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1. Lessons from the Biggest Business Tax Cut in US History

   https://doi.org/10.1257/jep.38.3.61  

   Chodorow-Reich, Gabriel; Zidar, Owen; Zwick, Eric (August 2024, Journal of Economic Perspectives)

   Williams, Heidi (Ed.)

   We assess the business provisions of the 2017 Tax Cuts and Jobs Act, the biggest corporate tax cut in US history. We draw five lessons. First, corporate tax revenue fell by 40 percent due to the lower rate and more generous expensing. Second, firms with larger declines in their effective tax wedge increased investment relatively more. In aggregate, we suggest a loose consensus from the literature that total tangible corporate investment increased by 11 percent. Third, the business tax provisions increased economic growth and wages by less than advertised by the Act’s proponents, with long-run GDP higher by less than 1 percent and labor income by less than $1,000 per employee. Fourth, provisions that increase foreign investment by US-based multinationals also boost their domestic operations. Fifth, some of the expired and expiring provisions, such as accelerated depreciation, generate more investment per dollar of tax revenue than others. 

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2. On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software

   https://doi.org/10.18653/v1/2023.nllp-1.23  

   Srinivas, Dananjay; Das, Rohan; Tizpaz-Niari, Saeid; Trivedi, Ashutosh; Pacheco, Maria L (January 2023, Association for Computational Linguistics)

   Due to the ever-increasing complexity of in- come tax laws in the United States, the num- ber of US taxpayers filing their taxes using tax preparation software (henceforth, tax soft- ware) continues to increase. According to the U.S. Internal Revenue Service (IRS), in FY22, nearly 50% of taxpayers filed their individual income taxes using tax software. Given the legal consequences of incorrectly filing taxes for the taxpayer, ensuring the correctness of tax software is of paramount importance. Meta- morphic testing has emerged as a leading solu- tion to test and debug legal-critical tax software due to the absence of correctness requirements and trustworthy datasets. The key idea behind metamorphic testing is to express the proper- ties of a system in terms of the relationship between one input and its slightly metamor- phosed twinned input. Extracting metamor- phic properties from IRS tax publications is a tedious and time-consuming process. As a response, this paper formulates the task of gen- erating metamorphic specifications as a transla- tion task between properties extracted from tax documents—expressed in natural language—to a contrastive first-order logic form. We per- form a systematic analysis on the potential and limitations of in-context learning with Large Language Models (LLMs) for this task, and outline a research agenda towards automating the generation of metamorphic specifications for tax preparation software. 

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3. AUDIT STATE DEPENDENT TAXPAYER COMPLIANCE: THEORY AND EVIDENCE FROM COLOMBIA

   https://doi.org/10.1111/ecin.12864  

   Alm, James; Cox, James_C; Sadiraj, Vjollca (November 2019, Economic Inquiry)

   We develop and analyze a dynamic model of individual taxpayer compliance choice that predicts “audit state dependent taxpayer compliance,” by distinguishing between forward‐looking versus myopic versus naïve behavior. We then test experimentally the audit state dependent model by reporting the results from the first tax compliance experiment run in Colombia. We find that subjects' compliance rates increase with greater enforcement. We also find more novel results: fine rates should be increased after an audit, and “nudging” myopic individuals toward reporting a constant rather than a fluctuating proportion of income would benefit both the taxpayer and the tax authority.(JELH26, C91) 

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4. The Tax Elasticity of Capital Gains and Revenue-Maximizing Rates

   https://doi.org/10.1257/aeri.20200535  

   Agersnap, Ole; Zidar, Owen (December 2021, American Economic Review: Insights)

   This paper uses a direct-projections approach to estimate the effect of capital gains taxation on realizations at the state level and then develops a framework for determining revenue-maximizing rates at the federal level. We find that the elasticity of revenues with respect to the tax rate over a 10-year period is −0.5 to −0.3, indicating that capital gains tax cuts do not pay for themselves and that a 5 percentage point rate increase would yield $18 to $30 billion in annual federal tax revenue. Our long-run estimates yield revenue-maximizing capital gains tax rates of 38 to 47 percent. (JEL E62, H25, H71) 

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5. High Marginal Tax Rates on the Top 1 Percent? Lessons from a Life-Cycle Model with Idiosyncratic Income Risk

   https://doi.org/10.1257/mac.20150170  

   Kindermann, Fabian; Krueger, Dirk (April 2022, American Economic Journal: Macroeconomics)

   This paper argues that high marginal labor income tax rates on top earners are an effective tool for social insurance even when households have high labor supply elasticity, households make dynamic savings decisions, and policies have general equilibrium effects. We construct a large-scale overlapping generations model with uninsurable labor productivity risk, show that it has a realistic wealth distribution, and numerically characterize the optimal top marginal rate. We find that marginal tax rates for top 1 percent earners of 79 percent are optimal as long as the model earnings and wealth distributions display a degree of concentration as observed in US data. (JEL D15, D31, H21, H24, H55, J22, J31) 

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