Technology and Engineering > Automation

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Mr. Sakai Ando, Mr. Ravi Balakrishnan, Bertrand Gruss, Mr. Jean-Jacques Hallaert, La-Bhus Fah Jirasavetakul, Koralai Kirabaeva, Nir Klein, Ana Lariau, Lucy Qian Liu, Mr. Davide Malacrino, Mr. Haonan Qu, and Alexandra Solovyeva
In 2020, the COVID-19 pandemic caused by far the largest shock to European economies since World War II. Yet, astonishingly, the EU unemployment rate had already declined to its pre-crisis level by 2021Q3, and in some countries the labor force participation rate is at a record high. This paper documents that the widespread use of job retention schemes has played an essential role in mitigating the pandemic’s impact on labor markets and thereby facilitating the restart of European economies after the initial lockdowns.
Mr. Carlos Mulas-Granados, Mr. Richard Varghese, Vizhdan Boranova, Alice deChalendar, and Judith Wallenstein
We exploit a survey data set that contains information on how 11,000 workers across advanced and emerging market economies perceive the main forces shaping the future of work. In general, workers feel more positive than negative about automation, especially in emerging markets. We find that negative perceptions about automation are prevalent among workers who are older, poorer, more exposed to job volatility, and from countries with higher levels of robot penetration. Perceptions over automation are positively viewed by workers with higher levels of job satisfaction, higher educational attainment, and from countries with stronger labor protection. Workers with positive perceptions of automation also tend to respond that re-education and retraining will be needed to adapt to rapidly evolving skill demands. These workers expect governments to have a role in shaping the future of work through protection of labor and new forms of social benefits. The demand for protection and benefits is more significant among women and workers that have suffered job volatility.
Mariya Brussevich, Ms. Era Dabla-Norris, and Salma Khalid
Using individual level data on task composition at work for 30 advanced and emerging economies, we find that women, on average, perform more routine tasks than men?tasks that are more prone to automation. To quantify the impact on jobs, we relate data on task composition at work to occupation level estimates of probability of automation, controlling for a rich set of individual characteristics (e.g., education, age, literacy and numeracy skills). Our results indicate that female workers are at a significantly higher risk for displacement by automation than male workers, with 11 percent of the female workforce at high risk of being automated given the current state of technology, albeit with significant cross-country heterogeneity. The probability of automation is lower for younger cohorts of women, and for those in managerial positions.
Mariya Brussevich, Ms. Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar
New technologies?digitalization, artificial intelligence, and machine learning?are changing the way work gets done at an unprecedented rate. Helping people adapt to a fast-changing world of work and ameliorating its deleterious impacts will be the defining challenge of our time. What are the gender implications of this changing nature of work? How vulnerable are women’s jobs to risk of displacement by technology? What policies are needed to ensure that technological change supports a closing, and not a widening, of gender gaps? This SDN finds that women, on average, perform more routine tasks than men across all sectors and occupations?tasks that are most prone to automation. Given the current state of technology, we estimate that 26 million female jobs in 30 countries (28 OECD member countries, Cyprus, and Singapore) are at a high risk of being displaced by technology (i.e., facing higher than 70 percent likelihood of being automated) within the next two decades. Female workers face a higher risk of automation compared to male workers (11 percent of the female workforce, relative to 9 percent of the male workforce), albeit with significant heterogeneity across sectors and countries. Less well-educated and older female workers (aged 40 and above), as well as those in low-skill clerical, service, and sales positions are disproportionately exposed to automation. Extrapolating our results, we find that around 180 million female jobs are at high risk of being displaced globally. Policies are needed to endow women with required skills; close gender gaps in leadership positions; bridge digital gender divide (as ongoing digital transformation could confer greater flexibility in work, benefiting women); ease transitions for older and low-skilled female workers.