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12. Working Papers
Social Policy and Labor Discussion Paper Series
Artificial Intelligence and Labor Market Adjustment in Türkiye: Evidence from LinkedIn Data

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Artificial Intelligence and Labor Market Adjustment in Türkiye: Evidence from LinkedIn Data
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Published
2026-04-01
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Date
2026-05-06
Author(s)
Fatima, Freeha
Özen, Efsan Nas
Raju, Dhushyanth
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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.
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“
Fatima, Freeha
;
Özen, Efsan Nas
;
Raju, Dhushyanth
.
2026
.
Artificial Intelligence and Labor Market Adjustment in Türkiye: Evidence from LinkedIn Data
.
Social Policy & Labor Discussion Paper; No. 2624
.
©
World Bank
.
http://hdl.handle.net/10986/44814
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CC BY-NC 3.0 IGO
.
”
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https://doi.org/10.1596/44814
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https://hdl.handle.net/10986/44814
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(
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Show more
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Cash assistance to refugees is often viewed with concern because regular transfers may be assumed to discourage work. Evidence from Türkiye’s Emergency Social Safety Net, one of the world’s largest humanitarian cash transfer programs, provides little support for this concern. Comparing similar Syrian refugee households near the program’s rule-based eligibility threshold, the study finds no large or statistically detectable reduction in employment among either women or men. The findings suggest that, in a labor market where refugee work is often informal, unstable, and constrained, cash assistance helps households manage economic insecurity without producing meaningful employment disincentives. Policies to strengthen refugee economic participation should therefore focus on easing barriers to job access, formal employment, and stable livelihoods, rather than assuming that reducing income support will increase work.
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</reference>

<statements>
1. The literature converges on the importance of proactive policy responses—targeted upskilling, inclusive AI governance, and social protection reforms—to steer AI-driven restructuring toward more inclusive outcomes.
2. The net effect is a reconfiguration of job composition and internal labor markets rather than simple elimination of employment.
3. Aggregate impacts of AI-labor substitution on employment and wage growth remain small and difficult to detect at this stage, but restructuring at the establishment and occupation levels is clear.
4. These roles often command wage premia, reflecting scarcity of relevant skills and the strategic importance of AI capabilities for firms.
5. Systematic reviews emphasize that a majority of empirical studies report net positive or mixed employment effects, with relatively few documenting large-scale technological unemployment.
6. Another gap relates to detailed occupational and regional analyses in emerging and developing economies, where data limitations hinder precise measurement of AI exposure and impacts.
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.