Bias in Tax Progressivity Estimates

IF 1.8 3区 经济学 Q2 BUSINESS, FINANCE National Tax Journal Pub Date : 2023-05-19 DOI:10.1086/724186
Johannes König
{"title":"Bias in Tax Progressivity Estimates","authors":"Johannes König","doi":"10.1086/724186","DOIUrl":null,"url":null,"abstract":"Tax progressivity is central in public and political debates when questions of vertical equity are raised. Applied, structural research demands a simple way to capture it. A power function approximation delivers one parameter that captures the residual income elasticity — a summary measure of progressivity. This approximation is accurate, tractable, and interpretable, and hence immensely popular. The most common procedure to estimate this parameter, a log ordinary least squares specification, produces biased and inconsistent estimates. A nonlinear estimator solves this issue and, using different data sets, I find differences in estimates between 6 and 14 percent.","PeriodicalId":18983,"journal":{"name":"National Tax Journal","volume":"76 1","pages":"267 - 289"},"PeriodicalIF":1.8000,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"National Tax Journal","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1086/724186","RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 1

Abstract

Tax progressivity is central in public and political debates when questions of vertical equity are raised. Applied, structural research demands a simple way to capture it. A power function approximation delivers one parameter that captures the residual income elasticity — a summary measure of progressivity. This approximation is accurate, tractable, and interpretable, and hence immensely popular. The most common procedure to estimate this parameter, a log ordinary least squares specification, produces biased and inconsistent estimates. A nonlinear estimator solves this issue and, using different data sets, I find differences in estimates between 6 and 14 percent.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
税收累进性估计的偏差
当纵向公平问题被提出时,税收累进性是公共和政治辩论的核心。应用和结构研究需要一种简单的方法来捕获它。幂函数近似值提供了一个参数来捕捉剩余收入弹性——累进性的汇总度量。这种近似是准确的、易于处理的和可解释的,因此非常受欢迎。估计该参数的最常用程序是对数普通最小二乘规范,它会产生有偏差和不一致的估计。非线性估计器解决了这个问题,使用不同的数据集,我发现估计的差异在6%到14%之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.40
自引率
11.80%
发文量
38
期刊介绍: The goal of the National Tax Journal (NTJ) is to encourage and disseminate high quality original research on governmental tax and expenditure policies. Articles published in the regular March, June and September issues of the journal, as well as articles accepted for publication in special issues of the journal, are subject to professional peer review and include economic, theoretical, and empirical analyses of tax and expenditure issues with an emphasis on policy implications. The NTJ has been published quarterly since 1948 under the auspices of the National Tax Association (NTA). Most issues include an NTJ Forum, which consists of invited papers by leading scholars that examine in depth a single current tax or expenditure policy issue. The December issue is devoted to publishing papers presented at the NTA’s annual Spring Symposium; the articles in the December issue generally are not subject to peer review.
期刊最新文献
Territorial Tax Reform and Profit Shifting by Us and Japanese Multinationals Public Housing Authorities’ Housing Choice Voucher Policies Can Affect SSI Participation Three Decades of Tax Analysis, 1992–2022 Agenda-Setting and Tax Referenda: Implications for Regression Discontinuity Identification Strategy Using Election Outcomes Automated Tax Filing: Simulating a PrePopulated Form 1040
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1