The Effect of Artificial Intelligence Investments on Economıc Development: Panel Data Analysis

Abstract

In the 21st century, the relationship between economic development and technological innovation is becoming increasingly complex. In recent years, with the emergence of artificial intelligence as a pioneer and leader in technological innovation and its increasing use in many fields, unemployment is rising on the one hand, while on the other hand, unit costs are decreasing due to increased productivity. This dilemma has become a question that needs to be explored, especially for countries with technologies that make intensive use of artificial intelligence. To this end, this study examined the effects of artificial intelligence (AI) investments on economic development in Germany, the United States, China, France, South Korea, India, Italy, and Japan for the period 2012-2023 using panel data methods. The Human Development Index (HDI) is used as an indicator of economic development in the models developed for the analysis, while AI investments are treated as the main independent variable. Per capita income (PCI), inflation (INF), and foreign direct investment (FDI) are included in the model as control variables. Second-generation panel methods, such as the Pesaran– Yamagata homogeneity test, Pesaran CD tests, and the CIPS unit root test, are applied in the study, and long- and short-term relationships are analyzed using the CS-ARDL model. The research indicates that investments in AI have a beneficial and substantial effect on HDI over time. However, INF was found to have a negative impact on the HDI. It has been observed that the FDI variable has negative effects contrary to expectations. The heterogeneity of the variables' parameters suggests that the impact of AI investments on development varies across countries. In conclusion, AI investments appear to be a supportive element for economic development, but the appropriate macroeconomic and institutional framework must be provided for the sustainability of this impact.

Keywords

• Artificial intelligence investments
• Economic development
• Human Development Index
• Panel data analysis

YAPAY ZEKA YATIRIMLARININ EKONOMİK KALKINMAYA ETKİSİ: PANEL VERİ ANALİZİ

Abstract

21. yüzyılda, ekonomik kalkınma ile teknolojik inovasyon arasındaki ilişki giderek daha karmaşık hale gelmektedir. Son yıllarda, teknolojik inovasyonun öncüsü ve lideri olarak yapay zekanın ortaya çıkması ve birçok alanda kullanımının artmasıyla, bir yandan işsizlik artarken, diğer yandan verimlilik artışı nedeniyle birim maliyetler düşmektedir. Bu ikilem, özellikle yapay zekayı yoğun olarak kullanan teknolojilere sahip ülkeler için araştırılması gereken bir soru haline gelmiştir. Bu amaçla, bu çalışmada panel veri yöntemleri kullanılarak 2012-2023 dönemi için Almanya, Amerika Birleşik Devletleri, Çin, Fransa, Güney Kore, Hindistan, İtalya ve Japonya'da yapay zeka (AI) yatırımlarının ekonomik kalkınma üzerindeki etkileri incelenmiştir. Analiz için geliştirilen modellerde ekonomik kalkınmanın göstergesi olarak İnsani Gelişme Endeksi (HDI) kullanılırken, AI yatırımları ana bağımsız değişken olarak ele alınmıştır. Kişi başına gelir (PCI), enflasyon (INF) ve doğrudan yabancı yatırımlar (FDI) kontrol değişkenleri olarak modele dahil edilmiştir. Pesaran–Yamagata homojenlik testi, Pesaran CD testleri ve CIPS birim kök testi gibi ikinci nesil panel yöntemleri çalışmada uygulanmış ve CS-ARDL modeli kullanılarak uzun ve kısa vadeli ilişkiler analiz edilmiştir. Araştırma, AI yatırımlarının zaman içinde HDI üzerinde yararlı ve önemli bir etkiye sahip olduğunu göstermektedir. Ancak, INF'nin HDI üzerinde olumsuz bir etkisi olduğu bulunmuştur. FDI değişkeninin beklentilerin aksine olumsuz etkileri olduğu gözlemlenmiştir. Değişkenlerin parametrelerinin heterojenliği, AI yatırımlarının kalkınma üzerindeki etkisinin ülkeler arasında farklılık gösterdiğini göstermektedir. Sonuç olarak, AI yatırımları ekonomik kalkınma için destekleyici bir unsur gibi görünmektedir, ancak bu etkinin sürdürülebilirliği için uygun makroekonomik ve kurumsal çerçeve sağlanmalıdır.

Keywords

• Yapay zeka yatırımları
• Ekonomik gelişme
• İnsani Gelişme Endeksi
• Panel veri analizi

References

1. [1] M. Mutascu, "Artificial intelligence and unemployment: New insights," Economic Analysis and Policy, vol. 69, pp. 653-667, 2021.
2. [2] F. Bordot, "Artificial intelligence, robots and unemployment: Evidence from OECD countries," Journal of Innovation Economics & Management, vol. 37, no. 1, pp. 117-138, 2022.
3. [3] Q. P. Nguyen and D. H. Vo, "Artificial intelligence and unemployment: An international evidence," Structural Change and Economic Dynamics, vol. 63, pp. 40-55, 2022.
4. [4] M. Qin, Y. Wan, J. Dou, and C. W. Su, "Artificial intelligence: intensifying or mitigating unemployment?," Technology in Society, vol. 79, p. 102755, 2024.
5. [5] N. Kudoh and H. Miyamoto, "Robots, AI, and unemployment," Journal of Economic Dynamics and Control, vol. 174, p. 105069, 2025.
6. [6] C.-H. Yang, "How artificial intelligence technology affects productivity and employment: Firm-level evidence from Taiwan," Research Policy, vol. 51, no. 6, p. 104536, 2022.
7. [7] D. Czarnitzki, G. P. Fernández, and C. Rammer, "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, vol. 211, pp. 188-205, 2023.
8. [8] S. Zhai and Z. Liu, "Artificial intelligence technology innovation and firm productivity: Evidence from China," Finance Research Letters, vol. 58, p. 104437, 2023.

9. [9] L. Lei, H. Feng, and J. Ren, "Artificial Intelligence, Human Capital and Firm-Level Total Factor Productivity," Finance Research Letters, p. 107897, 2025.
10. [10] X. Wu and Y. Zhu, "How artificial intelligence applications affect the total factor productivity of the service industry: Firm-level evidence from China," Journal of Asian Economics, vol. 97, p. 101893, 2025.
11. [11] P. Aghion, B. F. Jones, and C. I. Jones, "Artificial intelligence and economic growth," National Bureau of Economic Research, 2017.
12. [12] P. Aghion, C. Antonin, and S. Bunel, "Artificial intelligence, growth and employment: The role of policy," Economie et Statistique/Economics and Statistics, no. 510-511-512, pp. 150-164, 2019.
13. [13] Y. He, "A study on the impact of artificial intelligence industry on macroeconomy: Evidence from United States of America," The East Asian Journal of Business Management, vol. 8, no. 4, pp. 37-44, 2018.
14. [14] C. A. Makridis and S. Mishra, "Artificial intelligence as a service, economic growth, and well-being," Journal of Service Research, vol. 25, no. 4, pp. 505-520, 2022.
15. [15] C.-H. Lu, "The impact of artificial intelligence on economic growth and welfare," Journal of Macroeconomics, vol. 69, p. 103342, 2021.
16. [16] P. Zhao, Y. Gao, and X. Sun, "How does artificial intelligence affect green economic growth?—Evidence from China," Science of the Total Environment, vol. 834, p. 155306, 2022.
17. [17] A. Goyal and R. Aneja, "Artificial intelligence and income inequality: Do technological changes and worker's position matter?," Journal of Public Affairs, vol. 20, no. 4, p. e2326, 2020.
18. [18] M. A. Trabelsi, "The impact of artificial intelligence on economic development," Journal of Electronic Business & Digital Economics, vol. 3, no. 2, pp. 142-155, 2024.
19. [19] D. Mhlanga, "Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?," Sustainability, vol. 13, no. 11, p. 5788, 2021.
20. [20] R. Vinuesa et al., "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature communications, vol. 11, no. 1, p. 233, 2020.
21. [21] F. Filippucci, P. Gal, and C. S. Jona Lasinio, "The impact of Artificial Intelligence on productivity, distribution and growth," 2024.
22. [22] V. Capraro et al., "The impact of generative artificial intelligence on socioeconomic inequalities and policy making," PNAS nexus, vol. 3, no. 6, p. pgae191, 2024.
23. [23] C. S. Saba and N. Ngepah, "The impact of artificial intelligence (AI) on employment and economic growth in BRICS: Does the moderating role of governance Matter?," Research in Globalization, vol. 8, p. 100213, 2024.
24. [24] J. Li, S. Ma, Y. Qu, and J. Wang, "The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China," Resources Policy, vol. 82, p. 103507, 2023.
25. [25] Q. Wang, T. Sun, and R. Li, "Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects," Energy & Environment, vol. 36, no. 2, pp. 1005-1037, 2025.
26. [26] I. F. o. Robotics, "World Robotics Press Conference Report 2024," IFR, 2024. Accessed: 25.08.2025. [Online]. Available: https://ifr.org/img/worldrobotics/Press_Conference_2024.pdf
27. [27] C. S. Saba and M. Pretorius, "The impact of artificial intelligence (AI) investment on human well-being in G-7 countries: Does the moderating role of governance matter?," Sustainable futures, vol. 7, p. 100156, 2024.
28. [28] M. H. Pesaran and T. Yamagata, "Testing slope homogeneity in large panels," Journal of econometrics, vol. 142, no. 1, pp. 50-93, 2008.
29. [29] M. H. Pesaran, "Testing weak cross-sectional dependence in large panels," Econometric reviews, vol. 34, no. 6-10, pp. 1089-1117, 2015.
30. [30] M. H. Pesaran, "General diagnostic tests for cross-sectional dependence in panels," Empirical economics, vol. 60, no. 1, pp. 13-50, 2021.
31. [31] J. Fan, Y. Liao, and J. Yao, "Power enhancement in high‐dimensional cross‐sectional tests," Econometrica, vol. 83, no. 4, pp. 1497-1541, 2015.
32. [32] M. H. Pesaran, "A simple panel unit root test in the presence of cross‐section dependence," Journal of applied econometrics, vol. 22, no. 2, pp. 265-312, 2007.
33. [33] A. Chudik and M. H. Pesaran, "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of econometrics, vol. 188, no. 2, pp. 393-420, 2015.
34. [34] N. Samargandi, "Energy intensity and its determinants in OPEC countries," Energy, vol. 186, p. 115803, 2019.