The economic impacts of Russia–Ukraine War export disruptions of grain commodities

Abstract

Using the Global Trade Analysis Project (GTAP) computable general equilibrium model, we analyze the economic impacts of grain export disruptions caused by the Russia–Ukraine War during the first year of hostilities. The simulation results indicate that these disruptions not only affect Ukraine and Russia but also generate significant economic impacts across other world regions. Ukraine is projected to experience the largest impact on its own economy, with a real GDP loss of $859 million. In contrast, Russia's GDP is projected to decline by only $3.8 million, primarily due to its much lower dependence on grain exports and to favorable terms of trade effects.

The Russia–Ukraine War is already having and is expected to continue to have significant negative impacts on global markets for major commodities, particularly in terms of grains, in addition to metal and energy products. The War has forced the closure of all Ukrainian seaports until recently and has resulted in the United States, European Union, and other countries and regions imposing selective sanctions on Russia. There is a concern that the situation is leading to a world food crisis and could compromise the attainment of some important sustainable development goals (Banya, 2022; Ben Hassen & El Bilali, 2022; Legrand, 2023).

Supply-chains can transmit these direct impacts into cascading effects. Shortages or delays of a given good or service lead to ripple effects downstream to all other links. Extensive sectoral interdependencies across supply-chains within industrialized countries guarantee that these impacts will affect all facets of an economy, and further interdependencies between countries through international trade spread these impacts worldwide (see, e.g., Itakura, 2020, for the case of the US–China Trade War).

In Ukraine, the War prevented the harvesting of 20 to 30% of its winter crops in 2021, and/or transporting them to markets. As the conflict continued, farmers' capacity to plant crops for both the Spring and Winter planting seasons in 2022 has also been seriously curtailed for sunflower oil, maize, and wheat. As the top exporter of wheat and sunflower oil, sanctions against Russia add to the supply chain disruption. The ongoing conflict leads to a shortage of cooking oil globally due to the suspension of oilseed crushing operations. A shortage of palm oil, an alternative cooking oil, has caused its price to triple in the past 2 years. Moreove, international food and feed prices are rising by 8%–22% in different parts of the world. The conflict could especially affect countries in the Middle East and Africa that rely heavily on food from Ukraine, potentially leading to shortages and civil unrest (Benton et al., 2022; FAO, 2022).

The purpose of this paper is to analyze the impacts of disruption of exports of grains from Russia and Ukraine on their economies and those of the United States and the rest of the world. We apply a multi-country computable general equilibrium (CGE) model in our analysis. This modeling approach is ideal for the case at hand because it characterizes an economy as a set of interrelated supply chains. Specifically, we utilize the GTAP (2022) CGE Modeling System, the most widely used of its kind. We aggregate it to 17 countries/country groups and 44 commodities, with a detailed delineation of sub-categories of grains. “Shocks” to be modeled in the study include output reductions due to the Russia–Ukraine War and export restrictions due to Russian actions that disrupted relevant seaport, land transportation, and some planting, cultivation, and harvesting (Aloisi & Polityuk, 2022; Polityuk & Evans, 2022; Welsh et al., 2022; Wong & Woods, 2022). Our methodology utilizes the “phantom tax” approach and limits the mobility of critical factors of production to better reflect the realities of agricultural production. A major data source for our analysis is the United Nations (UN) Comtrade, maintained by the United Nations Statistics Division (UN Statistics Division, 2022), which provides import and export data by detailed commodity type for over 200 countries and areas.

This paper consists of five sections. The following section presents data on grain export disruptions from Ukraine and Russia. The “CGE Modeling” section explains the CGE modeling approach, including description of the Global Trade Analysis Project (GTAP) model and methodology to simulate export disruptions. The “CGE Modeling Results” section presents the results of a based case and sensitivity tests, as well as a comparison of our results with the recent literature. The “Conclusion” section summarizes the analysis and its limitations, which serve as the basis for suggestions for future research.

ESTIMATION OF GRAIN EXPORT DISRUPTIONS

The war between Russia and Ukraine is causing significant impacts on the already stretched global supply-chains following the COVID-19 pandemic. Although Ukraine and Russia are not major importing countries, they export many essential commodities, such as agricultural products.

As one of the most important players in the international food market, Ukraine was the largest exporter of sunflower oil, the fourth-largest exporter of barley and corns, and the fifth-largest exporter of wheat, accounting for 40%, 11.9%, 13.3%, and 8% of the global export values of these commodities, respectively, in the 2020 marketing year (United Nations Statistics Division, 2022). Due to strong demand and bad harvests, wheat is expected to be especially vulnerable to supply shocks (Leiva, 2022), and both the United States and United Nations predict that Ukrainian grain exports will drop precipitously compared to pre-war years (UN FAO, 2022; USDA, 2022a, 2022b).

Prior to the war, more than 90% of Ukrainian agricultural exports were shipped from the country's Black Sea ports (UN FAO, 2022). Damage, occupation, or blockade of these ports is currently preventing this commercial activity. Before the recent signing of the Black Sea Treaty, the Ukraine Ministry of Agriculture stated that about 20–25 million tons of grain were held up in-country, and Ukraine's capacity to move these commodities by river barge, rail, and truck, either into Europe or to Baltic Sea ports, has been severely limited (Hegarty, 2022; Wong & Woods, 2022).

The sanctions against Russia further severed the grain supply-chain, because Russia is the world's largest exporter of wheat and third-largest exporter of barley (United Nations Statistics Division, 2022). Table 1 presents the 2020 export values of grain and crop products, as well as sunflower seed oil of Ukraine and Russia, at the 4-digit Harmonized System (HS) code level.¹ Together, the two countries accounted for about 25% of global exports of wheat and barley, about 15% of maize, and nearly 60% of sunflower seed oil in 2020.

TABLE 1.

Ukraine and Russia export values of grain and crop products in 2020.

HS code	Commodity	Ukraine export value (M$)	Ukraine percentage of world total (%)	Russia export value (M$)	Russia percentage of world total (%)	Global export value (M$)
1001	Wheat and meslin	3594	8.0	7918	17.7	44,796
1002	Rye	3	0.6	2	0.4	449
1003	Barley	878	11.9	899	12.2	7367
1004	Oats	3	0.4	15	1.6	907
1005	Maize (corn)	4885	13.3	395	1.1	36,713
1006	Rice	4	0.0	67	0.3	25,463
1007	Grain sorghum	21	1.2	4	0.2	1709
1008	Buckwheat, millet, and canary seeds; other cereals	30	2.5	41	3.4	1187
1512	Sunflower seed, safflower, or cotton-seed oil	5320	40.0	2472	18.6	13,316

Source: United Nations Statistics Division (2022).

In order to analyze the impacts of supply-chain disruptions of grains exported from Ukraine and Russia to major world regions, detailed data on the trade values of 2020 exports from these two countries are collected from the United Nations Commodity Trade Statistics Database (United Nations Statistics Division, 2022). The database contains detailed trade statistics on both import- and export-side from 1962 to the most recent year reported by the statistical authorities of about 200 countries or areas, and then standardized by the UN Statistics Division. For each record, the database contains the information on commodity classification (up to 6-digit HS code), trade value (in US dollars), weight, reporter country, and partner country.

For this analysis, we collected the export data of Ukraine and Russia at the 4-digit HS code level, which categorizes products into over 1000 different commodity groups. In 2020, Russia exported $337 billion of commodities to 194 countries/areas and Ukraine exported $46.7 billion to 199 countries/areas. Next, according to the regional and sectoral aggregation classifications for the CGE model used for this analysis, we aggregate the 200 or so countries/areas into the 17 regions and map the over 1000 4-digit HS commodities first into the 65 GTAP sectors and then further into the more aggregated 44 sectors adopted in the CGE model.

In this study, we simulate the impacts of export disruption of major grain commodities from Ukraine and Russia. Specifically, we include all 4-digit level grain commodities for which the total value of exports from the two countries combined represents over 10% of the total global export values. Appendix Table A1 presents the list of the disrupted export commodities included in the simulations. They correspond to the following GTAP sectors: (1) wht (Wheat); (2) gro (Cereal Grains nec); and (3) vol (Vegetable Oils and Fats).

To determine the level and duration of grain export disruptions from Ukraine and Russia in the Base Case simulation, we collected data on the actual reductions of export from the two countries since the start of the War until June 2022, actual and projected shipments of grain exports from Black Sea ports since the signing of a treaty reopening them in late July, and other near-term projections of grain production and exports of the two countries. In the Base Case simulation, we assume the export disruptions will last 1 year, as presented in Tables 2 and 3 (see Appendix B for the details of the calculations).

TABLE 2.

Export disruptions of grain (for major 4-digit HS commodities) from Ukraine by region ($US millions).

ID	Region	1001 wheat	1003 barley	1005 maize (corn)	1512 sunflower seed
1	Oceania	0.2		a	10.9
2	China	0.6	249.8	623.1	401.2
3	Japan			a	2.7
4	India	0.1	0.6	0.2	593.9
5	Rest of Asia	777.1	1.9	125.0	68.0
6	Canada			1.3	1.3
7	USA				23.2
8	Rest of North America	3.1			1.1
9	Latin America	3.1		0.1	7.8
10	NATO countries (except USA/Canada)	180.2	14.1	789.0	704.6
11	Rest of Europe	2.3	3.6	41.3	8.6
12	Russia				a
13	Rest of Former Soviet Union	0.6	0.5	8.8	11.3
14	Ukraine
15	Middle East	205.3	107.0	223.8	263.4
16	Africa	641.5	89.2	388.8	100.2
17	Rest of World
	Total level	1814.0	466.8	2201.4	2197.9
	Total %	50.5%	53.2%	45.1%	41.3%

^aDenotes a value less than $50,000.

TABLE 3.

Export disruptions of grain (for major 4-digit HS commodities) from Russia by region ($US millions).

ID	Region	1001 wheat	1003 barley	1005 maize (corn)	1512 sunflower seed
1	Oceania				0.5
2	China	0.9	0.6	a	235.1
3	Japan			a	0.7
4	India				138.9
5	Rest of Asia	89.3	0.1	a	53.4
6	Canada				a
7	USA				0.1
8	Rest of North America	1.5
9	Latin America	5.5			0.1
10	NATO countries (except USA/Canada)	148.7	9.2	a	199.0
11	Rest of Europe	2.9		a	8.9
12	Russia
13	Rest of Former Soviet Union	44.0	6.0	a	239.8
14	Ukraine	0.2	0.3	a	9.3
15	Middle East	53.5	85.4	a	45.7
16	Africa	271.2	12.4	a	98.3
17	Rest of World
	Total level	617.7	113.9	a	1030.0
	Total %	7.8%	12.7%	0.0%	41.7%

^aDenotes a value less than $50,000.

CGE MODELING

The GTAP model

The GTAP model adopted in this assessment consists of 17 regions and 44 economic sectors (Center for Global Trade Analysis, 2022). This modeling system was originally developed by Hertel (1997). It is a static and multi-regional CGE model that has been widely adopted to evaluate the macroeconomic impact of trade policies (see, e.g., Wei et al., 2019). The model is based on Walras's general equilibrium theory and has been extended to provide a realistic representation of economy-wide national activity and international trade (Mukhopadhyay & Thomassin, 2010). The GTAP model is based on two sets of simultaneous equations. The first set consists of equations that reflect microeconomic theory responses to the behaviors of representative economic agents: producers, consumers, and government. The second set of equations reflects the accounting relationships among the agents and the Rest of the World, extending the linkages to the macroeconomic level. Rose (1995) noted that CGE models have advantages over other approaches, such as input–output models, because they have behavioral content, reflect the role of prices and markets, exhibit nonlinearities, and incorporate explicit constraints.

The GTAP 10 database was adopted for the CGE analysis in this paper. The data represent the world economy, with 2014 being the latest year of reference. The database describes global bilateral trade patterns, international transport margins, and trade protection matrices that link individual countries/regions (Aguiar et al., 2019). In this paper, we evaluate the economic impact of export reductions of major categories of grain commodities as a consequence of the outbreak of the Russia–Ukraine War.

Overall, the GTAP model enables us to analyze the economic impact measured in real GDP change, employment change, and the change in economic welfare. The latter is measured in terms of equivalent variation (EV), reflecting the level of disposable income necessary to attain the new level of aggregate consumer utility. Hence, the model enables us to evaluate the extent to which the export reductions from Ukraine and Russia affect major macroeconomic indicators and welfare changes that are likely to occur in various trade-partner countries/regions.

Our analysis includes the following steps. First, we estimate a Base Case of impacts in terms of real GDP and economic welfare changes stemming from export reduction shocks applied to Ukraine and Russia and extending to their corresponding trading partner countries/regions. Second, we conduct two sets of sensitivity analyses: One set applies to three key substitution parameters, including the factor substitution (ESUBVA), the import-domestic (Armington) substitution (ESUBD), and the import–import (Armington) substitution (ESUBM). The other applies to the potential of Russia to back out of its agreement not to block Ukrainian grain exports. Third, we discuss the findings, limitations of the modeling, and future research directions.

Methodology to simulate commodity export disruptions

There are a number of methods used by CGE modelers to analyze the economic impacts of commodity trade disruptions. These include the phantom tax, direct commodity output constraints, constraining flow variables through adjustment of tariff variables, productivity shocks, willingness to pay shock and margin shocks (Chen & Li, 2021; Walmsley & Strutt, 2021). We chose the Phantom Tax approach with closure rules that fix capital endowment of the economic disruptions from the War (a type of “medium-run approach” applicable to simulations that cover only a couple of years). These settings were deemed as the best for estimating the impacts of constraints on exports. Further considerations relating to commodity trade, such as tariffs, would likely best be served by some of the other methods.

Phantom tax

Phantom taxes are types of virtual taxes that have the effect of changing behavior or responding to mandates, such as reducing demand, but collecting no net revenue (Giesecke et al., 2013). “An indispensable part of the phantom tax modelling is the zero-revenue condition, which makes sure that revenues from phantom taxes on imports and subsidies to the domestic producers are evened out, i.e. there are no tax revenues gains/losses from a change in domestic preference margins” (Kutlina-Dimitrova, 2017, p. 13). The approach is often used to assess the impact of non-tariff barriers and external shocks (e.g., Dixon et al., 2011).

One of the early examples of utilizing a phantom tax was found in Dixon and Rimmer (2002), in which the authors endogenized a phantom tax on the production of fruits and nuts to generate a price consistent with a 10% reduction in demand to match the target reduction in output. In another application, the phantom tax was adopted to model the effect of both the government's policy of land designation, and the associated economic cost measured in terms of the land rent foregone by the policy impediment to the allocation of land to its most valued use (Giesecke et al., 2013). More recently, Walmsley et al. (2021) and Rose et al. (2021) also adopted a phantom tax shock to estimate the national and global impact of COVID-19 due to mandatory business shutdowns.

In this paper, the phantom tax shock is used to model the impact of the war on supply-chain disruption by restricting domestic export volume in Ukraine and Russia based on actual and projected trade statistics, while maintaining zero profit and zero government revenue conditions.

A possible disadvantage of this approach is the potential issue of double-counting when applying the phantom tax (as well as some other methods below, such as the trade margin approach). This pertains to a situation where two or more commodities have their production, exports, or imports constrained and one of the commodities is a major constituent element in the supply-chain of the other. Solutions to this problem also require trial and error adjustments (e.g., Walmsley et al., 2021). However, the double-counting issue is not applicable to the current study, where we are constraining trade in only four HS-code grain commodities (bridged to three GTAP sectors) that have very little interaction with each other directly or indirectly.

Compared to the approaches in other studies, we use a simple version of the phantom tax in applying the GTAP CGE model. This is to reduce exports (qxs) by a given percent, thereby transforming exports from an endogenous variable to an exogenous variable and then “swapping out” an export tax (txs), which was previously exogenous to become endogenous. This approach does not require trial and error to adjust the commodity price to obtain the necessary reduction. However, this facile approach is not likely to be applicable to all of the various trade disruptions that would have to be modeled in a complete compound/cascading analysis of the Russia–Ukraine War.

Shocks and closure rule settings

The closure rule of the CGE model refers to the specifications of variables to be exogenized so that the number of endogenous variables equals the number of equations. Table 4 summarizes the settings of shocks and closure rules for the scenarios modeled in this paper. Our assessment adopts the default standard closure rule with three additional statements to implement a direct shock on the export quantity of grain commodities. The default closure rule in GTAP is neoclassical, which assumes the economy will return to full employment or to the pre-shock level of unemployment, as the labor demand adjusts to endogenous wage changes among the various sectors (Hertel, 1997). Specifically, the export disruption shock is implemented through the qxs(Trad_Comm, ORG, DEST) variable, which represents export sales of commodity i from the region/country of origin to the region/country of destination. Hence, in the first swap statement, qxs is swapped with the txs(Trad_Comm, ORG, DEST), which represents the specific change in tax/subsidy on exports. The latter is considered a phantom export tax, since it is determined endogenously, and rents are assumed to be paid by the exporter.

TABLE 4.

Shocks and closure rule settings for the computable general equilibrium (CGE) simulations.

Closure and factor mobility	Full employment closure rule with limited capital mobility
Swapped variable (originally endogenous, becomes exogenous)	qxs(Trad_Comm, ORG, DEST), represents export sales of tradable grain commodity i from the region of origin to the region of destination qfe(“Capital,” Prod_Comm, Reg), represents the demand for capital for use in grain commodity production in industry j in region r tfav(“Capital,” Reg), represents the average tax rate of capital in region r
Variable in the model used for swap (originally exogenous, becomes endogenous)	txs(Trad_Comm, ORG, DEST), represents a specific change in subsidy on exports of commodity i from the region of origin to the region of destination (the phantom tax on export) tfall(“Capital,” Prod_Comm, Reg), represents the tax on primary factor capital used to produce a commodity in a region tfendw(“Capital,” Reg), represents the tax on primary factor capital used in a region (adjusts tax rates to keep the average rates equal to zero)
Binary parameter for endowment mobility (SLUG)	Capital is treated as immobile in the model, while land and natural resources are sluggish (partially mobile)
CET between sectors for potentially sluggish primary factors (ETRAE)	For the constant elasticity of transformation function (CET), the default setting is adopted (i.e., parameters of capital and labor are set to zero; however, parameters of land and natural resources are set to −1 and −0.001, respectively). ETRAE is the parameter of the CET function for specifying the level of factor mobility.

The second swap statement concerns the limited capital mobility constraints. The demand (qfe) for capital in all sectors except one is swapped by tfall, which represents a tax on a primary factor used by a given sector in a region. This closure method is used instead of the SLUG mechanisms in the GTAP model, because we sought the more realistic assumption of capital being immobile (SLUG allows some mobility through the setting of the parameter ETRAE, which is the parameter of the constant elasticity of transformation function for specifying the level of factor mobility).

To ensure we do not inadvertently subsidize capital, we also assume that the weighted average of these changes in the phantom taxes is zero. This is achieved through the third statement by swapping variable tfendw with tfav. The former represents a tax on the primary factor capital used in a region, while the latter variable represents the average tax rate of capital in a region.

One caveat of this approach is that the specification does not work well when combined with the unemployment of capital because all the unemployment will be forced into that last sector, which may lead to a biased impact estimation outcome. Hence, we have adopted the full employment of capital closure rule as well.

CGE MODELING RESULTS

Base Case

The Base Case CGE simulations were conducted for export reduction scenarios in Ukraine and Russia based on the data in “CGE Modeling” section. To eliminate the influence of price inflation, the impact results are reported in terms of GDP quantity change. This is the more important macroeconomic indicator of the change in well-being, given that it represents the real value of a market basket of goods.

Three sets of simulations are conducted for the Base Case: (1) export disruptions from Ukraine alone; (2) export disruptions from Russia alone; (3) export disruptions from both countries simultaneously. As shown in the first set of numerical columns in Table 5, in the case of the export reduction of grain products from Ukraine, its economy is estimated to experience the most negative impacts, with a real GDP (GDP quantity) reduction of $859 million, or 0.65%, in part because of its relatively large trade dependency. Other countries/regions are estimated to also experience relatively significant reductions in GDP: Rest of Asia (−0.01%) and RFSU (−0.005%). Conversely, the impacts on countries/regions, such as India, Canada, and Latin America are estimated to experience slightly positive impacts of less than 0.01%, due to the increased production of other goods and services in their country substituting domestic production for Ukrainian imports and the inherent substitutions among factor inputs as commodity prices change. Note that the results could be optimistic given that we have not made any adjustments in the import trade (Armington) elasticities of substitution, which could be lower than those embedded in the model given the medium-run nature of our analysis (see the sensitivity tests below).

TABLE 5.

Real GDP impacts of grains export reductions from the Russia–Ukraine War.

	Export reduction from Ukraine		Export reduction from Russia		Export reduction from both Russia and Ukraine
Country/region	Quantity change	Percent change	Quantity change	Percent change	Quantity change	Percent change
USA	−20.0	−0.0001	−6.0	a	−28.0	−0.0002
Rest of North America	−5.1	−0.0004	−5.0	−0.0004	−10.8	−0.0008
China	−111.0	−0.001	−16.0	−0.0002	−133.0	−0.0013
Japan	−30.0	−0.0007	−9.0	−0.0002	−43.0	−0.0009
India	171.0	0.0084	2.6	0.0001	171.5	0.0084
Rest of Asia	−502.0	−0.0099	−46.5	−0.0009	−573.5	−0.0113
Canada	22.4	0.0013	6.4	0.0004	31.9	0.0018
Latin America	19.5	0.0004	5.0	0.0001	25.5	0.0005
Oceania	27.6	0.0016	3.0	0.0002	32.8	0.0019
NATO (except USA, Canada)	−50.0	−0.0003	−52.0	−0.0003	−116.0	−0.0006
Rest of Europe	−17.0	−0.0007	−3.3	−0.0001	−21.8	−0.0009
Russia	−8.3	−0.0004	−3.8	−0.0002	−15.5	−0.0008
RFSU	−27.3	−0.0051	−3.6	−0.0007	−31.3	−0.0058
Ukraine	−858.9	−0.6516	7.8	0.0059	−864.1	−0.6556
Mid-East	−2.0	−0.0001	−4.8	−0.0002	−8.8	−0.0003
Africa	−40.8	−0.0017	−8.3	−0.0003	−53.8	−0.0022
Rest of World	a	−0.0001	a	a	0.0	−0.0002
Total	−1431.7	−0.0018	−133.2	−0.0002	−1637.8	−0.0021

^aLess than −0.00005.

The Russian economy, in contrast, suffers a negative GDP impact of only $3.8 million, or 0.0002%, from the ban on its grain exports. The simulation also projects relatively small reductions in GDP in its major trading partners Rest of NATO, China, and Rest of Asia. The simulation also projects a very small increase in GDP for Ukraine, Canada, Latin America, India, and Oceania, partly due to trade substitution effects. The overall negative impact on global GDP is projected to be only $133 million. There are two major reasons why the global impact of Russian export disruptions is less than 10% of the outcome for Ukrainian export reductions. First, although the total value of exports from Russia is nearly 1.6 times of that from Ukraine, the percentage of export disruption in Ukraine as a percent of the scale of its economy is nearly 10 times as large as that of Russia. Second, the difference of impacts also reflects that the global economy has a different dependence on grain products from Russia and Ukraine. For instance, the reduction of grain products from Ukraine has a more substantial impact on all other trade partner countries/regions, such as the Rest of Asia and China.

After simulating the impacts of grain export disruptions from Ukraine and Russia separately, we simulated the combined export disruptions in the two countries and present them in the third numerical column set of Table 5. The total $1.6 billion decrease in global GDP is $73 million larger than the sum of separate impacts discussed above, indicating some minor negative interaction effects, or synergies, of the combined trade disruptions. Figures 1 and 2 present the cascading gross output impacts across countries and Ukrainian economic sectors caused by grain export reductions from Ukraine and Russia.

FIGURE 1.

Cascading impacts of gross output quantity change on different countries/regions due to grain export reductions from Ukraine and Russia.

FIGURE 2.

Cascading impacts of gross output quantity change on Ukrainian sectors due to grain export reductions from Ukraine and Russia.

The economic welfare effects of the simulations are presented in Table 6, in which they are decomposed into three aspects. The allocative efficiency effects track almost perfectly with the GDP impacts on a country-by-county basis and for the global economy as a whole for the individual Ukraine and Russia simulations, as well for the combined simulations. However, the standard terms of trade (TOT) effects are especially strong and of the opposite sign for Russia, resulting in an estimate of a positive overall change in EV of $256 million, or more than 0.01%. This outcome is qualitatively similar to impacts on Ukraine for the Russian-only simulation and stronger in relative but not absolute terms. Similarly, the TOT effects further reinforce the basic allocative efficiency effects negatively in both directions in some of its major trading partners, such as China, Japan, Rest of Asia, the Mid-East, and Africa. The investment-savings TOT (ISTOT) effects are relatively small for all countries/regions except for Russia and China. Note that the totality of the three EV components in relation to their impact on the global economy is very small because they cancel each other out on the world import/export stage.

TABLE 6.

Economic welfare impacts of grain export reductions from the Russia–Ukraine War (in millions of 2014 US dollars).

	Export reduction from Ukraine				Export reduction from Russia				Export reduction from both countries
Country/ region	Allocative efficiency effect	Terms of trade effect	Investment- savings terms of trade effects	Total equivalent variation	Allocative efficiency effect	Terms of trade effect	Investment- savings terms of trade effects	Total equivalent variation	Allocative efficiency effect	Terms of trade effect	Investment- savings terms of trade effects	Total equivalent variation
USA	−20.7	482.0	11.1	472.4	−5.0	86.8	−18.8	63.0	−28.0	608.9	−13.0	567.9
Rest North America	−5.2	−20.6	−2.5	−28.2	−5.0	−16.9	−2.9	−24.9	−10.8	−42.7	−6.3	−59.7
China	−110.8	−264.2	−13.7	−388.7	−16.2	−55.3	−20.2	−91.7	−133.3	−340.2	−37.1	−510.6
Japan	−30.1	−133.0	−7.7	−170.7	−8.8	−60.1	−4.0	−72.9	−43.1	−218.0	−12.9	−274.1
India	171.0	15.4	4.7	191.1	2.6	9.9	−5.2	7.3	171.6	32.1	−2.3	201.4
Rest of Asia	−501.9	−241.7	−0.4	−744.0	−46.3	−35.5	−5.7	−87.4	−573.3	−300.8	−6.7	−880.8
Canada	22.3	167.6	9.4	199.4	6.4	46.3	0.4	53.2	31.8	235.9	10.5	278.3
Latin America	19.6	207.5	9.4	236.5	5.2	50.9	−3.2	53.0	25.7	276.0	5.1	306.8
Oceania	27.7	202.4	−6.5	223.6	3.0	26.2	−2.8	26.3	32.7	244.9	−10.1	267.5
NATO	−50.5	138.1	−30.7	56.9	−51.9	−78.5	−16.8	−147.2	−116.2	51.8	−52.4	−116.8
Rest of Europe	−16.9	−37.7	8.6	−46.1	−3.3	−12.0	1.7	−13.6	−21.7	−53.5	11.3	−63.9
Russia	−8.3	204.8	14.9	211.5	−3.7	179.5	80.1	255.8	−15.5	442.2	114.6	541.3
RFSU	−27.3	99.9	−6.0	66.6	−3.5	−40.2	−2.7	−46.4	−31.2	43.2	−9.8	2.1
Ukraine	−858.9	−25.2	−12.4	−896.4	7.8	47.5	0.8	56.1	−864.4	28.9	−12.5	−848.0
Mid-East	−2.2	−425.9	18.3	−409.8	−4.7	−76.6	0.9	−80.4	−8.8	−541.4	20.3	−529.8
Africa	−40.8	−376.6	3.2	−414.2	−8.3	−71.9	−1.7	−81.9	−53.7	−473.6	1.4	−526.0
Rest of World	a	a	a	a	a	a	a	a	a	a	a	a
Total	−1432.9	−7.0	−0.1	−1440.0	−131.8	a	a	−131.7	−1638.2	−6.2	−0.1	−1644.4

Note: The total welfare effect is a money metric measure of the value of the effects of price changes on real consumption and savings in a region. The allocative efficiency effect refers to the excess burden of each tax. The terms of trade effect reflects the value of changes in world prices (fob) of exported goods and services relative to its world prices (fob) of imported goods and services. The investment-saving terms-of trade effect reflects the changes in the price of domestically produced capital investment goods relative to the price of savings globally (Burfisher, 2011, p. 177).

^aLess than −0.00005.

In contrast, the overall TOT effects have little influence on the bottom-line impacts of the Ukrainian economy in its grain export disruption simulation. The allocative efficiency effect is almost identical to that of the GDP impact, and the other two components have only a minimally negative additional effect in the Ukraine-only simulation and in the combined simulation. This is in sharp contrast to the outcome for the Russian TOT and ISTOT EV impacts in the combined simulation which are substantially larger in the positive direction than in the Russia only simulation.

We also examined the model results in terms of the price effects and found them to be fairly minimal. For the combined Russia–Ukraine case, the largest increases in world commodity prices are projected to take place for grain crops in the Middle East (3.2%) and Africa (2.8%). The largest import price increases for vegetable oils are projected for the Rest of the FSU (4.5%) and India (1.2%).

Sensitivity tests

We acknowledge some limitations of our analysis with regard to intrinsic aspects of the CGE modeling approach in general and the GTAP model in particular. Generally, CGE models with their equilibrium basis tend to render adjustment to shocks rather facile. This is the main reason we have invoked various modifications of the basic GTAP model to limit factor mobility. Also, key parameters in the GTAP database, such as various types of substitution elasticities, as is the case with nearly all CGE models, are taken from the more general literature and are not necessarily country-specific. Therefore, we deemed it prudent to undertake sensitivity analyses of these key parameters in order to test the robustness of our results on this basis. We also present sensitivity analysis below relating to a key political consideration that has a strong bearing on the results—the treaty allowing some Ukrainian grain exports from black seaports beginning in the summer of 2022.

We first conducted three groups of sensitivity analysis with respect to variations in three parameters in the GTAP model: Armington elasticities of substitution between domestic and imported commodities (ESUBD), elasticities of substitution between primary factors in production (ESUBVA), and the CET transformation parameter (ETRAE) between uses for sluggish primary factors (see Table 7). The sensitivity analysis examines the variations in percent change of the quantity GDP (QGDP) impacts with the same level of shocks as for the Base Case scenario of grain export reduction in Ukraine, but varied by adjusting each parameter individually for each sector down and up by 50%. For instance, the value of ESUBD for grain sectors varied in range from 1.254 (a 50% decrease) to 3.762 (a 50% increase). The sensitivity analyses for the ESUBD, ESUBVA, and ETRAE parameters were executed 88 times, 90 times, and 10 times, respectively, depending on the number of parameters involved. Table 7 summarizes the mean, standard deviation, and confidence interval of the QGDP impacts based on all the simulations in each sensitivity analysis. For example, results for the Armington elasticity sensitivity analysis for the Ukraine indicate that the mean value of the low and high sensitivity analyses is only 1.2% different than the Base Case. Also, the 50% downward adjustments in these elasticities result in only a 22% decrease in gross output for that country, while the 50% upward increase in these elasticities results in only a 24% increase in gross output. Essentially, the results are inelastic, thereby tending toward robustness because the percentage change in the outcome is much smaller than the percentage change in the parameter.

TABLE 7.

Sensitivity analysis of the results to changes in key parameters.

Sensitivity of gross output to changes in Armington elasticities CES (ESUBD) between use of domestic versus imported commodities (% change)
qgdp (%)	USA	Rest of N. America	China	Japan	India	Rest of Asia	Canada	Latin America	Oceania	NATO	Rest of Europe	Russia	RFSU	Ukraine	Mid- East	Africa	Rest of World
Base Case	−0.0001	−0.0004	−0.0010	−0.0007	0.0084	−0.0099	0.0013	0.0004	0.0016	−0.0003	−0.0007	−0.0004	−0.0051	−0.6516	−0.0001	−0.0017	−0.0001
Mean	−0.0001	−0.0004	−0.0010	−0.0007	0.0084	−0.0099	0.0013	0.0004	0.0016	−0.0003	−0.0007	−0.0004	−0.0050	−0.6597	−0.0001	−0.0017	−0.0001
SD	0.0000	0.0000	0.0001	0.0000	0.0001	0.0002	0.0001	0.0000	0.0001	0.0001	0.0001	0.0004	0.0004	0.0303	0.0001	0.0002	0.0000
CI_low	−0.0001	−0.0004	−0.0014	−0.0007	0.0080	−0.0108	0.0009	0.0004	0.0012	−0.0007	−0.0011	−0.0022	−0.0068	−0.7952	−0.0005	−0.0026	−0.0001
CI_High	−0.0001	−0.0004	−0.0006	−0.0007	0.0088	−0.0090	0.0017	0.0004	0.0020	0.0001	−0.0003	0.0014	−0.0032	−0.5242	0.0003	−0.0008	−0.0001
Sensitivity of gross output to changes in CES elasticities (ESUBVA) between primary factors in production (% change)
qgdp (%)	USA	Rest of N. America	China	Japan	India	Rest of Asia	Canada	Latin America	Oceania	NATO	Rest of Europe	Russia	RFSU	Ukraine	Mid- East	Africa	Rest of World
Base Case	−0.0001	−0.0004	−0.0010	−0.0007	0.0084	−0.0099	0.0013	0.0004	0.0016	−0.0003	−0.0007	−0.0004	−0.0051	−0.6516	−0.0001	−0.0017	−0.0001
Mean	−0.0001	−0.0004	−0.0011	−0.0007	0.0084	−0.0099	0.0013	0.0004	0.0017	−0.0003	−0.0007	−0.0004	−0.0050	−0.6521	−0.0001	−0.0017	−0.0001
SD	0.0000	0.0001	0.0001	0.0001	0.0001	0.0002	0.0002	0.0001	0.0002	0.0000	0.0001	0.0001	0.0001	0.0240	0.0000	0.0001	0.0000
CI_low	−0.0001	−0.0008	−0.0015	−0.0011	0.0080	−0.0108	0.0004	0.0000	0.0008	−0.0003	−0.0011	−0.0008	−0.0054	−0.7594	−0.0001	−0.0021	−0.0001
CI_High	−0.0001	0.0000	−0.0007	−0.0003	0.0088	−0.0090	0.0022	0.0008	0.0026	−0.0003	−0.0003	0.0000	−0.0046	−0.5448	−0.0001	−0.0013	−0.0001

Overall, the results show that the mean estimates of GDP impacts for each country for each sensitivity test are generally consistent with the Base Case simulation. The results are slightly more sensitive to the variations of Armington international trade elasticities CES factor input substitution elasticities. The results change imperceptibly for the case of the CET elasticities, so those results are not displayed in the table. In addition, the results suggest that the variations of GDP impacts on trading partners are less sensitive to the changes in elasticity parameters than for the two countries being directly shocked.

Note also that the sensitivity tests that call for an increase in the two types of substitution elasticities can represent a type of “resilience” in terms of increased ability to find alternative suppliers (Rose, 2017; Rose & Liao, 2005; Wei et al., 2020). In this case, the inelasticity is a liability in terms of countries attempting to cushion the blow of export disruptions.

We next conduct a sensitivity test in relation to the impact of a key political consideration. On October 29 of 2022, the Russian government announced its intent to suspend the Black Sea Grain Export deal for an indefinite period, citing a drone attack at its naval base (Polityuk & Nichols, 2022). Although after 4 days, Russia rejoined the deal after Ukraine agreed not to use the Black Sea corridor for military operations against Russia, given the volatility of the situation, we have run a sensitivity analysis to simulate the impacts of a scenario where the Black Sea Treaty is suspended for the last 4 months of the 1-year simulation period. The grain export disruptions from Russia remain the same as in the Base Case.

The results, presented in Table 8, indicate that the resumption of the grain export disruption from Ukraine would increase the negative impacts on its GDP by about $367 million (or a 30% in the base case), and would have a slightly smaller relative impact than that on the World Economy. The results indicate, however, that the impact would be rather minimal for the Russian economy (only $3.3 million), indicating that Russia has little to lose economically from breaking the agreement.

TABLE 8.

Sensitivity analysis of GDP impacts for continued cessation of Ukrainian grain exports.

Scenario	Export reduction from Ukraine		Export reduction from Russia		Export reduction from both Russia and Ukraine
Region	Quantity change	Percent change	Quantity change	Percent change	Quantity change	Percent change
USA	−28.0	−0.0002	−6.0	0.0000	−36.0	−0.0002
Rest of North America	−6.5	−0.0005	−5.0	−0.0004	−12.3	−0.0009
China	−146.0	−0.0014	−16.0	−0.0002	−163.0	−0.0015
Japan	−38.5	−0.0008	−9.0	−0.0002	−52.5	−0.0011
India	216.5	0.0106	2.6	0.0001	168.3	0.0082
Rest of Asia	−627.0	−0.0123	−46.5	−0.0009	−706.5	−0.0139
Canada	28.6	0.0016	6.4	0.0004	38.8	0.0022
Latin America	26.5	0.0005	5.0	0.0001	26.5	0.0005
Oceania	34.6	0.0020	3.0	0.0002	40.3	0.0024
NATO	−70.0	−0.0004	−52.0	−0.0003	−142.0	−0.0008
Rest of Europe	−22.5	−0.0009	−3.3	−0.0001	−27.3	−0.0011
Russia	−14.9	−0.0007	−3.8	−0.0002	−18.8	−0.0009
RFSU	−33.8	−0.0063	−3.6	−0.0007	−36.2	−0.0067
Ukraine	−1300.6	−0.9867	7.8	0.0059	−1231.5	−0.9343
Mid-East	−4.5	−0.0002	−4.8	−0.0002	−11.8	−0.0004
Africa	−52.0	−0.0021	−8.3	−0.0003	−64.0	−0.0026
Rest of World	a	−0.0002	a	0.0000	a	−0.0002
Total	−2038.0	−0.0026	−133.2	−0.0002	−2227.9	−0.0028
Differences in GDP impacts (by countries/regions) between the scenario after adjustment for continued cessation of grain exports and before the adjustment
Region	Quantity change	Percent change	Quantity change	Percent change	Quantity change	Percent change
USA	−8.0	−0.0001	0	0.0000	−8.0	0.0000
Rest of North America	−1.4	−0.0001	0	0.0000	−1.5	−0.0001
China	−35.0	−0.0004	0	0.0000	−30.0	−0.0002
Japan	−8.5	−0.0001	0	0.0000	−9.5	−0.0002
India	45.5	0.0022	0	0.0000	−3.3	−0.0002
Rest of Asia	−125.0	−0.0024	0	0.0000	−133.0	−0.0026
Canada	6.3	0.0003	0	0.0000	6.9	0.0004
Latin America	7.0	0.0001	0	0.0000	1.0	0.0000
Oceania	7.0	0.0004	0	0.0000	7.5	0.0005
NATO	−20.0	−0.000	0	0.0000	−26.0	−0.0002
Rest of Europe	−5.5	−0.000	0	0.0000	−5.5	−0.0002
Russia	−6.6	−0.000	0	0.0000	−3.3	−0.0001
RFSU	−6.5	−0.0012	0	0.0000	−4.9	−0.0009
Ukraine	−441.7	−0.3351	0	0.0000	−367.4	−0.2787
Mid-East	−2.5	−0.0001	0	0.0000	−3.0	−0.0001
Africa	−11.3	−0.0004	0	0.0000	−10.3	−0.0004
Rest of World	a	−0.0001	0	0.0000	a	0.0000
Total	−606.2	−0.0008	0	0.0000	−590.2	−0.0008

^aLess than −0.00005.

Comparison to other literature

There is little literature to which to compare our results. Ben Hassen and El Bilali (2022) focus on the prospects of food shortages for the first 4 months of the War in countries of the Middle East. Their analysis is basically partial equilibrium and includes fertilizer production as well as grains. In contrast to their concerns about the urgency of the situation, our findings indicate relatively slight overall (general equilibrium) impacts of Ukrainian and Russian grain export disruptions on the Middle East, in part because imports from other countries and regions offset grain exports to that region. Simola (2022) also used a partial equilibrium approach to find that Russia's trade in foodstuffs decreased in the first 4 months of the War, but its export revenues increased due to increases in commodity prices and the shift of exports to markets where it was not subject to trade sanctions.²

As to the comparability of input data between our study and others, Ben Hassen and El Bilali (2022), as well as Benton et al. (2022), both used as a primary data source a rapid assessment report by United Nations Conference on Trade and Development (UNCTAD, 2022), which is based on the same data source, the UN Comtrade Database, as we use. Jagtap et al. (2022) performed a qualitative analysis of the War on the global food supply chains from six key areas. The authors first collected pre-war data on grain export from Ukraine from FAOSTAT (a food and agriculture dataset maintained by the Food and Agriculture Organization of the United Nations) supplemented by a large-scale literature review and syntheses, using methods such as customized Google search and information collection from news reports, targeted websites, and peer-reviewed articles. Because Russia ceased the publication of its trade statistics after the start of the War, Simola (2022) applied the method of mirror statistics that relies on the trade data released by Russia's major trading partners since 2015 to estimate the recent trends of Russia's foreign trade flows. Primary data sources the author used include Macrobond, Comtrade, and Eurostat. Since this approach requires the collection of data from individual trading partner countries of Russia, the estimations are primarily conducted at a very aggregated export level and for a limited number of major markets.

CONCLUSION

This paper contributes to the understanding of the macroeconomic impacts stemming from grain commodity export disruptions of the Russia–Ukraine War. Using the GTAP multi-regional CGE model, we demonstrate that this event generates significant cascading economic impacts both across sectors and regions of the world.

In the Base Case scenario of the Ukrainian export shock in isolation, for example, we estimate that the Ukrainian economy would experience the most negative impacts, with a real GDP reduction of about $859 million, or about 0.65%, for a projected 1-year period of the War. In addition, the analysis estimates that other countries/regions, such as China and the Rest of Asia, would experience relatively significant reductions in GDP through supply-chain disruptions. In contrast, some other countries/ regions, such as Rest of NATO and India, would experience economic gains by providing commodities to fill the gap of Ukrainian exports.

On the other hand, the disruption of Russian grain exports has a relatively small impact on its GDP, on the order of only $3.8 million, or only about 0.0002%. The main reason for the difference between the two countries is primarily due to Russia's much lower trade dependency.

Interestingly, the economic welfare impacts on Russia, as measured by EV, are positive because of the strong influence of terms of trade effects. On the other hand, they are negative for Ukraine, with the EV impacts almost as large as the GDP impacts, because the terms of trade effects are relatively small.

We also conducted two types of sensitivity analysis. We first performed sensitivity tests of key substitution parameters affecting this issue, such as factor substitution (ESUBVA), import-domestic (Armington) substitution (ESUBD), and the import–import (Armington) substitution (ESUBM). The results indicate that our findings are generally robust. The second sensitivity test indicates that the sudden breaking of the agreement to allow Ukrainian grain exports through the Black Sea ports would increase negative impacts on its GDP by nearly 30%, while having an imperceptible impact on the Russian economy.

We acknowledge some limitations of our analysis. First, we have used a CGE modeling approach, which is generally best suited for long-run analyses. However, our analysis has provided reasonable results for the impacts of short-term shocks because of the way we have specified some of our closure rules and assumptions regarding limiting factor mobility. Still our findings should be viewed as lower bounds, because we have not explicitly factored in supply-chain bottlenecks that have arisen as a result of the war and their interactions with bottleneck conditions related to the lingering effects of COVID. We have also not included interactive effects with other types of export disruptions, especially those associated with energy, and have not incorporated the effects of various types of trade sanctions as a response to the war. Second, we have taken resilience into account only to a limited extent in relation to input and import substitution. We have not expressly factored other resilience tactics, such as the role of inventories/stockpiles, though their influence is limited in an analysis of a year's length (see Dormady et al., 2022). Overcoming some of these limitations will be the basis of future research.

Overall, this paper is intended as a start in the evaluation of various economic disruptions of the Russia–Ukraine War and, eventually the effect of other considerations that make this a compound event, such as the potential disruptions from increased cyber-attacks from Russian sources. Our analysis is intended to provide valuable insights into policies and strategies to reduce the negative impacts of the disruption of the war on individual countries in the global economy. Work is underway to develop a more comprehensive model of compounding and cascading supply-chain disruptions (see Egan et al., 2022).