Search Results

You are looking at 1 - 10 of 14 items for :

  • "probability of automation" x
Clear All
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

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

Countries 3. Relationship Between Female Labor Force Participation and RTI and ICT Use Indices 4. RTI Decomposition: Drivers of RTI Gap 5. RTI Levels vs. Gender Gaps by Occupation and Sector 6. Wage Decomposition 7. Gender Gap in Probability of Automation 8. High Risk of Automation and Age 9. Gender Gap in High Risk of Automation Across Countries 10. Automation Across Sectors 11. Gender Gap in High Risk of Automation Across Occupations 12. Changes in Occupational Shares by Gender (1994–2016) 13. Occupational and Task Differences in ICT and Health

automation are also highest for less educated workers and for clerical and sales workers ( Figure 1 ). Figure 1. Gender Gaps in High Risk of Automation by Generation and Educational Level (Difference in automatability between females and males) Sources: Organisation for Economic Co-operation and Development, Programme for the International Assessment of Adult Competencies; and IMF staff estimates. Note: The probability of automation is estimated using an expectation-maximization algorithm that relates individual characteristics (age, education, and training

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

variables. Statistical significance levels: *** p <0.01; ** p <0.05; * p <0.1. Risk of Automation and the Future of Work for Women A. Quantifying the Risk of Automation for Women 16. Estimating the risk of automation . Our analysis in the previous section suggests that women perform more routine and less-abstract tasks in the same occupations as their male counterparts, placing them at higher risk of automation. To quantify the potential impact on jobs, we estimate the probability of automation at the level of each individual, accounting for differences

International Monetary Fund. Asia and Pacific Dept

. Around 60 percent of the labor force in Bangladesh are employed in industries that are at a high risk of automation (above 70 percent) . This estimate is obtained by using the probabilities of automation estimated in Frey and Osborne (2013) and applying them to the occupational categories in Bangladesh. As the data on employment from the 2016/17 labor force survey (LFS) is only available for single digit occupations, the calculation uses average over the probabilities of automation in Frey and Osborne (2013) which are estimated by 4-digit occupation codes. It

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