The Counterbalance Economics (CBE) system represents an alternative economic model designed to reduce inequality within any given population. It incorporates the principles of in-kind transfer vouchers discussed previously. The spreadsheet and computer modelling developed for this study encapsulate the core elements of the CBE system, which include:
1. Paying the lowest-paid workers the Average Weekly Earnings (AWE).
2. Exempting employees paid in in-kind transfer vouchers from taxation.
3. Adjusting the AWE in response to income increases among the top 50 percentile.
A primary objective of the modelling was to simulate various global economies, encompassing their economic and inequality levels, and to compare these with the projected levels under the integration of the CBE system. This comparison is feasible as all global economies share three fundamental attributes:
1. A level of Gross Domestic Product (GDP).
2. A taxation system.
3. A social welfare framework.
The existence of an economy is predicated on a market that facilitates the sale of goods and services. The distinguishing factor between countries is the degree of government control or freedom, as regulated by the government. This spectrum ranges from highly regulated systems like Keynesianism, Socialism, and Communism, to less regulated mixed market and Neoliberal economies (Heywood, 2013). The position on this spectrum influences the required level of taxation to administer regulations and provide social safety nets.
To simulate an economy, a dataset is needed that includes a country's GDP, Total Tax Revenue, Welfare Payments, Gini index, and the Average Weekly Earnings (AWE) for the years intended for simulation. For this study, the economies of Australia, the US, the UK, Switzerland, and Germany from 2006 to 2018 were chosen due to the availability of data. The data were sourced from the OECD database and normalised to the US dollar for consistent comparison.
The quantitative methodology employed in this study utilised existing OECD statistical data and Agent-Based Modelling techniques. This approach emphasises objective measurement and the statistical, mathematical, and numerical analysis of collected data. After confirming that paying the bottom 50 percentile the AWE as described would not result in uniform high-income levels across all employees, the focus shifted to testing scenarios outlined in the spreadsheet modelling. The computer model, economic data, and spreadsheet model are accessible to anyone with a computer or smartphone, enabling individuals and researchers to validate, verify, and replicate the published results. The initial download and setup process is estimated to take approximately 5 minutes (Malliaros, 2021).
Detailed results and a discussion of the outcomes are presented in the results section.
Discussion on Assumptions and Parameter Choices:
In our study, we explore the potential of the Counterbalance Economics (CBE) model to mitigate income inequality using in-kind transfer vouchers acknowledging various underlying assumptions and parameter choices with inherent uncertainties. We propose the adoption of a theory-based equivalence scale for extended income, incorporating public in-kind transfers, to adjust economic measures effectively and stimulate informed policy debates and practical explorations of alternative economic models.
Assumptions:
1. Potential of CBE Model to Reduce Income Inequality: We assume that the CBE model can significantly lower income inequality without raising taxes or disrupting market stability. This assumption is grounded in preliminary simulations and existing literature but is subject to real-world variables and economic dynamics not fully captured in our model.
2. Effectiveness of In-Kind Transfer Vouchers: Our model hypothesises that in-kind transfer vouchers can lead to increased employment, improved well-being, and lower poverty levels. However, the transition from theory to practice involves complexities, including public acceptance, the scalability of such programs, and varying market conditions that could influence the outcome.
3. Novel Funding Method Efficiency: The assumption that in-kind transfer vouchers offer a more cost-effective alternative to traditional cash transfers is based on the differential between retail and wholesale pricing. The effectiveness of this approach is contingent upon market dynamics, such as the variability of markup ratios and consumer behaviour, which may introduce variability in the anticipated outcomes.
Parameter Choices:
1. Setting the Average Weekly Earnings (AWE) as a Benchmark for Payments: This choice aims to elevate the income of the lowest-paid workers but introduces uncertainties related to the changing nature of the AWE over time and its reflection of actual living standards across different regions.
2. Tax Exemption for In-Kind Voucher Payments: While intended to incentivise the adoption of the CBE model, the long-term implications of such tax exemptions on public revenues and fiscal policy remain uncertain and warrant careful consideration.
3. Adaptation of the AWE in Response to Top 50 Percentile Income Increases: This parameter aims to ensure fairness and inclusivity but may face challenges in implementation, particularly in accurately tracking and responding to income shifts in a timely and effective manner.
Acknowledging Uncertainties:
The implementation of the CBE model and its foundational assumptions are subject to a variety of uncertainties, including but not limited to:
· Economic Fluctuations: Changes in the global and local economies can significantly impact the effectiveness of the CBE model.
· Market Dynamics: The variability in markup ratios across different industries and changes in consumer behaviour can affect the cost-effectiveness of in-kind transfer vouchers.
· Policy and Legislative Changes: Future changes in tax laws, welfare policies, and labour regulations could influence the feasibility and impact of the CBE model.
· Technological Advancements: Rapid technological change could alter the labour market landscape, affecting employment patterns and the relevance of certain assumptions in our model.
Considering these uncertainties, our findings should be interpreted with caution. We advocate for further empirical research, including pilot studies and longitudinal analyses, to validate the CBE model's assumptions and refine its parameter choices. Our study aims not only to contribute to the academic discourse on income inequality but also to stimulate informed policy debates and practical explorations of alternative economic models.
Acknowledging the Radical Nature of the Proposal:
We fully acknowledge the unconventional and exploratory nature of the CBE model and its in-kind voucher system. The implementation of such a system represents a bold step towards reimagining economic interventions to reduce income inequality. This initiative requires careful consideration, pilot testing, and a willingness to adapt based on empirical evidence and stakeholder feedback.
· Challenges and Uncertainties: Introducing a new economic model entails navigating uncertainties, including market dynamics, regulatory considerations, and the socio-economic impacts on different population segments. We commit to a transparent evaluation of these challenges and an iterative process of refinement and adaptation.
· Pilot Programs: To address the practicality and efficacy of the CBE model, we advocate for targeted pilot programs. These initiatives would provide valuable insights into the system's real-world application, allowing for adjustments before broader implementation.
· Stakeholder Engagement: Recognising the importance of broad support, we propose ongoing engagement with policymakers, businesses, community organisations, and potential beneficiaries. This collaborative approach is crucial for addressing concerns, ensuring alignment with public policy objectives, and securing the social license to operate.
While the CBE model and its in-kind transfer voucher system represent a radical and untested approach to addressing income inequality, we believe it offers a valuable exploration of alternative economic policies. Our ambition is to illuminate the potential of such proposals, grounded in rigorous analysis and open to adaptation based on empirical evidence and societal needs.
Operationalisation of the Voucher Payment System:
The in-kind transfer voucher system is designed to supplement the income of the lowest-paid workers, enabling them to achieve a level of purchasing power equivalent to the Average Weekly Earnings (AWE) without direct cash payments. These vouchers would be redeemable for goods and services at participating businesses, with a value grounded in the difference between wholesale and retail pricing. This system aims to increase the disposable income of workers while maintaining cost-effectiveness for businesses through reduced cash wage obligations.
Key features of the voucher system include:
· Flexibility and Choice: Vouchers offer recipients the flexibility to select goods and services according to their needs, akin to cash, but within a controlled ecosystem that maximises the impact of each dollar spent.
· Transformation into Cash: The model envisions minimal direct convertibility of vouchers to cash to preserve the system's integrity and focus on real consumption. However, secondary markets or redemption options might evolve, offering liquidity under specific conditions, albeit with safeguards to prevent undermining the system's objectives.
· Digital Implementation: Leveraging digital platforms and technologies, the voucher system can be efficiently managed, ensuring transparency, reducing fraud, and allowing for real-time adjustments to meet economic conditions and policy objectives.
Agent-Based Modelling (Netlogo)
Economists and professionals from various fields utilise models to analyse real-world phenomena, aiming not only to comprehend the underlying mechanics and processes but also to conduct 'what-if' scenarios. Whether it's an engineer employing a computer model to assess a bridge's structural integrity or a biologist modelling nucleic acids, the significance of computer modelling is immense. Another critical application of models lies in decision-making and policy formulation. This involves creating computer models that are "sufficiently explicit and concrete," enabling governments and individuals to derive conclusions and make informed decisions (Hubbard et al., 2015). Wilensky and Rand describe the utility of Agent-Based Modelling (ABM) as follows:
Agent-based modelling (ABM) is a modelling tool that assists in a deeper understanding of the natural and social worlds and in engineering solutions to societal challenges. To begin, let's define what an agent-based model is. An agent is an autonomous computational individual or object, characterised by specific properties and actions. Agent-based modelling is a type of computational modelling where phenomena are represented through agents and their interactions. ABM is a versatile methodology, applicable across a broad range of content areas. It facilitates exploration, comprehension, and analysis of phenomena and scenarios in diverse contexts and domains. Over the past two decades, scientists have increasingly adopted agent-based modelling methods in their research (Wilensky & Rand, 2015, pp. 1-20).
Below is a concise guide on utilising the Agent-Based Model to simulate the economic conditions and inequality levels in Australia, the US, the UK, Switzerland, and Germany. This methodology is adaptable for simulating any economy, and a comprehensive analysis of each country is intended for future research. The CBE Wealth Distribution and Bidding Market models, as examples of Agent-Based Modelling (ABM) tools, have been employed extensively to explore economic behaviours of populations and to evaluate various hypothetical scenarios.
Note. NetLogo Agent-Based modelling software is based on the model described by authors Epstein and Axtell (1996) in “Growing Artificial Societies: Social Science from the Bottom Up”. For more on the calculation of the Gini index, see journal reference “The Small-Sample Bias of the Gini Coefficient: Results and Implications for Empirical Research” (Deltas, 2003). Note that if one is calculating the Gini index of a sample to estimate the value for a larger population, a small correction factor to the method used here may be needed for small samples. For an explanation of Pareto’s Law, see Brakman (2001).
How to use the Agent-Based Model
To effectively simulate an economy, it is essential to gather a comprehensive dataset that includes key economic indicators. This dataset should encompass the Gross Domestic Product (GDP), Total Tax Revenue, Welfare Payments, Gini Index, and the Average Weekly Earnings (AWE) for the specific year or years under consideration for the simulation.
Upon determining the calculated values, input the Average Weekly Earnings (AWE) for the specific country and year under examination. Subsequently, execute the model multiple times, making incremental adjustments to the Income-Growth-Volume (IGV) slider. Aim to identify the highest and lowest Gini index values within the data set. This process facilitates the simulation of varying degrees of economic growth or stagnation within the modelled economy.
Example: To simulate the economic conditions in Australia, begin by setting the Tax-Collected to 0.27 and the welfare parameter to 0.17, as per the 2007/08 figures. Next, enter the Average Weekly Earnings (AWE) for that year into the 'Enter AWE' box. Then, adjust the Income-Growth-Volume (IGV) slider until a Gini index of 0.33, the highest recorded score, is achieved at the end of a simulation run. Repeat this process for the 2017/18 figures, adjusting the Tax-Collected to 0.285 and welfare to 0.165, and entering the corresponding AWE for that year. The IGV should be adjusted again to reach a Gini index of 0.30, the lowest recorded score. The appropriate IGV setting for a country is identified when both the high and low Gini values can be achieved by modifying only the tax-collected and welfare parameters for any given year. In the case of Australia, the optimal IGV setting was found to be 7. It is important to note that the Gini level calculated by the wealth distribution model is a result of running the model. While striving for accuracy is important, minor fluctuations are acceptable. For example, a Gini score fluctuating between 0.31 and 0.35 for 2007/08, and between 0.28 and 0.32 for 2017/18, or a variance of approximately 0.02 points on either side of the actual Gini score, is considered within acceptable limits.
Typically, the initial setting for the Income-Growth-Volume (IGV) slider is placed at 15. Subsequently, it should be gradually adjusted backward, one increment at a time, until the correct setting is determined. For additional insight into this process, detailed explanations and sample data are available within the model, under the tab labelled “Info”.
The econometric model
The dataset used to construct an econometric model for inequality comprises panel data for ten countries from 2006 to 2018 for several economic variables generated using the Agent-Based Model.
The objectives of this econometric model are to:
(i) Determine the impact of implementing Counterbalance Economics (CBE) transfers on income inequality, measured by changes in the Gini coefficient, to assess the effectiveness of vouchers in improving income distribution.
(ii) Investigate the relationship between the implementation of vouchers and changes in total income, particularly examining whether any increases in GDP are correlated with improvements in income inequality, providing insights into the broader economic implications of the CBE model.
Determine the impact of implementing Counterbalance Economics (CBE) transfers on income inequality, measured by changes in the Gini coefficient, to assess the effectiveness of vouchers in improving income distribution.
Investigate the relationship between the implementation of vouchers and changes in total income, particularly examining whether any increases in GDP are correlated with improvements in income inequality, providing insights into the broader economic implications of the CBE model.
We have chosen the Gini Coefficient as the measure of inequality because it has wide coverage. The Gini coefficient takes values from 0 to 1.[1]
All statistical analysis has been carried out using R v4.3.1 (R core team, 2023). REWB models (Bell et al, 2019) have been fitted to the data, with the general model equation of the form.
All variables have been scaled.
For model (i), the response variable is the difference between the agent-based model estimate for the Gini coefficient and the historical Gini – representing the improvement in income inequality. The historical Gini coefficients (Gini) serve as the explanatory variable.
For model (ii), the response variable is the difference between the agent-based model estimate of total income and the historical total income – indicating the improvement in total income. The explanatory variables include the estimates of the Gini coefficient (CBE Gini) and the historical total income (TotInc). A one-year lag has been applied to the Gini coefficient explanatory variable.