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threat of automation varies by workers’ gender, age, and education in 30 advanced and emerging market economies. The worker-level microdata from the Organisation for Economic Co-operation and Development’s Programme for the International Assessment of Adult Competencies (PIAAC) permits analysis of exposure to automation at the individual level. The Gender Routineness tGAP The index of routine task intensity ( RTI ) evaluates the relative importance of abstract skills, such as reasoning and interpersonal communication, and nonroutine manual skills against

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

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

Salomons, 2014 ; Das and Hilgenstock, 2018 ). 3 The standard measure of job routineness―an index of routine task intensity (RTI)―quantifies the extent of codifiability of tasks performed on the job and serves as a proxy for substitutability of workers and machines. Jobs with a higher share of tasks that can be performed by following a defined set of rules, and are thus easily codifiable, are more susceptible to automation. By contrast, jobs requiring analytical, communicational, and technical skills, are less prone to automation. 9. Methodology and data for assessing

International Monetary Fund. Research Dept.

to access higher-paying occupations that benefit from automation. Data for 15 European countries are consistent with this possibility. Figure 4.11 shows the changes in employment shares of different occupations by the level of their routine task intensity ( Autor and Dorn 2013 ), an index measuring the extent to which tasks are “routine” and thus potentially automatable. 1 Two patterns emerge. First, overall employment shifts away from occupations (many of which are medium-paying) with an initially high routine task intensity ( Figure 4.1.1 , panel 1). Second

Adrian Peralta and Agustin Roitman
This paper uses a DSGE model to simulate the impact of technological change on labor markets and income distribution. It finds that technological advances offers prospects for stronger productivity and growth, but brings risks of increased income polarization. This calls for inclusive policies tailored to country-specific circumstances and preferences, such as investment in human capital to facilitate retooling of low-skilled workers so that they can partake in the gains of technological change, and redistributive policies (such as differentiated income tax cuts) to help reallocate gains. Policies are also needed to facilitate the process of adjustment.
Ms. Mitali Das and Benjamin Hilgenstock
Evidence that the automation of routine tasks has contributed to the polarization of labor markets has been documented for many developed economies, but little is known about its incidence in developing economies. We propose a measure of the exposure to routinization—that is, the risk of the displacement of labor by information technology—and assemble several facts that link the exposure to routinization with the prospects of polarization. Drawing on exposures for about 85 countries since 1990, we establish that: (1) developing economies are significantly less exposed to routinization than their developed counterparts; (2) the initial exposure to routinization is a strong predictor of the long-run exposure; and (3) among countries with high initial exposures to routinization, polarization dynamics have been strong and subsequent exposures have fallen; while among those with low initial exposure, the globalization of trade and structural transformation have prevailed and routine exposures have risen. Although we find little evidence of polarization in developing countries thus far, with rapidly rising exposures to routinization, the risks of future labor market polarization have escalated with potentially significant consequences for productivity, growth and distribution.
Ms. Mitali Das and Benjamin Hilgenstock

and wages, and what they may imply for the future of labor markets in developing economies. The measures we propose begin with a set of ordinal scores in Autor and Dorn (2013) which assign to all 3-digit census occupations a score reflecting its routine-task intensity, i.e., its likelihood of automation by information technology. By weighting scores with the corresponding employment share, the measure reflects how intensive a country is in the labor input of routine tasks and thus, the extent to which jobs are exposed to the risk of being substituted by

Benjamin Hilgenstock and Zsoka Koczan

; and Chapter 3 of the April 2017 World Economic Outlook) act as proxies for the initial share of jobs within a geographical unit that are at risk of being automated or offshored and thus allow for a more granular analysis of local exposures to the global forces of trade and technology. The two measures are constructed as employment-weighted averages of occupational scores for routinizability and offshorability. The routinizability scores are based on scores from Autor and Dorn (2013) . The scores measure the “routine task intensity”, or how intensive an occupation