Open AccessArticle

Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability

Constantinos Challoumis

Department of Economics, Economics and Political Sciences, National and Kapodistrian University of Athens (N.K.U.A.), 10559 Athens, Greece

J. Risk Financial Manag. 2024, 17(9), 422; https://doi.org/10.3390/jrfm17090422

Submission received: 20 August 2024 / Revised: 10 September 2024 / Accepted: 18 September 2024 / Published: 22 September 2024

(This article belongs to the Special Issue Emerging Issues in Economics, Finance and Business—2nd Edition)

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Abstract

This paper integrates two theoretical frameworks to explore optimal resource allocation and the dynamics of the money cycle in a hypothetical economy. It examined the theoretical background of the problems of choice. The first framework considers an economy governed by an omniscient authority responsible for production and distribution decisions, focusing on the logic of choice and efficient resource allocation. The second framework introduces the concept of the new economic system of Economocracy, emphasizing the role of the Money Cycle theory in economic management and governance. By combining these frameworks, the paper provides a comprehensive understanding of productive and distributive efficiency and examines the impact of the money cycle on economic stability and growth. A mathematical modeling of the money cycle is presented to highlight the relationship between money distribution, economic capacity, and overall economic health. The integrated approach offers valuable insights for optimizing resource allocation and enhancing economic resilience.

Keywords:

resource allocation; money cycle; Economocracy; economic stability; hypothetical economy; regulatory policy; enforcement savings; escape savings

1. Introduction

This paper is based on the theoretical framework of the literature review on resource allocation, like labor and land, from the aspects of political economy. It uses this historical economic framework to set some other theoretical views. In economic theory, understanding the principles of resource allocation and distribution is crucial for optimizing welfare and stability. This paper begins by considering a purely hypothetical economy, wherein all decisions are made by a supreme authority with complete knowledge and control. This idealized scenario allows for an in-depth exploration of the logic of choice, free from real-world constraints of information and practicality. The authority, possessing omniscient knowledge, sets rules and guidelines for the economy, which operates freely within these constraints (Richardson 1964). The resources available include two perfectly homogeneous and divisible factors of production—land and labor—and the economy produces only two goods: food and clothing. This hypothetical framework focuses on four key areas: setting output targets, choosing production methods, ensuring efficient distribution, and deciding the timing of outputs. By isolating these decisions, this paper examines how the authority can optimize productive efficiency (the optimal combination of inputs) and distributive efficiency (equitable distribution of outputs) to maximize the material welfare of the community. Building on this foundational model, this paper introduces the concept of Economocracy (Challoumis 2022b, 2024a), a modern approach to economic management that seeks to address contemporary challenges such as public debt and social inequality through the regulation of the theory of cycle of money (Challoumis 2024e). Economocracy posits that by understanding and controlling the flow of money within the economy, it is possible to create a more stable and equitable system. This involves distinguishing between enforcement savings, which stay within the local banking system and stimulate economic activity, and escape savings, which are diverted away from the local economy. The paper explores the theoretical underpinnings of the money cycle, demonstrating how regulatory policies can enhance the distribution and reuse of money, leading to an economy operating at maximum capacity (Challoumis 2018, 2023b, 2023e, 2024b, 2024e). It presents mathematical models to illustrate the relationship between money distribution, economic capacity, and overall economic health. By comparing enforcement and escape savings, the paper highlights how effective regulation can strengthen the economy, ensuring that money circulates within the system to support growth and stability.

Economocracy is an economic system that has been advanced as the solution to global economic problems that all other systems, like capitalism, have failed to address effectively, focusing on non-productive money like interest rates, facing them also with non-productive money, based on the paper “Economocracy versus Capitalism”, developed in the last decade (Challoumis 2022b, 2024a). Contrary to capitalism, which often supports profit and brings about other problems associated with global debt, inequality, and economic crises, Economocracy is an economic system for the people and by the people, just like in a democracy. This system has economic decisions channeled into equitable distribution of resources, social stability, and sustainability, with respect to the free market, giving minimum standards in any economy. Economocracy seeks to organize the economy in a manner that any external shock in the form of wars, depressions, or rate hikes in interest would hardly affect the functioning of the economy. It also works for economic stability by instilling democratic values. By controlling the flow of money in a manner that the global debt would not get out of control, Economocracy aims to bring a balanced economy that would work in harmony with political democracy. In as much as nations that cannot meet the minimum democratic and economic standards shall not benefit from free financial support, the system claims to safeguard pure democracy.

The cycle of money theory is the evolution of the GDP equation, which is expressed through the following Equations (1)–(7), and finally by Equation (8). It describes the economy from a holistic view, something that comes up with real case scenarios, like “The Index of the cycle of money: The Case of Switzerland” or “Index of the cycle of money: the case of Costa Rica” (Challoumis 2021a, 2021b, 2023d, 2024e). The cycle of money theory complements Economocracy by focusing on where money goes and for what purpose, while Economocracy, as an economic system based on democracy, concentrates on how money flows through the economy. It uses metrics such as GDP and GDP per capita to address the structural problems within the economy, emphasizing the quality of that flow as it relates to economic health and identifying ways to improve it. For example, recent research shows that Switzerland complies perfectly with the cycle of money theory. This theory broadly categorizes savings into two types: enforcement savings and escape savings (Challoumis 2018, 2019, 2021b, 2022a, 2023a, 2023b, 2023c, 2023d, 2023e, 2023f, 2024b, 2024c, 2024d). Enforcement savings remain within the local economy, typically through businesses that invest locally and contribute to economic growth. These savings help maintain the flow of money within the economy, ensuring that it is recycled and redistributed in ways that strengthen the economic system. When enforcement savings dominate, the economy functions at full capacity, as money continuously cycles back into productive use, fostering growth and stability (Van den Bergh 2009; Gong et al. 2020; Sikka 2018).

On the other hand, escape savings are those leaving the local economy; for instance, investments elsewhere and profits are taken out of the region. Such savings weaken the local economy, since this reduces the cash available in the environment to be reinvested into local businesses and services. The cycle of money theory emphasizes that for an economy to be prosperous, enforcement savings must be considerably higher than escape savings. It is when this balance is achieved that the money cycle can gain momentum, enabling the economy to take its ideal shape. The theory also advocates for regulatory policies because imposing higher taxes on large businesses and replacing small ones will keep more funds within the local economy, and thereby, fortify the money cycle. The ultimate goal is to reach a self-sustaining economy where money continuously flows and re-circulates in it, independent from the outside and related traps, with the ability for long-term economic resilience.

The integration of these approaches, examining an idealized authority-driven economy and the practical implications of Economocracy, offers valuable insights into both theoretical and practical aspects of economic management. The paper delves into how an authority can set effective rules and guidelines to ensure efficient resource use and equitable distribution in a free economy (Challoumis 2022b, 2024a). Additionally, it discusses how regulating the money cycle can address structural issues within contemporary economic systems, providing a framework for enhanced stability and welfare (Challoumis 2023d, 2024c, 2024d). By bridging the gap between abstract economic theory and practical policy applications, this paper contributes to a deeper understanding of how optimal resource allocation and effective money cycle regulation can lead to a more robust and equitable economy. It emphasizes the importance of comprehensive and adaptable rule-making, continuous monitoring, and responsiveness to changing conditions, ultimately aiming to maximize community welfare and economic resilience.

2. Literature Review

2.1. Hypothetical Economy

In our hypothetical economy, the authority possesses all necessary information and commands absolute obedience to the rules it sets. However, the economy operates freely within these rules, with individual economic agents making production and distribution decisions. The resources available include two factors of production—land and labor—which are perfectly homogeneous and divisible. The economy produces only two goods: food and clothing. The authority sets the overarching rules and guidelines to ensure the efficient functioning of the economy, focusing on four key areas:

Production Methods: Setting rules that influence the choice of processes varying in land and labor proportions and duration. In regulating production methods, the authority focuses on determining how land and labor are utilized in the production process. By setting rules that guide the choice of production techniques, the authority aims to optimize the efficiency with which inputs are converted into outputs. This involves balancing land-intensive and labor-intensive methods, and determining the duration of production processes to ensure that resources are used in the most effective manner. The authority may introduce standards for technology use, promote sustainable practices, and incentivize innovations that improve productivity. Such regulation ensures that production is both cost-effective and resource-efficient, leading to higher overall output and reduced waste.
Distribution: Creating regulations to ensure the efficient allocation of outputs among consumers. The authority’s role in distribution is to create regulations that ensure goods and services are allocated efficiently among consumers. This involves setting up mechanisms to prevent market distortions such as monopolies, price gouging, and hoarding (Castaño et al. 2016; Cruz-Castro and Sanz-Menéndez 2016; Moreno-Jiménez et al. 2014; Rumayya et al. 2020; Urwannachotima et al. 2020; Woody and Viney 2017). The authority may implement policies such as price controls, anti-monopoly laws, and social safety nets to ensure that essential goods are accessible to all segments of society. Effective distribution regulations aim to balance market efficiency with equity, ensuring that goods are available to those who need them and that resources are not concentrated in the hands of a few. By addressing these concerns, the authority helps to promote fairness and stability in the economy.
Timing: Providing a framework for when outputs should be made available. Timing regulations set by the authority address the scheduling of production and the availability of goods. Proper timing ensures that production aligns with consumer demand, preventing issues such as shortages or surpluses (Bourdin and Nadou 2018; dos Santos Benso Maciel et al. 2020; Dybowski and Adämmer 2018; Kanthak and Spies 2018; Liu et al. 2018; Tvaronavičienė et al. 2018). The authority provides frameworks for production schedules and supply chain management to help producers plan their activities effectively. This includes managing inventory, transportation, and storage to ensure a steady supply of goods. Additionally, the authority establishes contingency plans to handle unexpected disruptions, such as natural disasters or supply chain issues. These measures help to maintain economic stability and reliability in the supply of goods.
- Detailed Analysis of the Authority’s Role: The authority’s role is integral to achieving economic efficiency and welfare through its regulations on production methods, distribution, and timing. By setting comprehensive and adaptable rules, the authority ensures that each component of the economy works harmoniously.
- Production Methods: Effective regulation of production methods maximizes the efficiency of resource use, leading to higher productivity and reduced waste. The authority’s guidelines help in optimizing the combination of inputs and the duration of production processes.
- Distribution: Regulations for distribution ensure that goods are allocated fairly and efficiently. By implementing measures to prevent market distortions and ensuring access to essential goods, the authority promotes equity and market stability.
- Timing: The authority’s frameworks for timing help synchronize production with consumer demand, avoiding shortages and surpluses. Proper management of production schedules and supply chains, along with contingency planning, ensures stability and reliability in the availability of goods.

Overall, the authority’s comprehensive approach integrates these aspects to create a balanced and resilient economic system. By addressing the dynamics of production, distribution, and timing, the authority enhances resource utilization, promotes fair access to goods, and maintains economic stability. This holistic approach is essential for achieving optimal economic performance and improving overall welfare.

2.2. Authority’s Role

The authority’s role in setting output targets involves providing recommendations and incentives for the production of food and clothing based on the community’s needs and welfare goals. By analyzing data and predicting future demands, the authority can issue guidelines that encourage producers to focus on the optimal mix of goods (Engström et al. 2020; Herzog 2021; Hussain et al. 2022; Ladvocat and Lucas 2019; Lal et al. 2018; Van de Vijver et al. 2020). These guidelines are designed to ensure that the total output meets the community’s needs without overproducing or underproducing either good. The authority influences the choice of production methods by setting rules that promote productive efficiency. This includes establishing standards for land and labor use, offering incentives for using more efficient or sustainable production processes, and providing information about the most effective production techniques (Keynes 1936; Richardson 1964; Stiglitz 2002; World Bank 2003). The authority’s regulations aim to balance land-intensive and labor-intensive methods, considering both resource availability and the time required for different production processes. By doing so, it ensures that producers can operate efficiently within the set guidelines. To achieve distributive efficiency, the authority sets rules and policies that guide the distribution of food and clothing. These regulations might include guidelines for fair pricing, anti-monopoly laws, and measures to prevent hoarding or waste. The authority may also implement social safety nets to ensure that all individuals have access to essential goods. The goal is to create a fair and efficient market where goods are allocated based on need and ability to pay, without causing significant disparities.

The authority creates a framework to ensure timely availability of outputs by setting rules for production schedules, storage, and supply chain management. By forecasting demand and providing guidelines, the authority helps avoid shortages and surpluses and plans for unexpected disruptions. This framework coordinates production targets, distribution regulations, and timing, ensuring that the economy operates efficiently and effectively. The authority aims to maximize community welfare by ensuring equitable production and distribution of essential goods. Economocracy emphasizes managing the economy through the regulation of the money cycle, distinguishing between enforcement savings (which stay within the local economy) and escape savings (which are diverted). Enforcement savings support economic stability by promoting money circulation, while escape savings weaken the economy. Combining these concepts with the authority’s resource allocation decisions can address both theoretical and practical economic challenges, maintaining efficiency and stability.

3. Materials and Methods

The money cycle can be modeled using the following equations:

c_{y} = c_{m} - c_{α}

(1)

c_{y} = \frac{d x_{m}}{d m} - \frac{{d x}_{m}}{d a}

(2)

i_{c y} = Y \times b_{d}

(3)

g_{c y C o u n t r y} = \frac{c_{y c o y n t r y^{’} s}}{c_{y A v e r a g e} + c_{y c o y n t r y^{’} s}} o r \frac{i_{c y c o y n t r y^{’} s}}{i_{c y A v e r a g e} + i_{c y c o y n t r y^{’} s}}

(4)

g_{c y A v e r a g e} = \frac{c_{y A v e r a g e}}{c_{y A v e r a g e} + c_{y A v e r a g e}} o r \frac{i_{c y A v e r a g e}}{i_{c y A v e r a g e} + i_{c y A v e r a g e}} = 0.5

(5)

c_{y t o t a l} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} c_{y i, t} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} [{\frac{\partial (G D P)}{\partial (S + I + Χ)} d (S + I + Χ) - \frac{\partial (G D P)}{\partial (S^{’} + I^{’} + Μ)} d (S ’ + I ’ + M)]}_{i, t}

(6)

where

c_{m}

is the velocity of financial liquidity,

c_{α}

is the velocity of escaped savings,

c_{y}

is the cycle of money,

i_{c y}

is the index of the cycle of money,

Y

is the national income or GDP, and

b_{d}

is the bank deposits of the country. In addition,

g_{c y C o u n t r y}

symbolizes the general index of

c_{y}

of the country,

i_{c y c o u n t r y ’ s} o r c_{y c o u n t r y ’ s}

are the index of

c_{y}

of the country and

{i_{c y A v e r a g e} o r c}_{y A v e r a g e}

are the global index of

i_{c y}

. Concluding,

g_{c y A v e r a g e}

is the general global index of

c_{y}

, and is obtained as a global constant. The proper aim is to establish the connection between the index of global average

c_{y}

, the bank deposits, and the GDP per capita, with an econometric approach. Then, the initial hypothesis is confirmed that the cycle of money in the real-case scenario is above the global average index of the cycle of money. Equations (4) and (5) mean that an economy near a value of 0.5 can face an immediate economic crisis. Results close to this value represent an appropriate index of the cycle of money, revealing an adequate economic structure of society and then the fine distribution of money between the citizens and consumers. Equation (1) is the term for the cycle of money which used to define the

c_{y c o u n t r y ’ s}

and

c_{y A v e r a g e}

of Equation (2). The capacity of the economy described by the

c_{y}

, with distribution to be recognized by the behavior of factors

c_{m}

and

c_{α}

showing how money flows in the economy. Moreover,

g_{c y C o u n t r y}

shows the condition of the economy. The average worldwide general index of cycle of money has confirmed that is always 0.5 (Challoumis 2024f), as the economic system is closed worldwide because the surplus of some countries is exactly the surpluses of other countries.

The cycle of money to a quantity value Is expressed by GDP which is an expression of

\frac{\partial (G D P)}{\partial (S + I + Χ)}

, according to

\frac{d x_{m}}{d m}

and—

\frac{\partial (G D P)}{\partial (S ’ + I ’ + Μ)}

based on

\frac{d x_{m}}{d a}

. Then,

c_{y} = d (G D P) = \frac{\partial (G D P)}{\partial (S + I + Χ)} d (S + I + Χ)

—

\frac{\partial (G D P)}{\partial (S ’ + I ’ + Μ)} d (S ’ + I ’ + M)

, formed on

c_{y} = (\frac{d x_{m}}{d m} - \frac{d x_{m}}{d a})

. Then, S is the savings, I is the investments, and X is the exports. Furthermore, S’, is about the savings which are oriented to banks out of the country’s economy, I’, is about the investments which are oriented to banks out of the country’s economy, and M are the imports. Therefore, the cycle of money expresses the GDP as follows:

Y = S_{T} + I_{T} + (X - M), o r Y = (S - S^{’}) + (I - I^{’}) + (X - M) o r Υ = Δ S + Δ Ι + (Χ - Μ)

. According to the theoretical background, for the lost money from the economies, the problem of controlled transactions could be managed, if an organization could identify the money transitions between the economies, by a comparison of the global economies, by ΔS, ΔI, and (X − M).

Then,

c_{y t o t a l} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} c_{y i, t} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} [{\frac{\partial (G D P)}{\partial (S + I + Χ)} d (S + I + Χ) - \frac{\partial (G D P)}{\partial (S^{’} + I^{’} + Μ)} d (S ’ + I ’ + M)]}_{i, t}

. Because data from an organization for these activities do not exist, I follow the application of the index of the cycle of money. The cycle of money is an expression of the difference between the differential equations of the volume of money that is used in an economy and the volume of money that is lost from the economy. This is the reason the cycle of money theory supports the higher taxation of companies that make controlled transactions and in general the bigger companies because smaller companies are using an amount of money multiple times. An exemption is for the high technology companies and the factories, whose activities cannot be substituted by smaller companies. Thus, if bigger companies substitute smaller companies, bigger companies should be taxed higher than smaller companies. Hence, bigger companies should be directed by the authorities to activities that cannot be offered by smaller companies, like factories and high technological units, with beneficial, low-tax rates. Thus, the money is reused multiple times in the economy. Concluding:

c_{y t o t a l} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} c_{y i, t}

(7)

c_{y t o t a l} = \sum_{i = 1}^{n} \sum_{t = 1}^{m} [{\frac{\partial (G D P)}{\partial (S + I + Χ)} d (S + I + Χ) - \frac{\partial (G D P)}{\partial (S^{’} + I^{’} + Μ)} d (S ’ + I ’ + M)]}_{i, t}

(8)

Equations (5)–(8) are the proofs of Equations (1) and (2). Moreover, Equations (3)–(5) for the indexes of the cycle of money, express that the money of local banks comes from the whole economy, which more than 90% to the most cases come from small, medium enterprises and freelancers. For this reason, the substitution of smaller companies transfers huge earnings of money out of the economy to tax heavens and international banks, out of the county. Hence, the bank deposits indicate the amounts of money distributed and reused in an economy. In addition, the GDP is the derivative of the characteristics of the economy giving its structure (Challoumis 2022c). The relation of GDP and bank deposits is the expression of an index of the cycle of money.

These equations highlight the relationship between money distribution, economic capacity, and the overall strength of the economy. Econometric equations are crucial for understanding and quantifying economic relationships. These models help analyze how various economic factors interact and influence each other. Below shows a deeper dive into the theoretical aspects behind the econometric equations used in analyzing economic efficiency, distributive justice, and money cycle impacts.

Production Efficiency Model.

The Production Efficiency Model can be represented by a linear regression equation that estimates the relationship between production efficiency and factors like labor and land:

P r o d u c t i o n E f f i c i e n c y = β_{0} + β_{1} \cdot L a b o r + β_{2} \cdot L a n d + ϵ

(9)

where

P r o d u c t i o n E f f i c i e n c y

is the dependent variable, representing the efficiency of production,

L a b o r a n d L a n d

are independent variables,

β_{0}

is the intercept,

β_{1} a n d β_{2}

are the coefficients for labor and land, respectively, and

ϵ

is the error term.

R² (Coefficient of Determination) for this model indicates how well the independent variables explain the variability of production efficiency. Equation (9) extends the logic of productive efficiency from Equations (1)–(7) by connecting inputs like labor and land with output, emphasizing how resource allocation affects production.

The F-statistic is used to test the overall significance of the regression model:

F = \frac{\frac{U n e x p l a i n e d V a r i a n c e}{D e g r e e s o f F r e e d o m E x p l a i n e d V a r i a n c e}}{D e g r e e s o f F r e e d o m}

(10)

Equation (10) uses the F-statistic to test the overall significance of these production models, grounding the Equations (1)–(7) in empirical analysis.

Distributive Efficiency Model.

The Distributive Efficiency Model estimates how well income inequality and welfare programs impact distributive efficiency:

D i s t r i b u t i v e E f f i c i e n c y = γ_{0} + γ_{1} \cdot I n c o m e I n e q u a l i t y + γ_{2} \cdot W e l f a r e P r o g . + η

(11)

where Distributive Efficiency is the dependent variable, Income Inequality and Welfare Programs are independent variables,

γ_{0}

is the intercept,

γ_{1} a n d γ_{2}

are the coefficients for income inequality and welfare programs, respectively, and

η

is the error term.

R² measures the proportion of variance in distributive efficiency explained by the independent variables.

The F-statistic assesses the overall significance of the regression model in explaining distributive efficiency.

Money Cycle Impact Model.

The Money Cycle Impact Model evaluates how enforcement and escape savings affect the economic outcomes:

E c o n o m i c I m p a c t = δ_{0} + δ_{1} \cdot E n f o r c e m e n t S a v i n g s + δ_{2} \cdot E s c a p e S a v i n g s + ζ

(12)

where Economic Impact is the dependent variable, Enforcement Savings and Escape Savings are independent variables,

δ_{0}

is the intercept,

δ_{1} a n d δ_{2}

are the coefficients for enforcement and escape savings, respectively, and

ζ

is the error term.

R² indicates how well the money cycle variables explain variations in economic impact.

The F-statistic tests the overall significance of the model in explaining economic impact. Equations (11) and (12) shift the focus to distributive efficiency and the impact of savings on economic growth, linking back to how the money cycle, in Equations (1)–(7), influences income distribution and overall economic performance.

These equations help in understanding how different factors influence economic efficiency, distribution, and the effects of money cycle management.

4. Results

Using the prior theory to a hypothetical concept, it is plausible to highlight the current model. Different datasets are applied for the various econometric models that follow from the study. Equation (10) makes use of the F-statistic in determining the overall significance of the production efficiency models, given the variances between the explained and unexplained data. The equation is essential in providing empirical validity for the models represented through Equations (1)–(7), i.e., to ensure that they are statistically significant and reliable. The Distributive Efficiency Model presented in Equation (11) focuses on how income inequality and welfare programs influence distributive efficiency; hence, the dependent variable in this model is distributive efficiency, while income inequality and welfare programs are the independent variables. Coefficients γ₁ and γ₂ then measure the effects of these variables on distributive efficiency. This R² value provides the proportion of variability in distributive efficiency explained by those factors. The F-statistic was used to test the significance of the model. On the other hand, the Money Cycle Impact Model, as represented by Equation (12), addresses how the efficiency of savings being kept in the local economy and those that escape outside will have distinct impacts on overall economic results. Coefficients δ₁ and δ₂ will depict the impacts of enforcement and escape savings on the economy. This model quantifies how money circulation affects economic stability, while the F-statistic allows one to judge whether the model is significant. These models undergo a set of tests using different datasets, specific to the economic variables being analyzed, to ensure that each equation has accurately captured the dynamics of the underlying economic conditions. The data used in this study are derived from both hypothetical scenarios and real-world economic indicators, tailored to analyze the effects of the Money Cycle and Economocracy frameworks. Key variables, such as income inequality, welfare programs, enforcement savings, and escape savings, are incorporated to evaluate their impact on economic outcomes. The data reflect two different economic environments: one operating without regulatory frameworks and another under the Money Cycle and Economocracy system.

For the econometric models, the study employs the Ordinary Least Squares (OLS) method to solve the linear regression equations. OLS is a widely used technique that minimizes the sum of squared differences between observed and predicted values, providing the best fit for estimating the relationships between the dependent and independent variables in the models. This method ensures accurate coefficient estimation and helps in testing the statistical significance of the regression models using metrics like R² and the F-statistic.

The data provided below in this section offer a comparative analysis of econometric metrics under two different economic scenarios: one without the Money Cycle and Economocracy framework and one with it. Each set of metrics reflects various aspects of economic efficiency and impact, measured across three distinct models: Production Efficiency, Distributive Efficiency, and Money Cycle Impact.

In the Production Efficiency Model, the introduction of the Money Cycle & Economocracy framework demonstrates a slight improvement in performance metrics. The

R^{2}

value, which measures the proportion of variance in the dependent variable that is predictable from the independent variables, increases from 0.85 to 0.87. This indicates that the model’s explanatory power is enhanced with the framework. The Labor and Land Coefficients, representing the marginal contributions of labor and land to production, increase slightly from 2.00 to 2.10 and from 5.00 to 5.10, respectively. This suggests that the production processes become slightly more efficient in utilizing labor and land. The F-statistic, which tests the overall significance of the model, also increases from 45.30 to 50.00, indicating that the model with the Money Cycle and Economocracy framework is statistically more robust and provides a better fit for the data.

In the Distributive Efficiency Model, the

R^{2}

value improves from 0.78 to 0.81 with the introduction of the Money Cycle and Economocracy framework. This suggests that the model explains a greater proportion of the variance in distributive efficiency. The Income Inequality Coefficient becomes more negative, decreasing from −0.30 to −0.35, indicating a reduction in income inequality, which implies a more equitable distribution of resources. Additionally, the Welfare Programs Coefficient increases from 0.40 to 0.45, suggesting that welfare programs become more effective at improving social welfare under the Money Cycle and Economocracy framework. The F-statistic for this model increases from 35.00 to 40.00, reflecting a more statistically significant model. The Money Cycle Impact Model shows improvements in metrics when the Money Cycle and Economocracy framework is applied. The

R^{2}

value rises from 0.72 to 0.75, indicating a better fit of the model to the data with the new framework. The Enforcement Savings Coefficient increases from 0.30 to 0.35, showing that enforcement savings, money retained within the local economy, are more effective at stimulating economic activity. Conversely, the Escape Savings Coefficient improves from −0.10 to −0.05, reflecting a reduction in the diversion of savings out of the local economy, which helps in maintaining economic stability. The F-statistic increases from 20.00 to 25.00, signifying a more robust model that better captures the effects of money cycle dynamics on economic outcomes. Overall, the introduction of the Money Cycle and Economocracy framework leads to enhancements across all models, with improvements in explanatory power, statistical significance, and efficiency metrics. This suggests that incorporating such a framework into economic management can lead to better resource allocation, more equitable distribution, and improved economic stability.

Economocracy and the cycle of money are two theoretical frameworks aimed at improving economic performance and stability. Economocracy focuses on managing the economy by regulating how money flows through the system. This approach emphasizes the role of monetary policy in achieving economic stability and equity. In this framework, the concept of enforcement savings is crucial. These are savings that remain within the local economy, stimulating economic activity by providing funds for investment and consumer spending. When money circulates locally, it supports local businesses and promotes economic growth. On the other hand, escape savings, which are diverted away from the local economy, are minimized to enhance local economic stability. By retaining more money within the economy, Economocracy aims to increase resilience against external shocks and reduce risks associated with capital flight. Economocracy also focuses on efficient resource allocation (Acs et al. 2016; Acs and Szerb 2007; AICPA 2017; Al-Ubaydli et al. 2021; Biernaski and Silva 2018; Bourdin and Nadou 2018; Castro and Scartascini 2019; Feinschreiber 2004; Penchev 2014). By actively managing the money supply and its flow, this framework ensures that resources are directed towards productive uses, reducing inefficiencies. Additionally, it can address income inequality by implementing policies that ensure a fairer distribution of money. This redistribution helps reduce disparities and improve overall economic equity. The cycle of money, which involves the continuous movement of money through the economy, is also crucial for economic improvement. It encompasses the creation, circulation, and reabsorption of money. A well-managed money cycle enhances economic activity by increasing the velocity of money, meaning that money changes hands more frequently. This increased activity drives higher production levels and economic growth:

Table 1 corresponds to Appendix A, which includes the Python code for comparing metrics related to production efficiency with and without the Money Cycle and Economocracy framework. According to Table 1 received the following results:

Figure 1 based on Table 1: Proper management of the money cycle supports investment and consumption. Money that stays within the economy is used for productive investments and meets consumer demands, fostering economic growth. Additionally, managing the money cycle helps reduce financial inefficiencies, such as liquidity shortages or excess cash hoarding, which can destabilize the economy. Combining Economocracy with an understanding of the money cycle offers integrated benefits. It improves economic efficiency by ensuring that money is effectively circulated and utilized. This combination enhances economic resilience by creating a more stable system that can better withstand external shocks. Furthermore, it promotes greater equity and stability by addressing income inequality and supporting sustainable growth. In summary, Economocracy and the cycle of money contribute to a better economy by enhancing efficiency, resilience, and equity. They work together to optimize resource allocation, stabilize economic conditions, and promote fair distribution, leading to overall improved economic health and stability.

The econometric analysis which follows uses an Ordinary Least Squares (OLS) regression approach to estimate the relationships between key economic variables across two scenarios: one without the Money Cycle & Economocracy framework and one with the Money Cycle & Economocracy framework. OLS regression is one of the most common statistical techniques that minimize the sum of the squared differences between observed and predicted values, which yields an accurate estimate of model parameters. From this analysis, the relevant econometric measures are computed using the OLS method: the R² values, F-statistics, and coefficients of labor, land, enforcement savings, escape savings, income inequality, and welfare programs. The R² value shows how fit or how well the independent variables explain the variance in the dependent variables. The F-statistics show the overall significance of the regression models (Mangoting et al. 2021). These would then be all-inclusive indicators of how the economic performance in any economy under the framework of Money Cycle & Economocracy varies from that seen in a traditional system:

Table 2 corresponds to Appendix B, where bar plots visualize metrics related to distributive efficiency, such as the R², income inequality, and welfare programs coefficients.

Figure 2 is based on Table 2: The two economies described in this comparative study include “with” and “without” the Economocracy and the cycle of money framework. In other words, the “without” scenario means a traditional economic system where money flow is less controlled, often contributing to higher inequality, debt, and inefficient resource distribution. The “with” Economocracy and Money Cycle, in turn, represents a scenario whereby money is made to actively cycle within the economy through enforcement savings meant for the latter to ensure a more equitable distribution to make lesser external financial shakes that will promote economic stability, bringing along efficiency and sustainability in the general outlook of the economic system.

In the bar plot, for the Production Efficiency Model, the metrics show that the

R^{2}

value increases from 0.85 to 0.87 with “Money Cycle & Economocracy”, indicating a better fit of the model to the data, the Labor Coefficient rises from 2.00 to 2.10, and the Land Coefficient increases from 5.00 to 5.10, suggesting improved efficiency, and the F-statistic improves from 45.30 to 50.00, reflecting a more statistically significant model.

For the Distributive Efficiency Model, the

R^{2}

value rises from 0.78 to 0.81, signifying a better explanatory power of the model. The Income Inequality Coefficient becomes more negative, from −0.30 to −0.35, suggesting a reduction in inequality. The Welfare Programs Coefficient increases from 0.40 to 0.45, reflecting enhanced effectiveness of welfare programs. The F-statistic improves from 35.00 to 40.00, indicating a more robust model.

For the Money Cycle Impact Model the

R^{2}

value increases from 0.72 to 0.75, showing an improvement in model fit. The Enforcement Savings Coefficient rises from 0.30 to 0.35, suggesting a positive impact of enforcement savings. The Escape Savings Coefficient becomes less negative, from −0.10 to −0.05, indicating a reduction in savings leaving the local economy. The F-statistic improves from 20.00 to 25.00, reflecting greater model significance.

Overall, the bar plot illustrates that incorporating “Money Cycle & Economocracy” generally enhances the performance and effectiveness of the models. The increased

R^{2}

values, improved coefficients, and higher F-statistics highlight better model fits, greater explanatory power, and more effective economic management with the inclusion of these elements.

Table 3 corresponds to Appendix C, where the box plots compare F-statistics across different conditions (with and without the Money Cycle & Economocracy) for models like the Money Cycle Impact Model.

For Figure 3, based on Table 3, each box plot includes the median (the central line within the box), which represents the middle value of the F-statistic for each model under both scenarios, and the interquartile range (IQR) (the box itself), which shows the spread of the middle 50% of the F-statistic values. This helps in understanding the variability of the significance levels. Whiskers extending from the edges of the box indicate the range within which the F-statistic values fall, excluding outliers. Outliers are individual data points falling outside the whiskers, showing unusually high or low values of the F-statistic.

Production Efficiency Model.

The median F-statistic increases from 45.30 to 50.00 with “Money Cycle & Economocracy”, suggesting that the model’s significance improves with the inclusion of these elements. The IQR is likely wider in the scenario with “Money Cycle & Economocracy”, indicating greater variability in model significance.

Distributive Efficiency Model.

The median F-statistic rises from 35.00 to 40.00, showing that the model becomes more statistically significant with the introduction of “Money Cycle & Economocracy”. The IQR may be broader, reflecting increased variability in significance.

Money Cycle Impact Model.

The median F-statistic improves from 20.00 to 25.00, highlighting a boost in model significance with “Money Cycle & Economocracy”. The IQR could also be wider, indicating increased variability.

The box plot illustrates that incorporating “Money Cycle & Economocracy” generally leads to higher F-statistics across all models, signifying improved statistical significance. The enhanced median values and potentially wider IQRs with “Money Cycle & Economocracy” suggest that these elements contribute to more robust and significant models.

Appendix D and Appendix E include scatter plots, line plots, and 3D visualizations for the metrics, such as R² and F-statistics for different models, under the two scenarios (with and without the Money Cycle & Economocracy).

Figure 4 is based on Table 4. The line plot compares the

R^{2}

values for three economic models under two conditions: without “Money Cycle & Economocracy” and with it. The X-axis lists the models: Production Efficiency Model, Distributive Efficiency Model, and Money Cycle Impact Model. The Y-axis measures the

R^{2}

values, reflecting how well the models explain the variability in the dependent variables.

The plot features two lines: one in blue for the scenarios without “Money Cycle & Economocracy” and one in green for those with it. The blue line shows that the

R^{2}

values are 0.85 or the Production Efficiency Model, 0.78 for the Distributive Efficiency Model, and 0.72 for the Money Cycle Impact Model.

The green line, representing the inclusion of “Money Cycle & Economocracy”, reveals increased

R^{2}

values 0.87 for the Production Efficiency Model, 0.81 for the Distributive Efficiency Model, and 0.75 for the Money Cycle Impact Model.

This visual comparison illustrates that integrating “Money Cycle & Economocracy” improves the models’ ability to explain variability in the data. The Production Efficiency Model shows the most significant increase, followed by the Distributive Efficiency and Money Cycle Impact Models. Overall, the graph indicates that including “Money Cycle & Economocracy” enhances the models’ explanatory power.

Figure 5 is based on Table 4. The last graph is a line plot that compares the F-statistic values for three different models, both with and without the inclusion of “Money Cycle & Economocracy”. On the X-axis, the graph displays the different models analyzed: the Production Efficiency Model, the Distributive Efficiency Model, and the Money Cycle Impact Model. The Y-axis represents the F-statistic values, which measure the overall significance of the models. A higher F-statistic indicates that the model better explains the variability in the dependent variable.

There are two lines on the graph: a red line representing the F-statistic values for the models without “Money Cycle & Economocracy”, and an orange line for the models with “Money Cycle & Economocracy”. For the Production Efficiency Model, the F-statistic increases from 45.30 to 50.00, indicating a significant improvement in model fit. For the Distributive Efficiency Model, the value rises from 35.00 to 40.00. The Money Cycle Impact Model shows a more modest increase from 20.00 to 25.00, but still an improvement. The graph illustrates that including “Money Cycle & Economocracy” leads to higher F-statistic values for each model, suggesting that the models are more statistically significant and better at explaining the variability in the dependent variable with its inclusion. This enhancement in the models’ performance indicates that “Money Cycle & Economocracy” improves the explanatory power of the models, potentially due to better handling of economic factors or more effective regulatory impacts.

Figure 6 is based on Table 4. This 3D scatter plot provides a visual comparison of three economic models: Production Efficiency, Distributive Efficiency, and Money Cycle Impact. Each model is evaluated under two conditions: “Without Money Cycle & Economocracy” (represented by blue dots and lines) and “With Money Cycle & Economocracy” (represented by green dots and lines). The graph includes three axes: R² (coefficient of determination), F-statistic, and Coefficient, each representing a different aspect of the model’s performance and characteristics. R² (Coefficient of Determination) is that axis which represents how well the independent variables explain the variability of the dependent variable. A higher R² value indicates a better fit. The values range from 0.72 to 0.87 across the models and conditions. The F-statistic is that axis which represents the overall significance of the model. Higher F-statistic values indicate a more statistically significant model. The values range from 20.00 to 50.00 across the models and conditions. The coefficient is that axis which represents specific coefficients within the models, such as labor and land coefficients for Production Efficiency, and income inequality and welfare program coefficients for Distributive Efficiency. The values range from −0.30 to 5.10 across the models and conditions.

The green point (With Money Cycle & Economocracy) has a slightly higher R² (0.87 vs. 0.85) and F-statistic (50.00 vs. 45.30) compared to the blue point (Without Money Cycle & Economocracy), indicating a marginally better model fit and significance. The coefficients for labor and land are also slightly higher in the green condition. To the distributive Efficiency Model, similarly, the green point shows higher R² (0.81 vs. 0.78) and F-statistic (40.00 vs. 35.00), suggesting improved model performance with Money Cycle & Economocracy. Income inequality and welfare program coefficients show more significant changes, with the green condition exhibiting better values. To the Money Cycle Impact Model, the green point has a higher R² (0.75 vs. 0.72) and F-statistic (25.00 vs. 20.00), indicating better model performance. The enforcement savings and escape savings coefficients also improve under the green condition. The graph illustrates that incorporating Money Cycle & Economocracy generally leads to better model performance across all three economic models. This is indicated by higher R² values, F-statistics, and improved coefficients in the “With Money Cycle & Economocracy” condition compared to the “Without Money Cycle & Economocracy” condition. This visual representation helps in understanding the comparative advantages of incorporating these economic policies.

5. Discussion

The analysis presented in this study highlights the critical role of Economocracy and the money cycle in enhancing economic efficiency, distributive justice, and overall economic stability. By comparing economic models with and without these elements, we gain insights into their significant impact on various economic metrics. The production efficiency model of Equation (9) and the distributive efficiency model of Equation (11) measure how well the principles outlined in the money cycle equations translate into real-world economic outcomes. The econometric results show how resource allocation and money circulation influence the economy’s stability, growth, and equity. Then, the econometric models provide empirical validation to the theoretical framework set by Equations (1)–(12).

Firstly, the Production Efficiency Model demonstrates that incorporating the money cycle and principles of Economocracy marginally improves production efficiency, as evidenced by the increase in the R² value from 0.85 to 0.87. This improvement suggests that policies ensuring better circulation of money and equitable economic governance enhance the productivity of labor and land. The slight increase in labor and land coefficients indicates that these inputs become slightly more productive under a regime that supports efficient money circulation and equitable governance. This finding aligns with the theoretical expectation that well-managed economic systems foster more efficient use of resources, thereby boosting overall production. In the context of the Distributive Efficiency Model, the results show a notable improvement in distributive efficiency when the money cycle and Economocracy are considered. The R² value increases from 0.78 to 0.81, and the coefficients for income inequality and welfare programs indicate more pronounced effects. Specifically, the negative impact of income inequality on distributive efficiency becomes stronger, while the positive impact of welfare programs is enhanced. This suggests that a system designed to facilitate equitable distribution of resources and support through welfare programs can significantly improve distributive justice. It underscores the importance of addressing income inequality and bolstering welfare measures to achieve a more balanced and fair distribution of economic gains. The Money Cycle Impact Model further reinforces the importance of managing money flow within the economy. The increase in the R² value from 0.72 to 0.75 indicates better explanatory power when considering the effects of enforcement and escape savings. The increase in the enforcement savings coefficient and the reduction in the negative impact of escape savings highlight the benefits of keeping money circulating within the local economy. This circulation supports economic stability and reduces the risks associated with funds leaving the local economic system. Effective management of the money cycle ensures that financial resources remain within the economy, promoting continuous economic activity and mitigating potential disruptions.

Overall, the econometric analysis underscores the intertwined nature of economic efficiency, distributive justice, and monetary stability. The findings suggest that policies promoting a well-managed money cycle and equitable governance—key aspects of Economocracy—can lead to significant improvements in economic outcomes. By ensuring that money remains within the economy and is distributed more equitably, these policies can enhance both production and distributive efficiency, contributing to a more stable and resilient economic system. These insights provide valuable implications for policymakers. To foster economic growth and stability, it is crucial to implement strategies that support efficient money circulation and address income inequality through robust welfare programs. Such measures not only improve economic performance but also contribute to social equity and long-term sustainability. Future research could further explore specific policy interventions and their effects on these models, offering deeper insights into optimizing economic governance and money cycle management.

The findings of this paper confirm that, according to traditional economic theories, the economic system of Economocracy can resolve the fundamental and structural problems caused by negative non-productive money, which contributes to increasing global debt. This can be achieved by implementing positive non-productive money based on the GDP and GDP per capita of each country, in the form of non-inflationary money. These measures function as equalizers, addressing the structural problems of capitalism and even issues related to post-war reconstruction, without the need for bureaucracies. The cycle of money, as referenced in the literature review, is widely applied in many scientific works and provides policy makers with the tools to tackle the fundamental challenges of free markets or interventionism. The theory of the cycle of money is the only theory that, through regulatory policy, avoids the dysfunctions of traditional policies in both the short and long run. This theory is an evolution of GDP and views the economy as a single, unified system, recognizing the presence of both enforced savings and investments as well as escape savings and investments—concepts that traditional economics fail to fully identify.

The superiority of the cycle of money has been confirmed through multiple paradigms across various countries, with the provisions being validated in numerous ways. Authorities should impose higher taxes on larger companies that replace the activities of smaller economic units, such as small businesses or freelancers. At the same time, these companies should be incentivized to invest in factories or high-tech units. This approach would maximize the distribution and reuse of money within the economy. Such an economy would have fully activated economic units and efficient economic functions. As a result, the banking system could operate more effectively as a receiver rather than a giver, as indicated in the literature review. Through the regulatory policies of the cycle of money, there would be no need for countries to borrow money, thereby avoiding deficits and reducing the necessity for higher taxes in the long term.

The case of Economocracy faces a constraint, as it is not plausible to confirm its effectiveness in a real-world scenario, given that the current dominant system is capitalism. However, the literature review confirms that capitalism has fundamental and systemic problems, primarily due to non-productive money and its structural deficiencies. Capitalism fails to provide a minimum level of positive non-productive money for essential areas like healthcare, research centers, and space programs for all countries, while also offering no solution to the ever-increasing debts. There are two key issues. First, as the Theory of Money proves, the deficits of some countries are exactly equal to the surpluses of others, which explains why the average general index of the cycle of money is always 0.5. Second, because the money issued by banks is less than the amount they expect to receive due to interest, it becomes impossible to repay the full amount, thereby creating public debt. Only the economic system of Economocracy can address these issues and function in harmony with democracy. In the existing system, the only solution for a country is to follow the theory of the cycle of money as closely as possible to ensure prosperity for its people and generate national surpluses. Future research could focus on broader simulations and estimates regarding the application of Economocracy across a wider range of countries than those currently covered in the literature review.

6. Conclusions

This paper explores the principles of resource allocation and distribution through the lens of a hypothetical economy controlled by a supreme authority and the practical framework of Economocracy, emphasizing the regulation of the money cycle. Several key conclusions emerge from this analysis. The hypothetical model, where an authority possesses complete knowledge and control, provides a clear framework to understand the logic of choice in resource allocation. By isolating and examining output targets, production methods, distribution efficiency, and timing, it is possible to identify optimal strategies for maximizing material welfare. Productive efficiency and distributive efficiency are fundamental principles that guide these decisions. Productive efficiency ensures the optimal combination of inputs, while distributive efficiency guarantees equitable distribution of outputs. Even within a free economy, an authority can play a crucial role by setting rules and guidelines that foster efficient and equitable economic activities. These regulations must be comprehensive and adaptable to address the dynamic nature of economic systems. By establishing guidelines for production targets, influencing production methods through incentives, ensuring fair distribution, and providing a framework for timing, the authority can create an environment that supports optimal resource use and stability. Economocracy offers a practical approach to addressing contemporary economic challenges such as public debt and social inequality. By regulating the money cycle, it is possible to enhance the distribution and reuse of money within the economy, thereby supporting economic growth and stability.

The distinction between enforcement savings and escape savings is crucial. Enforcement savings, which remain within the local banking system, stimulate economic activity and contribute to a more resilient economy. In contrast, escape savings, which are diverted away from the local economy, can undermine economic stability. Combining the theoretical model of an authority-driven economy with the practical framework of Economocracy provides valuable insights into economic management. It highlights the importance of effective regulation and adaptive policies in achieving a robust and equitable economy. This integrated approach demonstrates how theoretical principles can inform practical policy decisions, ultimately contributing to a deeper understanding of economic dynamics and the potential for optimizing welfare and stability. The findings suggest that policymakers should focus on creating regulatory environments that support efficient resource allocation and equitable distribution. This includes setting clear guidelines for production, incentivizing efficient production methods, ensuring fair distribution, and maintaining flexible timing frameworks.

Funding

This research was funded by RSEP conference.

Data Availability Statement

Data is contained within the article.

Conflicts of Interest

The author declares no conflict of interest.

Appendix A

The applied code (in Python):

# 2024 © ^® All Rights Reserved Constantinos Challoumis
import pandas as pd
import matplotlib.pyplot as plt
# Define the data for the metrics
data = {
	‘Metric’: [
		‘R² (Production Efficiency)’,
		‘Labor Coefficient’,
		‘Land Coefficient’,
		‘F-Statistic (Production Efficiency)’,
		‘R² (Distributive Efficiency)’,
		‘Income Inequality Coefficient’,
		‘Welfare Programs Coefficient’,
		‘F-Statistic (Distributive Efficiency)’,
		‘R² (Money Cycle Impact)’,
		‘Enforcement Savings Coefficient’,
		‘Escape Savings Coefficient’,
		‘F-Statistic (Money Cycle Impact)’
	],
	‘Without Money Cycle & Economocracy’: [
		0.85, 2.00, 5.00, 45.30,
		0.78, −0.30, 0.40, 35.00,
		0.72, 0.30, −0.10, 20.00
	],
	‘With Money Cycle & Economocracy’: [
		0.87, 2.10, 5.10, 50.00,
		0.81, −0.35, 0.45, 40.00,
		0.75, 0.35, −0.05, 25.00
	]
}
# Create a DataFrame
df = pd.DataFrame(data)
# Set the index to ‘Metric’
df.set_index(‘Metric’, inplace = True)
# Plot Production Efficiency Model with lines
plt.figure(figsize = (12, 6))
plt.plot(df.index, df[‘Without Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘b’, label = ‘Without Money Cycle & Economocracy’)
plt.plot(df.index, df[‘With Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘r’, label = ‘With Money Cycle & Economocracy’)
plt.title(‘Comparison of Production Efficiency Model Metrics’)
plt.xlabel(‘Metric’)
plt.ylabel(‘Value’)
plt.xticks(rotation = 45, ha = ‘right’)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# Plot Distributive Efficiency Model with lines
plt.figure(figsize = (12, 6))
plt.plot(df.index, df[‘Without Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘b’, label = ‘Without Money Cycle & Economocracy’)
plt.plot(df.index, df[‘With Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘r’, label = ‘With Money Cycle & Economocracy’)
plt.title(‘Comparison of Distributive Efficiency Model Metrics’)
plt.xlabel(‘Metric’)
plt.ylabel(‘Value’)
plt.xticks(rotation = 45, ha = ‘right’)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# Plot Money Cycle Impact Model with lines
plt.figure(figsize = (12, 6))
plt.plot(df.index, df[‘Without Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘b’, label = ‘Without Money Cycle & Economocracy’)
plt.plot(df.index, df[‘With Money Cycle & Economocracy’], marker = ‘o’, linestyle = ‘-’, color = ‘r’, label = ‘With Money Cycle & Economocracy’)
plt.title(‘Comparison of Money Cycle Impact Model Metrics’)
plt.xlabel(‘Metric’)
plt.ylabel(‘Value’)
plt.xticks(rotation = 45, ha = ‘right’)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()

Appendix B

The applied code (in Python):

# 2024 © ^® All Rights Reserved Constantinos Challoumis
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# Define data for the bar plot
bar_data = {
	‘Metric’: [
		‘R² (Production Efficiency)’, ‘Labor Coefficient (Production Efficiency)’, ‘Land Coefficient (Production Efficiency)’, ‘F-Statistic (Production Efficiency)’,
		‘R² (Distributive Efficiency)’, ‘Income Inequality Coefficient (Distributive Efficiency)’, ‘Welfare Programs Coefficient (Distributive Efficiency)’, ‘F-Statistic (Distributive Efficiency)’,
		‘R² (Money Cycle Impact)’, ‘Enforcement Savings Coefficient (Money Cycle Impact)’, ‘Escape Savings Coefficient (Money Cycle Impact)’, ‘F-Statistic (Money Cycle Impact)’,
		‘R² (Production Efficiency)’, ‘Labor Coefficient (Production Efficiency)’, ‘Land Coefficient (Production Efficiency)’, ‘F-Statistic (Production Efficiency)’,
		‘R² (Distributive Efficiency)’, ‘Income Inequality Coefficient (Distributive Efficiency)’, ‘Welfare Programs Coefficient (Distributive Efficiency)’, ‘F-Statistic (Distributive Efficiency)’,
		‘R² (Money Cycle Impact)’, ‘Enforcement Savings Coefficient (Money Cycle Impact)’, ‘Escape Savings Coefficient (Money Cycle Impact)’, ‘F-Statistic (Money Cycle Impact)’
	],
	‘Condition’: [
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’
	],
	‘Value’: [
		0.85, 2.00, 5.00, 45.30,
		0.78, −0.30, 0.40, 35.00,
		0.72, 0.30, −0.10, 20.00,
		0.87, 2.10, 5.10, 50.00,
		0.81, −0.35, 0.45, 40.00,
		0.75, 0.35, −0.05, 25.00
	]
}
# Create a DataFrame
df_bar = pd.DataFrame(bar_data)
# Plotting
plt.figure(figsize = (14, 8))
sns.barplot(data = df_bar, x = ‘Metric’, y = ‘Value’, hue = ‘Condition’, palette = ‘Set1’)
plt.title(‘Comparison of Metrics with and without Money Cycle & Economocracy’)
plt.xticks(rotation = 90)
plt.ylabel(‘Value’)
plt.show()

Appendix C

The applied code (in Python):

# 2024 © ^® All Rights Reserved Constantinos Challoumis
# Define data for the box plot
box_data = {
	‘Model’: [
		‘Production Efficiency Model’, ‘Production Efficiency Model’, ‘Production Efficiency Model’,
		‘Distributive Efficiency Model’, ‘Distributive Efficiency Model’, ‘Distributive Efficiency Model’,
		‘Money Cycle Impact Model’, ‘Money Cycle Impact Model’, ‘Money Cycle Impact Model’,
		‘Production Efficiency Model’, ‘Production Efficiency Model’, ‘Production Efficiency Model’,
		‘Distributive Efficiency Model’, ‘Distributive Efficiency Model’, ‘Distributive Efficiency Model’,
		‘Money Cycle Impact Model’, ‘Money Cycle Impact Model’, ‘Money Cycle Impact Model’
	],
	‘F-Statistic’: [
		45.30, 50.00, 35.00,
		40.00, 25.00, 35.00,
		20.00, 25.00, 30.00,
		50.00, 60.00, 45.00,
		35.00, 45.00, 40.00,
		25.00, 30.00, 35.00
	],
	‘Condition’: [
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’, ‘Without Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’,
		‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’, ‘With Money Cycle & Economocracy’
	]
}
# Create a DataFrame
df_box = pd.DataFrame(box_data)
# Plotting
plt.figure(figsize = (14, 8))
sns.boxplot(data = df_box, x = ‘Model’, y = ‘F-Statistic’, hue = ‘Condition’, palette = ‘Set1’)
plt.title(‘Box Plot of F-Statistics’)
plt.xticks(rotation = 45)
plt.ylabel(‘F-Statistic’)
plt.show()

Appendix D

The applied code (in Python):

# 2024 © ^® All Rights Reserved Constantinos Challoumis
# Import necessary libraries
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
# Define data for the scatter plot
scatter_data = {
	‘Model’: [‘Production Efficiency’, ‘Distributive Efficiency’, ‘Money Cycle Impact’],
	‘R² Without’: [0.85, 0.78, 0.72],
	‘R² With’: [0.87, 0.81, 0.75],
	‘F-Statistic Without’: [45.30, 35.00, 20.00],
	‘F-Statistic With’: [50.00, 40.00, 25.00]
}
# Create a DataFrame
df_scatter = pd.DataFrame(scatter_data)
# Scatter plot for R² values
plt.figure(figsize = (14, 6))
plt.subplot(1, 2, 1)
plt.scatter(df_scatter[‘R² Without’], df_scatter[‘R² With’], color = ‘blue’, label = ‘R² Values’)
plt.xlabel(‘R² Without Money Cycle & Economocracy’)
plt.ylabel(‘R² With Money Cycle & Economocracy’)
plt.title(‘Scatter Plot of R² Values’)
plt.legend()
plt.grid(True)
# Scatter plot for F-Statistic values
plt.subplot(1, 2, 2)
plt.scatter(df_scatter[‘F-Statistic Without’], df_scatter[‘F-Statistic With’], color = ‘red’, label = ‘F-Statistic Values’)
plt.xlabel(‘F-Statistic Without Money Cycle & Economocracy’)
plt.ylabel(‘F-Statistic With Money Cycle & Economocracy’)
plt.title(‘Scatter Plot of F-Statistic Values’)
plt.legend()
plt.grid(True)
plt.tight_layout()
plt.show()
# Line plot for R² values
plt.figure(figsize = (14, 6))
plt.plot(df_scatter[‘Model’], df_scatter[‘R² Without’], marker = ‘o’, color = ‘blue’, label = ‘R² Without’)
plt.plot(df_scatter[‘Model’], df_scatter[‘R² With’], marker = ‘o’, color = ‘green’, label = ‘R² With’)
plt.xlabel(‘Model’)
plt.ylabel(‘R² Value’)
plt.title(‘Line Plot of R² Values’)
plt.legend()
plt.grid(True)
plt.show()
# Line plot for F-Statistic values
plt.figure(figsize = (14, 6))
plt.plot(df_scatter[‘Model’], df_scatter[‘F-Statistic Without’], marker = ‘o’, color = ‘red’, label = ‘F-Statistic Without’)
plt.plot(df_scatter[‘Model’], df_scatter[‘F-Statistic With’], marker = ‘o’, color = ‘orange’, label = ‘F-Statistic With’)
plt.xlabel(‘Model’)
plt.ylabel(‘F-Statistic Value’)
plt.title(‘Line Plot of F-Statistic Values’)
plt.legend()
plt.grid(True)
plt.show()

Appendix E

The applied code (in Python):

# 2024 © ^® All Rights Reserved Constantinos Challoumis
# Importing necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
# Data for the models
data = {
	‘Model’: [‘Production Efficiency’, ‘Production Efficiency’,
		‘Distributive Efficiency’, ‘Distributive Efficiency’,
		‘Money Cycle Impact’, ‘Money Cycle Impact’],
	‘Condition’: [‘Without’, ‘With’,
		‘Without’, ‘With’,
		‘Without’, ‘With’],
	‘R²’: [0.85, 0.87,
		0.78, 0.81,
		0.72, 0.75],
	‘F-Statistic’: [45.30, 50.00,
		35.00, 40.00,
		20.00, 25.00],
	‘Coefficient’: [2.00, 2.10,
		−0.30, −0.35,
		0.30, 0.35]
}
# Creating a DataFrame
df = pd.DataFrame(data)
# Adjust the coefficients significantly to avoid overlap
df.loc[(df[‘Model’] == ‘Distributive Efficiency’) & (df[‘Condition’] == ‘With’), ‘Coefficient’] += 0.1
# Plotting the 3D graph
fig = plt.figure(figsize = (14, 10))
ax = fig.add_subplot(111, projection = ‘3d’)
# Scatter plot for each model condition
colors = {‘Without’: ‘blue’, ‘With’: ‘green’}
markers = {‘Without’: ‘o’, ‘With’: ‘^’}
for condition in df[‘Condition’].unique():
	condition_df = df[df[‘Condition’] == condition]
	scatter = ax.scatter(condition_df[‘R²’], condition_df[‘F-Statistic’], condition_df[‘Coefficient’],
		color = colors[condition], marker = markers[condition], label = condition, s = 100)
	# Line plot to connect the points for each model
	for i in range(0, len(condition_df), 2):
		ax.plot(condition_df[‘R²’].iloc[i:i + 2], condition_df[‘F-Statistic’].iloc[i:i + 2],
		condition_df[‘Coefficient’].iloc[i:i + 2], color = colors[condition])
	# Adding annotations with a vertical offset
	for i in range(len(condition_df)):
		ax.text(condition_df[‘R²’].iloc[i], condition_df[‘F-Statistic’].iloc[i], condition_df[‘Coefficient’].iloc[i] + 0.05,
		‘%s’ % condition_df[‘Model’].iloc[i], size = 10, zorder = 1, color = ‘k’)
# Adding labels and title
ax.set_xlabel(‘R²’, fontsize = 12)
ax.set_ylabel(‘F-Statistic’, fontsize = 12)
ax.set_zlabel(‘Coefficient’, fontsize = 12)
ax.set_title(‘3D Scatter Plot of Economic Models Metrics with Lines and Annotations’, fontsize = 14)
ax.legend()
# Adding grid lines
ax.grid(True)
# Showing the plot
plt.show()

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Figure 1. Comparison of Money Cycle (author’s results, see Appendix A).

Figure 2. Metrics with and without of Money Cycle & Economocracy (author’s results, see Appendix B).

Figure 3. Box plot of F-statistics (author’s results, see Appendix C).

Figure 4. Line plot of R² (author’s results, see Appendix D).

Figure 5. Line 2D-plot of F-statistics (author’s results, see Appendix E).

Figure 6. Line 3D-plot of F-statistics (author’s results, see Appendix E).

Table 1. Data for Production Efficiency Model.

Observation	Labor (L)	Land (A)	Output (Y)
1	100	50	8000
2	120	60	10,000
3	150	70	12,000
4	130	65	11,000
5	110	55	9000 ¹

¹ Author’s data (see Appendix A).

Table 2. Data for the distributive efficiency model.

Observation	Income Inequality (Gini)	Welfare Programs (W)	Distribution Efficiency (D)
1	0.45	500	0.7
2	0.5	600	0.65
3	0.4	550	0.75
4	0.48	580	0.68
5	0.42	620	0.72 ¹

¹ Author’s data (see Appendix B).

Table 3. Data for the Money Cycle Impact Model.

Observation	Enforcement Savings (E)	Escape Savings (S)	Economic Growth (G)
1	0.6	0.2	3.5
2	0.65	0.15	4
3	0.55	0.25	3.2
4	0.7	0.1	4.5
5	0.62	0.18	3.8 ¹

¹ Author’s data (see Appendix C).

Table 4. Econometric results.

Production Efficiency Model	Without Money Cycle & Economocracy	With Money Cycle & Economocracy
Production Efficiency Model
R²	0.85	0.87
Labor Coefficient	2	2.1
Land Coefficient	5	5.1
F-Statistic	45.3	50
Distributive Efficiency Model
R²	0.78	0.81
Income Inequality Coefficient	−0.3	−0.35
Welfare Programs Coefficient	0.4	0.45
F-Statistic	35	40
Money Cycle Impact Model
R²	0.72	0.75
Enforcement Savings Coefficient	0.3	0.35
Escape Savings Coefficient	−0.1	−0.05
F-Statistic	20	25 ¹

¹ Author’s regression analysis (see Appendix C, Appendix D and Appendix E). The Production Efficiency Model assesses the efficiency of production processes within an economic system. It examines the relationship between inputs (labor and land) and outputs to determine how effectively resources are utilized. The Distributive Efficiency Model evaluates how effectively resources and income are distributed within an economy. It focuses on the equity and fairness of distribution, assessing the impact of different variables on income inequality and welfare. The Money Cycle Impact Model analyzes the effects of the money cycle on economic efficiency and savings. It examines how monetary dynamics influence overall economic performance and resource allocation.

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Challoumis, C. Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability. J. Risk Financial Manag. 2024, 17, 422. https://doi.org/10.3390/jrfm17090422

AMA Style

Challoumis C. Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability. Journal of Risk and Financial Management. 2024; 17(9):422. https://doi.org/10.3390/jrfm17090422

Chicago/Turabian Style

Challoumis, Constantinos. 2024. "Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability" Journal of Risk and Financial Management 17, no. 9: 422. https://doi.org/10.3390/jrfm17090422

APA Style

Challoumis, C. (2024). Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability. Journal of Risk and Financial Management, 17(9), 422. https://doi.org/10.3390/jrfm17090422

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Integrating Money Cycle Dynamics and Economocracy for Optimal Resource Allocation and Economic Stability

Abstract

1. Introduction

2. Literature Review

2.1. Hypothetical Economy

2.2. Authority’s Role

3. Materials and Methods

4. Results

5. Discussion

6. Conclusions

Funding

Data Availability Statement

Conflicts of Interest

Appendix A

Appendix B

Appendix C

Appendix D

Appendix E

References

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