Author
Listed:
- Fatima, Freeha
- Ozen, Efsan Nas
- Raju, Dhushyanth
Abstract
This paper examines how artificial intelligence (AI) is reshaping Türkiye’s labor market by documenting patterns in skill supply, employer demand, and labor market adjustment using high-frequency digital labor market indicators from LinkedIn. The analysis focuses on the mechanisms through which AI-related change is associated with shifts in skills, hiring, occupational mobility, exposure to generative AI, and international migration. The evidence shows a relatively broad presence of foundational digital and AI literacy skills across sectors and demographic groups, alongside a persistent and increasing concentration of advanced AI engineering talent within a narrow set of occupations and industries. Measured skill penetration follows non-monotonic patterns over time, while frontier AI talent accumulates steadily, indicating a divergence between the breadth and depth of AI capability. Entry into AI roles often follows strongly path-dependent pathways, and employer demand signals for technical and AI-adjacent capabilities are only partially reflected in realized hiring, with no sustained positive divergence in AI-related hiring relative to overall labor demand. Potential exposure to generative AI varies systematically across sectors and demographic groups, with the balance between task augmentation and disruption differing across sectors rather than uniformly favoring one over the other. International migration emerges as a salient adjustment margin for highly specialized AI talent, operating alongside domestic reallocation mechanisms and influencing the availability of frontier skills within the domestic labor market. These patterns indicate that the central challenge associated with AI in Türkiye’s labor market lies not in whether AI-related capabilities will spread, but in how reallocation unfolds across skills, occupations, and workers over time. The findings highlight the role of skill formation systems, hiring and credentialing practices, occupational structures, and cross-border mobility in shaping the trajectory of labor market adjustment. The analysis also illustrates how digital labor market data can complement traditional sources by providing timely evidence on emerging skills, evolving demand, and early adjustment dynamics in middle-income economies navigating the AI transition.
Suggested Citation
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wbk:hdnspu:209921. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Aaron F Buchsbaum (email available below). General contact details of provider: https://edirc.repec.org/data/wrldbus.html .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.