Study on the current situation and influencing factors of corn import trade in China – based on the trade gravity model

Hongxue He

doi:10.1515/jisys-2024-0040

Article Open Access

Study on the current situation and influencing factors of corn import trade in China – based on the trade gravity model

Hongxue He

Published/Copyright: June 19, 2024

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From the journal Journal of Intelligent Systems Volume 33 Issue 1

Abstract

With the rapid development of China’s economy and society, the income level of Chinese residents is increasing day by day, and the dietary consumption structure of domestic residents is constantly optimized and upgraded. The consumption demand for livestock products such as meat, eggs and milk, and corn starch industrial products is increasing day by day. Therefore, as the main raw material of feed industry and deep processing owners, the consumption scale of China corn market is constantly expanding. However, the corresponding domestic corn production scale has not expanded, and the domestic corn production presents an obvious problem of insufficient supply scale, which leads to a sharp and rapid increase in the number of corn imports in China, which seriously impacts the domestic corn market and affects China’s healthy development of corn industry. In recent years, the continuous spread of the COVID-19 epidemic has seriously affected the global grain production and trade. The fierce military conflict between Russia and Ukraine, the two major grain exporting countries, has once again brought a serious adverse impact on the supply and price of the global grain market, resulting in an increase in the import cost of corn in China and a change in the import pattern of corn in China. In this regard, this study analyzes the current situation of corn production and import trade in China, and based on the relevant trade data between China and major corn importing countries from 2013 to 2022, establishes a trade gravity model to quantitatively evaluate the main factors affecting China’s corn import trade, and analyzes the trade potential of sample countries. It was found that in 2022, China’s import and export trade volume and trade value were 20.62 million tons and 7.11 billion US dollars, respectively, compared with 2013, trade volume and trade volume increased by 6.3 times. Statistics on the potential types of various trading countries showed that the proportion of potential countries was 14.2%, and Brazil had the greatest potential in China’s corn import trade. The proportion of potential expanding countries is about 71.4%, and Laos is a mature country with less opportunities for further development. In order to promote the benign development of the domestic corn industry and the development of China’s corn import trade, this paper puts forward a series of countermeasure suggestions based on the above research.

Keywords: corn import; food trade; gravity model; analysis of trade potential; corn production and trade in China; factors influencing China’s corn import trade

1 Introduction

Research perspectives on this issue have been summarized and categorized through research and analysis of relevant articles. Mao et al. [1] predicted the future import demand for corn in China against the backdrop of increasing consumption demand for corn. Chen et al. [2] examined the spatial correlation of inter-provincial corn production in China from 1985 to 2012 using the global Moran’s I index and constructed a spatial Durbin model to analyze the influencing factors of changes in corn production distribution in China. Gong et al. [3] further analyzed the factors affecting the fluctuation of corn prices in Jilin Province, indicating that corn production costs, the international corn market, and corn demand positively impact corn prices. Li et al. [4] empirically analyzed the behavior and influencing factors of Jilin Province’s farmers in selecting new corn varieties based on survey data from 129 households in the main grain-producing areas of Jilin Province, using the Logistic model. Zhang et al. [5] employed an expanded gravity equation model using panel data from 2001 to 2017 to analyze the influencing factors and trade potential of China’s tea exports to countries along the “Belt and Road” initiative using the Poisson Pseudo Maximum Likelihood method. Yang et al. [6] predicted future cost changes based on detailed comparative analysis of the composition of corn production costs, trends, and key influencing factors in China, the United States, and Brazil using actual data. Wang et al. [7] applied the gravity model to analyze the empirical data on the relevant influencing factors of countries exporting corn (substitutes) to China from 2001 to 2019 under the premise of import tariff quotas, state trade monopolies, and changes in industrial policies. Lu [8] indicated that the conflict between Russia and Ukraine will have far-reaching effects on global agricultural product trade and agricultural cooperation while affecting agricultural production in both countries. Russia and Ukraine are both sources of agricultural imports for China and potential agricultural cooperation partners. Kang and Zhou [9] used UN Comtrade statistics and the international trade index correlation analysis method to analyze the current status, structural characteristics, and trade complementarity index and revealed the comparative advantage of bilateral trade development between China and Russia. Combining the analysis results and the background of the current Russia-Ukraine conflict, the study analyzed the impact of changes in EU-Russia bilateral trade on China-Russia trade. Qin et al. [10] studied China’s trade situation with other major partners based on an expanded gravity model and trade data since the first quarter of 2018. Wang [11] analyzed the current status of agricultural product trade between China and Kazakhstan and conducted qualitative and quantitative research on trade efficiency and growth path between the two countries. In a previous study, more attentive spatio-temporal feature extractions by using the 3D-CNN, convolutional LSTM, and attention mechanism were presented. The proposed models show that the architectures offer more precise crop yield prediction. New-generation Deutsches Zentrum für Luft-und Raumfahrt Earth Sensing Imaging Spectrometer images were used earlier to classify the main crop types (hybrid corn, soybean, sunflower, and winter wheat) in Mezőhegyes (southeastern Hungary). In a previous study, drought indexes were combined to investigate the spatio-temporal variations in meteorological and agricultural droughts and analyze the association between them. A supervised BS method that allows the selection of the required number of bands was examined earlier. The authors embed a deep network of 3D-convolutional layers in a genetic algorithm (GA). The GA uses embedded 3D-CNN as a fitness function. GA also considers the parent check box. The parent check box (parent sub-bands) is designed to make genetic operators more effective. In a previous study, the cadmium (Cd) concentration was accurately mapped and the most suitable regression model was introduced among different models, including support vector regression, partial least square regression, and artificial neural networks. Various techniques were used earlier to achieve the desired performance. The model is a UNet assisted with attention blocks in the decoder part and trained with a patched, rotated, and augmented dataset that has been extracted from the DeepGlobe dataset. In a previous study, the ResMorCNN model, which utilizes 3D convolutional layers and morphology mathematics to extract structural information, shapes, and interregional interactions from hyperspectral imagings. These features are then incorporated into the model’s layers using residual connections. Zhu et al. [12] suggested that the fluctuations in the world grain market caused by the Russia-Ukraine conflict and their impact on China’s grain security must be highly considered. With the possibility of future recurrent or even normalized wheat export restrictions, the import cost of wheat in China will significantly increase.

Regarding the gravity trade model, most existing literature analyzes trade along the Belt and Road countries based on conventional variables such as population, exchange rates, GDP, and other relevant factors. In this study, new variables such as corn market openness and trade share are incorporated into the analysis of import trade data from the top seven countries regarding corn export to China. A gravity trade model is established to explore the significant factors restricting China’s corn imports. The aim of this study is to identify suitable trade partners based on the degree of influence of these factors and to develop corn import trade further.

2 Current situation of corn production and trade in China

As one of the three major staple crops in China, corn plays a strategic role in the stable development of the national economy and holds a crucial position in the country’s grain security strategy. In 2022, the planting area and production of corn in China reached 65.869 million hectares and 277.20 million tons, respectively. Compared to 2013, this represents an increase of 6.3 and 26.8%, with an average annual growth rate of 0.7 and 2.9%, respectively. The planting area and production increased continuously before 2015. Still, in 2016, with the cancellation of the temporary corn storage system, the planting area experienced a continuous decline, while production remained stable with a slight increase. In terms of consumption, the demand for corn reached 284.01 million tons in 2022, showing a growth of 32.9% compared to 2013, with an average annual growth rate of 3.7%. However, this consumption level only accounted for 93.6% of the peak volume reached in 2018. Among the various consumption sectors, the consumption for food and seed purposes remained stable, while industrial consumption experienced a slight decline. Meanwhile, there was a significant increase in corn consumption for animal feed purposes (Table 1).

Table 1

Chinese corn production and consumption from 2013 to 2022

Year	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
Planting Area	61,948	64,495	67,452	66,266	63,598	63,195	61,926	61,896	64,986	65,869
Production	21,862	21,581	26,516	26,378	25,926	25,734	26,096	26,088	27,276	27,720
Consumption	21,377	20,301	22,528	27,257	29,752	30,351	29,101	25,201	27,501	28,401
Food Consumption	1,048	1,065	1,092	1,150	1,150	1,150	1,150	1,150	1,150	1,150
Industrial consumption	4,600	4,400	4,700	6,500	8,500	9,200	8,100	7,000	7,000	6,800
Animal feed consumption	15,205	14,300	16,128	18,850	19,300	19,200	19,000	16,000	18,300	19,400
Seed consumption	178	185	188	180	180	180	180	180	180	180

Data source: FAO database, Unit: 10,000 hectares, 10,000 tons.

In terms of corn trade, in 2022, the import and export trade volume and trade value were 20.62 million tons and 7.11 billion US dollars, respectively. Compared to 2013, they have increased by 6.3 times and 6.3 times, accounting for 56.2 and 11.8% of the total import and global volumes of the three major staple crops. The import volume, import value, and average import price were 20.61 million tons, 7.1 billion US dollars, and 328.7 US dollars per ton, respectively. Compared to 2013, they have increased by 84.2% and 6.7 times. The peak import volume, in 2021, accounted for 65.9% of the total import volume of the three major staple crops that year, an increase of 36.4% compared to 2013. Due to the continuous increase in import volume, the price rose to 2823.9 yuan/ton in 2023, an increase of 3.1% compared to 2022. The export volume and export value were 10,000 tons and 3.82 million US dollars, respectively, a decrease of 87.5 and 88.5% compared to 2013. The trade deficit was approximately 7.1 billion US dollars, 12.3% of the total agricultural trade deficit (Figure 1).

Figure 1

Trend of corn import from 2013 to 2022 (10,000 tons).

Regarding trading countries, there have been significant changes in the main sources of corn imports since 2013. In 2013, the United States was the main source of China’s corn imports, accounting for 90.9%. However, from 2014, due to factors such as bilateral trade frictions and genetically modified organisms, China’s corn imports shifted from the United States to Ukraine, accounting for over 80%. Since 2021, with changes in the international situation and the impact of the Russia-Ukraine conflict, Ukraine’s proportion of corn imports to China has dropped to only 25.5%, and the United States has once again become the largest source of China’s corn imports. However, in 2023, the import trade pattern of China’s corn has changed. The main source countries have shifted from the United States, Ukraine, Myanmar, and Laos to the United States, Brazil, Ukraine, Myanmar, and Russia. Brazil is expected to surpass the United States this year, while Ukraine’s export volume will still maintain a certain proportion (Table 2).

Table 2

Proportion of corn import countries and import volume during 2013–2022

	United States		Ukraine		Myanmar		Russia		Argentina		Brazil		Laos
	Proportion (%)	Value	Proportion (%)	Value	Proportion (%)	Value	Proportion (%)	Value	Proportion	Value	Proportion (%)	Value	Proportion (%)	Value
2013	90.92	84,717	3.34	2,618	0.80	731	0.13	103	2.02	1,922	0.02	15	2.51	2,649
2014	39.52	29,344	37.1	25,797	1.58	1,124	0.03	489	0.02	51	0.00	0	4.25	3,585
2015	9.76	12,089	81.5	87,681	1.02	1,250	1.38	1,440	0.00	13	0.00	0	2.64	3,930
2016	7.04	5,620	84.3	50,831	2.66	1,786	0.34	1,023	0.01	60	0.00	0	6.08	4,080
2017	26.78	15,988	64.5	36,958	3.30	2,441	0.08	28	0.00	23	0.00	0	5.33	4,514
2018	8.87	6,875	83.2	63,999	2.85	2,658	1.10	545	0.00	13	0.00	0	3.96	4,063
2019	6.63	7,411	86.4	89,628	2.43	3,183	1.45	1,062	0.00	89	0.00	0	2.96	4,041
2020	38.44	95,983	55.8	135,324	1.07	3,892	1.22	2,172	0.00	15	0.00	0	1.17	4,279
2021	69.94	558,553	29.1	234,840	0.11	1,113	0.31	2,127	0.00	47	0.00	0	0.07	690
2022	72.08	528,404	25.5	164,973	0.94	6,556	0.46	2,969	0.00	82	0.00	0	0.25	1,786
2023	44.10	83,537	22.1	36,982	2.38	4,404	2.09	3,452	0.01	120	27.7	58,763	0.53	1,058

Data source: The data are from Brook Agriculture and are taken as of April 25, 2023. The units are 10,000 tons and 10,000 US dollars.

Based on corn prices, the average price per ton of corn in China is $250–440. The average prices per ton of corn in the United States, Ukraine, and Russia range from $190 to 330. This indicates that the average price in China exceeds the average prices of these three countries by 25–40%. The price difference between the supply and demand countries largely determines the scale and direction of China’s corn trade (Table 3).

Table 3

Price trends of corn in the United States, China, Ukraine, and Russia from 2013 to 2022

Country	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
United States	285	286	262	212	211	220	233	221	282	355
Ukraine	240	267	228	212	203	218	217	217	285	313
Russia	208	189	175	212	126	141	153	158	238	314
Average price of three countries	245	248	221	212	180	193	201	199	268	328
China	378	387	359	277	250	280	277	325	425	439
Higher than the average price of the three countries	35%	36%	38%	23%	28%	31%	28%	39%	37%	25%

Data source: Domestic corn price data are sourced from the National Bureau of Statistics, while the import corn price is calculated based on data from the General Administration of Customs, represented as the ratio of corn import volume to corn import quantity in USD/ton.

3 Empirical analysis of factors influencing China’s corn import trade

3.1 Model specification

The gravity model originated from the gravitational law in physics and has been widely applied in empirical analyses of economic trade. It suggests that the volume of international trade depends mainly on the economic size of the two countries and the geographical distance between them. If the two countries have large economies and a small distance between them, trade is highly likely to occur.

In this study, we construct the econometric model as follows: The gravity model typically assumes that bilateral trade volume is directly proportional to the economic size of the trading partners and inversely proportional to the distance between them. It can be expressed as:

(1) M ijt = A GDP it β 1 GDP jt β 2 POP it β 3 POP jt β 4 DIST ijt β 5 U ijt ,

where M ijt represents the trade flow between the trading countries, GDP represents the gross domestic product, POP represents the population size, DIST represents the distance between the trading countries, i represents China, j represents the importing country, t represents the time, α represents the constant term, β represents the slope term, and U represents the random error term (other factors that promote or hinder trade flow between the two countries). For regression analysis, we take the logarithm of both sides of equation (1) to linearize the gravity model

(2) ln ( M ijt ) = a + β 1 ln ( GDP it ) + β 2 ln ( GDP jt ) + β 3 ln ( POP it ) + β 4 ln ( POP jt ) + β 5 ln ( DIST ijt ) + β 6 F ij + μ ijt ,

where Ln represents the natural logarithm, Ln(A) = a , μ ijt = Ln ( U ijt ) , and F ij represents some dummy variables reflecting heterogeneity. If heterogeneity exists, the volume is 1; otherwise, it is 0. The coefficients β 1 – β 5 reflect the impact of GDP, population, and distance on bilateral trade flow.

Based on the standard expression of the gravity model of trade and considering the actual situation of China and the major source countries of imported corn (such as the United States, Ukraine, Myanmar, Laos, Brazil, Russia, and Argentina), we introduce some new explanatory variables to expand the gravity model. The analysis focuses on factors previously qualitatively analyzed to influence the layout of China’s corn import sources. Therefore, after incorporating the new explanatory variables, the gravity model for China’s corn imports can be constructed as follows:

(3) ln ( M ijt ) = a + β 1 ln ( GDP it ) + β 2 ln ( GDP jt ) + β 3 ln ( POP it ) + β 4 ln ( POP jt ) + β 5 ln ( DIS ijt ) + β 6 ln ( YIE it ) + β 7 ln ( YIE jt ) + β 8 ln ( PRI it ) + β 9 ln ( PRI jt ) + β 10 RESTR ij + β 11 MAO ij + β 12 RATE jt + PAR + BOR + VT + μ ijt ,

where i represents China, j represents the source country of corn imports, t represents time, and ln ( M ijt ) represents the corn import trade volume from country j to China during period t (units: US dollars).

GDP i represents the size of China’s economy, using China’s GDP (in constant 2015 US dollar value) to reflect the demand in the Chinese corn import market. The larger the economic scale, assuming the domestic market size remains constant, the stronger the potential import capacity, resulting in a larger volume of corn imports. Consequently, China’s corn import trade value would be more significant with a positive expected sign. GDP j represents the economic size and export supply capacity of the countries that are sources of corn trade imports. The larger the economic dimension, the stronger the potential export capacity, with a positive expected sign.

POP i represents the import demand capacity of China for corn and is represented by the total population of China. Since corn is both a grain crop and a labor-intensive product, the larger the population of China, the greater the number of people employed in labor, and consequently, the smaller the comparative advantage in imports. This is unfavorable for corn imports, with an expected negative sign. POP j represents the supply capacity of the countries that are sources of corn import trade. Increasing their population would enhance their export supply capacity for corn, leading to a positive expected sign.

DIS ij represents the straight-line distance between China’s capital and the corn-importing country’s capital. This variable reflects the transportation costs between the two countries. Generally, the greater the spatial distance, the higher the transportation costs, and greater the trade barriers. Consequently, China’s corn import trade would be smaller, with a negative expected sign.

YIE i represents the corn production in China, and this variable reflects the corn production situation and potential import demand capacity in China. Under the assumption of relatively stable domestic market demand, the larger the total corn production in China, the correspondingly smaller the volume of corn imports, and thus, the volume of China’s corn import trade would be smaller with a negative expected sign. YIE j represents the corn production in the corn-importing countries in China. This variable reflects the corn production situation and potential export supply capacity in the corn-importing countries. In general, the larger the total corn production in the exporting countries, the greater the domestic market demand and, consequently, the smaller the export volume, resulting in a negative expected sign.

PRI i represents the price of corn in China (annual average), and this variable reflects the consumption situation in the Chinese corn market and the potential import demand capacity. The higher the domestic price of corn in China, the greater the willingness to import corn domestically, leading to an increase in torn imports, with a positive expected sign. PRI j represents the import price of corn in the corn-importing countries in China, expressed as the ratio of corn import value to corn import volume. This variable reflects the consumption situation in the corn market of the corn-importing countries and the potential export supply capacity. The higher the price of corn in the exporting countries, the corresponding decrease in the export volume of corn, resulting in a smaller corn import trade value in China, with a negative expected sign.

RESTR ij represents the proportion of China’s corn import trade volume to the corn-exporting countries’ total corn export trade value. The balance of corn trade value directly determines China’s position in the corn trade with various countries. When the total corn trade value is more significant, it indicates a greater possibility of China engaging in corn trade with those countries. Consequently, the value of China’s corn import trade would be more significant, with a positive expected sign.

MAO represents the market openness of the corn market in China, expressed as the ratio of corn import value to GDP during period t. Market openness reflects the degree to which foreign factors of production, such as labor, capital, land, and entrepreneurial talent, are allowed to engage in exchange activities within a country. The market openness of the corn market is directly proportional to the value of corn import trade, meaning that the higher the market openness of the corn market, the greater the value of corn import trade, with a positive expected sign.

RATE jt represents the exchange rate between the currency of country j and the Chinese Renminbi (RMB) during period t. The large exchange rate indicate a greater amount of j-country’s currency exchanged for RMB. The more devalued the RMB is, the more it hinders corn import trade. The expected sign is negative.

Vt is a dummy variable used to represent the impact of the strip intercropping of corn and soybeans on China’s corn imports. Years before 2021 are assigned a volume of 1, while years from 2021 onwards are 0. When strip intercropping of corn and soybeans is adopted, it leads to a corresponding decrease in China’s corn production, thereby promoting corn imports. The expected sign is positive.

PAR is a dummy variable representing whether two countries have a comprehensive strategic partnership. It is generally believed that if the source country of import trade has a comprehensive strategic partnership with China, it will increase China’s corn import demand. The expected sign is positive.

BOR is a dummy variable that represents whether two countries share a common border. If there is a common border between two countries, it reduces trade costs and increases the possibility of trade, thereby promoting China’s corn imports. The expected sign is positive.

3.2 Data source

This study selected import trade values and other relevant data from seven major corn-exporting countries to China, including the United States, Ukraine, Russia, Laos, Myanmar, Argentina, and Brazil, between 2013 and 2022. These data were used as panel data. Table 4 presents a partial overview of the panel data for China’s trade values with the corn-importing countries from 2013 to 2022.

Table 4

Panel data of China’s trade values with corn-importing countries, 2013–2022 (partial)

ID	Year	Country	M	DIS	GDP_i	YIE_j	POP_j
1	2013	USA	847,169,025	10993.68	9.61958 × 10¹²	351,271,870	323,094
1	2014	USA	293,435,567	10993.68	1.03339 × 10¹³	361,091,140	325,697
1	2015	USA	120,887,975	10993.68	1.10616 × 10¹³	345,486,340	328,208
1	2016	USA	56,200,654	10993.68	1.18191 × 10¹³	412,262,180	330,745
1	2017	USA	159,882,321	10993.68	1.26402 × 10¹³	371,096,030	333,259
1	2018	USA	68,748,645	10993.68	1.34934 × 10¹³	364,262,150	335,561
1	2019	USA	74,113,546	10993.68	1.42963 × 10¹³	345,962,110	337,713
1	2020	USA	959,827,576	10993.68	1.46165 × 10¹³	358,447,310	339,314
1	2021	USA	5,585,529,783	10993.68	1.58019 × 10¹³	383,943,000	340,354
1	2022	USA	5,284,035,616	10993.68	1.68619 × 10¹³	348,751,000	341,642
2	2013	Ukraine	26,177,657	6460.799	9.61958 × 10¹²	30,949,550	45,307
2	2014	Ukraine	257,967,195	6460.799	1.03339 × 10¹³	28,496,810	45,148
2	2015	Ukraine	876,809,212	6460.799	1.10616 × 10¹³	23,327,570	44,983
2	2016	Ukraine	508,308,900	6460.799	1.18191 × 10¹³	28,074,610	44,834
2	2017	Ukraine	369,580,172	6460.799	1.26402 × 10¹³	24,668,750	44,657
2	2018	Ukraine	639,990,532	6460.799	1.34934 × 10¹³	35,801,050	44,447
2	2019	Ukraine	896,277,904	6460.799	1.42963 × 10¹³	35,880,050	44,211
2	2020	Ukraine	1,353,239,737	6460.799	1.46165 × 10¹³	30,290,340	43,910
2	2021	Ukraine	2,348,400,919	6460.799	1.58019 × 10¹³	42,109,850	43,531
2	2022	Ukraine	1,649,734,305	6460.799	1.68619 × 10¹³	27,000,000	39,702

The corn import trade value (USD) and volume (kg) are sourced from UN Comtrade, the United Nations Commodity Trade Statistics. Population data (in thousands) are obtained from Statistics – UNCTAD. GDP data for each country are sourced from The World Bank. The geographical distance between countries and information on whether they share a common border are obtained from Data – CEPII. Corn production data for the corn-exporting countries and China’s corn production (in metric tons) are sourced from the grain and Agriculture Organization (FAO) of the United Nations and the National Bureau of Statistics of China, respectively. Import prices are sourced from UN Comtrade. China’s corn price (annual average value) is obtained from the Brock Agricultural Data Intelligence Terminal. The exchange rate between the country j’s currency and the RMB during period t, represented by RATEjt, is sourced from the World Bank’s World Development Indicators and the International Monetary Fund database.

3.3 Model testing

This study analyzes the gravity model through Ordinary Least Squares (OLS) regression using the econometric software STATA 16.0. Due to the inclusion of multiple dummy variables and distance variables that do not vary over time, a fixed-effects model with two-way effects and OLS estimation are employed to examine China’s corn import gravity model. After multiple adjustments, control variables, and iterative corrections, the final results indicate that China’s GDP, the population of corn-exporting countries, the distance between countries, corn price in exporting countries, corn market openness, the proportion of corn exports, and dummy variables representing comprehensive strategic partnership are statistically significant. The model’s overall fit is good, and the significance level of the remaining variables is low. After excluding these variables, the final modified model (equation (4)) is obtained and tested further.

(4) ln ( M ijt ) = a + β 1 ln ( GDP it ) + β 2 ln ( POP jt ) + β 3 ln ( DIS ijt ) + β 4 ln ( PRI jt ) + β 5 RESTR ij + β 6 MAO ij + PAR + μ ijt .

3.3.1 Correlation test

A correlation test was conducted on the modified model variables, and the results are presented in Table 5.

Table 5

Correlation test

M	GDP_i	POP_j	DIS	PRI_j	MAO	PAR	RESTR_ij
GDP_i	0.0540	1
POP_j	−0.212*	0.0120	1
DIS	−0.512***	0	0.587***	1
PRI_j	−0.844***	0.110	0.198*	0.651***	1
MAO	0.481***	0.253**	0.210*	0.0750	−0.179	1
PAR	0.172	0	−0.436***	−0.839***	−0.467***	−0.295**	1
RESTR_ij	0.310***	0.0960	−0.718***	−0.567***	−0.256**	0.0820	0.383***

Note: *, **, *** indicate significant at 10%, 5%, and 1% significance levels, respectively.

After taking the absolute volume of the correlation coefficient, if the coefficient falls between 0 and 0.09, it indicates that the two variables are essentially uncorrelated. If the coefficient ranges from 0.1 to 0.3, it suggests a weak correlation between the variables. A coefficient between 0.3 and 0.5 implies a moderate correlation, while a coefficient above 0.5 indicates a strong correlation between the variables. From the regression results, it can be observed that both China’s corn import demand capacity and corn price positively impact the corn import trade value. Moreover, China’s economic scale and the openness of its corn market are positively correlated with the corn import trade value. Specifically, a higher level of market openness leads to a higher trade value. However, whether the two countries have a comprehensive strategic partnership does not significantly affect China’s position in corn import trade. Additionally, there is a negative correlation between the straight-line distance between major ports in the importing and exporting countries and the corn import trade value. This suggests that transportation costs rise as the spatial distance increases, creating greater trade barriers and a smaller volume of corn imports.

3.3.2 Collinearity test

The Variance inflation factor (VIF) test was conducted to examine collinearity. A higher VIF indicates stronger collinearity between variables. When VIF ≥10, it indicates severe multicollinearity among the independent variables. If multicollinearity exists, it could excessively affect the least squares estimates. Therefore, it is essential to perform a multicollinearity test before estimating and interpreting the results of multiple regression analysis.

The larger the VIF value, the more severe the collinearity. Generally, a VIF more significant than 10 (or strictly 5) indicates a considerable collinearity problem in the model. The tolerance volume is sometimes used as a criterion, where tolerance = 1/VIF. If the tolerance volume is more significant than 0.1 (or strictly 0.2), it indicates no collinearity. VIF and tolerance have a logical correspondence, and either of these indicators can be chosen. After conducting the collinearity test, it was found that the VIF values for the variables in this study, as shown in Table 6, are all below 10.

Table 6

Collinearity test

Variable	VIF	1/VIF
DIS	7.44	0.134368
PAR	4.53	0.220798
POP_j	2.80	0.356653
RESTR_ij	2.53	0.394864
PRI_j	2.21	0.451548
MAO	1.68	0.595164
GDP_i	1.15	0.868340
Mean VIF	3.19

3.3.3 Results analysis

After estimation using the fixed-effects model with two-way effects and OLS, the model’s overall fit is reasonably good. Although the dummy variable PAR is not significant, most of the estimated coefficients have signs that match the expected signs. The results are presented in Table 7.

Table 7

Regression results

Variable	Coefficient	Standard error	t-value	p-value
GDP_i	2.210028^*	1.113662	1.98	0.053
POP_j	−11.05295^**	5.430581	−2.04	0.047
DIS	−3.608834^***	0.5984563	−6.03	0.000
PRI_j	−0.8691089^***	0.187332	−4.64	0.000
MAO	9720.649^***	2913.67	3.34	0.002
PAR	9.01844	6.461645	1.40	0.169
RESTR_ij	1.668038^*	0.929504	1.79	0.079
_cons	107.4223^*	57.04678	1.88	0.066

*p < 0.1, **p < 0.05, ***p < 0.01.

The regression result shows an R-squared volume of 0.9479, indicating a good model fit and a solid ability to explain the panel data. After adjusting and eliminating variables, the coefficients of the variables are generally consistent with the expected signs. Most of the regression coefficients are significant at the 1% level. It can be observed that the degree of market openness and the population of corn-exporting countries have the most significant impact on China’s corn imports. For every 1% increase in the population of corn-exporting countries, China’s corn imports decrease by 11.05%. When the corn price in exporting countries increases by 1%, China’s corn import volume decreases by 0.87%. When China’s corn import volume accounts for 1% more of the total corn exports from the exporting countries, China’s corn import volume increases by 1.67%. The regression coefficient of the dummy variable representing a comprehensive strategic partnership is insignificant, indicating that the existence of a comprehensive strategic partnership may not be a major factor affecting China’s corn import trade. GDP, population, and distance variables all pass the significance test and have coefficients with the expected signs.

3.4 Trade potential analysis

Based on the estimated results of the model, the predicted values of China’s corn import trade with seven corn-exporting countries are obtained from 2013 to 2022. The final expression for the corn trade predictions is as follows:

(5) ln ( M ijt ) = 2.21 ln ( GDP it ) − 11.05 ln ( POP jt ) − 3.61 ln ( DIS ijt ) − 0.87 ln ( PRI jt ) + 1.67 RESTR ij + 9720.65 MAO ij + 107.42 .

Using the above equation expression and inputting the data of the explanatory variables such as GDP, distance, population, market openness of corn, and corn prices for each corn-exporting country and China from the panel data, the predicted values of corn imports from each trading country to China can be obtained. Then, taking the logarithm of the actual export values of the seven corn-exporting countries for each year, averaging them, and comparing them with the average predicted corn import values, the resulting ratio is used to calculate the trade potential of each corn-exporting country. The average trade potential of each corn-exporting country is calculated, as shown in Table 8.

Table 8

Predicted values, actual values, and trade potential of each trading Country

Country	Predicted values	Actual values	Trade potential
Brazil	14.07	15.00	0.898
Russia	18.94	19.36	0.979
Ukraine	16.52	16.82	0.983
Myanmar	16.00	15.66	1.030
Argentina	19.43	19.29	1.007
United States	17.11	16.69	1.027
Laos	8.73	7.38	1.193

Countries with a trade potential volume below 0.9 are considered to have a likely latent type, indicating significant trade space. Countries with trade possible values above 0.9 but below 1.1 are believed to have some opportunities for trade expansion, known as likely expansion type. Countries with trade potential values above 1.1 are considered to have mature trade potential, indicating mature and stable trade markets with limited further opportunities for expansion. Statistical analysis of the trade potential types of each trading country in the table shows one country with latent likely type, accounting for 14.2% of the total. Brazil has the most significant potential in China’s corn import trade. Five countries have potential expansion types, accounting for approximately 71.4%. Laos falls under the mature possible type, indicating a mature and stable market for corn imports with limited opportunities for further expansion.

4 Conclusion and suggestions

4.1 Conclusion

This study takes China’s corn production and consumption, and the current status of corn import trade as the starting point. It qualitatively analyzes the characteristics of China’s corn import trade and the factors influencing China’s corn imports. By comprehensively analyzing the relevant data of China’s corn import trade with seven major supplying countries from 2013 to 2022, the explanatory variables of the gravity model are expanded, and an empirical study is conducted based on this model. The study proposes a gravity equation that fits the characteristics of China’s grain import trade and calculates the import potential of corn from the seven major supplying countries to China. The following conclusions are drawn:

4.1.1 Changes in the pattern of corn imports

From the perspective of China’s corn import trade in 2023, the corn import volume from Brazil has increased significantly. Apart from the lower production costs compared to China, China must further diversify its major grain import sources, eliminate the dependence on individual countries, and increase imports from countries like Brazil to ensure supply security.

4.1.2 Significant promoting effect of economic growth on China’s corn imports

The research findings indicate that GDP has a significant promoting effect on corn import trade. As China is a developing country and the world’s second-largest economy, economic development and improvements in the standard of living directly impact the supply and demand capacity of products, including the increased protein consumption by the population.

4.1.3 Significant hindrance effect of geographic distance on China’s corn imports

Geographic distance represents trade costs; the higher the costs, the less likely trade will occur. For example, as one of the world’s major corn-producing countries, Brazil is farther away from China than the United States, which increases transportation costs and weakens the export competitiveness of its products.

4.1.4 Significant hindrance effect of rising corn prices on China’s corn imports

The Russia-Ukraine conflict has led to a significant increase in grain and oil prices in international commodity futures. In China, soybean prices have risen by nearly 8%, while rapeseed meal, soybean oil, and corn prices have increased by over 6, 5, and 1%, respectively. On February 24, 2023, corn futures prices closed at 2,837 yuan/ton, with a daily increase of 1.14% and briefly reaching a high of 2,863 yuan/ton. Due to the price increase, China urgently needs to find alternatives or reduce the usage of corn domestically to mitigate the impact of rising prices.

4.2 Suggestions

4.2.1 Optimize the pattern of corn import trade and promote the diversified development of supply sources

At present, China’s corn import market is controlled by Ukraine and the United States, and China’s corn import is highly dependent on these two countries. However, the international economic and political situation is complicated now. The Sino-US economic and trade friction, the COVID-19 epidemic, and the outbreak of the Russian-Ukrainian war have all brought great potential risks to China’s corn imports. China should broaden the sources of corn imports, reduce the risk of excessive dependence on a single country, accelerate the formation of a diversified corn import pattern, and enrich the sources of corn imports. Corn import should also continue to implement tariff quota management, improve the efficiency of the use of corn quotas, and should always pay attention to the domestic and international political and economic environment, and flexibly adjust tariff quotas in light of the actual situation, so as to reduce trade friction and form a diversified corn import pattern. In this way, we can reduce the risk of corn import and deal with it more calmly in the face of risks and shocks, from “trade,” a big country has changed into a “trading power.” At the same time, different measures should be taken for countries with different types of trade efficiency. In countries with potential trade potential, China has not yet formed a stable import market, and there is still a lot of room for trade expansion. China should improve trade efficiency with these countries, reduce trade resistance, introduce relevant policies, and establish more convenience. Trade environment, fully tap the trade potential; for developing countries, different cooperation mechanisms should be established according to the types and characteristics of different countries, so as to weaken trade resistance, establish stable long-term cooperative relations and reduce corn import risks; For trade-expanding countries, because the import scale of corn is already large, there is still room for trade development, so it is necessary to further tap the market potential, pay attention to the demand for corn in the domestic market, and further improve trade efficiency.

4.2.2 Further develop the advantages of comprehensive strategic partnership

According to the regression results of trade gravity model, the member countries that have a comprehensive strategic partnership with China have a significant role in promoting corn import trade. Therefore, we should give full play to the advantages of comprehensive strategic partnership. China should make full use of the signed regional trade agreements. Second, we should strengthen agricultural cooperation with countries along the “the belt and road initiative,” improve trade efficiency, reduce trade barriers, and improve the degree of trade facilitation. Countries and regions along “the belt and road initiative” have great potential for increasing grain production, and compared with many grain import countries located in Europe and America, countries along “the belt and road initiative” and China have more advantages in geographical advantages and political mutual trust. Therefore, China can establish good and stable mutually beneficial and win-win cooperation relations with countries and regions along the “the belt and road initiative,” develop corn trade, expand the import channels of corn in China, and ensure the trade security of corn in China. At the same time, we should strengthen agricultural cooperation with the member countries of RCEP and broaden the import channels of corn in China. China can take advantage of the closer industrial chain and supply chain in the region to increase the corn trade volume with RCEP member countries and reach a stable corn import cooperation relationship with RCEP member countries.

4.2.3 Improving corn production efficiency, establishing the price advantage of domestic corn

The empirical results show that the price advantage of foreign corn has an obvious inhibitory effect on the scale of corn import trade in China. Therefore, in order to realize the self-sufficiency of corn, it is necessary to reduce the production cost of domestic corn, narrow the price difference between domestic and foreign corn and establish the price advantage of domestic corn. First, improve the relevant supporting measures for land transfer. The rural land contract in China is based on households. This land system inevitably leads to the inability to use large-scale machinery and equipment for corn planting. The household-based production system inevitably leads to low production efficiency, which hinders China’s progress towards modern agriculture. In view of the above situation, the government should take relevant measures to actively promote land circulation, centralize scattered land for unified planting and management, build corn planting bases, carry out large-scale and intensive production, make full use of cultivated land resources, improve corn production efficiency, reduce corn production costs, and form internationally competitive corn prices. Second, change the production mode of corn and reduce the labor cost of corn production. At present, the level of agricultural mechanization in China is low, and most of the work needs labor, so the labor cost is high. Therefore, we should speed up the process of agricultural mechanization and agricultural modernization, realize all-round mechanization of corn planting and reduce labor costs. Only in this way, can the production cost of corn in China be greatly reduced, the planting efficiency of corn be improved, the price advantage be created for domestic corn, and the international competitiveness of domestic corn be enhanced.

Under the background of economic globalization, the trade relations between countries are constantly strengthened, and countries have formed various economic alliances due to geographical location, and internal members can enjoy corresponding trade preferential policies, such as tariff preferences. As a member of many economic organizations, China can make full use of preferential trade policies to strengthen the trade relations of agricultural products with internal member countries. It is to eliminate trade barriers and deepen corn import trade. For example, Russia, Ukraine and Brazil are all important importers of agricultural products in China, so we can give full play to the advantages of “neighborhood” by means of geographical relations, strengthen friendly trade between neighbors, especially promote corn import trade and enhance economic exchanges.

4.2.4 Strengthen infrastructure construction and economic development, establishing safety management standards suitable for genetically modified organisms in China

Due to the high cost of corn production in China in recent years, compared with the main corn importing countries in China, China’s corn trade competitiveness is at a disadvantage, which leads to the increasing dependence on corn imports year by year. In the long run, China’s corn market will be difficult to resist import shocks. At present, there is still much room for improvement in China’s agricultural protection, which has limited effect on promoting corn production and supply, and it is difficult to fundamentally solve the dilemma faced by China’s corn industry development. Therefore, it is urgent to strengthen infrastructure construction and economic development and optimize industrial structure and competitiveness. At present, the final import of genetically modified organisms still needs to be examined and approved by the Ministry of Agriculture, and the subsequent changes in the Regulations on the Safety Management of Agricultural Genetically Modified Organisms need to be paid attention to. With the promotion of the relevant agreement between China and Pakistan, if the examination and approval methods are revised in the agreement, it is expected that the import procedures of Brazilian corn, including corn in South America, will gradually clear the obstacles in the future, thus further widening the trade channels and further improving China’s ability to cope with the risk of international food price fluctuations.

Acknowledgment

Jiahua Le has helped perform the analysis with discussions in this paper.

Funding information: Author states no funding involved.
Author contribution: Hongxue He contributed to the conception of the study; Hongxue He contributed significantly to analysis and manuscript preparation; Hongxue He performed the data analyses and wrote the manuscript.
Conflict of interest: The author declares that there is no conflict of interest.
Data availability statement: The corn import trade value (USD) and volume (kilograms) are sourced from UN Comtrade, UN Comtrade, [https://comtradeplus.un.org/]. The United Nations Commodity Trade Statistics. Population data (in thousands) are obtained from Statistics – UNCTAD. UNCTAD, [https://unctad.org/]. GDP data for each country are sourced from The World Bank. The World Bank, [https://www.worldbank.org/en/home]. The geographical distance between countries and information on whether they share a common border are obtained from Data – CEPII. CEPII, [http://www.cepii.fr/]. Corn production data for the corn-exporting countries and China’s corn production (in metric tons) are sourced from the grain and Agriculture Organization (FAO) of the United Nations and the National Bureau of Statistics of China. FAO, [https://www.fao.org/home/zh/]. China’s corn price (annual average value) is obtained from the Brock Agricultural Data Intelligence Terminal. Brock Agricultural Data Intelligence Terminal, [https://www.brockreport.com/]. The exchange rate between the country j’s currency and the Chinese Renminbi (RMB) during period t, represented by RATEjt, is sourced from the World Bank’s World Development Indicators (WDI) and the International Monetary Fund (IMF) database. WDI,IMF, [https://databank.worldbank.org/source/world-development-indicators].

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Received: 2024-01-11

Accepted: 2024-03-01

Published Online: 2024-06-19

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/jisys-2024-0040

Keywords for this article

corn import; food trade; gravity model; analysis of trade potential; corn production and trade in China; factors influencing China’s corn import trade

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