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Cited by 331

The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.

The lessons of failure: learning and blame avoidance in public policy-making

Howlett, Michael·2012·International Political Science Review

Abstract

Recent studies by Hood have underscored the significance of the desire of decision-makers to avoid blame for poor policy initiatives, highlighting the importance to policy-making of learning about how best to avoid policy failure. This article examines several different concepts of policy failure in the literature on the subject, such as policy accidents, errors, mistakes, and anomalies, along with recent work by McConnell and his colleagues on the general types and sources of such failures. The article distinguishes between ‘thin’ (technical-strategic) and ‘thick’ (political-experiential) policy learning and links them to McConnell’s three categories of political, programme, and process failures. The analysis points to the significant and underappreciated roles played by process and political problems in the analysis of policy failure and the need to draw lessons in these areas as well as in more technically oriented programme-related ones if the prospects of policy success are to be enhanced.

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Metadata

Authors
Howlett, M.
Venue
International Political Science Review
Published
2012-10-24
Volume
33
Issue
5
Pages
539-555
ISSN
  • 0192-5121 (ISSN-L)
  • 1460-373X
OpenAlex
W2169552471
Cited by
331
References
77
FWCI
69.20
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The lessons of failure: learning and blame avoidance in public policy-making. https://app.cochat.ai/papers/10.1177/0192512112453603
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Howlett, M. (2012). The lessons of failure: learning and blame avoidance in public policy-making. International Political Science Review. https://doi.org/10.1177/0192512112453603

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.