Documents Georgieff, Alexandre 3 results

"Recent years have seen impressive advances in artificial intelligence (AI) and this has stoked renewed concern about the impact of technological progress on the labour market, including on worker displacement.

This paper looks at the possible links between AI and employment in a cross-country context. It adapts the AI occupational impact measure developed by Felten, Raj and Seamans (2018[1]; 2019[2]) – an indicator measuring the degree to which occupations rely on abilities in which AI has made the most progress – and extends it to 23 OECD countries. The indicator, which allows for variations in AI exposure across occupations, as well as within occupations and across countries, is then matched to Labour Force Surveys, to analyse the relationship with employment.

Over the period 2012-2019, employment grew in nearly all occupations analysed. Overall, there appears to be no clear relationship between AI exposure and employment growth. However, in occupations where computer use is high, greater exposure to AI is linked to higher employment growth. The paper also finds suggestive evidence of a negative relationship between AI exposure and growth in average hours worked among occupations where computer use is low.

While further research is needed to identify the exact mechanisms driving these results, one possible explanation is that partial automation by AI increases productivity directly as well as by shifting the task composition of occupations towards higher value-added tasks. This increase in labour productivity and output counteracts the direct displacement effect of automation through AI for workers with good digital skills, who may find it easier to use AI effectively and shift to non-automatable, higher-value added tasks within their occupations. The opposite could be true for workers with poor digital skills, who may not be able to interact efficiently with AI and thus reap all potential benefits of the technology."

"This study looks at what happened to jobs at risk of automation over the past decade and across 21 countries. There is no support for net job destruction at the broad country level. All countries experienced employment growth over the past decade and countries that faced higher automation risk back in 2012 experienced higher employment growth over the subsequent period. At the occupational level, however, employment growth has been much lower in jobs at high risk of automation (6%) than in jobs at low risk (18%).

Low-educated workers were more concentrated in high-risk occupations in 2012 and have become even more concentrated in these occupations since then. In spite of this, the low growth in jobs in high-risk occupations has not led to a drop in the employment rate of low-educated workers relative to that of other education groups. This is largely because the number of low-educated workers has fallen in line with the demand for these workers.Going forward, however, the risk of automation is increasingly falling on low-educated workers and the COVID-19 crisis may have accelerated automation, as companies reduce reliance on human labour and contact between workers, or re-shore some production."

"This paper looks at the links between AI and wage inequality across 19 OECD countries. It uses a measure of occupational exposure to AI derived from that developed by Felten, Raj and Seamans (2019) – a measure of the degree to which occupations rely on abilities in which AI has made the most progress.

The results provide no indication that AI has affected wage inequality between occupations so far (over the period 2014-2018). At the same time, there is some evidence that AI may be associated with lower wage inequality within occupations – consistent with emerging findings from the literature that AI reduces productivity differentials between workers.

Further research is needed to identify the exact mechanisms driving the negative relationship between AI and wage inequality within occupations. One possible explanation is that low performers have more to gain from using AI because AI systems are trained to embody the more accurate practices of high performers. It is also possible that AI reduces performance differences within an occupation through a selection effect, e.g. if low performers leave their job because they are unable to adapt to AI tools by shifting their activities to tasks that AI cannot automate."