Combining data from the United States, Japan, and nine European countries, Michaels, Natraj, and Van Reenen (2014) find a shift in the demand from middle-educated workers to highly educated workers in industries with rapid ICT growth. Other studies also find that shifts in demand for certain tasks accompanying workplace computerization contributed to an increase in the relative demand for skilled workers in advanced economies (Acemoglu 1998, 2002; Autor, Katz, and Krueger 1998; Akerman et al. 2015; Atasoy et al. 2013).

The relationship between the first wave of digitalization and labor market polarization is less conclusive in developing economies than in developed economies.<sup>13</sup> On one hand, Shapiro and Mandelman (2021) find that a composite measure of ICT adoption (density of servers, download speeds, 3G coverage, and share of firms with websites) is negatively correlated with selfemployment rates but uncorrelated with wage-employment rates. Their results suggest a possible boost to shared prosperity following ICT adoption given that self-employed workers in developing economies tend to be at the lower end of the income distribution. Das and Hilgenstock (2022) similarly find little evidence of polarization in developing economies since the1990s, but caution that these economies, thus far, have been far less exposed to routinization of tasks than advanced economies. On the other hand, based on a sample of more than 50 countries at different income levels, Jaumotte et al. (2013) show that an increasing share of ICT capital in the total capital stock increased the premium on higher skills, thereby exacerbating income inequality. In China, Zhou and Tyers (2019) find that skill-biased technological change has contributed to rising inequality. In Africa, Hjort and Poulsen (2019) find that faster internet connectivity increased employment in higher-skill occupations more than in low-skilled occupations.

The rise of robots and AI may lead to additional substitution of capital for labor, by allowing more tasks to be automated. In a study of EU economies, Graetz and Michaels (2018) show that the share of hours worked by low-skilled workers fell with increased use of industrial robots, while the middle- and high-skilled share rose. In contrast, Maloney and Molina (2019) find no evidence of polarization, on average, in a study of 21 developing economies.

AI is increasingly able to automate prediction- and decision-related cognitive tasks that could previously only be done by people (Agrawal, Gans, and Goldfarb 2019). The diffusion of AI may therefore increase income inequality to the extent it complements high-income workers. In the United States, Felten, Raj, and Seamans (2019) find that high-wage occupations experience employment and wage growth following the introduction of AI, while low-wage occupations experience wage losses.<sup>14</sup> Albanesi et al. (2023) conduct a similar study of 16 European countries for 2011-19, also finding a positive relationship between AI-enabled automation and employment, particularly for high-skilled and young workers. At the same time, AI could automate certain highly skilled, decision-making tasks such as medical care, legal services, and software coding, potentially reinstating middle-skilled jobs that were previously diminished by ICT usage in advanced economies (Autor 2024). One study projects that U.S. workers’ exposure to AI will reduce wage inequality, defined as the ratio of the 90th to the 10th percentile of wages, by 4 percent (Webb 2020).

> 13 Martins-Neto et al. (2024) review the literature on job polarization in developing economies.

> 14 That machine learning systems will increasingly be able to replace high-wage cognitive tasks is reinforced by the deceleration of employment growth in abstract task-intensive occupations after 2000 in the United States (Beaudry, Green, and Sand 2016; Mishel, Shierholz, and Schmitt 2013).

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