The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach
Oil price and its shocks bring drastic influences on any economy. They are considered as significant predictors for numerous macro-economic variables, nevertheless their role in terms of affecting food prices is not widely explored. Therefore, the study aims to examine the nexus between food and oil price by applying the advanced econometric technique quantile on quantile regression (QnQ) and monthly data from January 1993 to September 2020. To attain a broader aspect, we categorize oil prices into supply and demand shocks while food price index into sub-indices such as Meat price index (MI), Dairy price index (DI), Cereal price index (CI), Sugar price index (SI), Corn price index (COPI), Soybean price index (SOPI) and Wheat price index (WOPI). The findings reveal positive association between the food prices and indices across the quantiles. In the case of an oil demand shock, a stronger relationship is observed between the extremely high and low quantiles especially in dairy, meat, food indexes while corn, soybean and wheat indicate a stronger relationship in lower quantiles. According to the oil prices, a stronger relationship is observed on the extremely low quantiles, in the case of cereal, dairy, meat and corn price indexes. Conversely, a stronger relationship is observed between the extremely low and high quantiles in the food price index. In accordance with the oil supply shock, a stronger relationship is observed on the extremely low quantiles in cereal and dairy indexes; and a stronger relationship is observed on the extreme middle quantiles in the food prices index. While a stronger relationship is observed on the extreme middle quantiles in terms of corn, soybean and wheat price. The result confirms variation in variables dependency, and the extremely positive effects are noted in the lower and middle quantiles. The outcome of this study will benefit the policymakers of the agricultural sector in developing reliable and comprehensive policy designs that will control the influence of oil prices on food prices.
Oil prices, quantile on quantile regression (QnQ), food prices.