JOURNAL ARTICLE

Gender Differences in Financial Literacy Between and Within Generations.

  • Published In: Journal of Financial Counseling & Planning, 2024, v. 35, n. 3. P. 381 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Potrich, Ani Caroline Grigion; Matheis, Taiane Keila; Vieira, Kelmara Mendes 3 of 3

Abstract

The study seeks to identify differences in financial literacy between genders, between generations, and between genders within the same generation. For this purpose, 2,888 instruments were collected in different Brazilian regions, and structural equation modeling and multigroup analysis were used. The results demonstrate that the model for the measurement of financial literacy presented no variation for all groups (gender, generation, and gender and generation). Results show that women are less financially literate than men and that the differences are statistically similar between the generations. Generation Z (aged 18–26 years, in 2022) presents a lower financial literacy level than Generations Y (aged 27–41 years, in 2022) and X (aged 42–56 years, in 2022), and it is more similar to Baby Boomers (aged 57–78 years, in 2022). This study shows that, despite efforts made to expand financial literacy, there are still significant differences between genders and these differences are not decreasing between generations. Thus, the need for improvement and specification of the strategies adopted in the search for reducing these differences is evident. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Financial Counseling & Planning. 2024/11, Vol. 35, Issue 3, p381
  • Document Type:Article
  • Subject Area:Economics
  • Publication Date:2024
  • ISSN:1052-3073
  • DOI:10.1891/JFCP-2023-0058
  • Accession Number:181090479
  • Copyright Statement:Copyright of Journal of Financial Counseling & Planning is the property of Springer Publishing Company, Inc. 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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