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.