Publications & Documents

The digital economy is growing, with producers increasingly using digital technology to revolutionise their production processes, and with new business models being created based on the digital transformation. To improve the visibility of digitalisation in macroeconomic statistics, the Digital Supply and Use Tables (SUTs) framework has been developed under the auspices of the OECD’s Informal Advisory Group (IAG) on Measuring GDP in a Digitalised Economy. In the Digital SUTs framework, three dimensions are introduced for measuring the digital economy: the nature of the transaction (the 'how'), the goods and services produced (the 'what'), and the new digital industries (the 'who'). The OECD Handbook on Compiling Digital SUTs explains these three dimensions and includes examples. It also presents the high priority indicators that have been agreed by the IAG and includes recommended templates for producing the outputs.

This paper estimates the data intensity of occupations/sectors (i.e. the share of job postings per occupation/sector related to the production of data) using natural language processing (NLP) on job advertisements in the United Kingdom, Canada and the United States. Online job advertisement data collected by Lightcast provide timely and disaggregated insights into labour demand and skill requirements of different professions. The paper makes three major contributions. First, indicators created from the Lightcast data add to the understanding of digital skills in the labour market. Second, the results may advance the measurement of data assets in national account statistics. Third, the NLP methodology can handle up to 66 languages and can be adapted to measure concepts beyond digital skills. Results provide a ranking of data intensity across occupations, with data analytics activities contributing most to aggregate data intensity shares in all three countries. At the sectoral level, the emerging picture is more heterogeneous across countries. Differences in labour demand primarily explain those variations, with low data-intensive professions contributing most to aggregate data intensity in the United Kingdom. Estimates of investment in data, using a sum of costs approach and sectoral intensity shares, point to lower levels in the United Kingdom and Canada than in the United States.