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跨国企业利润核算:境外附属企业收入的重复计算与错误归属

Accounting for the profits of multinational enterprises: Double counting and misattribution of foreign affiliate income
Journal of Public Economics · 2025 · [{"name": "Jennifer Blouin", "affiliation": ["Wharton County Junior College", "William P. Wharton Trust"]}, {"name": "Leslie A. Robinson", "affiliation": ["Dartmouth Hospital"]}]

中文摘要

• 过去十年间,围绕国际税收政策展开了一场根本性的辩论,并催生了近期已实施(或拟议中)的、与现状截然不同的改革。 • 为给国际税收政策提供指引,研究人员既需以忠实反映收入产生地的方式衡量跨国企业(MNE)收入在各司法管辖区间的分布,也需衡量跨国企业就其在各个不同司法管辖区的收入所缴纳的税率。用于报告跨国企业活动的会计方法会影响对这两个概念的衡量。我们所记录的衡量问题,不同程度地影响了所有按司法管辖区分列的跨国企业数据。 • 忽视指导跨国企业数据编制的会计惯例,可能导致两类衡量错误——收入的重复计算与收入的错误归属。第一类错误,即重复或“双重”计算,是指研究人员将同一美元的跨国企业收入计入两个或更多不同的司法管辖区,从而重复计算。第二类错误,即错误归属,是指研究人员未能在跨国企业收入实际产生的司法管辖区确认这部分收入。 • 本文所强调的会计问题,会影响跨国企业总利润的估计、有效税率的衡量、政府因利润转移而遭受的税收收入损失金额,以及收入对税收敏感度(或半弹性)的估计。 • 例如,在美国 2017 年《减税与就业法案》通过前夕发表的一项具有影响力的研究估计,美国每年因利润转移损失的税收收入在 770 亿至 1110 亿美元之间。当我们重新审视该研究(Clausing 2016),在保持其研究方法不变的前提下,纠正跨国企业收入的重复计算与错误归属后,这一估计值降至每年 110 亿美元。 鉴于人们普遍认为跨国企业从事大规模的税收筹划,终止税基侵蚀与利润转移活动已成为许多国家议程中的优先事项。然而,此类活动的实际规模仍存在争议。本文就如何利用利润转移研究中常用的数据集,准确衡量国家层面的跨国企业收入提供指导。这一问题具有全球性,因为任何按司法管辖区报告利润的经济数据,都必须采用既定的会计方法来报告跨国企业间接持有的境外附属企业的活动。我们说明了该会计方法可能如何导致研究人员对收入进行重复计算,或将其归属到错误的司法管辖区。此类错误不仅影响对跨国企业国家层面利润的衡量,也会使研究人员对收入相对于税收之敏感度的估计产生偏误。尽管我们的分析聚焦于美国经济分析局(BEA)的数据,但我们通过多项采用不同数据来源的研究,展示了此类衡量错误所带来的后果。

Abstract

• A fundamental debate about international tax policy has ensued over the past decade, leading to recently enacted (or proposed) reforms that represent radical departures from the status quo. • To guide international tax policy, researchers must measure both the distribution of MNE income across jurisdictions, in a way that faithfully represents where the income was generated, and the rate of tax paid by the MNEs on their income in each different jurisdiction. The accounting methods used to report MNE activity affect the measurement of each of these constructs. The measurement issues we document impact to various degrees all MNE data that are disaggregated by jurisdiction. • Overlooking the accounting conventions that guide MNE data can result in two kinds of mismeasurement – duplicative counting of income and misattribution of income. The first error – duplicative or “double” counting – arises when a researcher counts the same dollar of MNE income more than once by including it in two or more different jurisdictions. The second error – misattribution – arises when a researcher fails to recognize a dollar of MNE income in the jurisdiction where it was earned. • The accounting issues we highlight in this paper affect estimates of the aggregate profits of MNEs, measurement of effective tax rates, the amount of revenue losses governments experience due to profit shifting, as well as estimates of the sensitivity or semi-elasticity of income to taxes. • For example, an influential study published just prior to the passage of the 2017 U.S. Tax Cuts and Jobs Act estimated that between $77 and $111 billion dollars in U.S. revenue was being lost to profit shifting each year. When we reexamine that study ( Clausing 2016 ), correcting for double counting and misattributed MNE income, while otherwise holding constant the study’s methodology, this estimate drops to $11 billion a year. Given the perception that multinational enterprises (MNEs) engage in extensive tax planning, ending base erosion and profit shifting activity is a priority on many national agendas. Yet the actual level of such activity is subject to debate. In this paper, we provide guidance on how to accurately measure country-level MNE income using the datasets commonly used in research on profit shifting. This issue is of global concern, as any economic data that reports profits by jurisdiction must use an established accounting method to report the activity of the MNEs’ indirectly owned foreign affiliates. We explain how the accounting method could lead a researcher to double count income or to attribute it to the wrong jurisdiction. Such errors not only affect measures of the MNEs’ country-level profits, but also bias researchers’ estimates of the sensitivity of income to taxes. Although we focus our analysis on data from the U.S. Bureau of Economic Analysis (BEA), we illustrate the consequences of such mismeasurement across a variety of studies relying on a variety of data sources.
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