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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

Front Matter Page Fiscal Affairs Department Table of Contents ABSTRACT I. INTRODUCTION II. MEASURING EXPOSURE TO ROUTINIZATION: THE RTI INDEX A. Measuring Routineness B. The Gender RTI Gap C. Decomposing the Gender RTI Gap III. RISK OF AUTOMATION AND THE FUTURE OF WORK FOR WOMEN A. Estimating the Probability of Automation B. Gender Differences in Probability of Automation IV. NARROWING GENDER GAPS ACROSS GENERATIONS V. CONCLUSION FIGURES 1. Relationship Between Female Labor Force Participation and Size of

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

automation. Section IV elaborates on intergenerational differences in the gender automation gap, highlighting differences in educational attainment and occupational and sector differences. Section V concludes. II. Measuring Exposure to Routinization: The RTI Index A. Measuring Routineness The standard measure of job routineness―an index of routine task intensity (RTI)―developed by Autor, Levy, and Murnane (2003) quantifies the extent of codifiability of tasks performed on the job and serves as a proxy for substitutability of workers and machines. Jobs

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, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar

intensity (RTI) index, we follow the method outlined in Autor and Dorn (2013) and modified by De La Rica and Gortazar (2016) to match the content of the PIAAC survey. The RTI index evaluates the relative importance of abstract skills, such as reasoning and interpersonal communication, and of non-routine manual skills against the importance of routine tasks, which can be easily automated. Specifically, we calculate RTI for each individual worker i as follows: R T I i = R o u t i n e i − A b s t

International Monetary Fund. Asia and Pacific Dept

are more prone to automation. In Singapore, the routine task intensity (RTI) index level of female workers is considerably higher than in other countries in the sample (text figure). 2/ Women have a higher average probability of automation than men. The probability of automation is estimated at the level of each individual worker using detailed information on worker characteristics and task composition at work. In Singapore, the probability of automation among women is estimated at 36 percent, 6 percent higher than among men. Moreover, close to 10 percent of women

International Monetary Fund. Research Dept.

and percentage points for labor shares. One explanation for the higher aggregate elasticity of substitution in advanced economies is their greater exposure to routinization, as shown by their higher aggregate routine task intensity (RTI). (Details of the construction of RTI indices are in Box 3.3 .) Using data on routinization scores by occupation and aggregating up for each country using employment shares from population censuses, a distribution of the aggregate RTI index is obtained. The distribution of the RTI index for advanced economies has a higher mean

Ms. Era Dabla-Norris, Carlo Pizzinelli, and Jay Rappaport

1986 to 2019. Figure B.1: Shares of routine, abstract, and manual occupation for non-college females: 1986–2019 Notes : The figures plot the shares of routine, abstract, and manual jobs from 1981 onwards. Series breaks due to changes in the occupational classifications are marked by vertical lines. Source : LFS and authors’ calculations. We also check the alignment between our occupational categories and the RTI index constructed by Autor et al. (2003 ) based on the US Census OCC1990 classification. We followed the mapping OCC 1990→OCC 2000→US SOC

Ms. Era Dabla-Norris, Carlo Pizzinelli, and Jay Rappaport
Labor markets in the UK have been characterized by markedly widening wage inequality for lowskill (non-college) women, a trend that predates the pandemic. We examine the contribution of job polarization to this trend by estimating age, period, and cohort effects for the likelihood of employment in different occupations and the wages earned therein over 2001-2019. For recent generations of women, cohort effects indicate a higher likelihood of employment in low-paying manual jobs relative to high-paying abstract jobs. However, cohort effects also underpin falling wages for post-1980 cohorts across all occupations. We find that falling returns to labor rather than job polarization has been a key driver of rising inter-age wage inequality among low-skill females. Wage-level cohort effects underpin a nearly 10 percent fall in expected lifetime earnings for low-skill women born in 1990 relative to those born in 1970.
Ms. Era Dabla-Norris, Carlo Pizzinelli, and Jay Rappaport

), but the original UK SOC occupations resemble more civil sector clerks. We thus repaired them with the ISCO 08 occupations corresponding to the US SOC 2010 codes that are matched with them in the CASCOT-based mapping from Dickerson et al. (2012) . ANNEX 2. Occupational Categorization Measures We first check to see alignment between our occupational categories and the RTI index constructed by Autor et al. (2003) . The RTI is calculated using the task-content measures produced through the 1977 US Dictionary of Occupational Titles. The formula is RTI