Automation exposure and implications in advanced and developing countries across gender, age, and skills

This paper addresses three main objectives. First, the analysis estimates and compares the average share of workers at risk of automation in advanced and developing regions. Second, the study investigates the possible structural implications of automation across the Gender, Age, and Skill labour market structures at the sectoral, country, and regional levels. Third, the paper extends the analysis of the Gender structure from possible job implications to potential wage consequences; in particular, the potential effect of automation on the gender wage gap at the regional level is studied and the sources of the differentials are identified. This study uses data from the PIAAC dataset, which comprises detailed task data for individual workers including novel data for developing countries. The results indicate that, from a purely technological feasibility viewpoint, advanced countries are more vulnerable than developing countries on average. Male and middle-aged workers are also likely to be more affected by automation, whereas high-skilled workers are likely to be the least affected by automation. The results also indicate that automation could reduce gender inequality not only through jobs but also through wages.