Gender, Technology, and the Future of Work

Mariya Brussevich, Era Dabla-Norris, Christine Kamunge, Pooja Karnane, Salma Khalid and Kalpana Kochhar

No 2018/007, IMF Staff Discussion Notes from International Monetary Fund

Abstract: 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.

Keywords: SDN; labor force; task characteristic; labor market; automation; technological change; jobs; female labor Keywords: force; occupational choice; gender equality; routine job task; work responsibility; employment incentive; task composition; work arrangement; job task characteristic; service worker; nonstandard employment; work contract; task frequency; gender earnings Gap; job routineness; Women; Gender inequality; Global (search for similar items in EconPapers)
Pages: 36
Date: 2018-10-08
New Economics Papers: this item is included in nep-big, nep-gen, nep-ict, nep-pay and nep-sea
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