Assessment welfare of the population in the synergetic system of socio-economic exclusion

Abstract

The research confirmed that in order to carry out a comprehensive multi-level diagnosis of the well-being of the population, it is necessary to systematize the most important social, economic and other types of indicators. The authors developed a multilevel approach to implement the tools for diagnosing the population welfare: at the international, national, regional, territory and local levels. It is proved that socioeconomic exclusion is the result of the law of the synergetic development of the system, that is, a law that expresses internally necessary, stable connections between elements of the system that determine the level of system synergy. The system of estimation indicators of population welfare is generalized taking into account its multilevel dimension. The complex of factors is determined, under the influence of which the level of population welfare changes. The authors develop methodological and applied aspects of the choice of a multilevel approach to estimate the population welfare through the neutralization of the negative effects of exclusion and the promotion of population improvement in different regions. The conducted studies allowed to confirm the results obtained regarding the choice of instruments to overcome existing problems and disproportions in society. The introduction of a multi-level approach to evaluate the population welfare could directly affect to the level of life quality of the population and progressive socioeconomic development. Avoidance of regional disproportions in development and rejection processes taking place in the state is possible taking into account from the different point of views on the specificity of socioeconomic development of each region. Monitoring of the coherence of the public communities actions and governmental organizations could ensure the achievement of a high quality living standard of the population, which enable each individual member of the community to implement their own rational-selfish development goals, which trigger the loop of feedback with a multiplicative positive effect.

Creative Analysis of Innovation as a Catalizer of Socialization of Structural Change

“…Historically, Kremenchuk is included to Kremenchuk agglomeration (Horishni Plavni (Komsomolsk) -Kremenchuk -Svitlovodsk), the one agglomeration in Ukraine that is located in two regions together. Table 4, that confirms general inherent tendency for Ukrainian economy as a whole and for machine-building industry in particular and dependence on external factors, that are determined by city`s specificities of urban functions (Druzhynina et al, 2018). There has been a reduction of output in 2015 relatively to 2013 PJSC `Kryukovsky Railway Car Building Works` and PrJSC `Kremenchuk Plant of Road Machines` that caused reduction of average number of staff members and capital investments; including project works, R&D, modernization, equipment renovation in machine-building enterprises in Kremenchuk, in 2017 increasing of output relatively to 2015 caused increasing of capital investments.…”

Changing patterns of business behavior: The Ukrainian experience of adaptation of global accounting and financial mechanisms

“…Methods of measuring and evaluating business behavior are focused on the use of accounting engineering tools [13]. The pattern of business behavior and its accounting methodology is focused on the use of an integrated system of financial, managerial and strategic accounting with transactional managerial and strategic accounting (Figure 1).…”

“…The system of integrated accounting with transactional management and strategic accounting is built on a structured plan of accounts with integration on three levels [13]:…”

Country’s health profile: Social, economic, behavioral and healthcare determinants

“…The most informative indicators were for countries with a convergent socio-economic trend (Vasylieva et al, 2020;Bondarenko et al, 2020). It is also necessary to consider the multilevel measurement of socio-economic determinants (Druzhynina et al, 2018;Kyrychenko et al, 2018) and the existing relationship (Halaskova & Bednar, 2020;Luchko, 2019;Pupavac et al, 2020). Antosova et al (2019) and Baranowski & Jabkowski (2021) identify indicators depending on household income clusters.…”