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Tax Credits and Household Behavior: The Roles of Myopic Decision-Making and Liquidity in a Simulated Economy

Kshama Dwarakanath, Jialin Dong, Svitlana Vyetrenko

TL;DR

This work considers a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government, and proposes an innovative tax credit distribution strategy for the government to reduce inequality among households.

Abstract

There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL) based models that focus solely on a single type of agents, such as households, firms, or the government. Such an approach overlooks the adaptation of interacting agents thereby failing to capture the complexity of real-world economic systems. In this work, we consider a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government. In particular, we focus on the crucial role of the government in distributing tax credits to households. We conduct two broad categories of comprehensive experiments dealing with the impact of tax credits on 1) households with varied degrees of myopia (short-sightedness in spending and saving decisions), and 2) households with diverse liquidity profiles. The first category of experiments examines the impact of the frequency of tax credits (e.g. annual vs quarterly) on consumption patterns of myopic households. The second category of experiments focuses on the impact of varying tax credit distribution strategies on households with differing liquidities. We validate our simulation model by reproducing trends observed in real households upon receipt of unforeseen, uniform tax credits, as documented in a JPMorgan Chase report. Based on the results of the latter, we propose an innovative tax credit distribution strategy for the government to reduce inequality among households. We demonstrate the efficacy of this strategy in improving social welfare in our simulation results.

Tax Credits and Household Behavior: The Roles of Myopic Decision-Making and Liquidity in a Simulated Economy

TL;DR

This work considers a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government, and proposes an innovative tax credit distribution strategy for the government to reduce inequality among households.

Abstract

There has been a growing interest in multi-agent simulators in the domain of economic modeling. However, contemporary research often involves developing reinforcement learning (RL) based models that focus solely on a single type of agents, such as households, firms, or the government. Such an approach overlooks the adaptation of interacting agents thereby failing to capture the complexity of real-world economic systems. In this work, we consider a multi-agent simulator comprised of RL agents of numerous types, including heterogeneous households, firm, central bank and government. In particular, we focus on the crucial role of the government in distributing tax credits to households. We conduct two broad categories of comprehensive experiments dealing with the impact of tax credits on 1) households with varied degrees of myopia (short-sightedness in spending and saving decisions), and 2) households with diverse liquidity profiles. The first category of experiments examines the impact of the frequency of tax credits (e.g. annual vs quarterly) on consumption patterns of myopic households. The second category of experiments focuses on the impact of varying tax credit distribution strategies on households with differing liquidities. We validate our simulation model by reproducing trends observed in real households upon receipt of unforeseen, uniform tax credits, as documented in a JPMorgan Chase report. Based on the results of the latter, we propose an innovative tax credit distribution strategy for the government to reduce inequality among households. We demonstrate the efficacy of this strategy in improving social welfare in our simulation results.
Paper Structure (21 sections, 6 equations, 7 figures, 1 table)

This paper contains 21 sections, 6 equations, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. Agent-based modeling in Economics.
  4. Agent-based economic modeling and Reinforcement Learning (RL)
  5. Agent-based economic modeling and Multi-agent Reinforcement Learning (MARL)
  6. Myopic decision-making in humans.
  7. Myopia and household decision-making.
  8. Tax Credit studies
  9. Economic model
  10. Households
  11. Firm
  12. Central Bank
  13. Government
  14. Experimental Framework
  15. Multi-Agent Simulator
  16. ...and 6 more sections

Figures (7)

  • Figure 1: Training rewards with quarterly credits (first row) and annual credits (second row).
  • Figure 2: Household observables over time with quarterly credits (first row) and annual credits (second row).
  • Figure 3: Distribution of average household observables with quarterly credits (first row) and annual credits (second row).
  • Figure 4: Training rewards without tax credits.
  • Figure 5: Household observables and government reward for social welfare in absence of tax credits (first row), presence of equal tax credits (second row) and presence of learned distribution of tax credits (third row).
  • ...and 2 more figures