Annex I. Taskforce and Other Contributing Staff
Staff engaged also with external shareholders during a Hackathon on the Strategy on Data and Statistics—a crowdsourcing retreat held at end-February 2017. The Hackathon participants (about 80)—including Taskforce members, private-sector representatives (e.g., Google, Haver, Bloomberg, IBM), IFIs (BIS, IADB, Eurostat, and the World Bank Group), and academia—were asked to identify innovative solutions to the Fund’s data and statistics challenges as input to the development of the overarching strategy.
Artificial intelligence is a data-intensive technology that emulates human performance by machine learning and data mining to automate execution of routine tasks and extract information from large amount of data.
Data (text) mining is the process of discovering meaningful data correlations, patterns, and trends by sifting through large amounts of data (text) in repositories through pattern recognition technologies and statistical and mathematical techniques.
An open data policy implies easy, universal access to most of the Fund’s operational data and the data underlying research and other publications.
Challenges include: methodological, such as selection bias; analytical, in extracting information from noise; and data management, in processing and storing the large amount of data.
Some data categories listed as minimum required in Article VIII, Section 5, have been superseded by innovation in statistical methodology (e.g., CPI in lieu of the retail price index).
The Articles do not specify the periodicity and timeliness for the reporting of data categories under Article VIII, Section 5. Members are required to compile information on a regular basis in as up-to-date a manner as possible and to provide the Fund with such data as soon as it becomes available.
To this end, the Fund is partnering with Statistics Netherlands and the United Nations (UN) Statistical Division on Big Data, including to take stock of countries’ experimentation and emerging best practices.
About 60 country teams already use monetary data from STA databases and STA data are used for benchmarking BOP data and government debt data supplied by desks to the WEO, with FAD also using STA data to complement desk data on tax revenue and functional expenditure.
Current statistical capacity development priorities include closing data gaps, improving data quality, and broadening data dissemination to help detect economic vulnerabilities and improve economic decision-making. See Annex II of 2018 Quinquennial Review of the Fund’s Capacity Development Strategy Review—Concept Note.
To increase effectiveness, planned changes to the data ROSC include shifting to a thematic approach (by sector) and focusing on operational data.
Reviewing and augmenting internal access to TA reports—an important Fund knowledge asset—is fully aligned with the priorities of the Knowledge Management work stream. However, some pilot work is needed to understand better how to best surface and share the underlying economic data.
An explanation of any qualifiers used in the summing up can be found here: http://www.imf.org/external/np/sec/misc/qualifiers.htm