The impact of economic shadowing on social development: challenges for macroeconomic stability

Abstract

Purpose. To develop a model of investigating the link between the level of shadow economy and indicators of social development of the country. Methodology. The study of the relationship between the analyzed indicators was conducted using general and special research methods. The established hypotheses were tested using VAR/VEC modeling. Dickey-Fuller test, the Phillips-Perron test, Joansen test are used in the work. Findings. The paper identifies the risks of the shadow economy for social indicators of macroeconomic stability. Based on the analysis of the countrys social development indicators, indicators were identified that are most sensitive to changes in the level of the shadow economy which are: Gini coefficient, average income ratio of 10% of the richest to 10% of the poorest, average income ratio of 20% of the richest to 20% of the poorest sections of the population, Human Development Index, gross average wage. The EU countries and Ukraine are identified as the statistical base of the study and the assessment period is 20052020. The results of modeling proved the relationship between the level of shadow economy and indicator of social development. Originality. The approach to assessing the relationship between the level of the shadow economy and indicators of social development of the country by considering the indicators that most fully characterize the level of social protection and material well-being of the population has been improved. Practical value. The scientific contribution of the paper is that existing research on the impact of shadow economy on the level of social development of the countries remains fragmented, as well as studies assessing its effect on the macroeconomic stability. The impulse response function constructed by the authors may provide some insight into better understanding of the indicators of social development, the most sensitive to the shadow economy shocks. The results of estimation can be used for practical or scientific purposes.

The public debt of Ukraine in the economic development policy in the war and post-war periods: Bibliometric analysis

“…For public debt not to burden the country's economy, it is essential to increase the rate of economic growth, stimulate business, and create acceptable conditions for business operations. Therefore, Lyulyov et al (2021) and Tiutiunyk et al (2022) focused on reducing the economy's shadowing level. This help to establish an effective policy of economic development and reduce the public debt due to the receipt of all (shadow) funds to the budget.…”

Technological Innovation Efficiency in China: Dynamic Evaluation and Driving Factors

“…The scientific and technological output is expressed by published scientific and technological papers [39][40][41] and the number of patent applications. The economic output is expressed by the sales income from new products of industrial enterprises above the designated size, and the environmental pollution index calculated by the entropy method is used to express non-expected output.…”

“…where srd i,t represents the stock of investment of region i in technical renovation at year t; rd i,t represents the annual added investment in technical renovation or R&D activities in region i at year t; srd i,t−1 represents the stock of investment of region i in technical renovation at year t − 1; φ represents the depreciation rate for investment, which is generally set up as 15 percent. The scientific and technological output is expressed by published scientific and technological papers [39][40][41] and the number of patent applications. The economic output is expressed by the sales income from new products of industrial enterprises above the designated size, and the environmental pollution index calculated by the entropy method is used to express non-expected output.…”

Interaction between health insurance, household income, and public health financing in Ukraine

“…The innovative activities of insurance companies are based on four industrial revolutions. All this made it possible to transform healthcare services and adapt to new forms of medical care organization, for example, telemedicine, the context of their impact on forming the national health insurance system (Tiutiunyk et al, 2021(Tiutiunyk et al, , 2022Yarovenko et al, 2021). The use of artificial neural networks, artificial intelligence, big data, and smartphone software is another promising direction of innovation that will improve the quality of information, consultation, and reference services in the health insurance field (Starostina et al, 2020;Vieriezubova & Levchenko, 2017;Zhuravka et al, 2021).…”