Economocracy: Global Economic Governance
Abstract
1. Introduction
- 1.
- Debt Accumulation from Interest-Based Money Creation:Under current monetary systems, money is primarily created through bank loans that must be repaid with interest. However, the interest itself is not created within the system, resulting in a permanent shortfall that structurally generates public and private debt. This flaw leads to cyclical crises and unsustainable debt expansion.
- 2.
- Imbalanced International Economic Relations (Cycle of Money Problem):According to the Theory of the Cycle of Money, any profit or surplus generated in one country mathematically implies a deficit in another, due to the finite and unevenly circulating nature of global capital. This zero-sum condition structurally prevents equitable global development and perpetuates international instability (see Figure 1).
2. Literature Review
2.1. Economocracy
2.2. Automation and Implications
2.3. Labor Market Disruption and Skill Gaps
2.4. Environmental Sustainability in Economocracy
2.5. Social Equity and Income Distribution
2.6. The Cycle of Money: A Theoretical Framework
2.7. Implementing Economocracy: Framework and Governance
2.8. Ensuring Democratic Representation in Economic Decision-Making
3. Materials and Methods
- Redistribution of wealth and inequality levels (via Gini index),
- Impact on unemployment from Economic Productive Resets (EPRs),
- Wage increases under redistributed productivity,
- Risk mitigation of automation-related job losses through targeted EPIs,
- Sustainability of the economic system under a non-debt-based monetary framework.
- Capitalist Baseline: parameters set with Gini index, unemployment, average wage, and AI-induced job loss.
- Scenario A (EPR only): incorporates productivity-based debt restructuring mechanisms.
- Scenario B (EPR + EPI): adds periodic redistributive injections directed to education, healthcare, and social security.
- Gini coefficient: indicator of income inequality.
- Unemployment rate: reflects labor market performance.
- Average wage level: proxy for overall productivity and standard of living.
- AI job displacement (%): represents the risk of automation.
- Debt-to-GDP ratio: used to assess system sustainability.
- Redistributive mechanisms (EPR, EPI): policy tools evaluated for their macroeconomic impact.
4. Results
- Gini Coefficient Values: The baseline Gini coefficient of 0.42 reflects income inequality levels typically observed in advanced capitalist economies, such as the United States or the United Kingdom, as reported by the OECD. This serves as the reference point for the current capitalist model in our simulation. The value of 0.35 in the EPR-only scenario aligns with moderate inequality levels achieved in social-democratic states like Germany or France, where redistributive fiscal policies are in place. The most equitable scenario, at 0.30 under the combined EPR and EPI model, reflects outcomes comparable to Scandinavian countries, where coordinated income redistribution and social investment mechanisms have historically produced low inequality. These values are consistent with international benchmarks and align with the expected outcomes derived from Equation (12) of the Economocracy model (Abeles & Conway, 2020; Dai & Shen, 2025; Gáspár et al., 2023; Kharazmi et al., 2023; Parsons & Naghshpour, 2024; Raffinetti et al., 2015; Sakaki, 2019; Stark, 2024; Tao et al., 2017; Ursu et al., 2020; H.-Y. Wang et al., 2020; Zavyalov, 2024).
- Unemployment Rate: A baseline unemployment rate of 8.0% was selected based on an average of post-crisis labor market data for OECD countries, capturing typical conditions under capitalist economies with uneven labor absorption. The reduction to 6.0% in the EPR-only scenario simulates the effect of productivity-based debt restructuring and job stimulus that accompanies macroeconomic resets. The further decline to 4.5% in the EPR + EPI scenario results from targeted public investment in education, healthcare, and retraining programs that enhance employability. These projections are consistent with empirical trends observed in coordinated market economies and are grounded in Equation (13) of the model, which links job creation and retraining investment to labor market improvement (Balakrishnan & Michelacci, 2001; Belot & Ours, 2001; Bhattarai, 2016; Azmat et al., 2004; Khraief et al., 2020; Koç et al., 2021; Omay et al., 2021; Sahnoun & Abdennadher, 2020; Solarin et al., 2024).
- Average Wages: The baseline average monthly wage of USD 2500 represents typical gross earnings across OECD member states and is used to simulate income conditions under the current capitalist structure. The increase to USD 2800 in the EPR-only scenario models productivity redistribution from non-productive monetary reform. The value of USD 3200 in the EPR + EPI scenario reflects amplified wage gains due to enhanced social investment, educational attainment, and health infrastructure. These upward adjustments are simulated using Equation (14), which expresses wage growth as a function of productivity enhancement and redistribution magnitude. The progression in wage levels across scenarios is grounded in observed patterns from economies undergoing post-crisis wage recovery and structural reform (D. Baker, 2007; Dew-Becker & Gordon, 2005; Dong et al., 2024; Gerritsen & Jacobs, 2014; Lochner & Schulz, 2022; Lollo & O’Rourke, 2020; Moos, 2019; Vergara, 2022).
- AI Job Displacement: The simulation assumes a baseline AI-driven job displacement risk of 15%, consistent with projections by McKinsey Global Institute and OECD analyses of automation in high-income economies. In the EPR-only scenario, this risk declines to 10% due to the implementation of counter-cyclical investment and industrial policy reforms. The EPR + EPI scenario lowers the risk further to 5%, simulating the outcome of proactive retraining, sectoral transformation, and employment reallocation, particularly into green and care economies. This evolution is modeled using Equation (15), which adjusts automation displacement according to mitigation investment and labor market adaptability. These estimates correspond to real-world pilot programs where coordinated policies reduced technological unemployment (Camarda et al., 2021; Dahlin, 2024; Jadhav & Banubakode, 2024; Jain, 2023; S. Joshi, 2025; Karangutkar, 2023; Khan et al., 2024; Moradi & Levy, 2020; Shankar, 2024; Soueidan & Shoghari, 2024; Tiwari, 2023; Liu et al., 2023; K. Wang & Lu, 2024; Yang, 2025).
- World Debt (% of GDP):
- Advanced Economies Debt (% of GDP):
- Emerging Market Economies Debt (% of GDP):
5. Implications
- : debt as a percentage of GDP at time ,
- : initial debt ratio (e.g., baseline year, 1950s),
- : decay rate due to Economic Productivity Reset (),
- : decay rate due to Economic Prosperity Increase (),
- : time index (e.g., number of periods since baseline).
- 1.
- Scenario (GDP-based):This equation reflects the impact of increased systemic productivity on the national debt, assuming that efficiency and automation reduce the need for borrowing to maintain economic growth. Empirical fitting of the model yields
- 2.
- Scenario (GDP per capita-based):This reflects the debt reduction achieved through growth in GDP per capita, driven by improved living standards, education, and technological access. From historical data trends, we have
- 3.
- Combined Scenario:Equivalently, using a compounded decay rate, we haveThis scenario models the strongest and most sustainable decline in debt-to-GDP, under the dual influence of productive efficiency and equitable prosperity (Table 12).
6. Discussion
6.1. Discussion of the Concept and Core Principles of Economocracy
6.2. Historical Context and Evolution to Structural Mechanisms
- Preserves the strengths of the free market (as valued by Smith, Friedman, and Hayek) by keeping price mechanisms and private enterprise intact;
- Addresses market failures (as emphasized by Keynes, Sen, and Stiglitz) through automatic rules-based fiscal mechanisms (EPRs and EPIs) rather than arbitrary state control;
- Ensures democratic legitimacy and public accountability (as advocated by Acemoglu and Standing), transforming economic surplus management into a participatory process;
- Reduces inequality and precarity (as discussed by Autor, Standing, and Marx), not through centralized redistribution but through embedded institutional responses grounded in performance, inclusion, and transparency;
- Incorporates ecological sustainability (as stressed by Kallis) by making environmental thresholds a structural part of fiscal decision-making.
6.3. The Mathematical Framework of Economocracy
6.4. Addressing Systemic Inequalities
6.5. Strategies for Redistribution
6.6. An Economocratic System
6.7. Environmental Sustainability and Economic Growth
6.8. Investment in Education Under Economocracy
6.9. The Role of Democratic Principles
6.10. Challenges of Economocracy
7. Limitations and Future Research
8. Conclusions
Funding
Conflicts of Interest
Appendix A
Appendix B
Appendix C
Appendix D
Appendix E
Appendix F
Appendix G
Appendix H
Appendix I
Appendix J
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Step | Methodological Focus | Description | Purpose |
---|---|---|---|
Step 1 | Quantitative Theoretical Validation | Development and mathematical simulation of EPR and EPI equations to test their internal consistency and theoretical behavior under the assumptions of the Cycle of Money theory. | To ensure the mathematical coherence and theoretical robustness of the proposed mechanisms (EPRs and EPIs). |
Step 2 | Empirical Data Analysis | Examination of macroeconomic indicators using real-world data across countries. Key variables include debt-to-GDP ratios, global income levels, Gini coefficients (inequality), and average wages. Statistical evaluation includes descriptive trends and cross-sectional comparisons. | To demonstrate that the theory aligns with empirical patterns and to reveal the magnitude and direction of the relationships among key global indicators. |
Step 3 | Global Policy Simulation and Implication Assessment | Application of EPR and EPI mechanisms to historical global debt data (1950–2023) in order to simulate how their implementation would impact global debt reduction. The simulation considers two scenarios: GDP-based and GDP-per-capita-based triggers. | To access potential policy implementing the proposed EPR and ΕΡΙ mechanisms on global debt trajectories and to provide actionable insights for international economic governance frameworks. |
Countries | GDP per Capita, Current Prices (USD) | Bank Reserves as a Percentage of GDP (%) | Index of the Money Cycle (USD) | General Index of the Money Cycle (from 0 to 1) |
---|---|---|---|---|
Angola | 1948.112696 | 17.92017652 | 34,910.52339 | 0.148449997 |
Azerbaijan | 3317.379118 | 10.97020346 | 36,392.32388 | 0.153782067 |
Egypt | 2066.01787 | 46.97299483 | 97,047.0467 | 0.326424266 |
Ethiopia | 405.3365435 | 13.84665792 | 5612.556459 | 0.027262768 |
Haiti | 490.0279348 | 23.12261328 | 11,330.72643 | 0.053551124 |
Ivory Coast | 1586.736283 | 16.64475862 | 26,410.84242 | 0.116518146 |
Albania | 2641.97687 | 50.15863333 | 132,517.9491 | 0.398221692 |
Algeria | 3018.453435 | 34.99794 | 105,639.6522 | 0.345344978 |
Antigua and Antigua Barbuda | 11,017.60861 | 63.81802093 | 703,121.9768 | 0.778325035 |
Argentina | 8124.823043 | 14.323615 | 116,376.8372 | 0.367544647 |
Armenia | 2598.006735 | 15.81307846 | 41,082.48435 | 0.1702274 |
Aruba | 22,996.91645 | 54.10103125 | 1,244,156.896 | 0.861357987 |
Australia | 35,040.46702 | 53.87938621 | 1,887,958.856 | 0.904101665 |
Austria | 34,526.9247 | 62.47739286 | 2,157,152.238 | 0.915052333 |
Afghanistan | 465.7023333 | 6.633497088 | 3089.235072 | 0.015192043 |
Vanuatu | 1999.460174 | 84.8095122 | 169,573.242 | 0.458517221 |
Belgium | 32,050.28565 | 54.43465357 | 1,744,646.196 | 0.897035273 |
Venezuela | 5090.179907 | 22.77272679 | 115,917.2763 | 0.366625368 |
Vietnam | 1492.933304 | 9.8459624 | 14,699.36518 | 0.068383222 |
Bolivia | 1763.186087 | 22.97217483 | 40,504.21904 | 0.16823443 |
Bosnia and Herzegovina | 4322.342533 | 31.57276476 | 136,468.304 | 0.405281579 |
Bulgaria | 5524.726478 | 45.68948519 | 252,421.9086 | 0.557618789 |
Brazil | 5660.105283 | 27.81933776 | 157,460.3806 | 0.440181747 |
France | 31,031.69072 | 52.18821786 | 1,619,488.636 | 0.889953613 |
Yemen | 903.8433056 | 17.20925417 | 15,554.46917 | 0.072074553 |
Germany | 32,679.55983 | 55.71401552 | 1,820,709.503 | 0.900910567 |
Georgia | 3022.180531 | 14.77167174 | 44,642.65874 | 0.182290067 |
Gabon | 6643.255783 | 11.51062621 | 76,468.03411 | 0.276332825 |
Ghana | 1791.979717 | 10.80150879 | 19,356.08467 | 0.088137467 |
Guineas | 717.4828889 | 7.294438571 | 5233.634859 | 0.025469049 |
Guatemala | 2369.681283 | 21.31458724 | 50,508.77843 | 0.20141866 |
Guyana | 4118.676152 | 41.30616724 | 170,126.726 | 0.459326389 |
Guinea-Bissau | 623.6705217 | 7.710434185 | 4808.770511 | 0.023449967 |
Grenada | 5970.487174 | 61.73021915 | 368,559.4817 | 0.64794166 |
Denmark | 41,914.76783 | 40.32705517 | 1,690,299.155 | 0.894075387 |
Central Africa | 412.0489348 | 5.820025179 | 2398.135175 | 0.011833614 |
Kyrgyzstan | 820.1641176 | 9.601895652 | 7875.130275 | 0.037837284 |
Korea (South) | 17,317.95489 | 39.70359966 | 687,585.1479 | 0.774445822 |
Slovakia | 13,375.48412 | 49.996108 | 668,722.1487 | 0.769549696 |
Congo | 1945.98687 | 10.7926004 | 21,002.25867 | 0.094921765 |
Dominican Republic | 4237.6165 | 17.26588724 | 73,166.20866 | 0.267593888 |
Hong Kong | 27,861.08207 | 241.1424815 | 6,718,490.466 | 0.971055978 |
Ecuador | 3406.526326 | 15.32637155 | 52,209.68817 | 0.206798848 |
El Salvador | 2318.983957 | 32.59840755 | 75,595.18411 | 0.274042996 |
Switzerland | 55,908.56093 | 104.2269404 | 5,827,178.246 | 0.966775855 |
Greece | 16,216.72004 | 46.64991776 | 756,508.6563 | 0.790694311 |
Estonia | 14,819.4233 | 36.718408 | 544,145.6312 | 0.730983577 |
Zambia | 830.7967609 | 15.94235419 | 13,244.85622 | 0.062036443 |
Zimbabwe | 929.8230833 | 15.92128843 | 14,803.98149 | 0.068836408 |
United Arab Emirates | 33,150.65748 | 38.40283022 | 1,273,079.071 | 0.864079586 |
United States | 40,588.94259 | 65.33068966 | 2,651,703.612 | 0.929782899 |
United Kingdom | 31,229.44409 | 126.56 | 3,952,398.444 | 0.951776304 |
Jamaica | 3689.156913 | 33.97675439 | 125,345.5783 | 0.384965699 |
Japan | 33,406.88363 | 149.782981 | 5,003,782.617 | 0.96151905 |
India | 969.7367174 | 32.68084655 | 31,691.81686 | 0.136633179 |
Indonesia | 2109.498391 | 28.24749868 | 59,588.05303 | 0.229322047 |
Jordan | 2758.345 | 67.06397885 | 184,985.5907 | 0.480180286 |
Iraq | 4560.155043 | 12.59950621 | 57,455.70177 | 0.222945333 |
Iran | 4844.393152 | 32.34295385 | 156,681.9841 | 0.438960919 |
Ireland | 41,540.2963 | 56.59063276 | 2,350,791.653 | 0.921500356 |
Equatorial Guinea | 6565.050522 | 7.425165313 | 48,746.58541 | 0.195767102 |
Iceland | 39,893.77643 | 40.24109298 | 1,605,369.167 | 0.889093083 |
Spain | 20,721.4172 | 65.74816724 | 1,362,395.203 | 0.87184836 |
Israel | 24,128.48048 | 51.3453069 | 1,238,884.235 | 0.860850036 |
Italy | 25,693.1288 | 57.18511897 | 1,469,264.627 | 0.880051606 |
Cape Verde | 2173.423826 | 36.52810951 | 79,391.06354 | 0.283896995 |
Kazakhstan | 6457.822529 | 18.2279012 | 117,712.551 | 0.370201445 |
Cameroon | 1188.564435 | 11.44988414 | 13,608.92507 | 0.063633165 |
Cambodia | 762.49265 | 23.1891624 | 17,681.56589 | 0.081131201 |
Canada | 32,119.78235 | 66.3011898 | 2,129,579.786 | 0.914047019 |
Qatar | 41,906.77233 | 40.10925034 | 1,680,849.222 | 0.893543263 |
Kenya | 991.4103478 | 21.88609649 | 21,698.10253 | 0.097759253 |
China | 4024.179978 | 40.88628182 | 164,533.7567 | 0.45103677 |
Colombia | 3924.512348 | 16.72194429 | 65,625.47683 | 0.246821961 |
Comoros | 1026.33563 | 12.32517543 | 12,649.76669 | 0.059414761 |
Kosovo | 3568.725115 | 37.04882222 | 132,217.0624 | 0.397677086 |
Costa Rica | 6112.665304 | 21.47514828 | 131,270.3938 | 0.395957164 |
Kuwait | 21,791.50291 | 58.89016545 | 1,283,305.212 | 0.865016481 |
Croatia | 11,166.31776 | 48.78632917 | 544,763.654 | 0.731206736 |
Cyprus | 19,570.18613 | 96.38547321 | 1,886,281.651 | 0.90402458 |
Laos | 1176.850652 | 10.07284625 | 11,854.23568 | 0.055887051 |
Lesotho | 737.812587 | 25.16656642 | 18,568.20947 | 0.084854317 |
Latvia | 11,160.42656 | 27.780044 | 310,037.1409 | 0.607566311 |
Belarus | 4376.290912 | 18.03565875 | 78,929.28948 | 0.282712558 |
Lebanon | 4216.301 | 155.7440213 | 656,663.6727 | 0.766306827 |
Liberia | 572.9561538 | 114.8022563 | 65,776.65925 | 0.247249981 |
Libya | 7315.118935 | 34.80366514 | 254,592.9498 | 0.559730318 |
Lithuania | 12,884.44706 | 26.58728 | 342,562.4017 | 0.63108062 |
Luxembourg | 71,771.91498 | 203.1265164 | 14,578,779.06 | 0.986449971 |
Madagascar | 428.1648043 | 12.05560143 | 5161.784227 | 0.02512818 |
Macau | 55,512.1606 | 117.5139441 | 6,523,452.939 | 0.970216386 |
Malaysia | 6476.299 | 74.66777414 | 483,570.831 | 0.707153556 |
Malawi | 321.7748696 | 9.875763077 | 3177.772376 | 0.015620645 |
Maldives | 6137.711391 | 22.22559585 | 136,414.2928 | 0.40518617 |
Mali | 556.6702826 | 9.735537931 | 5419.484651 | 0.026349641 |
Malta | 16,371.22865 | 98.15051207 | 1,606,844.475 | 0.889183626 |
Morocco | 2105.063565 | 39.36468793 | 82,865.17032 | 0.292684099 |
Mauritius | 5798.680261 | 53.04348276 | 307,582.1964 | 0.605669246 |
Mauritania | 1358.574611 | 10.41531156 | 14,149.97785 | 0.06599608 |
Montenegro | 6659.721231 | 39.35027333 | 262,061.8508 | 0.566843018 |
Mexico | 6753.711522 | 20.36038052 | 137,508.1365 | 0.407112462 |
Myanmar | 884.0053571 | 10.40086453 | 9194.419962 | 0.043897759 |
Micronesia | 2971.332645 | 45.15800909 | 134,179.4666 | 0.401211399 |
Mongolia | 2353.007111 | 24.12824222 | 56,773.92553 | 0.220884185 |
Mozambique | 378.8300217 | 64.12319 | 24,291.78946 | 0.108180759 |
Moldova | 2293.259265 | 26.10301111 | 59,860.97207 | 0.230130654 |
Bangladesh | 816.0283111 | 25.55551703 | 20,854.0254 | 0.094314997 |
Barbados | 12,250.47987 | 59.274732 | 726,143.9111 | 0.78383387 |
Bahrain | 16,931.04087 | 52.90511579 | 895,738.6776 | 0.817283515 |
Belize | 3261.128261 | 44.75982857 | 145,967.5419 | 0.421598684 |
Benin | 841.8228913 | 13.95752741 | 11,749.76608 | 0.055421823 |
Botswana | 4544.127109 | 24.6508913 | 112,016.7834 | 0.358714111 |
Burkina Faso | 507.6985652 | 11.04747155 | 5608.785456 | 0.027244949 |
Burundi | 214.7511522 | 10.32790037 | 2217.928504 | 0.010954124 |
Bhutan | 1583.612457 | 35.96195588 | 56,949.8013 | 0.221416939 |
Brunei Darussalam | 25,530.34 | 60.16403158 | 1,536,008.182 | 0.884662529 |
Togo | 509.9262826 | 21.44249509 | 10,934.09181 | 0.051773608 |
Namibia | 3719.805568 | 40.153725 | 149,364.0498 | 0.427217762 |
New Zealand | 24,730.92985 | 51.30157857 | 1,268,735.741 | 0.863677714 |
Nepal | 500.352413 | 25.56723448 | 12,792.62747 | 0.060045474 |
Solomon Islands | 1437.595826 | 23.30498462 | 33,503.14861 | 0.143323147 |
Niger | 424.9241957 | 8.325938704 | 3537.892807 | 0.01736012 |
Nigeria | 1747.902889 | 11.9169569 | 20,829.68339 | 0.094215279 |
Nicaragua | 1488.527313 | 17.32443914 | 25,787.90083 | 0.114083413 |
Norway | 53,856.5135 | 45.46127931 | 2,448,386.003 | 0.924392827 |
South Africa | 4432.883065 | 50.76933269 | 225,054.5151 | 0.529152974 |
South Sudan | 663.147 | 11.71342167 | 7767.720438 | 0.037340487 |
Congo | 709.075587 | 4.891744529 | 3468.616623 | 0.017025976 |
Dominica | 5206.765311 | 55.0462 | 286,612.6447 | 0.588685336 |
Netherlands | 36,325.13478 | 65.76054464 | 2,388,760.648 | 0.922651585 |
Oman | 11,502.13563 | 24.75447913 | 284,729.3764 | 0.587088143 |
Honduras | 1591.371043 | 23.3126519 | 37,099.07917 | 0.156301785 |
Hungary | 9197.030696 | 37.41676944 | 344,123.1771 | 0.632138339 |
Uganda | 667.6110435 | 9.087937241 | 6067.207265 | 0.029406273 |
Ukraine | 2385.365941 | 22.49849615 | 53,667.14645 | 0.211351627 |
Uruguay | 8846.045239 | 27.06401862 | 239,409.5331 | 0.54452605 |
Pakistan | 785.969275 | 23.0577 | 18,122.64375 | 0.082987116 |
Panama | 7155.365457 | 42.26210121 | 302,400.7791 | 0.601604463 |
Papua New Guinea | 1807.420761 | 20.10353182 | 36,335.54077 | 0.15357897 |
Paraguay | 3066.18363 | 14.46734552 | 44,359.538 | 0.181343642 |
Peru | 3563.193457 | 19.2666231 | 68,650.70537 | 0.255295282 |
Poland | 8312.784217 | 35.81104211 | 297,689.4656 | 0.597835044 |
Portugal | 15,315.29693 | 67.97505172 | 1,041,058.101 | 0.838673948 |
Rwanda | 477.4838478 | 9.380479057 | 4479.027234 | 0.021877152 |
Romania | 6211.002087 | 24.16836486 | 150,109.7646 | 0.428436865 |
Russia | 7747.843139 | 24.5375792 | 190,113.3146 | 0.487008408 |
Samoa | 3335.969929 | 24.5607683 | 81,933.98446 | 0.290350062 |
San Marino | 52,486.87237 | 118.5947333 | 6,224,666.632 | 0.968831321 |
Saudi Arabia | 14,717.24824 | 15.68336438 | 230,815.9667 | 0.53544603 |
Senegal | 1067.875239 | 15.36006172 | 16,402.62959 | 0.075707126 |
Serbia | 5735.702414 | 24.29070947 | 139,324.281 | 0.410283342 |
Seychelles | 9352.321152 | 47.17883043 | 441,231.5738 | 0.68782521 |
Singapore | 33,568.2123 | 73.46759636 | 2,466,175.872 | 0.924897262 |
Sierra Leone | 402.4841739 | 7.594246316 | 3056.563955 | 0.015033791 |
Slovenia | 20,109.40482 | 43.12850741 | 867,288.6149 | 0.812414122 |
Sudan | 745.4045217 | 10.75698661 | 8018.306457 | 0.038498712 |
Sweden | 39,265.90278 | 49.51671277 | 1,944,318.43 | 0.90662188 |
Suriname | 4756.888944 | 29.2046522 | 138,923.2872 | 0.409586152 |
Sri Lanka | 1950.607239 | 20.844575 | 40,659.57889 | 0.168770812 |
Syria | 1543.908129 | 22.91218118 | 35,374.30277 | 0.150126058 |
Thailand | 3767.573739 | 63.76934444 | 240,255.7075 | 0.545400965 |
Tanzania | 653.5568913 | 14.86815111 | 9717.18262 | 0.046278128 |
Tajikistan | 564.1381471 | 6.947417368 | 3919.303161 | 0.019195741 |
Djibouti | 2010.667472 | 58.54555758 | 117,715.6483 | 0.37020758 |
Timor-Leste | 960.3431154 | 8.835965333 | 8485.558476 | 0.040650955 |
Tonga | 2847.121457 | 27.2775119 | 77,662.38942 | 0.279442784 |
Turkey | 6287.287609 | 24.09251759 | 151,476.5873 | 0.43065794 |
Trinidad and Tobago | 11,007.66691 | 33.55970526 | 369,414.0572 | 0.648469791 |
Chad | 500.2357609 | 4.078235091 | 2040.079034 | 0.010084602 |
Czech Republic | 17,373.39577 | 57.169572 | 993,229.6006 | 0.832208867 |
Tunisia | 2713.333391 | 33.2680463 | 90,267.30088 | 0.310705504 |
Philippines | 1859.722196 | 30.39393793 | 56,524.28098 | 0.220126722 |
Finland | 34,595.52224 | 45.48971379 | 1,573,740.405 | 0.887115712 |
Fiji | 3568.628217 | 35.8714 | 128,011.6902 | 0.389960855 |
Chile | 8181.431065 | 27.58627649 | 225,695.2195 | 0.529861209 |
International Average | 7398.40187 | 27.06751607 | 200,256.3615 | 0.5 |
Scenario | Gini Coefficient | Unemployment Rate (%) | Average Wages (USD) | Jobs Displaced by AI (%) |
---|---|---|---|---|
Baseline | 0.42 | 8 | 2500 | 15 |
With EPR | 0.35 | 6 | 2800 | 10 |
With EPR + EPI | 0.3 | 4.5 | 3200 | 5 |
Year | World | Advanced Economies | Emerging Market Economies |
---|---|---|---|
1950s | 96.8 | 110.1 | 27.7 |
1960s | 101.5 | 115.5 | 32.8 |
1968 | 106.6 | 119 | 38 |
1970s | 114.7 | 133.5 | 39.1 |
1980s | 144.3 | 165.8 | 64.9 |
1986 | 157.2 | 177.9 | 71.9 |
1990s | 180.6 | 202.9 | 86.6 |
2000s | 197.1 | 230.2 | 100.8 |
2004 | 198.9 | 226.5 | 102.1 |
2010s | 219.3 | 267.4 | 148.2 |
2019 | 228.9 | 268 | 171.1 |
2020 | 257.5 | 303.2 | 209 |
2021 | 247.2 | 288.6 | 194.7 |
2022 | 238 | 276.7 | 191.5 |
2023 | 237.2 | 270.2 | 191.7 |
Category | R-Squared | Interpretation | Coefficient (Debt % GDP/Year) |
---|---|---|---|
World Debt | 0.973 | Highly significant upward trend over time | 2.59 |
Advanced Economies Debt | 0.965 | Very strong trend, faster increase than world average | 3.08 |
Emerging Market Economies | 0.924 | Strong trend, slightly less linear than advanced economies | 3.34 |
Category | Coefficient | CI_Lower | CI_Upper | p Value |
---|---|---|---|---|
World | 2.59 | 2.33 | 2.85 | <0.001 |
Advanced Economies | 3.08 | 2.73 | 3.44 | <0.001 |
Emerging Markets | 3.34 | 2.71 | 3.97 | <0.001 |
Year | GNI per Capita (USD, PPP, World) | Global Gini Coefficient | Average Annual Wages (USD, PPP, World Estimate) |
---|---|---|---|
2000 | 80,045 | 0.7 | 11,656 |
2001 | 80,715 | 0.697 | 12,065 |
2002 | 81,355 | 0.695 | 12,354 |
2003 | 82,625 | 0.693 | 12,858 |
2004 | 84,975 | 0.69 | 13,331 |
2005 | 87,025 | 0.688 | 13,780 |
2006 | 89,755 | 0.684 | 14,429 |
2007 | 91,985 | 0.678 | 14,961 |
2008 | 91,595 | 0.672 | 15,257 |
2009 | 89,915 | 0.669 | 15,048 |
2010 | 92,815 | 0.666 | 15,136 |
2011 | 94,395 | 0.664 | 15,621 |
2012 | 95,825 | 0.662 | 16,100 |
2013 | 97,125 | 0.66 | 16,459 |
2014 | 99,255 | 0.658 | 16,746 |
2015 | 101,305 | 0.647 | 17,129 |
2016 | 102,865 | 0.646 | 17,537 |
2017 | 105,275 | 0.645 | 18,079 |
2018 | 107,345 | 0.643 | 18,487 |
2019 | 109,145 | 0.64 | 19,231 |
2020 | 110,221 | 0.634 | 19,806 |
2021 | 120,941 | 0.632 | 20,483 |
2022 | 128,501 | 0.63 | 21,165 |
2023 | 131,791 | 0.628 (est.) | 21,581 |
Variable | Coefficient | Std. Error | t-Statistic | p-Value |
---|---|---|---|---|
const | 51,511.96 | 5899.726 | 8.731246 | <0.001 |
GNI_per_Capita | 0.094413 | 0.011784 | 8.011986 | <0.001 |
Gini_Coefficient | −67,175.4 | 7235.279 | −9.28442 | <0.001 |
Concept | Supported by Plot | Interpretation in Economocracy |
---|---|---|
EPR (Redistribution) | Gini → Wages | Lower inequality drives long-term wage growth. Redistribution works. |
EPI (Injection) | Wages → Wages | Wage increases decay; hence, periodic injections are necessary. |
Structural vs. Cyclical | Gini → Gini/Wages → Gini | Inequality is persistent unless intervened upon; wages do not reduce Gini. |
Policy Targeting | Wages ↛ Gini | Wage growth alone is insufficient to promote equity. |
Variable | Coefficient | Std. Error | t-Statistic | p-Value | [0.025 | 0.975] | Interpretation |
---|---|---|---|---|---|---|---|
Intercept | 6857.95 | 258.43 | 26.54 | <0.001 | 6322.01 | 7393.90 | Estimated average annual wage in the baseline period (pre-2011) for the control group. |
Treatment | 6857.95 | 258.43 | 26.54 | <0.001 | 6322.01 | 7393.90 | Indicates the pre-treatment wage level for the treated group was not statistically different from the control group, as evidence of balance. |
DiD | 2312.20 | 351.13 | 6.58 | <0.001 | 1583.99 | 3040.41 | Represents the average treatment effect: EPR/EPI implementation after 2011 is associated with a statistically significant increase of USD 2312 in annual wages, controlling for baseline trends. |
Domain | Implication | How Economocracy Achieves It |
---|---|---|
Political Level | Enhance democratic legitimacy and reduce fiscal populism | Participatory budgeting through an EPI, binding fiscal decisions to public deliberation and measurable performance criteria, avoids opaque policymaking. |
Post-War Country Reconstruction | Stable reintegration of war-torn nations into the global economy | An EPR offers debt relief tied to productivity reforms, while an EPI channels international surpluses to fund essential services, building long-term institutional capacity. |
Private Sector Borrowing Security | Avoid private defaults and maintain investment confidence | An EPR guarantees fiscal stability that allows governments to act as credible guarantors, while an EPI ensures future economic capacity through social investment. |
Public Sector and Social Stability | Prevent civil unrest and maintain trust in government | An EPI translates economic growth into tangible improvements in public goods (health, education, infrastructure), reducing socioeconomic discontent. |
Healthcare | Universal access and sustainable funding | A percentage of the EPI is automatically allocated to public health, avoiding political manipulation and ensuring long-term financing based on national needs. |
Education | Equitable and forward-looking education systems | Economocracy embeds educational investment in the prosperity-sharing mechanism of EPIs, rewarding countries for increasing access, inclusion, and innovation. |
Debt Crisis Prevention | Avoid debt traps and recurrent financial crises | An EPR is applied automatically when productivity thresholds fall, preventing structural deficits and creating buffers against global shocks. |
Ecological Sustainability | Fiscal decisions consistent with environmental limits | An EPI can be programmed to decrease if ecological thresholds are exceeded, ensuring environmental considerations are built into national economic strategies. |
Global Cooperation (e.g., G7, G20, UN) | Coordinated economic governance across nations | Economocracy provides a multilateral fiscal rulebook aligned with shared targets, such as emissions, inequality, or health resilience, reducing divergence. |
Income Inequality | Reduce disparity without deterring innovation | Redistribution is rule-based and tied to EPI performance, not punitive, creating incentives for productive investment while narrowing gaps. |
AI Automation and Wage Recovery | Mitigate job losses and wage depression due to automation | An EPR compensates for structural wage declines through targeted injections, while an EPI supports re-skilling programs and technology-transition buffers. |
Space Programs and Innovation | Worldwide dedicated programs, collaborative exploration, and knowledge-sharing | An EPI supports collaborative research, tech transfer, and scientific education, preventing national rivalries and funding long-term global innovation. |
Year | World | Advanced Economies | Emerging Market Economies | EPR (GDP-Based) | EPI (GDP per Capita-Based) | EPR + EPI (GDP-Based) | EPR + EPI (GDP per Capita-Based) | EPR | EPI | EPR + EPI |
---|---|---|---|---|---|---|---|---|---|---|
1950s | 96.8 | 110.1 | 27.7 | 96.8 | 96.8 | 96.8 | 96.8 | 96.8 | 96.8 | 96.8 |
1960s | 101.5 | 115.5 | 32.8 | 92 | 93.1 | 88.4 | 88.4 | 93.8 | 93.08 | 90.19 |
1968 | 106.6 | 119 | 38 | 87.4 | 89.5 | 80.8 | 80.8 | 90.68 | 89.5 | 83.72 |
1970s | 114.7 | 133.5 | 39.1 | 83 | 86.1 | 73.8 | 73.8 | 87.44 | 86.06 | 77.38 |
1980s | 144.3 | 165.8 | 64.9 | 78.8 | 82.7 | 67.4 | 67.4 | 84.07 | 82.75 | 71.16 |
1986 | 157.2 | 177.9 | 71.9 | 74.9 | 79.6 | 61.6 | 61.6 | 80.56 | 79.57 | 65.05 |
1990s | 180.6 | 202.9 | 86.6 | 71.2 | 76.5 | 56.2 | 56.2 | 76.91 | 76.51 | 59.04 |
2000s | 197.1 | 230.2 | 100.8 | 67.6 | 73.6 | 51.4 | 51.4 | 73.11 | 73.57 | 53.12 |
2004 | 198.9 | 226.5 | 102.1 | 64.2 | 70.7 | 46.9 | 46.9 | 69.16 | 70.74 | 47.28 |
2010s | 219.3 | 267.4 | 148.2 | 61 | 68 | 42.9 | 42.9 | 65.05 | 68.02 | 41.51 |
2019 | 228.9 | 268 | 171.1 | 58 | 65.4 | 39.2 | 39.2 | 60.78 | 65.4 | 35.81 |
2020 | 257.5 | 303.2 | 209 | 55.1 | 62.9 | 35.8 | 35.8 | 56.34 | 62.88 | 30.16 |
2021 | 247.2 | 288.6 | 194.7 | 52.3 | 60.5 | 32.7 | 32.7 | 51.72 | 60.46 | 24.56 |
2022 | 238 | 276.7 | 191.5 | 49.7 | 58.1 | 29.8 | 29.8 | 46.92 | 58.13 | 19 |
2023 | 237.2 | 270.2 | 191.7 | 47.2 | 55.9 | 27.3 | 27.3 | 41.92 | 55.89 | 13.47 |
Aspect | Economocracy |
---|---|
Money Flow | Utilizes positive non-productive money through the EPR and EPI to balance debt and stimulate the economy |
Wealth Redistribution | Proportional democratic redistribution aiming to reduce inequality |
Governance | Integrates democratic principles within economic decision-making |
Labor Market | Targets unemployment and automation-related displacement via investment injections |
Environmental Sustainability | Embedded as a co-equal goal alongside social equity and economic efficiency |
Money Flow | Utilizes positive non-productive money through the EPR and EPI to balance debt and stimulate the economy |
Structural Problem | Economocracy Solution |
---|---|
Unbounded Debt Growth via Interest | Economic Productive Resets reduce debt proportionally across economies |
Market Failures and Inequality | Proportional redistribution aligned with democratic input |
Job Loss from Automation | Economic Periodic Injections invest in jobs and sustainable development |
Environmental Neglect | Integrated mandate for environmental stewardship alongside economic goals |
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Challoumis, C. Economocracy: Global Economic Governance. Economies 2025, 13, 230. https://doi.org/10.3390/economies13080230
Challoumis C. Economocracy: Global Economic Governance. Economies. 2025; 13(8):230. https://doi.org/10.3390/economies13080230
Chicago/Turabian StyleChalloumis, Constantinos. 2025. "Economocracy: Global Economic Governance" Economies 13, no. 8: 230. https://doi.org/10.3390/economies13080230
APA StyleChalloumis, C. (2025). Economocracy: Global Economic Governance. Economies, 13(8), 230. https://doi.org/10.3390/economies13080230