Statistical capacity building for sustainable development: Developing the fundamental pillars necessary for modern national statistical systems
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Date
2017-11-24
Authors
MacFeely, Stephen
Barnat, Nour
Journal Title
Journal ISSN
Volume Title
Publisher
IOS Publishing
Published Version
Abstract
This article argues that to prioritise the data requirements of the Sustainable Development Goal (SDG) monitoring framework, requiring 232 global indicators and spanning the full spectrum of development issues, over the development of national statistical systems would be a mistake. Rather, countries and international organisations should prioritise the development of efficient national statistical systems that are sufficiently flexible, responsive and affordable to satisfy the enormous appetite of the SDG monitoring framework but also national and regional information requirements. The growing recognition of the importance of good quality, independent official statistics to support development and progress, provides a unique opportunity to make a real and long-lasting investment to improve national statistical systems. But this will require coordinated investment and political support from countries, donors and international organisations. The three core pillars necessary for a modern statistical system are detailed: a robust legal framework; functioning institutional coordination; and a logical data infrastructure. Without these pillars countries will not be able to build statistical systems appropriate to a data driven world. Nor will they be able to meet existing and future demands for information, including the SDG monitoring framework.
Description
Keywords
Legal framework , Institutional environment , Data infrastructure , SDGs , Sustainable Development Goal (SDG)
Citation
MacFeely, S. and Barnat, N. (2017) 'Statistical capacity building for sustainable development: Developing the fundamental pillars necessary for modern national statistical systems', Statistical Journal of the IAOS, 33 (4), pp. 895-909, doi: 10.3233/SJI-160331
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Copyright
© 2017 the authors. This is the accepted manuscript of an article published in Statistical Journal of the IAOS. The final publication is available at IOS Press through http://dx.doi.org/10.3233/SJI-160331