Public-Private Partnerships for Statistics: Lessons Learned, Future Steps

Report by Nicholas Robin, Thilo Klein and Johannes Jütting for Paris 21: “Non-offcial sources of data, big data in particular, are currently attracting enormous interest in the world of official statistics. An impressive body of work focuses on how different types of big data (telecom data, social media, sensors, etc.) can be used to fll specifc data gaps, especially with regard to the post-2015 agenda and the associated technology challenges. The focus of this paper is on a different aspect, but one that is of crucial importance: what are the perspectives of the commercial operations and national statistical offces which respectively produce and might use this data and which incentives, business models and protocols are needed in order to leverage non-offcial data sources within the offcial statistics community?

Public-private partnerships (PPPs) offer signifcant opportunities such as cost effectiveness, timeliness, granularity, new indicators, but also present a range of challenges that need to be surmounted. These comprise technical diffculties, risks related to data confdentiality as well as a lack of incentives. Nevertheless, a number of collaborative projects have already emerged and can be

Nevertheless, a number of collaborative projects have already emerged and can be classified into four ideal types: namely the in-house production of statistics by the data provider, the transfer of private data sets to the end user, the transfer of private data sets to a trusted third party for processing and/or analysis, and the outsourcing of national statistical office functions (the only model which is not centred around a data-sharing dimension). In developing countries, a severe lack of resources and particular statistical needs (to adopt a system-wide approach within national statistical systems and fill statistical gaps which are relevant to national development plans) highlight the importance of harnessing the private sector’s resources and point to the most holistic models (in-house and third party) in which the private sector contributes to the processing and analysis of data. The following key lessons are drawn from four case studies….(More)”