Discussion Paper

No. 2018-71 | September 28, 2018
Data, measurement and initiatives for inclusive digitalization and future of work
(Submitted as Global Solutions Paper)


As the pace of digitalization and automation accelerates globally, and more disruptive innovations in machine learning, artificial intelligence and robotics are expected, new data sources and measurement tools are needed to complement existing valuable statistics and administrative data. This is necessary to better understand the impact of technological change on the labor market and the economy and better inform policy decisions for inclusive people centered growth. In accordance with G20 Roadmap for Digitalisation (2017), points 10, 5 and 7, the authors propose to: i) track technological developments globally in a multidisciplinary and coordinated fashion; ii) develop new methods of measurement for the digital economy; iii) harmonize occupational taxonomies and develop new sources of data and indicators at the international level; iv) Build International Collaborative Platforms for Digital Skills and the Digital Transformation of SMES.

JEL Classification:

E01, J23, J24, J31, E25, F6, O33, O4


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Cite As

Beatriz Nofal, Ariel Coremberg, and Luca Sartorio (2018). Data, measurement and initiatives for inclusive digitalization and future of work. Economics Discussion Papers, No 2018-71, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2018-71

Comments and Questions

Anonymous - Report
November 21, 2018 - 11:01

The article addresses the issue of measuring digitally-driven technological change and the implication of the digitalisation for the labour market. This is a topic of high policy relevance because, as the authors note, our mismeasurement of the digital phenomena impair adequate policy responses.

The article starts with reviewing ...[more]

... some stylised facts on the implications of technological change for employment and skill demand. There is no clear explanation why certain examples were chosen. The authors proceed then to make a statement about future development and to make four proposals how to address the policy challenges. In my opinion, the discussion on each of them is rather underdeveloped and often fails to provide practical guidance on how to achieve them. In most of the cases, the discussion is rather limited and does not go beyond a short elaboration on the headline (e.g. proposal 1 or 4). Also the ones that are more elaborated are limited to the discussion on what has been done so far (e.g. proposal 2 and its reference to, among others, the work of the EU KLEMS group).

The article touches upon the issue of the relevance of new types and sources of data, e.g. AI, but also does not provide a clear vision of what data to collect and how to integrate with the available data and, most importantly, with the existing policy making processes.

All in all, although the authors address an issue that is high on policy agenda, they do not add much into the ongoing discussion on how to capture and navigate the digital transformation.