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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.
International Monetary Fund. Monetary and Capital Markets Department

flexibility to schedule tasks in a manner, which ensures that areas of particular concern can be prioritized. CBI has stated that supervisory engagement routinely takes place at levels well in excess of the minimum engagement as set out by PRISM. Ultra High/High Impact Firms Medium Low Impact Firms Supervisory Task Frequency Supervisory Task Frequency Business model analysis Every two years Summary risk assessment 10% sample each year Governance reviews Every two years Financial risk reviews (“FRRs”) Approximately Six every two years Regular meetings with key firm officers

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, and Salma Khalid

-country comparisons. We use the OECD’s PIAAC database to develop a new cross-country index of job routineness from task composition at work. The PIAAC survey covers adults 16 to 65 years of age and collects detailed information on task composition, task frequency, and extent of ICT use in the workplace for 28 OECD member countries, as well as Cyprus and Singapore. 6 In addition, the survey contains demographic information and measures of literacy, numeracy, and problem-solving skills for each respondent. To construct the routine task intensity (RTI) index, we follow the method

Mariya Brussevich, Ms. Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar

job routineness . We develop a new index of job routineness from task composition at work using data from the Program for the International Assessment of Adult Competencies (PIAAC) survey. The PIAAC survey covers adults 16 to 65 years of age and collects detailed information on task composition, task frequency, and extent of ICT use in the workplace for 28 OECD member countries, as well as Cyprus and Singapore. 4 This data set is uniquely suited for cross-country comparison of the nature of work and its susceptibility to automation. 5 To construct the routine task