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220928 ||| eng |
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|a 9781484379769
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| 100 |
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|a Brussevich, Mariya
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| 245 |
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|a Gender, Technology, and the Future of Work
|c Mariya Brussevich, Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid, Kalpana Kochhar
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| 260 |
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|a Washington, D.C.
|b International Monetary Fund
|c 2018
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| 300 |
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|a 36 pages
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| 651 |
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4 |
|a United States
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| 653 |
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|a Social discrimination & equal treatment
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| 653 |
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|a Women
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| 653 |
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|a Gender studies; women & girls
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| 653 |
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|a Research and Development
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| 653 |
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|a Labour; income economics
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| 653 |
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|a Technology; general issues
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| 653 |
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|a Aggregate Labor Productivity
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| 653 |
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|a Skills
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| 653 |
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|a Labor
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| 653 |
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|a Sex discrimination
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| 653 |
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|a Gender Studies
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| 653 |
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|a Macroeconomics
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| 653 |
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|a Occupational Choice
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| 653 |
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|a Technological Change
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| 653 |
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|a Human Capital
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| 653 |
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|a Women''s Studies'
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| 653 |
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|a Employment
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| 653 |
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|a Technological Change: Choices and Consequences
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| 653 |
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|a Intellectual Property Rights: General
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| 653 |
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|a Gender inequality
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| 653 |
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|a Economics of Gender
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| 653 |
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|a Technology
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| 653 |
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|a Non-labor Discrimination
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| 653 |
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|a Unemployment
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| 653 |
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|a Diffusion Processes
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| 653 |
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|a Aggregate Human Capital
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| 653 |
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|a Labor Productivity
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| 653 |
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|a Automatic control engineering
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| 653 |
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|a Labor Economics: General
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| 653 |
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|a Innovation
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| 653 |
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|a Labor Demand
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| 653 |
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|a Wages
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| 653 |
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|a Intergenerational Income Distribution
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| 653 |
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|a Automation
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| 653 |
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|a Gender
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| 653 |
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|a Labor economics
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| 700 |
1 |
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|a Dabla-Norris, Era
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| 700 |
1 |
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|a Kamunge, Christine
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| 700 |
1 |
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|a Karnane, Pooja
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b IMF
|a International Monetary Fund
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| 490 |
0 |
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|a Staff Discussion Notes
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| 028 |
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|a 10.5089/9781484379769.006
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| 856 |
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|u https://elibrary.imf.org/view/journals/006/2018/007/006.2018.issue-007-en.xml?cid=46236-com-dsp-marc
|x Verlag
|3 Volltext
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| 082 |
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|a 330
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| 520 |
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|a 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
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