Authors

Rajeswaran Ayyadurai

IL Health & Beauty Natural Oils Co Inc, California, USA

Karthikeyan Parthasarathy

Principal Data Engineering, LTI Mindtree Limited, New Jersey, USA

Naresh Kumar Reddy Panga

Engineering Manager, Virtusa Corporation, New York, NY, USA

Jyothi Bobba

LEAD IT Corporation, Springfield, Illinois, USA

R Padmavathy

Anna University, Coimbatore, Tamilnadu, India

Abstract

The intricate interaction between tax rates on labor and employment in the economies of post-reform OECD is examined here, based on reinforcement learning (RL) to solve for tax policies. Policymakers seek the equilibrium between generating tax revenues and preserving labor market participation, but the nonlinear effects of tax policies on labor supply elasticity and revenue efficiency are not yet well understood. Employing a multi-step regression model on a two-decade cross-country panel data set, the research introduces RL algorithms to maximize tax rates and welfare spending. The findings present a nonlinear, inverted-U-shaped Laffer Curve with higher tax rates raising revenue at the beginning but decreasing employment and revenue thereafter. Public spending on complements like childcare and eldercare offsets the disincentive effects of taxation on work, highlighting the need for coordination between taxation and social policy. The RL-based optimization more precisely improves tax policy suggestions, providing a model for balancing social spending and tax rates in order to improve fiscal discipline and labor market durability in post-reform economies. This research offers important information for policy design so that tax systems are not only efficient but also equitable.

Keywords

Taxation Labor Elasticity Revenue Efficiency Public Expenditure Reinforcement Learning

Citation of this Article

Rajeswaran Ayyadurai, Karthikeyan Parthasarathy, Naresh Kumar Reddy Panga, Jyothi Bobba, & R Padmavathy, “AI-Based Reinforcement Learning Framework Enhances Fiscal Policy by Modeling Labor Elasticity and Tax Revenue Tradeoffs” Published in International Current Journal of Engineering and Science - ICJES, Volume 2, Issue 4, pp 5-20, August 2023. Article DOI: https://doi.org/10.47001/ICJES/2023.204002

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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