Rutin İşlerin Türkiye İşgücü Piyasasındaki Dönüşümü: Yapay Zekâ ve Teknolojik Değişimin İstihdam Üzerindeki Etkilerine İlişkin Nicel Bir Analiz (2014–2024)

Yıl 2025, Cilt: 12 Sayı: Özel Sayı 2025-1, 180 - 199, 31.10.2025

Hakan Saraç

https://doi.org/10.46868/atdd.2025.1010

Öz

Bu çalışma, 2014–2024 döneminde Türkiye işgücü piyasasının iş görevi yoğunluğu açısından geçirdiği dönüşümü nicel olarak incelemektedir. Mihaylov ve Tijdens’in (2019) ISCO-08 görev veri seti ile TÜİK istihdam verileri kullanılarak, teknolojik ilerleme ve yapay zekâ destekli otomasyonun yön verdiği önemli yapısal değişimler belirlenmiştir. Bulgular, rutin olmayan bilişsel işlerin payının %5,2 arttığını; rutin manuel ve rutin olmayan manuel işlerin ise sırasıyla %3,3 ve %1,8 azaldığını göstermektedir. Bu durum, bilgi temelli sektörlere geçişi ve artan istihdam kutuplaşmasını ortaya koymaktadır. Yüksek vasıflı mesleki ve teknik roller genişlerken, orta vasıflı tarım ve zanaatkârlık alanları daralmıştır. Ücret yapıları, rutin olmayan bilişsel işlerde yoğunlaşan yüksek vasıflı grupları lehine şekillenmiş; cinsiyete dayalı ücret eşitsizliği ise sürmüştür. Çalışma, mesleki eğitim reformları, hedefli teşvikler ve kapsamlı yeniden beceri kazandırma programlarının işgücü piyasasının teknolojik değişime uyumunu güçlendireceğini vurgulamaktadır.

Anahtar Kelimeler

Rutin İşler , İstihdam Kutuplaşması , Yapay Zekâ , Otomasyon , Türkiye İşgücü Piyasası

Transformation of Routine Jobs in the Türkiye Labor Market: A Quantitative Analysis of the Effects of Artificial Intelligence and Technological Change on Employment (2014–2024)

Öz

This study quantitatively analyzes the transformation of Türkiye’s labor market between 2014 and 2024 in terms of job task intensity. Using Mihaylov and Tijdens’ (2019) ISCO-08 task dataset and TurkStat employment data, it identifies major structural shifts driven by technological progress and artificial intelligence–assisted automation. The share of non-routine cognitive jobs increased by 5.2%, while routine manual and non-routine manual jobs declined by 3.3% and 1.8%, respectively. These findings indicate a transition toward knowledge-based sectors and growing employment polarization. High-skilled professional and technical roles expanded, whereas middle-skilled occupations, including skilled agriculture and artisanal work, contracted. Wage structures increasingly favored high-skilled, non-routine cognitive occupations, while gender pay disparities persisted. The study underscores the need for vocational education reforms, targeted incentives, and large-scale reskilling programs to enhance labor market adaptability to technological change.

Anahtar Kelimeler

Routine Jobs , Employment Polarization , Artificial İntelligence , Automation , Türkiye Labor Market

Kaynakça

• Acemoglu, D., & Autor, D. (2011). Skills, tasks, and technologies: Implications for employment and earnings. In D. Card & O. Ashenfelter (Eds.), Handbook of labor economics (Vol. 4B, pp. 1043–1171). Elsevier. https://doi.org/10.1016/S0169-7218(11)02410-5
• Aedo, C., Hentschel, J., Moreno, M., & Luque, J. (2013). From occupations to embedded skills: A cross-country comparison (Policy Research Working Paper No. 6560). World Bank. https://doi.org/10.1596/1813-9450-6560
• Alabdulkareem, A., Frank, M. R., Sun, L., AlShebli, B., Hidalgo, C., & Rahwan, I. (2018). Unpacking the polarization of workplace skills. Science Advances, 4(7), eaao6030. https://doi.org/10.1126/sciadv.aao6030
• Autor, D. (2010). The polarization of job opportunities in the U.S. labor market: Implications for employment and earnings. Center for American Progress and The Hamilton Project. https://www.brookings.edu/wp-content/uploads/2016/06/04_jobs_autor.pdf
• Autor, D., & Dorn, D. (2009). This job is “getting old”: Measuring changes in job opportunities using occupational age structure. American Economic Review, 99(2), 45–51. https://doi.org/10.1257/aer.99.2.45
• Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333. https://doi.org/10.1162/003355303322552801
• Cramarenco, R. E., Burcă-Voicu, M. I., & Dabija, D. C. (2023). The impact of artificial intelligence (AI) on employees’ skills and well-being in global labor markets: A systematic review. Oeconomia Copernicana, 14(3), 731–767. https://doi.org/10.24136/oc.2023.023
• David, H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the U.S. labor market. American Economic Review, 103(5), 1553–1597. https://doi.org/10.1257/aer.103.5.1553
• Davis, D. R. (1998). Technology, unemployment, and relative wages in a global economy. European Economic Review, 42(9), 1613–1633. https://doi.org/10.1016/S0014-2921(97)00102-5
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280. https://doi.org/10.1016/j.techfore.2016.08.019
• Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8), 2509–2526. https://doi.org/10.1257/aer.104.8.2509
• International Labour Organization (ILO). (2013). Global employment trends for youth 2013: A generation at risk. International Labour Office. https://www.ilo.org/global/research/global-reports/youth/2013/lang--en/index.htm
• Jaimovich, N., & Siu, H. E. (2012). Job polarization and jobless recoveries (NBER Working Paper No. 18334). National Bureau of Economic Research. https://doi.org/10.3386/w18334
• Jaiswal, A., Arun, C. J., & Varma, A. (2023). Rebooting employees: Upskilling for artificial intelligence in multinational corporations. In A. Varma, C. J. Arun, & A. Jaiswal (Eds.), Artificial intelligence and international HRM (pp. 114–143). Routledge. https://doi.org/10.4324/9781003313268-9
• Keister, R., & Lewandowski, P. (2017). A routine transition in the digital era? The rise of routine work in Central and Eastern Europe. Transfer: European Review of Labour and Research, 23(3), 263–279. https://doi.org/10.1177/1024258917701758
• Lewandowski, P., Park, A., Hardy, W., & Du, Y. (2019). Technology, skills, and globalization: Explaining international differences in routine and nonroutine work using survey data. World Bank. https://doi.org/10.1596/1813-9450-8887
• Macias, E. F., Hurley, J., & Rafferty, A. (2015). Upgrading or polarisation? Long-term and global shifts in the employment structure: European Jobs Monitor 2015. European Foundation for the Improvement of Living and Working Conditions. https://doi.org/10.2806/099856
• Mihaylov, E., & Tijdens, K. G. (2019). Measuring the routine and non-routine task content of 427 four-digit ISCO-08 occupations. WageIndicator Foundation. https://wageindicator.org
• Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh4451
• Santhosh, A., Unnikrishnan, R., Shibu, S., Meenakshi, K. M., & Joseph, G. (2023). AI impact on job automation. International Journal of Engineering Technology and Management Sciences, 7(4), 410–425. https://doi.org/10.46647/ijetms.2023.v07i04.060
• Tolan, S., Pesole, A., Martínez-Plumed, F., Fernández-Macías, E., Hernández-Orallo, J., & Gómez, E. (2021). Measuring the occupational impact of AI: Tasks, cognitive abilities, and AI benchmarks. Journal of Artificial Intelligence Research, 71, 191–236. https://doi.org/10.1613/jair.1.12846
• TurkStat. (2023). Earnings structure statistics. Turkish Statistical Institute. https://data.tuik.gov.tr/Bulten/Index?p=Kazanc-Yapisi-Istatistikleri-2023-53700
• Tyson, L. D., & Zysman, J. (2022). Automation, AI, and work. Daedalus, 151(2), 256–271. https://doi.org/10.1162/daed_a_01911