Ekonomik Politika Belirsizliğinin Gıda Fiyatlarına Etkisi: Seçilmiş Ülkeler İçin Zamanla Değişen Nedensellik Analizi

Yıl 2023, , 409 - 433, 02.08.2023

Veysel Karagöl

https://doi.org/10.26650/JEPR1212094

Öz

Küresel ısınma ve iklim değişikliği gibi olgular, son yıllarda yaşanan Covid-19 Pandemisi’nin de etkisiyle gıda fiyatlarının artmasına nedenolmuştur. Gıda fiyatlarının artmasına neden olan enerji fiyatları, döviz kuru, arz ve talep miktarları gibi birçok itici güç bulunmaktadır. Ekonomik politika belirsizliğinin de bu itici güçlerden biri olabileceği, yakın dönemde tartışılmaya başlamıştır. Bu çalışmanın amacı, ekonomik politika belirsizliği ile gıda fiyatları arasındaki ilişkiyi araştırmaktır. Bu amaç doğrultusunda çalışmada Çin, İngiltere, Almanya, Macaristan, Güney Afrika, Türkiye ve Amerika Birleşik Devletleri’nin gıda enflasyonlarıyla küresel ekonomik politika belirsizliği arasındaki nedensellik ilişkileri incelenmiştir. Simetrik nedensellik ilişkisi bulguları, küresel ekonomik politika belirsizliği ile yalnızca Amerika Birleşik Devletleri’nin gıda enflasyonu arasında iki yönlü bir nedensellik ilişkisinin varlığına işaret etmektedir. Zamanla değişen nedensellik analizi bulgularına göre, küresel ekonomik politika belirsizliğinden ülkelerin tamamındaki gıda enflasyonuna doğru zamanla değişen nedensellik ilişkileri mevcuttur.

Analiz bulgularına göre ayrıca, ekonomik politika belirsizliğinden gıda fiyatlarına doğru nedensellik ilişkisinin Covid-19 Pandemisi döneminde yoğunlaştığı gözlenmiştir. Ekonomik politika belirsizliğinin gıda fiyatları üzerindeki potansiyel etkileri daha fazla kanıta muhtaç olsa da politika yapıcıların, etkili ekonomi politikası müdahaleleriyle gıda fiyatlarında istikrarı sağlayabilecekleri düşünülmektedir.

Anahtar Kelimeler

Gıda fiyatları, Ekonomik politika belirsizliği, İklim değişikliği, Covid-19 pandemisi, Zamanla değişen nedensellik

The Effect of Economic Policy Uncertainty on Food Prices: A Time-Varying Causality Analysis for Selected Countries

Öz

Phenomena such as global warming and climate change have caused food prices to increase alongside the effects from the COVID-19 pandemic. Many driving forces have led food prices to increase, such as energy costs, exchange rates, and supply and demand quantities. Economic policy uncertainty has recently been discussed as one of these possible driving forces. This study aims to investigate the relationship between economic policy uncertainty and food prices. For this purpose, it examines the causal relationships between food inflation and global economic policy uncertainty in China, England, Germany, Hungary, South Africa, Türkiye, and the United States. Symmetric causality findings point to the existence of a bidirectional causality relationship between global economic policy uncertainty and food inflation only in the United States. According to the time-varying causality analysis findings, time-varying causality relationships existgoing from global economic policy uncertainty to food inflation in all countries. According to the analysis findings, the causality relationship from economic policy uncertainty to food prices was observed to have intensified during the COVID-19. Although the potential effects of economic policy uncertainty on food prices require more evidence, policymakers are considered to be able to stabilize food prices by using effective economic policy interventions.

Anahtar Kelimeler

Food prices, Economic policy uncertainty, Climate change, COVID-19 pandemic, Time-varying causality

Kaynakça

• Al-Thaqeb, S. A., Algharabali, B. G., & Alabdulghafour, K. T. (2022). The pandemic and economic policy uncertainty. International Journal of Finance & Economics, 27(3), 2784-2794. Doi: https://doi.org/10.1002/ijfe.2298 google scholar
• Bairagi, S., Mishra, A. K., & Mottaleb, K. A. (2022). Impacts of the COVID-19 pandemic on food prices: Evidence from storable and perishable commodities in India. PloS one, 17(3), e0264355. Doi: https://doi.org/10.1371/ journal.pone.0264355 google scholar
• Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The quarterly journal of economics, 131(4), 1593-1636. Doi: https://doi.org/10.1093/qje/qjw024 google scholar
• Balcilar, M., Ozdemir, Z. A., & Arslanturk, Y. (2010). Economic growth and energy consumption causal nexus viewed through a bootstrap rolling window. Energy Economics, 32(6), 1398-1410. Doi: https://doi. org/10.1016/j.eneco.2010.05.015 google scholar
• Barrett, C. B., Fanzo, J., Herrero, M., Mason-D’Croz, D., Mathys, A., Thornton, P. K., ... & Majele Sibanda, L. (2021). COVID-19 pandemic lessons for agri-food systems innovation. Environmental Research Letters, 16(10), 101001. Doi: https://doi.org/10.3929/ethz-b-000505271 google scholar
• Beckman, J., Baquedano, F., & Countryman, A. (2021). The impacts of COVID-19 on GDP, food prices, and food security. Q Open, 1(1), qoab005. Doi: https://doi.org/10.1093/qopen/qoab005 google scholar
• Birgani, R. A., Kianirad, A., Shab-Bidar, S., Djazayeri, A., Pouraram, H., & Takian, A. (2022). Climate Change and Food Price: A Systematic Review and Meta-Analysis of Observational Studies, 1990-2021. American Journal of Climate Change, 11(2), 103-132. Doi: 10.4236/ajcc.2022.112006 google scholar
• Caspi, I. (2013). Rtadf: Testing for bubbles with EViews. Journal of Statistical Software, 81(1), 1-16. Doi: 10.18637/jss.v081.c01 google scholar
• Davis, S. J. (2016). An index of global economic policy uncertainty (No. w22740). National Bureau of Economic Research. Doi: 10.3386/w22740 google scholar
• Dorward, A., & Giller, K. E. (2022). Change in the climate and other factors affecting agriculture, food or poverty: An opportunity, a threat or both? A personal perspective. Global Food Security, 33, 100623. Doi: https://doi. org/10.1016/j.gfs.2022.100623 google scholar
• Erdoğan, S., Gedikli, A., & Kırca, M. (2019). A note on time-varying causality between natural gas consumption and economic growth in Turkey. Resources Policy, 64, 101504. Doi: https://doi.org/10.1016/j. resourpol.2019.101504 google scholar
• Fasanya, I. O., & Olawepo, F. (2018). Determinants of food price volatility in Nigeria. Agric. Tropic. Subtropic, 51(4), 165-174. Doi: 10.2478/ats-2018-0019 google scholar
• Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424-438. Doi: https://doi.org/10.2307/1912791 google scholar
• Hacker, R. S. & Hatemi-J, A. (2006). Tests for Causality between Integrated Variables Using Asymptotic and Bootstrap Distributions: Theory and Application. Applied Economics, 38(13), 1489-1500. Doi: https://doi. org/10.1080/00036840500405763 google scholar
• Hua, J., Li, H., He, Z., Ding, J., & Jin, F. (2022). The Microcosmic Mechanism and Empirical Test of Uncertainty on the Non-Linear Fluctuation of Chinese Grain Prices-Based on the Perspective of Global Economic Policy Uncertainty. Agriculture, 12(10), 1526. Doi: https://doi.org/10.3390/agriculture12101526 google scholar
• Irz, X., Niemi, J., & Liu, X. (2013). Determinants of food price inflation in Finland—The role of energy. Energy Policy, 63, 656-663. Doi: http://dx.doi.org/10.1016/j.enpol.2013.09.023 google scholar
• Ismaya, B. I., & Anugrah, D. F. (2018). Determinant of food inflation: The case of Indonesia. Bulletin of Monetary Economics and Banking, 21(1), 1-14. Doi: https://doi.org/10.21098/bemp.v21i1.926 google scholar
• Kido, Y. (2018). The transmission of US economic policy uncertainty shocks to Asian and global financial markets. The North American Journal of Economics and Finance, 46, 222-231. Doi: https://doi.org/10.1016/j. najef.2018.04.008 google scholar
• Kirikkaleli, D., & Darbaz, I. (2022). New insights into an old issue: modelling the US food prices. Letters in Spatial and Resource Sciences, 1-15. Doi: https://doi.org/10.1007/s12076-022-00319-3 google scholar
• Lee, J. & Strazicich, M. C. (2003). Minimum lagrange multipier unit root test with two structural breaks. The Review of Economics and Statistics, 85 (4), 1082-1089. Doi: https://doi.org/10.1162/003465303772815961 google scholar
• Li, J., Li, C. & Chavas, J.P. (2017). Food price bubbles and government intervention: is China different? The Canadian Journal of AgricutturalEconomics. 65 (1), 135-157. Doi: https://doi.org/10.1111/cjag.12106 google scholar
• Long, S., Li, J., & Luo, T. (2022). The asymmetric impact of global economic policy uncertainty on international grain prices. Journal of Commodity Markets, 100273. Doi: https://doi.org/10.1016/j.jcomm.2022.100273 google scholar
• Mawejje, J. (2016). Food prices, energy and climate shocks in Uganda. Agricultural & Food Economics, 4 (1), 1-18. Doi: 10.1186/s40100-016-0049-6 google scholar
• Organisation for Economic Co-operation and Development (OECD) Veri tabanı. google scholar
• Pesaran, M. H., & Timmermann, A. (2005). Small sample properties of forecasts from autoregressive models under structural breaks. Journal of Econometrics, 129(1-2), 183-217. Doi: https://doi.org/10.1016/j. jeconom.2004.09.007 google scholar
• Phillips, P. C. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75 (2), 335-346. Doi: https://doi.org/10.1093/biomet/75.2.335 google scholar
• Rasul, G. (2021). Twin challenges of COVID-19 pandemic and climate change for agriculture and food security in South Asia. Environmental Challenges, 2, 100027. Doi: https://doi.org/10.1016/j.envc.2021.100027 google scholar
• Rehman, F. U., & Khan, D. (2015). The determinants of food price inflation in Pakistan: An econometric analysis. Advances in Economics and Business, 3(12), 571-576. Doi: 10.13189/aeb.2015.031205 google scholar
• Said, S. E. & Dickey, D. A. (1984). Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order. Biometrika, 71(3), 599-607. Doi: https://doi.org/10.1093/biomet/71.3.599 google scholar
• Samal, A., Ummalla, M., & Goyari, P. (2022). The impact of macroeconomic factors on food price inflation: an evidence from India. Future Business Journal, 8(1), 1-14. Doi: https://doi.org/10.1186/s43093-022-00127-7 google scholar
• Sims, C. (1980). Macroeconomics and Reality, Econometrica, 48(1), 1-48. Doi: https://doi.org/10.2307/1912017 google scholar
• Toda, H. Y., & Yamamoto, T. (1995). Statistical Inference in Vector Autoregressions with Possibly Integrated Processes. Journal of Econometrics, 66(1-2), 225-250. Doi: https://doi.org/10.1016/0304-4076(94)01616-8 google scholar
• Van Bodegom, A., & Koopmanschap, E. (2020). The COVID-19 pandemic and climate change adaptation. Wageningen Centre for Development Innovation: Wageningen, The Netherlands, 1-24. google scholar
• Wahidah, N. L., & Antriyandarti, E. (2021). Impact of climate change and Coronavirus Disease (COVID-19) on inflation in Indonesia. In IOP Conference Series: Earth and Environmental Science (Vol. 724, No. 1, p. 012105). IOP Publishing. Doi: 10.1088/1755-1315/724/1/012105 google scholar
• Wang, J., & Zheng, Y. (2019). Economic policy uncertainty and grain prices volatility. The Frontiers of Society, Science and Technology, 1(10), 37-64. Doi: 10.25236/FSST.2019.011004 google scholar
• Wen, J., Khalid, S., Mahmood, H., & Zakaria, M. (2021). Symmetric and asymmetric impact of economic policy uncertainty on food prices in China: a new evidence. Resources Policy, 74, 102247. Doi: https://doi. org/10.1016/j.resourpol.2021.102247 google scholar
• Xiao, X., Tian, Q., Hou, S., & Li, C. (2019). Economic policy uncertainty and grain futures price volatility: evidence from China. China Agricultural Economic Review, 11(4), 624-654. Doi: 10.1108/CAER-11-2018-0224 google scholar
• Yılancı, V., & Bozoklu, Ş. (2014). Türk sermaye piyasasında fiyat ve işlem hacmi ilişkisi: Zamanla Değişen Asimetrik Nedensellik Analizi. Ege Academic Review, 14(2). https://www.policyuncertainty.com/, (Erişim tarihi: 28/11/2022). google scholar