Effect of Grain Corridor Agreement on Grain Prices

Abstract

This study investigates the impact of the Grain Corridor Agreement (GCA), particularly in the aftermath of the Russia–Ukraine conflict, on the prices of major grains (wheat, maize, and barley), pivotal for global sustenance. By delineating three significant shocks: the initiation of the conflict, the enforcement of the GCA, and Russia's subsequent withdrawal from it, we employ an Integrated GARCH (IGARCH) model to investigate the impact of the Russia–Ukraine conflict on grain prices. Our empirical findings reveal that all grain prices surged at the onset of the conflict, with barley experiencing the most pronounced increase. Additionally, volatility escalated across all grain prices during the conflict's inception, albeit subsiding upon the implementation of the GCA. Price volatility spiked initially but decreased with the GCA's enforcement. The evidence suggests that the conflict is driving up world grain prices and causing global vulnerability, and that conciliatory policies such as the GCA offer a short-term solution. However, long-term strategies should focus on reducing external dependence by reviewing agricultural policies and promoting domestic production. Moreover, policymakers are advised to consider both domestic and global market vulnerabilities when designing sound policies.

Highlights

• International grain prices (wheat, maize and barley) spiked during the onset of the ongoing Russia–Ukraine conflict.

• The conflict triggered an international response to resume safe maritime humanitarian transportation of agricultural grains via GCA.

• We develop an empirical framework to assess the impact of the Russia–Ukraine conflict on grain prices.

• Empirical findings indicate that Russia–Ukraine conflict increased all grain prices.

KEYWORDS:

• Military and political shocks
• IGARCH model
• Russian–Ukraine conflict
• Grain Corridor Agreement

JEL Classifications:

• F53
• G40
• Q11

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 According to Buyuksahin et al. (2016), agricultural production reacts more quickly, usually within the next growing season, considering investment costs are generally higher for oil and metal projects which can have long gestation periods (e.g. Radetzki, 2006; Cuddington & Jerrett, 2008).

2 Ginn and Pourroy (2019) and Ginn and Pourroy (2022) show an endogenous fiscal policy response in regards to sizable producer and consumer food price subsidies designed to cushion the effects of rising prices, which may be a policy-induced price smoothing mechanism that is different to, yet in parallel with, the classic Calvo price stickiness approach.

3 Wiggins et al. (2010) note that once food prices started to increase in 2007, there were amplifying reactions that accelerated the price increases such as export restrictions, country-imposed increase in import taxes on food goods.

4 Both Russia and Ukraine are major exporters of key fertilizer components, including potash, phosphate and nitrogen.

5 See https://www.fao.org/worldfoodsituation/foodpricesindex/en/.

6 See https://www.fao.org/3/u8480e/u8480e07.htm.

7 Food accounts for 48%, 31% and 20% of consumption on average in low-, middle- and high-income countries, respectively (Pourroy et al., 2016).

8 See USDA, https://www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/definitions-of-food-security/.

9 Source: Peterson Institute for International Economics (PIIE) (2022). Russia’s war on Ukraine: A sanctions timeline. Retrieved from https://www.piie.com/blogs/realtime-economic-issues-watch/russias-war-ukraine-sanctions-timeline.

10 Ukraine’s first grain shipment leaves Odesa on August 1.

11 Specifically, there is a tendency for high residuals to correspond with high values, and low residuals to correspond with low values, indicating heteroscedasticity in the residuals.

12 GARCH(1,1) indicates that one lag is used for both the ARCH term and GARCH term.

13 Our specification follows from economic theory such that Russia–Ukraine conflict created an international shock, which may have had a systematic impact on the average level of the commodity grains price time series (hence, it is included in the mean function).

14 We include Shock_2,t and Shock_3,t in the variance function, where the GCA and Russia’s withdrawal from the GCA is posited to impact the conditional volatility dynamics. Accordingly, the model includes these two exogenous variables in the variance function to estimate whether there is volatility clustering in the data.

15 Source: USDA, see https://ipad.fas.usda.gov/countrysummary/Default.aspx?id=UP.

16 Source: USDA, see https://apps.fas.usda.gov/psdonline/circulars/grain.pdf.

17 Source: see IGC, Supply and Demand, https://www.igc.int/en/markets/marketinfo-sd.aspx.

18 Source: USDA, see https://ipad.fas.usda.gov/countrysummary/Default.aspx?id=UP&crop=Barley.