Search Results

You are looking at 1 - 6 of 6 items for :

  • "job routineness" x
Clear All
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.
Ali Alichi, Rodrigo Mariscal, and Daniela Muhaj

since the 1970s in the United States and that both upward and downward polarization have contributed. The econometric analysis at the state level suggests that technology, measured by job routinization, and international trade, measured by job offshoring, can explain more than half of the rise in income polarization, with broadly equal contributions. Household characteristics, including education, age, race, and gender have also been important drivers but on net have had a countervailing effect on income polarization. This is mainly thanks to the rising education

Ali Alichi, Rodrigo Mariscal, and Daniela Muhaj
Data show that middle-income households have continued moving down, and less so up, the income distribution in the United States since the 1970s—a phenomenon that is often referred to as the polarization or “hollowing out” of the income distribution. While the level of income polarization is generally lower in the richer states (i.e., those with higher median household income levels), there have been wide variations in the changes in income polarization over time across states. The paper develops two indices to measure income polarization including a novel hollowing-out index. Another important contribution of the paper is to examine the proximate causes of income polarization. The econometric analysis is done at both state and household levels. The results suggest that technology, measured by job routinization, and international trade, measured by job offshoring, can fully explain the non-trend rise in income polarization, with broadly equal contributions. Household characteristics, including age, education, race, and gender have also been important drivers but with a net countervailing effect on income polarization. This is mainly thanks to the rising education level of households, which has led to better incomes.
Mariya Brussevich, Ms. Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, and Ms. Kalpana Kochhar

―tasks that are more prone to automation. Moreover, women perform fewer tasks requiring analytical input or abstract thinking (e.g., information-processing skills), where technological change can be complementary to human skills and improve labor productivity. 2 The selection by women into specific sectors and occupations explains most of these differences. Interestingly, the gender gap in the job routineness level and use of information and communications technology (ICT) is lower in sectors and countries where female labor force participation (FLFP) is higher. We also

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