Evolution of the Spatial Patterns of Global Egg Trading Networks in the 21 Century

Abstract

Global agriculture and food system is faced with increasing uncertainty from natural disasters, epidemics, financial crises, and wars. Agricultural trade can provide a powerful supplement to global food security. Eggs have been recognized as the most important protein source for many years as they contain the highest quality protein naturally available while the price is competitive. Egg trading plays an important role in enhancing the resilience of the global food system under uncertainty. We empirically investigate the evolution of global egg trading networks by social network analysis. And, the quadratic assignment procedure (QAP) is applied to detect factors that impact global egg trading networks. The results show that in the 21st century, global egg trading networks are becoming more complex and the clusters are undergoing dynamic differentiation and integration. Additionally, compared to cultural difference among countries, factors including geographical distances, natural endowments, economic developments, trade policies, and political stability have a more significant effect on the evolution of egg trading networks. Our work provides suggestions for participating countries to develop more resilient egg trading networks to resist external shocks.

1. Introduction

1.1. Background

Eggs are high-quality and affordable sources of animal protein, which play an essential role in people’s diets by effectively reducing hunger and improving nutrition [1,2]. As the world population grows steadily, egg production has become one of the most important agricultural sectors and an essential part of the global food supply. The demand for eggs is still growing rapidly [3]. In wealthier regions, eggs constitute a healthy component of the diet, while the relative cheapness and abundance make eggs an important animal protein source for people in poorer countries. However, in recent years, the continuous increase in breeding costs and the outbreak of highly pathogenic avian influenza have emerged as crucial factors contributing to the shortage of egg supply and high prices. Under the Sustainable Development Goals, the global and regional egg trading is likely to become a means of balancing food supply and demand, improving the resilience of the global food system and dealing with uncertainty.

The volume of the global egg trading has been on an upward trajectory since 1970, increasing from 0.4 million tons to 5.48 million tons from 1970 to 2021 [4]. As the scale and scope of global egg trading expands, the interconnections between countries and regions have formed a complex network. The egg trading in Europe, Asia, and the USA is particularly active, with high trade volumes in developed countries and rapidly growing trade in developing countries. The use of social network analysis can effectively depict and analyze the evolving characteristics of global egg trading networks, helping to better understand the supply and demand situation for eggs to respond to uncertainty.

First, we use social network analysis to quantify the evolution of global egg trading networks. Through this analysis, we capture the changing status of major countries in the network, and multiple clusters with regional characteristics. Second, we use quadratic assignment procedure (QAP) to assess the influencing factors of global egg trading networks, including nature, geography, economy, politics, trading relations, and culture.

The study is the empirical assessment of the international egg market based on social network analysis, one of the most successful and widely used methods in quantitative trading network. Our work sheds light on the evolving features of the world egg market and the influencing factors. We do not analyze the impacts of trading policy and other market issues in global egg trading based on theoretical and/or empirical perspectives. Our work also contributes to provide a scientific basis for decision making on egg trading and food security, particularly for developing countries. The findings will provide a better understanding of the overall situation of global egg supply and demand and the potential risks it faces, which can help to eliminate hunger and promote food security.

1.2. Literature Review

With the backdrop of complex global industrial division and diversified trade linkages, the connotation of trade is becoming increasingly abundant. There are many statistical indicators used to measure a country’s trade intensity and trade quality. However, these trade indicators can only reflect a certain aspect of trade between the two countries. The study of the international trade system using social network analysis or complex network analysis has emerged as a new area of research, which more comprehensively reflects a country’s relative position and network characteristics in the global international trade network. The current studies in this field mainly concentrate on two fronts: First, the analysis of the topological structure of the global or regional trading networks [5,6,7]. Scholars have shown a particular interest in studying the “Belt and Road” region and Europe [8,9]. Second, the examination of the trading network characteristics of specific industries or sectors, such as energy, minerals, manufacturing, and agriculture [10,11,12,13,14,15]. While there has been some research on agricultural product trading networks, research on the global livestock trading networks, particularly in eggs, is lacking.

Food security and agriculture development is an important goal of national development. The study of agricultural product and food trading networks gains attention. Some research has recently commenced to investigate the traits, essence, and progression of the worldwide agricultural trading networks. Shade and Rachata utilized the social network analysis and the ternary analysis network tool to examine the international trading network of agricultural products. Their aim was to comprehend the network’s characteristics and its correlation with the international development model [16]. Cristina et al. analyzed the contact network between Southeast Asia by investigating the dissemination and adoption of rice, beans, and other trade crops along the early Maritime Silk Road in Southeast Asia’s local agriculture [17]. Drawing upon complex network theory, Qiang et al. established two types of agricultural product trading networks, which were weighted based on both physical quantity and monetary value. The findings revealed that the expansion of scale and structural complexity of these two networks reflected the heterogeneity among nodes and the trend towards global agricultural economic integration [18]. On the whole, the existing studies have primarily focused on the agricultural trading network. However, existing studies of agricultural trading networks are often limited to the analysis of major categories of agricultural products or specific planting industries (such as wheat or rice) and have yet to address the global spatial scale of the livestock trading network. As humans continually strive for better nutrition, eggs, being a readily accessible source of animal protein, have yet to be studied in depth with regards to their global trading network patterns. The spatial characteristics of the global egg trading networks and the position of developing countries in the networks are pressing questions that require further investigation.

2. Methods and Data

2.1. Methods

The global egg trading networks are abstracted as social networks G = (N, E, F), where N is a set of nodes representing egg-trading countries, E is a set of edges representing trading relationships between countries, and F is a set of functions describing the trade quantity between countries. Firstly, we use density, average degree, average path length, and average clustering coefficient to analyze the overall characteristics of global egg trading networks. Then, we investigate the trading relationships between countries and their positions in networks using degree centrality, closeness centrality, and betweenness centrality. The community discovery method is used to identify the grouping structure within the network and explore the potential relationships between trading members [19], as shown in

Table 1. The meaning and formula of main index of social network.

Indicators	Meaning	Formula
Network density (D)	It refers to the ratio of the total number of existing relationships in a network to the maximum number of potential relationships that could exist in theory, reflecting the degree of connection between nodes within a social network.	$D=\frac{M}{N(N-1)}$
Average density (AD)	It refers to the average number of connections that each node in a network possesses, reflecting the level of trade links between countries and the overall complexity of the network.	$AD=M/N$
Average clustering coefficient (CL)	It refers to the likelihood that any two nodes within a network have a trading relationship, reflecting the level of cohesion within the trading network.	$CL=\frac{1}{N}{\displaystyle \sum _{i=1}^N\frac{2M_i}{k_i(k_i-1)}}$
Average path length (L)	It refers to the shortest path between all pairs of nodes in a network, reflecting the transmission efficiency of traded goods within the network.	$L=\frac{2}{N(N-1)}{\displaystyle \sum _{i\ge j}^N{d}_{ij}}$
Degree centrality (DC)	In directed networks, degree can be divided into two indicators: out-degree and in-degree. Out-degree indicates the degree to which a node sends objects of the trading relationship, while in-degree indicates the degree to which it accepts them, reflecting the importance of exports and imports in the economy.	$D{C}_{sum,i}=\frac{D{C}_{out,i}+D{C}_{in,i}}{N-1}$
Out-degree (OD)		$D{C}_{out,i}={\displaystyle \sum _{j=1}^N{x}_{ij}(i\ne j)}$
In-degree (ID)		$D{C}_{in,i}={\displaystyle \sum _{j=1}^N{x}_{ji}(i\ne j)}$
Closeness Centrality (CC)	It refers to the proximity of nodes in the network to each other, reflecting the degree of independence in trading relations.	$C{C}_i=\frac{N}{{\displaystyle \sum _{j=1}^N{d}_{ij}(i\ne j)}}$
Betweenness centrality (BC)	It refers to the measurement of the shortest path between all pairs of nodes that pass through a specific node, reflecting the degree of resource flow control exerted by that node.	$B{C}_i=\frac{2{\displaystyle \sum _j^N{\displaystyle \sum _k^N{b}_{jk}(i)}}}{N^2-3N+2}(j\ne k\ne i)$
Cluster separation (Q)	It refers to a subset of nodes within a trading network where the connections between nodes within the subset are dense, while the connections between nodes outside the subset are sparse.	$Q=\frac{1}{2m}{\displaystyle \sum _{ij}\left[A_{ij}-\frac{k_i{k}_j}{2m}\right]}\delta (c_i,c_j)$

. Finally, we use QAP analysis, which tests the independence of each explanatory variable, to assess the geographic, political, economic, natural, and cultural factors that impact the egg trading networks, respectively. Moreover, we use UCINET analysis tool to characterize the global egg trading networks.

2.2. Data

Trading data series, including table eggs and egg products, of 213 countries between 2000 and 2021 are used in this paper. We collect bilateral trade volumes from CEPII BACI database, which reconciles the discrepancies between trade values as reported by exporters versus importers in the primary bilateral trade data from the UN Comtrade database. To avoid inflation effect, we take the average price from 2014 to 2016 of the Food and Agriculture Organization of the United Nations (FAO) as the benchmark price. Furthermore, to assess the factors impacting egg trading networks, we calculate the laying hen breeding scale matrix (data from FAO, 2021). Matrixes of GDP, per capita, and institutional distance are obtained from the World Bank database. Preferential trade agreements matrix, language difference matrix, and geographical distance matrix are sourced from the CEPII BACI database.

3. Spatial Patterns of Global Egg Trading Networks

The value of global egg trading and the egg prices were rising in fluctuations. With the development of globalization, the volume of global egg trading has been on the rise, increasing from 2.318 million tons in 2000 to 5.480 million tons in 2021, a growth of 1.36 times [4]. The trading value has also been constantly increasing (Figure 1). However, factors such as the avian influenza epidemic, changes in supply chain, and rising costs have led to fluctuations in trading value and egg prices, which reached peak in 2012 and gradually declined in 2016. As raw materials for feed, corn and soybean meal play an important role in laying hens breeding. Since 2020, COVID-19 and extreme weather have affected corn and soybean meal production, driving up the egg price index.

3.1. Findings of Global Egg Trading Network Evolution

The complexity of global egg trading networks is gradually increasing. The relationships between countries have been strengthened, and the degree of interaction has deepened. From 2000 to 2021, the average number of countries participating in global egg trading reached 192, accounting for 82.4% of the total number of countries in the world. The trade links between countries increased from 1733 to 2498, an increase of 0.44 times (Figure 2).

The density of global egg trading networks increased, and the transmission efficiency improved. From 2000 to 2021, the density increased from 0.031 to 0.054, with the average degree increased from 9.17 to 14.53. The trading network became increasingly dense, and the relationships became closer (Figure 3). The average clustering coefficient reflects the degree of aggregation and dispersion among countries in networks [20]. A larger value indicates a stronger aggregation. It can be seen from Figure 4 that the average clustering coefficient of global egg trading networks was between 0.388 and 0.505, showing a fluctuating growth trend, and the networks tended to be more cohesive, especially after 2011. The average path length reflects the trade accessibility and network efficiency between countries [21]. The average path of global egg trading networks decreased from 2.821 to 2.577, indicating high connectivity. The trade distance between any two countries has decreased, and the transmission efficiency of egg trading networks has improved. A small average path length along with a large clustering coefficient indicates that global egg trading networks display both small-world and scale-free network characteristics [22]. As seen in Figure 4, the minimum value of the average path length was 2.53, while the maximum value of the clustering coefficient was 0.505, with a four-fold difference.

Degree centrality reflects a country’s position in the global egg trading market [23]. Further, we calculate the node degree distribution of global egg trading networks in the years 2000, 2005, 2010, 2015, and 2021, as seen in Figure 5, which exhibit a typical long-tailed distribution pattern. Notably, a power function fit of the node degree passed a significance test (

Table 2. Power function fitting of ranking–node degree distribution.

Year	Fitting Equation	R2	p
2000	95.31532 × 0.9742782x	0.9516	0.0000
2005	106.5343 × 0.9748419x	0.9530	0.0000
2010	103.6603 × 0.9746435x	0.9442	0.0000
2015	115.0746 × 0.9722739x	0.9576	0.0000
2021	123.5967 × 0.9699222x	0.9726	0.0000

), and the degree distribution followed the power–law distribution, indicating significant heterogeneity among nodes, consistent with the characteristics of a scale-free network. The power–law index of the fitting equation, as seen in the years 2000 to 2021, showed an increasing trend, indicating that the scale-free character of global egg trading networks was generally on the rise.

3.2. Main Countries in Global Egg Trading Networks

While the positions of some Asian countries in global egg trading networks have weakened, the trade advantage between Europe and America has persisted and continue to strengthen. We compare the top 10 countries in degree centrality in 2000 and 2021, suggesting that developed countries, such as the USA, The Netherlands, Germany, France, Belgium, and Denmark, continued to be among the top 10 countries in degree centrality. In contrast, India, China, and South Africa dropped out of the top 10 in 2021, and were replaced by Turkey, Brazil, Russia, Spain, and other countries which are from South Europe and Eastern Europe (

Table 3. Top 10 countries or regions in degree, out-degree, and in-degree in 2000 and 2021.

2000						2021
Node	Degree	Node	Out-degree	Node	In-degree	Node	Degree	Node	Out-degree	Node	In-degree
USA	0.527	USA	0.405	The Netherlands	0.131	The Netherlands	0.574	The Netherlands	0.430	Germany	0.186
The Netherlands	0.511	The Netherlands	0.380	Germany	0.127	Germany	0.570	USA	0.430	The Netherlands	0.177
Germany	0.477	Germany	0.350	USA	0.122	USA	0.561	Germany	0.426	Russian	0.148
France	0.435	France	0.333	Italy	0.114	Turkey	0.553	Turkey	0.392	China, Hong Kong SAR	0.148
UK	0.363	UK	0.274	Austria	0.110	Belgium	0.460	Poland	0.380	Belgium	0.143
Belgium	0.342	India	0.245	Switzerland	0.110	Poland	0.460	Spain	0.346	Mexico	0.135
Denmark	0.287	Belgium	0.236	Belgium	0.105	Russia	0.426	France	0.338	Saudi Arabia	0.135
India	0.287	China	0.228	Kuwait	0.105	France	0.426	Belgium	0.308	France	0.131
China	0.283	South Africa	0.207	France	0.101	Spain	0.418	China	0.300	UK	0.127
South Africa	0.278	Denmark	0.203	China, Hong Kong SAR	0.101	Denmark	0.388	Brazil	0.283	United Arab Emirates	0.122

).

Out-degree and in-degree reflect the import and export position of countries in global egg trading networks [23]. The top 10 countries in out-degree have been stable over the years, with the USA, The Netherlands, Germany, France, Belgium, and China who consistently have long dominated the global egg export market. In 2021, however, Turkey, Poland, and Spain rose rapidly, surpassing France, Belgium, and China. In in-degree, the top 10 countries are mainly comprised of Germany, The Netherlands, the USA, Belgium, France, and China, Hong Kong SAR. However, in 2021, Russian, Mexico, Saudi Arabia, UK, and the United Arab Emirates entered the top 10, replacing Italy, Austria, Switzerland, and Kuwait (Table 3). The out-degree and in-degree also reflect the impact of external shocks on the export and import of eggs for countries. For instance, the outbreak of highly pathogenic avian influenza from 2014 to 2015 was concentrated in Asia, Western Europe, and North America. As the major laying hens producing countries, the out-degree of the USA, The Netherlands, and France significantly decreased due to the impact of avian influenza. On the other hand, Brazil, India, and Ukraine did not experience serious influence, and their out-degree significantly increased, making up for the global egg supply chain.

Closeness centrality reflects the degree of independence and control of nodes in networks. Additionally, a higher closeness centrality value indicates a country is closer to other countries in trading networks. In closeness centrality (

Table 4. Top 10 countries or regions in closeness centrality and betweenness centrality in 2000 and 2021.

2000				2021
Node	Closeness Centrality	Node	Betweenness Centrality	Node	Closeness Centrality	Node	Betweenness Centrality
USA	0.235	Russia	0.118	France	0.267	USA	0.174
Italy	0.228	USA	0.103	USA	0.246	France	0.171
France	0.228	France	0.095	The Netherlands	0.242	The Netherlands	0.116
Germany	0.225	Czech	0.085	Brazil	0.241	Germany	0.088
Belgium	0.225	Kazakhstan	0.083	Turkey	0.237	Turkey	0.087
China, Hong Kong SAR	0.225	Ukraine	0.057	Germany	0.236	Spain	0.042
Kuwait	0.224	Germany	0.047	Spain	0.233	Russian	0.039
Spain	0.222	United Arab Emirates	0.036	Ukraine	0.232	Mozambique	0.028
The Netherlands	0.221	Brazil	0.031	Italy	0.231	United Arab Emirates	0.026
United Arab Emirates	0.219	The Netherlands	0.027	Belgium	0.228	Denmark	0.019

), the top 10 countries in 2000 were primarily developed countries in Europe and America, as well as some Asian countries or regions. In 2021, the top 10 countries in closeness centrality have undergone significant changes, with Brazil, Turkey, and Ukraine replacing China, Hong Kong SAR, Kuwait, and the United Arab Emirates. Comparing the ranking of these countries in closeness centrality, out-degree, and in-degree, it is observed that nearly 80% of countries entry into the list repeatedly, demonstrating that the global egg trading networks exhibit a “point-to-point” trade characteristic and has an agglomeration effect.

Betweenness centrality reflects the degree of control over resource flow by the country [23]. A higher value indicates the country has a stronger control in trading networks. Table 4 shows the ranking of countries by betweenness centrality. From 2000 to 2021, the USA, France, Germany, and The Netherlands consistently ranked among the top 10 countries, which occupied a central position in global egg trading networks. Notably, despite not being among the top 10 in degree ranking, African and Middle Eastern countries have made significant inroads into the egg trading networks in recent years through expanding trade with China, Europe, and the USA, which enter the betweenness centrality ranking.

To examine the regional agglomeration characteristics of egg trading networks, we select the USA, The Netherlands, the United Arab Emirates, and South Africa, which are from different continents. Figure 6 illustrates their positions in regional networks.

Given the unique geographical location, the USA occupies an important position in trading network but has a low closeness centrality, indicating that there is a long trade distance between the USA and other countries. In contrast, the primary trading targets of The Netherlands are concentrated in Europe, while the United Arab Emirates is located in the central of the Eurasian continent, which places it close to major trading countries in Asia and Europe, resulting in higher closeness centrality rankings for both countries. The United Arab Emirates has even held the top rank in the world for closeness centrality since 2016. A possible reason is that the United Arab Emirates encouraged trading and investment constantly and joined The Belt and Road Initiative in December 2015.

While the closeness centrality of South Africa does not rank highly and shows significant fluctuations, betweenness centrality ranks among the top in the world. The finding suggests that as an important economy in Africa, despite the fact that South Africa has a long distance in egg trade, it exerts strong control over the flow of resources and can serve as an intermediary in trading networks, which influences its position in the global egg market.

3.3. Clusters of Global Egg Trading Networks

Considering the requirement for fresh, egg transportation radius is greatly shortened. Therefore, egg trading reflects extremely regional characteristics. To find the regional evolution characteristics of global egg trading network clusters, we collect egg trading relationships in five time intervals in 2000, 2005, 2010, 2015, and 2021. We identify five major trade clusters in the world, including America-Asia Cluster, Western Europe Cluster, Middle East-West Asia-South Asia Cluster, Nordic-Eastern Europe Cluster, and Africa Cluster (Figure 7).

America-Asia Cluster is the largest cluster in global egg trading networks, centered around the USA. The cluster consists of 72 countries and regions from the Americas, Asia, and Oceania, including China, Thailand, Canada, Brazil, Australia, and other Pacific Rim countries. As the second largest cluster in global egg trading networks, Western Europe Cluster is primarily composed of developed countries in Western Europe and involves roughly 40 countries and regions in Asia, South America, and Africa. The cluster exhibits a fluctuating trend of differentiation and unification. France, the UK, Italy, Spain, Greece, and other Southern Europe countries split in 2005, forming two clusters: the German-The Netherlands Cluster and the France-UK Cluster. The cluster reunified in 2015 with Germany, France, and The Netherlands as the core. Ukraine, the United Arab Emirates, Turkey, and India play a key role in Middle East-West Asia-South Asia Cluster. From 2000 to 2021, the number of countries included in this cluster, as well as the trade value, increased steadily. By 2021, the trade value had risen to CNY 1.104 million, and the number of countries had increased to 40. Nordic-Eastern Europe Cluster is centered around Russia. There has been a significant change in the cluster. From 2000 to 2009, Denmark was the core country, but since 2010 it has been part of the Western Europe Cluster. Similarly, Poland transferred from the cluster to the Western Europe Cluster in 2005, and Ukraine joined the Middle East-Central Asia-South Asia Cluster, becoming a core country of that cluster. The number of members in the Nordic-Eastern Europe Cluster decreased from 28 in 2000 to 15 in 2021, indicating a shrinking trend. Africa Cluster is going through decline. In 2009, it absorbed some countries from South American cluster, such as Brazil. However, since 2011, some North African countries, such as Egypt and Tunisia, integrated into the Western Europe Cluster, while some West African regions, such as Mauritania, Senegal, Ghana, and most regions north of the Republic of the Congo, integrated into the Middle East-West Asia-South Asia Cluster. In 2021, Africa Cluster has been reduced to being centered around the Southern African region, with only CNY 0.127 million of trade value.

Additionally, the global egg trading clusters underwent several changes in the 21st century. For example, in 2002, two clusters split from America-Asia Cluster: South American Cluster, with Brazil and Argentina as its cores, and East Indian Ocean Cluster, with Australia as its core. The two clusters have established closer trading relationships due to their geographic proximity. In 2004, Malay Peninsula Cluster, comprised of Singapore, Timor-Leste, Brunei, and Malaysia, differentiated from the East Indian Ocean Cluster and persisted until 2021. The core countries of South American Cluster gradually integrated into Western Europe Cluster, and some countries rejoined America-Asia Cluster. A new South American Cluster, with Costa Rica, Nicaragua, and Panama as its cores, emerged. Furthermore, Nordic Clusters, including Denmark, Iceland, Finland, and other Nordic countries, briefly emerged between 2011 and 2013.

4. Factors Impacting on Global Egg Trading Networks

4.1. Selection of Influence Factors

Natural endowments and differences in natural resources influence the formation of global egg trading networks, which determines the pattern of global egg production and thus shapes the initial feature of egg trading networks. As agricultural products, eggs require a substantial amount of land for breeding and are affected by temperature, both of which impact the rate of egg production. In the 21st century, the countries ranking among the top in global egg trading networks had ample land area and a suitable production environment, with temperate and subtropical climates that were free of extreme hot or cold weather [24]. However, countries or regions such as Malaysia, Singapore, and China, Hong Kong SAR, lack land so they require a large amount of egg imports.

Geographical location is another crucial factor in the evolution of global egg trading networks. The formation and development of egg trading clusters follows the first law of geography, with closer trading relations between countries that are geographically closer [25]. From 2000 to 2021, more than half of the global egg trading clusters were spatially adjacent (Figure 7). The trade within Europe is active, and Turkey, which is located in the middle of Asia, Africa, and Europe, establishes close egg trading relationships with the three continents and has a great net export volume of eggs.

Economic factors further drive the evolution of networks, primarily by supply and demand which impact fluctuations in egg market prices. When eggs are at a low price, importing countries tend to increase imports, while exporting countries reduce exports to minimize losses. In recent years, rising costs in feed, energy, and labor have driven up total production costs, while strong demand has also maintained high international prices on eggs. As a result, exporting countries are expanding exports to increase profits and controlling their own production costs. In addition to the basic supply-and-demand dynamics and price fluctuations, other factors such as GDP and industrial foundation also impact the global egg trading networks. A well-developed economic and industrial foundation might be profited to increase scale of egg production and improve processing efficiency, creating greater competitiveness in global market. In 2000, the top 10 countries in degree centrality were mainly developed countries, with only 3 being developing countries. By 2021, the number had reduced to one. In both 2000 and 2021, 5 of the top 10 countries in degree centrality were also among the top 10 countries in GDP [26]. There is a strong positive correlation between the domestic economy and the position in global egg trade.

Political relations and internal political characteristics also impact the global egg trading networks. A typical fact is that following the collapse of the Soviet Union, Russia and other Eastern Europe countries have continued to maintain close trading relationships [27]. The division of egg trading clusters in 2010 and 2021 showed that they maintained a united front. Similarly, the Europe cluster expanded in scale with the European Union. NATO continued to expand eastward by absorbing countries like Poland, the Czech Republic, and Hungary and strengthen their trading relationships. The “dual-track strategy” was employed to attract new member countries [28]. However, the Ukrainian conflict in 2022 disrupted the transportation of eggs from Ukraine to Europe and the Middle East, forcing these regions to seek alternative sources of imports. Internal political stability, egg trade policies, domestic willingness, and ability to participate in globalization and regional grouping also impact the development of global egg trading networks.

Policy factors shape and influence global egg trading networks. Trade liberalization policies facilitate the changes of global resources and enhance the efficiency of egg trading networks. Conversely, trade protection policies obstruct cooperation and hinder the global egg trading networks. Membership in the World Trade Organization (WTO) has a positive impact on increasing trades, as demonstrated by several new Eastern Europe countries in 2000, China in 2001, Saudi Arabia in 2005, and Ukraine in 2008. Since joining the WTO, China has strengthened its links with the global egg market, leading to a rapid increase in the number of its trading partners. There have not been any egg-related trade agreements in major egg-producing countries, making the egg trade policy relatively flexible, primarily influenced by egg prices, avian influenza epidemics, and other factors. In 2003, 2005, 2008, and 2014, there were significant outbreaks of avian influenza, resulting in many countries issuing policies to restrict egg imports from affected areas, thereby weakening global egg trade links. Many countries had to adjust their trade targets to prevent and control the spread of avian influenza, leading to changes in the global egg market.

Cultural proximity is another crucial factor impacting the global egg trading networks, including cultural identity, religious beliefs, values, and languages, which are stable and persistent over time [29]. Countries with similar cultural backgrounds are more likely to have trust in economic and trade dealings, thereby reducing transaction costs. The “Confucian Cultural Circle”, comprising East and Southeast Asian trading clusters, and the “Islamic Cultural Circle”, represented by the Middle East and Central Asia trading clusters, are significant players in global egg trading networks due to their cultural proximity.

4.2. Estimation Strategy

QAP analysis is a method used specifically for network analysis, including QAP correlation analysis and QAP regression analysis. We use QAP analysis to empirically test the impact of each independent variable and construct a weighted directional symmetric matrix of global egg trade, used as the dependent variable. Then, we analyze the influencing factors of global egg trading networks from five aspects and select seven indicators.

Firstly, to explore the differences in natural endowment between countries, we use data on the scale or production of laying hens in FAO database. Secondly, according to the gravitational model theory, the distance between two countries is inversely proportional to the size of bilateral trade. Therefore, to evaluate the impact of economic factors on global egg trading networks, we use the total GDP and per capita GDP in the World Bank database to measure the size of domestic economy and the income level of residents. Thirdly, considering political factors, which can affect trade costs, we use Worldwide Governance Indicators published by the World Bank such as control of corruption, government effectiveness, political stability and absence of violence, regulatory quality, voice and accountability, and rule of law to measure the different political characteristics between countries. Fourthly, to examine policy factors that impact global egg trading networks, we explore major regional trade organizations and free trade agreements in the world to estimate the impact on global egg trading networks. Lastly, we use the CEPII database to investigate the impact of cultural differences by confirming whether the two countries use a common official language. Additionally, we also evaluate the impact of geographical distance on the world egg trading network by selecting the geographical distance between the two capitals and whether the two countries are adjacent to each other in the CEPII database.

We construct a model to empirically test the influencing factors of global egg trading networks, which is given by:

$$Q=f(layer, gdp, pgdp, sys, fta, lan, dis)$$

where Q represents the volume of egg trade between countries in 2021. The variables layer, gdp, pgdp, sys, fta, lan, and dis represent the difference matrix of egg production, GDP difference matrix, per capita GDP difference matrix, political system distance matrix, trading policy difference matrix, language difference matrix, and geographical distance matrix.

From the results of the QAP correlation analysis presented in

Table 5. QAP correlation analysis on trading networks.

Variable	QAP Correlations	Significance	Std. Dev.	Min	Max
layer	0.032	0.035	0.012	−0.011	0.078
gdp	0.079	0.000	0.012	−0.013	0.067
pgdp	0.018	0.059	0.010	−0.026	0.047
sys	0.023	0.28	0.030	−0.079	0.026
fla	0.18	0.000	0.029	−0.043	0.091
lan	0.006	0.067	0.016	−0.042	0.084
dis	−0.016	0.026	0.008	−0.030	0.028

, it suggests that the egg production, GDP, signing a free trade agreement, and geographical proximity significantly impact the global egg trading networks. A possible explanation for this could be that with the increase in income and improvement in living standards worldwide, eggs are increasingly becoming a staple food in dietary structure, and the income expenditure elasticity of eggs is gradually decreasing. As a result, the income level and institutional differences among residents may no longer have a significant impact on egg trade.

According to QAP regression analysis, from

Table 6. QAP regression analysis on trading networks.

Variable	(1)	(2)	(3)	(4)	(5)
layer	0.032 ** (0.000)	0.036 *** (0.000)	0.032 *** (0.000)	0.033 *** (0.000)	0.031 *** (0.000)
gdp		0.103 *** (0.000)	0.089 *** (0.000)	0.078 *** (0.000)	0.069 *** (0.000)
fla			0.123 *** (0.001)	0.105 *** (0.001)	0.121 *** (0.001)
dis				−0.033 ** (0.001)	−0.029 ** (0.001)
lan					−0.023 (0.000)

Note: **, and *** indicate significance at the 5%, and 1% levels, respectively. The values in parentheses denote standard errors. As the sample selected in this study represents the overall size of the global egg trading, the standard error is close to zero.

, it can be observed that the difference matrix of egg production shows a significant positive impact at a 5% level of significance, which is consistent with expectations. The result indicates that differences in egg production between different countries can significantly impact the global egg trading networks. Furthermore, it can be inferred that a larger difference in egg production between the two countries could lead to stronger trading relationships. The technology gap theory can be used to explain the egg trade under resource and supply factors. As industrialization progresses, the world’s resources are becoming increasingly scarce. Consequently, the egg industry has evolved from a labor-intensive and resource-intensive industry to a technology-intensive one. Major egg producing countries such as the USA and The Netherlands have already developed standardized and efficient technologies, contributing lower production costs and competitive advantages. In contrast, the industrial system of Asian and African countries still relies heavily on labor and resources, leading to higher production costs and putting themselves at a competitive disadvantage. Therefore, it is essential to strengthen trade with major egg exporting countries in the world to achieve a more rational allocation of resources.

The GDP difference matrix shows a significant positive correlation, indicating that global egg trading is more likely to occur between countries with large differences in economy. Therefore, developed countries will be easier to increase the intensity of trade, which also reflects the “big country effect” in global egg trade. Small economies are also more inclined to trade with larger economies. A high economic level means a greater attraction in international trade, so large economies play a leading role in global egg trading networks.

Trade environment differences, whether belonging to the same regional trade organization or signing free trade agreements, are significantly positive, and the coefficient remains at a high level. The result indicates that the development of global economic integration, the join of regional trade organizations, and the signing of free trade agreements have reduced trade barriers, improved trade inclusiveness and reciprocity, and significantly promoted the expansion of global egg trading networks.

Language differences have not had a significant impact on global egg trade, confirming that language barriers and cultural differences are no longer major impediments to the global egg trading with the prevalence of multiculturalism and the deepening of exchanges and negotiations. From the perspective of geographical distance, geographical factors have a significant impact on the global egg trading pattern. The global egg trade mostly follows the “proximity principle”, exhibiting distinct regional agglomeration characteristics. This verifies that egg trading networks present a centralized and contiguous trade feature.

5. Discussion

Currently, the global food system faces various economic, environmental, and health challenges. External shocks and uncertainty exacerbate these problems and reveal system vulnerabilities such as poverty and hunger. Simultaneously, the global food system is transforming towards a sustainable and healthier future. The reduction of hunger and improvement of nutrition are crucial for sustainable food systems in countries, particularly in developing countries. As a cost-effective animal protein source, eggs play an important role in the food system. Exploring the global trading patterns of eggs can enhance the resilience and sustainability of the global food system in emergencies by exerting the role of eggs in diet and trade.

In contrast to other studies, we construct an optimization study of global egg trading networks. In this paper, we undertake two interrelated sets of analyses in order to explore and describe the characteristics of global egg trading networks. First, we implement social network analysis to explain the spatial structure of global egg trading networks, using egg bilateral trading relationships data of 213 countries and regions from 2000 to 2021. Second, we use QAP analysis to assess the influencing factors of global egg trading networks, including nature, geography, economy, politics, trading relations, and culture. Therefore, we propose several countermeasures.

For sustainable development, establishing long-term and stable trade relationships with key countries (such as The Netherlands, Germany, France, and the USA) in the network is crucial to maintain the stability of market and ensure national food security. Long-term cooperation agreements should be reached with these countries to address the specificities of eggs trade, tariffs, and trade facilitation, with the aim of eliminating trade barriers and reducing friction.

To reduce hunger and improve nutrition, it is necessary to strengthen trade and investment cooperation among developing countries and form a mutually beneficial bilateral trade pattern, which can be achieved by utilizing comparative advantages and providing technical assistance. Developing fair trade based on dialogue, transparency, and mutual respect in trade partnerships can enable poor farmers to gain more value chain surpluses, and enable producers to obtain better trading conditions, allowing them the opportunity to improve their lives and better plan for the future. At the same time, it is important to enhance cultural and human exchanges among countries to promote better communication and reduce trade and investment risks.

6. Conclusions

From the perspective of spatial and temporal dynamic evolution, the global egg trading networks are becoming increasingly complex, with a steady increase in the number of trade links between countries. The density of global egg trading networks has deepened, and the trade links between countries have become closer. It is found that the networks have a higher cohesion, connectivity, and efficiency. Additionally, the global egg trading networks exhibit small-world network characteristics and scale-free network characteristics.

From the perspective of spatial national differences and regional difference, countries with important positions in global egg trading networks control more resources. The USA, The Netherlands, France, the United Kingdom, and Germany are the most important core countries in global egg trading networks, occupying pivotal positions and having more trade opportunities. Some developing countries, like Brazil, restrictively control and influence over resources, which does not match their status as major egg producers in the world. In addition, due to the short transportation radius of eggs, there is a strong regional agglomeration feature in egg trading networks. In the 21st century, the global egg trading networks have undergone a continuous process of differentiation and integration. There are five relatively stable trading clusters globally, distributed around core countries.

From the influencing factors, the formation of global egg trading networks is the result of the interaction of natural endowments, geography, economy, politics, policies, and culture. Natural endowments are the prerequisites for the formation of the network, while economic conditions are the fundamental driving force. Political stability and geopolitical relations also play a role in shaping the networks. Geographical proximity is better able to contribute to reducing transaction costs and improving trading efficiency compared to cultural identity. Trade policies can either promote or hinder the development of global egg trading networks. Trade liberalization policies encourage the rational flow of global resources and improve the efficiency of the trading networks, while trade protection policies obstruct its healthy development.

This paper focused on the worldwide stability and the trading networks of livestock products, especially on eggs. For sustainable development, it is imperative to establish enduring and stable trading partnerships with key players in global egg trading, taking into account geographic factors, which is crucial for promoting global market stability and safeguarding domestic food security under uncertainty. Future research will analyze the impacts of trade policy and other market issues in global egg trading based on theoretical and/or empirical perspectives to ensure food security sustainability.