Jeopolitik risk ve gıda fiyat enflasyonu: Gelişmiş ve gelişmekte olan ekonomilerden yeni kanıtlar

Öz

Amaç: Bu çalışma, 1995–2024 döneminde 32 gelişmiş ve gelişmekte olan ekonomide jeopolitik risk ile yurt içi gıda fiyat enflasyonu arasındaki ilişkiyi incelemekte; farklı jeopolitik risk göstergeleri, ülke özellikleri ve dönemler arasındaki heterojenliğe özellikle odaklanmaktadır.

Tasarım/Metodoloji/Yaklaşım: Çalışmada, jeopolitik risk ile gıda enflasyonu arasındaki dinamik ilişkiyi incelemek amacıyla panel vektör otoregresif (PVAR) yaklaşımı kullanılmaktadır. Ülkeye özgü jeopolitik risk (GPRC), küresel jeopolitik risk (GPRG), jeopolitik tehditler (GPRT) ve jeopolitik eylemler (GPRA) olmak üzere dört alternatif jeopolitik risk göstergesi dikkate alınmıştır. Etki-tepki fonksiyonları ve tahmin hata varyans ayrıştırmaları kullanılarak, jeopolitik risk yenilikleriyle ilişkili tepki örüntülerinin büyüklüğü, kalıcılığı ve dinamik özellikleri değerlendirilmiştir.

Bulgular: Bulgular, jeopolitik riskteki yeniliklerin gıda enflasyonundaki kısa dönemli artışlarla ilişkili olduğunu göstermektedir. Bununla birlikte, bu ilişkinin niceliksel katkısı petrol fiyatları ve para politikası koşulları gibi geleneksel makroekonomik belirleyicilere kıyasla daha sınırlı kalmaktadır. Ayrıca, elde edilen tepkilerin oldukça heterojen ve dönemsel koşullara bağlı olduğu görülmektedir. Gelişmekte olan ekonomiler ile net gıda ithalatçısı ülkelerde enflasyon tepkileri daha güçlü ve kalıcı iken, gelişmiş ve gıda ihracatçısı ekonomilerde daha sınırlı ve geçici örüntüler gözlenmektedir. Bunun yanı sıra, jeopolitik risk ile gıda enflasyonu arasındaki ilişkinin 2008 sonrası dönemde daha belirgin hale geldiği görülmektedir.

Özgünlük/Değer: Bu çalışma, jeopolitik riskin farklı boyutlarını bütünleşik bir Panel VAR çerçevesi içinde birlikte inceleyerek ve yapısal heterojenliğin gıda enflasyonu dinamiklerindeki önemini vurgulayarak literatüre katkı sunmaktadır. Alternatif jeopolitik risk göstergeleri arasındaki farklılıkları ve bunlarla ilişkili tepki örüntülerini inceleyen çalışma, jeopolitik risk ile gıda fiyatları arasındaki dinamik ilişkiye dair yeni bulgular ortaya koymakta ve gıda güvenliği ile makroekonomik politika tartışmaları açısından önemli çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Gıda fiyat enflasyonu
• jeopolitik risk
• panel VAR

Geopolitical risk and food price inflation: New evidence from advanced and emerging economies

Öz

Purpose: This study examines the relationship between geopolitical risk and domestic food price inflation across 32 advanced and emerging economies over the period 1995–2024, with a particular focus on heterogeneity across different geopolitical risk measures, country characteristics, and time periods.

Design/Methodology/Approach: A panel vector autoregressive (PVAR) framework is employed to analyse the dynamic relationship between geopolitical risk and food inflation. Four alternative measures of geopolitical risk—country-specific GPR, global GPR, GPR threats, and GPR acts—are considered. Impulse response functions and forecast error variance decompositions are used to evaluate the magnitude, persistence, and dynamic response patterns associated with geopolitical risk innovations.

Findings: The results indicate that innovations in geopolitical risk are associated with short-run increases in food inflation, although their quantitative contribution remains comparatively limited relative to traditional macroeconomic drivers such as oil prices and monetary conditions. Importantly, the observed responses are highly heterogeneous and state dependent. Emerging economies and food-import-dependent countries exhibit stronger and more persistent inflationary responses, whereas advanced and food-exporting economies display more muted and transitory patterns. In addition, the association between geopolitical risk and food inflation appears to have become more pronounced in the post-2008 period.

Originality/Value: This study contributes to the literature by jointly analysing multiple dimensions of geopolitical risk within a unified PVAR framework and by highlighting the importance of structural heterogeneity in food inflation dynamics. By distinguishing between alternative GPR measures and examining their associated response patterns, the paper provides new evidence on the dynamic relationship between geopolitical risk and food prices, offering relevant implications for food security and macroeconomic policy discussions.

Anahtar Kelimeler

• Food price inflation
• geopolitical risk
• panel VAR

Kaynakça

1. Abrigo, M. R., & Love, I. (2016), “Estimation of panel vector autoregression in Stata”, The Stata Journal, Vol. 16 No.3, pp.778-804.
2. Akaike, H. (1969). “Fitting autoregreesive models for prediction. In Selected Papers of Hirotugu Akaike” ,New York, NY: Springer New York. pp. 131-135.
3. Akaike, H. (1978). “On the likelihood of a time series model”, Journal of the Royal Statistical Society: Series D (The Statistician), Vol.27 No.(3-4), pp. 217-235.
4. Akpan, U. F., Abubakar Sani, I. and Question, E.M. (2024), “Geopolitical Risks and Food Inflation Dynamics in Nigeria: A Time-Varying Granger Causality Analysis” Available at (May 21, 2024). SSRN: https://ssrn.com/abstract=4836073 or http://dx.doi.org/10.2139/ssrn.4836073
5. Andrews, D. W., & Lu, B. (2001). “Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models”, Journal of Econometrics, Vol. 101 No. 1, pp. 123-164.
6. Attílio, L. A. (2025), “Geopolitical risk and food price inflation: a GVAR analysis of Brics and the g7”, The International Trade Journal, Vol. 39 No. 6, pp.622-647.
7. Barrett, C. B., & Carter, M. R. (2013), “The economics of poverty traps and persistent poverty: Empirical and policy implications”, The Journal of Development Studies, Vol. 49 No. 7, pp. 976-990.
8. Bouri, E., Gabauer, D., Gupta, R., & Kinateder, H. (2023), “Global geopolitical risk and inflation spillovers across European and North American economies”, Research in International Business and Finance, Vol. 66, pp. 102048.

9. Caldara, D., & Iacoviello, M. (2022), “Measuring geopolitical risk”, American Economic Review, Vol. 112 No.4, pp.1194-1225.
10. Caldara, D., Conlisk, S., Iacoviello, M., & Penn, M. (2026), “Do geopolitical risks raise or lower inflation?”, Journal of International Economics, Vol. 159, pp. 104188.
11. Canova F, Ciccarelli M. (2013), “Panel Vector Autoregressive Models: A Survey”. Ch. 12, 2013. p. 205–46. Emerald Group Publishing Limited.
12. Ceylan, F. (2024), “Time-Varying Effects of Geopolitical Risks on Global Energy and Food Prices”, Eskişehir Osmangazi University Social Science Journal, 25(3), s.775-791.
13. Chowdhury, M. A. F., Meo, M. S., & Aloui, C. (2021), “How world uncertainties and global pandemics destabilized food, energy and stock markets? Fresh evidence from quantile on quantile regressions”, International Review of Financial Analysis, Vol. 76, pp. 101759.
14. Darwiche, J., Badran, M., Awada, M., & Boungou, W. (2026), “Geopolitical risks and inflation: insights across time horizons”, Policy Studies, Vol. 47, No. 3, pp. 397-411.
15. Eissa, M. A., Al Refai, H., & Chortareas, G. (2025), “Climate policy uncertainty, geopolitical risk, oil volatility, and global food price volatility: A time-varying analysis”, Economic Modelling, Vol. 152, pp.107272.
16. Gong, X., & Xu, J. (2022), “Geopolitical risk and dynamic connectedness between commodity markets”, Energy Economics, Vol. 110, pp. 106028.
17. Goyal, R., Mensah, E., & Steinbach, S. (2024), “The interplay of geopolitics and agricultural commodity prices”, Applied Economic Perspectives and Policy, Vol. 46 No.4, pp.1533-1562.
18. Ha, J., Kose, M. A., & Ohnsorge, F. (2023), “One-stop source: A global database of inflation”, Journal of International Money and Finance, Vol. 137, pp. 102896.
19. Headey, D., Fan, S., (2010). “Reflections on the global food crisis: how did it happen? how has it hurt? and how can we prevent the next one?”, Research Report of the International Food Policy Research Institute
20. Jawadi, F., Mallick, S. K., & Sousa, R. M. (2016), “Fiscal and monetary policies in the BRICS: A panel VAR approach”, Economic Modelling, Vol. 58, pp. 535-542.
21. Kao, C. (1999), “Spurious regression and residual-based tests for cointegration in panel data”, Journal of Econometrics, Vol. 90 No. 1, pp. 1-44.
22. Karagöl, V. (2025), “Global Food Prices in the Shadow of Uncertainties, Risks and Volatilities”, İzmir İktisat Dergisi, 40(4), s.1001-1019.
23. Kilian, L., & Lütkepohl, H. (2017),” Structural vector autoregressive analysis”, Cambridge University Press.
24. Kornher, L., Balezentis, T., & Santeramo, F. G. (2024), “EU food price inflation amid global market turbulences during the COVID‐19 pandemic and the Russia–Ukraine War”, Applied Economic Perspectives and Policy, Vol. 46 No.4, pp.1563-1584.
25. Lee, C. C., Lee, C. C., & Li, Y. Y. (2021), “Oil price shocks, geopolitical risks, and green bond market dynamics”, The North American Journal of Economics and Finance, Vol. 55, pp. 101309.
26. Li, C., Zhang, W., Huang, X., & Zhang, J. F. (2025), “Geopolitical risks, institutional environment, and food price inflation”, Applied Economics, Vol. 57 No. 42, pp. 6753-6773.
27. Liu, Q., Xu, C., & Xu, Z. (2025), “The impact of geopolitical risk on food prices: evidence from the TVP-SV-VAR model”, Journal of Economics and Finance, Vol. 49 No.2, pp. 450-473.
28. Lofaro, A., & Di Bucchianico, S. (2025), “Impact of monetary policy on functional income distribution: A panel vector autoregressive analysis”, Economic Modelling, Vol. 151, 107227.
29. Lutkepohl, H. (2005), “New Introduction to Multiple Time Series Analysis”. Springer, Berlin.
30. Minot, N. (2014), “Food price volatility in sub-Saharan Africa: Has it really increased?”, Food Policy, Vol. 45, pp 45-56.
31. Nogueira, I. L., de Araujo Moxotó, A. C., & da Rocha Oliveira Junior, A. (2026), “Geopolitical Risk and Food Inflation”, Thunderbird International Business Review, Vol.68 No. 2, pp. 165-176.
32. Ochi, A., & Saidi, A. (2024), “Impact of governance quality, population and economic growth on greenhouse gas emissions: An analysis based on a panel VAR model”, Journal of Environmental Management, Vol. 370, pp. 122613.
33. Özçelik, O. (2023), “Determinants of Global Food Prices Overshadowed by The Russian-Ukrainian War: Evidence from Fourier Bootstrap Ardl and Fourier Bootstrap Toda-Yamamoto Approaches”, Turkish Journal of Agricultural Economics, 29(1), s.29-47.
34. Pedroni, P. (2004), “Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis”, Econometric Theory, Vol. 20 No.3, pp 597-625.
35. Saâdaoui, F., Jabeur, S. B., & Goodell, J. W. (2022), “Causality of geopolitical risk on food prices: Considering the Russo–Ukrainian conflict”, Finance Research Letters, Vol. 49, pp. 103103.
36. Saidi, Y., Ochi, A., & Maktouf, S. (2023), “FDI inflows, economic growth, and governance quality trilogy in developing countries: A panel VAR analysis”, Bulletin of Economic Research, Vol. 75 No. 2, pp. 426-449.
37. Schwarz, G. (1978), "Estimating the dimension of a model," The Annals of Statistics, Vol.6, pp.461-464.
38. Sohag, K., Islam, M. M., Tomas Žiković, I., & Mansour, H. (2023), “Food inflation and geopolitical risks: analyzing European regions amid the Russia-Ukraine war”, British Food Journal, Vol. 125 No.7, pp. 2368-2391.
39. Sun, T. T., & Su, C. W. (2025), “How is geopolitical risk associated with food prices?”, International Journal of Emerging Markets, Vol. 20 No. 8, pp. 3149-3169.
40. Uhlig, H. (2005), “What are the effects of monetary policy on output? Results from an agnostic identification procedure”, Journal of Monetary Economics, Vol. 52 No. 2, pp. 381-419.
41. Ullah, E., Sohag, K., Mariev, O., Zada, H., & Esquivias, M. A. (2026). Food price dynamics in Russia: assessing the role of ICT, oil prices, and geopolitical risk. Cogent Food & Agriculture, 12(1), 2622206.
42. Westerlund, J. (2007), “Testing for error correction in panel data”, Oxford Bulletin of Economics and Statistics, Vol. 69, pp. 709–748.
43. Yang, T., Dong, Q., Du, M., & Du, Q. (2023), “Geopolitical risks, oil price shocks and inflation: Evidence from a TVP–SV–VAR approach”, Energy Economics, Vol. 127, pp. 107099.
44. Yıldırım, Ö. F. (2025), “Yükselen Belirsizlik Çağı: Ölçüm Yaklaşımları, Kaynaklar ve Makro-Finansal Aktarım Mekanizmaları”, Zafer Kanberoğlu, Mustafa Türkmenoğlu (Ed.), Makroekonomide Yeni Eğilimler, Ekin Yayınevi, 63-82.
45. Yilmazkuday, H. (2025), “Geopolitical risks and exchange rates”, Finance Research Letters, Vol. 74, pp. 106769.
46. Zhang, Z., He, M., Zhang, Y., & Wang, Y. (2022), “Geopolitical risk trends and crude oil price predictability”, Energy, Vol. 258, pp. 124824.
47. Zhang, B., Liu, Z., Wang, Z., & Zhang, S. (2023), “The impact of geopolitical risk on energy security: Evidence from a GMM panel VAR approach”, Resources Policy, Vol. 86, pp. 104222.
48. Zhang, W., Sun, L., & Nguyen‐Van, P. (2025), “Dynamic Relationship Between Global Economic Policy Uncertainty, Food Prices, and Maritime Transport: Evidence From the TVP‐VAR‐SV Model”, Agribusiness, 1-15.