Overall, a total of 26-38% of jobs in Latin America and the Caribbean could be exposed to Generative AI.
Generative AI could result in a productivity-enhancing transformation of 8-14% of jobs, with a higher likelihood in urban, educated, and formal sectors, and among higher-income earners.
About 2-5% of jobs are at risk of full automation due to the current capabilities of GenAI.
Up to half of the jobs that could improve productivity with GenAI – about 17 million jobs – are hindered by gaps in digital access and infrastructure.
Governments should implement policies to protect jobs, enhance productivity, and maximize the transformative potential of GenAI to promote more inclusive growth and sustainable development.
OVERVIEW
In this new paper from the World Bank Group and the International Labour Organization, researchers found that generative AI (GenAI) could have transformative effects on jobs and livelihoods in Latin America and the Caribbean. Nonetheless, gaps in digital infrastructure and other inequalities could hinder the potential impacts of GenAI in the region.
KEY MESSAGES
Latin America and the Caribbean has long grappled with a persistent productivity gap, which has partly been the result of barriers to innovation and technology adoption, especially compared to other regions that have narrowed their gaps with high-income economies in recent decades. It is also one of the most unequal regions in the world, with millions of workers in low-paid jobs in the informal economy. Historically, new technologies have shaped the evolution of labor productivity, inequality, and prosperity across the world. Understanding whether GenAI can help remove some of the barriers to economic development is critical for the design of policies.
Who is exposed to GenAI?
According to the study, between 26 and 38 percent of jobs in the region are exposed to GenAI. GenAI is more likely to augment and transform jobs in the region than outright automate them. Anywhere from 8 to 14 percent of jobs could become more productive by adopting GenAI. Meanwhile, 2 to 5 percent of jobs face the potential for full automation. However, the potential impact on people’s lives and livelihoods should not be trivialized. A large share of jobs are also exposed to GenAI under the “Big Unknown” category, where it is uncertain if their exposure would tilt toward automation or augmentation. The final outcome would depend on the evolution and uses of this new technology in the future.
Workers who are women, working in urban areas, younger, non-poor, in formal sectors (especially in banking, finance, or public administration), or have higher education are more exposed to automation through GenAI. The potential loss of well-paid, formal, and skilled jobs in industries that are dominated by women due to GenAI automation would have negative impacts for the already highly informal and unequal economies in the region.
Will the low level of digital inclusion act as a buffer for the automation impacts?
The short answer is no. Most workers who are exposed to automation from GenAI are already using digital technologies in their job, thereby the potential negative effects for this group of workers may not take long to materialize.
Which jobs are most likely to benefit from GenAI?
The potential transformative benefits of GenAI on jobs are more equally distributed among workers in terms of gender and age, but they are still more likely to affect formal jobs that are in urban areas and held by higher-educated and higher-income workers. Salaried and self-employed workers – such as hairdressers, salespersons, architects, or real estate agents – and those working in education, health, or personal services are more likely to benefit from the transformative effects of GenAI.
What is preventing workers who might benefit from GenAI from realizing this potential?
A large number of workers who stand to benefit from greater productivity from GenAI are often in jobs that do not use digital technologies at work, particularly in the region’s poorest countries. A lack of access to digital technologies and infrastructure could hamper nearly half of the jobs that could benefit from greater productivity from GenAI, preventing these workers from realizing its full potential. This is equal to about 7 million jobs held by women and 10 million jobs held by men.
The potential loss in productivity due to this gap in digital access would have a greater impact on workers living in poverty. For example, in Brazil, while 8.5 percent of workers living in poverty could benefit from GenAI, only 40 percent of them would be able to do so because they use digital technologies at work. In contrast, 14 percent of workers who are not living in poverty could benefit from GenAI, and 60 percent of them could reap such gains because they use digital technologies.
Exposure by country, exposure type and access to digital infrastructure
How should policymakers prepare for the impacts of GenAI on workers and jobs in Latin America and the Caribbean?
Governments should enact policies that aim to protect jobs, minimize disruptions from job losses due to GenAI, and maximize the potential benefits to productivity as GenAI becomes more widespread in the workplace. This includes:
Lifelong learning programs to mitigate job losses and enhance productivity.
Strengthen workers’ foundational skills to work with new GenAI tools in their jobs to boost productivity and creativity.
Enhance social protection to stabilize transitions and address gender gaps, since jobs dominated by women are disproportionately exposed to automation.
Improve digital infrastructure and incentivize workers and employers to adopt digital technologies to ensure equitable benefits from GenAI, especially in developing countries and for those who are most in need of its advantages.
Help workers in the informal sector improve their chances of transitioning to the formal sector.
Through these efforts, countries can minimize the negative impacts of GenAI on workers while maximizing its transformative potential on jobs and workers – promoting more inclusive growth and sustainable development.
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