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Mariya Brussevich, Ms. Era Dabla-Norris, and Salma Khalid

occupations using nine specific job task characteristics contained in the ONET data that are deemed to constitute bottlenecks in automatability. The resulting dataset contains 702 occupations and their associated probabilities of automation on a continuous scale between 0 and 100 percent. Probabilities of automation provide an intuitive and forward-looking measure of the likelihood that an occupation can be codified and performed by a computer. However, there are two key drawbacks of assigning probabilities of automation solely based on the individual’s occupation. First

, among others) and job task characteristics to occupational-level risk of automation. Bars represent the gender difference in automatability = (share of females at high risk for automation) / (share of males at high risk for automation). High automatability is defined as probability of automation ≥ 0.7. Individuals between ages 20 and 39 are defined as younger generation. Individuals between ages 40 and 65 are defined as older generation. Statistical significance levels on bars reflect t-tests of the differences between proportions of male and female workers at high

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.
Mariya Brussevich, Ms. Era Dabla-Norris, and Salma Khalid

both during and after the lockdown period. Recent IMF research has estimated the distribution of tele-workability across sectors, occupations, age groups, gender, income, and education levels in 35 advanced and emerging market economies, including 30 OECD member countries and Cyprus, Ecuador, Kazakhstan, Peru, and Singapore. Worker-level microdata from the OECD Programme for the International Assessment of Adult Competencies (PIAAC) allows the authors to unpack differences in job task characteristics—and therefore tele-workability—among workers within the same

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

estimated using an expectation-maximization (EM) algorithm that relates individual characteristics (age, education, training, among others) and job task characteristics to occupational-level risk of automation. Details on the methodology and variables are included in Annex III . Differences in probability of automation and share of workers with high automatability across gender are statistically significant at 1 percent level. High automatability is defined as having probability of automation >= 0.7. 18. Age and probability of automation . Among both men and women

Where do we go from here? The Fall-Winter 2020 issue of the IMF Research Perspectives considers this question and analyzes the impact of the COVID-19 pandemic. The research showcased in this issue looks at the history of social unrest in the aftermaths of pandemics, discusses the inequalities of telework, places COVID and lending conditions under the microscope, and examines how economic activity has been shaped by people’s reaction to the virus and the resulting policy measures.