Proceedings of selected IFC-sponsored sessions at the 63rd World Statistics Congress of the International Statistical Institute (ISI) , The Hague, Netherlands, July 2021 (virtual format).
The experience of central banks has underlined the potential of alternative data sets to deliver statistics that are higher-frequency as well as more timely, flexible and granular than traditional ones. These are urgently needed to help policymakers follow macroeconomic developments and support policy decisions. In particular, the new, unconventional sources of information that have emerged with the digital transformation of our societies show a lot of promise (Hammer et al (2017)). They can cover many realms of the economic and financial sphere that are still difficult to capture through more traditional data collections. And they are potentially available in near real time, facilitating the conduct of economic policy especially in the face of unexpected shocks.
Yet these new data sources can come with huge numbers, multiple formats and high noise-to-signal ratios, making them difficult to use systematically in policymaking and statistical production. Some of these challenges might be addressed with appropriate engagement rules between public agencies and private data providers; others require further adequate improvement in our statistical and analytical methodological work.
Meeting all these challenges will make life easier for the statistical and policymaking communities. It’s worth noting here that what may at first sight look like an information gap does not necessarily reflect a lack of relevant data, but rather a failure to transform existing indicators into useful knowledge (Drozdova (2017)). This is even more the case in today’s evolving information society: torrents of data are constantly generated, collected and stored by both public and private agents. This means that perceived information gaps do not necessarily require new reporting exercises, as they may arguably be filled if statisticians and policymakers can quickly tap into existing data that could be turned into useful information, for instance to get timelier or higher-frequency measures of common phenomena or to cover new, unexplored statistical domains.