Labour automation and challenges in labour inclusion in Latin America: regionally adjusted risk estimates based on machine learning

In recent decades, rapid technological progress has generated a growing interest in the transformation of the world of work. This concern is based on the potential of emerging technologies to replace tasks and roles traditionally performed by human beings, either partially or entirely. It is, therefore, essential to examine and understand the social, economic, and ethical implications of this process and seek solutions to harness the benefits associated with the automation of production processes and mitigate possible negative impacts.

This paper seeks to estimate job automation's probabilities and risks and analyse its potential impacts on labour inclusion in Latin America. To this end, this document implemented a machine learning-based methodology adapted to the specific characteristics of the region using data from PIAAC surveys and household surveys. In this way, the aim is to build a probability vector of job automation adapted to the region. This vector can be reused in any source of information that contains internationally comparable occupational codes, such as household surveys or employment surveys.

The study provides novel estimates of labour automation based on Latin American data and analyses the phenomenon in different aspects of labour inclusion and social stratification. The results show that the risks of automation vary among different social groups, which points to the need to build adapted and efficient policies that address the diverse needs that this process imposes. To this end, the document addresses different policy areas to promote effective labour inclusion in an era of rapid advances in intelligent technologies, ensuring that all individuals can access decent employment and so that these inequalities can be addressed effectively.