MODELLING STRUCTURAL RELATIONS BETWEEN FINANCIAL, SOCIAL-ECONOMIC AND HEALTHCARE FACTORS

: Relevance of the article topic consists in the fact that it is critical to analyse relations between financial, social-economic and healthcare determinants for the post-pandemic recovery of macroeconomic stability. Consideration of these relations can provide complex recovery strategies, which will contribute to economic, medical amd social resiliemce. The latter is extremely important when there are new global challenges to overcome. The manuscript aims at identifying structural and functional connections between social-economic determinants that influence the healthcare sector. To achieve this purpose, we used the following research methods: induction and deduction, analysis and synthesis, comparison and logical generalisation, table and diagram visualisation, observation and assessment, method of principal components, structural modelling. The research data are taken from the World Bank. Totally, the paper analyses statistical data from 15 European countries in 2000-2021. The modelling input data comprise 18 indexes (financial, social-economic, healthcare ones). The constructed structural model provided the following conclusion: if healthcare indexes increase by 1, social-economic development decreases by 0.014. One of the reasons for that is the negative mortality value. The most important aspect is an efficient reaction to epidemics and pandemics. Development of containment plans, including the healthcare scaling and inter-authority coordination is critical for effective crisis management. We should develop a stable economy and policies to provide an affordable and equal access to healthcare for all population groups. Only a complex approach in each separate country may drop mortality and minimise impacts on different aspects of society life. The obtained results can be applied to improve efficient recovery strategies, raise healthcare standards, make proper decisions for social-economic development.


Literature review
Analysis of the latest publications shows that experts pay a great attention to the financial, economic and healthcare influence over macroeconomic stability.
According to Scopus (2023), the macrostability issue has been quite relevant for 40 years.That is represented on Figure 1.Besides, we produced a Scopus diagram of branches where the macroeconomic stability topic was studied (Figure 3).Let us review some of home and foreign works on the given topic.Macroeconomic stability is regarded as an important aspect of the International Monetary Fund and European Union.In her conference speech in Montreal, Anne O. Krueger (2006) as the IMF first deputy managing director underlines macroeconomic stability is the main requirement to implement IMF reforms.Fischer (1993) conducted research on macroeconomic analysis: inflation, foreign debt, budget deficit.He related the country's macroeconomic stability to its long-term growth.
Kantsir (2016) treats macroeconomic stability via financial one.The latter is a key factor to promote economic growth, price stability and population employment.These processes contribute to national macroeconomic stability.
In his paper on state influence over macroeconomic rise, Kostyk (2016) argues it is state authorities who are responsible for determining main factors of macroeconomic stability.Kuzior et al. (2023) prove that effective informatization of innovative technologies significantly increases macroeconomic indicators and socio-economic development processes.
Rozhko (2017) considers state finances within keeping the macroeconomic stability of the whole country.Inverstigation of financial stability and resilience, especially within the budget security, allowed defining main ways to achieve macroeconomic stability.That concerns a stronger impact of state finances on the socialeconomic development, more budget investments.
Studying the evolution of theoretical pluralism approaches to macroecenomic stability, Mutalymov (2014) singles out the most important directions the modern economy theory focuses on to probe into macroeconomic balance.These directions deal with balance and proportionality of economic processes within the whole national economy (including the aggregate demand and supply, production and consumption).
Nominal instability creates price indefiniteness because inflation does not impact on goods and services evenly.Simultaneously, real instability leads to indefinite and variable consumption: there are no perfect financial markets that provide unlimited use of savings to smooth consumption within income variations.Finally, foreign instability leads to country's solvency indefiniteness.That can create drawbacks on international financial markets, sudden delays of foreign financing with a subsequent crisis of external accounts.Within social welfare, macroeconomic instability will be efficient only in that case when it promotes a much higher GDP rise to compensate for indefiniteness.So, indefiniteness may raise the GDP significantly.That was confirmed by Chomicz-Grabowska & Orłowski (2020), Dufrénot (2023).
Telnova et al. (2023) discuss the notions of macroeconomic stability and stabilisation.The stabilisation policy influences behaviour of all economic entities and efficiency of national economy.Any marketeconomy country aspires to macroeconomic stability.It explains efficiency of all economy development factors and that of state economic policy as a whole.A great attention is paid to macroeconomic consequences: better foreign image, global index ranking, home manufacturing competitiveness, firm enterprise income, budget profits, higher salaries and social benefits, etc. Lagoarde-Segot (2023), Miklaszewska & Wachtel (2023) review the index dynamic change and the country forecast of further economic stability.Today's macroeconomic system in Ukraine is characterised by instability.Therefore, we should urgently develop an effective national strategy to economic growth.The authors analyse the international experience and arrange main principles of macroeconomic stability that are suitable for use in Ukraine.
Moreover, Guo & Zhang (2023) study banking stability and its relation to economic development.They assess key risks, macroeconomic shifts, regulations and innovations.Such results will be useful for scientists, economists and other stakeholders.Ho et al. (2023) and Kuzior et al. (2023) research connections between pandemic and financial stability.Crises turn out to increase risks of no carrying out obligations among enterprises.Governments are offered to found reliable institutions to soften such risks.
Hirose et al. ( 2020) note that poverty is a complex problem.It goes beyond economy and includes social, political and cultural aspects.Thus, poverty overcoming may not be based on economic policies only.On the contrary, it requires a set of properly coordinated measures.In such a way, all-purpose strategies of poverty overcoming can be introduced.Consequently, we should focus on macroeconomic stability because it is important to reach high and permanent growth.So, macroeconomic stability must be a key component of any strategy to reduce poverty.

Methodology and research methods
Macroeconomic stability of any country concerns that state when economy functions without any serious challenges.It is important to ensure a stable economic growth and welfare.Impacts on macroeconomic stability are critical to understand economic situations and make effective political decisions (Fischer, 1993).
The input data for the model generation include 18 indexes.They are divided into three groups: 1) Financial indexes; 2) Social-economic indexes; 3) Healthcare indexes.
The input data table is shown below (Table 1).
Let us analyse the input data change among countries by their means and line diagrams.We will construct diagrams and assess index means in 15 countries.In economics, structural modelling is a method to research relations between economic system elements and impact of different factors on them.It consists in generating formal models that reproduce structure and interaction of key economic components.
Main principles of structural modelling are: -Module division: System is split into separate independent modules.It can simplify developing, testing and supporting: you may work with separate system parts.
-Hierarchic order: Modules are arranged.Upper levels are general abstractions or big system segments.Lower levels reflect interface and realisation details.
-Module independence: Modules must be maximally independent, that is change of one module must minimise impacts on the others.In such a way, the system management is more flexible.
-Interface setting: Each module should have a clear interface with certain relations to other modules.Via this principle, we can support the system with stable interface functions.
-Composition and decomposition: Modules are combined and divided in case of necessity.
-Data flow control: Structural modelling focuses on data flow ways via the system.It determines how data are processed and sent between modules.
Structural modelling allows understanding, analysing and projecting the system.That ensures a proper management of its functions.The approach is applied to improve development processes, which makes the system structure easier and more efficient.
Main stages of structural modelling are: 1. Identification of components: Those factors and relations are defined which influence economy.
2. Formalisation of relations: Mathematical and statistical models are created which reflect interactions between different variables and factors.
3. Calibration and testing: Model parameters are set via real data.Their accuracy is tested.
4. Scenario analysis: The model is used to assess different scenarios and forecast possible variants of economy developments.
To find a single decision for the structural model, we should assume that some model coefficients are equal to zero because of weak relation to endogenic variable on the system left.In such a way, we have less structural model coefficients.Also, we can reduce them via equating some coefficients with one another and regarding their influence on the formed endogenic variable as the same.Limitations, such as structural coefficient overlay, may be used too Calvo (2003).
In terms of identification, there are three types of structural models: -Identifiable: Each structural coefficient is unambiguously determined via coefficients of the given model form.Number of model parameters is equal to parameters of the given model form.
-Non-identifiable: Number of given parameters is less than that of structural coefficients.Structural coefficients cannot be determined via coefficients of the given model form.
-Super-identifiable: Number of given parameters is more than that of structural coefficients.Via the given coefficients, we can get two or more values of the same structural coefficient.
Structural model is always represented as combined equations.Each of them should be checked.If at least one equation is non-idenifiable, the whole model is regarded as non-identifiable.A super-identifiable model has at least one super-identifiable equation.Identification conditions are checked for each equation.An equation is identifiable if amount of conditioned variables (absent in the given equation but present in the equation set) must be equal to endogenic variables in the qiven equation Fu & Riche (2021).
Since structural modelling can detect functional relations between latent variables, we should include latent variables in our study.We will define connections between them and establish a set of explicit indicators.The general model visualisation is shown on Figure 4.
Before generating the structural model, we should identify latent and explicit variables.The model has three latent variables: FIN (financial indexes), SEC (social-economic indexes) and HEC (healthcare indexes).Let us discuss them in detail.

Mathematically, the regression relation between latent variables is represented as:
Formula 5 shows a practical realisation of structural relations between variables.It is implemented via the Statistica software (2023).Below, there is a corresponding program code.
The model is written via the Path1 language.We can process the model in the Statistica software.It will assess unknown parameters.Figure 9 shows iteration results.StepLen -length of full step during the current iteration; Asterisk -maximal step size has been achieved.
The row "Solution appears to have converged normally" stands for iteration finish.After clicking OK, we obtain the structural modelling results (Figure 10).
Therefore, the healthcare index increase by 1 leads to the social-economic development decrease by 0.014.That is explained by the negative mortality value.

Conclusions
We analysed the notion of macroeconomic stability and its determinants.Modern approaches to macroeconomic modelling were discussed within financial, social-economic and healthcare factors.The paper aimed at modelling structural relations between them.We selected input data of 18 indexes for 15 countries in 2000-2021.The healthcare increase by 1 leads to the social-economic development decrease by 0.014.That is explained by the negative mortality value.Thus, to minimise the mortality impact on population, countries should take measures to raise the healthcare system.
Firstly, access to the high-quality medical aid is an important aspect.Secondly, disease prevention (proper nutrition, physical activity, vaccination) is a an efficient tool for mortality fall.Thirdly, better infrastructure and sanitation contribute to healthcare improvement.Investing in clean water, sewage and waste recycling prevents disease spread and raises sanitary conditions.

Figure 1 :
Figure 1: Publication dynamics on the macroeconomic stability topic for 40 years (Scopus)

Figure 2 :
Figure 2: Top 10 countries in macroeconomic stability research

Figure 3 :
Figure 3: Diagram of branches with the researched macrostability topic

Figure 5 :
Figure 5: The financial indexes Source: Generated by the authors

Figure 6 :
Figure 6: The social-economic indexes Source: Generated by the authors

Figure 7 :
Figure 7: The healthcare indexes Source: Generated by the authors

Figure 8 :
Figure 8: Regression relations between latent variables Source: Generated by the authors

Figure 10 :
Figure 10: The structural modelling results Source: Generated via the Statistica software (2023)

Figure 11 :
Figure 11: The structural modelling results Source: Generated via the Statistica software (2023)

Figure 14
demonstrates a normal probabilistic diagram.It can assess the model adequacy.

Figure 14 :
Figure 14: The normal probabilistic diagram Source: Generated via the Statistica software (2023)

Table 1 : Input data № Index Index full name Unit
Source: Based on the World Bank data(2023)