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Cited by 332

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

THE OPTIMAL SUPPLY OF PUBLIC GOODS AND THE DISTORTIONARY COST OF TAXATION

KAPLOW, LOUIS·1996·National Tax Journal

Abstract

The conventional view of economists is that the optimal supply of public goods must directly take into account the cost of financing them with distortionary taxes, notably the income tax. However, this article demonstrates, under standard simplifying assumptions, that it is possible to finance a public good in a manner that results in no additional distortion, by using an adjustment to the income tax that offsets the benefits of the public good. In this case, it is optimal to supply the public good whenever the simple costbenefit test is satisfied. The article also shows that if a different tax adjustment is made, the difference in outcome will be purely redistributive; thus, any change in distortionary costs will be accompanied by an opposing change in redistributive benefits. In this case, the cost-benefit test should be modified to reflect both of these changes. Finally, it is explained that the present analysis is fully applicable to determining the optimal level of environmental taxes.

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Metadata

Authors
KAPLOW, L.
Venue
National Tax Journal
Published
1996-12-01
Volume
49
Issue
4
Pages
513-533
ISSN
  • 0028-0283 (ISSN-L)
  • 1944-7477
OpenAlex
W3136089047
Cited by
332
References
1
FWCI
15.10
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: THE OPTIMAL SUPPLY OF PUBLIC GOODS AND THE DISTORTIONARY COST OF TAXATION. https://app.cochat.ai/papers/10.1086/ntj41789224
The paper
KAPLOW, L. (1996). THE OPTIMAL SUPPLY OF PUBLIC GOODS AND THE DISTORTIONARY COST OF TAXATION. National Tax Journal. https://doi.org/10.1086/ntj41789224

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.