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

task characteristics included in the PIAAC data set (see Annex III for details). This allows us to recreate estimates for probability of automation at the level of each individual worker. Our estimates thus incorporate rich information on demographics, skills, and responsibilities in the workplace, as opposed to simply relying on occupational distribution as most existing studies do. 13 17. Higher probability of automation for women . Women have a higher average probability of automation than men. The average probability of automation among women in our sample is

Mariya Brussevich, Ms. Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar
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.
Mr. Yasser Abdih and Mr. Stephan Danninger
The U.S. labor share of income has been on a secular downward trajectory since the beginning of the new millennium. Using data that are disaggregated across both state and industry, we show the decline in the labor share is broad-based but the extent of the fall varies greatly. Exploiting a new data set on the task characteristics of occupations, the U.S. input-output tables, and the Current Population Survey, we find that in addition to changes in labor institutions, technological change and different forms of trade integration lowered the labor share. In particular, the fall was largest, on average, in industries that saw: a high initial intensity of “routinizable” occupations; steep declines in unionization; a high level of competition from imports; and a high intensity of foreign input usage. Quantitatively, we find that the bulk of the effect comes from changes in technology that are linked to the automation of routine tasks, followed by trade globalization.
Mr. Yasser Abdih and Mr. Stephan Danninger

globalization of trade and capital, and developments in labor market institutions and policies. In this paper, we build on this literature and make three main contributions: First, we shed light on the key drivers and their relative contribution by exploiting cross-state variation at the industry level. To our knowledge, we are the first to do so. We follow closely the empirical methodology in IMF 2017 , which mostly focused on labor share drivers at the global level. Second, we carry out the empirical analysis utilizing a data set on the task characteristics of

, 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

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, 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

. Second, to quantify the potential impact on labor demand, we derive the likelihood of automation for male and female workers using detailed information on worker characteristics and task composition at work. We derive probabilities of automation at the individual level based on worker characteristics, including age, education, gender, literacy and numeracy skills, and a broad subset of task characteristics included in the PIAAC data set. This allows us to evaluate differences in the probability of automation across different demographic groups unlike most of the