There is growing concern that human jobs are being replaced by the rapid technological progress of artificial intelligence (AI), robotics, and automation (Acemoglu and Restrepo 2017, Brynjolfsson and McAfee 2014, Ford 2015). It is often emphasised that whereas mechanisation has so far replaced blue-collar jobs, recent AI technology, which plays a similar role to the human brain, is mainly replacing white-collar jobs (Dauth et al. 2017, Graetz and Michaels 2018). In fact, pattern recognition based on deep learning plays an important role in companies that collect big data, including image, speech, and texts. AI consulting services are already in use.
In a recent paper (Hamaguchi and Kondo 2018), we explore possible labour market problems that will be caused by computerisation based on AI technology. Our main concern is that the geographical distribution of occupations in a particular country is not uniform. Some occupations are relatively concentrated in urban areas, and others are concentrated in rural areas. This heterogeneity is highly relevant for male versus female workers. When discussing the impacts of AI technology on employment, we should consider these geographical and gender aspects of the occupational distribution.
Fact-finding in the Japanese labour market
To clarify which groups of workers are exposed to the risks of AI technology, we propose a regional employment risk score for computerisation, which is calculated using the regional share of occupations and the probability of computerisation by occupation based on Frey and Osborne (2017).
This risk score takes a value between 0 and 100, with 0 indicating no employment risk of computerisation and 100 indicating full replacement of employment. For example, when all workers are engaged in an occupation with 0 probability of computerisation in a region, the risk score takes the value 0. When all workers are engaged in an occupation with probability of 1 for computerisation in a region, the risk score takes the value 100.
Figure 1 shows the relationship between the employment risk scores of computerisation and city size at the prefectural level. Panel (a) of Figure 1 shows the negative correlation for male workers. By contrast, Panel (b) of Figure 1 shows the positive correlation for female workers. A big question raised by Figure 1 is why gender inequality expands in larger cities.
Figure 1 Employment risk score of computerisation and city size
Source: Reproduced from Figure 3 in Hamaguchi and Kondo (2018).
Our main finding is that female workers tend to have little opportunity to advance in their career and tend to be engaged in occupations that are susceptible to computerisation, such as receptionist, clerical, and sales work.1 Male workers are more likely to get decision-making positions (e.g. managers and supervisors) and professional jobs (e.g. engineers and natural scientists), which are seen as more difficult to replace with AI technology. This tendency becomes more prevalent in larger cities. Consequently, larger cities show a greater gender gap in the employment risk of computerisation.2
Policy implications for AI technology and gender inequality
Currently, policymakers face the twin policy challenges of strengthening the global competitiveness of firms using AI technology and of mitigating the negative employment impact of using AI technology. Importantly, AI technology is essential for firms to survive amidst fierce global competition, but the promotion of AI technology may simultaneously accelerate labour substitution. This study contributes to tackling the latter challenge.
The important policy implication from this study is that supporting additional human capital investment is important, but insufficient, as a means of alleviating the risk of new technology. As shown in Kawaguchi and Toriyabe (2018), there is no significant gender gap in skill levels in Japan. The problem in the Japanese labour market is the gender gap in skill utilisation, which suggests occupational segregation between males and females.3
Corporate managers need to recognise that simple clerical, data-collection, and processing work will be computerised. Human capability within the current vertically centralised decision-making structure, which creates the above-mentioned occupational gender segregation, will no longer be able to deal with the high-speed information flows that such a technology presents. Therefore, decision making needs to be more horizontally decentralised, incorporating the sensibility and perspectives of women. AI technology will amplify the unequal risks of computerisation between males and females if these structural labour market problems remain unresolved.
Finally, we should bear in mind that nobody can exactly predict the future progress of AI and its impact on the labour market. This research field, therefore, should continuously incorporate updated information and contribute to policy debates.
Acemoglu, D and P Restrepo (2017), “Robots and jobs: Evidence from US labor markets”, NBER working paper 23285.
Brynjolfsson, E and A McAfee (2014), The second machine age: Work, progress, and prosperity in a time of brilliant technologies, New York: W.W. Norton & Company.
Dauth, W, S Findeisen, J Suedekum and N Woessner (2017), “German robots – The impact of industrial robots on workers”, CEPR discussion paper 12306.
Ford, M (2015), Rise of the robots: Technology and the threat of a jobless future, New York: Basic Books.
Frey, C B and M Osborne (2017), “The future of employment: How susceptible are jobs to computerisation?”, Technological Forecasting and Social Change 114: 254-280.
Graetz, G and G Michaels (2018), “Robots at work”, Review of Economics and Statistics 100(5): 753-768.
Hamaguchi, N and K Kondo (2018), “Regional employment and artificial intelligence in Japan,” RIETI discussion paper 18-E-032.
Kawaguchi, D and T Takahiro (2018) “Parental leaves and female skill utilization: Evidence from PIAAC,” RIETI discussion paper 18-E-003.
Kondo, K (2018), “Job substitution does not equal job disappearance: Employment and education policies in the era of AI and robotics,” VoxEU.org, 24 March.
OECD (2017), The pursuit of gender equality: An uphill battle, Paris: OECD Publishing.
Schneider, T, G H Hong and A V Le (2018) “Land of the rising robots”, Finance and Development 55(2): 28-31.
 The probability of computerisation is 0.96 for receptionists and information clerks and 0.92 for retail salespersons in Frey and Osborne (2017).
 Todd et al. (2018) provide an overview of how AI and robotics reshape the social and economic situation in Japan. OECD (2017) shows that Japan’s share of female managers in 2015 was 12.4%, which is the second lowest of all OECD countries. In addition, Japan’s share of female bachelor’s graduates in STEM (Science, Technology, Engineering, and Mathematics) in 2015 is 15.4%, which is the lowest of all OECD Countries.
 Kondo (2018) also discusses educational policy for children in the era of AI and robotics.