E‐Government and Tax Evasion: Does the Free Press Connect the Dots?
Published In: Journal of Public Affairs (14723891), 2025, v. 25, n. 1. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Kuzey, Cemil; Uyar, Ali; Nimer, Khalil 3 of 3
Abstract
This study, for the first time, tests the moderating effect of press freedom on the association between e‐government and tax evasion. It aims to suggest policymaking better to combat tax evasion through e‐government and the press. The study sample covers the period between 2002 and 2017 and includes 2202 country‐year records affiliated with 138 countries. The time‐fixed effect was executed to test the proposed hypotheses. The conclusions drawn from the study are as follows. First, the study finds that e‐government practices with four proxies curtail tax evasion through a long‐term vision of public administration, governments' adaptability to change, delivering online services to the citizens by governments, and crafting a legal framework for digital business services. Second, the study finds that all three proxies of press freedom (i.e., legal, economic, and political) are significant predictors of tax evasion implying that tax evasion is curbed in countries where the press is free in terms of highlighted dimensions of media freedom. Third, the free press, with its all three proxies, moderates and strengthens the relationship between e‐government implementation and tax evasion. To fully realize the benefits obtained from e‐government implementations, states worldwide need to build a better institutional environment one dimension of which is the free press. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Journal of Public Affairs (14723891). 2025/02, Vol. 25, Issue 1, p1
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2025
- ISSN:1472-3891
- DOI:10.1002/pa.70002
- Accession Number:183822181
- Copyright Statement:Copyright of Journal of Public Affairs (14723891) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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