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Microfounded Tax Revenue Forecast Model with Heterogeneous Population and Genetic Algorithm Approach

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Abstract

The ability of governments to accurately forecast tax revenues is essential for the successful implementation of fiscal programs. However, forecasting state government tax revenues using only aggregate economic variables is subject to Lucas’s critique, which is left not fully answered as classical methods do not consider the complex feedback dynamics between heterogeneous consumers, businesses, and the government. In this study we present an agent-based model with a heterogeneous population and genetic algorithm-based decision-making to model and simulate an economy with taxation policy dynamics. The model focuses on assessing state tax revenues obtained from regions or cities within countries while introducing consumers and businesses, each with unique attributes and a decision-making mechanism driven by an adaptive genetic algorithm. We demonstrate the efficacy of the proposed method on a small village, resulting in a mean relative error of \(5.44\% \pm 2.45\%\) from the recorded taxes over 4 years and \(4.08\% \pm 1.21\) for the following year’s assessment. Moreover, we demonstrate the model’s ability to evaluate the effect of different taxation policies on economic activity and tax revenues.

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Data Availability

The data used as part of this study is partially published: https://rosstat.gov.ru/compendium/document/132822. https://rosstat.gov.ru/compendium/document/132843. https://vestnikramn.spr-journal.ru/jour/article/view/41. The business’s yearly reports are protected by privacy law according to clauses 2–11 of part 1 of article 6, part 2 of article 10, and part 2 of article 11 of the Russian federal law.

Code Availability

Upon acceptance, we will publish all the source code used in a GitHub repository.

Notes

  1. Under the Constitution of the Russian Federation, all state bodies are divided into 1) federal entities, 2) bodies of the “constituent subjects” of the Russian Federation, and 3) local (municipal) bodies. The holder of the highest office in the Russian Federation is the President, which appoints the Prime Minister and the Chairman of the Central Bank. The Government of the Russian Federation exercises executive power at the federal level, with the Prime Minister acting as its head, and the Parliament exercises legislative powers at the federal level. The Russian Federation consists of 85 “constituent subjects” as regions within the federation, and they granted a certain degree of autonomy over their internal economic and political affairs. Regional powers include the authority to manage a regional property, establish regional budgets, collect regional taxes, and maintain law and order. The lowest level of the political system is the local government (Municipalities) that have their budgets and may enjoy certain limited taxation powers.

  2. https://rosstat.gov.ru/compendium/document/13282.

  3. https://rosstat.gov.ru/compendium/document/13282.

  4. https://vestnikramn.spr-journal.ru/jour/article/view/41.

  5. https://www.statista.com/statistics/1269963/russia-minimum-wage/.

  6. https://www.statista.com/statistics/1010660/russia-average-monthly-nominal-wage/.

  7. https://www.ceicdata.com/en/indicator/russia/gross-savings-rate.

  8. https://www.statista.com/statistics/248023/us-gross-domestic-product-gdp-by-state/

  9. https://tradingeconomics.com/united-states/

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  1. All three authors contribute equaly to this work.

Authors and Affiliations

  1. Department of Information Science, Bar-Ilan University, Ramat-Gan, Israel

    Ariel Alexi

  2. Department of Cancer Biology, Cancer Institute, University College London, London, UK

    Teddy Lazebnik

  3. Department of Economics, Western Galilee College, Acre, Israel

    Labib Shami

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  1. Ariel Alexi
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Formal analysis and investigation, and manuscript editing were performed by Ariel Alexi; Conceptualization, formal analysis and investigation, software, original draft preparation, and original draft preparation were performed by Teddy Lazebnik; Formal analysis and investigation and manuscript editing were performed by Labib Shami.

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Alexi, A., Lazebnik, T. & Shami, L. Microfounded Tax Revenue Forecast Model with Heterogeneous Population and Genetic Algorithm Approach. Comput Econ (2023). https://doi.org/10.1007/s10614-023-10379-2

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