Local Income Tax

Local income tax research spans diverse fields, from analyzing its impact on property values and migration patterns using advanced econometric techniques like double machine learning, to leveraging graph representation learning for detecting tax evasion schemes like circular trading. Current research emphasizes developing sophisticated models, including transformer networks and contrastive learning approaches, to improve the accuracy and efficiency of tax-related analyses and predictions. These advancements have significant implications for both economic policy, informing optimal tax design and enforcement strategies, and for the development of more robust AI systems capable of handling complex real-world data.