Evidence is also indicative of the differential impacts of robot usage on jobs in advanced and developing economies. De Vries et al. (2020), using data for 37 economies, conclude that a rise in robot adoption relates significantly to a fall in the employment share of routine manual taskintensive jobs in advanced economies but not in developing economies. This reflects the fact that higher wages for low-skilled workers provide larger incentives for automation in advanced economies. It is also possible for automation in advanced economies to displace jobs in developing economies through cross-border effects. Kugler et al. (2020), for example, find that robot use in the United States has lowered employment and earnings in Colombia among workers in sectors characterized by a high level of automation in the U.S. labor market. Similarly, Faber (2020) finds negative spillovers from robot adoption in the United States on employment in Mexico.

The literature on the effects of AI on jobs is nascent. Using data for the United States for 2010-18, Acemoglu et al. (2022) find no detectable effect on employment and wages in occupations and industries exposed to AI. Felten, Raj, and Seamans (2019)—also using U.S. data—find that, on average, employment increases and wages decline in industries where AI is used. In general, there has been an acceleration in the displacement effects of automation and a deceleration in reinstatement effects during the past 40 years in the United States compared to the 40 years before (Autor et al. 2024).

Importantly, none of these studies captures data from very recent AI developments—in particular, the rapid uptake of generative AI. It is possible that the economic effects of AI will take time to fully discern in the data, just as the effects of the first wave of digitalization took time to measure. There is consensus that the advent of AI is poised to automate a wider range of cognitive tasks relative to ICT which made it feasible for machines to replace labor in routine tasks (Brynjolfsson, Mitchell, and Rock 2018). It is therefore expected that advanced economies will experience the benefits and pitfalls of AI sooner than developing economies because of the larger share of cognitive-intensive tasks in total employment (Felten, Raj, and Seamans 2021). However, much like other technologies, the gains in productivity from AI adoption could result in higher average incomes if the resulting increase in labor demand offsets the replacement of labor in certain tasks (Cazzaniga et al. 2024).<sup>12</sup>

# **_3.2 Does automation increase job polarization?_**

A related concern about the effect of digitalization on shared prosperity is the potential for job polarization, whereby the automation of routine tasks results in rising employment of low-skilled and high-skilled workers relative to middle-skilled workers. Multiple studies provide evidence of job polarization in advanced economies. Autor and Dorn (2013) and Autor, Katz, and Kearney (2006) conclude that computerization in the United States was associated with falling numbers of middle-skilled, middle-wage jobs and increasing numbers of high-skilled and low-skilled jobs. Goos, Manning, and Salomons (2014) reach similar conclusions for Europe, where middle-wage jobs fell as a share of total employment between the early 1990s and mid-2000s. Similar conclusions are reached in studies of individual countries, including Germany, Japan, and the United Kingdom (Dustmann, Ludsteck, and Schönberg 2009; Goos and Manning 2007; Ikenaga and Kambayashi 2016; Montresor 2019; Spitz-Oener 2006).

> 12 When using different model assumptions, the potential impact of AI on output growth in the literature can range from a negative impact to a continuous unbounded positive impact (e.g., Nordhaus 2021; Trammell and Korinek 2023).

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