publishing).
- Bielientsov, V. M. (2014). Metodychnyi pidkhid do doslidzhennia rozvytku natsionalnoi ekonomiky na osnovi strukturnykh proportsii [Methodical approach to studying the development of national economy based on structural proportions]. Ekonomichnyi visnyk Donbasu – Donbas Economic Herald, 2(36), 50-55. (In Ukrainian).
- Bilozubenko, V., Yatchuk, O., Wolanin, E., Serediuk, T., & Korneyev, M. (2020). Comparison of the digital economy development parameters in the EU countries in the context of bridging the digital divide. Problems and Perspectives in Management, 18(2), 206-218.
- Bruinshoofd, A. (2016). Institutional quality and economic performance.
- Cavalcanti, T., & Novo, Á. (2005). Institutions and economic development: How strong is the relation? Empirical Economics, 30, 263.
- Efendic, A., Pugh, G., & Adnett, N. (2010). Institutions and economic performance: System GMM modelling of institutional effects in transition.
- Falat, L., & Pancikova, L. (2015). Quantitative Modelling in Economics with Advanced Artificial Neural Networks. Procedia Economics and Finance, 34, 194-201.
- Haleshchuk, S. (2016). Shtuchni neironni merezhi u prohnozuvanni valiutnoho rynku [Artificial neural networks in forecasting the foreign exchange market]. Visnyk KNTEU – KNTEU Herald, 3, 101-114. (In Ukrainian).
- Heritage Foundation. (2020). Index of Economic Freedom.
- Jalaee, S. A., Lashkary, M., & GhasemiNejad, A. (2019). The Phillips curve in Iran: econometric versus artificial neural networks. Heliyon, 5(8), 1-6.
- Kharynovych-Yavorska, D. O. (2017). Zastosuvannia neiromerezhevykh tekhnolohii dlia prohnozuvannia konkurentnoi stratehii torhovelnykh pidpryiemstv [Use of neural network technologies for forecasting the competitive strategy of trade enterprises]. Mizhnarodnyi naukovyi zhurnal “Internauka”. Seriia: “Ekonomichni nauky” – International scientific journal “Interscience”. Series: “Economic sciences”, 2(2), 25-30. (In Ukrainian).
- Klepikova, S. (2018). Neural networks application in managing the energy efficiency of industrial enterprise. Neuro-Fuzzy Modeling Techniques in Economics, 7(1), 62-73.
- KMU. (2019). Reformy v Ukraini 2016–2019: Nezvorotnist zmin [Reforms in Ukraine 2016–2019: irreversibility of changes]. (In Ukrainian).
- Koilo, V. (2020). A methodology to analyze sustainable development index: evidence from emerging markets and developed economies. Environmental Economics, 11(1), 14-29.
- Kurnikov, D. S., & Petrov, S. A. (2017). Ispolzovanie neyronnykh setey v ekonomike [Use of neural networks in economy]. Juvenis scientia, 6, 10-12. (In Russian).
- Mints, A., Marhasova, V., Hlukha, H., Kurok, R., & Kolodizieva, T. (2019) Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks. Banks and Bank Systems, 14(3), 86-98.
- Mints, O. (2018). Neural network methods for forecasting the reliability of Ukrainian banks. Neuro-Fuzzy Modeling Techniques in Economics, 7(1), 74-85.
- Morseletto, P. (2020). Targets for a circular economy. Resources, Conservation & Recycling, 153.
- Muradkhanli, L. (2018). Neural Networks for Prediction of Oil Production. IFAC-PapersOnLine, 51(30), 415-417.
- Prokopenko, O., Slatvinskyi, M., Biloshkurska, M., & Omelyanenko, V. (2019). Methodology of national investment and innovation security analytics. Problems and Perspectives in Management, 17(1), 380-394.
- Pryimak, V. I., & Skorupka, D. (2011). Neironni merezhi v upravlinni rynkom pratsi [Neural networks in labor market management]. Aktualni problemy rozvytku ekonomiky rehionu – Actual problems of regional economy development, 7, 260-266. (In Ukrainian).
- Puhachov, M. I., Hrybyniuk, O. M., & Melnyk, A. O. (2015). Prohnoz dynamiky vnutrishnoho valovoho produktu Ukrainy za dopomohoiu neironnykh merezh [Forecast of the dynamics of gross domestic product of Ukraine with the help of neural networks]. Ekonomika APK – AIC Economy, 4, 82-88. (In Ukrainian).
- Pyroh, O. V., & Katan, V. O. (2018). Modeliuvannia ekonomichnoho rozvytku natsionalnoi ekonomiky Ukrainy [Modeling of economic development of Ukraine’s national economy]. Mizhnarodnyi naukovyi zhurnal “Internauka”. Seriia: “Ekonomichni nauky” – International scientific journal “Interscience”. Series: “Economic sciences”, 4(12), 15-20. (In Ukrainian).
- Rudenko, O., Bezsonov, O., & Romanyk, O. (2019). Neural network time series prediction based on multiple year perceptron. Development Management, 17(1), 23-34.
- Sergi, B. S., Popkova, E. G., & Ekimova, K. V. (2020) Dataset of balance of Russia’s regional economy in 2005–2024 based on the methodology of calculation of “underdevelopment whirlpools”. Data in Brief, 31, 1-14.
- Siedaia, A. V. (2011). Neironni merezhi yak zasib doslidzhennia finansovoi diialnosti avtotransportnykh pidpryiemstv [Neural networks as a means of studying the financial performance of motor transport enterprises]. Zbirnyk naukovykh prats NTU – NTU Collection of scientific works, 24(1), 389-392. (In Ukrainian).
- State Statistics Service of Ukraine. (n.d.). Informatsiia ofitsiinoho saitu Derzhavnoi sluzhby statystyky Ukrainy [Information of the official web-site of State Statistics Service of Ukraine]. (In Ukrainian).
- Strilets, V. Yu. (2019). Zabezpechennia rozvytku malykh pidpryiemstv: teoriia, metodolohiia, praktyka [Ensuring the development of small enterprises: theory, methodology, practice] (457 p.). Poltava: PUET. (In Ukrainian).
- Sułkowski, Ł., & Dobrowolski, Z. (2019). Implementing a SustainableModel for Anti-Money Laundering in the United Nations Development Goals. Sustainability, 12, 244.
- The Verkhovna Rada of Ukraine. (2015). Ukaz Prezydenta Ukrainy “Pro Stratehiiu staloho rozvytku “Ukraina – 2020”” [Decree of the President of Ukraine “On the Sustainable Development Strategy “Ukraine – 2020””. (In Ukrainian).
- Ukrainian Evaluation Association. (2017). Ekspertnyi vysnovok pro system monitorynhu ta otsinky Stratehii staloho rozvytku “Ukraina – 2020” [Expert inference on the monitoring and evaluation system of the Sustainable Development Strategy “Ukraine – 2020”]. (In Ukrainian).
- Vitola, A., & Senfelde, M. (2015). The Role of Institutions in Economic Performance. Business: Theory and Practice, 16(3), 271-279.
- Waas, T., Hugé, J., Block, T., Wright, T., Benitez-Capistros, F., & Verbruggen, A. (2014). Sustainability Assessment and Indicators: Tools in a Decision-Making Strategy for Sustainable Development. Sustainability, 6, 5512-5534.
- Williamson, O. E. (2000). The New Institutional Economics: Taking stock, Looking ahead. Journal of Economic Literature, XXXVIII, 595-613.
- World Bank Group. (2019). Economy Profile of Ukraine: Doing Business 2019 Indicators.
- World Intellectual Property Organization. (2019). Global Innovation Index 2019.
- Yu, L., Huang, W., Lai, K. K., & Nakamori, Y. (2007). Neural Networks in Finance and Economics Forecasting. International Journal of Information Technology and Decision Making, 06(01), 113-140.
- Yurynets, Z. (2016). Forecasting model and assessment of the innovative and scientific-technical policy of Ukraine in the sphere of innovative economy formation. Investment Management and Financial Innovations, 13(2), 16-23.