Recent years have seen impressive advances in artificial intelligence (AI) and this has stoked renewed concern about the impact of technological progress on the labor market, including on worker displacement. This paper looks at the possible links between AI and employment in a cross-country context. It adapts the AI occupational impact measure developed by Felten, Raj and Seamans—an indicator measuring the degree to which occupations rely on abilities in which AI has made the most progress—and extends it to 23 OECD countries. Overall, there appears to be no clear relationship between AI exposure and employment growth. However, in occupations where computer use is high, greater exposure to AI is linked to higher employment growth. The paper also finds suggestive evidence of a negative relationship between AI exposure and growth in average hours worked among occupations where computer use is low. One possible explanation is that partial automation by AI increases productivity directly as well as by shifting the task composition of occupations toward higher value-added tasks. This increase in labor productivity and output counteracts the direct displacement effect of automation through AI for workers with good digital skills, who may find it easier to use AI effectively and shift to non-automatable, higher-value added tasks within their occupations. The opposite could be true for workers with poor digital skills, who may not be able to interact efficiently with AI and thus reap all potential benefits of the technology1.
This paper develops a model that combines intrahousehold bargaining with competition on the marriage market to analyse women's and men's incentives to invest in education. Once married, spouses bargain over their share of total household income. They have the option of unilateral divorce and subsequent remarriage. Through this channel, the marriage market situation (the quality of prospective spouses and the distribution of resources in other couples) influences the distribution within existing couples. Individuals differ in their educational attainment, and more educated individuals contribute more to household income. I use this model to study the impact of changes in wage inequality and the rates of educational attainment of men and women on intrahousehold distribution. An interesting development of the last decade in western countries is women overtaking men in terms of higher education. Within the context of my model, an increase of women's participation in higher education over and above men's university graduation rates benefits men without degrees; educated men, however, are not able to translate the increase in educated women on the marriage market into a significantly larger share of household income. Hence, men's incentive to invest in education decreases if more women become educated. Even without assuming any heterogeneity in tastes between men and women, equilibria arise in which men and women decide to become educated at different rates.
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