Artificial Intelligence (AI) has the potential to reshape the global economy, especially in the realm of labor markets. Advanced economies will experience the benefits and pitfalls of AI sooner than emerging market and developing economies, largely due to their employment structure focused on cognitive-intensive roles. There are some consistent patterns concerning AI exposure, with women and college-educated individuals more exposed but also better poised to reap AI benefits, and older workers potentially less able to adapt to the new technology. Labor income inequality may increase if the complementarity between AI and high-income workers is strong, while capital returns will increase wealth inequality. However, if productivity gains are sufficiently large, income levels could surge for most workers. In this evolving landscape, advanced economies and more developed emerging markets need to focus on upgrading regulatory frameworks and supporting labor reallocation, while safeguarding those adversely affected. Emerging market and developing economies should prioritize developing digital infrastructure and digital skills
As a consequence, labor income inequality increases. Last, when the AI productivity impact is also considered, labor income rises for all workers in the economy, even for the workers who have low exposure and those with high exposure and low complementarity. The main reason is that higher productivity leads to higher demand for all factors of production in the economy, leading to increased labor income. However, labor income inequality rises because the increase is larger for workers with high AI complementarity.
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Unlike labor income inequality, capital income and wealth inequality always increase with AI adoption (Figure 10). The main reason for the increase in capital income and wealth inequality is that AI leads to labor displacement and an increase in the demand for AI capital, increasing capital returns and asset holdings' value. In all scenarios, interest rates increase by almost 0.4 percentage point, with the potential to partially offset the decline in the natural rate of interest in the UK and advanced economies in general.17 Since in the model, as in 16 Annex 4 discusses two additional hypothetical scenarios that disentangle the importance of exposure and complementarity. 17 The increase in the interest rate is approximately of the same magnitude as the decline in the UK natural rate attributable to demographics (IMF 2023). INTERNATIONAL MONETARY FUND 17 STAFF DISCUSSION NOTES
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Gen-AI: Artificial Intelligence and the Future of Work the data, high-income workers hold a large share of assets, they benefit more from the rise in capital returns. As a result, in all scenarios, independent of the impact on labor income, the total income of top earners increases because of capital income gains. These model simulations abstract from possible changes in the definition of property rights, as well as changes in fiscal and redistributive policies, which can help reshape distributional outcomes (see, for example, Berg and others 2021, in the context of automation; and Klinova and Korinek 2021, in the context of AI). Figure 10. Change in Total Income by Income Percentile 1. Low Complementarity 2. High Complementarity 3. High Complementarity and High (Percent) (Percent)
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Productivity (Percent) Source: IMF staff calculations. Note: The panels show three scenarios from the model: (1) low complementarity, (2) high complementarity, and (3) high complementarity and high productivity. For all scenarios, the calibrated change in the capital share is the same: 5.5 percentage points, based on the change in the capital share during 19802014. The panels show the change in total income by income percentile, decomposed into the change in labor income in blue and the change in capital income in orange. For more details on the model see Annex 4. P = percentile.
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