Extreme weather events and high Colombian food prices: A non-stationary extreme value approach1

被引:4
作者
Melo-Velandia, Luis Fernando [1 ]
Orozco-Vanegas, Camilo Andres [1 ]
Parra-Amado, Daniel [1 ]
机构
[1] Banco Republ Colombia, Bogota, Colombia
关键词
extreme value theory; food inflation; Relative Risk ratio; return levels; weather extreme events; NINO SOUTHERN-OSCILLATION; EL-NINO; INTERANNUAL VARIABILITY; CLIMATE-CHANGE; ENSO; PRECIPITATION; RAINFALL; WATER; SECURITY; RIVER;
D O I
10.1111/agec.12753
中图分类号
F3 [农业经济];
学科分类号
0202 ; 020205 ; 1203 ;
摘要
Given the importance of climate change and the increase of its severity under extreme weather events, we analyze the main drivers of high food prices in Colombia between 1985 and 2020 focusing on extreme weather shocks like a strong El Nino. We estimate a non-stationary extreme value model for Colombian food prices. Our findings suggest that perishable foods are more exposed to extreme weather conditions in comparison to processed foods. In fact, an extremely low precipitation level explains only high prices in perishable foods. The risk of high perishable food prices is significantly larger for low rainfall levels (dry seasons) compared to high precipitation levels (rainy seasons). This risk gradually results in higher perishable food prices. It is nonlinear and is also significantly larger than the risk related to changes in the US dollar-Colombian peso exchange rate and fuel prices. Those covariates also explain high prices for both perishable and processed foods. Finally, we find that the events associated with the strongest El Nino in 1988 and 2016 are expected to reoccur once every 50 years.
引用
收藏
页码:21 / 40
页数:20
相关论文
共 37 条
  • [31] Statistical analysis of extreme events in a non-stationary context via a Bayesian framework: case study with peak-over-threshold data
    Benjamin Renard
    Michel Lang
    Philippe Bois
    Stochastic Environmental Research and Risk Assessment, 2006, 21 : 97 - 112
  • [32] Analysis of non-stationary climate-related extreme events considering climate change scenarios: an application for multi-hazard assessment in the Dar es Salaam region, Tanzania
    Garcia-Aristizabal, Alexander
    Bucchignani, Edoardo
    Palazzi, Elisa
    D'Onofrio, Donatella
    Gasparini, Paolo
    Marzocchi, Warner
    NATURAL HAZARDS, 2015, 75 (01) : 289 - 320
  • [33] Assessing trends in extreme precipitation events intensity and magnitude using non-stationary peaks-over-threshold analysis: a case study in northeast Spain from 1930 to 2006
    Begueria, Santiago
    Angulo-Martinez, Marta
    Vicente-Serrano, Sergio M.
    Ignacio Lopez-Moreno, J.
    El-Kenawy, Ahmed
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2011, 31 (14) : 2102 - 2114
  • [34] Estimating Non-Stationary Extreme-Value Probability Distribution Shifts and Their Parameters Under Climate Change Using L-Moments and L-Moment Ratio Diagrams: A Case Study of Hydrologic Drought in the Goat River Near Creston, British Columbia
    Dekker, Isaac
    Dubrawski, Kristian L.
    Jones, Pearce
    Macdonald, Ryan
    HYDROLOGY, 2024, 11 (09)
  • [35] Time-varying copula-based compound flood risk assessment of extreme rainfall and high water level under a non-stationary environment
    Song, Mingming
    Zhang, Jianyun
    Liu, Yanli
    Liu, Cuishan
    Bao, Zhenxin
    Jin, Junliang
    He, Ruimin
    Bian, Guodong
    Wang, Guoqing
    JOURNAL OF FLOOD RISK MANAGEMENT, 2024, 17 (04):
  • [36] Analysis of non-stationary climate-related extreme events considering climate change scenarios: an application for multi-hazard assessment in the Dar es Salaam region, Tanzania
    Alexander Garcia-Aristizabal
    Edoardo Bucchignani
    Elisa Palazzi
    Donatella D’Onofrio
    Paolo Gasparini
    Warner Marzocchi
    Natural Hazards, 2015, 75 : 289 - 320
  • [37] A non-stationary bivariate extreme value model to estimate real-time pedestrian crash risk by severity at signalized intersections using artificial intelligence-based video analytics
    Bin Tahir, Hassan
    Haque, Md Mazharul
    ANALYTIC METHODS IN ACCIDENT RESEARCH, 2024, 43