The Future of Employment Revisited: How Model Selection Determines Automation Forecasts

TL;DR

This paper demonstrates that differences in automation forecasts are largely due to model choice rather than data or task differences, highlighting the importance of model selection in predicting technological unemployment.

Contribution

It shows that the diversity in previous automation forecasts is mainly driven by model selection rather than personal or task controls, based on expert consultations.

Findings

Routine clerical jobs are highly susceptible to automation.

Complex professional activities are less likely to be automated.

Model choice significantly influences automation predictions.

Abstract

The uniqueness of human labour is at question in times of smart technologies. The 250 years-old discussion on technological unemployment reawakens. Prominently, Frey and Osborne (2017) estimated that half of US employment will be automated by algorithms within the next 20 years. Other follow-up studies conclude that only a small fraction of workers will be replaced by digital technologies. The main contribution of our work is to show that the diversity of previous findings regarding the degree of job automation is, to a large extent, driven by model selection and not by controlling for personal characteristics or tasks. For our case study, we consult experts in machine learning and industry professionals on the susceptibility to digital technologies in the Austrian labour market. Our results indicate that, while clerical computer-based routine jobs are likely to change in the next decade, professional activities, such as the processing of complex information, are less prone to digital change.