Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/250605 
Year of Publication: 
2021
Series/Report no.: 
IZA Discussion Papers No. 14944
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
AI is transforming labor markets around the world. Existing research has focused on advanced economies but has neglected developing economies. Different impacts of AI on labor markets in different countries arise not only from heterogeneous occupational structures, but also from the fact that occupations vary across countries in their composition of tasks. We propose a new methodology to translate existing measures of AI impacts that were developed for the US to countries at various levels of economic development. Our method assesses semantic similarities between textual descriptions of work activities in the US and workers' skills elicited in surveys for other countries. We implement the approach using the measure of suitability of work activities for machine learning provided by Brynjolfsson et al. (2018) for the US and the World Bank's STEP survey for Lao PDR and Viet Nam. Our approach allows characterizing the extent to which workers and occupations in a given country are subject to destructive digitalization, which puts workers at risk of being displaced, in contrast to transformative digitalization, which tends to benefit workers. We find that workers in Lao PDR are less likely than in Viet Nam to be in the "machine terrain", where workers will have to adapt to occupational transformations due to AI and are at risk of being partially displaced. Our method based on semantic textual similarities using SBERT is advantageous compared to approaches transferring AI impact scores across countries using crosswalks of occupational codes.
Subjects: 
labor
digitalization
machine learning
artificial intelligence
skills
developing countries
JEL: 
J22
J23
O14
O33
Document Type: 
Working Paper

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