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DAC Peer Review of the United Kingdom

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Abstract

This review contains the Main Findings and Recommendations of the Development Assistance Committee (DAC) and the report of the Secretariat. It was prepared with examiners from the United States and Italy for the Peer Review meeting held on 31 May 2006. The review welcomed the United Kingdom’s strong commitment to the Millennium Development Goals and to increase development aid to 0.7% of its Gross National Income by 2013. The Committee recognised the effectiveness of the United Kingdom’s distinctive development co-operation model, characterised by a strong legislative basis, clarity of mission and unified programme under a Cabinet Minister. It identified several opportunities (e.g. new tactics in working with donors, identifying policy coherence priorities, more strategic involvement of civil society, greater institutional clarity around humanitarian aid) to make improvements that would better place UK development co-operation to address the future challenges of scaling up aid volume, moving into more difficult operational environments in fragile states and clearly demonstrating development results.

Suggested Citation

  • Oecd, 2008. "DAC Peer Review of the United Kingdom," OECD Journal on Development, OECD Publishing, vol. 7(3), pages 7-114.
    • Handle: RePEc:oec:dcdkaa:5kzsmg05fcnw
    DOI: 10.1787/journal_dev-v7-art37-en
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    Cited by:

    1. ederico Bianchi & Flaminio Squazzoni, 2022. "Can transparency undermine peer review? A simulation model of scientist behavior under open peer review [Reviewing Peer Review]," Science and Public Policy, Oxford University Press, vol. 49(5), pages 791-800.
    2. Cathal O'Donoghue & Karyn Morrissey & John Lennon, 2014. "Spatial Microsimulation Modelling: a Review of Applications and Methodological Choices," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 26-75.
    3. Robert Tanton & Paul Williamson & Ann Harding, 2014. "Comparing Two Methods of Reweighting a Survey File to Small Area Data," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 76-99.
    4. Sun, Zhuanlan & Clark Cao, C. & Ma, Chao & Li, Yiwei, 2023. "The academic status of reviewers predicts their language use," Journal of Informetrics, Elsevier, vol. 17(4).
    5. Siddarth Srinivasan & Jamie Morgenstern, 2021. "Auctions and Peer Prediction for Academic Peer Review," Papers 2109.00923, arXiv.org, revised May 2023.
    6. Maciej J. Mrowinski & Agata Fronczak & Piotr Fronczak & Olgica Nedic & Aleksandar Dekanski, 2020. "The hurdles of academic publishing from the perspective of journal editors: a case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 115-133, October.

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