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From policy to politics? Exploring feedback effects of social protection on state‐citizen relations in Ghana.

  • Published In: Social Policy & Administration, 2023, v. 57, n. 6. P. 902 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ibrahim, Mohammed 3 of 3

Abstract

This paper addresses two interrelated questions: Do social protection policies generate feedback effects on state‐citizen relations? If so, how do such policies change the perception and interaction of both citizens and the state? The paper is based on an in‐depth mixed methods study on Ghana's Livelihood Empowerment Against Poverty cash transfer programme (LEAP) conducted in two implementation districts. I find emergent positive resource and interpretive/policy learning effects on state‐citizen perceptions and interactions. This is evidenced in increased state presence in service provision and changing citizen recognition of the state as a legitimate provider of their welfare, with these impacts being higher among beneficiaries with previously limited access to or negative perception about the state. Notwithstanding, these effects may be limited, unsustainable or undermined partly due to the way in which LEAP is promoted and delivered as a clientelist resource. The paper highlights the ideational and relational impact of social protection by arguing that when implemented well, social policies can generate political feedback effects beyond their poverty reduction and human development impacts by fostering policy learning opportunities for reconfiguring the perceptions of and interactions between the state and citizens. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Social Policy & Administration. 2023/11, Vol. 57, Issue 6, p902
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2023
  • ISSN:0144-5596
  • DOI:10.1111/spol.12938
  • Accession Number:173657239
  • Copyright Statement:Copyright of Social Policy & Administration 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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