JOURNAL ARTICLE
Divine Equity in Islamic Inheritance Law: An Analytical Exegesis of Qur'an 4:11 and Its Jurisprudential Applications.
Published In: Manchester Journal of Transnational Islamic Law & Practice, 2025, v. 21, n. 4. P. 223 1 of 3
Database: Legal Source 2 of 3
Authored By: Estaity, Mohannad Fuad Mohammad 3 of 3
Abstract
This article critically examines the Qur'anic verse in Surat al-Nisā? (4:11), which outlines the principles of inheritance distribution. Focusing on the shares allocated to lineal descendants, grandchildren through sons, and parents, the study argues that Qur'anic inheritance rules reflect a deliberate balance between justice and equality. It contends that these rules uphold the dignity and rights of both genders while proportionately adjusting share allocations based on familial responsibilities. Employing inductive, descriptive, and analytical methods, the article traces classical interpretations from the Companions and early exegetes, identifying areas of consensus and divergence. These views are then assessed in light of the Qur'an and Sunnah to draw jurisprudential conclusions. The findings affirm that Islamic inheritance law promotes gender equality in principle, while tailoring distribution to the social and economic roles within the family. The article concludes by advocating broader recognition of these principles in contemporary legal discourse, emphasising the protection of women's and children's rights within a framework that respects religious particularities. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Manchester Journal of Transnational Islamic Law & Practice. 2025/10, Vol. 21, Issue 4, p223
- Document Type:Article
- Subject Area:Law
- Publication Date:2025
- ISSN:17423945
- Accession Number:191252373
- Copyright Statement:Copyright of Manchester Journal of Transnational Islamic Law & Practice is the property of ElectronicPublications.org Ltd 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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