JOURNAL ARTICLE

Brief of Amici Curiae economists in support of respondents in Dobbs v. Jackson Women's Health Organization.

  • Published In: Perspectives on Sexual & Reproductive Health, 2024, v. 56, n. 3. P. 211 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Myers, Caitlin; Srinivasan, Anjali 3 of 3

Abstract

A pillar of Mississippi's argument in Dobbs v. Jackson Women's Health was that there is no evidence of "societal reliance" on abortion, meaning no reason to believe that access to abortion impacts the ability of women to participate in the economic and social life of the nation. Led by economist Caitlin Myers and attorney Anjali Srinivasan, more than 150 economists filed an amicus brief seeking to assist the Court in understanding that this assertion is erroneous. The economists describe developments in causal inference methodologies over the last three decades, and the ways in which these tools have been used to isolate the measure of the effects of abortion legalization in the 1970s and of abortion policies and access over the ensuing decades. The economists argue that there is a substantial body of well‐developed and credible research that shows that abortion access has had and continues to have a significant effect on birth rates as well as broad downstream social and economic effects, including on women's educational attainment and job opportunities. What follows is a reprint of this brief. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Perspectives on Sexual & Reproductive Health. 2024/09, Vol. 56, Issue 3, p211
  • Document Type:Article
  • Subject Area:Women's Studies and Feminism
  • Publication Date:2024
  • ISSN:1538-6341
  • DOI:10.1111/psrh.12268
  • Accession Number:181226184
  • Copyright Statement:Copyright of Perspectives on Sexual & Reproductive Health 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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