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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.

The shipping industry is essential for international trade, but it is also an important source of CO2 emissions. To make progress towards climate targets, countries need to monitor CO2 emissions from vessels owned by their ship operator companies. However, most shipping activity takes place outside national borders, making it more difficult to monitor than activity taking place within countries. The OECD’s experimental database on OECD.stat provides a new source of data for CO2 emissions from global shipping, which is available monthly in near real time. This data will help national statistics producers to compile their Air Emission Accounts (AEAs) for the System of Environmental Economic Accounting (SEEA). This Working Paper presents some initial results from the new data source and describes how they were produced. The method is based on granular and timely ship-level data provided by the United Nations Global Platform, and it uses a bottom-up estimation approach to produce results broken down by country and type of ship.

Trade in value added (TiVA) indicators are increasingly used to monitor countries’ integration into global supply chains. However, they are published with a significant lag - often two or three years - which reduces their relevance for monitoring recent economic developments. This paper aims to provide more timely insights into the international fragmentation of production by exploring new ways of nowcasting five TiVA indicators for the years 2021 and 2022 covering a panel of 41 economies at the economy-wide level and for 24 industry sectors. The analysis relies on a range of models, including Gradient boosted trees (GBM), and other machine-learning techniques, in a panel setting, uses a wide range of explanatory variables capturing domestic business cycles and global economic developments and corrects for publication lags to produce nowcasts in quasi-real time conditions. Resulting nowcasting algorithms significantly improve compared to the benchmark model and exhibit relatively low prediction errors at a one- and two-year horizon, although model performance varies across countries and sectors.