The fiscal response to revenue shocks
- URL: http://arxiv.org/abs/2101.07661v1
- Date: Tue, 19 Jan 2021 14:51:28 GMT
- Title: The fiscal response to revenue shocks
- Abstract summary: We study the very volatile revenues from the immovable property gains tax in the canton of Zurich, Switzerland.
We apply machine learning strategies and implement the post-double-selection LASSO estimator to identify the causal effect of revenue shocks on public finances.
- Score: 0.12891210250935145
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study the impact of fiscal revenue shocks on local fiscal policy. We focus
on the very volatile revenues from the immovable property gains tax in the
canton of Zurich, Switzerland, and analyze fiscal behavior following large and
rare positive and negative revenue shocks. We apply causal machine learning
strategies and implement the post-double-selection LASSO estimator to identify
the causal effect of revenue shocks on public finances. We show that local
policymakers overall predominantly smooth fiscal shocks. However, we also find
some patterns consistent with fiscal conservatism, where positive shocks are
smoothed, while negative ones are mitigated by spending cuts.
Related papers
- TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph [41.897301755940454]
We propose a novel graph neural network model to improve the performance of tax evasion detection.<n>Specifically, we use heterogeneous and complex related party transaction groups to filter low-level noise information.<n>We apply our method to the real risk management system of the tax bureau, and evaluate it on two human-labeled real-world tax datasets.
arXiv Detail & Related papers (2026-05-26T13:09:37Z) - Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards [110.47944672736723]
We argue that gains are often overstated due to three forces - an RLVR tax, evaluation pitfalls, and data contamination.<n>We propose a tax-aware training and evaluation protocol that co-optimizes accuracy, grounding, and calibrated abstention.
arXiv Detail & Related papers (2025-09-26T05:06:25Z) - Can LLMs Identify Tax Abuse? [53.3007576756411]
We investigate whether large language models can discover and analyze U.S. tax-minimization strategies.<n>We evaluate the most advanced LLMs on their ability to (1) interpret and verify tax strategies, (2) fill in gaps in partially specified strategies, and (3) generate complete, end-to-end strategies from scratch.
arXiv Detail & Related papers (2025-08-10T15:15:45Z) - A Taxation Perspective for Fair Re-ranking [61.946428892727795]
We introduce a new fair re-ranking method named Tax-rank, which levies taxes based on the difference in utility between two items.
Our model Tax-rank offers a superior tax policy for fair re-ranking, theoretically demonstrating both continuity and controllability over accuracy loss.
arXiv Detail & Related papers (2024-04-27T08:21:29Z) - Fairness Incentives in Response to Unfair Dynamic Pricing [7.991187769447732]
We design a basic simulated economy, wherein we generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours.
To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and as a full reinforcement learning (RL) problem.
We find that social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings.
arXiv Detail & Related papers (2024-04-22T23:12:58Z) - Learning Optimal Tax Design in Nonatomic Congestion Games [63.89699366726275]
We study how to learn the optimal tax design to maximize the efficiency in nonatomic congestion games.
It is known that self-interested behavior among the players can alleviate the system's efficiency.
arXiv Detail & Related papers (2024-02-12T06:32:53Z) - Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax
Audit Models [73.24381010980606]
This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS.
We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity.
Our results have implications for the design of algorithmic tools across the public sector.
arXiv Detail & Related papers (2022-06-20T16:27:06Z) - Measuring the Impact of Taxes and Public Services on Property Values: A
Double Machine Learning Approach [0.0]
We use a novel dataset of 947 time-experimental local characteristic and public service controls for all municipalities in Sweden.
We find that properly controlling for local public service and characteristic controls more than doubles the estimated impact of local income taxes on house prices.
We also measure the impact of public services, education, and crime on house prices and the effect of local taxes on migration.
arXiv Detail & Related papers (2022-03-23T19:33:54Z) - Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features.
We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
arXiv Detail & Related papers (2020-12-21T01:01:56Z) - The AI Economist: Improving Equality and Productivity with AI-Driven Tax
Policies [119.07163415116686]
We train social planners that discover tax policies that can effectively trade-off economic equality and productivity.
We present an economic simulation environment that features competitive pressures and market dynamics.
We show that AI-driven tax policies improve the trade-off between equality and productivity by 16% over baseline policies.
arXiv Detail & Related papers (2020-04-28T06:57:18Z) - Corruption-robust exploration in episodic reinforcement learning [76.19192549843727]
We study multi-stage episodic reinforcement learning under adversarial corruptions in both the rewards and the transition probabilities of the underlying system.
Our framework yields efficient algorithms which attain near-optimal regret in the absence of corruptions.
Notably, our work provides the first sublinear regret guarantee which any deviation from purely i.i.d. transitions in the bandit-feedback model for episodic reinforcement learning.
arXiv Detail & Related papers (2019-11-20T03:49:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.